How many hours were spent on employee learning in 2021?
22.5 million hours
IBM │
│ 2022
What was the rate of fatalities in 2021?
The rate of fatalities in 2021 was 0.0016.
IBM 2022
How many full audits were con- ducted in 2022 in │
│ India?
2
Starbucks 2022
What is the percentage of women in the Board of Directors?
25%
Starbucks 2022
What was the total energy con- │
│ sumption in 2021?
According to the table, the total energy consumption in 2021 was 2,491,543 MWh.
Starbucks 2022
How much packaging material was made from renewable mate- │
│ rials?
According to the given data, 31% of packaging materials were made from recycled or renewable materials in FY22.
│
│
Table 1: Example question answers from the ESG reports of IBM and Starbucks using Deep Search DocQA system.
│
│
ESG report in our library via our QA conversational assistant. Our assistant generates answers and also presents the information (paragraph or table), in the ESG report, from which it has generated the │
│ response.
│
│
Related Work
│
│
The DocQA integrates multiple AI technologies, namely:
│
│
Document Conversion: Converting unstructured documents, such as PDF files, into a machine-readable format is a challenging task in AI. Early strategies for document conversion were based on geometric │
│ layout analysis (Cattoni et al. 2000; Breuel 2002). Thanks to the availability of large annotated datasets (PubLayNet (Zhong et al. 2019), DocBank (Li et al. 2020), DocLayNet (Pfitzmann et al. 2022; Auer et │
│ al. 2023), deep learning-based methods are routinely used. Modern approaches for recovering the structure of a document can be broadly divided into two categories: image-based or PDF representation-based . │
│ Imagebased methods usually employ Transformer or CNN architectures on the images of pages (Zhang et al. 2023; Li et al. 2022; Huang et al. 2022). On the other hand, deep learning-
│
│ Figure 1: System architecture: Simplified sketch of document question-answering pipeline. │
│
based language processing methods are applied on the native PDF content (generated by a single PDF printing command) (Auer et al. 2022; Livathinos et al. 2021; Staar et al. 2018).
How many hours were spent on employee learning in 2021?
22.5 million hours
IBM │\\n",\
"│ 2022
What was the rate of fatalities in 2021?
The rate of fatalities in 2021 was 0.0016.
IBM 2022
How many full audits were con- ducted in 2022 in │\\n",\
"│ India?
2
Starbucks 2022
What is the percentage of women in the Board of Directors?
25%
Starbucks 2022
What was the total energy con- │\\n",\
"│ sumption in 2021?
According to the table, the total energy consumption in 2021 was 2,491,543 MWh.
Starbucks 2022
How much packaging material was made from renewable mate- │\\n",\
"│ rials?
According to the given data, 31% of packaging materials were made from recycled or renewable materials in FY22.
│\\n",\
"│
Table 1: Example question answers from the ESG reports of IBM and Starbucks using Deep Search DocQA system.
│\\n",\
"│
ESG report in our library via our QA conversational assistant. Our assistant generates answers and also presents the information (paragraph or table), in the ESG report, from which it has generated the │\\n",\
"│ response.
│\\n",\
"│
Related Work
│\\n",\
"│
The DocQA integrates multiple AI technologies, namely:
│\\n",\
"│
Document Conversion: Converting unstructured documents, such as PDF files, into a machine-readable format is a challenging task in AI. Early strategies for document conversion were based on geometric │\\n",\
"│ layout analysis (Cattoni et al. 2000; Breuel 2002). Thanks to the availability of large annotated datasets (PubLayNet (Zhong et al. 2019), DocBank (Li et al. 2020), DocLayNet (Pfitzmann et al. 2022; Auer et │\\n",\
"│ al. 2023), deep learning-based methods are routinely used. Modern approaches for recovering the structure of a document can be broadly divided into two categories: image-based or PDF representation-based . │\\n",\
"│ Imagebased methods usually employ Transformer or CNN architectures on the images of pages (Zhang et al. 2023; Li et al. 2022; Huang et al. 2022). On the other hand, deep learning-
│\\n",\
"│ Figure 1: System architecture: Simplified sketch of document question-answering pipeline. │\\n",\
"│
based language processing methods are applied on the native PDF content (generated by a single PDF printing command) (Auer et al. 2022; Livathinos et al. 2021; Staar et al. 2018).
│\\n",\
"│
│\\n",\
"╰────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"from docling\_core.transforms.serializer.html import HTMLDocSerializer\\n",\
"\\n",\
"serializer = HTMLDocSerializer(doc=doc)\\n",\
"ser\_result = serializer.serialize()\\n",\
"ser\_text = ser\_result.text\\n",\
"\\n",\
"# we here only print an excerpt to keep the output brief:\\n",\
"print\_in\_console(ser\_text\[ser\_text.find(start\_cue) : ser\_text.find(stop\_cue)\])"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"In the following example, we use a \`MarkdownDocSerializer\`:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 6,\
"metadata": {},\
"outputs": \[\
{\
"data": {\
"text/html": \[\
"
╭───────────────────────────────────────────────────────────────────── chunk\_pos=17 num\_tokens=261 doc\_items\_refs=\['#/tables/0'\] ──────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ Apple M3 Max, Thread budget. = 4. Apple M3 Max, native backend.TTS = 177 s 167 s. Apple M3 Max, native backend.Pages/s = 1.27 1.34. Apple M3 Max, native backend.Mem = 6.20 GB. Apple M3 Max, │\\n",\
"│ pypdfium backend.TTS = 103 s 92 s. Apple M3 Max, pypdfium backend.Pages/s = 2.18 2.45. Apple M3 Max, pypdfium backend.Mem = 2.56 GB. (16 cores) Intel(R) Xeon E5-2690, Thread budget. = 16 4 16. (16 │\\n",\
"│ cores) Intel(R) Xeon E5-2690, native │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"╭───────────────────────────────────────────────────────────────────── chunk\_pos=17 num\_tokens=261 doc\_items\_refs=\['#/tables/0'\] ──────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ Apple M3 Max, Thread budget. = 4. Apple M3 Max, native backend.TTS = 177 s 167 s. Apple M3 Max, native backend.Pages/s = 1.27 1.34. Apple M3 Max, native backend.Mem = 6.20 GB. Apple M3 Max, │\\n",\
"│ pypdfium backend.TTS = 103 s 92 s. Apple M3 Max, pypdfium backend.Pages/s = 2.18 2.45. Apple M3 Max, pypdfium backend.Mem = 2.56 GB. (16 cores) Intel(R) Xeon E5-2690, Thread budget. = 16 4 16. (16 │\\n",\
"│ cores) Intel(R) Xeon E5-2690, native │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"chunker = HybridChunker(tokenizer=tokenizer)\\n",\
"\\n",\
"chunk\_iter = chunker.chunk(dl\_doc=doc)\\n",\
"\\n",\
"chunks = list(chunk\_iter)\\n",\
"i, chunk = find\_n\_th\_chunk\_with\_label(chunks, n=0, label=DocItemLabel.TABLE)\\n",\
"print\_chunk(\\n",\
" chunks=chunks,\\n",\
" chunk\_pos=i,\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"
\\n",\
" INFO: As you see above, using the HybridChunker can sometimes lead to a warning from the transformers library, however this is a \\"false alarm\\" — for details check here.\\n",\
"
"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"### Configuring a different strategy"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"We can configure a different serialization strategy. In the example below, we specify a different table serializer that serializes tables to Markdown instead of the triplet notation used by default:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 6,\
"metadata": {\
"execution": {\
"iopub.execute\_input": "2026-05-20T20:00:51.204590Z",\
"iopub.status.busy": "2026-05-20T20:00:51.204378Z",\
"iopub.status.idle": "2026-05-20T20:00:51.588172Z",\
"shell.execute\_reply": "2026-05-20T20:00:51.587577Z"\
}\
},\
"outputs": \[\
{\
"data": {\
"text/html": \[\
"
╭───────────────────────────────────────────────────────────────────── chunk\_pos=17 num\_tokens=262 doc\_items\_refs=\['#/tables/0'\] ──────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ │\\n",\
"│ | CPU | Thread budget | native backend | native backend | native backend | pypdfium backend | pypdfium backend | pypdfium backend | │\\n",\
"│ │\\n",\
"│ |----------------------------------|-----------------|------------------|------------------|------------------|------------- │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"╭───────────────────────────────────────────────────────────────────── chunk\_pos=17 num\_tokens=262 doc\_items\_refs=\['#/tables/0'\] ──────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ │\\n",\
"│ | CPU | Thread budget | native backend | native backend | native backend | pypdfium backend | pypdfium backend | pypdfium backend | │\\n",\
"│ │\\n",\
"│ |----------------------------------|-----------------|------------------|------------------|------------------|------------- │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"from docling\_core.transforms.chunker.hierarchical\_chunker import (\\n",\
" ChunkingDocSerializer,\\n",\
" ChunkingSerializerProvider,\\n",\
")\\n",\
"from docling\_core.transforms.serializer.markdown import MarkdownTableSerializer\\n",\
"\\n",\
"\\n",\
"class MDTableSerializerProvider(ChunkingSerializerProvider):\\n",\
" def get\_serializer(self, doc):\\n",\
" return ChunkingDocSerializer(\\n",\
" doc=doc,\\n",\
" table\_serializer=MarkdownTableSerializer(), # configuring a different table serializer\\n",\
" )\\n",\
"\\n",\
"\\n",\
"chunker = HybridChunker(\\n",\
" tokenizer=tokenizer,\\n",\
" serializer\_provider=MDTableSerializerProvider(),\\n",\
")\\n",\
"\\n",\
"chunk\_iter = chunker.chunk(dl\_doc=doc)\\n",\
"\\n",\
"chunks = list(chunk\_iter)\\n",\
"i, chunk = find\_n\_th\_chunk\_with\_label(chunks, n=0, label=DocItemLabel.TABLE)\\n",\
"print\_chunk(\\n",\
" chunks=chunks,\\n",\
" chunk\_pos=i,\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Picture serialization"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"### Using the default strategy"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Below we inspect the first chunk containing a picture.\\n",\
"\\n",\
"Even when using the default strategy, we can modify the relevant parameters, e.g. which placeholder is used for pictures:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 7,\
"metadata": {\
"execution": {\
"iopub.execute\_input": "2026-05-20T20:00:51.590464Z",\
"iopub.status.busy": "2026-05-20T20:00:51.590180Z",\
"iopub.status.idle": "2026-05-20T20:00:52.315495Z",\
"shell.execute\_reply": "2026-05-20T20:00:52.314689Z"\
}\
},\
"outputs": \[\
{\
"data": {\
"text/html": \[\
"
╭───────────────────────────────────────────────── chunk\_pos=0 num\_tokens=133 doc\_items\_refs=\['#/pictures/0', '#/texts/2', '#/texts/3', '#/texts/4'\] ──────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ <!-- image --> │\\n",\
"│ │\\n",\
"│ In this image we can see a cartoon image of a duck holding a paper. │\\n",\
"│ Version 1.0 │\\n",\
"│ Christoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta │\\n",\
"│ Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar │\\n",\
"│ AI4K Group, IBM Research R¨ uschlikon, Switzerland │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"╭───────────────────────────────────────────────── chunk\_pos=0 num\_tokens=133 doc\_items\_refs=\['#/pictures/0', '#/texts/2', '#/texts/3', '#/texts/4'\] ──────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ │\\n",\
"│ │\\n",\
"│ In this image we can see a cartoon image of a duck holding a paper. │\\n",\
"│ Version 1.0 │\\n",\
"│ Christoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta │\\n",\
"│ Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar │\\n",\
"│ AI4K Group, IBM Research R¨ uschlikon, Switzerland │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"from docling\_core.transforms.serializer.markdown import MarkdownParams\\n",\
"\\n",\
"\\n",\
"class ImgPlaceholderSerializerProvider(ChunkingSerializerProvider):\\n",\
" def get\_serializer(self, doc):\\n",\
" return ChunkingDocSerializer(\\n",\
" doc=doc,\\n",\
" params=MarkdownParams(\\n",\
" image\_placeholder=\\"\\",\\n",\
" ),\\n",\
" )\\n",\
"\\n",\
"\\n",\
"chunker = HybridChunker(\\n",\
" tokenizer=tokenizer,\\n",\
" serializer\_provider=ImgPlaceholderSerializerProvider(),\\n",\
")\\n",\
"\\n",\
"chunk\_iter = chunker.chunk(dl\_doc=doc)\\n",\
"\\n",\
"chunks = list(chunk\_iter)\\n",\
"i, chunk = find\_n\_th\_chunk\_with\_label(chunks, n=0, label=DocItemLabel.PICTURE)\\n",\
"print\_chunk(\\n",\
" chunks=chunks,\\n",\
" chunk\_pos=i,\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"### Using a custom strategy"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Below we define and use our custom picture serialization strategy which leverages picture annotations:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 8,\
"metadata": {\
"execution": {\
"iopub.execute\_input": "2026-05-20T20:00:52.318089Z",\
"iopub.status.busy": "2026-05-20T20:00:52.317869Z",\
"iopub.status.idle": "2026-05-20T20:00:52.323521Z",\
"shell.execute\_reply": "2026-05-20T20:00:52.322732Z"\
}\
},\
"outputs": \[\],\
"source": \[\
"from typing import Any\\n",\
"\\n",\
"from docling\_core.transforms.serializer.base import (\\n",\
" BaseDocSerializer,\\n",\
" SerializationResult,\\n",\
")\\n",\
"from docling\_core.transforms.serializer.common import create\_ser\_result\\n",\
"from docling\_core.transforms.serializer.markdown import MarkdownPictureSerializer\\n",\
"from docling\_core.types.doc.document import (\\n",\
" PictureClassificationData,\\n",\
" PictureDescriptionData,\\n",\
" PictureItem,\\n",\
" PictureMoleculeData,\\n",\
")\\n",\
"from typing\_extensions import override\\n",\
"\\n",\
"\\n",\
"class AnnotationPictureSerializer(MarkdownPictureSerializer):\\n",\
" @override\\n",\
" def serialize(\\n",\
" self,\\n",\
" \*,\\n",\
" item: PictureItem,\\n",\
" doc\_serializer: BaseDocSerializer,\\n",\
" doc: DoclingDocument,\\n",\
" \*\*kwargs: Any,\\n",\
" ) -> SerializationResult:\\n",\
" text\_parts: list\[str\] = \[\]\\n",\
"\\n",\
" if item.meta is not None:\\n",\
" if item.meta.classification is not None:\\n",\
" main\_pred = item.meta.classification.get\_main\_prediction()\\n",\
" if main\_pred is not None:\\n",\
" text\_parts.append(f\\"Picture type: {main\_pred.class\_name}\\")\\n",\
"\\n",\
" if item.meta.molecule is not None:\\n",\
" text\_parts.append(f\\"SMILES: {item.meta.molecule.smi}\\")\\n",\
"\\n",\
" if item.meta.description is not None:\\n",\
" text\_parts.append(f\\"Picture description: {item.meta.description.text}\\")\\n",\
"\\n",\
" text\_res = \\"\\\\n\\".join(text\_parts)\\n",\
" text\_res = doc\_serializer.post\_process(text=text\_res)\\n",\
" return create\_ser\_result(text=text\_res, span\_source=item)"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 9,\
"metadata": {\
"execution": {\
"iopub.execute\_input": "2026-05-20T20:00:52.326112Z",\
"iopub.status.busy": "2026-05-20T20:00:52.325715Z",\
"iopub.status.idle": "2026-05-20T20:00:53.121340Z",\
"shell.execute\_reply": "2026-05-20T20:00:53.120668Z"\
}\
},\
"outputs": \[\
{\
"data": {\
"text/html": \[\
"
╭───────────────────────────────────────────────── chunk\_pos=0 num\_tokens=144 doc\_items\_refs=\['#/pictures/0', '#/texts/2', '#/texts/3', '#/texts/4'\] ──────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ Picture description: In this image we can see a cartoon image of a duck holding a paper. │\\n",\
"│ │\\n",\
"│ In this image we can see a cartoon image of a duck holding a paper. │\\n",\
"│ Version 1.0 │\\n",\
"│ Christoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta │\\n",\
"│ Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar │\\n",\
"│ AI4K Group, IBM Research R¨ uschlikon, Switzerland │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"╭───────────────────────────────────────────────── chunk\_pos=0 num\_tokens=144 doc\_items\_refs=\['#/pictures/0', '#/texts/2', '#/texts/3', '#/texts/4'\] ──────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ Picture description: In this image we can see a cartoon image of a duck holding a paper. │\\n",\
"│ │\\n",\
"│ In this image we can see a cartoon image of a duck holding a paper. │\\n",\
"│ Version 1.0 │\\n",\
"│ Christoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta │\\n",\
"│ Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar │\\n",\
"│ AI4K Group, IBM Research R¨ uschlikon, Switzerland │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"class ImgAnnotationSerializerProvider(ChunkingSerializerProvider):\\n",\
" def get\_serializer(self, doc: DoclingDocument):\\n",\
" return ChunkingDocSerializer(\\n",\
" doc=doc,\\n",\
" picture\_serializer=AnnotationPictureSerializer(), # configuring a different picture serializer\\n",\
" )\\n",\
"\\n",\
"\\n",\
"chunker = HybridChunker(\\n",\
" tokenizer=tokenizer,\\n",\
" serializer\_provider=ImgAnnotationSerializerProvider(),\\n",\
")\\n",\
"\\n",\
"chunk\_iter = chunker.chunk(dl\_doc=doc)\\n",\
"\\n",\
"chunks = list(chunk\_iter)\\n",\
"i, chunk = find\_n\_th\_chunk\_with\_label(chunks, n=0, label=DocItemLabel.PICTURE)\\n",\
"print\_chunk(\\n",\
" chunks=chunks,\\n",\
" chunk\_pos=i,\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Chunk expansion\\n",\
"In this section, we demonstrate how to expand chunks to include additional context from their containing document items or pages. This is useful when we want to ensure that chunks include complete semantic units or when we need more context for downstream tasks."\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"### Expansion to containing DocItem\\n",\
"We can expand a chunk to include the full content of its containing document item. This ensures that the chunk contains the complete semantic unit (e.g., a full paragraph, section, list, or table) rather than a truncated portion."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 10,\
"metadata": {\
"execution": {\
"iopub.execute\_input": "2026-05-20T20:00:53.124048Z",\
"iopub.status.busy": "2026-05-20T20:00:53.123835Z",\
"iopub.status.idle": "2026-05-20T20:00:53.148365Z",\
"shell.execute\_reply": "2026-05-20T20:00:53.147354Z"\
}\
},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Original chunk (partial table):\\n"\
\]\
},\
{\
"data": {\
"text/html": \[\
"
╭───────────────────────────────────────────────────────────────────── chunk\_pos=17 num\_tokens=261 doc\_items\_refs=\['#/tables/0'\] ──────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ Apple M3 Max, Thread budget. = 4. Apple M3 Max, native backend.TTS = 177 s 167 s. Apple M3 Max, native backend.Pages/s = 1.27 1.34. Apple M3 Max, native backend.Mem = 6.20 GB. Apple M3 Max, │\\n",\
"│ pypdfium backend.TTS = 103 s 92 s. Apple M3 Max, pypdfium backend.Pages/s = 2.18 2.45. Apple M3 Max, pypdfium backend.Mem = 2.56 GB. (16 cores) Intel(R) Xeon E5-2690, Thread budget. = 16 4 16. (16 │\\n",\
"│ cores) Intel(R) Xeon E5-2690, native │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"╭───────────────────────────────────────────────────────────────────── chunk\_pos=17 num\_tokens=261 doc\_items\_refs=\['#/tables/0'\] ──────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ Apple M3 Max, Thread budget. = 4. Apple M3 Max, native backend.TTS = 177 s 167 s. Apple M3 Max, native backend.Pages/s = 1.27 1.34. Apple M3 Max, native backend.Mem = 6.20 GB. Apple M3 Max, │\\n",\
"│ pypdfium backend.TTS = 103 s 92 s. Apple M3 Max, pypdfium backend.Pages/s = 2.18 2.45. Apple M3 Max, pypdfium backend.Mem = 2.56 GB. (16 cores) Intel(R) Xeon E5-2690, Thread budget. = 16 4 16. (16 │\\n",\
"│ cores) Intel(R) Xeon E5-2690, native │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
},\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"\\n",\
"Expanded chunk (complete table in containing doc item):\\n"\
\]\
},\
{\
"data": {\
"text/html": \[\
"
╭─────────────────────────────────────────────────────────────────────────────── chunk\_pos=17 (expanded) num\_tokens=431 ───────────────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ │\\n",\
"│ | CPU | Thread budget | native backend | native backend | native backend | pypdfium backend | pypdfium backend | pypdfium backend | │\\n",\
"│ |----------------------------------|-----------------|------------------|------------------|------------------|--------------------|--------------------|--------------------| │\\n",\
"│ | | | TTS | Pages/s | Mem | TTS | Pages/s | Mem | │\\n",\
"│ | Apple M3 Max | 4 | 177 s 167 s | 1.27 1.34 | 6.20 GB | 103 s 92 s | 2.18 2.45 | 2.56 GB | │\\n",\
"│ | (16 cores) Intel(R) Xeon E5-2690 | 16 4 16 | 375 s 244 s | 0.60 0.92 | 6.16 GB | 239 s 143 s | 0.94 1.57 | 2.42 GB | │\\n",\
"│ │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"╭─────────────────────────────────────────────────────────────────────────────── chunk\_pos=17 (expanded) num\_tokens=431 ───────────────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ │\\n",\
"│ | CPU | Thread budget | native backend | native backend | native backend | pypdfium backend | pypdfium backend | pypdfium backend | │\\n",\
"│ |----------------------------------|-----------------|------------------|------------------|------------------|--------------------|--------------------|--------------------| │\\n",\
"│ | | | TTS | Pages/s | Mem | TTS | Pages/s | Mem | │\\n",\
"│ | Apple M3 Max | 4 | 177 s 167 s | 1.27 1.34 | 6.20 GB | 103 s 92 s | 2.18 2.45 | 2.56 GB | │\\n",\
"│ | (16 cores) Intel(R) Xeon E5-2690 | 16 4 16 | 375 s 244 s | 0.60 0.92 | 6.16 GB | 239 s 143 s | 0.94 1.57 | 2.42 GB | │\\n",\
"│ │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"from docling\_core.transforms.chunker.chunk\_expander import TreeChunkExpander\\n",\
"from docling\_core.transforms.chunker.hierarchical\_chunker import ChunkingDocSerializer\\n",\
"\\n",\
"# Create a chunk expander for expanding to containing doc items\\n",\
"tree\_expander = TreeChunkExpander()\\n",\
"serializer = MDTableSerializerProvider().get\_serializer(doc=doc)\\n",\
"\\n",\
"# Reuse the chunks from the previous table serialization example\\n",\
"# Find a chunk that contains a table (reusing the variable 'i' from earlier)\\n",\
"table\_chunk\_idx, table\_chunk = find\_n\_th\_chunk\_with\_label(\\n",\
" chunks, n=0, label=DocItemLabel.TABLE\\n",\
")\\n",\
"\\n",\
"# Expand the chunk to include the full containing doc item (complete table)\\n",\
"expanded\_chunk = tree\_expander.expand(\\n",\
" chunk=table\_chunk, dl\_doc=doc, serializer=serializer\\n",\
")\\n",\
"\\n",\
"# Compare original and expanded chunks\\n",\
"print(\\"Original chunk (partial table):\\")\\n",\
"print\_chunk(chunks=chunks, chunk\_pos=table\_chunk\_idx)\\n",\
"\\n",\
"print(\\"\\\\nExpanded chunk (complete table in containing doc item):\\")\\n",\
"ctx\_text = chunker.contextualize(chunk=expanded\_chunk)\\n",\
"num\_tokens = tokenizer.count\_tokens(text=ctx\_text)\\n",\
"title = f\\"chunk\_pos={table\_chunk\_idx} (expanded) {num\_tokens=}\\"\\n",\
"console.print(Panel(ctx\_text, title=title))"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"### Expansion to containing page\\n",\
"We can also expand a chunk to include all content from its containing page. This is particularly useful when we need full page context for tasks like question answering or when working with documents where page boundaries are semantically important."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 11,\
"metadata": {\
"execution": {\
"iopub.execute\_input": "2026-05-20T20:00:53.151179Z",\
"iopub.status.busy": "2026-05-20T20:00:53.150947Z",\
"iopub.status.idle": "2026-05-20T20:00:53.238019Z",\
"shell.execute\_reply": "2026-05-20T20:00:53.237188Z"\
}\
},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Original chunk (partial table):\\n"\
\]\
},\
{\
"data": {\
"text/html": \[\
"
╭───────────────────────────────────────────────────────────────────── chunk\_pos=17 num\_tokens=261 doc\_items\_refs=\['#/tables/0'\] ──────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ Apple M3 Max, Thread budget. = 4. Apple M3 Max, native backend.TTS = 177 s 167 s. Apple M3 Max, native backend.Pages/s = 1.27 1.34. Apple M3 Max, native backend.Mem = 6.20 GB. Apple M3 Max, │\\n",\
"│ pypdfium backend.TTS = 103 s 92 s. Apple M3 Max, pypdfium backend.Pages/s = 2.18 2.45. Apple M3 Max, pypdfium backend.Mem = 2.56 GB. (16 cores) Intel(R) Xeon E5-2690, Thread budget. = 16 4 16. (16 │\\n",\
"│ cores) Intel(R) Xeon E5-2690, native │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"╭───────────────────────────────────────────────────────────────────── chunk\_pos=17 num\_tokens=261 doc\_items\_refs=\['#/tables/0'\] ──────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ Apple M3 Max, Thread budget. = 4. Apple M3 Max, native backend.TTS = 177 s 167 s. Apple M3 Max, native backend.Pages/s = 1.27 1.34. Apple M3 Max, native backend.Mem = 6.20 GB. Apple M3 Max, │\\n",\
"│ pypdfium backend.TTS = 103 s 92 s. Apple M3 Max, pypdfium backend.Pages/s = 2.18 2.45. Apple M3 Max, pypdfium backend.Mem = 2.56 GB. (16 cores) Intel(R) Xeon E5-2690, Thread budget. = 16 4 16. (16 │\\n",\
"│ cores) Intel(R) Xeon E5-2690, native │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
},\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"\\n",\
"Expanded chunk (full page containing the table):\\n"\
\]\
},\
{\
"data": {\
"text/html": \[\
"
╭────────────────────────────────────────────────────────────────────────── chunk\_pos=17 (expanded to page) num\_tokens=1209 ───────────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ torch runtimes backing the Docling pipeline. We will deliver updates on this topic at in a future version of this report. │\\n",\
"│ │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ │\\n",\
"│ | CPU | Thread budget | native backend | native backend | native backend | pypdfium backend | pypdfium backend | pypdfium backend | │\\n",\
"│ |----------------------------------|-----------------|------------------|------------------|------------------|--------------------|--------------------|--------------------| │\\n",\
"│ | | | TTS | Pages/s | Mem | TTS | Pages/s | Mem | │\\n",\
"│ | Apple M3 Max | 4 | 177 s 167 s | 1.27 1.34 | 6.20 GB | 103 s 92 s | 2.18 2.45 | 2.56 GB | │\\n",\
"│ | (16 cores) Intel(R) Xeon E5-2690 | 16 4 16 | 375 s 244 s | 0.60 0.92 | 6.16 GB | 239 s 143 s | 0.94 1.57 | 2.42 GB | │\\n",\
"│ │\\n",\
"│ ## 5 Applications │\\n",\
"│ │\\n",\
"│ Thanks to the high-quality, richly structured document conversion achieved by Docling, its output qualifies for numerous downstream applications. For example, Docling can provide a base for │\\n",\
"│ detailed enterprise document search, passage retrieval or classification use-cases, or support knowledge extraction pipelines, allowing specific treatment of different structures in the document, │\\n",\
"│ such as tables, figures, section structure or references. For popular generative AI application patterns, such as retrieval-augmented generation (RAG), we provide quackling , an open-source │\\n",\
"│ package which capitalizes on Docling's feature-rich document output to enable document-native optimized vector embedding and chunking. It plugs in seamlessly with LLM frameworks such as LlamaIndex │\\n",\
"│ \[8\]. Since Docling is fast, stable and cheap to run, it also makes for an excellent choice to build document-derived datasets. With its powerful table structure recognition, it provides │\\n",\
"│ significant benefit to automated knowledge-base construction \[11, 10\]. Docling is also integrated within the open IBM data prep kit \[6\], which implements scalable data transforms to build │\\n",\
"│ large-scale multi-modal training datasets. │\\n",\
"│ │\\n",\
"│ ## 6 Future work and contributions │\\n",\
"│ │\\n",\
"│ Docling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an │\\n",\
"│ equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of content, as well as augment extracted document metadata with │\\n",\
"│ additional information. Further investment into testing and optimizing GPU acceleration as well as improving the Docling-native PDF backend are on our roadmap, too. │\\n",\
"│ │\\n",\
"│ We encourage everyone to propose or implement additional features and models, and will gladly take your inputs and contributions under review . The codebase of Docling is open for use and │\\n",\
"│ contribution, under the MIT license agreement and in alignment with our contributing guidelines included in the Docling repository. If you use Docling in your projects, please consider citing this │\\n",\
"│ technical report. │\\n",\
"│ │\\n",\
"│ ## References │\\n",\
"│ │\\n",\
"│ - \[1\] J. AI. Easyocr: Ready-to-use ocr with 80+ supported languages. https://github.com/ JaidedAI/EasyOCR , 2024. Version: 1.7.0. │\\n",\
"│ - \[2\] J. Ansel, E. Yang, H. He, N. Gimelshein, A. Jain, M. Voznesensky, B. Bao, P. Bell, D. Berard, E. Burovski, G. Chauhan, A. Chourdia, W. Constable, A. Desmaison, Z. DeVito, E. Ellison, W. │\\n",\
"│ Feng, J. Gong, M. Gschwind, B. Hirsh, S. Huang, K. Kalambarkar, L. Kirsch, M. Lazos, M. Lezcano, Y. Liang, J. Liang, Y. Lu, C. Luk, B. Maher, Y. Pan, C. Puhrsch, M. Reso, M. Saroufim, M. Y. │\\n",\
"│ Siraichi, H. Suk, M. Suo, P. Tillet, E. Wang, X. Wang, W. Wen, S. Zhang, X. Zhao, K. Zhou, R. Zou, A. Mathews, G. Chanan, P. Wu, and S. Chintala. Pytorch 2: Faster │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"╭────────────────────────────────────────────────────────────────────────── chunk\_pos=17 (expanded to page) num\_tokens=1209 ───────────────────────────────────────────────────────────────────────────╮\\n",\
"│ Docling Technical Report │\\n",\
"│ 4 Performance │\\n",\
"│ torch runtimes backing the Docling pipeline. We will deliver updates on this topic at in a future version of this report. │\\n",\
"│ │\\n",\
"│ Table 1: Runtime characteristics of Docling with the standard model pipeline and settings, on our test dataset of 225 pages, on two different systems. OCR is disabled. We show the time-to-solution │\\n",\
"│ (TTS), computed throughput in pages per second, and the peak memory used (resident set size) for both the Docling-native PDF backend and for the pypdfium backend, using 4 and 16 threads. │\\n",\
"│ │\\n",\
"│ | CPU | Thread budget | native backend | native backend | native backend | pypdfium backend | pypdfium backend | pypdfium backend | │\\n",\
"│ |----------------------------------|-----------------|------------------|------------------|------------------|--------------------|--------------------|--------------------| │\\n",\
"│ | | | TTS | Pages/s | Mem | TTS | Pages/s | Mem | │\\n",\
"│ | Apple M3 Max | 4 | 177 s 167 s | 1.27 1.34 | 6.20 GB | 103 s 92 s | 2.18 2.45 | 2.56 GB | │\\n",\
"│ | (16 cores) Intel(R) Xeon E5-2690 | 16 4 16 | 375 s 244 s | 0.60 0.92 | 6.16 GB | 239 s 143 s | 0.94 1.57 | 2.42 GB | │\\n",\
"│ │\\n",\
"│ ## 5 Applications │\\n",\
"│ │\\n",\
"│ Thanks to the high-quality, richly structured document conversion achieved by Docling, its output qualifies for numerous downstream applications. For example, Docling can provide a base for │\\n",\
"│ detailed enterprise document search, passage retrieval or classification use-cases, or support knowledge extraction pipelines, allowing specific treatment of different structures in the document, │\\n",\
"│ such as tables, figures, section structure or references. For popular generative AI application patterns, such as retrieval-augmented generation (RAG), we provide quackling , an open-source │\\n",\
"│ package which capitalizes on Docling's feature-rich document output to enable document-native optimized vector embedding and chunking. It plugs in seamlessly with LLM frameworks such as LlamaIndex │\\n",\
"│ \[8\]. Since Docling is fast, stable and cheap to run, it also makes for an excellent choice to build document-derived datasets. With its powerful table structure recognition, it provides │\\n",\
"│ significant benefit to automated knowledge-base construction \[11, 10\]. Docling is also integrated within the open IBM data prep kit \[6\], which implements scalable data transforms to build │\\n",\
"│ large-scale multi-modal training datasets. │\\n",\
"│ │\\n",\
"│ ## 6 Future work and contributions │\\n",\
"│ │\\n",\
"│ Docling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an │\\n",\
"│ equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of content, as well as augment extracted document metadata with │\\n",\
"│ additional information. Further investment into testing and optimizing GPU acceleration as well as improving the Docling-native PDF backend are on our roadmap, too. │\\n",\
"│ │\\n",\
"│ We encourage everyone to propose or implement additional features and models, and will gladly take your inputs and contributions under review . The codebase of Docling is open for use and │\\n",\
"│ contribution, under the MIT license agreement and in alignment with our contributing guidelines included in the Docling repository. If you use Docling in your projects, please consider citing this │\\n",\
"│ technical report. │\\n",\
"│ │\\n",\
"│ ## References │\\n",\
"│ │\\n",\
"│ - \[1\] J. AI. Easyocr: Ready-to-use ocr with 80+ supported languages. https://github.com/ JaidedAI/EasyOCR , 2024. Version: 1.7.0. │\\n",\
"│ - \[2\] J. Ansel, E. Yang, H. He, N. Gimelshein, A. Jain, M. Voznesensky, B. Bao, P. Bell, D. Berard, E. Burovski, G. Chauhan, A. Chourdia, W. Constable, A. Desmaison, Z. DeVito, E. Ellison, W. │\\n",\
"│ Feng, J. Gong, M. Gschwind, B. Hirsh, S. Huang, K. Kalambarkar, L. Kirsch, M. Lazos, M. Lezcano, Y. Liang, J. Liang, Y. Lu, C. Luk, B. Maher, Y. Pan, C. Puhrsch, M. Reso, M. Saroufim, M. Y. │\\n",\
"│ Siraichi, H. Suk, M. Suo, P. Tillet, E. Wang, X. Wang, W. Wen, S. Zhang, X. Zhao, K. Zhou, R. Zou, A. Mathews, G. Chanan, P. Wu, and S. Chintala. Pytorch 2: Faster │\\n",\
"╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"from docling\_core.transforms.chunker.chunk\_expander import PageChunkExpander\\n",\
"\\n",\
"# Create a chunk expander for expanding to containing pages\\n",\
"page\_expander = PageChunkExpander()\\n",\
"\\n",\
"# Reuse the table chunk from the previous example\\n",\
"# Expand it to include all content from the containing page\\n",\
"expanded\_chunk = page\_expander.expand(\\n",\
" chunk=table\_chunk, dl\_doc=doc, serializer=serializer\\n",\
")\\n",\
"\\n",\
"# Compare original and expanded chunks\\n",\
"print(\\"Original chunk (partial table):\\")\\n",\
"print\_chunk(chunks=chunks, chunk\_pos=table\_chunk\_idx)\\n",\
"\\n",\
"print(\\"\\\\nExpanded chunk (full page containing the table):\\")\\n",\
"ctx\_text = chunker.contextualize(chunk=expanded\_chunk)\\n",\
"num\_tokens = tokenizer.count\_tokens(text=ctx\_text)\\n",\
"title = f\\"chunk\_pos={table\_chunk\_idx} (expanded to page) {num\_tokens=}\\"\\n",\
"console.print(Panel(ctx\_text, title=title))"\
\]\
}\
\],
"metadata": {
"kernelspec": {
"display\_name": ".venv",
"language": "python",
"name": "python3"
},
"language\_info": {
"codemirror\_mode": {
"name": "ipython",
"version": 3
},
"file\_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert\_exporter": "python",
"pygments\_lexer": "ipython3",
"version": "3.13.12"
}
},
"nbformat": 4,
"nbformat\_minor": 2
}
---
# Unknown
{
"cells": \[\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"\[!\[Open in Colab\](https://colab.research.google.com/assets/colab-badge.svg)\](https://colab.research.google.com/github/docling-project/docling/blob/main/docs/examples/rag\_weaviate.ipynb)"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "Ag9kcX2B\_atc"\
},\
"source": \[\
"# RAG with Weaviate"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"\\n",\
"| Step | Tech | Execution | \\n",\
"| --- | --- | --- |\\n",\
"| Embedding | Open AI | 🌐 Remote |\\n",\
"| Vector store | Weaviate | 💻 Local |\\n",\
"| Gen AI | Open AI | 🌐 Remote |\\n",\
"\\n",\
"## A recipe 🧑🍳 🐥 💚\\n",\
"\\n",\
"This is a code recipe that uses \[Weaviate\](https://weaviate.io/) to perform RAG over PDF documents parsed by \[Docling\](https://docling-project.github.io/docling/).\\n",\
"\\n",\
"In this notebook, we accomplish the following:\\n",\
"\* Parse the top machine learning papers on \[arXiv\](https://arxiv.org/) using Docling\\n",\
"\* Perform hierarchical chunking of the documents using Docling\\n",\
"\* Generate text embeddings with OpenAI\\n",\
"\* Perform RAG using \[Weaviate\](https://weaviate.io/developers/weaviate/search/generative)\\n",\
"\\n",\
"To run this notebook, you'll need:\\n",\
"\* An \[OpenAI API key\](https://platform.openai.com/docs/quickstart)\\n",\
"\* Access to GPU/s\\n",\
"\\n",\
"Note: For best results, please use \*\*GPU acceleration\*\* to run this notebook. Here are two options for running this notebook:\\n",\
"1. \*\*Locally on a MacBook with an Apple Silicon chip.\*\* Converting all documents in the notebook takes ~2 minutes on a MacBook M2 due to Docling's usage of MPS accelerators.\\n",\
"2. \*\*Run this notebook on Google Colab.\*\* Converting all documents in the notebook takes ~8 minutes on a Google Colab T4 GPU."\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "4YgT7tpXCUl0"\
},\
"source": \[\
"### Install Docling and Weaviate client\\n",\
"\\n",\
"Note: If Colab prompts you to restart the session after running the cell below, click \\"restart\\" and proceed with running the rest of the notebook."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {\
"collapsed": true,\
"id": "u076oUSF\_YUG"\
},\
"outputs": \[\],\
"source": \[\
"%%capture\\n",\
"%pip install docling~=\\"2.7.0\\"\\n",\
"%pip install -U weaviate-client~=\\"4.9.4\\"\\n",\
"%pip install rich\\n",\
"%pip install torch\\n",\
"\\n",\
"import logging\\n",\
"import warnings\\n",\
"\\n",\
"warnings.filterwarnings(\\"ignore\\")\\n",\
"\\n",\
"# Suppress Weaviate client logs\\n",\
"logging.getLogger(\\"weaviate\\").setLevel(logging.ERROR)"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "2q2F9RUmR8Wj"\
},\
"source": \[\
"## 🐥 Part 1: Docling\\n",\
"\\n",\
"Part of what makes Docling so remarkable is the fact that it can run on commodity hardware. This means that this notebook can be run on a local machine with GPU acceleration. If you're using a MacBook with a silicon chip, Docling integrates seamlessly with Metal Performance Shaders (MPS). MPS provides out-of-the-box GPU acceleration for macOS, seamlessly integrating with PyTorch and TensorFlow, offering energy-efficient performance on Apple Silicon, and broad compatibility with all Metal-supported GPUs.\\n",\
"\\n",\
"The code below checks to see if a GPU is available, either via CUDA or MPS."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 2,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"MPS GPU is enabled.\\n"\
\]\
}\
\],\
"source": \[\
"import torch\\n",\
"\\n",\
"# Check if GPU or MPS is available\\n",\
"if torch.cuda.is\_available():\\n",\
" device = torch.device(\\"cuda\\")\\n",\
" print(f\\"CUDA GPU is enabled: {torch.cuda.get\_device\_name(0)}\\")\\n",\
"elif torch.backends.mps.is\_available():\\n",\
" device = torch.device(\\"mps\\")\\n",\
" print(\\"MPS GPU is enabled.\\")\\n",\
"else:\\n",\
" raise OSError(\\n",\
" \\"No GPU or MPS device found. Please check your environment and ensure GPU or MPS support is configured.\\"\\n",\
" )"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "wHTsy4a8JFPl"\
},\
"source": \[\
"Here, we've collected 10 influential machine learning papers published as PDFs on arXiv. Because Docling does not yet have title extraction for PDFs, we manually add the titles in a corresponding list.\\n",\
"\\n",\
"Note: Converting all 10 papers should take around 8 minutes with a T4 GPU."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 3,\
"metadata": {\
"id": "Vy5SMPiGDMy-"\
},\
"outputs": \[\],\
"source": \[\
"# Influential machine learning papers\\n",\
"source\_urls = \[\\n",\
" \\"https://arxiv.org/pdf/1706.03762\\",\\n",\
" \\"https://arxiv.org/pdf/1810.04805\\",\\n",\
" \\"https://arxiv.org/pdf/1406.2661\\",\\n",\
" \\"https://arxiv.org/pdf/1409.0473\\",\\n",\
" \\"https://arxiv.org/pdf/1412.6980\\",\\n",\
" \\"https://arxiv.org/pdf/1312.6114\\",\\n",\
" \\"https://arxiv.org/pdf/1312.5602\\",\\n",\
" \\"https://arxiv.org/pdf/1512.03385\\",\\n",\
" \\"https://arxiv.org/pdf/1409.3215\\",\\n",\
" \\"https://arxiv.org/pdf/1301.3781\\",\\n",\
"\]\\n",\
"\\n",\
"# And their corresponding titles (because Docling doesn't have title extraction yet!)\\n",\
"source\_titles = \[\\n",\
" \\"Attention Is All You Need\\",\\n",\
" \\"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding\\",\\n",\
" \\"Generative Adversarial Nets\\",\\n",\
" \\"Neural Machine Translation by Jointly Learning to Align and Translate\\",\\n",\
" \\"Adam: A Method for Stochastic Optimization\\",\\n",\
" \\"Auto-Encoding Variational Bayes\\",\\n",\
" \\"Playing Atari with Deep Reinforcement Learning\\",\\n",\
" \\"Deep Residual Learning for Image Recognition\\",\\n",\
" \\"Sequence to Sequence Learning with Neural Networks\\",\\n",\
" \\"A Neural Probabilistic Language Model\\",\\n",\
"\]"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "5fi8wzHrCoLa"\
},\
"source": \[\
"### Convert PDFs to Docling documents\\n",\
"\\n",\
"Here we use Docling's \`.convert\_all()\` to parse a batch of PDFs. The result is a list of Docling documents that we can use for text extraction.\\n",\
"\\n",\
"Note: Please ignore the \`ERR#\` message."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 4,\
"metadata": {\
"colab": {\
"base\_uri": "https://localhost:8080/",\
"height": 67,\
"referenced\_widgets": \[\
"6d049f786a2f4ad7857a6cf2d95b5ba2",\
"db2a7b9f549e4f0fb1ff3fce655d76a2",\
"630967a2db4c4714b4c15d1358a0fcae",\
"b3da9595ab7c4995a00e506e7b5202e3",\
"243ecaf36ee24cafbd1c33d148f2ca78",\
"5b7e22df1b464ca894126736e6f72207",\
"02f6af5993bb4a6a9dbca77952f675d2",\
"dea323b3de0e43118f338842c94ac065",\
"bd198d2c0c4c4933a6e6544908d0d846",\
"febd5c498e4f4f5dbde8dec3cd935502",\
"ab4f282c0d37451092c60e6566e8e945"\
\]\
},\
"id": "Sr44xGR1PNSc",\
"outputId": "b5cca9ee-d7c0-4c8f-c18a-0ac4787984e9"\
},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"Fetching 9 files: 100%|██████████| 9/9 \[00:00<00:00, 84072.91it/s\]\\n"\
\]\
},\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"ERR#: COULD NOT CONVERT TO RS THIS TABLE TO COMPUTE SPANS\\n"\
\]\
}\
\],\
"source": \[\
"from docling.document\_converter import DocumentConverter\\n",\
"\\n",\
"# Instantiate the doc converter\\n",\
"doc\_converter = DocumentConverter()\\n",\
"\\n",\
"# Directly pass list of files or streams to \`convert\_all\`\\n",\
"conv\_results\_iter = doc\_converter.convert\_all(source\_urls) # previously \`convert\`\\n",\
"\\n",\
"# Iterate over the generator to get a list of Docling documents\\n",\
"docs = \[result.document for result in conv\_results\_iter\]"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "xHun\_P-OCtKd"\
},\
"source": \[\
"### Post-process extracted document data\\n",\
"#### Perform hierarchical chunking on documents\\n",\
"\\n",\
"We use Docling's \`HierarchicalChunker()\` to perform hierarchy-aware chunking of our list of documents. This is meant to preserve some of the structure and relationships within the document, which enables more accurate and relevant retrieval in our RAG pipeline."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 5,\
"metadata": {\
"id": "L17ju9xibuIo"\
},\
"outputs": \[\],\
"source": \[\
"from docling\_core.transforms.chunker import HierarchicalChunker\\n",\
"\\n",\
"# Initialize lists for text, and titles\\n",\
"texts, titles = \[\], \[\]\\n",\
"\\n",\
"chunker = HierarchicalChunker()\\n",\
"\\n",\
"# Process each document in the list\\n",\
"for doc, title in zip(docs, source\_titles): # Pair each document with its title\\n",\
" chunks = list(\\n",\
" chunker.chunk(doc)\\n",\
" ) # Perform hierarchical chunking and get text from chunks\\n",\
" for chunk in chunks:\\n",\
" texts.append(chunk.text)\\n",\
" titles.append(title)"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "khbU9R1li2Kj"\
},\
"source": \[\
"Because we're splitting the documents into chunks, we'll concatenate the article title to the beginning of each chunk for additional context."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 6,\
"metadata": {\
"id": "HNwYV9P57OwF"\
},\
"outputs": \[\],\
"source": \[\
"# Concatenate title and text\\n",\
"for i in range(len(texts)):\\n",\
" texts\[i\] = f\\"{titles\[i\]} {texts\[i\]}\\""\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "uhLlCpQODaT3"\
},\
"source": \[\
"## 💚 Part 2: Weaviate\\n",\
"### Create and configure an embedded Weaviate collection"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "ho7xYQTZK5Wk"\
},\
"source": \[\
"We'll be using the OpenAI API for both generating the text embeddings and for the generative model in our RAG pipeline. The code below dynamically fetches your API key based on whether you're running this notebook in Google Colab and running it as a regular Jupyter notebook. All you need to do is replace \`openai\_api\_key\_var\` with the name of your environmental variable name or Colab secret name for the API key.\\n",\
"\\n",\
"If you're running this notebook in Google Colab, make sure you \[add\](https://medium.com/@parthdasawant/how-to-use-secrets-in-google-colab-450c38e3ec75) your API key as a secret."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 7,\
"metadata": {\
"id": "PD53jOT4roj2"\
},\
"outputs": \[\],\
"source": \[\
"# OpenAI API key variable name\\n",\
"openai\_api\_key\_var = \\"OPENAI\_API\_KEY\\" # Replace with the name of your secret/env var\\n",\
"\\n",\
"# Fetch OpenAI API key\\n",\
"try:\\n",\
" # If running in Colab, fetch API key from Secrets\\n",\
" import google.colab\\n",\
" from google.colab import userdata\\n",\
"\\n",\
" openai\_api\_key = userdata.get(openai\_api\_key\_var)\\n",\
" if not openai\_api\_key:\\n",\
" raise ValueError(f\\"Secret '{openai\_api\_key\_var}' not found in Colab secrets.\\")\\n",\
"except ImportError:\\n",\
" # If not running in Colab, fetch API key from environment variable\\n",\
" import os\\n",\
"\\n",\
" openai\_api\_key = os.getenv(openai\_api\_key\_var)\\n",\
" if not openai\_api\_key:\\n",\
" raise OSError(\\n",\
" f\\"Environment variable '{openai\_api\_key\_var}' is not set. \\"\\n",\
" \\"Please define it before running this script.\\"\\n",\
" )"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "8G5jZSh6ti3e"\
},\
"source": \[\
"\[Embedded Weaviate\](https://weaviate.io/developers/weaviate/installation/embedded) allows you to spin up a Weaviate instance directly from your application code, without having to use a Docker container. If you're interested in other deployment methods, like using Docker-Compose or Kubernetes, check out this \[page\](https://weaviate.io/developers/weaviate/installation) in the Weaviate docs."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {\
"colab": {\
"base\_uri": "https://localhost:8080/"\
},\
"id": "hFUBEZiJUMic",\
"outputId": "0b6534c9-66c9-4a47-9754-103bcc030019"\
},\
"outputs": \[\],\
"source": \[\
"import weaviate\\n",\
"\\n",\
"# Connect to Weaviate embedded\\n",\
"client = weaviate.connect\_to\_embedded(headers={\\"X-OpenAI-Api-Key\\": openai\_api\_key})"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {\
"id": "4nu9qM75hrsd"\
},\
"outputs": \[\],\
"source": \[\
"import weaviate.classes.config as wc\\n",\
"\\n",\
"# Define the collection name\\n",\
"collection\_name = \\"docling\\"\\n",\
"\\n",\
"# Delete the collection if it already exists\\n",\
"if client.collections.exists(collection\_name):\\n",\
" client.collections.delete(collection\_name)\\n",\
"\\n",\
"# Create the collection\\n",\
"collection = client.collections.create(\\n",\
" name=collection\_name,\\n",\
" vectorizer\_config=wc.Configure.Vectorizer.text2vec\_openai(\\n",\
" model=\\"text-embedding-3-large\\", # Specify your embedding model here\\n",\
" ),\\n",\
" # Enable generative model from Cohere\\n",\
" generative\_config=wc.Configure.Generative.openai(\\n",\
" model=\\"gpt-4o\\" # Specify your generative model for RAG here\\n",\
" ),\\n",\
" # Define properties of metadata\\n",\
" properties=\[\\n",\
" wc.Property(name=\\"text\\", data\_type=wc.DataType.TEXT),\\n",\
" wc.Property(name=\\"title\\", data\_type=wc.DataType.TEXT, skip\_vectorization=True),\\n",\
" \],\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "RgMcZDB9Dzfs"\
},\
"source": \[\
"### Wrangle data into an acceptable format for Weaviate\\n",\
"\\n",\
"Transform our data from lists to a list of dictionaries for insertion into our Weaviate collection."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 10,\
"metadata": {\
"id": "kttDgwZEsIJQ"\
},\
"outputs": \[\],\
"source": \[\
"# Initialize the data object\\n",\
"data = \[\]\\n",\
"\\n",\
"# Create a dictionary for each row by iterating through the corresponding lists\\n",\
"for text, title in zip(texts, titles):\\n",\
" data\_point = {\\n",\
" \\"text\\": text,\\n",\
" \\"title\\": title,\\n",\
" }\\n",\
" data.append(data\_point)"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "-4amqRaoD5g0"\
},\
"source": \[\
"### Insert data into Weaviate and generate embeddings\\n",\
"\\n",\
"Embeddings will be generated upon insertion to our Weaviate collection."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {\
"colab": {\
"base\_uri": "https://localhost:8080/"\
},\
"id": "g8VCYnhbaxcz",\
"outputId": "cc900e56-9fb6-4d4e-ab18-ebd12b1f4201"\
},\
"outputs": \[\],\
"source": \[\
"# Insert text chunks and metadata into vector DB collection\\n",\
"response = collection.data.insert\_many(data)\\n",\
"\\n",\
"if response.has\_errors:\\n",\
" print(response.errors)\\n",\
"else:\\n",\
" print(\\"Insert complete.\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "KI01PxjuD\_XR"\
},\
"source": \[\
"### Query the data\\n",\
"\\n",\
"Here, we perform a simple similarity search to return the most similar embedded chunks to our search query."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 12,\
"metadata": {\
"colab": {\
"base\_uri": "https://localhost:8080/"\
},\
"id": "zbz6nWJc5CSj",\
"outputId": "16aced21-4496-4c91-cc12-d5c9ac983351"\
},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"{'text': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding A distinctive feature of BERT is its unified architecture across different tasks. There is mini-', 'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}\\n",\
"0.6578550338745117\\n",\
"{'text': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding We introduce a new language representation model called BERT , which stands for B idirectional E ncoder R epresentations from T ransformers. Unlike recent language representation models (Peters et al., 2018a; Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications.', 'title': 'BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding'}\\n",\
"0.6696287989616394\\n"\
\]\
}\
\],\
"source": \[\
"from weaviate.classes.query import MetadataQuery\\n",\
"\\n",\
"response = collection.query.near\_text(\\n",\
" query=\\"bert\\",\\n",\
" limit=2,\\n",\
" return\_metadata=MetadataQuery(distance=True),\\n",\
" return\_properties=\[\\"text\\", \\"title\\"\],\\n",\
")\\n",\
"\\n",\
"for o in response.objects:\\n",\
" print(o.properties)\\n",\
" print(o.metadata.distance)"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "elo32iMnEC18"\
},\
"source": \[\
"### Perform RAG on parsed articles\\n",\
"\\n",\
"Weaviate's \`generate\` module allows you to perform RAG over your embedded data without having to use a separate framework.\\n",\
"\\n",\
"We specify a prompt that includes the field we want to search through in the database (in this case it's \`text\`), a query that includes our search term, and the number of retrieved results to use in the generation."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 13,\
"metadata": {\
"colab": {\
"base\_uri": "https://localhost:8080/",\
"height": 233\
},\
"id": "7r2LMSX9bO4y",\
"outputId": "84639adf-7783-4d43-94d9-711fb313a168"\
},\
"outputs": \[\
{\
"data": {\
"text/html": \[\
"
╭──────────────────────────────────────────────────── Prompt ─────────────────────────────────────────────────────╮\\n",\
"│ Explain how bert works, using only the retrieved context. │\\n",\
"╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"\\u001b\[1;31m╭─\\u001b\[0m\\u001b\[1;31m───────────────────────────────────────────────────\\u001b\[0m\\u001b\[1;31m Prompt \\u001b\[0m\\u001b\[1;31m────────────────────────────────────────────────────\\u001b\[0m\\u001b\[1;31m─╮\\u001b\[0m\\n",\
"\\u001b\[1;31m│\\u001b\[0m Explain how bert works, using only the retrieved context. \\u001b\[1;31m│\\u001b\[0m\\n",\
"\\u001b\[1;31m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\u001b\[0m\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
},\
{\
"data": {\
"text/html": \[\
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╭─────────────────────────────────────────────── Generated Content ───────────────────────────────────────────────╮\\n",\
"│ BERT, which stands for Bidirectional Encoder Representations from Transformers, is a language representation │\\n",\
"│ model designed to pretrain deep bidirectional representations from unlabeled text. It conditions on both left │\\n",\
"│ and right context in all layers, unlike traditional left-to-right or right-to-left language models. This │\\n",\
"│ pre-training involves two unsupervised tasks. The pre-trained BERT model can then be fine-tuned with just one │\\n",\
"│ additional output layer to create state-of-the-art models for various tasks, such as question answering and │\\n",\
"│ language inference, without needing substantial task-specific architecture modifications. A distinctive feature │\\n",\
"│ of BERT is its unified architecture across different tasks. │\\n",\
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\\n"\
\],\
"text/plain": \[\
"\\u001b\[1;32m╭─\\u001b\[0m\\u001b\[1;32m──────────────────────────────────────────────\\u001b\[0m\\u001b\[1;32m Generated Content \\u001b\[0m\\u001b\[1;32m──────────────────────────────────────────────\\u001b\[0m\\u001b\[1;32m─╮\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m BERT, which stands for Bidirectional Encoder Representations from Transformers, is a language representation \\u001b\[1;32m│\\u001b\[0m\\n",\
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"\\u001b\[1;32m│\\u001b\[0m pre-training involves two unsupervised tasks. The pre-trained BERT model can then be fine-tuned with just one \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m additional output layer to create state-of-the-art models for various tasks, such as question answering and \\u001b\[1;32m│\\u001b\[0m\\n",\
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"\\u001b\[1;32m│\\u001b\[0m of BERT is its unified architecture across different tasks. \\u001b\[1;32m│\\u001b\[0m\\n",\
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\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"from rich.console import Console\\n",\
"from rich.panel import Panel\\n",\
"\\n",\
"# Create a prompt where context from the Weaviate collection will be injected\\n",\
"prompt = \\"Explain how {text} works, using only the retrieved context.\\"\\n",\
"query = \\"bert\\"\\n",\
"\\n",\
"response = collection.generate.near\_text(\\n",\
" query=query, limit=3, grouped\_task=prompt, return\_properties=\[\\"text\\", \\"title\\"\]\\n",\
")\\n",\
"\\n",\
"# Prettify the output using Rich\\n",\
"console = Console()\\n",\
"\\n",\
"console.print(\\n",\
" Panel(f\\"{prompt}\\".replace(\\"{text}\\", query), title=\\"Prompt\\", border\_style=\\"bold red\\")\\n",\
")\\n",\
"console.print(\\n",\
" Panel(response.generated, title=\\"Generated Content\\", border\_style=\\"bold green\\")\\n",\
")"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 14,\
"metadata": {\
"colab": {\
"base\_uri": "https://localhost:8080/",\
"height": 233\
},\
"id": "Dtju3oCiDOdD",\
"outputId": "2f0f0cf8-0305-40cc-8409-07036c101938"\
},\
"outputs": \[\
{\
"data": {\
"text/html": \[\
"
╭──────────────────────────────────────────────────── Prompt ─────────────────────────────────────────────────────╮\\n",\
"│ Explain how a generative adversarial net works, using only the retrieved context. │\\n",\
"╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"\\u001b\[1;31m╭─\\u001b\[0m\\u001b\[1;31m───────────────────────────────────────────────────\\u001b\[0m\\u001b\[1;31m Prompt \\u001b\[0m\\u001b\[1;31m────────────────────────────────────────────────────\\u001b\[0m\\u001b\[1;31m─╮\\u001b\[0m\\n",\
"\\u001b\[1;31m│\\u001b\[0m Explain how a generative adversarial net works, using only the retrieved context. \\u001b\[1;31m│\\u001b\[0m\\n",\
"\\u001b\[1;31m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\u001b\[0m\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
},\
{\
"data": {\
"text/html": \[\
"
╭─────────────────────────────────────────────── Generated Content ───────────────────────────────────────────────╮\\n",\
"│ Generative Adversarial Nets (GANs) operate within an adversarial framework where two models are trained │\\n",\
"│ simultaneously: a generative model (G) and a discriminative model (D). The generative model aims to capture the │\\n",\
"│ data distribution and generate samples that mimic real data, while the discriminative model's task is to │\\n",\
"│ distinguish between samples from the real data and those generated by G. This setup is akin to a game where the │\\n",\
"│ generative model acts like counterfeiters trying to produce indistinguishable fake currency, and the │\\n",\
"│ discriminative model acts like the police trying to detect these counterfeits. │\\n",\
"││\\n",\
"│ The training process involves a minimax two-player game where G tries to maximize the probability of D making a │\\n",\
"│ mistake, while D tries to minimize it. When both models are defined by multilayer perceptrons, they can be │\\n",\
"│ trained using backpropagation without the need for Markov chains or approximate inference networks. The │\\n",\
"│ ultimate goal is for G to perfectly replicate the training data distribution, making D's output equal to 1/2 │\\n",\
"│ everywhere, indicating it cannot distinguish between real and generated data. This framework allows for │\\n",\
"│ specific training algorithms and optimization techniques, such as backpropagation and dropout, to be │\\n",\
"│ effectively utilized. │\\n",\
"╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\n",\
"
\\n"\
\],\
"text/plain": \[\
"\\u001b\[1;32m╭─\\u001b\[0m\\u001b\[1;32m──────────────────────────────────────────────\\u001b\[0m\\u001b\[1;32m Generated Content \\u001b\[0m\\u001b\[1;32m──────────────────────────────────────────────\\u001b\[0m\\u001b\[1;32m─╮\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m Generative Adversarial Nets (GANs) operate within an adversarial framework where two models are trained \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m simultaneously: a generative model (G) and a discriminative model (D). The generative model aims to capture the \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m data distribution and generate samples that mimic real data, while the discriminative model's task is to \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m distinguish between samples from the real data and those generated by G. This setup is akin to a game where the \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m generative model acts like counterfeiters trying to produce indistinguishable fake currency, and the \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m discriminative model acts like the police trying to detect these counterfeits. \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m The training process involves a minimax two-player game where G tries to maximize the probability of D making a \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m mistake, while D tries to minimize it. When both models are defined by multilayer perceptrons, they can be \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m trained using backpropagation without the need for Markov chains or approximate inference networks. The \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m ultimate goal is for G to perfectly replicate the training data distribution, making D's output equal to 1/2 \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m everywhere, indicating it cannot distinguish between real and generated data. This framework allows for \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m specific training algorithms and optimization techniques, such as backpropagation and dropout, to be \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m│\\u001b\[0m effectively utilized. \\u001b\[1;32m│\\u001b\[0m\\n",\
"\\u001b\[1;32m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\\u001b\[0m\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"# Create a prompt where context from the Weaviate collection will be injected\\n",\
"prompt = \\"Explain how {text} works, using only the retrieved context.\\"\\n",\
"query = \\"a generative adversarial net\\"\\n",\
"\\n",\
"response = collection.generate.near\_text(\\n",\
" query=query, limit=3, grouped\_task=prompt, return\_properties=\[\\"text\\", \\"title\\"\]\\n",\
")\\n",\
"\\n",\
"# Prettify the output using Rich\\n",\
"console = Console()\\n",\
"\\n",\
"console.print(\\n",\
" Panel(f\\"{prompt}\\".replace(\\"{text}\\", query), title=\\"Prompt\\", border\_style=\\"bold red\\")\\n",\
")\\n",\
"console.print(\\n",\
" Panel(response.generated, title=\\"Generated Content\\", border\_style=\\"bold green\\")\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {\
"id": "7tGz49nfUegG"\
},\
"source": \[\
"We can see that our RAG pipeline performs relatively well for simple queries, especially given the small size of the dataset. Scaling this method for converting a larger sample of PDFs would require more compute (GPUs) and a more advanced deployment of Weaviate (like Docker, Kubernetes, or Weaviate Cloud). For more information on available Weaviate configurations, check out the \[documentation\](https://weaviate.io/developers/weaviate/starter-guides/which-weaviate)."\
\]\
}\
\],\
"metadata": {\
"accelerator": "GPU",\
"colab": {\
"gpuType": "T4",\
"provenance": \[\]\
},\
"kernelspec": {\
"display\_name": ".venv",\
"language": "python",\
"name": "python3"\
},\
"language\_info": {\
"codemirror\_mode": {\
"name": "ipython",\
"version": 3\
},\
"file\_extension": ".py",\
"mimetype": "text/x-python",\
"name": "python",\
"nbconvert\_exporter": "python",\
"pygments\_lexer": "ipython3",\
"version": "3.12.7"\
}\
},\
"nbformat": 4,\
"nbformat\_minor": 0\
}
---
# Unknown
{
"cells": \[\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"# Line-Based Token Chunking\\n",\
"## Overview\\n",\
"The \`LineBasedTokenChunker\` is a tokenization-aware chunker that preserves line boundaries. It's particularly useful for structured content like tables, code, or logs where line boundaries are semantically important.\\n",\
"\\n",\
"Key features:\\n",\
"- \*\*Line preservation\*\*: Keeps entire lines within a single chunk when possible\\n",\
"- \*\*Prefix support\*\*: Add repeated context (e.g., table headers) to each chunk\\n",\
"- \*\*Overflow handling\*\*: Choose between splitting lines or omitting prefix when lines are too long"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Setup"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 1,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Note: you may need to restart the kernel to use updated packages.\\n"\
\]\
}\
\],\
"source": \[\
"%pip install -qU pip docling transformers"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 2,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"from docling\_core.transforms.chunker.line\_chunker import LineBasedTokenChunker\\n",\
"from docling\_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer\\n",\
"from transformers import AutoTokenizer"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Example 1: Basic Table Chunking with Prefix\\n",\
"\\n",\
"In this example, we'll chunk a table while repeating the header in each chunk."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 3,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Max tokens: 50\\n",\
"Prefix token count: 34\\n",\
"\\n",\
"Total chunks: 3\\n",\
"\\n",\
"=== Chunk 1 ===\\n",\
"| Name | Age | Department |\\n",\
"|------|-----|------------|\\n",\
"| Alice | 30 | Engineering |\\n",\
"| Bob | 25 | Marketing |\\n",\
"\\n",\
"Tokens: 48\\n",\
"\\n",\
"=== Chunk 2 ===\\n",\
"| Name | Age | Department |\\n",\
"|------|-----|------------|\\n",\
"| Charlie | 35 | Sales |\\n",\
"| Diana | 28 | HR |\\n",\
"\\n",\
"Tokens: 48\\n",\
"\\n",\
"=== Chunk 3 ===\\n",\
"| Name | Age | Department |\\n",\
"|------|-----|------------|\\n",\
"| Eve | 32 | Finance |\\n",\
"\\n",\
"Tokens: 41\\n",\
"\\n"\
\]\
}\
\],\
"source": \[\
"# Setup tokenizer with a reasonable token limit\\n",\
"tokenizer = HuggingFaceTokenizer(\\n",\
" tokenizer=AutoTokenizer.from\_pretrained(\\"sentence-transformers/all-MiniLM-L6-v2\\"),\\n",\
" max\_tokens=50, # Small limit to demonstrate chunking\\n",\
")\\n",\
"\\n",\
"# Create chunker with table header prefix\\n",\
"chunker = LineBasedTokenChunker(\\n",\
" tokenizer=tokenizer,\\n",\
" prefix=\\"| Name | Age | Department |\\\\n|------|-----|------------|\\\\n\\",\\n",\
" omit\_prefix\_on\_overflow=False, # Always include prefix (default)\\n",\
")\\n",\
"\\n",\
"# Sample table rows\\n",\
"lines = \[\\n",\
" \\"| Alice | 30 | Engineering |\\\\n\\",\\n",\
" \\"| Bob | 25 | Marketing |\\\\n\\",\\n",\
" \\"| Charlie | 35 | Sales |\\\\n\\",\\n",\
" \\"| Diana | 28 | HR |\\\\n\\",\\n",\
" \\"| Eve | 32 | Finance |\\\\n\\",\\n",\
"\]\\n",\
"\\n",\
"print(f\\"Max tokens: {chunker.max\_tokens}\\")\\n",\
"print(f\\"Prefix token count: {chunker.prefix\_len}\\\\n\\")\\n",\
"\\n",\
"chunks = chunker.chunk\_text(lines)\\n",\
"\\n",\
"print(f\\"Total chunks: {len(chunks)}\\\\n\\")\\n",\
"for i, chunk in enumerate(chunks, 1):\\n",\
" print(f\\"=== Chunk {i} ===\\")\\n",\
" print(chunk)\\n",\
" print(f\\"Tokens: {tokenizer.count\_tokens(chunk)}\\\\n\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Example 2: Handling Wide Tables with \`omit\_prefix\_on\_overflow\`\\n",\
"\\n",\
"When working with wide tables, some rows might fit without the header but not with it. The \`omit\_prefix\_on\_overflow\` parameter provides flexibility in these cases."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 4,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Prefix token count: 47\\n",\
"Max tokens: 30\\n",\
"\\n",\
"Token counts:\\n",\
" Line 1: 11 tokens (with prefix: 58 tokens)\\n",\
" Line 2: 11 tokens (with prefix: 58 tokens)\\n",\
" Line 3: 17 tokens (with prefix: 64 tokens)\\n",\
"\\n"\
\]\
}\
\],\
"source": \[\
"# Setup tokenizer with a very small token limit\\n",\
"tokenizer = HuggingFaceTokenizer(\\n",\
" tokenizer=AutoTokenizer.from\_pretrained(\\"sentence-transformers/all-MiniLM-L6-v2\\"),\\n",\
" max\_tokens=30, # Very small limit to force overflow\\n",\
")\\n",\
"\\n",\
"# Create chunker with a longer prefix\\n",\
"prefix = (\\n",\
" \\"| Name | Age | Department | Location |\\\\n|------|-----|------------|----------|\\\\n\\"\\n",\
")\\n",\
"\\n",\
"print(f\\"Prefix token count: {tokenizer.count\_tokens(prefix)}\\")\\n",\
"print(f\\"Max tokens: {tokenizer.get\_max\_tokens()}\\\\n\\")\\n",\
"\\n",\
"# Sample lines - some will be too long with prefix\\n",\
"lines = \[\\n",\
" \\"| Alice Johnson | 30 | Engineering | San Francisco |\\\\n\\",\\n",\
" \\"| Bob Smith | 25 | Marketing | New York |\\\\n\\",\\n",\
" \\"| Charlie Brown with a very long name | 35 | Sales Department | Los Angeles |\\\\n\\",\\n",\
"\]\\n",\
"\\n",\
"# Check token counts for each line\\n",\
"print(\\"Token counts:\\")\\n",\
"for i, line in enumerate(lines, 1):\\n",\
" line\_tokens = tokenizer.count\_tokens(line)\\n",\
" with\_prefix = line\_tokens + tokenizer.count\_tokens(prefix)\\n",\
" print(f\\" Line {i}: {line\_tokens} tokens (with prefix: {with\_prefix} tokens)\\")\\n",\
"print()"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"### Without \`omit\_prefix\_on\_overflow\` (default behavior)"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 5,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"============================================================\\n",\
"WITHOUT omit\_prefix\_on\_overflow (may split long lines)\\n",\
"============================================================\\n",\
"\\n",\
"Total chunks: 5\\n",\
"\\n",\
"--- Chunk 1 ---\\n",\
"\\n",\
"| Name | Age | Department | Location\\n",\
"Tokens: 8\\n",\
"Has prefix: False\\n",\
"\\n",\
"--- Chunk 2 ---\\n",\
"\\n",\
" |\\n",\
"|------|-----|------------|--\\n",\
"Tokens: 30\\n",\
"Has prefix: False\\n",\
"\\n",\
"--- Chunk 3 ---\\n",\
"--------|\\n",\
"\\n",\
"Tokens: 9\\n",\
"Has prefix: False\\n",\
"\\n",\
"--- Chunk 4 ---\\n",\
"| Alice Johnson | 30 | Engineering | San Francisco |\\n",\
"| Bob Smith | 25 | Marketing | New York |\\n",\
"\\n",\
"Tokens: 22\\n",\
"Has prefix: False\\n",\
"\\n",\
"--- Chunk 5 ---\\n",\
"| Charlie Brown with a very long name | 35 | Sales Department | Los Angeles |\\n",\
"\\n",\
"Tokens: 17\\n",\
"Has prefix: False\\n",\
"\\n"\
\]\
},\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"/Users/anish/Desktop/Programs/docling/.venv/lib/python3.12/site-packages/docling\_core/transforms/chunker/line\_chunker.py:83: UserWarning: Chunks prefix is too long (47 tokens) for chunk size 30. It will be split into multiple chunks and only included in the first chunk(s). Consider increasing max\_tokens to accommodate the full prefix in each chunk.\\n",\
" warnings.warn(\\n"\
\]\
}\
\],\
"source": \[\
"chunker\_no\_omit = LineBasedTokenChunker(\\n",\
" tokenizer=tokenizer,\\n",\
" prefix=prefix,\\n",\
" omit\_prefix\_on\_overflow=False, # Default: always include prefix\\n",\
")\\n",\
"\\n",\
"chunks\_no\_omit = chunker\_no\_omit.chunk\_text(lines)\\n",\
"\\n",\
"print(\\"=\\" \* 60)\\n",\
"print(\\"WITHOUT omit\_prefix\_on\_overflow (may split long lines)\\")\\n",\
"print(\\"=\\" \* 60)\\n",\
"print(f\\"\\\\nTotal chunks: {len(chunks\_no\_omit)}\\\\n\\")\\n",\
"\\n",\
"for i, chunk in enumerate(chunks\_no\_omit, 1):\\n",\
" print(f\\"--- Chunk {i} ---\\")\\n",\
" print(chunk)\\n",\
" print(f\\"Tokens: {tokenizer.count\_tokens(chunk)}\\")\\n",\
" print(f\\"Has prefix: {chunk.startswith(prefix)}\\\\n\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"### With \`omit\_prefix\_on\_overflow=True\`"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 6,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"============================================================\\n",\
"WITH omit\_prefix\_on\_overflow (keeps lines intact)\\n",\
"============================================================\\n",\
"\\n",\
"Total chunks: 5\\n",\
"\\n",\
"--- Chunk 1 ---\\n",\
"\\n",\
"| Name | Age | Department | Location\\n",\
"Tokens: 8\\n",\
"Has prefix: False\\n",\
"\\n",\
"--- Chunk 2 ---\\n",\
"\\n",\
" |\\n",\
"|------|-----|------------|--\\n",\
"Tokens: 30\\n",\
"Has prefix: False\\n",\
"\\n",\
"--- Chunk 3 ---\\n",\
"--------|\\n",\
"\\n",\
"Tokens: 9\\n",\
"Has prefix: False\\n",\
"\\n",\
"--- Chunk 4 ---\\n",\
"| Alice Johnson | 30 | Engineering | San Francisco |\\n",\
"| Bob Smith | 25 | Marketing | New York |\\n",\
"\\n",\
"Tokens: 22\\n",\
"Has prefix: False\\n",\
"\\n",\
"--- Chunk 5 ---\\n",\
"| Charlie Brown with a very long name | 35 | Sales Department | Los Angeles |\\n",\
"\\n",\
"Tokens: 17\\n",\
"Has prefix: False\\n",\
"\\n"\
\]\
}\
\],\
"source": \[\
"chunker\_with\_omit = LineBasedTokenChunker(\\n",\
" tokenizer=tokenizer,\\n",\
" prefix=prefix,\\n",\
" omit\_prefix\_on\_overflow=True, # Omit prefix for lines that would overflow\\n",\
")\\n",\
"\\n",\
"chunks\_with\_omit = chunker\_with\_omit.chunk\_text(lines)\\n",\
"\\n",\
"print(\\"=\\" \* 60)\\n",\
"print(\\"WITH omit\_prefix\_on\_overflow (keeps lines intact)\\")\\n",\
"print(\\"=\\" \* 60)\\n",\
"print(f\\"\\\\nTotal chunks: {len(chunks\_with\_omit)}\\\\n\\")\\n",\
"\\n",\
"for i, chunk in enumerate(chunks\_with\_omit, 1):\\n",\
" print(f\\"--- Chunk {i} ---\\")\\n",\
" print(chunk)\\n",\
" print(f\\"Tokens: {tokenizer.count\_tokens(chunk)}\\")\\n",\
" print(f\\"Has prefix: {chunk.startswith(prefix)}\\\\n\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Example 3: Chunking a DoclingDocument\\n",\
"\\n",\
"The \`LineBasedTokenChunker\` can also be used directly with \`DoclingDocument\` objects."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 7,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Total chunks: 11\\n",\
"\\n",\
"=== Chunk 1 ===\\n",\
"Text: # IBM\\n",\
"\\n",\
"International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and present in over ...\\n",\
"Tokens: 57\\n",\
"Doc items: 12\\n",\
"\\n",\
"=== Chunk 2 ===\\n",\
"Text: IBM is the largest industrial research organization in the world, with 19 research facilities across a dozen countries, having held the record for most annual U.S. patents generated by a business for ...\\n",\
"Tokens: 99\\n",\
"Doc items: 12\\n",\
"\\n",\
"=== Chunk 3 ===\\n",\
"Text: systems. During the 1960s and 1970s, the IBM mainframe, exemplified by the System/360, was the world's dominant computing platform, with the company producing 80 percent of computers in the U.S. and ...\\n",\
"Tokens: 100\\n",\
"Doc items: 12\\n",\
"\\n"\
\]\
}\
\],\
"source": \[\
"from docling.document\_converter import DocumentConverter\\n",\
"\\n",\
"# Convert a document\\n",\
"converter = DocumentConverter()\\n",\
"result = converter.convert(\\"../../tests/data/md/sources/wiki.md\\")\\n",\
"doc = result.document\\n",\
"\\n",\
"# Create chunker\\n",\
"tokenizer = HuggingFaceTokenizer(\\n",\
" tokenizer=AutoTokenizer.from\_pretrained(\\"sentence-transformers/all-MiniLM-L6-v2\\"),\\n",\
" max\_tokens=100,\\n",\
")\\n",\
"\\n",\
"chunker = LineBasedTokenChunker(\\n",\
" tokenizer=tokenizer,\\n",\
" prefix=\\"\\", # No prefix for general documents\\n",\
")\\n",\
"\\n",\
"# Chunk the document\\n",\
"chunks = list(chunker.chunk(doc))\\n",\
"\\n",\
"print(f\\"Total chunks: {len(chunks)}\\\\n\\")\\n",\
"\\n",\
"# Display first few chunks\\n",\
"for i, chunk in enumerate(chunks\[:3\], 1):\\n",\
" print(f\\"=== Chunk {i} ===\\")\\n",\
" print(\\n",\
" f\\"Text: {chunk.text\[:200\]}...\\"\\n",\
" if len(chunk.text) > 200\\n",\
" else f\\"Text: {chunk.text}\\"\\n",\
" )\\n",\
" print(f\\"Tokens: {tokenizer.count\_tokens(chunk.text)}\\")\\n",\
" print(f\\"Doc items: {len(chunk.meta.doc\_items)}\\\\n\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Example 4: Handling Large Prefixes\\n",\
"\\n",\
"When a prefix exceeds the \`max\_tokens\` limit, it's automatically split into multiple chunks and only included at the beginning."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 8,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Large prefix token count: 130 tokens\\n",\
"Max tokens allowed: 25 tokens\\n",\
"\\n",\
"⚠️ Warning issued:\\n",\
" Chunks prefix is too long (130 tokens) for chunk size 25. It will be split into multiple chunks and only included in the first chunk(s). Consider increasing max\_tokens to accommodate the full prefix in each chunk.\\n",\
"\\n",\
"Number of prefix chunks: 6\\n",\
"Prefix len (for single chunk): 0\\n",\
"\\n",\
"Prefix chunks:\\n",\
" Chunk 1: 25 tokens\\n",\
" Content: \\n",\
"This is a very long table header that contains a lot of information This is a very long table heade...\\n",\
"\\n",\
" Chunk 2: 25 tokens\\n",\
" Content: \\n",\
" information This is a very long table header that contains a lot of information This is a very lon...\\n",\
"\\n",\
" Chunk 3: 25 tokens\\n",\
" Content: \\n",\
" of information This is a very long table header that contains a lot of information This is a very ...\\n",\
"\\n",\
" Chunk 4: 24 tokens\\n",\
" Content: \\n",\
" lot of information This is a very long table header that contains a lot of information This is a v...\\n",\
"\\n",\
" Chunk 5: 24 tokens\\n",\
" Content: \\n",\
" contains a lot of information This is a very long table header that contains a lot of information ...\\n",\
"\\n",\
" Chunk 6: 7 tokens\\n",\
" Content: header that contains a lot of information \\n",\
"\\n",\
"Total chunks (including prefix chunks): 7\\n",\
"Content chunks: 1\\n",\
"\\n",\
"Chunk 1 \[PREFIX CHUNK\]:\\n",\
" Content: \\n",\
"This is a very long table header that contains a lot of information This is a very long table heade...\\n",\
" Tokens: 25\\n",\
"\\n",\
"Chunk 2 \[PREFIX CHUNK\]:\\n",\
" Content: \\n",\
" information This is a very long table header that contains a lot of information This is a very lon...\\n",\
" Tokens: 25\\n",\
"\\n",\
"Chunk 3 \[PREFIX CHUNK\]:\\n",\
" Content: \\n",\
" of information This is a very long table header that contains a lot of information This is a very ...\\n",\
" Tokens: 25\\n",\
"\\n",\
"Chunk 4 \[PREFIX CHUNK\]:\\n",\
" Content: \\n",\
" lot of information This is a very long table header that contains a lot of information This is a v...\\n",\
" Tokens: 24\\n",\
"\\n",\
"Chunk 5 \[PREFIX CHUNK\]:\\n",\
" Content: \\n",\
" contains a lot of information This is a very long table header that contains a lot of information ...\\n",\
" Tokens: 24\\n",\
"\\n",\
"Chunk 6 \[PREFIX CHUNK\]:\\n",\
" Content: header that contains a lot of information \\n",\
" Tokens: 7\\n",\
"\\n",\
"Chunk 7 \[CONTENT CHUNK\]:\\n",\
" Content: Row 1: Some data here\\n",\
"Row 2: More data here\\n",\
"Row 3: Even more data\\n",\
"\\n",\
" Tokens: 18\\n",\
"\\n"\
\]\
}\
\],\
"source": \[\
"import warnings\\n",\
"\\n",\
"# Create a very long prefix that exceeds max\_tokens\\n",\
"tokenizer = HuggingFaceTokenizer(\\n",\
" tokenizer=AutoTokenizer.from\_pretrained(\\"sentence-transformers/all-MiniLM-L6-v2\\"),\\n",\
" max\_tokens=25, # Small limit\\n",\
")\\n",\
"\\n",\
"large\_prefix = (\\n",\
" \\"This is a very long table header that contains a lot of information \\" \* 10\\n",\
")\\n",\
"\\n",\
"print(f\\"Large prefix token count: {tokenizer.count\_tokens(large\_prefix)} tokens\\")\\n",\
"print(f\\"Max tokens allowed: {tokenizer.get\_max\_tokens()} tokens\\\\n\\")\\n",\
"\\n",\
"# Create chunker with large prefix - will trigger warning\\n",\
"with warnings.catch\_warnings(record=True) as w:\\n",\
" warnings.simplefilter(\\"always\\")\\n",\
"\\n",\
" chunker\_large = LineBasedTokenChunker(\\n",\
" tokenizer=tokenizer,\\n",\
" prefix=large\_prefix,\\n",\
" )\\n",\
"\\n",\
" if w:\\n",\
" print(\\"⚠️ Warning issued:\\")\\n",\
" print(f\\" {w\[0\].message}\\\\n\\")\\n",\
"\\n",\
"print(f\\"Number of prefix chunks: {len(chunker\_large.prefix\_chunks)}\\")\\n",\
"print(f\\"Prefix len (for single chunk): {chunker\_large.prefix\_len}\\\\n\\")\\n",\
"\\n",\
"# Show the prefix chunks\\n",\
"print(\\"Prefix chunks:\\")\\n",\
"for i, prefix\_chunk in enumerate(chunker\_large.prefix\_chunks, 1):\\n",\
" preview = prefix\_chunk\[:100\] + \\"...\\" if len(prefix\_chunk) > 100 else prefix\_chunk\\n",\
" print(f\\" Chunk {i}: {tokenizer.count\_tokens(prefix\_chunk)} tokens\\")\\n",\
" print(f\\" Content: {preview}\\\\n\\")\\n",\
"\\n",\
"# Test chunking with the large prefix\\n",\
"lines = \[\\n",\
" \\"Row 1: Some data here\\\\n\\",\\n",\
" \\"Row 2: More data here\\\\n\\",\\n",\
" \\"Row 3: Even more data\\\\n\\",\\n",\
"\]\\n",\
"\\n",\
"chunks\_large = chunker\_large.chunk\_text(lines)\\n",\
"\\n",\
"print(f\\"Total chunks (including prefix chunks): {len(chunks\_large)}\\")\\n",\
"print(f\\"Content chunks: {len(chunks\_large) - len(chunker\_large.prefix\_chunks)}\\\\n\\")\\n",\
"\\n",\
"# Display all chunks\\n",\
"for i, chunk in enumerate(chunks\_large, 1):\\n",\
" is\_prefix\_chunk = i <= len(chunker\_large.prefix\_chunks)\\n",\
" chunk\_type = \\"\[PREFIX CHUNK\]\\" if is\_prefix\_chunk else \\"\[CONTENT CHUNK\]\\"\\n",\
"\\n",\
" print(f\\"Chunk {i} {chunk\_type}:\\")\\n",\
" preview = chunk\[:100\] + \\"...\\" if len(chunk) > 100 else chunk\\n",\
" print(f\\" Content: {preview}\\")\\n",\
" print(f\\" Tokens: {tokenizer.count\_tokens(chunk)}\\\\n\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Summary\\n",\
"\\n",\
"### When to use \`LineBasedTokenChunker\`\\n",\
"\\n",\
"- You need to preserve line boundaries (tables, code, logs)\\n",\
"- You want to add context (headers, metadata) to each chunk\\n",\
"- You're working with structured text where lines have semantic meaning\\n",\
"- You need fine-grained control over how lines are split\\n",\
"\\n",\
"### When to use \`omit\_prefix\_on\_overflow=True\`\\n",\
"\\n",\
"- Working with wide tables or long prefixes\\n",\
"- Token budget is limited\\n",\
"- Line integrity is more important than consistent formatting\\n",\
"- You can handle chunks without the prefix in downstream processing\\n",\
"\\n",\
"### When to use \`omit\_prefix\_on\_overflow=False\` (default)\\n",\
"\\n",\
"- You need the prefix in every chunk for context\\n",\
"- Consistent formatting is critical\\n",\
"- Downstream processing requires the prefix to understand the content\\n",\
"- Working with narrow content where the prefix doesn't cause overflow"\
\]\
}\
\],
"metadata": {
"kernelspec": {
"display\_name": ".venv",
"language": "python",
"name": "python3"
},
"language\_info": {
"codemirror\_mode": {
"name": "ipython",
"version": 3
},
"file\_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert\_exporter": "python",
"pygments\_lexer": "ipython3",
"version": "3.12.11"
}
},
"nbformat": 4,
"nbformat\_minor": 4
}
---
# Unknown
{
"cells": \[\
{\
"cell\_type": "markdown",\
"id": "15674164",\
"metadata": {},\
"source": \[\
"# Information extraction"\
\]\
},\
{\
"cell\_type": "markdown",\
"id": "932f12cd",\
"metadata": {},\
"source": \[\
"Docling provides the capability of extracting information, i.e. structured data, from unstructured documents.\\n",\
"\\n",\
"The user can provide the desired data schema AKA \*template\*, either as a dictionary or as a Pydantic model, and Docling will return\\n",\
"the extracted data as a standardized output, organized by page.\\n",\
"\\n",\
"Check out the subsections below for different usage scenarios."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"id": "f52005fe",\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"%pip install -q docling\[vlm\] # Install the Docling package with VLM support"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 1,\
"id": "f97abf2e",\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"from IPython import display\\n",\
"from pydantic import BaseModel, Field\\n",\
"from rich import print"\
\]\
},\
{\
"cell\_type": "markdown",\
"id": "cda07006",\
"metadata": {},\
"source": \[\
"In this notebook, we will work with an example input image — let's quickly inspect it:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 2,\
"id": "15846b44",\
"metadata": {},\
"outputs": \[\
{\
"data": {\
"text/html": \[\
""\
\],\
"text/plain": \[\
""\
\]\
},\
"execution\_count": 2,\
"metadata": {},\
"output\_type": "execute\_result"\
}\
\],\
"source": \[\
"file\_path = (\\n",\
" \\"https://upload.wikimedia.org/wikipedia/commons/9/9f/Swiss\_QR-Bill\_example.jpg\\"\\n",\
")\\n",\
"display.HTML(f\\"\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"id": "dbbc173c",\
"metadata": {},\
"source": \[\
"## Defining the extractor"\
\]\
},\
{\
"cell\_type": "markdown",\
"id": "e9871c8d",\
"metadata": {},\
"source": \[\
"Let's first define our extractor:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 3,\
"id": "7a8c6ff0",\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"from docling.datamodel.base\_models import InputFormat\\n",\
"from docling.document\_extractor import DocumentExtractor\\n",\
"\\n",\
"extractor = DocumentExtractor(allowed\_formats=\[InputFormat.IMAGE, InputFormat.PDF\])"\
\]\
},\
{\
"cell\_type": "markdown",\
"id": "e2b1933e",\
"metadata": {},\
"source": \[\
"Following, we look at different ways to define the data template."\
\]\
},\
{\
"cell\_type": "markdown",\
"id": "20e62dfd",\
"metadata": {},\
"source": \[\
"## Using a string template"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 4,\
"id": "4c5119b0",\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"Only PDF and image formats are supported.\\n",\
" return next(all\_res)\\n",\
"You have video processor config saved in \`preprocessor.json\` file which is deprecated. Video processor configs should be saved in their own \`video\_preprocessor.json\` file. You can rename the file or load and save the processor back which renames it automatically. Loading from \`preprocessor.json\` will be removed in v5.0.\\n",\
"The following generation flags are not valid and may be ignored: \['temperature', 'top\_p', 'top\_k'\]. Set \`TRANSFORMERS\_VERBOSITY=info\` for more details.\\n"\
\]\
},\
{\
"data": {\
"text/html": \[\
"
\\n"\
\],\
"text/plain": \[\
"\\u001b\[1m\[\\u001b\[0m\\n",\
" \\u001b\[1;35mExtractedPageData\\u001b\[0m\\u001b\[1m(\\u001b\[0m\\n",\
" \\u001b\[33mpage\_no\\u001b\[0m=\\u001b\[1;36m1\\u001b\[0m,\\n",\
" \\u001b\[33mextracted\_data\\u001b\[0m=\\u001b\[1m{\\u001b\[0m\\u001b\[32m'bill\_no'\\u001b\[0m: \\u001b\[32m'3139'\\u001b\[0m, \\u001b\[32m'total'\\u001b\[0m: \\u001b\[1;36m3949.75\\u001b\[0m, \\u001b\[32m'tax\_id'\\u001b\[0m: \\u001b\[3;35mNone\\u001b\[0m\\u001b\[1m}\\u001b\[0m,\\n",\
" \\u001b\[33mraw\_text\\u001b\[0m=\\u001b\[32m'\\u001b\[0m\\u001b\[32m{\\u001b\[0m\\u001b\[32m\\"bill\_no\\": \\"3139\\", \\"total\\": 3949.75, \\"tax\_id\\": null\\u001b\[0m\\u001b\[32m}\\u001b\[0m\\u001b\[32m'\\u001b\[0m,\\n",\
" \\u001b\[33merrors\\u001b\[0m=\\u001b\[1m\[\\u001b\[0m\\u001b\[1m\]\\u001b\[0m\\n",\
" \\u001b\[1m)\\u001b\[0m\\n",\
"\\u001b\[1m\]\\u001b\[0m\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"result = extractor.extract(\\n",\
" source=file\_path,\\n",\
" template=Invoice,\\n",\
")\\n",\
"print(result.pages)"\
\]\
},\
{\
"cell\_type": "markdown",\
"id": "2bd8736b",\
"metadata": {},\
"source": \[\
"Alternatively, a Pydantic model instance can be passed as a template instead, allowing to override the default values.\\n",\
"\\n",\
"This can be very useful in scenarios where we happen to have available context that is more relevant than the\\n",\
"default values predefined in the model definition.\\n",\
"\\n",\
"E.g. in the example below:\\n",\
"- \`bill\_no\` and \`total\` are actually set from the value extracted from the data,\\n",\
"- there was no \`tax\_id\` to be extracted, so the updated default we provided was applied"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 8,\
"id": "b531a20d",\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"The following generation flags are not valid and may be ignored: \['temperature', 'top\_p', 'top\_k'\]. Set \`TRANSFORMERS\_VERBOSITY=info\` for more details.\\n"\
\]\
},\
{\
"data": {\
"text/html": \[\
"
Invoice #3139 was sent by Robert Schneider to Pia Rutschmann at Rue du Lac 1268.\\n",\
"
\\n"\
\],\
"text/plain": \[\
"Invoice #\\u001b\[1;36m3139\\u001b\[0m was sent by Robert Schneider to Pia Rutschmann at Rue du Lac \\u001b\[1;36m1268\\u001b\[0m.\\n"\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"print(\\n",\
" f\\"Invoice #{invoice.bill\_no} was sent by {invoice.sender.name} \\"\\n",\
" f\\"to {invoice.receiver.name} at {invoice.sender.address}.\\"\\n",\
")"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"id": "6c1dbe41",\
"metadata": {},\
"outputs": \[\],\
"source": \[\]\
}\
\],\
"metadata": {\
"kernelspec": {\
"display\_name": ".venv",\
"language": "python",\
"name": "python3"\
},\
"language\_info": {\
"codemirror\_mode": {\
"name": "ipython",\
"version": 3\
},\
"file\_extension": ".py",\
"mimetype": "text/x-python",\
"name": "python",\
"nbconvert\_exporter": "python",\
"pygments\_lexer": "ipython3",\
"version": "3.12.11"\
}\
},\
"nbformat": 4,\
"nbformat\_minor": 5\
}
---
# Unknown
{
"cells": \[\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
""\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"# RAG with Haystack"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"| Step | Tech | Execution | \\n",\
"| --- | --- | --- |\\n",\
"| Embedding | Hugging Face / Sentence Transformers | 💻 Local |\\n",\
"| Vector store | Milvus | 💻 Local |\\n",\
"| Gen AI | Hugging Face Inference API | 🌐 Remote | "\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Overview"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"This example leverages the\\n",\
"\[Haystack Docling extension\](../../integrations/haystack/), along with\\n",\
"Milvus-based document store and retriever instances, as well as sentence-transformers\\n",\
"embeddings.\\n",\
"\\n",\
"The presented \`DoclingConverter\` component enables you to:\\n",\
"- use various document types in your LLM applications with ease and speed, and\\n",\
"- leverage Docling's rich format for advanced, document-native grounding.\\n",\
"\\n",\
"\`DoclingConverter\` supports two different export modes:\\n",\
"- \`ExportType.MARKDOWN\`: if you want to capture each input document as a separate\\n",\
" Haystack document, or\\n",\
"- \`ExportType.DOC\_CHUNKS\` (default): if you want to have each input document chunked and\\n",\
" to then capture each individual chunk as a separate Haystack document downstream.\\n",\
"\\n",\
"The example allows to explore both modes via parameter \`EXPORT\_TYPE\`; depending on the\\n",\
"value set, the ingestion and RAG pipelines are then set up accordingly."\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Setup"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"- 👉 For best conversion speed, use GPU acceleration whenever available; e.g. if running on Colab, use GPU-enabled runtime.\\n",\
"- Notebook uses HuggingFace's Inference API; for increased LLM quota, token can be provided via env var \`HF\_TOKEN\`.\\n",\
"- Requirements can be installed as shown below (\`--no-warn-conflicts\` meant for Colab's pre-populated Python env; feel free to remove for stricter usage):"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 1,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Note: you may need to restart the kernel to use updated packages.\\n"\
\]\
}\
\],\
"source": \[\
"%pip install -q --progress-bar off --no-warn-conflicts docling-haystack haystack-ai docling \\"pymilvus\[milvus-lite\]\\" milvus-haystack sentence-transformers python-dotenv"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 2,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"import os\\n",\
"from pathlib import Path\\n",\
"from tempfile import mkdtemp\\n",\
"\\n",\
"from docling\_haystack.converter import ExportType\\n",\
"from dotenv import load\_dotenv\\n",\
"\\n",\
"\\n",\
"def \_get\_env\_from\_colab\_or\_os(key):\\n",\
" try:\\n",\
" from google.colab import userdata\\n",\
"\\n",\
" try:\\n",\
" return userdata.get(key)\\n",\
" except userdata.SecretNotFoundError:\\n",\
" pass\\n",\
" except ImportError:\\n",\
" pass\\n",\
" return os.getenv(key)\\n",\
"\\n",\
"\\n",\
"load\_dotenv()\\n",\
"HF\_TOKEN = \_get\_env\_from\_colab\_or\_os(\\"HF\_TOKEN\\")\\n",\
"PATHS = \[\\"https://arxiv.org/pdf/2408.09869\\"\] # Docling Technical Report\\n",\
"EMBED\_MODEL\_ID = \\"sentence-transformers/all-MiniLM-L6-v2\\"\\n",\
"GENERATION\_MODEL\_ID = \\"mistralai/Mixtral-8x7B-Instruct-v0.1\\"\\n",\
"EXPORT\_TYPE = ExportType.DOC\_CHUNKS\\n",\
"QUESTION = \\"Which are the main AI models in Docling?\\"\\n",\
"TOP\_K = 3\\n",\
"MILVUS\_URI = str(Path(mkdtemp()) / \\"docling.db\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Indexing pipeline"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 3,\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"Token indices sequence length is longer than the specified maximum sequence length for this model (1041 > 512). Running this sequence through the model will result in indexing errors\\n"\
\]\
},\
{\
"data": {\
"application/vnd.jupyter.widget-view+json": {\
"model\_id": "80beca8762c34095a21467fb7f056059",\
"version\_major": 2,\
"version\_minor": 0\
},\
"text/plain": \[\
"Batches: 0%| | 0/2 \[00:00"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Note: you may need to restart the kernel to use updated packages.\\n"\
\]\
}\
\],\
"source": \[\
"%pip install -q docling\[vlm\] ipython"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 1,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"from docling.datamodel.base\_models import InputFormat\\n",\
"from docling.datamodel.pipeline\_options import PdfPipelineOptions\\n",\
"from docling.document\_converter import DocumentConverter, PdfFormatOption"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 2,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"# The source document\\n",\
"DOC\_SOURCE = \\"https://arxiv.org/pdf/2501.17887\\""\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"---"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Describe pictures with Granite Vision\\n",\
"\\n",\
"This section will run locally the \[ibm-granite/granite-vision-3.1-2b-preview\](https://huggingface.co/ibm-granite/granite-vision-3.1-2b-preview) model to describe the pictures of the document."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 3,\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"Using a slow image processor as \`use\_fast\` is unset and a slow processor was saved with this model. \`use\_fast=True\` will be the default behavior in v4.48, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with \`use\_fast=False\`.\\n"\
\]\
},\
{\
"data": {\
"application/vnd.jupyter.widget-view+json": {\
"model\_id": "93a634699bf1434c9bc8e384d6db1a28",\
"version\_major": 2,\
"version\_minor": 0\
},\
"text/plain": \[\
"Loading checkpoint shards: 0%| | 0/2 \[00:00Picture #/pictures/0
Caption
Figure 1: Sketch of Docling's pipelines and usage model. Both PDF pipeline and simple pipeline build up a DoclingDocument representation, which can be further enriched. Downstream applications can utilize Docling's API to inspect, export, or chunk the document for various purposes.
In this image we can see a pie chart. In the pie chart we can see the categories and the number of documents in each category. \\n",\
"
Picture #/pictures/2
Caption
Figure 3: Distribution of conversion times for all documents, ordered by number of pages in a document, on all system configurations. Every dot represents one document. Log/log scale is used to even the spacing, since both number of pages and conversion times have long-tail distributions.
In this image we can see a graph. On the x-axis we can see the number of pages. On the y-axis we can see the seconds. \\n",\
"
Picture #/pictures/3
Caption
Figure 4: Contributions of PDF backend and AI models to the conversion time of a page (in seconds per page). Lower is better. Left: Ranges of time contributions for each model to pages it was applied on (i.e., OCR was applied only on pages with bitmaps, table structure was applied only on pages with tables). Right: Average time contribution to a page in the benchmark dataset (factoring in zero-time contribution for OCR and table structure models on pages without bitmaps or tables) .
In this image we can see a bar chart and a line chart. In the bar chart we can see the values of Pdf Parse, OCR, Layout, Table Structure, Page Total and Page. In the line chart we can see the values of Pdf Parse, OCR, Layout, Table Structure, Page Total and Page. \\n",\
"
Picture #/pictures/4
Caption
Figure 5: Conversion time in seconds per page on our dataset in three scenarios, across all assets and system configurations. Lower bars are better. The configuration includes OCR and table structure recognition ( fast table option on Docling and MinerU, hi res in unstructured, as shown in table 1).
In this image we can see a bar chart. In the chart we can see the CPU, Max, GPU, and sec/page. \\n"\
\],\
"text/plain": \[\
""\
\]\
},\
"execution\_count": 4,\
"metadata": {},\
"output\_type": "execute\_result"\
}\
\],\
"source": \[\
"from docling\_core.types.doc.document import PictureDescriptionData\\n",\
"from IPython import display\\n",\
"\\n",\
"html\_buffer = \[\]\\n",\
"# display the first 5 pictures and their captions and annotations:\\n",\
"for pic in doc.pictures\[:5\]:\\n",\
" html\_item = (\\n",\
" f\\"
Picture {pic.self\_ref}
\\"\\n",\
" f' '\\n",\
" f\\"
Caption
{pic.caption\_text(doc=doc)} \\"\\n",\
" )\\n",\
" if pic.meta is not None and pic.meta.description is not None:\\n",\
" html\_item += f\\"
Annotations ({pic.meta.description.created\_by})
{pic.meta.description.text} \\\\n\\"\\n",\
" html\_buffer.append(html\_item)\\n",\
"display.HTML(\\"\\".join(html\_buffer))"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"---"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Describe pictures with SmolVLM\\n",\
"\\n",\
"This section will run locally the \[HuggingFaceTB/SmolVLM-256M-Instruct\](https://huggingface.co/HuggingFaceTB/SmolVLM-256M-Instruct) model to describe the pictures of the document."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 7,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"from docling.datamodel.pipeline\_options import smolvlm\_picture\_description\\n",\
"\\n",\
"pipeline\_options = PdfPipelineOptions()\\n",\
"pipeline\_options.do\_picture\_description = True\\n",\
"pipeline\_options.picture\_description\_options = (\\n",\
" smolvlm\_picture\_description # <-- the model choice\\n",\
")\\n",\
"pipeline\_options.picture\_description\_options.prompt = (\\n",\
" \\"Describe the image in three sentences. Be consise and accurate.\\"\\n",\
")\\n",\
"pipeline\_options.images\_scale = 2.0\\n",\
"pipeline\_options.generate\_picture\_images = True\\n",\
"\\n",\
"converter = DocumentConverter(\\n",\
" format\_options={\\n",\
" InputFormat.PDF: PdfFormatOption(\\n",\
" pipeline\_options=pipeline\_options,\\n",\
" )\\n",\
" }\\n",\
")\\n",\
"doc = converter.convert(DOC\_SOURCE).document"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {},\
"outputs": \[\
{\
"data": {\
"text/html": \[\
"
Picture #/pictures/0
Caption
Figure 1: Sketch of Docling's pipelines and usage model. Both PDF pipeline and simple pipeline build up a DoclingDocument representation, which can be further enriched. Downstream applications can utilize Docling's API to inspect, export, or chunk the document for various purposes.
Annotations (HuggingFaceTB/SmolVLM-256M-Instruct)
This is a page that has different types of documents on it. \\n",\
"
Picture #/pictures/1
Caption
Figure 2: Dataset categories and sample counts for documents and pages.
Annotations (HuggingFaceTB/SmolVLM-256M-Instruct)
Here is a page-by-page list of documents per category:\\n",\
"- Science\\n",\
"- Articles\\n",\
"- Law and Regulations\\n",\
"- Articles\\n",\
"- Misc. \\n",\
"
Picture #/pictures/2
Caption
Figure 3: Distribution of conversion times for all documents, ordered by number of pages in a document, on all system configurations. Every dot represents one document. Log/log scale is used to even the spacing, since both number of pages and conversion times have long-tail distributions.
Annotations (HuggingFaceTB/SmolVLM-256M-Instruct)
The image is a bar chart that shows the number of pages of a website as a function of the number of pages of the website. The x-axis represents the number of pages, ranging from 100 to 10,000. The y-axis represents the number of pages, ranging from 100 to 10,000. The chart is labeled \\"Number of pages\\" and has a legend at the top of the chart that indicates the number of pages.\\n",\
"\\n",\
"The chart shows a clear trend: as the number of pages increases, the number of pages decreases. This is evident from the following points:\\n",\
"\\n",\
"- The number of pages increases from 100 to 1000.\\n",\
"- The number of pages decreases from 1000 to 10,000.\\n",\
"- The number of pages increases from 10,000 to 10,000. \\n",\
"
Picture #/pictures/3
Caption
Figure 4: Contributions of PDF backend and AI models to the conversion time of a page (in seconds per page). Lower is better. Left: Ranges of time contributions for each model to pages it was applied on (i.e., OCR was applied only on pages with bitmaps, table structure was applied only on pages with tables). Right: Average time contribution to a page in the benchmark dataset (factoring in zero-time contribution for OCR and table structure models on pages without bitmaps or tables) .
Annotations (HuggingFaceTB/SmolVLM-256M-Instruct)
bar chart with different colored bars representing different data points. \\n",\
"
Picture #/pictures/4
Caption
Figure 5: Conversion time in seconds per page on our dataset in three scenarios, across all assets and system configurations. Lower bars are better. The configuration includes OCR and table structure recognition ( fast table option on Docling and MinerU, hi res in unstructured, as shown in table 1).
Annotations (HuggingFaceTB/SmolVLM-256M-Instruct)
A bar chart with the following information:\\n",\
"\\n",\
"- The x-axis represents the number of pages, ranging from 0 to 14.\\n",\
"- The y-axis represents the page count, ranging from 0 to 14.\\n",\
"- The chart has three categories: Marker, Unstructured, and Detailed.\\n",\
"- The x-axis is labeled \\"see/page.\\"\\n",\
"- The y-axis is labeled \\"Page Count.\\"\\n",\
"- The chart shows that the Marker category has the highest number of pages, followed by the Unstructured category, and then the Detailed category. \\n"\
\],\
"text/plain": \[\
""\
\]\
},\
"execution\_count": 6,\
"metadata": {},\
"output\_type": "execute\_result"\
}\
\],\
"source": \[\
"from docling\_core.types.doc.document import PictureDescriptionData\\n",\
"from IPython import display\\n",\
"\\n",\
"html\_buffer = \[\]\\n",\
"# display the first 5 pictures and their captions and annotations:\\n",\
"for pic in doc.pictures\[:5\]:\\n",\
" html\_item = (\\n",\
" f\\"
Picture {pic.self\_ref}
\\"\\n",\
" f' '\\n",\
" f\\"
Caption
{pic.caption\_text(doc=doc)} \\"\\n",\
" )\\n",\
" if pic.meta is not None and pic.meta.description is not None:\\n",\
" html\_item += f\\"
Annotations ({pic.meta.description.created\_by})
{pic.meta.description.text} \\\\n\\"\\n",\
" html\_buffer.append(html\_item)\\n",\
"display.HTML(\\"\\".join(html\_buffer))"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"---"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Use other vision models\\n",\
"\\n",\
"The examples above can also be reproduced using other vision model.\\n",\
"The Docling options \`PictureDescriptionVlmOptions\` allows to specify your favorite vision model from the Hugging Face Hub."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 8,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"from docling.datamodel.pipeline\_options import PictureDescriptionVlmOptions\\n",\
"\\n",\
"pipeline\_options = PdfPipelineOptions()\\n",\
"pipeline\_options.do\_picture\_description = True\\n",\
"pipeline\_options.picture\_description\_options = PictureDescriptionVlmOptions(\\n",\
" repo\_id=\\"\\", # <-- add here the Hugging Face repo\_id of your favorite VLM\\n",\
" prompt=\\"Describe the image in three sentences. Be consise and accurate.\\",\\n",\
")\\n",\
"pipeline\_options.images\_scale = 2.0\\n",\
"pipeline\_options.generate\_picture\_images = True\\n",\
"\\n",\
"converter = DocumentConverter(\\n",\
" format\_options={\\n",\
" InputFormat.PDF: PdfFormatOption(\\n",\
" pipeline\_options=pipeline\_options,\\n",\
" )\\n",\
" }\\n",\
")\\n",\
"\\n",\
"# Uncomment to run:\\n",\
"# doc = converter.convert(DOC\_SOURCE).document"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {},\
"outputs": \[\],\
"source": \[\]\
}\
\],
"metadata": {
"kernelspec": {
"display\_name": "docling",
"language": "python",
"name": "python3"
},
"language\_info": {
"codemirror\_mode": {
"name": "ipython",
"version": 3
},
"file\_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert\_exporter": "python",
"pygments\_lexer": "ipython3",
"version": "3.12.9"
}
},
"nbformat": 4,
"nbformat\_minor": 2
}
---
# Unknown
{
"cells": \[\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"# Hybrid chunking"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Overview"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Hybrid chunking applies tokenization-aware refinements on top of document-based hierarchical chunking.\\n",\
"\\n",\
"For more details, see \[here\](../../concepts/chunking#hybrid-chunker)."\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Setup"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 1,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Note: you may need to restart the kernel to use updated packages.\\n"\
\]\
}\
\],\
"source": \[\
"%pip install -qU pip docling transformers"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 2,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"DOC\_SOURCE = \\"../../tests/data/md/sources/wiki.md\\""\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Basic usage"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"We first convert the document:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 3,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"from docling.document\_converter import DocumentConverter\\n",\
"\\n",\
"doc = DocumentConverter().convert(source=DOC\_SOURCE).document"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"For a basic chunking scenario, we can just instantiate a \`HybridChunker\`, which will use\\n",\
"the default parameters."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 4,\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"Token indices sequence length is longer than the specified maximum sequence length for this model (531 > 512). Running this sequence through the model will result in indexing errors\\n"\
\]\
}\
\],\
"source": \[\
"from docling.chunking import HybridChunker\\n",\
"\\n",\
"chunker = HybridChunker()\\n",\
"chunk\_iter = chunker.chunk(dl\_doc=doc)"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"> 👉 \*\*NOTE\*\*: As you see above, using the \`HybridChunker\` can sometimes lead to a warning from the transformers library, however this is a \\"false alarm\\" — for details check \[here\](https://docling-project.github.io/docling/faq/#hybridchunker-triggers-warning-token-indices-sequence-length-is-longer-than-the-specified-maximum-sequence-length-for-this-model)."\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Note that the text you would typically want to embed is the context-enriched one as\\n",\
"returned by the \`contextualize()\` method:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 5,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"=== 0 ===\\n",\
"chunk.text:\\n",\
"'International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and present in over 175 countries.\\\\nIt is a publicly traded company and one of the 30 companies in the Dow Jones Industrial Aver…'\\n",\
"chunker.contextualize(chunk):\\n",\
"'IBM\\\\nInternational Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and present in over 175 countries.\\\\nIt is a publicly traded company and one of the 30 companies in the Dow Jones Industrial …'\\n",\
"\\n",\
"=== 1 ===\\n",\
"chunk.text:\\n",\
"'IBM debuted in the microcomputer market in 1981 with the IBM Personal Computer, — its DOS software provided by Microsoft, — which became the basis for the majority of personal computers to the present day.\[12\] The company later also found success in the portable space with the ThinkPad. Since the 19…'\\n",\
"chunker.contextualize(chunk):\\n",\
"'IBM\\\\nIBM debuted in the microcomputer market in 1981 with the IBM Personal Computer, — its DOS software provided by Microsoft, — which became the basis for the majority of personal computers to the present day.\[12\] The company later also found success in the portable space with the ThinkPad. Since th…'\\n",\
"\\n",\
"=== 2 ===\\n",\
"chunk.text:\\n",\
"'IBM originated with several technological innovations developed and commercialized in the late 19th century. Julius E. Pitrap patented the computing scale in 1885;\[17\] Alexander Dey invented the dial recorder (1888);\[18\] Herman Hollerith patented the Electric Tabulating Machine (1889);\[19\] and Willa…'\\n",\
"chunker.contextualize(chunk):\\n",\
"'IBM\\\\n1910s–1950s\\\\nIBM originated with several technological innovations developed and commercialized in the late 19th century. Julius E. Pitrap patented the computing scale in 1885;\[17\] Alexander Dey invented the dial recorder (1888);\[18\] Herman Hollerith patented the Electric Tabulating Machine (1889…'\\n",\
"\\n",\
"=== 3 ===\\n",\
"chunk.text:\\n",\
"'Collectively, the companies manufactured a wide array of machinery for sale and lease, ranging from commercial scales and industrial time recorders, meat and cheese slicers, to tabulators and punched cards. Thomas J. Watson, Sr., fired from the National Cash Register Company by John Henry Patterson,…'\\n",\
"chunker.contextualize(chunk):\\n",\
"'IBM\\\\n1910s–1950s\\\\nCollectively, the companies manufactured a wide array of machinery for sale and lease, ranging from commercial scales and industrial time recorders, meat and cheese slicers, to tabulators and punched cards. Thomas J. Watson, Sr., fired from the National Cash Register Company by John …'\\n",\
"\\n",\
"=== 4 ===\\n",\
"chunk.text:\\n",\
"'He implemented sales conventions, \\"generous sales incentives, a focus on customer service, an insistence on well-groomed, dark-suited salesmen and had an evangelical fervor for instilling company pride and loyalty in every worker\\".\[25\]\[26\] His favorite slogan, \\"THINK\\", became a mantra for each compa…'\\n",\
"chunker.contextualize(chunk):\\n",\
"'IBM\\\\n1910s–1950s\\\\nHe implemented sales conventions, \\"generous sales incentives, a focus on customer service, an insistence on well-groomed, dark-suited salesmen and had an evangelical fervor for instilling company pride and loyalty in every worker\\".\[25\]\[26\] His favorite slogan, \\"THINK\\", became a mantr…'\\n",\
"\\n",\
"=== 5 ===\\n",\
"chunk.text:\\n",\
"'In 1961, IBM developed the SABRE reservation system for American Airlines and introduced the highly successful Selectric typewriter.…'\\n",\
"chunker.contextualize(chunk):\\n",\
"'IBM\\\\n1960s–1980s\\\\nIn 1961, IBM developed the SABRE reservation system for American Airlines and introduced the highly successful Selectric typewriter.…'\\n",\
"\\n"\
\]\
}\
\],\
"source": \[\
"for i, chunk in enumerate(chunk\_iter):\\n",\
" print(f\\"=== {i} ===\\")\\n",\
" print(f\\"chunk.text:\\\\n{f'{chunk.text\[:300\]}…'!r}\\")\\n",\
"\\n",\
" enriched\_text = chunker.contextualize(chunk=chunk)\\n",\
" print(f\\"chunker.contextualize(chunk):\\\\n{f'{enriched\_text\[:300\]}…'!r}\\")\\n",\
"\\n",\
" print()"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Configuring tokenization\\n",\
"\\n",\
"For more control on the chunking, we can parametrize tokenization as shown below.\\n",\
"\\n",\
"In a RAG / retrieval context, it is important to make sure that the chunker and\\n",\
"embedding model are using the same tokenizer.\\n",\
"\\n",\
"👉 HuggingFace transformers tokenizers can be used as shown in the following example:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 6,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"from docling\_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer\\n",\
"from transformers import AutoTokenizer\\n",\
"\\n",\
"from docling.chunking import HybridChunker\\n",\
"\\n",\
"EMBED\_MODEL\_ID = \\"sentence-transformers/all-MiniLM-L6-v2\\"\\n",\
"MAX\_TOKENS = 64 # set to a small number for illustrative purposes\\n",\
"\\n",\
"tokenizer = HuggingFaceTokenizer(\\n",\
" tokenizer=AutoTokenizer.from\_pretrained(EMBED\_MODEL\_ID),\\n",\
" max\_tokens=MAX\_TOKENS, # optional, by default derived from \`tokenizer\` for HF case\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"👉 Alternatively, \[OpenAI tokenizers\](https://github.com/openai/tiktoken) can be used as shown in the example below (uncomment to use — requires installing \`docling-core\[chunking-openai\]\`):"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 7,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"# import tiktoken\\n",\
"\\n",\
"# from docling\_core.transforms.chunker.tokenizer.openai import OpenAITokenizer\\n",\
"\\n",\
"# tokenizer = OpenAITokenizer(\\n",\
"# tokenizer=tiktoken.encoding\_for\_model(\\"gpt-4o\\"),\\n",\
"# max\_tokens=128 \* 1024, # context window length required for OpenAI tokenizers\\n",\
"# )"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"We can now instantiate our chunker:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 8,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"chunker = HybridChunker(\\n",\
" tokenizer=tokenizer,\\n",\
" merge\_peers=True, # optional, defaults to True\\n",\
")\\n",\
"chunk\_iter = chunker.chunk(dl\_doc=doc)\\n",\
"chunks = list(chunk\_iter)"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Points to notice looking at the output chunks below:\\n",\
"- Where possible, we fit the limit of 64 tokens for the metadata-enriched serialization form (see chunk 2)\\n",\
"- Where needed, we stop before the limit, e.g. see cases of 63 as it would otherwise run into a comma (see chunk 6)\\n",\
"- Where possible, we merge undersized peer chunks (see chunk 0)\\n",\
"- \\"Tail\\" chunks trailing right after merges may still be undersized (see chunk 8)"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 9,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"=== 0 ===\\n",\
"chunk.text (55 tokens):\\n",\
"'International Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and present in over 175 countries.\\\\nIt is a publicly traded company and one of the 30 companies in the Dow Jones Industrial Average.'\\n",\
"chunker.contextualize(chunk) (56 tokens):\\n",\
"'IBM\\\\nInternational Business Machines Corporation (using the trademark IBM), nicknamed Big Blue, is an American multinational technology company headquartered in Armonk, New York and present in over 175 countries.\\\\nIt is a publicly traded company and one of the 30 companies in the Dow Jones Industrial Average.'\\n",\
"\\n",\
"=== 1 ===\\n",\
"chunk.text (45 tokens):\\n",\
"'IBM is the largest industrial research organization in the world, with 19 research facilities across a dozen countries, having held the record for most annual U.S. patents generated by a business for 29 consecutive years from 1993 to 2021.'\\n",\
"chunker.contextualize(chunk) (46 tokens):\\n",\
"'IBM\\\\nIBM is the largest industrial research organization in the world, with 19 research facilities across a dozen countries, having held the record for most annual U.S. patents generated by a business for 29 consecutive years from 1993 to 2021.'\\n",\
"\\n",\
"=== 2 ===\\n",\
"chunk.text (56 tokens):\\n",\
"'IBM was founded in 1911 as the Computing-Tabulating-Recording Company (CTR), a holding company of manufacturers of record-keeping and measuring systems. It was renamed \\"International Business Machines\\" in 1924 and soon became the leading manufacturer of punch-card tabulating systems.'\\n",\
"chunker.contextualize(chunk) (57 tokens):\\n",\
"'IBM\\\\nIBM was founded in 1911 as the Computing-Tabulating-Recording Company (CTR), a holding company of manufacturers of record-keeping and measuring systems. It was renamed \\"International Business Machines\\" in 1924 and soon became the leading manufacturer of punch-card tabulating systems.'\\n",\
"\\n",\
"=== 3 ===\\n",\
"chunk.text (51 tokens):\\n",\
"\\"During the 1960s and 1970s, the IBM mainframe, exemplified by the System/360, was the world's dominant computing platform, with the company producing 80 percent of computers in the U.S. and 70 percent of computers worldwide.\[11\]\\"\\n",\
"chunker.contextualize(chunk) (52 tokens):\\n",\
"\\"IBM\\\\nDuring the 1960s and 1970s, the IBM mainframe, exemplified by the System/360, was the world's dominant computing platform, with the company producing 80 percent of computers in the U.S. and 70 percent of computers worldwide.\[11\]\\"\\n",\
"\\n",\
"=== 4 ===\\n",\
"chunk.text (59 tokens):\\n",\
"'IBM debuted in the microcomputer market in 1981 with the IBM Personal Computer, — its DOS software provided by Microsoft, — which became the basis for the majority of personal computers to the present day.\[12\] The company later also found success in the portable space with the ThinkPad.'\\n",\
"chunker.contextualize(chunk) (60 tokens):\\n",\
"'IBM\\\\nIBM debuted in the microcomputer market in 1981 with the IBM Personal Computer, — its DOS software provided by Microsoft, — which became the basis for the majority of personal computers to the present day.\[12\] The company later also found success in the portable space with the ThinkPad.'\\n",\
"\\n",\
"=== 5 ===\\n",\
"chunk.text (36 tokens):\\n",\
"'Since the 1990s, IBM has concentrated on computer services, software, supercomputers, and scientific research; it sold its microcomputer division to Lenovo in 2005.'\\n",\
"chunker.contextualize(chunk) (37 tokens):\\n",\
"'IBM\\\\nSince the 1990s, IBM has concentrated on computer services, software, supercomputers, and scientific research; it sold its microcomputer division to Lenovo in 2005.'\\n",\
"\\n",\
"=== 6 ===\\n",\
"chunk.text (29 tokens):\\n",\
"'IBM continues to develop mainframes, and its supercomputers have consistently ranked among the most powerful in the world in the 21st century.'\\n",\
"chunker.contextualize(chunk) (30 tokens):\\n",\
"'IBM\\\\nIBM continues to develop mainframes, and its supercomputers have consistently ranked among the most powerful in the world in the 21st century.'\\n",\
"\\n",\
"=== 7 ===\\n",\
"chunk.text (59 tokens):\\n",\
"\\"As one of the world's oldest and largest technology companies, IBM has been responsible for several technological innovations, including the automated teller machine (ATM), dynamic random-access memory (DRAM), the floppy disk, the hard disk drive, the magnetic stripe card, the relational database,\\"\\n",\
"chunker.contextualize(chunk) (60 tokens):\\n",\
"\\"IBM\\\\nAs one of the world's oldest and largest technology companies, IBM has been responsible for several technological innovations, including the automated teller machine (ATM), dynamic random-access memory (DRAM), the floppy disk, the hard disk drive, the magnetic stripe card, the relational database,\\"\\n",\
"\\n",\
"=== 8 ===\\n",\
"chunk.text (12 tokens):\\n",\
"'the SQL programming language, and the UPC barcode.'\\n",\
"chunker.contextualize(chunk) (13 tokens):\\n",\
"'IBM\\\\nthe SQL programming language, and the UPC barcode.'\\n",\
"\\n",\
"=== 9 ===\\n",\
"chunk.text (59 tokens):\\n",\
"'The company has made inroads in advanced computer chips, quantum computing, artificial intelligence, and data infrastructure.\[13\]\[14\]\[15\] IBM employees and alumni have won various recognitions for their scientific research and inventions, including six Nobel Prizes and six Turing Awards.\[16\]'\\n",\
"chunker.contextualize(chunk) (60 tokens):\\n",\
"'IBM\\\\nThe company has made inroads in advanced computer chips, quantum computing, artificial intelligence, and data infrastructure.\[13\]\[14\]\[15\] IBM employees and alumni have won various recognitions for their scientific research and inventions, including six Nobel Prizes and six Turing Awards.\[16\]'\\n",\
"\\n",\
"=== 10 ===\\n",\
"chunk.text (19 tokens):\\n",\
"'IBM originated with several technological innovations developed and commercialized in the late 19th century. Julius E.'\\n",\
"chunker.contextualize(chunk) (23 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\nIBM originated with several technological innovations developed and commercialized in the late 19th century. Julius E.'\\n",\
"\\n",\
"=== 11 ===\\n",\
"chunk.text (44 tokens):\\n",\
"'Pitrap patented the computing scale in 1885;\[17\] Alexander Dey invented the dial recorder (1888);\[18\] Herman Hollerith patented the Electric Tabulating Machine (1889);\[19\]'\\n",\
"chunker.contextualize(chunk) (48 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\nPitrap patented the computing scale in 1885;\[17\] Alexander Dey invented the dial recorder (1888);\[18\] Herman Hollerith patented the Electric Tabulating Machine (1889);\[19\]'\\n",\
"\\n",\
"=== 12 ===\\n",\
"chunk.text (31 tokens):\\n",\
"\\"and Willard Bundy invented a time clock to record workers' arrival and departure times on a paper tape (1889).\[20\] On June 16,\\"\\n",\
"chunker.contextualize(chunk) (35 tokens):\\n",\
"\\"IBM\\\\n1910s–1950s\\\\nand Willard Bundy invented a time clock to record workers' arrival and departure times on a paper tape (1889).\[20\] On June 16,\\"\\n",\
"\\n",\
"=== 13 ===\\n",\
"chunk.text (39 tokens):\\n",\
"'1911, their four companies were amalgamated in New York State by Charles Ranlett Flint forming a fifth company, the Computing-Tabulating-Recording Company (CTR) based in Endicott,'\\n",\
"chunker.contextualize(chunk) (43 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\n1911, their four companies were amalgamated in New York State by Charles Ranlett Flint forming a fifth company, the Computing-Tabulating-Recording Company (CTR) based in Endicott,'\\n",\
"\\n",\
"=== 14 ===\\n",\
"chunk.text (55 tokens):\\n",\
"'New York.\[1\]\[21\] The five companies had 1,300 employees and offices and plants in Endicott and Binghamton, New York;\\\\nDayton, Ohio; Detroit, Michigan; Washington, D.C.; and Toronto, Canada.\[22\]'\\n",\
"chunker.contextualize(chunk) (59 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\nNew York.\[1\]\[21\] The five companies had 1,300 employees and offices and plants in Endicott and Binghamton, New York;\\\\nDayton, Ohio; Detroit, Michigan; Washington, D.C.; and Toronto, Canada.\[22\]'\\n",\
"\\n",\
"=== 15 ===\\n",\
"chunk.text (42 tokens):\\n",\
"'Collectively, the companies manufactured a wide array of machinery for sale and lease, ranging from commercial scales and industrial time recorders, meat and cheese slicers, to tabulators and punched cards. Thomas J.'\\n",\
"chunker.contextualize(chunk) (46 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\nCollectively, the companies manufactured a wide array of machinery for sale and lease, ranging from commercial scales and industrial time recorders, meat and cheese slicers, to tabulators and punched cards. Thomas J.'\\n",\
"\\n",\
"=== 16 ===\\n",\
"chunk.text (50 tokens):\\n",\
"'Watson, Sr., fired from the National Cash Register Company by John Henry Patterson, called on Flint and, in 1914, was offered a position at CTR.\[23\] Watson joined CTR as general manager and then, 11 months later,'\\n",\
"chunker.contextualize(chunk) (54 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\nWatson, Sr., fired from the National Cash Register Company by John Henry Patterson, called on Flint and, in 1914, was offered a position at CTR.\[23\] Watson joined CTR as general manager and then, 11 months later,'\\n",\
"\\n",\
"=== 17 ===\\n",\
"chunk.text (50 tokens):\\n",\
"\\"was made President when antitrust cases relating to his time at NCR were resolved.\[24\] Having learned Patterson's pioneering business practices, Watson proceeded to put the stamp of NCR onto CTR's companies.\[23\]:\\\\u200a105\\"\\n",\
"chunker.contextualize(chunk) (54 tokens):\\n",\
"\\"IBM\\\\n1910s–1950s\\\\nwas made President when antitrust cases relating to his time at NCR were resolved.\[24\] Having learned Patterson's pioneering business practices, Watson proceeded to put the stamp of NCR onto CTR's companies.\[23\]:\\\\u200a105\\"\\n",\
"\\n",\
"=== 18 ===\\n",\
"chunk.text (59 tokens):\\n",\
"'He implemented sales conventions, \\"generous sales incentives, a focus on customer service, an insistence on well-groomed, dark-suited salesmen and had an evangelical fervor for instilling company pride and loyalty in every worker\\".\[25\]\[26\] His favorite slogan,'\\n",\
"chunker.contextualize(chunk) (63 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\nHe implemented sales conventions, \\"generous sales incentives, a focus on customer service, an insistence on well-groomed, dark-suited salesmen and had an evangelical fervor for instilling company pride and loyalty in every worker\\".\[25\]\[26\] His favorite slogan,'\\n",\
"\\n",\
"=== 19 ===\\n",\
"chunk.text (49 tokens):\\n",\
"'\\"THINK\\", became a mantra for each company\\\\'s employees.\[25\] During Watson\\\\'s first four years, revenues reached $9 million ($158 million today) and the company\\\\'s operations expanded to Europe, South America,'\\n",\
"chunker.contextualize(chunk) (53 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\n\\"THINK\\", became a mantra for each company\\\\'s employees.\[25\] During Watson\\\\'s first four years, revenues reached $9 million ($158 million today) and the company\\\\'s operations expanded to Europe, South America,'\\n",\
"\\n",\
"=== 20 ===\\n",\
"chunk.text (60 tokens):\\n",\
"'Asia and Australia.\[25\] Watson never liked the clumsy hyphenated name \\"Computing-Tabulating-Recording Company\\" and chose to replace it with the more expansive title \\"International Business Machines\\" which had previously been used as the name of CTR\\\\'s Canadian Division;\[27\]'\\n",\
"chunker.contextualize(chunk) (64 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\nAsia and Australia.\[25\] Watson never liked the clumsy hyphenated name \\"Computing-Tabulating-Recording Company\\" and chose to replace it with the more expansive title \\"International Business Machines\\" which had previously been used as the name of CTR\\\\'s Canadian Division;\[27\]'\\n",\
"\\n",\
"=== 21 ===\\n",\
"chunk.text (29 tokens):\\n",\
"'the name was changed on February 14,\\\\n1924.\[28\] By 1933, most of the subsidiaries had been merged into one company, IBM.'\\n",\
"chunker.contextualize(chunk) (33 tokens):\\n",\
"'IBM\\\\n1910s–1950s\\\\nthe name was changed on February 14,\\\\n1924.\[28\] By 1933, most of the subsidiaries had been merged into one company, IBM.'\\n",\
"\\n",\
"=== 22 ===\\n",\
"chunk.text (22 tokens):\\n",\
"'In 1961, IBM developed the SABRE reservation system for American Airlines and introduced the highly successful Selectric typewriter.'\\n",\
"chunker.contextualize(chunk) (26 tokens):\\n",\
"'IBM\\\\n1960s–1980s\\\\nIn 1961, IBM developed the SABRE reservation system for American Airlines and introduced the highly successful Selectric typewriter.'\\n",\
"\\n"\
\]\
}\
\],\
"source": \[\
"for i, chunk in enumerate(chunks):\\n",\
" print(f\\"=== {i} ===\\")\\n",\
" txt\_tokens = tokenizer.count\_tokens(chunk.text)\\n",\
" print(f\\"chunk.text ({txt\_tokens} tokens):\\\\n{chunk.text!r}\\")\\n",\
"\\n",\
" ser\_txt = chunker.contextualize(chunk=chunk)\\n",\
" ser\_tokens = tokenizer.count\_tokens(ser\_txt)\\n",\
" print(f\\"chunker.contextualize(chunk) ({ser\_tokens} tokens):\\\\n{ser\_txt!r}\\")\\n",\
"\\n",\
" print()"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Table chunking with header repetition\\n",\
"\\n",\
"When chunking documents with tables, the \`HybridChunker\` can repeat table headers in each chunk to maintain context. This is particularly useful for wide tables where the content spans multiple chunks.\\n",\
"\\n",\
"Let's demonstrate this with a CSV file containing customer data."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 10,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Document has 1 items\\n",\
"\\n",\
"First few lines of the CSV table:\\n",\
"| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website |\\n",\
"|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|-------\\n"\
\]\
}\
\],\
"source": \[\
"# Convert a CSV file with a wide table\\n",\
"CSV\_SOURCE = \\"../../tests/data/csv/sources/csv-comma.csv\\"\\n",\
"\\n",\
"csv\_result = DocumentConverter().convert(source=CSV\_SOURCE)\\n",\
"csv\_doc = csv\_result.document\\n",\
"\\n",\
"print(f\\"Document has {len(list(csv\_doc.iterate\_items()))} items\\")\\n",\
"print(\\"\\\\nFirst few lines of the CSV table:\\")\\n",\
"print(csv\_doc.export\_to\_markdown()\[:500\])"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Now let's chunk this table with header repetition enabled. We'll use a small token limit to force the table to be split across multiple chunks."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Total chunks created: 5\\n",\
"\\n",\
"============================================================\\n",\
"Chunk 1:\\n",\
"============================================================\\n",\
"| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website |\\n",\
"| - | - | - | - | - | - | - | - | - | - | - | - || 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | ...\\n",\
"\\n",\
"Tokens: 131\\n",\
"Has table header: True\\n",\
"\\n",\
"============================================================\\n",\
"Chunk 2:\\n",\
"============================================================\\n",\
"| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website |\\n",\
"| - | - | - | - | - | - | - | - | - | - | - | - || 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820...\\n",\
"\\n",\
"Tokens: 132\\n",\
"Has table header: True\\n",\
"\\n",\
"============================================================\\n",\
"Chunk 3:\\n",\
"============================================================\\n",\
"| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website |\\n",\
"| - | - | - | - | - | - | - | - | - | - | - | - || 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)97...\\n",\
"\\n",\
"Tokens: 141\\n",\
"Has table header: True\\n",\
"\\n"\
\]\
}\
\],\
"source": \[\
"from docling\_core.transforms.chunker.hierarchical\_chunker import (\\n",\
" ChunkingDocSerializer,\\n",\
" ChunkingSerializerProvider,\\n",\
")\\n",\
"from docling\_core.transforms.serializer.markdown import (\\n",\
" MarkdownParams,\\n",\
" MarkdownTableSerializer,\\n",\
")\\n",\
"\\n",\
"\\n",\
"# Create a custom serializer provider that uses Markdown for tables\\n",\
"class MDTableSerializerProvider(ChunkingSerializerProvider):\\n",\
" def get\_serializer(self, doc):\\n",\
" return ChunkingDocSerializer(\\n",\
" doc=doc,\\n",\
" table\_serializer=MarkdownTableSerializer(),\\n",\
" params=MarkdownParams(compact\_tables=True),\\n",\
" )\\n",\
"\\n",\
"\\n",\
"small\_tokenizer = HuggingFaceTokenizer(\\n",\
" tokenizer=AutoTokenizer.from\_pretrained(EMBED\_MODEL\_ID),\\n",\
" max\_tokens=200,\\n",\
")\\n",\
"\\n",\
"chunker\_with\_headers = HybridChunker(\\n",\
" tokenizer=small\_tokenizer,\\n",\
" repeat\_table\_header=True, # Repeat headers in each chunk\\n",\
" serializer\_provider=MDTableSerializerProvider(), # Use Markdown table format\\n",\
")\\n",\
"\\n",\
"csv\_chunks = list(chunker\_with\_headers.chunk(csv\_doc))\\n",\
"\\n",\
"print(f\\"Total chunks created: {len(csv\_chunks)}\\\\n\\")\\n",\
"\\n",\
"# Display the first few chunks to show header repetition\\n",\
"for i, chunk in enumerate(csv\_chunks\[:3\], 1):\\n",\
" print(f\\"{'=' \* 60}\\")\\n",\
" print(f\\"Chunk {i}:\\")\\n",\
" print(f\\"{'=' \* 60}\\")\\n",\
" chunk\_text = chunk.text\\n",\
" # Show first 300 characters of each chunk\\n",\
" preview = chunk\_text\[:300\] + \\"...\\" if len(chunk\_text) > 300 else chunk\_text\\n",\
" print(preview)\\n",\
" print(f\\"\\\\nTokens: {small\_tokenizer.count\_tokens(chunk\_text)}\\")\\n",\
" print(f\\"Has table header: {chunk\_text.startswith('|')}\\\\n\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Each chunk starts with the table header row, ensuring that every chunk maintains the context of what each column represents. This is especially important when:\\n",\
"\\n",\
"- Feeding chunks to an embedding model for semantic search\\n",\
"- Processing chunks independently in downstream tasks\\n",\
"- Working with wide tables that naturally span multiple chunks\\n",\
"\\n",\
"For more advanced control over header handling in wide tables, including the \`omit\_header\_on\_overflow\` parameter, see the \[Line-based chunking example\](../line\_based\_chunking)."\
\]\
}\
\],
"metadata": {
"kernelspec": {
"display\_name": ".venv",
"language": "python",
"name": "python3"
},
"language\_info": {
"codemirror\_mode": {
"name": "ipython",
"version": 3
},
"file\_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert\_exporter": "python",
"pygments\_lexer": "ipython3",
"version": "3.13.2"
}
},
"nbformat": 4,
"nbformat\_minor": 2
}
---
# Unknown
{
"cells": \[\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
""\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"# Visual grounding"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"| Step | Tech | Execution | \\n",\
"| --- | --- | --- |\\n",\
"| Embedding | Hugging Face / Sentence Transformers | 💻 Local |\\n",\
"| Vector store | Milvus | 💻 Local |\\n",\
"| Gen AI | Hugging Face Inference API | 🌐 Remote | "\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"This example showcases Docling's \*\*visual grounding\*\* capabilities, which can be combined\\n",\
"with any agentic AI / RAG framework.\\n",\
"\\n",\
"In this instance, we illustrate these capabilities leveraging the\\n",\
"\[LangChain Docling integration\](../../integrations/langchain/), along with a Milvus\\n",\
"vector store, as well as sentence-transformers embeddings."\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Setup"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"- 👉 For best conversion speed, use GPU acceleration whenever available; e.g. if running on Colab, use GPU-enabled runtime.\\n",\
"- Notebook uses HuggingFace's Inference API; for increased LLM quota, token can be provided via env var \`HF\_TOKEN\`.\\n",\
"- Requirements can be installed as shown below (\`--no-warn-conflicts\` meant for Colab's pre-populated Python env; feel free to remove for stricter usage):"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 1,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Note: you may need to restart the kernel to use updated packages.\\n"\
\]\
}\
\],\
"source": \[\
"%pip install -q --progress-bar off --no-warn-conflicts langchain-docling langchain-core langchain-huggingface langchain\_milvus langchain matplotlib python-dotenv"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 2,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"import os\\n",\
"from pathlib import Path\\n",\
"from tempfile import mkdtemp\\n",\
"\\n",\
"from dotenv import load\_dotenv\\n",\
"from langchain\_core.prompts import PromptTemplate\\n",\
"from langchain\_docling.loader import ExportType\\n",\
"\\n",\
"\\n",\
"def \_get\_env\_from\_colab\_or\_os(key):\\n",\
" try:\\n",\
" from google.colab import userdata\\n",\
"\\n",\
" try:\\n",\
" return userdata.get(key)\\n",\
" except userdata.SecretNotFoundError:\\n",\
" pass\\n",\
" except ImportError:\\n",\
" pass\\n",\
" return os.getenv(key)\\n",\
"\\n",\
"\\n",\
"load\_dotenv()\\n",\
"\\n",\
"# https://github.com/huggingface/transformers/issues/5486:\\n",\
"os.environ\[\\"TOKENIZERS\_PARALLELISM\\"\] = \\"false\\"\\n",\
"\\n",\
"HF\_TOKEN = \_get\_env\_from\_colab\_or\_os(\\"HF\_TOKEN\\")\\n",\
"SOURCES = \[\\"https://arxiv.org/pdf/2408.09869\\"\] # Docling Technical Report\\n",\
"EMBED\_MODEL\_ID = \\"sentence-transformers/all-MiniLM-L6-v2\\"\\n",\
"GEN\_MODEL\_ID = \\"mistralai/Mixtral-8x7B-Instruct-v0.1\\"\\n",\
"QUESTION = \\"Which are the main AI models in Docling?\\"\\n",\
"PROMPT = PromptTemplate.from\_template(\\n",\
" \\"Context information is below.\\\\n---------------------\\\\n{context}\\\\n---------------------\\\\nGiven the context information and not prior knowledge, answer the query.\\\\nQuery: {input}\\\\nAnswer:\\\\n\\",\\n",\
")\\n",\
"TOP\_K = 3\\n",\
"MILVUS\_URI = str(Path(mkdtemp()) / \\"docling.db\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Document store setup\\n",\
"\\n"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Document loading\\n",\
"\\n",\
"We first define our converter, in this case including options for keeping page images (for visual grounding)."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 3,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"from docling.datamodel.base\_models import InputFormat\\n",\
"from docling.datamodel.pipeline\_options import PdfPipelineOptions\\n",\
"from docling.document\_converter import DocumentConverter, PdfFormatOption\\n",\
"\\n",\
"converter = DocumentConverter(\\n",\
" format\_options={\\n",\
" InputFormat.PDF: PdfFormatOption(\\n",\
" pipeline\_options=PdfPipelineOptions(\\n",\
" generate\_page\_images=True,\\n",\
" images\_scale=2.0,\\n",\
" ),\\n",\
" )\\n",\
" }\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"We set up a simple doc store for keeping converted documents, as that is needed for visual grounding further below."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 4,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"doc\_store = {}\\n",\
"doc\_store\_root = Path(mkdtemp())\\n",\
"for source in SOURCES:\\n",\
" dl\_doc = converter.convert(source=source).document\\n",\
" file\_path = Path(doc\_store\_root / f\\"{dl\_doc.origin.binary\_hash}.json\\")\\n",\
" dl\_doc.save\_as\_json(file\_path)\\n",\
" doc\_store\[dl\_doc.origin.binary\_hash\] = file\_path"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Now we can instantiate our loader and load documents."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 5,\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"Token indices sequence length is longer than the specified maximum sequence length for this model (648 > 512). Running this sequence through the model will result in indexing errors\\n"\
\]\
}\
\],\
"source": \[\
"from langchain\_docling import DoclingLoader\\n",\
"\\n",\
"from docling.chunking import HybridChunker\\n",\
"\\n",\
"loader = DoclingLoader(\\n",\
" file\_path=SOURCES,\\n",\
" converter=converter,\\n",\
" export\_type=ExportType.DOC\_CHUNKS,\\n",\
" chunker=HybridChunker(tokenizer=EMBED\_MODEL\_ID),\\n",\
")\\n",\
"\\n",\
"docs = loader.load()"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"> 👉 \*\*NOTE\*\*: As you see above, using the \`HybridChunker\` can sometimes lead to a warning from the transformers library, however this is a \\"false alarm\\" — for details check \[here\](https://docling-project.github.io/docling/faq/#hybridchunker-triggers-warning-token-indices-sequence-length-is-longer-than-the-specified-maximum-sequence-length-for-this-model)."\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Inspecting some sample splits:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 6,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"- d.page\_content='Docling Technical Report\\\\nVersion 1.0\\\\nChristoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar\\\\nAI4K Group, IBM Research R¨ uschlikon, Switzerland'\\n",\
"- d.page\_content='Abstract\\\\nThis technical report introduces Docling , an easy to use, self-contained, MITlicensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.'\\n",\
"- d.page\_content='1 Introduction\\\\nConverting PDF documents back into a machine-processable format has been a major challenge for decades due to their huge variability in formats, weak standardization and printing-optimized characteristic, which discards most structural features and metadata. With the advent of LLMs and popular application patterns such as retrieval-augmented generation (RAG), leveraging the rich content embedded in PDFs has become ever more relevant. In the past decade, several powerful document understanding solutions have emerged on the market, most of which are commercial software, cloud offerings \[3\] and most recently, multi-modal vision-language models. As of today, only a handful of open-source tools cover PDF conversion, leaving a significant feature and quality gap to proprietary solutions.\\\\nWith Docling , we open-source a very capable and efficient document conversion tool which builds on the powerful, specialized AI models and datasets for layout analysis and table structure recognition we developed and presented in the recent past \[12, 13, 9\]. Docling is designed as a simple, self-contained python library with permissive license, running entirely locally on commodity hardware. Its code architecture allows for easy extensibility and addition of new features and models.\\\\nHere is what Docling delivers today:\\\\n· Converts PDF documents to JSON or Markdown format, stable and lightning fast\\\\n· Understands detailed page layout, reading order, locates figures and recovers table structures\\\\n· Extracts metadata from the document, such as title, authors, references and language\\\\n· Optionally applies OCR, e.g. for scanned PDFs\\\\n· Can be configured to be optimal for batch-mode (i.e high throughput, low time-to-solution) or interactive mode (compromise on efficiency, low time-to-solution)\\\\n· Can leverage different accelerators (GPU, MPS, etc).'\\n",\
"...\\n"\
\]\
}\
\],\
"source": \[\
"for d in docs\[:3\]:\\n",\
" print(f\\"- {d.page\_content=}\\")\\n",\
"print(\\"...\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Ingestion"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 7,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"import json\\n",\
"from pathlib import Path\\n",\
"from tempfile import mkdtemp\\n",\
"\\n",\
"from langchain\_huggingface.embeddings import HuggingFaceEmbeddings\\n",\
"from langchain\_milvus import Milvus\\n",\
"\\n",\
"embedding = HuggingFaceEmbeddings(model\_name=EMBED\_MODEL\_ID)\\n",\
"\\n",\
"\\n",\
"milvus\_uri = str(Path(mkdtemp()) / \\"docling.db\\") # or set as needed\\n",\
"vectorstore = Milvus.from\_documents(\\n",\
" documents=docs,\\n",\
" embedding=embedding,\\n",\
" collection\_name=\\"docling\_demo\\",\\n",\
" connection\_args={\\"uri\\": milvus\_uri},\\n",\
" index\_params={\\"index\_type\\": \\"FLAT\\"},\\n",\
" drop\_old=True,\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## RAG"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 8,\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"Note: Environment variable\`HF\_TOKEN\` is set and is the current active token independently from the token you've just configured.\\n"\
\]\
}\
\],\
"source": \[\
"from langchain.chains import create\_retrieval\_chain\\n",\
"from langchain.chains.combine\_documents import create\_stuff\_documents\_chain\\n",\
"from langchain\_huggingface import HuggingFaceEndpoint\\n",\
"\\n",\
"retriever = vectorstore.as\_retriever(search\_kwargs={\\"k\\": TOP\_K})\\n",\
"llm = HuggingFaceEndpoint(\\n",\
" repo\_id=GEN\_MODEL\_ID,\\n",\
" huggingfacehub\_api\_token=HF\_TOKEN,\\n",\
" task=\\"text-generation\\",\\n",\
")\\n",\
"\\n",\
"\\n",\
"def clip\_text(text, threshold=100):\\n",\
" return f\\"{text\[:threshold\]}...\\" if len(text) > threshold else text"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 9,\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"/Users/pva/work/github.com/DS4SD/docling/.venv/lib/python3.12/site-packages/huggingface\_hub/utils/\_deprecation.py:131: FutureWarning: 'post' (from 'huggingface\_hub.inference.\_client') is deprecated and will be removed from version '0.31.0'. Making direct POST requests to the inference server is not supported anymore. Please use task methods instead (e.g. \`InferenceClient.chat\_completion\`). If your use case is not supported, please open an issue in https://github.com/huggingface/huggingface\_hub.\\n",\
" warnings.warn(warning\_message, FutureWarning)\\n"\
\]\
},\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Question:\\n",\
"Which are the main AI models in Docling?\\n",\
"\\n",\
"Answer:\\n",\
"The main AI models in Docling are:\\n",\
"1. A layout analysis model, an accurate object-detector for page elements.\\n",\
"2. TableFormer, a state-of-the-art table structure recognition model.\\n"\
\]\
}\
\],\
"source": \[\
"from docling.chunking import DocMeta\\n",\
"from docling.datamodel.document import DoclingDocument\\n",\
"\\n",\
"question\_answer\_chain = create\_stuff\_documents\_chain(llm, PROMPT)\\n",\
"rag\_chain = create\_retrieval\_chain(retriever, question\_answer\_chain)\\n",\
"resp\_dict = rag\_chain.invoke({\\"input\\": QUESTION})\\n",\
"\\n",\
"clipped\_answer = clip\_text(resp\_dict\[\\"answer\\"\], threshold=200)\\n",\
"print(f\\"Question:\\\\n{resp\_dict\['input'\]}\\\\n\\\\nAnswer:\\\\n{clipped\_answer}\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"### Visual grounding"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 10,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Source 1:\\n",\
" text: \\"3.2 AI models\\\\nAs part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements \[13\]. The second model is TableFormer \[12, 9\], a state-of-the-art table structure re...\\"\\n",\
" page: 3\\n"\
\]\
},\
{\
"data": {\
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",\
"text/plain": \[\
""\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
},\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Source 2:\\n",\
" text: \\"3 Processing pipeline\\\\nDocling implements a linear pipeline of operations, which execute sequentially on each given document (see Fig. 1). Each document is first parsed by a PDF backend, which retrieves the programmatic text tokens, consisting of string content and its coordinates on the page, and also renders a bitmap image of each page to support ...\\"\\n",\
" page: 2\\n"\
\]\
},\
{\
"data": {\
"image/png": 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",\
"text/plain": \[\
""\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
},\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Source 3:\\n",\
" text: \\"6 Future work and contributions\\\\nDocling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of ...\\"\\n",\
" page: 5\\n"\
\]\
},\
{\
"data": {\
"image/png": 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DHvCA4VyTG/UC+SRn2h/9pnzva8OsJ9dBfWQx9f36r//6aE9oqK9k2fdoqN518or/L3/5y1cvetGLVve///2HfGo3eUBL7Sc3nPzoL3SdF+W0w7glU5l3lvpQm17wghcMGbeYpgztVo539Rmt0SLjGD31yXgnAxnn2kanhFczPciaMpS/1MtpS9os48YcoFz1iIDL5LjFLW4xaLYcZ5mDtTPzSPRR2kBHzDKzRHSN54xF7USzdamweCh11Q/bwPjXN7/RwXggz/S0v/VFZJj+Vy55U4a+qUOb9dtz9EHkXFQYH5RDHrURHdFfX6LXi2Ivog5ksWfBmJE+YlUyex8oWyksJh5GgNVlqVtW7aXVvOxlLxsGwx/90R+N3ze84Q1HqhMlzyiRqnmve91rGPPwuc997uh0OhPPE57whKHIZ1DqVrKlD8VhUI6okMn/mte85urKV77yqFMa2n3ve98RgRO9MVmd85znHO03aVpBZii85z3vGelmJk4Tioncu1e/+tVHZMKq9BOf+MTV+c53vtEGZWvXJS95yZF+K43rzGc+8zA8TFImrziQ9hbe/va3H/UyGkxIjJAXvvCFgx72DW2C1FLt//SnPz0mL44Y2jJ0vvSlL40InPS7S13qUqvLXOYyYwJ/wxveMCZLdNKO293udqsLXvCC31euiCBacaw4WHj1vOc9b3Xve9977CuRFmX19mIXu9hI6fP8jW50o/Gj3De96U2rP/mTPxl8f+1rXzt4cIELXGDQarkarQ/ogy74yjEwCX/hC18YKVDox1B76EMfOiI9ZMDqt2fI2xnPeMaRQhjnfBsoDz30SSorPpEZf3NG8Ff9DCTtYXz4jpPib21CC/sNZ7nDB6nNjAlyi7dWwrWbTHOGOAAPfvCDxzNkCp2Vo0xj51znOtfqSle60g/wnVxI61TmOc5xjlEXA12Ew5gis2Tw0pe+9HC4pDc/+9nPHvzTZv3TDuMHP7Tli1/84jCM3vve944oBTmZ4TuLJ+pVB3lAM/KF95xt7bniFa+4ustd7jKex29ypi9Jjcaj/d0Dxbi+/OUvv7rb3e426kA3OgbvjDfjmHGsD2hG18gS0Fe0SmqpyOXSqGNk/v7v//74+7GPfex49n73u9/qcpe73EhvZrCSPVFNxi5dQu4tIIXX+4PoQTTBV/KbBSfGKP1kDIUXeHqVq1zl++phTNMPeOF7fH7KU54ynmfM05/kifNx61vfejhE9BB4ho6Tbqluukt/8Ac96Yh57yhHiFySkWc961mjXRbQtJWetLcPffAVr2UPMMQ9Q3cZ++TAWMKPr3zlK6tzn/vcg5ZSo6Uakh/88p5xpgzp68YJPUAfkj0OEbrRZ8YUfUy2syDy9Kc/fWQWzAt6gfaqg96WYaEOn93ylrccToL26r92apfP0MaevSXIxLve9a5BM2ONk49/nAoLp2QID20tEHXTB+VbBJFijQZ0r7FLl2gP+V7CnIDuxpY20c10I76ae8iEjAA6QoRamRZVyLk5Rb0cT/opcow25jJ8RQ9jxvxkrlUGfbFOf5JLckt3kyHlmJP0nx7GL5+JMs96nazST3SN/Z90wAc/+MExjsgMB5ts0oHalwXUGej6tKc9bcxV+EOP3vzmNx9ytBzPZERb0Y7sKQ990YkjSd+ip+wTut4CIhpzODNGyAi+qIdtQS9YvDDGyI2ylKkOcpJxwHlUjrFnAQTt60QWexFNYS32JChMExKjhFHHoDSxmIxN0BwHBigjkJI10XEwTbiMWWl/JsrnPve5Y4JnPFHyQDlnNZIRyjhi/HFcGJbr2qLsrDqbaOP4KNtkw1iSBmoyYACZmK08MlJMhiZbk5DJmUHDmNFOBjQHzSq1dxmBDF3GGafDBM0YYriaoEzgjB4GC+OP8cFR8HccBY61iVJ5HDrlmdRjOGwCmqAH59NErV8cauUoH/2luumHcrXV5wwdDjQaW4034c6pdNrIYGNIS4fUb/3NgTScKEawCRPdOK36whhBX044RwZNpBJxWhgsjLJ1/eHgWAFG44te9KIjWqJvnmWs4EXohp7Pf/7zB68ufvGLD15xZtfJwb7klZGgXxYKOBwMBsYOWcVDbWAAMoBF1RkN5BrttHHd/jXloQEHhZFk4YN84bG+WEBgqHCaGUgcc3RVLufFeFDGusNqyCGa4A0jlEGDXmSH3OK3ehk0xlwcVP3kHHHE1G3RJgsEjDEy6Bk8I8MzEvHSRv3Xb8Zcor/GsnYz6NUr7Q4vpHZzvoxRDuccAdkf6Cf5Uy4aMsKvcY1rDBowXvUXfcmx/xn4Fk2MU+Ofccp5W6erjH+89z86qMcYSpQO78gufihL/QcCZaE7GdJ28sN5ylhg2PqOXrGooT5GLF7MhqixTi6id4xrz2szxzp947DQfxwOi0rqyqJP9C06ahfdYdFnuaiTSB3Zp3f9Ta9rk/dkUxiT+nD3u999yJf/LVh4x1hCX2OJntGeZIVwYOkb+i0LifQKPaDvHGFyzTjnqNAL+IlXjHlzh3c8K6WZrly391R9nBw6UXnaEUfVmKG79Nv8go6ccHy2KLVugQDtyZZ6jRk6K5k3+oFnHC1tww/jkWzil3lBvy34RD43HUamHouNxp0FSuVwkuk+9LQnVn2cIjTRD+NBeeZFP5wp+hivyX+ybTh1xr0xqw8i+sbopvHJgcUzzil+Gof6hCfqNT/4exmBJ4ece88ao0C3klu0wQcLTvhLF66D/qqPQ4Y/aGj+Wre1Ac30SX8t4uqbhQZ6jW4H8xF5xQ+fR7cYj8aMRTzyjZ9knf40l+ExnqOxhRRzuUUCNDYn4bmFAPLPkT9QXVcUhxqNQBZ7Fkm5S5qXicOkyYkxUTM4Gf1Wa03sJkarg1b1GHAUsAnLBMiJ8o7JnzHD4GCQc0IYf4xvk8A6o079jCtGi98mB2WZvOcoyJwG6re2mFBMJBxM7ctezhhySV8zwcypN5wqPwwtZfg+/3vOZJTUP0aulU2Tp8nJhMfQSwoj40v/RBiSPrMJaMfIBZMm2pogGfYmXKvR+g0mNnWLqCX9kuE3p5Zy9KT1MAoZKaEXWiRF0Wfai16cCXRmTJlMk5acdCHPMoYYCfq2jEKlvJkfASdVuXisjqSzilaqn4HhnTh0+4OkWKGRssieNt7hDncYbSJzjCP0YXSJQDMaknLHsfT3si/ayblNCqtVfc8xuMkAaCtnLKmeOciHw8IQyp7NZdlkkiGjDnxHGzwXzREt5SiQOc+SUWWRf4aTNusnR4/coTNjiZGePbCR7dl49j/ez6l2SevEe7Kub4wu/BGlYDSSM7LA8Ev2wP5CffpANzDqtdsYYlSLcL/97W8fNKQP0CR7skQ64hSixzrngj4QieC4oQEHA684egxO8mDsMsrRhawf6CmSxjLDV1nZk4WPObWTU2fccbx9JoK3LpqmT9E7gbHFORSRpUcsJDC4GekMXLKXSCeHXr/RShl0KWdmE/R37vOcWq39Gf/kULs4cuiHDxxUbfO8uhJhUr+FRPJiIYvzwmlLenDq9bdImYWLHAplHlEPGqnTvlQyh3fr9CS+0xX0R06q9j5niPxYiMxWBDSnm7OIYAwso0hzWnlo4zn9y5505aC7/miTz/KuNjtgS8aIaL/Fr3U8nlPXvUefi8Re//rXH+0FvLYgoH/0Odk1hpNGSSYsqFgcyLaQ7OPXXrKtDs4oXbXJmRVJTTYBOUUjY27eErBuXNAv+o4G6Qu+5SReNMcXunDTHkR61FykfDLACaYPNh1UNbcp80hkNrSdtzPkJ5/N9kA+z3f6ga7mWnyjvy16a1N0newNdO2hPMVeRR3I4oiBicnkwHHJhJxV3xjY82SZ1dp8F8WdvToz8t6+UsnW7X/aKb1kuddwU/n7qnfu11xuolNWY0WkEoXIHjmrmyYtkRNGp1VmRtq+oJ+MFzCBiTxxHq0wM/BzaiXjmOEYOjDs5sM/4miLXjB0lo7MJlqFJn4YyRYKGGmMBU4SZ5hBcCCYackY1l6nczKYgpxYuD+pQ9qFHtqWPZ2znDFW8CdXSqgD8CdyuSnysa/+eIbxxOkTSWQgoj2j28829PATZ25fUB8jk+Gv38YZ3pPBRA/Sl1yfsak/m/qHPsoVSWIgB2iLZvvLH+AAiTJJLZNmyYjmkKKbFG/yILsgC1bkzTNJx9tkbGoH+eFAic7HiWb8S2njRHAwd+MaluxdW0c37UAzzpiU3IyRZFzkCppNIEMia7IAOBPabXxzzJTBgZ/lCY9ErWB/HeK5/cu+kF3OmXotHtH5m/a1GXN0mwgQ3bSJxvjJGebEZIEoelL0SlnS2ukZGRgctGVd5MNP6KjPnJf59M1tdf42yPvLMtQr4iobRYq7hRYLpaJm+5Ix8yHHjW7KQqqxyyGb9VV0Qvat4sMMtEPTlEO3cUiNlbR7yTO6wFYFaacWVg/mhOd12EkfZAGUvFicwLf9dc4OlpfrPss+b+NWlJOTHh7GuS6KvYhKZrHnMTuLVj9FR2LgMyySHrVU7iYzk6LV9OzRs8JnVX3TxLEvg3STERNlr9xtHcq8tz+G8PK5OB9W6qUBZjISvTNRcmLsSWRkmLylIe00Cc6HrHiXY6o8BqXV6ERfgcHA8GC8MEhyYIOUrvCHcS791kq1fW5z+t86h3j+f47GStFKJIQTK5V1eU/a/iBlJ+osapTPOUQMjW2uUIhxRw45yVbJrRzjiXbP6VQMrUQVGGVSknMgBOPBivxORsY6Ws1/kwXGPVqRd327853vvDb6tK6OHIbCuJ73Ym5qD15yMBiDZEMEyNhKWfqTaPXc1mV/1pUP2kG+yI56fK5fZDtRnW1PKl3SiHPACWOwMXgZ48rxfaJLdE0iiGmT/5eGdGBccLA4jfbrSQ80Xp7znOeM99W5qd/74wiTHW1D62Q0zFF3MkZHWvQJrTlionjb1I8+6C4tEy2SHaAu/Y+OM05EOfeH/rOui/M2R3Ty92xQS720GJitB4Fn6R1ZJWSRXpgP11rWiS6iyXSI//VDFJqM6psIkH1o6sG/Zb/IhnnGWBdRztjXNmN6mba7LU+X9czjY5M+QB+Ou8UQaY4ilviVQ2U2AV3IDpqSiegfeptcZTEwzyarwnP07zKCTLfQ92TRs/hg3K+7usmcIC1XWTmZeZaH/L3pFNpN2JbOtlZYXLMglX2T6w772an82TEObZZz+rxokIPC8izMNMy7aG+cyYyYD88z3pJOXhR7DY1AFnsaJrukT5nArM5JmzNJMcg4L/YLMDQ8w8Dk+FgRNklKR5S2kku5rZhapTXRmcgoa3/7bYJLSs28ty4TbCKffvue42qVXLRH26TReo7hpg0mc2X6zKSREyzVlwOBYpDlc/WnbX7rj754N2l9ytRe7dQWkQ3RE6voImlJ0bQab38aI0MKEiN5U9RO+xjSaO1H+VItRX4YRowVEQlGGGOBM8qIQnsr3w7TsE+NsS01TJ+0TVsZ0dLMpNJmD5V++i7pZuFD3vE9uvnxnWgRGksz9Zm++z8pljOyEp4j/DmaKQcN0TKRBCvrUtAYjvrtfwal6M1yJZ+zor14gD9oos2isSJa3hO5kA6qPg6EaBR6oDtj1d5E7RblcQCE9xnsnBKyqv/aF/rFQPVc6JGfyHpk0/8MXwYdByDprDH01q1ko50y7JuLU2vPmOiZsZI64vjndELfcY610z48n9tvTA78H+cODTgC2hce423KxWu09L9+kPXU533RXAeGSAUmx+hErhmAHDMRDLxD320NSXoDT9QTRxlP7L0S6U60USojXcMJl66ZkxjXpQoGyhBp9sOpUI/omPRX4yhOOnqEd2Q5RmNo4P/wdHlxOXm2SKFcBrkxL8qJzvSJca6P9IGIcE6rlF6YcqNfMibCC3VlLyDZ1VdyIwvAOGGAe8cCiQUrh++kLONCWUlNX8qZcn3vagS04eSiKdmzAKHfnFw8Nqb0x+KTuvCH/sjChVRU+tYiAFmk8xjfHFp98Tsn6RoP5JnTYg8cflpkyQnGaGUfn4NfyL5+rkvBVLfvpXtK7be4ltNS0db4QduM1fA5PF13AT19qr10A72BttFP2uA7NFOWn3ynXIte9Kn2kFvz0LrTe/GPPsgWEONH+jz9Y86kn8mNckRx8YBMJm2Yo0mG6PrMTdpEXrKFRPqu9Hf9xMt1C1DKxC/txyvtz0F49BM95TMLXxYuzK+hl/qjw43PWQfOc0bahy9L/Y2W5M22lpza6zPzGfth1o/ZOpFTaJVr3FlA0HZ61vNklT40d9AN2oHGysu1WCLa5kvzqefUl/lf2fqhbFscbPWwj9gcST7xaid9UxTHJOpAFnsanBIrhyYxe+hM8owz+wT8ptAZOyYuBgfnMEY+o5LDyaAxaShHOpp9ByatrIKaVCh2k2H2UGSvSSYTzoIJ3t8mViu+JgEGs0M9GN6MRAfF+M4kZiK0Km6SMdHnNDUTA2eWk8L40C8TmvbY08EJM9GaUEws+mWiZTSabHKwCcPR5KlexoDT7bRDOeiQa0M4UtqvHgbtOkObgZETFUUp0cHeMAYF2jJGGRkmXYcR5fADbTTxM7Q5mAwzaXyMwuyvQl9GrTbktD701kYTblLU9E+/9JfxpK2MVDzWD4ag6BPjQB84XAy3paGgTnxnUHk/B2nEqfKjPZ7Tdif6MXrIhIgpB4hxuKRTVtcZXP5mtMcoVB4+5LAahit6MrwZEGSRIRAek0P1M0bINgeF0cQoJmfKzKXVjDF9JhN+6wvZIydoyskCxhRjOyeX5nAI5eLfujvM4ujhnb4pV//xX9kOdPBZ0lAZTRxl/AbOHf4AZ4YTSYbQiJyQ+xyC413lpM3qwAuybRFEu8lKTrTVX/WLftuXSbYZVuSEbBu75GKng6HWQfnoHaMQ0I1RLQITvuOXvaki5/QNx4Ljv9MddGQMrcmT/nF0GYUZ+2it/8aI79FSO9RLjtEj6eP6Zhx5d3YMlMPYdxgUuhivZIbu4TSRYamY0UvGvXGCF9mfST7xwg/ZJZNob2zqHxorKwtOnjdOOFvGoTrQ0PPq8Hx0W/bDzfA/mZANYZwpj8NBl8VZi3yQXXSSfq/t6KKvFgz0FU0zD6AR54XuI7/kjBzl9GuHq1hkzJi32KB8n6Ehp4gOwi/RSXRwgFf6s+wDXeJwHAfbqJMM0pP4qQ5jTZvILv2oDrKVQ3CWMD+oizPNWSDLxgIHGs/RicxnEUbfOVD+xwt9poeMdY7HupNP0Q/POTr0J/4YX+TPnEj/0kXGck7O1Veyid54QZeRB06Q3+qjn8it7REWMM0P6G9OWNcODiHnne6w+KRf9A8ZQAeLlYmqz1dAgTZov7FAPuk+44YuzJUa3sNPc7T5d5mKj0fGuDFFfp2izi5YF7VNlBkvOIE5yTwLSRx+uomM4hM9Ql9YUEFTOpudgaZ0Kx1q8dAznkVbdgt6m1PNN/43btHcmFJ+5vGi2Is43vcOJqm7KA4xEv2D7K3IBOF37nT0dxyLpKLNq5cmBIo+e1+Snphn55TW5Ub+3Dk173FJ+YlI+puiV24ODkkd8/7KnHTqxwRocsqhPqkrqUNze+bvl+3NfjB08nnu60obTPaMpFyUvilSkxQk/dGXeS+LcpSfz5I6HDrhR6KYoXlS3fKTdJ5l/csL0HMPWspxci2DjEOCXjFeOF4csXWr7onOZhV7pv2cOpw0JmV6Xvs33QG5TJmcU72Wd2suV7J9R1ZnucpeXDIZp2SWszy7/D/1LtO9GGLS2BhRjEs851QykkQSLGws4VTjF7/4xeMADcYx2cmpocsxIrLOucwJx2RkPgwnJ0iiZWQw/dwk28s9YktZiGxnjM38jCxEJvcH4WOcz5QXeZh5nkiweva1vyx0i47QF+VGppYytInH8/+b0uwSzfQsuqQ/eTZRIt+l3Zt4MevMOLrZ1zbXvRwn68rcKS0w4zI8C53ILkeKM0h+c+fvLFuJ7OXe0fBK/co0hvygid/al+9Cg/ATor/CE7SKPt40nmdZUY86cvjVPGbmsbqk7ab5LfNHnlmOhTw/l599rXFidmpzxtA8j/pMX+imjAULBHStxROLJnjhneU8OM+TmXvmOWEdMneHP0md9UO28HiW17n9yzllpu9Sr2ziX+5dzHhBu3Xjek5PTfmRH2VE7yR7KH1WXmTIZ/5OhHFOM980ZjKmw6fufyz2MhqBLPY0YjTOyEl8y882KVsKfvn8Mmqxk6KOsbLOePTdfN/Zuv08mzCvLO7r2eX3604fnduRz/TTzzZ3GsYJXq54ppyZhkt65jS/YB29durjpv5lT6UollVaq/kmWCvCVnM3GfTau81hMAEDJpeKb0LosD/IiZvroO3LNq6j27YHrzDCpTXijZX5fGaFfnlyaYzdpH/5f760etnXGKCMLz+J3s6I0bOUtQOh2bLcpXz5LIbngWBJ05S3xLpxtROWdNOXOWK5SYYOhOfr6DJj3Vjelheb9N26cbI//F03LuNEJWVfn5aLOJt0U9o666OZJsv6NslS9OQ2iKws01y30fn7mt/2pefX9X2niPhOcqzsJS1mpzK8iBzsNA8u5+hNWM7dc5uyALCp/Zvq3h8s69g0fnbSBxzqLEQv27NuvK07OG6TrO1rTBfFXkIdyKIo9ixMyKKMfttnaDKWHiTNTPpUV2j/H6RMSTXLwSBS6aS8iSKsM2KkCzIUpQmKLnp+k/GSlC5piNLnpOutS4ktiv0Fx1Fqu7R+0XopfTkBujh8iCMv28PWBWmh2Se6vxH+oiiO/WgKa1EUR4SRmb0qnKFNaabHdSQtVlSR0500wHWpc6FpUuxEajalfuWgl6R1KXfbqENR7EtmyZa0wESx5/TV4vAgWQnzOBch3Ck1tiiK4y7qQBbFMYh5P1tRFEVRFEVR7HU0/6s4TsPpeE7CdPrn4XYcpRCq28XKTqLcBCvzTuWzFzD3Wc4QcVKO/W5dDyqKoiiKoigOJepAFsdpOHL7wQ9+8NiDczjhKP8nP/nJw4F1x6Gj5ecT93IKnN+ecby3+xbXObr2uzkZ8xOf+MTqSEqVyqmsRVEURVEUxZGDHqJTHKdxtrOdbVyO7QCSwwVOkwNhOJH3ute9xmmiy8u3RRVzp6O72FxM7I6sdddguHvMdQzr7l/bi7A/z310aJ57E4uiKIqiKIojA3Ugiz0L6Zrf+c53htPEwXJJtw3+Dv5wWqTj5DkjHDGHfzhFMkdvOyAkdz6JdnFUOGWe9S6nTJn+dsXBfM+i95wE6D0XTM/3fIkAes9vJ1OqM5fJr4uySSvVh1/6pV9anfjEJx71+MyFzPqhHm3TjtwDpd8uZn7jG984TiN0p59jv3N/lHRWfzsdz2995kTOdwK6VFmKLJq5xHg+dCbP5I6qHLziomvPuCJD3/Q9VzLk/jZ9dUqfA1fQMGXmont98t58dL7PtNNvz7tE2qXzLllW/nx9RFEURVEURbG3UQey2LPgSD3nOc8Zd9vd8573XF3kIhdZff3rXx+Xnt/4xjcep3G+613vGk7ZBz7wgXG9w+1ud7uxV/AJT3jC6nd+53eGU8MhvPOd77x65zvfOS5U/9SnPjWuIHBxtUjgS17yktVd7nKXEe376le/unrlK185HD6O3+c+97nVTW9603FlxGte85px6fqFL3zhkTbqbsKrXe1qq5vf/OY/cP8Th+xFL3rRcNA4cNp37nOfe1yS/fGPf3xEEzm3b3vb28adfa5SCBydbl+kttjbeOlLX3o4WBy45z3veaN/rlS4+93vvrrkJS85+vXUpz510ORyl7vccNB85goHfXHlhYvfZ2dPGY973OPG5fHo841vfGN11FFHrT772c8O51P7XNdwv/vdbzwvSosf6OaaCG2/613vOtpm/+Zzn/vccWIfR/eDH/zg6kpXutLqQhe60OrNb37z+M5VEvrNUXaXIwdahJXzKAJbFEVRFEVRHBnoHshiz8JFzRwUjhxnJg6QdE7Rtxe84AUjssaJ4oQ9+9nPXn30ox8dkb4vfOELI0p4s5vdbHW9611vOI2f//znV+c85zlXl73sZUfZIome5SzlImuOpwgdx/AmN7nJiODd+973HtEz0UAOpSicPYcXu9jFxt5FDte8l49D63PO7dWvfvXVta51rdVFL3rR4bBpB8fq1Kc+9SjvOte5zujPDKmoHF199L4op/I5pe7he+xjHzve4fhy/kQov/KVr4z+cvbe/va3jzu8OI6XuMQlfuC6BXQU9fSOaKP2cYI5fpxP71z5yldefexjHxsOPCdPH0U+0Vr9HMmHP/zh430OPYdWX25wgxuMNj7kIQ8Z9Obk21+Kfne4wx3GnY76z4m84hWvuDrrWc/a6GNRFEVRFMURhDqQxZ6GKKLoFaeII8npOc1pTjOiYSJYonSidZwrjiGnUPRQeisnjcPI0eOUibrd//73H86YaCBHkJPmN4hWvv/97x+RTBFF0TLvco5EzDidnDH1S9/kACZVdoZI4Rve8IaREspRkl4ryqZNoqnSOf1wnJKaGicqd/blZ76Xj7MrqqhMdXMYOav6kAvglaXeN73pTatHPepRo51nOMMZvs9JU6bIo/KUo23nPe95B31EP0VFHS4kQss5TdSQ06jveMLB5KRyMjnKaCYCqf4LXOACwyG3z1FbOKAirGh+vvOd7+g04013DhZFURRFURR7F01hLfY0OFjSIe973/uOFEj7+kQbRcQ4kZycc5zjHN93cuk//uM/jv/jmPncOyKJTjN9z3veM9I/lTtDlIxTlqsyvM8Z5ejkwvX5UvblnsKAQymtk+MkIuh9ThiHTdsOxGlaHp6jbal3rt/nl7nMZYYT65RXzh3acerm9/OO54Ej/Ld/+7cjOnijG93oaLql/TO8I4LJWeco2peqvrRR1FHffZ7PlmUURVEURVEURyYagSz2PETQRNns8+O4iJ6JBCbKJQrHQeG02Zc4X4cBnBiRNU7Us571rJE2KVV1vhKDkyPKJkoo5ZJDBSKbImsnP/nJt3b8PC+y9+Uvf3lETXOXo5/Tnva0W19dMTuN+6o7jm0O7hEh5EBKwX3a0572A1HS5XuisNrN4eRAo6ey0DMO+dwGDrzopv5wsu2dDM30k+MvKrlTu+fvcmVJURRFURRFsbdRB7LY0+BkcFTsIbTfTkoq58Zn0iHdoWhv4Utf+tLhMHEsk1YqAuY3h5Ij9N73vnecuOqgGamVORnUM36LEF73utddfehDHxr7Ijl//hbRs+dQlI2jw8HiJHlHXXGcAs6Ycjz/jne8Yzhb9k5qs7I8zzH1PSd4eR+iPnP87PuUImrvoWfzvLq9rx3+z3c+0xdpuPZacu6ucIUrDId7GX1Mu1NO0nWlrjqw6BWveMXqiU984nC8OZbAMfUj1dd+yatc5SojnVVf0Uu9nHK0Ri+8yt5SvNBuwCPtzL5UacgOAJL2WyeyKIqiKIpib6MprMWeB4fRCawcPJHIOCFOThU15Mw4FfUa17jGuFvQiaf2TXJG7F885SlPOSKXDthxqinH0LvgWg+OE8eHk+PgHGW6QoOT6cfBL8qSPnvxi198PGf/HyfIQTVOMFXHHC2UVvuIRzxiOK0vf/nLh1N5t7vdbRyIw0lz1QWHyqE1Zz/72X/gFFcH2fiOA5srMdxZqT+cWA6f/0U5OYFOkFWeKGwigpxKuOUtb3n0Pk/wnO9FdkVc7Su1t5KzmQOA/DgwxyFGuaZEPfZWcrztafSdSCOn2G+ns7qWxN9OvXX6LN4k5Zgzqt94hFecY1FK7dWf7I0siqIoiqIo9i6O970u+RdHAKRT+pnvM4REAt0naE9enp33+PnhGPmcU6UMDmj2+AU51CX3PfptPx+HKPsrg5QJy8NuAu+IwPnhIGlf6pyHnfLXvZv7E/Ut7UzK6Vz3XJZ2+B9d1Msx9bOMQM5tmNuvXA6y/9Eoey1dj8IJdNWHvoQugfcSAeXwovFMM38rM3suE8VVDuirdtaJLIqiKIqi2NtoBLI4IsD5WHcQC4dDuueM2bFZvj9H4jhMcWhm+NyJq8vPls7Nvg6G8Q4nbL5GIyev7gs5jCZYtnNfdft+GdXcpg3qEZUM4owmLRj90Gbp8Hpvbm/q2eQQcor9BMt3i6IoiqIoir2J7oEsimIjOJD2YHKCc2fmHIktiqIoiqIojltoCmtRFBuRVFo/kAhkr+UoiqIoiqI4bqIOZFEURVEURVEURbEVmsJaFEVRFEVRFEVRbIU6kEVRFEVRFEVRFMVWqANZFEVRFEVRFEVRbIU6kEVRFEVRFEVRFMVWqANZFEVRFEVRFEVRbIU6kEVRFEVRFEVRFMVWqANZFEVRFEVRFEVRbIU6kEVRFEVRFEVRFMVWqANZFEVRFEVRFEVRbIU6kEVRFEVRFEVRFMVWqANZFEVRFEVRFEVRbIU6kEVRFEVRFEVRFMVWqANZFEVxLML3vve98VMURVEURXEocPxDUmpR7AL+8z//c/Wd73xn9d///d/j/+Md73jj5//+7//G/z/yIz+y+rmf+7nVCU5wgvF58D//8z+rf/iHf1j90z/90+pXfuVXVj/2Yz929HcM6//4j/9Y/f3f//147td+7ddWxz/+/g8D5Xz3u98d7/7kT/7k99W/r/e0y8/cF238mZ/5mdGn3YY60eNb3/rW6jd/8zdXJzzhCbdu727V/6//+q+rv/mbv1md6EQnWp34xCc+rPUfLP7rv/5r9ZWvfGV1kpOcZPULv/ALW7/3b//2b6svf/nLq9/4jd8Y/T5cbcVnP7/0S7/0ffLtu7/92789Wubw5Yd/+IeH/P7UT/3U6od+6ODWE5X37//+76u/+7u/G3X8+q//+vjsq1/96upHf/RHV7/6q7960HUcaLu0Sd9PecpTDn0xgx7wPX6d7GQn2299QJ+QbbREc7/3OvSZ/vJDR/74j//4AdOWng7P8fhw9Z88q9dv9a7TndqXvtKBnvuJn/iJ/arnf//3f1df+9rXht486UlPekC6Szv++Z//ecigsfbzP//zg270gzH6sz/7s6u9BH0296KbcbwcM4cC5sQ/+7M/G2P0QOWxKIrDh0Ygiz0JE+4Xv/jF1Z3vfOfVwx/+8NVTn/rU1R3veMfV7W53u9VTnvKU1SMe8Yjx/+c///kfeJdz+MQnPnF173vfe/VXf/VX3/cdY+JP/uRPVne9611HOQze/W2Xd971rneNMj784Q/v9/uMzac//emrG97whqtHP/rRqxe96EWrhzzkIeNHn3cjepQoVMp697vfPdrLsTgmjJH3ve99q1ve8par9773vcdIdGxJj/3BX//1X6/ue9/7rl772tfu+P5ch58vfOELg+Yf//jHV4cDZBuf3/Oe94y23v/+9x8Ga8BgxQdj6E53utOQu2c961njuSc84Qmrr3/96wfFG+9+4hOfWN3tbncb4zXOFbl+7nOfO/4+JkD+3vzmN6/ufve7H02PmU/G40Mf+tDVgx70oLHQsQ3m9z/3uc8NXfSCF7xgODO7jaVc7QbQgY78/d///YPSCZzGP/3TPx3yhMeHov/rgA5//ud/vrrf/e63esxjHjOcj03P6d+Tn/zk1V3ucpfVX/zFX+x3Xcp+1KMeNWT6QOlP9t/5zneubnGLW6ze8pa3DLoZb/e85z3HeN1rMIf+wR/8weArJ3I3sJTh5f8f+9jHhr6kN4ui2PuoA1nsWZi4fvu3f3sYuLe5zW3GJGwV92Y3u9nqPve5z+oMZzjD6h//8R9/4D0RLpEEkY9ELwPRhdOd7nRjNZljsL8GwRzNePvb377fk6vV61Oc4hSjX5zf3/u931vd/va3H31ifPu9dHoPBP/yL/8yJuREm8597nMPA/oXf/EXV4cbIhK/8zu/M2iFX8eEA8mJsHAgCrG/EHXkdF30ohfd8TnyqQ60ByvpnClyejggYsAhPPnJTz6MZbI0Rz5FGn/3d3939e1vf3uMAw49x+dGN7rRMNqufvWrD+N2OWb2R7ZPc5rTjLI5ZSDyqJ4rXelK4+9jAuTvAhe4wDBORZHDK+ODU01fiAhxLjJe9gW64zOf+cz4+7d+67eGfNELh0q2tU20arfK1186UmTtQPkNIsp0mXbRiYdzbIuMydrYiW9kUoQVjw60ryKGN7nJTVbXuta1Djhzwnxj7IlyRweiPwftXOc612qvQUTUPPWXf/mXQ7Z3AxlzFhmSlfLHf/zHRy/invGMZxxjlN4simLvow5ksWchteeqV73qMPCkeDJM/fibAcQoZUQwnKW+cC5NTHmGceN/BrMVVRMhA4Ah6/ulMWBi/+Y3vzkm+U1QpjQmDplUqNlgyqSYdqyDOhm02uBvhoW2MMLOe97zDiORMS+apM2cLn9rEwPVajhjSfmMIZ8zgjmd/k4bXvjCF44oJ9p4X2rd2c52tu9L89LOpPuiEfokIjOnOoK/tYextq8oQ55FyxgH+qtu9NM+hgmDYl6NRn98XDp5ylCnvoS23lW+6II+zDzIdzHoff/BD35wddRRRw36Ki+fo5u+z2Wgq2e0R1u0Hb/jfACaowU6eR7tRP9EQxiqvue8WSCYU+bUqx8zbUIzfZn5uAnKFn2Z+67PIhpSbTmw6HyOc5zj++omb+GB3/rFqTz96U8/Fmmk2orCWdhI/dr4jW98YzhMS0NSv32uzf6OPM8p0upiODOWA23VR2VHDpb8Rh+yv4kO6OX7jBHl+dtPxgje+fG3MXvWs551jD31vepVrxoROO33ffSFv322yRnMAtLjH//41etf//pRtzLTZ3TQp6UO8Dl+kbV9RbHJFNkyTrRHfbIw3vGOdxztfKQdxgt+z+NI3epTl/4vdZTx7btlKjv+qhu/lzpQHeTOuxn/6tBO3+1rccCznvObvIQ+kQF9ndu5aXyH9z7H62W9yoieUob+mQ+kRO6UPu09fY6eIQNxNr1PdmYZ1iZt9472z2M2Zc0yrG4yhuaBsXme85zn6EWe0EMbyPDchpm3yvV7J+Cl9/FyHl8+xwftJwP6OtM98oa+0lZ3Skee9bwy5/mC3OFBdJzfL33pS0ckmAz5X5bEk570pPFcFnLo2aTLKi9tzaLtXEe2kcz1rNOn0ff7s8XEuNK3dTKcbQI7zfPLOQ2d5/KVTUaU6Ts/aJC2RxbRKp8XxV5D90AWexacQ1i36ptIniggw9mK6ac//enVzW9+82EIg4njla985eqTn/zkmISkjF72spf9gVQw30kz9D7DnDFz61vfeuwX3LTivG7FWzlSnUzSVpatju+ElK0sk5GJkJFhIlUWA1XKoTQzBv9znvOcMfFIpzQpPf/5zx+GsfqkQTHU73CHO4xyXvKSl4xJ6I1vfOMwUj70oQ+tPvrRj452WVF/xSteMaJlF7rQhYZzhYbXve51h6HjHROa9GHvKp/xyjkR1WXwSMXilC7pY0J861vfOowT9WvLTW9606P3iXKuOCmiN2c/+9lHhI5xJxXYDyNNOy0ciNxJPZQuzIB7wxveMPooEsABEFn40pe+dPRCA+NM+dqKjvgu8sSRkl6oXG3TFgsQUsoYJVLw9PNqV7va+JuhI4rH2TSJ+9x7omvXv/71hxHx8pe/fOy/ZTz+8i//8nhfSqg63vSmNw1aKPs1r3nN6opXvOKgM0MOn2J8+C1KqB8ifwwFP4wvUbvlviMyq39owvhkHPqNvupSL4ND/8nwJS95ycHrTZjHgMg0/uvf+9///tFX/H7b2962+umf/unBd88YQ/jFCMIPskF2fHbjG9/4+xYokoZuMYPzLdJJ5tD31Kc+9SiDfJ/znOcckVBtZYwqFy/JCF6d6lSnWl360pce42Ief8bHq1/96lEuJ1iKIbm71a1uNWSHbiB/Iqto9qlPfWp1j3vcY/DB2EF/sn75y1/+aNk1bqRZ0x2yHCy6LJ0sURT1iqpb+Dn/+c8/nklaKDk2Nh74wAeO34zI173udWNM6dMFL3jBIRPLRSz0QntyR59x5NFbfeSIXCjvEpe4xOALXqOT5+k9xv6LX/ziQUPRHM9wfoxj/+sf2fA8nqJ9oL/amBReck2ulGms4aMonmfoGOWhgfLJrO/XRdLoMVEm7SI/6jXujJlrXOMaYzzon/H2gAc8YPCbDtZfesbffl/nOtcZMqR92mMMGh/0dhZJ9AH9yCq94L3b3va237fwsw7kEK3QhhzRG2h4hStcYYwJtCCzZFtknwyTE/3BU5kjaCMd1Vg2bugsutp4NcbJ0VJP0sn0MHkwVo1fMksvohma0j3Xu971hpzQTX/0R3805IxzSEdo71IH44f2oj15Jef0NTz72c8ezpjxYfwYR+YT5XgWHcgH2uHNpsVC72m/Z81z6I2f5PMjH/nISOumF5Qpw4a+owfUTdepj07m4BmbF7nIRY7WNze4wQ3GmCczFlPRw+foKYvGohzQteoig8aVuUO5dK0y0dhYx1/939f5AvqkTPOh8acP5o5LXepSQw6f97znjfGG78rXV7TD8yUP9Jse0D70UZY54uIXv/iQCY608WLeUh95MtbPfOYzj7/Vx46hC41t8nEk7K0ujltoBLLYk0ikzs8mB5JxaO+WiYMRSDFTvgHDjZF/r3vdayh7zgpFvoTPXvaylw2Ffs1rXnMYCA972MP2O91Jmxi5DJZtlL3JWV3q5lSZQO3pMQmaqDjQjCv9cPiDvpi0vWdyMdl94AMfWF3sYhcbEySDjpHMsWbcSgViPJvwGFdxLExo2moSNNGhj3fsVbOiry2eEcH0vQmfUc2guva1rz2ME6mSy5VRzzL8GKYMZAYFp5xR5LtEYaSDMRIYFElheuxjHzsMAXV4lqGjrX6UyWFWHsPHpIoOJuSznOUsoy0MKs+IAJ7pTGcaTiYj5JnPfOYwpPBWKqqoNWOVMYCOJnCTtj4zRtGFYcUgMdl7j6GPzowlUDfjyneMC+UzYhk2aM3oY1AqK840MBDRxiIGeVVeDDbOp74ojxG9jLokMkUufc+gZnAwVEVW9e0yl7nM4BtnCx09tz/gNOorY5ABTR6MLXUxpMmpfVEWBv7wD/9wyOVVrnKVkdqrb0npnIEuDHJywEBDH3+jgzaSW333DL4rl1GJL2hoPCtjGWlKWrQxzzi1iMCZI/+JNpEncu19kQq00nZjhwOkb2QqByNxyCzI2A+p/Kc97Wk/EB31ufGpPk4BXnJ8Y6gzhBm5xqZxQ7b1CYwdzjKjkwwtoY2e1S7laov3vUN/yVDAW7zhLFvg4kzSIWSW3uFYcYo5ahxYtERD5WgPHtJxfryfRQRl6i/Hnj5h1KIXOtJLokWcCvz22/5xY5NzY6xvkjX00h9tVKYIkwUx+sS41Q7p4cYJmnDI7F1nwKOXBQ1665GPfOSozyIaR8rnxjh9m4wM7eVgoYnxxdGkR/YVfeJYkAs0VAeH26KA/enGOV2s/XSVMa2vdBrdTJ9YbFSv58mjRQQLXcanuvWXQzBDOWirzeYxzjCnmk62cMDpok/Ig4UDziSaWxCzUINfD37wg49OFZ+BPuSDXtM+fNcPMkHn4aU+kA9jx95V7TQm6WS0Q1uysIl2xoX3yILxT5aUbRzTz9ppDNObdHucO2OPDGmb8eNzz5nrvE8f6q/2oTvdry7zovZzvHK4nvYbI+YSco8nxgD9xfnEA/qUTlvq0yX0k/5HU/0hX+hA31o0MffiE/7Q08Ywnvt/XVnGPz1CFpXFAaRP8cJYNifgKV1lviJfZMR48dvCNxqYS9Y5qEWxF9AIZHHEQhSJU2hSooxNInOaC8PQRMYwtppuMrNCzfCcYRK3ap0DcRiiJnQT1f6cPkf5q8cEMqcq7QQTuJVYhgfnhwGaVKuk7M7ph3FMc5qpvsXwZyibWD2jDD/az/hmDORkO2WinQnZZOhvZZhA0cb/DG/00j6TnsmdIccJtCKsLctJ2cTPieUYmHAZ1SKy2hqD3ooux1Z52mxFWhvtcdUfzpSVZrRHR05gUpsufOELj3o8w5FkFFvlZngzMjgwDEB90T7OgffIgbrRUF0cH31hmOubd0Q4GZHopD7GDmPdO4n8ZBKPU8TgZrxoF36H7urWN233bFIbGW6eVT5HjwGENtquDgYoOqh3ndxZjddHTpf+ML4YJoxqDp56Iyf5e3+QU46V/dnPfnbQFj38j2dZ2UdTBinjSj8ZQDESl+VxtBIlh5z4etrTnnaUjW7KT6op45fh53Pl4p26lydnKpvRSp4YnQxldSlHRIhByuBDHzQ2piP/GRsZH0l1j5OmjcYBGpDD+RTn0Mc7+O19sux/fSI35JEzwKFk6Ett5pRx7IwnxrN39HdeaMqCGUdB/xmf2kLOUy++aicnld5jLOd0T21BL0Y049xvNDKePIN33jE2lTVnWHDss9CmTGOELqEjvKMskTLfk0PGM0cvJ2Ezctc5G8pHf8/gTyLPysEjn6GDfor06b8FBpFi/VGuekWMr3zlKw8ngayHl9qGT8rIWEZvfDOmyd2+9rXiL1pop0Up+kP56uL04h3ZtkilP+QZvehOMkoX0Z0iq2TAXGQhw1jVT7JhjM9OduiPvhl3aEw34B3aKIsjpE+caxkHIlqepVvo33URQgtkHC40xyvyY1ygLfkgmxa/6CBjixOqfbIqvJtxjLab5r+kez/jGc8Y5Zhn0dFiEL1hnvW9PigDH6Ifkxo7j8FkGmTe9D0aowdHGs3JDrnDW/Kek3H9mJPodP0xp5B586lsBAtr+4o+0s/4p810hXYZy8qjt8mAtqAJuUJT7Yu9MY9jZdFJPtMnv8kJ2irLvDPr54xroBvNveRGv5OFVRR7EXUgiyMWJiyTPsPMRGUSmo2FTFAUtUnT5Lk8dCdplZS2VX+TEUMle1b2B9kDti1MnIxxxq/VatEBkxMHbl8nLvouhkfqjiG86fl17V3Sai4LLRkvDHHRuKT6oU/2maYd8x7SGB2JEmXfVerJd6nDb5MxA0DUSfSB8R0axPkMTOQMqaS3+U45DCzGRdrjuaQ9zv3XTosNogwmdrCqj3c5AGee4Je04wCIooqgiKQw1hgF62iedzmnDKtcOaN8MqvdjCuHRyjHarXUNEby8joJNMz+vvCJgZJ9twd79D3eMUgZ0ElBnnkQY4qDwbkIjdQrogeh374wy5ofdZAJxjPjG60YveSHMbUOjG8OIkdcdEgmgfHEgWAEo3UMsG3Gxdwmbdk0BjeVtXwfn7Jv1/iJYY7HFnWWe/LoL44h58lClOcsKCyB3mSV3jNe/D+3KTTNOM5+PYtkjNh1yJ5sThMjedal2VceI1lUU78O5FCktHOTviHj837CLJRwcIxvda9bTMiprBbgLGD5TPbFpgyWTe0K3cgW53Xdfvj5WcAL8wcHVhYE2nFaZvrg0XLf4lKOluWGPvhnbNKRdLDv8zv78Oc+Zk9l0u7p6ZmXS73vM+PWuEa/0HMnGFsiryJ2nDTRYjKLRxy9y13uckM/iDZGf897RLdB9E7amzEJaEEfiZpy4MmHhSd9xQsLy/Z1G0PaqX07IdkxOeAnfeS40kezzl3SbonskVRmvicLFku2OWU9461Rx2KvoymsxREJSla0grHlVNUcVjIbpDOyApq0nFk5W/VjfHI+rKb6sRqeje/rMDtuc5tMNNue5JhJiNGsHyKhSRFM2XNbU+5Oh0EsJ51tJ6HZGZ1hEjUpS9fyN+eOMbTuoA2TJGNK5DLfcW48Ox/AMtfjbyv72Zsp2hADaF170EBalzqkGjGIY4Ay+qR/zUf1MyqXh/JoP4NJap669Ils+H8b3ilP6p7In6gBB4aBB5uMA7TzI7UpTpbfjHqGJUfW6j0DzhU1IpxLMJoYJYzU1JPDJ0QbtsFy0WHuk0i4sSQKJ2WaszqfCKwu/WVUabtIH+M2B7rg47ZtWAc85DhzGhmGDDGGoAWVTeVoq/5LZUdf+4VEqEWsOJP7ijwcaiPN2MnpvGQt+6I5yMsIPifP99KrOY76IIVv2VZ0t1/QYhOjead7DWfj29gkb7OMRx7IpYiyMi0GzHfrLmlknBvXZH7WT5t0yL4wv2ORT/vmE2fJAdnPCdJJJV+WoX+il/pqTNMnFge3vZplbr9xhj+iUevaOf+fw20SUdU+40kb8DMH4hzoCbXGBdkRadUX5fpf9Mu4m4F2FuLw0gIZ+djGic6CIB2YA2nmhYgl6HM6R7RcCrI0aamedKt5WTv0nU4mJxbtNsnTJmxawANlS/PFp2QVycyIoy46TJ/KmkALC0w5OG1deeSdzsuhaqlXP80xS2dxpz4oixzodxZOUxbndllWbIb9pU9RHNOoA1nsaSQKkFV0RmwmAQqagUDhM9A4fSbu3PVmcokhYPK1ss7gzHc5dfR85zvfWBl83OMeNyJb0jBNSutW2Of2zCuMcR4Zfdm/t1N/9CUpSCaU7DvxvlQi/TQZqoMzIR1KH6TH6ae65vqzx3A+aRYdGIzSeTyvrjhyfqf/6U/S6sBvn2kD+nBuOW7SfEVKtWfpyHIupGiin2c5ASbxOCEzzXPAQ05ClWrFYccHBhijyypu+pl258AjhhPH1OpwDiDi/DCWrDxbfRctY0wlSsbp4VBybhkc2qit0qI4g+FZaBna+Ikc+ZuR6h1On/1S2WPjt7bgT64WSB/xA21y8qDIJYeN7HpW9EKU3Gq5lfV1J+9Z/GDge5ZTQoY49lJeRWnUNfN4neylHzmwB53Jl/056O4gEEaOVX3Gs7RHZaKdMUZORVt9j374Szbs2UnkKH1Ond6f6RkZCD8jCzkYh+Gvbn1KJG+T8c2IlV6mbXiKRpwQbRGdjTEWnka+yQN548glDS3yv+T3sm5OKSMR33IK6vx8ZNb/nG1yaV+cA1OMH/vMjOPl+GFk27enbaI50gyVnZRZ9WmvxS56j1PCWDdOjAc/6kwfQnt952Qqj9wZl94x1tSZMo0n8mhs+834RaMl7RjUfpJ67ycyn1Ntl/ouPM+i2Kw/M078cF4Z/2Rc3fpvUYiM4y25ox85BOpUN/752yKi8W7/H4eYbHoXr0KXmTZLoJX5QztE4/DZglbkYda3OYHUO2hFN8vMkFKNXxaVpERqj0WA9H+pYzNmZz0cumVsAEeQzIj44aFxKVq87Icy6dKcQEo/ZE5M9sKs9yOn5EMaqpRd6Zf4SD7IFFoszwOgOziM2m6PNEfNM+YKOpm8q5vM44Xy6Uc0y6mkdGJOa9XeyFnGUfRIZGaeP9DCWDG26Ak/0SN0vD5wCOlT8772WnR0CBL9vaQbntHPxp65Q13q0H/RVN/P6dAZ43Nkd9YPFjf9zraPnBadLCdRamMOr9AHb9CB3lrqoqLYq2gKa7HnQfE6pIMhQCmbPKV9MuApasa/zfImMY6fCYKRy2BirCV1THqYFVEr+DninQEmUiECyOBgmHAynZy36QRLE7PJW7kmAD+MGwqf8RVDYV1kwHcmDka79DMGJWPZROh0QxMqg8NEpj/SsKzuWhlmECuX46CPcVgYUQzo7CH0PXroi2iGCVCfc0KefjEatV97TWiMR8a4duUoe7RSho38jElOgolZeYwGbZxXS/HHKjCjlOFg8kRHRh+jx8q5fqhL+xnX/mccOYiDc8VgZAhy7Ez4+MQh0G40llbG0bbynUMllK8u0SoHF9j7wokkAwwGq9X2yKCzPUXaZL+XfXuifeggoogHjD71oSmjkDOHdowd/cJr9IphwDCT/ir6kD2bFhDQh1FrJVof/S3tlSGjb+gh2sYhRA91oS8jhZO2PLUxe6Qc9uNgIH1ET8YR/pAD9GI0MUg4gct0xez99B5DxeE7SR1EG1EvPOG0cWZdHeGgDHRi8GiTcYfv2uF9EXNyLI3Nu+Q5Rm/uISVP2q+PMVYZZgxUcov+2cvqWbLDONQ2dJeSKMqxLpqYdD7f2bOkbPIxX5kQA18ES/vQiNNJV1iwkW6Y1Ei8JmPaop8cEHI073EiT1LzyBKZlSboe0YgXqsPvRiN5FlqMnm0HxiNOIdkfulAGj/kiR4yFqU454AazglH3Rimr/xwlIxFeoLR7ntypp/661njRz36J03e/2Rem0WxODzaTucxnJWJHsqkG8kUg5xuIbP2ZSpXf8gFnaqd5J58LvfkkWfl4I+6OSPog9Z+oxedpc3a7n1ypx0WKIxF8qVNaEJPa78TctVLThPVdSBPnHDjWFv1GU3puaQj5mClJYxnep1cGk85ids7dCo6WrxCOyAb2uk79COn+mlc2M9sjyiZpNfRjO6ke9ETHfyIrus3vUInogN65BltJpPklVNq8YnuIpPG3PIkbLyUxYBX6Mdh8T95UD4nBb30yTjIAqY+oxW976AadEBvDuF83VLgHWMYrZVDT3LyvUPWLDLat8kBz1yKHz4zJ9GFZMz/5m/zHDmhs9CVrkcT9DAnxWE3FvQFD7SV/sl+ZHXLZKFzlZX0bTrLogT6m0fIqcWmWZ+goa0JFpGNaw6wMu2j14dch+I5cyL5MH60AU3n/d+eMb7paIsdib6aE0SF1Wu+IccW7jiueKT95p2ckmzsaev+HoZWFIcLx/telziKPYx5JX1OaUmKlcmbMqd8szLJQLZimJU/n1PE3pmjX2CSyP4M7/o8729qT1YfszI67w/KnWwmzHWpKHP9vldWDsvxf1adc+CLejiVJqukInl2LmOO0oQ2oO+MaMZS2ps+p//zPql539T8vQkvE7i60WenNBtlZYU5Tvgcgcqexage5WfF18SZQ1XybuicQ4S0zff6qW8maN9loiUPJnvtjJHgfQa+dxlQOdhCO9E2BuXczsjGsu15TnnKDz30Kav86phX7RNFUv9MG+9lpZ3sJDVsk/ylbg69/ns2NJllcj5wKZiduazwR8421Ze7yLQ3qb6hJ2c4PMt+2OXYyniJXKUd6+QghidDEz84uTld0b7QnJa6RPoVhzF0yJiaaRN58zc+eIbc5PvleMj+raW8557WeQ9u+pQ+hw/KCK3UlVT7df3IHaRkE28jv9ED5Aq/Qps4bWQef+a9Wul75C9RlMiOduQQk6RhJiVaG/zgbeQktAsftQENM55ykNSMyETatdRVSxmILtUnDkoO6ZnL1UbfoWVkN3SK7vGZtmeBZMn/pfPOWOfYc7o4w2ip/KW+jt7glHAsOAVkf25jaIk+Mw/jXEe3zJHaWSeHDlmIiQ6GLLLg0U46gh7ynH5orx+yOstH9NE8h/kfbf0f/bTuQK7MyZET9SRjJ/oZljrDmMEzZfs/h/to26wvl/RY/i8DhKNpoTTtsBhBtjmsnsUD/Y0+9Zx9w/qz6VoMbYpcaxMZiq5M/Us9FnlaRyNZLfqonGzNyHfmLWXkdFz0i6wGOeSrKPYi6kAWRVEUxzhcDyPCIk1PhIGjkFN5RaD254CqotgWTCDRaI6HU0WdxAqbFgA57qKNIkdS2Of9osWhBwcv97nKOuGkcqwTwZV5tI4fIr2il6L3jeoVxcGjKaxFURTFMQ7pcvZgSWEUNWAMSpsVkdyf63SKYlvEIZQumitMpDdyStZBhMgWBPIoUifVVPpmcfggUudUVSnUTq+mK2whEBGWOrrJmZeaLuW5zn5R7A4agSyKoiiOcSQt1oEaogscSOld61LNimI3kJTFXLsgXZAjKd1wnaMhrdSz0iNz3UedksOPbElw2A+ecQ6Xe/KLoji0qANZFMdCZFh3Qi1mVC6KoiiKojhYNIW1KP4/iH7YbwWiH0di5CMHE9jvwUlwiqBDHubvrZ47mdD3Vs/n79HAqq7fDi2xwj47G1Z95+suHIawabW+OHQRE3LqEBTpW/b/4KcTDHPghUiJAyWcdCkdz8mEonpODHTghJMo8bY49OmR+IDmxgq9Yi/ncrwYS3iae/1y6AY+SpfMSY7zQVmeWXfASeBdp9viv0NX9neMqofM5PAsbdcP1zBIGdzp/slN7aE/9MXJnXNacupyeIm/fb9Ne3O9A51HZ3lvJ70d/agumA/iCm1FstBWudojbTUHVh3IgSbep3OVhYbKPyYi+/iINtqwW/pauU4OFQE8NumTHJxDt5JZ/Vt3rddOcDAO2ohoL+fRojg2oA5ksacxB8gPVAFvE3UxyTvVzfHrjhB39PpOBtJutGtf5R5I2RwG10EweFwXoR+uOonx6e5MR5W7YsFnrglwfLsJzkTp2hPv+dvR5w4rcES5ZzkuLmx3RDx45773ve+4/uK4ioPl14EYbO7xc9jM9a9//XGYhysLHI3vSPtcJ+Loe1dk5AoLR/87wp7D8YY3vGFcf+Jk00N9wt/hingeqvF4MO1x3L8rSTgMHDhjz9/4FgcpjpPnnCzJUKV3OJ2cT7rIAULGJD7jL+ffggGD1vUerrNYdwokh8E1G7l2Z6dTOzf1wZUwZM3VOq55cT2CKzyMe4eV7E9Z+vT0pz996Fk6iGE9f++6CtdiuJrmAQ94wNbl2rdIL3G4tW1felv9Tlul8528in7uH3UlC3qjlfso8cV9lO9+97vHFSGuWtjf8ZJUS1ezKOuRj3zkuDLncCBjgiNEH6C5ce+qpYOdSwOLCQ4ScuWJE1EP5dg73GOcvLoyyRggX8trkfal76Q63/GOdxz0dp3KXtBLRbGb6PnAxZ6Gyc/palZwDwRWvRk9JvGdQLnnnkXXCewrs9sqNkPkUPTXnVw5Bn1/oM3ujHSwg/urGI/zAQ8MUoaMO8Tcc+XuMgcRMJw4Ju7J0n/3V3EuGKwcarRTNofE34xWx9i7b2++rP24CEboV7/61WHQHw5YGOCwiyTmMnoHzTDu3SkHDF9GP4Pa5ze60Y3GPXj4687Ee97znhtPKtxt2jhFlRN7KJGoknvj5qsRjiloA2fQPX0i/H5zGm9729uOKJSxRXek3ZwxjhUnhcHpe/de5l5ZEUR31OUOQ6eFeobxzgGiH9fB9Rrq5vwdSDYF+WA0J2MBjHfOgt/7C1eO6Ivxkisq5rrc90j/Mbz3p42car9lXexLb+e+P4tiDmzSJpkWrnXwPiefEw9O6tR/jq57ZA80I0XkWZkcXXr2cAAd0BGtQyOylgj3gUJU1jjLfGwhhDy4k/lQw/xF1g/XriuygW7ugJyv1tgEMq192gnk6va3v/1YiD0uz5HFsRd1IIs9C0rb6u/DHvawYTCbfHP/lDRLK+zz3VBx/nzub+8z5FwLwDBYdyFy7vky2YqwLdOLUiYj2HMx+kR7XvOa1xx9l1PudVKPyXWuR8on44TBH8PJ99LBlJ30KeUwEl0uzMBMmtU6umiPMvNM7hcUWfSZOjmKDM5MXlZUTfQuQbfCLjopxZWDbdJjMLncnoPiomPRLROidiuPAyL9zoTIefT9prQevPLjvaTEzjTniKLV0rH3HWM19J6BPtqCZjNtvKPvPlfftk6E59RDtmbZSFQo9+uh2zoDwnMcJPzCNzT0PPnQL/z1t7blDkq/fRa59Qw66PNO7U7qqj7CnP7HGOYk4E3SFp0Uqf3eczKhz8iZNoqAMKJhHT21TV1JlU279I3MofFMK2Xm/r0874eh6fJzTtD8zgzvon/okzvY/Pgud2uiWfqjHd7x2zvKdhn5C17wglF/xmkO5PHu3F514KfyI2eRraXcqVMZDO/Uvy+QdwsznCULOX5LE+eIcOgY9dqrftFg15dwBJ1Cy9mggxjmTpgUEVOncSZ67Ls4NsZfLqBfJzvkwcXlygrQBA/RKfebroPnyEXupwv0RSQud7SG/8oMD1Mm/pBtcgfan7sntZfMhzd0FBr5mY3t6ArlGysz1IXW6p/T8HdC6hHVNYYskvkMjUSJjGNjJaCH0VC/o/tDl03IfKJ/ntNffEuUWBl0ylK/oZPFP3KYFMrIfvRJxkZk3zt+lmWRV3PUm970pvEuWcBH9Rvr2petCAF5yLy6Tp589uEPf3ickiw1Uzn4KcNFem/4pZ25z1R5GTc59GZJO+1HU8+vk8fImDHjZ+5vxqefnXgSoC1Zyh2RKR8tyCq6RR5zF2YWDqKb/I6+SDq09qANfZdtAuh9oQtd6OiFvdTlHXxe2gm5zzLbSDbRoyj2CprCWuxJUJyUqAnj4x//+OrNb37ziJ6Iqpj0TcAidRe72MVWl770pccEIw1JWiUFziA4xznOMVL7OFXeZzQwyOY6TGrSx+IEWiXmIIH6X/e61430M0affWQib1JaGH3Kcsn5xS9+8WF4MObUr12iCVbpTUocL5EIqYTuDlM+I0U/GBnae5Ob3GQ4cy9+8YtHiiIDkdERQz/QT46rycXfJj5HmmvjO97xjlG3MvVJu+ZIAZpYCZ8v1M4KvIlSRHI+yU6ZvmdwmewYqh/60IdG2YyqOb01QEeXcruwWfQTPTkQVvBFVRga6CddTb2cVxFNTiujQx/QwyRrguYAe9e9gPjIMWK8aBOj234e5XHkGDPoKuIWg2aTbOE7mcieTnXd4AY3GPTB03e9612rs571rCMNUR+udrWrDWdgjkKY8MnHW9/61qMvDGd8cQr0SWRQSh5Z5ZDjpfRfRqnIICNCf7Vbn9BLPTH05vZaBRcpJv9o48cz2k7u0VOZ2qBfZNfddoCn0lcZTtKbtUtbjCttIdvacPOb33wYZ/irDQwjvGMwoo8y4uxxhES41I2HnNT3vOc9Qya1Q/nklIyTO+XhYxxffVIvWSFn+Mcx8dxTnvKU8YyIHfnSBvzAH23SL0YZWSfjPnv+858/jHzOsWgS45rxjB/qseBh3PmMDJN18mTMSuMWacA3z4oW0inGmLRDNMVD5UsH3ek+yqRI4gc51LcZnMjTn/70g+++J2tkmfO4jFKc9KQnHfpmuaiVRa+kxOLDuj2VZBe/LRYpB2+NS7JqnNANrkhZls2Ixjuyju9kU5vRw4IceSffFqiUQ1/RE/iBh8ay/pMvfMI/zysj44aMGbfqu/GNbzyuYFiCLlGO9Et91Scyilf6gpfkTRl005wSuxOyN1w7lZEUfg6Deixakudc06Ht6iDf5AU90FC76bCZ9tpj/CnL+7lyIv0h7+YB48hiXVIbjS388jx9Q67dR4lfZPDqV7/66gxnOMNIvdV3dHDdiPElEi0qnbsNtZWMv+xlLxv9IEfGHnC0ZJ+IvnrfGEM3c4ayLKDgI7nQtjllV99sccBvugo/jD16iaPkx1xHpskzXY13PtcOtNZ387j+oBH+ymzRB6A7lqfaZkyp29hTjnnfM/QvGJ/k1dglc8vxoAxySOZ8rx3oTzdpp/FoDvzKV74ydJfxjxezA4dGT37yk8ecg95kznxNdvVR9g8nEs98ZryENtqrrOh/epCuoU+MI3QyJsx35lo6TjtkEW27OFIUhxuNQBZ7Fox5Rp8JhQHkt4ue4apXveqYGDkxJnUTM+VMWXNuTHYmdxOX34zEZdoVBW9vg4khETeK3URvcmRQmliUx9gweTMK/G3i4WCYZBlzIg4UvdVYRodUUZOrSZwx73MTQqKUVodFAxnFVjSlFXIKRJIYJybZ5d5C9Zj8TVz6710Tp0ii79TBuOJsMVpMRjNMiPMKv1Vfk6739JsBEoPBJMaQYDwxgk2az33uc4eRYfL3G+3XpRajHceCEcPpsqfI5CpNj/MhLdZkykjRZ4sEeOF5/bOXBl3VwahiuHPQTcZ4oUzlS9NE22c961nDudcPxsGmyG3AgNUWBq6yGIIcMQ6u8pRhz5lJnaGAz/YPLdOKORYMJcazNuMdB4ZDRU7QzCIDo4Xhw2BCG4sDnDmRcfu9GDIWHDhOT3va034gWotPopyMXs8yTsho9tCpi9GCtsYIunIq7HcSbWa8kDWGonqMCX1nmCrL2GCIMY6Ui+6MNAYmI5Y8i+4xQjm4xtv97ne/ITPayklBP8YO2to3ZDGAYYSWaGMxB71mHrz85S8fDp4xax+aMvBR241lskoejQ9jghwyIo0h73BI9Jmco7v2GjcgDZt+YFByDkTzGKnKeNWrXjWM9KR3Jmpwl7vcZThb6MDQZtiiER0Ums8ZD+vgGc6+SEVSK2eQAboDTzlf2oQv6xY8PMuBnp1uzgvePOIRjxjjQYrcOufTs95nzOOXdnOAOCrkAl+XESjAT7QzhoxzshQDVh34ZmxwNJRpXOMh+uCHMvGHXvW9MUlHGG+J7HBKLVhou77RI4z7Jeg5C4DKpuvIhLYZh3QRw5884zke70+0hlxy0tCG3tEHPHfJfDJLjH88NL7RkXNEL6nTeKB3limpZAZ98ISMzlHeZDWQMzo6+kv96OV58xCZtK9QfzmNFjrIk3mMPNCFdCLdrcx1BxpZJPKscaefWfTQR4uYytdOdRhD+kJe6EM6CU+WqdHqIDf0mnmBbiPvdAc51h5tNGa0ma4yb5sb6Rtp1+TBHG4O5DyidfSUttgjuhxjZMYYN3+rU92cPQtb5m46SV3mCvPoMlIN6aMFF/Sgg9kIZOje97730Ilorx3GFsdwGdWlm5TD+dZvOku/6XtzJ/1oHGsf/qInvWyc65OFF/qdvFq0wFsp6xZl0M08webAH/2h69CxKPYq6kAWexImDRMSJcyI5PyY5ClhSptjZ2K2b4ZBwcgxeT/72c8exkQOPJjf93cMrRhjjKqk/zEuTbqp32Rpf0fS7NTDQEqZDGLvmVgZoyYQxjbjg4GlDu2yIm2S5RCaNBgAViFN0Ix1Rr8fEx+DStnKnQ1uYGwwGjkDjGw/jHOToDK9E5qpd6c9OwwfNLTSarKbDVATJ4ecIaF85WiTSdKzHAUGBgOLkznDcyZntEQ/xrcJnkFlomRMcso4gowIxmTS6fDV33iljFzozeA2EeOBFV6GBzqaxLXNu5wvNOUs7RR9BPzBE+XjnaieSZ8xqWzGpfZzgDhtnIpEfJcyuuQXR4YRiq+MDfIUY9RvBg/+qYcTqR/6wDDyHgNi3k+ZA3HIFaMQbxmM6KL/2o8nMSDTFm1L2lraph5yoQz9R288YQAx/tExsijCYHWc0cfZImMMHA4IY44hlrsaySFaaRcZ59AqKyeEpv5ZxjK+OXOMczTRj6QX6puxnn1Fxlbkm8PqO449HqZv+qW/+KitDDfGbFI+ybz2op3yGITonn2ljDjlJdVXucqywOEZBv5O0cclNo2/nPYZQzn/L4FGS8dQ2yxKcXJFMDlh+rPOUSW7nNOUo+0MUs4PGTcmlxAJFmEia2Qqi0dJo8WjRMjDQ44IebJAhn5kQ9sYwtGb0Yd+OGSixGRG+40tcjXTAP85bBxtY56c4pG6LZr5Tn05iTh6e1ugD72vrfSgPhu7nENt5szQbfQXOee0c+LMF+YZdScSPAONyR3n0Pf6Gd6QbXrPOEFfupCckWXv5WRadEN3usAiKlkVkUIn79IPHLKkXC/3M/ub7NIFyvOTxSa6TNs5P+Q9JzXrr/kQXdEeH5cLZnO52ot25t9E2X2Pt/SBsZnFTPWjLb1ivKEn3YiWaIW35i/PArrgO/2UOSFzEL6p29yBb/pjfKOTuQqd6Cr9yvvZU6sMY1l9+KA95jn9VI6y9ZtONs9auFrSVd1ZZFVe/o89EFsjtEn6qrLIEFoZk57lTKszW0PIMRnPj36Z84tir6IprMURldJqBVf6GeUMokcUrUlPmpqVXMbMfe5znzFB7gQTS65DCDJh+E3RWxn3HEVvwli3N8RkYAIzCSf11HM+55gwRO52t7sNg027TI7q5OyYOKyIqo8jI4VmE7L/JfRIG01Y2Ru3DR0Z90k7E3XVhpSn3RwGRhTaatPyJE31mfw469nftIRnM9GiHQMiK7FoJCXRb7zLpdwM3qR5xVliwGmvdxkYDDIQCcAPiwWcd3SV0iryyunZ6cj1XBw+G6ycCXxjeCS6F3qlH9tEONDSCjMHW5oUw4hRQjYYKcpikGe/5kxbMu25eQU9h/TEIZvpO/N0E6/X/a1M9GSkxMDVZvSMkR/HGL9yTL9FGTSy6OC7+dj+tGfbg0b0xWq/vlmQMBZEZpTJuGVEcy7xnOOHhurAX2PEOxxN0XcO1FJPeFbfZocvKWoz7WL85bPsz1NO9gI//vGPHzJpDIuW7ATvJ4rKyM24CtCTwW7siZAavxwS49pY2wnK4WhzGBjG0uhF55Ypz5vepTct5ojycFqczDvvzwK8ZtwnHXLpxM5ypK+ilHj4+7//+yNaiR8cBzQwxjl2ZGZefJlprv/GQ/aBB+RdW+iEpNniJ/mymGL8zOn2B3JIifFGzyTlW5SavGiP1HO6xyJa9sehtbZoJ9kjw5zC8NhvsksfPfShDx39J9NpfxxuyG+ypj/zvKJf6tUm8mK8kUH0MG4t7nBGstC15OGMpW6ILktb1MuBonPIRw5dswBgjlrK77ozBJZYjq95Lshv/BWptxhl/qPro9O1x7kH+gvoZy6ay6Wns38+bcwiAnpacDRmfUd3yOAwP4g24g99d6c73eno/d/ph/rR3mLkuj2VO/V/kx5O5N6cM9sk6Euvhs9L/vi7eyCLvYxGIIs9j0wQjJrccWjipIxN4owpk7x9hFYYTepSWfZ14hxjweRhIlvWRaEzIqwOmsBMdDF85meBccG4NJEz7nLnUw7nOOqoo4bRxjEzMWozo4sjow9JuzQpzuUuwVA26XBIs/KtncrbdgVee0RnOG1SF7O/MZMxWlrdtpprhZbxnrvZluXggWc2YZ7w9ZVhqTzpU4xeBkto5ceqtUle+6wSM3bj2DCqrCwDmqFFDnzQd6luFhCkyklvTYRnHS1zzxt+ZOL2W5uy/2Z/Ju6lkSViIJogKprUWPyWssloJccxmslygKc+Xy5oaJe+ZvFgdnYOxHD2Hr4zkhhu6Gn1O07tctU9MqefZNXzuXvyQJG79aSLuXpBGh8jOfceSuEii9LGRUfnNEopcQ960INGBEOkZ3a4094cjpTxldM9t+Fr+m9MShWXjo4+2hmnME7mOnAiOB+iIeFZoF0cxqTRcUrIAWdjmbrnfzSeD3DSNhEShjD5lQbtmX31S3sZsfbOWfjxPx25TEEna+i5PNF0nZwlTVYqoXborwU8BrwFFONMpGeOUK1rF35xFJeOB4dfdNznGfN0elJBsy9ybmPG7k78CcgZ/UL/67MUSe2weCFNkdzkrk10sRikrbkf15ik12b4n8NHZvAXHdBjE/STrvDMvKCk/Zxwci8NVX1Sw/WP4yMrQVYLWdjf61mW9NK3HA6UPfH6jC7rUrb316lZx/fUS6ejAVr7yfU0TiY2//qRhh6+pm7Pei+LkkB3oJe52tUZ9K/3LTaZQ8iTFFKLHGjHVqDvydR88m9ovy7bYLlgMS827kQbsoauFrDmLRbetZC0aeGtp7cWexl1IIs9DZNJTvMzUZqUGY1W0E3y0rFM2vY/mFSlRTkYwLOMDJOA1W/vmyDnlV5l+bHXgIFvEmc45VQ3aYYMQIaoiU47ctqbCcHfypXmJtrEEDRBJI3V9wxDZTJURDMYKkkZtSrK6BJ1kjKTEw89413lzBO41XqOHefKdyZMTgAjQnQLGJvzSa8z/O9dNNMvq7SiGCbZHJZhDyKnTNqYTf2MFimEaKGNubBbyhlHabnPMkDnnGjHKEAjhhVHNdeBqI+T4G+GNWPRcww4hhzeoEX2GlpR1tbsnZGOl0MrPCM11nP4xLjjrHPalivJIj2iGfoYp0KbrL5zYuN8xsDy9yaaqtd3ZAtP0IaRKd1R+eQRfxiJ+ov3uXqD4SDFEq0YFWQIfxlxgWe97ze+6Zc0Q/xDu5zkl5NH00ZyMMuOv3MaYtKzyQKDyrgR7bNwkD3A2bvlWW3VP/t20R69tTt7OjfVm++Nn5wYnGc9l7EhSs/5zynLicZqo/EkEhgDUr3kyTvSDVOmce5z/GRccvjwXyoxPZF9mqHFbACi3ZxO6m+fc5rJHIdQqqWoIhpqE8cXH5ZQrkUAh4Foj/FFPykXz8gv45QuYNCKZHEmGbtSeUMD+oOOU3/GtLp9TxZyn59xkL14y2hI+JGFLLzjBEnd9C6jdZmCST7RTposeSXTFrbouZzOGXr5Wxoh2ZDJYK+zNuonnaEPaKScjEnvpk3aZ7wbB2QsvPGdcYX/xoT9xxaVRN3Qgx6UComnyZbwQ4doi/Zyasn1Ticb45W9b+QrkdxkhFiUEu3VDgs+2uJwnegfabtouTTwtYc+VyaZ4TzTYaHXvPCHDt4XfafH9BGMCXqWvuQQkScLk8aL9nIo0UA/ydImBw1/0Twnri71GF7533ixuIfOFkzpLbpGm+YF06RwkmdtVHZ0xXyi6ax34szP9fpe2/SDzpeObf42v8iMoT/1yxzgR/uSLqrPZJIONwbwJKeukzkLFrJd0Czv073aTG95jkNqPtY/dFW+77TRc8qnh5NJlP4kS4ismXe1OfQl39pnHBorPguP/c7eS+03b6GDcvw235Gzpb7OuG0UstiraAprsafB2OEEMJI4CKIAjAMr8Ax+k4EIhUmbQ8QRosAZSFYXGfAmI+9Lg8lJfSYDnzsMRHk2szPUGQ4MO0qbUcRZNbHan8a4Et2yAizdhuPJyTLZmLisaPrbJG+y55QwcB00kQN0pM/43kXZj3rUo0ZZjA31c5gYMQxW7dWH+Qh+kwyjhFHB4GRkMwxEEkxejCuTDseN0SzCN6c9oovoQxzCgPFk0uYscipmoysr8owlB1gwqtDJaqpoxqZUUfRVh6gSY4ihnJMtTaQMa5+jBcPBs2iPZiJL2e+Ct1LL7MdjuHuWUyCdUpoVw4LzrzyOF8MSbzilJmoGCUOFgRSgFdpqO6eIwYE3Vr0ZE4xyhgLjlpNm8UDZDDzyM5+Khz+MS6eAcr6SZqd9jBWOvbKka5IpBmXSdTk4+Iyu2qSdDp2Yr+jwrGiWFF3GKwNVSpZyySkjBO3UgeciFfrMCWDg5PRMz6En2nFuRZ99xghnNGqvgx0Y7GSW4cOQVLcxhAe5VBvPRFX1k4OANgxB5SsTGFgiOmRQNIYcMpTnfuUEZDKFNsZyoino4XnOxZz2Rd7pAzIIUlpFpjzLGTYmRS9FxEQpRR+ME3uZyZd30Yic4QEnEc85z2goZd1v/aEfGKiMQrLKYdUe9ObsoUtS6WfogzFlMYZcGut0EdowJBnM5JGMK48eEEH3OflGT7SlF/DFeOZMag+e4oGFDvxiyPvOO9JJc70G2SMP4BlOXA4GyzUCUr05xTPUYWFLlFdWhfEkpY/cc844ZfiBTuSVEU6G8JDe4ETSY7I2MsbJKt1MRzPYLT6RZfwgZyJCxhHdk4gefUNO6Tv6k05AU1kmaCb9VlYH/tKXeIoG3tdP/MGbfd1PqF/02HzPI/mmW/SdnOKnBQ50xCf6gT4jr/TmMnKqn9qg72RRuzgo6EbO0Fz/8Ao/6TbzGgdZHfpAP5lTso+RzvR3nFoLEH7P6dsztAsPlIm2iXJxeIxN/Ncv+g3fpCCbB9EV/9DZ2J8dSDDujGv8I3/6yMGmg+km40rd6hDB9D9nzpxDz6bf6GB+NR86sMY4M9dbeFmXym3c6I+52AFKaM8GkHFCh5MlbSWz6+Ykfee80UXkmbxYhKID6F9zt7kqexPN4xaA8MezxhL+0ePGH355jn6jq4w3tKFv2Qx0BV3lOzoSX80NdInFGXOL7/XXOKJT8CELhOhFh/ubgzwvKhbFXsHxvtfljWIPg3jmrjCTZU5ltMpNwcaYz2ofB9KEm2e95zPf5+S6ZfnKY6QpLyudWZ1Xj/JMIv42wZjgfGeC8bk2MNI5IRyE3BGV1C2Thu9MtN5NNCVXSHhuPujA594xaSwncIiTmNXj7GGc76nLAS/L/VdZ9Z6RAwrW3XUYR867uestp0ZuSruRVmSCZzxlH5o+hh8xyHPYTU5llFbkN8MZXTgyDA7GQY5BxwPtT/1ZAcdDPPB56GHyFbHSjnUH63gmdzyiYxy3mY76ntXzdTT1nLYypLNSPtM6B9rktMZlWpQ+ipzkMJB1/E496KQecpFDTdLeYG5vUgzTniD7Gz3HOPF36p6jCTkYInKZuxjx0udJiwxt5ohW3sUXq/PaPKfaJTqGLmTKOJqNceVyyhmrDMvITqJwymSIJ91XWWTD/8aZZ9FK2fqWI/lnWiV6nKjn3P78r1xj19+RuegZ+mRf6YO5R1Zbkna/btzkvktjzPfG2JwKn3Grrb4PXyJnicLO+3WXcpz2aDt6oN86eQvt0DM6KHK7pF+uctD28EO7fJYx7v/oa+9ob9L10YM8Rh4iezkUyf/e9TuO9XLsqDcHmkUneIejsq9Dj6KP1BUa+UzZMz1BX+lddWQhaFlW9BGZ8X2e2zROZp2ChtpNrjJPBJGBjPmZ55uQ7QcZ29EBoVFkI4dQ0Z/0gfrn/aXLPuqb+vVtPmV2mZGw7v854h09rU6/tXOn8ZSU7oxx7VO/z3KAz/LguSBR0ByGlrtZM6Z8jrfKjVMeXgbhk/cz1yRaHrlEG7zOd3Pf/YR+sRWiv+YsiOxFz1iYyy+KvYQ6kEVR7BqoEw6kFXKr+I7fXx7EsQ6MACvaVtpFUkzWjEwr2FZ1rV7vz34QjosImpV3q8ybHLNib8kOZ0tElOHGORCJ2emQkKIoiqIoDj+awloUxa7BqqnIHwdQ2hwnQArTvpw/K+7SKqXDScG0CizVSJqTVLT9PUwgl7Nbue1BBEcOLBpI8bJaL8W3qVtFURRFsffQCGRRFLsGqTgikFJ8OG45tXObCGBSxDid0s/sG0sKaHHckR8HUEjV4zyW90VRFEWx91AHsiiKoiiKoiiKotgKTWEtjgjkYAcRCfvb5oM+cmR9DiSQ/iYCtukZ0Q0RrhxcYP9dDt4RBZNOmWPxHSZwXEWOXM9hPyKC+zrkwHMOCBA9mk8rPVzI4QPaTF721ebdrlvfHQrhlM3DVW9x8CAzZNfBL/PhFocKOTRGfeqW5r3TAUp+HBxEvhywM5/Uuxf0g7HmUvTK/O4gB7uQD3PQptNWE7V36I/nDnavd+5Lxcfl1oMcOOcQpvlAuCMRGX9ORjZXxR7YX1qhB7rvdMhNxonMGvzMXZvdl18c6ai2L46YfXWOBWeoOEY99185PjwK2cRnIrB3zsXkjHiK27uMG4ah48t97/hsR2Y76dEpZwwyxpmJ2tHxFL0j6x07n5MEj3TMJ7RuC6dCPuMZzxi0dd3A8tj/OYGBIenIfdeJOGLdtSfHBHLfnqPSXUTvNMvdwk6Xq5NTB8A4ll69ZK44MsA5s//WtQeuNHDtzqGGaw9c9cEhdH3C0ilcji3XF7gigmxZGNkLoB9cDWSsub5g3WnHxf7DSZ6utnC9hKtq7AVfBzKSKyBc7XKwCwvkzBUu5lFXXM0wl9Jv7oh0JZYTtg+FHj1c5bEFXAPjoDdXTO1vOzigrqQxfl2ttJND6Ioadohrg+iaBz7wgd93PVFRHInoEkixp2FicKeU6xgcpmKPnHu6OIDuCXNPE6fGnWZObOT4ubfPtQ+ecXcY5c2Yd9+XyZgCt4rq/ihRB+/d9KY3Had1moytKlLujCHOqkn12EBHDrNJc3+y1q1CM0pMfOsu5bb6zXi0As7o4Mw7OTXXVhwTsDIugqTN8/UVuwH9cgfeunIZEBwPBslOEYNi74G8cMrc/yjyczjA8DRu3MW3bkySMXef0j/aR88Za+uu2zmmQD9YoDkUY+24DIuaFkkdRGZOWwf0fsc73jGuOuLAbLrCYn9AztxLuc45pN8svppDzJ8HAzJtAWU3dlApg11ggXndHLUJ5ni62infB+LE2p/vrlCOPudwU19kpFgoch2V+3PdIXpczmwqjj1oBLLY0xBxdNm6C7VNqC6lZnhxCB35b1KjjB3WwuFzBQQH8QIXuMAwuPyYHEyMnnFhtIlWig5H0yXUuVfKfYG5U1BZLga24m/V1aXD2x4EY3LlWIl4zildnFXfaS+jK+V5h2NiQmIs+K09uW9O+3IvlwnSs/rgO+VzmHyOVsr0vBVsxqnnvGuCtbLMCb/a1a529D1ajGWTOedvTtub71/z+bq+azfn3AXzVqvxR1vybOiAB/M9dLlfy4Q7pxLjm//9JF1KHVKOve+Z8DvGUu7/Q7Pcmxea5D4t3+eesdz7pR3KQiM/2uh5dWqz9Ntlnd57/etfP6JULjFXn2difPjt3kIXd6d9+qp8TjjeKnd5+fhM83W8TQq27/HR3/Odk6Gr8pOePfMrqZLKzXiA3McZeZv7skyl864Ivd9z+bkfEs3me9zSF/A+Ws3tzf17uZNQ/TkxN3Lv//m9tCV3qkaGQf3akfte5/vx8FS7w+vl/Y1J0daO/Tm1N/fE5o7ClOlz/MDz9G/meWTWb31Q9zpZcGG56L/L1hm5kel5bC3vDjWGlIfP6y5UX94Hqxz8zJ14c79y92U+j27TNzRUPjrPYx6tM8aV6Zl1aZA+107tyH2UueN1vp8y6ehkJfdIzjRWf+5c9J42RqbntMTIacb63N/oInX4O3VHl/qM3OHnrCO1QR+UQz/knkFyEJ2ZMesZdMpdgj6f7wycnb+kruZO4nXQXosJouaiaMmy0IbcP6iv4U3uqs29xSnDZ37QKnrVvavenevSpozJWa58N9+XHP0SHZt7TCOTaKPfotVw61vf+gf0Vfi+iW7R57lbWHkWgzmEFojX3W8aHehz5eU+Z9dGxenE/2x9ydVT6tXfzBWZvyNb5jyLhjJeZBHMp0aHBhZXOMvKRf9LXepSozzf6SOazHc+R3dm/lHXXkhZL4ol6kAWexYUsBQRq3t3vOMdx+RFWW/ab0ABu7qBAl5e+B5Q/pxIk9opTnGKMQEz+t1ByJjjXMVI8NvK7vOe97wRydzpSoFMFiKlJjTRPhN1JjRR1Pe///1jMnJKqX6YSKTgvva1rx2fcwqsKKvnWte61kjD/eAHPzgmKUaCNr/hDW9YffOb3xxOyrve9a5BC+lE0pfe+MY3jrsP73rXuw5avOhFLxoT0c1udrORVvryl7980EfUQH+kpboyQ1u12b2N7lv0jlVVK83oyJBdt7LrnRe+8IWj7Ne97nWrS1ziEuNzRpy2cyztL3Un5IUvfOHVpz/96dFGkV6RHvy5733vOyJ60oVN6mhj9fuc5zzniL4owyRvQeDtb3/76j3vec+IFp/pTGcaxvKrXvWqQRerzyZnCwsWG2LE4Ac6MVrvcIc7DLprr8840+oVAdI+/Sdr0sZEt0Wx0cGPxQV8IQue11/vWFUO/032+IdeDCOfkS/ts+ihLXhpFRr/ZvlkSJADPBEJUw7e4t15z3veYYApi0HpGW25ylWuMuTGCveb3/zmo0/AZXCc/OQnH+953neuxyBrV7rSlcbn6OFzMioN8ZSnPOWQifkydXLhGbR1nyaaqY+84Q+jjHwyjvBKnb7zHvroH1n67Gc/O1Ka1THLDllmxJFn4+z85z//+Fw9FofwQpuME7LBELNgoVzf6f9ZznKW0ReOPRrhATpc85rXHE4OecG/c5zjHCNbwXvkkUyhr2i5PqCZMZoL3/cFdZI9ZeQCcrqDvLuGRHsZuHhuXEuLZeAyQr2nrcYJ2qDbEoxdC2fGJz01Oy70CDrgO/lANzC+9JF84qNxQu4iZ9kKoH7tZSzru/EobR+PORN4TsY8i/YXvehFx7s+930ccTpqmdJunxenV2TG+EiqbS6Mt/hi4e485znPGIdkz9hCi/Aw49Q7+oovInHKwjvtpsvIFn311re+dbTHmPOcsUNOjTMyh6dkKhEqbbTQ6N2PfexjQ694js5CNzQzjlwrRO94Xlo6OtvSEJAzdWmj7Ba0kMJPdsm7uUbZoZnxkntx8ZUDqL10FhpwJI2FjEvlkKd185g+6bfnvI9WxqCMGXqQTCrLXEBWzaNo7T16jswZTx//+MfH89ohTRYfjF1yjZ7mEfMAvkVXxLGL3tJHYz4044DiLdqc7nSnG/WQ88tf/vKrq171qqNO84Z5lw42b8wLLPiqTDQjD/qGPuhtMVd76Jyzne1sQ5/Rt7aimNfNbSKos5PrPe1Hf+WZ0/RV5Nb4JItk1ZjSNuPVc3QD/aMOskuP6Ce+kB+ZUOhkUfsxj3nM6LM+zosWZA6f0Omd73znKNeYis4mb/Zhmg/QJvRHZ3OFdrNNzGndM1nsNVQiiz0Lk52JxMTKGMuK4CYHcrliuNMzJkNO6bnPfe7VUUcdNQxOk5OJLJOPZxkZJkaTyb7ayuAzWZqcTEj2ijDQGHQPfvCDhyFo4jFhPeQhDxkGNEfApGXiZ8Axnhgk9huaiBkmJhoGEKNUO0y+2iYyYbK9053uNFZGGTD6wPhkVDASY8CIYJikGNLalrQajhijRrvty2CwcPJMXCZiEyLarwOj26StDwwHBgFw7Bh5DDjvWh3mGGgjw0//1MuIYdg8/vGPH04NZ9vnjFnPMGz1l7OJZ9KnTPIMI4YgR4/Rx3BnqFjVNlGjE/pYqQZGlMlcOxLNsG9LujK+4wcD4NWvfvVokzoZ4+q0j1MdDCzGiYg1OjIq/F4Crzl6aM4QYTgxKjkz+MUIwfdluhNDQp0MIbLLeGPw3OUudxlGCD76jrxaBHjCE54wHG/GEaeWDF32spcdNHryk5886ved5xiyaKs9aMGAZ+ygMUfQD7osUxC1iYFtIUIZ2o+H97jHPYaBQ9YYb+SEw02e8DeRWvQk03izTK/TH+/qC5nEK2MIT7UFffHCqj66oCP+eRa/yZj6yLr+Wggw7vSTo3znO9950I+uwA/vcz7QRzkcpLlMzihjdtuUOg4AR4kRqw9Pf/rTh/OGz4xbPCEvxq9njW/88Bz6y2jAL3poXZ3GsnYZW1LejAVIxIieoBfJrcUBzsgrXvGK8Y6xiL6+Wy784IOFCkaqscYwJwf0EbqLpGgn59B4vtvd7jbGifLRmw7Da+97b95XrV2Mcs4jmeCILfUFPcAZw7dEnzjX6iTrZPApT3nKaDedR8bsT0Ovpz3taWMfKFlkdBsP6IcWyuO00XvajLfoEYfd3+Tj+te//njf9gZ94lwpU12cQ3qSQ8DY11ZjINkc8wIIWJyhA9CaPBlfFkS02fMcXwtgxiwnnPOgLI6VH/3CKzSn0+neZDZwJozLTfMYWtOF82FdaIYv+op3FmWMUzLH2SH/5qOHP/zhQ0+SUe9wkvTDmEJ/fA1vjXfzAlk2d2lTZMp32m6eMe7IKh6aS+h9Cylk39hSvwwY9ZIPfCJ/nKllOifeoYe6zFXoRmfTQ/Qr2hhz97///QftLSJpv0VUzugyaosP3vUcR0x5+IMXxiXHW5/011gju+TJ3ITn6qZH8de48K4FXePOO0l/p9vJU2BM0DcWHsiFui2OkivjjZ2hPD9sBbrI+/Sd+Un7zE3oWxR7EXUgiz0LTghnwYS6G/s7ZlDunAHOC6Oag8BAZtzMUYikqmjHTsal7xkOVqxNFgwRBpxJi7Fg4jShM3gYeSZRdemXyZmxbNXdhCOq5TPP+GGUWnVlVJrQTFYmVoYJA9WExIDm8MTg0OakKvnbpJr0Iz+MDxM9Y1aarno9zyhgcDFATJjq9ve6VXB1KXdOK0Uj/UcHxibDIalajGC81E+T5k1ucpNBG+3WZ/UzAEyYHBBG5XwPpOciB8rjNOlTosoMVm1NSi96KQ8NGRdWj03+/tc3hrD3RM2kK2sLA2o2QPyd9CF1Jf2Z0bBMX9U3DmfSjf3GK04UY0i9/mZsLmXJu0l/xVuGg329HCu8Jasccm3QD8ZeUjY5LqA+Rpm+KYuBg7dWzBla2s5w8672MZAYm2SNAz+ndeYOTzzTJsaPNtkvrDxtsrKuLwynRBGzeKGf+M9gYlQydGfoR6LinlU/2cvquzLx37gQAWLg659+kQ+LJsZrDr8hU4xWbWYEcnosBIj6eQ/9OYh+q4cBbrzqpz6m7G1TxZTFcSGzaJD0dGNLneQZvTjP+qdPnB5jy7jl/JFP/Vs3tpK+GPnO2PK36Kn6OTwWgtBAX0QMjQnyQE/Qn3MqonGqXSIb5B3NGcsMcvqAQ4/O6E1eyJb26ZexxenyG6/VSadFVhi+HDpt5OAnRX8eH/pjPPiODkQX7UBDTpu26Rddp3yyQP4ThaRT9QlNlOMHDfDPQp9yPKN/5E07tZ3DTL9xcIxbixP6yLmJLqAftFu0B138b+GOU20BkWwso63zqd+hb9Kg0cHnxirHl3xwFsm09qRfdDAZ0S+849SSZX1BH/p3qSuSnqrspO37QT9jDy04pxx0+t3cxikx3nyXNHFtsbilXn1GM2MdrbJIi0b6j1/a5Bn0Ap+TG/zicKOF7+mAnCHgPXrBPEBno2cWgcmDMmc5AXTDO44v580iJnrhh7Lpac423sXJT3nRvcuxZFGJw6zf9AM5QAt98bxn6AZ8tjDHsUxmj//RjvNKL+kXupPRAJ+M76UDadymj377wRNjFE3Qgp6U7WLOU6Z2oZ95wJijLxp9LPYimsJa7Flkr9z+7EvaFjmIwoR529vedkQSRDuskjLCTeKZWJKKtxMYcia1RC9NDBQ/mHhM1umDyYvRY9I14SwNBJPF7Aj6O+3NVST5YTCYzEyky2sz1hke2bOiTQxuq8/KsUqavXYiKNumzKxzquf2z6vjQWjkM0bIvDiANgwYRvlONEdfxqJoKcOZcYIW88mns9xoR1aZl6fRoh+DW1vQ5mAx02TmV2iz6aCHJW8ZhJwlRgWaMIIY+PP+Tr8ZUlI1OSkcI4YnI5qhQm6s1udQH1EIhjDaWv1/3OMeN4wjUZtNKdox8CJv3uecciQZY2THd4zWGEvh0aZsAQsMjM9HPOIRo35RTbTXfkZnTvAVISYfUs3mfWvo4YeTaOyFHmkjYxI90CZ9mGUCDxiG2V8LM5/2BTQWuTCGs392juDO49d36kMvjst8wNJO9W0aW6Fpxk3SFxPt8LmIl35t2geZ9nmW0aztdAJ9JPMh40jUibyhJ6OWnPmNt7Mcc2hkLWjLTqdzhkeBvqQ/M63IAceEHkqKrr+zADbTLjpySfPsLWfYc9IC7af76Bh1eW9eOFC+qJkokewH7TAfrLuiZKdFRTqFA/CgBz1oLORYqOQ0KA+N5n6RbVG37L/el3xot2eX4ytObfYech7NcdHzxp1+6L/osyieNuKzBavs9U7fOLRxfpY0N+7UwfmM7oh+yb7UWe8t6bWJdhwoC6MPe9jDhgObBSg6x4IfvQ90D/24r3mKg8YZFEGnc0S8yfgS5p7szzSW6B70xS/yj18WlNEQ5sycRKp30u35zfHnSM+8NQbpeXKfcVmnsdjrqIQWexaUNiVt9XY3DPsZIiiiH4lyMbKt2FqxNmEEDBCTQiYLRsm6SUIZjC+ruZ7JvW0MRivlVlCT8gLqZaRtexrb0pDI/zmsw4S3nJQ3TWY5HIJxoR0MDG3M4SVgBXg+7XFfxvWBOPjeUS96W4Gf2+7zORKYPY0xQPVB6i3DTqSCM3DPe97zaONiX22c+4NO6mdoMB7nenJ4zfIgkN1e0FjXxhysxFmR4iRlUvRJH2cHXfqYCE6idxZCOJ/ZH0kG41wrz/94K1XYKj+5tBfVAsgmoy60IC9x8sm5CIpVfDIkkqmMbU4x1Tcph9JPpeg+4AEPGO8xoBhYIl3KZLD5X7ulRzLwQFv0NVEMiwjREfiVUyx3gmc4dXOUbnl327ox5DPpeQxSUQK032aRC6/iqB3MNQbrjHC85kSiR3jNSN10Wua8T0v/0Ru90NfYTxl4TT44+VL9s7g2ZwbkgCqOiChK9v3uVO+++o43fqQzKpsskDN6dH9OeFYG58ai2FI+6Jh19ftM9Jg+wGPzD3nfhk/z3cPmA5Gll770pWMhQ9qjdqCdxZ1lv3LgV/6enbVl+3JoWPYI7qTnyQVEz2sXvcCRFh0VyReJtAVjlvfUI9JsXK9rg899r2zyYizSL/tzGuoS2mcxSoaOhVZ0y/5V+iaH6KEf2dzXqcTKE2WXxupdi1Ui2ksoRxQdj2xvMefTR2SFDkp2hR+09dlch4WhTRkMMy/x25ib7YwcxNV01eJIQh3IYs/CZMRgMUHNqSEBg4BBts4AXDqAHK15omWYMsrtb1OGn0yqDIfABGmilFrFuBIxkYK0rJORz7BnENv7Ze+Y9EATnBVLZTDCtFmZJg8pTRzIOJxBDPX8rf3zhMzQzmmhUpOs2KobvRJZYTSYJBmR2mDiSkqV+kV4fC7NVuqayIIoj3ZaJfe3NBvGgMmbIcqwnA3DrHZrC0Nceb6f26tvy77kf+1hYCmb4w2iAv63Qp+UH84BZ1e/TOIcB/SXQmnStgIuFQ7vUnZOmpxlJacTwnyJO4MeXXL4AqNLtA8NOS0MOu3yTk7WFNVAx6WhlP4mGjXzNnzdKWqR/TnqkoaJT37Ik3qNBXTwnL8tUlilz545qa4MEe2yqs1gwmOpV8pj3Ou3fuEv2ok+coA2GWLkTX3KFPFjDIvoSM+W6mUhhhxmryGDK3ze1FfjjhHNAct+xaRoM2rJH5lg2OqjiAE+PfrRjx4HRYk828/FIOZUSwckF+pkZJIHP8uxEzmIk6ANoohoScbIMnk3bkQiGJTLU1Jz/xs6+hGl8jx58H4i3eG5530misIgRi9jk8xpszE2Ly4FZE3dvid/ka1ZhrPYoS94apEBL6T7iXptSv3PYUNoazGNwc4RlrJqL5YUVvKhrerHdzJPBzKw6Uo/iZhkL52oDacLb9Zd6xF+zIs3c5/87z1jUAaH1Fz7YrXFAgkZXqcT53Lm75VDx5DLOFL0iagqmUqkbuk4MfLtu1Qf52iZvhrk9FX8jM5FoxwChY7GJKfFWNQ3/cKf9Mt84rd0SfOV+SN6V1nmCvya5UO9xmGiqMEsI8a0FN/cN6ov2kPe6RhjDP+k7dofmjpSBjrIzCGDUq9zUA6+GyscOHSyX5+sKd/CSk7TneeKZbvIpT6SKTI4943uNR+hG+dRHeYB4xnN6Bxjjmx6VnnmEuPPeJpTSwFf6D9zpIUyc0Ycb2Mocmr82A/rkCG6DY3M2eZ25dsHSa5FMdFjzgoyfunp+bCrWTZjYwC+6RsdlvqNWeOHczqfUl4UexlNYS32LKzySbOj/BmP8xUEJjApUxQ248C+CEZUUm2yWukZE5DJwaRESSs3136YhBjnJhTGpEiIPRvqodgZTvZGcNJMEspj/JrscwInmJQYCSYoPwwxK/ImC2Upl4Gd48sZNVJwGBy5CkC6UNL4GCD6xbDVXsYEGuRwGOlOVqA9ywBnEDHqGYKMBak+DFYGOaMW7TiyjAd9t6+E4+AQBP1hKEgb0m577aQPiRCZtDkQnBEOzDJyYlI2wUqFtMrLELKKyihFExO99pt09Y3h4Bn95pSL/KKbCTl7n6QbSRdiGDBIXZyOpgwv/TK55kQ9q9QMLs+SBQeTSD/De/VyLPCAoeF/PEYTvCVXaMkgsicTn7XvOte5zjCKRPLIC9rEoGJ0MrwY2QyvRKaTmkTO9D8GND7GwPQ/Xugnw2m5LxC0x8IGfjLopL3hF3nh6Eur8zfaSWdFE8+JcpBlhg7jkkGIn+jmihV7BjkvTuYkJwxz/UBvCwPonP1A69rEkVKu50UuRRsdSGJMkBX/c/oZX0kr1RaOVtLmZuCfBRZl4w2aowea66NosoUc7TLW0C4RUzIsRZOcGgP2IhpX5MQ4JacMT7wkf57xG98ZuflcexnE3qUbjBGykqt86BTt45hn7xh4n9PKkXU4lr9zH5w2M+qNGUahhSs/PjNWpQziB77SM7mmhKOwjJiK9KGrMUo+6QMyTqa1MQc9WVAgp2hPx1hYyR5aC2XrYHwoV9tyuqnxiu5kJmmDxiJjWp+NM4e8WGjhbFq4YAiTbQsvxjxeq58+0F+yivfGBwPf+NMmOsDndB7QwWhmXNCFvpdWiT/4apybC2Qd4An9TybUacGEM8L5Mv7Q1DPK9z4ZQi98RlOyif54xRkB71lMwWfAE3xHx50OszEH0Dn0MTqgmTrobfTkWOV6IPre4hyaaX/65fOkftLJnCRjQtkin1mkm3lpLJJfehPdtM+CCzlCP3ThPOqTLRqib2ScvqAD0My4pzc4Psq2Xxx/0BLvjF19QSv9I290SA6bIpvGqxRdY1Nb6Rxybezk5GRzF36SXWWaJ4wZZVogokfmyJtxRrfl+g+6jN43PsiQ8cMZNyYy59MHnHI6jIzMQH+OMyjP3Kcf6KEf+Kad5DZjkZ5Rl/+NI2PC4hVa0p/27ycqnZRUqcjzgo2yjXtzH/qaZ8mcfqCZw6LMM+TE4id5J5v0p3e1zyJGo5LFXsXxvtdljmIPg3haeaZYr3GNaxyd8pl7wLKnLPtY5r0Wnsm9Xp4xGWR/COODMSzixsg3YZqw53usOGwmc4crmBTBBMuA46Qs94zlKg/GAwcgij/lMQJMzvP9lIlW5dCFrABD/k9Ew+QkwslI5MCowySeu9bSZxNfDh7yWdJqGFjoyOnR7/luNBNYDoVJP0yMuYPSd5kcl+mcuQdQuXMUyzv5f053y3fLu/jSNv3KO7n3C9BMefqrbYwfDh064592cNwY0niTNFX8jwPob4YT/tkLwzhGK5P4fA/Xss6k7Gl/7u7yztyvRLrz2bwvbr7zE3IYz0xHjj+D1j7cmbcBvirHwkdSE5XHCNWPpFz5YbhKbeU0MZrQxnvz+Mk9jAywuf8zGDhWyhk7OQQlYyiySqa1M/SYV/UzLpeOqWdSvzbN+wIZxGisvTneP3JDRpSde9PmdnBm/c4evUQYZpmLPKZdeOY9v9P+0Nz452RzmEQGljKc6wy8R36VgW+JvGR/2jx+40yoU/9yMNS8DzDw3nyn7Dy2PB+ZTiaA59En6XDr0mq9Q49ydKTs4yk5y7Paif7KIfuz8aoteIa+ZMpz+p7+Zn9moi2zvs34mPVBot6bxim6KIfc5668yPzMx7w303zWoRln+KXd2p+FRv2Z5WPeV5qrNjhcmxzIyBh65b6+6NzoAw6Jz9Q76wEykBTb0CQ8J09Z0FnuPwz0Rdopp1ckf46kJsqXNuaUY20Ijfyfe4DJl3E4j92UkffpFbICcwrzOv0yZ/yg6xyN9G70BhlbHnwT/kbHRZZTLnmgFxIVzhkFeJD7SefyYivgQxaP53MN5rl3GRHX1tARncKX9B/9OOfKtQi2dCDnOWHWAaCPHEyygq6xS+ZxnkPqimIvohHIYs/DaZlWNK3giVjF+F53qEFAGWcfzTpQ4pmAckT+DJMXZ8TEbMU1yp3jwmmZDd65zhztvu47k96cHgs7tXHGcp1H20X+lvv6ctLmOphY57Z5ft0VHenHugjZumcZDsHyUIedeDSDE5fLsGeYPGdHPRO9iIGFBfKgT3gjZcxq+nxZdjAbUzHU9HHd3qZ1dc79ZYSto4O+b6p3G4TH63gLc72huaiXSJ6IkJXtHJDEqIwTzQlYrmJH7jatbs8pmKkv5c19RpulvG9zkulO9TMo5+h+6oJ1B/2kHUu5jxzvJI850XOJRA9EjNB92W/IaZQw64N98Tx7SPeFLBbMbZ2xrMf362Rz7tPcB/1eGtv+Rv91e7PnPnKWEhFbtmPdmM/42Jd+WFfWkj/rdP824wy/Zp7B8pAhDoG5hv4XlbLQtK+93+R9nQxFHyx1QvqwTk+H5zPfd5IjiwEibyLqImNLeU8b140bz/qZ03PXjd28v+mQrXX6ZV/jbp3eCCIny2tgUu66vc3Z+7sOkZelTG86YGrbOSqp7MqR+bOUwU1zQpBTa9e1tSiOBFRSiz2NGMDSaxjGB7M5f3/AEOcomqxi9JjcOSz+PyZWBa2wSn9Uv7Qbq5brDIbjAqTWcpqkKjKaGUGicNK6NqXtZdKX8meSRkNpXpsMo8OFrPBL9fI3w3Vb3kqHEx2XSpnTJRmS0rQ27dvaFiIE9hQZgyLvUvnWRROPrZC6PJ96eqSDbEmNFEmiS+jTnFJb/D8HUsRdyqV0zJwGvFdBdxnroqtNJju8QG/2AT2x7WF4RXFsQlNYi+IIgGHK6GPQc6Jzf9Sm1c3jAqQGcbqSWpVUvJ0gXde+KL8ThUla1jHNW/u/kv62LW89n6sKpGTrjyjLwTo9ZIxRmkM6rLKLms2pa8WRBVF6ckLW8FC6LZ42Re7/ISnqxhLnYNt7QYuiKI5rqANZFEVRFHsUB3PlR1EcKlQui+K4jaawFnt6xVx0SVqRVJ0D3RuQKI1N9CIp6/ba7fSuaJV37TmRTnsg97Ypw2ED832G2ZexG2mBucvQJn/tlMaZg3DQbnlQwV6BlFJtFA3xc7BtVN585H0OLkCT3MOXA0/yvQiMyFoObMnnDjtB0+yZnCHCRxaUPR+SpOzswTyYvkRmlbc8sGe3MMsM2h8KGclVEWRReu26MYwnUngdxKEduxFBhRwGZezi03yK817DrKNyx1/2Xec+R7QULTe2D1U/crgYfYVf+7OHd5OMRX+Sr90Y45vmCeOPrjvYNm/KECBL9uUdqohtDo4hp7vdB8gBSXhBhmZ9twR66ucmPUZGyKW5i5zsxNPI9eEag5lvtU/7D+V4KYrjMupAFnsSJgGnozoF1Wl1ju4+0L1qDC9HaSvrSle60rh/cVvkugcnrTlq3VHdBzIZ6YNDX1zFYLO/AwQ4lCZpe/lcW3Gg6ahxXlzN4CqH6173umMvoPROV2S47kD5exGcR1cboKsDIQ4W6ODIdqdnMlalgsapdg2Aw2nwwJUEeGv/YBwpV5igUw5pkL7pmHl7JZ1mSv7QGt+02zUeeAeMyze84Q3jFFVXZjh85WCNSVcbOE7eXlzHxu+2EaQf9ntpNxmxl2e3gYbudWRE5kqSGehpv5kf8up5R+bj08HCuHe1gfrtoXZM/l41JPGbjnr2s599tI4il65OcEAKIxivHOjl2p5DtS+TI+8uSbrXFQ3rDofZH+C7a1foT9cuuLJiN5HDjtzfp81kZ7dT0vHG3CH13TUmh2q/m0Pi8Ngl9usOkNmN8eDAHXPErW51q3HFx7oFF/LmehA62fUwS1q4IsT9iA66ov92OvAnB545xdlheGTgUB8SQ+acQv285z1vnERN7xdFsfvo5odiz4LBbtIzoc3HgG+DXO7LuMipmg4CsZK8zbu54Nu7HD53sOVqhwOBMhjF7rdiHDBm/XAkH/GIR4z7v3KIyoFAZFX5DohJOxnr7txanvx6TEL/7MNyFDuItOQS892AqKC7yzhfnEmnKDLG8Z4Tho8O4HGYCIPQPVvuiXNfnXu33EnG4NVOBhJj1KmMTjXFL88ySuwdm+WBUYSvuVT+YKE8dTjohCweip0GFiw4CGglonAonCvjjqNOLufj6Wdjz5U05MChJe5zOxgDPeMezXKSo4vUOfx7GTltlHxmjyL5euYznzl0hHvzXE+zzcnIBwOLLrmrlTNxsBChMo7wP/3abYiA4fuBzBPb8sb9iRblDsWhZZlvXOPjntJ142Q3YDzQZ64myRUZSxg7eO+KJPPfzC/ywCnjRNOhFmWcYL4uIovX5rPMfYdSjy1Bfi0CRuaKojg0qANZ7Fkk3TT3ODKoGLrzJORvkyEDkcOQ70yEJjpOgc8ZRonw5Z653AM1Qz0cHFEx0QupMNIHc3iIsrw73y+X9DNOkfYtkes1cml4Lg7mIHBGHvjAB67e9KY3rR7/+MePqFBSXJWlX9KJlhOvyVwfk66pXCvBMXDUKV2IgzSvZus7I0uZ6Jk7vwKf68d8P9omoAH6eHYdPROpC8/QllEhmmxVWltEVlzEzclN+qkf3yXaJ/0p94ppL6NAuuOy7UBWOJHowBFB51z8LJLIgWfU5qoCDg4Dx0XVLrBnrDvZlZGFZ3iVFNJcA8DQ55g6AXWud6Z/Trqc2zjzVL9m+E6/8RQd8Y+8JGU676IFeU+56OSdyE3KCp+VuaSV79M+9M/9n+vgPbwlE7Nxnqitz5Qz15/vfW68JG1teX8ofnPiXZwN2oKu6Khc/VLvsu3o4/vcSzj32/Pu0eT05/L1pOFljJLZpRz7PJew75SKqV7jJuNullk0nVM2U496PYdGaLl0cjynfnKZqwU8b9GDEewz8i9azpHMO+rAA3UmpXCmh++Uk3sdo7M8u5TNQBtyJ2hOB55TviMTPudgLJ2djE8/+qmc6E+ITGr7XC7a4Ld+zfzEw/B6Tk3P92Q7+n1fqaVzmjk6JT0YPWZ9jo8ZU9pHdpzWLIIWGfZu5CFyPrctn2v3TnrUd2hBL3HG6PFNz2fcZk6YP09botdnfqXfZMxY2On6Co7XumwY/ZXZYbFHhgtaGFvLu0YjM+6PlQWChp6ja/En9yQv+ex/3+VO4eWcOtMkc9+yn6Ac5ZO3uZ+eQ+eM/8N1mntRHJvRFNZiT2K+PJzCl1IjUmIyusENbjCubEhq6le+8pWjDV0G6KlOdaox2Uk/NIFboc5dj1nplYYjhYcDN+/LUddrX/vakdbH8GXE5xJxzg+n1H2DJlApjCZFE7qoFYPKxKZMTstOl0+njyDdUbqQ9CWOjqP1tc+PCZpDKw3nrGc963hHH97//vePPjPApTia9ON4zimQ2uUuSyvG0nqkSZ3jHOcY91e5EgQtvatc0Q/vxCEU0URL7ZtX3pVttV86lOiCidmdmdKdROgYC8qSKoq2on3S8jheeMKw4NhlH85b3/rWwR9G2vOf//wxyTNSOGgMaCnE0ls5FlKntFuZ5znPeYbzuU06X2g9p08t+aBN2i8Spm+ijrk3MvdPAueSnCxTPvO9tEMyRBZcQk8mOaOiC+imz2TJ59JmyTFafe1rXzvaeZYuzYibjSz8s6iBzq7UQAd0/+Y3vzkMR+9YLEAvsiMS63uGFln1Dpl0TQiDlXPq3XURjzgaeKX/ZNs40mZRfenYubAe/0TGpMWJcJBLcifqwIjTlnUOqnIsnOA3edYO9NJ/9aKL7zggV7va1UbbOYbGn8UX4+UKV7jCiGCHr/pOxtAILeLkq0saHfnGN/Qwthn53pH+iJ7kVLRpbq82vP71rx/GLRmRKq5ft7zlLUe/X/CCFww6inRbiPA3fSWaii9o7ToIjjK5FcmysIP+2qWv2o9PftTtczrI2KfLyJT2Mcgf8IAHjHJFgyxoGBvGCZkzhugSevF973vf6AtH1PvSR9HNc8r1nijSOt7ghT6TN7JLL9EbeILuHJLsk8MbY5nsWrDTRm2lP8jLrJe8T3Z8jz5nOtOZxsKOsUFe0AFvOLLkim6nu/BdO29xi1sMR5oTQRegJ17g675AXj0vnZYM0KnKwB9yhHa+1+eMDfKo/+qnd/GczKCheo1l/CEHUk/pKG3TR/TAR2WRRfMNHbh0uPQd/egbf69DIqyZA+kKtEVjbfM52TOO0BYN6X2gazyDpr4zJ67j+bz/ewljQDp9UpqlwdIvUvvJQN7FX3rKGDSvaJ+52vfo9uQnP3noA/Pj7W9/+6O3HNAv5lvtx2P01GZzqx9zqiwV8xLdjKbmvtOf/vRjrJMb/CIz+KK96J/962hHznwXvXyw6dlFcVxHI5DFnkaMV5OUFELpaNI9TYQmEg6H9EOGv4n+Nre5zTCQTVqMK0YzwyBRTJPOBS5wgeF4xJGcYYIxuZnMTPjK8S6HgeFq4mFIWWH1P6NS5NDKrf0dJi57hxik26braLfJ2OTIMPjwhz+8evSjHz0cJEYe58oeNc6AZ5Lyx3BWx8Me9rAfiHzqqwmUcatMxgPD3P9SlNDSJPyYxzxm0JehI5WWAaRcEy7arru2gcPGiHGBPSOXMcahftKTnnR0qi/6qJ+RiBZPfepTh0OEnt7FA5O/dtuPyLjGLw6IMrM/VN05FIjRop14x/FC500GF+CZ5xnXjB91M5g3HVChPoa+uvSfcZPIFcME7ewPs8+KMbkO3kFvbWSs3+c+9xmGuH6KNOOLNqDTC1/4wtFGBpQ+M/gYQ+p65Stf+X1RO0YU49Yz5I8RZL+cyC2nh1OLHok2+M5Y4YirA2+0wdh4+MMfPmjBifH+ukiUchjWUiiNH7wid+ohIwxWDozFAnVwBDkWyrIPFE8Z5caLMbVuxZ8Bx8hEb44Og57xTfaNIwayH8a6NpMnvPzDP/zDIVMMymU0xWeMdca0MvE64x7d0AoPtd/Cx7Oe9axRhnrIp/2rnJYlLThkaIEfHHX13+UudxlOlEUUffe3sUFe/NAJ2Z+nPo4juj/oQQ8aMmGcGH/aqF2+m2ljXFmA4RBy0nzGQUMji1pkgpHN2cNHBnIcPn1+8YtfPPQbWaRfyK2Fihvf+MbDQLcYNkeyZigbL+kJhj9jn/zJHMAfezGNbXJmUUp/0Ud/8dHCk0gwOYccCMRJpG88wzFEa84GR1I7LTzQQ3jCIVA+OnFgyS5+aTMn3hhRjh902ldUyfjGE7oGHdDJeOdoo53xQabxm1zSsWTE/3QvuUZr8q/P+OpvNPKdcexZsm8BQbTYoh8emyuWe/88q//Gqblmp72B5hl3vRrL6ESe0Vt79EtbtFFbLPQYLzmYSmaFhR4yZm47kO0g2mjsWcyhe8gzp06q61we+efgo4u+uzfZHIL3dIf52L2V6GxMKRvP8dUzHFI0NzeTd/M6fmfOJyv0o3JkizzhCU8YskruyLM5hF6jB9CG3Bkv9kGbR+hX7VFHURQHh0Ygiz0NE4yJgjGaE/Bufetbj8mH8cQgMNFwkOz34/SYkKy2m5BNGn4YpCYTEw/DLO8wZhJlgpw853dSb3znWU4PQ8yEySEycTFyRPNMmv5XrslpXSrrTshJrCY9xrhJLncBcuhMgIxUDqTogcmZkyuCaJJe7htTHqM/Bw9lj5XnGEe+46AyCLWVw8ZAYzShNzqhIyPPxD47xEnB9dtEHUMXTdBZ1Cer4/7nuHof9CcOKAeWwZBDL3zOGGRMWIlmVDKw/GYcWtXXb98xCjiRaMFZWAItGYUMDn3jhDP4Gb6bjObQKae1pr9kAV0YZnjP+N6U8olvDDxGv8gtQxXfyM51rnOdEWUgc2hCXjhFHCS8QAd0FWHKvi4/jHDOK2cMbRO1wDs0YGwbBwws5XEu0FZkmlyijzo4lnjK2GdIGhd+r7vrTv+0VTQWPUQt/JA/sqMOdakDb9Sfva0WFji6+gKMdEbnsnwGvHair7aQIf0SFWIAZ+wyOBmKnCh1M04tGmnfzAd/k++cbuzdnKKLBmSaoake8igywyHzHNqQTQYo/nCAAvWRH4sGnEC8AdEoCw14a7GAU8PZ5iTps/eUo4/kgZNgvIjQqJc+48xw6DyjDmN6Pjk4Y0VZfuJk+Bzf03889JN0dmNdf4xNdMI3RruxpK3awdHhYCh/hjLwl84lX+hFz+ALvuo3OiUVm4OJnvokPV0fLCgZM96PLuR0qC+Rd3Ij+mucJo3Z50klpoeUgb6cdLpIPeSO8+h/YwatZG3EWd2EyNws72gY3ZstCi972cuGXtKHyCV+iuKCz/AueyO9k31+9Lcxj4baTu7oC7yli2d55eCRBY44mcqC2brMFe3kHCpn1h/0ETqF5vhqoQSv0Ml4ohd8Fn0b3bI/yLYGvDff4LvFWk4s2c5Yz/xJBo1BdSbiqQ1+zJnmDuPPONam/G3OIBPmVH1VL1lLyip6GlPkI/oI/UXZ0V/56qfn1REdSu7oJbQyxxyX708uit1CHchiTyPXXZiY/JiMGVKMORMjwyKTcvaoMTI2rUbPBliikjvVnd+eTapk2pL9kpwGhnQmSd9lQt0GytFmhon2m0zntCATsYlVXf72fIwe/d/2xMo4yjFQ0h+fm2wZkqKfHB+GEOcCvUQxRAWsfntHqhZHLrSAlKtt2sjRFsGKYbSk8yYDxvMidAwrjhjDgWHECGUsMBpz4A7HIkbC0qHzGedJdIzxta+9fnknBkmc98gfmiTdjxG+bv9s6BG6Muq0AT85pIwf/YrRmz10IpY53de7nBCIo8vAxnvOSwwixpI+cRAYaYmykzvlreOzupRFXue03000YcAlyhXazXxc8j6Gmv7OV+Us9z/uBMaxMubnGYPZuwZoty9ezghd87d39YNxaiFC5C9RS1kEOW13Wf5s2JMPcp4rUKSvWuRBe3rJgsC8ny31o73+6Cde4MkcRd3Up/B9fw3/eWFsjnCFX+uiz2lv3mXgJ92Xo8QB5SyS4/SR/mLgpy/GZVIos4dNdDQHV6Uf6MXZpTfxlR7Vzhj4s+yEhpwnC0ue3c0rNfAT/0Vqb3azm40ImwWUTYiOjnzFeeewc+C0kSyZrzguM2/RnX4UtbSo4TuLEegh0u8wr3mPNXoY4xYp4rxmr+BOep1jha7hy4EeYqQ+5WSxQXnaw1lOave2Y9BvP5G9LJhkXOYALOWb54zLHIQH9JEIcHSRckRI/c61SqlT2cacCDa+3vSmN13d9ra3HYvN68Z4URTboymsxREHBhujxsRskp73cJlAGDbzHspN2J/JY1NZJlLGE0OUYWUF3YS1Pye2MiCk5ViBtbrK8WCEM5SCRBX0nZHBocjBNN7PoSw79WmnfS+cUI4hekq/8r/rAnzPCZI+xYmULsvg3kSP7N9yfYgJnWMUfmxDb8+IHjPMRTQtGDBOGBecCHTlNKM1Q4qDuSklCx9iwM2GxSZ6KF8qKUOSczqnus79Vf8codoJDB8ygT/SkBmCIsB4nDbg4Xzqpd+J+PpehIIDK0WV45i+Wcn3W/no4W8O8E4gRwy0fZ2wiaait1Jf1U8G1qX+rnOycljSgYDjkVTHAB3Qi/G6m6c4Mk7jRKKhH/X7bN3C0txX9MkptvqMRuRMpDQO5jI6mr7oH7mOnkia5Gz47i+iCw6VQayv2iyCLRU7Ed3IBD1I9uiP0E6EKKctaxfHEY1E2HM6pvfx2iJIeIA+m04JzWKeZ8j/cuxH788ZBJvKWdLNO8a2dFCOpOiaFNr9gbbZdiBKJ8LLSbzDHe5w9KLQDDrWIl1SnvVFW8jjUv6MWY4PmeWY73R1xkwL9MUDTt7yu/2BDAM8SiZJeG4+2ulQnm2wHCfGhTlknlPpZPKVA7IsKtLBcZrND/qZA+fmsZTrmugzc4t5LHu4/eymTimK4xLqQBZ7HhzEKHsrjVbwrVibSO2ryJ4le3QYOlLFkqZkUhFFMRmZSOZVz5S7RKJD2cBvYvdenk1bMjGBfUFWY6XemJyWp2xCDAO/lWlSt5JqTxRHxyEkInCcLoalFXvIiYD6K2XKij5DVX3SfbL3Zp3hFKNkbvf8XeiR+kTZGDUclkQsGAn66XOr4nF+MvnGaIsxJ7oivVI/GYecXe1XBwcmJ2zmWHdtyLvKYqhKVRQNFWHLSY4MBnsZc6iIPZHKXkYhtEt5Obl1CZ/5Dv89x1HXThEkxon9XiKd4Vmemw/d2enU0vSFscXo4YwydrVZWSJU+s5J5rQyxDnt9hNxJKXnkeU4l+htb55yyVkiseTHfkDlSulWRvi9PLgkq/P4agFCJIMR729Gl/bNJyPqN/nK6aTak3TFnPw5G+EZHyLoeGcMiLBpq7I5BIzCJT8yJiOHop6i1xZU0h40IfcM7sjZpsyB8IV85VTPWc7ncc8oZoijqXGoHumLOchmCf3P6Z1oI60RHzwrMofPIsz6wNie5Q2v/EZPP/Zi5SAwtPJZUpDJtDEeHbMcY6FhDuER1aMHjTvlzCc5z5jptlP0calzyReHmJzqd/SR6Jb2kgl8R096yRgydskj2YocW/CRRi5V3oFG+mh8kxPOmvI4XHifhYjZKU77OSxkhAxLW9UeMkZO0cMPntrbuHQws63BeEc3fcObRL7Vre+cNRkQWZCJjIamy5OBZ9rai8gJyiFB5MHzs0zRg+gpKmaPogU6KdoWRi3cGUMzclqxeqI/8CCO4dyeua0cfX2wMEY2LOwYi8qYFyhnLB0r7dYHY9ACQk6nVQZ9kpTumcb6h6Z0YJzjtDH6eZ6XZjmXjux9++jNp+Yle0z1w3yn3cYomSdfPjc/aReZ8x3+6ic6+d4YM17QljOfTBNpreSop7IWxf6jKazFnoZJy5430ShGGqVvD6SVfxOuSUOUhCHCkIjxzwA2eZogOJXZI8jg4HCalExYDgYw0cwruhwYE6N9Z5lMTXhSObXHbw6q3+5mE22zgd9+ExOZtCcrpzEYvK9OhpVJzKmLJt84J9JBrYzqk4mTA8kwcAgGA59hZP+cVW1lupCd42BPkcmQ4wmcCN/rEyNN+3Lxs+f0PadioiUnL6f7cch9j16MH0YAg9CVFmg7Gz/KZHihiYla3xjg/lcmJ1N5Vu8ZEVb11aH/9jMxvF3MjS7qYgTopzZbHEAj9GC0ZH8ko48TxdhihCrTXir9mtMx0Y2xkQMqUqf3gdFqj2mutmC46SujhCH00Ic+dPCDAZsTYEW+GBnag7+bnEdtVRcjhqGGbyIZFjQYNeSSrNqDw0hSNjraA8cIP+qoowZfyDVHnhEe59ZvNHEAjFV4KW72IDHEGayce3LA2ecI4bOyRW/1w/v4q1zOIAObsazNZNW4Io85bddv8qZsESN/ew7tLHbkRNCkYXIk4oyKtHrHWGQIc+K0Y460+a3PUiHVxVAkF2h8z3vec9DJmDImGKCi49qnPUn/47RYWJjB2TZGGcxoYVxrn4UnB5mghXLQxt5iYwnd7e3SP6lu+LQuNZLRSabRFj8d7BG9QR/gs3HAuVnKSE6UNB7sLfSsZ/TLWNA+egf/gUOUA77oKv3CP+1CA+nM2q/ce9/73oM/xp1FAnV4NzJA1rIvD5054sYjupMxdJwdXu039jl1nsVbY07bpI0aX8ahNhgPeMFZ0g60NE7VSQ8aU2TGGCUr+s15NkaMO2PY/kq6zqFo9C45xmPPoyudQtY4UX6jjWfUhQdS2o1rbTWu0QhN9VFfyEGgz8aXce9d2QCZL+gvMox+0lCl7Rpz9JK20Ff4y5mOY8SJyZUtxhXnH73oUXufsx8XfYyH5X7TGfiFF+tOlVYnPewUU7pF+8i4uTFOMjnJnZvKIAPqxQeLU+YLqbTkDB31ed47Tz703QIdOtEjHODQggzQG3SOxQDvOzE1ujUwJvAYP9Fem7LHHo0sFKCl/zn5ftM/0n7t86YHzDv2QStf/X7TcfSXcw58R08r3/5u45hMmLM52mTBT65bMXacvkuujXPZPjktV19ENHczHboojgs43vcavy/2MIhnTtM0KTJisv8popu7ohjDJrN8l/vPsj8jK6CMrKzkJ2Vmnjx8ntVd7ybNCRg0uTMsh+v4zQnVBs8v95tkhdqEldV4P/oz70dbHqOufoYqg33eU5bvtHHeTzffATf3Mf/PK+homNRffWekmWQZ3+jmJ6vVjoRfGhqJpmb/ylyX/uc+rhhEnHv90N/cmYdWWX1OGxP11Daf56CQlK3cHPme61eWe4s8k3f0beZv7ukLtDt8XB4mow055VC5vl+XCjvzzHNJpcIbBmPaEplCE0jkJvTTL3TOvYn4GbkLXRJdQBd0JR/KRUv9nOUg+zHDZ3/noJVE1rPfb+Zv2puImf89p66kk6bMXG8SHio/J5EysnMQledC35Svb/Ne0pn/2pa9UFlYWT6PBkujL+Oe7GXxYZavOZMgcpE7MTk7uXdzhrZIe2PgPu5xjzv6QKr5EBtlWmQg5/bd5nOLShwkzg4Hj8wuIzboapylvegVPoZe2p60Y22O3uG8hP/a6TnfRwby7qyzsn8uZSsrdIwce5/O9bl25TTmOP6RHbTzrL89m3ty/T/3Y9ZDGfN+Z69j7vvMeEimRuTYT9qc/Xi+JyN4pq6MT+9yjDl9FnCWB4zljlL1aaf2hZfolP22+GQ+Ue+sK5dtmeUfL2QQcIL0JXe3cjw5rhzrfWUvhIdLuc59ocpVL1mbD9tax1t801880bbcCZz+zm1JP+b09tA6vEcrjjwkbX7d/nNl0GfoO+93nPkfemY8pr0pL3d/6m/kxHvRk3QLPiaVFvQzc0v0mvLQzud4mzk6mQHZ29n9kEWxf6gDWRTHcZjARUJMpKIHojq5MsEEL+qw6eqLoji2Y3YgRT9EWGJsMspFkhmnFmAcvJLTYU2tFmZc+yFqLLMAaqgeWnAWRLZF7XJP4eGC6LAIugiZiBnIIBB1k53AGSr/i6I4NqAprEVxHAeDhnHr7i2pQowcK8fSkHJJc1EcVyFCKG1S9EKqqTTLLKhYaHE9hu+lFHNa5iiMlD2RMQ6ElLtlym2x+xDF4qzNkdXDBfyXvs+JlC2T/ePSfZfXeBRFURzJaASyKIph8DKUpa1yGO2TSYpoURyXkfteQeqbqFZS6owbqZ6iXvZ6ZbElKYccSCm1HBkOhXTQ7rU69kespXlmC4R9d12EK4ri2IY6kEVRFEVRFEVRFMVW6FJoURRFURRFURRFsRXqQBZFURRFURRFURRboQ5kURRFURRFURRFsRXqQBZFURRFURRFURRboQ5kURRFURRFURRFsRXqQBZFURRFURRFURRboQ5kURRFURRFURRFsRXqQBZFURRFURRFURRb4fjbPVYURfC9731v9X//93/jd1EURVEURy6Od7zjrX7oh35o/C6KYjvUgSyK/cR//ud/rl71qletPvOZz9SJLIqiKIojFJzG05/+9KsrXvGKqx/90R89pptTFEcM6kAWxX6C0/jd73539a1vfeuYbkpRFEVRFAeBk53sZF0MLor9xPG+11FTFPsFQyY/RVEURVEc2VHI/BRFsR3qQBZFURRFURRFURRboaewFkVRFEVRFEVRFFuhDmRRFEVRFEVRFEWxFXqITlEUx1okQ39fmfr2vriaxc/xj3/8Q74XJu1R3//+7/+ufuRHfqT7b/ZxbY7Tj3/sx37soOk00343+D3LGF66DuCHf/iHD6qNO9XxP//zP5WXPYbwB2/wBf/99vl//dd/rf7xH/9x9ZM/+ZNDfuFQ8G6Wkf/+7/9eneAEJ9iVsTLL9W5fdVE9WBRHLupAFkVxrMYHPvCB1atf/erVT//0T4//v/3tb69+4id+YvUzP/Mzq3/6p39a/ezP/uzqTGc60+o1r3nN6qIXvejq8pe//HAqDiUYmh//+MdXz3nOc1YXuchFVle+8pVrOO1gEL/3ve9dPetZz1o96EEPWv3Gb/zGQZf7uc99bvX0pz99dbrTnW513ete96CP7/+bv/mb1ctf/vLVJz/5ydUDH/jA1a/92q+tdhPa/JWvfGX17Gc/e8jx7W53u9WP//iP72odxYHj3/7t34aOed/73re6/e1vv/qd3/md4cR95CMfWX3pS19a/fM///Pqwx/+8OqWt7zl6vd+7/cOmQP5hS98YYyTX/mVX1nd8Y53POgyOb4ve9nLhq66853vvPqt3/qt1W7DWHzqU5+6Os1pTrO68Y1vPBzfoij2PprCWhTFsRZWtT//+c+Pvy90oQsNR/ENb3jD6u///u+P/v/LX/7ycOg+9KEPrf7iL/7isJyuy4A84QlPOIzKv/zLvzzk9R2JfPvmN785oo5o9fM///Ors571rMPx3w2IBnH2OGW7wW8O6L/+678OGRJxOhQQvSLLnAQRm2LvQGSOzP7RH/3R6l/+5V/GZ3/6p3+6euYznzkWpW54wxuuzn/+8w9H81DpF+NEBO+zn/3scFp3A8qDD37wg0O+dxtoYey4U/mrX/1qTzYviiMIjUAWRXGsxm/+5m8O483qOYeBIf5Lv/RLqzOf+cyrM57xjCMC+eu//uvjdwxBhow0NP/DfG1L0h7z95y2mLS1pH3BspykTooSqPPYGnmc6bUuRTi04rzP9Pa3iIeIsCjKSU5ykhHRwUfGZmju/aTU+SxlbOJL4LNf/dVfHeWG35y+1J92pb0wp7nmu7l8UcGTn/zkR0cy8z3McrH8LOWFVr6f2zGXpb2//Mu/fLRTXexfFHsdjdfxJLI0y5bfeW4pCylPZDwpqmSa0/Uf//Efq1/4hV8YZYl0R2a9o9zUmbJn+fOzrHdu7zL1Ogst6lNu5GYp19vopqThinLrV+Q674Y+eTdjWJtC17kPvt+kI8n1L/7iL1ami+IIQx3IoiiOtWCwnO985zvasViucDP4pJBK1fKdyORLX/rSEZ0Snbz61a8+DPaPfexjIzrJURA1vPnNb776qZ/6qdUnPvGJEcGUrnalK11pGFvS1aSyiUQoT2rWec5znpGa9Xd/93cjSvWd73xnGGpSaI+thhOjkcP+7ne/e3WKU5xi0E+k4bKXvezgCeMR/UQa0eFEJzrR6nKXu9yg0YMf/ODVX//1Xw+n/8IXvvDqb//2bwfdLnWpS433OJeM1Ste8YrDEVfHN77xjcEDvPjjP/7jEYVBY/VxPtfRGR/w+1Of+tTq1Kc+9epqV7vaMMK1QdqsstQt7dBigzLSD2mreH2BC1xgpMLO8MyrXvWqYRif85znHL9FhkQP//zP/3wsXlzwghcc9b/nPe8ZxrWoqL9Pe9rTrq5ylasM+RL1IXt/9md/NqJBaMVBKLaDMf1Xf/VXg5dZtODY4TUaG/dST8nSFa5whbGQ9NGPfnRED0UOv/Wtb42/LQ6ILnIIpZuf7WxnG2WJmklTJZOezcLGpz/96dU73vGOISNSQMnh1772tVEOGSMLb3/720cWhMURfCdT2kSe6RjPkzm65Lvf/e5479///d+HrGuHqKby1sm18aRefSX73rX4QB8ZR+TK3+TwHOc4x5A/443e8zn517alXMuWkMFBNs91rnONhRgpqPpj/J3+9Kcf+pTu0070pzPf9a53Ddpe//rXH+Pc97YWqJNuRqfdTvsuiuLQoimsRVEca5G0rk2HP/gsq+aMvy9+8YsjTZKR/qhHPWoY/IzMZzzjGasnP/nJw4DiGPn9whe+cLwrusnIv//97z8MPYbjH/7hHw7jj4H16Ec/ehhXjCb7kzgDnCJ1+OzY6kAyHvX1kY985Optb3vbiDRI4UNXRirD8773ve/qpCc96XDW7YNiwDI4GZP2qDKupfoy3h/zmMcMQ5MzptxXvOIVR0cdGcT44uclL3nJqJ/TzoDmjDK4l2DUx/k/8YlPvHrCE56wetrTnjYWDJ7//OcPx4Kjiz+PeMQjhlHtx95J73AqOZ6+805AjjgWjH0ywCF4//vfP5wCBruo833uc5/hqDD0lfeCF7xgREHR4vGPf/z4Th2ve93rVm9961tHXZweDuuxVV4OBcgHZ/6xj33s6sUvfvHg+clOdrLVE5/4xDGmOUJkwIIEeSNLeILPom6cJfz9kz/5k9UpT3nKIbN3u9vdRplf//rXh0xacOBsqStyYGyTd+WTAT8WNTiK+PpzP/dzqze+8Y1Db4jycaqe+9znHp3GyfkiB5xKiyicWGOFw3WWs5xljAcyM8tdQD60jW7htD3lKU9Z/cEf/MHQTa985SuH3uI00ova/w//8A9jkYR+0zYLG943btAhZQKnFn2MS7LKGeUckmuLZ9rof7KvP2Tb8/SgdnC2ybkxajzQneRaG4qiOLLQCGRRFMX/lz5mn52IFaOGscVgsqougsYgu+pVrzqMQo7JO9/5zmH4Mb4YhFbhRYgYS1b8OTreZ/QxkPyIJjz84Q8fDhIDTirtsXU/m4gr4xeNQlcG+v3ud7/hCHGkRHM8wyDnBDKgOUvoic6iJ5y7M5zhDCNCx5BVnkgGI5qBzYAXyTvvec97dGRHZJnxyynwmegQHs5QFsf1Wte61vib8ewgnGte85qD/wxizixHgsNqIUHE1CFM2qj9d7rTnUaEKynNHNg3v/nNo823uMUtRv0MZsY+eeCIcGbJmiiVKJLPPZ/oK+NalNLf2nOd61xn0EE5v/3bvz3qKLYDOnP8OHSiacYlPlvA4MA5tEWWgVRpvOXQcBIvcYlLjIidd8jTJS95yREZ5nw6CMc4xh+85Dziv0hzFjU4j3jKaeNskXsLCRwrsEiV/by+83z2G5J7fCZ7FkG0nwyTKwtPvreoYD+sMbM8AEr93r/e9a43/ic3ouzXvva1x1gjt8aQNpNrY9HCjUWue9zjHsOhRQ+LZ+oCMocO2olmxqfxoFz94OAqR1vItYUXOg6t0RIdOOkW6Hz+ohe9aHXUUUeNMUn+9bEoiiMLdSCLoiimg20YnYwrPzmWP98xkOJQ+J5zybERFfA9h9CzIgOiDZxL4CQynjihDK6UmT1Ex2boJ2dSX2MkowdHm6HJGEcvkTsG53LP2nwtQsCw51QypDlX3hVtERFRl+8Y4L/7u787/ucMzMjeLDzTJjzl5HIAGLscRNEh0WTORvaGiSIpz3ve51T4STsZ0aKX0iGVmc9EdDgnHFafScVlhOc9bdDH/GifhQcLEp47LsnLbiJ0m3mNvvjwlre8ZThX+C4llZNv/BrznL6853k895ujgx8cfJHLOGKwzHLY6UCYba4VUl+utfBbPUnF97+xsm7xKQfT5DRTfeOwkWt6KpFQCyHeVw7dNI9Piyc57Vj9oogiihZ8MpaVhw7SyI034EwbdxkfabMffxundCe9yBkNKtdFceSho7YoDgI5eEB0yQqu3wwQK9OME7/9MJCl7/mdQxr8+M6qdL7PYQSe8b/v83mxNxDDUtSLE8TYFLES5bAqL+2QMcq5ZJzOB+4wxPB7do6Oi9Bv6Z+isaI69lMl2jE/s+kuTwayqJAy4kRy5IFhy2EMX0Q4jMt9Ge2MXBFDERjXZShXu5Qzt8k4x8dAfcZp9nvZxybaIkKdOo1vf2uPPbEiRMZ1HOZ1IC8iTMqPw3tclZdDAQ4MRwfP8czBTSK+pzrVqcbn65CDYsiUv0UhcyDN/vImPJ1xKPjLKRR1NL5Et1/72tcOvcTpi8PIgePYWeCKzJK96Crv3+hGNxopvfQbufUOufaMMeJHRDGHUm3qC7n2Dv15XNeDRXEkoxHIojhAmESl7piUTYYciDiMjL673OUuI3XO3i6f2SeSFeNzn/vcI/piNdhdgCINF7/4xUe6mhQfjog9OyZsqXlS9qRAFQeG2ZDnjDNwcpog4IvP8TSnA2Z1PqcZ+j4nbzKWOCX2Fklp84x9bowyzkOcHCv7HAX85Dwo7/Wvf/0wQBmfHBERKu1KxOrYgvm00tA6NPRbCqrUPIatiASD1Y+xIsrBmDWu0GV+L4Y3546jpxzjw2cik/ZJ2m8lhRSf8cVYmxE644cy8cihO5e+9KVH+qsDcKQkek4kUDl4JaXQeFfvNa5xjdE+kSh3h6aPl7nMZcZ77oPknIj6iFIa51IIGdn6ra9SEtWfCGf6qF2cYD/k6exnP/uQOzIXx5Kje2ySl0ONLOChrdRnhx+JIKKrfX85zIYOnumaBUHvSevkeOagGLrdYTAimuQOX4z1yHx4m5NI87/FCk4qve89zps66IL5uXncRBfN+mk+6TRIOnbK0GbzDVmyr5f86bMIJEcOXezRJNP2XUrpRicyii6pyzzE4aTzRGpF9y2Sec+443g7NCtXJGXRM+0NPUQ2pZ075EfWgLaaLy2+aMsm570oir2FRiCL4gBgQhVhcGm0Cc8F537s72KY2MtlsmQgMhYYBo5xv/Wtbz0czbvf/e5jJZhha2JnpJp0HUzAAGB4MkT9zzhdpuAV+w9OikMcrKajt+hVjCzOAz5x+Bl09i5KNZWiZU8Uwz13SjJ4GFxOYpXiyFF52MMeNowqjgIng1ORw3PshXKIDF7e7GY3W73pTW8az+M/PnNM1HlsAzqho8ggeqO/cWG8MDRF4vztkBLpohZOHKzhGYd8MNBFctHfZ8phDOcgGWPCvkHpoHgKHPib3OQmY7/WrW51q3FIiHKlHi6v8sAnbcIPCzUcx5ve9KYjRVEKK2fCYSuMWoazg004gze4wQ1GqquxbA8Y49kznAHOsN/ahbccWXvryIpyHcDix8KTOshbHEL7QC04kEP0Qpvb3e5248CmBzzgAavnPe95g6baQi6PrXtnDwXwmxxxxkUZOd/0cfYP4r0TfMkEuYqsJNOAPHnX4UbGsKi5dE5OlSi6Q3pEMLPvGS+NawsPZJeDSEb8j5/kxIKhvdQOmaJ/RNHJI5nwjDbhM0dPG8gF+c+pxdrmuWVknrNojFgEIdccU20mf/pn0fJJT3rSKNOY8gy9RfbpJPrM4Tei5GQtVx/pgzZzAB24g55oyAkk0/YDSyHP2OWc6q89wvSgRSH9ow+z59TBY9JryTqnVV+LojgycLzv9ebWothvcCikPZkUneYnOhijw6RpAhZxFDkQHTHpO/2OE8GgcKABQ0VU4nGPe9wwSBmI3glMpsqxIu649uLAQc0x4BjmDBsGJQM/94+JBnLiGDL4wkDDYwYZYy/XbjDCrJ7n5E972zyrHIaU9Db/M7rIBn4z8nKYByOJIeYZMsOBFQXhAC33+R3p4HAzGEUkOOPoliiN/vpJ5AXt0MQPOqEXZwo/OIDKYJTiBUfd53iKL4xcn+d+OeWHxniJB0vaeo6Rzbj1PENauXgByjVOIyOcAn9zHMJ37fa/BSF8Zfj7TL9EU5TtczLjGd/HydQmdWkjmdQ29ZNNsshg12+fe8czyiBf5Aotsz+u2Blkhw6WAcD5x3up51l0AJ/Zu4quDn3JnjzOjUUICz74yPkxbpP6iVdkgXzhD92fvX251gNfc00GkCeyRP7IWA7TIgvaRLYjS+ojm/5Xtvb5Xln0kbLmu2Sj58hMotTK9ozv6LQs6vicg+pv4099OT0YffyQX3LsM/KKBuYldUQXGtPK0S7Opbr0jRMdudZ2tCK7ykUvdWnPLNf5uyiKvY+O1KI4AIiYOLnunve855hEZ0PORCi9bZ1xl6PeRRIYEbkcehN6uMDuAC84MVb514HhMt+vx6iaU4bX3b2nvBygkjqAg5CFgJwamr/xO3vqju3GPyOR8egnyMEcgShkaDEfKmNMiPLm/+X+yHXv5DfezQfbrEP4Mpc7P6vclA1SFOf3cmhI3mGoL2VLSt8MTmPuust7dICfgOM50wukHK47QbbYHpwtTvlyvFooEDmkjzmCUphnnes9/+ObiNyS7hw8PylvRsZ9MPM530euUlbK2JcskZOd9Jy2zp/lN5mO4+z/lJP3pKTO71hAW8re8n96Mrpy1oEZ25t0KId+2faiKI4c1IEsigOAVVwGsgjHuhXTGBXZB2LFVlTKqqy9H5xMq+IMz+LIxaa7Jffn/+Mylnu31v297fvbfL5b7x8oDw/kvcrLgUMEUPqkCJpImkgZZzCnmYpU23Zg0UE6ZfQ22MtIzyfFXZSOE7qbvDlUvN2p3J3G2eGUz8p1URzZqANZFAeAHF6wbQZ49tEwQKwmS6WyGt5JtCiK4tCAfhYN4xxyGudDbECUzH5ADqTo+KyPOZ8Xu9jFxj51ZfQk7KIoiv+HOpBFcQCQxsOgkPYk/WlfJ8dZ9XY4gTS2JZLGus4Z7dH9RVEUB4bc3bjponopmnMa6Qypng6EKYqiKH4QdSCL4gBgfxajxBUCoou5QD5OoJQpK9hzytMmZ1AarAMMHHqQI9n95pjmAuziwJBDVXJqpX03B3JIQ/auOpzCwQ/uUUsUYy+gZ6EVewUHsuB1uOV3U337akfH2bEXXagtiv1DHciiOAA4OOce97jHuOvNdQ23ve1tx4EEDAwn5L33ve8dd2g5XIADw/lwMh2ncul4SJF6whOeMO7octqqwxU875oQ78+nBRb7B06j+8wcM8/pO+qoow74lD97qeyXsjfKcfcH6kDmXjfv78YhSTl50SmlRXFMgTzTgTmhdH9gwY1+LIrDDfrTQq805R5aVxTbow5kURygseR+RseouwvuoQ996DhkwaE40qKkPjni371hTt9zwp37tziEy9P4pMM6Jt4F5Y6Mz7HnfrtrrBHIAweDwKmn6C6iezDg3Nsz5eLwgwFHzwKDewCXsnCgcNjHpz/96UZIimMM9hG6F3E+iGZbOFyMriyKww060/5Xc3YdyKLYHnUgi+IA4SRVBpN0VndhcfqsZIpOMqZEv3zHOQSRL47kOmf0ohe96IiQuStLlDL3dOWi6+LA05LQN/cA5mRFR8o70l46au4XRHP3FqK/qK//IamrooZJMQ44pbknzf1m+M8ISRqyO9PcQydNWZlSlZ/73Oeu3va2tw2nVkqtdw42feq3fuu3xgJGURxTIPfLU0r3Z3FmeX1JURwuiJr3/smi2D90xBTFQSB3aa27l4vTsm2EybMcTz/F7iJROUfxixT/yZ/8yTB2pSA73Oi+973vOBFXmuvnP//51YMf/ODVTW5yk9W1r33tkY784he/eKQVWzCw59XCAHAGn/Oc54zfynvLW94yHLnLXOYyq3Oe85yrd7zjHWNhgYP5qU99atTHkXzXu941rg/40Ic+NPiduwEPFNkn20h1caTC2PJTFEVRHBlovL4oiuMEOOnXve51x55VK85+i5hwvER+/XaRtv2E3/3ud4fjyXnkBF7taldbXelKVxrpsIkWShl93vOet7rwhS88rmXJxeRSkqXjcRTPda5zrS572cuu/umf/mn1+Mc/fkQ9z3zmM49opfIO1nksiqIoiqI43KgDWRTFcWafi0jjGc5whuEQfvWrXx0RRs5knMKkvPrN6XvDG94wnEYprhxMEcPsk+F0cjSlsUpN5nyKREtV/eAHPzhOfn3/+98/nEmpzmc729lG9DKn8aaenv5XFEVRFMWRhKawFkVxrMfsqHHcsl9xeejM7EjaL2kP46aDFexv5TS+8pWvPPpE1otf/OJj36qo5W/+5m+urn/96x+9L8zeysOBXAGj/alTH3LXqL7Z76Odoq2bnuFYS93VN8/pE4iicoQ51bu5b0i99hFrd/4HqY32ox7I6Z4HC/teLTJYFFi3f/lwYEkXfEOPpVzio0UPz/pOm7NIcaRBH/QF/SF9Tfo4WdiX/JJNWwgiv8ay78gvOPBst9Nmw6e0w486jJVjQn7VL4Ve/cbyMYFZH/nbQty6E0995xl7zvGP/B4TNCuKYjvUgSyK4jiBGNIMFQfnnPSkJx2H6TBK46wwRmOQchgYO1/4wheGUTNHDBk/TuCVhsqxclDOta51rbEH0nOMVAflOJQnh9t8/OMfH3WmDfPv3TbyP/OZz6we97jHjf4xlL/+9a8PQ1ZbtFV/pNe++93vHvsyGWpf/vKXRwTV4T76xEC/4x3vOAzxZz3rWeM6Go4Lwx7udKc7rU5ykpPsarsZkK6zkf6rXVJ8HXSkHntL3b16uBwivPnLv/zLcQjWne98542X0R8OuOKCPL35zW8eKdK3u93tBk1mmXbNjFOc0fByl7vc6pKXvOQx5vQeDPTFXmRXG5FHjo9sAePKYTv6Z98x+X3rW9865FU/v/jFLw5ZN8ZyGJb9zWTbwVUyBjiU5Bfd7HNW3m7zSZtc80M/GG+yFPRBKrtTnA+nQ0/PkV+p+2c/+9kPW73r2uH06pe//OXjdPI73OEOgz4zLfDnAQ94wNgGcKlLXWqM9906pbooit1HU1iLojhOnA7JkGNYWpH/0z/909X1rne9kZLKAP3EJz4xDsHxw8D8yle+MoybK17xiiON9WUve9nqYx/72HDMvG//Y/ZAgugGJ40TxnC9yEUuMoxchtLzn//81WMf+9hhFLviwI/nP/zhDw/D+FCAA8h4vdWtbjWcXG1jxN3iFrdY3f72tx/GOBqIoLrDlMPhoB99vtGNbrS6+c1vPspBC3108JDPb3azmw3D3bOJiO0m0MbJtGgtAnHDG95wdY1rXGMYn+rXj8MJTj8Hlowck0ALe2c5SQ5ukiI9R89FeDjdouGe8Sxn6khETi92KivZtDAjHdxnxuxtbnObsUDjWiROPWfa58ajMSfqT04t/hjznJb3vOc9YyyQ/wtd6ELjWQ7LboPDI1XdAVkWbK5zneuM9ruLlvzSH4frqh310GsWFo5p+UUXafz04zOf+cyxcDXTwSIeHr3iFa8Yh53J7jimIqZFUWyHRiCLojhWgyF5y1vecvWmN71p9ZrXvGYYJhe84AXHirwoG4NTqhwD5vznP/94VhRD5I3RKd3K6aucT3c3OmHVZwwghr33pK4xgjiqjF7PPPzhDx+Op+/9f9WrXnV8LzLEyOdQ2o95KKD9HC/RKgZkUv5ErTjNDG537/3u7/7uiPS5hkR/tY8TJ3KpvWjF0WSY+05/L33pS48oprTA3UT2hYom+c0BUh/j032onHGOkz5xbEV7tE9UNJEMPPCdCKt35xRF/BJF5UiLbKFHrltRlu+Ul6tY/P+Rj3xk3A/nWTIiOuI7/Ff24YgmhS7oIYKVKC1ZzenP2mXRQ3SHvHs2KYJpN/56PunWZNZih/85Wuio73gd2irrmEiBFXnmeOkPBx6ttYVsaufVr3711ec+97khv57F86Rleybyrz/Gmv6k/+g2p0nvZv8ivxlvZND40l5OLV1gT7Xxpg36gs5zJFlbfU/u55OVfWfM+Z78Jbocuc7nkV+LCuqzmMAZNzY4zerKnu7DJb9+0ONUpzrVkDWHk4nI5qokC170zOlOd7rR91l+ySj5VYaFg6Qq+5xs46vv0cN7xj7aZiwXRXFoUAeyKIpjNRgcjCgH6DDAGHeMjRhPv/3bv7164AMfeLQDyDBhlOR7EQ9OByPWu8pjpDzjGc8Y0T0GKWONQS4yx5j1rCjkuc997mHszAaRyOBDHvKQ8X+cmN2GVNplufOK/1nOcpbhvG7aA6afngERQamRUmKlcnJARSwP5/6keT+k6KToEwNRRIOjYL8piPwwRPEnUVhRVk6gKBCHMHd6ikZxPkQ8LBDoF2cDTx24xNgW+br85S8/nKp3vvOdg8/4q+8c7MN59QT55IxI7ZMiKbVav0DfOCv6k72qaMawfu973ztScTmYom/26YrAiayjT6I/HBuLA1Iw0Zdzdutb3/qw30VLbjka877lWXZ9xpEWfQz9l1E9jpx7dY1lsvGSl7xkLOjc5S53GdE4Y/NwpkLn8CxOG/kkpxZz8M2BXvrjmU9+8pNDh9Al3/rWt4YDTW/hs/trHcrlc/pICi4aiMqL0OO/Zy52sYuNMStiqy76SxukQGdxhTyLiGYx4XDRwcIUGRNpFN2nO31uMUD7yaOFtTyvn65DsthlrJ/vfOcb49EzFlGSfm9sSkfmqHuejiDPotONZBbFoUFTWIuiONYjxhsjZY5Y5TuOI6eSUzefygo5jIMT6DtGl9V+KahSXqV3MnAY9AyeRBWVoczlgSf5XJ2HwojNATibDrjJ9+rfdMjKXIYrSkQApbJK6RU98F6iB4cCOVBD9JRzxwG4wAUuMCIUj3rUowY9OQEcvGc/+9njHc9aCGA8X/Oa1xxGNf5wgKQOvuAFL1id5zznGQa7Mo866qgRuXnyk588+sPwdrgSpws4Wd7lgDBwX/WqVw2nRBSaAZ4DXQ4ntPMqV7nKoL3UaI6wiA0nkTE+R1w4Cvqsj/jHGbn73e8+HGaRnLe//e3D2BaJFx3DX3sFPasOdOWAHG7MsrdJNhMt38kBUoYxj9/udJUNYOFDP8nXUg/sJuL8kE/OIj5YkCF/j3zkI4f8cITokSc+8YlHO4z2K4qeWsTgJFkoEC10b+yTnvSkkckgY4I8P+hBDxpZEcrmGMqqIKu5gohzqn9nOtOZxsLI05/+9JF+ftGLXnTIjDoPN/AEDzh+ZM0Y1xaptpzkeVEKjci4/uChhaB73eteY+wap9JgX/e61w1HkTNtf7CxzEH1P53h2cOVMlwUxzU0AlkURbGfkCbnEBn7q6ymc0w5NyKOokBH4smX66AfjD0RU8YnI1YU0t5NKaWM20MBzhkHzp4+UWNG4SUucYkRtWFAixDai8pI50QxEkXYRNq0iYOEFznEiNMrOuNKFX0SdVaH9wEf8Y1Tyoj1HcOfM4q3jHt9ZvQ6kEQa8jF1qqY2iSRKybZowQnh6Pp8Npb1nVEtasPYFo0TsXKqrOiifnmH3EpJFoHlgKBtDo1C4yMZeO2wFofp2Fcna4ATTVYcEGUcH8qDgKTMc4QsOpAZjg9nlqyJhHPuRYzRG63JqIUqcm6xwuIUHpFf8ud/fZLG7Z2cQisa5zMOJr5xDjllopdJCVfPU5/61DF+LAQdU/KrncYyB09/RCXRyJicob0WOMiq3/qKFmRVKjK+oifH0qJPUpdFr5Pajqa7naZcFMX/H3Ugi6Io9hMiIAw8q98MG/8zWHbzWou9AM4ZZ4px5kAQRviDH/zg1dOe9rRh2DEED4VxJsLoMBJpdmgaI1hbpOdKuZSKKIUxp+ZyFBnfnEROEOCP90UnOcKcKe9wwHyuXw4M4hRLaWVc2/dq353oGyfD84xb34l+SiP0PGfzmIA+2j/GOeFYcJhFH7Vz6UByUNARHcC+Qsa3VFzIHtBE9RIp99tnuUbjSERSR/WBQ6HvHCxRatFVjrM05EORxol2xoq9xvjlRz3aJENBZJzzg2eJZNuniDfSrPHL5571m/xyKvFZ2eQYvyyU6JeFBAta+kN+9ZkDK73bcxYG7M12yun73ve+o1N5Dzf0X3u002KMCCJnmi61oDHLr6iqcWghiFMIouPkfalzcjJ2eJn05ywuFUWx+2gKa1EUxQGAgZJogRTXveo8HsxVIVLnXOEhQqB/0uFEHsGepENlnOXQDXTNXZTqkhJ4t7vdbXzOmJ4jSIxkRrTUPZE3KXFOcZXGmOtYpD0y3P3oEyeL4y+tkVEtKqK/Ut/UL7oBIjqiNr4XMbnnPe85okWHG3E2OEL6L6Jmrxu+LKH9jHB95JzoMwdFlPbYZlRvkm1RLI4KR4T8ckTucY97DHkiv4fKQdYezmHkN+m4aJ+rcexFna/B4dD5jrNov6LooQO9PEvPSEP1Nz56VgRSv3wnM0C/pLxa3Mk+YGWo1yLXhS984ZHG6zNRWNG/w43InT2fFkFe+MIXjnRcDvF8By+gn3bTQfrrBz1F0I+J9NuiKL4fdSCLoiiOxRChE3FiUG6C7/MzOxeMX9GSN77xjcNo8x2jVUQwxulug1Gvzdq7dHQYj4xjhqU0RA6lFE4H3kgJ5PgxlEUpRZgSWRPhYIjab+aQEvv/HBajTyJ5cOMb33gYtdLeRDhFWOOgqkvkhuHL0WT4i+4dTnB4OUR4hP4ODPFbhIazGJr53t8irvY9ci6kADq0hMGOf+CZRB/9Dt3B//k5pqFd+pWTjtdBu32v7/MzeC9i/PrXv36Uo685sTPR6UPR3hyqtaQfB5LDRH6lpHIWyRH5xVunDHOmyK/xlT7YP2khwP5JEXYpyeQXXSyW2CcoNdbeX4sG+ixd2cKBPjtoh1NKfjmPnFDvHE5oqz5kMcoeUAsa+psTZX0X+TX2RP7Jb/aav/SlLx00FG2MrAaR4SDyXRTFocHeXDIviqIoDhqMUsYzY4rxZq+U1Mtc/8DAcgCQVDKROE4Wg9RF3qJ1HEj7ihirubPS3sS73vWu40CQ3XQgtYXzxrnTPoa2uw2TngkcIg6TKIs2cgKcwOjAHFE4ziXnz/4u79hvJVooZVVqnzsUc3CM+zBFEzmlolSMdtEcBu2LXvSiYZAn8umH0c4pYKCLRnr+cEDdjGaOLh5IW3XICjqIxtqbyvB2qirnEE/8LX1RVOqhD33oeE5ar3RHUVWnVHK07O0UiRNNzb49zjInm1Evmuv+PnQ63PvIslih3+SXo0V+0Z58xPHFP5E3UWZ/W+zgnGg/x1q/Of9ki1w4Ydbpsg6TmQ+32g2IBmuv1FK8IsvzflltcdiNg2+MR3v6jCcOn0OiHNak/WSXk2URwP5NvLZw8ZSnPGX0RVqyPvjenkCRcSmz+mO/MHnmTOYEXZ97jzw7tEablHE47/W0z5h+wUP7iJ2MnVRwvDVuySO+ooFnZBRIOSa/Ul19ZuzSDfYvc7zRGW/JM9kVqSXTaO47ZXKYuw+yKHYXx/tel2iKoiiOlUhUajaeGNoxnHOfGsco96vNz+Q0VM4kI1D0htHqwJX5moXd3LOmver2N4N3vm/RMwxgjkXS/3KohpNIGeWMbQapKA9nQdobYxoYnKIU3k30Sd9zv57P48i6FoARmnZ5LvcHem7TCba7jZlHcZpmHuaZZYQ59/yhl37nBGJIlAfQQf8Sucv/kYWcPHy4Ecedc7yU31n2IuPr5DcHJZEhNPDDGSbDsNv8W/Ippz/P8qut2iHChrYWefDG3sdcVYFn+s0BsmBx05vedPCArHtfH2b59aznlKNMkU0LPzmVN1kIOaHV+D1c8rvUQ5vkd47Yzs8kcmkxK1dyLOU3Y2SdPON99vQWRbF7qANZFEVRHNFgYLrzzaEjUlFFn3zmXj2H60gBrAFZ7FVwnhzixDEUGecoWSSRtmkBRJR5t6OlRVEUB4M6kEVRFMURb4BL1ZXil2s8RGCkKdrLuLzbsyj2Ephh0jbt0RV5E+F2aIzUa1eAHBNXbhRFUeyEOpBFURTFEQ9TmXRAe57s/RJ5TApfURwJ8itF1X5XaZcWQETSu/BRFMVeRB3IoiiKoiiKoiiKYis0qb4oiqIoiqIoiqLYCr3Goyj2E053kyrndL+iKIqiKI5c2DftZNoeVFQU26MOZFHsJxwr7sAOJzwWRVEURXHk4qxnPevqOte5ztH3ZhZFsW90D2RR7CcMmdxD1QMOiqIoiuLInc/didmTmoti/1AHsiiKoiiKoiiKotgKTfguiqIoiqIoiqIotkIdyKIoiqIoiqIoimIr9BCdotgFyAT3Yw/FbuyjSGa53059tUdjm3Lznncg7Vm+m/Yu25z6/J8T6eZ35/fAM+valXJ2asMxhbRtU9uPDZh5C34veReENzt9t1fptM24yzOzXKe/EDkN5rL2ar+P7VjqmBn4teTl/J696Xlvr8ruLHvZgwc7tXWTvp7L2ct9Lori2IU6kEVxkDBxf+tb31p95jOfWZ3//OdfnfCEJ9yVcr/zne+s3v3ud6++9KUvrW52s5utTnKSk2z13l/91V+tPvaxj63+/M//fHWa05xmdd7znvcHTpf79re/PU6SZWyd+tSnXp373Ode/fM///Pqve997+pTn/rU6sd+7MdWV7jCFcb7S3zta19bffzjHx9XmZzjHOdYne1sZ/s+Iw89vvzlL6/e+c53rk5wghOsznjGM67OdKYzrY5//L2hbvDpE5/4xOpKV7rS6kQnOtHq2AY8/Yu/+IvVRz7ykUH3U53qVOPz//j/sXcfQNIVVd/Ar5/6+r7mnHNOmLOigkQJiiBRcs45I0hUESQnJWfJoiAiKiIGDJhzAMWcc9bnq19Xnaea653Zmd3Z2dnl/Ku2dnfm3g6nT3ef1Kf/9rcy5j/+8Y/L/6FEPfOZzyx8++lPf7qMa61Q3v/+929WX3315p73vGczSdC2v/zlL83Xvva15tZbb21WXXXVnhkUf/vb3zbXXntt86Uvfan047WvfW3pM5794he/2Hzyk59s/vWvfy1+/r73vW955sEPfvAYe5So+fdnP/tZGZenPOUpzbOe9azF3/35z39ubrzxxubXv/514YGnP/3pzRJLLFEUMNmxrXuf+tSnmt///veFp0e5Ho8K2v3Xv/619OMTn/hE6e9SSy3VvOIVryjrZZexT7/xun1mmWWWWTwfvfvlL3+57BESu9397ncva/lDH/rQVCITicSsIkNYE4kZgvX3hhtuaA488MDmBz/4wcjKJRxQ5s4///wicAwKShGh45BDDik/FMnas0RYvvrqq5s999yzueiii8rzlItrrrmmKB4PechDiuJ6wAEHdN51eb/73a8oj/p7xBFHNL/85S9v9722nnfeeaV85VBCJuV+rV/96lfNO97xjqJI6fNCg3EmfL/3ve9t9t133+a73/3u4u8ImXiJ8kzoxFtnnnlm85nPfKYoWcaM0uk7itWVV17ZXHbZZf/llZwEEJa18e1vf3tz2mmnlf+7QMm88MILi2KsH+iy2267Nd/61reKknH55Zc3H//4x0uf/TB6oBFjSmJuYG254oormr333rv55je/ufjzf/zjH4Uf/VAqH/nIR5b1Bz8bW3yMf603FC3vU7omDdrKoPGBD3ygrPEMN9tvv31RKLuetU4xfhx88MHNBRdccLt16ytf+Upz4oknNo961KOaF73oRaW/5vRCXNsSicRkYTJcAonEPAaFibWcsPP+97+/2Wmnnf7LIxehRxFeVYekRghdO1yVx5F38IMf/ODt6vNMXCHS9ur5zKXIT37yk5vHP/7xpU0f+chHmic84Qml3FAweOHuc5/7FC/LYx/72CJweOcZz3hGsdhTIt/61rc2v/vd70p5dfkPeMADyrOPfvSjFwttK6+8cumz8imht9xyS/FiEmwIevGddmu/dkf/gyZ1WFZXeNoorP7XX399UTYiZGwhAt+89KUvbd71rnfdrv+MAfvvv3/hC3Tl4d5nn32aV77ylUWZWmeddYpgjjaEde/z6hnHNmIsI/xu3GFz+Id3HF8Trrugbbzl+qDf+HqllVZqNt544yKsv+51ryueH8J7eC8vueSSolymB2fuQAF87nOfW7xp9ViKrGDkcF+fdco6cumllzZnn3128VRaLzbYYIPC/yIjtt5662LQe97znjdR/MtYIwpg1113LevvN77xjWabbbYpSvPSSy/9X89HFIf1lnJdtxMfK8+8vcc97lE864yC1vOueZtIJBKjQiqQicQMQUkjtAiJvPjii5s3vvGNRXECYVXf+973ikJFWODl4BUhtAu/CmWOF5B3TOgngeclL3lJEWrbQo1QJs8TjAj5QrQe8YhH/JeHj2CkHN5CQpcwxAc96EHlc1brhz3sYUVIDvjOD1CwtJGwHZ91Ybnllisem/e85z1FEKdoKp8yLWySYhkgrOkb5ZIio7/6SAgUdqY/hECKq/9DmSU8jQLaxQsF6FK3bSEBv6AZ4bGtJFMq6/BUipdnCKdC4iLUFXiVhYYSyLuUbeNlPI0fPn3BC15QBNhxAb+rryvkr4Y5JxQbb3pOHx/zmMeUdvsu5oDvzCe8t+yyy461L4nbg6KP/m2+o2jhuwirxwPWCB5o66jQzeAFxiJ87aeNMHLhX9EYeMiaMK5wdsYPPPnwhz+8tJcBz3rXPosLvkcHynRXKK61n9dRRIXQVvsL5btXOHcikUiMCpMRV5ZIzFMQQITAOWdIcSTI8HKFV40lmFeStZm1nKdHGN0uu+xSQkuFEe61117NqaeeWoQawoDveA3bAoW6WKkppISOD33oQ8VLGOeBavjfWa5VVlmlKK/ONkY4lNC/5z//+eXi5EAIXrxSQv4ohYSvXgqcthFeeB71nxKtfB5LoZIUiloAZHEX8qr/PJgnnHBCUeiUTxA89thjSxuFnhHiCXOj8hLWXleeiUk5izlOtD26eIkCycNNOG0n38CbnsFnXUaMk046qfA4rwhexYPjxlShtdpNSeTRCqVZaCrh2tnQus++M094eHyX3sfJgvG57bbbyu86IoKBzJrCC2fMrG8MWEcffXRZ/7rOjTPq8Vpaq6xhQkitD+OCyA/1Bt+ZTwx2jIHDguGOIe+www5rdt9998LrG264YSqQiURi1pEKZCIxTUSyGN4ayhaljHXcuSqfhbATSR5WXHHFZrvttmve/OY3F0XpuuuuKwI8hUYims0226wIAkI+nXXhEakhtM7nLNGEIMK7hBHC9LqgzuWXX76E+Z1zzjlFOaSgUv5Y7rtAENM2CoF23nzzzT0Fde3mpSQQnXvuuaVNlEAeHm1rv6df6MPr84c//KF4FJTx8pe/vIQVCh/UP3QQZjkKBTJCN5355KEIwQ3uqEpCKPpf//rXy7mpLjo70/vEJz6x0wONnjy4DB7Gco011pgXHjsKL/589atffbvELGBOSGjyuMc9rvBuopnIpEnhkQswQDF0xBlxzzB+4NHTTz+9RF+01yHrFP61bjIwiByhiM4FtI3XEN+95jWvGfp9a++6665beNZ+wihHIZ3Ec8uJRGJhIRXIRGKG3kdeC+dOIiENDxovH/ByUJJ4+yLkTqimDV+IoO8IRL6jGLIgU0Sd91F+rexE4g9lEZhk7ZMQhvDRBUKEUNrVVluteDp5ivx4j9ep68oGSsMee+xRwsL0RRKc8KZ2la8vBB/nNJ3H0W8W8bZyRnl0NpQnUlsIOZH4BA3WWmutIsTpd4R2jQIUA4IV2qlboiB/GzOeYkr1HRF4Dw8Zvzat8RhBlLe2y5OBR3m2JTDhWcenvD2TjEhIRbHYaKONSshu3W99Nm9FEtSe+cRkwFiFJ7nOmEsZtKaGAUN0w9prr12URzwq+qOdUMazjHnWOPzLaEYRmwtQdhlyGNCif8NA5McZZ5xREmYdddRRZf1lhORpTyUykUjMJlKBTCSmAZuzqzCEVRFYnOdzxoxwSogRatrODBkbOoWRskjo7lLifEeZap9rJAQTmHg0ndOiqBF4+6WpV4YwU57Q4447rihMFNR+woU28BQ6F9RLeQwQxFnwWf6PPPLI0idevjYobzJmUlqECNYWf20hxFEyCVOU3FEJPxRI3k7ZSAmVhEYhbsKAheqOM3RtkkBZMk5tDyO6GwNCd9tLV/MHPn/LW95SvDg777xzCYcdt8A61b2P9R15wqp5vNdcc83S57hLMJ4nyONBvDmXiOQu5okogK75F8+IUKBM1X2NRFy+8xP3A/arzzPKsbZ4R9nxE3X5vv6J78Y15saaoYyySNkPODcuTLUOVY1EX0JCGae61ljHDaxHDFaODEgG1nUGcbavfmKgc2VMnD+u6ToVbbVXpmRz1ZrOUCiMVVZavDxOBJ/Y8/xMxXfBc/ETvFqXF3xsHrSjcbrKQgd1D8O/iURi+rjjHQZKJEYAm4+zMxQhoZGR8U4yBNZtyokzd5EBMDa5EBxsdM6vRFmxgRIcKVuEH8JSLUgIReW5c35HmCdFlLBAIHbmsBaolc/bokyClyQ6zq2xuLPAs1BHRlTlEx4plyzx+kLpUr6+dYU4aq+2qJOnSlIgbaFI8+BEufGbF1C4LcUj+hlCrr+dC91qq63KmTuKKM+Y8N6ZeiIJkOutt17xcAZOPvnk4i095phjSjKhhYpeAlJkDaZMtTM14lFhnhJx9EqgxHtLAcVTDBjOXPHuzdXZwcjaG3/jbQYD84UXnbGAoM3zHkmt/FCQheDiR56bLoV6nDBntYtnXD/MpbZnV1t5neJqEuvPJptsslhZIkRfddVVRSG2fui/MPMu75Y6hNqrjwLF2MQYJTpBOeo3vtY5nnuKm8gJPONd8xNNtXE2xr3Nv6IjrK/GSsg/egn/tI6ax/hS+/UZH1vPJJZp8zj+MP8Z4KzP22677eK1axzXDUVGWSH7DHXGSXIgIeHG03ptjB0FoAjX53Tb8DmeRgt9t24Kyx3ntUl1fxj/tIlRVaKgrggGPOwaE+fsvautjJArrLBC+du+ZI/El5RH85RxtqteYyzcXvi5etWHf9EDf8q0bN/Bv/gDbfFD3H/s+ETQOJFIDIdUIBOJIWED5KVx3xZBm0AeQoq/bWKUN9cgCC0CCpnN1YbHW0Ooc26S0KA8AhwhiFeTsGfzdU6N14Tg5uyi82quWoi7+whzPiPE1QK0zVd2VEI+QYlgZCNVDsFL2yL0NrKgCnU9/PDDi9Bts2bBdlaMclhvrsonnNu0nf/kCXWekpKmDAKMPtrQKZgEIVlfZVdVv2Q5hDaCAnpoD0GYBxMtKZ/OU3rOPZKEqJls7nHVSZ04h0Ls/0jgs9CA/sY0zjlS7PBW0FEIr7HBf23aUijwnJC6XnQnnFPICGqEdePfVdZsggCIt/AiIZOxhmdem5zb5VXiYcKPPKU+E84Y7WdwITwCAZwCzIszV5fOM6S4F9AZYHPfvKJMtGlqXhK8KQnmtDsrzRO/ebHMe2UceuihZR056KCDCj+4JqKdEIv3br/99itrzpZbbrnYCMW4gHYUQ0oWIxLlwJwXtkwIx1/KXnXVVYsHOi62HwXwrfGg6BljayT+pdzjS8Ym65f2m8eu9fCb4cna5ty3/xnKePfaNMQ7FBhrmXWJImHsx7UWmHuiQZyV5zmN9jFmSP7DiLD55puXcWXkwRs86D5HE3PaummfEV1CoTbmFC3rLoWaUjTOs6n6op14Em3xlb5R4Nuw7zBSGU99t3fKSG7MjI25evzxx5c1Rf+NfVeWceOPT+0vruKxr+BxvOloh7J4mu2R5hbDinBfewq+koCO4UUSu7k6A5tIzGfcaVH68ROJoWDK2ARZjQkpYdUMBdLnFEabWQhbhKzYVAl/FDXCrhBA1lqJSCh6cUaS8GZjVhahiHDhbKDybZiURIlmWOS1oVYg1S201vs26Qgp9blybay+J1jGfZOETZ4G3k+CKE+gdravSYiN23OsvHEdSFz9YSPWRh4Q9REKfK/eEAoJ+uoWPkpIoACgBYVYXymmEbI2G94NtNMO5S/E824EVEI3QZyCh2/8Djoae+PXzsQL3qGMGZc622UNCpcxMuaUseCxcdKS4cMcNJb4GY/hV23ATzxKFCL8R8kk3MZWhw7ay+AR13d43nxSxri9EdpF8N9tt92KIscYox9d7TBHjZH7AhlCePYl5iIMO4ssMsH8kgALKBannHJK+S1EPGDsKFySazk7R9APIT0MBJRVCpvPlecz0Q94g4dSe7VHohpr1qhgHQn+pZgG/2qHsbJmWhfjLtC4isX6wiNpPfO5sYzrW2roO/4Nzx1lCy/0ovmooV4KjH7W4pc12Nrrc0q8Ox31HX9bc3n5IK6fCYXLWsso5G8KMU+tSJJx9CWy4+IDkTf2IrSl1FPeDzjggNspf+atK0c8j7esHRRhhhvtNa6MHfpuv+wavyhHJm+KOP5Vd9SDDgwp1jHn7rXxne98Z3mWIklZ9b7yKZWML4xg6YVMJIZDeiATiWnAJu6nDYoki3YbNntKI6EW2psVgUAYaHxn07MZ2+xq2FB5BAJdm154hdoIqzTBU1v81OBtqNFrQ22fOYoyI1yJMthFg/D4AAGeoAs1HfWv6+LvUQKt6zswFxrQn2GhFygStTJRg+BZj1MXGBXa/DVu4Ut9hGU/7c/xec3LU/ETxYHxJt4fNyhjPC7mj3khdA9/UsraHlHrgefCm+g5c9G8oVhRTIQWm4/WEEYZHkZe5XrMXVnCk2kOWpNqId9axYvHmFPTow6lZKQQUcBgNOoL65VX30laIwxstcIabTLO7bHuGs9QPKd6bragD/3OF1Oag38j62wXr4PvGPv8tMsZFyiBlMUwZGonnuy6V5NyLAMugwaDhD0vFEZjSJnjaaVcek4ZeL6d9Ipx4ayzzir7TPv4BpqInon9pUY8Z84pg4EyrzxJJKaHVCATiSExzObMYh7eOF6f+m5FllJeON4wVlwCW4SCTVcAmG3BYSFYaRdCH2bSv37fD0KbSaDfTPswk+dHDesDYZlSwQNDCRTGx7PobFjdvloo50WxpvCeEZZ5XAjokVHUe4wJBOrwXgWsNzy1vFV1+CnFkBdPOUAB9YyyRA0Q+nmPPMMrxgs56nPE0+HfYcZwrsd72P7NZD7PNiIENUAJ5D32OS95O/SUQWSDDTYooaO8hEJf8bwEZ3jVZ3g8QumFulKmeRJrYwq+N28ct6gjcCiGeNNeCvbduMs2ztrz9itbJuodd9yxGFbmmicSifmIhXcAKJGYILDSs9AKASPI1dnxIouiMxgs6jbFcWYCTCQSc48Iw3Wf3/rrr1/WA14XIaa+6wXCsfPDBHJen0hYVQvtBOMI/awRSbt4X+rnCek+j2shCPWgXJ5aaxkFlRePkC+cnrCeJ2ESeICS5qysjLC8im3gIV5DSuGBBx5YEpnxhlMc7Y0MqvIDCIF1jlk5p556askb0A4Dtnfi17b3HJx7PuSQQ8p3kQTO36ITwusYZ6fzypNEYnpID2QiMYsgcEk00QWWW5ZYP4lE4o4Jgm1kTI3zYBJjyRwZ96K2QVAX6iqU2NlHQnIIxr4DQnFca9DOLuu8nXooqITxELz9dhaUkkgpFSIb3hmeSh4fnlKCvYQvkurw4EhW0pWtOXHHAX5zlpjRQSKoOnFZL8+lhD/CjimO+IeCaR4ow/uSuJkfzjFLhhOgCArbdg4a/4aho+Zf/M2DHiHOMa+EL8s5IFycoum3kNnk30RiOKQCmUjMAHHNBot+HWITghuBrp2EJr5nwbf5EcDaGRLnAjwP2hMJDRZSWE99Tx4Bm8DRda0BGvAas04Tnttnb+ZTX40l4audHKT+ngDmfzwYCkcN701qttqYe5EFuVcbY37ycKBFZDftda0FRWyc85GCiM+0D/+FcB1nHdveEWMkyYr+8uR4zmf4ldJHKYx38LHv22dinX/m5ZTBU8ih74Me/eZ+lIuOPExCXoUgavdsCOBxj5/wWX10tq4emxh/c9a4RTjlpM9Z7eZB40VGOzyJfv3aHdct8fgyABj/GI/6HkVjEl7icdFBO3ijhZWussoqpf64JqpXcq3IkE1h1B80EA4tNNpY+854+t0+p+g5xoxIKFcnWwuve7SrRh3hE+cunR1OD2QiMTwmTypIJOYRCABXX311Sa1uc3IGwxmhLbbYorn44otv92wIOx/60Ieagw8+uGSEs8lOiuUzrhNxuXV4MRYK9I2gIGxKiGD7gnb/E0Z4VIRMEVj7WdAnFcFjeEtoo/M+bQjZuvTSS8tVD84YEfAIs1Li82ZRSvxIRCGkctyXkg86ns7xabNzTITmXvRw3mnvvfduNt1009KvE0888b+eN/5C79ypSAgeJ3hJJBNxXx1aUwacz5IlV6Zg7ZF0hLJPsLbeGDdGEJlHeSJlUyVsu0YhrnCxFjlLKcLB1QU1eHD22WefwisS+AgjjLBACoq/Q/CPS9rRyNlI/EPpdF0Dw4Prd2ZLedRn2TP133oqq6eMo4AWPnPVA76VyAXfa+8kQ7+MrzBhbeYVcz8vL1w/RUa/JI4xbjX/OjPreifePD/m9TjXb+1i0JCNF18ZH9dHuavUmkvBM3686ZRDa5LP8Lm2W2eNH37Gv8JK8ZdyGSd4CPFYDUZONGN4cWWH5/Bo8K+/g3/xbfA2/kU7tP7gBz9Y3ufZnEQDWSIx6Zh/ElIiMSGwUUkCwHoqrMsmZOO2ORIGbUy1BdiGKVkAQVW6/Qg9mxTYyLVZnyiRhO2Fcs0F4YF3SYhV27tEGSHwRDp4wliv9PHzAbwxBG+KxEYbbXS77whwFAYCrMQUrPB4kKCHP3fYYYdi7Sd0uaeTQDvKO/5GBe0znjxolP2pzgmutdZaxTsn8yhlS4IPoWwR3ub8se/CmzZOmGOu73j3u9/dXHbZZSUrMQXQWKC9Ow9PPvnkEppHwWTgMI7mKGgvJYp3zjlKz0gWwguE76017Uyp+m2uO4N20UUXlWsO0EN91i8eSWGqeIJA7zNzwrUH+IpSqw1CAGXtnQ0B3LgYE3fguqZEH/x2X612M9q5MoJxwLr7tre9rSgTwnojdHESgabuJNVOoZQMAIwakhVJnNQLFDMKszEKRdPYM1gaS4qWPvMsd0VYzAbiWieKrXOK2hJtE37qPKOkNq7c4GmX8AbP4GVzkGLH0CURFEgahdfMA3uqd9GkbQDRN0qlNZviiiecrcTzlEPj7+iI5/CveW1eSQLFSBpJeLRFOyaVVxKJSUYqkInENGDjpiQSbvbYY4/FiqAwHBtb+x4uz7uz7V3velfZtHgcpgpXImgQjCKUJ1LzE55ZWFngfR53NaqDwOjv+E0ZCM+ozyIsznfeD0u1zwmyfkuLTqAR5ia5z0LYXPVZpkoZ+QgYAfQkhLoUXUgUATzO08xHRBp93qt2Gn38xCtAiGLICOURPLv11lsvviqAlZ6y7ZkuIwIexDsRpt2+L3S2YU4QlF33QKHpBf1bdtllF1/bEtkfa4WHEMsrQgAf9ZUUgwDdKG88Urw02kb4jovWl1xyyeKJJESjN6E5wnfjfcJxXPcg+Qgh3Hzn3eIV6lLwPE/5My8YHCgCPvOOkEA/QNjnuY/w2vihDFBaZ8sIhgcprK5YobzyOlGURBBQChjwKB+UBW2jdFtbXVvis67wz/BMKWuuIgwo+MbSVSnmjb9l4eVN1s5eodUUTUoVz3vAOFNGGfvwCSVtXPdZgnrsecYEv9U8GTxkLbLemF94Bf8Krcar+DZCygHP418eSPsTJVu4aq/rWJTN2IEOca+xdVyZDLv+t+YzMpg7dXg2RdwzC8VImkiMG6lAJhLTAGGcdfw5z3nO7TZA6NrsCDWUR4K6jcyGSsghwNXnN4AwJOxOWJBNkiBPiGKtJgQRqghLNmyWVF4z33uOgkDgoxR5V8gXiyuhg8WecsSzoU5hjLWiKpGA74WleZe122XLCwG14FsDPS+55JJi3XeBuzEl1AkFI/DMx9CmXmfYKM6nnXZaEbi+/e1vF/5iIHCvaPteT8oE3qJQt8vCv8IoCbUULsIeT2c7Uctsoj7n1E9YpuT4wecEd6Gfa6+9dlHIoi+8z4RIyTx47uYC+oJ+9TjUgi7vSvxPmeqFWnBvl9OrXs8yeNV3L9bvUEDjDtlxgrJHKTBGoVihEcVCyC2vcs0D6ESBsh63gZeNLWXEPYPWbYaFuQClaeWVV17cdryn7dbtrrESdsnogT/Rog6xFjlA+RfKfdhhhxVPtuMTxmtcSiRlXNt7wfyLM7jaVGdn7bqyBC/Wxq+pruzxbHtO1O9QEv0kEonRYv5JR4nEBIBg48wcZWsqCzzhh2eBUlffuybNuLC1uHMtniUgnHLKKUW4F5pGeKTYEBQIQULXnJ1hfSco7L///iVEhzIkcyNlkVJJOfWsML8VVlihZMYTnkrJJERREFlrhfoQYiLVP48S5ZZg3ets2UIBL4ZzYqzhBE9jQ6GkOBM6F1JyBeeN9MnYMhRQEnkO8FTtXQK8TSjrsv5TxM4444zCn3gH3ebizOwgYxMhqhQOobsnnXRS8fRE2KvsjualMLZJCCfvMnQMm9CqLmPQ96bzzmzDXMSrojyEZUN4ufzUSWfwL55lDKFEtvvgvCajiZBKHlZr7lzNbYpjneSFcZFSbM1uQ7/wK1C82uHVPHBnn312MQyZi0Kd7RXtZFhziTZfDcJnC4F/E4mFjlQgE4lpgPfF+ap+3oAaPIaEFgqh0CXnkggMNnsenPaZFgIuizPr7YYbbliSuxConFfiPXFmhKVWWcp2Xk0olM+EORGUfOdzisMVV1xRzoL5jqLIw0hwcdecvqy++uqlLrABs9jyWCl7IYPSTZHicUQz1xFIykJ59rOQ7uU03pQ9Z9sYIPRT+CJvOONEgJeDp46XpvZkBRgb0O3YY48twr3yeFUmPazX2VZhuhRmHlRhdM4jC/1jNAnlxG8C+EIyHsxHGBPeNOGJPGznnntuMX4ZG4a7gDnKEGA8GdW6QtCt09ZCihaldFKuTjKPrDPWauGaNfCfNVhkiZDmOKYAkdTImm0/kPBKYjZKpOQwC93wl0gk5h6pQCYS00CdxXMQi6cwHz+E77hagYLIc1MnAVEuD6LP6xA2IUAEJwJFCLq+j4uRQwHwGcu8OjzneZ5IGSi33XbbIoQ5YyJcjZeNd5JgLSy2tlorg7DSzla60KCfkSre336cR0NT3rqFpEDG3WpxRkroFx7EO7WhgEGDQC4suiuE13tvfvObiwGFwH7mmWdOdNbeOK+H57faaqtmmWWWKV5Hyi/DCq+kM1KiAcw9iVgI9alAzv24GSsGjhe+8IVFCcS/IjIiDNIY4VfnA61xvcJAIzmWjK54gDdyrseXIc/ZW7zJgNe+8gne9773Fe+ppEHOyDJ+SFZDoWYYbId+UkT9vdDX7UQiMfdIBTKRmAZ4cuLS7kEEER4a55uEp8bzhHPCeJ3lMj4TYkqYjXCtSHuuHN+FhTkEBYkCoG4LZYjAJUQz7tIiXDgL6ewM5UFGR8KVxAYy3gV4S7vOliw0EN4InUKMQ1k0BpRKYzwfz0D2gqRIjBg8znWyC+Mc3mef81Tru+e7hHHXOEhKQ/HiIeLV4bEct0De765CYxlzp+4r5dnZVj/G3dlNYY88PJGF9NnPfnYJ3U3MPaxhkiVR/ngdrVEiMvCncWV8c5aXscPxAGAQqRUovMA76XygMGbvibzol713NhFebusyw43IB/OPV1E7g3+B8c/ZbMmD/IgyER0iURsjV83f8R4P6yTcK5xIJBY2MolOIjENCIkjcLJ+E1YoaLGBO1fHm0cJIxQQ2oWcumBZMgTnEQntLMgEn0hhHgKTz1w1sPvuuxdreYQyCRV0L5/QQYlxWOWd+yEwSAhB+FBv3ONGKBGqJfyVBdvfLO+EixDGCCfSpPs7zkAqg6LrfE1XCON8RNyPaEwiGyOPHBoJ+5IGnjfWmBoXijqlYj4qkIRTBgZ99GNcjTkBm0CKB/GOvuEHCT0iSyneEdYpS3D78u76DKTQ680337woYARh9czVeEY/jac+SYjEK8o76jycbMmURWMqXNlc5dkyBySmCrif1Y/w3jqhTGLuYD01xvjUGmauuiuQMQAfWtsiy7T5aw1k9HKfZw0eSuukOY1v3Us4V1467XU1i2gQiiAveJx917+4m9OZde2N0Na4igg97CWR6MwarRzrtuRrvguDUCKRSMwWUoFMJKYBwjVBRjhReOtCABcex7tnw+dFdEaFR8u5R+FYkrREunXKW33lR9zjJXuqRDpCrpQVmfXUGaFPIUzstttuRYiQbIEgoQ0UToqpM5c8RpQGniVnZWTWdB7OD2VBeTJTxmXNhPxw1J8AAQAASURBVGzvy3K4UBISGAtCKDpRpghaBDNKB8WaJ0ACIslW9N35wDrz5XyCvlCinGHEIzwyPG6EShl4nSMTCsewITTaOIfHQrISvEzR7NV3NCOwO1OGr9Fv3HepEf554rWbskfpx7/6KLRWUil0iHPDcR2GOWRO6He7vRRMirMy5uO4LzREeCpjmYgJxi7jg+eMP4UxLpC35gKe3n777W9n+ImrJpwltA5Y35yFbWceHhcYd6y7cXY9ENfS6LN7WsNDGryoTwwhDGD2Dgqj60xcY2Ndt96jD8PfJCSESiQSCxt3WjTXBwESiXkI04a1mxWZsG3TjrMnBASbe4RCRuiodyh8LOcROkjA7RJWlUOAoNzVzylD2cpgySbsRggsxdHz6iXkE6a8E3X6XPgToZsg5flIkR/3rPn/Ax/4QFGkXOvRq33z1ZNB+NJfYxJ3awKaEizRJcJX56sQFl5oY6kPeCHuadR3BgWKou8YL+pLx/EPBYyi3euePOUqI87ihid3nIi5RHnwN94ND6Q+4He8ro/mqfYac/3tpSB6T5nmwnwd+4WEuHoFLxsTY1dnLw0vew3fx1nJGGPPes7YWgfi6pK5GmPzxhxrn6/Gw9Zb88tapL/1vaTRD/M71mXl4O84+qBf8zFqIpFIzD+kAplITBNxNpF3T2p451MG2bzrM1lzjbotBBeeOZZ9yRhY6CehjeNCvRQu9H5PEg8mEnf0+ZhIJBLzDalAJhIzBGsxy7Cw1Pls/WUZl3mU52ahJ89JJBKJRCKRSEwPqUAmEjPEQrGUp1cqkUgkEolEIjEVUoFMJBKJRCKRSCQSicRAmL/xdolEIpFIJBKJRCKRGCvyGo9EYkjInicF+7jvvkskEolEIjFayHZbZ/lNJBJTIxXIRGJISPd/9dVXlzsg87xgIpFIJBLzE05xuTN39dVXL1f9JBKJwZBnIBOJaXggXdDuHrJEIpFIJBLzF+7VdG1VeiATicGRCmQikUgkEolEIpFIJAZCmlsSiUQikUgkEolEIjEQUoFMJBKJRCKRSCQSicRAyCQ6icSQEPXtHKQfZyb8lkznzne+84yT6ijbzz//+c/mX//6V3P3u9/9dmX67t///nf5re543t9+4tn43I929apLWeC9iGb3fPQvPr/LXe6yYBIG6ZcMuhImLMQzL8YL7/z9738v2QWDL2qeaKN+Bn2iHAmjJpVO2vnXv/61/B397Ifo+5/+9KdCn/vd736L+4wfYr71mi+J8SDWJfx7t7vdraw9XeNunHxfr0u++8tf/lI+G4Qn5rqf+qHN2tqP79BDv9DCfOxai4OP73rXu/4XzRKJRGLUyFUmkRgSNupvf/vbzYc//OHmxz/+cfOgBz2oedWrXtUsscQSzf/8z/9M+S70UsYICp///OebK664onn605/evOlNb7qdMH/zzTc397jHPZpvfetbzRe/+MVSH0HY99rxspe9rHnMYx5T3vnNb37TfO1rX2ue97znlSQB7ToJzJ/5zGeaG264ofn973/fPPShD21e/epXN89+9rObH/3oR80111xT+idD3XLLLVfKmO9AJzT81Kc+1ay//vrNfe9732ahIBSkr3zlK83FF1/c/OAHP2ie9rSnNVtssUXzgAc8oAirZ511VvPd7363PI8f/LzoRS9qVl111cJL3/nOd5r3vOc9hR+UhafXW2+9iRPE8e51111X+khYfvCDH9y85jWvKYJ4m89jzv30pz8t7xDYn/vc55ax991NN91UeIJgToFceeWVO+dLYvZhPL761a82Z555ZnPbbbeVtcg8fdSjHlW+/9WvflV42xppvDbZZJOyvgEF66Mf/Wjzi1/8ovD6Ix/5yGb55ZefuMyaYSD8xCc+UfYRCp8rJFZcccX/4l/P/va3vy18+8c//rH00Zx8xStecTsl0XNoc8YZZzSrrbZa86QnPWmOepdIJO4omCypIJGYB7DBP/7xjy9C+WmnnVaE2Wc84xlFEOgHCiDhpl/eKmUTYj/5yU823/jGNxZ7SGR8veyyy4ow8ZSnPKV51rOe1VxwwQXlmVe+8pVF0KLwEfZdLwKEEu++//3vX+ypqUEAUQ5Bi8BGkH7IQx5SvpORjnDOKk4p1ab5DrSgRLz1rW9tPvaxj5XxWGigABI2X/rSlzZLL710UQZPPfXUYpjAKx/5yEeK8Ao+Q4dQwtADD339618vwiiBlTFi0jxyxlEfTz/99CJIr7DCCs373ve+oliER72Gz/R71113LXOVssw4A5/97Gebt7/97c2LX/ziksb/C1/4QnPCCSd0zpfEeAxz1tTHPvaxxfhhjA844IDF/Ggto9xTkmTC3nvvvZuf/exnxShw/vnnNx/60IeKsYsx4cILLyw/4VGfJFjfTznllOYlL3lJs8wyyxT+POecc/6rrebqscceW+iCb1/4whc2Rx11VHP99dffbh/hrUWr8847r+wRiUQiMdtID2QiMSQodbw1PH5CqChdEUpVb+oRWhqCwLXXXlu8Q7vvvvti70+Ea0UIKmGdtf3+97//7coh6H/ve99rdthhh1IX5ZUnkqLH2uxv77LWX3755UVA1kaC8Yknnth84AMfKEJX7UlS/73uda9m7bXXLgIKhZQSyRLOu+q7bbfdtrn3ve99u7bU7a3hcz8U6bYVvf47+j5uEEAJXvrFkr/QECHHxvPhD3/4Ym8rBTEUK8rS4x73uPIcZRov8p7jHc/hgx133LF46GIce4XLRSj1uMObeUff/e53F8/4U5/61NKG5z//+UUAX2mllcqcqMGgsv/++zdrrbVWmR9h6MEPFAzP6y9+JswfeOCBxXOl/PRCjg+Ue5EVxii8itZYCr1ICOsib/mTn/zkMobGi3JpHBm4KE+MAA972MMWe9bxibH0WVd9c8G/DDUiARjunvjEJxavI0Xy5JNPLmvvox/96MXP8sKKRjn44IMLn1rnvadfYdizt1BI0WHSvK2JRGLhIhXIRGKaqBUjwvsPf/jDEhJqo//1r39d/iaQLrXUUsX78453vKMofyzrvIb+5gHh8SEEEH55NtseSoLBJZdcUr5vh5HW59rivfp9wglB6phjjinWa0JYW1giQAkFo2wceeSRRTH48pe/3Gy44YZF2YoytUOf9MX/r3/96xeHln3/+98vyqdnKK7LLrtsUWLQxOcUXmfPbrzxxtJ3XqOpwn1HCX363Oc+VwQu3mJtWmggUOOfUOx5IgjaQjLRWuhbCMvGj7Du2VDChP/hM6F1hNOtt966GCK6QkIZM3jr8D2aCn0OXpltqPub3/xmUQgpvtrDU4rXfFcrkDz3BHNzjZIopJsAzkAjxBvPE9qDZvjZO57j1U8FcnwwBjznIicC+M+aZ5yF6gs79pz1klfyda97XRlPkR2UzDgz7hmK2M9//vPCE7UCiV8YS/CvcihdSy65ZCl/HOAxxXdCy/FlzFv9Eb5br9GOEtSKod+8s44d6Ju/KZn6wxDEM583syUSiXEgQ1gTiRGBAPCWt7ylOemkkxZ74vbbb7+iXBFOCK3Oagkb9R2Pic2fgskzudNOOxVBqA0KJmE/PEc1KGW33HJL8/GPf7ycfyFQsMLX4bQ8lMK9KH9dwoUyCeBC/NR/2GGHlRAwZYWyQYg5++yzm9/97ndFAaSA7bzzzsUbROjR70c84hElzCo8pX4Tct75znc2xx9/fEnwQDjcd999i/A2LkFHPbfeemuhIcVooSaYMFbRNwoWr4XfxgUNKJGhKPGCMF7gxQhPNubCByn3Quq23HLLYkhojxOe48nEyxRHwjBlbBzQFgI4Ja8W+MNLTpCu4bwnrzPB2xxhIHGuWKgj3sWftcJJWUEj3tlJDH1cyEB3Y1FHMFhDeJcZoyJRGeOctehd73rX4jWKoYwyZi0Uzgn43Rji1xrWIeuR7yldvO5d6+5sAf/iPUa1CA+nGIcRsob1kqfU58GP+qkPwqz1jdL4ghe84HZRK4lEIjHbSAUykRiR8ONsIkv3M5/5zBIuykNi8ycwEAQIuYReiiCBxZmtP/zhD0XIJ0ywNrcFYKCAUki93w4NJVhQWFmzCV/+phTUzxGKCUuEMe3p1X6hYfpAeI466zY4H8erRSgXWuYzZ+6EvhLQX/7yl5dEPM5h6stVV11V+uozZa+55pqLE7J09XO2QOnlXeMNpvS0M9YuNBA09ZlAic6Ue4pSzTe+N3aE86AFYZyXm+KJj4T5CodunyskvBK6P/3pT5d3hRzWXqPZRoQehvAdHie/ta0GXuZlojTusssuRfElrDNq4GW0qg0KkUk5zokmxocIl455iWcZ1vBXZMwFxgPj5rNDDjmkJMTyN95lGHAeFm/ypOOTtmfR2OJf3yvHumRdGBfU386OXRt2ajzhCU8oXllnmR2BEMERkRR+HG3gsRTVEnNg0hJeJRKJhYlcaRKJESHO90X6eAKCnzgrVoeZUsIIC5Q9IZUbb7xxSQIRyT1qsDLX3qWAz4QfbrrppkWApxB0JbvRBu8qp8urEh5GZ214oVizJW7g+Yz2suzrh/YJhdxqq61KeymSnos09NpEkeX1ilBXCJpoh59eiuyooR5CJeWZB5JSKzzxJz/5SfGyjctzNk4YB2HLzjcSsAnLkXk1QPknyBJQ2zA+zs4KfeUhb/MMxZQhQNKOzTbbrBhDxpWhF38xpDCIhLAd15aEUlEjzrgJc2TkoCgwJPBI43nzplY6Q7h31myuDQy9rlyZq3LGhViPGKwohXi5VoooTBtttFHxIjp/7vyfsRIRYR3lWXfuleFOZIVzhjWskW984xtLQh78a23w2bhoxIARXsSoEy/rQzsrtHl16KGHFp6lLFrDzDfh2NYuSiWF+oMf/GDxsDMM+e25+TTmiURi/mFhxnIlEnOIQTZuwoIwJoJEpFwnCPD6tLO5EnIJx11ZQ8NzKPEHAYqwcfjhh99OEInkNs6odVmnfUeYIrQJW+SVcv5NWc6PhVBOYNGWur0UMcohj6O2E7zVQcDvdUXGOAXzGIs4H6duiraQ3ksvvbScc1tooV8R6hfnXynz7XHnfXHuikLVNR6ULoaASMxTg7L2hje8oQjmEjThGYlOeKDHMba82jz2vIuhHAkfx3s86DVEBKCD7yOpFR427/RdWXg4wCvpGTw+FwpkKE8UAesABalXO4QwWhN6zWvzUbin90UB9ArdVobnREN4xryNJDVoZ52iWCsn+IEyHmcTRw39EnaMb4X3q8NaE0ao+HHG0VoV/UIrvKiNFEgJkjbYYIP/mt9o7NyrMT766KPL0QFrpnPb4xhzxow4n2ntVSel0JqJf+s2RMZvxiDPMoYZJ/+ji7G76KKLynN417p29dVXF9rZF8YJBkrzTLuMRTuZWq938ClDZH0nq3KMo8+7ruZpr0eeRxd8Yb9St7LMDfwbCnrwL1ozRE1ahulEYj4hFchEYkjUdznakOL/EOIilCg2xPjb5zZHmxnLuN8yBxImbHjOsvAI1RZz7/ufkBR387Xr9rezPISnI444ong0N99888Ubrw1aXQSR9t1hINyLFV+In82XB5Lg5bwYLyMrvXbZ7LWX8KJuVm8eUHVTIChmFDIbOYHT+bigSU2LOjvtbEN/nQmV8CfAc0GBIjwSrBcK0BXtCZqh9PMg4p+ap/CDe/SEdVKkQminYOPFyFJLqHVNRlvIIqzhGUqk0DlenzhfOg4B3Hk4POcc7TrrrFPq5FWWJIrgrH3O6PKi4nlefl4pSgN+0C/CNQVC/3hszEtKUSTZEYY+F4YO4YkyIPOymYdxrU79nPE1VsIa9bceRzA3w9tO0ZZNtl1OlMVLJ5Sel9rYE6zRj7HIWVieL2VRxign1hZ1UTgp3zx5FM5R0Cr6xvNIuTI2Ih8oRsaMMmkNirODFG3nHdvnmj3L8GXcJdnpOjfOm/fa1762JDaz7umj6ItxrEsUF1fsqFNbKOLOkYvsEOGhX5LkWEuNb8CYO6O8yiqrlLBW/CpSJOBeYGfPDzrooML740KcMdc266qxcIRDYrZ+WWGNHb4yXq5jUQ6+c7aVAcEeYkwYM+sQ5rpevMoQKNTZvLXn4U08g0b4U/SFuSKc31zQJrwsZB9/2APmOtogkZiPSAUykZgGbEbOo9gsCauEV1ZyAjyhnWBGKCCIh3ArNb1EN+7xssHyGB533HHlDI/NTwIagq3NUDnq8MMqTZiIhBLqEX5JKfQ94VgWQRs2T5vQQpBFlXBHsWOF542qN0obNGHVeTCKgL7YlJVL4OSp0V5CpY2WUkpIcJ6QEO/sECGNIK4dviOU2bCFnvFIEUwpJzyUsiRKyuJ/zxCUKCuzuXmHN65WgoSrEdrCy7JQwBJPmDr33HOL4BQZGrfffvtiyQ9EaLEwuDrboyRK+AVvEu7wrIRJXYIbjzVBzPMEX8LauIQw4yaEmoHjve99bxHICY2unCFAUm55z9/85jcXJWi33XYrPE5Rwrd4laCNv/Gw/xlKKNkSTbnGhOA+rv6E14WQ665XCg3ao28Xf1obKFbOp+pfHfFAAWPYoUBRLM1H5XR5WsxHdEBPtIrrI6wXEl0Jc6YsWrd4uczdbbbZpqwVlBXPWNucma2v+pkuwihl7Kyb+qBv0T5rm7G0HrrrkfJgzcR7wGsroZMriygK5kBXdEHcI4pW+EF5XYaS2QK+cq6TAUtb46yyvlmXGEbQ1LxlIKHs2COs+fpkTMM4WGeyNlbWU7/H6VmzjuNbdLTum0vu2kXbLgU+xoCX2F6Fn8B4yAJtXgq950nFA/qEFu338S8eNPfxTCRUosy6tsfeg3cYgxhTwkiqnXjbPGew0VbRFqlEJhLD4U6LMlA+kRgKYfkU+hYhSBGCY7O3adk8WVN5DYXWsdZTnAgHQucoXXGVgnJ4JHlLbPz+tykSHpXD+mrzY92VLIfwQOBQnjJsuDZP7xKibI7awGuofTxuFNP2PZAUDptthK4RmgkzhFmKB0U1BDjCtf5J+MNirL3KD6XTO4RJFnUWXX3xnmcp095VPmGDdVgftDnuzxwntEnfKO3tcOH5DOOgb4RoAjj64rO2ko6/8C9vUgiajAbGDz/iJ/yCZ7sUGM9SYNQRQrifcfeVl4qQqA2RwEo/zQF9IThG+DdhU9/wq34Z++i7OcpoQ2n2Hd4dd18oEpSkffbZp3h2Bwn9c9+nPkqSFJ4eQrlIBAm8eG56eYDMV+9JOMPww2MVdaIXI5G1jIKNB7TtyiuvLNlPGbMYn/bYY4/ynCRaoQTMBDFOEW4coCQFL+qfNctYG6cIz/a8s9hx7tHa1IuG1j38aw3AO9bjrjDv2YT2WuOtp9qjL3hSG8wrCrq5az0WLeAzffJMr355hiFIX+w54+pHGAZ5VfGbvQP/+Z83tIuu5i6DiX2N8eGUU04p9OB5dPY6vISMQuasjOW1UmzcKH4+57FsG7oYkbRJ9Iw2MiAx1lJKrXvg2AejEv5lBEsFMpEYDumBTCSGRCiMtVcnUIcctRN6EISEddawcfmpIXwt7lcMCGcjGFMiXYwtdLQLPEI8BuFJFApF6OCpaG/kNuZ2ggmg3PU6P8Oy30YkDtLGNngy6/A54WcUm7nEODMujhPGAa2FavZDl8KHN3kk/UyFOKs1l4hzfTVvhQBoDvB+B/C9+WQe1M8FKJnx/FwIkQR/SlycGSYUE3Ljup8u1OHrAQowBY9ihC68hpQO6wGBvH6WEhaeOnM9vosyefUYqCLkHGoFk9HJGVRr4Kju/4xxaq99NV7ykpeUny5YE7vuLW3DuhfnuOcKkWysi+9ESAjLBbSvMyX3g/fGfe4x+kGBC2MFLyqlsMurF8ZGnma8R+G1rwE+srdFOYyLFOg27wIjpGgLhgs/Nf+CdaxtCIpEdJ5hAFG38selbCcSCw2pQCYS8wA2UYoB7xLBjZV9KvD2UWKFt3ZlZ00k5juGUfj6PTuX3gfeMGHowlbjehUeEx5EGZb73Vtae+oIxM5zMtJQSnnyRB8I93TGrBaUhYOKkGBAqhVA7/Hc8ORRWiiykanX80KXhVOqhxeJh2nc3ud+oerzDVPx3XzoV+3lxo+OJzAoCrVtg+eQEdQ7DBv4KUBhjLO8EeXD0x7nnGvgdZ5OZ6FrvmZE4aW2R3qHoSvuT2ZYEWprfghV104hsPbS9D4mEsMjFchEYh4grKeEzEFho6wvSU8kEpMHoXrmt6tRJHsi6PKQnHrqqUUI7+eRq0H5JHA7+0zxFBpJIJcpN8JdA3G3pzWlFp55PH3nvkzeRWfQ6u+sJzz4vH2EdFlBeb26kvQk7njAuwwMzmq2w4gphY5eCHkVERNn0LuUN15uYajO7fPAdmWC9kxcHRUID6Pz0coX3hqRN57Fu3gY/2qLHADmxTjPPCcSCwWpQCYS8wiDbnK5GSYS8wMEXYJvJEYRAshQJEEQT+CgCqQyCMlxh6X/hZUTtHlragWSEE25dB5WSGHc4yk8meAvBJECWWeo9IzwUaG1FFuhv4cddlgJB1133XUn3lOWmF0wPEgox3DpHGOcTYXgIWGnEtc4j4wvHbHAf7LhOjfJG+hzZ0CFSeMr/NwuB2/iUV5w78fciWMZcVVPnCv1vv+FZgtzpoAKX91rr73KkQpJuZJ/E4nhsHBSECYSY0Kkm7cJxW8eAz/xWR1aFs/Hs8Js+uWusoHGHW/92uAZXoe4/Dw+i+QQbaibh6JfuXcEoEMv+vvc+Dhjg07zMceYNutjJLnp1Yd+vIgHCWZ+Tyq0TR/9TDVO6ID39amNOC8cSanGDUIzoZuyWN9TR3CmtGlTexy6DESUPcllKIvxPIE6zqTV4DV0/o63SCKXut8RNtm+j7D+mzBO8Eb7aPeoocyudSzgO+PZXmvxtLGcZN5tz9Wp2lrvIX6CV9ufx8+4+dj84TGUCIcXPa4CshdB9I/xwQ+FTxhrGD3ijk9tF0ItU7TQVfPCGEt0VUPYtCMdktJJpDVV2G97vqgX/8ZVNvNxnU8k5hrpgUwkhoTNUJZHadVtbCyZBKpQ4ED2QsIcCPNiaY2NjXVWWE4XbMDSoNsYeQlYZWU9rM9Bscw6RyKEjCDoLIk2OdchA6vNXEIOG2wIoCy12gE2aUkaWGoX0jUW/YAGhBkWcnQjnLSFjAi/EtoUyUfmW5ZWfCCjqPvVeJckqnBlQDuUmdAqkQUhT9bPmhZxjunaa68t/NeVLGquof0yNjrLFxeXS+TRddaXMUBfCKLGmHdPkg1zylzxneyM/jZnXEXQ7/66UUPbJdfCe4RviqMre4TvuVqDt0bCG/eZxlUboXjUwjFh2x2BxtXYE7LRp30PKFAoXWPgx12CzoJFAhZrGGW75n1KmXUDjfxQUq+44ooS+oeeo/Te4GFn3IQXWvvMw65njL924G/1a7essD7Xfmc/eUrrqy4mCfjReT17iXHvunIkxtoz6B3jgDdckeF917nUirY1H02M/zgiUfChM7E85rL5uiLF+FDM3ONonsnga9+TJEf4KuiLH2drt9tuu/KZtevMM88sXkLjqO/GmLGjTvClTF5D8+Twww8v13jwhPscHzj/H5nRAc+GwYzBVdg4uvFU8kreUfbBRGKUSAUykRgSNhsH822Qsh5KeCGTqs3OxmSj9x0FkrIiG6INLbJk9hLICbfOPXnW4X/hPoRK55BsqGAzFe5DSHAvozoISIRgz7tLjOBnU6UI7bTTTmXDdCZEvRtssEFRPqVAd9fWuISMuQahgkBirAhqzoQFYtzQlZCBrqzTXdn/JhkhaOJJgj0hWhIVZ44OOOCAxclSPMdq75wbgc/1LjW85446yVwoJJMG7WeQefe7312SwzCWCKWkKDp7VRtbKMh4X9833njj4tnA+7vvvnsx4ri0ntJBeXKlwJ577ll4BX/0S14zSpiv7ld0VtGZLXPaGJjLFGJGIWsKQVc4KwMS5YowTFFkOBJaqhx3P6KDaxGsGZRPd9+1DQj4mnHBcwxWrjRgNLGuMDYJfWU8oOSgi3KUj+YSc3kGfU444YRijBqlAG6Nc8UDepiTbQXS+ONN36GHPlNYtJM3lbJhrrt6xPpG2Zo0GDsJ0dwJjJaU3V53VuIFyWYoixRl4+D+S7wtKQxjT2T8do2P8oR+jgPaZ4+zb7n32LwM/tInvKyf2snzKDNqvabqc8wzxlj8b+z8BLxHIW57wYVk4xHXcLiSRtnWAjTA++aw9n3sYx8rtFPXSSedVAwtngHGE8ab+bTOJxITA/dAJhKJ4fCvf/1r0THHHLPoMY95zKKbb7550X/+85/y4/OvfvWri3784x8v+sc//rHo7LPPXvSOd7xj0c9//vNF//znPxf9+9//Ls+14bMvfOELiy644IJShv8/8YlPLHrCE56w6NRTTy3v/exnP1u0zjrrLNpss80W/eIXv1hczt/+9rdFa6+99qI99thj0V/+8pfy+UUXXbRoiSWWWPSVr3xl0fXXX7/o+c9//qLPfvaz5bsf/vCHi1ZYYYVFJ5xwQmdbFiL08/e///2ibbbZZtEb3vCGMhaBX/3qV4u22mqrRWuttVYZt/lKE3xw2WWXLfr85z9f+OXvf//7ooMOOmjRs571rEVf/vKXFz/329/+dtFRRx21aLnlllu022673Y4Wf/3rX0sZ6OT7W2+9tbOu4HU8Pm56/e53v1u0xhprLDrwwANLG7T/xBNPXLTsssuWsazx6U9/etEzn/nMRR//+MdLO727/vrrL9piiy0W/ehHPyrz86c//Wn5Dl3MC3zw61//eqx9Ur/x+/a3v73oW9/61uJ5DGhs7sc4GVvj5Bk/3otn/fb8LbfcsuhrX/vaoj/84Q99x8d3yvvlL39Z1p8vfelLhYaxBkVdf/7zn8vvP/7xj6VM9cZaNurxVzcefvazn73oPe95z399bx6fcsopi1ZcccWyFqKLn5tuuqmMr/Z885vfXLTkkksuuvzyy/vWMxf8C+rU5iOPPHLRy172skW33XZbz+c+85nPLNprr73KeGpv0B3f3nDDDYv7oD8XXnhh2QeM07j6oV78gD/qH/2Lftr/rEddNIj261dXOf3GKPgPz1rjvvjFL5a9Mfg35lWbf/1fP5NIJIZHeiATiWkizqDU51dYNsPTKBxV6BkPAS+HcCqW1F73prGm83yxMivX/zxJ/ucZEyIkAQGPinp4Fll4WbOF8sgsF1ntJA9guXa5Nmu9ENvwNgpf8zdr8SabbDLWcL25QiRYaGf9Q1feW+G94cXyWSQumU+Waf3jpWKF10+eaVZ6fBOJJIRyCTHrCmuM8FceOJ5Jno0uRDZFiS7QitfqFa94xeJELLMNHjmexDXWWGPxXDFv3G3oOx6GgFBInv1ILIMm5obwc+AlifvqhLO5U44HZ9whbXGusOt+Qh6nOsuptvWas8rx/KB3rXo+7vLrytjsu3GvD8a0nR225tEbb7yxjLH1TrRGvBNhyeBz4a9d95riF3yBf80HXt6YN3OxHvWD/pqv5557bgkTXWaZZUoUiQiYGLM4r2o/cPTB1SvjGrM4luEMYy/oZ9fdu0GD+v9+5fSqH/BDPe9rxNUgiURitMjA70RimohNm5L2+c9/voSbHnPMMeV/ggFFceeddy5hdv53Z5qwUaFHXWUJsSHEBtyDRagRauecCGXUZi0cR2ii0K3TTz+9tIGA7LxTJJUgKEdoJkWA0hjnw3ynLs8Lh7sjg5LtzJgzRMLfhAu7BuG4444r382n5Ap4jKIRwqM+UagogxK16AuhGf/5rC2g4xUGCsK0Mnr1nUIm/DXOD+F7xpJxhsxpQx3ypy2+o0TWiDvifB4JagiUkdyDYBvn98wdyuNSSy3V08iTGA96JXay9lH+KFL1Gc1QnI2hc87OxEUSojY8I/TWO/hX0hbjPqlz2llHRkPGoNNOO62cF0QDyletaOsDw47Q5MwomkgkZhupQCYSMwAPDG+Icx6svzb2SGjAmirBBC+fc2nOIjm4T4hvZ88L4TaEAeU6uyHhAE8Ri7rzTpJ8UEoJFARdZzh4HyVhoMTyNmkHK71yKaCs7ASKsHaHsOXzflkO7wjgbYsLqSUUcXaUZ8vZGoLofFIga/6B8MhtscUWRZDmtXZOSbKZSHsfPIEOPB3Om1E2w1Pb5QWigDKS4HmKJq96O8vnbILBJO6Ai35rr59IYlWfnyJ4m3/mkzGlMKAHg0r0L87QUSgJ7CmATxbCGGZtozxG1s42jxp/Z36N73nnnVc8d+0Mp2H0++xnP1vGeVITRYG+Oc8nKZu1/ogjjijrvaRPbTD+WO95n+dT5EQikZifSAUykZgBCDKSkFASCepxr1SEt4LNXJIDSTyk2qewsBZLkrHllluWH4plZFUk8BB0eQplCw1vkB9ZGAm+6hUSSwkUouoS8u23374okZRHdRDqhXEJ7amVxbiigZB1Rw/viRBkYY3oga6UdpZ9QuZ8uAqgq088y0KUGRYoUD7DY5I74ZfIYEnJ5EEUxks4dy2M8E5KFq9iZPutwfNHyeat3XrrrUtd41IgzSXewTCAtK9CaIchmm+8pYRwHnzJg4Qp89KEd9L7vPGUa8lsukI5E3MLY4tHGejwLx7Fu5RFPIpvjaN1lgGAouV3ZN+tYf303SWXXFL4NxTOSTcW2Q+s+YxdDEM1zAUGEpEF6T1PJBLjQJ6BTCRmCJbw8Fg4f0JgYSV2TqU+40HI5uGIcFLnxljMCcUyIPpNyeOhFIokRJUwQDig2HhXWGtdHsFH/YR6CmqkwOftdD2Dc0KUSNZ2We4IWNrHmq+t4/QcTSLQ1XlQylIIkHFv3nxN7c5D6MwtTyIew1f4jKLHMOFMLj6LUGteGp46CiXPK/B2U6rOOuuscs42rqQBPCbLo/N6QgE322yz8lsm0HF4Phho4t7EGDPjZyzjKoqA/pkDwsc9iy5owaMTIZA8s4wuyy67bJmHERVAYE9PzmTA2InCYCDzgwdlhrU28i4zxgljjnN11krZWPFIFxhW8K8sqEJCZXRdeumlJ368GRP1rT7qAPYb85URcdL7kEgkFgZSgUwkponawxi/CZ4uVCakO0vnHB1BnrDKck6BI6xTUNzr1oYkJoRcgj9lkSBPUBIKGxd/E6QoljwpBAmCUIRyqc+5SIIGryhhynuELFZrbVEuBVKa8ztSqF49TqEcUhbRR5p5XgzeJ4o25SGSEs0nUI6Et+mD9jMmUK7wxbbbbluudQklc7/99iseaOfF8CdFMMBbKTW/s7YMITXwD0U0vJsMF5TRrnOVswGJpZZccsliaMHDxkj9rp6Q/Eh/KRnODlM0Y7yFmkvj76oP88nn5oFrdvQRLW655ZZivMEH+pbC+Nwi6G+dEuGx6aablv8pjowCzoY7r2zsjDvDnB/fi/JwD2E7oYz1k3fecQBn1s0J19+4zmEu5ns7DNdcFU6N/+wXPK0iT3jMzT3fu1MxYM8RTs7bHveEJhKJxGwjFchEYkjYsCWmIaQSVHhwJHcQRkcAdUebsFRCivA5d3YRZn0nRI7HqwsUPBd6E3TPOeecIlSwtAupJOzwSHrGXWzOsUn+wpMiPI/iQIh+3/veV4QtwlUIE85QUmaFcxGuKKHCoAjhdxQB2dhQzikHFEV/u2uT4MlTGwl0ZOUUCibEbVwK0Sj7aPzxHH6g/AFjg3sPeejCYOBZgrX+Ux4jIUcYRnzueYp028iA/5219b2QUZ7sUMjGAW2iTDgHfNVVVxVjjDBb4YjaxIDj3kv3AJo7FAtzkWGG8mhsPUfJ5jl1DyJaRKZaXkgZeefT2C8kmJ/CqWWXthYKMaUc1TwKxswP/sXP1k7KoTUxjg24x7StFBrjSJzFGMFrLZnOOI1p2ksxtA7ZQ4SMm0vWZ2HWeNeZbGeSGWgok/rC4EEprrPLSghl/3Efp7mRSCQS48Cd3OUxlpoSiQUCSh2BlWATwkDtjSR8E0I9R5hlNeb5o+jFlQFdYE2nILbPT/KqRBp/wlUoroQfZRKqKLTCmIQaEugjtTvE9Q2UJCFdvJZLLLHE7ZKILHQQFoVoRqiqcaDIR7ZaVn1JKCheaI0+BNP5RB99pCDXYc5AyHbJdlxlEc/yThKunf+s+4kevNuEVeGi7XOycW0MLybe9z5eHCetIhsrntYX129oQ8wRAjlPP0WRd0bYNoOB+REKhc/Mm3ZWZIqHftdZPhPjg7mIj42L+WlchejXiqC1VXQG/rO2Ai8iRUpUgbEO3m3zpSQ6+Fc9eIdyiofbV/zMJtTLQ2o91hdrsnVee/XbWiSCAC/y9jM+WrOs7e224mPGQ3RAp/m0ZiUSifmLVCATiXmGesoSFiIRTB2a2QvtbK+J3nRNzP8xi7kxTuUgkUgkEomFjlQgE4lEIpFIJBKJRCIxEOZXhohEIpFIJBKJRCKRSMwZMolOIjFHyJDJRCKRSCQSicR8QyqQicQMIemBJDVxD5kECRKZSG7jGoE24nuJSiSJkDxhEhJ2OCumTZKttFPfJxKJRCKRSCQSkCGsicQMIHOqlPAyQkY2T9cF7LLLLs0111xzu2d9L8vnFVdcUe6jk0FykhLaaJ8ssNqvH4lEIpFIJBKJRBupQCYS01S2/vCHPzQXX3xxuQ7DXYsUQSni/bjk3D1zNXzubr4LLrig3OvoR6r5+m6zuYRMle4bc5faddddV7yqmWMrkUgkEolEIlFjMiTXRGKegWLFU+ceubXWWmtxCKrLoN3N6C6y2rPovrLLLrusOf3005vjjjuuecELXtD3agHlu8/OXXzKdvdX1OEePh5PobNCZN0PpiwhqJRa4EH0mbsO1e3ONM/738Xr6uUNVQ64P8znQldf9KIXNUcccURRbp/97GePgZqJRCKRSCQSifmCVCATiWmAd+5973tf85znPKcoi+17GdueO4rghRdeWM5JChP9xCc+US6FXmaZZRafnQx410Xon/zkJ8ul9tdff31R5NZdd92iDF500UXlOQrlF77wheLJXHLJJZsbb7yxOf/884sCKzyWZ/OQQw5pPv7xjxfF0mfavdtuuxWFUXv8ppBSMLfYYoviTX3wgx9cLmC/8sorU4FMJBKJRCKRSNwOGcKaSEwDkuB86Utfah796Ec3d77znfs+SyH80Y9+VBQ4IaJPecpTSsjroYce2hx55JHNn//858UKp9+8mieccELzkpe8pFlppZWKkkqZ+9nPftacd955zWc+85lm5ZVXblZfffXmyU9+crPffvsVpVTyG0rnL37xi2aTTTZpVltttebaa68t9S611FKlLP+fe+65zbe//e3m8ssvb572tKc1yy+/fPFiCrEFXkjexxtuuCHPQiYSiUQikUgkbodUIBOJaYBHkXJFaRskCY5zhX/729+aNddcs3nVq17VrLPOOiX0lRfzhz/84eLnhKF++tOfLuGmFM273e1u5bl3vvOdxVNJ6Xvc4x7XPOhBDyp18zzyKn74wx9uHvrQh5Yw1CWWWKJ5+ctf3rz0pS8tSiDvot8U09e85jWlHO/ySlJieTspl7yPoD9CcfVRGG0ikUgkEolEIhFIBTKRmAbqENVBEs1QBP0EKGlCWCl/tZLmig+eRgpklEuxe/zjH1+e/fWvf11+UzSBokdpdP1GPO/sI/Ao8kbyMq6wwgrNqquu2hxwwAHNeuutVzynb37zm4vyutVWW5VkQMJjA8roCsVNJBKJRCKRSNyxkQpkIjENCPmk2Ak/baPrao6HPexhJYHN9773vdspZfe73/2KElgrbhLjfOc732m+8Y1vFEWRUnnLLbcUBY/i5/NIlkOZVBdvJdRlOwPpLONNN91UzksqlxLrfe3wjjOT66+/fnPsscc2n/vc5xaXwWOqbZTTRCKRSCQSiUQikApkIjENCCGVrObHP/5xUfBC8aLwUb4kpqFcxrlCyiMPoFBT5yF9/9WvfrV52cte1jziEY9YrHA6TykLKuVtn332aU477bTmzDPPLCGowk55D2+99dbmU5/6VAmhdV0I7+SrX/3q4rUUJku5VC8F1+eS67ztbW8rXkZZYG+77bYSnur/e93rXs0GG2xQFNPac+mZ5z3veeX7RCKRSCQSiUQikFlYE4lpgCdPkprPfvazRVEMT51Q0ptvvrl51rOeVRS8r3/960XR9Pymm25aPIEUQtlVKWcUwtrLR5F86lOfWhQ+d0Y68+ico6Q4PJUyroLMqrySyt1rr71KJtiPfexjJWsq5VBSnSc96UnNG9/4xuKlfP/739/84Ac/KMl3lltuuaL4aqvkPL7faKONisJICfa+0FdtC6UykUgkEolEIpGAOy3KQ06JxNAwbX7/+9+XqzBkVuVJbF/FAfFZ/OahpFgKR6UQdoW7xvs8m54XihrPRbne58XklWy/V9cXiFDXuEsynvU5xPlM3keJfSiXFEghsIlEIpFIJBKJRCAVyERiBpDw5qMf/Wjx/PEczmePHYWV91SIrGs/nPNMJBKJRCKRSCRqpAKZSMwApo+zh7yBPIrzXYHkeRQOyyM5yPUkiUQikUgkEok7FlKBTCQSiUQikUgkEonEQJi/7pJEIpFIJBKJRCKRSIwVqUAmEolEIpFIJBKJRGIgpAKZSCQSiUQikUgkEomBkApkIpFIJBKJRCKRSCQGQiqQiUQikUgkEolEIpEYCKlAJhKJRCKRSCQSiURiIKQCmUgkEolEIpFIJBKJgZAKZCKRSCQSiUQikUgkBkIqkIlEIpFIJBKJRCKRGAipQCYSiUQikUgkEolEYiCkAplIJBKJRCKRSCQSiYGQCmQikUgkEolEIpFIJAZCKpCJRCKRSCQSiUQikRgIqUAmEolEIpFIJBKJRGIgpAKZSCQSiUQikUgkEomBkApkIpFIJBKJRCKRSCQGQiqQiUQikUgkEolEIpEYCKlAJhKJRCKRSCQSiURiINxlsMcSibnDokWLmn/+85/ld/3Z//t//6+5613v2tzpTnealTr//Oc/N3e7291KHbOJf/3rX+UHok/wj3/8o/nDH/7Q3Pe+9x1LG373u98197znPUufZ4OmiUQikUgkEon5j1QgExMNityvf/3r5pRTTml++MMfLv6cgvPiF7+4WXPNNZt73OMeI6/3L3/5S3Psscc2yy67bPOCF7xgVhWqL33pS83ZZ5/d/PWvf20e8YhHNDvvvHPzv//7v81JJ53UXHvttc1GG23UrLHGGkW5HBVN//Of/5Q+Rb+uuuqq5t3vfnfz8pe/vNl1112b//mf/xlJXYlEIpFIJBKJhYVUIBMTjy9/+cvNCSec0Pz85z9f/Nn//d//NU996lNnTdG5+eabiwLJC/nMZz6z1DdbePKTn9z8/ve/b84555zmKU95SrPllls2f/vb35qzzjqr+eIXv1gU5NVWW20kCmQo5Oeee26zyiqrNI9//OPLZ5deemlRIr/97W83m2yySfOQhzxkJH1LJBKJRCKRSCwspAKZmGhQbig1r3jFK5oHPvCBi8NY7373uzfLL7/8rIR2Cpd9//vf3/ziF79oPvjBDzZbbLFF8+hHP3rWvJBCRh/84AcvDsuF+9znPsXzeP311xcv653vfOeRhapeeOGFxaO79NJLl8/0a/XVV2/+9Kc/NS95yUtKyGwikUgkEolEItGFVCATEw3K3C233FKUuKc//enF4+iHQjUbXkFKnPoobvDVr361/L3BBhssfkb4J89knFukAP773/9u/vjHPxaF9t73vndpo888F+GinuNZFB5LAb7Xve7V3OUud+l55nOttdYqXsIHPOABi5VX3ymDsvf3v/+90EB9UY7PtMNvNHKmUV0UU+3laTzssMPK385XKsdzL3vZy5ollliitCm8uupyDlN56hRWG9/rk3f9Bu3QX2UqjwLsOe32uWeDPp7VXr/zrGUikUgkEonE/EIqkImJxo9+9KPm8ssvby677LLmYQ97WPO85z2veB5f9apXzYryQbH69Kc/3bzyla8sdf/kJz9pzj///GbVVVdt7ne/+5VnKFU8lM4oOreoLRSpG264oShab3rTm5rNNtusKGAnnnhi86EPfagoU5777ne/23z2s58tXj5nDSmI7dBUCpezj8J2KV0rrbRSs99++xXF7Dvf+U7zrne9q3xOIXV+cu211y5hp7/61a/Kd7/97W+bhz70oc0nPvGJ8o62vPa1r22+/vWvN8cff3zz05/+tChvxxxzTPOa17ymedrTnlb+1t8nPvGJpQyK589+9rPiqeQBFlr7jW98o3nGM55RQmwppWeccUZz0UUXFZrxYKr/ve99b+nD5ptv3my11VZF6bzuuuua008/vXnCE55Qvvve977XvO51ryvt7lKgE4lEIpFIJBKTi7zGIzGxoIB9/vOfL4oNxevjH/94UYC23nrrorxQtEZd329+85tS1xvf+MYSNgtf+MIXitIX4bMUN8oghelzn/tcc+ONN5ZkO/vuu285y3j44Yc3V1xxRfHCPec5zykK2Cc/+cnymyd1ww03bL75zW82Bx10UOlfG5QqHkFeO/XyiAKFjiJJeXUmkldUYqG3ve1tza233lqUbEqnUN9tt922PCME94ADDig0pMC94Q1vKIo3BXDTTTctihyl/P73v3+p62tf+1pRCHlOjzrqqELv5z//+c0OO+xQzoIed9xx5WwoWqCPer1HWX3Ri17UrLzyyqW9lFB9vO2220o56LLHHns022+/fUkURAFOJBKJRCKRSMw/pAKZmGg87nGPa/bcc8+iLFF0eMYoTW9/+9uLYldf7TEKfOtb3yoKosQ2Mp8KH+XRoxCG0kMBEzZKCQNK4nLLLdessMIKzbOe9azyPM8cTyVPI4WQJ46376UvfWnxoD784Q8vCiUFj9eyjQg9jfoodRRHnknvCud95CMfWbKmPvvZzy7PC43Vdkowj6jn1E0hpkB6JryoPJMUOZ5KSXoou1EXUArPO++8EnLK6+h7dYIzlDyf+qY+oExSTnlLvfPLX/6yKLwS9lAyJUJ6z3veU5T+HXfcsTyf4auJRCKRSCQS8w8ZP5aYWFAwXvjCF5YfChHFiBLFK8a79ZnPfKYolaNMMENB48HjOaNwSZ5DUXUOUggnL5t21YorhUkYKmXKu/CDH/yg3KtY90U7/VYupS3Od3YpkG1QDnkr67spZUoVRqtM5VGyKXu+Fzb7sY99rNQRZxbb6Kd8f+UrXynt1584q0ihpZDyvFK060yt2qN/vg/6UBYlBxIia9x22WWXopQKb+UdHdW4JRKJRCKRSCTGh1QgExONUHIoaJSZddddtygy7krk4RslKI08b5RGf1OAKHuUIl60j3zkI0Vh7ZX5leI0SFbYULTq/k2FCCuNBD7OXoKkNlGOuinWlDVeTtecTEdJi+Q56kL3OKPptz76fFDa84IKu/UupVYoL+VaGeutt17eN5lIJBKJRCIxz5AhrImJBSVF4heeRqGQlA4Kh5BRSh7P1ijuRgTK4jXXXNOsuOKKxcMpU6kw2aOPPrp51KMeVRS2iy++uIRm9lPynPUD70RYaBvK8kPhe+xjH1vCW7tQh3h6RsgqxVMIr0Q0oXyqV9iss5cHHnhgCS11ztIdj/0QobHts6RoKnSY59H3kW3WeIRSPci1Jr53FYoxO/PMM8s9l8J43ecpsY4znolEIpFIJBKJ+YVUIBMTC14/SVde//rXN3vttVfxDgpjlbRmySWXLIlmRnWOzhlBiWBkXxUCGj9CQiWRAQlmPvCBD/yX15BCR3HkWeMBpOzxAMYZRnCthu8pjsI/KVHCO50FVE+EmEbZ4emLz+IMpXd+/OMfN6eeempRIoWZfvjDHy7Xjdx0003FOylsleImwY+/o37KX3gk/e99ipy2tOsXqvvc5z63lCdklRJJeaZECimmxNehvF2/ff/973+/Ofnkk0uYrrBVirlzpb2uMEkkEolEIpFITDZSgUxMLCgZPGGUGElpNt5445JdlNIkEUuEb84EFB2KEU+jLK/CVCmucc+hz3zP68aTFtdyhFcOXPUhA6trKySOWXPNNctP7R1Vz6c+9alyDvDQQw8tytx2221XFGFKFuXYOUaJepy3pBRSTNXLC0sxpWxSpCW+4S11dcb6669fkvHIsCpLqjJcQ6IedzK6+kTdriJxfQeF2LMUwf33378k2KGQOl+pLn2loFNUDz744JI4x9Uc+nz11VcXpdJ7vKuUae31njqdEdX2uP/RtSYU65tvvrl5xzveURIGUVh5L9HHe4lEIpFIJBKJ+YU7LRp1GstEYkTAmhQQSWx4wSiM7iOMUM5ReB/VQQniXQwvnLBSZ/d4CylFfocy6HkeNErcMsssUxSvbbbZptloo42aL37xiyWzKc8oBUv7JLJZZ511igfOdRtCW3k7hd+++MUvLv2gwFFaow4ZXtVDAQwvH6XLuxRXbaWU+ZsncIklliheTF5H9VF8KXoyyVKAneekqPpfWTKoyoqqPFlh0dYzUZf+uQ9Se3gnhRB7hlKpzQ960INKf7SDJ9N7nkUTocY+A587t8przBNK0aU4U4SN46jCjxOJRCKRSCQS40MqkInENEBJe/WrX10USF5R9yN2KbShQFJCjznmmJI4Zr5lH7VE+JmpwleH5yYSiUQikUgk5ifSBZBIDAmeSl7LyIQqhNX/9VUZlCUeQuG2vG5CRj3nOo75ZrMJD+MoyknlMZFIJBKJRGJ+IxXIRGJICNF0vs/5wFVWWaVkK3V20lnNGpRHZxdf/vKXl3BXoatCR+ebAplIJBKJRCKRSAQyhDWRGBI8is5mRobTuIPRucf6fsfwQNYZTimbzjimJy6RSCQSiUQiMR+RCmQikUgkEolEIpFIJAZChrAmEolEIpFIJBKJRGIgpAKZSCQSiUQikUgkEomBkApkIpFIJBKJRCKRSCQGQiqQiUQikUgkEolEIpEYCKlAJhKJRCKRSCQSiURiIKQCmUgkEolEIpFIJBKJgZAKZCKRSCQSiUQikUgkBkIqkIlEIpFIJBKJRCKRGAipQCYSiUQikUgkEolEYiCkAplIJBKJRCKRSCQSiYGQCmQikUgkEolEIpFIJAZCKpCJRCKRSCQSiUQikRgIqUAmEolEIpFIJBKJRGIgpAKZSCQSiUQikUgkEomBkApkIpFIJBKJRCKRSCQGQiqQiUQikUgkEolEIpEYCKlAJuY9Fi1a1Pz73/8uv0dd5n/+859mEjHK/vbqp/L/9a9/le/nol0zHTvQr3/+859jbZO61TkItK8ffdF/ruk5iZjO/Ax+TnpOHozJP/7xj4lbb/utjeb4ONs7Fe/Oxj44Lvpqc/DAuDAXY5hILCTcZa4bkEj0wk9/+tPmgx/8YPOXv/ylufOd77xY0I6//+///q951rOe1XzpS19q7nGPezSrrLJKc/e7333Gm8ovfvGL5iMf+Ujz61//ullnnXWaBzzgATMuE+50pzvNuAy0+PCHP9x85zvfaTbbbLPmPve5z7TL/Pvf/97ceOONzWc+85lmo402ah72sIf91/dnnXVWoe1aa63V3PWud+3Ztl/96lfNBz7wgSLkbLDBBs1d7jLepUUb/vznPzc33XRT89nPfrZ53ete1zz1qU9tvvrVrzaXX355s+GGGzaPecxjyhiMYjz6teO9731v88Mf/rDZdNNNm3vd616dz/ztb38rbfWz9tprl7bV33v/uuuuK/TceOONm//5n/8ZeTtnkwazWfbvfve75qMf/Wjzk5/8pNDugQ984JTv/PjHPy70JCzOBX8m+uNHP/pRc9JJJ5X19pnPfOas8M6gwC9//OMfmxtuuKH55je/2Wy++eb/tc7+9re/bU477bRmiSWWaJZffvlZa6+2/PKXvyy8q84tttjiv9Zhe+H3v//95tprr20e97jHNSussMKM26M8/bem2m+tP34e//jHN894xjOae97zns3/+3//b9r12Mc+/vGPN1/84heb1VZbrXnSk55U1uoLL7yw0Puxj31sM5tA15/97GfNqaee2iy33HLNi170olnfG6Zqz1zUm0jMBOmBTEwkLKjf/va3m8suu6z53//936LEnXvuuc0ZZ5xRNnOfXXnllc3Xv/715qKLLiqK5qBen6nqJWTa3GxmNrqZ4ve//31RxmbSJmXoH4WC0oEOf/3rX2fULoLH1772teaUU04pQnlXvX/4wx+KMDWVVVtbrrrqqub973//UB7LUcK43XrrrUUoYHwAdP/Nb36zmDf0gyCGjrMB5f/pT38q9Oxn2fYdgeld73pXaU8b+O6aa64pyuioLeTKU+eox0nf0XsQfpkJjJ35ecEFFxQBd5B24U9rCSPHfEYoN/oznzxNU8H8NE9nsk6OEubGJz/5ybLOMvZ0fW9NHoT/Zgr1WwsuueSSzjGPdfrd7353MQaOAg9/+MOLMvOWt7ylGCuf+MQnFsXVHsxogy4z2RvN4VirGYLA2FuXxuWF1AY8h77hAbVuz9beMO71OJGYbaQCmZhY8CayRvIYLLvsss1DHvKQ4m1Yeumlm/XXX79YY1lDWStZQ0cB5fDEPf3pTx+JNZAXk4LGqzldUIZstLx8PFr6zPs6UyhDP1mTu0BJ33bbbYsHrJf3EdDpwQ9+cLFOj2ochoU23Pve926e85znLG6rz573vOc1hxxySGmb/3miTj755FkT/PSft3b33Xcv7enVVl7dpz3taZ2eRd/zJDzlKU+ZFU/Zt771rSIAjlpQIwjxgvAOzlZYGNo86EEPKnNgmDHh4X3CE55QvCnzGYRdUQE8YwsJ1vDDDz+8zN+59sKo/773vW+JYLjb3e7W+QyD5l577VWiXma7LfYja0GvNdgawYP30Ic+dGTrr34/+tGPLnsAL+srX/nKZs0112wOPfTQZplllmne/va3FwPrdBV++9hzn/vcYgyO8fb/2972tqKszjbU+YhHPKI5+OCDm5e//OWFbjySvOAMNOPEd7/73eLNnhTjSSIxKDKOJzGxICRScAh9YSEEv22aNrXYfAis3/jGN5rPfe5zZeN7zWteUxRQlm1etu9973vFsugdGy0vkedtyix/n/70p5vnP//5JZSlLbSzwH75y18uQr+Q2fvd737lf8oI5evZz352UaBqwSc8UTxMhHXfq5ugRBEUdmvD0pYXvvCFRWDpEpxYSI844ogSUkuBpjyDZ71PYLfxCNkkWIfSqn233HJLoSGhjEDQVb52+pwF9OKLLy7hUiuttFLzqEc9qpTLSswqq41o5TNtVzYFyfigM9rE+FBQWO/RiYBFUe2q23jYPLXVGBPyCSw2dmGovCyElZ///OfNV77ylSJsGB/PoD0PnnYTOEJxrC30eAKttccz3jvqqKOKt5pApu36QWAwLpRNiv7nP//55slPfnJ5pp8wi7e0DY2MH+v/i1/84iIcCQFTnr895398qE36gL71GPMuoL1wOG1p98O7+ovflKu+m2++ufDYkksu+V+h23GeyDvCYSPcW1kU6ttuu6307wUveEEZUyGExss8UD/vinEgPBqfH/zgByW0kNIbYXX4AL2UYw54bv/99y/9wxvKM77eMSeFq3nPPCMU8zxox/3vf/8y3q961asKD5uXIgv8TbhTVoSWa58+eLff2MR5MO3+whe+UOa0eVF/ry2+M4bmIVpoMzAw+E77KAuveMUrCi+huWfxG9oae4qGPiofn6IfmlPyllpqqfK8+et94XIRnofv0ASNGDhe8pKXlHngXe1mROBp1U/z2xw+//zzm2OPPbbMDf+rt02HGHvrljagl/ZrB6CjfmjPIx/5yDIe+JQXK3hM/z2Dt7QN3fGpvwn66I8+FBfj7zv8j5bWCnMLX+mbcUAbPMUQ6F1z0piYZxR7dRtzbVSHNhsfc1E78eJLX/rSwsfKwSPabG3Ch/gED/ZSoNDDO2hi3fG8MtEOLX2uTQw66rFeB7TXOmsev/a1ry3rMO+bd/QN/SJKBM3Mdf3HI+ivXDz+spe9rDyPTuqxVugnOoq2sSagt3H1uTmpPH9b6/sh1gt9EXpqLlon8DQ+E7WizfgFv+IxY21+9zPAtsM6zY9NNtmklMeoqU/GX/uMgzFDV/WGAQ0vxVwxZt6xfrXXOGPpOXxvfbDGW5+171Of+lQZQ/uJ+Rj0Nn8YWNXpPfuQNk5lhAivrfHRFnx3/PHHN5deemnZK4yN9Qcdla/d1j9j31V2eBHRwLgZb/PZnmVs8R6esMdZ69AJHZR12GGHlWeMS9Q710aURGIQpAcyMZGwgBIWep1p9L3vKAU2A5v5xz72sbLZswwLVbOo2xBYTQnPNoI99tijCDMW7K222qpYICk7NgreNp/XdSiDEGnRtyn4cabOOUSLvQ3PJtarjaH82tgoONqpPTYvAo8w3F122aUISb2gDO1QBqFLuYQCIaMENgqqMgkP4a0kLBBEdtxxx0KDfh4hG7MQKf0UMol+aETw2WGHHYrAGuGzvB8UTYIjCzRl88wzzyzConbZPCloBIoDDjighDx1heb4zHO8gQRGGyuv3Yknnlj6aBy0w6bM6+wMEOu0/wnXO++8c2kToevAAw8sgmYbBBft5cVWHiEpPFBoaXNX1t57712EkfhOGO4gGzgaUcbwEZrgOXR885vfXH4iHMt3ykQzvOJsZCgzvtc3dEP7fffdtwgXNbRbe48++uhSlnFFIwI3Ab3LS2K8tUeZ+NRzBNc4t+Q3RUE7KH14nxJ79dVXl7EzbjznxjUUb7yqXHNFW8w/Y4O+eBF/+55wrL3a5TnzxVzWZv37xCc+UfgWjdSLP9/3vvcVQVq9+I5Ap05jjpfNM4KWcgmUFOqpEooQpPGTcDx9ZUSKdwj5wvMo4DEPd9ttt9Iugus73/nOQhvKDc+YfigDjQia5pr/zQ3t979+8tafffbZpS7K39Zbb92cc845ZUzRVp+NeUQmBH/jdcYm64Ayt9lmm+Y973lPEfTf8Y53lGfNP7T1Ptr38o6hFzpSWgn3hG88qg1oa16af5SH8847r9lnn30WK+eiOtDdWDDGbb/99oU3tctz+AjvmHvmrzYQwnlQjI260YAX2px805veVMbQs/jRmWv9UzfgT+/gH8+qEw8RxK3NeMaz1ho00k5tc/6bF4yCpd3obOx68YL5Z7yVhfbWGmVZI04//fRCIwI8PrS28PTGOmtOe1aIKF6wdqsT3zs+AeG98h6FZLvttivjp/1vfetbS//Qzfjhf+OO94yrcaGwGhe0UYY+4jlKGKUllNV+0E80N+72K+sMPlCn//fcc8/Sp9hD7GFhQBwG+scwQiHE59Yh+w+eYLAUWm6eUwj1U588RwHTX8+2vXz+11bKKX4yn+xj+NHn1mnzDk3QwbpkLPCJ9UB96623XuGxQUK7zSVGAWf/8Wq9V5vXaKasD33oQ8UgQgG0fvYKITZH8Lm1g+JOpsCT/jeGjMBC6CN/g7HG6+qN9Tj290RiviAVyMTEIhbXXhtc/TmBU6grhYegbiON84wsfayTNhqbkw2bJZbywdJJ4CGwedZGXW8yBAuCyU477VQ2EiCEeY7AvMYaaxQFqKttNiLf2RhYZNVHACUg8KxRarXZ5k7A6FLyKKzaHt431knP2WgkH9h1112Ld4JQb7MmpBKugdBuI/TZVOdDeQYom9qDRvqs7bWHg5eBsOxzm6S+EyBZzUORt1lKWENRZ5k3Djb78CDHD0WXMPHqV7+6PKce42Nz1TdCU2ymyuQpic1b/9GEQGL8whvQBiGbp8E4RNsoqqzJrMPelXDCuAS/EIoIQazQUwlWBH/KBz5gGT/uuOOKQBIeCe8TfAgiPOKEV0lCeJHD4k+g8D+BE90I6F1nIrX7jW98Y/ne2BB8CVno1+U9QEfPUpz9/YY3vKEoE6z3rPXajp/NFXTEJ54hpOkLgTbo7W98CIRnChJhG23VT+jzPb5g/ad04Q91oXVEDKCzuWD81I8eaIcmJ5xwQhFMCfCe1WY8QIBjFDJvzAFeSmXzqPWDOaad+Mq8sQaYb8ZE2YRcz6y44orlcyHxlHtjRbAk3OMNY0kQRH98VNPFXIwEPnjNHPeb58maYjytNTwPBGHzhbBtfAmtBFL9N4f0TV+VrRyfGQfGJTQOAwmPnTrwHTp3eR/NX8Y0xh1jiyeNVRiAlM0Tqp36TdGl2HlWn8xDCpq1lBLAsGS9QkseVuVQwtDVGFkTKJjWGO/jy1Be0YsCTzmnEFoLrE/4iBKib9pTe5o9Y3ysKTHelDUKz/XXX1/6rh5rALr60U5zrRe0mSEFj/KGU2jMIwqI9ddY679xMy/C82ZevP71ry9K5corr1wMGZSd8CbFXMM3+mU+Gx8/+Ej/vW99irPR+NyaaW/xEwYhffKOtRHvWp88pz14YpDwa2OE1pQr46Mc823VVVctPBbrHFqZf+bksAqk581t483wxkCCj42nscJT+BsPMI5ZU+1V1lUGI21sr1nWCeMTRhH/a3/wKvr7TbG23lPMGCWUhTb2L/yjDYP0B73RFf+G0ojv/dYO8oSxNm6+t/Za83rB+FMitREfWRPRmMFPu4y5cdA2e1nsSdZN/6OneiMCor1fJhKTiDR3JOY9LMo2R5s2QYGQSoix4VKuCECsyIS38KTFJuI9mxRhxSZYnwvjpWD952WKEFUCBWWANXfdddctgr//e6EOu1U2yzUhMEJKCYE2ENZIZ+e6zjbWZQS01+ZjI9YH/VI+YcgmFOFDrO6+72fZ1G+CMHp510YX3o76jF5k4yN82cQ9awOuw2/Vqz7t8jchnbJDyLDBAkHCO7wrtaLWbmMvQYDgRrilHBFOlN91fkQb0KkWvGJDjs2ccEtQJwRLDkH4plwOcpZIucon7BiLCBH0WbSdUEnIIiD4jOBJaQu6KiNoH2FdvTwNeFk7CU5CXb2Dd3qNKeGcZ41lnoGEkBx9DzooAy8aK23Uh34CC4WIEqEt+uNd3i30isQutdBTG4Dqcut6Cc76zoMXQhUa8qDgCbSibBFCYyx93w+EcMqBMfVO1Af4RXl4L+YhpQIvUKbQjqKhbj8UgFCCo09dCA83fghFwnz2f0RUmKfmDs+ZdSrOH1tP/I0Owbf+Vqfn4oqLqYRJ31vnzIc4X0ZRIPxS+igQlDU09Z1+4j/tEbqpbvWqPzwi2hzh6BRQCr01gCJHmcLjwlq9g4aMENYF76vHeHofrdVhLaCo8ThSNtQR81SbwgNHeYrxpowqB+9REoxR0Me7fvc7Q0aZJtCbO9qrT36sl6B9+q59FJc4b6jN6tUuv70TGcDjmRhP7Yn1+6CDDir09xnF0/6jLp/hNc/pizLwiboZKX1uDCmWFEHfW4t7eZvb0EZ8hPYMU7yflFZKjfnPkxsGCcpOv7Pt/XgML8X42ruMVfCUsUJPXrigbdCFIQL92vUGz9frbnzmfXygvrh2w3j7bQ8Jw4W1yzODKJCeCb6JPrX3WWus/R2PUmDRs9cxkNoYBBRz+zy+6ErCVCPmtd+e5Xll3FAX4xb+GUXOg0Ri1EgPZGLBwsbLai20yGIeqboHRZzlYM3ldQzwShCSfC+sSojNIFbCEKYJ4SHs2HQi3G+mCRDieggbub46c0m5paQOmjClH31s5LyUlCyhWwQxQj5PwFRlhnDsh8AUiiWrfK++1O2p20UQFBJEMSNMhnesF9pjUys16E64JNQIZSMMoteogO7C3yIzaYzxsBkMI+kDIVDYH+GM8B/KSRvqoqziU1Z6YapC8EJxHqbe+m90Mm4Et/iMcBeZDNttqD+bql60MmcjaUd4NHzOY2W8uxTTLmgPmnclSwpFT9hhPQ8JaYRK7zI6RUSAZ7Vhunfstd9RbvSVsUJfCaeUvK4ohGE9RPpEycNjdYiiOiNZSKwH+q3P/RTy+sw5WvAGMxYw5MT1OeY0JcIzFBSKZBjq6vZToIQhMkCYb8LPa497tFc7CdFhTKnDooelB5ryiNkHzBmenlBMjbV6IqTS5xSSYRKaxLoLPN3BuwwU6qZQUbCFtPJoGWf9U4dx0J6gG95XlnWxzmY8bJ8jRD2MAejG4IkvYr+a6ox3L5iLwsP1gzFQGda44CljZS6pM85X+y7GFV8Mk+m0iwbWQp5nXk4RNsrnae+VEG46sG6G7CCihuGjF1/gm8joCmH4qfm1vZZ2wefW9HqvzPOQiUlFKpCJiUbt0agt8O0D+F1gJXU+0AZKKag9JL3qqcu2AfCuUFCcb+DVsIGwJPP8UGJWX331oqT28hqF0O3HJsP6T4hQViQCIICFBbdXGbHpRl/rtsaPzdlmJ2yIRdSmKtRHeE0/gaguq6ZlmyZ+tIGnVPgPZUZSibrdNW3jHZuoMEUWdj88GeiHHkLSbLqEtlqZDM8HIThS/Mf4EwZZdkN4qdOw9+pbm5YxJr4n6GkTAY8AwisWXgXC5SBKQzvkKH6UzQPAc2icCYXGR3+66N+vH+go5JIAp4wQRLuAZjwDLPO8EMIp0RofhBDX5dGK/8OjiIdCGfM8I4Kxk8yFF0o9wqd5++Ldmk+NsXH1GTp0XT0TfdYfXmX8iv6eF9Knbl4bYZbKMm7apI5eXjkRA5QVnpyoP5RAQhlPU3iwvY+mEVquHea0ea5/5pCQULTTn5ou3hvEM1gjkscoHy8r11qlr9aDLvoEjEvMwy6FNqIaKIk8utqK74THaidlhedLn2L9CcWnrqv+HQKsED8hpQRp3moCLmE3QvODHxh48FooDTVf4xWfE8idhUMDbanrxOcMB57FAz6Pa3EY7yJZVi/DSRv6SFk1duhu3IJ2jCzWY0qIzxn30CoUgfZ61v7bb/uLqAJjGclerBvKwXtoZJ30nXXf/LFmGifzBq+FkQQPGFveNM/H9Q54JPaYfrwWNDEO6mKgME6g78aZAkl5HORu2aBzzLU4q4iHRDVEwhle71gr8bK2UqKt0dYh61Uo63G/ZFdd7fWvHuf2no+uMsNSuoWYMpqaH5E8rD7z3FVXF+1iX1C+s6p43vppz4v1p+s9dKYoMzwp23PogN6hBFrTjK3v9L9ea8Ozird5PO2TjLXW+0G9z4nEuJEhrImJh83TIfZIPGKzIuTbuH0WwrjNPxSR8IrYrG3khEmLt7KcN7ShW8QJFzbpyOroN+ErQki8YzG3WfJEUigpFrLDsRoL1WKJ7xUiGqnQCTBCYihSNjYCuDNnyuJ96Hf5M+HE5iN8TJ3hXdAGmwsBzKan3TZUVlkhnjZWwoPP2hdhg43Wpodm2uTMDYEa7dThXbRFIxZmQqMzHZExkJUZfYX4edezaElwUYZzUZGBse0pE1LlTFgkHEFDm29cIh5p6XnOnNVSBmGY91PInfbwZMT5U0IYKz/h1SasX4RO/fGe3wSd8MY6f8k7G9llCQgENiFD/jcuEogQWI888shiTGiDMKXNITSjSZzPIRz4Ls6ZSTTEa6hPQsfQUhvVpW36YTy1De3RCw/iZc/x8qKpMXXeB+3rTK5tqJ+H2LyhFKGZ95RLQaKkSXyCJsbLXNAOz+Flgo8kJYR8nxE20V5fzQXhdWhmnFjqnZnD58qmPFDg0JfBBM8oM7L1UgycYdRPQrNQcf3yrDkibFz70Ai/K5N3QVigeo2ZLJDmspBw3q62kMXo4+wiDwJ+IgjiM2sC44oxEVXgrB3+1SZ1oREepNjoEwHYHFdWKJ6EfHQxlvpFAUE744RO4dEy/sF7+hBGI8+Z79YlZxz1XXsZMRgw8I+++R3eHePjt7aYyyIA0E45bUXAPLDO8D4L2TQWPH7WIl4b9Lb+WAP1k5BKAI5zxOatuYMftUO78SWeUw4+iIyn+Mm5aePCw4UX9MV4WQ+00fvajl+tUcZW+KryKBmMHDE2MZ+cIcMf5k0oX+jDS8f7jR7We2sTempLeLravIA+6qbgUQasSeqQWMcY67+13b6AvtZlcw39PYcOytC2WGfxQrTBWFNg8L0zfsaToYVxMcJf8TsFx9jE8QK8Zt1CO/S03oqGQEOhk4yWki4pj8KmrwwZlPj2mFtHzEVtxd/WIbS3X4V3GV3sBdqsHf1g/K0f+Fu4qzGyBgsttsZbE60r6I6njAmeREtzUri5+WT+UBj1kQJrvzD22q+txtzaqUzzJMYxxjUMOHge3c0nv9HK+qV9+Nu6QpnF9/ZsIeGetWd2rZPGMfhIPcbQGmus7A36FpnW7c/WmEjG1QW8aYwkpTIW5oh1BL3xivXUcQLl+968MfeskdYGUSVCV9VrvOf7dUOJOwbutGg6MTmJxJiAPSMLY1wOb5O3yNo0CBmRBdWCbWOgYFqACf82EeeabDK+J4jYDG0GhGsbGaXBpmpDs/ELjyFk2dzqM0KEKYu9DcrzNq8Q6m0sXQqgMtRvQ7SJEDq9qzzlRDKRfmc3bHIEDbDxE7YIZ5QhtLDReyZS4ocQEc/3uk8QbeIaA32OJEPKptDZtP2Ppiz1hKFIyhDjQtDQdx4uAjkaobP6IpkP+qJ321umHkIdJUkbZaG0uR5zzDHlfeNqLAlG2oKGhCvfEaT8Tygj8BIC/B0GgLiXEi38b8wpc6BO7aewhIBHKaboUBoJeIQlwjfruXOkXXc6qhf90J5gpN+ENoIH2qAnusWZO4JSXJ0BcZVL0F5f0cTf+BDtCcT6TLjRb/wk4QwhMsrpgjHDJ6HUAD7TD8IajwAhDG31HS9QMAh8ntdmAnFkC9YfAr/243ltNz7GXlvQG18oi8AZYXvqMlbo7X9z2BzwnjKCTvpIwDU3WP7RwWfGLK7jIaB5J85qog369krbr2zjRzHCzxFWp1xlUmp8r23mobIikQvhksfB3xRWcyu8COiiX+iC39FLH9RDydA289D/xpdxxf/mB57Bl+ijfnUow9iYJ/qkTfpPINZX/dZ/z+AV7yiXMmvMuvpuXjJYoLc1K8716Y/PjJH3jJt+o401D8+hDT4gPKODNYZHEO3wnzH2fawpcc2MdqMFZRS942qMoLnPzE91+AyP4j/jZy2mQJiPoUzpY2SqRAe8QBmSHMpcsWaiSXh+0B0NuzJ364d1WPmeIbyrw5psHbEvmPMxDuYD/jbOxg6dzGufGxdrmXaYC543xtYaypN1Hb3iCqraoEOZqY156KFd5hua2rPQFR8YI3RRdpwz90zXXqP8oJd30RTN0aT24OknfqL09ztXFwmGrM2RuTXOyhsz86QuN6518Zm11loY1zfhIzyLd6xZ2hXnXM0BtEVj6yPe9L75hnesB+YWGmg3+isb/1C40CWiAbQZfRiHjAOvOAONstown/CQMcNH1ibttVYpz96gbWiqLuNoPLsMsQFzFv21EZ/hpXjevFc24G//67e+RURJXHHS61hCIjFpSAUysaARITH1ZjfV+alBy42w1KnKqkNM688IXnGtwiD1waDtjrNuo9qICEUszDZmnqAQAEMYZYHt8tJNhaCNTVfIDnpSICOra9Ct3f9ey9YwtAyhSz9YgAnaLMLK0B6Kps08LpqeCbp4YJh3IySKoEYY46HodcVN+93ghai7pl0/enWFR7b70+axrs+H5d2pyq7LGqRM49trnvWahzWPtMesH12GRYT6DjNXB6VnP54LI9Cg689Udfcas0F5cqpnh21nVzldYa81j053ftaozxh30agXD/aiXT/eHYReEe4cV5wwklHw/YxaSek3VsOuAf1AWRSlYI8QDRGRQgwWlFB7kT3J2t4+YjFIH2o+GGafn+r5XuvGoOtxIjFpyBDWxIJGe1Me1QIdVtnptCE+GyZMZTpJFEYJ9YfXSOILFmN1sEo7vzndLHEh5BBwKEjKoSxFJtNewthMxjHeVY+zWCzdQiclBKrLZSnmfRkFLWeizKOPcEf3IPI4ubplqiyk/fhs0Hb0e65Xf3rx+jAYpuxB0G+e9ZqH/RTUUQp50+GtYcav17PT5ele5Q0zNsOsfcOuk/3KmQ4/D4t+dJ0O7Ybtexe9KFKyiDI+CTuP62xGjX5jNcr6lMXzyKDJ084rH2ci4yiIiBBe4GGzzHbJC8Pczdjv+X7jn0jMR6QHMpFIDAReOWFcwonruxiF4sxEyRIyJFxM2TZf55yE9sw2WK2FFVFehWEKs53EzTzOzTnbKnwu7glMJBKJfojoDmHXzvk5dzlV1ur5AGGfomHifKqwWGHh9ZVSiURidpEKZCKRGBgRogODhvUMUmad1j2uWZht1Fn/ZhoiN9sYVShfIpG4Y6Ed+r1Q1o/Z2IsSicTgSAUykZgF1GchgILUpRg5zO9Mh9DNYUJlpmOFjkvZE6OHsR7kTFditIgzYhBXDUzncvReCMNGCqiTrRjNl3nXNgRZ//HWfGn/XCOMjb1oNhc8Mew51URioSDPQCYWNEZ5eH8Q2NxkGZS6XLihkCGhQy5xl2HPeUGbn3bJZiiznLMpsvBJQS5T4ajaqg4CioyDzi5KeLPeeutN+wLpxO0RmWiNtYyMaDvX4WHj5ve5giyGwvKcmzVvJHCSNdc4uAJiFBeKC62WXMk5K2VOSmp9PGdOxz13tXJr/ZHFUjKo6SS1mg/QZ9lRZRRFA+f6nFWeCSLrsPOCymeE8CP7q7PQaDoTJYHSKMmLxFzO5zmrx6gnk6jsnq72WehzdqaIOyZdieWKkLj3MWCeymgre6rrZmSMnU2oR/ZUcCVVGmcTdzSk2SuxYDcbnj3C1jhBULDJURjjKg1Cnexw0nyHgE95PO6444rwsOqqq5bvnS0cZUCAsmxwV1xxRREoCcTOjCRGB1cwEFpcQdJ1yfQ4gc8YK+4IQSXmNsXdPW8EcX2W8t9Z0TocehgowxwxjuEpIiTGvZ6TAudfXQUio+axxx5bBFeGJ4oJSC5CwV7IME4USHf2URxmCkmp0NC9kyeeeGJRSCng1vCNN9643MfoqpDpAi+5LuTcc88tSmTcRYlfF/pYjXq9tVeiZRdP2O/Q2Do421DHpZdeWvbX6a45icR8RnogEwsSNmfeA/dg8fqNy7pLmIvEMnHexJ1cLh4n+AkjJUy4tNpvXkqWbpe2+26U7bTZulPP5ibhzQEHHDAxXpSFgMgGyEMhS+pcexAklSBcudR8oVvD3QvnnjV3yYG5wxOF12fifXTfKuWUB8N85Xk0Tydp3miLhE+yT1J2XclAoQSKibsGp5sVeT7APLOuG3/r6KhoSmkUQSCbJ4ObdXmllVYqCp+1k7J3xBFHlLV9WOBP1wTJohxXdtgX3DFrrs712jEfgEbm/dve9rai8LdDVI0Lnoh7oWcb5p9s5O4vTSTuiEgFMjHRZwiFKvHWCSFySa9NXfgKAUkYF+HO5bu8Dy5ddnGvUFHhSIccckjx7hEIpC7vOmOoHuFLyha+RPhySTJrvpBPIVIuIiawCTfVHhda+5/w5nJolmmhTi4cbwuvcf6QYG/DozC6wJj3BFhMbYyyyrGAy7BJmPEO4djl3i5u1id951F1x5V2oImLk7suuadAs54rg4BCCOLttOnxkPoczQg0cVk7wVl/tUdmUsL4MsssczvhOc6g8Mx4VpvVo2/CddXlf+0UYkSJRTdlGCfvo4X+Ub7QQZsIwbyj3hVi65J07+ABl4ALwfWuMggK3hcSpj6CnWdlUuWNMJb6GPQEbdBmF0YTPtBQ2e54NGZxCTS6aQ8jQK+zdNrheTRkCde++t5AbVOWdhFI9Q0P+gxPKR9/yTqLPviLAuB/vIhX9UN7CbU8nIRNQi2+10bPKt/Y4wH95SVRNt7ELzwb+ARt0EqYnD4JsTZXPItnjT1amGeuNAk6q1tfCEis+/qgLdquT8bfeOGrF7/4xT3P8Aa9YgzwnHHQLv1UpzFFT+2QhVfdLPzarY94Rjte8YpXLL4YvPYKoomyhLBS6I1p8E5kDcYLaONvz6EB6Iv6CJ4Eeun//W9eoKEyPBMXvKMTGoIyrRnKNGfM15gT+mf+oJs54z0049miTMw0629cgRPQBnUYUzSzPuE5IfLWCvNRW/QhxhS/aTfeiIvf63HzY8xuvvnmQl9l6J/3jZs+Wp+UoT68S7lTLrpbG80B/GHemS/WPzygDPQy3p6xRlkHrSPBe8YJz1qr1Kd+/N7mtQjXV561Ao3xsHkzLI3rc3TqWWqppQr/4Q1h0uuuu27hLWNp/Vc+fkYX9AjPNX7H9/gRjaLs+G29jeMLnkE//cW75i366QNesb7iIXXiW8YNdPCsse61t0U7veN/653QTm203uJjaxCewcfaZFy0SbnunLUeB/931VF7Ur3DaIkHll566TJ21iZzSFvNcXMY7xsr9FO/OWcNNP72de1Qr7mOJn6sM/ZZ7+GloClaffKTn1x8HVR9v6I1xFqpr+iIztrU6z5OPIfXjYu60D+ulOrFK4CO+mZNxS/2JbRXL77VH2sFvlcW/sCnaGc9NZZ4Bw/ov6gHexCFeLZyIyQSM0WGsCYmFhZZlt8NN9ywnHsg4LkLb6eddiobgw129913L8IFZYQ12h2FBJ0IR7Px9jtMb9PjHbRBWeAJyASEUPgOP/zw5pJLLinClTJ32WWXsqla+L33gQ98oGxKZ599dmljO5TFJuJ5ZSrH98qxacal0zYXlm3fK9cmKDRLv3y//fbbN+edd175zucEMHW+733vKwJjL2h/XBgeSQc8TxBCP8rsGWec0ey7776FDvqy+uqrN0cddVQJzTnnnHM6Q4DR98gjj2w222yz5vzzzy9C/sEHH9ycfPLJiwVO42LjDMVpt912K4KEDXWrrbYqdV599dXlGd+9853vLHTy/XbbbVcEAm3nEXr/+99fBBPPe5cwcuWVV5ZzJ8cff3yh24UXXlg23oMOOqi0gTCCVvpKGIqx46WjMBlv9ylGaDG+sukTANC6XwgUJfCtb31rGR9CI0Ex6MQIgWcJbQQA14S47Fp9hBJ99Td+0rf99tuvCDSEBP0ifGkPZdB7F1xwQSnffZXGRD/wubApQiuhxTvBU3EPmXeMz1577VVCHYV6GnvhXfgHDdDq6KOPLvPksssuK+2mABgD7UQD/dhiiy1KO/EHQRTdvKdewty2225b6NoV5ukzvKX9ymKxRzseW/3095ve9KZyFozghq+Mv3F5z3ve06y55prl0nCGBmNuHuGTuq645kQbtZtAad6JQBDOSUDDg+75NAZop6+ENu3XNoJrGJPMFT++01e8at6ik3mO1/TDmBuja665ptRpDm+++eZlzNDaWiS02Xto5XM8oVz9GUXYmzYQwhlEGKNOOOGEsp7gw/3337/wlDopPtYRdQv93GCDDcr4mzt4Ai93gfDtezylT9Zi9THUGUO00Xe8TOiluKGhcTVf8ZP3TzvttPK5v423dQePGiM0RFdjbu6hm35p8xve8IaytrjCBv85Z+zvNoy3cWU4pIwq37iOIrxV3xktKW3WJbyinX6sM4T+rbfeurRXO/Cx4wnmGJ7SX/tKzbPGnpFBf9DEWmcOrb/++mVf8fy1115b5p754119s04yBlC2GZRErvQaO+sB/rdPuBYJD2655ZZlnPTD2hCKunlD2TfG+GSfffYpfG3P9U57ztWw/luXvac+vGiPtqaa8+oxL+wVfqzTxt1+YW6i04477ljmh3mHDu6tRFfrmfkTETt4zplnvOB/+wd+sYZSWtE+2mkO2JPRx965ww47FH7vBTxnXcDLDHB41vve7QV7qjllTUBTaxcPqbbrx5577lnGq6ax/d0eaw9g4LCumxsUdzTxrP+t3+ZQZJpNJCYNqUAmJhI2jghJsdAKyyPwEApsQAQbm4/N3YZBgWRBjWyY7oUi5Hi/l/fRszZXArPwN0Ihz1coXt6nuIQCZhMmRISFWpkst9qhfsJ8e7PxHusqxSTKYVUViuM9HgoJFYSYRpsIBzZu/SYMaQPhiAJG6LABqY8Q2CucyoZKyYxQNwIQYYEiASuuuGJRmEMgs/kRNNWlnxQvQmA7EQfasmDHJc3aTkDg/SM42ITViU5o6P0I2/JjXNAZbY0pocO42eyFC1I8CRSESwqMM0lh/SWsERAoQMaVcopuBC4CD+GAtVhbWMwJLZdffnkRftDbd9pEUdTH8DCiq3IJwqzUlJZeIYvet+ETENCQEsfirGz9s+ETqCi3+I4ASDDQNkKJ/hPStJ2nhSCp78A7o+3aplztXG211QrP66+xVz/epxhoP2GKIcA7BBX04KXRD8qtdqExwQQ9Cbn4yud4B1+pl5CmfcpRBuMMQ4D2ojt+M0eUhZa+o5AZL2MdXr4u6KP38RteRFtzBW+jkbboJ0GQ4mzszXE0QCf9oRw67+dZyi4Bq+ZJc8nzATSlgBobnsS11157cSi7OepZfcWb+Ikyi44EOvNTnfqtfWGAIqyaPzHur3/968szlDY08Jkx8f3ee+9d6jMn8Ep4Lc3vVVZZ5XblzgSUGZ6Mm266qSileA0fUrbxCD7EO/hQG8wH/TRHzXVzlzBrTNtAJzREG21FY3OFYoPXCOS8SpQl3ho0VmZkwjUH1YOmxtOz6Iqv0YrSYQ3TTmsV3mLAwhuEaPxinfCdOcCYZ60IY1oN400Ri3BQPMx4Q7gfBbTdWmYOoTHjmjVVP7SZd+yYY44p9TGGaDvv1Wtf+9qiBLdDiu0deAD/hSHRO9ZW85YiQimlPIa32BqiDeapea9NvLfGog08gK8patZZc2CjjTYq76rLOq3NeAGt1EHR8jeeNo6eZ4RQZ7/z+cbEnDHO+IzBx7lRdeNP+4r+muPWIeX7TQG2f9pDeKgZ/NDXc9YGhoow2JlLaOV5+1h4Cylu6GAtxVvGJPpvH40IHG2LtYOBpQuR3RXfqksbghd7QT3KpgBT7tGMEs3QVtNYnxlI0Nh6a3/3N962jphfFHbGRWuJPqKZeTzXZ+sTiV5IBTIxsbCAWvQpFBZzC6sN0CY7VTKYEGZ7CbVgYWbVtQFG6GnbY9kO6QoQkGwaBC9KAwHGxtTeZL2vD/WZtPr+wfbzBCMCofK1w7ssl34I0wQSghLhixA9VfbBug6bOUGTsIeWEVKLtpSTaKcyKRfo0kvI9bkyIkSMgkDIISB3CRo1HY0reqtPP42vvilP/X7btHmVCGSe9T7hQYILAolnvEtAJUBpByVQO+IsKQFNuejZZcWNNlFqCHu8KQQm/NXrHF3wjHeiXfpB8NB/Aoe26RNoG2WOIkEYJjSxcBPK/egDqzrLNGGRsB9eROXrD5roRwg+hCX1EFb0mXASnuagvXK1Q/1+CP8EMm0kzPrO+7wEBERn/gjCeNlz+NBP3Zbgj/Z4+SEs9RIw8RL+9Yy+UtSNL5op0w+eVE6Eh5vfUb73/U2oIyRSONpCnf7WQjqBF315JfAqRZg30XxdeeWVS18p055DV31tz0tl1uFrhEJjFNkdQ5gn9DHqRH9iThjvKFe7tQfNeQCNxygUSHOVkkhgp8zz0KoTX4ShQFs8Z67jQ+1EV7RBd/O86xye9cXcoUxoK4Wbl5WCDP5XL+MTHgz64xUKkPJ5inh9ecPCgIaf0MbfaOjH/+r3t2f9xPhro35oJ0WDstOOjDA2lFt8qa1CchkcvDMKqA+fmEvWJOMd46zNxhfP8jppC1r7TntEPoTiFoh1Qx8D2h2ZX2NdRKOILPA3WlJAjAn6Bh17KZDme/ArZcYcML8oWjyj9gPeeXVQoKIe5fvRRuPq+y7E+qAf2uIHPSJpG573PjroVxghKFOxd3mf8oym5rbQVeGvFFCfWY/Q13PKCsOfdcS+VWcuR7P4W9/wj+f1xfywZ/cyDsZYaSePJyOZPvSTIUKB9KPv9ufYu4Tq8ihrIwMKGipPvymOjEw8yNpmfzLXYt/wGW8oI2jNI4nEJCEVyMS8QCQesBnYoOpzf7FhDHvWhaDF4mlD7nX9QdTb/s5mwDsmbIzFMgSGfu0ftJ+EFcIEqysPCgstS7eNzJlOQhxFhgeIMtNvg+sqPzZGsMHalP0edabJfuPSr654Pu7IRFt04K0lvHZdTxBZM1l1Q1m08Xp2qoQyeIpgxaPCa2HT7uW5QDeKOPr3Ci3ShlBwCCsE6lC09MP48iZ4jpdBuJr/CXiEmH408hnFmLWeAClUTOhXVzhk+31twleENe1Yfvnly5kofdJvniTnighr7XOvvdozCChdQo0JtDwmBNiutoYwGgpFGyGQo2ev81gBfENQjRDGCOFGA546faX0MEoMem+k+s175YLytDUMCP3AQ8NDRUHm9UOPXkL5MNCmmMPWRN4mRofIShtGh+DDYe7IxN9hpOIZpxRIKsN4Ym7GfYaUcuF+cXQAfwtJ5alkmIkzq8OizW/6oJ9hVKkRwrk5hLcZWeIM3kzXNf0MJYtSYF1B1+hvKJHa5FnrA0UPgqdDge7Xv37t1A/rPf4Vkk454rkSht1rfVUn7114sEI5RCcGBgo2nrHPDKKk9Gtf+zvjFHOjbSjVDrxlH4r/49m4q5iBh0Iu5NWcpyT2yjrd67gBPtQOPIgX7B0Ut16h43hdhIGIHJFIvLHD8o51yzyjCPIyorH2Rwg06Kv9xtpNUbUHUDijveZatJfhq8vQnEhMAlKBTEw8YpOOe6Assjw1QJCxIdoUYkMHz8fF4r0WYIs8CzVvDotonIGK8Ki4GD68KwSDEPp4jAhJhCMWy7hKYBihoOs5GyjLvrBL3oKol7eEMOAcC+u6sDmeLYJ/KINT1UtYoByw6CorykY7m2z7/UHa3raqB80ICHHOk4LeVramUvbVHRZqYZbRzrjTsv0snqBk+y7OPAY/ROKEEKq0RXlBN8IerwFvCg9nJCrqAuGAMEMACxpql58IzVNe3NEXCicjgzZQhiktBG7eDAI5YdQ4Ui6mogt68vgQlBkShJUKfdJm78YZ2zaNfSZ02NgL7fIcwZKHiKDGq03Awc8xbvFevwQSMeb1Z22YX+rhhVFHzKG2UBljRshFi/qZoLPzWxTg8BDXCTNqoDO6CwHDD+hDUTfPCaXGgrKszAh7D9Q0rOcBYY7whzdizHlICPFdIaD1mOFLbZbYS6i2+Y3++iu0tN85qy66h8Gk/j8MbNY1fMibY2yD79VHMO5KQNMF5fD+CnvF055jWCFgoxkBmRdI2DF+F7qnH9ZG/IRHKXuxNnbxUfuzfvyvHO2IxCY1rJm8XQRyY23eGW/eYQoG+sc51140bbfBZ3GmUVgqQx7BHh8YS6Hk4aHSLusx3rQf4DGf4RH8L3y5npf1nhaf9ep7fG5dpVToP0+s4wdxxKKN8BjjL8apoAnjiTkkBNe8wLtxpUivyJu6fYPsCZ7hJWYoiuzANU21y3zhiQ/6mUcUtvAmijIxP/GUv7WtvSeZi4xo5lLwWJTnb2tAHAeJ67VEWPQKCbUHoEvc+1lHOfTjm7pvFEGREtZac0Bd2h80trYxqJmXzhNbw+OsObpYJyORWYSL49tEYhKR6Z0SEw2LM8HAxkcRscCyvApziXOKhDJembC2EpIpmDYXZxwJrTZ+YSY1KB3O7RA0hITamC3uIVCyHBJ6CaERbuk3YYSwQGGQrMFmoG7ClRC9SLDBwhqhitpuk7Shh/Vau6I+mwhhzHtC7CgUkheoXzv1WX9OOumksgmpn7BP0O7ysNmA1KdeNPG3TVs/CdHOjqhHyBXBiGDp7Flk1NOWNr2i3EjSYbP2mxATYWo2PYKDUCUeF5Z79UFk0VU2IRYNbNJBK+9qr//RJa5R4K0hgPF8UPh5CEMgI6hESJekA87ROSNFyeaddf6IRTfOXxov76jLGOs/GhNUImQLTQm/XdAm9fBWEpyVTaEjjPFGq5fAoh7hioR4tBBmaNwI5azrhDj1ElS8gx4RaoYm6GEseJEIq9prnNGOQIrfePPQhMBqrAhMhDKCC8EwPKWeNfas4BRZYboSExHAfBbnfbRfEps4E4nvhbYaJ+32W3nah5+Ct2O8InNjGz7zDn4wL5VBCcMPxkB9+F055jd+NJ8JVea4uU+44sHUV2dU8SA+VSda4is8qN0+N4bO14kSQBP95z0iHKIVJcjZP3UrS18J5OauEGP1mBP6aW5Sigi0knugnfln7VGnsDgwF8wBv60d6O5/Y0Yo5DUSQRBnko0n4d55MIKrNW0QWC+sNTHnnLlVZihVeIkn0nzGh8LoIoGRdQRPobX24St97VJelIeHnT8V7qh9eCYSWSnb32jBEMIoYpzUY83yv3q105xDYzwdWVXxgbEzhtYI76IdpUvbrCOeRzfj6RnzT5iud4xzKG8MY4wpkhaZ0+aVOaz9eMsZaXwjbLDLe61O6zh+UDbBXdnWGbzKq+l8H4UYP/Me2VsiTN54CNNGI0mB1Kc9FChzES+aI+Yk2qB77APWBfMdn+C1eE6/jAsaBY9SkBkRQ7kXMtmOGADvWV+sOxJeUZwY5MytyMSq/ea78cBTDAXOVsb81w7t1KYYrzpCoq7LDz6kzFmvzC9nWtFK+5WHZ/CpcXamVRSFdqGRPZsnLtbAyHvAOBZKaIy19vitz8bXmq88/Y0kR+a8s8YUSOHUYbyy9kYdbaCpH+0yX/WdAQL/KKtNY0qt/phn1vzIds67SBlWD3qgcWRlRWNlob3PIvwZr1qHPY9X8Lv2mnfKyWteEpOIOy1K33higkFIsQE6v2Cjs2BL4GKhJ1zY3C3gNm6buw05EqywHLMSE5ot0F1njmxKzigoh5CpDmcPWJxZ8W3sBMrYkAmSlBiWUt4wQi0BgRBG0IlEHrwKBDBtsaERRGwI2k5YibuqCLRhzVd3XDVA4LJxERCV6XNtVQcBJ86DxLUUbSjTJhhXFVCe4s5JG3pcvUEIIQTZzNCKIGRT8xlat2lmuYhU4wQMQrUyKAPq9F4I++qwKSoHHdGJIITWytV3Y+gcmv9tmgRb/xNC9Nk4Ewr0hSJG+CG4oxcaeU45xs4YESrjCgUbrzIieQ5hgPBFgUAPQgglXH/xSnit9YfVuNemrZ9oFYlB9JeCZKzVabwZJYydurSvFlrQj7ClDu0kWOk/vlInHg6lFN+iQXhd8aDvjWGEh/mMoGbsCDDGmbBoDPQH3X2GZ/SZQkZY1V7eUHzpXfwW/EAAw9PGj3CnnfqhT8ZHWf6P6xrUH96XNt0ibNTcISwZc2USyvVTcizZCvVHu5VjTNBY2B7Bn0CGdxl5KAcUOzyHlnjQZ8pEe33VZ22NzL6UHF4btMQ32qOt+hrngpWNDhQQ75uH2hDJMPCKvuEvfIYPIwlJXAVkTnjOePsf/fF1XKcQczXWDHytDoJ9V9huF/RLX/Fz8CuatZV3yj0+RG91hfKML6wLxlT/rWW9rgpQF94zZ5VPqcan+Fd//K/djBr+RzNt0SfrMh5nnEBjfQ4F31w2bvoR1yehJV4iuFvPvUcBwifKtEbhdXM9riIyF9BfPdpqXCmayta2UGAkTzE2lOEug5t34+odNEWPOAuoLGtYTSPPqx8v6T+l2U9cPYSv9QXdhd57xp6gv8ox99DCWERCHfPEO/Yu67qy9UXdaMdwgT/NVzyJ95XBINQrmZr5wDCExmjKcBHGFp/bQ8wL66k1CJ9rgz5oQ6zH6GCtMCbt+e0ZxjQ8xbinTeYAfrPuo6u+xZVRkWDNmotnrKHG0DtBY+uaNcr7sXbFXoH3zONoj3FQBxqglT0GPckD6GUNsOfiHXTvFcbtGXwaSdrMFetgJCXr2gvjHKa5Zty01bNoxAjCoBw0No98rjz9CFjz4h08Ys7GmX/9SOUxMalIBTIx8QokC71zXjYgG3TXXWUR4gJ1SElXmF0bEQ7mGRs/y6n03RFSWNfRfq8dwjJVXYOiDqFr1xuhuW1aDIMoYzbvmAqaBmZCFwINOgxSRoSeddGnV5vq5CmD1hOe6q6znv3a0MWn7fen08dh+b1tUa9pM0pebretLp/nV9p72T55Gur+hAJJOOXhivDoYeuNs3p1X6KvYTSoQ/QG6XuEZE919rFLSK0TdQ1a33TRjw9na/519aum8aBgJOABxwM8TXGOddjx5rHiUWToYcAbJZ3rxDPtdoxyXClBPGOMqYwrxoMC6WgD40MvBbJfW9rHPabbVgokxZyibr+OBG2DrqHQ9XxXWHGv/rXb357rUcdUaMsTg66nsRd08UG0p93Grj24DtMdRZKtRGI2kSGsiYlEhEqyGrNoskR2ZdTrtWm0v+uHWPhZSeOSexbuWknrdX5nVApSV9m9ypzqbstBMIoyBqljVBhGUEezXopxrzZNR3jq16Z+bRjm3Ncw5Q/L7230OwM1U3S1jRfH/I67Twn40S4Cme8iEYjf7asQBq13qr62+z1I34dVHAPTHbPpoh8fDoNh+9vu17BrgfHHE9biCLntCo/uqrcrnFMUiiMAo6Z1vzk+yrrUE3ed8uxFYh4KMS9nP/Tbv0axVlNkhb3ydvLERTTIKNbQmczx6fStLU8Mup4Osta329G1B8/2epBIjBKpQCYmFsKTKI2Sm9ikBhUipgvhKMJbnHWhwMah90QiMXoQOHlOhL1FSF6cu43kNs7ACkWjTAp7TOHqjgFGQ55Da7/wZAbE6a79QiR7XcszXyD8UsKVuA7CvKFICj+eyz3KPkl5pKAbM6GxFNrpGHsSicT8QoawJiYSEZYSIUL+p9zNpoWunZGRVTHDSBKJ8czvmG/xP4E0/iYkzzQMMzF/ELwBEVIY97veURGhkhH2OCl7U1zpUnvjJqVtiURi9pAKZGJeIK6FcL5itiyuvc6GTRddZxV7naccpKxhzudNKqZ7dmxY1ALNfKbXJCNpPLuY7XPKvc6HzgWGPWM5FfRLmV1KZ1zlI6HRKBWdUPDGcTwgkUgk5hoZwpqYaNiUZTiUDU1SjUjvLaxpVGDplkFOHUJxZD4bpF3QS+DxvSxzsn5Ko09YkelOH2Tlk61tEPDCyNwnC6IMpLK1zVewVEtfbjyluO/KhjhTRIZU9JKFsdd1HPMF00mwM9sgnONJ2QdlF5QhMjF6yMYoI6Srhrqu1JnJeif7pYRhEpO5DmYuwiDxtoya2uHc3LLLLjsSPleuDLXWGnuFPsaRBGuDrMe+Fwbqexk9p1tvbX93bt4VQq6LkcE0lchEIrGQkStcYqJhg3ZHGkHDxkwpk5J81AIxBdJ9TdLqD5LcJy6h7/ecczvSdcfzhJd3vvOdJRnCMMKeK0De/va3F6Fn1NB3Z8/CMzibQBNXKqBxeK9GDfSSCt15obiQeb7CmFC2J60PcSUKGps3k9a++QA0dMavH+1ktURf68eo+coaetxxxxUlda7GDw20w32Rrp8Z9Xl26y0aB9xN6J5RV4LIsO16Jol6ZgJndyPUVsIfV5dY9xOJmQBP2ZdzbU1MMtIDmZhoCDdy/xUrvHuR3N836gP6PGG8joMkaSAwuAzY8+6K6gUWbZ5GSq8EDqzRBBeW/mE2BdZx7yljNjYTGS7dkSZZxWwnmkBnF4/rR9fdlaOqwz1bg96pN6lAI8oDD/Zmm23WTBKEBfKwoPEkeUbnExiFrr/++mbLLbfs6f1z6fnKK6888nnpGIAELO7Mi9D4uYB+u78x2jEq4ElJZ4499tjFF98TyC+55JKyNqjPfZUymM5knZDkjXHTVSNx3y0Dof0pvY+J6cJccLcl4/OGG26Ya2xiYpEKZGJiYYN2qbfLgIVysZYTDGzOPCCsvUCxJBAQhLzDwydzI28lS7PrAbrumBLSRJCLC7n9X1vpXenh8l8CVyiClFmeFws7ZUUoKmu3BZ+1m2LpQmlgQeRx80xbOY2zkLLWqUN9wmcjNJfAI+RVXwZROn0flz67gNql0RFaKLRKX9BI2CGhSjisd9yxefXVV5c7+CgFUt3LeKluWTJdru4ztNEW7Yp07RRbKfYlN2LJR0fjI8wWPZXDGm+8hMy5xNr72kL5Rldt4ylQvoyC2qE8n6OLegh9svH2OmsXY4kf9N241PRCW5fY65N26ae6IkGLPngX/QjWwun8rw0RiiZk07O+JyBSvI07muJLNH3Vq15VPCpCoX1uPKMt6nDZNU+00Frhc4wjwum0zzPCbgm1jCT4gtcZDYXhErTxdBcfq9O4+xvvEWYpedqHhuo2LuaBvqgXLdSB37TT5/gV/fTFe/rqHWOIHryhygmBX1uMp/4bU7yi/kEEnkiSY1y0J/gdX5hDeN7nvMnmv/mD37TNu9pJwTYWLnqXoZWnDo3NQ/RVFvrrF9qipfYpPwR8Y6xe7+Jb9GDciPd8xxOID8zjXvfVaSsa+B69/L/kkksuvhxeP42heWm8jK3fPjNP49oK/UFraxrjkX5qs/6juf+NjbsAzWFtxJu+cxE7OljT8Kd+WrPQCZ898IEPLPyl3EEQaxT+ifVN+/Gn8UEzY6M844R21tsuhVj/tAktzFV8ZF3pqjPq0zc8aE1Ac/9bK2T9xIPGPeaictHehfTmNrprm7lMSbTuGUsXz/N2hndfOyLE1f/KV56y9dM88jka4wPzWh+9f+aZZzbvfve7S9ZRvK//nsE72qHN2oEnlaktxhV/6Yv2aZv+GjN7TNdeVUeimLP4VEgzPlC+Pvjf+qUu7dVOz1lj/R97ibbrWy8+jrXSfmsczTn9853P0dkzPtcfsM7Ya31uncaH3sUveNl7db3moc/xAdqbV9b8QXnS/hPjhN/xCB60nqC/uadN+m1N7joqEVFHsd7ha/Szh+mDcdBX+w864jdl2s+tLfqLfso3hupSlnmqDG3DE/gA9NXnomL01Zg95CEPKf1RrrXDu+iiT2QM76ClNWK+G0QTCxNpJktMLELItHnGD6HlggsuaE499dSy0Fu4eWhcqEwAc0/WBhtsUL5/17veVYTbrvBMm8zpp5/eXHXVVWWRt5lH6JF6KFWURRvTWWed1ey6665F4LNhq9MzNiGKkWs//G2R32677crmZuM5/PDDi6Jp0+91OfTFF19chBTt4I2wydt4hZfxPtnIlEcx7mXVRid91x+btQ126623Lp5Fm/Vb3vKWchk3azlBeo899ihndfSFIIOm0R+CkpCysIKipU1NW3iBlXX22WeXS62V8frXv75c9P7e9763XAbvTNENN9xQNmd932233Uq9J554Ygkf23777Zt99923bNbaaaM0PpQj5WkvQYlwZowIMsqxYfeCfpx00kklJI2waeM3ljZ4mzCPMXoSqtTFC6q9vqPsUaIJWmjnvKpN3qa+ww47FIEMDwihNrZ+e27HHXcs31900UWFTrvvvntz8MEHF74hTOMX/UVXwuu5555bhCq0QVPChj6tt956zf7771/G/7rrris8gHb6jg+0URm9DAjohc+0kaB0/vnnF7riS5d6h6CKhjfddFP5zhiqi2B0wgknlPEhxGr/uuuuW+aOHyF/xt3YUVgIZ9pLGAVtU44+oTEe055BgL8I8muuuWbhB7yKXuuss07hoxhTfdDOK664ojnqqKNKncaEd8nnxuLKK68szx9zzDGljeYzepsT2oPn0ABvqMul8hFOjRbeJfDxJBnXCJPXbwomhQVfGNsuT5nPtH2NNdYoiqH1gmKhTnNSO9DX2F5zzTVlTVNmZH0259VrHdFf65a6rGXarm3ecaZP2/Gq8nfZZZdCD7Qxz/QDz6vrQx/6UPnuHe94RxkbArJ2GddBw9XRiAHvsMMOK7xLwD3ggAPKuFjfzNdVV1210N18NW7WxjbUZ25aVyhw5qeIB/Okzdc+Qwf9INhvu+22i5VO9Roz42vd9DfFWP3WDzS69NJLS9vQ0Fpk7YpjB9Yc5Zhb5hiaxbpiDMwj/EIZc7WMOsxB89M+QgFSF09mKKDK9be6jZk6g0+U6V2/GQGUZ8z8b27h9be+9a1lDvkOf/QKqUV/iod1Rvn2DLxuTdJHfXIEA9/ic2u0762D5rh+6Yu1x77Wxcf4Ea/oH+XenqpN5oC5qu0UPvxo7bMn4eNDDjmktM/Y4nef6Yc9Qv2eM97WOLyJhsbEmoVnPD8oPxpv9VG47I142n6mXB59+9Ghhx5a1jT84vOucvCjZxjnrGfGw7ziQcY7YSDba6+9ytzWD/vcm970pvK9uqwdO+20U6Gz9eYNb3hDqdscN4+d81c2nvO/vjJu6AMed/bX+oWu/tcXY2Wdtm5PtfYnEnONVCATEwsbOSseQYI3Yemlly4bJMFlxRVXLFY8izThyMJrwWWNtKETbAhZQsC6LOKUBBvpCiusUKyjyy+//GLvn0XdYs7yapOzyXzsYx8rygOrPmGFV4yF2qbMss9DQcCwWRJOKJPabqPv2gAIMzZbSiEruj4QZih4hDYCi74pd5VVVimehF5hXsq3obFIE/JYhpVpw9IO/VPPa1/72sUXURPCbZjqReelllqq9IuQh34UBv1mNdUWAhDLq7FQBmFeef73nM3VZ6ynhBcWW8IGpcbmSElbaaWVFntbAK1CqWFltflrEwMBoUUbWKcJIwTnXn1Xhu/RS1+NfSQdobwTFHg/Wf9XW2214gGkUFJObeze4c1BZwlFCA76ioZo7rd2RFZM/dJeAp0277PPPqUM407JJtBQVo0l4ejCCy8sggBeYlFGU/yElwhq+A6N9t5778IXlD/WafUYS1ZuwlmXZ4KA5G44yoP+4Wc8R5AmqOIbghaBhAIS3j5eR5+jEyUarWPuCL0m/KETfkA7vExA0pa6Hd7Zc889iyBLsNT2QYAfzR9zRz8J88cff3yhY4w/4Rz/MxShE54NS765CMaMVw0PUdQp/soj6PH0EPLxII9JeBBDqMM3yvK+9QQ9CHj6RCnTF3SnsKAZARidu/piDpg/xpKAS5GkWOMxc8S8AOOiL/oUY0sRRgu8jH/MFbQ2nuGdNpbeNb+VZ62heFgPKSJhzAoPDT429/VT39HEWOOVQRVIfTX+5jIewBcUfgK09VP78IK1+eijjy5KMD5qg4BtrbPGekcZki/1uxs0PFbGk0EpjD34Au0isZD+M+AYZx6817zmNWWe4e8I+/e/vcL4mLfmyHLLLVfWdzAnrBGes6bxHCvfvA9a4RE09AwaxpptPNSrrfYE/BzRLMbL+iMU2Rgw6lnb8REeUz++2HnnnYtCRrFgWOoCOlsTjL02WXvwqvUyzrGjj/1IOxl68D9DGR7Hx/jbs/Y960wb6qY8GifzjjEpoliUr4/WL3uB8Teu1h/zDv3xAb61/qiXMcAYaac2q5ciG+simtqfhznOwGCHB4wTXtpoo43KWspgon7jgU4Udwo1XmoDDSjd6G1tsG/oFxoZE2OH3vhMeea+fliv0R3/mt/2QUqg/uBnPIEHGOLshYCeDMAhs+iz+tRjD8KT5j8+VS4DD0OU/QNv4EXPJBKTiFQgExONUJpCeLcZUihs1GCzsoH7nLBBgCE4xQLcS/DmWbEx2GB8byOJeyaVxeJoYwvBnHXaxhkCRbTLpsWrYUOlxBF6CSSUVsJGr/NNBB8Khzq8Q4Bk/SSUEDK0J8JWPNMvxEdbbGKgT54lkGyzzTalfjSyuaGF/5WrjXH+KdL56zMl1Ial3zY5mzAh1/vaROhFe0IBYYYAg4baiD4UDZ4Xm7DPlEGoZhVXb31+VTkEb/QjgBKstV3/0U4bvU8gJ9R0QT2e9x7BAbRJWwn2jAERvhf8YlMmkOMB46YvnsUvLM6xofeDcgioaKBuf2sz/sND/g4vKyVCv40zIY5XiMARd6YRTnzvHW3X7xiTqYDPCYyEUAYT5SuTUKc89DGeBCoeNjzAi4EePBnmEu+J/mu7fqAjWlDwWfqNHRgf9Imzv+rBD36jg99dClYX9D3GCX94X9soAOYGGlCqCVyMA7xN6IcuhFt/82rwktRGEHxEoUVPn+mHvhsLzxoP/Q36hmfG8/qtDWiGfmgRCWzC+9AVDqcvMT8oW/qjXmXgDWuG73mleYJjzsWPZ2J+ehc/Gp86O6j+U9D9r03WPHNTfWhEuOX5IFybfwR/71PqjJ/vIrxvUPDkWQ+0xdhaOwjI6GftQh9zHA/iDW3pWu88a22JsHEKuTGi0LfXZv1gkPEOxSASkKGttYWRwXrh/1iHrBH6yfCA7vrtp77+JNa6rjmln9YQCpJ3/FD2KCnWLAYhv9FQP4LH67Uz5nzwB36zJlNAYu1GH31nCIkrRqyd3sH/5mC/5Gzq6hUern34n2JjjqoXT+FB8zrmDtrxQmofD521CB8xdjBgUhTNaW0xz4xTzEs04Yn0XOxzEdKLRuq2/ugvxUq9+oMnGMfMH3MMnY2xscTv2jmIl828tGZH39AiwtcZg9ATP6pDuWjdtW/qmz0Aj4leMh7WkK42BL31wXpljGO/Y3TEexRv7fGZeeA5+xr6kSWMAYU5DNTGikGBZ93nygzDrzaj21yeTU4kBkWegUzMK1hUbXKUxYAF2aI9TOIC70dIUxs2RkICK3GcZ7QR+pwQV4OCwpLIOs/qz4I4yMKvLJuwDdDGD6HEaRuhdpjEEp63EfEUhBeAVXiY7Kpo4R0bspT60aYIpWyj3U/0r5WqQC+hh+DEiosGvJYELv8bF5spoSXGlDITQlcNn1EKIuQHH7TbqDxCYsBm7TljqlzfxR2b2lQLce22D5vQIGhKiMcjce5Sm7vGZtjyjfnb3va24kHhLeKNQUN1EnTwVtCMsKK/rOHq5qUgOEfodns80QFtKFlxDg3dal6o3xm27fFuXQb+MT7KEppKGeJlYATw41lClogDoXAUO4IYgXSTTTYpwi7vKSHZb14EgrJ5zNvBKx8Cv/nK6CPagLfVMzzKaIOfKCc1D0ZGzy5BPpSJ+n9laKt6QrEw7pG1cxhYj4wHoTSu8ol2Kju8IrzMhFrKijUmQgQJ77wuhOZhoAzjry5CtLWFsDzMtR/KwEd+QjnR5nZ4XoQoWk8ZjNCNUSTmJoOBfvHQGmvKD8+iMFDrB36gSJgPwwA/oyNvYYyt9lmf/YhSoBzwDFFg+mXFrXk6jhbUd0Pi76Bdm2cGRdc885nQfEoKby9DBbpYB9Bc9E7Ui499rs94I86z+p8BAo8qx7oR4bm89njJvDJXrDeATsKaI2yccqlea675U9erXPsCBZfxwHpF0WT0oXgOAu3RRjyFD61H9pthr6HhibY+4DXGVsYdc6QXfbugL9aqOslVjKf2hLfYuJu/IbP433dh5OxnGEgkJhnpgUxMLNqbq0WWIGvxDY8bUOpsSHHYv9f7NZTDIi8ULi6dDqufDdcGKwTJwu87VkrClzbEcwQgApu28NLZUGxsdbKXXlbN2KC9b2NVplAXQp6Nl8fBT9Tjp9cm43PvCDWNxELKZDEnIEy1OemfZ3iq0FDYDcGnvqetXX+X4kAIIQDyUrQzR3aNJcWF9ZbgQ4jwP4stq7Ywr7juhBcsEl+0geY8cOqNK1jQMoRTAidPhTEKgY2HyzuR5Ea4IuGWIOk5dK/75B10rAW+qRTL+J/A7QdN46wPmjr/GopMP7qGstFL2MTDxso5GgK1sDFGDTTkYYyQOMKxcz/4g8IU4XY1v7b5NjzCBEfCD/oTEOsw1S7e0q84nzaIESSU6kjUEWFzQiXxdShDURaPLpo6m+cMkn5pEyFXCJ2zi5Rnn+NdiiiPnXVDOZFAhJCHByjZ1g/n3iiclHICLY+luuJKHuekukL/QgCs6acOZXhHGSDcz5wMmteKZJsH2mNtPI2BeRHnlSn/oRBSpMw5IX48kvqG/sY6kgOFZ7tN+6B/G5RxnhleSP0OOkR4+6BrLZpSQpxzDQ+wMFDtq9uBv4Q+GkeGMOujZ8Prqh1C0HmujKX+WxvMKyHQzqQJd/VZm7d60TkMkPgswj3RluFCO5QnBJ0xMSI3oox6L2iPGRrxpkZClUj0hY4iWiLapVdSsC6016QwKvicYcjaxShk3qifMq1fPPjBx3jQWkohFrVA+WOEYdwSxml8nflThz3QnqL9aEDxRIdYX9Vr/TH3hLAbF+87R29/FWIdycdi/pjf3jHXRPVoj2fQRRtjne0Cmlm3IgrJc9pE8RXREYayfnsvaDv+M+d5X+NcqLIiyZg+oi0adK3tyrZnmGf1XAhjk/7YZ4x1JP6z9uJx9Wu/SI6IjGnzasyH6Ed6IxOTiPRAJiYWNlxChg3CpmOTJNAIpSH0x7kWipfPCBWeswl5jxDZK/STZ0HiAtZqAodNMjZMVn7WZtZviqOQGMKgRBraYnMmDIUwYTNg/bcZ2Bx4NYT1REIcmyaFxW/Pq8uGx0rMam4DJmRR3iQtsOnbfIUPScqgfcoR0sX667l6U7M5OQtmg0aHyKrJ40Lo0T4bIsstwYFHQdv1hXCubEkwtIm3hzdHXwkU3meZRXvP2xQJstGGOEumXTZ2m54yPK+eeAfNCI9BZ59pI8GE4BsZInkj0UXf/aZMsDTb5LvCB23gzpYQenjejJs61E+BIHBIxMFDgX7GkhcBnfCPECaKF0ER3QnbPB3oo/+8HZSh8MLw8BlLtCQkoEV4Mv1NINKmCE3SR2eJ8Ac64iOfaxN6hFcwaOM32qCtZylA2m5s8H5bmPG+xDmUcH3TJx5k40dA5KkhyBKuIiU8euoXgUad+JJiQ9k0nuaQceCRkxhHoqMwbBgPQipFVVvxuPEMIZmiSlhk1UcvyW56ZWaNz9A0zrFpgzrxnfE2R/GQ8UEXgh7hFq2dJdJXoY1oyitivlOWhISbg2GAYaAg6ClLSJl57W8RA9on3BWPxjk6Y4Z/8If1wLzhnbTGdHkfzTE8gx7owjMRCaXM6bjCh4KrLh4ba415p53ej7mi/foYIae+84z5TDmmxFv3lKHsMEg512etIBhDrH3oYox9RzFyXsxY4h18jP/iPGANfGIcrZGMEXgLn5jf+MC8p8RYa41dhG+3ge740XrqnJ4xM9edM9Rf/Y5QYmtBZCCmwBo7Cg/eN/b6ZK3E09qg/T63PlCyzRN9oVibn/qtjVFP0Nk8xevqMrYUGnPIHMU7jIj6GUl3eMvMAeVQHBiAwpvK88aAaExiHlsTeIXtTXgMP1GQ9ENorHZrl9+xv4H2xTGENnjbeM7sPdqlLvVTpPXd5/jOvofmeMaaYC8JPjamzoiH164eL2PiO0cp0BxPmQ/G3djaX+xXkUSOMcO6T/HG0+huXPCf9cPzwtDr+WOuMKbpizWfgoWP8RIjANowBLQjTQL4CB0ZtfCP98wtewAaookxNUYRMtoFxgH7VZx91i59QAMeXPwXUU3mW3hqrTv2EbRlYNZef9sj8Ik5rf/WF+NgDUBr9LdHGX/lWCMlyDLP8VSspbHuaRO+trbbw7rCvROJucadFqVpIzGhsLGyOlvoLaw2p0hoYdO0aNswI7U8ocDGbjH2XGx8XQhvByHRhmjDJXBYqP3Y3HkgCLSELRZaG4xNQh0EaIKmTdNz3nWuwvM2B0pcWEptBhSTSAVPabPpERIImELwfCZki9fHRqFvLMcUP0KDDUw/9au98YPneLZs/IQXm446CGWENhsfgQsdbbr6b/P3v/ajqbBVm7oN0gZLINJvAh9BSDu12SbLwkzoIxA7C8IybePTb8IMQZVFXLuMjboJVj5DW31SrraFsEQgMw7esQFrp/FF10iA0guEY/3Qd3yCd7QxhGlCAKFSeT73TKTZD89thFwRXIOe+IMgor36FFdA4D+8QHgglGkvGqIN6Cd6EGjR1PPa4G8eL+OtvEjIgVfRRxsJHjwwxpzgjicIqF3KCwEUP8Y5In2g+FLejCEF1HcEQfwTc8d4xj143jdGocwS0Al2hHn0RANCm/GSgMSc0m5zAP97lvcX73uGwIivKSoE86B1G/pLwMQv5hKhGL2MmfajIR4gjBoz44SPQkGIM57hHYpkQhFqiReNkXaYdxRNApuxxuvmJuE3ku3oq3lC6UYvNEIbMB7q7gqVQ1NtRQ+0jqQ1xiPO8um/9oQyT8glgBPwjZc2RUg7+hkLfTce+o7GeNr8xjeUXjSLcN9Yz/Q3hE3tMv74QL34mPEkPCLGVN/jKouu+WUc9CuyEZuj1hZtDS9i8ECcJ++CcbGeWROj7cZIv62LaGLs8RxBO65B0VfrjOc9Z4w8a7yNOzoxCBD20QCt4roi9NAm/SOcq8sarq141fw21401Opqf6GXe4nNzCX9RhPC3cY1zhdZqNLPm4Bn8YS56Tr+srWhlT/K+9URdcUettdTeFufh0MUapkz/d+1b+qfd5qNxs/bga+NtjbCWaq9x1B/9xK/6bT56XzutZV187Hv8RuFEOx4ya7s+G2fzSLv0AT8E7+hHJItDe+WDPqoXTyjH55HsJ3jN89YeeyZjkXplMm0fRajbaN3Ax2iKp7VBG9WHR/yNNnihi6e9h/fjChrzRJ+Mj7EUFYS/zVeKqDEy94X+M4hRdJWh7fqlrcpjcLAfRhQD/rXehwcdbxkvPEgesJfrSyQhMvfxNLqiN97CD/izl3EmkZhLpAKZmJfAthF62XWn1aCIEKTYaNohTr5vlx8hJXX4V5RR/z0o4pxEVx1+hjnfEf0Z9h2INvfqdxsEcBkGebZ4ACiC07lAOzJNthXjLrrMdCyjn+0y+41B/dl0+WxQmnb1qVeoW40IKWuPez1H2m2J+0Xj71G13fNx3s5Ztl5ZBFneWeYpDrzeXWG09Tyrx6ie/3XbIxydQFuXU/c3Qt8k9uAVIVTHexRGSgyhLUIUYZAx6IVeY1D3bVD0G4uu8Oo2D0+HB+O9oOFsz4F+62nX+Pbj/2HRK6y8pmGvtb9fmV1tnmkbA/XfvcZpGD7uNU41bdp0mmoNb7czng9PIwWKYkyJo5QN0kZjPhOaRhna3GtuRru1VySI89SMThTX6KtnKZAMT8KBrXntMqfDp8Puf4nEuJEhrIl5CYtqrzCXYdBv4++10Lc34fr/6Qiag9QxKKajwLXfGWSDi5BYntcIie0KMR0EveoaVhiczlj2q2smSsOw9c90PHvNhV79is8Hadd02s4DIjyM17QLca6UZ4qVHx+1rext+tdt6DX/I1lJr/bXAjC+JRAKIdfeuEKC56Aub6YYJW/1G4teYcK9aDgMuug6LAblo37raa92jGIviPL7tSf+7/VdrzJHwUf92jhVXcPU32uc6jLa5Q27hnd9zvvGqzfIvPBMv8zkg6BXGV0GhEgoxpPIq1yfXfR9hCT7LhTbLgzDpzM1hiQSs41UIBOJxNCwSdosnfMRhicEqZeykLhjgfA1VVZFCpxwOXeDUhyFrgnhGqWg3Q+8okLlhMXiXYKdsLc4r5VIJMaHSDY2qQjjkrOsIm+ElsY1W5RLobm+E85KmXREIZFY6MgQ1kQiMTQiSx1E6FJkFkwkpkJY9euwSx7scfNPZEWM+1ITiUSiRoTL1xmx4wqOiGao98K4SzT3wsRCRyqQiYlGncJ6XN6Jqc4IDorpnIecz4i04+M8txGb+0zOZnWdfanPzU2aINA+Czjdc229EOeo5qtC1et8VHwu0Qgv4x1lXs4W4mqKEKin4r/6HF6coWufPWuX57kIj8+QvvFjFGcNZ3tPnI01MJFITI35KSEkFjxsCsJCZBCULU2Kelntxlm/RBvqd0ZKum6hmoMgMj26ekD2TOEuCx2EvEizLyxxHMJe3GsmnbpEKDK4TkeA0HYJX2QTlAFRVkDZ/GTPFJYkscNcQ19l5pNBVeiUrJRCqWTwI1zLljuK0Ev1yEapXNd1RJjWfIIMp5FiX4bKEFrNR3NZJktZEqXYd/ZxWHQlq5kpZqPM2azTu7KYyj4re6jsxbJ+dpXlfKt1VGZpGYiFD7vKQbZK89ZaQQEwPp4zPtYQYydzpQQlEhu5tqV9v+wkoZ1wa5TlwlwoR7KGGivXe8hqOl3YE62pfvCKbM2jMKQJfZetWMioLMvzWYGcy3FOJKaDNMEmJhY2HWnr3UvlnNK44VC8FOwuPHauYRiFhODjfkHp4+cahLO4CHyUiDvSwnPg2gOK8yCXx48KjAxoLaPndCH8SPp9Z+Li4mvp1WXk7Lo4fi6gTfroLj1p5tFbUgeXhkt/HyFUowCFQKp9/D8fIRU/nqBgB+3054ILLijp+F1p4aoIVycMC94YPD8b4bzGdJyI+Tud9pov7gR0NQM+ca1CL6jD3bhHHXVUGRP1xjUwsSZRLiU0cg7WeTPXP+BtnzMMMeaMYw+glOjPMGulZ+1VEeI4SkTZ41xTa9j3jK2EU9NtO5oac+XYS62towCaMKq5p9RZ5vkMtDJPIoonkZgPSAUyMZFghYs7nuZi81Q/j6OkGsOG8sXdXpMSHe5+rIsuumikdLTRuaMw7smTAGH33Xdvdtppp7GFPhojd7Udd9xxxZMxXUj+4942vyO0zj1vJ598cskKOAkQnkWQ1t+4EsD/4WEbFfTdhfVoOqjHfdLAg0wZcT0IUM6uvPLK4r3i+ZBl1ffoNwzwBoMSZWiUIDheeOGFRSkbFwj0DBE8zcMCv8UdkO6w3HPPPct9nr3g3rt6HTfPGGtc/RORCgyFFAtergMOOKBcheDeQYlLeNvdv4f3ZxP6Za10Sf0wgjwjlgvfecNGDUrzmWeeWYwicwEGglNOOWXx/bbDgIHr3HPPLcqnuSfLan0kZaYQ7cIgJDJprhTsUUbSMHCF0SuRmA/IENbEvIHFlZAVF6ZT1IQ7CV8R5sRyTfCPbKAEM1ZuYWuEGJugS4Z5HnhzhCa6gFkoFoGKwEwg97zNT1bGrrMWcSm1MDiKYtRHUBX+511tab+nrcK01OtSdxnbhB0S2oXd8eARqEKZ6brnTeZKdQivddExJZvHSNmUCpcTo5N7tQjIfhPW1O/yYhtuXJ3geX0naNuMhXCyOGtbnRFTndqmTpdLo7dQSuW6cJuQR5D0vfcJ8N5VJ/r70U8hbsaBcEaoIDDa/IUgCWsTgtR1fx/hCc2lTie4CjtEI2Np3AhZyqccsHjHtQy8F9rjWeMMvATCVfEAHiLUxCXYNZ3xDrp6hvBjPP2vnC984QslbBT9I3MgOvtcv/BNZCGNz9FcKLPx6XVJdt1nbdR+fGB86vDU9j2J9bvogd76Z34Q3PEpmuAxdEYn8wi/4h98IUycEGN88SB+0lb90yffeda4KwNN4/J2Xlq8Y5zwDhrhp/a5uOAHZSsDz2gj/jIf9ZVC42J6fTa+gPZCJV374fMIZY+5iFbawtijvOAJ/MgAoG34Bl+GwcN4qB+f64P/tY1SZJzMnToplLqUceCBB5Y7IvUR3cw1PKWN2ocnIhzT+HkGTSJMM8YGT+mLz4XbirLYaqutylhZh4JGyqTUWGeMm3moXO9GaLxyjacf7YpQUooNnlWeMvTRHXvoTAk+7LDDysXn6K2NteEnaGKtiayT+Noz6sEHaMzzg9fxSvu8qTmEZsah9h6a/8Zc+d7De9YAz/AcK08kgLb7bQxD2Tc+1l68EWtvzFVzxG88ixfMfXMPP6CXfnoWz8Q5X7zhWeu+9ejII48sbcMb1so6hDuONagfzxsP68MVV1xRvKtxz6j2Uibxv3G0Fvpbv9HQPIk1Sr98V/OG/UHfrB1HH3108cYK98VL2qjvFDv98BNzWD/Viw4x/3nbYw1Sbz1/jCv6ajfFXbvbPKBMz1lX8RZ+VIe/8aIxdEyjvV9ZWyiP7k7Ex+ZFwDvufDRn8KP2x9qMT/G39sRa1F4f/Rhnz5r3dfSF7+xtxlX5+NrYRr/0x3vohqfR0dqDXuiC96wnyvCevc5Y49GYl9ZRe5U+i2xQj7Dc4BX9I49oo/GNNUY9eAsfeg//mKtoS0kXFWFsyTCxXyUSk4z0QCbmDWzewvUs7hZaHqJ11lmnOeGEE8qGdMghhxSlhpDgWVbr6667rizUfhPQnOmwsG+zzTZFWCXIEaZ23XXXsjnasChVvGvtg/42AeW4SBh4MVjfbczaRPiwOYWgHeFhhAPnAz2vPmGINpzjjz++bCqEWlZv5bNk77DDDmWTqpUDZdiUhHXZoAhc66+/ftnslHnOOec0++23X+mDNhIMCBI2P4KZtvib8OfydnVoBwGFEMhzSKixifIkElrUSYBExzhv8qY3vakII+hLsbD5+SFMbLrppoX+EeZ3+umnF88nwUQ71UugNAZ77bVXs+WWWxYBA/2Nh7FtW6dt3Nq68cYbl/ZeddVVzfbbb98cfPDBheaXXXZZs9pqqxVvhTbhide97nUlfJjX6Ygjjmg23HDDItDb6NGJcOFZfT7jjDNu520gBKGh95yR02f14C0eE/RHGzTyLoGEUOFzAqt6jEuMv3LQiZKt/VNZmLXl1FNPLf0geOifMgYJNaQw4KlrrrmmCD7C/jbffPPSPny27bbblrEmYAsn22WXXYpAg+cJbZdffnnpQ/QVD1IcjZVytB9vbbfddkVojjoPOuigwm8EudVXX72cnfzgBz/4X+3THvTXL/WbsxQC8xLvmMfGVzuNr/LMRbQgBDsfF/yHx7QXzzBK+K1d6Mub9/rXv770FQ9715jif7/x4m677Va8iWB80c3cNZeMLd6t+cLfNc8rC9+6xoZSSQAU0omOPEbmGVria2MY80E9lBO8+fa3v72UZQxCoVIuz6DzfuamsbTe4GH0x3vmlPXK2Hie5wK/ode+++5blFFrkjZFObE+CvdTJzrEvXZ+2oYIayRPIEHbmmDt2n///Uv/tRNNY52NMPb6fWvOW9/61jJ/KUTWuAjxNPZrr712mTPmT5QX1yH432/f+dyPd/UBLYypvhlD9eApZ/R4zfEQOn3uc58rCowyreUuedcndFL3W97ylsLDxgb/mbfqqfvX9kLqq/KNK+XPWm0M0NLa4h10t5bz4hsHdDM+FAb1WE8oHvphrNSn3NNOO62MLV62j5hTaFC3hxJEybDn+N/6fuKJJ5awUGVoDzpYL3x+1llnlXmpDdpH8TU3GWMootpnLVCWNvrd7i8aMjLYb7QD/9pz0dp4aKdxbnsA0R0tlKEfoeQpA3/rq36++c1vLvTzrPlhbpoL5o3+tMcAH3jfGNtn0dOci71aH60d+kK5VL7+q9+46bO5Sdmzr6A7Bd4cMX/xsvlgbKwv2u4ZewGaWsPUbQ8yz+1naGFueRedPc8YoB/WTTzmf+vTjjvu2Jx33nllfbTmWYO8Zy6ilfk87lD2RGK6SAUyMfEgkLAQEjxswjZJAvmLXvSi8j0rHoGAYBDnNViyhUXZyH1PgCAQE/pZnHkjCKcRshdWcRuUDUTSnnZSEhsQ4d7mw4rIukhxsSERrG1+a6yxRmmXBB5xSbENgoBLkGKJpeDYJNQBhCHl+dE2gilhuRbKbGQ2cxs1T5zn0ET9hA4brk1U/1h80YKgwqJKmWDpZ9lkqfa3DZVCF4qKfrOOs9j6LpQswpnPWJlXWWWVYjFGK/QjRGkHa7kyWGxj86N4Ug5tvL4nAFNUbMLay7qsHMofoZa139i1FSXKv3K9awwps3iA8Ifu6g+vJuE/Qq1YiCmzhHRKBcXEZk8AIBDzHOAhm3l9vgfNwztEYENvzylXHcLpCNI8cIRCNNIWAhmBBk8QiAgN+IWghJ7ojhb9vI+gDsqesUBTY4WX8eZUIBhee+21hffQXFihvuEv9PaD5/WJ5xh/aqM6CS7CBnk60DSyYUYYt2fwrXmmHMqZ7xkOCEHB8zy66ID+beAJAh8vG/4MCz/+IsRRWo3vRhttVBJ3EAYJkdqmTDTxPp7XDzTWZvObUItX9CkiDbRZuWhhDPGsuW79MBfDm0GZo4hQOo0rJbh9wbj/vY/XeHU8h2f9b52gbBFOKaGUFHQzduqnUBCc0dq7T3jCE8q8xBuAnuYqmvmb19jc4oUgqO6zzz5l7VIPoRf/hycEjfC2cfOj3DCyKc9Y6z+eNdfwLMT89Z366v6agxQKZVAelKNO9RB88bgf69WKK65Y6FB7rqwlaEr5tB5a8/BbCPne1VbzQr3B68Zo2WWXLbyEvtqnbRLveJbAbm6qV/u1BR14kawP6GoMGEZirfQsmuJT64u6jL1xY6igYFBeCPjhgcJH6mwnz7IGU6TMeX02H+wFyvdbGD3ew4/qZijgmWSAwev40ef6xcOGzv7HL+au8UErRhJzRNvRBe9rj3ZpYyhV+MNPZKmNMGHvUIb0j3ebMoW25jGDjfnjNyVbWUK67Q3toweUy/AcKlebIlGNPlLUzBXGATSp4Vm8bi+yZpoLcd0TOlHQ7AnGxdpmbPAmYyPaeM/cx4OBOJvuc+3FJ/iPJzE83vYX+6N1AS/Zsyh85ol1WnnarM/WIO3RT/QJJRdvmo/WQOOKF5VvnPGW9cn+aKyNrXVd26211kKGaeOkH9Z781H59gAwvpRu44qf1IGH8Zt+qT+RmA9IBTIx8QjvHliEI6zFbwu0TdTfhIJI029TttFFWAlBlXAbAhRB0qZlI1V+hNWwKBP0bPhtEDIIsAQhQocNmQXWhkWgVn+E3dlAQ2CKML9IIOF5m4VNUnms9AQA7bDpUHwI1XVIkO8iZMffymf1tBF6Tt8IazZJgou2RN0RFhnp8b1rQ/NjY/c8LwrFmVBGGLCZqocF2watLzZ3G399nikyDxJu9T1S79uwQ3DxmU2a0BGKKcGRMGgT9bf2GoO2N9B3yiV0KcvfMo5qDwFVX/QJfBb/28C1iaBHYKXcRygpQdB4ECBYhNuGAoKUttXhUz5Tt7Hzt7FDo/CMeZYg5Lu99967CC6eIaBTuggM6Ego7AdtZwwQJsn7xLqunkESdOB59ItwsghnDO8BxYmgSuim5BpvwiSex5vo1KvvytR27TOn0M84axd6RpKpCC0PXqhBGKVEUkYogOZyPb7KVw9jhfo9i+eNj/mmbnxKmI6xxJPGXdl4Ex/htbYCWIf71oYZtCX0abf31EWwpZB2hc/V8Kz5SLDHm5SPOFOobWGkIXAq298ExEjg4xn0a7dLf9SN12Ke4Gt9MxYRNm0M8DX6+8F/spcSaj2rDLSlzConwntjLejqEyiLcmGNChpYD/XR59ocZXSdPdMuSoU5GAYTfYj1UJsidL9N2/bvKN8aaS54Rz8pgtZJ4482+md+WefxkHXGs/jGM5QdCo02oCHaxuX1+Cq8sP3O6OFRPGvdpaBZu/SrpoXyfaZ/2qJuvNV1bYX2ma94Rh+0RduMIUOBz+r1u1/Wzlhj/ajT3Pe3tclva7k22K9Ev6CVz61/PG32gNi7AqFc1Ypl8FS95/Zan7p4BK29q0z8Fde0aGeEguMfc8Y62Da44XeGk5j3yot2MwLhT3yHH9SBT7QBP9r7zdEITRfVIsv2VEY9/VRPzD/8FQp+8EVE+FiDPW9u++GB1Bd1+Nye4D38gU8iYVO9nyYS8wV5BjIx8bBgszjzllGUCAJdqa7rz+rNCGwaFu+4e4yFUqgJz6HneGtY2EMRJGC066AsEMBsSiyGENkwCdA2265U3DYLm4j2UwrUJ5SI4CmsyiaiX3HOR9vbGTCjPzZA1vxQ1KJ//tbu8Kppn7+70N6kKBM8q5RXFl3We7Apsqr6HtBNHwcJp9ReiqJ30dK7BNgQ7PsJsFO1l8AQSspUz4ZCZMMnGKufEB+GhfACTFVOG/XZOONi/PBUnIMyLvos3Iywg6YUOR4JQmivVO34iUJDoBLiREmmxA+CUOaCd9RBWIm69BvPM5SoB0/y4vF4mxvGadD+BxgT9JuVnTCMvsI622OjPAYX4WS8jAQrtBHu24axCkMA2poneD7ojba8yeac/uqjekOhHUYI8zwepUT7WznmaygUw6TUjzNyeIuSGzTQRnwmIoICr+88Kf4eBF1noeO3sv2mdIfRaFCDQ79+GEs0ibrMI4LyIPSIaI7w8o4CEdpn7TXXoh5rcu2lArT2OSNcrJXagZfacz2Ur6ngfeO58847F+WLV180A17uNT6DAq0oPxG9EQaxtjFtUMSzysOPFFnrej1/jKe1yLovAoQSy9DVte919WeUio42aScli7cyFOcIja7nYYTFds1z5USoaiQas/YrN0LQI1t3GD3rzKdtXuhH8+CpNk3UYZ5Q0kMxrcOhB91XEon5gPRAJiYeFmKCJiWS1bmd7a696Po/kiiwWsdmRCDiESLg2SiFpjh34X+hWoRH54vUE5t4DaElNghKYHgPCIHODvqO1y3CISMEMNpDmVMHZUIYkP7Y4ELJI4ATBiOxg59a2LEREhC1LwQm3lNeVG1RNwHJOUhtcu6nFiJtYEGnNr0oELwivDgUCbQK7616WajVqT08Qs4L1QpUGxQnNAT9CIE0QvhssLVQMMymDYQtbaPwTrXhEpbQVbgT5ZvnKgR3/aSssWhH+b1o1Av4hIfHuRzKnvcilMn/zq1uvfXWReDUDlbwSHSDHm2BwjvOWQnH4/UhEPWjVf2/kCrjxMMYNKeoUPIoWXheCLLQYoquUC7jLVxR2Jt5NlW/298ba/US/JQvzNgca3tc8Am6M5I4MyTsS5hjV3ZJQh5e5z2lhFByIwmL87NoaL6hFQ9qeIJ5GQYJ9W0bd/SdUh1X0OAHyn5XJs6ga69yefS1I0LQtYeRigdNKB3lA9/G3Aw61V7BXrRuf+435Zo3Vzis95WrfoapqfpfRybUnjdGIp573p7wLMc1I5GVOnivq43mhHXOXAvjU6yHwyoeUT6vkjG29sbVELxNtXElnrW+UPbMSWuiz3l88U2Xga/9fy3s1+3VF3OL4YUH0tqCp43hVN7LMPiFklgb5cxz3nb9CSXEeOpvvFd7p8OL7HcYELrGgeHV2mT+xBpnztgz7FnKF8JLcVR/L56ZyljbC9H2ft+Dtch8NufCc2yMraHtM+OR1MY+FImQYi5FMiFzL9aBiNax51r/Rdjg60hqo87Y7/B4eETrc8FtXu+1R+iHOsw/vB/z39pinNpltN+v56J10RGNUV8ZlEiMEumBTEwkLKIWcYJjLKqsv3F+QKILm4vNhGJoMyRchqLC2+KHYmZzomxZsIWRURSV54yEzYa3gGDHik85Fc4D6rfJRFZHSogwQF4U79mwhOMQmp2fsnEIoxOCxENi0yAIKM85KMIOqzilzMauLAInYZolmLJhgyTsUzBrIdxnQi6dtXJWLLIm8mQSRiQOoRCw0OuHZAeUONZ6IVS8HzyTFDibqo0NnbRFOf4n4BJmCDDO3Tmroh3Ch9Hd5kiQE1oUSjGBzgZNsLTpKdNvtPIOhTcykqIhrxraU7LQJxQIY6kdBD7ltRUlAlBkriMYCtdFO8osBZqihBc8638Wdc96R/8pOOojTAnbQiPPRobWUMC10W/leR5PoQkei4ywPo8sn4QXhgFhgwRL9Pec5CUEaeNi7NTBK6zNhFljznMnJDOy+Nbhi84u4Ts8gzYUJeHKxgn/amN4ifCpdumLMoPmeNd3+hvWcHTAk3jfvGApVwclCoKWcd7IvDBWBBl1+zws/PofCi8eNx8I72igHrxVC0yEbYqUs5IRih4Xw5t3hC714VW0xON4fo899ih8SKnxjrAzSpj5bS1gAImzSn7QlzCo/3gCv4eXMRKloK3vPWcO8SZJ6oSP9SHOR9WIeUshpOCicYSmGw/KuDktQYoQxDgvFhkajRtjjDHGb3iIQMtYoC7h5+HBDE+K8fBczeP+x4PqpbwzEOA/RiDjjM98xkgR/ddv/VU+OsQ5blEX5p01MMaC8oUm2ugMNF4VmmftcG476tY+89KY1SHD5q/5ycPs/CZeENlhDNAZvwf/+o0eysNffvvebzTAX+pDa2HhDDGUB8YK/GY9p3Qoyxqvv94Xguw8pDBF75qLoky0AQ1iHqOpz9DUWJkT1gj7Bl7A18HDxsB3EYbpe3yov2jmHbTVH+2wdqC7761BfvP6GTM86llzT0g+gx+ewb/Kt6YYD7Q1bvYP8z/WZ4lztFUZ+mKstM936lU/HrUGSxZmPpk/ESGA7hK7oYk+aFM7vN4cQZ+YS/oSvBfrd/AkXmDsiT0rruHyrrY7Ux7Zb42ttuKdWHPxiPZE+D7Yfxgi6r0Ab5v3FPgIE9dfCrDxR0Nrqrlg/uE7vKh/+m5tEaJujcIn1hLjgjb2Px5l80HZ+EqZkWk5jrrEHqH99utYT9AAj1q7rbmRZdncUqbnvIfPY832g67K8Tf+wgPGk5xhv8cL6ZlMTCLutCiDrhMTCGxpc6QA+E3AJVBS0uKMokXdAk8o9x2lgTBA0KE42eAIl5E62yZioa7DYVhiPW9zYV22QYT3zILPWqt+9fGIEFRsTqz+2mTzV7b2EmwkMSFM8LQoX502Ugf/vROKsU3XhkvIsoHZPAl8lB4CtnfaZ4TUTfhHE5sfRZVwZLOhSNlwvYdGyiK0UJRs/oRTgipa2WzRTlIAG2lktNRPz6MXZZYwTFBDA5ZatIu2gXYQLG3UBFIKAkWD8oiG2kvxseESrtGZsGIjJkgGffSFwhpXmGhjCCLaJqMqodymbLM1FjxpaGLM9Z3QT/AgTBB+hQlqu8/RJcIzjbHxwyeRoCOuGzA+lGJCCusvgQd9jSVhgiBB0CA8EkwIDdoRHgQWc3xEYcB/2uqzsDZrA+8cIY/BgPDF4FALbnFdBPqgIb6mZBgrNGS4UBYah1cPHfTF88r2bgh06EvQDF4ytngjPEn6aByUD3ggrmjAL3g8UtXjFfT0vToJtNpA0CH0mjtoqW6CGf6pvV3aTnA23ngjrlZwnQRDBCHX8+rUv7jeAD/jQZ4xIYlh4DE/jaU+6L/2AE+OsSRA86p7Dp9433gZR3NGO4I3CPIUQ3xHuY6raNprknK0x1rAe6kc7dRefKD9+DE8o+aGZ5VFQTY2xlQfrBXojg76j3etJ3F1h/FTLp41Hymv+Md3PsOb+uN560dcw0LJ01Y0E4FhPuFhc8zcUyY+Nq74B92MrflXj5f28J6EMqAvxt+aiQ74O4xUtREEjK/+4v84goD3wtAUfGy+G2Njhl5BG/NNH+NOXYoU3jYXjC1ao1VkFNbPSFJG6bC+mqPKRTc0sX5511pmjfas/QNtzWnl4RW0VAalJs4To4f+2k8iGQy+QTd9RSdtsAbhf4YSvB3t8Y72qFsfrc0UELyhHO2gROojOnsvDHsUMO213vtNCeJFQxfPoJvxxRfGRr3KYOxTrz3DnPWuUFbzy56gPXGGEz3RveZ5+6e1yJy1LppjaGVcrOOMQPpjPH1n3awNCfqiT3gujLP66V3tjXXG39qrXM+jM7rEWei28hSGF/ypTuuwNQ5Pm4too114w5jHVUb1Hg3Wkjg2oo2MKX6bU/pk/bAG4lV0NY/MLzxlzUFHNDOH8I368YNnY/7jB/XjCXPfGhn305pDxsQaZBzs0dZPyZmsUQy+2i+CZVx3KycSwyAVyMSCBva2wViAuwTCrjMNU1n7Qplrb271Yfjae2EDl1WSNZ5A5F2bC6WNUEB4qZMw9Ks/nouznIPSYKqyw8tbh2OF4jNVf9uemna5lEDPDNreGt5lbSeg8TwSELqEigCBh4eWUkMQqO+VHKbdw6LfuET2xWiLPhFEfU6BbYdL1+MFg/BF+311hDez/d10eL4LlEoewMjAqj9+jAEjCWG1i/Y1P/ifAkmYdK1DJI4ZlLaDzptBoO2Dzr+pnouQ8S5e6BrXev5NB9OhwyDzIEIE6zsxh20TjGqu9VqLZvrsMHwQ87nm00HXwl7h6L3KqMNi66Rsw6xf/XjD3uiz2VJQYvwHOWtahwFP1bcY217t7hqjdpu6wuyjrfXv6SDm/yD97moXRZZxLDLGpwcyMYlIs0ZiQcPC23WeMb7r93+/Mrs2Lp93bXys4az/7priESKQRZhnePN6vTtoHTN9p9/5jGH7236maxMfBIQEFtv6jjXKdq9xijCjCLPsdx5plMrjVGX6vC0UxvUqXbRplzWd8e5F8+nyfC8hifcr7rfEyxFiG0k7+rUtziZHRljjzFvU9U4vGoxyLEc5//rxQtffwwiaM2nXsO9EpstxtWmQMgdVdqarGE3V5vZ8ns4a2+v7rnK75uwwdO33/HTX5kExjEFkmDkw1dh2jdFUbZqu8aZX/cOirj8iPEQkpfKYmFSkBzKRmGUQknmc4gJ0oTBCdIQNjXLTWmiwNEnswBJLIYx71uKsVhsUEOFdwt+EFnleGFduwLM3PsLwjI9wMuF+whmFbPG0T0V3FndhbTzLDARC8niN29eqJBKJRCKRmCykAplIjDGUltDMoj/JimM7zHGuaVZnk437tHp5xCKjXoQvTSf0LjE4IuRMSDaeHobeXeNLeRzF3JhJaG4ikUgkEon+SAUycYcAhSLC5OIi4EkRLiOxjlA+7ZKooutORc8IExz2TM8wUFdk2CPIC7Oc6qLlhQ5KqaQTaG98EpONCK3Fx8YrLj2flPk+KvDaxn2j7esS9Nc6Mt/6HcYI46dfdWbPO1LmcaHcs+mJt75b19vnOO2PcS7S/5FhdjZ5KM5o4mftMubaFlm6RZF0nWWfDiJruzXhjr6vJRIzxR1jZU7c4WFjlulwt912K5kTJwk2Txnadt1118VZ/moIE5Re3H1dXffmjQoEB5kohYHK0ChDrP973Xt3R4EshK6NkdkvMdkwl2SKdMekdPoSV7XvjV0oEN594oknNttvv3357coYP666sFbE3X/zCdYaGY333nvv5txzz72dd/qOANmAZZvGw7MFCvqZZ55ZspW25476ndXXBonfnG0eh4+BUufKGPtz3N1sH3KFD+PdqCDBlzoiI3oikZg+UoFM3CHA2sjqaAOJy6XHAQKQaxMosL3Awi5Nuud4TdqQll1KcopvWIdHDZupdOQulXcNhbunXIdR3+V3RwEhVkr5uOybN8A9n1K6jwuEPFcBSCI0akGHFR6v9bvke76C1+Kss84qc8odeKutttp/XTGxUCARk6tdXEvg+gB3V/pxhUpcuD7fYNxccUGhMAcXupBvrXHtA74F4+beTclTZgPo6ZqZk08+uWQBr/cltHctBq81w6HrRsaVxMWVGfY5e5A1T52yj7rb2HnqmUSPWOvCCOoKEWV2JeuaK5ARXBulrYnEfEIqkImJhg0vLm4X4mKx9T8lkJBNoYrLoX0XAocNgzIm+UqkMLdJRSiMMnzn/X5QjucINPWzkenTj4W/S9DxPCvqoYceWtqivZF5Un9kDA0hXriZkCVts4HG5cQghChCuep72pSjXfoe9UcYFMuxz7V/KiHM93FvGrqgkbpd2CxpTY1ou+ei3Ai/1W79Ua//gy6RTVX/ZemMfvmtnz4LOsS5OHX4vB7TqRBZWI1J0DrGkOfW57VHI8Lloq4Ipfryl7/cHHLIIYs3dYKNy6hrocNzeDAuq6/bqP1o4l39m0owiDagmfZpr7FwfQnvmfJiXP2trUFjdWtLzAnP1G2JuROfh4Llx9/1e9GnmFtBoxg7NAz6+R/veXaq8Yk5FJfD10AbfVJOm441rbXPT/BK8Ky+hZKtbbxu7m3ztznlYm/jF6HZaFbPFfX7Tv11OfjIT+1913Zt8LtrXL2nn95r85l6Yw2J+vVDf3we9B/WKEa4FoZofRBi7RoUd+KttdZaRRkBdMKn2lDXETylH9rRFWkQz/gOjeo1KxDl+77+zt9oGpesW7/rEFX/e8b7sX5Y3yj8baU/1ls81KbhVH2I980ZtO5ar+OC+DYP+j/4o16nom6f1Wt13e/g11gD/Y1OMReVK6rh4IMPLt5kZTByWmsYBrQz5mOsrWiGVlFfzEN9G4R/PO/+QfVIfkV5rc8L22vMGzylDb3ONMe6YDz0q12H9qFd0KZei2PO+y6MHPICmKf1+XZJ5szf+tiAPuJB79ZjHWt8rDHBZxJ0HXXUUYv3HQoxo5K+dfFWzRv1+qCd7f2jC2gS/NKeK3UbY+3QRuPgrmPP17RKJCYdeY1HYmJhIWWFJkxT/mx2Lie3CV922WXlb/ckWYBZLjfbbLOSfdPi7SJfi7mN2YW/LueNjVx4kFBRoTI+t6F03cVHuFZOCIa8gCz8FDl3O4YCahNYf/31/+uqBhvFGWecUdrmkmIXVROMhNLy7PEw8Ry4MBls1PrrXjxWU1lEleudelPxNxq4pNtl9OrxnEuObZTo4R3Kh8yWMr5OBRcs65PyLrroorLZUaDQbuONNy7tVSdvR2TPpFC54JtVV7gTAYDXkgJm43ZNCW+m8FzlEwi0zTMydVKuZe90ufTKK69c6GDMXORNONYvGWtd6D2VFdwY4ZPY5LUff1CGXaFirNGUhZ2Fn7KufJb2CFOlMKPh6aefXmjLw4MvjK9+yxDq3kblC/FFH1COexCVLQRMH+vLzF0mzRsWl5LX8IznCWrK4eVUjjbI2ssLoF3GRWif8cCTaBuGAyGMQrKMgXfxs357XxsJnfhinXXWKQqWss0nl3EbE3NJPd4jXOJPl2zjTXTAkwROd3Fus802ZUz8rU3KUy5DQ9e9dmgXl4nrq/my5pprFo+7C+BvvPHGkrkVfV3Evfnmmy9Oz4/PzF/t8Zn+GidhbQQw44p/jMcmm2xShF/P4k3jQ/A0XsZa+fhXefji/7N370HbVtV9+C9/f3Tamd9M80cnnWpjTUYnpsZzBFSExgMoVQQMAgYVFDkIykFRUBTkIKAgRzkflIMoBxVRDlplGkKMikiisSZmTKZtmrTTyXTaTmd+f7T+5rPvfl+3m+u67/t53ud93/t5371mnnme576va++111p7HfZae+/f+Z3fKX2bm/olry6157zDi0zqy6XfeIu+5rDgDD3xFQ/e+c53Fr1A59ApvtM/R1Vb8NaHk4H1LcCDK1kQ6MoGwRtuns+1PmuBXNMQ/Ybn5N/c9LdxwsPl5+bda17zmsI7NBHMeB+OStbpz1o2zQ+4u3AdzznAMpyuadEn/PFB+9E35jy5wAttoA8dgCZ0tGBXpYO2TjnllEKXu+66qwS8dEMLcCTz+O+H/oUDmSJbgFyZeyeffPIvjSFyqHrDXaNZSDC3zSX0wGuBnN/kii7FB/QxdjJgHuj7mGOOKbJOrr1DLsiJeQQnfDSv0FRb+j3zzDOLrtc+mTZufWjD3afkjd5BT0EsOrNLZOWWW24pNPQ8fPGJbL/97W8vY6OzzQH9qhohk/Pu+sV3OvKkk04qZar6NvfrKzGWWXA0FjzBSzJGpugL+gOt8IB8ow29Dl+BLt1unOhgDtElxx57bMk612AekT30O+KII4rsRA4E0OSLDpFNzLzPQhge0/Hkg65DG/2a3+zAY489VnSdDCfaskNZCGR/8JFtg58fPkZ0qXbxpr1yg69g/mv7ec97XmkzOpfdT0muz7Ko4js0cQ2SOUCW0ZGO6dBhM0DPQHZYWWAMGFjOECXMWDF+HA3KWiDIOWGEGBDGkZF3iTxjouzw2c9+dtk/KIBhWBkJBpcTyyB/6lOfKu+NAUOt1EcAxijYW2TfiEDE34w148hQtZkVIKDieDF0HC6GgePECDJmDgeAm8ABMKradv+T762cMoA5WTTA6NjzxEGUaTBWTjUH8d577y1jhRfHmKFdZkWTE4EeWQEXKGuXk5QV2CuvvLL0wQFnQNGOMWRs8YXBh6uAkmOpXNiYOBX4iE76YTxTZsgBEJza/2nsHE30QYM99tijOKXLrKzjoWBd38YOX04IZ44TuM8++xQ+XnjhhcUxRPOLL7648IbjBQerwJwTTqUAizEX0HE48S17c8gFGcN/DoV3OWOcSYHBl7/85bIHj0OF72SIAzIGnG900xb8ODICHu9yIAUvnCfvoyHZJdP4xBEl9+EfZ43Dg7749oUvfKHQkEPPef7c5z5XHGdyw8Hbd999y3sCREEGOptLnCCOW5weMki+yQeZgAe6mUP6sqeIA9SC72644YbCU04XxwyPTzvttC0litoSGJsn7fUsyUbb12dRg1OPFvigXY49OTXm888/vzimFhvwTiAjCKYf8ArAV8bOXDGPtfPpT3+6LHigN94p60NHCxrmD+cfjzip+AQPAa/v6QCyjTbwIYP4JZgwd43Vu5xXuKA3WvohZ1dccUXhMdk0v1MyvVYwPzmf9gxqW0bLPDV3VD/Ql+Y0R9h4yIx3yOntt99eaMSBHws8BD7kF03Q2lxHP4GH9j/2sY+V9s1jOtL/2sc7c4AMklXvCnbszQR0uoAcHuZYFnPGsvX0yEUXXVSCLItq5rk5qA+LJuRaRmmq3JKc6Zfsswl45h36J3oDDoIKugKNtEOn2StItgUxnH/yYt6ZF+YYu/Syl72s6EKLEGwWnATE7BWdT84uuOCCQgeLQfSPseM9mrET9IRFJ/jgCb3NPpBlz9Kz8EW3ZM/w2pyhv/RLh87bL2h+Gx86wdkPeW4ziIvAnCS7dLzxCz7tm0QXMoyegmDyzo75jv4Mv8xndCEz6Hj22Wc/yX7CFd/wKdUsxksOZSXpNXJJrwnsVGvQa3Q2mSOf6EtXouu//tf/uugP9BWUJ5OtffJKPwnofG9Rw2/zwrPskT7ZFv0I8lsgL/CgzwTXdA3+28tL3ugCMkK/oon92XA0n+hgOJIBPO/QYbNAz0B2WGlgMDl1nDIGibFiTJIxpLhlGnKanCwlwy4YYAAYFCU5nAdGgyPIoWa4/c/JYfjaFVDAqbDirG3OBEPLeDDADLwgyIo5Q9heapyLjgUDfqfUjNPLqNUnRaZ0C8iYcCj9toLL2WCM2tV0ThWDBg+GUTsCNN9bAeYQG+cyB+DoG55+UroEZ3TLoT1WWAUxHAbfM4IyV1Zxja0+MRBv/MDFs8brGU40PnA8OGLGiZfZn8pow0UAg+ecdM8scwpjVsU5brJCDD5ec5AYZ448/pEjNCMjgjGOCPmRIZRZEBh5TzCSUrrgDzg6nA5On7a8y/D7TPB+/PHHF8dB8IHXHATOFrmB41iWDi7kUGCODmgJh/AAf+Hgb/Qnu/AQ+KQ99E4JdBw1tI5cf/jDHy7fey5yaWwcePQPbv6OLOuXc6OfOLjoGMeHzHFw0UZAqL8a0Bce+MGZ04dARhZTACdo9K6An7y3gAdk3HyDK+cfXTj1HEl4AYGtcVgwQCP45zRji0wyvAIYi07Gr08LDAIS+As6yYHFJ0GuBYSUc3NKOeWCRu+hh2cFTXChQwRrHGGBG3k37/w2Zs6sZ9CMrOMjuuVEaPJK5gQv2lsP4Jv+yCS54JyjHceX3BmnQEMGHa0Emhxd/EIncocOLcAHj4zDM3CnE9HRgoOFKjqHPGrfoo/gIIEs2sHJvMcTuMQB13bmNZ0z76RP4znuuOPKvBKM4TOa4psxGDNdUZcl1iDQJS/GSIYFoQJQvESjZPbhga/4LbjwrHHhp7Fb4MFnckH3oZ128FFb9IpAhbxqi942n8wNWWf6ImWy5oZnYh/IN3mlN3IKt+/JvMNlBEr6oOPoKX3BhUyyI37DFV2mghAyR9eYb2SdfrKgRjYFUMsC2aKX8czYww92DI7kBc3Q2cIYGqGzQJc+YpfxztjpfLbAHKohd8pGLgTxbKFg01jhbnGE/YEP+UgAjj9ojq7onzmPntpMSSzZtYhCdsiGdthzi7LsK377nFyzp3hGx7OJ9HQ9/9De2Mmg9siTYNHiGfq+7W1vK9UV9CZ5RH9yBqechptTk+vtMsavzV3lJOIOmwt6ANlhZYGBUT6pLOnoo48uyv29733vk1apU7oFshcnR3TH6QaMXMq9QI64n8pwJVAUkDFCKb3h1Aos7ZNjIGRgpspO6v0UgDPHmDKq6dfv4BLnnUFhtDjg9epsLm/3HOPMGPrNwHD4GSg4nXHGGcVxg9ta7uWbAoaMs8awZr8SOnAWlmkXvgn6EzRzhNA4maS0xTmxYi8j8dGPfnQp/Bl0WQKZCiu+siTa55hoTx9p21g4uoLryIL3lzlYgfPHmZHBC16hu89Dw7TrGbyZoq1MGWcK3hwNZYRjZYze12Zdaj3Wpv7IixVvzpW+vacfz6/1FF/v5D7NyJ7fnCtOGdlDdzi3ATIHjTNUl3Zz8tCfM8bhgt+84/Qztz3jWY6VIEJ/KdfDW/glw1CD/+kLQR9n0Dsce7ImG9PKJXzNO9k6/coocuCyEJP5Gb6mnBtdEwDqhwPtMzIhKAiunGrPCawE0rKxggs6TnA9tsiwCOCBBhxd78M9JzvDO84nJzcBIKd/0b2qoX+991qwKzDMvvB8n/bpBIFN9hLmO78FUPiUvpcFulXgRW+iW3ttiTbnjYFcsAN0rhJv80NgjA8WHAWm6MfeyMppP3sww+ea9+QJzwX95ApYqDT+zMncZ+p/OJMBdEc/uM7bq1kDvSJryQaRKbaNHhZQAjpOwAgPtlL7UwtVFm0suqAdXWNBDY4WMeC/7BVFkTeZUTJel0+3NMsiBHxjx2pbJ9ACYzJR04K8mZ+ZY+QWPYDxkGuLPua1dtu9//We2eCoT/qhlm/2CB187tl6LJGxeXbSs3me3jBX0Nnf5oY5mbMY6n3/+W3eyJbLvupbsC843lkPAuuwuaEva3RYWeAsMu5WtAVssjzKxsYCvhjN7G3jQOdzCpwBqp9dBJ5RknTZZZdtKQdltLK3hWMpu8CYK3nhjM6DBKrKWDhYggYOlc+nHEaGKAatBnhwYHxvlTOZhBz6oQ8lfTmggZHXdw4fmYdji0v+Z8A4vJwk5Uj6sho8FkDmMJq827bJYccPQSTcjYGTlcN5lP0ol9WHADhByzz88cTqv4yjVW4ZNxlc73AGBC76kWHQLgPOeUzAoV0OipXheUC+jFkQw5nLu8a4nhMLybjxKkkW7MJ7rdcvpM/MC44O2ZCpjmPGedN+67S0EEdwzAGN7MW5iuyRDbRr2yQrfpR21U5S7mZc6/jyNxzwTgALh2RZw48aPGvBg7xx5nJyaX3acT02dLIIRU60zYFPoDRPJrRfn/ZI5+QuQ6XNnHztwYGTaE4KZjjvdIl9z0pdl5mnY1A/n3GmtLzmjd9oH0d8rf0EN+2Td3MWfervzbVUJNQ6BW3Ncz9jsjumf3yuDF1prADZwsVa7++z2GNBiT6odTY9pC3zgj7wuR/zp7YXLWQh0XtojK+CXDwdK8G1b9FCgSymyo2xvdDz+pIdTKmtgAv9YufIG/mHg/mQSoex7KMyZ2WuSnTpHDJn/6FssgCvDgDnAR3pehw60OKsudL2WR/2lqy4zGMN+Y4+ndr7W1dYmN8C4OgoeNDlFgYEXRZnsxBQj2FqUcYcoItkp2ufwucC8XoMa4FapvGPTJEB9lK23gLPWLVBDjEimzL1ssMqIvrdwx1WFXoA2WFlgaMliKNYBS/KeeqyD44ZIyZ4Y1wYWYaKg6SE1YqvIEq5UZxLBrfO/E2deqYkTiaLQfYuh9VvzpjDGqyGcwA/8pGPFIdpzOGIsysogrfAwN4QgZPnc8CJPoJHSldyYEhKs+AKd21yiBgmRgaOSj4zxhxMo1zIvjxOLOeBc2rPiH6nDKL261Pm6v/hbIVXdoYTl/KetJVDdvBDiSrnhmOZkxa1E7qjFwNtb4yVcKWxHDu0leFEX+XKaKtfcqB00r6yOstXA97nUBhZS32gpXJEAa9SJQGV0lhyw4hzXmQ6OSBWhpU6ohX5yqmM2tUfWvjhnCiPVUqmhC8HveCLjG+ejdOS0xanaK58jvxaUJA1hnNKtHOCagKYWnYFJmiOZgIXNINrDquR6TIee3g4zsYvA5GMm/fII/5ohzNs1dsz2sC/nPAa/I3HSj8nR8bU3IK/LPnYPioBhnHloA9toD0nmLPXzscx0Gfk0Ptwl3WBq/3DaKd9+5iS4UDDBHLwTRmx59DC/iPzQNs1bziy8FIiK7vCWTWvyDSoA7vwFZA5TrRSPFlz8uQ9z8j8ki372vAInil7JItwE1wYE574XPmnebCo/DwOuPFGbyQIS/aCnjB3cngMWaVH8TwyvchB1kf0Z+4n1LasVdrPqZpkSmYuQR49loNN0F6GT7DAKaab6Ao/FsTIUH0acvQh/RBZpTO1KVjz/zJj8K4ASRlugoxkiegH39ON+E3HKSfPgl8tm9Fh9IP3yBEe0gPRL8mORjb8bXzkHh3odLJn7MZKnnOKrTmXPsP76HsyRtfrN/OQLiLXPicvqirojLHFH/PEHCSP5q8fMqA0HRhzDuXK/Jk6oRyegk5jpJ/skzW2nIya02m15Xu20+KeADIHzuSUVjKhKoRMhOf1HItdNL8E4BZ06TV6xxyDi/HTfeSBLqS/Ih/oi9a+D07hI7sqADYfcw6BOWoBJoeCxeaCyNo80Hb2MqOL+WHRgAyzGcbs8+CIFvhY45iD1PzQofOy6x067EjoAWSHlQXGmGNh5dX+B86vFTkrsMrQBEucbkrXyh6FbKXf6iiDdsIJJ5Qgi8Mqk8nRYVAYH+0yuowDR7i9p1FfTs+k8GXxOBnaEYhl/wkDxuFghBjmFlI6mEMGGAgGW3/w4ohYjbavwuqx77Qny+pQDs4WZ0x/cGZMBVxWm5U6cnhkZjnzyoTgB18ZW0aWw6Ds19i169AY7bTAqHLIBRFoKSDVdo5o5zQyjNpzIIlVa6fqKV0U+MFLwIe2+MFhZuw5kQwyJwEtBM8MJR6deuqpxcBbGLB6zHkXnDLs2rRwwOjaO2JRAN6ykgL3MSOOj/qVgcRn2QrOlvIftJHBsLLLURNc4YtVeO0aj0DLZwkiBLj4gA4cFMBR5ATgk7JDfd14440laLC/RZbaMxxuz+UQFfTRzlh207MCMHKFzvbfCH6zSm3MnF94kiOLIhxQ9LRvEJ6ytLIL+M9J45SYJzJbMpsWEuI8chqVxOGLQAVOFikECOYNXil9Bf4O79GWbHFkyR5HmOzJdPss5bI1CBKcEKt8U/AvMONMOwHSd2TZO+YS2WuDgFxzwDH1Pbn0jOAFz2Tv0B0fOGnGptTPe57VF/qQSUG48ndZAM4qh9SYALnk6JJfGRrvyAQbJ70Af7xM+bJ24QXQB06e16/3jZUTmoNCLDhY0IIzeVB6Z76TCfvbyA/eCcjwQbApYzEvsE5QgIbaMt8F//XcII/wgitZNq/wHr/xFk3RlgM9dghY5pXv0FqwIkghT9r2Y5FH+wIwfNC+8eZd88Apk/SfdmxFwCc0JTPw07YsGr3OsTZ2/KCjBV7KM+kSmTNjpFvwzLyhG+kpc2TqGhLzD58sLOnfiabkkowIFOggesV3aC/A1J93OPeeg4e+yYMg1vw3n2TxyLNFBjrH/CdraJtTX+kTsuBZ/Dfu6Fg6Sh/opz+0NLfpnFQiWKgT/JIlNEq1g6wq+pIrNkDggTZt9g0ubAJ65oReQP/S+9qEK51r/pANdklQacytHFqgJauCZ/rJfDE3vJug02KNBRXf05fsQ/CiO9g33xm7OYxmcPC/sXuGbMPfPKULzUf6zRwzpy2+0D3sJN0oY8dmoK85xd6SHbhZbKLz2MHa5uubHkU/+hAO7AJ+5RC4nKCMX2w23k3dsYrf5NJcMd/IFvtD3o2BDFhoiAzjH/zxAr2WPfSuQ4dVgKf8vEtrhxWFZAgpa46VQCgnNfouG+WtJHJO6tIVCpkzwjhyVol57njyDEMQpymZwvZobn3kHklGMvcSwiP38eW73C/ZgnZS4iU48TyHB04p/cvdW57Vn0AY3vqp785KZiHlYfpHFw5xDhvwXO5E034OlkALho1BFWy3ToY+2j0qWUU3NoZNVoYhzKq5cTGq9t4IeuAieEEP9PFccKrpXPPI+I0VX+Os5s5Bz6a0SUbQKr+2Gfx2VRb+HILcB8q5qPcsobk+OGP12NEJHvoJvuiRk3UzljhR2Qub/YR+9BW5zOp99g7WGbbsi6ohd0bCw/dxECMLfpOD2sGvZRW9BeXwN+4ckhOaGLfntVGvpHOuBB65Ikb/6ERG4ZSDddo5kjbw2tiTTZp3dUDw0JbFg7RdL9qErq1ceicOb70HNPPK9+ifPZr13aPZuwlS7mZ8nFA8iVx6Fu3DV3gJYNAnhyehST0f6mwknPCDHOFZ5mPAsxYutO+77I3TP/6ZS8YQupgTcFxE12Qek/UK79vSO3jpny4g/9lLmvFkH+pY5srCnQypQ3e8i35oEhmNDOKv7zO/8EXAngU4IGgPftnvFZ6EX76vee5/n3sOvcxH/eERPILD1BiMUSAiCxZHnS6xCJDrQFJSr03t5wCwWm/hXYKjyCE8o8tD1yl5zX2O5D/35uK5Z1JinEOQarky/swDPK4PyqrnYQ7/Cr7t/Eu7qUBIhrW+97AtJ/Y7h2q1baJrZCqHSfnbb4uWFm/ZGvoFf4OzRUeybTEvh6dFT9a6JncR5//wNncko3dsRq2vza/wOPtBszgUPyHjrWlJH/ouh5gBtInuhmPmi3cy52sQgJsrFiGMGy7Zuxh7oU18r+e99tBNH5k/a90K0aHDjoAeQHbosJODKW7luT51dC0GyvuMogCUM2jVlyGUldGmMsWcHLitQD9WgnMYSjewHTpsWzDvZeZkumRT6oOjFr3H+RdE0BMykFMX0m9rsHAgc2UBRUDLoRe8yVTRWbKN7QnaHdYH+C4TKyiXJXQoVL2Y4UflCrqrwFmrHVpViAutioSsCSQdmLMzjK1Dh3nQNWeHDrsAKG/yM5UpXQRKvZQ0KZe0Sms1V/mVvS3b44Q4K7UC1ayed+jQYduCLI6Fp/r+wPYglCmHOnsas9CkZG9HBGqySfSGElEl1wJJmSElmHRa31+2cUBOyIvFA79ldeuKCmWgMoUyeQJNpZ07iy6X9ZTlJk/mCntlruws4+vQYQx6BrJDhw4LISVn9swoweJMphyvQ4cOOx/I3tn7x+GXQXSa5Nh9kS0oR1QxkBNBZZzs266voNmekHJwAQwc7Ldcy0moHZaDXB/ETrALynVtbchhNBYVsp0D/e373RkC+JwIK4DMXCFjFlm7jHXYmaEHkB06dOjQoUOHDh06dOjQYSnoJawdOuyAUh9ZPGUvVufXeq/ZjoKs5IOUJm1k2zkER+nPMqVyOzuEJg5ccGBEe7/ZZoAc4iHzIOuwLcq66sNActhU1kWVTdprVV8W3kLezeEZY2OQVSGbyvM8hx+yDQ4QydULU+/vLGDs+Ciz0t5NZ/xo5Mf3OyrbuAicb/M//6fyXFcPDQM1QyyIizNbHJTsLBdnd6m49bzv+jbJDq2+oZfNg51lL2eHDmuFrhY7dNjOwBF1oqi7tOwp/M3f/M1hMwCj6Xhy4KjzjXSY7bdyR56rURyR76qDXR04406+dUWD4+DHrorZDKCU0RUhrqRxxcNGO1uO5+fM2d9mn67rEJRb+j8nX7pGwCmgY+BqFLLteou2JDv7/1y1IxC2cCJwzOm5rgEQpDqNOHej7qygBPTcc88tV83k/sAA+n/uc58r1yO4bgP9VxEc/Ov2lrvuGoZjjhmGl7/8F9/91/86DLfcMgyu/bzoImWWw/DII7PP99vPtTQ7EvMOqwJslSs43APqmqluqzrsqtA3MHVYaeDA2XcnW7ezgKyFFXp3gq3KuGRZnE5on8pUVbu9HU4t9JPrETYK0MNx705HTJZzZwJBhwukp+7bm6KJgMS9aLmcejNA7p5ziAqQrXNXqtN7NzJ4pBsELO6Js58q+46chmiP7hvf+Mbh5S9/eblr0YKHfXmtbFvMcZenu2azZ6/mmeDdaaKyDE6QdEWB0yVVDbh70/sCVYspgv1cxr4zAll0hY4TJltAD/LqDkcO9qqCmy4Ehn/5l7PfNThImuqRibQ2ho1PPOFOw1ng2aFDZN0hOe6MZKt69rHDrgo9A9lhZYEDxyFxCTYH0Kq21X6ZAJkFf3NOkzWQFcgR8pS6QI3DbqO+37IM7g7L89rn8HlWad2iE0q1baVdW57nMMlC5L6s3FOl/dz5l/u0EgDoX/AlWMr3cDAmJX5jhwrknj7PeD94alcACh900G57l6Uxehae2oaX94zDbyW03vE/R9qzRx111JYS0tzFhXb6kHnQVk2nlONysrXv2byXe8Ry8XLo4nnvGXPu4EOT+v68RWWdns2dfuieOzJzJ6R+cz9m5AKtPVOXHaVNuMLT+7mWhJzBVz+hbQ5NQFvP5a42ePgs9NQHGdGv7Ko79VxmLuAwXv1ox9+1HGvLeODCac+9dGOQ5+DX3oNHLsbGG9qMzYm2zTrLHBnyPbqSt5Qdey4y4xJwF2Yff/zx5c5RPJa1qi8kT1koOWhLltEQbWo6tmXeuTJA1svpmjKDAG3Ryx1wLgP3Q2Zlzl2K7lCPAHwef/zx0pcL4DmELmyPvDgQxLU1Tos88cQTt5wATE5lhHPnHzq88IUvLAtCLjA/4IADJuXX8+iXew8zXv8bI/qiC/6kZDnza4zHoQU+ege9tBt65g7B8M0Y/Bg72Y6s+wweuZszOozsel+baOuKhpaPuXcPXepTkoNXrRvm69fZ7/X44+30qNuo2/Xzox8Nw7/4F7Of+jkq6q//ehhe9Sp3OM4+e9e7ZgHl/70OdClAnvS1GO/ZPMh8i75C49A9i4y1jmjbCA9yf2g7l0AtQ4HoxMhM2o+OiC6PnZiC6Br8N4bc6Ro5i67UT6uvfRf5jX2Ac+aF/nPPLjp51/+ZC7Fz+vHZWJl85oE+vB98MiY4wd8z9GFdgg0X9tuzsdG5Nzg0zpyOrQC5r9Q79bxNX7G/tc0PL9EjtqUHqB1WFXoA2WElgSJ1ue7VV189fPvb3x4eeuihcmUEh1rppwyHbJnj2N0NyDn81re+VVbHZbEYItdMfO973xt222238tspaYcffviw9957l/aVrlHklD+ju88++zxpb0+Njz6UszGQLh5+85vfXJxlZXOc1Be84AUlSND/W9/61nJvmvb1w5jA2VUaxpGAw7N33nlnWck87LDDhr322muLQYnRe/jhh4sBi4E8+OCDy/jgw/HNRc7KA12xARjdxx57rGRoZGeU3MBHBoXTjBYyYs997nMLPgL1m266qZTToqFsEZoluNLXoYceWpx2Y0FHtPr3//7flz7wgtFDE8+7N44BdHG0rJAx/Kf/9J9K9sbJjvpnbB3r/qY3vams6C4jE97Df0YZ/hx+mSe8Nwbj068SYfu1OP6edYmzjBFjDw+BhD6f85znFMdfeR450Q6c8I+zIgPFgXjnO99ZAgk8Qxdlk95xuqQxGz/e5zJwOLom4MgjjyzfkWM81p5ySjyLHKOhqwbIJZoYI5nBY99pb8wh8p3smzHJnsm6Pf3pTy9yD/c/+IM/KGN18bby0d/6rd8aHnnkkTKfZJFlNvETL8mdPtCU3GhbHwIGDpW2BFTGKxgmC+REexwmVQJKufDCnaFo4eoGsswJEliRNeME5hF++d6F5DKU5o/5ZK4LRtDd+H77t3+7lIrGiQT4gEbkXGlqve8R1EGMucPZa4NQjho8lEx/8pOfLLyhT4wn+gE9Tj311C2BUfrQFt5mwQNu6GvsaJmAtgV8Mc/IJrqbK0rhjJcckVX6QNktmpAP8oYf6IHuaBxcZFXNLTIrwEMvbcFN8IxvSnrxQmaWjtMuvpEZcxmYg8atVFc57mtf+9phzz33LO1/6UtfGl7xileUMdMj+IhOeOBZ+kRgQJfRAaG7+fHoo4+WNnwugwunJ8vyMPzn/zwMjz0225PIL/+VXxmGf/bPhuEHP7B4NQyveMUse/j447N3XvKS2W97Fn32f9erSuD3spfhD8d+lkH8+7+f7WV86Utnbejnec+zOKR8edan9hwQ6tnnPGe2R1KgqZx1991n7WrLM3vtpZx39v/v/u4w/Pqvh7fD8Ed/NPttz6R2jMF4xmIANPrhD39Y6EaO6Am6nY0zB/CcTJlL5vjRRx9d5kJNP21EH2WPOh7TVa7SwC9yQT7Ma7rBnBYA+Sw6yBxkB7SPZ9r0LN3O1inVZGfbBc4EPPrxHZliZzyL5+SMbfIM+TU++oTMmv/mALmzdYGttBhEX9EX5oJ5QoelggC92Gu4mwt0nr/1bwz6slhlHAkOPW9ekFXzwLxmD+CCVp6DD9yzYHLQQQcVPtAdxhD7zT7ut99+v6RrPA9/eMjQo72x0R3A4i9c6QRzBs19p23tsEXPf/7zi801vieeeKLwHt/QvZ8Y3GFVoZewdlhZYOQobA4Rxew3B00gRYlz6uwh5HAzvjKVAgBBVLJJn/3sZ8tnHB6BjMwIg8i5ueWWW0r7L33pS4vhyer7GDA4V155ZcGBo59N9IIQxi6BJQeeEwYfRlNWjwEUYMqgXnTRRcWQJfOoX4aMYfnUpz71pFJFTv4FF1xQHAlG2RgZdQ7Fpz/96RJk/Kt/9a/K+3Xpp//hcPHFFxfjyPHn5LkYXKaFI29PnQwLZwXeDBl6KPvjDNx2222FRmjJiDGUyvRclMzQMvTGx9m1f4wjZP8TQ8nwCzTQJJkRTjYan3XWWQUXPGH4OajGswjQ5sILLyyGXBBmvJdffnn5Tp8ccWP2N/z9Twbginc/+clPtmSY8IHDAT/Bz3XXXVccFM4H+XJ5uv44W75X4qgdtMRv/XOwBR533XVXoY/+0ItTw+G+6qqrCk3II/qjryBLgCtzRqbIBRwEKQJrjsX5559fgn4lmNqKU14DpwRvjU8bHA7y41kOEzkhG7JhHCMOGH757jOf+UxxqDhPeKI/cwI9jBOu+MnRInve4fTI4qG9ceAjmnJcjZUs44U+ySp6mCecIuMkS+YIh0nAoU8BI0fNHHLxOKeVrJFReAg2BDsuotd/67SZ13Bp9+LmHkJyDif8EoDKhrbPoJd5gNbaEyTne/jiVV2ymaCNk23sFlaSIeI445/3piAZFosZZNS8I18czlQXkDv9mkP0l2foO4G/+Zoy6GRe6BtBKX2Ib2hlHuK7di+55JKCp3bI/uc///lCEzrAohOn1QXo2hKcWhCjs+CXBSR01p5yYAEpEOxrm1Mu+KCvU3lAHs0psmDxgs46++yzn6TfBHbf/vYwXHih7PHsYJvTT599Jth78MFh+PznZwfb/Jf/Mgznnz8M3/veLOj8j/9x9p4g0jbyT396tr/Rd5695JJh+MlPZgHerbcOw1e+Mgz/4T/8IigUQP7hH85KV60b/tmfzfp8+tOV6w/Dn//5MHz84xYaZllFuFx77TA8+ujsvRtuGIYHHpiNwzMf+cjsc4HjPfcMw8knz/qaAvMRn9AXf8kTuc+iivlGZ1lYQE/6vp4D/mYf8MBcQmfz5+STTy5ybN6RH/JBr1vYMGfNp3zHTuCdeWAOwkkgdMMNN5R5Sl/67tJLL53cWiDAoivMI/ymI8iohRC2hc4jZ2SQnkvZNzn0bBZb2AF2gm5jV+hT88BCpeCXbhXU0iv0l2fIE7qYOxZB/E93s++hld+CabqA7aUvtQkXc8oiGtlk2+kbeNMFcEEH85C+9709wOQ+gH76wi922Pj9f9555xUd+LKXvawsALCZwFyjty1c4od5S99pxxjRwPi0ZXy+X8Y2duiwI6AHkB1WEjg6HCKKPuVTHHsKlrPLOUxgIwhjIK1kOshCeRljyBninFPGskt+MwaMJ6XMOFuVZ7AZuHknf6YMlXFh9Di+cIID/OrylZSmcQwZV/0ycBz2M844owSSnoe7IMrqIyec8Wa0aieB4yZDwngxvH44d1lt5uwJoAUKgo0A2mnXKqq27eOyCsooGz/Hw/ucdOMxjpT/GBfH33dwZZgZRIaNkxzgFOIDp1mgJJDEI/SUaeRwckI42PpgMI1ZppUB1T/nyXhSZlXTG5547Qdt0JXxR0dOBmeVsebgkJOsjtcloclAcfh9ZhEBzT0voOC8yGBlf5eVa7LiPWWJHAI/xmhsHAG4GDenQUZZQGFcyia9z2kQ7OsvtBU4+I22cMcH76OhcXGUOImcDfSSIdQmHLXTlrBy8tCWXAlQ8UY7eA5X7Wifg2SMKcPCI2Ov5wQ+wYnTZPVbICsIwn88IktooC8OJadX8OFdji+ZFWRyxAB88QBNjcu7cEwwYrVeoMTpNUY89T+a+B+NzGdj8qMNwV4NAmef4WddWkdvkAv4m2sCafhyquFfl5HhIRw5u/gWHOKwpazOTw3obCFBqbd5lP7TPjpMlRxri5yRB/3DH485lvQFeqNdMuOCavzg+JJTfE/Aql+fmaOCXEEw/pjrAkdyqm14CSxPO+20wjOLF7IkdIug0vw2P+BDJsivgIC8kG1znmwLFMlSAlcBAP2Kj8aBhtGh5E5AJJChGyxKkJE6CEEisehllw2Dc0hkCE1h2b/nPneWxZMR/K3fmmUJf/jD2d7FF75wdrDNBRdYOBoG5/n4jW2//duzbN/11wtih2H//Welqu94xzDssccsoNSHvn/602E48cRh2G23WV/f//4wWCvQFpbq+9d+bRaA2i9JRQkwZRRf/3p7Qunn2Vi++91ZNvOgg4bB+UHi6Gc+88llsjWgHdpbwMRXNE3mH18E5HSdoIkuIh91MEE+LcyYH7LCZNlcQmcVGGTCXMJTGWXVIOTAPBfMybjTT2Q4JaFsKZmiI8ipfultuoT+I3v+ppPpjZTBk1WLgfShOWt+qkIhM3QOXcL+kAtylpORo69TPutHG5E3mXQHcMmk+4wc+TGns/hBVuENBzTTHnrWdEYfsstOChK1aR57Fy5oYx6RT7hrD721SZejo/4sfqbKB5+MBx1VvqAVXWkusHHmmDmEH/gK6CX0Y8/pOPraPPRjwbWuOLCIaP6tyjkJHTq00EtYO2wKoKwZLc4qR4URyaorhU4pZ09FDT6rA4s4jwy0lV0rjIyAFXiZEP2MlYswCkqIZDw5jv5WXpISsDGAEwMSnDhy9emFjGX2isRJrfcXAePk3DPOHIXsK2H8ZFU+/OEPF3xOOeWUQosaf39rP6V7AjJOt9VUtPO9LBq88nxL8+xPrD8LXQWuHH6G03N+exatlG8ygIxrDoNhJI1ZnwwzI5pLptu+GU0ZPAEWENDKvnBOBEecF+/l3THcA5wTdBQceY/DI9iGZy0nAWPI/tTwRlCSQNiKerJZCfRC47wXZ6jlZ+TCQgA55uRxIjg22hGgZi/cGG71mDhLnA3vcuTQiCMoqNY3fvgfr8mqMcse1XMicpc9f9r0PJ7hoe+zz9RnWRzJgUocMM5azcP2dw0CSM5RxuQ3B858hLd+Irf53t/5PO/lJNTMn5rv8OQwcxQFkYIjAW/9PmdYEEgG8JRMGKcMO6eVvNArnjNvAuia0mftoXnK3iM38yoZ9K9tQQGHUUCnD/OHrHNyObLmpEyHeYOPZAI/9EFO6vFm8SryCiftZTEKr7JfC6+Mtw6K0d73dINnBaEyxAJhwavMUntolrmgfN5Y2is7kpXJAkgWC/wWTIQPpsbtt8/2F+655yzLJ7toWsooChKtSbzhDbPvvvrV2bOCOgfbyBi+//2zgFAwKBP5rGcNw9/+7TDcf//sO6QSp7zmNbNS2C9/mR4chmuuGYb3vncYHMxLJFKyavuqIFF2UomqYBV7JU6Vvmr/xS8eBtWJgkTB7WzMs75kUAXF9lIKJufdZkLe6BK6nS4EtT6r50jmQA0CHTxKSTMgz/Rsyicjl5FdizkyXd4zf+ggASP+qL7wroWpPF+/Dye4qmCAp6DKFgELDmTzYx/7WNHrbJF3BT+1HksVDDsuoFwEAj/tmOsyiOZKbGZOpBakAgvHPqdLVY2QsxrqOQIEo+hmPphr2jP/jCkLj+ZDto0APIr9FmiigcDWfFWKG33nOUE7vyI2Jguk5pqA0uKK78xT7xujgNXf5gwcjIluqUv3O3RYJegZyA4rDzGmHBUKPc6ylfGsyq8FtEVhW3VkDP0vMJSRBHFWa/A8YysDyZDJyFnlXYSvgEWglc8FDhzSZfY0eF55C8NsFZRByUEUDBJ87rjjjuIsK61RqjQWsAQ4cL4XwDBU6Oc3R2TsvToQHQPj0xYDK1jUHmOHHxxfDgn8BUWCDQaeYyMbxFGWGc1hNS09OA9WrK34Wm22NwbtBPratzLP2YnTPEa78CElovCUfcHnj3zkI8WhWQvkcAcBkMDPeK2Wc8TqMtNFp3DCw1i8U8txAhU0TXu1E1kDB4ijJqOurNCcICc54ZeDg6f44ge9x+ZJjav3OHcc//CTvJlzbf8+E9CTbYFa9rbNk5kE42QDDbWRz8kGp2+Kny0YJ9kZk920JzuqGkGWRjlYTgeFm+CR02veqFaQkUA/NBJE4rOgE03IcFtGlsMt6uwnHsAlV3mM6RHAKRT0C8CUqAkmLf7IfsrOmOvajbMt0Ag//F0HtGMAd85psoU1P3IwEmc15aaRSYsHxsT5JV8pn9P3GE98pnogB2TlmeCecnJ4a0Pf9YKbfY/f+MYw7L33LDCkJl2vISPof4GYaSBIsxdSTPSCF8wCRuWkPpcMkqARiIrBxCXeE0wm+2foRMRnqotlD8UE9isige/0LWCV+fT/3/3dbJ+jrCjfX2WzstgDD5xlKOEjRlF2q33ZTc8qk1VGe+aZs36IR/a7t7KAPjmcyRya2jc7BXjmHcFg5lLoTzeOyR7ZMM/IaLLBkS2fz7Ol+Es26U6HgdE/9KD+zKNPfOITJfj0nUWUyFnmThZ4x8ZZL0yO9Quy/zHjoCf1TQeib+RMEJj9hVMQ20BvycbKzhuL7RtZEEJffVhwSVvazfwj42yUeW/BJTwwr20ZoNO1V1c+yDZboLJ4pPrJYq5qpizQWHgRZGYs2WPaocMqQg8gO6w0MK4pKaHYOTbK0qx0Kuuy/4yTlYChLoWM4Q7Uq7sMGyfSyqN9kZwbxsGKu30ijEptgBhbCl/2QdZPWQ/DksM5GHFOMePBkKQUyPP2PNiXxLgqwU3GJheQg5zgWQMHT3kRJy3XQGSPCKMpgLXaCR+GHR1aZzr9AIaV0yyIUvpqjMakrCbOvbI3bTNa3mtLS9Me2li1FiDbU2PcVnDhlFNwZQu1yQhb7Y0TIAuWPZr6MiY0C2/8eE9wBl8OtnIfdEAPbRsr+udABXyAm4CGEZa90a72cxARXgtaZUDxJnRp5ST/17zJiXmyQWiHjwJR2VDjDq1+kV2ZvZOVb/iRL3IhyOas2SNGLtDk7rvvLo4JHMmSvUP+Nxa0Mfba4ReAyiQox0Uf48pqt9IpOMjgKmVUQphDKto5UmcOBfmykAIHPJK5E0yFDjkVEcDN6nscObwML3LCrLH6SabYjznMWfYcXqYU2xznTNXZxvAi/dbOJUfUOPG3njfJTPotyJQZEEjaJ5ZDlMisPYApLRcw+0FDTqQFD9kTgZRFJvz2WTJ6KQcOb2sdQW45f9k/hY/tHlbjwDMZYbKjvE65IJ0EH4EcIPt0inbwQ0Yj91G27aFfToTGb3NT+6FfgA4gMwJVvPE8OTZu/NcWB1/wjEbKCHNQUMbuh76kc8iXMn06iQygC1nFG3PUPj7ybZyh4S9keJbpMxwVykpAZfrsT8T+P/3TWYAmsHN4jeectSWwE8wp7NCGrKWgUTbR53xxz2bvoupB72jD95JIgjtZTO8JHmUXTZ8c6OMd4qYPwaSknHJViS9tyk4KXmVITUu/lcASB3dGKsmFx89/PrtqxiFa7TUydJSqCDSny9ALH9Gw1j+he6vbzSX7F72rHd+Hp3WlS05BJR/kiNyn3JiOUMXgfYuj9H9b0VHrfPaXDiS/5Na8MT4ypIRaxj+nsZrnvsv1OOTAgqV3c/oqfU3G2V3v6T86N3IruJLllP20YMrWqGwh5+yCtthY+pAtY4PG9mvmpFggo6l9tPAePet/upduIs9sN51v/yddSF9ln2YWcY3HoiZdaE8pu8mu5lAj7cIFD/CVDcdntJJpzJVYxmC+a0dWFT3ZLBUe7Xzv0GFVoJewdlhpYGwEKAydUk0b5GX/ZBYEGA6gYUQ4eRQtgyVTZ2VRgJnN6Z7l7MSQeYdzzAHk+DJ2Vvw4aQ4j4RRbVa0zQAwWI6S0zQovx5TDJ3sAR4aB08mB5NgytjJfNugfe+yxJdvkagNGFy7Abw5b7k9jzOGe0ysFFJx/hplTx2Bx7BgbRj/7ZTiLHMOUpjFkDFBOwmPstYtuHFVBp3c4GvZvGAeHkPGVscn+C+0xnIIuRt4PY4im6Cwz6OAHhk8fVldzVQJn1V4QwVL2Rimh8r1DbOzHwRdj5tCiGecfLazcJsMY0A56CHDwh6PNOZEhktERsHK2ORGMP1pxrtCdIWeYHcRgTHBz0qqFAA4JvhoTJ4vx91vAjyecHAETp0N5lP9lrHIojlJiNM0+Td9z1LSBR7KlHCDyFjkmF+TLfjXOu71QeMph54BYzRd8yBSQN04IXuZKD86O5yyk5EoNz5EvvJJR51TBDc2NFf/RwPvGij6ZEzl5U6mrRRmBMUfR/ls08Jz+7Z/CN8Eq58ez5IFMc37NQ3ywYGCsZCv7UNEZH8ipucIZTCZSm3iBxllUMD60REef47U5lcUOAWcOSuKAecbcQAv4m4cCGbTmuDlEA5/1jbdkmR7giMd5REtBNDk56aSTyg/+WHQxdllrMqovtOGIA3oGz/2vT06orKe5jf5ZQAmYb3QXR9Vihv+zPysyb37pQ4ZZ3xxOpwG3JxYnE6g/+owc0jf4RZ8ZM3roj9yTOYtkgjv4Cvw4wdnbRcbwED0EorlKhB5A65xALPtCl8q+mPt4Yk7QG/QK3WCRRN/6oXvqU1hl8J7//GG46aZZyafuBYwOoRG/CMzEAfY9KnH1mSBQfC3jJ9t36aWzU1G1JTEkxlJ2Kgi97rpZEOo7J7MK8rzrkBzscACOg3ccdiOJ6qAe8Yv+/a1/B/XISiqRFRQKMn3ueZXKklXw+pu/mQWwd989DF/84jB84APD8NrX/iKAdNiWhUb8C5AlpdCXXXZZsSVkBK1kp+gT/DPv0FWwaZ7QJyktR0cBJHqTT/yiJ+goz5CD2C2LruYgWc5JrzLv8LIdgx42V+HkoDpyZI6Yb37jv/nr/fYkVnOVroKfeUaXkQP8N68iZ+a6TF/KOMkY+Ug/7JFnzE9tmf++I1v21BsffMkpnXrccceVeWMcfAK6jt1CY3O/vXbEPKAvtEl+4adtfgPekGM6EC3MN/bWvHCQmL7MY/MqWw3QFk3MU3ZfNROgN40fTviDpmwcPrCDaIXP2RvveRlLfgTdY0HbGNljc7XeatGhwyrBU36+qN6qQ4cdCMRTsAAYGUaBcYkzmf1ADFJWaHOwDaOX1TtOUU4vTHDmHUaVIeCU5W48AYnPBT8xQtph0K1gcqL0nVKXZFtSnqeN7N8A3uEUC3hyIArcksEIrsGtvrsQ5LCC7MnyLHxzP5wf/9d3/aEFPNNGTkNN3ymNy32UwPh8zunN/YUg93PBIVdK1HdKclTgAL9kK4I/Gnu2vgaB05wDXfTB2YY/Z0N7KXUa29+mvQSPnuE8GYOxwB8fOBXBLc8IwgVxuVtQG4y/ctoEZZELY0wWNHeIZi8Z2YKzNv1NDvJMaJNsaO7gDD+9kxKu+hReY8n+1vDOs97Xvv9zyERoaIXaSYScF33htSDCe4IOcpiVdHxJAJ95oh3jqeeE8fiOrJIR/PFZ7i5MeWlOEvUZ/HP/H5oam75y2Iu+Q4vMzdxD6BmyY4ycpByAk4xdnsscrmUi5eAWLzinMnh5N3tPPZ/SMN8ZU33Xpr8zT/0fHgZyn1zkTqCV8mxBn7kcnqG7RSQHbVj0ArlaiPOb4KyW5dwjCQf/JxCux+j7nJ6rjeynTr/asJgh0LZg4HlBWvgWPRM5zMmq+GGuows+R0YDvodPrhBAz2Q4szc6c4XceSaZ01p/0V3a0Ud7DyTVJCFJvXv16qtnwaLTU6l1ySJBoQDQupjgUemp8lYiImgj1r/6q7PAD2tTHUmdyjp6XmBHDIggUcptLtr2nPbhoQ1rX/Yt+lx/YvX8D4dZVnEW2BqKCnzltMbw+78/a8eJrw89NAw33jgM//yfz0oQUy1i0aMu806VBP2HN5lP0QM+I8PkutZBdRs5gdsil3ayp51MyqBblLCdQFvktg6s6OH0n7124XN0Wfqu7yZt5Zh80jV4n1Lo2Eby4Zkc4BXZqPW19xLIebbOPmYPYtrKPcr1fcjGnvFnHDWNzEOLaha1zF84+p25X88x8oov/kYzNtf32s4VP7UehR98g7MxeieLQ8ZJb8BZ4Ii+dFYOJbLIo7TVwpoxZu8ovWis/QqPDqsKPYDs0OH/gqnACFHoNuPnfrkOmxcYeRkcAaQMFKPMQbHSKyDINRqbbUyCBUGkzDTHjNPjf06Lsqyp+0x3tvnKAbWiL2iTvW1PTN0ewBGUNeIIC2SzgCArb2GALtlWeAkAZS9kfpyK2h5os1lARu+tb53th3z3u2fB2mYA6w1XXjkLZJ30KsBU7uq6j3PPtS/z56UKQqZWkJCFku0B+nX3Jh0neNpVIQGkTKVMrUzijrDrsquynqpBZCbNVfpLdlImlP7q0GEzwSZR0x06bB/g8ClLrFd5O2xewEMOvNJKxluwJauiFNaKb3tq72YZk8yjck0lTsZEbpWUKsHabAHxeiGVA7J+smmCth0RQMookDHlfcl4ymzgBydxW+HEMTZuTqhMkrJaTmh7Yuqqw3/7b7OMnX2QyUpKom0G9YvU++47DPfe60TQWSDpHBonvCYbSkZlvLanTclhX7JfyjXJCTzass5dAdDA3LBwqFRfqfC8K7u2FajOsLinfJw9spiQrSfKZjt02GzQM5AdOnTYaSHqTYZO1pEzwYHgTC174ucqQvYVyjzJSBpTSmM365jWC7UJ2xFjn3eC5LYEfJdlyr2wFkZyhcdmAofdONxGeantdcpGrYFsFjHOfk0HAsFfBaU1gx2Jv3JKe3xVJKS8mI7YEQssOxpUCAggZestrljoWeuJtxsBOZwo+zwFkPZH9sXqDpsVegDZoUOHDh06dOjQoUOHDh2Wgl1vOarDTgfZyJ9DKNoSvhy4YQXSKYRTq305YMYGeav52aS/PcE4rBpbnVx2A30OxcihOTlEwX5Om/HrA1pWGXJYS318fQ57aQ9vWA/kUBJ9+J0DQnJ/V31f13rbJmfkb62ryjlEBS54Rk43W7bA/CJz6JhDk6boZKy+rw+iWXYFH/9kkvXTngjZYdeEyAb97aeVjVpHgq3RifQTGaSrtVNfUN9h2j6rmHBwzaqVWMOP7oIffk7prnnvs9nkYCNsbe3PsCO7wn72DpsTdr2C+A47HVC27ktyXL8juse+dzS5axPqUxZb4JQ6Lc+hFI5R3xFgj4aTQZXcrAVcIeAYcveNAeN05LkrL3LC66oDp8xVAS50P+uss8q9Wq5TuOaaa8o9iSnVWy9411HwrmlxLyCHQZ+OuHeU/tbet+WofQc1OMykvbNtETjB1Bhds+GCbgfibDYwZ9773veOzsEAujid9MwzzyxXgKyVTk5DdFWLy8rry9M7dHASKX3novvc9xfI9U1sAPnZGj3iRE3XT7huwd8dFoOrX1yJs6p6zfYGBw25amOtQIexJ2SCPdlaYLudsO0KEQf6deiwqtADyA6bHmRqrBq6lH3sAmEr0e57c3DKvIxFjkzXTn3h9fYEp4S6A9FKbYyT00IXBYEyVhx4V1OEJu6XcuLcZslkydw5jMQdhYIQh5A4YMBdae5QdM9XLq9fL9gL5NCR3OmJNtrX79YeMEF+8MnhNvMWKlqQGcmdiK58cNDCevax5XqR9uL67QWy5mSXDE+B1Xmyjb/2b67VkScjAkcLDTk2fzNCqiIfNZHMAAEAAElEQVQsHGz0LpJcnyErsrVtR6Zylc1UX3iyo3fD5PoEd6O2shG5I3MWMLYGVzIoUHB3rbm2WbOPyZzRhxsNuY4iGV97Dl2jtCP2Hs47wdn4AdlwSu483TUF+O++VPe/boStZUfI8qOPPloWzDp0WFXoAWSHlYWUurkriaHOvYeCO44NJ8H//nbHUsocPccwxImgkDnlBx100C8FCb73HEcu91y5+yn3TulblmpRlgSedTvt537qNnyeu660n7vagDvcXIQMD+844vucc84phsQ4U95i5Rsd8p4A0k/G57cLnY3b+ENL7/hBt7pc1Pecr9YRDI4+9/eU45XyMQG8tls64F893jGAp9IzZUBKGx364EJoQaRsqpPzZPisFtf4KZsc41Oc9MgC2dB+fU+ZhQfH3Nf3s2mnxrcNyMLXdpzPeMYzSvb4jW9845brFIwVfjWta/AZB9yiBZ4Jwhy971S+mid10JwDdLQbnpAN95e6qoSsZK7APSV3/k8gkHe0Sx5y11vd5zy5z/uZg943B51066Lw+jnfw9XzaExOU+qVObgo6IVL5j0nr3bU5sne2Pz0f+Q/c1Hb9Tyckln9B992nvi8npP1XY8pT849inDBq9wLNzWvvBteZ/77Hfwz9vDA/xz3Cy+8cPiTP/mTLZ/BL3dhai9t5d3g6W+4et7/grGLLrqoBLrtwo333T+p8kFmqZYtbeBHAoh5kDGiSS1z0ZHBuZ0DcKUHIzsCu8hGaOyZALkTKIzdLYu+ixZ94KZNz+XexvquwchgLQOtfGSMNa2jbyKDoYF3wpfaTmSbQztnfB5dU8tg5Njn0SW5sko1huqdKf0UefOTMtTcDxwIr9Ov9p944omS7XXIk//pBAuAdG/kt5ZZ7xp/bPaUTIxB7hJudZa/Q7/cuZh7M80RVRAqd7z/1Kc+dTjyyCOL/c24I39whEt0be0XZJsFnf+qV72qyF7uYK5trf8jv/N0rL58nrsxO3RYZdgcqYkOuyQwksr6GKcDDzywKHBlb64t8L+A8Utf+lIxTlk5lK1Txsmh2Weffcp9SxS4zBWjITijmBmmGE5GTgZqzz333GIQrFIrZ3XJ8sEHH1wCsbGVZkaY889AaF87nmV8OPRw1gbDxMBY9fzmN79ZAgzfKVFhvI466qiyOiv79v3vf7/g7n/lVspZdtttt3LHnJVJYxHkGiuc9VmDMSj3ND4GzTUH8PzqV7+6Zc+ZbCUn3kXT2tKm495lwfbee+/SLkg21vgEN2PZMf058U9WCAiIrDa7mw9N3MGVvZj+dgqeIIlDtwhCc7R429veNrz73e8u2UjXFTC0sn1x3NAR3fTDEKOl/n2HbgLqXDIdwI+HH364jM/9XGQBjsaILmjo0ue3vOUtxaijhe/Jj3HCS7BvrN7hHKCfYFDbjzzySOkbHq6HaY9rhzt54Ijrz9iSgUZPfMKTPfbYo7xPZqxMmxvGL6g2FzyjjIo8yzq/6EUvKnJjvIcddliR5wceeKBkpckR2shW44UgRvvmiqyKtvSLh3vttdeT+E22ySgn0SmC6GH1HU8seHhP/3AVxBgbh4ucyLCmPTKp3Nc44Xz44YdvybzXoB39KesmR/6ugwlZJfOIrKC5zLX5opoAncgwHZD56QoUNLcQceihhxZe3XfffaVN8yFzhxzhnb8tZuy3337Dd77znTJGY/Ws78kEWpMdpyuab7La+ITG5AddBWH6gxua3XjjjeWSd/oMT9rsBdkgm5xj8kY+tA3gi35HH310wde1AOTf9/6+8847i54zNjIiECSn5jjdaH5bUKNDPP+85z2vzG28ILMWVSyIKO82BvyxyIIOAY63kms6OHt2taP9lDGbe9r1+Vj1h3kHNzIXfUZ+VAj43HtkV5sHHHBA0aH4TPbIFTAPXv/61xcbkACBXsArMmDuamcMyBRc9W1PNPkcy0JpF98FHHS3d+ogjQzCt9Z/+Iz+ZNB3kUHy4Dv2gQy+4x3vKLSkn8EhhxxS2iAjcNEG/ezKB3OdrOOTdjxLL5Etn5NBskLXsTnmuR/z01zUv3dU4+jfHbnmvYDYvKmDFjgpPdU3vusD3S3oeQdt0cO2C3/DGX7wveqqq4r8mweuFiLD5iH9DF92nY4mg3QQevqBG/1gvpBVsk3vkYsx+wsn+MGVnaVfySlamzsW0+hoc9j8YUPIta0R5hAa+dzc1yfdS9bhQn7cz4i36AB3vgY9is5kha6HA14Zt/mH3/fff3/RDXA2Dn/TF+ai+RW9LutpfpApY6VHosfI9WbNbnfYNaBnIDusLHDgOFGcIQqesudIcCAZOg4Lg1iXezJoHEQG7xOf+ERxEDjVMZZZiWXgODeMk7bOO++8YigobAZYPwwl58Bel7Y0Nlmg66+/vhgMzzICZ599dmmHg/rpT3+6GCNZNCu92uRsKlcU5OqXI+GSexkDRtYePfdEMVCMDYMocOOQcAA5dIyM/zny9u61+3CyGs45Z+w8Z0xWfxkrjpILlXOsuaCcM8ZA6sNeUk4HXOAKfw5Y7hVr+2IgjZuzw/H0+0Mf+lAxyHgEB/tD/K1/jjMHby373/DFFQXwg5vxoCkc4Z127ZfkPOhTHxwC5cs+x5MWtMOBvueeewo+DD3nxl4qjozACu6cIfTynSAvgSq5IXPexU+BZFbZ0Y58cn60xUltgfMi2OKAC8Y4JP7WB+fSuPxvPwxH3tj1yTEjW2SFUycIMT/8JhucTTw1No6Nto2BI0UeOJNkjtOpffLCoTIWwYz3P/CBD5S5NpbR89wFF1xQnDfvk2vBk6yavaDo4Tt05awKXO1DJusBz6HVK17xisI7uI1lkQUTN998c3Hm0CeLL4BM2jNLB3BUOXhnnHFGaSvzU6BlfqK1Z+FJ7m+77bbyNxzIgXmADgIdeNIX5gqZ1o65Zj6SCf+jjfkAN4EQpxpe6EYH6B896B6BOBqhOfo/61nPKvKDj/jfBlfoAD8yS34ETsYEf7KkbfIFX2PGa/yDj+BW2+hO9jn2sojmhO/MITrt3nvvLbpFcINX2tUWmSFT5Bme+DtWmgxn7QkI6DHzk47TNjqhBx1mb5kgo5UjY0RnzjQ97NnTTjtti540BwVV5hQ+aNccootlt8wx/aKRxT7t0RO+54xzzvVLn5G1Fix6CSQsKuBx9lu3+9jgTZfRA/ojS/rJIgbaww1djdnYjVlQgT9kRXBDBryHh9HZ9LC/BXD0FluHt2QcP8lW7g+0J9x8177/4SPQESTBHR/ghn4f/vCHS3tsE7kVtJhngpJLL7200Fcg5R2BKP2eqolADgdixywSRHYuvvjiovMFTvigH20LuMgsmartFvmhy9hc85UcmQPmG1rrJxl+NoKONSZj8R1a4lXLE3yGizHhH7tPzukZ7VkYM1Z40huRBc+Ta4tr8RWMB+/JCd1AHxg3W+sZ40bTLK6YG/wLvNMXnMmq+Rdbq0/0ZYeN0zvklO40NjqWXkc37VxxxRVlXAlUN3OJfoddA3oA2WFlgTHZf//9ixHh0HDuGXuGgMLlJDBODBtg7DhanB3ZAoqd8aD8Gcms8HNIOWOeYZxlGB1cwohT4PrRr3aSNRzbW8mh50Qy5hzYN7/5zaWEUSkf4yDzw4gylt73w8n3nRVhK+rKZmRFORtwZaz0Dw+GjOPEsMPN55wpq+0pFeJQtfs10YsB9xOnjbOQS+Y5nhxLGTfOJUPOARaceFc/DD2jh9ZwYwjRq81GwYETyOnhWOMH4whvTg26wJ1B9D7j6H+B31oMZE5j9ZMAyCqvYAe90VPbHDLOCkONVgIqeDlYgwy0gB/kJ6W/DD/8OMN478fY0YOjZBWd46xPDkLu3vN/cPQ75W6CB6vXaILmLeiXDAputKVv48MjvMmhDuhoMcT3MiroHGcFbniWHw4aPvtJRp1znv/x0J4d4+K4C/bJhj61lwyfIEQgRA7g4ocjDGcBofc5xQKm448/vjjsxpAyL3KFthw089bBN3gRgINsARzQj0yMlf595jOfKXMYj/FTIJkyczJgfpqraKItNCJ7ZJdMZ35mnqMPPAN4ZqwBMgAffDU+AZzsnvfIlCACLQUOeMLRJ/8yVNpFO3NZ0G7+RyfBw3wlR/rTL7zGMvHGJHjQn7kDF2VygkDyVGfS0cL/oZ0+6QxyZSzohVf6NwdPPPHEwj8BpOdCS3ICf3iRX9/lVEpjb+d+Sq49rx/fC4C8i0bGpT9AtlreCm44+4IFgRD9jK4CSvQmywITtEsWiwzqR6aHTJFNPwKAlM/ql26jj7OgJ0iuFyfIFYdfX/SFAIW+ZFPaA3joV7zATzJINtAvWwPMcfwSsOOnMaOXwIHdojMEUuQSToIGbZiTaAjn0DpBqTHgH74bPzp4X/tshLmGVsYtILEAQA+wSeYKeTRX6X30IsfmHp1l0YRtjG3Rph8Beea5HzSiH8mOPuFAx9I7vte/YErmMiWa6Jj5FLnRtgA1c87nMvHkzfyEi4U27WRBDz3JhD7IQbLNNdCNMrGp2EATdBZoa9MCG33BlsvI2v4AP7bDmOCJD+hMF2Uhmhyac/hDji3wmDv4hA7GgpZwJrvZapEFDDSn18xJGXrfyzJbyDIPyJNxe978wUNBJN7FhlpY8t2O3lfcocM86CWsHVYWKGPBFiUu4BMsMIqMQ5y1XMSb53P1ht+Mc/Z71Jd9c1Q56SkxYUSsHAe8l4xA9tOM7f/hfHBecuw3Y8nJAwwoR9GKZfaM1NdTJJj1HoOUI+YX7XvgjFpxtyKe/TLz9iUGtOtZBo0DaqWWoWJM0ZSx5SxYmU22IQ6CVVz9cb45dglKAGcimdusWOMLI8ogBofQ0/f+Xqth9DzDy8FgZDkw/g7ttQl3NOckyODmmg58No6WJmN0CmR/U42vvtBIaZT+cy0MJ6UtNSKTSpzQzd4fQZ8gq6Zd6FFDMija59AKOjknHBg8IavkhAzAK/up6r1YtbxPjTMLFJk7ZEDQI+DTnx/OmMDAXLM6b7zk2j5H8pv3Q6d6jpELTl+yNZ4V3IFkeHKcf2g8djgSR5iDrDSuLfE0ZxJ0hl94nWyrbII5MjY/l5G/mjdwjXzry9/6jzMusOCUkkELPOZJPS9qXTLGn1YOONB4kqyQ7zm18KB32vfn8bueg5mf+CuoWqadKRzb9/DVmKN7c02LOUMWyGq9B934OOBwiZzTbwIgsljjnN/6gb/nZM+1nzlQjzP6hh7SHnrWY8MzOJEJ/QKnb9N5cdwzXoGrIFMgERzynX7JYMYW22M8dKuTX80F7fqOXNAXY7Qeo336yRyq9WhkUODob7IH95RXJzNd27P8bnWB9gQxsoRZPHBqKh1Uy3DG6B19CShl6fAk+2Zb+R7TcejAzlicpNvMc7yitwVaAmelrXgjGGvtDjBuNiFjgpvAy8JADpDKXPWesQgKtZ9y50Vyne+0U1/rNeUXgCzUWcS0gKBSgx5ld+l1/MErP9Gxgt4sXtc879BhlaFnIDusNFDUnD5lOUrKrPz6X6bJZwz1Wk/PZIg5KPWqtbKWtR7BnRLanKSYIEdbsnLKjaxYZrV6Chghxjj3N06BoFepFENvRZmjvKyR4RTILMHJ/kMr0T7LASvwFkQywAytvnx3wgknlHJbY1T+w1lqaWDlliNdl9IaL8dto4yg4CV7VvFfIOMzPGwzIn6MR3YoQbvxcFbnOTaLnDnBkpVkhl8pscyC8iqr12M8lTFQmikbQnbtUVwm64p2xulZ8i1z6rfA8aGHHipZlayMxzFdBsYcptrpzmEyHBx9GmdKImWtLCAcd9xx5fNFoA/t2b+WMfvd8qB9Zwy8JwDIISe1U4fXbYWAzzmI+JV9ifX8bDNMU7RZBFkIykmOgkjzR8BUH2TS9rHoMyAw0jb5jgwniKgPgaoXDlq+zrvvVpvwnLqrcz3zFm74wbEXEAQ//DAXWj1tfDmwh+4hcwKHRQfvaF+pK/7KWiWDO4ZzAmYBSP19giqyIXDBN/wTYIwd8pXDieosZhZO6G2Zy7oSRNv0ovnpO+/WtiaH+9S0Xm+2CR2zH9sY0NF413qSuHcFjapo3vOe95QAbhEfZFPhb3GXXIJlZCd2nT1hy9kyi2PoRnb8zljIBHq1wRobYIz1KbLZaz92d6L3yQy7tF5aL/OeZ1Q/WHhVhYQ2sbVki/6JvLO1OcxMYNsewNSDyA6rDD2A7LDyoFSJMbanzYqnFckcxR7HIE5RneWrnfV6lZphYthl4WRXlMIohRMA5b0o8bzXgj4ZAQbLPgzlNtpRFsaZffDBB7ec+shIcorqk/MYipy+pwxHeZBSl6xqphyGU5ZT7qwmW13l/DCo2ZPI8LTjB9rJmDnuAkFOgYMEGFIBJUeXgZZhki2VtbH/TnDub1kKK8T2boE4hgEr1bIBDKNsW068hK9MTMo5a0dp3mmuIAbVT8qb7DGxn86hIYLyGGABFVpoU5bAii5nUNDjACIr3DJYyqIS/IZOLY/rrEKglgef47EASkZTEC97PLaXM3twfMchE7TLzrQnGKYPz0VeOVIcKnvA8EQgTK6MA43RA6QcMvdZZu8OXpMvMu5vQYisLJ54Ng56nbXhVFntF/CimT7Jmj7hI+uLtmgfhx2+9dhDq3o/qIy3dsxXcqW90DJ0rmW+Bc65QFnWV0aHbFnM4IQbWw57Qid9Gz+HUkWB0k2Oqr2y9fzUhrI+ASnaCDLJhnezCNJm9uu5VP+YT4Im+5fMJ/NGSau5GdkP1PMz2YyUZbZ7i1VekC/jygmo5iO5t2ABf7wnUzKSZAJNjCEZc/TRPpnLKbbayR5BpZbw0BZ8fYbv2smpr2Qqp536aXnkfXT3HRl0KJC/6VXP+jv7Q1tnmMzReWTOONEOf8hq5kRN//yfvcAJnIwTjjlBun4WfSwmCDTpzfDAmMkn3Sv7Y9yy1jkVt4aUOLI/AgPyg1bmElrKpOnX+ynfJlP0onlj3HQvmpBD8oEXdDDa0evmMlxjJ8ay1PWcqeUplRd0uPllntCXsWetDKaNZHXRTzZY4JV57neu3MjzkefoSviiHxsgENdOTnolN/72GRoF17ot8xP/zW3lvz6z0ODH/kNtkwn7L83PVn74BU972tNKNUZOXJXlU+mQjCVe5jRVdKHj8DL7qDP24NbSt87i1/oydM3/kTn/G69MrnHk0D6ZaHPTQmD2e7NX/A48gxNcyJ+5h554SpeRiV7K2mEVoZewdlhpSImpoJGS5gxYXeSoMHSMMCPBOeTwMNKUMWNO8fqcM0aB50RGBl/myPUYHBGByLHHHltW/Rktxt37+k27DD+nPiWm8GKMZGYEVzJTghanhOrPfgyb7B0V7m9OpiAGbhwZxoMhybHd3jM+5UAMh0CBgyMY0hcn2P4ZjpBAijFioPxmhLIPKHsZOQcMvPYZVcbJmKyGyo56niNmpdkYZBfhoOzJ3kxGmCOnDCer5soR61MYQwfjc4G8QCGZXDTGM84tZ8qY9MepgYvxM45oFcfAWAQvnDN9OvwhJ+aimaxDDqThINi7x3F3UIn/jV15JcdUpuyss84qGVR8U07K+eaQCGYYeeVF5InzyIlAcwsV+peJMH6ONZzJj3c4ehxOPOCEyG44SdCeHu/UmSFjxwNBF5p4ZuxEU6V4nAZ9kDcLHPhiz6Df2VOjDBY/9e1Ah+yrslhhAQIfOLkO1xBoC/zJhr0/vlM6l5I7Dju6kjer4LIHsg/mhd/4zOknc23miOMcOdUOfM0d+7B8JpAS/JgT6GWPpPYsKMCTHKAl+vpBJzJBTvEB/wKCBGNBR4fjcBq9az55T3vGZyEh2SwZf3vk4NTOT3JBVvBKW7LCnGU6Be/hixdkE63JBr0BryyqwDnyLAizQGCecxbR0kmSdIM9oJ6jO3yeazXMT3IMT7wk47//+7+/pZwYkBPtwo8zjcbmN97Ak8NtHJkTnH0yRlazGGABxTziqOdQI2BsdAsdikYWQ1zFQQdoV2bSPIard+0nNcfQsM22exYecMQnlQ1+2/9nHqEd2SUPLaC5MdbzlMwJvMix/s1RdKKXzRVzhA4kr672yQmiqjLQ2Rz3v2CKLuOI0wna9kwCe7JmX6V26VZ7MY3dGNGyDlbIijbIoCw8Gpg7aGeMdCoZFPyiG3tkztOjgjLf4aNSdvS0751tyWIA2pvL5MDzgmK8tHCQkvzcWUge6U64+B890Iv9wgMLfmRNCSu+5HAwga+FQjSCozlq7qCtYBMv6LH6IB30po/gQvazV8/Y2VmLufpCP/MAT9hXOsE42U2BlCycMeAnHZtDaSxEkT3jpIfRHO3pPLaZHNEb6Nje4Zy9hjKg9kjrx5wxDguk0cXoZt9h9rGzU9lbSk4ccETHpXLI3CRf6EOXkBNzxXjxgq6mR9HT+LIIiyf0os/RBw3oXDJBHugK/Nc/HUveoxPNGQE3HWCvLT1Bv5EFukh7/UqPDqsIT/l5X9rosOKQ/TUMQhxwxpPzXt+7ROFzdj2Tu68YHUYxd2j5O3sUOKLaYcxyCEVKZbynnewz1A/jVDvTWSXWThy37LvThsDJewwrBzmHmXAC4cGxyD5Pfekn/ef4fb85Kb7jEDBanFzGNw4MhzJjBtnzlf/jmObOrayUwi34MlKcLYYrpUTox7AyjPAwvhwS0/In2QbOTw4mSFlkaJ8x+iz71ep9Jbkvr74LTL/69G79bPr1PHoYI/pk7HUmwJiCT+4TbenkeW3UJwLmWo7cXwjvHP3OIUEzBp7TwPhz2oNfypWSIfQ8nrWlzJG5jBkO4Zf2c1BU8NcuPnneeNEcXllxRwtyk3Iysskx4vR4LnTUZ/ZteT57ejwbmdJG9g/VkLsj837mRT0Hw1s0yInC2stn9d19uaOwLt0co5GxaRctc9l2skrJTKKVfuq9Xu38zMEl2kRL8pqj/LWZ+xIjA54dwzcyrC/tCzI5wuTNd/Vc1k7KWsmzdtI/3uR6nXbc8MAP7STwDeSgFI4wgEdKmsllDu2iewRaHHMLO2iAFqER+qGdcfkcH/UDT/3q3xhS7dHOQe3DJXs0vZPME3rkftqx8dUyh2/kDl5wQOPQrtbDxmh8fuhOvNGf/lMWS1a86/uUM2rDDzzwW7v6kL2Fs2ezp7eF6JMcWJUDV+qDcOCDjr4z7pq+yeC2vNamsecgrZwEba7WMpf7SmMDWxmEA92LD/rIgTWZk3DRfu57pPu8R8fAOXiBuqTW8+kXHrVdNXa8069MoGeMxd/68jcgU+iezKV+yUnah08CvsiEceSE5MjEGF9iM6P3jCN7q5WQqnqwgAR3/K0PzzJ36LHY1eAXucg4M3fH/ILst015fW1TYmdja9HXs9Hrta0F+sML3xuH9wSR9b7XDh1WCXoA2aHDdgTGwSokIyTD1h6d3mE1gZqUdVOu7ETXZHU4lDKayQZ2I99h1UAAL4CUrZHF7tBhV9DXAkgVGDKUgtoOHTpsLPQS1g4dtqNRy+mKViStQsoQzDtgp8PqAF5ZNXZao/IpK+n2H3HO64NNOnRYFZBJUT7ot/LbXKzeZbXDzgrJYCrVl2H0W0Y4mckOHTpsDPQMZIcO2wlMNU6c0hygtEsAUu996rCaEDWpdAkPlUxa1RZUtqW1HTqsCihhFECmDF0Zn1LPtZ5c3aHDZgG6Wiloro5RvpoTdjt06LBx0APIDh12MLRTcL3ByNhUXvaI+FUMgNZLl42i51rb3Vb9rgWnjep/FcayijK5SvK2kTDv6oC14rHMNS3rfWYRbvPw26zyNQ/WMv7tCdub1svI0/bGa1vp5g4dVgV6CWuHDisADmBwqqTyMhmC9RgYm/yVx9rz5HqBXLdQl/XowyEkTiW0Smv/nhPucpjOqkAOWICvVWTHn6+lBMlYnRro4AInBW4EPvBwMIh2HXIgm1ODQxAcqKMk2Qmf2wtydUcOHcHLnGq4HsjhLQ4XMV4nNW6vvbpOK8R3fW6WkjMHZqCVQ1Kc6Gx/89bOJTxQfpd7IJ3GvC1L3XMqsRMncwhQrVfoCuX3Tq+cd18tvB0oosoCXbRjDpLHHI4U+cpJt7mrsd1D7Bn9esb35lwbYOYQHM+knzFZzSE3srGeJ19OwdwZMrEOkclJ18a1o/V4Dqgiu2SWzLQHY20LyPVROezGATVkuYYc6uVZOnpb6zV0YJNzEFE7Fzp02Myw+bVnhw5rhPb+uh0NcHFAi7sC3Ym1NcbK8fmOC1dmWTsSHC1HvLvU3lUZvnNEu+Plc6/gKgEnRPDoGgZHtI9d7j0FHAjHxaPnN77xjQ3DSWDjqhInrjq+fex79Hcx9vaUL86Q4+L333//4dxzzy17fnLv2qI7N6fAqYZnn312OXhl0cXuGwXwdW2MayPWegn6jgSnULpT1hUPgpSNcODxzBUCTmy+8sorR+8P3UhwPYQxmHOtnsQLV8M4xZVcLALBnHvuXFvjwDBBW30nb3QVXef6B9fx0FetnHrG1SOu4HG9hkWSFuiwAw88sFwVYfGhvU8zYAyuuVDWqF9XnEw9u9kA7VwN4dqoVbFreO5qkVtvvXWby2592rK54noWVy3lTtcaBI/0mn3s9PW2BnJnLtDNrjOyYDt2r3SHDpsRegDZYWWhvbS7/Wmfmfd+/mdU3DEmazPWRtvOvO/m9bUMLvnbKrgrIKxOxgFYNKaxz73vXrHWWcvVAe4dy7UEwJH+ThV1IMw8mi/T/6IxL8Of+nNOuAyBLF87nkV9WX2WdbTnpT5KfYz2a+G7Q3Oe97znlVXrMTrpj8PsJ5mNZem5FrrWgE4y1njJIZKplUXSfy5cX6btFgd0t29oCs+1jmWZMcLZXYruI6yP2x/j2zLjWFZ2lx3PlMy4poBctM77WmlRf46veIm3cTineLDMuBb9yMY4FVqfcXxdgUBP4gXdkUBgHh/g7eoB8uhv+4SdUFzff5q7fd2fqE3VEO09f0A1hrtM9e/+PQtfdb/+dv+nbCfd517DWm5qXFUGWKRzN5/7VS2yJfvUjmeR7KxHf6z1Z5m2A/S3ipPWdiwrJ8vgPfbd1HuA/sW/VnYXwXr1B90h00mOLDzgM7lqIXqtnqvr0WmL5lzknG5wz6P+2DN3TObMg7XwpUOHVYSeR++wsmCFmHPAychdfhwaJSEcEKVUueRbkMFZ8RnF7R0lUp7nKLjrCVh5fvTRR0tbDImgQLmjLIIyE/0opcp9Te65EhjkjrmxqxoYh9x7Bhys4h1OmIyEvzn3+vC+vuHmeTg7wZNRUdZiHFbJ9Qtn5S6Mo8+1lfskGcLcnSU7l5Ix2aK6LMt76INODGt9TyFIQMnQa98zaCgrqR2OSZ4xnpTjcNQcTuBnrGRP+9oTsMPBGI3buz7TnvHqR38c5TiQ6GKcY3fHtaCdlHCigzvD0BjPQ09XbcAbPX2XUif81DdeoBtjbzzkIXfhOSlXO5w04/QTPFOqR9a04wcffQ4v+Bhr+JF7zeChDc+Ep/7O+7mDby2ZrBzkk/fIkMvtOVRkxdjqsqncnQo/c8Bv/CW7+KSd3IkHN9+3NCPT5FPAADKPtO2ZXOytX/THp5SWjZVMk0F8S9Af+kVWcj+l8YyVgNUyqi1BRe6ENFafm1f6TxmZOZP74Dxn/HAwD/Amd98ZVy4/J7PGCo/cbdneH5h5B3/fcx5zvxxdgnbe8X7uhIwegzcc4eHHO2gCB7LrPXIcmuiXTNU0QQvPR//AG8+iS7UPNzwxrhw2kvtLb7vttpJNNy855rkjFZ4WJoA5MXZ/Jcj9ePMOmPKdn3myrg0LIvqFk4ASvslyGZPgN+XbYzKBZhZSzDMZKAeX4T8+oC+Z8JzxoEfuVjUP0AlPohPIemjueXzUpnYETVlc0kbKysmUeeA9tM38wh8Al+ia3LerDWODWz7TN7mGi361oQ/f5f7B3IlqruIn+YoswAUePpsq8yWD2kf33C9Mb+GTftBH2/DNvaCR3dAn+OSuR++TQzJY6/kWMs+0p7/cj4z+0dHaxJPcWdmCftFrqkS01muBlFNrH83wC6ADXOgqeMFPUIrGeG5s/s9Cpedb2Qku+s1caPUafeJ54+0nsnfYTNADyA4rCxT2F7/4xXKXk1K6V77ylWVfhRK3I444oih9K9MMgtVlSvi0004rRsA7cfq+8IUvlFXAffbZZ/jOd75TlL/yMAaQMVRyyBlgBJSbvP71ry+r2T5/8MEHS79KIV/96leXMq52bxaHSruM89e//vWyD8oKtzLHK664omQFGW2Bqz4OOuig4rz43+ccfcC4KL1U5vInf/InxUk844wzhmc/+9nl/+yv++Y3v1nuITzhhBOKQ3HPPfcUB5gBeuSRR7Zk7Pz2P+dJZuFnP/vZlgCUI3DHHXcUfN1ryLALrj2v3EZJ2+OPP17K8tBD+8qRGFPvoje8XGFhVbUGhlEAo+wN3QVwnD39aMfFztrgGNp3hRYXXHBByeLAkeMqIwt/fEK/MdAP46usFB3xFR/0gxfGmT11aMAZ+djHPlbk6sMf/nDh65FHHlnK4JQ+KRc0FnTAB86qkr4f/vCHhe/oUoN2b7rppuIYKcXjRF1zzTWFx0qDlZJ+/OMfL86ODA5ZhZMyU4E5fG+++eZCR/TWD5ooyePkbg2gv7GbE3/xF39RcKz3oKGBDJM70l7ykpeUsXzve98r/SrvihMVMNfIIpocffTRpW0Zq2OOOabQxffGYlxoRvbtx3rjG99YZPaBBx4ofPQeWSM3bcDDgcYHTjM+cTrRkUMmEIaf+Yqm5lgbdHhOkMFx49SRX7iSBTJirijBhANem0P33Xff8OMf/7hkLNBDtgzOZEJJIJztvTNXzXt4a4N8cPzxst0TSG59T+bJgjmmrM4djD43l9HGHHrVq15VeH7nnXcWnAV3xikTm8A89DEXzj///MID8ugz+NN/b3vb257kNBvnWWedVco7Dz744KIH6TPzj1zQM8as1PAP//APy/fKScmA+cLxJ5NkJzqDHhSgoA895t1t6fQaI/orY73++utLGT7+4KVSVHwjE+Rvno5Q5kn30KGAvNNPFhHpH8ED+aYT6SFjRO/3ve99JQi49tpri0ybm+QbH/FUkIXOeIp2dIp5r8qFrlSCi37vfve7S3k2ftFP5hc5FtTRfWyW79CSbZNZhTM9SI7hqw2ypaTfmMwB/CSDdfD83e9+t7yPZmSTLdRP7MPhhx8+ui9cm7fffnuZa/SwYOyAAw4odpB+gq+AStUKWfXbXL3rrrtKP3DU1/HHH190CjAGso1G+GQ+03ctmLvoox9zUEk+20HGyBq8d9tttyKnfqPzRoDxKHfVP7mHg60PdANZQEd8hjvZk1m3oEKnwsWWEPPLXGE3LGbQW3QK2cnCdQ3k51Of+lThpbnPZpE3clBXfXTosOrQS1g7rCxwoF73utcVxzyrvpSuYIjzxthxngR7e+yxx3D33XcXJ51CZoA49fvuu28xZgyYZyl4QSUDxMDbF8N54AQzVgwHJ41zz8niQHF4OV2MaessMSxXX311ySRy7F70ohcVR4dzxmBnFfrNb35z2c/HMTMWziQnkbHn7APOpnExPOedd15pg1Hi4HDOOcb6EIwIVBhnTijnnLOAVnvuuecW3GLU4OS717zmNYWmnCoOC5y1wfD7nBPgfzQSRBg3OnIcGXEODlomoOQ8wGUMOGqMKIPLIeBI+d/44qzgAZpkP5QVXAE3J/oNb3hD4R8cpsp7yILFBM4kPnvesxw4kKwe59r+E44YRw//8cQCg6CAQ+Bv/aMDp5CDwEE7+eSTiwPL0a73rpAZiw0CDA6HNowtq9NA1gmNOAcCD20JKjii2kJb+BgrxxS/w5etBU4a+cNLTrZ2a+Ackif85uRYKLCQgU8WJOp9S8l4Z6UezTg6xsVZMhZBEgfLOI0FD1JeK/gybvSM0z3GU/PNHM0eTnzi2Jkv2Sunf47b2D4ic+1LX/pSGQ/nM2WlSh/NO7Krf/zhNOK3xRgB/l577VUWIYydXHHcszeYXjjqqKNKG3QRB9/cNfd+8pOfPAkPzjfd5OAbi1ayFAIQNMBzdIQLmRX0oaGFMuM3f83XthyW3vE5mtBl2duKT2OHHKE92aTjLOAYIzlIhiRZWfoOvcgqvH1mzvicfMA/8phSV/qJvkWrtexNXi8YC51DbvDXnMJD+q1dvGpBYGUuwNu46EC0N1fRhe6xf9ACpYUB89HYPUNf4C++CJg4/toRvAvABEv6V7KOVhYp4CYwpUfIFB1HFuh5/5NR/EZTbVkQTdaMTAvezElBIhqT10MOOaTgpV3BKP6RJd8JRMhqsvZ0CLkVxHnfXBXgwR///UwdHES+6SbzFL2NKVlMeOo32VR9kEGLfexT5j0ao5mxwMd8tnhBzxu7OTMG+CBYw2NzV9/sOdvkc/MudqzehrG1ACdBuIUvvDImfEU3PEJn31kogRda0vVkwLwzn+lBPGPz6Byyg454MQbGlkoEdKbXyB691m656OWtHVYZegayw8pC9gFZpafkOVxWKBk4jgtnlzHk9MioCNIYLA6jICSrf8cdd1x5nuHzvB9GiAK3simL4juK2r4cwZmASeCgHY5g9ga1INBgeDkpHBptOzyCwU55J6eP0WCAU1bHKZFhBBzZXO6tH4YL7oylYFhGggPCGedAa5dTwQhZ6dZeMkwpzTMWeBkjB9ZnKUkCDGRKqQCHEs4cTgZSm/AQ3KZ8NqWB3uNAGQtHaaz8jKOGT5x/QTgHIeXE+kEPY9S29hhhDqwFAMF1SkWNa8qACt45JBxAbcIJ79And20ae8o3tYnWGW9kzN9xqjyXcXGW0JMccDaDByMvGys4FhTq23fe40BlsSMlnPoXgKU8VfCNjpyXlPhy4PWnX/hv7UEsKZcytrZky9/GS7Z9Z+XdGOHOIeY4tweMeF5baSfzKGVzKTs2FmOV0SdLxs8Bu/zyy0vmRRaFk9xmy7Rj3GTU337ISkru0CfBDSd97LAQtDMm2WVzXsUAnnBAyS99gU/0ibnn+WTFZcI5yeQDb/QLH8+RAXzSFjqRyegWcluD9i2UcKLNWzKgDY4iWgu8ZWGMhZwbF5qZD7KF6GPu6KMNzoxdxlzAkmwxOpD7MXnxvH45+fA319GUgyxIkmGjM7OYkCA08pGS6OgT9DKf0IMuonvotG19v56+0RCuAnHBFLobtzHOOzimHkNkCh+8h7/0bbL95BQ/jInOSomv98mycZJtvCEH5jnZjIySfzIkO0mO6TxtyVLRcdoXdJBHwYr3BA4WXtgY46AzVTWQSXNI39kjygbSN3CnU8gS+SJLxkZ/ygoK1owpMpEFUPhZKIXrGOhPIGSBwPwRMOm/XjSNTvHbnGdrZeDMDXwi27VuQE9zBa3RbexgG0C2zjnnnDJuttcCgblobrCtFrf8bfFGILlRADe6ib4T9LGvKY8Nzz1jfqA52YkMsI90pXlKb6pY0A7ZoW+mDgLTBnqYR3R+to3Qa2y6uZ13yRk+b5YTqTvsWtADyA4rDQyR7ANHj5PHaDG+DBFlbYXcil+cHsrYynj2ofmM8vXTOhqescJJWXtWGwwGA5IT2rIHLH+3kP0zHDXGIHgADsYYjG3Ub//WjvZS3sIBQgerzenD5wIlzslYW/o3xnYcU2PJ+2MBm5VVzoBVYQ44fNB+CjheVuThy9Bmb9A8QMsEeMsAvpGHZMvQpC77y3gy1rUGZVP4asf4OYJoYaV/XtvtwQrA+wIcmVelg5w3/NbW9rhaYOy+Pf1ylLI3a1ngUKoCkKWVbeHwcLA4oNo0JqXlylMt1sgGmy9rcYrmzcGAeUtPcGKt6nNEZWLoCplyp+dmsSD7imUYOGwy+xzFRaeMLnMIiXlJ3i3yZIElDjX6ci4tQAg0lB9yiGXAOLIOeLHw4yChVpa9L5CwwKPqQDsJosbAWGWcZFsFKQINc9Yijex5AuLtee1D9luj/Twd1J4ebCwybsoKjce4lbKux7Ge6hd9lOhy7AWsY/vsajkc0+Psj3aygBbc8z3bpWJF8E5/qc4Q0NOXnjcen2dRFPhM0EfP1QeK1botgYnMoCwtec/+YwGlNpyyS7bMDfqmpYP+lY6bP+aNQFL2cArYSPrX79p+guDe2pyp+eNzcxF+5iK5zD5DC8cWL1XT+F4wSd+spXQ6e6pb3UoW6Ql00yY6twfsjLVV/+2nlh2B5rKLKlksC+Cvag5j97nA1HzvAWSHVYRewtph5UGpj1VYe66s/uXgDgZVBioHKFg9FHAx/pxCRppCZpCtWic4SZmIVWJOu1XEOCzJVKY0c5HDKHjjNHDIYkRjCBcdqd4GNq1h4ogy6gwIJ1gWMm1aKTYmDqmStgTBub7B3+gkGIghzuf5PYXPGKApZ4Jh5xxwSmTgxsD3N954Y3FaBViL7iQMLlZ8GUoGPSVQ+T0W2HKcOerKEDnTeMgRsAI8NaYY7JRDowX5qftZRA+fCw4Yd86Mlf9FctLy2P9W1mVVssrveHkBt2cE/hnTWkuYaqdkEc9rnNBAEG+xICcF1riHZimdzN8+J6NoYu4AdJFViaxyCgXKyiplWIxtPTCPFpxM46QnlPZxzM0ZsquM1jxAZ3PcfDVv7Ovi8MnIyQosWzI2Jlf5nZI/GY18LiNJz6hYkGUUCOG7YCgLPZ/85CeL8y6rY2FhTH8IamR84C8DSAanFhzCF0GkoAVvleJp195X2bDWCa/HsUwJ3TLBZ/0MGYM3WtTf18/QHxZn0CyyBmQLVWUopRcYbe0+4RZkajnvaJoMczv2ReOlk/BDtjD3EVqUELT7X8BIziymyEjaK0822S0ymJJbNiVXm+QQH/OpXlRoecU2qsbQt32H6Ca4k3mU3aaT6VbzYqwEnN2ymGHPo+BcyayyXTjFrkRf+i1IYkMtHLFVuYfYvMrC5bJAh9J/7DEdUZfcw4vM0x/mqi0ibeZ/0QKZhVb7GwXq9Xfk0HYRi9CL7hqex3uyI4AkO1mUGVsYXgQyyhbY6AE6zH7V6NQOHVYNegayw0oDRSxIs6JrBTIOk8+sSNsbyCgo0+GUMnz2lFmhV+rlmVyT4cd7jIlyNoGp/WscTkaX42wfgpVIz3ImGCoOHmNfO9UBz9nbYnVXmSNnldGGh3eTJeMQMOR+6z8n13EM/E5JKIPN+RQw+lyZG8fBuHNnY8qjlMrKSnImlc8I1jjM+mXEOe2yht5L/3DhmGlHeatnBd7BJ6fsGWtOAfQDL0ZS0MgR96zgdGz/Ve6O4yxz9owxpUmcJO3rx+ccDb/hxvDKyuFpSn2V0DLqaJIV/YAsl72f9gvBX9DP+bRXVR/6NEZ9GQua5pRQjgrHjVMRvnDYvOcZPPQ8WqfUNN8JQPxw/vwIljhuZAtdUs6JLt5DSzjkFE3PxEkW/FphNz6fow+nH26cfhmBsUMn2pNH7RuEExk0ZjTjzFpI4RRyjtCrBbQgK+SbE8fhtZ8JfmiW0mXOqUUceHHk9IlvnLvc12fOyXDBwVhT+m0xBZBVGRhl462jlnHoMzQy99EvmWm/Q1tjbU/v9L1DdJTfmdtk05jNRU6zLKg5oh80Jm/GJ2DjmOO/ucmplDXXp+9lFyJP9dyNc+45NM9cNm79y/RYdMn1PMoHBYzek6WVoeWMGwun3V2H+C/LSO5ymqYAwt90C11G39iLBcecSDoF+kZ3C23axkc8sBUg1xzkNGFj9Ttl5f4X7NGLmUf4mlL6nCCMdvW1NcZEX3ieLMi04pVxK++lV8l1DrYhdwIozrNn0N+PrCxdZaGMfDtwBA3puOwfpYvghO4CNZm1Wh9FruhwfPMbzXLyrv/hiU5+w4ktMA/wM4dB5XRQ8ghP48aTlKD77XmLYIJcwZrvzStyyX6hAdrZJmGBQNCS0nu8VH5NnvFXoEk34VdOECYf6BMdgwe+16//s+BI5ixGCMIcOGO+KqPVj3YtOo4FQxZc8ZlOzeFnghfBNF6r7CEzsU/ooS9ZfwuK+ENvkK/YPbIRm4IOOa21DYq8lz3JaEIm4MJmCL7NKQshaOe7Mf3hffYCjcgM/edzuJv/xm1ctY6Bi78Fwfoj/wJhOjF2Ea89l7lOFvA572vf2OhCsmMu1LKT0niy5sfYo1fybuwLXZDT1Tt0WHXoAWSHlQfGXDCUazQAI85R9D9jQSlzlAQRnDUBFSeL8mbQGB4GlSHndAigBEOcZQqbY0OZy5YJQLXPQbECyABS9mMBJAPpwBmGXF8cEeV5HAXGj8PDYUkQpT04Z3+HIFBfHEZBCKNjJRNOHAbjMX77uoyHI+U77QqCBFkO/eGUcdQ4CYw5BzYn/Slp4uAzwkoIOYTJLAhQASMGTyu9DCqcrAR7h1FjjNEfrf2fUipjb0s4vatcMKexMtoOHeHYaVu7xoWm2hHkM8jGiQaCAIGPvmW1tFeX6QYYagfkwI2TgaZoL2jOKZG5tgNv4RQnHD4CYo40/C02cOpz5QjHyN8cCE6AQCiBn3bw3Y9sEieXw2y8aMuJzV5VAYvfZAjd4ITO2iFraII3aMHx1jdek6NcHbIIjE3bThwlR2Qb/8kPRzX8HHNK4uwIooDDIYyVo4OODsbgOJElsiNzZWGAk0vOsk8Lf8xBCzDhK4fYarpFFgdK5E5KeLblmQAe2vOduaI9TiNawcFngkGBn/G2d/4Zu8CQjAKyzIEmV/SBOcLJo0voA/Pb4UFw1rZssD7xnDNHBsxjvONUkkVjytwle3DwLL5xvM0j9LOooJyX/JPjN73pTQVvfBHo5MoUOsh8hjenFa5kxGE69ImFozjBuZJEn5x182oR4LlARUmshQT8hidZTsCCvpxuQQZ59L9FNOOHqwUMc4yuolvRQ8Djf3j5rM6C4g2Z8732HS7ie/LNUaan6RK8IAtkU/CSZ7QvCDNOfKcv4Os7YxB8mqs5iMSikyAYvdC4DSATfNN1+JMyUwtNaJr9rXiUA3rYCQcneRavyAH+Z+EEj7Xpf2MkZ3DQriwv3NkU8kRmBXYpndYvnQJX+NE9ufoCoLWMoQUmi4PmFtzRwdwmT+hv7LEl5Aqf4E+fwgXt/I9W9Bz60Ed4O5a1JoNk2Tw1bmWsgkdjYWvJsoCYDTNuNGMrjV/gBzdjpQM9i4ZkO7rPs+ao/9sAUrtKZlWt+D7VDGTR2PCfTaAbLNq15cXo6Bm0g7f/2QC/ybbgET5khl6DJ77DNYebGQe7YIELDuw9HRKZR2syjabkPu3A0Zz1TGRHO2Qhi17+R8csYNBr4RFakj08RfeeceywWeApP+9HPHXYBJASvPpuwJQ4+sl9S/V3KX3KnWQR9QQ/eV7b+aw+UCVljfXBAWNQ45G+grOf7IFKKWH+z5jSfr33Z2w8bR91maKx1u3UpWkp1cxnoWE9vhqf+vvQwYq2DAbnJOOQCeAY+KylTU1/40hbcMjfoXNK9TKmmqcpb5u6EzK4GGN9/9c82ocOobV34gTXdGnpkHZqmtXtRpZaGmds9bMcCScAc9o5bimfFshxTgXY3o0MLNqzWpekxVHVV+RljH4WK2TgL7300hIQZm9nLW91e6DmaWjGCbJ/j4PI2QvvZb04SzlVNDQdm0vtOBbRr5aNdh7WezrzXuZWcK/njs9Do/yd75aZuzlBs6VVPe9anQBqWWzxrssG8xmaclYFAuQE7xIEzoPgXmcJ039K7VpdNzY/pnRFq5Pb8bTQtjEGLf9rvOq9f3U/Y3q6las8U9O2fqcds596/GM6oZXTVoe1d2GmrTGZrvH2fq2zal7VcjJv3tQ6q35nTGaiLwKZE7XtqQ+/qvvKe8kMtvTNOOt2p/T41Fyc4vEYn8egnms1LdqxRWe2ur6VgXYOLJKd4F/ri2X0WocOqwo9A9lhU8DYimkc3rFLg303drQ9aMtftD12sfLUZcTL4lE7jvVnU2Maw3lRH2lnLDsamNrX0bbV4pP/raRalc5+mKwqJ1MxhW89lrrttp+WRlPjnOpnis7zaN/SpcZpEV1aqNtt+xgba8qtrKjjm2yTcfgsp/wl+FkEdYA9htdUG1mZT8bHmOs2ptpseZoSQfJBHqzM61ObvlfytsxYpsaxSFbaNqZ0wdTcamlUP7OWuTtGq7F5N8WTMbzrZzmnSoQdTEVGZD6mTkBuoQ6+8n/7/Vj/U/Nj7P+27WXm7zKHRU3p/cCifqbkaooPU2NeNP72/3k6rJ1nY3jMk+Uxu7Esf+ZBvfgxhsvUeNarf9s+1jIXx96f0lktLOLVVF+LaLys7Ix9ti3vUu3QYVtBP0SnQ4cOc0G5kLJKJTwcWGVVQNZqe5/iuLOArKPyTsGXvUU5+h9NlWFtS5om6LOXyH47CwO5e3E9oPTKWODsPkP7JJVhKRXN9TQdtg7QMHKh7NVPdzo7dOjQocOOgl7C2qFDh7kQFdGWQoEeHGwMTbOCvj3oWZd0pjQrWYb19L8jx7KrQGhcl6J2+nbo0KFDhx0FPYDs0KFDhw4dOnTo0KFDhw5LQd8D2WHTQI6bV37nDrCp/YJrgdxdpW0r+0ru/uE//EfD//pfw/B3fzcM/+N/ON1xGJyW/w/+waz0z6l+MgFKO5UdLtp3kWyPfpycl/fsP8sx/E6p9FxOXd1WYLy5/LndH6bs0M/UIQvLgDGgpbE6XMWJgTJbTuVzIp3TCDeCb+nLaafKL51QOHXS6Bjgh+PY0d2eMu+v4vHp5CwnQzq11U89fvsOc8WCw3c2irbLQO6NdPqi0ywX3fdZy0eOyO+XZE/T9oknnij7HJ2svJ49bZGPXN/jlM55e6W3F0Qf5toQp1k6DGij+8iBQ3SPU1SNf9m91ZsZjJ0+cFAXfhu7fezmHH1izq1HntYLeE0G9U9HLXOydD0WV6vQMw7o2pH8y3Utucpm7CTpKWDr3U/sHSfOks1cm4Uf9bzMqa/0qrJ1WwQ6dFhF6HsgO2waoFDttXLvlEBuI4Ciduy3kygd/z87dnsYrrhiGB56aBj+6q+G4aqrhuGOO9xVNRTj4Y4yd8q5IsPJmcs4g/YOutfMvWr2vDk63EXBnGnGxfUILlhPYLetAC7wdyWDUzPdS+fSaXdFoqs7wxzmsjUgoLNn0pUQoY8+nDq66ALotQDnwhUM+IAvywIDbc8hmnOyLrzwwnLk+1ohp+5tDSxqg2y4D84x7/Xl6wH7F8mi61Q2ak4sC2T1K1/5Srn+womuy4Cxul/uhBNOKFdrLDN/tgVsBO+2JXAwzcm77757y8m3OSVzWbw9Z58r2XBFAl23KiCgvfLKK8tVKIK8jQZ6lS5zvYRA3CX1AtZdAci2fciu23C/qDnmnk3XD91yyy1zTypdBnI67LLAnjjF27Uo7OtagAyzHWRla+3S1owB6B8eruRYq8yae+4rznwWFLOP5NJCWiu7Dsyi811r0qHDqsLOvxzXYdNDHCaHuFiNd19a+91aoD1SPfdouRNSc/fdNwzf+c4w3HTTLPvo5/LLHRrzlNK/FUQnaAo+lumfYy9YcYmzO78YEHc9upeOcc99gtpyZ+DUuNrj6Rd93n4HZKjce4iWgi7XSNSXhgtq3WsowM0VAYvarI8oBzIKVpqt/Odz/Qg4ZF6XxbVtd4we7vOTtc39oPNok+845wIfd9G5v8s9eXl/7N32M/9bUbfg4DCTZTKfY20Ipjl6rkGpj+mvn5OdS+ao3oMacJegexZdqVJ/N48G8/Aa+3ysjciqzG19xP4yfVi9d/hSAt4xPs/rf1mcp/Am6xxqd+Khb8vnZbLQi+b9WvFqgUydccYZJSsORzSm97SVe1yn5kva9+MUXPIhg701tG1hakxTNGyfdy+fcTh0aUyu5+mdZWTTYqP7b+mx3J+Yi+UXjW/ZMaxlvky1MzbOFqb00FR7dAna0m8CJfKj8kOVQBs8rmWsPhMAOnHZ4VtTpwC3+HiODnOtT8vrRbLnt0UGtgP/5j07hXeLmwDbwiOcUgE0pntb0D9bc999943K7BRoyx2QFr9V+OCHCipz02Jaix8brR86f70Hm3XosD2gB5AdNkU5jhXr+q6+fGf1jjNv1Y6BpOQZOc65Z/2fS9o5LQnQaqiP/2YTJAH/+q+H4S//chhe+MJhkDRTYVJXB867F3Kq9NYF7HBT6ufC+9zf6Dc8/W1M8M2VDrkaQYDDIci9Wxmj542LgfIdQ4seuTw577TjzdHlHFXv+mGoGVeropxOlyajC1xyYbigUpu5wgGuucieo+A7BrIOqrSp/ZQqooc2teczpU2+F1TkjjY8Vy4EfMeo4l1d7qh//wtK01dK9vSBfmSifgftXI7OGedUKSMS0AQP8oQu6B0DTvZ87hnj89nVV19dAm0BnIB5XlmW8eNVrstIG9dcc00JZNyvqT/fpS/0JSfG7AddyIeMI96iFV6gSxvA6k87aOw7z46ViaIRGmfMnCm/8TSXfeOt55RRpcQXHmjsObjMK4eL/AoW4WKckQ+f+Q4/tF3jmTmvL3SI86g/OJM3peBkHE7ayiXcyUYH51pe4HzjjTeWlX2LHPqES+TNWPVlUaUtl0NX/aTc3fNk0/9+k390MWY8xjft6tP35AuuZGZeKWmcXGOLo+qi8zPPPLMEvXCGX13qTq7JGL3geXKPPq18RGfCvS3Dh3fkD37o3spW9HEukidj/iY7nkV740V7Ywb+15/2vQM3tMWrtB29Bci1d+EBH+95JwtaLf7RRwGyYW7KEkUXctjDs+hN72kzcmEcxu0dcloHSKFbdF1kJEEVHI0Hj/EFj8fKyfEJzeABn+i86Cwyg45w1F7GlnkEB8/gjbmivYzJ/9ozBs+kf2Pwfy1z8DYO9DVu/RgPuUumD17Gg0bo4TsZzIcffrhUq5g/9aX32qzlEN74Zjx+/I8XZBmgEbxS1l7r2eg+7cV2JGCL/UMPYzNPyIG/8REtjU3ftW0AZMYp4uecc85w1llnlT7wOXbD+3AN3u2VMZ5PP8bh76c+9anlO+9rHw9iF9E88zh63zvabn0Rug4vzS90WLXtFB06tNADyA4rDZTyrbfeWpQwZWz1M4ZRBpAxA0roGBblfIzURRddVJSxUkrZPqCkZyyArIEfsscew/CMZwzD6acPw1veMgw/+9kwvOtds32Q6wEGyvUMykWN4dhjjy0rjEpIGR5BgfIWxuWjH/3o8Gd/9mfDFVdcUZweBvAP/uAPirFltBk2mS8ZJ0b929/+djE0xskIP6TudhhKG2inbFbWdBmAiyysciOlX/CDG/oJsjgT9gwqRXrhC19YDKRVfk46pwEuyoF9V4M9etdff315X1aFcb/sssuKkbWS7T3vX3DBBYUusiXKzzih+lMS6zL6Aw88sKzaBjgRt99+e7lU3bvPeMYzSlaXE+suRXRTqgv3gL7RL3tRBXAysuiFpi996UuHBx98cNhtt92G3/u93yuOqPYFm96zso+e2mbkyR1nMivZLXActG31XRtKZQWM9r3IPsJDNttdkD/+8Y9Lu/p+5JFHyjPKzgAek3X8/dM//dPSBt5yXmogA1bXBWXoox3Z39e97nW/FBB57mtf+1rpE7/wmIy+5jWvKSvsSt9e+cpXFh4LtpRTHXnkkcUJ9KwSLIGzsU2VoXKE7r///tIXOijLskixzz77bJm/d911V8HBAsuHPvShkqWXLUJzDiyecB6NlSMp+PO5ceM1nPBD+TcauzqEzGjzgAMOKPyvMxd0QuijbfeY0hvK5PCQI4lHZP/Vr371L11eD19BP1zNTTgefPDBhS+y2GTfPifl8EcccUQJ9lQe+F578NL2SSedVP6f2usMnz//8z8vOsAceP/7318WPJRick61s/vuu/8Sbr5XHkd2/O2744477pccfGB8t912WwkW0NWilpI8etH75iRH17zAX9/VQbggggxqQ/Yfb8mxeRna+19mXpmysdx8881FB8KFPKrCkP2pMzh0A7qZ5+hOdjj6noluJx9kgD2g++hLTrxSQHM5QGbRWSBkzsLfvDZWbdIT+C/7c/TRR5dghbz7znPk++1vf/vwFsq/0jUWjehf4yBD+vX/tddeW8rL4Y32fpQNk/M6CMi+VjKCjuY6Pprj2tMOecJDcq5/ckXPmEfeQW/yBD/zkz3UH1r4XJ8W/sb0UA3aNB/NXTpeqfOJJ55Y7NPll19e2lOVg5eesU1AIIlG9DGehBY1eJ7ORFPzU5DJXkT27r333vI+/rzjHe8oY0QX2zrYAO/FtuENHqIFnfCxj32sfI7W+vc3eYIDepNdupuOoiPxjN7A7wD6kW36Cw5k3Vxmb+DHz2CPVBmZw7Vc1XrNtgK6W5XNfvvtV8ZCl6niga+2yLPfAlW6g/yYv2xguzgleKS/4K1v84iu6kFkh1WGvgeyw8oC5csBEwgJHijqBEMMgeCF0eG4MN6UN0PLYChNpNw5AwJKwQCDt4xC/rVfG4azz7bnYRg++EFO0zDstpsV2fWNg1HmTHGeGUpO73XXXVcMJ2PHiBkrIw4YRkGUleFDDjmkOMGMS4wKh8wYBQV77713cSCs3irL5BRx7DionBKOsbaXBU4Cp5XB05c9H4yojKlAhOGzesuJZnT9hof+OB05DKimc1aBOcWcoIyXI8HJZuSzGOBd4zAmDtK+++5bjGoyFzVwerWl3WQ1lcR5Fl0Ehm3JFmdD0CBYEKwqH9UGx8bY4CP4xCftCRLwgiNHxqwcczIEg4J6z7d41RBHEV84hmQV3njMiRYIwUGbHHNOkGCSE4EOyQYYB5ztZbNI4tk77rjjSbxFfzQQmLtsnvMsEMGnGvTDgSSb5EWQJYDwv+BCYOi3IERQhz7mGudX0M5RtdiAzmOBEHwtQgiOBaWCOfhzDsOTlCAbEzpxOMk8ucJPtEVzzprvyLi9xBYEfCd7TGYEQvCGM6eXg2be3HDDDcVZrJ1nvHawE0eObJFNizecQjjaz4n/svBksgb04JQKTgVIHFr44YFnUyoogKGzyJqAB08sBggEzVuyNW8/Ip1gDhqvQCdlq3AmQ37a7BbHnYwJSARh9GAyejVYXEFPeJhf8MIPgQHa4ie5IZMWvDjkNf3wWmBJtxovnSDosRihHQEnOuqfgy0wxDuybt6ZS3RZgAyQcYEAHeJddKbb0RE+0e0WNgQ75oX24GiekJ0aBCH6ErT6rQ0OuvJ8Mqgf8qF9C0/RVYJNwQTek/ka0BYt/uW//JelX4GrhRU8pm/IHrk65ZRTSh/mYDs39WfuRPbQ3twkO+YTnrBXeGHOmMN0EvrBjczD3VwVRMEJj+BkLgriBSnJ8M0DfQjCBMx0u4BKdpH+Nwe1DSe6xndsibliscn44NIumrFfFnLoSjbBb3iyv2iMHha+lHK++MUvLrYJTQRsdA9ekT1j8V22GQA6nnyRX2NWIcMfsOiFNmyVeUXeYv/obp/RJQF6Qt/aNQY2wHPkF831b9z6wIuxMlV2Dh0s6MKVLWev6HTySR7pEPRCE7JHpulPgfBYm7Ll+jR3M642OO/QYdWgB5AdVhYoW44Ihc8Ri+PNweIAcyoYS44PRU2ZJ1ihgBkRmS6BBKdz2ZPnbM165JFhkPw58kgHwAzDpZfOPl8PwIfTdf755xcjwZhwcmVJjSOleMGP08/QeYcjyUniuDHsDJXAiwEVzMTYeYczzfCjR7Iogpy1HJqQ8jm05gBy+Dk6/ue4oLEggsPKoeOwc9Q4RYJNzmTKWwMMYU7o9GNsxpw9rcbJoHMmvMfJ5FRqB/6Mse/rTAjgRGsjzjQctcUplKWw96k9wU6bKadC0ziAMrhw5wgy5gIFjge54TgKmuHNwOvPu9rw/rxTcz0Df04g/sNRG95JG/Dxt8yMlWxOCKcNDcI7zwkiyALnU5ApOKoDEc/KSCTTIkDEO45Z67SYT2Rw//33L2MWqCTgwE94ckg5iXjECZSJSJCPrqH/WHmsZzlgxp4Sa465PWlZfRc8c9g9429OoKBQMGH85AsvLAaZ19pJZp7T6Fk0QjvfeYYTzxGX2cAbzyXAyP4w/Yf3ZByeMiXkwI8gX/BWH2Dh3ZQUookARpBh/HU5e/iav9FSm55HM/IJ59qpbUFbxlKXn6M1GYA7OWx1mYCIs48mAlxzaWzhCB/gxflFZ862vugM40sJpEAUvnhQH+yV/VzmormJFsYEL89bTEH/lDf7TkbL56mKQNu0hRZ0tPezkFHrdo59dHvKLD1r0UEgyulPqWzA+EIjskmW6U16y3ww38ibAELwQvbJj+CJ/Bi7MdSLYGRO9ln/dCv6mnv6wGN408lkGV0ECzn8KCDo1p+gyrjgIXgD3rfAQLebw4JmYzCnzePQFp6ygapYjAdNPY9P2kCPRacaC4DYVXPLIg+c2EvzHK3wNvMeHdAl+jg6y3N1Fo1OoBvIdfbvyaziLfnyPTpZYEF7bdOx9ARcyDfa+S1wzgIaqE93lsEVcOfkUsGWBVW41PbPoqNgEN3qfYT1fbf68h1e5LRTn9HPcMxexxr871lBOxzpJvgJsEMbENmLzUPDlGCPgblIpowLDvhsPq7yQV8dOvQS1g4rCwxM9oq0wCn0PeeYEfZM9kfGSOTKCMrdc8scYc/WfOtbsxNYb7jBYRYyD7NTWF/1qll563oCYc4C50PAx6ni7MkgMYCCsDFDUWfy8psDZtwxioyUdvWRbKxS3ezrrPd3LgMcam1x5rLXJCWK2sr+JXhk305onkMq1nrKX94PDTh2nCJZNoaUUUWjMWgDVdlajuvFF19cAgPBJEO/CGToBMWyO5wHZViAI2fVW3kZxxHf2uzEPOBUGA/eCLBlIwVusiIt6Efgl8C3DcRreglsBUf199mvyWEjAwmUIwe1PPmbAykLwWEWCLUZtxbwlWwsw1/PyAZwCtMvRz97+sYO08gBRxZKyJ+fWo61yTGkE9Aoe9Aynro9TnD9fQ31Z2RFwKKkL3im3LI91bbdCxi5XQss87xn1np4BvkyXwQxcYTHxs6JtZfSCa+CeVUR5owAQcCTYDuLPvUixiKoD2TJ39ozl+kQuNWHaAG8FYgp/8NvASw86B2LJBbBahn2W0mgIEq2j5wrJR27BqQeP16SrQTV2Tvnd1uCPcYjNLCYhi8WctoDeZYBcosvAhsBVBY0AuiNRhYALCKxX/qlmwS2wY18+lzAoT2LB/Cp6TR12rXvskfaOFJVknfXe5IzWpjv5hPcslDYHgTTHtrjfzLmvQTc2Sst4G6zy3Rkbf+M3zxHL2NGW+X2y9q/yFrOIxAYk31tRidMjTc2L3tHF9FnHtCr2uglqx02E/QMZIeVBYqcMUkWgRKm6P1meBkPDjnFzwgyQv5nBClkq7n2GyknUqayjFPGV3KADrvumieL24cdxvESvM0O2WlBu3FQxgwFh0CJTg7fkO0QOMKb8xpYxiHhZAjyOGW5AkR5D2dCpsM4Qw8G0HUVyexNQejKkCtB0gdDzPnn9FvljoHk7CXTw5Gxgs0x4jTAy6r4WOC7DORZwaL+0RP/Tz311MkgsG4/h7LYD6MsUWZBOeQyxtveU+MRTGXfi/esaCuf/vznP1+CZ2WRcc4StKAd2eOItM42unEA8Vv5msUD15nEWQpu3j333HPL8zLm9b69FnLoBwez3qNDtjjF5I3zlQwB/slK1sGWYFFwnbI07SyiU+5JNa9yWEvmY36nDX17VpYoB2CRR0F4StrGIAdXJNOpHX2RY6v0yhCNm/zNy/yij/fNtfa5BGg5gEq2RWAKL5/5bWFGlmEZSHCbxZNlr9rIgSXZIzqvfVDTt/5bf/Zb4YvFDWMaex/IUOK1+SHrbn7giQxpstA51AQN0XnRvvF5p5nKbinxk1USNEUmgzv9kut+XOuSICu6HR7R7clMCxZlA81z81KZ9CLcUoFiHsSW0GV4v+iePc/KiFpEQl8LWutx9M0HOlI2NNk8gY9FnJRwKytGJwtg9Eyy6xZBvRtbIxNn7tJd+JcsuHlPB7cnI9f4ZtHRnBKQoS86o6+5tmhs4V3mUNpHR/SFj/HQ33Cpg9KxtsmecQhAtUc3eofstVUn+KVCRR+e82OxD96eZY/MB2PCX2OMrmrH4Ie+EbSbO/oHaEHeBNhjwWfdVhYfVEll4QRdEpAuc1qr9/CDTsbjdpGtZyE7rCr0DGSHlQUBjOyi1WmON8OaAzcoWk65lXTOBmXvcxkkxoejL+voR6kfgyww4izPW5Hk3zgD5sEHh+Hb3x4GC79uo3AOyzOfSdk/+R1OD2dGpsley3aDvNIr+6U4S/ZOMU4cAM4IBxe+OTUzp/x5hgPhu2QdPSPA4YxZged8CHoYGCV76GF1Hi6e8R7jXBthho3T4DvtCy6yV4nTjr4CC3RiALUrCMheP05GDnpwWINnPWe/C1xlQAUKHNVkkNNnTlXkkOrbeP3kxLqMXeZQ+aDsn3a8xxlpDwPKhfTa8BxaWCyQteB84D3DXENOLcw+NXjkdEXOBjnjBMGXo6eMzmq4cdnXI9DIaaycY+9wKO0r48hyyvUdR0l/xpKDlIxJ8J/TWDntHGVOMT4ob+ME4YsxWTyREeXo4DfnyXPGLnukfTTlLKF3sonK4eznS3CDZ7Xzhp7aMQbzguNpzORSvzn1kRz6XPt+yIH9kMqvybrFC/TnxMmqJqCVeVAea7+TRRyOMfzIVS4WD/9BSi7JqhJdQTYakV94mitwMcfRC9/gCWRSzWl8RDftkGULEd6rx+05jjdnTTs55IO+sPjiHXzEpzrrbY5lnmYumg90D5khB/btRZY5pDltMwsb5n/kOfvMlJIq9TvssMO2BLpZzEEjNPA3mbOgRnYEtmQuF5kn2wsnY8BL9CFLZCz6w2fkxkKGrKP5ITAyH/HS3FbOpx9yT+bM/zoAh1tOiox85ITVZKAyxswz46RDOP9wNL8Egjmx1jjIibLMZM/NNUExOYpuJ9vGoKTTXksZS/i2QQZ9BBc4kh08wWdjpFs4+/o0txxGQ0fA2XPew8/6epfoeEGOuZly65z0mnJWv70X2c5JtWkHz5RHKmXXFnlADxkzfMjhO8pllbJbzLNfXpmkfeJolIOt0MJcUi7qcCGZRO1lL69nYk/ggW7+zqnL5qZDn+grZa++Y2/17Vl8yfz0f3iMP2hEb+CLMvdk4vFDOSlc6R6y63ntokl0Ln5FRpI1FDwrPbVwRs8BY9JnTvZFK3PSPkr2lAyRVXPa/FHmfN555xV6oI2+LDa1CyDRUeQd3ujrbyWrOTiNfPi7DXhzaiy+G5t5rxrC2BM4W9RgA8g5utHZMus5yTzzGQ2iG8xFB1Mp+cV7Zds+p1/RaF75a4cOOwp6ANlhZSEliZwI5UMUL0eeE8RJZjQ4cEoPOQPKQxlWQRCFy2mgqBlUQSSlzwDNCyDp6N13H4YTThiG7353GP7qrwQqw3DsscMgfvF9uyCYPUv1sfQ1cDgEwvBnpBgG+2QYWU4oZ1OGzUozo6cNBzVw7OOAcjJyPLsSSEaKs8xQGp/xcgo4k5xIjibHAL3q0l0GjvPCkeEsaCPfo9273vWugktKf/wPVwYWrgyyNjmXghIG0eo4Y8kRYDg5ewwmJ5CjnqsYBAbGwsnRDueDMyJoxDsOpu/QgSNqbwwHwpjwW7BaH9zAAdG/DCEHmSxo06EleM2wo0F7xUUOWogz5B37D5WPeo8jQsa0L2jNiZ0cIifzec8zHADj5tDDS9v69n+9Jy6HjpBBtHYwTfbvcC7IK/kQaClD5EDBG12MSxAkIORM+B7tOduCK845B41TRnaM2V5D5dH6Ix85QKoG2QIHOwlujQPv4EQ2gLK4lMJx6vCSE6c9Tpr9SZw+q/dOSzSv6kDD32hsocOzAlP8d2gHh5ATmDI8/ViAyHUFnPrMef+jte/RAv3RjOPsOdmblCV6n/PHscQrJY4pUwzQB2QNn/FKO+aPeeVd+JBVixCt44m+ZAUt/Q1/Msfhw3u4CKLxBk9yBQH55PzSD+SJrOTKEcGL98yl0M975o05Cl/ves/YOaz6hne9P1KfsokCP7TiFOOdd/Ea7b0HL+PTJ0fYIhCekin7fM1lQRbcnMpJR7TzxxxFH7jlYnjyYi4mO4+m5pCFJgsd8EI7QQG6C+yMl2ziH30gKCRj5pv2yECt280D7wmK6Te4kAEyX0P266IBHPCUPpLpFHAaI7ngtCuTNR7zB5/oaO/W+wiN34KC+ZdxCOyyV5ruNX/Rmtxlfxxa0Fuhn+eS9RXc4yGdbTzGhW/4INAUROdaB32rTrAX2wIW25CDlCwYOmhOe/olR/htEYSeJl/a8GO85jI5MG5tmsN5ls7xXBZ5Ui5KT5EbMmsBiS5Dh2TdArK8ssh0D57pQ5tAQEbHaNOcQBc0wyt0gIsFHbKHF+5Fpt/YEvON/oGLZy26OGRHdjOncxsXG0kG2Sr0IPPGXB84BV86i9zQsxZaBcL0VGRf/3Q0PrRgzBacyDB+69fcMz50M2eMnXzhLfzZi9AVfvQt2YcH20A+2U00sBBJx5t3ZIxMtAvSHTqsCjzl5z0/3mGFIYe6cATqAyqiVDlbKQ1lKJJxyQXK2fORE0/HVpYFchwWRiX7QVQi8ktVAumKDXrKU2blKdqzAsroO9SDs8UJygEdbRCZd0DKWuARw1Z/X+/3yjhTEuPzODbZr+T/us/QKocEtOV7oWddfpR+00671wsuKbnUX33p/Rj92/H4mfq/dpoBB+7CCy8smQ8LAClpsjrNUWTsW7qmZBb+2qnLnFt+1ONP/34yjhywkr/r8sSaRtmvl3FzbgRKHIzauW/LGtOG33Co26hx8OO7/O3ZlDqmfDu41bT1bEoQc8/mmEyGDmjrGe+m79B2jFd+MvbIcUpNx/YEZkz1QTC1fLT8D22CW2iT/aCR+8yd4K9KQVYUDzhs2mnnei038M8+rXwWPEPzVm5auW5lwe/wMXMk8jlGyyy+eFeAVF/LEZmp6R6aZF7XMhb8M+/zNxxrnNNOSuMyn0MD7acEcIx+9fxp51z29NWynvtLfZb9cNFbmac1Xj7LvB3TLe38rfGf0gv14SZ1m5H7yFV7yErL/8ypHL4SOazHW/M4sjzWTk3jjCl91/jUuiI8Dd6hR9seqGmUNts5Vsv8orkZ3EDewc92bC2doi/mzYP0k3H4P7I3NufqZ9dq/4JnZDLP1HcyR/6mylczD0OTWg/UOjWfRZbr+Ry61jSoaZdxTuHRocMqQF/a6LDSEIM5dbJcFPi8z+a973MrnTleXpBixZOOb0/RFm95TqbCyqkVxDhy8y6SjwMExi6Xro1oPYb6+xayT2gZetRQB6HLQG0kl/1ubDyL/k8bVmetRFvBzWXUgkqr1u0+yJqu9WeLxjM2/nYcdfZpauz1MxYiBLeC3tqpmke/nGa4DA7oNbYXbYzWi+Qxz7TzYmqv21j7i/bFzcN7kbxPzdk4hTXP45zJjsheCcZy2fcUtHSfwrPtewzvMXrXz7TyWQeJMl2yHRYdarmdkpkpPRbcavyn8BnDKQCHRQeNjc2fds61/dVtJjgYgxavMRpEBubBmF5Y1OYy454axyLdtqivdkxjz0zJ5xTuUzpuI+bmlG5J0N1+P8azMRqNjWNqzk3xa5H9m8JnGdnPu/Pm4dR3U37KWvV2hw6rBD0D2WGXBs5n9nzlEJIpRzIHLwhorBLaSyHb0VcINw441fbYKSe1GqtcTLlW7u5a1X0gyXCvKn47MygTc3m3skzVBMrm2oNkVg3qg0jmBRwdOnTo0KHDKkIPIDt06LAykDKotixsrDyyQwegbMzPrPT8F2VtHTp06NChQ4dtAz2A7NChQ4cOHTp06NChQ4cOS0Ffpu2wacDmdCfIOcnzGc94xpr28m1myMXPTvUz7hwosQwokXNqnsyMPZtbW26bvVtO09M2fKZwyZUZcFeKuswek1WEHGmfMlUn7uWevOxXcxqfUkSymc/tb7Nf1gmG9WmMAO085yAm7TkZcx7NnY6aY+l3luxaZMmJjjlZ1AmlYwe3mPu52oIsrWevEJrbV0t29WNfbX0QTU699IwTJft+pNUFvHTaLr7Ra2My45TN3Ce4rbYZ2Avv+g37s1epxD76wpyq9/vlwCff0WHkfFvoE9tCnFJLX23NPMJnOtQY0DiHHsHdScX64Ac4BZq+3Z70hwM60uN0Ui9F77CrQd+81WHTgOPdXXVw2mmnlb2ImxlycuEy4KoJV3e4akIQuRYjychdf/315X6pXHq8NcB4u17B3XXuzlvkRHz0ox8t1zzkeP957a6FJmsBbS57ufsYwP3qq68uR8K7wkIA4gh596g51t7R67lShFPrCHj3GDr237H9joav97zBhRNnr6ej7d2ptmgBwR2Drudw5Duoy3w3KwjW0MlVMo7+J6M5cbEGY3TVjXvw0Dx3R7bPLKIHR4/MOnHZ9Ss14IuAw9Uf73vf+wpuWwvbkkdbK9ObHcy36667rpyCnWtcanpbDHDVkesZ8H1bAZlhj1yZE15sKz22FqCTjJ3tqAFurgKhv6+99totp71uNLgv88wzzyzXDG0NsFnuerbHOae/WhT47Gc/W67r8LfrndyhuK1oXp8iW4O7Gukj44wMduiwK8HOsZTdYaeGKG5ZA6uQHM6x78HYSvQy0B5HPvZ9Pp/Xx1hwV78XY+T4fnfh5aL7qaDQ855xuqfgZK39Wrl1l6M+rQQvokc9zrbdHIuPB3DKMeRTeLv3zAq4YCtttv2nP06e+8jclSUjtwiPto32u7zvxz2L/ndP2hRv54HsofvrBDnoKNPgbk/3sQmm/S8DqR339ck4uo8Nzzhxns2VDu4I87yVf6f/1nhO4SJD4F69vfbaq9DUs7K6VuYdLoSv8+g1RZtl6LrWAGXZeQLMYycfcw4F5xaF6lNK6zbdlybT1M6BPJtshAOX6mtm6jYcjiVTccstt/ySPPpOZgNP8MzdmPW7i+g69oz/ZTLx2111yY6kv7Hnp/oYk2ntuj4ITcb4ulbeLpoDwXuqrUW0mvdOO8axz9v3jN3CVK6L8JwFRgETHWLOuOPSXYP1oWhr4eUyC3Xk0rVDMlCeF5C5u1LmbUy3z9M/LR/H6D3FgxYsTJlTdWWDd8wNMk4HtcHj1tq3+nt3wJJNdwivtY/6e7rt5JNP3nL6sud+8IMfDP/m3/ybsqjnEDs6t6bXvL6m+pln8+la881dmfVBafQ6XZ8gfdHYxj6fpy87dFh16AFkh5WGBBeyQPXdjvnOCqXvcnw/o517r3LptVVKxofR9JkMhv85476zms3BU9KmvVzy7bfvtaltWQm4MMpxCLXHUfST/oHnrJgq2/QevF0P4p1vfetbZdXUpcsMpLbGxg0XGSd91HeZAW0bt36cBmts3lFSlXvOjBM+Au/cmwVPtIIP4+sz7aCZ//WlT2NHD+3mfjC09D6aTJUPp0TKmDko7X118IOX7+CNB/6+9dZbi1PgYmyXKMNHf370bxwpgdV/rvjQhh+gbWMxVs+jq4DDnZ3u2WPwBafBAV7erUsZp0Bw4UJ7K+uuchH8yT5eeuml5YLvV77ylSWIlNnSDycNcCxTIvbEE08UXFw8j67kJff0cULQnIy0pZVon+czTpk7ZWouSIeb5zJ2MoWXdTCmD+2nPc+ihXZzBxm6plQXDniT+YXWmS/6iPz4Gy88b87keb9973P9JUML/G0sSv9kqf0Yh/fRAR3RhRzCC45T8wToT/aS3HHo4zT7HK3M9egFbeTOS3yEkxNb9U2+/K4hOgRd4KCdVvZDT3PRd/AHMv8cT1lopd7aQhf4eD73IqKRtjPX0Rl9ovv0nc/JK7qZJwIk8pKSPt/jZegGV3KkjWR28Y8+QIN6XGhQlzLCC67oZ0zazJyKvtO297IwFV2Iv5HBtFmfXk3u0CjliPiGHvRA+JD7EbWlDd9pGw7o5TvjyL262nYRu8+e9rSnFbpkLtVjig7LAhd6Ri/5Qfdk+cnRvO0C3jOGlFSbN1O6PfocL7QJD+Mm2+hRz89UKOhf32gTOmQuGG/9GYC359HW2LKwlLHjfSpYWjsa3Yme2mt1KjCWLO4F4AqXWvaND93ppdis2o6nrVx5Yey+g2/oYux4AW/8zGKl72V7taNfcq+vyEtwqm1EaOb72JToFfKmDc/Xcha++FsGlI6yAJl5kqtAUp4LF7rL/+QuPkjtn4SuNa/NVwGqMZDtfqJ7h80EPYDssNIgELn99tuL4uVYyFLFgaN4v/jFL265K8ox/lY+OfoUMWPOSaTUXQvhO6uynH4Gi2FQgsTYW7U+5phjynM333xzCQgYT5lCGTf/y5jITuyxxx7De97znmJA/K9MR1mjAEFwILN40003lZVSfXIiPfP+97+/ZIwefvjhUt7oXbgxTO3+CUZOxitBjjLHOOCMHTzRRhmN8j5tM1Cf+cxnStAk8/f1r3+9XGuABmh1zjnnDH/8x388XHzxxYVGSi0ZsiuvvLKM/9WvfnUpC2IsBScyXMpm4Sww+trXvlYcfQZRHwK9FrSndIyR5EygX5xXOMCPETYW9Dr11FPLGO+///5CI89zPI1JQPk7v/M7hU5WmY844ohiqAWbeAJPxv+oo44qztNDDz1UnAd0xxs8euyxx0qWkFGHL7q4dN7KeJwQdFi0nxZ/BKF33313yZoJIL2vH7zBB3utlIfJOHEOHn/88S2lr3BHW7iRYU4RJwodyK0sKd64SxJNshABjJMsGAvnlFwKWs0H2Q7ywakiD2RAv8cee2xxpgGakF18Jm8+hzPclYXuvvvuhd5KamVb0QQ+ytzw6oMf/GAJgJR24o/A6LDDDhve8pa3FLk3j8g9RxofOUvkWztKztFCuaHPtQ9nGV20IBNkWcYGnhdddNHw7ne/u2Rbze0HHnigZHF9PwXmMFnhdBqXd/02ZnPRPCDvrvcAZOarX/1q2X9q/6WFAKWrHMYaPIeueAtPdDXu/fbb75ecaPOE7MHRM+bLM5/5zEILesZ4jd3/gj8yfd999xWcyKZMtow1uaJ7zGV6w3zQrmAAPfHXfCQnZIY8W6zgpF5++eXDCSecMLz85S8v5c7mKr1GbvGLzjB36R7Pkl3jsocLznj9ute9bktGRF9f+MIXytwjz2h45JFHFj3hs1RRkJ23ve1tRZbIJDpxnvWz//77lwwgGhu7/n3v/7e//e2lLfS55557Cn3INVyMy/iBhRJzn4PtfljfKec33+lYepJ8oA99r83MI/P83/27f1f0PZ1C35A1eMhWmld0B7m58847C63JArqYx+yBzP/UHkHyoT06jVzA2f/R7eYnnYAHnkVD2W84ogP5p48+8pGPlP+vuuqqEtDQRebfJz/5yYIL+YIz2UEHugQNzz///DLO008/vfRBL9Ip+Iimxq68lq6s9Tea098CPGAeoB2ekittyPoZt3bQx1wmE0o2oyvJiTnmXeOk1172spcVnLyHX/SLeeGqHfKkLX2bs+aJrB7clKDSk3hiTsigoj+9pNSWfOG7KgN6EM7mPDtvUc/cJQfmijHgtb/JKF1vbmjrc5/73PCSl7yk4G18dD9+sYnmrHbMQzRLCa5+0B1PyUcWiID5Qgdec801pU+0pUvYUrJpTqAreaLf6EPzlVz7nH5CJ9/ZHjFmUzt0WFXoyx0dVhYYXUZRedqb3vSm4ozIMAArhZx5DofP7WtiGBhVBoSx5UgJ9t7whjcUhz3BiZVNB8swPgIBbTFMgHPNoHBorPBzDDibHBaOEsPGqWXAGHZGmUNw4IEHFoPAECXLo3ST88No6MuzDH3uNBSQ5GCblKZlBZoBZnR+7/d+rzhiKVXzwzlmzBhcDg4ngwFj2Bl5jgkjznl46UtfWlZRGWnGTokfJ4UxDZ7owRFEN84Ao86hFUQKgBhIzg0jqj/GsQ5wAtoyxjgA6M5BAvrmOIRexs7gGougQmkh52bfffctRjzOKKcfDRl+wSmawhG+HPCs3nMAOcOe5+DbA4RPDDQnmxwYOxy0nfeThV0GjIWB5/xzyDkyxshRECToH63winNiXGTJZ8ZPPn229957F/74LBkosuUHbuFNQICYINBYOUQWHQR1xuuzOHr+N+a6PI18CaDhgqbkSVYM3oIJn+Gz+UaODz/88IIThwxwNM0X8iWYyAp/Mhzahgt668Niy0EHHVQcI048R5oskWd8F4iSMYEU+pu/eGMu5vAPY0Zr81Jf8yDlrRxAfRsveSDXgjNt0RVpxzi9w7m1wGKBinPfQoJqtE4gwalv90cKPDjIAldzA03IMj7glTH7zQknMy94wQuK0+9Zn9FvaClrzWGla+Ao6NYneRYUkF2LQd4nR3vuuWehGzrVdNMOBz3VCb4TkOiPTkAH4+LY47c+BO7JvAFBBB0ogEYr7+GxoI0uJff0GL1ggUzwQHbhhJ/0Ax1FtjjLAgA0tHBAti2EGDc9jVfGQS7NFbjjJZrRW+hNnwiczH3fCw6ykGUOCWDg6TcZIJPwF0SlyiKBJDzodjwTZOcCd/oIXQUb+mY/5skeXW7OoS0cyZv5Ed2OD8kooTGasFcJXPwYj4US72jH2IzV4he+oa/x4JMg/ytf+UpZTCLv9Br6wEObxknfW/QxJvMtlSr2/pIF7ZCnVKz4MTfwnnz4gQ+82BWybUz0CttY7zPUnzkv6DOfE/SjgznPDgmWPEc2BPb0u7bYCHqLLKF56CKgJ0NwYreMRfvmor4FduQWf9GJfUQHvDQW8mN/pDlBDukYullb7D95QE/2QB/mG/2Wg7zIID1sIcBn+mN//OBpndEOGCM9I+CEF5mCL15ZqNCf+ULHsB1sFnqTXXSzYEhvWmCs/YD6p0OHVYSegeywssCAUsIyElb9GBzGQvCWLEwMNueWglfKZvWb0RIocUQ4L5zWlN4xVDHsjG9WVBldDgZDK5tif4P3Pc/QcQp9zjHhWDCCAhoroPpgtHKAQvbFcYoZFQ4JJ4MxSPkQQ+tvTkOCDIaXo8F5Yejhoz3jzko245YVZe0JhhhhOHBo/J3ACfif4QTwEhSjEwcOThwvuHL4GT7tAsYXLpw8gR7DiqZw8nlr2BhFRplxjrMBh5TzMPyyYMYtGOLY+Ekpk/FxXP3IoAkYBS7owxFNaRNnQVbMM4JAvBDkc9gEjmhEFhhq9MRrtNEvXuhTlocDx8moTymcArxBR04Sp05GBR5oiT7klJNBdvShT8+n5CslTz6HU8px0UZgigc5HKY9IAo9yF4yIdpJGZXv0BZPHLKDZhYWUtYGUgLM6Sa/+vMuR9pYOJqcLEFNymQ5Z4I/csbpNH/8DU/tmHuCAU4hp1UfeAAvTpJ5YLU+JYcpIc3+NLKB597Tpx/jzrhAZGIR5LmUqPqtVFgf5B6fyEiCajwS/GRummvmFCcvgKaCIk4w2gswOJoc2vYwKp9z0PUtK5uTL+ERmTa/9EUmLCT4zNyrS7yjl7LYwuEV6HhW0Ch4I0+czcgRvNJPLWeRFf2mDN189rlsj2DJ3NQG/M0rOjWyrl3jxmu6RMCC1/SA9jjXnjcfPC/oMDZBpj5k1ciABQDvWVAIn9FZkGYBDi2V/iXoRjt8S9aL7JBZjrz+UnprXPrTfkoyfed3snjaC13oabw05+FAtwuWLJJoI3qfDqGrzWV4jx3qFNA2Gqbcsy4NTRBVP0snwpseZCvgIJAXOFi0QVfjJJvAAUHk1rPaQgff0c8yuIInelAAr13jttiB/sYeWRIIWhy0WIKv+kmQiZbRnXDBDwFetnjQJ3SlzKPP60qN7Bu2aItPFnnZSX34nbmLnxY+2Afvo21sjj7g433z0efJ3tP36EsWvB/d50fb6G7MfmLP2WS2VFt4YMyCVnOPvhOcpvJAUGmRRd/mVeTMmOEZfRTdop9WH9ETaGABVH/OG1CNgd70oH7ZBrqHPtSX5/zOflT9kb9s+zC/MxfRJnOqQ4dVgy6VHVYWOBApiWqBc8IgUbQJZLJnj2GgjLPvgaHMymFOclsW2v0vKfFiFLN3i/FngOpL78feHQPjEIAKegEng9Oo7dqZrp+PgeREJCjJzxiuIL89I+BkDK0uM3DayuotA8oA1m1yWLLfZt64GFzOervvscads+M3o8gByIp2G4ziPbpwtjg72afHkbXSKxOkxEl2l1MgKJTZE3SD4CD4rNvmvAguOI6cSYsT6FgftDEFaC04c+iLrIrAirPJiYaPbLksCdqu9wCatR5uAsjeiSeeWJwPmUX4OP2WEzcF6MMB5yhxUvEuTgvI3jbfcVo5dlbQZdlkeQSQshT4zRH3brKbSh3TR/bgZR7mZwo2arWdLMv2WWAi49n31gInlpNKL7SnOJqD3hUU5oqGWrYDHFSZBqWMAnG0F1SPjWtZ3YCW2Y/qWXMv+x3XA3V/aMMpNy6Oa6u3pvAjIwIqTnkOhkIPQYeFIbojfCaT/hYkGkcddGdvIdkyv80hCz8Wg0BKjReNYxHUtAoO0f/Gm72y7Smti2R0nn5dFh+QIJ3stPKRfZ1olCA2c1a2jqySbXpUgOJvQUxrM6LXtdUGP56j1/GVzmh1pz7oE4cE0ZMWEGVGoyvREM+0b3HDadO2SbSyDyel1fS5wNAYjNtC46IDhubRdew7i6LZb52FkATy+E9noVn2MtZttO2tR3+T5SyG8U/YCNU8NV3hV79T04B8qoggEz5HSzalB5AdVhF6CWuHlYWs4srw5Hj2/HB8rfzJMOQIbcqX0rWCycGxyp7SF88IXhjjGEeQktG1QrKVMmkMaFae4SNrtggyDkaFgVFC5kepGAObLF2chzzPaHN6lZblgJDst+PML3J8fG/l22qoUigG18prDkXRLmfb/ynD5WAxvlayfVaX1dRGlrEWFHIa62Akz3Ew7PEShAgEQ7P6NDo/DK+AjBG2p4bjHNxTwsdZTwbNuNGL8y6I0q4xyJbEYUzbnrXqCw/ZQ6W/6GxccB47rr2mHYfXDzz8lh3iHOGJjPmiO+fG6LbIiVrUlrHCW2AsG2AVXlZhHuTgl5RoyQSgQ9pEC0EAWZE95NTjn/1BFjiU4OYURM/hvd9ojm94QGb8r61FJ162uGWRZmqBYaotP+TUIoN5ZLEkGaIxIC9khPPd3ldnbLIB+Jospyysz4IP/JTBWYQgk2hgcaG+bmUeRBdFD2UMAlb0UwZKdyWLU1+L0LaddsjCvCsNtG2OmvvJ3BlTPa4xsAhH1mRmk5WRzaHztEnuOL6541RmG+3NjVa20NYChx/z0KKLtiw+0E3rhamAjmOfzG7meA6tSqXGFOBLfRBRDWMLAlPlh+0cIHvmIJ3Ryh7aCnQshGZ7RQ67MV9TqUIHmuvKX6f2z+Gb98yLmj5+0F1feJoKHUGr7De7ZuHIPFdtIEhMJQtADzJE9i2q0SEqW+oAKeM2v2Qoc1CPagc2YBHMs2dj35nzxkmW8Q2OmcN0PV9CSekYz8cWiwO1Hpo3R4wdXyzKoi39lwOGsh86fsoYkAMZX74AG6hseZnFzQ4ddgT0ZY0OKwucV3sLlEUymgIdBoyjoeyJI8thtsndSp2yEUZX+aLsgX1h9hcI0HL6oBIpxoNBUbJDyfubsbEHIieQZsU6J1vKCORUVN/7XJ/Kweylouhzyp7+PMfx40zrw/OMGUOek9iUSDGmcK435utPmZeyOI46B0Mgx6Bz0GR8ZAUFm+jDydAH+gQ/nxlvMkMZk344HzJFys7QmDMHOG7KbpQrWo1GM88yuII2e7Jy8AuacTzwIZe/W9VV+qj0yj4U5YqcSLzgsOIbB5gRDU045D6Hg7Y4MpwA7cOdAeYccVwZf7/RAO85Tz5jsI1HFgROVuO9j3Y51dFiglI8GUlOJH55R3+CcAsNDjGwyi7AnTLw8EQbTje+eI6zINCV0bCyXp9OCF88IRfJTMqMeRY/wx+y43dWrtE4WVff5cJq33G64UHe/QhaBHdoLxsLl6kTS9GFQ6pNTj06mFuySko5lfmZH8rqBI3GiKZkgAOpX+NFO1ncfE+mfaZ00QmhMrX4yQHNSa0pWRZsZNw+91u76IOXykmNEZ3Q0IKMeeu5nKCbrDTwm/PqOYtEvidTaIMfKRHH96zkC8bIPvkkbzIFaGLe6ANegk/Ot3kmC2DOwp8zXYOsBhrKqCjVJN85/VFQZRzmeA5tSnUEp1LfZFwFAhk05+ArIySzaY+q+SPQ9z69kFNKo+9SpqoNc9Hn8DdevIwMeh994WkBxbg4/+GPSgpA5uiulI2TPeOzMEfnKZVGX7ijIx1Efuxf0yY5yOKEueQ7Otp48A19vEOOzH962F6wBMn6910y437Cm8yPjMlvNBUIogM9YzHN4qLn6T3PWTQyP2TRlfKaM2QFL9GMzIUO5pmxp3984dCjvaxtFohSBRMZRivy0ur2ukTeZ+TUOAU2+kSfnBqa9vCJDAgO7f/OgWd4AYeUbbJ1DklKKSiILom+wDdVBA6jQX9AD+d0ZPpMAGge0ZG+I/t4TA/ATz/RlW31DF7RubYDZG8jPM2jlGWir3kgY2nMgjz9mYM5Zda4az77nF6peQF8Hr6TFf9HTuBtsUDmlGwny2iM0X14Q060ybagSU7nTf+Rm+h8z3tPkF4vaCbbiJ/+pgvYf2Xq5oHsrbkOL/3TYzmtGH/Sb8aDVjk9uUOHVYceQHZYWeAE54RMipmhoogFMwy1DBAjKjigiCldASPngePkoAZBEseKA8wga4sjw0lj1Di6gpGUcVHiHGd/5xoAjkecBU6C7/XHEZH1cQiE0hxGlLFltBh0z2lPn1Zb4cdQwNtzHJgcolMDIy2Q0aeVYI64d4zJuI2fE6d0SSkhp47xzpUjgggOM8fBuxw94+QQaEP7HCrlSN6NseJYGo/AW3mr7xh8bcroOEFQn4IGBlkQVONu3LIJjDBngSHWD4PLYRP4orssJ9oIXuCEJr5jzAWEjKw73gSzHHx4GQ8jjV4cMG3ggeeMEy4MMyeH8eegppSKY+xz7wtMOK3e9xynF46+W3QSK0CrHCiRa1k4c5w6DkSCGrgIdNEd/TlUHBuyhgbZx4tvubKCvDkshRORK1CA7zghvuNMaZtM+lwQmD0+uRvQuI1/DPRhPmRPjjmhbY40mTXPBF14LhjK/ZYcYfugBKfwxRMngqJ9yrA42OTdXlrtcDq1jw6eM3c4p2QhQRS+c+r0mVJc5YyCKlmLd77znYXW6Ac/8ziZkdBHQCW74fRfMqRP+2M5s4IoeJEltIM3mbEQlet8zIOUEnLCzR/PcgLNB6dHOizEvLPvLOWskQcLN8aQ7J/SariaI9ok03Hw4SYgyZUYysWVPtNF+pOBI+Pwcppjqhw43frmcJtfZNrnMup4g24WANDNwk1OFxV8or130B4//Z9x0Vuer8cVpxx9yVYWODjaFssEIoJA46bHzB+6yoICOpMv75F9AQAeetcih7mAdwJG3+U6JO+RyRzqRY+TZWXp2beceU2OOOPmMt6bS8ZKhgVc9rcZJ3kig2hk7jlZVLAKP22iNzy0R6bNxQRdZM48IiP60Ba5RqdkC4OHeQRX+pdudKDblG4H+ECeyIo5JTgnI/BBE1k9fEcnNDUf8Ml39AcbmHbRE+/tfYz+yrVMsT9kz15Ji510OxzpEu8am8UR7Vl4oFPR2KFGdKLP6XL8Z5PqLCf5yAFbaIGP9rYaF3kNPfEVP+h5wXAqhrxLxuHLzmTRDX3hTnYB+njW/2QT3qo+8BVevotOEQC7zkV1jWoJNMUz8iGgNNfMETSEH71ijlroiJzRy2iQigAybvEMj+rxo7eFJ3OVvTRGc8IcJ8P6NX6BJ1zIYhbW2Dg0MgZ0IYdw9Hebje7QYVXhKT/vRzx1WGEgnhQtQ8Eo1geJMFiUvO9yKAeDWO9TzHc5bCLf+Zyh87nvGUoGkbHTZg5k8Ez9f5wcz8IhfXgmWQWfwVk/ORgjpZQ5ACDv1EFHO259eS4H/fgsK8DeTZloxuaz3IsW/OrPPFOXjfq8PlCobtf3Nc1SBmwcoX3K2NryreAdmmXcoXvGHdz8nb1yCchyZ572vQun8D/jAfidTF1NL7jXn8PdZ+FF3s+BIylhrQ+omQLPwr1+LuVtkc/0m5LplEDCw+eRuzHZygEptRwH51oW4Ot/z0Zuw+f2/jp0sTJuIUL5mD7C30Ay5DmIpc42tPICJ8+398mFb9l/BD+f5QCbyGAtlznkqJ7POVAqfaT/Vo7Tb+Q2OiCyEPnwXTJ3kQeQ5yP7bR/1/PZcS9eMOWWRwTtzJge/eK+emykdzXgz3zLezMnMueCZE5t9ljs/M/fTl+cin3BoaV/TeWxcrczV/IkspVQ8J/HmnVYnBcKP0CcyoKyQ4y5gSBsWATj2AvDsZcvzmS+Rq5pX9T2b0eX198EDTUOn6Ijsgc2VUHk3sqeUGE4WVcKb0Db7VLMffUq3W8wwBwXvAgZ4hX7RFZHJMVuVuV63GfkLTcb0RXTvlP6u+RPdmTkenRRd2e5vb2U/eq3WZQJ/Zd0WiwS6+V5QJeBSKTJlY30W+c28aG1rzav6/sXY91rm6zkSGxVZGpOz0I5tqvlV24LMxbRb8y7ylmxl5nwrX+k/MtQzkB02A/QAskOHDh12crCyLUsn+6Dsu3WEOnTY3sCBl7mX7SObspGCDRlP1R2Ci1WQUVmp3Cna3hW6FrANQQCpekR2ale5NF7Fh9NJZeBklHOSrmBOtr+XbHbosDlh19BgHTp06LCLgtVxzqsyQivdyuvmHbLSocP2ANkW5ZeyMEoLbTdQEip4VJa7KkGFoFHp89gdgMuCwNi8U46p5DMH4+wKoOzaKa5Kd+29Ve4po6cMtQePHTpsXugZyA4dOnTYiSFlVjmlM+Wr3XHrsCMhJ1qmDJJc1nda7kygbDFl0/V2i10BUlab7SApne2lmh06bG7oAWSHDh06dOjQoUOHDh06dFgK+imsHTYt5PADp8X5cRiDU9KWOU2zw3ywUuxUQScS5l7KQA5kyHUTuVR8e0EOJ7CvD45OWBw7iGjsPePynj04Tt6sD4rZWnxyVQt6LYNPh2lwGqMyP/N5I+9By+FDOSjKgU2z9h16Y18e2Xd3nhNW887sx1kerrj7lV9xifryfaZd72s372rT2VoOm/SdGxZmt6/MrlfIYS+5imbRHDMXzQeymEN8cjBIDq7Zmn13mT+5YsHeRZlsp6D632msG7mvT19OyzWflF9vjY5JpjPXySiR3Yg5isaupEBfp8LuLPsajQuf7UdVgro1ehLNzWWn/44dQkNmnaDqFFKyXkPuccR/369XBiK79Iq5xWbVhzx16NBh7bBzaLsOuxwwCI7DdsmxazzsK7HPwrUXgZxyOZVkry/v3p54b4Y+OYXu0nLlgaPGaxC0O9Le9QguT9/e++mMxZHzrgFxZHtO6VsExk9OjMl9hblXbGtBu64bcdy70wZTqrYzAl6nFHZbyqhrLVyojV8bfXDLJZdcUq75cNUI5/9//28X3w/Dt789DEccMQyf//wsuAsIKN2fftppw3D66bOAcFkgCj/+8TC8973DcOGFQ+lnNt5h+Pu/H4ZLLhmGO+4w3mE47zwHrcycZvfOuZLCITO33357oc0iMA/oQleDuO7EReT04znnnFOu4XFVhkWfreGdKybOOOOM0oe/4eo6ENdECAQ2ElzXQgbgvcz4F4HTXfH9pJNO2rA5Sp7cH+vO3py0vQg2g/534iy5cS2TIHJrgE12hYrrVQLBRz8OFXLVC3kaw4Ns2R+7XpqlL7rENTnGldOCO3TosH7oAWSHTQmMgsupOYDutBJMMOQOKQCcXCuXjNaY4fGZ+9g4FdsrANKngwQ4edvTgbC6y4ivZZzJ5o4ZWpkMR6/n0uztDTIH7tGSAVk2eARWr90ZJvO4kUGedt35hh7a3Vl3BZhzDuNxj+VGAyfVVQkJFHJhvUzRRgLe6IvsuMPNtSazaxsEbrMArhV504a4/NVfwXNt/SXu+dnPZgFjDQ8+OAyPPz4Mb37zMBx66CyzecUVAsF/UO6vO+CAA8oBLrImy4AsmHs6ZeTNS/f2uZfPwpoTTT/xiU+Uu3FlYNYLsoFoFjmXxXEnqQUZp6huJDhgxZUXTu/ciGyhexV/4zd+Y006YxmaC0jZn2WydO4ydA/h9tKbWWh9/PHH19SnOw3p2I2gFVm20EcGgYUGV3gIuN13yc44VGdMb7qb9KMf/Wi5J3I92UeLn+5h1Dbdz64J+jt06LD10HP4HVYaUnLG6AiAXIrMaCtD4nACfzPeDJESGe+4HJgDyvngLOZC9LRpZZNREwgpPdIu0A8HSykS523qsJGU+OQuPu1zpnzGULv02XtZlY8jqE94MqaMp3YYT+9px3NK6nL/HANubNrhXDOovjNmOGojd0l5Xju+Tzv+v/LKK4vj4goH4/QevNMn3HNXFqMOT+1NHfLgfc5ivs/F47mrzNg5mPDVbi7jTt8pgdW/8SlbEnylnCl3qGVM8NJf6KuPlB4mwM0qu/fR0/PwyN1o2oGHNupxpbRNu8auzdx3ljvB0mbwbJ3Z3F2ZC6BT4gcHfMidamnH82gRhzN8yzgjc/AJjbSjjylZrNvwbMY4RufcpZY789DQ33BKuWjeS5mnH1mxs846q1zIrZxMH7mfM3wIfnDQp/FGrlNW6TNzzHt57rOf/WxZzBGIwyMObO5hC52Uw+EJOhmLNuGaOx+1Hcd0quwzd+DV8I//8TA897mzEtMWVMT/5m8Ow9OeJis2rAmQ07tPfeovfy6Gu/POYXjOc4bhV3/VvYnD8OIXD8Nttw3Dj340DLvvPsNz0Z2kY5C76Mg/OvrBM7S3yEbnySxpN7rVHEBPdE0ZJr74Dm0jO/ih3TyDN/QreoPctZd7/PDLO5n7dZ+Rrcy19o5Bf7s3sL53Me9Gz4zx2HPkiyyAzDWyHb0cyLwJLgmCoxN8bz4a+xgvvGfRMjKKZrlL1WfaQP9cUSJTaRHRO8oytelzYzJOuGYLBjlGP+OBW+7BjT7L/aaeh19NV33CW9sy0caeAGomf7M+vavPtJutAbkvcqwkN/cixvbBTXvw1A5c4GdeRgeay3ilXdlEP0qe0cAz0XcqX/wdu6Z/74bumeu+8zccjLXVjXCk71WEwPXZz372Fh2qH58ZJxpFNoH2fB4dPWUDYxeMGy7RufU9ykBbPjee2hbFTodP9T2caZusRsd16LCK0APIDisNVhC/8Y1vFKX/3e9+tziYyl0cia5skEFQ6sUgPPjgg8OrXvWqUi7zyCOPlFVOCtqKvPunYpgpcSWQ2qW4rUq7yPoHP/hBWR3mHCt38TzHS4DZGqfvfe97pT/OmBXOF7zgBWUl3vtXXXVVwcF3d9xxR+nnwgsvLPs0v/71rxdc4Se41ed3vvOdYthTrqfEklG57bbbSj8uYP7Rj35Uxuu+NIYNLdxNJsOw7777luPvP//5zxcnQSkQw3b88ceXLCv6oIOSPeP0rBVpxu2xxx4rl1pbQYfXl7/85WIAGS84LoIEBXfddVfJBlsplqVQ2ipIt/qs3JXBFDxzCAWz995775YLtAUmr3/964cXvvCFwxVXXFHGqUyOA2MRwB6cU045peCEthwP5bM15GJqY7N4YM8NOroSwAo8hyUXe5Md2Q3ACbn//vuLnBmH72S9XvGKVwy777574S2+GIsAR+meu8umMiIuC7/88suLfMH39NNPH1784heXzB0cnv/855fvOJDKE8k1vlkYyOID554zoYwTj9CDY6acj7NeA5zJARniiJEX8ksuyMCXvvSlLZdz209GRh2fj1aCthe96EXFWbEY4/PTTjuttAmnXDhObo2bHOvHXiYOEdqRUbJvfJ43PnL28Y9/vND7+uuvL7y46KKLCs3ND3iYt7LirkfAIzJqzKoK/G8c3/zmN4f3vve9pX1Ot2eMEZ3QGf30Z0597nOfK1lLvPauS+jf8573LLXPaZF/5vv1+nBT7/3N37gfbxhe+1rXWcyew1rZTgHkbrsNWw1xYnPBu7l/yy23DPfcc0+hD76TSc4vmuKTewp322238p3rFrLQRGZlMslLW2aqhJU+uuyyy4o8X3PNNYUP5jO50pYSWvpQNQSZwX9z1Y+MpioSchUwD+hG8mMeyqYKLr72ta8VfQxXv+mBds87+br77rvLghm5VHHw1re+9UmyQN/Rz+ap+WEsxxxzTJnb5g1cyZv5RwbRpt0LrsIFjuavTCRdZvz6JO8PP/xwWSxUhql8+Ctf+UrBw+dslfltHpB9umavvfYaDj300IKPMeiDvqSDjMf3MujKmt1LCTc6QWmm5+lXOBkD/Y8W5g2dqU/62ffegw++HnTQQcP+++9f+sy42QvPT5Xl0jmyzkqljzjiiCIDeKPP1772tUVX+sx3dCpZQh+4wgedzNvf/d3fLe3RVeypccKP7oYTu4e+xn7ccccV/XHjjTcWmYHvo48+WuRMljILeAE64oEHHih8oEv5AOYC2cR3to8M0Hl0DvtFXuFCh2j3qKOOetIebHpPe8aA72gRGbNwqGzcvOF3aA8N6W5jJWP0qDbQ3jPGymaxbdph3+lndlTFgD46dFhF6CWsHVYWKHKBCIPLmDC4lCylzHimJIlTzAimPI2RYBAoYw6qn9p58D3jIDDhEDEsFLi9UQwTR+Hwww8vStyKcVvywnBxwhhawZuMIgeWQeYEaQseDAEcldIaCwfK5n3OCMPLIWJsvONiaX0am8+MhUPOkDEgHBsrsYypNjjHDAwHkIMnIIQvJ9oP48wgoo8ffTDs2uXAWflVwifgtELNQecAcGIEqcrnvLcItMfICzoYUM6eYBgtlOoaq2DaWDkRaMex8A4nVuDKiAoUGVd8ZZg5kOhqrALiZEbQBB3bVVntCQQ5v/jHMSUrHDTj5YShMTlCtzjXHNGbbrqptIkeaInm+MaRERgbk2BFW3jDScgVBPmp99wal4DXeAROVsI5TWhMXtAETmgiwOdQ+MyCAplBI46PUlFOP76hHeezLfOCExkVNBqb8XNuOElkkuwZF9kKnV3sjUeca4GxgJVjaazoJmD0N5r43LP6RQfOGCfWvMEPASVecxQPOeSQIk8ctxxUAS8OljEZq6BV0GfOcqwFjMbM8YWfsXoPn8liDrzBI2Mil+TMyryxwJUsc8b0CQ8LIgLgZUs/dwT89/8+O1hHwitJHv6vv7dyy9kkCPrpRPMJ380PNKI7yTeQoTRXOPMCOPywt9e8EOi0exEFgpxwAYnv8JJMmvsW+wQUdCH5NT/8tjBgwYiesbgEh9ZJNr99RkazuGNe6sdCBxmU2Wnng//NHXOXw84OkOV2HzegHy2YWeARiOqPnJo/Aik6mkyzH6o4BCttf4Jv8zqLf8ZP/tkA81nQQxcnkBUIWaAyrwV59LmgCi3YnksvvbQEX8ZKZxirOUi/GUMOf0E3dLeYikdwE8RbbPSOYNJ8M6/MM7ZDn3hhbggo6Ulzh46CL/uBP/qjM2TtpjL4eIv+3hOAoyF9RickoGdf/Xgu85MMmv90MBmwCAAEVOhiwYjuhSMb4Hn6g14C9EloQZ/TB3iIBy3QRfBk+/gOeJOD98iFgJbuIutsuaCSXjF2+o2uZ1drnvsbL+lcC4loKOCna8mZYJPeFrj73gKiOaJ8HM4Cf/2TN7wSDKM73PCL3OKnhWs8No86dFhV6AFkh5UFxomzwQmmnP3PKWCkcqpgSnAYmpT++fE9A+2ztrzF3ylT9BylzxHmjApgvMOYCSwFJQkYAvp0GIOgjjMmCPFMSlBSDlrjkT6DO5wY1Rh5nwnYOC2MuOCD485BYezh43vOA6PI2fY/Z4/xlTE7//zzi6PFgeEopHzMO3AShDFQVpatvgo+4SPoyOo+p0GQwClgyBeBLCXjyZBzUvSDnvgBf86CNjlJnFaOqOyjtjM+DovvGEyGnqPFqKI5h4HTyNljmDl5xt4CmnG8BGP+1mecZTLEIeAcwCtOC+A4kCv80Rd8jZ2jBh+yJ6PGSeGI4AMnEz4cCT/4n3vs8MbiBOeAU47HnBtBj1XoBI2cZg4gnHzPcUcDTicaOjhEIK1fDpNFgZRA1cDBwG/BHpkSmHFu8J0jnZJQY+K06MMqPjqktMy4OLbGAK9kZGSN0F5gmnKunArqGTwlU4JfDr3ALfKGnpH1lIdxpAUZ5pj3ONccq5RQp/wtspcsLycPX1OGBhfyhjccPE6wNswjYzEmtE0J4yqCoSFLvS05rN3WB3miNYccP8gN2pnzHFeOrzntOzKBtn6UAsoCtlk8/BIkJGgw97VlLkW2zCX6DO/JfkonyaZFInMaT2sdjffeqwNLMmtR4eKLLy7yQ+eNnbgt6JPxI8vmRnRzC8ZNH8mk0R3mjvEJHvVlrhuPQMD/5mIN8DVWwVD0PflEE+8aP1k3J+ASGU/JqYUcgZ85zL7R4+a5OU6nmKueFQixAWhBp6GL97XNRrGJ5qisv7EKZv02ttg5v9GMLhGc4rFxasf89bygTbAJd3Ncv1MnmifQgruFnegYOseCj8/xB421k2A/408Jcto3NuNh04zVnKeP6Bg0Dn39jcZ0Adkx57OFouVN6N2WXbMf9Lz3/c2Gmg9sDrwF32yn/cTtvlEy7DnBMDrxFeBN57Ofvif7xuxzi250P9uF1+ZVFibZEDoztDAu44eXuYdfZKBDh1WFXsLaYWWBAeRsMOwUavYuZR9NDXUmaD2XHDNAOQIf6EOgkX2ILVhFFJgwdtlXslYQvGR/WvrkOBsbZ6Jd/Y0RrUvTciKm3zIGHJ7sDxyjB4PEmFu9ZawYVT8p/WQ01wJoJtDiyFhRbvGt8QboadzGl0N9OKgcEjzgcHBkOTiCMI4XB1WmStkn4wr/1rALFDmkSi3joBgXh4CDMOUIMfScEdk9OHAMZAmBFWMOhjZz8bV2jcHqtADPuJTpKiGrx+w5bXsPf+ErcM/ep/DRM067VfYqe3biiSeWIBKN0NMKd3g0dvE2vuVQFN/he0r8OGH1POEEGyPnqJWNeu8Zh1jwyCG1SCLL4WcKav7OAws05k32t8HFTxvotW3VMhO8zbmUs84by6oClWHbtYOATQMBJTb62/rGttj2ZO7jgcUU8pmrQuqMH4dYQED2M8eyuDKPnlPf1bJhLgjGBBmcbRk3fDR/2r12Y7qPM25+yJzKhlqQke1vq0s48fSHuav9sYUXQK/Y00vOlSpa0JAJJGt0aMZPRrVDP2Vv5yKoxz22bxKYnwl4cxVS9JbfKjUEHLJlgotaN6f9tG3M2Qrh2eyLb/slA4IVlR+CsegVn9MZdOgy4B2Z2ZtvvrnQ2lyU+cQXARa7UO9dXAby7LyrUKbm+rLPt++mr+ytlo1n02tetO1ZnKWzYns9Q9YEhq2voB1BP37gN31FZi3a1v7MVLXEKuuwDh16BrLDygIjTqnLmGQlkREXKI2dojllqBcFlwwAp5tRsEIe4Hxz8Nu7qRgQGSPPy7yMBZB1WePUdxwYznFOtfR5Sv9SbjNVQlT/rQ/lN5wNTkYCw7E+OReyAVbms0IrKyYYEnzI6rTXNMyjn9VvAb69GsucLsvRsKrM2Ca4SbAMb78FjoytrIfAlFMna2nFPs+0wBmSDTS2HKwBnwQYOY23HQveKe/kVFkRtpLPiWDYOdQyE4K0ZI9laASk9oMp/ZVBU27k/ZonOWgCjwXFsiaccivOHIrgQJZkgZRociw4xwJ5vJG9zGEWeCL70l6VAH9BAbzMFc+RYbTVj/KvlB2mLYsU85w04yOD11133fCud72rlJHLWC/jDGbsGV9d8ghXckb24Jn9r+1cbmUIbQXdnq3HQl7HstHbGn5xP+TsAKYExWuBf/pPh+F5zxsGlXeG73VVlkpan//88XeMfT195SAypXocVZlxsoGuaJo2c+iJIILOEwgkaCfLZGxrTy9W6mh+kXuLPqeeemrJ3iwTCHjH4oqgRfbHnsvW8TZOZaDmAN1EjqdAoGxBRxmpBSDyjh7a9r5AEpBTNLLQtFH3PBqPwJSes0fQvDTn4USPydwqjUR3wZ19cgK2MTBmpZC2P6jgML+n6EkXCRT9pFoAH+g+8kD/j2XuxwI3/cDp1ltvLfSyAMa+XHvttSUIa/cOtucIjLW7CNZzoMwyfZB3QSRe4APa+F+FQ70PFP+Nm81MdjDziy5qTyL2nXbYczSh9wT6yQrzZ/SRu1s7dNhM0APIDisLgjdOgINV7EFRTmrvGGWbEwJzsmVOduRQ5ERGhoADzSlojWJOj/OdH+Umgq+UyvoRsDDi9QmuQJ8Mru8Fs4KMlEulDE+2iUFnHHJABWBg9MdQKb+xaquUSBAgUFEyyMHjbDM8OcSC45ZT3+rPjdXn2le65H0/noGfcWd/BVxllziIAmC0VMaqBFVgxjlTKugzToxyJwZOgFsHwvWJopxaq/cCcAffCHqSOc5JiP6Gjx8g4EIrpVQCPqvWSopyoAKnzuo1Z0qA7nP9oAnHF4QOwcP+HgGawxQEm5watNeOrIexyhin5FPbobnxwh9vtGW8AgPlxP7/0Ic+VJ5R4qUMiZOUMiU/gpuUa6KXttEbDTmmHKkcDqF/n+cgGA4bfMmivWYcR887VERW1P4kcs8pI2OtgyKbJCvrugNBKBwFCvisPU6/BRhBoQBU1oec6z+8IVvwzqmvOZCJg6ONnIQJRw4R+dIu59c7HO0sBpA18kGO0IoM6UObFgbQVXmh7AqekBdtm2P4oW0OrTbJtj7MJ/s07fHyPZmR9TAO44dzTpU0Fu/kZONlrq7hX0ociBeoCTG63/E7NUF0/cgSyhqKY+HnwBTl7HAfA8+JcfwgUTKOEuKHHz4LIH/yE8HM7B5KSfyxJBC6C+jsERvbz5d5SUaSISHHZMw8iP6U7UY3QZKFE3Q0D8ks/YBHMn3kz15AzyvdFKBoN9UaOa3S72S6o3ujr6KT8SWfmYPwIefmCyfceNq9lTnl2o92tQ8/NBAUyswna1eD54zZfLPoYb5FF2onF8lHZ5JDc4qepwPJPP0LHwty6KINz6jaaIOg6KHovNii/K2/jN/zdAfbwi7RGxaP7OU1d5Wt33nnncWGkHP0twCVQIOM5ZTUZOX9Ngfx2pjhyrbAy5j1bW6hibko+NGmvXn6otfYPPpPWTia0Z36p6f0QUeNXQFCTymLx7/sN6SHjZXOAPAlH/BJBtd8Jksp7/d9TlgmI2gW3oe2fufk1PDPO9FbySDWEFtMF2cfZfhRy6V38cLihoXQ8MLvlNwG6Cp2gfyiW7Yk0Fl0FFrn4CP8Mj4yS4/KOgrwLVhY5JCFtycyFUeZLzkxPHNpI6+d6dBhI6GXsHZYWaD8HRZDaVPCHHCr11asKW0Gi4KnrClfezkocA4mB5pCZ1A92x4Iw/gp49GugE72RzkTo8px1w5DIWPUlj/KoCnt45BxkhkGBpzBsIdFKZQ27J2DI0ctVyTYR6Qck0GzhyTBCYNlvIIlRphRZLTtI2HEcyIg548hFAArSeRA5yAMRlngKuiSSfOcNjg+HEAOuGyePT8OMxG4CLA4DoI2eDLwDBtnhFOgnTZo0SbDyOAyvGhvn4cxcEZTtgNHzpI2BeieZayVZnomjivDLxOR0k4r4Rw6NNA3Zxe9ZSO1qX+0tl9H28ZJJgRRnF2GHR/QV9DJ6UZbzqJS6BwAkwwsg2/fSxwQfHKKKd67dJpM5DAd9EP31nGFl8CYPAngjAEOwVmQrQ9OoWDZd5wONOZAWGzgRJBJzgkZM37BNRpxPODTZkBkSTmgTtckj8bGWYFj9nRpAx/QzeE+2uZQwsE4Uh6qdBh/fK8fOKEv2VJG51myxDGVjfEOefWb7HHorbIrdyVrHF80g4u/fWefruDUogk51Sfe+A1HfRonp9I8yPU9nHqyIADhABuXw5nMU0GItozPe/gpEEK3BCqLQGD4l385DHvvPbt6QzwokMNmYqFIwJZgJ6V6jn/sO+OymMRpH8uGSti5A/JZz5L9n7X7T/7JLIB8+cuVMQ7DY4/N+vS6AHKsQjKHf0QvjIHPzVv8Q0PBQA4wwgPZcvM9JZ8yRhYF6LDsLyYz+IHn+GohgHzQEUpNsx/cwhe96x3yTVeak2itGsB8xAtzTDChH+2YF/SzwNtYyJR57CRM8zzzCs5wIs/a8y79QK4siJBT2XEBWQ3+N9fImNJ6OooOhZtABx3gRn/RyalwgQv9x4aY/2yBTJFyW+OzSGChpuWJAALe2iFveBC9aJzmOF5km0G9DxmN6WLl8OYuHSwwNk8EcGgtmAu/0eEDH/hA6Yd8o5F5ZzHL/FApIOg1r80nehEP6VI2hi3Up0UB+p9OIw/eFVjSbWwJ3PShXbpk6n5LvBIAq8aInmNHBaPe9b35i3dokOtBzFW44A+5oWvwiLz4ISNsKn2gTfoS3/CPzMl6wimBpy0HWUytbRU+sgEWSPFSO8aJV3gD4kMA+oT8sH94ih7sVq1zI6/sguccdJRFBwuV2WaQrR14jp9snjmAVr6n58g3eTCvzFt9kUnygQ+hjaAy14x16LBK8JSf9yLrDisMKQ/JfVfZDM8QpZwqG/TjkOQeNAbFe/WdhXW7nqecc4df+tJuMpRT1wB4l5HQrh9/564xBt//uXPQ/zlMJH16LseOB0/Pp09Gzue5Wyv3Xvk/e/xSXpZSpJQ3Gm/eRa+s5gc/IOjzPhx8lvsZM66UDIe+tRFNuVvKbnyvj5T6wM+Yg1volesEctcVOnjG+/V9mykNBKG//jLu2pkG+vCT+7Nyp1uuoQhfjTmHGuVEV8EhPBzkA4wjB/bYm+R932tbm1N3C8ZB1Ae6hJfZ45KMeQ4Zwg9/aw+voobRIfuXIivawNP6nrwa0m/KOmtaRha15/PIXM0/n3nXGGtZS0Dt+9zHmH7g43vt13MuvIZ3PoNf6JY7+kLP8KieM/qLrOcgLPyCb/Z7+ixzGv1yiJHP8m7aqmkGf5lybSlBTv//5/+4T3IWLM4Cw1mQ52+s8V0SAT6fXb0xO7yDI86B5ig+mTfo8cvvQjvtEnND0q9K+Rl7ZiXA2uZwCsItpEUn5H7TMRkMX/M/SLlcuxCWe/9SnZE7UEOv8BFdo3eDQw5E8RM91P6ftlKiTuZldQQ/FvRSkmuBgyMv8Kp1QKtjIrd4C5dcy9NC5lrmYP42/pTrZvEo7WV+RF/BDV3IVfTm2LaAVg+FbsHZ+5HN3O8bfBLsZC9qDlfzvPJcwR3ew8WP4FkbAjD/t/o0d0nmZFzfZ57GVmVvvD7hWR8yV+uczKXQZap01/ep+Mn8Ds2yHzW2KDTwDnyyZ1ufY3Yt8hoZSCl/LRP+1ketJ2rcolci/+F/LU/RJ3iCbrkft74TdWzcsaHaja6Hj9Nd0VCVT+7QrW1Rbe/Th/cyT3KfpM98Fx536LBq0APIDh067JLAuZDZBDKoVp8ZdlkFGTaZk115X0p9WNPOApw3e2tl5vBcNkHmcD0OGmdWNpazKKu13v1xscAhc648sZAhA6WcVDXFZncilVCqBpDNlCVDL0GyzJTsTSoQdnU5z5VDsvsyiQIUQRC9JHhMGX+H1dQvrq5hR+iZ9nThDh12JugBZIcOHXZJoPqUazmAiCPLgZVF4sxa/d/sDnuH8aCPg670D3+VBSrDXE/wR35kCnKS5UZBSuwEVvBVDgjHze6ICoKU/LlmByi1VRotmEzZdIfZAoItEMpaLXIpDc/e5brMt8Nqgblqb7h96/5WIq7Ue6oEuEOHzQ49gOzQocMuCTkttC6VSzlsd9I6dNhYSMCd8ui6PK/PtyeXw6c0O/cV537VDqsJKU9NSTP5nlcG26HDZoceQHbosA7jvrNlpzKusbsGfc7hm7pLcT1gmxY7a9/XtvaJ4qyOXfOS/YqLjPw8+ow9u2y764E4ld2ZXBs9puRgrfxdJkja2fiTfcTZT7nZx1bf4beWseSqnDqYy0m/Y3N9kTzsbPKyUfNoW+oE9IbnjqD5WuiTfa49AO2wqtBPYe2wsuA0MqeVOUGPInWyqv1KjhV3SlmcGauzDmHIaX+5VNk+J8raCWhObqzB506C81xOi5xXtmjV3EmLTlZzOqQT3TbTvpp5htaJnMrKHGyRPTf1OJwc6qRGJ5M6DW8eeN61Jp7HC3vMlKnNDmSYHRjyb//tMDgE7wUvGIZf/3Wn/v1ROfUP7Z2op2RvI0AJoHE5BTAnk9ZAhi6//PJyAqC9RVMHk8DN6Yb+1k57v1kLnnefoxP2HAyyEQ5A7sFzsq8DfpwK2Z5AuSNgR8m5fu0TdCor+jrdeGqBw2mNTsZVpuyk3PpeV7qCPnHaqBMaHaa0noUS7dizSN7IvHm0sywy0REOuqH/nF7qahollZtR9ozF9RR0mpJQJ2Au+7536QHv04Whi1O8nW6K77V9YbfIFXnzfX2QmwUm9sl8djK0K5Q2MxgvvWS8TvG1z7W9P3l7yIOTW81BPLXHuT08T3YwB6cpMV3mhOaNwk1ZOn2llNtp2+3p5nmOnuJnkA8nCm+UPezQYaOhL210WFmwAV2g5oJn9yUlSFT25IQzhzI4Wp0xdrIipzqBiOcEMieeeGI5Jr29S8mJgOeee245MY1id4T6PEc/10VoixM1D2IENsP9TcmWOdbdfsD2Li3A2HLC8x3HyfjGntWe4NHJlAJHx+JzyD3qnruTThqGv/iLYeAvPfe5rmr5f8s+EQsDDh3ghGwUwJMhjsPQAvwFl2MXZ9egLMkx+I70n7pCoe03d7RtJHB+XH3iyo6tvdB9IwCvBeH1Rdvbs298s6fOXrF59z3m/kHXuLS4puzMUf/uRByT6WUgp2u6LgePFkHuqVwvzbcX/43LtUP33HNPueLDaay503ZHQi5oXysdckefBR771dZSgJXTS83tVGVYfHCyrDbHMrauExEktnojfHT1hoXMzQ458dT1PBZrdoROAGguyKcXQvPcq5gtC7n7cXsW38XOuvqDLZnnG8DbYg1dkpPVO3RYRegBZIeVBWVCDhCwAueAExlIq3YyBbKG7nizimyl0d1z7uDzk7sEnaTp/dxrWEMuSfaclWNZpXkr0b5351V7LcAYCDRdFrweB3F7g+BO5lWG1rjasflfVtAdiw4cYQgF7pzxsaAs77ifz8Ef+PWUp/w/w49/PAxnnTUMz3zmMDj49GlPw9/ZFQMyafrf6BVrCxBkYGphAH7u85JRnsoWGYt7zWRnly3L8rz7Dt1XtlHlR/p10IjMT67i2NEgI0rOW+d5ewC64okFpEWZPnLrcKSxzCIdQ0YsYGwNr7StHSdkLpIRzrX7Si20rBXorBtuuKHcL7g9INdsCJjo3Q9+8INlvuxosIAlcFsrHfDJfXvrOZTIu+7FdXcju6ENC5xjVz2RSfI5degN+yXzmJNnV71SZREYL3scPbkjAA3JaL0YLMh3P7H7kUN3GXQnpW6v7COADxtHD82jjzG4u5ivsajSpUOHHQ29hLXDSoOAZcrA1iVMghAGvs76CEiUEbrAXZaCcWNArP5bFVa6qHxzXt9WNGUUXGKcO+3yHcfZhc8ymIIG5VBWD6+88sqyAsqYCSI4/o7715cVc+WyvmvvrNKXsjyro75TXivr6lRQxk529ac//WlxOuDkmHdtW0kXEHNojIlzw9EU6Am6fadNl9ELlsfuMwswuBxbR8YnYEEv2VxXFaCvoEt72lb6qd8p3gBJgk9/enbn3WtfK/s7ux8v+x9nd+IttxrsObT8wQ9+UAJEF40r9cEDQYCMpwBeWZkMaN7BPzLAKCtTVM5qdRc9OT+cOXQR9OOpUiiG3AmRuQMxp+y5oF2g4KJn5Vq1bHoGbWQm4Efm0NJ7Agw8kWV1NQP5lCG1Oi5IJ0PeNR7twlk7+M/pHQuU8MNYtWtscBK8K//GM21qx5jIBhq5T86Cimc5NPDHY214x9j0aT6hpdLMpz/96UUu4OUeP/Ioe+JCePIoi0ymtSFDDxc0zcJEzT8LD9///veLjJq35o12BSvGi7+egwMZM2e0K/OlLYtGZHNMNswRGR3BLVlPCXGyD/itb7xRZopWLeAhnphj5MA4tDN1jyL641mugQn4G9/Nac9GFyifvOCCC0ogJrg1x8gdvPENj8huu1hFNugWGXV94R+cyBNZ/eM//uMtNIMP3PGBDJujqgjoAgtyZBKv4SAIB4Ix3+MDfpMNOujxxx8veMgsWWyCn/fha95ZpIMvnPCWnjNez5BPl8Ljqzb1gZfuzSQbaOE5MsXx957xwAPfBGCZm7/QJ//fcNtttxVamEN0ED0pqISTPtCVXI1dDRJdA1d6WqkpmTJedNM/nOlRsmIMaEhmooPpcN+D+u5K+hO/0Qxu5GjqEvhW57Ed3iUzdBNdBif9o6nvyQC7Y1sGOtCDcEJLtIITmSZPcDB36R38Gdu7mtJ44ySv5j5dk7tq8Y2u0CaakCPPRY+xieiP3u2YzCNzHB5orF0Bd3sqKXkzt8krWbRgYWzGiA5kxFxQ5kveySTc2DMyLVtnodL8qnWk/uks1Up0n7mnMgm9yBwetrjAX5vkgI1Ae/2xM+QQncwbfeFP7nDEMz94hE50T/wFY6dL4F4vtkVWvGc8aDNvwbNDh1WDLqkdNiVQ9BQzZ1hgwKFgEBiVGhgOnyutYaS8x5kGjOq8wIUhufTSS4vBsCLo/WTdKPz3ve99xbAzJJdccklZ6cyBCIBRY6AEq1atBRPeP+2004pjXgNDo593vOMdw4c+9KFShuN/96Zde+21xeDJArpY3Fgc886wKZHyHKdQea1nOATesd+Lk88hV6L3rne9a4tzNwYJJDhhnAMBAQN+8cUXFzw4i3kuF1Yvs8/rpz8dhq9/fRhs2XvggWE45ZRhOP74YfjBD4Z1w6c+9alSUorGHILTTz+94JfLpa04p0zI555lvPHJvkcOCodb9poTiW/kyXeceGOXRVTqlkUMxp4zg75Kpi1MtCWPnCH7gLQrOERPtLeXBa8EV/boKLdWFqgv5b4XXnhhcQ7JkP0xLhLnrHCm8JE8tH1xbrTJQRMoKvMmO8ZL5mTlyQEHRVtHH310wcU4lSt7llyRcyXMcOMICm7QF26f/OQny34vGTPPKvtWQou2HCK0wQN056y5EkWwhSdwaGUNzkqzOOGeUzFgXnFK3XmIvpyo7LkVDBiX8mZz0FjOPvvsEiy0gC8yC8ZvTnoO3cwt/IM/mmvDPJFN4+CO7a1UNh++m3Pm2xivBXN+BIHGhrZxAMkHeeN4csLPOuusgncuWOcU+xFknHzyyYUO8CYLqibGynLNN5+be+SFc0yWyBDZEVAkyERf5ad0mHmsjJdMkGkOMlrrl+OOdnDFS442nar9XM7uR3CC5+j38Y9/vMwhQYFxoiW6k9dDDz209In26E1G6bXoInoLzmSOnMCRLKIf2pt3AkIBqbbHysZb2bNIgr7kxUKX+W/7wtR2A/JLPtkPgbE5Rx/oSzv23lvUQIMPfOADJZAyPvQ+5ZRTCs9awFNz26KKbBzdj+/LBgR49/73v78ELvhLp+kXvdAPXeB67733Fp594hOfKPqc7kc/esJ8IoNsAd0jeKW7za2xskk0N8f9JqfmOVqSU23gNV7Iels4ZL/oFJUHgij08/kYj4z95ptv3nLS9XHHHbdlL3kN9A8c4GmeobM+2FeywdaSJ2Mg/+ZJ5E1AS1eY6/RVDTkkybjJsaCYPOAn+R3bYmBukWG6ki0QnKKx//HVmATP+ETW6C1jNP/QjzzSF3ikXzJlXplTFjPwM0E8P8T8I2cCdH4BW96hw2aBHkB22JRA0TOiVvYYVYrXSvjssJaZgYqDccghh5SV2Dj0DLUV5XklLNpnHDneypasvMtYpMwy97NZmbRqa7WRU8ZQWX3k/Ahcfce4cuYEkFbvGUDGrjakDF1W9wUvLrjnrOy3336lXJTh5KxxShzIwAk0Xo4eg6QtRooDk4vN4SLTxTBxmDlrKeUZA06RQIvDoH9BiWxGfdCEMcPRbyutYxmcFvhwfO83vGEYjjhiFkD+7d8KAmVp1sT2godMhswJnqKtRQL4+B+gLdzQA104vmjG8ZO1kUHMAgL+JLtIjsiHbIjv0MBYA/o4/PDDi9EX5HAuWqcMf5Lx0Sb5Qz/OC4eLI8JZ5chYcebQ4zHZ4YD6DE31D1+OlX45dq3DwxnhYOsDHuTGPj7zQp8cURkMwasfwRKZUcIlwLWIgVbmA8czgQKeC2CAgIYcwdF+Yf9zfDwnY5Tx6VtwACd4mjNkcCyA5IDhlTnylre8pQSt2uRcHnzwwSUYQxd0zGmXaIcXZFIfY+XhAjrfm5N+69uzOXGTo2YRhfOItpxT+qPGkTyZQ5xG76WywSJDvR/JOxxI+5nIIpzJDdms2yOH8CZngmZzU6YIj/HGnA7N6Qj88X+yGTXA5f9n7z7Ari2K+/HfmPySmKox1SSiJjE9UWOMxhi7olEUC4IRUcACikgRRYqoqCAiKDbAioUgQVSwY6+xJdYUY2KiMUXTTCyx8b8+y/9L1tv7tOd53vc9L8xc17nOOXfZnZ2ZnZ3Zmd299rWv3dpBPhis3kej6AS4RAbVibboDTzLoaBnyDaDnhxyrBOx4jxoC/7pN+pQjr4kuuk3hxBtZECQBxsY0a10C/lVBx149NFHN/n1HDlRP9njDHFYyQ9+4Dk+kU+4ZHkBuSJ3Y8jyBWWiGdljsNNvnocznDij+DM1YYbGaMRJM2GiXRwUbabjvaMe7YizKsqL7v5PZcRwhNAGTmSfXhLxmrc+twf8hrsoHJnRHn2UPiYXHBCOUxw+EwB4pR3JPKHf8AENyYW2oJO+1a8DzIe+w3eyjAZ0EznDT30APU3c0B9wC10iC/CiP8bLGbTF2MTh8jsbfpnkGK+PJKP6LD0jE+Pggw9u0Ub1k2cy4j59i5ZkMZFDfaHX0T0YM/U1daMp2dR/tCcwpge5cF9/JFf0JHzcQ0tyZfMdvMabTM7KdCLfslt8c7DpYRNqZIgtQIeRjcgjnY2m9IA2mOD0TkHBzgKVwlqwU4LBgVI2sDG8DZpmFilnM6JJN3PP4EpRU+YGEQMpJW/gnBWN857ZQ+Vcuo5vl+ZwJuJmUDJDyhDmeJipNjD2g1EMB4algcugzNE1cIyNQ+Wry8DIuFSnActArXw4G2BdZ8hwKhjADADPMx4MdmahGRpwVR5HRpkcHgZbIpBTqUycR0autUVpMyMkUYj+2VU2IOBjYcev/uow/PRPMxguTWV9znPsWGon1hUYPwyXpTMxUERLOWdoIKLAkGbQMESyNklbGLXogW/hU2gUfovKkZNcv9vd7vZt/GSs+KCFcsyqj+ngXs5s66/5D8c+kpMzwvA5MoFXOeBeOxlp5NxExlhmGC8MN/KgfJuc2P2QrJGXvk40Umfw9z+RAQ5GHG3XRYc8692cQae93ktUppeByDlHQSYAI4tzApdx9MX7JjVMaHCSGGecrmyEk51yY1hpg51v9TNGMPlFqymjnGEpMuBZDrm+0fNI+9AV3fVJUQ1OhvTFAMOcM6u9aOFZkR94jdeeon9SvQFck+4MtF/5+qlnyV2OEAD51l/pEsY255UzFxkdw9jYxRs0VUYmTDjfaICmolEpy/P4qS3w9B4gV/QJh02mhP6jDO0lX72+UJaIWHZWdp2Do276lAPjOjrTPeozSZE+4z95R1v88L7fcEB7zi3em2jhWNLvU2sMe9kTBTLZh1dZy84gZ7jDderYJf/RzPOcC06YSRSTBOOU6/6dsS7sgfFPPtWtjDy/LHCS0A398Q+toxP0RzxK/8h4Q744PN6jK4D3I79ARNV/OJF5zrnfnBkZO3SlLBP8M0mk34vqeSc848ihIzwyASZaCM+c69mD/0n1xxeyJrIOzzEvktGCVtFPZIMckZWcp5jNi1bd+KjXVfCP3AL9UkYPOVcPB5AsR9ehfaKXcEof9z65M+GSSChw36QRXSW1HS/oxRwZ0suDyRf85BjL+MFPum17bu5TULAZqAhkwU4LOfidomdkG2Cln4xTWRgKe+yxRzMqpcIxGCj8eYo6a7UM1FPGqhQfqSkGCQNEBu+pcgzG0oE8Y61YZrSXgTiUmRXvwcBtwGFAWIcCD7OcjI4px8bgNbVeMbREQ++Z4TbDulXwkz956YY5X/zipf/9/vEfZ5Chz+rlwdVEAAdDqh5DRUQFv0QADNac5UUwppH3zSqjaQxARm1vHG1mcO/X7PZtyb2psmNgMzTGhis+w5nBjf83v/nNmxPQG2hTdY5BG7WZQZly/F52R9KULRohzYsxLnoulXK8i6BnOf/6DpCmysHSFlGQPJ+oOiNN9E8aK+dCZCdrGscgYsMQxkOGsX4/C9CUcRyDtacXx5MhqK+mX3FGeucPeI5+6CPDPZ1N/Ej35WBxuKYcIZD0UQ6kuuixRXI2i5/4KCKCZmQBzWatAeyBUc15MAnHkfJ7akfkrEcXLckOqJkM6Q3zRZvCzLpPz5kANBEm6m5CAF/nlZO+CqfsqIzWeLxoc7Q8Sxbo2UwALNuOHsgBeZi1udgiEDXk4NHT+uCUzg9OiY6Z9JMyT4Y528HDeyZGIr9Z55p0SZ/QRlQYzXM8EIcmzhF6ZiKRg+O6tGSTsRwlenisl0Izz2qDMQkeJhfgvcoOrfP6wUZ4NIZMlOUzXhM5rmuMm7ajUW9zaHMylcgDnTbVDpPJlgqgbaL05TwW7ExQDmTB2kI/Uz/+9EqdYWewM0MpmmGQ9Exm9ChoA7KZYTOmjFDXZpXb7ypnRtlHHZlp9REREi1goBmIM1Mf44NBnNlxayTgwuGJ07dMuz0n3VW0VJRpPLgwag36Ig3KNVAxWvudCYNTZqwN5rNm2JUnpUtqFCM0hsiYNmlfaLFo0LvOdS6NMsqkYzvwS/infDyO5BiUieYiY7PSvwz2NlnQdgY4Yx8v4G0mP5HFHvfxd38/KcScGTPxaG+WnTEdR2rczimnb6qOnm5Tdff3x/fwDD859+OUa/Is0siYE7ViyMA3m7b0Zc5qO+Bsm1zRdu0mR1JYpwz3Mb5oE1kQwTVBIeJh/ZmI+3iNITxFhTio1nmSR/ImaoqPJgQ8oz/JKuBUWTOJ3/q2/jQlc3BRn/ZnB8M4E1M8oi9EpUUBeronbZFhzqBHDxGGRAbH9M86p9Ah0T59x7oxzgCjGd6JIEcOvJtMB06AdESGZzbimZV66Z2pSIX/eGZtoRReOkNZ+tA4cjOWPW0UjZGi/eQnP7m1tV8v3UdwOCYmzrzjusk07XW937Boqp4pOZqK6loPZzIA/6c2OutlT79QN10Z2afvfOj9eRuwAXKijzHiszEO+ctxPONo8Cx9Iu0RLiYxE22ml/uN12a133/r6Ex+SHvNu2OdkWfdN0EiBdwEknWnZDkRVX3BOk54cKzJl/ZwFk0O6J+i8Pq6CTjZFlKgyZ+sDrIovVtWB97CS3/MmmRROhOi7o3bF32qT0tZJSvk1bv09TgVf5EOHd83NmsLvil3XmQyEwzpa2N51NdFu9GEzPXprfNwCGijfobWwYH8GktMHOEReUg/TEaED0eczKK1e+gy5vcU/wsK1gUqhbVgbYFCtTYmZyoaiAwCjF2GnlQzA5IBUsoIZW69n9lkhgfDTMoM45szaJD1HoPOjKFyOJJS6fz3bIwNA6A1j4xbTpV0NAYuMLBKLTWIiWiKLsCPU+medRfuMSDtNmiwZZxba6M+96zNMXgZfHowWMDLgAQMMBblZxA3aGob45cRLuWGoWWDDEY2vDiqBmtGUM4gEwVlIGVn0kA2j2FIiDqGTjYxUJ53pGbBm7OqbdnkxJohqU0Mnnkg6HnIIcNw9tnD8Na3DoMsLEfJHXSQtMLvfJ7xKgrKGWBUcaDHgE+iTAZhES8DtfU6DCaDN9D+7ATJYWD8+u+6NsdZYghzxPbee+9mPIlmGtQZhGgPH7wnh95nJKIV58C1/kBotEYnfMoaQ9+hM/ploxXfZFGZDAvPJbWU0eFZBjFeS+djhHBqtUcdcLS+V3Qbn+ErKsAp43x7TtkMafKg/eTJfzxFK9+eZ2gfd9xxLerDiJbWRm7RDa4cEzKADuim3QxIuHHw9BXGv3ql0oqYkp1xxDtnc6IPA0v7Gb76qFRVERipccqWNcBJgwdDNGct4pVnRDL1fe1BO/1X+aJ5MQQj93apRFf9Sp/TX0Q/yTIaKxNPycbd7373lhLJ2FYOeoiIea93RvQzMmO9E7owvtGFnqK3RGQZ7nSOcrXZmlW81M846UknhbdNOxL5zfprEyN9nZx9z0oV1tfJUHY+TZotmptUIHv4i4cikknNi7xnzTAZQTu8z8ZY+hR9mok5OJEdjim6kEOTNdZKmhDQ9+g6bSen+IE3cPFbG8mQvgQnsqk8eKgDb1zXNpNg1r3FWYXHGMi6Pm9yjg700XetFwdS8U0Uwm/sQOq/JiNEgKQYagveWtum7XSkDX/gDS80xQvy6lo2+0ErNPSM6/QO2lgCoE44Klt9onpkPNkB6Omefo8uaEzmyQTdlyOTrO2M46EP4512kzc46pNwpzfgJ71UGj99YEMpupNcuOa98VFEylYHHaZO8u4dYxP9a3MeskaWle05eNLL9IB2k3njmSgjvLL7qr6RdGhletf616ThBvQLMpA24rt20k/kG630TXKV3ZXpDRvn4JV34ZK1vuSOniDX2qyfkim6SlRe2eRQXeNjgOCuPN/qIp/kEm5wgYf3yGnWmj/84Q9v/Y0uwYtEcuHIOTU+23TNfWM7eukzMg30AZshkRWgn5J3ZWeX1uztUFCwbrDLJTW9UbCmQMlz7gxSBj0Kl6KldLObYQZEUUeOjGdy3AJHK0c0MMYNLJ43qChThMHg1R85ME5n8xxjnjI3EBlQDDrwcN1gwgD2LIOEUWegYiQbABmAvg3GcBQdY+gwqBhcfcqM5xgxgGOgXYwSA4oB2XuMRcaPwQjejESRH22DlwGNwcNwtb6M8wsfg7a6s+4pYOBlBDH8lIeG6kA//w3W2ZWPMe8+vnCslat9/fmNaM+hELExo40fl9YzDB/+8KWOo6U5zoHk56X5DG7OmvVmDD9RQMYhg4zhNQXqMiDjrXYZ3NEBvRhWcDa7rj0MdsYsnjMA0Y8RwNDUTgZOZAs9AVoqi8HBAPS89pOpbCSTTZTGkx4GfjiRGTJETrJBEkcLnv7jZY5ZwT88l5rGmVKXdnAqGCYMGjKA/vDSBoYNXihP2dkcCl3goM3KYawySsg+Q1O7cwQD+SVfnC3yrVxtz4ZTwU170IGs45GyTdKQQYY+A4/xRP48o95s7x+Ar36inqyvlAbnWz3aojx0i8HNOcITdNYfGF/wxi+0Dl/ct4kQOuELvtMBDFvGK9ox1v2OLMNT+crJGYHK1icyiZMt/GdtnIIG+gi6wgWvOHr4Dm//vU8W0Z/Drj60RXvPoj3c0DXHmVi7PT5XEv04v77DV8Zy2oP25Elblcvh44zCLbuC6rfoSz7g7V3yhLfojSbq1fc9Q1b0NTLgmnIY51lDrk3ohn459oFOgI97dAk86QJluK8/4J//6Q/6sef9VwedS6ZdH4N+pl3wJ3vaiRdojDbKISvaOuabtsBBHwFkFH/oSM/ClRyZiNQuOgEtySjakl10ICf0Zpw/coz/JhHJTzbgMQGB/mgSXOhdtPJc9I++Tv49YyIIHfRB41ImnUwa+R8HXtna64OGHEBjiPtkTz2R36kds/VZNMtOxWiAbp7FI33AM8qgFzwDL+/gjQ/5Jz/eRQ/PeNY9fQuflKeMqXMQ6WmyRK9leQlnUL30Nj7Qt+Qh7cc78qpe/MMDeOsL3sWHrPsnv/D1rrLJI/mBi/L71HJ0wBd40xPohq7+m1D1nx6NPGeTsRxVgh9wynFZ6tHPc6RS1pTCFY6iuvoQfquDzkInv5NR5dmtPiO5oGAroBzIgoIF0Kc5Tl3v0wSTMtOnEM5KSRkbNnEgGZzWkGUDgY3gJyIrLYeRkZ0et/Kw6hgcU8YZB1Jk0Bo4xmk2HdDs7AOTI/U8r92MAeudzNaaVWaIMIJFs/oI37jdU2ma/bWNtg3siPO4tFtamki6tLJl+NbL4WbaPUvOV3nHb/RbhHfS8LK5xLy2zOtn8/CZ6neR20Vt7FPdlnl2qs3jtvR9Zopus/Ce18Z5uC9DrzEkDW+Z43mW5fWq0NN+Ht5TdJglV6vKYGAj7erpsmo5U+PG+F0424SKkyy6lyUFnH0OqR1BcwbhsusEw/dZMjyWtV6WZ40F/bNbrU+n+ssqcrLV0OuWMS3mycMy/C4oWFeoFNaCggUwa3AcX1/m99R/kJQZUZikCi476zg2ts1GixAAs6JmYrc6BWbWYAwPURT4ixT5Nkt76QYb/+c4BswYm6GGp9RBs7SAccQQmkeDZei6EdhRBzkzDEWhyILZf9GHrMmaB5t1HDdTzlQfWMb5yMYcy+Awr5/Ne3+qnGVwm4XHvGenyh2XMTYgx21a1thfhNsq9NqM7K9Cz1VgWdrPkpVZmxWtIoObgc3QZRm+uW5STdqoLAI6k+6QISCVN3Wvwst5+nwKj/75RfVsC326lf1lq/CZxfNF96Z+FxTsDFARyIKCNQAzyFKDpB0xarIWZVljKJBNLaQeSbWR/iUFkkO2PQaoqJNs3JHBc97scDYXyXNX1IGU48/plmIlLUskVgR3WxjpBQUFOydEx9MV0muzcY6U4RzXUVBQULCtoRzIgoI1gKRT9d0xh6ivWk7KClzRHbOdBXpnGixyvgsKCq64ED2flOzSEwUFBdsTyoEsuNwb5Ms6YnHgVo36bQSvbbF2qKCgoKCgoKCgoGBbQ50DWbD2MD67aVmwnsxBvXbFWwR2q7M1uTPUVjnoeBXQBilHtnl3/MisMw7H79QcT0FBQUFBQUFBwbpAOZAFaw2cJ9t796l9q6wps9229SLL1GPjGVvvL+PYbRRszX322We37bvn1QOfnFO41Q6kejdK04KCgoKCgoKCgis2lANZsNYgiuigbmd8rQo2IHGu4C1vecuFzzpqwqG/hx9+eDvXaVuAdFXnUjnbaRmnkJN58cUXf9t6xq0A51o5YxFNK7pZUFBQUFBQUFCwCtQxHgVrCRwb0TopqA7O5gw6MJoT5vBou8198IMfbIeEO+DXAc+ihw4zdgC5bc5zsLk1jY6ycESGQ4IdJuxZZ2bd/OY3bzuVisapT4qpXUtF6bwrismhtKtpDjd3OHSOWnB+oUihHfCU69iFsQOaM7o86zgLxzNk7aN7Dg2286Y2O2TcodTOQDz22GPbERjOhXSwuwOslQFPByDbqVXb4ChNl0MIBwda54Bp5fo49Ngh89ZdPuc5zxnOPffcVq5rtoFHP7u22greYckO+S4oKCgoKCgoKCgYQ0UgC9YarEcUgeO0cRwdNv+gBz1oeMlLXjK89KUvHT70oQ8NT3jCE4ZXv/rVzXF60Yte1CKWnLTXve51w1577TW89rWvbeWI5t3znvccTj311OZwWfOoPA7Zpz71qeGQQw5pBzRzPDmX6nGgu7I9/8AHPrA5dsDzxxxzTHMEncV1u9vdrpXlIPgeOJrqffazn92cYG3hxObehRdeODzqUY8afv3Xf72dhXjYYYc1RzHnOWYnTs4d/KSeXv3qV2/vfOADH2jO6xlnnDF8/OMfbw4lXD/5yU+2d6XKfuQjH2lO9Yknnjg86UlPak40HNAjmwuh0/nnn9/Ob/T8O97xjh3C64KCgoKCgoKCgvWHciAL1hI4Ns7Cu/a1rz1c5SpXaZE55yL+yq/8SnOoOIacskTVRAY5aCJwiQqK3v3UT/1Uc8ZEBZXhYHrO2kMf+tBhv/32axFHh92rR/lZl+hcLemm6r7Pfe7THDtRzziQb3zjG1sqqGjlHnvs0dJSvS9K2QPnkoN305vedLjhDW/YIp6iodkch6N3/etff7jWta7VPp/4xCfaOyKM2gIP5wHCS1TQs9qpfdrpfU609Ztw3XfffRvd3HvVq17VorPSc3/2Z3+2OYpf+tKXWv2u3+QmN2nXRR/RAV333nvvVmdBQUFBQUFBQUHBFJQDWbDWkDV6OV6DIyiiJv1S6iVnSWTObzubWjMpuuZ56aIilwGOpv8cM9/SPJNGyskap56qj6PF6XTP56tf/eplZXE8v/CFLzTHS1QQLsrpQfqo9FXOYc7rSj1+3/GOd2xOG+dO9A/uwR/EofX+kUce2TYFsjZSJFKkFG53uMMdWkT2oIMOahsGcZo5kNqljepVxwknnNBwVGZ/lMitb33r4WMf+9hwv/vdrx1OzXGutZEFBQUFBQUFBQVTUA5kwU4Bs85L5NBJW33961/fIoyLnJ/NOEY9DrvvvnuL1FlL+K53vatFIjlinMkepNLmMwappNJFTzvttOYgwr93eHucpbdaD8pBFsXkQAc4odJyOZX777//8La3va1FJkUpRS9vcYtbDLe97W0vW0M6LtuaxzPPPLM54SKtL37xi7fZUSYFBQUFBQUFBQU7N5QDWbDWwOHhaCVq1oP/1iq+4AUvGK5znesMP/ZjP9aicuNn+u/NQF9WNrzhmElblQ4rCjmuxzVRvve85z3NodMO356z1lHkUBullWa9p/8+nknbOYWcTam6nNQ4eI75sMEP5/UZz3hGS9u15lJKrcinNZGelbr65je/uaW6wic7u8IFbpxhayTvfe97tzWjcLM21AY/5UwWFBQUFBQUFBQEahfWgrUGEUVpmS984QvbGkIROBvB2FTGmr+kl3IipXe6b4MbaxR33XXX5vzYfEcZdl7139pF0TpliAy6LqLnufznjHpHSqrdTTle3rU+kZP63ve+t23Ww/GzcylcrHPkUPapsHZn3WeffVpUj7OXHWA5aDa7UQ/H7pRTTmn3OXbWLnpH29/61re2XVk5evA5+eST266xcBH5FGF8wxve0OriRFs7aS3mzW52s3Z8yeMe97i2UY+03+y4ikacUkd5/P7v/37bjActbnWrWzWHV9qr9Z6ikhdddNFw+umnf8fazoKCgoKCgoKCgism7HJJLXYqWGPgVFnbaD0iJ4ZzKG01m95I+eTMWcMnFZMjKKJmR1YggnblK1+57VAqomaXVM/YSMfOpZwp6aOOufBf2XEKbU4jEiiqJ4JnPSPnitNmvaLjRayRdM971kPanCd1923gJEpD5eBxSh0zAn9Oq+hijt943/ve15xRG9zY2EZ0Ubvg9M53vrM5vyKfnFoRRseYOE6EQ4oWyoafb8/C0bM2HxK95HDDh2PKEZfeii4cSI4vx1I6bo5G+eu//uvmWMKpoKCgoKCgoKCgoBzIgrWGPm1U6uWsZ3yyvi+/Z62b3CxwYp3ReNRRR7XUz6SlchL955xO4ZhNa0CPm+vBt//dt2v8f0yTpLtKbx2Xnfr66ylr3vvjZwoKCgoKCgoKCgoqhbVgrSHOzDxncOwcbSvHMZA1iHY1tTGNaKXUV5HMWUdg5DzHKegdtP73VLtmtdNvUdp5ZY/xWfT+tnTCCwoKCgoKCgoKdk6oCGRBwYogqmfzHiml0kOlv0qR5TyOjwIpKCgoKCgoKCgouDxBOZAFBRuApKQm7VOkr6J1BQUFBQUFBQUFl3coB7KgoKCgoKCgoKCgoKBgKajdMQoKCgoKCgoKCgoKCgqWgnIgCwoKCgoKCgoKCgoKCpaCciALCgoKCgoKCgoKCgoKloJyIAsKCgoKCgoKCgoKCgqWgnIgCwoKCgoKCgoKCgoKCpaCciALCgoKCgoKCgoKCgoKloJyIAsKCgoKCgoKCgoKCgqWgnIgCwoKCgoKCgoKCgoKCpaCciALCgoKCgoKCgoKCgoKloJyIAsKCgoKCgoKCgoKCgqWgnIgCwoKCgoKCgoKCgoKCpaCciALCgoKCgoKCgoKCgoKloJyIAsKCgoKCgoKCgoKCgqWgnIgCwoKCgoKCgoKCgoKCpaCciALCgoKCgoKCgoKCgoKloJyIAsKCgoKCgoKCgoKCgqWgnIgCwoKCgoKCgoKCgoKCpaCciALCgoKCgoKCgoKCgoKloJyIAsKCgoKCgoKCgoKCgqWgnIgCwoKCgoKCgoKCgoKCpaCciALCgoKCgoKCgoKCgoKloJyIAsKCgoKCgoKCgoKCgqWgu9e7rGCgvWASy65ZPjmN785fOMb32i/v/d7v3e40pU2Nw+inK997Wvte3wd7LLLLu1bPf/v//2/y/4XTAO64Q8+ge/6ru8avvu7v3uH0m3M43XAaTPy/5WvfGX4nu/5nvbpab2tZDT087nyla/c6Lez0a6goKCgoKBga6AcyIKdBr70pS8NH/nIR4Y///M/H774xS8Ov/IrvzLc6la3Gn7gB35gU4axss4777zhfe973/A///M/7drVrna14ad/+qebwfxv//Zvw7//+78P17rWtYajjz56+P7v//6ly//617/e8P6RH/mRK4zBrc1vetObhj/5kz9pjs5Nb3rT4X73u9/wfd/3fTsMJ3w955xzhg984APt9w1veMNhv/32G374h3942JFA1tDIN7laJCMcxfPPP3847bTTmuwfdthhw4c+9KF27b/+67+G3/iN3xge+MAHDj/6oz+6KbzUg076Fkdb2Y9//OOH97znPcPBBx883PWud22OakFBQUFBQcEVDyqFtWDtgXH96U9/enjiE584HHHEEcO//Mu/DLe97W2Hm9/85i0asllguN/iFrdoBvO5557bnMmrXvWqw93vfvfhbne723DrW9+6OZmcIg7lsvCtb32rGdwvfvGLvyO6eXkGDsc1r3nN4cMf/nBz2t773ve2KNmOBHLCafzbv/3bxuN3vvOdw//+7/8OOxrIE3zQaBkZ+epXvzq8/vWvb5Mdvv/zP/9z+Pmf//nhc5/73PDHf/zHw1vf+tbhy1/+8qZwgsdf/dVfDc9//vNb+eCf//mfh1e84hXDu971ruEtb3nLSv2goKCgoKCg4PIFFYEsWGtgzP7FX/zFcOSRRzZn7Pjjj2/RLJGRrYjoKUMkRXSRIe6/9Lxf/MVfHK5zneu0SIzvn/qpnxrOOOOMlfD+67/+6+GEE04YfuZnfma4IoE0yh/7sR9rTjhYh8grp/Znf/Znh6tc5SqX8XhHA6f64osvbhMjxx577NKO8B/90R+1qLYJlJ/8yZ9sMore6O6zGXqT23/9139t/YyDvddee7Xr17jGNYYHP/jBw/vf//5hzz333KHR5IKCgoKCgoIdC+VAFqw1/Md//MfwqEc9anjNa14z3PnOd95S57GHWdEfzutnP/vZ4fd+7/eGO9zhDi26IxrjeUa0NNcvfOELLUIJJ7hxGEW6HvGIR7So5e677z785V/+ZTP2RXREbzzLKfX9T//0T608To13OTv/+I//2Ax4DoE6pDn+3d/9XfvNmAcMfZFZDsS1r33tVv6YLsqVgvv5z3/+snucDZGs//7v/77s2o//+I+3CCzH5Cd+4idaCq8I6j/8wz80/OJkS430jndd94x7nDN0Qotf+qVfamX2uMDxM5/5zGUpwhwhbbWGbwyeVZby0Uqd6hYp5nTBKTTUZimzn/rUp9p6WI7/D/7gD7Z76sGvT37yk61ebVpWbuAg0q0ubdS+n/u5n7vsffWjPbpqC/pLU1Yn3qkPcKLdhwPcf+EXfqHh4f03vvGNbWJEO33+5m/+pvEXX93HezRCa3XBIXR7zGMe036jH9noAU5kJfSAF7nTnqwbVpb/8NQmNCOPeHTiiScOr3zlK4ff//3fb5FIeJCX293udsNuu+12mQxEvvAcfnCGm7LJMNqLjOIP2b761a/eUsHRVOowOYELIOt46D588Vo9mYQoKCgoKCgoWB8oB7JgbYHh/oY3vKE5YQxlax6tYRNF+uVf/uVmfG7L6BbD9+1vf3sz/BnPHEjGLwP7Yx/7WItMWhfGOXja057WnIDf+Z3fGU466aThta997fCOd7yjteHjH//4cNZZZ7V1YyI4UhY5KKJO1qy5J/WQEf/kJz+5Gf0XXnjh8NKXvrQZ3ve5z32Gt73tbcMHP/jB4Td/8zeH008/vTkpnt11112bsc3RPfzww4ff/u3f/o5NhTgmxxxzTHPGGfiPfvSjm6Nw6qmnNgfiete73nDooYcO7373u4dXv/rVzWH/oR/6oZbKa23dTW5yk+ZscAasf5MKyim1Dk8qJadHqq+UR8/d9773Hfbdd99vw0F7/+zP/qxFcfFNFOuOd7zjdziQnnvZy17WnCv1oDc6+s0hRyOTCS95yUsaX9CUs4l+2n3Pe96zrQtEE44XXqElZwSt//7v/34pvuOfOn7rt36rOTbohV9owTHSDmmnN7vZzZoswPvhD394cyTJKHmwblBqtIkGOHPOpUPjBccLfZXtt/rwAq8vuOCCRgO0MWHyute9rqUD/+7v/m6LPr7whS9sMnfd6153eMITnvBtadzwfO5znzt84hOfaPyy/vS4445rtHjsYx/bHEJO7DOe8YzL+GFCRDTzqKOOarRWH2cQbs973vOGvffeuzmJz3nOc1rb/T/ooIPaxIH1l+SIXJED6a1Sv/FXe8jwRRdd1Phxl7vcpaXAWsfs/0Me8pDWHu1/0Yte1Po5+sIbbuQQfQsKCgoKCgrWC2oNZMHaAgOcQ8No5dAwdBm5t7/97ZsBy9DcFmsLswbs6U9/+vDUpz71smgXo9faPpGoj370o81IF5m50Y1u1KImjF6GvWf32Wef5rAAhv/jHve49hyDnpHvWUY5B1B0k4Pgw+hWFseDs/Onf/qnbR2hqCGHiaPoXZErzts97nGPYf/9929Oi3RZ0aueJnDhIHJsvKduhj6HAXAAtE+7RIg4wL/+67/enBvrTTl79773vdsHLujOIRUFVa733/zmN7dUTO9zUtGOE9YDR1hZops2IuL4TW1+JJLFcdBOONroxrvPfvazh7PPPrtFtmweQxbUDSeOCTp4h3Mt2qtNHB5OLqfzKU95SnsGXvNkxj3tka7J+X/AAx4wXP/612/O4jOf+czWPrhwysgBh8/EAqeL42oSAe9E1NCbY8upf+hDH9pwjqMkcupdkyEcTL/xVKSV04TG2mZdI5rBW7vcxz8bSUmR7tciJop9r3vdq7VX2ZzNJz3pSY03nDw4kVvAWeN8usZZBNoi8qgccmCy4Za3vGV7VhQTzTnsHGY4mFAgh5xGckjeTGS8/OUvb5FWjr/6pJ+Tb3TFV9fQUb34xtEn4yYfHvawhzV51LcKCgoKCgoK1g/KgSxYW2CsS8VjyEt3EwESReGIiGyIeG0LI5MhztFixIsa9cd59MckJPrJqRlH0jyTSCBjnPPpOZ9+/Z3fHIhx1FB57qmbQyHS9KxnPWs45ZRTGm6ikRxNtEAf5TPk4TwGZe2xxx7NoGewi4IGR23gCEgd5LBycEUXRd/QnyGvHsY9p0f0yO6qIOmHcD/ggAOasy2yxYHod6pVBwfMZkKe43jCaSp6rL1JBc2REcpKiqNveOcYCbSxodIf/MEfNDpKJeZEi9CK5HG8OPAi1r/6q7/a1rnOA/w+88wzWySVA2XiAr5xrJTLkYUfpxxuHDrOsAgu5zAyAkwicHA5YRw6kyHKGMuR38qKo+0bX29zm9u0iDa6nnzyyc1xn3X8iGucRM+IMiZ6JyrIMQ+/+jp7uXVN2WO59YxnU68P3rzqVa9qzqf0Zw5qUnkTrUTL8Nn3ne50pyZfHGr/OaT4xbGWdisKy2FWNicyqdAFBQUFBQUF6wWVwlqwtiDilp0yGbIMYAYqI1lqI4Ndit9Wb1LDgGbwW7vIOdjWxxVwRmZFxeBibRoHhaOEHjY4EakUgZUqiTaMeA5izgMcA2dIJI2DI51SuYx+ZVuvKUVWmRwPUTSOO1AvR8KHg8Cp4TRwhIKz6+rnYIpEAU6E+5wHTqcIKWfB/XkRQA4r51X0WaSLQyHSFjqNgVMTOmXdow+nRDvgDbeeh/PSnr0juqy8OPY3uMENGp3RQkRR2erNsRvkUvnoJ5rWRwXH+AE0XEYG4E7+RFA5sGDR7qcpAz54G0eNU72VYOIGncgj/oe+aKSteEY+A/3GRWNekbmkp0tTNtEgUilKXVBQUFBQULB+UA5kwdoCw1Q0KkZ3NgBhUOf8vHGq5FYBI9cGHtY+it6sA/QbuGg/Otz//vdvkbXA1O6Y2WxG6i+nW7ql6JB3RRmlCVvrJl1UpFGEDL0TcR3Xz7GfcoL6Z/I7Dg1aWgsqzVY0cFZ0iQMsBVa0lTMCJ+mPq/KAXMBz3IZFgLZ5lxMMHzSVRhr84hin3ETlXPde7whuVYr1qmt9E00MbqvQYBlIW8fOceohH8sc3aIckwZSbqUbW3NsnaQUV/znOG817gUFBQUFBQWbgxqZC9YWbHzya7/2ay1yIeLBeM9RBYmgcaK2JajfmrZZEAN97DhNOQ/j/73RvayDgBZ2ueRI2xhFuikaZOfRWWcbikRJ8xSV4iCmbTe+8Y3bPVE1UUqRJHTnMCWNNJEijpU6RIz6FNVZkPba+MeaQdHjbLqS8wXHYN2fFFhRSw6udMeNHBlh4gFNyIzIp+8xn6ZAFFfE0vPShPszFbWHzNlBlIOUyQuOUhxNztDUzrKLYF50cqPlwB1e+InvU88uI3dxmnsgf9KBySM6BG/91LMipyYtlsHXumH8ssaUbFgTbILDZMe2miAqKCgoKCgo2DiUA1mwtsBJsZsj45ejJFrGQRJ5FNFyrMdmt/mPc8RIjvPlP0OYYwAHUbne2WPsiu4wcqU8SqdNhAxucTqS1mc9naiKg98Z3iIr6pCiKbUwu3iqj1OY34m6wiu/1WtDEzu1oofIoTWMIjbWnSX1dAqkCXIiORSikdbk2diHU2Rtmqhg1tFJ34WnNERtytpCzqs0VI5DIky+42iGfvBNOq1vO4baSIbzZYMeaYrK7R0T73Da1Okd6zJFLePwqgMt+ghofveRP79tTsQ5dk2qpTLxyCfO1bh+wHm0oywnUOTTekjvWh9qTSWnyO6xOWoETiKV0lfRV+pz5Cm4JFoXnMNLNCRPnCQyJDKs/T3v45Dl00f21NGnLGdtovLIpkiua9Yect5zBqb0YBMG1pRqQ+pBD/iQMdfIF/pb84pewUmd5PgP//APmxOp/XilXuVxpO2uSs6Ca0+D0CRlqYPsAhsd2Z01jvw6nNdZUFBQUFBQ8O1QDmTB2gJjl4PjoHUGpbV7jhngmFkrxcnZivQ2xrGjHjhiHCQOlbWCHL7x2jHGtc1JRElE0ewYKv3OdU4dp4vjAS9HNihPuRwRzoedO22qIkLzghe8YDjkkEPa7pocHu97l7Pyzne+s0XQPG9tGCczaaWcMfWKGNox1LEKdjZVvujgrKgSw55zhKY+jHNOo01mrDdLpI9zYEfNBz3oQc3hciYgJwIP7JZpTSDHWTQR3hxqvOHoxSnIjrLuczC0iYMl3TZHhHhmzG9lw0kZdg+1sQq6cH7QgCNuvSYHz2YxHGfr7WwgJGqKrvAiIzZiseOnesgLJ8V7nB6OqGjneM0ommi73WaVZdMiR4PYRddEgmuO60Bza0G1WwqwI11sduMbDzlj2s5Rw391mYzATxE3kwqcOpMBrtstVcRNHWTcxAjZsfmQNsZ54xRas6ocDptILcADGyXZpdXRN+hrPabjVOyUirfW0OK1SYoDDzyw7YIKkk6MhtLGybdIKro6KgZd0VD9cCITnGqbCtnUiqP3ile8om2qY4MnEWRrkzmqyjQBRHb8NsEBL7Kq3Zx7DqV7+IOe6Gf3Vv2kHMiCgoKCgoL1g10u2RbnIBQUbCGIqjC6c4Yfg5Yjwtjd7DmQifAxakVtesiukoncBBi8jGvRIoa5FM0+Miry4iOC4xkGO2cvKX+cUjuhiqpyOBjTDHL1iNBxBhjp3gPq5rhwSGJQcx44VPCGj/MKOQKL0j0Z9XD3PGdKOYx4jqe6e1A/x8EOmerlyGkD50zEyY6vSTF0Da04QJwdvOJkZr0gx8Nv7UwkjrPDWe9pqzw0w2985ixzRDlg6GRXU2W4BrRBxJRDlsgvGkjP9S3KxtFTrjbjEXqhNaemjy73MmFyAF04PBxe/EMjEwPu4x36o4N68FH73YM7pxkk1RpungVx3MkKZxB+nCnpuiYN8CeymPc5ywBN0TY0RKM4exw57SQTZC/0Uo82cpbhrF1kGz3Ig7LIHJlFE7Q1aYHveM7xRwdtjjy6ru4cLUNe1Y0Xnkcz5aBF+BKnX5QyuyeTOW3Ppkt4iz76FPnYlue8FhQUFBQUFGwMyoEs2GkgUZhV1gxuK0g64az1j4EY+uPn+rbMendZHMCyNEnqYL/xS/9/Xju312YmqbOvb6M49Omfq9J6zOMpR3OztOnpvyxeU2UEvD+vnywjt30q8jLrJMc0XqUN4+FnHr0LCgoKCgoK1gPKgSwoKCgoKCgoKCgoKChYCmoNZEFBQUFBQUFBQUFBQcFSUA5kQUFBQUFBQUFBQUFBwVJQDmRBQUFBQUFBQUFBQUHBUlAOZEFBQUFBQUFBQUFBQcFSUA5kQUFBQUFBQUFBQUFBwVJQDmRBQUFBQUFBQUFBQUHBUlAOZEFBQUFBQUFBQUFBQcFSUA5kQUFBQUFBQUFBQUFBwVJQDmRBQUFBQUFBQUFBQUHBUlAOZEFBQUFBQUFBQUFBQcFSUA5kQUFBQUFBQUFBQUFBwVJQDmRBQUFBQUFBQUFBQUHBUlAOZEFBQUFBQUFBQUFBQcFSUA5kQUFBQUFBQUFBQUFBwVJQDmRBQUFBQUFBQUFBQUHBUlAOZEFBQUFBQUFBQUFBQcFSUA5kQUFBQUFBQUFBQUFBwVJQDmRBQUFBQUFBQUFBQUHBUvDdyz1WULB94OMfH4aXvez//u+yyzBccslqZUy9sxXlzCtjXp39vVXLmFfWMu9uBmewLB2Xoe+yuMyj+7LPLfN/WfxWLWfV55atY1k5mlf+rO+tqmdZ/LfF/43isWx7wGbpv1k8xu8to3cW6Y7N1jOr3lXLW4TDvPdW7durPL8R3bQqfvOe2YjeWoQLGOO9qP7Njl2rXNuZdMFW6/Z5cKUrDcORRw7Dla+8CsYFBdsGdrnkko0MYQUF2wbOO28Y9txzGH7wBy9VltsHZnWBXbZhPbvswLK2Eo+N1Ls965yqe0e1/4oMO57mlw51lwy77HKlnUBet5qmi9oyLmvR/8sLXLLB9mwVPa4odN4WfeqS/9/p0qd3uZzS6//ga1+79POFLwzDVa+6o7EpKKgIZMGawoc/PAw//uNfHj73uX8avvrVr7RrDL/v+q7vGn7gB35g+PEf//Hhe7/3ezY1aHzzm98c/v3f/334x3/8x+GqV73qcI1r/Nzwla98Zfjwhz88XOta1xp+6qd+aksHpW9+81vDhz70oeGqV73KcO1r//xwpQ14yIzg//mf/x4+85nPDle60i7DL/3SL/3/g+fSJQz/9V9fHD760Y8Ov/qrvzL86I9ebdjW8I1vfL3xEa1//uevPfzQD/3wsH3gkuHLX/5K4+8Xv/jF4Td/8zeH//3f/x3+8R8/O3z9698YfvEXf2H43u/9vu2EyxUb/uM//mP46Ec/NvzWb/3m8CM/cpXtWrc+o/5Pf/rTw7/+678Ov/Zrvzb8zM/8zMz+R17/5V/+dfj85z/f9MCP/MiPDOsG5Phzn/vc8N///d/Dz//8zzedOAVf/7q2/Etr9667XmO42tV+7Dto86UvfWn47Gc/O3zzm98YfuEXfrHp2L/4i79oeuVXf/VXhy9+8T+Hz3zmM8MP//CPDNe4xjUui8DsrKDN//Vf/9Xa9P3ff+VGv1X0vPf/6Z/+afj7v//0cN3rXm+48pW/b0OTeF/5yldbOXTTL/zCLww/8APfP/zd3316+MIXPj9c73rXH/7f//t/w44A4+KleP1Xays8fvInf2r44R/+4RXHmv+Db3zjG00Gfa55zV2Hq1xlsx7QJcO//du/D3/3d383fOELXxh+4zd+fbj61X9mw/jtDHDWWcPwkIfsaCwKCv4Pag1kwVrCd33XMHzrW18f/uIvPjoceOADhhNOeMzw9re/eXjd6y4aTj/91OEJT3js8OEPf2i45JKvD9/93cOGPt/85v8OH/rQ+4aHPewhw5ve9PrhSle6ZPjCF/5lePKTTxo++MH3NRw2WvbU5xvf+OrwjGc8dXjNay40pG6ojCtd6VvD3/zNXw3HHnvU8OIXv7DhvMr72vSZz/zdcOKJJwx/9VefGL7ru1Z7fyOfr3zlf4aXveylw9FHP2L43Oc+Mwe3S7YUH239l3/5x0bzRz/66OFrX/vy8M///Nnh5JNPHE499eThS1/64jZv+8722Woe5ENmn/jExw1/+7ef3O5t+spX/ns455wXNbl/5SvPb7+//vWvzHz+f//3y8MFF5w3HHnkYcNnP/vpHc6Tqc8Xv/jvw1lnPWs44YTjh//4j8/P5OWXv/zF4dWvfuVw6KEHD5/85F9O6pPPfe4fhic96QnDaaed0p5HGzR60YueP3zrW18bPv7xjwyHHvrQ4Q1veM120Rfb+qMNH/vYnw9HHfXw4VWvumBlPe/997//PcMppzyp0X4j4wS97d3nP/+s4bjjHjV84Qv/zHVr49DTnnZq01U7ij5k4t///V+Hpz/9qcMBB9xveO9737VpfLx/4YUXDIcffsjwD//wd5vG8ctf/u/h7LOf1/r0+eefO5x77kubrC7i284sv9svI6ugYDmoCGTB2oIZzxvd6EbDv/3bvw2/9Vu/Ndzznvccvud7vqfNOJ511lnD/e9//+Goo44a9thjj+G7adgV4fu+7/uG6173usO3vvWt4ctf/nKbbb361a8+PP7xj28Riq2GK1/5ysPRRx89/NAP/dCGZ5dFTURFRBxEHzYCv/iLvzg86UlPauVsLz6KZLzsZS9rs9tT8NWvfnX44Ac/2Gbif/Inf3LL6v65n/u5Ydddd21lm52+9rWv3SLLn/jEJxrfC/4PRNxEnn73d393+N7v/d4tLfvXf/3Xh5NPPrnJ3vYGGQVvfvObh1NPPXW48Y1v3LIM9P0eXPvTP/3T4XrXu97wgz/4g00ORUtmyeuOBhkY17zmNYf3vOc9c3G8ylWuMvzGb/xG05miQFP6RFt/4id+Yvibv/mbpgO///u/f3jQgx7U7tO3v/IrvzJ87Wtfazry8gD0gKijSOtG2uT9m9/85i37Y6O6Shk//dM/PVznOtcZ3vjGNzYeGsPucpe7DDe72c1mRpS3B8CDvtY2UW595tJsnNWATn/f+97X+j45JGd0zJQcrgrvfe97h7e97W3Dve51r2YjiLTPGlPJtCwUH/2bTBcUFGweyoEsWFswyBrM8uGAMe44JBxHaVePfexjm3NpIOYQGCSkSnpOulUGlaQtGRBd41gwkhmS/cCjHgN7wMDE+GJUef8///M/mxOi/IBBUZoYx9BAyyhTzlQ6DcfUwApXz3ECpQsxZn7sx36stW0qtQ7+npV2xdjoy3ZPmejhm2MIv7yT8hmdcNRubewdKO30PoNKG5KupAxt9r5nlIWuP/qjP/pt6YHoij89zeH593//98049+6s9CL3XvOa17RJgcMOO2y44Q1v2NIGXfe+FDu04dzPKiP8hR+AByMMLtobmjIe/B/TDw//+Z//ud1nmPsmR9ruee2N8UNm0M896WeM7ymDT7lJH2RA/ezP/mzDIzRVH1Af2gFtRUvPaDN+aZOURXjgDbp61zt4ijfjesPP//mf/2m0QE9yHF7CR10cA2WSm5NOOqld0zZON9rhrT5F/pVDrtEgfUJ56J4+ET7Az/uRabgqt3d21KlsOGoHgxV9lB88tRsN0UgbZvHfO54z2YQX+jdc8ZCTBV91MfrhOpa/iy66qMnfCSec0J6JjLj3V3/1V+07EzeRbXxSPrx9xrihhefISByEf/iHf2htRXPypAxyTYbS3/RZuHqHzPTtxi+puPruePsCNCArcFJ+ZAPtZ6XrKnfcJ3zDJxNU+Ah39cEXLeF1tatdrV3DI/zSL/CdnPgdfuA/vQcXeLumTOXEmEdf8gRncqHPeI/8jOmaMjzjffX5jtwom85RPzm2PGGsq+A8T5eoA998R++R3zhSZD3tj/5VtjbQ0dqv7rRPfdGvZBNP4TmeyNAvUyb+awc+KJcu9B7ap8/DUbmZWFSfdkUX4huarpJ+6jnlwS8f+KTfazvZzVgDtzF49vWvf/3wzGc+czj22GObExkZc++Tn/zkt41V4Sve4C2c0XsW70324LV+gs6e0y/wB65kQxv8RyO6DS3ocbSgc9DSx7Nwia7CK/TTb5QPD9f1KzSFLx4smgS+dLnJ/zRawY+8+E8G4AK3pPaSUR//yQ+e0wveuTyn5Rbs3FAOZMFOBxQqJX7HO95xeNOb3jS8853vbAPRq1/96jaIMCje/e53N0Px3ve+d3vWmj/PMWTe/va3t0HhoQ996HcMTiIVL3jBC4a73vWuwx/+4R8Or3vd64YXv/jFw01ucpNmILz//e8fdtttt+GBD3xgGzj/7M/+rA2UZrTf8pa3NNx+53d+Z7jzne98mZMFDAwf+chHhmc961ktCnPwwQe3weTlL395w5MRAPbee+/vMCoMRIwHzxq4DJwiRb/3e7/X7hsg3VP3n//5n7eB75BDDmnGxLnnnttwi1Ny97vffXjDG94wvPSlLx0e8IAHDLe97W0bbqKDBk0DGxqg5y1ucYtWxhlnnNGiEIwWxjj8RVK17+Mf/3gbzBk773rXu4bb3OY2bVZYma94xSva4Inm6pwVKTFYqpNRAX/8gyucMnGAZ/gBXwP3eFC1Fubss89uzifaeF/7Fs3kw1ObtMEMufqV/eAHP3j427/92+HRj370cLvb3W7Yd999hwsvvHB47WtfOzzkIQ9pg7/7f/zHfzwcccQR31GPcs2+ow+nTPn777//cMtb3nL41Kc+NbzoRS9qM/vkEo3dA3/yJ3/SjCdGBgMYLc20P//5z28yJ9pOTvHPWj59YAyMIbT3jabkg2MOJ5FnTshjHvOY5jThyX3uc59Gv7e+9a2tbjLtm7OjDd7DW3K/++67N16mTzCqPvCBDwy3v/3tG73J5qte9arhl3/5l4e//uu/bg4wHNGNLJJLERY4aUP4iwfoTH70U84coxO4J1Pg8MMPbzI35r326IPoypgUndC/0ZQ+IA/pu5lI6cvAHzRGK+2Nsc2Q1FYG68c+9rHhbne727Dffvu1d/R1fYrDJJpNBkxk9eWiLdo8+9nPbsagfkAO8E8/RB+0JjsnnnhiMxbhS+71P30HTuoURaQv0DCOBpx7nutDZAZeoomHHnpoe28V0G/gpc9rC7xy3fptekpEVx991KMe1XTgc5/73KafRK3IlMk9Tpdy1E92yQC9SA7OO++8FuWmo00M4BU6ey+yTwfr62RsPNmDpvSye9pM1u5xj3sM73jHO4bnPe95rU7146V7xx9/fBsD0PKVr3xl07ccA/ICpykakPfnPOc5jW9A28kFZ0jb6RpyJluFDHtWefok+nAKDjjggMZn7SGf5El/J69oN47qkSW0/Mu//MvhqU99ausj+EAOrN+GA11J94qkobn2A7RUN73lGf0THfRr9Lvf/e63YUfEe2TvggsuaH3793//95scGM+MV8bYcVSP40tnkwH8Sr/Fl4svvrjpCbS4053u1DKJ9EvyTOdor3sPe9jDhhvc4Abfhre+Q++a1NGnM/ZwbNHuEY94ROM5OUMb/EEPZdMN9Lvx8OlPf3qjF12ORk9+8pNbO0SWlaNPo7H26n90EXkiG+QM3nTxomgmvODAeT7uuOOaLpKJQV8ql/4888wzL8uu0g/QBk5oTE7gW05kwTpCZVUX7JRgdtGAwEA1oBggKH5KnXL/oz/6o2ZMMFYoYgYpA+8Od7hDM9wZgJT3VFpRNtvgyDE2DJQGKQMax/D8889vM5EGMAMRA1jq0fWvf/1WLgerj1CmbIaHchNBYJQyVBiLt7rVrdqs/9SmyIxD9RiEGGIcKY4kgBdjCy7KYOBzakT0OC8GcW1gmCsfiJYwpgxUwKDGyGVM7bXXXs3gYYRyGtHYbzRkhHAADHKMJPRjOMGFsWdm/YlPfGKbQfUMY42BAec4u1PAGGZQmgE2sKrzJS95SRuw99xzz2aEcbyOOeaYZjBMAbozvBgcPgZ+dF4EjL7TTjutOZ7oalAnS0972tMaHxnIjHFGIoOVM8GhYvAwkqbS2PAQLuecc85l5ZIRhgQjEW3g5h6ak1HGIBozfBitDBnGtXo9l1n7RHEYoYzKcfTRPZMJeHrrW9+6TYRwDBmUykVfxhm58CynyMw3o4sc4rk+QuYY/q5x7Mgu3sLfNfzXTk4K/uoT7kkVZjByxsmjshOJZJQlqsOB9qz+BIff/u3fbs46w1D5+jT5YoQzijkm+DAFDGt9Hc3gos3ai65opF9yTOgFtBwDHUL+PKMf43uiuHhA7ji9HDt9hkPLIWBIe57T9pSnPKUZ2T3gjbYwIvFX33zc4x7X+j2ekDUGLMcWzdDGffgolw7TvzkMaIHGaKZPoRt9Fn3hfYY52efokyVOyEbSBRn7nB3ylvJ904d4wSEjX4xu/YOsoAna4iE9ANcsQUAHOomB7Jo+7Bpjn775gz/4g0ZDeoNeJ9v6Cz5MRXXJN1rT43QbpxqNMxHHOTzwwAPbhAs9SN49D1/6nYyQ8Vnpp4nAcmbovoMOOqgZ/eQbTROtwke/yThZ0SduetObNlkw4XHKKac0PUAX4b979DDe0w1jXY/v6ElvJ/qMTpwe8qn/0fPaRB70TxE0ZeL7M57xjKYz0ZdDyYEk82i+mVRseBrPyLLyTXg88pGPbPxG+7HcA8/gPTqRe3oyfYp8kGl466N4pw+YAKZjybZ+hub6RA/oQVdw7tJf6VC0wnv0MZ7Alb4xOWRig2xwAvUNfRrf6GC6CZ3InzHdGEvnmsTyjv7IgTPx69s4R4ZMxHl+0SEG8KILyYpn0UydnORkc5BDOEllRl+6Bt+8Y1w2zhQUrCOUA1mw0wJD1CezlxQzI9m3lFYfs5AGp+yoyIhhPBhsDUBjwyFpLPlvIGTMGADdUwalbwDiaDFW/FanAcrzDIrxzCQcRRj6qKTnzDQyDAyiBs+ptWeMDwaagZCBYTBPWhaHhuEoesgp8KxB1SDqY5AURWD8MHi9P065dE85aKmtBtS0g9EIJ4Mg2hrotJXRwNhnvDPsGSzaxyiACwOb06GMpOuMnZ0Ao9EHvVO2yDJDAz6uMTLd18Ypo5iTI0LFQWfcKgNfFgHD2ztJSYKzAVz9jDTGDMOQcYIWcNA2vDe7zogdpwcyFBh9nCAGP4OOLHoW7owDjjgDBA0ZOQx2NGcwkQfXyQNeo7l3GI74y7Agu+gzNrC1m4OqHDPljChOY1IIOZV4xLjlNLqujP6DXxxr0TG0DG+9S0am+gTDkOEfR1HKGBppM/khc4msw1E0V7uU6zpjE6C7sl0jc+TGc2iMTlNA/hnI6fv6oegCp9M74U9SMcc0S7sBGkVO0V45+ixjMn0ePfQZfCX/Jkf0Te0fl6vt2oPO8FMW3vnN+A9d6Cc8p6uUFWeLE47f6mFg4ln6RJ/ihwZ0EZ5xQDgcHDB8WQWUl9TRXq79Rlc8UbZJERE4dNce7WDYm+gh6yLe5JfzhlYMe3RlLJMfOkY5DGpOvvc4hvqiCTs8H0eKg5+JLJF5Mkq/kSf9B83Qklx610SUOukjNNEn8RNPOGuzdtdVB92J/+itn8HR5KRy1If20fF0Kr5qm/LxFn5wwjtjEEeU3OARJ8TzY+dDOcpIaiRZRSu05bzASX/gXJEX5dEx+rmyOMb4BDe0kWXAKdffZ+neZUG56OGDv/DHI/1hLPc9HYG6I0vaiEbGBX3Kuxwk4xva6FN0ZuRiLL/K7MvL8paMH1N1pu3RbRlv+rblf3QV+eGkkmH8JsdojeZ4YIxY5DxmPKN/6WPOYeioHGMu+UUH1/GR/LuXCUA03oo1owUF2wIqhbVgpwSGLOVr8GHYcCwMZpRtDEUDqVn0rAXL2hKDMsU8awDor897xiBqIOG8GiQ4FAYcBsesd/ryGCdHHnlkm2mV/icaIbowdiLNjjIasg6pB+1inImywAWIwKCBQUlamJl3s/FSdkX0xmBARw8OCqMKTc2WM8amNurJIJ1ZVPWmze5lraZyNwKMBu3tZ16z3i8RrDFdtVc012DMWJ+i1RRwEtXDEPY8/DmTnAUGIH6Kbhn84WCmn4HD0CdrjNQxwI8hwFBgBCQl0kcZJgvIDEOXsZl1keQVv8xui3owlJOGa1JA1MIsPeCYTa3B0RZ84ZCZKAGcYEYUuWL0JurJMeaYj1OmARzR0H1ONYCnZ016hO5jPjC6OPJkjnMuQkkee8h6sX4tcNYjJSq+CsAVr2JoZY0qo32WcbsqROazZokRadIia860AW8jQ4v6fn7n2ax9xD9tyT2OHD4nQtIbvn15WZsMp6w18+xGN0Ma49tfz7pD5affBK+0X1s4mCat9Fv6oL/f0wi/6D0px/Q4vc5Zm+f0cKbJcVJMxzj2tAWcE7RddrO1qfbTcemrY172H8BB4vCRcfKJDpxuOIm4a9sycjLVpqSUao/Ji6R6Z8LCt6g5PYKeon3q3MixUVM4bBUEHzKkT3HcyG8m6hb1qW0F4/qy1tZkTiaAo8uXAeM8Z9hEgokLMks3GUOUbazNuk1jAluCfERvhwaX93MuC3Y+qAhkwU4BY6NDJEZalAE06X4cRYoZMEwZYhSygZ9RYmbbdUag1CIKe1zHVL2z/hvoGPZmS81uM1qldxkklgGGBcdPO7RBVFTa1Hig5oBwqswqx2DLM5wLeJi9ZBxl8xltRQsGvTQjg58NDXpDLsDAkyrofTPmUucY/THgZhkOBlPRBU66NvtwXtAaHiJRiQLOMox6yDNmqTlmcIkxnY2D8HNsBOKniJpZYpEMRveYV7OAwylCge4Bxi8DWXTMYM+JlFKrnRw7xiFe9Zuq9JANE8wi4zFacCbVQe5E5/AyuIYu6iWr1ldZj8NxRUMgAsGgkKaNr6JUU0AWGK5SsJSbKEvS+ES5GNPW4ErJNPM/5kF46znOIvnz0V/gPwu0G/8Zy2RORMrOp+N+Rk7QDn3iMIbPs9o1D0RE4mBl8xGRR7KybF/s2z4PsgER55gB7zcDz+RRNkVaBFM6JRMI8E3KPFAHWUo6tQhYHOUeX8+IUpJlPOdI6D/4sVGY1X/oUPTluJCLscNGbqSy4rFIYwztWeW5Lh3XpIjUcY7DeKOjgAkk68LgwPAeLxWYVQ8+oaV+lw2IVnWIRIyymRMYv9+PUfoa3PBNn+RMex4e2pf1jBsFeseEKVkhF2mfSaGky+qDnFaTORk7TNxsJCq9EYhcLwL0oFPRBN20JUsHyNKykL4PTBzh8zyZy7O+59FE36Jb6KtEYeEFv2yyN+99fZEDb4KTzr3vfe/b7BbjMRz1E5NE+lJ2VUYDTqs+TO4KCtYRKgJZsLaQAY/RkF0UXWPIW9BP4Yp2UPDSgqSsSYMStcnujhanc0ZEpTh3BtKkspj1ZnAZyD2bdBoDs+sMtexU6Zq687z/vjkW7ivPfwo/RwH00O9WqJysMZIWk3V+HIypWXfGFAPLWkdGpgFXO9ShPClKBiODEAOcIct4Z0gw+Dkj6uDYeRc9OXbBxXVr0kSbtJmDYgBn/CQSp70GSXQKvaSgoa2USwa8+kX01CXyJoWKw2OwNDAyZqXYZZfLHtIW0T1t5Zhbf2K9EOeawc6ZE+0cz6SnDcr3fDZY4NQbnLU3PFMvHibSg1eceHJj3ZL2ctrIRtL41O++9YFwh4O1ijkKYAzeUS4aMnbRIymeZpc58XAyI40/8OHgZUMfm9pwFtEizjKZ5dSTcUbsrMgSOlrnI+rAiBelQRcy5D3va68ZcUat9VpmyPWhbDQijSwpZk94whOaPGlneOs7fQXOfZ/gcKI9/sE3OxAmzVC7GUZk0ppQEx9khVyoz8ZNns1uhcoP//TFqWgE+acPRNDJLLmHA2efTJIrfFVmIp5jSPqwSDMee0cbvZM2pt/EOLcOLOlp3tfe8YYv2py2kAH/tcU7/qOherQNf0TepKMqVyScUS2STPbIobXcjFA8c49zTnbQzQZMdqUWxcEjjg4HLnXBQ19Jul9wTPvgon1kyzuu+Z8UPzpYGVnTqY8k2uh59ziLvt0XYeE8kSkGODzxRXuV3eNCJhjZ6A/nWdFHONKT6pHurI9rq7LpvqQZR27QG231R/JlUia7cJpwVI5vtJ5yNugVOHtef8Rjxr2yI5eRSc/p22TOmmzZCrIwTBTpZ/oSHYMW+KnN6IA3kY+UG0fKb/eSous/vrqnb1sbjyZ0lXLJCv2lXGOf/oo29Ijrp59+ehsfjQ/zdqFNloD24bcJtX6MjMwkEjrul1n+4T45wIO+T43H0kzSycCQiusZ7TUujvtU5MczytWn6VqyZnMl3+RI2fSrtOvoNuOItkgVpmtldChHeSaByFIvz8o2Hhl7jHPkGn89Y+MmTqE+ySmkh2bRlM6FA97RL3SItbEmYcggeujDMpJs1GQSjuNovNcfahOdgnWEciAL1hYoUCk4DCdKlpNDeZulFyWjlClkRgjjwQYHUvPsambwsuGDAdt96wA5M4wODo33DapmcJVjYOLM+Rj0DeZmGBkrlL3Bz28GG8eJYaFOhrc1ZwyZRAYY8IzXPvqRTRYYKgY49TDSGeeMeoOwgX3KkGHwcKZsWGNzEMZ/NsVRvzPbDHQGRPhZf6ONBkQGfF8+Q8MgyrjRJkZHjrsw+GqDwQyODGQ0ygAGZ4OwwS3b9qOrAdS6QAMto9IAzdBSjjLRyqDJafA9ZRyK4poEMPDjBxrCC7/UqS4Da7Zs74HBa9MMGw5wSLzrk405tEl7OSnaryxRK+Vymhl2jG87HqI1wwDPgycZsqaLIega50gbZ6Uqw4+BKDUZbdCfscfIwLPgaq2LNuMlvqEBY4ojhJ9klKMQYMRoF97OSkeDX3Z+RHtGIF5w0siH62iItqLScJBOy2HdZ5992mYR+kQcPPjDh8y5ll1a0U3ZjG8GZvqE1E59QZsZWdKyE02Ad468gY82MMCUAR/Otj7B0EWLHAPjXQamvkKOx1FfjoEoi51n8VC/Uy+nwbsMTrLJkNa/GN9jfpEP0RoTEKKvSTskP3jNoMRDMoNn0gNFirWT4cfxy9rpAPw51Jxa9/Qf5TIatR29tE/dmbSSZs5o5CSiZZxqbZLSrJ+qk2FMNvDDMyYzyDH9Z3KDrFo3m6gbfuXYkUToATlTFh4xtLUvESDl01l4YcMbaaMmquCivfqpNtCd+oi+S1ejFxk3QQE3jqE1r1mbrG+hq0m+PvXds3igrFmgvTYzM6HDodMfjBM5Rglt6Vp4qUNd5FSfJyPGhhe+8IVNHjgqnPapKGZwwndOJzrqR+iasYA8ox2d5lm01Od8w5MuJEu+8ZuOhAu+4GnWhqIvHhnjyID0c3TnNLqmTeQIT9CLLHGc8AQvySz+0xdxok1k2sRMeXYI1k79SpmLooL6SzIT0JeOoGPR0+QZXYV3mYAjM1N6mc7QVmM4vZd0zWx0hG9pO77TlyZJOXWeIzfjvoq2xjXf6qZr0IDMkQubN9EpZFP/I1NwRxtjpwg+3tvACu3Vxek2aZdsFNf1dRM0+iBnk+zQLcZ6Zdv8iT7Wf9HbOIWnU8sK0IU86Nt0gWeUbx18ZJ18mDRCe32fHJkIQpepY1IKCtYBdrlkqxPbCwo2AeedNwyW6dlA8xrXWLxIfer8rpyZN55pBxT0Vh0kbGbVwMExoOSVzUCWPmjnuX6b9nndLGeSBa9Za2Py7KzoE8NpapMQAxzHYmpwU55Bm6FjINUGH06me+P1a7NgFl05b9n4YBnegfAtKUnKzrqYebPmSXPN2qz8XhbQKZsyjGe9e7yT/jYPn7yXCE9o37drFq5J5wyfkx7FAWKUM3YWpWam3vGGEYtgHKHaaJ+ZJ3Pj+rLr5rIwr38kyjxv/dys95PauOyGI9nEq48ST+G0EUAT70cG+nRA9/rNfnrIJNaitX5jvbgMaCu+4tUyERG4BI95B71nnWp2xjYBMk/eIttZRzgls/PaAJexDh2/a2LN8RImEUweJWtlKi1T/VK1Gf2WI+Ss3ZSb59Xbb+6yVdD3z74+ekR9Pd49T+bp0o3ALJ2+PftU374xTEXfswnfsrgpv5dNNM6O1hy+VWg6HkPyTJapjMehM84YhoMOsvTFGtulUC4o2KZQEciCtYVV0zbiqEzN2KWsjW4qMQZKXqTGbL9olllis8IiHmaDxzv8zWvL1CYmY8j7856dZXTNM85FiTjBZnHNIpvxFakwC5udMZfhwyy6LnIgArOczDh0y7zfP7fsZhnL0GlqzdoyNMlzY74swrXnMePQeYkiCpwjUcJZEZNxHcvSvocpum2kzyzrEC7L30UQfiw693Pe+6vuVDnLiRvjtBGYovkyOmxVnq+Co7auQt8el1m6ScRHRFBUinFuI51FbRjL9irys4hnvWOR3bb7yZ/+O8/meIopp7//3qqJyzHMcobHY8WyOmGr0iV3RJ+a176xnKw6wTiFmzGfPl6UZrrK/gpboQ8LCrYH1CY6BQUbAAOP9BIfKSdSTKVVSTHKDqY7A4g8WnfBYJKmI41IipA2zNrIomD7AiPWuk4pp1Kfaj1MweUFsj7WxJs0ammzO1q+OYH6G90oXVXq7byjFDi/npPqKHNj1nEzBZc/kHIvfX+ZSb2CgssbVAprwdqmsE6ckLB2oPtYJyMiKXVpI1GfHQ1JbcoGJwbDHW3EFfwfiG5wIkV/tjr9raBgR4M0QDJOvtdB79CHUmmTSkinyyiZioy5z2GUHRCQxeGddWhLweUHKoW1YN2gYuUFOzVMnfm1PUG9sw6kXicYr+ObSm2yIc/2wmFc/46CeXQZP7Oj8MSbzU5MLNPOyzusQoMdzfOdDa/N0FoK5lYtLdgojM9ZXFYXejZHO8wqM89NXR/f29n5eUXVM1fUdhdcsaEcyIKdEqRcmiW2s5sZ36kDpQsupZP1jHYptM7RbnBTEVSb/+TcrKzRs9tfzi3bDIgu4JM0Lym+IrXZIdVOfTvKeNR2OwHasdGuqdbO9oM/vO2UKMJgl7+dcTc8bUBrfUVK8kbXCO7MkK3/0cGaXzshTq1HSyReX8jRJ6ucI7mtIMcq2NhFVMxuqesKcLVjqV02c4SLvmWSbR0Na9FPckE3Wcu+VUsP8Mmuu+SuP9sUH9Xno2512qF0HWkDjAl0JP0tRXeWY+054wh9af2z3Y5XXf+4MwM+S3U2xjoybF35WVCwlVD5UAU7JTCMbV/uuAzn6W0L2Mhh0+tWL4PFMQ2O+rCD51RdjCdncNnd0zb9tpv337lXjj6xxmcz+HBgbQRj23Lb0TM27FznzLrNHHS+FWDNkvMq+wOrQ38GkeM2jjnmmGZA7YyAt46/cazFFYOr9AABAABJREFUKodyX96Awe6YH+fMWXM3C2zqQlYdeeOohnUBuJBTbchuxesInCJrwh31QvasqXbe3bw1hDsS6B99nG6C71bSgRw5p7E/3N5OrcYtcuZYJp915qdx1njgSCzHpswaB7TNkRqOqsD/7Bx9RQFjnKN98LdWhRVcUaAcyIKdEqT0mdk2292vP9kqEHVyHAcHbHsbNM5T26oB2Iw6OjGO0GoMZkpFBZ1lxrlzLpfz/mxh71gP5yMefPDB7d5GB0abUThbkxOWoyWcS+ege7PvOxJsSgMPs8YAjTi3ZErUxEw65yNG4M4GIsgiIAycdTXitwfkPDfnMM6TY/TSF3LQ/boA3EXA9eN1NlAdbeScSudBOi/z+OOPb31sXdfuysiwa7YI/VY6cvSuswdN3CQSZ3drzpUdu50Z6NxTZ/+tc7TKOAtfkft5Y6GIvvMVgYm3yzuQFQ61cyCzmY7N6NadnwUFWwmVwlqwtpDt0Rm/0mKkxxiMGfXSHhlU/dowz4suMPgpeE6BZ/x2PWeRicRwahhlU+dMedahwQYIzsW1rnWtNkByKhgBDEvvMjbnrZszKyvtjFMCZ+tkpAMZjBlUHBSOKsOeI6V8h4G7p/ykASUFVfqhNrnvHUaP9rnv3T7NUv0cttybtTV4fzQA2qCz1FXXpeO4/sAHPnA46aST2llnykdH7UInNIBrjCS0hisDXPsY7jlapTci0T+Hb+fsTnTRRhFP9Wpr+IvmaI+W0qiSEjc+/y3t1mZGnLaguTrQ30CPbupjPEpRRDflMJBEDS644IK2I63r6OGeNjs0HB1dn7VJRtJic3g5GiYtLileynBkijbB1bd2owmachKSPhyZU653yGHkORt4eF8Kd/hC7hxGj0bqQvtZ/M+RBeiqTPXqH2mn66JfaKeOlKMOz7mvjT7hp3biORlAO7gklRa+yomMTeGSvopu0pzVrS8or++7kafQCE7kzf0AnrrXp2cH9KHIqvfIhvbhRZ9+593IHnkhf8usSdUeuKlDvfDSpkSztQF+ZAVf6QD14icZ4OzCxXPq84xy0Bef4WLyZ54O8mzqiz4E2kIWPUPOZqWqu69fpg14N3UOZDIZnOWoTr/xWn3aHKfXt/aaKHMvfISPfuMaOumj3ifD+IIm+IQO3nUdH7yHFniZ/joLPIMW8FMHmVWeOnt+wzFprb3OJQdwwBdtUr/60o/hG/kgR2ilrsjBe97znpYuj57k2jPortyUr2wf18kkOusLylEnnil/PPbkfEs4wwGNyFZ0m/rgKS07squ/KE9/Q+PIX8rL+JHzcce8RiM4kUHvoUVkdBVAt/RR/Rw/4Bod5zoeo4e2zysff6Jjo6d73ruPdvCGL76grWvqRXt0VA/ehyfoQA/lbOcPfehDbQLABAF6k0d2Qj/p5F24RI6iP6Pjcn6n8U59U/ZIQcE6QzmQBWsKlzoCDmaWZmitnLPwDDR77733cK973es73jDIOFNMxMXMIAV9wAEHNCX+rGc9qw0alPi73/3uNrCceOKJbcAagzqc72hw+dM//dNmaBjkXvnKV7ZBlqFj23lROtG9KaXPsDET7z2Oh2cMNgYiaWgGEul0nBXOkjZ98pOfHC6++OI2yGuzQUXkz2+4+L7rXe863PKWt2ypMueff/5w4xvfuLVPytjd7na3Fj3UNlFM7TSoqd8guWoUwPPo/nu/93stKmdNj/VXopLaxQlQjyM/7nSnO7XBXjox+hkYpWpJi73JTW7ybeUaUM8777xWprQ8A/ALXvCCdnzIb//2b7fBmQHkAG9lG5Rf/epXNyNIuQyxa17zmq1cM/njtTZ4duaZZ7YIAFop25mdUlEZCh/+8IdbOq8zFZ2BKcX3EY94RMPjjW98Y6O5OrIrpLZeeOGFDT8ODId69913n7n9PxqIuMIDfxxGznBUF+NB3SI0joBBS/WL8t7sZjdrxsTzn//84X73u1+TIfgwfkR2HHGw3377DR//+MeH5z3veS1yEjl/+MMfPrzmNa9p8gxfvFLmeM3rFHAQlae/oTeeuoZ+jF3/GfRHH310kwfGj36J7vgBN/TQTm3H2+te97ptAgZNTcKI5jPe0Ae+eKsvjvuOdUTS+vRVaxXRirzc9773bTjpA+h87LHHNhmA87nnntscApMCH/3oR1sfuP71r99wf8UrXtHkh3GorESZGIloq+/gqW9pevpzD0m3/tSnPtXqe8c73tFketERN3He9G86I5MWd7nLXRqflGOyCA3Rnk6R0s0QJXv6LFnVh0Trd9ttt0Yr+oM86+/K139EfqZ0ENkwIeI+uqLpvvvu2+5JK8UbMoaG5Gq8PlYbyBE9IwqlDHqL/I/XCqKTfqNv6bvwBr5NfDzykY9s+kK79CVOB37QMWRFP8F35YvkwF06/Q1ucINGb/2fzrve9a7X+ovn9V/plfhLZyhT6vHUxB5+08fRg+SRTImO9qAsTrB0TG0mI3QbHtBryrBu8ROf+ETTrbe5zW1aO8iH+sknPawd+ITvJqPQ2HOcEf2Jro9eP/300xvf9SN6Ck/0HfiR43POOaf913a0I0P41U9i6Fv6HbmiW+BDT5Ab44iy8Q/v6B1OId1DJvUNuNCl7qGPsYP+0kY8JXdoSiaU454ytOWOd7xjG5dWBWXpe1Ls0ZqOpFuMk+gjtdjkE7lEO448ee8niMaySt7oS3jRR3SYPvaGN7yhlWn8ooPQhpxp/ymnnNJoZgymP84666wmR67RZ2SW7iUXIozGKPUoA23gRaexPcgrm4MckU18oOfgI7Nhzz33bOOY8Qn/9A0805+0zf9yIgt2FljPvJKCgmFoBgcFz6BiTFKwlLcBmfIeA8PCwEeJ3+hGN2rGm0HDzJ4B1qAnrerwww9v73PWptLBGIcGDMrc4MhItpbFwO4/A9WgxAGcWh/ImDI4q5uB4Xwzzqf0JWVzfAyI8DFYMFC0leOrTs4HPBnOZ599dht4DOwGTudNMkIMWtpqoDLgcvIYGgxRAyR8OQPec34gfDeS+sa4UD+nxGDP8GTMKZch4wysxz/+8c3AYWi7pz0+BkUG1zj1yXUGFqPFvURmGHWMMMYmfBlE2sdgecpTntLSa/faa69mBLqGVlNOMYPZMww+ZXN6Ej1UrsEeHzhXDDd4MFwY+g6ENssMf8ZLonzk77jjjmv4MQyn0kEZqdrPGSGncX7x5BnPeEabEZcWzFgiw+SD0cbI8A5ZgBsnkMyqx+TC7W53u2YwmwR517ve1fDGewYT4/qe97xnm9DAGwYnw4ezymhcBtSlneRR+6UdMm6sf2Vg6y9xomMo4ffd7373ZixzujiJjGrywlhiJFl36z7Di2GGHtrC+LLGdgoYW/qq8rUj6085ifrEwx72sObIM7rwjPHNSFcPA1A/8g5nRv/nCOiz+EmOY5yho/7Pab/HPe7RHDJZB+M0b+3WXtEHfQx9l01rh2cmhLQlkUtyhlbKTqQYnmjrHXzXr9DKfX2IbMGdTrj1rW/deBQH1XtTwGjmkNCHnDQ6iRPuQ045xPAi71Ppm67hHdoog17CB7I6Bv3QfbKgPDTFD/2PY6AfwJde0pY99tij4WSijvwlOoMuZNHkiOfJOP1LP6AlGlivSBekH6ITPcvhmNLp2qEeckdvmezAQ87BmHZkT7nqNPmEX2TVZIT34aeP0KnukRd6il4x5pAPOHHOySf6w4fe0JfQg1Onz3JEsyacLjCR4Bm0ARwbukOb9WcyaKJIPWO9R+7pBTpUZA2NlKV/kCGTP8Y04wln0HikTLTj/MHb+MoRpytPO+205jRn/EgEEu/1B/zRp4xDxiR82gjA17htTNQuZynT7Rw9coPexpkDDzyw8Us/GQP6GhPpK3KkvxtL0c/zdAna0rV0CDqol/NOVr2rLjzHW20RXcU/Y6q207/4QBbQF71MVKCN8Ubf9p6JD/jQLyZA6Ht00gb6hQ7FJ/0Yrb2PN/DcVns5FBRsK6gIZMGawi5NUVO2lDMlzWEQeWQMMGoo3x4MeJ6h+DlROZ8rZxu6Z2AygBrIzZRT9v2MX9IikxrJgDdoMlhFqZKKySk0m89AGEcxGRuiDAZXRgcjhdGXlELvclpEQA1yBr3UmQ+ctUE7GaAGJ4Ynw0l5DAa4xGDj4KALw8jgpe0MFPgz0MzibnTtlPeSYsVg1S5GCaOJoatsAz9Ai6QCcrjgOk4r81+bsxOmZ5OSxshXNoOL45B0sqTQMczMxHOYkjb5bVKzyy6tvYwFA7IocVLgGJeMZgYKhwMe6skusH1KLNzidJNBhgW8GE4MLO0apzEm9fjFL35xq5Oh6l1GEH57Vzu01+y45/GIYyK6wljTLsY6p4iRAwcGNzlOtBW/s7aRkag8RtAJJ5zQ8I1zkvS5eZC26h+hP3pwdk0Y4JM6yR6HXJ8hZxwPMqh8eJlR10cY8uERg5AxLeqhDk4vunEk1TfV99SddC/RD+8ndQ39tUm9sg3gh8eimYmIiRAw6E3QcLzgpq9rg/cjx/on+YITPBiwU9FaPHadIU6G6Jxld2bVRo60zTVM8nBWyRxc0m749HIkauG//owWNgnTZr/hTX7x3W+yix/kY2onY86ByROyRK6yjg0tOUo2yeJo4OXUDsNwY3DTvWhFByUlfkqOtCv6K0dz9CmNZJLDhJ74xQjH66TK93TxO/0PPfCOzGsPOsIFXfUjTi5HTnuVw4gnq+EfOtFPeKwuZdK9kbce8PbQQw9t+OlH6Iv+SWs0SUYWRD/1WzjiM+dVPyRv8PQ76fsgk1fqVDZ6qzvtJa9oYzxBP+OU9+FP36qbXpiV8aJsOlTf8C4aG+/UpU/DSZ/mTHFe6HH6iL6Chz7JuTexoL9orwkB/PMMusCLHBmTlGUiQrl4sNG9AtRLpskoJ9rYTTb1E3XCEf/IO1nx3BQkUwfNON3oTU60w8QafYwOruNXxh5tCo96GQbqM2GVsZjujk4NL5Najt7qAHScyRvXtU2d+IYvcDGBoGzt4szqF+iobfA1uZ0+plzj/6wlCAUFOxJKKgt2CoixaVChqMebzLhPKYt05LiKrDfI/d6YNkgs61BlXVo/Wx0HilE2BgMCw4NBk9QzM5AGFAOOwYJxzcDljDBsptZUGbAM7gw8g3jw1k6Rih5iVGivgZ3hnWub2XSDYWZgZ1RkcEZf9cR4cg+ueNKfiTkvfXJqp9nxWVrBnzHtY/BVFwOD0TLrSIrQzCy7WV7GpskFzryB3Htx+lfdOGNeGjB6cAI5N2abpb9JS4Qv2nA+sg2+chhJ+E5GpDMyHj1rtptDSO60M5Ez0VcGYXgbXNCNQWoWnSHF4IjDsSyEH3lnLDtZ38hQZETJDEifJANkut/gJe97jvOhL2SDCRGWRQetj2Uh+EUusm4zuATIJlrgAedtqh684GCgU3AStaAvxqmZ6Ci1FT9EaDjJ0men1gGOQfmit3YiFY1VjhS2eZC1amkrXZe1sGOYp8MiE+SfA2ICIPrQf063dc1S9fbff/820TLVJjqL08Ghz9q+jeoT7eAw6IciLvQKp4gzsgh63vc6mR7lVAIRcfyiV0WOvIN+WcbQy1DWso3bEseVI68fej9yTU8rS9/m8FkDp3+iH3xMmJkoNNFIvyxLp6wbpzvJY/o1POgqDtsqyw/GZ1qmT6cM/YUej2MGMoGXtZxj/RLQD+DFOcvRWZ4jaxuRCzJnzKbXx/I1/p7X30xs6Neixsbm4OVd0dR+zfSykL0GREgTPaafZ0HwhAf60k+hbxxx4/0UnXp9KQJuEsQ1/UUfnTpntKBgR0OlsBbsFBAjklJmJIjc9PcoammClL1BfNG5kOPox6xngIHNzK4Z6aQuMuhcy+6d40HRwC+VD64GnqRnwV8EjIPJ8JAOZB1F2tHXy3jhDBrUleG/gdIs8CzcXYcXA9kgtkp7exyAtjKYpTZaS8KRMaPKec1MsMGSwcuIQydRs9zTVjO3U7u/rgIMAgY4HsNFBDdrPWe1AT5m0J/2tKc1x996QE6FFC5G63jTjK04FBq98MeAzwhggHAe4MnwsIaXoYaXHB901CZRXPIqRdV9jjcZYkxwAPIOWpOhcdqdMqRHq5MBKnI33jBmKyATOPihLZzJnJ1Ivjn5Uw5IHBl4Jl2Z0Tg1+TKv7vH/pO1xRmKooRljy2y/ujkpcTDjQOAHWeUYoSWc4EhWxwaifq6djFC85DhIY8vGJu7PMpzJgiibtE8TGmRR341hm08mw7RJe+gDeMdhj94ZR2vnGez6pOwGZTGqMwHlvZyFKVLO6RI9NnkxBnXSqRwYEzCcrvGmVasAvnOg9T+6kYxLf+wnmrQ5ExnZkGSqvmQ14Ec2oIIv3SoKLmXRWmJ8M2nEeRWppJuyPhW/xxORJmCkidLrya4IDvSwSRzrf+lim4plEyzRXPQWBbPudJUdfNEU/nhObytbn/fbEom0fyv6s7ajnegk/UNGgX6AfnQmWpnI6I8uyvgBL3oUXvCMLou8jsePTLxOyarrZE+0zgTO1JroZdtNF2iL6B384aVv6m/a6zvjYWSsh/DLt486OfUmBci9dOveiev733i8oHfoYDRKJFyd2msMnTfxQw/pE2TXR/bGznj+cMEVA8qBLFh7MLtLGftmMEj7kFZlgMjunb4NEhwuaWnWTRq8GF+MBoaYD4PBt3eyA+pYmcc4lSLHIPCMCCLnSDTLu1KZRJSsZxsPcN4VcZEOJLIkpY5xbxacgeq6NXI2QhAxYXhkZtdgoR0M7hw9IPLB2GO42hSEUQP3tCXri7Kzm5nzONSMPzTjsBpc1d+3Nw4AGnmfcWDQY1CqT1oTg8yGDnBjQKGr1EHPoy9DglOHHgwJazpsjGBNCoMsRit++agP/tn50//svImP2uB/7omsKc+MOWPAdTO0846lgBNnikFpsoFDwalURzZAyY67cMvZgNnBMFu0B290Vh86JxVwLDfuM1LRjuNgbZHy1E1OpM3ZoIFDYWOSpEzhcXBl7CbtWLqy1DhracgQGsTQg29S6/AajcgruSeneMRAZASlfWg+5Sz3/Ej5Sf/GD3W4l42TrG+zblM9rnNSGExS+tAlkUFyyVnTF0zscMCsw7NZSg4cH+OSdF0fv+GjXm3wGz4+romq6ZfazCFVtwkGfUtkijyKAlt3BEdOL95yHDhVZFffwg/9U3QAvmilfnX4cM7RUzuSRqwfWfeJN7OOLcATfZ3haQJGOl52X06aG3nBd8av5+GsfmWbXGJEwjt0Ua93EwlOnx/z1fP4r8xsiKIOZWk/mnD6OXRSYqeiM+SKLsJr7yf9URnjMxMjR+m36S+9XPlNp5ALBr53lO357PoMXzTxoYv0QbKi7dEX3qcbObXoY1My/YOOdD9ZKnHEOBTWoaEDh8BzJpKy82f6hzroQc4TeTJ5Rt9zpNABDVyjP0xgJROGToZn1tjpv64rF4/gDeeMU/73tPKfs0qu8B1+PvoMefRePrMcD+1A3/QP9aXf9Do3fKHH9QXLHeBgnKBzOHLo6jmybXJGu/HAmMTJy7pH+oizrE+bHMhYnLEJDY844oiW8j4FnkE3PKdLjFPeJ59wC08i++H/OLOC7NA/dCcdY9zRpzn68DJ+6esm6Kz71GblJYXZBAbZpivc18eMY9pMPrWD7JABskBPhMdooj8EP/grlz43SUU2lKeNnjEmhFf+6xe+wyd8i/ySW2P9uh6BU1BQKawFaw8xzClxRqdoEsVqUGD8UL4UuzWJjAnpRxavi0RyPH0YvlnLwEgw2GR93XgNowGAQ+RZAxuHVVTJQGPQNIiq30w1JT8GhgADw2AHb06tgYOBwShwnTMEJwaj2XjX4WzjCUaRgUkZBmpOhMGQ02zTFAOTwZbRAX/t44ByUhiNDF3r4eDqXev3pA36Hq+lQAPvGCjhZzA0cGZWXLoeWmXdl5QtG3jA1yBvIJWmZwBHlxgW0k3RkDHCiDL4cqp8M/44OyIRaKw9eGj220CbdGQOArwYgfBhqJAFAzB81CuyMzXAuoa2iQLhPSM+kWM8zoZD7qOBb44Fh8R6SemoDGXtZ1AowwBP5hj9nJSenvhu4Cen+KIussrgYhiKWKCL6I9dVslVcGfkwMk9AMes7WOASfMjD+pmAPlmhMAL3zlSfkvfQ3frL8kEY5gTb02iCQQz4H2aMcAL4J5ntMFkgUgj+utzeARX/GFk+o1GMabgil/6Cz4y/j3L2CZ7+MtAc52c9xtz9MCghJ8y0ZiBlaMjtIMxliNQ8M8kDFoxGPVjz1nDBhdpmeogN2gtem7tq/aJKNhsx1ow96UR+zACcx6qtuEdWUNXDg4nUHuUi78cCrSfOpbEe9rrXcY1/qGHMk3IoJV2cLxzpIzyTTRwcKRgM0T1yxzjo13ZQMxvdOIIjXcedV379X90J1/6Ip6Qc/2Cc6t+EwJ4Nsaf80KnmvRQBjyUgy/j9ef6bHZJ1ka6GR/xg2OCpmiOTnQHnZczYdFdei+5oh8Y3conw+Qwu/eaGIgRT4c4tB6YzCKr1iXSiWO5UgcekUH9j9xYS6//0mFoq39wlqSpygzRh5XJ8ZQOSyfShyYUI3uiQ2RCGzkenidjnMgsIyB3nDOTSMYddOaUmlQgE3QLftKr+C4iZ8LCJCActUVZdLdJEPKRNPge8NM4gEaJfNFdaO69TDbSG3hB5sg/GdQ2fJN6a1xCW+OHdnMijTvGr2zyRv9zxvQ5uoeM6KP0pvv0YDaToYOsNybHWXcYyIQkHugjxi0f+JuEo/PIMblSHhrkSI0xDfQFE5cmTa13NWkILzjjiTK0lc5Ubo6W0aeM7SZxvUcW4YRvnqOnOdDapw36ZJZE0CXkQV36uX6t7e7jq43STBSaMCMj0p+NeXQqHRQbBp+yrnnKHikoWFfY5ZLNLJAqKNhiOO+8YbBM6NOfHoZddx2aM8UIMtNpYBuv0xmvj0hUysCbtJR+B9KptV39d19u0sb6dEcGLUWftVLzzmDL81lX09+bhQcw2PeL+T3D2Og3XRi/O1WO9muD96Y2LOnLmLcuY6pO5WYL+KRL9veSKjh+bxa+s/6jBYMKMAITRWaUMQYM4uNjPPo6c8bauI5+Xd24vb0MrSI3PS05IgwmZaSunNWGLlPnVwbXvi4fMpRNOMZ1jfmtDOUnBdDvWWtLxzgvw4+xnMItMjAPN0BetCEG/lSfWxWX0BYe2qtfjuURL9Ch3ywj4D00H59z19MrMuFZ5UeeEpViWOZc06m2aHeiHT2MN2Iap//N6kMb1WH979CMnCQSPIsfkb+chxrdtIocAU4BZ4LznBRtDgHDXt/maPZyO4XLGM9x/1ikj+GfjVPGOqDHOf00vOj7cZZR9P0RznRTvy57I30qE2T92t15/bevayP1Be++D6edaNDL4Jjui8Yk7yf7xaTLrHNGe327rO6dRYMpHdvrUddNwJmwMNHJwXYv645T75QsuJazG4G64Jv/8+ib84Sn6DT1f6p9Z5wxDAcdZB2qDJvvuF1QsN2hIpAFawqU/qUDagaysaEKpmbNA71RuOi9Mbg/tcZuarfDWe/Pen7KkethPFPrmakDsheVo/2zjPXxe8uur8lzaDOmT39vHq6r/BetFf1guJulFdXIbnhSUuel94yd/6k6pto9y6if+j91z/d4g5/gMsvZHePa4zfe2GUWHuNo6JTDNA/neeXPktOxrM6rb6odW4HLlHPWPzdvDdGi/hkYyzvjUeRAxGvq3MG+LbMOt5+nSzbSh6ae78sZy/Wy/OjxnOJ3/+wsHBnSUgFF00Wt9OVs0CViqV/P6yPz9NeidvTvT+nWRfLUvzPVt0F295yH8zL8W4bvUzCrHYvKmcI7z87i9bJjEseJfpYlMo9/82R0Ef7j+1Py0+vROISZcIqzOMtWGMtC/3tc16r0XaVtBQXrCOVAFqwtZLG+1CApndK8plLFCi6/wDh3lIF1Lc6bxH+zxtJLpc0WFOwIoIPIYZ8pUDAbklIuHdPaTs6JdEn92A6XyziBBTsXcJySUrwuY7Z0XWmnUlKzBGbWBE9BQcF8qBTWgjVNYb1kuNrVvnTZBgtm/K0bGKfMFFwxwKyxtVBmqGsSoaBg5wTmhn4s+qMfz9pJuaBgW0A2I0rkkE0xL9K6TlAprAXrBhWBLFhTuPR8uXU+/2i87mRqDdGidRvrBP06pnmprVPrnbZ12xiaNqzYSDvWne6rwOVZ5hbhvD3atjPLznhN7xiWWUuWMrZl2/sJoEVr93ocltVPW62vdrY+tQpslKbbA5ZZE70qrjaqyoZwy+Iw7hPrQp+Cgh0N5UAWFGwQrMOzEN8ub1KxxmCXRrunSpGxu+q6Q46DsIut3UDtuji1U6bB1C5/dlMUGXSW3Lo4+tmUwY6K2dlWmtLlBfDITpJSu+1AOl6Ppe12FSWbdhDcGWbXbbJhnStZslPuVFQKXz1nV2MRBDI3tQPyFVV24G6nSscLZZMY+se39FBrvughO2q+//3vb9ezy2kP5MqukTneY6uNZetGTz/99JbOaqdRayDx1DIFOlSKq/+cTDug9qmtopaet+NpjmpZBrxnwyPl0tV2514l8mmtpl1xyds973nP4fIGdg11pAydbxfqdXGQ8I1M22U3R0XZuMcZn9bN2rxq1pryrepT1tvbsM1OtrKf7OwKl3WhUUHBjoRavFFQsEHI+Yk5fDw7veW8Lsa+Lcd9doZMcThyPOx6y8ichXPayVHhzIwPtt/RgO4MTYZqDnK+vADaM8IdI5BdhpPem/PR3Le75fi8tHUFu+k6usHZhPNwztmJzvzjHG0LUL8jXXY22SEXDF3HDJALMuEYAecuAvLh2AXOgl2NHeEx1W8Z6I7J4DRtK52p/Jy7Sn8689BOrGgfZ43TMD7nVRutYXva057WjvVY1ohXrvockWFX0PSbZcHz8NxZ9PgyEJ2hPXSJNebWBq5T++CCb3ZiN6GDjz50W38mJpmddRbrZsAmQPqPY46s5aQP1FtQUHApVASyYK2hTx/pjznI1tv5P06JyrPjrbO949lZxyiA8T3XU6dP7puJfNjDHnZZlE7ZDDgpMs4CE5VzVtg47SUD4RiPcVtzfxFderz6e1PtmFVvwCy041LGdOtp42MDG88ySJc15FKO73lb7s/jR1/GmPe5r2xnc4li9NfDo+zWN6/uKT4skpFePnt8++NklpG5nh9jXipDFIQBSL6U7Uw9fHCkiSiTs0Jd73cMnCX747ZOtW1Mw77Nvcz5P9XGRX3O5ljkSfR7Hm1sfOF8NobcojTXcf+f1V/myc6iOnr5yLP9/ymZ7XfGHfM511POPF5M4SQabcMpu15yCpyJ50w55yuq1zEZNq4RheMMgfAmH3ywWVWfUTCW7R7PPsUv+M7677fz+5wVm+OY0Nr5eZFtOsX5e7N2tHSeIv2qrOA1S+aCr2ijaKoo5zJ6fQxo4TzXqXFmSgZS95Tsjfnay8CUrgleY/2T+qZSjufJTsp43ete1xx0UWjyQDZSXs7nnNJjU/14Xv8b0ys0meon4/EJv51NS2ZTFl4cccQRlx3bQg+ayKRDRJZTV9rf/5+VfjrW0QFOqai1TA7ngzo/dWoH3nl6PPfGY84qY31BwbpCOZAFawtmAKU0+ThnTRqJtCcGhxloBiel6xBmBwlTyGYLHZRsxpgRKNWJwSHK4WBwM9hm3qXA2E4+50U63Fd5ZsAZOZxDZYp0ODib8apsuDhEmOEj0iNaQfnbSdAsqQPjDcreZYwpVxlwBGYwHRzNwDP4MYgZRWZRRc3MzjOgnC9nsHJw9zhVz3uMaO1hFCkLHg4pRhsOhfvwdKBxBkO7zqVeA7OPQU3dzmhTN2ML3QPuoacUMLg7cNt7q64FUSccpQSZVYang6HHKZZ46Fm0sAuvOvGKAYjXabcyGKHabG1kInMf+9jHWlulOyonOOIrPkqJwu+b3vSmlxlNPZANPHbwNDmw+6/jQuAKf7RQvhlphpf31YXPaCvlDk54hnZwJTfqJw8OEvcMHKQLaifZJG/q0xY4qIvMK9Mz6pN+iDfkFG1cwzMHaDOgyCiZdg2dGT3qUr62u4Ym8PCcerQHHhwNEazslkl+x8a2dsALjpFj5egL3kUDOKEXuerrVZdD0D3vnvanD+OdqEKAAYtP7nN0tDuppIsmHfRvOKpb377hDW/YjE4Oto+26wfqM8mjr4FedtAPnaYAPtoER/wSqddPOW1khrzokze72c1avcrRRv2HDOApnSR1lCyjGd454gLNHfCOH3CFB3nxWZSKjG/ezfl3Y1qRAymiMXDRX3t9YnxrB/qRN0Y7fuln2osu3tP3yVkP2iGyR88yskNrMkUvkmF0wUd44KtjO8hCr0cWrTHr78NHKite00n6HBrBF/7qJw/6QBzH3lEhX951z7f+hH5wGp+9KVKHf/S48siAfk2O0Yy+NekmpVZZeIl2dL66tJd80QnkBe/Rm/xkvCIXdDAHGs3Ihevk2HvoCi+RWmObPka3e1/f0z/oI7IW2clxKb3swOc973nPcMwxx7QlCnjkgzb0lfL1a3w2fqhT301fJcP6uH4zdnjQCm5kCh+0MxMyrisX3uRHW+kFZfkfmtIrZFN7p/RzntMmZV188cVtQsKkWnSW/o22+j55I3v6of/jHVfpU/Ki72qbccnuyn5LB0dLdoey1Tde0oFm6OJ9Mkm/RP8ZF+BCfuClXOX7r0/hBfkhE3iH3rUjbMHOBDXlUbCmcOnA/YIXvGB4+MMf3lKuHEJtsJY2SWkbWM8555zh5JNPbgMto8D6HYMC4+34448fzj///HbPc9ZZUeDuH3vssc0AMVgZJKRFJeVUVFFKj0HB4GzGU1oVI8P6wBNPPLEZoIyFxz72sS0lLNEFhqXIjw8cn/jEJw5nnHFGK9tAfNZZZzXn0KBtgIGHNhlwtZUz8Na3vrUZXSeddFKLaI5TuZRlkD7ssMOGl770pc3oM0g+4hGPGF7+8pe3Qfjd7353w82gpl50Ua/2x+kwcLl3wQUXtNRAA7mBUztjZDp0+R3veEejNVqog4G7EicvuaQNmAb6HGaubTHyxiB9zXlx6vQM+jPgGA74pAy4kgfREnRk3Fkbgy7u4U/SmrRRiiQjiPPjN76g8RiUQ2bIAFqSEfwxOcCg4OgxKNCBgYeW6mUgMQae+tSnNllhWKhHGYxUhgF++B8n9dRTT22yqM5HPepRwzOf+cwmMwwabX7Ws57V/uPBcccd1/DVLs896UlPusyoBwwbxhJ8TjvttOEpT3lK4yVakX3tgIe+YJKDjMFROqi2oCXZl7rp+bEDReYYtfoieYYXnj7ykY9sZcAL7fU58qGN0r/wksxpI1lXr7LIK3lnZKIbemRW/oMf/GCLkOA/GqOFuhYBWuKrVDwGrtRIPNBO9SpH29BI1EKKJ7x89PfIDjr3tO2BI0AGybL+gGZ4ge/kS9+Dgz6D9ox1KeEmsxi1+iG+eo9MPeQhD2nHWoh0eIdhLZWXvJDrI488stFjUTpyojOzdql2DZ3jTKArOuEbfOk1dILvoYce2vijTvyDZybt4DeFizRDejB9iI70PJ2IL8onUwzqww8/vBn+G02XjPyQQTzjDOkz6jVOqFc/pdfoh3HaoffVf8ghh7S+S6791+fxdBwVVM8JJ5zQZCRjhHLx3XpZjpC+LUXWs+SHrLtHL5AX/cT4Y4w65ZRTGs3JoX4nuol2aK2v44s+jlfk0Tp78pVzMx/zmMdcpqPJGN0dHRvZUba+SnbGdOb4eU/5dEbkBW/pWDqXPvIfwNtYQX71I7pIm8flGsPINvrvuuuuja5kyTfZ0gbvq1dbtIOzTVfQKXDniKsPX8apxuoz5tIxdE4iohlztUXb0Jv+yDnI6Gn8GGedkEf0t0+BSRTv4ylepEzAiUbTscPsGX3DGE3e6D1jMhyjh11DC86zMRce+jX9TIbICDvDPePMOqUQFxQsgnIgC9YWGPuiTxT97W53u2aI+X/RRRc1ZU+pMxQZX5Q2I4YRZWbVTPG9733vNnNoADPgmBE2CyjVzwwxBW/23KBm0DRja2bYbLCB28y/mVgDhVlF5dkMxyDLMDBbbFA1OBqczD6ahTfjauYXrmbys9aIA8eoEVU0G3/Xu961tYHRzlhOqo7NIx784Ae3yKTBr48IAgOw8g02ZqG15173uldzJA1WZmNtMsK4MkOtDIMcumi/ehnu6mUkcHhsSGAmX/SJc6tshhcHFf7wUjY6a8eqgEZoijZmWw2anIYxMGgZamaLzULDFU/Ql0MCFzO17t/3vvdthqmPCAg+elYb8TqRW4aV9V85NgBfDPx4Trbg5qNs8oOm/otSMkaUSbZioOA7Q9OHs8lwEnESHScfHDWGpHYoA13Jr8gCGdZ2BhnjiRyQB3JKdjyLt0nfwlcpqcrDX4a8+hlgeKQuRhfaeldEm2xnDRt+oZvoCHraiAIfOU+JxCvH5hlStUTHGDLj9XHoDy808A6+wI0xzXi6xz3u0d5HU3z1zTDTD9Pn1Mtw0v6nP/3prb+gD16K8AKTRt5D/6RUcnbwdjyRMoakM+qr6tJPGNAALQEewXOPPfZo7SRv5NnkwFh2pow5UZFMCOgzBxxwQJNn/UyqqE028JGDw5FgCOOZSIu22pyGLOmb6mIEazuHS5/n4OaYAboNz+m3VdftzYPIDZ494AEPaLTSf7SJjKC7foGeZBSu9IUIJtnqHdSkFttwiKyRB+3BN46Eujgb5Eo95F09m1k3nWiOfmGJAJlJxI6ew3fGv/rwFR496FuJftIlnHhyie4mePr1n/o7PayNkUnl0vvGCe3eZ599mryYXCDnaIpfdKvnPI9+e+21V3MU8NkEGWfQGKNc8gEHPOGIGvui55RpwoOzsdtuuzXexdHloKAtnU3WIjvqJDvjfhM9ThdqVzIClEU3a4s0aPxPVgo9lrMc8VW/IRNjmeL4uScDR7v0w8gRHaEefMcbfca93Pe+58m8Z5Uz7u9JeebsGRPhpC10djJq6Eb6VP+jl33Ih3Gtj8RGLk3ekEk0pKNlEtBNaEfGlK1OunW8UZz+TVd51/273OUurR54sVHwCy2UT1filUkt+OMvHhirTWR4x8RkJpozJqFPOZUF6wrlQBasKVy6ZoDBb/BNWqkZXM6bSIoBm1NkdphhywgyoCQaw7gzwJv1YyjGoTCQSFdilHMgPWswMDOoPkYig95zWYsR5yODiMHN/3mHJLvvk7UlBgiDgvQt1+BjkGKkGeSCNwNU3UmBG8/492tE0CYzpN4VZUhqVTalMJD5Vp930dGAZ1ZUyhI6Jo3JuxloXed8MYYYhmjL0IXzSpzcZZdmmD70oQ9thh6HFv+m0gQ552bXGUnayNAQhfGfM4AmoSljAZ/QlaGUoz7c04akG+E/mUEP34w1EQDtYcj57SM65D4DB13RyQcdOERo4z6HzeQD4wv94OB59GcoMAI5SQwGRibwLgeSQea6Z9GU4wFX8k3u/I7ca6v/eJmogTZNpf2GzrkfmRTxgXNkTpnwYGyrO8+HrmRvymiJzMGNrHnHt3fgl37qPTKuXu3LWkLt5cTqvxw2xiljNOvXgjNcyUB+w1uGgX6+KGVa/aIT8BLNS7p2sgNyhqi6tJM8kG087GUn/XqWLKefhg7qTdaBd33rb4x5dCBDWcem30XOlaMMBqW+CVeyrEy0U44oBR221eujckSSdvhmkIcPidT4ZlBzwPbff//Wb8frCAHcONP4ro9mcxZ0JetJrw8PNnuwfHRn+oYy0S6pm2ivz9Lx9BXdNgbv+igHDegCjoAxoZ+wizz0aa3pB97zwSd89OnlAB55n4ygs28TCWhAzyVyFl2jbnqZPlCHujhGytZn4MmRF2lFW46cdmovPb9IdualB4cWkQv8pDuTcaJck0gmn0xU9cDpEUGl7+Ae+emjfuP05PxXFyeKPhcBpzvRYSrSvahvardJMn3PxCLZhdOUDGhXMpnSr00wsQPGk5tTqdWeoc/0XzQ2AWyCwGQDBzby6T044GMiwhm7fTJ2x3HksBuPyK5sq/EEckHBukCtgSxYe+g3aqBks4aNMQP8ZxBLYTIoZ3E6yKxxHE4QQzopLqIvZqpFahg6ZkIZrLNw2QjEOcuMqAHNoJMBJANtvwHFRuqaekc9jJkYdqnXAJddY8eDVFKDXBeRE52JIyy9byq9qH93fI8jKn3X4M6IZsBP4QoffOp3PMyukvA2EZC6e+cFXfF/KrKhHeSAA5O1sok8ep/hBjgr440O8j76MZo4X4Bxw0FRZ9b44aN3Qkvl92myPa/hwjiXxkn+OMpmsKdouhmZC+/xLP0ijk+M4qnNXOaVOYapdyLr2S0xKZTZ+AKN9Mmx3KArmnIuGb+5Nn52CrRTmhyDjpEt8tdHMWa9D0fvrhIVG5fV99seyCqDNPcyOZFJgP49z6ILvNP2ZAKM69yMA9bXu6gc0RNReCmV0hrJuom58TEYJlU4YKKl2iBSJuovIqi943WTWw3agX/4zQkgP9FhidZPQX89R5rMclAWvb9IBwbiUGbCZdwOkBTRODZxTv22dldmiKwaelTUjp6hb2bJzqptCSRKKKpMb4em+mMPZNc1n0x0LQv4JhJofBKh50ia3NoIwI+eFr0VHZTNkzWJY6AbySrahdaZCFxmkgNdjFUZV0Nz7VGfsYouy6ZPKXteue6Z5FJGdOZm+3pBwbaCikAWrD30u6oxDAwuUnpEKChpqTNmns2OMlqkvxgUzOaLdpj54xxkC/c4nCJpBl/l+83o9G22nUE0rn/qf28AZgDJurak5uR+ZuHN5MPBgC8CKmUOfn05fdmz1jTNw6v/r16/zYrGSEVD9YoMGgjNHmc9mPs+BjKONsfarDB6o63IQu/UAwOemXOzslMzx/hiNpujlCgVQ2OcpiRFDp8ZSGZ38Zdzj7+cLtFaBjmAj/YwGBk46sbzOCcxHhmv/kvV9a4ytVcUSOqplDIfKWaJGPa0FBlECxFKdGA0JIomCmm9jZlz+HCURUNFCqR6MqjTRjPeSeF1jVxKYYL7/e9//2+bJR/TcJbMAfRCT+3Gh543jCc8JHMxsLWBsR/ZnydDY5i6P2VEo4t6peqqE5/M9utfnPCsp0IzuOMT/DlWHECOdWgtyoKOnpuHI36QG7RntJFldfc0maKhCRK4RXa8p09kDelGnEf/9WnttR45x46Y4BLxwJfsrBkwkUE2tEGbTaKIKlmfp+3okU2HFkGiq7PWTs5yfMYOLbkm/xxzxj0+TDna2aTH8/SMvmQSTlql9xPlmUWvVcC7fbuye2ccKWvYsl7Y2t9xumVfTviLP9IW6cTxTpuz8O1185R89ZBxJ2uJsynLWI+ilb5Jv2TiQR8hz/R45FXUkT6jLzmWZIfckx16uJedqVRQH/LtA6/xxGXoSx795owZM9CVPlZ2D/qtlFh1i7KZPIJDZCUReM6We3iTiBtnmW5FE3KSfpd+O0XPvi0g/VZbOGqWmxjjjVfwmgIRQzpa/0xdxhw2Rt6Zt/aYvjYZgA/6Jn5ZGuF3IpnGq9gE2m7SMDsIj2UpdNR3jEdSiaXj9rtpFxSsE1QEsmBNwc6Ml+5AKXpiYOIIGHRFscyK23CAsWnAFZE0My5N8sADD7xsd0gGuoGCMrZIX+qP2VzOhs0LpK1xQBms2SLefWtPzF5Ki6L8DUYGBE4Ho5YzZNAx+BlUDRreVZd6/DYTymlgmDAaOAqcU5seGCiTWgRvbTR4+VaX+941GHKeGON9VFVqjgEzmzagkwFKypz1J/7DT73WdqhXOzlIMRas1WGwWGNj8wNtYPTCQbuVb72YDW88a2DVPuu8snsgQ8A3Byvl2fRhvF4E7ziR1l0ySgBeMLDzHyjfuibP4TkDCS84HdYXZm2htSbum43nBJppl45qEwSDNh4xJjgF1vVYE6qNjIXstCeVdTxTjobK9c3w5OQxFPBIShgeMTrgZaBXFmPPBhyZkHBdm6ytst5NpMBzeJPJCYY2Y08UnawwEszwW5uH9urHD/TFazzJJkB46r7UQG0g53jLcIaD97WfDIggwUM6F/4x9Mks2Sd/4bN2uZeNgUxsZN0gYNCReUahvgOvvMtpZjD59h9++MUIIuvq1T7Ok/6pn5EVacBSTuEfnBnLf/RHf9Q2+rHG1T00YhDisTbBIUZ4H1kgu9rAGNUO/Va59IQ+wnDVBvxL//EcWbKZRi87DDwOqXYlJQ8kBVn78Yf80AvwyE6O/ntGP9VO5WatFb7rP+THJAY+qhO98JZ82GSGDjMhpt+QXQ6tctCHrpq1K2syBDKhga+ceA6Hd/Ae/9DCNxmgn/AHLchUdh2N846vDF908D2OPgLlaB+9y3Hk0NA71uolApTdPPFCXeSBnMEJoB+euKZP+E8GoyfRAF7ZaTX6mDwol/Nnba5MB04M+YAT+mUHaG3WXkA/MPjpVeXR15zgXqbIgXfwEz3JHZ4rA46+I7d0k+fg5Zr3shmTtuoL6KJOckEXc3CzozFc6VFjlX6BzybNTP7QC8aPRCS1M+vw0YfTS0eRnQc96EHfJjtjfqlDn6QvyRzdhCdom7EqY4A2GgOs3aa/OKl4Qhf3ACcZFMoUqRYBRLtM+mg3GbcHAF0CXzKnDpOYaGGikuxF/ygLT+AUPuB9duc2BnmPzlWuvp/js8gc54vu9sx4EtZ/tgPdSF5MSHvWJLNr7kmDR2Pthk+OmwnQ4/qrTZF8o2nW5mZXa2MhmUZftMNHZWoT/aR/kl/XMmE2hW9BwTrCLpdsZiqwoGCL4bzzhmHPPYfh05++ZLjqVf/7suMjKGtGedZ4GFQNNAwxA2vOxGMoMPwMUNkKnEGQHewMOnH0kkbKwGTMGTSSzmaw2m+//dogTLFT6t6Jwnc9jigwkHuGscWoYQyox/MMH3gyaAwanmFkpEw4qhPuBkjPGvThm7PR+kFFeXDKURMGUAOcQSn/tVMdjG90Y7zBK/Vqe9YzGbQYmQYxBrtB3zMGPDRl3HhXm9EanWJwGNiVz+FlDPnYXW989IiyOW/qM7jHwLfJSj/jTx2JFnL8PJMjJxg97uG5AR1e8ECrrGdjvJkZZ6B4D03Rl+woUxsZkTnCxXvjgdpzcZSU71l8VlaOqlAeQwGt4cRwdA/P8B1tGVRoriwyogy4+OCpKA2nj2HFWEs02qQF3LSdQ6QdMbLVq07lMUqUxajBd23Dd3VnDRH+4Rk8yJJy0DFb9+e4EEabd93zH/3IAXkNoDeZ0lZ9Dd05Ua7BU114w7gLX7wzVW/6Ix4zILXDRAmaoTc6kjeOlfaiabb6R0+0gm82nQroF+iqz5lEUQ9Z4ZhrKzppUzY5Qnd4oyEcZ8lOb4CTYzTSNo4w+SETQJu97z/jGA3RCo20RznKy9pPbYcT+mXyKjuVctz0IW2Hb3Ye1Q59Zt75sHDPhFfOf1Qn+urrcFFeNsSBr2+0j2EOJ/fxNJF+bVJO1tOOQdvxG4+1BR74xQnIOkXygfb6g/oyeQE39WUtI7zd74+TUT7cPK989+Hnmn6kHuWhE366T1+RLfWiCSdDPfoQx8iEBv7TQZwu5fRtQyd09C4aKBPt0Swb7OQ4nLQh9Md7z0v/FQm02zH+oA/ZyPFR+hE592zSWvVhtIRLxj73knlCT9F33slkwizZGadvel/Z+p9+gpZ4ohx4ZTIzG8KhAf3mGXh4Z8rJydE8+hG+ec/6dZOBnHu8MrGB1pzQOGXkU58yzhjryBfcRFqDG9qSPTLgf9bAj8fc8AcuHDsTHiYgZzlkeJejT8gkPZOoa45LgoNr4zajI3lDb/Skk4xt6R/0lTYZ0znqKSPHf6lDG+gz9aNLdMYUvmecMQwHHWSjOQ75ZHMKCrYrlANZsLYO5P9/RNt3QAbRPoWoTyUaPxuYeocBJxpj1pZh5hqFbybSYLLqhjGLYAr3Vd5NO7ai3nF5Y5yWSaUNGLBF4hhPIhBTRsuids16dszDMa7LtGN8b1ZbpvCcSheeqncWvmNcGX9muUU7EvFgiDA2PCtatJUz0LN4v0iOpui+FfX25U3J1RRui3iwURzHMKsPbLSOKdmb9+xUHbPoMQv/ZXBZFaZ4OK/ucV2bqXsVvbesfuXkcCAdpSODYVbbNitTHB6RY5OUolFxUOfRY5G+WkY/Tt3bbLsW6c5xuRx5kbk4kLNgs7LRv2uyIxFM9dOzi85WXLbuZWk5S8et0s5yIAt2BqgU1oI1BQp3zt0ZA9kiA218PwdrMyREyMwCmu00E2hGe1ukkiwahJd5fyvrXWQYLoOvgVHExkzr1IYFq+C8jJG9ajsWtXFR3cvgNOve1H1yJ9XS7LXoXCIujNmt3nFzFh5bxZNV6+3LW4XeG+XdKrAt6lhW9ubVsZl3N1LORt5bJF+b5c8qNFg02SWyJNImEilyN2ud2WZxlgmgLo6NSGYf4dysntsMvpuR42WeSVRNFM4krfFhnl7bqvHQenTHoYgGOq9ykfO42brnvT/Fw21hTxQU7CgoB7LgCg0cHhtESAHMOXtSCKUNzVpnVPCdIEXNoF2wGKSiPeEJT2hREAaslE9pYVLbljF4CgoKNg45IuKII45ofVG0SnrhtjDuOY3W/kmllXJqfJESeXkHDjOQwso5l3WxPdptAg7Qp8bxgoKCbQeVwlqwpimsw7DrrtunzkWpcQUFWw2LdhUsKCjY+fveFXVs2VHtXjaFd2eESmEtWDeoCGTBFRrG63S2dR3buq7tRZ95axEv77AV/Lyi0GqrYdl1cAVXXFjkREzJx/gYjq2SoSuqLC67TnLRs1tZ7zxYZV3pZupZVO9Wll1QsK2hHMiCKzTYPe38889vxwRYw7ctwC6Xdp+zSYpdAO91r3tddt7gzpCKZHt1R6XYZCgbQ1jfYic5O9XZAEab+nMML8+QHWXteGi3Q8dOjI8tKdg2kN12nUVqvbJNiKSe23Jfqtzuu+8+ecxEwRUHpEuSD3LiGKdFqZM5a9G5pOTrDne4w3ecBVmwNYDWxkMbrlkb6uiP8Y7dO3Kcs2Oro5HGONP1ZMrxJHaH3SrI8SRkzxp4x5GVA1mws8DW79ZQULATQc67sn39tgAbNjgDzvbmjhWxaYojMHYWsBkCJ9Fangym2uJ8Q1u12zGUE26A3cyaJDvf7gzZ9Ohx5plntnVT1thYx2hb9u0FdrnlsG+GVjkGAN13NmBw2ZjDmZ42zMh/Z+Y5f3V8aHrB/4HNYui7bdHP8EHZW01/5dHN8w50n+ojzvXUT/WVZcBzdkl1HuDO2C8CeIteeL099Cl9uOrYiT8mbfXZRfzBfw7ntm5LNv3J2JzjcOhJsmey9MlPfvJlR/ZsFSjbrrEnn3xyc6p3hjGwoCBQDmTB2gJlSsH6JMUo1/oUyv6Z/t3x81PlObfJhibOY5pV76wyx/fHuGe2lTHjrCuzlw6jt2HDFC7z8F6FPvNwnKLhvHbD++ijj24HNOe+g7AZi87ycvYW+jlwfVzPMjRyDtdFF13UDv7u35nHs/H1RW2YRat5MKte5zE6EFy0Gk1OOOGEtuHSLJhV56oyFoOGk5TjPpbh81Q5aH722We3s+qWocW4TJMii+g+VeYUPrP4PQt/OyTbHVkfyq6Ozt7LmYar4L9R+cizyplH53l8n7o+r5xV5HyW3rNRmMPZGeXLtG+enh3j6NxCEZw4qPPwCN0W1em3zA3npfY4L6Ktc/hsojJrZ9VxvT7OSzQGjHdwHfN5/N6ssWmRbp737BRd+vrnyYqJPo5wJrVm8XkZeVvUP/CELpSNMS5vFo99TDwaO/rdumfhwakyPkyVP29SYVVdY5yza+stb3nL9r7yzzvvvHYWLT3TnxHav7eMfM3jrbJFNG2kti123y4o2JZQKawFawtmNh1gbDA04DASpb2IdjEindMo/cNh1WYyOTMMS8+bTZTKZkA10EkNcTiy/xS3Q4ntiOe/Z/2XgpmDrJXLYGdYiDQxXNVtBtK1lCWt02HkU8of/pwtETwHHRsMnSuZGU1lmO3Ozq8GVJEtqVdSXD/ykY+0g6q1a3w0hjKUy3Dz2wCYg79ddzB1DomXloN+cEQbEUR12IH2Bje4QUu/RCPXtNs7aGHQNAOrzXYHZZR5901velOrk+GALiJAno8Tqd3egYs64W+AnDpjTerO8ccfP9zjHvdoqUzaKaKZg7DtkmgQxws0kq7o4G33Rf8M6njg26CPftqtnQbmpBtpG76q07MmDmY5HBwsZSgLf/CXvKHF29/+9lZWDrm3uyIajEE9ZrPJpvQo7RCBdmRHyncAuvLJnmNjtB39yQU5Fq1W561udavGK8baYx7zmGGfffa57DButMcTssmRlWqsXejvHmPNeWiMaedz2lmYgemQbf1DSrW6lgFyQY45CYDsoKU+oz34om7lcbCn6AtXB3/DVZv1ZXQiu/5rO5nWDm30W1vUidb6w9jIX/ZYHDoC/ugi4oAf8CDL+ISGQP9Wz9RxNJ7V7+FEzvWL613veu0eeVQHeUQrMiJ9UlvQWRu0XXu9p27yoP/ps3gdfuENnWXCwqHq5N0ElGfRm+zAmZzrl5wf9McHukP9ftsBlOx69thjj211kmd9aSrtmtySe31Q/8UTfQjO8CC7+iUauq8foRPjm+wqm1zAC53g7T00IYPJ9tAf6EI4qBPt3YM3XaFcNLZTqufoevzCN7iREzSUbkgGlaFfuoc+2rAI6At0Rxv0VveYz+TVdXVFj6lL++HrGe2CI9qQAW0mE+4rm2zjD5zUpy35TTfjl/foNjzPjrBo6H101f/JETz1Ce236zUZg+vNb37z1l8cDfTSl760yRHaTC0roEuTVWLsUQbcndVIfskfntI72mJs8LwJp/RDOov+x3cTafoMWSEnnpNRgrfoAK/oZv1P2WSn5wNakQHvoQ+Z0/ZHP/rRl5XtGn2Jx/Cgd9HEhMFU/9d3TJroe/AI3fQBbSCH2q5/4K/69Ed9/5WvfGWbHHQ+dH92J/wuvvjiJmv6lr43y/Ejh+QHndGVDEhThTceoAWe08noX1Cws0FNeRSsLVC0oi0iYAYlitpgToEzJMxOv+Utb2kK2BqoRz7ykU2xGwAPOuigluYm+ictyfqFl73sZW1gNRhy7ChxaXAPe9jDmiEbh0bKadZRSVsxIBu41HHwwQc3A9xgI4rj/EiDyiwYRxAMlvBXh4HV/Sc+8YlttlPbLrjgguHAAw8czjnnnDaIqdNANAYDzzOf+czWdmW/4hWvaO+brX3gAx84PPe5z22DIxwPOeSQ1maOjHINaAbDxz3uce0/WnLU//iP//iygfewww4bPvCBDzTjz1bsIl9pD7plhlmd6Pzyl7+83XdP2zg/BvbnPe95jUauT4G2ZfCMEeDg7ZTnGjwY3vAWwT3qqKOG17/+9Q03RrGDqvFAe7XfO+Tm8MMPb8YGw8QzDAk4K3uKpgAtX/3qVw/nnntuM4DgxpB41atedRkfp2bXxzxn4EvzhTODSZRWhEa9ykdrzoXyOIXKZ2TgI36RB0YVHqMv+QutlK+N+IR/DCE8RAsy47/0sIc//OGNb2hAxsiWdz27aqqhes8444xWPgMPvnirLMavlDSGFgPNoemzymd4o0XWLOIrI1RbYljqg+rDd04zPfC+972vtYcxvRGADz2gDQxIhiUnGs0ZltrCiOTE0Qmz0hjJ2UknndRwY2yeddZZ7Xm4owODlyOnLNG+0047rTkC6tEXTUC4h6eHHnpo02FkhHziH32mr9I78KPL0OSxj31si8SRPfUomz7Ub9EFj8k/h0vZ+h89o0w6UfvxKpGXWQ43/pF9z2sHPUB30jPvfe97my6Gr8kwdeIhGU7ZwH1HI8GF/NG76KVf+6YXpJdqg3cZ0vqKtqG7fmuSKjj3tCd/HEtt186nP/3p7R1t9Js+0q84z7P6OFCvrAfyoA34553QBR3wBO05b3iMB+RUHeRF/yQDT3va05ocwF8/eMYznnGZjlCGa+QZ/Rxsn/EDP9GIPuBc+SZb5MXHe65rx4knntjGIXJEd+CLPkhWnHuoTDrWs4v6tuc8T27wgPyjAz3HKdRmZeov2kEG6Vv9EP+Ms3jQ6+6MBfQz2UMzbQ8f9XO6zHPK0f7IobFdv9Fm/+kHMqH8Xq7UZSxRJqcSPcjWLD573jio35Ff45969B84KBsP4K1doQfZSN09oAXdBjfvPeIRj2jj8Cygs+hdbdIn0NdYpFx6Xl3o5B6a19rHgp0NyoEsWFswS73HHns0g8AAZCAy82gmmrFqAKHUGfpmDTkUBgKRGYOA2VUGTxS3gcOzZkzNwHPgzETGIVI2o8TsqtnY29/+9m1G2EDIcM0MqNnm/fbbr20AwIBjsI8dCoMBvM1aq9MspwgdA4Dxoawb3ehGbRMQkQV1MILMaHrG7DHj8z73uc/keZQcFMandipHdAm9zNwb/JyHxRAzYGo7Y4PTzIgROeMEMH4YeO6budZukQKbSJh9ZliZ+WXwGowZbWZRvWsARyMz35xy9AOMf4bZ7W53uzZDa3Mdz08BGpmRxUvRTrxAl9122605gAZ2tMAf7THjri7tRBdt23vvvZuzxZhl+HoHvc36M4w4INqHVnBE19vc5jYzZQ4P8EIUJfzBEwaV99GX3JAf0YOpcxs9h67kCX3gns08GHwMTGtHlU/GlK9Ozgs5TsTcRAJZZbRoFxkSEYCD5xgxnAbX0IZRxDBRv/YrR1uVow7lwBcP0Ug/Wjb6SM7goN1kQsQbTcm+WXZ9C19ufetbt2dmHYCuDfqPCRuTNJ4HDGX30Sx0ffGLX9z6m3K1Vz0cz40A/DmvdAP8yTr8RUz0bcae+vHFZ1ZUAU19yCoaczw4U/oK2cwZg2Sao4qnibDhjXbo7z4MWm0VtcM/dep397///Vs/YKjaFAj/9Df1cKLQxTc5xEvGPCdP2UB5+oeJJE6OfpA+qx5RGzQdA71Bp3KSyKry4MjQxQ8bjSmbo8YIvvvd797aSRZEnLVfe/Rp/9Hibne7W3NOyLryI7toTU+QKRMr3qVP9H1tpvPwiIGtPBEkfZwjBzeRQG1CUw4IJ4Hc609wUN9UZkCALHD6wg914yleKYfzhGdSGpW17777Nv3JgTVJpc7URQfRqfihL9JXeGn8QWc6jKxru/LpOrSkEzit2uk/eupH0VdkXT8lR8rhOGoTeqA/vUI24EA2yB35ptfhLPo41Q/xTLu1k6zf7373a2OTjBd6m9wYt9SPP8YG+l/79B0OIkeTLIieqlPb0j+iI5RvfCSreKwNcDbGJFMCPdBHO8m8spLpg79kKzJLBpWFT8rXTv12arJH2ehG/2ajN3Qil8pGK7omuoBegh+66sv6lfaig2cC8NA/DzjggFaO/jUL0A4OytYu/VnfIjscX7rPOEPHa2dsiDjmPovSzQsKdiSUA1mw1kCx3vnOd27GMqeJEjYAU/4GMoMH49NgY+bS4CLSxADxroGdEuc4UMYGbWUZNAzyno2xyNDiqDJaGIYGa06QQct1/xkCjASDjAHMQDRrxrdPrcvvpJ7BS73KZLyYgUxaloGLgQQPg/2UMWtQY8BKsTEDbpD1Hrx8DOwGxAyAaGUm2aCkLgOZVMiHPvShzQlgPChDuxmaHG+DpbL6XS3HqYNwC34MBkYHnIP3He94xzbrPuVoTaUeeochxfBOqpB3GWNww1fGBh4wJDnOBliGmPbhBQMELaTGMg7RiQFq0BfZUdaUUw7IFx4xaCMfZCApdsukTjIAGIDK8D4amnCwW6sykkalfHikfI4MeXAN/bynncqLgx5I1Nh1s+AcIbLA+EMf5eC/aA8c0Epf6Y2R4L/MzDdcRLMYQiYJ9BV4kX9ygl+cFvcYrrOMd/iQCYayPoXWcBNV0A5OImdX//YMeUUbfUHEXF0bATIsK4GBDEfOSAw0bVI++eCcMTLRbArwRcSYTOKxvpTo+rzUWrxOqh5QH/nQdnINPzQmt5Fxdfi45zoaowvHC+3RBT1FxjgMyvEhM96jn9SrncvIrbJNwHlH2YzuY445pvVHOHA66Is3v/nNlzlxU23G+8iuNtLD9DM80ZXzIepChjmove7RTpEgEx/joxRMOoiKegfdPaOPk0GRXZMh6vWe9s+aBEiarnqz7tGzcarxyUSb//B1X9s5THAXjdOe9LM999xz2H///S+re4rWoQs6eg+Po6/R0nXfibrig3FHW0XmTMShizKmdAQez1sT2ANc1Nfjou6MPcGFvJEDE7PKRjN1GWeNp+N24rVIKVpytuEOL+1AT/0K7vjcjwccTlkIykdf49FUSideiHAan5QPt3ntzgSV/kk+yJy2yj7QD9GYLRE+e36eLoQ3mfdsxrd5Gy7RNXQOuTU2wQN/TY6ijXYAOGU8wnuTIbIPTFrKoNqZN3UquHxDrYEsWGug0M3Ki6BJ76JMGQqMTcqWg2SWMbOZBp/s7JYUGd+cCOlBIj2+zQIyjsbAMKPcDUoxaDLwb1V7YjgFPwNY0qiCd56dBd5hyEpFYvQaEBloY4B7HIjMroqqaY96DMBZ12ZWPjhp/6rrMrxjkGTc4ZM6lYemcZaXoQ/+cqrN9jM0zLhrb+9EhUYxfrRHnQw9EQz1eYbx4p60KwO2mV/ONDmY2vxG/Qb58AfAAd2WwT9ADtEBzj2tE+FgCIfWysej8aYSi2itveSzby8eR26WnbkeO5VTgIdkDV0YzPpT0rfwR3qiyDqDnixKsZuKcsGT8WmCQyQH7+K4MZa8wwFNqhdnUkS879/LGso9oHk2a+LoinaYSAKMQVkKom+iw1kvaJJnTA/1P/vZz273TF6YjJhFs/462TNhI42YfJsseMADHtAcwFkwxT/tQBdRDRNrPV3wZtH7i2ik/+oXfdn0qd+pO2vE6GWO36z29xu/wE92B8dZVC9ZCYkEZU1c6kxf6YG8kx9OAWclfcnkkX6+7I6rQPkcJJ9Z9KIPkx4ZJ1793iH79MS4b28Ept7TVs4dJzkyoo7xRNKs9zcK47ISDdNfRFx7eevXMSbiZjKTo61vmDjod9KdtXt0sjLIhYm+WX0CHqLGeEDnqSfrlqcArvSUCTp9Hw6yhqT9mwTQNv9XSR1dRadyGtUli0q2RyalyGp2yp3CeepTULCOUBHIgrUHETSOojUUDCcDTGZfpS2KpjFCRGSkcvU7tsVIMYvKOBWBssbMAGJmt3+O4WpG2gxljpUQIWHsmCUF453ploG+DgYnA1LKUYwOdajbYDd+fhaI1HBCrMNAF21nSOX9rMHxDNow9rTNTLAITIw2KZ6eY9CjJccaXdCAozWFz9RucoAxJQLMSBelUI4IIsd/lnGXwZFhGgMJn6S+MbIZ+XjeRxP6He1Ewsxs4w+jXjqr9wzQ6KF9aIXGUno5jtoo+jAFolEMaNGl8Id8kUGG8zI85wh6XuqfFFO0FnVEd0YPeSKnmT1njOKNqHjoOeUExnhjePjteVFaaVzqYExJ8cuRK1M8SrQlzgBDlaEsIjfveBBOFQcArdHHu8rQBrLHCRaZEKEy25++NQUMS1FIa2WVkRQuqXLS20wK6OOcSw4/GdI+tETDjRjMaAN/ZWpDorq+RQi03dpak0vkd9ZaS32CUaivivD15eA7Y5mDoTx09T+GIudJFNq7e+21V3OEVjlwHXAq9FVyTqbQRURDf5t6fiw/SY2bOuYBbUwGSN9Upj6E7/qQNukz0vZEmfDXhEEmmchU1o1NGcZZY638bCSFbsku4LxzJLLREJnWN+CsPB99h5zS3ZwVONAPHGeOrOiVKJayx2uGxyCSRl+gG17BO2uDk+pOf+ib3ocXGkjBlHZIp4mCwYM+8VtdZCBp93lHmZn0mKU7x//pGjSgJ/CBHpcW3x+nNAXp22n7KpMtU7iQWX1RP4y84aX2BkI7uFq3mTTa6AjjAp0hhR7PXM+yEXh6x/jsPc9GNuIsR2bJHx1h/BQJTPnzjizhtBlL1C2jQZ8TreeI0rk5gzLv93oyE3L92uFZtBoDnsMV37Qr13zoHzzF315Wo0NMNJmA8+G0L7ObcEHBjoCKQBasNWQtoXQQhnzWTnAgRRKkeTAaGNMGFWtJstMf48c91/1mLDBcOSgcKhElRoIB333GK6VtNjTrlTh61pkwchjnBhPGpZRBxpQB0cA63nXSAGNgYAwxIrJTJlwe/OAHt00k1MHwYXC7ZpCNweA9s76zDsFWLyPS+hWDDoNHWfDi/CoHGDQNmKK0BjNOjc07EgUS6TPASbWRImh9h//aLm0KLRlo2siYSuqcgT5RYHUyVPBHdNNmECJ+DC3OknVNU9EooH2cZ8YRQwU+2cWQsSbSoIw+FThrGxnT2mnAZVSQB5EjkR1OJyPG2iX4MV4TLTAjnfViY8BHMiCNiGEDNxFa/CEv+JjdPK2pYWCNU+XUw0EwAw0XdGCswpPRgv6cJ5v5KJ8Meo4xnd04GaXeyW6fHGAySO6951nGENmRugoX9HMNrZXjPbRitOgH5ALv1MlJYxTiH2dBBJEDaD3XFCgTX0QKGZF4rWyGGSOIkycqQKas38SbeZE5MsH5JYd4LPogwhO+eJ9s24RCWi66KRsN1Uv28AFd1B/HwX9Oypgn2qsekTP1mDzRh+gE9JB6zblDV+vCOLRT+GdHScahdmbHYU6nSQP3nelGT6kDzfGC7Oiv2WWSjKhXdALfsvsx/PEYfX30Owak3/qeZ9BFX0UXfdXzcEcDsoM+HA1yhVfqJ4Powsmid6R2j3fo1PaktZNHa1XRnM5UHicPn0wS4R8aWPtGN+MP3WISiizCm77RT5ST9ExOp7rhmZ091SmFke5WNjm/733v22SAXs/Ei36rLm3HN3SLnqC3bPZlXbT6kwKqD4r+9tkD+KoO9dL1yoO/MQBedJ60XX1dP73Tne502Y62fuMBB9Lma/isLLKfNeiiXfoTfR59qX+kL+O1yQU0Ul/WQbrvv+v0tYg8PWpcQgvf+JHdu32rEw3paDKirTl7GK3gN07Xz9nH+g+88Ibc4Rcc9BP/9Q9tlSEgdZSeIW/GMHqCfjXmcOjRQJ/DY2te6fFshIXP+Kkvi/Sb7DMZhD/kI20ywUNG4W9iAC1NtpjMI7PaRka1DU3hh3ZkGl9mrbsm++jgG+5oa1xMGm5Sy9Ewu9N6Dl50bTZPyw7m1ouayMADtOcMj5doJKU3myplssnYiJ7kxR4H6MzJRWfy53924S0oWHfY5ZJaoVuwRnDeecOw557SWmwG8X/XDW45vL1PT2QgcLYMYNJEKG1K3YCQow4Y4gZKhlWf0sogprTd8x6ng2FnUPK+wZkhybhQp2cNdAxAA6fBz6DC6FVeb7QmrclAo17Gjuc4HAYSZRms1ZEjDQy+oiEMT/XCRxumAD20m/NoAGQMwsnsMEOMk2LAzQY1BkM4MV4Y/QY/AzR6GsAMggZVERa0jNPjeXgyQpRjIPUMOnJw1W1Q1XbGn3q8w/FmoBi48WDWLKo2m1nmxKgTjfCAY2NyQAqX6wA9OYiMdeuB/GdYop3ytUlZZrPhzrjMsS7omggR2qpn1hqpOFXkguGG3ww8bcVPcqidyvA9ax0kOWT8aA/ZhKc6lY+GjAXlJyoN4Mk50i544jE6ehce2o4fnBzObnY9xVtReimf2ogGDEU4qkO52QSI3DOAGbMmF9CNY0iGrOWaak/K9CGXDCw8VicDFM/RV/u0Be7zzjVDAzhlswoGIXzS17SHMR2ZJMv4iQaeJXPqI1tJY8wGJVlf2kN4hx/wyzEE+i06upcJBnTwmTrGA97eQ3f4KFc7OEVwpI/oBfxCFzxAL/STAgzQ24dcwsMRNmRF2ekr+E6G9DF4kEdOkPahFz4wwukteMCXnkEHukb95NcHzbznvvajMTnI+r4xn9GbnDGK9T04MqrpLOXSYTmiQv3KJqOMfnKhDQxx+plDENkmp3CAb3QdY947fnM26KtsdgK0h5yRa/qFkY/+JoXUoyx0Riu6D52yvpR+gK/fUzINP3XqT/qb9kam0FA/RGM81s/RjV6hA70jguReJqTwB7/RmIOYoyLwCw3wAl3IJ7kjQ8lwobvRlO6Hi7o8L2OErKT90RH4gbbwwi/txweOiskQ73A4p84XhFM2KYMHmpM39NB2+GbTukxQkGv1wp0uSwqycsgiPsAbvujmP3nWTzn4ykDLjC94hifRiSYE0FXZdIm+YZIXn8kNPLSPficT6AFPPCejypnldCkX7uQAPbQb7ZWnz8eB1Af1PXRGR3wkr8r3jPtkS12u448+r49OjW/GCXpWWcZZtFIfevRykiOi0FZfI4dTbTnjjGE46CBjv7T7yaYWFGxXKAeyYKdwIGet08oxGcsewrvM+sLxc3E6F8G4zFnvpLxlcJm3vmgKTwaR2V6zuZyM8TqKcb1jWiZ9ctk2z4JZdcx6NvUaWBkNBnhtEQ3J4MxIsm6VUZKIIr5PtaGvexatlsEpz8+DZdaAzaL1FJ6LYMyjzZbDiGHAMfoYpYva08vSuE1TbZ1VVmCM8yyZXAWmnt8K/HtZGpfT86N/n34SrWNwilrl3FXOA6OSoTwr02AeLCuf43cW8WejMrUMPovo1tfZ49JnICQts782hXdgET5JlR2X16fLZ3xZVY9uFjbLh1n9YKO4zKL5uP2z+tgUraf4tmz/msJpVV0zjx7L8nGevhnXk7aMZWteG0A5kAXrBpXCWrBTwLx0uKlIwarlLHpuI8bAIsdwMwbG1KA5TlU0azpOX5pX7/jeVuC37LMZYKWmOkPOLDXnMfi7l7VAHB7ffWrrvPYtMjDm4bQZWIXWq+A35tFmyomBJOozb0OYqbo200eWfXcr+LCV+E/J0jL8SJRFVEy0Cd1FJpKeuNX6ZTPvbFSmVn1mEd1mXZ81WbgRWVk0fmykrq2W2Xn/l3ln1furvLdIVsb3pmi9ShkbGaMW0XCRztsoLMJ12UnvgoJ1hXIgCwouJyB6x2k8+OCD2+ymdKjxOqd1B46MCKoUpf7IBga3NT7WrUiblEImLWpWim/BcsCoybErBduOxo4PkqrWp6JKhZOCWIZkQUFBQcHOBpXCWrBTpLAWLIaprrxVM+HbC+alMk7Bzta+gismlPwWFBRsBiqFtWDdoCKQBTsNzJvrWLTWY1sYass6bOPnlsFlI+9sa2N0W9NzqtxF81sbWQe2qPytbtv2oNvOBDszPTbSL1d5bkfiuCPqWlYWtlebtsV8er/+bXx9e8P2xGNn7ufLwEb2RSgouDxBOZAFO1WKph347FjmcHFr4ZxZ5rdd68ZgnZyjCmwpLu1xq0GaqLWGduOzVs8RCjkseIyHLfDtKGrHtkVgLaMdDu0wZ+c35+Tl+JIdCdph8xq7yNnFcXtAds9ED+vH0DnraLLGU9rrZlIw7crn3Es7/zmGZNaRIxsFqcTk0BEIUnOv6GCXRnKk3+aMtJ3JaLQrqJ0n7XZs/eK64WcTquze6ZiQVdaIrwo5msC3oy9W0VFw1a8dj3TXu951ck1zwI6edgqm5+nzrW4TXOgTGx3ZLdRacpt0wckGR3QC3W0HUDrZszb38oydOa1r9ZwUZTt0uk8nGXd80zH0uTHLDq92brUGdkfp8Rxz4qiPbYVHjppyjIv27ujxa1vZJHSBJRWOu7GDao4u8d+uvZfHdhcUgFp8UbDTgEHYGVAveMELmnGUQdmAGCPAdvLuAQNkjIFtARxIdTvPyflyUwdoA5u/2Bbf1uvLRNQcA/DMZz6z7YjJcDIg7YhM89AzBymjp23UOc3bCwy+1jk6j8vRJLaX50jCyZpI58oxQlcFBp7BHw+1yy6Z5CoHo28l4P/2pltftwmJ7QkmeCIz84yuHIo+D5ShP5PDjYL2o8NG+xAZyfpivzkZjjXQxnUEOunZz372cM4552y53lAePafP+I22Jngck7CRuuCKlug7D9TpSBeGeQ6Y30pQprMG6Rhny3IIHalgx2fXOEJ4z2mkKxwjZKdosnD66ae3MxLhZ0KLTjr88MPbkSPZBIyjiWY2CONE9udSbk+ILD//+c9vH311qwD/6U9lZuxwbAeH+vIK2mrsd+QUh9l/53eecsopjc8FBZdnqAhkwdpDDBPnJJkFZnAAs9EnnXRSO9cug5dIj41WbFjhLLHHPvax37ZF/tQ2/PlelN4z3kI8m2FMHVzeA2PE4eJmnufVFUOGw8jpFa3S3tQ5taX9vLKm2jKVCjZFE8AAEOG1U2TOy3MQdD9jvRl6LgMiDeiHzqIqt7jFLdpsdmjFyIPPqsYrwxCd99xzzxZlsKEJA3CM96y2rNJGZ8ChG/7PemejMA8PDhr+OWR+lSjDrLWny8gbR40hftOb3rS1e2obfLJ04oknXhY1nlceg1d5+lj4Pq8v9JByOQSMd9Hlvi9NtXkKl5xFaCMcDoTIqfML5+E/b/3uoj6wCm5T922cRTcumjgb991l9cnb3/725jjKqBBxecADHvBtxxGsktrn3D86fJEsiPQef/zxl0X9tpLunpNFYqwQRbX7c3S1c1I5gbIItI/sOu/QWYUyQ+gfZ/xxIE36Pe1pT2vXRCmdJ5gItUkwZRmbZJPMynJYRZ8u09bxdW3QlzKOLipjFo1nHd/x+te/vo3HosTGa+f29m3dyvFis2P5VoBzIGXjvPWtb23/RaOdG/m2t71t4btTOmxdUp0LCpaBciAL1hooVDOYUkSAWb5cF4E0kItQOLhcitOpp546HHbYYW3QYjQzovzmIHjHrLCyfBvUDaR2QmT4mlFmsDIYRC4ZCwYHBouZRe9JDzMIMxqSnjLPeVQenOEBT0clMEYYGAw9Bxgr04DroGOz+Rwl0SpGq8GeA6UM9Zux1x4OssGKk+c8ua985SvNqPOelE7f8JJSK/ploIOzNoriKVdqESdY2epFT2XnOno+/elPb3RDE464dqCVg7PH9GQswSv0xBvtV5aZaO8wqsZHiywDU4Oo2X/l4c3FF1/cHBa0Eg1BU7xFI5FKaZOMVPfwwAwxpwKP0QWt4Io+IppkBx19J9LM6UQjbUQ/75NNuGm/iBTHqT9UOjP+oRvjUlnqQSd44RWjA34ic+REaidD1XNkxX/ygdf+qxveOXDddbjgief0B5GRiy66qNHds3DxnHu+1cuY1Q/wRxtMFjD8ALzx1rOu5SDz4EymvIe/zhwlO1JT1QvQFH497zj9aKhM7XRfef7DW9vw7sY3vnE79Nts/mMe85hmoKMPuqMjHqEjWptcmDosHi0YyY94xCMaXzIRoV71oC1HgbyTXX0Ij5Wvz5MfuB511FFtIiN0RBf4cp4ih/ihLJMQ5ItjpO+hOfnjhJrgQjMypS1Tx3eItNEJaEKe8J2eIONwBtqsT2sP/uUwdPxPeuWiiF50BTpqN5zpyDhL2kS/uS+Swnknj/QieprIIRP6Dz6QHbgoCx6+yTd6aC96qI+8eE/fIjfeUxc6eI5uQn/0Ri/0IHdkC03IFOcYX9AJzdEefdWBR3kXLfDJO+iO3to0XmYAB86f9/bee+9vm+jzLKdS5DNps72+d40MaxN8tDtn004d26Ads8aLjGnKgRP5Im+Z/HEt98iPe55BI7JMx/iNj2SG7KEZ2sCL7NOR4zFLveRL2XQ2B1CfIGcmofBTfXhjUhMtMxYBY6T7JhaOPvroNimHzp6DE5miM9XTj2Nwj45WvzbgGxqRb3TF016f9jiTk9BDHyAneIcGeB+nX5tNEo6BXGfSEC2VE52AVvoRedfP0JNe0h98kzN0xv/+LOLQdpHjq25l4Te5lOqs3fo8fqGTNqmHHiwoWEeoFNaCtQWKlkH3jGc8ozlK/ltjktQpTsOBBx44vOc972nK3oCSFFYf1x1pYY0NoPilm3IiKO1XvepVzThl2BhwGEZPeMIT2uyhGUWOqAHEoPaiF71oOPvss9tgfuGFFw5nnnnmZQdazwMDgvIe+MAHtkiIshjFDGJlMnCf9axntQgl/D0Pd89pi8HQwMYo51xw4rTHzC7jyKDucHJ4n3/++S11TbsZvU960pPab3VIqYKztX4c1COPPLIN5AawJz/5ya292gYX7VMvI3RMzwc/+MHDG9/4xtY2A7QZdym3nBCRIlECRgd6HnHEEcPjH//4VvZb3vKW9n+jqW4ATQy66KANcGWg4IOIljQzA7fB95hjjmn0xnPpReQAfUIDRidaM6zCR3Kgbcr3/gUXXNDkjAyK5KGBlOlDDjmkGcLoKvpCptyXXp0JjoD34YFub3rTm9p/dDzggAOGM844o0VBpbyhE1o7KxCv4A3wBx5wZVRxCP1mrEmjY0Ax8KTVaTvDTEoVXPEvcuTjjE1yZ2JAG63LJQfkURul8KIVOcz6JQaVMsmbct2DtygNvJRz3HHHtWfhpU51oysZGvMaPvqDfvuud72r9Vs0h5c2oAcZxk/l4VNSYr2LLu6nL5ArRutUmhy+ajecvQuvpJzpN8oW/Xruc5/bcEUHssr4xSs8Vi8e5H19Uf3SGsk55/DRj350ow/64i2+WKutfn1MW9RLdu9///u3NEa/p/qBSSPRavfw1m9OgPaph74iI/DU7+knz6IH+kV/zCofoDk86dXQ6YlPfGKTB/zD1/3337/xmZz5fshDHnKZsxl6+E0+Hve4x7X3lYsmdACZQA8Gur7EQUMTMkwPaKf39Ul9Az/InHahsd/6t37B2KZjtFdarjZaEuC/CS5toXvoPDpRe8iYfoUG1mjut99+LTsl/aoH7+A1Z71fh0km8Fef5UBodyAOorr0Hfwx6WFcmaff5jkW+KY98FUOfZLUWTieddZZTRbQ2ViFdnA0HpBdDrm+RP7wga6KLs94xlkfA9lEv0ye4KX0VnRHQ33VxJBr+tu5557bZE0/EXE0NiV1tder+rJ+Qp4Anno2kwrkg5yQIY6rPkS+1UE3wHfW8g00QQ/0URb8jM9Z8gFH1403ZHiqDHSh80wmcgTJnT6m72cSAi/Q3AQCHU8GlAk3fW4jgA7GXG3kNOqHr33taxuO5JZdYvLFGOdTULCuUA5kwdqCAYnxQplKFxKlu971rtfuGSzNagIDPEVs1pBRaXbbLLWP5wwESYVjiEhBE2mRkiZ1icI2S6kMs6JmoQ3CWcNhoDEgm21XJyPDoDRrzWMPZk/N5qrf4GY21qyi32ZuDzrooOFud7tbM67UYUbWfYOzNDltMNAaFG9/+9u3SA+8DV4GT1EBdShXWQYjaVIcOm2xOYXBzoDIEVAX4xBttc1A77oZUDO43uPweN6sLHoqD06hN3oa7Bji6GkjBvQ086wdjBaRImV533VOEePU8xt1IBknyjfYqtsADESftB+/EikyC8wgMzOOX969853v3IxSG4uY3XYdPc04w4nB4R6jB90ZpnBmqDEczJSbgc/GH55hzLnOEePEk9UeyJ+ZavhkEyB0TZSRQ4GfjCr3bbzgnazrxBv0F+GIo2NGGh4cWDTgRJNj0RIbHDFktQV+2q/f4Id61YGPDGp8YWSSOTKBPtpsYuAd73hHM160zbuAg6GPkDnya1MgtFIuPOAGL/SUrqe/jKMtZNUz8NBvzbLrt/67zpkgs/BQB1wZ0yIR8NTvtPVWt7pV4512kGOTMeM1iWQBDmigHFFeRjLeuSaygF/axUhULlq6rp+4x3EQnSMv6Oo3vuk3ZCZl4Nld7nKX5hz5ja7ogY4Mzj/4gz9o9/FIhAc+U84EhwVu+iK6ahuc1Os9Ze+1116tT5O9rMVkTIss44m60HQKkjXAAdAmz9Ir6El3mPiJnOjTHBVOrOuMaTKlvXiRqKR+lDWq6EE+0Z7ONjEgEiWqQt7ICxpwIPUFegedyAJ9cac73anpE+1EN+nX+jOaJ0qcNEz00+b73e9+bYKIE8rphrs104CM7Lbbbq3/0rd0xRjoQvjTuX12BPrCAc4mjchOgCNK5vR5E0j6HSd7M2sb0SCZEz50J31HV9D16IQ2NmvbfffdG405ri95yUsar8jnPe5xj0YbbeJEoQV+kAeyZPKh179+m4TgCJI7/CRnylQfGaE76RDOn7FTX9GvEtHk9KgHzvQOOhunyYoxKOuhObkm76SSw9U4i74ce/XQG/QBfUifeodenFrz6p6+oo5EMekM7VamiWbtxvupDfbIjnf0F/1LG+GsX2adrTbBS1uNAZ5TpmfUbQzq8VkW8JMTSfbhrj8Yc9kIdBBnWp+hg/T7goJ1hXIgC9YWzJYaFChZAwvDgZGR2V/GbAbs3hhLGonBKwYBA4ExZ4AzMHvGwCClkFFqMPaswTPPUOyUvW+GobQthjLj3kC1TAQyaUvBQ73aAm+DERwNZIyHsUPqWdcN0EnJdc2gbRAz6PivDIYRJ8M9/9WRdnjXt497/jPkGVXeEf2Ei5nfpNqmbaF12pH1R/Ayq51yk1rGeEFn97WZUdDXnQ2ONgIM13322acZF6IevpOONk4b6tepqhdfDcYM3H49YJ9q5jpeeF5dibaIdvgtmsSwMEtNFtBQ/YxYdGRshD5j/ud6+O9dBmtwy+YkjC9GjdlujhzjjhGcnRMZY/DwnjVbjB48V/9973vfZviR6d6QDf/UGxlXP97kv28GpPJ9sm5Q5AMe97nPfZqzm36iTUkHzHt9f5i1NjH06HfRhFf6QcojP1Ob/5At7yb9Ez8ZYvREHyEa1x0+o6GJmqTNcp44SXjPiWJIc6b1A/phXB669vhrT9K70ZF+8iHnMSrVh39wZhiiG/pPOZCceJE1BjBcE8kN/9SRPgUP9+lJhqe+5xm4uTcr2sX4TSqpcj3PQcBvEzx44UMnwJnjzbnPBlY9PbXds6kr9MjkEX76Dl3g5aOu6PD0Dfe1gRNlKQIHlbMc+VBW36990NJ7+qt70aHZ7CiTSPqoZ8b9EygXP0wW9TLnmrZrs8hcUrhB0kutAxVd5UQmnXwWLHIy0Fqfpkf1P86hcYuzweHm3MHfh0OLNiK6IoX6gfZLIxa1xVOTGN6nL9BIZDgTsD2QM/IpA0OfMIlFt8In/MokiskCDjVH1nhhYkAdPd0iG/34iw+cYdcyjsHZ5Jo+Hfkm22Q88t33ox60jx42saRcTp++APCMDJl8QDf0nOoL6pTari6TkuioXE5r1sjTAfCII68e8sAeMEHSt3lZ0O9EebUTn+kaNFQG3Q9nupwOWGbX9oKCHQW1BrJgbSG7L25mB8YxmH1XbpwsA6sBYd7ie0aJ6KWB2HoYgx5jaqtgmZ1ZDW4GSIM6vA16i+gyLneqnuzIp0wzw4yUDMSLgJEGL/SMIQi/WWtAtmLjgjhDPgZbA/4ykeBsWLDqM0mDxHMzwur1DBoxKvt3V4WpdxitogsiTGa4yacomFlpcsqIFQXI+wxx70j/wkdRJNFpBsqqmy+MN1BinJOJbELVO/9buRFQyus3xZgHSaEE+MHo5BAu017tQlPGH6cCkB/lMSalm0oj5MSZpOAgLIv/GCKjJlpkUnCIRNPQdNY6OEa/iAwZYOwvs5OqNpnQWmUH4aQDBzKxFMe4rzMOO7ote3zGRuXDBI2IMNw4COpcFTgtolicUBMxHE30nIpEAXJg0oUzRI787nWN/sVB6uULrfBnPMkQ5107xhMgdEkmIaZA/zIBhJ/6ODz0++wizPEPXsD1OJjuJ7qq7uyebUJKWdmghZyMI3qexV9OYcpIhsCYn65Lo+fciCCKDC+TZpn3jb90J76ig2/jRj/+LiM72iDCyvEVtcZbE33KMblq+YblFdKnjdv06XjywLOcYtFjWT5oIDJK9iyPcd/EmbrgbHJDhkr4h5ah0SrybsyVGqzPmbTiSOd9DrH0VmnynNRHPepRLaq8kX0DCgq2NVQEsmBtwQwdI9YMpQEyhh6FPstpMEjnORDFTFkz4KRimUXMYJr0nP7ZHrJBCgMQLgYcz6lnlUFj2WfHjqzBSjogw8ZA6LoBn+MmbYlxs2h3xnltE4kwmDE0RBwMiGkf4ym0HtMzeFm/4hN6GhzhZcCbhZfrnjPjveyW/FNlMT5ED+JQGuThiaeJovbvjR2MRLmmIl293IiMSSETiUN7kQ0GxirHOMzDYwxm0Dny1ueIriXSwpGTmidKkBRk0XNpe/B65CMf2aIP0hrxxHsxZFc5ygN+HB2z42bjlc2okoKmLyxqG7qnzqko/TKOXsqM4UsGfdDGjD0e5LoIn7T17HI7VVdkmFEuqpt0N3Jo3ZisAhEHukB6p2+TRon+JUNgGXnt6aEf6A8cUca2FF0TAlNAnhiO+rb+yBkJDafkPzs4kk/Gr8hFomjB1Wf8rufx1/PRowxxEToTJeN2KDMbG/UZCFNrXGfRY95z/cSBDA9rdK0tZkzry1ISkzY/i85j0BZOo8gpXorozHJGkwaLBhxO7e1xmppUio4cgz5nkof+0S9Df/TSJ/FpSk6B56Wpk1FRqrxLx9EH5DHZL4k6cz70SenF5IYzqd9mklTqpXbpw94VNRzzjWNk0ok89H2C8zreGZQTax150l1Dh75fpK/2PNIGfTcbN2USzmRQxt9V9AMdYKInmR/pJ/oQvUhfWs8tm0B0cZyd0PdPqeXesfaRQye7RHoy+0P56Oib48x+QEvRYfqwHwOW2eU1+zgoXwSbnPa4i3SyU9gbHGP6fhbuBQU7GioCWbC2IG3GGicplqIBjAHK14DICDRoZXdCSlZ6GGPPon2Dk0HLdQO3gdVMHiPRgvvs0sdgkjbGOTAAe59BavA0MDPWDYYGEeUaJLznGelucDKocuQ4eYyUPrpgUGAYqB8eDDGDP/zUof7s3pcdY13XRgOt2VT4MRbMuAKDl0FTe5TlWQOT39nVEu4MUO1St0+eza6RvhkPBlFrqKyHyq6I6ststHajM3pmt1rPWdPE8IAXGsFXyo0BWb3aii7qREfvwF0ZogzeM+M+y6AGOdw7m56oj/Gbbf8BvKRuWXtpQ4TsfikdT73ex1sRBoM2I0/aJ2PRAG0tj+c845uRhxeJ6phpt1HLvvvue9muvCLRZCB8JQNTB6GjiwkLRpnn0MV/ZavD+5FffGc8kgHRTk5q1noxOm28w0G0roxccCjhpm6y6B3yx9DXF/Qf9aIx3io/aampL5u7cCrgFQdZ1IJRKlXOu2SEoadO972nHLTwH0/xmcygL96SaU5AdisF6GkiJP2WjMDD+8oJPd13HV54LRqrXzHwrKPiZAA0ZCyKFEylJ+IlIxst8Ydxz9k0s89IhhueoplURLTktJMPqXZ4IcrEeCQvmTRBP+1AF+/47ztrirPrrqgIWVdH0jDVb31zjngI4DOjFV7W6mZSRHQE3ZXrGfVEdnzjOQeI44sGcE9qIcPYGuxEDr1vosh66UToyBL6oKHf3vGuyQk4iIqiifVk+rP+CkfluE4O8BFd4En/oIf+4X33/Oc8ZP2XfuB/NvKiA+kuTgEa6OP4rA+jsXK8ox3a712yrP3RlWQmaZ90gYlHbUFzvBR1Jo/jKKqyGev4ZWyAHycC79XjOp55Tj2RT7qI85klEaGvejgXeKj/cT7gzhGms2c5sokykW1t9EEfbdDPpave+973bo4Yx0MKK/2g34u2cT70efymD6VwWqMp9VyEDh040nBKX8M7KfLKzO665BRfybo+qq3kST/St7WXM6u87ExOfkyUkAl6A+3oAjoVT+h+Dj0nFY2NV2hLltBEGfiYXYv9di18JhO9bk0qr4m96Cz1mIDIhJf0dPyht+ZFso3h2mpsoCO0gzPpvWTT6BuimDbDI0PqRvfo9+wiC8/INNpxssfRQ30omwCpC43Ih0gu+mgHnqA33TbWEwUF6wK7XLLVuUgFBZuA884bhj33HIZPf3oYdt310vQaM6oZwHKsAkMnhoPB1KBpQGNIU94UM+WeHUKlG3nOgGngN+tHOTNUDHSMHoaSAVJZBlcDNwPWgKEczqd6PU/RM0bd59QaaPw3II4dSLj7qN8gBQd4M/R8GBbako0TYsCJWDAQtMtAmo05lMOoNTgysj3PKDIQqp9RxdjQFu2OsYMODFlt45AxzNXpffh51mCnndL5DH6MVwaUwZTxhw5wRCPvq0uUCk/gxVBBH/iKhGWjDINkNjZg3Dg2xH/O5rwBEp8MqGikHcrWhvFmNfBg1KK9djBC1IVGJhu0Ge/w2wCOboyEbIpizQte5ZgRtNYms8E5noFTzUBgTKI/oxcd4Y+vnLYpBxJttRfdPIfX3kvd2oZe8MOPOP7akPW/iTwzErN+zeY6eKQ8tEzEER4mJhiKNoxRHgOJQQ/nrD3K7pE54xNOnnHPh4zqe8rEQ3TFD7RhoHG80YaTBD/3yYQoh3IYo+SxN9jTH9ADD8ks44/RBQfyoy3aj1baxyhkpDGq4KXsbB6DH95By6nIBR4muqIfawdac47IhDrwWB9DV9fSfz2rfLIiioFO8IU7/MiS/p/JLH0CTtkhllGuLZwJfS+7LDOITYzhyThqwQjV59CGnKM1Y5xckhPtSWRO2eiN7srBK/zNWkhyrn2eGdOGI68vkwG46Vf4yXnQ/0VuOJlZB6mOHCeA3z7wIGvwIJvwhWv0AZzVSxdFPvQH93Pkj36Lvlljl2fJlTI40CZVtMc9fVM96lMv/vQyRF60w6QXefaeZ5UDF07S1MYkiWaTeXQmY+rCB23EazhxCtAN/Tgb6s6avr4sOj7OgH6ZY3DI6qz0ZWWSdfWjFf6Rc7qX7LmOx3ikL9Ad6oUrZ1ld2p5oPBmhs9CNHnSdbJBdPCCL9Itr3tUnjJ3pE+k7nscD/MoRV5ydHBeibu/AOU6/uuCvr3uHrORIJTKtbuOvdqFNdo1Vp3LwTN1kVz/qnfR+J2Ft02bPqdvY4ln9CO+Vh3ez9EN0BFlCs2wapA05JgU/8QYv9A3lqwft6Fh4Gt/QA50zya1euPUOZCKv2UtB2wB+mKhDF7QI7v1xYWecMQwHHSRbYBi6+dOCgh0G5UAWrLUDGfE0YCw6X6lPOZp3NmOfornKOrGsYUma3qw6tvrA4qQRbRTvRTCmWd+2bUXPvtxFZ2aN/0+dsdanlM3Dtadnz8+ten4Kr83CWJ5m9YW+/bmXdLtFNJkF4zJXSVmcwnGja0XH5cGrl7lFMjTur+My+5TFcbmryEoPjGWboliPx6AGjEeGaBy/MV/Gda1ab2Rj3kZG47p8c/LTVkayqJUIGkdiTAswT/9tBayS8j0GjoijUkSZOZRowimwA6mJRRMo8+pNHwtdZsl+dNEUjvPKWdTunra9LI5lY7xucKrPzdMXs9q+DF/nyegi2djoODY1rk7Rqm/LmFaz8OnrGL87xhusIvuzJrbmjblTuq0cyIJ1g0phLVhr6HfdW+X5Rc9sxPjpIynLbiaxVY5eBpJtYbSNcezbtq3ouSxdxs/Nes/1ZXmyPZ7v39tKmLcJxxR+m5WXcZnLysNG7q3yTta9Lvv+onb0jsC43I3wPkap1D0bG4mUiGYwDEVtZkWixnWtWu8qx0hMtSsphKKlIrX+j3f03Qhe27PfcJhEVjmRosjBVTRo6kD5cb2z9NmyumhROYvqn8X/WXI4z0Gapy+mnl0W5snoItnYinFsSg5XHbdmPTuLnhsdA6Zg3P4x7ttycqagYKugHMiCgoKCgoItBEaglE9nKEr3kyIvHVIaY44GWEcQqRMllWIrZZ0zueh4inUDTqM1gSK9UiE565xH7djMOY0FBQUFBf8HpU0LCgoKCgq2GEQVOIvWmI1T4rY6Mr1VYD2fzUFmpYrvDGD9mNRb68v6NMGdsS0FBQUF6wrlQBasNdjYwCJ+s/nrehZSjqXIBjfbKv0k28GLEuQ8yI2UkXMvfaR7Jc1pq9JzsiGFNDibXKCHVD7f49301gWySQ2cydpmaREaxIDN2hk8y1mSeIn+6urXouFFT6McpeLe+PgAZXl+vL4Pj3Nu6GajLllHRcZtuGJzjK2Gscxs6xTJ7QXr6oRFRmxMZfOQfpfWdcR3FVh3J72goKDg8gDlQBasNdhF7jGPeUxLBctGFOsGDDHnt1l3I3Vqq7fdznoqOxaedtppbcc39LB73UZwtc26bdjtOGkXQY45Z0akxO6r2fVwo7iigw04pO0514wDc9xxxzXn2i6I67Iteb95gt+OirAtvKMEpo7kWAU4XHYrdJajHUTtyCdtMbsMOrTbzpG2tLd7oN148dMufAx6m33Y1ROt7OjnuBG7ebqHTwx+DpcyHEHhGIdMsNgp0kHadhYN3TdDI2mMjoiwAyS+5qD1rQI0sWMm2bbDoSN7NnKAfMHysmlNpnPyyJI+uQ4ptZvZOKegoKCgYPtCrdQtWGtg4DCO7aa3rsBBcq6b86y2Okqas9xyTAangEOyzIHmUwA/uHIYORr77LPPcOCBBw53vOMdm1HpnDBnw4lebRRsey8VLoe9c0Yd18Fp2hbRq62gLYPVVvPoMeug71WAg6c8Oz7a5t1B8uhsW3YO0hOf+MS2JkuanTVynErHJpB1EVDnTh511FGNho5YcKaZbfzRz1lkBx988PDQhz60nQtn6338DOT8Tlvzb1ROesiB2o5byAHhWwlon4PTbdxSG4NvW0BvG/lkjeM60BsOJkrotj7CXlBQUFCwnlARyIK1BUYFw9KByfmf72wfDpK+l3TMqRTSPkVwqp5+rUxSBjk+eT5boY+3DU99oqPBa4xnv416f18dU7vk5RlOgXMt3/rWtw4nnXRScx44kZmdd79vb/9eHLdc78vWJhEHu0I6Y8rZVejsXDHRXodPcwKdS5V3RcbU05eZdiQVM86hqFmfyuv71re+9bfRu4dZ9BEtTYpmj/9UOVMyMS7L7+AIZ1FYTrM2u679zvLqedZv1d/zO/+VgybjtLl8J4WUE+Yssj322KMdJ+BcQQd9JyXUsQ7OSeP8WTeHLyJDvo899tjGKx/Po23OJxMx5qT2EWO8VZ6zGtPWMX37VOZMeozvJa0ZfnBLHXFKQ4/0kTixY3nu+0GfKtnzRnvgzKkZQ79lPzmE71Q/Hh8nMMZnHNXq5dcnB9bPwn3WkSH9UUNTeiMyk1TgHJkxJbvjvjyvfakveI/7pI8Jpyl6kyMy2ePQ6y91pQ2zdKe60++nIoZjfTwrqqhsfHWwuskTkzh9H4NvaJoy0pYpvVRQUFBQsO2hHMiCtQXGpJ30HKx9pzvdqe2md9FFF7UUSYYz58qBzQ94wAOasSQ10zqtBz/4wS3S47Beh8uLBDl02CHbd73rXdtHpIMDwUAX5ZHOecQRR7QZeQdAM36svXTYt7ocsux5uyjuu+++zYCRnsn5uuUtbzlcfPHFbfb8kEMOaThIh/Qt3Q9eUiIf8pCHXDbz/7a3va3hIx2VYcRxcUC2iGuMIOU5AsD6wZe+9KUtihW6aBecgHJFsFx3hhunyCHFyhKh6g3FQO/kBhjwD3rQg1q6pIO4bURhvSUHljMCD9E559ox9BhvomfwRCuw9957N+ezLxeP8A2P7nOf+zR6Kz+RMvx1MDO+cbjQTftEJPBDNFNbOOnK7sGz3newucO13/SmNzWais7huzV15ES0UXnauOeeezb6SFVF+5e97GXNWRYplE4qwifahw4OixYVZKTiN+fNJiMOqlancsgKoxcPZ0FP7zj3vUHe00tdN7vZzRpO55xzTuMhGvROqw+6OvNO35iaNME7ciP1lJyLcJJfNEN/ZUkzJqMixBwBh7FLG0c3EUwyR/4DZBP/0AIvbnWrW7VvdHNouAO41XH3u9+90Vs92skBlYLLScdn/+HBmdYOz6A5/owBnmRav4Sj+u985zs3euOZcv1+97vf3b7xSz9QHv7Ah1z2aZpxWsiv9FkRd/igpee9JxKv33Jg4OlA9wMOOOA7UrDxn57ywRftloLseWnML37xi1vU/7a3vW2Ta/JKXsgW2fzkJz/ZUpwvvPDCFqX2XsrVPn2H7lO+FN8jjzyyyZ6ytYHuEqHWh7TrLW95y2VZCtY4art26Bf0lL6K3uqFA0dQ+9zTh9BNXX/4h3/Y7tNfrpFD9ShXHfimfvRxj6Ou/3mX/vXbffwgy7PS4uFMl0mPpm/pGBkRMiQcUo+mcP7d3/3dpms5xdL56SXRd3qJfJtonJKfgoKCgoKth0phLVhbYDgyFl/0ohc1A4KBwqhgFHMsOAJZX8dBYJQxJhhsDEIOmjVojB/pkxw0B3sz4hjXL3zhC4fXve51zYDmoDC6RPr85+hYH2itISPml3/5l5vTwNhkwIiYMGxEfxhAjFdRJcYYI5eDyOH0HMNGPYxF7zCEOYZwYljCF07jiAMjmwHH+PMspw1oI9Be9NHGGIEMuzvc4Q7t+dNPP70ZwcumHXIOpFVqEwOawWltJ8cArbVDu6RfMrYZiNrF2WHw+a0diZSARF0YoRyZRIUY0a4xnOH6vOc9rxnFAJ0Y0wxuhqh2MKSnAP/xmaMVmWDAc3AYzxwJZTNMGeZojYfaiefWHqqHU0BOrIEkO/jNQJbiOuY3Z/rss89u73Om1UFupqJnwHsmLNRLdv/8z/+80VLds/gAH0cPoLPU0USGTJ5YN6n+Jz/5yU02Z218wjlUjqgnx05aLNrgGzpw/m5xi1u0dbvKRT9GPCdZOjY++t+3y2/PilhzMERLlYd3nE10PvXUU9s1Ti2nBJ9NtHC80ZezwOnwmwPnOhmYtXEOPnL2yaO2+MZTcsXhtT5T+/Rvzod+iMa77757wwdvyNs4NZIs0wHkQuo2Wj384Q9vZeG5vkOeOaUcIHW6N+6n+Go9IefY5BL98ehHP7o51eTs4x//eMNH1M8RE9pPZtEADniJriaBEjFM36Fz4EjG6ALOun5EvrUtKc8mv/QRPLT+Fb058Zws/RIN6VG4kD2OWACfOHoveclLmvNHJ+l3+jknlbOun9JZ5Jg8OmPRsgK6Bo/pHmCijmzRx+on+8ccc0xr76xUWbKrPrTivKM1XXziiSc2WqKpSQy6mF7QJ8gDJ1JbyASen3LKKU2vFxQUFBRseygHsmBtQcTARiIxqhjxjHazzWbKRch8OEi+GWcMEZETxiKDy7PSJxmohx12WDN8Gd3SCX04CoxlUSeRFzPayjLTz5BisDHYfTOIGFiMG4Y454XBw4Bj7CcVUJ0cDY6fMjhYIo8MHgYVg5MT6D1GL0cVXtrWp3txRNFA+0XrfDPCGIeMfwakNmsvo0okhoPNYGQoMsjUyThcFtSvHejHaefMWaenTZwF0VvXOAycNvzhCDEwGa7am3S9tAMtRFDiIGiLj3cYi9qCtxwewFjGU5FTUURGpPe1m6HNGPbxG33UL/Kg7sgF548jCGfrBeGfKKrv0NU3XNxHy0SX8ItjwyhHU/wGniE/JgE4CAxn74oCwWkKGLUcKg4TvB71qEe1qPm8jUuSWpr0yQAHSZs4NRyWeZvNMPA59pxc8k0mOARk9dBDD20yjh5w9+HYcM5EejmRotGeG6f9ekfECV/hpl2iWeQCrdCTk+Fbv/Ech8VHufisnle96lXNafIcepOHKSdD/dogOoiWPmQeffRx0Vk4iw6KkJF/jqX+oK/qh/Dp14n6ff755zd89DvfJh/IhvfhTd6Uz3khp+r7/Oc//2246SfaQV61BU3pG7Irsq0v4HMcfPwi6/57hv6hBzjjaC2KGKBf8Jhsu87JNWnEiVKOPkEe9El9nKNOpkWa9Usyi1acSh9ONJ1BXvUZ/SqpqWiPBvobWor2AbpJu7yH7/hmYksd5Ai96B6OZd8efNhtt91aRoZnONDzZB0PRSh9K4csojU80BQPyD35I8ccb/0cbfAI/dCFHBYUFBQUbHuoFNaCtYVZ61kYZXG0GD/9f0ZX1gCmjKROcQ4YZAyerGlinOZds+R99Izhwrjk0IgWMMZFIcy4x/ljwGWtTyB1wy1OU45g8BzDT3mMJAYaA5ORtuzxH2lznAzlMhST2segdI+Tou2rbOzDkWAMisrF+OzX9cEdfUViGOkM2dA4xu+yG7dk7WDakwiRSQDOvOgf2nAu1MtBEwUR7fWsazakSQQutCYDSRXFJ0a1CEXw7PHr1371PISP6IroFseIMw4vxq0IXKKP2i9Cou4+etQDGREp4dxmTdiitVocAs4PmeUIBkeODWOeE8KZJbOzIMd7wC277XIAGN7e42Chh+fQk3OZ9WRx/H0CHHZGuvpNyAAywjmTRs1pABx2ZfRrcUPfyLjJCU4gh8G19KEpungfvTntaOx/ZCXv6MfaqV9xUDmTHDn34OadPoWSw4cG+nDAJE6czX59cs+zsYOrjfpBv1aWjHDOlAOfHqYc5OA2tRY6oN1ZAwhvfMl9Dh35RE/PuCcTgXNFTgCa+NBpU+UH//ShyE1w6o+QIZOca3owa3j7PtXrvTjiJubmQZ/ijaZo12/khT6cU+0yScEpDf5w5cySxaz7LSgoKCjYtlARyIIrFDBOOCRjp4rBIxJi1jvRJsBgYlQy4BL5s67LLDinb9VNGzxvVp3zId2LcyqyGWN33nvzIIaeNExGIsOcYceoZFgts9MiI1D0xTcHkvPFOGPMxWD3zWjMMRcidNmMhcGXnVc3s5mFyIW0Qo6iFD5pepw3POAc251UFM/1RGVngfZw+kVRObirONPaz1GynlUExfvaxSjn3HGo0Bm9yU3WgY4hjkg+ixxI7VG+Nbzqn9qB2Pvq5UQuM/EQvnGSrO2USit9VcSp3/BH6nLfDhFs6Yi5L5IpCi8qry/FWefUkLfInXKyydMUeI+TzKnpncEpWoh8Sae0LhM9xutge8jxJiJR8A0+6Jl2pP6sf06KbuSWbC97dqb66IVEVXt6K6ffoCjfm9n1FK/JpX4N734CBF2006RKsh9MwLivPfpnv+vqeMOnKZjC1XpU6dPkUj+ddSxPoudowMFdFtAUb+jhOIRxXrVdO5WbTBPgPzxqDWRBQUHB9oFyIAvWFrJhSHYb9O2TXQH7+7nef6cMEUfGFkfITDgnMFGPvM/Y4bgo1xojxiajkDEtlTHphKKQjOqkWoGU4xPcxr+Dv2ucPIaR1FkGvM/4gHiQCCOcOSgM6OCb8lM244mDw7m1dktbRc6kWY4jDZ738S6jkoOprdZAnXnmmS1iw7ngaNsMJBvToCGHUQSL4SitTBqrj3WSHK1swx88w4vUl2v59DxIxIqDot2cBbQRXYgRLHXTuYeiedKH8TG0yEddobuolYiq60n19cnulZwTtEWDnk+hv9Q4MsD5inGqbg6K9YGiT9bxicz1Tkfq5yT55mT3/M3aUDTNcyKseM1Z5QQwlB/2sIc1PD0HN+WkfYsg9ZJlfMNLqYpScEWalcPRywYy6EpurGEko54TZerlWfRxv/32a2tjyZc2S3nkkKIB/kszFKGOA5F+0MuByRq89Cy+4AHnRvt9elq5p0/mqAfPa5PnlBeeK1+/FFV8/vOf3yYPtEeKqQmfnj9+WyOJf3iJ/r7Jk7WccO/7cK9/xs4OGYEPOUBLacr6i8mOpEmLmmVNLdq73/dh5Y552u+onPrhLdKbsvAXzX2b9EF3EXL4o2f0BtkVmZRuHL3mg4bupy+O9eqUfhWpJh9opW/RZfqPtgdXZZJZ6fqWCuirdAS51r/HbU30MymxZIpOo3uSto6XdIJJExNC0sfR0n2bSdFZZAo9rNH0vQ5HlBQUFBRcHqFSWAvWFhgljCTpa4xGBgTnTcoWY0RaE0OMoc1wYkQxNBiRngWMHhthMHSUIa2TA8SI4RQwQBh31tFwVhzpwGhOJINxyLFM5EAKrPQ4RmYiPzHIOJRm560vYnjFcISX34xM+MIxxnmOSeD8cdykvsXhc93aK+mcDHYGEodH+7MJB6NNRAxdROQYYKIDWRN4//vf/7I0U6BN2o4uZvk5jXDznvY4wsN7ed4RErbXt1EPhxJPbIrhXTudMhRtcnPuuec2h9jaK7hkx1OOi2+4MqbhzSFiHDLaRYAYvKIZaMh4xBNRMg5JjhzhsNooJLuRBtTvPREXDgCZYBSrEx4mC6zXPPnkk5uDxPhklHKE8NUGMtJUGeWcCY4NAxk+6pUmi4bWsSYtTzk2W7HGDW+USSbQJIC+ymPEKoMjT44TTSRLZJSzKL2U84G36Kv9cCN7eO0/B4oDwFhWFh7NipLhnfWG5JJTL4WWnNi9E45Sc7Ubr9GBg84Yt0YYv23qos3aaVdQ7+o7okjaRA6tQeM0wsFaSTJt0xN9UTTdej30T/oqvnNo4ELW0Eck2eYzj3zkI1v/Ix/4SCZE9dK/RE3Rw5pF76KD8kyWqJ9TjPf6r2ft8goffVnESl/Vnj76DCcOJHqbcNE+9Dr88MNbZkEi+cqhO/TjnIWpH/drT9FJRFzEWL93X18Ij6xb1E79RvYBWcLH7F6MJjIRTMj05eoz2qXfoTt6csbIBbzx1vvoqs0mNjzHiZLqjU8cLc4zRxKOp512WpMDz+gj2giP0DzOvLrIKz2L79oND9/oQ6aVpQ7yz4Gjq4E+zGlPpNTaTnJhQx/rMNGZg9fLL12nXeQQn61HJZ9+Z002utktmO44/vjjW7+1YY968NOxN+6hpfRsOnWdzw8uKCgo2Jlhl0tqiq5gjeC884Zhzz0dYWH9zNeaUc3RYIgydhI9Ymj5ZCaf4cAI8TyRZpQxnBi3HCrGj+cZQ4wN73E+GKme5Zgk6sBgdE99HIpsIgIYMhwUZcUgZWSqN2lU/x979wFlWVGtD/z4zDlnfWb+ZsUsYnggZhRQQAURFRMKBgyYxQSIYkBUBMSAESWZUDAAYgZz1mfOOWf9r1/d943F8d7u2zM9Mz3D/tbq1d33nlNh166q/dXeVXWe85ynpZ9y5B0ERJo8I/J1KESOwc/hJgyyPiQxnlNlYPxLV3rSiQdNPjEGyYcxyDhLnXt4F7lLOF+8r0goAqzsY4+lNBmU0mSsqVMINXLjO+8zbpWJPJQz8lBv8gRlVD/vScPBHfEwaGPlRUwQCemRjx/hrAz+fs9aPDTq45kcUBKZqI//GbHKou0REM8yLOkR41mZlEOZfUYGiFv2Bqq/dKIHuQKCnP1mHMurbzfvqbN65o5ReSTcL/tW057xdEXPpRcZ0w/lzn46xFb5FgpdJRfEQv7aBDmQrjSzv1L9yJhB73N/kxPvljbRBtra8/KXJjmog3Rzf2NIrn7j/4R8Rw+in2RLltEjv+N5VF/yVdZpstSmnvOePJRRf4gXl9z6PX/ypZfKoR/MuqtQ3XIdhXz1d3lnIYPuSldd9JnxnZiBuqm/NjWWSCfjAzkJxSYHMs7Yojx0V/20Kf3oSZW6+R6BVw7f50Aeaaad5EcHs4/Y5/JQb89Jg74rM8JJ5skr7Um+qR85GK/IJGHJ8Vjnvs7UVZ9SPu9bOHPS6yGHHNIiGbyrvXJ6tOcs3njXgkvfxmQjT23meTL2mWd9rt7q2PfB6Bw5k3dkqh5kQNYLHVRVKGxIOOSQYdh992Gww6YitQsrAUUgCyuWQK7J4nFCUe3ZypH36/uSaWViIPHq5OoCRhSDhyeKFyYX2Z9VwWPJm8ITxptJFgxIXjeepBijhUJhZcH4hjzyDPJAInz9WGYRgpeWF9aCRqFQmB9FIAsrDRXCWtgoYWVeOJdVaeFoiJlV9/VNzqzuC6sUmiWMlReFocXzKBRvfZdvfUN4onBVIXhCBnkWeDmEZPJKFAqFlUkeE/rK6yecNREA/em0wqdn3fdZKBQKhQ0H5YEsbJQeSOFX2a8kjA0ZSZjq+oTuJpRL2GBCI3nVhIWt77KtBJCP8Dvy0XYJIxZmV/IpFFYm+lD3HOollHlW6HChUFgaygNZWGkoD2Rho4R9MTx6PVaCIaMMVugdlrNSyrSSkEvF/YyvGygUCisX6bc9qu8WCoXCxokikIUViQMOYJCsSQor3XBZ6eVbCSgZFQobBqqvFgprE5/+9PouQaFwZhSBLKwoOLjwYhcbhje9aX2XpFAoFAqFQmFlgG1UTv3CSkHtgSysKNDG7g7xQqFQKBQKhcIwDG6/KRJZWAkoAlkoFAqFQqFQKBQKhbkw+ybqQqFQKBQKhUKhUCgUOhSBLBQKhUKhUCgUCoXCXKhDdAorCqedNgz77be+S1EoFAqFQqGwsvY/vuENw3CBC6zvkhQKRSALKww//OEwvPOdw7DppsNw7nOv79IUCoVCoVAorF/85CfD8J3vDMPf/ra+S1IoTFAEsrAiccwxw3DFK/5z+Pvf/77qM+c9neMc5xj+8Y9//Mfz5zznOYe//d/I6vJqz9Ul1oXCysaf/vSn4a9//etw4QtfeFnTNVYYJ/75f0c6Gw/+y/L9Rgh1NPZlvBvXlSz6sfHsZz97+558fvvb3w4XuMAF2vg5hve0zZ///Ofhghe84FqTn3yM837PM3Z77je/+c3wxz/+cbj4xS8+nLtWGs+EWe29rsugTelmyuBnOdp/XUF5lF/ZlGc5y6ZP+THujcepjF35P213yCHDsPvua5x1obBsKAJZWLH40Y9+NLzuda8bvv71r7dB+5rXvOZw3/ved/jUpz41vPe9720DMOPnTne603Db2952OPHEE4eTTjppuNGNbjTc+973Hs53vvOt7yoUNkAwvkzi53IpaWGt4Ve/+tXwspe9rPXvpz71qcMmm2yybGlrvzPOOGN44xvf2MaJ+9///sMtb3nLYaWAUcpIXA7yo37HHHPMcOqpp7b/H/3oR7exss/rHe94x/DBD36wPfvQhz50uMlNbjK8853vHI444ojhAQ94wHDXu961Gat9ufSDl770pW28fdrTnjZc5zrXWSuGPZKq/MbvS1/60q38l7zkJafmxdB+z3veM7z97W9vzyK+6nO5y11utcq2MfZ1bfjud797eP/7399I9m677bbOdZ9MP/nJTw7HH3/88POf/3zYaquthu22227qQoXy0kVzujn7kY985HDVq151WAn47ne/23Tty1/+8vD//t//Gx784AcPF73oRdcozd/97nfDYYcdNnz0ox9t/Yr+64dkdr/73W84z3nOM7z2ta8dfv/737f/b3Ob2yxbfQqF5cTGuSRb2CjAQLj2ta/dBvA3v/nNzajwmQH117/+9fCa17xm+OxnPzvc9KY3Hc573vO23wbnG9zgBm0QLhSWCgb2K1/5ymbMFNY+gfzwhz/cfr797W8va9rIBFKBnB555JHt90oB0oIwMRqXA8Y6BOEzn/lMMzx/8IMfnOl7xPAWt7jF8I1vfKONmf/7v//bjPaPfexjw4c+9KHh05/+dPufIWucfdOb3rSqLyCdFuW8s7aAvDHOjfN+eBdn4Stf+cqw1157Dd/5zneGy172sq2+p5xyymrlq34HH3xwI1obE7Q3fdBm2vub3/zmOi+D/ne9612vEX76hCxNixxKeS9/+cu3ceAtb3nL8LOf/WxYKbjiFa84XP3qV291OOGEExohX1Mghqeddlpb8Pna177WdP9zn/tcG6f8z8ah369//euHr371q8tSj0JhbaAIZGHFwsRy61vfug2wJnvGg3AOK4A8jmCgNeH43EqnZ5HOjTVcbRxeMwu+m3XFq8/Pate/zlPnv/zlL20l/OUvf3mb5AtrFwje05/+9OElL3nJcLOb3WzZDVjEKlEIK2U80C8//vGPDy94wQuGH9rwvQx9Xd3U0yLatHBFsvCdn/xvbH3Qgx40vOpVr2peDv8zaF/4wheuMuDPf/7zD894xjOat2TzzTdfo3ov1BeVR4gsIqnsC3kSLTT88pe/bHVBip/85CcvqjvT+r6+ftxxxw2HHHLI8Ic//GFYSVisvZei+wkLXdeQJy+2EE1tOit8Nc8qa7zAfXnXdK5ak/fTT1KH5YKw6yc84Qlt8eJ2t7tdk5NIKvnJx//6XspQKKxUVAhrYUXjIhe5SAtRPf3009tKuIGXsSG0xHff//7322qekC2hVlY9DcBW29/2trc14nmJS1xiuNe97tVW9maFRVm1P/bYY5tXxOAt3OZCF7pQ+8wKrpCaS13qUi0UzATwwAc+cLjGNa7R3v3Wt77VjBEr5ybKHXfcsX3HEHvXu941/MTu92EYbnWrWw03v/nNW3pCe+z/uvOd79zeEZalrFtvvXVb8ZQvY0nYmPIISRIGRBYMUN4G3larune7291aSJrJh1FqFVdZeDouc5nLNHkh3FZ53/e+9zU5eZZR6PNpYUVgtVVYGS8vuVnV3myzzVqdeC18Jlz4ute9biuf9rjHPe7R6q1+6kNWPmMg8G5oN6GKPlfnlJuHKCvn//3f/93yUF7tZiUdseMh0bbbbLPNcOUrX7mtaqsP+ZmITz755Pas78lEesKgeXt+8YtftDKQr7YZGwTakQHNYNae8iO3O9zhDu1d/2tHdZYXo3Wa3KTDa/OBD3yglZV8eHm0tfBr71kIOfroo1t9ttxyy7b6rP6PeMQj2oo3mX/hC19o6dHnO97xju19q+D0XfnhIx/5SPubLvLO04XPf/7zTSb+Vv673/3uTZ7aUp76BWOFgbntttsOF7vYxZqsv/jFL7a6/c///E9rg1l74niqlE3bqas+oo30De3Bc8sQ3GKLLVo9lPsxj3lM8xb1kI5+y3uknejEl770pZau/0UZ8Egor/orE++UZ7SJ9lVm+dHzG97whq3u9J++yX9a+ZEPfZUclVN/0t+krV2UU1noM3nstNNO7TdPl99kLS9p8RZICxlRf3pn/DDuICX61/e+973m6dP3jQsWuZ70pCe18YzshNje5S53aX1nDP2HfOiHvYrkrT1vfOMbN/3Vz3gxjSfK5PkeysKr4X1RGVkUUQ/v0AfjhAU55UXGyFA/utKVrtT6qL/V1cJcntMX1F159F/9QTvL68c//nF7lu5pG31YiCy91o/oBJloB7pHvp5ZDMqg/9EP9aAX+hUSTJeVScgk3TFGGdf0AXMGffUOmdHVa13rWq2PPvOZz2xjrLHK2ECPjK3+FvLsb+Ocems7US/GZXXVT4Q1ksPDH/7wthhy1FFHtXlE3zF/kIN3yIWckV5zkDFoWhikPiHsWr8wtiszPfYjXTpKbtrf98YYi6UPechDmizl8YY3vKGlQR88sxDIxxiiTZTZYi19M4bSSfnQZV4w/USfUwc6QK6ZFzJG+Y5spKed6JC27glc3jV/GbfNhwnR7J+jy9qU7pr/tIkyXe1qV5tLV/QX7UpuZGYe1CbkT++NHcZMuqCssMsuuwybbrpp02v58gAawz23EAn1HVmbd8lOX9Zm2sFcYrw1ZtFDY746GCu1t/EbQe3TP6st7hY2XKyMZdlCYQZMULe//e3bBM3AQ34YDyY8kzqjwISFKCAnDBqDtn0xjKGdd965TRj77bdfm1TGMFgzbBBTE/Q973nPNtA/9rGPbROJSfaggw5qK/MmHRPkgQceOBxwwAFtAjDRMJBMlPYRvfrVrx6e+9znNs/o4x//+PY/g8FExNhhVCG0n/jEJ4YXvehFzZA0oTBUlJERxLhgzMjnec97XjNMGKomcwaduqk/Y8hkrI5kwsDaY489hsMPP7yRTRPv4x73uDaRIhCMC99f//rXbxOXvUYxEMdQ77e+9a2tDsi5n0c96lHN4DAJkxXPESDXZGuSZ8TYJ8K4UD7l3nfffVelybvnf5Ot7xhkDAllNmEzBhnj0mPMa0NGO+KgniZlcmS4XeUqV2l1kx5yqr0YQELc5M8AU39lRSoYfP7/6U9/+h91pmcMBT90SpuoFyP9iU98YvPC3Oc+92lllX9I2jR9VS4695znPKe1j3SUi6Gn3RnT2l25eaKERjEypUmmysiY007qQmb0R5npnXfonvaVxj777NPIgrTJSJnJXrraQr+gQ/vvv38zwpBWBgwyY4/xU57ylGZwIuWMYQbsNA+I/Bnt8qBP8mDkIb4IGZlZYFEme+fUh4dHHxmDHMkaefUsw4tOMub0A7pAlspC73jKtItwQ31V+9JDJPP5z39+0yNy1o/0D/1smtdJ2lb+9Q/GrLIjnQiPviotfVDbKru8GeogHBD5o1eIh71l9HD77bdv+qddkEI6S9Z0hpzU3x5P7cHIZlgrmzFNv5q1D1LdHvawh7V3GL/kycglKwar/VPKyzgmG+StN8B9Z0FE3ehKPJ7y1rcsWignMqXv0kvwG/lBpMnRM0iEPkn/5IukMbbJWtsYj9SV/r74xS9uOmc88jw5Kq/nlIdxbZzW7tqS7s4DdVA2JJGRbnHN38Y17WgLAx22YEG/LQIY4xDUHXbYoZF1+q3tjDHGYXpoAYHu0EfPqINxXvvoH4ceemiTHTkjPuRh7Ja+5+m8vk6vjDP0wzhLN4zhxig6oq2Nn7PCIJF6OqUvIdbGdO1tYUt7+Tx9S98155CnhSXtLS96Rk7afBzKPAadUW99ULrkCMYq5bc4Qb70zuKCMUufkb9+YYEEySRve5nVW13pBHnRc2Xux0ll1eZIp7yNdfr4GMZN4xqCacwi9yzELgZzp/JoEws3yvnsZz+7yYbsyUfd9A+LO8Y6Oup5cjW27L777m0ssjhKjv1hfrM8vsjhK17xiiYDhF6fYjewT+itcsmbjupzvjMnFQobKopAFlY8TPAGcgYgwmMiYyxY1TNQM9AYef5mWGSFk+FgYLfCx/Adr4YGnjVZ5FAGz5vUGE2MDJMoY4IHKYfz+M5kigghNwwqq8E8BFY1rV6bSEyiysEDwChhRJmwlDXhWgxE+fRhM4wVf/sciWOM7rnnnqvCgfz2XfZLMFYYHIxfn1/hCldo5fCcvBgUiJRJjvGkjibLHEY0BlLNeFIGMmUYIyYmQ21B9mSpLazwkhtPhImREc/gUU7veUd66sRYRpCRMpO2uiHYwtGkb/ImV+8zEBEfpJGxqk4MD88gaCZjZSMvK+DIUlb9GS7e0648KZ6Rr3bVXmOopzwZkcqtLcmPwYfo0kFGgPylzwCZtlenlxeZMH4YgYwYhms8ptqA/JBEBiu9QKAQJ22l7n54yRipSAP5SZMcGJiInLrRfYsbykTHkUF1YbAypJWfoWKRhbErffKXP6MxZaYndElfySmOPegJnaBnDEeeTfLWJ9VLumRMvy36MKbUixd2mpzIyOp8/qeT3qezSAZjWhvIFzl1uIZ+RO7qqf09T0YIN5nof+qpzmOCjzBZFJEvPSQ7ukSHtYd+6Dt9fNddd23P0EX6qQ31Ne2A8NArC01pE/UgA89LF9QbYdH2PrewJX1yU1+/PZPQ0jGMFzmFkV4qIx1SD2MeAsMIt7CBuNCLvq76iDL7nlz6g0mMYTmoxo+2N07523PkbBwh34TW6TeIss+0Oz3zncUPizLy0hb6CRJhgYHhTWbqoh3JSp08Q2bqMc+et4S50gNpkDEdlG4iTeQvTf1T+2RRS38ja/UhF0TbeC4N3+ln6uN/OpXx1f8JgQ5JkIbv0m+RRgtGFlL0IfmTJVkhYvRLf6EbZEof6MA0mN9yYqm+oD0tyFmQkHfaS5+wiGURhCzNRcin8V356C99WMxbl36vPeVpYUm60kf4jUf6nLHQXIEUygcJVB5jIdmRkc+MQcYTcqdzIhy0kbTlhWAaP4w7mZPMHRZWUp78Nl4Zw+k4IJrzHqxj/OlPDvae8uqvCCQZ6XPqTVbGGf9b9FBvcyKbwBhkzje3kPNCoPfkR2bGH+OTOvocAaVzibDR/jl1ukJUCxsyKoS1sKIREmUwtxKLlJloeQj9TugqI5u3hsGCTJkoEBnGDePBqt+sUE2eTRO1id4kbNA3Afcn8zEm/d8fge8dBgJS5jvGgxV3ZebpYNzEIDGBecZkb9KcZ+KIYcfYYbD4X515QhijVsZN6D5XbwYUI1C+yoa0xRAyeSMH5EKGJjAhSYjRNG8Tg5CxyvC1Qi0NhjRCbAJkkJIVso6smKQZIwwn8iB7hp3yMHwh5N3ELR1ENHIwUUtLXuobAxMJ9F72iPgtfXIkCyBfclbXTPTe8Yz6xrjyPaNmoQOWUsb8Tvhg8mcs+U1XGL4IxCzIj2Gi3Rl/2oiRwrjJ3iRGIk8PwwPp8T3D3DvK6X0GCWOq18XszVFvxh2dp4t0PDqnzNqWfguVFspr1duiBy9AjB1GE53Qfjwl+ss0eI4RJm3GmTLIg4wZj2kP5dN+DObo7TyIzLOnr18s6ds3e8TyfIx73+sD3tHm/f4vv7N/TvmQK98jOb1BT37SynH92kEbqKP0c7UFA1d96ScZ0H8/fWi0OkhHmv2+tnlD1JBwiwLKra/nIBtjj7/ppfEvutkbucYH/R+50Vbj7xeS/7Ty+UyaDGH9nVx8RjbGFUZyZJ2xLuOt8vqbV0u4ovGariLi+gW5LLRHbqEySoN8jHn6OJkLt9QWGa/0KWSPrve6s1B9F5JD32+RNP0qUQ0IdrYi6APIBy/X3nvv3b6z5xdBmwafG9uRXO1NtxIFE2QMIq/oqO+1jXHAeESffT5rvhvXx08/hvZ9zt9Jx3NkbSwyn2pjedMBn+kTyu5/Y376axYpjJf0Vv8x1pkHyEda4zLxHOrLogIQTERWpM88ezr1bx5SZM18pFz6r7L2bZq+7nP1zHU46dv0XB3mkSPQObpnjDWeqh99tyBn4Y4uap+Vtue2UFhdFIEsrHgYwHn4TEoMQ4ZzPHg8D7xECBNjKRNeVtKFgPUb1KfBROh7JEnoSoxXn/WG+7Swx5BJq7MmIBNSDKtZdYkRnDT637OQsiMKwlrlxYvEGMtJbYwZBopwJESMsceT4keeWQlGJsgt/0/LO/d2eY8XzZ6fGHnKIgQN0eF9khdvUyZbdVMWXghtJf14K3s59u3BS2NFm+cWgREqKO9pZC/G0az2jFEUg5nekEEMgnnQG8PjtCHynCcNoCO98Tft2RCX3rCNcT1rT6Lv4wEbh0ImHd8LBRNayAMgDFSYtdAz8pCvxQRt0LfxGMqWPCLjGHQ5AGVslK/rw2tCDtLnx+WPp9OCRb8X1hgSLEbweuNcGvQ8RjvDc7n2MCH+2kmb8NQLUWaA93121mFZeWZ1DhGZNRZl4WJ8yElIcv/ZOD36z1POuLYYyNAWmbCmyBgFdFgkQ/SQvISbMuSFsvJKLuVE0l6Gs74PSVIOfVtUAOLocwtGFlDoBs83ImHsNjZOu7IGwUV0LU4JTTX3qMM8iB6vyQE88+hJdACiByGgmXf7MoyfyXPmFPtUM88ibX3+PMK8uzya2s3CrPncokpO4LXgNU1XkUCRMMY7Yxoyn5OF56lz37f6/xeDMZAO2nJgscCCSa4dQyiVBykvAlnYWFAhrIUVj9wBaTI2cZgUTGQmImEjJhKEBlHwOSKCwAlDMdlYnRbaaYKeBulaCbdaycPC2LH6ayJfCAwDJAqsklqdtdJpNZ73haGaw2x4b6xMC71NWKwJymqsn0wq/er4eLXc3yZUITYMV6vg6ga+l4Z6MlCEN1m1dVAEY4WM/M7BHH5bNRfiGM9RD3K2Is5jYy+WZ9TL88ohPYeGkJWyWC1WJ6GmjATyI2/lQwpzUm7KOjZsyYRRSRYWB6zWSk9b+83TIC9eFW2rveMF6dH/r121AUMcydYOPB+LGZHSIB/Guna0aix/9eb18b16jg+GGUN5tQkdoIcMTOllcWFs7GkfhhNvMT2iM34znCyOpG5kqi4WLZSHHqgrWfk8OqfM+ogwMiv+2sn+JfuZ4jmgK9qYoarOjFYGz7Q9P0gZA12ZhQkmD7rgcKB47OchLfN4fab9Pe095SEn5dFnjRfK0xv/nlF/3m391NigjvSC93Shco3LQb+j5xZFLNRoZ54mchm/O6uudF2oG8/R+BnlRfaFPtIJupZQc9/RCeOP/uDHd8oQjxV9sSBGF6Sv/+YQnYVOaAbP0tdxXUQr6Jd0jg5KT57CGJGmWcQl4YsIAY8+75K+oP69fOf1CPbjh7Eip3QjDcpurBHNIHza2CXSQbsrc592iIF36Q39yV2YkVfuiRx7r/rFEx40kRbGR+NeomCEy4uMQSqURRi0/jJrD6Q5xPPGQmNBykuui3lNtbc5TNuM23tc9rE8F5J5X9fonX6UsUke/qYbxjbztM8TaUH/opPmZ21BX81B5ERmFm/G+dvTbOxGHJFu3xunhPg7p8B+xsx9YxgXXcWkb+unudN02vw6RkKaLaoZ/8kxY/848mEa1M9it+d4QNkH+ob50XcLyXtW+y7XglShsNwoD2RhgwBDwaolowlBzORvD5EVbauZVvd8jog4NIHR7OASA7fB3F6OMTzP+HYIg3AZHkiDvmcdb8/YZCCbSBh7yId8TEgIGM8WgmISzAXdJi0EjtfAKjsChXiZYO3lkB5j3/4O4YSIlomU540hY/KVD0PC5MwYyd4bhqQVdSRS2XMSJuOVwWjlMyGNDHrha8KE1MX+GwRBWBwDmuHj82mXaJu8hQnneXU0EVpRzaqxfZAIvMODTLjxbjKUhOzYW0L2jHlGhzohIerEaO9PMlUG6fFmCo/NKrf8hEUJiWUMWql2eJJQS3LSLurombRTTufVBvaNCgfjbdM2SLFDeaaBUavdtaU9p4wxe2SEQ5G3EGpyzsER007O7EFHhPEyZhmWwqIZkwyohL8ychwOwcAgX6GlvBC8hOTE8OVpZiBKA9Tbarb/1V0dGXD0jxwsItAT+kr/tT/PjzYUssmYQwTJmw54jsGVxYccFjSGNsoBGWShLZVFHtJKKKHPEXWG1KxLtxlF8mVE0jXpJPRcnYTkMVa1pzS0J4OOwep/eZFvwrf1MzLVp+gjvSFXMjR2SJ/89UkGKQ+PZ9XXiZtIc/qBtPXtnI7oXW2WkD3kyhhDN+grPWd0qi8jPqGEyuxZ/ysDoql+jGyk37vGkmn6mD256qqcIQGep5+8G/IV8mhRgP6Qvd9Irf3AxjKHvTgETJ4McEQnp7Iqj3GFnMhZ35CnUFDtL0pBeZVd3+X9oYsOKOFN0xb0zphmDNI+fpO5cUwbkoc0EAl1tkBHv40L8cboU/pSxhDpSHesL571nXZRV2kpo3FNXRL2aRzV/siuPq0Par9cjcATmj3GxmbkTvmEt5snhDfz/OaQHWkYr7SFdHyu3xq3tTPZkrVDlBxI5X2f0UNtYSxDmtXPmJTDisaQLnmpA8+p9iIXY7X+ZI7QvnRTH/Tje8+RiTIgXNqHfOkdmdtDqW9kK0EvU/JMn6P3nqcL5pLMQ77Xrr4nI+3twB5jomeNUTlsTRm0uzlD+eiatLIP0Jyi/OZa4xF9tHdZXtnnKh//axseXf1bBIXFV7L1vj2nxkptPAY5axcy07bmc2UwnltkgERSJBQ8e12VUx0tqFkAcLCOz7QNQkyW/V7jaXk78Eo7slOMGdqenZJ9j+RCppExOehLkbFxkT7K0/e+I5dCYaXhbP+q5Y3CCsJRRw3DDjsw4oZhPDcYeE3aCFrvzTJJG9T7/WgmTITD84wRBvOswwvAartJw4QZLycPTfbqMAIYtCZlE7b8TZgxwBFEz/kMUWGsmCQYA97JyiYi4F1GlsnMpMpwM9EwkHIwDiJgEjGpmmQZBNKQFgPIs4gMwxK5Mll5Tugn4y7Gr3qZ+BBLJJvRRV7SNSkjTLNCdEzcJkwGTLw6VsZD7hjw8iZfRmbag+zJw8SLOMY7xUg3Wau/spKVz+MB1b5WmBnHIR7SYzCQY07QY0jIj5HqB4n2vEk7l6STI5nkwvS0KwORITWtzvJSBoZDHzZtAicD5fC5ttCOs+RGPxBpZBcxg3hTGVvkwrBK+Cm98Ln0eGW0ESMsYdjqQWYOFELOHVYUco8AqRM55jqa7MulA3SEHvOu0wXypw8Mmux/JVvloV/qLM1Z4blkJA/tnjzoEdKoLehtQrgtzORwqDHkizCqp+e1nbZFuNKe+nM8Q4wtafmfzuvL2oAxi4g5GZSslEl59EvyYZzRAekpj3bQz7UN41t99TcyQA5y4AoZ5VRTadFRY0k8TvTa83QFAeMFo5d0hfyVMSebMgKzHzrXBHhPWxiXpDct1Je+GSO8a0xRN3qI/NEJ/UFf19/043jLyI1s1Junhxz0tehXDpvRJ9ST0UtPPI+cyk+51IH8pEl+dIb8yEqaPldvaUvHAoQy0Wl1Ui465x06Tjb6ImOYHORPxsYH7+WUWPLv90dH74x56m9czCFiZOE5+m180X7agTy0F/l4V/3I23iGUMg/fR2ZoAfSo3/mDelLI3dO5rAkRJJcvKPe+q1nE2lBXmSF2Oob+gndyViur866TsqcYEsAfVJ/7yekX1/VX8hZX6d/2aOeA220qfamb9pFuXJomr4RAt33QW2QMVQf86w+TBf87ydjqj6n/ORpjEo4dcYoso+O6gNkY5xWL+lkPy4ZkovyaSt1Q8jNo+A9+XlP/eiN9MlbmwoH1i+Q1Gnji7qoA11WZm1PdnTN2EAP9C3565/K3Pd1+RiX6EZknQUldoZ3Zo39ZKP9zNPSkYexJvO676WtvjlMLwtiGdfUX5tkcS1h0IccMgy77z4MP/+5cwSmZl8orFMUgSxsMAQyIR69sdWHEo0H9YT+ZK/fYkiokvSXundrVl5JM6frjcvdHyAxbxnHcsghAIwlBNI+yITLmKiE+zjGPCenJtRssUu7k1+eH5OK5ZA9I1PZGGBCk/zmpRvv+ZOPCXXaRenL3a69fJaav+csJPBQMWp5Yh2kEBktRQ+zxyqhmLzVvGX2o/IS5eCYabL3fvar9UP8ON3ktxSdmJbHukYWKkQOCP3jkRUNAIvtTV2Tfj7GUseY/j1YTJdm9fV+n9ZCY8hi369uuabp0WKInuW9hAPOe4DOPGmn7OO9ePLI/32Zp9U3h9bks6XUL3o1fmfaHDANkUnyHpd3MSy1vVcXC+nANDlMkwfMkkf0ZKwjyKTwVoszIgpmHQzV61q/F3heWY71aVodFnq3nxf7cqwJikAWVhoqhLWwwWDaILzQwLxUA3GekwpnYVZes9JMuZdqwE6rb9LnxbHKa/U3BxZY2RTOZeU47y/FYFvo+eWQffaB8lha/RZ6N0te856Gt6btOqsd5z3VMJesWz3m7eIliYd2dctrJd6qupVsBJUnhUdoWtuMZd+30SzZLtWIXw7ytabgCWVQki2vBu/EPJfSr0k/Xy45zPPOQn193vyXWr55y7U68hvr2XLqzzQdHn827ZlpZVhdQruQXOaV17g8K7VfLlTXefRjsXqlDuNFV947fdx8tlAa09p+KVidMbF/t89vfY+ThcLaQnkgCxuMB7KwMJAWYVkJLxN+I2xJCJQwrPXhKVoMOcFW+BLDQIjQcngk1hfIXViYMDKwUi6UabH9kotBiB/SmMOWyEro2OqS6g0d5Jx9kQkpFGqWu/IKhcLG1+ctNs5zAvbGiPJAFlYaygNZKGwksNKJgPVemLVpTGftaU3yYAjYUzLrYIkNDWRhD+Fil3gvFRYA7BVbV+0yTx7rm6jR9YUOtNiQsS7asLD+UO27dJDV+KqiQqGw/lAEsrBBwsEENqc7VMAm9P5Ot8Ug7I2XyH47hrmDYWx65wnj0eHZWVOP0TRkH50QRwcECHF0gMBye5HWhVHC22mPn8MFHFKQe/A2do9XLiN30AGiPivcdbkxb5p02yFJ2sMhIEK+HGRCzx2swQBb0/IJoRWaq58gyusjRGt16qDdjBdCgXl0yUT/Gx8usr6hDR1U41AU4c9r29uiL9tPmmsj+r1boH2Nhzy9DggR0bA+ZZYDjpRVWabJx3fqo7zG1xx2sxLgsCP9UtmUKwer0Unt0O+j420j+2n3mi4V2Z9tzM6hVGTk4B9lmHVi8upAPYSYi76YdpfvSgU7QFQDGD/JvVAoTMfKGFELhSVCKItTKR0q8vznP3/qXYazkNNDves6BhMqo9JR5I4hd4rc6kzM9h7mvqlZQCBdjPygBz1o2Gefff7jrrUNBerrPkEnprrSYV1EwssD8XbaYg5hWJdw6qYj8umNi6mn3ZW4NhDi45TLhfTcc2RDp+myBRG6/IAHPGCVnq9O3oxMp7QKnyV3p566OsGVARua/uqf9tu6I3WvvfZqe4RXEsgbedSvnDCLZKxtaFdjkSs6XIXh7lg67noQ+q6t3U/oDlp7lHNa5vqChSuycUVD7pIcQ9/UF5TdgVPrqq8uBuVwBYXTUv124JlTQQGBdGWLucHYan5TR3+7nB4hm3XX5jzQj4855pjWhk4oBQupZOm01+Ucw11DpG4J5V/pyP7K1772tW1hWtlz5UehUJiOIpCFDRL2lvGoMIqtGi5lYs09jIxfRjajmCcld7zlwu554XmGlysbZhk0YAXZaiyPZ+4CXB9EaDnAk0X+5Gbf2boAA+sVr3jFqqsx1jWspvN4MKAZfetq+zjS6FJ5dzkupF/gQBneBGXl4XZdAdK9usandxi17sVzpQEdjteE925DCylzjQCPOZlox8XkuT7AE0S2rvfJ/XRrE7ka4lnPela7DsUJzoi1+0B9hkwiPsYtOrDYItnaBtnwDBn3Z/VBnjv1UO6VtMiBpLgD0VUiiJs7ZuPN5RV0rYUoB/3Loo9Ttcnfooe7jf1e3TnD3OM6GOMJMgnGb4sH8l1OuGpo77333mC2JtAT94WyB9zvut9++7VDygqFwmxUCGthRYOBwGhB7Azy7uMyETJcx/uw/I+YWfVkGDJ03f2XEFETrxVXK+6z9nDlM8Q0d3f5MbkzRFKGhLYwvqwUu0aAYcrwMikrn3IItRJi5cLj3FmV8KSUyTsxZIVFMhqRpdx5x5hAevtrF9STFzBHlLtjyv/SSfk8Q26MKWXymfC43GfHUBU6xRBLmJrQR6Ga6qkc/pcGYkKeIRXKd7/73a/lx7AkG/lJF6EkOwSZAeF/hIs3Jfn43juRS76PrOWR+zblrc15QA4++OBmfOXSa/mTExnkTq1xOK02UgfpMdbIkyzoQWShTLk0XjrKJe/cpSk/bZqj62eB3BAT+hMyR/69HktXeyAzykbe4H/PKoM6KAM58Bq4pJz8fafeWTRRD+0kXXL2Hk+StPzE66jMaSPvMMIZkmSe+8eEdJKH8pGTdnEnHo8U+ZOB38qFUKYvgrSUQR4+zz5c6eduQOmRjToLf83l2OQibAyJ0X7aP0Y1eZIP0qJ8ZKpPKbP/6b08GMTyIAN5+F9Ye/opPckF3cjjuL/nMCc/0ojh6//knYu+lbEPiZQmOUsrYYF0Xxurh7+9p53IHchZ3eiFZ8hRm5KB5xjfyt6XP5eek3/uJyWLnBjp2SxsKBv5GhN8rk1mhXDKEzkhr5yg6XfKssUWW6y6czBXiNAj9ZRuf+Km/OWpnWZdfk5v1ZVe9DoE8tCe8iXLPu3c16d+Gf/H9++RKZ2gC8rvt3onXbrVk/L0CYstvst4qm7aVNsYGyITeq49/U9PZoXPek5ZPWcO8hy9t33BYg45+cyc0V+5kQNilDs/nkHyHvKQhwz7779/8wy7rzDyVh66SeZ9efo6qBs5kFsv0+ytDinNfZueo1/SIM8+Xf1Hn12ojXPXaN7LeJ5Tk5Vp2pUa2o98QB/L3J1y0RX6lXburzvRD8lDu6SN9V1yVx9yUqbx/CBtbeVuS+/RCYuj8oquKrd0+1Bi+qgc2jp3pfrOZ8oC5EO2ZKYuGeMTDp67IHPIXeZ2fUMfX44tB4XC2kIRyMKKhcFVqKlVcIa4gZ83xEB+r3vd60x7Bw2yTqk88MAD24Rr0H/ve9/bwoFcvm5gRkBMvrxIjLCFPI1IyROe8IQ2Ae+0004t7EfonusCnvKUp6wy7ExKxx13XDMIGXjuqMqKsee33HLL4X3ve1+b9BiFuU4jMJF49pBDDhnucpe7tLvsTLTCA5WZEcWAFlKDOGSSYay7TN4ktM022wx77rlnuw9P2NYznvGMFuZFFlZRQzbVx8Tt3kV5Cu9y5+K+++7bwiPlzUg5+uijWygPGfFkqR/SxvvHEMlVFe6XNHlqH0aS1XKhwNJkrNuLxwOGbJg0lctnt73tbVu9lRX59LmJVXra28r8rW51q+E5z3lOa1N3/W299datHAx6zyJVPhPiJW36IeRIHe54xzuukpOJXPiX+jMKkH2GCRmYnBEz77mrkbEjtIyekZ3LxXkMeGW0u8vV1W+aByDhta9//evbZekuvtYeO++8c9M/IaCMP3vaPEev1JORgBR7lqy0t/IwXsiVZ4AeMeD1B22z/fbbtxBaF2prX4aLfsGrLt83v/nNTcelGzCKeDHtd6I7Qji33XbbJj+ydKfaQx/60CYr/QZpkL9wa3eJMvTlrd8pj/stb3/727e2Uy7vWZxxUbfwNel5VggtAqqvur8SIVUXeWsD7UK3Q6iFQyPA8YiQtbzURd0s1CjHkUce2XSHV1bIJ10BOvGBD3yg1ZEHjc4zzrQL/c2iEnlknzMjk24J01RveqmsyqiuogsQSuTP/9pQviGZ0qK7QtjVm4zVO/ex6ss8P/qE/MlZGymT9tdv9O0XvehFbXygH7xUdJq3XTnpo3Ywtsn70Y9+dKuHMFz5uUPVdT36Ml0zJuy4446tXVxqT2dmEUhyRyKyYDL26tFJOqZtQB/RT7W3sZGO6zvkRgbGQv3UdgCy6he+jBfHH398qyO5GduE6yJE3qX7xhh1Ihvjt/J5VvvQI+2ljvQBkIaTTjqp5WncQyx47zIGGKsQMG1iPKMTxja6KHTTYoZL48mL/hrT3blK/sphL5x7XX2uPxhT6By50rH+hGt9wTygT5mnjB9IiTEACdE3pGNOUKetttpqJgnr5zZl0Yb6EtnTI/uQ/SAgyA+PtbZAnJWbLBFjY72xgU6kHcD8p02NI/SH19Df+o++TT7GO/oqb+VIG5lHtLc6Xuc612lpq2e2c5gHlc28ZNwVMqsd6JKxlK5qg378pDfyz8IXwvjABz6wvWO801b0VPnMe+rqx/ivHuqsj5EznZLm4Ycf3vRaXsZI8jBW+D9thvC5Q9n76mUsIC/9W5ix+VMevt9uu+3a2KTu7AljkJBX3z/vec9r/cSYRXa+N0dbmPE32RkT9FtlocfyVlcyzvxD5nTI98ZCbV0ksrASUSGshRULAzejG7lBChCzeBcQsx65aN3kYlBneCCRjC0TtkkUkTFBMMYYhwuF9DHUkCYDOUJnEkMs7n73u6/yxAHDD/GD61//+m3CNBHYO2RiQUwYtQwPhmhCh+LZzMEJjC2Tnjw8x9iXl7RNKibC7OPxHrJhIkN01ZMRwfDyjjQZpfLnJWTIM2gYl8qm/N4jX8YTsqGeWVmXtjozAEx2jKr+0AwTMsOY/BiRDDgeJ5OzyU85kQMGDUPJZMoYlC+DysqtfauMCOTgoIMOasYHY0S5GCDaEGmRvvqZmMkCsYqcfceoVH5p8aKMQ/6U57DDDmvGmmeUGwFhJEgTcfEMwsEopxcMHOFM5BjCwlBhhGblfxoYxgw8JCD6xZBi+Fs8YNwqI6Nbe9BHz6grQxqxQuDlxdhQBu2FkJM/Q4SOeJ5hzmBmmJEzw0p9GNXkJa1+35d2la88GZiMnRyeQY7SQlS8zwPghyFHdxnIZEw+SLVySN878qCrSJIyCP+yuMLwQaT1IXIkU31Ff6YnDCpy0f6MavlapNHPew+Jsqor+fC+al/tz+jyv/yRaP2KEa0+dM975MdolTeiq9/ol4zdPvRW/S0KyZehrK8jz/oA45g8EQY6bBFB+XuDLgcqqQ/jHvlEVOgcY1W5tKsxBYmVljZlINJ3+Wq7EJX0P30le+aMWWRkP6sxkF6ShfbQVgxqeqE/S5PM1cNnFmMWupdPf17o+/5OvHhb6Pkd7nCHRnC1g/EDqUeU6a8yqXvGO9DvkWb9gJ7QR38bHzyv3ZD0kFJ9x8KBurzkJS9p8kTEED3jnXKQob6kDyNB5GrxS9uHDCOr+h1j3jwgPJG8jKlkaMykL8YJZTRWK5eFrrSTz9VVenSErqvfeL+8elhMoIfKapEO4TIPGUvvfOc7t7amg/Kd95CWjLlIn76XBUL6aIxXJrIyHhrL9Ef9gpzplbHJeD9OU/3NL+RBBsbck08+ufUv9VBPOqsPyNu47R156g9kpV/3i7n+prfaFQn0o78bTxBmejzek2rMNybRNfO8RUN6jnBpP3pnYUu/1TeNE+azLGbQAf2OzJE7+qKfSpdNQC+lry9aBOpB97WLcc1YahES6aV/5i9juX7ne4t++jlZnXjiiU3vza8WnjKm+M54Ka/MzdJTTuTWWEY/taf+TubkQU+MhcZc7aYdtOlSzncoFNYlygNZWLEwcJr0EnpjYPaTvVjjVTkTiAnepGxiz16PnExpkkToGHyeYxjPgknQBG8SMQGZDJSF0dTn25fDb2VFAk0AiBWjh3HjO//3oUK+YwgibkcccUQjR+pmNdyEgjgro3eQQfVImAzwdoQ0m9RMViZoBqryMn7JwgTmt3Qjz94r0Je/rxcDlDHnJ6E2/XNJZywDBosVXoRQPRIynOcZudJmSPjfxOqZsYck/4/z8DmD1o/3GFKMWIYlA7SvB8Pe5G9CJyOr3ORJ9n04qjL5SagfWZNVVvFN9n7PCgVkYDLS6BySScd4LrUpEsNjwjhhRGlTaWkPxkTCApUR8aC7Cdki914OeTa6pn4MHkYOQwhZmtY3EsbmN9KMZCCp6QN9+41l3ec9foZxjQT6m0GpvjxAvDT0kkylQf8QI88kLDqhrYw8BI+MGGO9Z2Jamcb/5zN1YTQbK+SnLzF66Zd25zEiA+Xr+76xAYlkCPMMIMb0g4xiFNJl44GFKXWWxriMwFBFGrQH41L/JhMy0jbA+DcOhYDxnilPPH19HekVI5leGUcYytmrbWxDchAmxEh70ltl5rmnR9JDXJbDg5G7ZcmR3iH2iAEDnwyVz5hFT/RD5VTuLOoYnxjkFgrooTqTj4ULfYHcEV56HOKLPPMG0SXEgSyVQ/4hh76ThrajU0g16DvqrZzIu/+NjRaElMtijPeMrQiT8VnbGucY76JCjH3aSoijduKZkw85W1jro1iUh9yNM/SQ3mtTi508zrz+Iep+L0TaZ0F99Bk6Ie+cQmuhxsKKhTHjhvr5XxmMi7yLytKHb2vL/sRxaau7/y2C6Q/GLKSGXOWjbshSQmstYCFWIZDSMMYZYzJeZKxGRI170u4XJHN3Lg+jspJLFq+Q2OwJ1QfpuzLzTIomQRa1GeJOh7SxcUYd1E99eBLJh0z0hYT/j0OHycqPuuhbZGkOJTd9StuL2OCt5HnOgt00myB1Dvq7K6WlHZBxnnJztny1qT6gj5g3kXdyUu4N6STbwlkHRSALKxaMTSvHVlNN8iZGkwUDxkQ0DrUySTDShH9Z8ev3NzEQvMuYmedYfO+agK3wGtClyeM3z1HnBntGgxXu17zmNTMNBZMFgsI4YWAgAsqYPR7KyiCVjvqOvWsmN7JwyqBnGC0mOxOkCdAEmonMD6N+KYeGmEiVndG9FEwjpNPAgFQuxslSDniJZ4GhyHPL88GLyZBEJhmeyVvZGaBCoBgps8KWx2X1f/ZpksNiE7hJPgcykX32jqkXD1326IX8kqt2zj7bvm7T6jsL2dOURZGFnve5MiBXypU9RQthobz7PWExvGLgeo8e9l7z/u67/I2UIT/IN8+QUDSkguG6VNIz6/nsm9Mm08i/cmo3zzCaH/awhzWPoYUQBjOkzRY7MIo844HwbvYNei9eJwZt9h1aAEKSpC08rm+PEHREWPnIFblmeGp3fZ2hrB8YA3grkBpjFjJJpuqynNcE9cQ94xq58YpZOOEF1K/Tvj1Rp+v6SNrAbwsmnvW5sSnjgLQRBcTbAgwZjHU1BJv+iU7w4zPjqLSzQJRFFzJDbHOdkvSQQYtDiIby+c7YzQtmUY+XCXH1vj7MY0QvUr9+bM/+NeQgC4UJn1ROpHN1IW36lP3L5o14g4E+IW08giIL6E2/f3/WvDWtf/f9qO8vSJ05UB9Fqo1f9G0xL6p+hzzr3yJH9AHRDH0kj3GW/HvPpH6ScXhcruzJz955baI/eQa5y/NZuMz/fmbNNb0stLVxgx2Rz/U3+fanWWfMm4Vp3ym3MGoRLfQMGVZ2JJgu8ibTWYsXWRgrFFYiKoS1sGJhcGYUIARWIf0IBeLZ6feNxJBAHkz2iB8PYAZe6ZhETfYm93H4zDRvRjxwCU+1Is2gWcgYy4TFc2GCtELKExFCM/6xyi7UVNnsfzIxKyPirIzKj/xkxX9cRqRS+chCeI8J12TuWZ/FIJMWg0J9phlh0zw785DAaaRrnucCk38OJWIAzXpnWj7qJcTP5ItAmnQZWAy+/pRIq9qMaJ4BRgyZT/NgTQNPmR9yRHLHZejfZ5yqB8ODFybeD795q7SHcqU9pJm72OY9zTX6FeNwIfnMgryVUXnpy5hQTUs3XursYQvixUeW1Cv1UzfpSn+8T3lcF0aa0EXhgwx3HgWEcpZM+vLOq6f6E+j7yten7T1eCf2OXHgUGMe8K32Y6+p48Bib9IfukIV+zPviM2AsGhsY/NmjO85LGehPPNHKJR16zMiUFk8N8ALyWvCUIDIWSxC0yDokeW0AkbV4J19eEz+8pL2xbewhZx4cOqIsxgDP0SF/I5L9Hr2EByt77ucL4jnyW7h9PM90Dunsw2ch+sub5TfvFmIvcqX3wplnjBkW5dQDYVfeXGcjnxyAZczpF0a0VfZdBt71ee9dXiqEcwp75Em0UKoOZEvevbx8jrDK3zyUPo/g57qQ1QW57rbbbq2/GOP8RghnEcjU07hAZuY4oe3GaAsevS4aC+mHheJAG5FZ+u84zZwMrJ+JMlA/7SIdi4UI+zRZzzNPIb3Z6hGvbQ69QtTnReqYcUcexgNpWFxmH/DMikogR/OVZ3I4mnqspFOEC4UeRSALKxYGbofeMCoZEYwMhpiJPKefMSxM0FYpGZ4+YwDwGlq19h2jhCfPhMDIEdolXET6PASMWBNSbwDnEmcTtgnZgJ9TCsfISWlWEHkTkDkTgnQYGMindE0E8YRmhVpIjn0bCXcz6TEAGS5WwKUnjFb41PjwFgakFWDlSvgjI1MIDy+ElXneFeUiF2STIWPCVTd5MgTIRNqeN7mRbcLP+lXuIMZoT4jyP4S4+j+Ep/eOMRJyyIPJXn2R4ZTLpG0iJbt4j5UDyVQ/xpRQvhwkY1UcEddO0gjJkJe6IZqMK3vyEMkcXKD9U+bUpS+zMikbw0R4lDbN+1a9vZ96Ma54mBg8vMmMPXrLg8JjYCGgbw9ytu+GTkbfohfxEsfgjyeZngqrUqc8m76Qcvg7bdIvlEiTDLJ30CIL8oLAkpc+wmulzJ5FaOhr8k74ITn05aX3Vs+1o3RzPYb+yluTMkU//E55/e/wICFyZOFAGIZ8H6YNSFJOFRaCqIzkp+/6LAskySN1jxwQQp5F7Ud2iJa65RRannvyQA6EqOmvwkK1V6/L0/pC9Cxt1nvTeeQQRmOTvi1dix10H7InjPGs/lnc6eWkbTxjPOBdsiilHYwJ8abr6/q+esjT/4iENLVfTgC2B8z+r1nXQKRfG4PUZ1zf6GZk63u/fU7GdAjZQhDpv9991ISxB1nTZ43rIgZ45+kWOekn6iZ/n2kr3iT14mHyrPbXn5HOXKfjXeOBtrPvTrSIPpaTWvWRRHYIs7W4mJBgzyEfCJd28r+FPH3BuIJIagNkSSiskGP9m87az9kv5vRRK9pHftrIdgJtoZ0zb2WcGZ8km+/okR9yME5aNFUfe6kRKnLUT8wLmePIhbzsD9Qm9g/yQiNr5ru0Yfpj2jtt2o/rKdu4vdVdO+rfxg7168eZ/r286xl6p12FqFso6hdyyM38aqyl45mPtTW9Mrf1J+NK0/xgfCYH6ZnTHYhG55RRHzdupS/1dc3//ZyU76LT+qJxzRyp/aTjN9nLr58jx4tSoL+ay4xV3rPAkcUS9ol6Gtd4y0US0TGRBKkHHTRW0MPx4mqhsFJQIayFFQsDtEHXKpyQD4OswTr7y6yqIlCMToOykA+TlEHbpCMkzCTkOXsrHHrgVEKeSqu4JlqGaVbKkVOTvElDOgw8Bpm9hvKcFb7GEDcxSieTuL9N/H5MjH4gB07wJJgAkSSGE6JqkmKg27On7vbqOCgAyeTRnBZuqHwM9ezPMxnnsBIHhjC6GPXuG7N/i6x4LExM8nbogno7LMJ7Jm/fMYRMwAx8cpR+YPJkdOW6EHU2SXrehM4IQ+ZNoGQjTe3Y7+sScuodB1vkpEX5IJdIGgOUXNQ5e3ekpezys3eQB4fBYAXX93e9611bCF3CyuTFANF+3mFECStFHuI9YHjFIKA3CAmDVF20lfJlH5o8Im9GFILR7wuVj3RN/LnWxX4v9dCO6sx4ZgjZf2U1X77KJk/GtvanO/SBwehvCwJkg+DoC8goGTFUeQAZZwnfI1Ny8a76WaHXL+gvY1LdGSw8B7kehhGjn1gRR8LJUT21I6+YBQ7tqc3VCRHKPiblUzb9klFH3xhG2oTRr2yMw5ycSb70xjMInfaN3OVHXnQoiHEpVBkZ13+VXzuqk/cQMs/o88qgD4sWyBUvudcNsUBwhMeSYa65UB5jgvZB0JUzpz8y9BCHnPRILv1R/qD9jBf0yDiiPsiHcjDi1dVCDcJjHPJdvGfqqn+QZ8Ik0/+0ofZUV2H8ZEuu3rfPMp5MukiXGLf0kb4hPvpLEFkgQrPgGfLTt3hikRPpWaBC1pEsfzPO6U30S7r6pfFVGf2WtzL14wb9pvd03thGj7SrsZgsRJfYU6mOZEyexiVykKa285t+K59FG9+lH1lQQkCNAfal6cO+4/FC/DzLe0vm+qlxGhEEYywSSsfphbrTDW0mckG/sE9XGaSl7+g3dKOHtqBrnhFSTIe0JQKp79EtMkQokLCcrA30gI7RZX3BmIGY5CAtepDxRhsjGsYT8vKMg22yn44sjf/Kq75O+VR/bSdPc5D2zMFT8vOZvqTvIO/6hXnV/8Zl859+rI2QNOXWf81X8s7CnXf0bbrht/eRYeOPPqt+6tNHE9DlJz/5yS0SgdzMSdpBGyXayBhjQTWH8zjISdnJkTfZnCE/MlCehMTSJX0K2cs1O9o1B6Jlb6y6eN5Co0UL42AOsaLr/tafycEisffNEWQWT39gLHIQkBD1zCnmDXqOEHqPPMiALEQOKId5MifOqodQ+nkPWioU1jXO9q+1FdNSKKwGjjpqGHbYYRi+/W0Xav+2rVQzvhgNBltGPKPOYGxyTHhW9jWavEyUjDfPGpBzF2RC5kyCjC0GEwM8+2X6FVETKiMdQWVYMDwYrtOQwxxyV5S8TJi5C5Ch7e8cqtCv2Obo/KSTPWS5J8pvE3m/18JzJkDGDmOSYa18DPa+TFmZ9m7uIst35GJiy11m2eOVfVc9snevTzur0n09ErbZhzzmt+dN+E7Hc+KlyTX3yPV3fZn0eUu0T2TRHxwTOZNjvD4+I2/GyXi/SI6V90wOwKEjDJaElabs8unrELlkPwyjmW55nhEwLRQ294YxcMhW2yc95ZV3TsJNfgn5nCa3/rAgsqHX8u7fgaxS93tc833CoXMnIzn0945FV9WPka9Mff0Y27n3zefSG5cv+7+UUdvF6Is3Y5pexKuhjeNJHJ/oGCiD8uc+Ru8mxHS8zzK/85n00i7S0S65R9L30W16ZVGAZyfEp+8L8QaP2zwez16P0pcg+pO7Dfs92NLXtjnZtfdmJh9pSZeMlFH79Z69tKH8c+iW//v93jFOc4futEiKeIUTLut3DnvpywXSjSczMs64Ig0ynLb3O94efVJZxmQ8B6elnfsxS9t5j6xyV2K+J386mvDSPgqBzOhl7gDMd/LyufSU1fv5Xh28p4zS60O4EUHPjfek93VUnlz/kKiIeLKDHNyS+sf7l7wyFiUKYBrolnKr27R6k1nuh5zWhr33bNr/fRsjvxbsLBilPfQXZMcCZQ44iud/fCqr55U34+g0yE8/lZ+xLuU2xyHwFufSP8m/l132sEo/8k45+vsix7IfR8lEn/OddPUn+fZRAr2cMkf1kBd9VVf6rCyJCEi/V27l7efAHBKYegSHHDIMu+9uzzbbaKr4CoV1iiKQhRVLIP/yl6+1lXSryVaSDaomFyu8VqezyXy5oUtYwRUSZbK0+sxLtlJCSUw6Vrh5uXi9rOjymMyalFcCEDjE0Wq5FX3kvzeyC4VCobAyYc4xJ4qQ4IXj9UNQeSaRI97RWfeMrikQMZEBCKRw3PFJ22cVFIEsrDRUCGthxUKIiLAWe2XsSYo3R0ia0MS1CROi0BRkUijLcp5kuKYwUau/FWErn8JjVwq5nQYy5NFlCAi1tIrPeypMaHWOsi8UCoXCup1zLKYK13aXLO+YMNMc8ra2yGMWH21DEdlgj6DffWh0oVBYPygPZGHFeiBdB5dwDyEk2XORMLK1if6QkJXoKVvp5ZtW1pyK6H/eUm25kstdKBQKhQlykI1QcouBiJxF3bVJHsHcL/Q/4eHyPSvuCywPZGGloZb/Cysa2QOyrj1syXelYqWXbyW0YaFQKBSWbxy379DBNesS8lzK1RmFQmHdoAhkYaNfNbVZ3WZ/q6UO0FjfXq+EdOY+KN45h5hYXXXYzZqs6ErTqYgm+fEBFSsNVrO1y3LUe3ygkb2yDj0QarVUGSiX94EMp6WRQzscSqH8nsuF17kCxt4g+4RycEMOtclBLznsKQdF5FAK+43suwWnrnrO4TJre6W/sO6QA030e/oxvpNTP3bNhH6c01bXJK8c6kJHc5+tg30c8mEMOiss7uSgp1wQ329LSJSE/ue3UzTPSv3N2EPf7G003vWHma3JPEK3zUfGwv7E2fUF5XEAnf5gi0x/OFF/oBjSmoPcxshzdCUH1EnPKba5t3J917NQWBc464yQhbMkTBhOjnNcvOPc10XEdkjCrLwYKO4QcyS9idpR6k6xc8Jcf0re6uTlUANpObZ8pbeLfZDK6kj4pdZ7jMgA+XJKr/Z2OuDqtDcC6ph2VyU861nP+o8LuKXpCHxXfMjHkfyMLVe2+N+VM46TdwWK02adPOv6CQdBOUjI4UyOnXcqLQM+Zbe/x32Wjt9XD0ad61g8v6byKaydvrwm0Fe1t4Ol+lMrwf7rxz/+8e1KkOUAo9khKHQ2+uxAEvrNYN6Q2mF128KY45qPxz3ucY1E9ukCAuB6KP21P2F6Y8BisnMdj7HJeQMWvVzvZGx2fcWa5GUxzjzniqCVMIYpw6GHHtoOoLMXv4c++JGPfKSN++aQWfeles9VHw6Dc02H5yxMOOTHYT+1K6xwVkF5IAsbNawiuwIkR7mv7cE9x3C758kdYdNWIq1abrvttu1+Lt4F94IxWKxoLrV8SIbrRtylxosgLeTEXXcrGeTiWhRkmrdvTdrFu4xxx9hb5ZZurtJYHbhqwb1c7iRjwDtQyUFK8UgoM3KIZJK3QySs2rsIGhng6XYPngUBp+S6I5TB6t5R99cx4u0jcmdeLoMnD14RK9++A3tE3d9m9f6s4CFaaWAYWijgVXC/33KDB0Qb042x/jtp0kKEOwSXAzxLvJnuPQw5cjehMYq+bwjgjTd28g6uDvRffdUVOrxEgEw7GMYVSDxJaY+NTY95F801s+4BNW4+5jGPaWMQj6P5w0nfGZ/mBT029yFZxkZjl0U1ea+EMYz+OIQPkRyfWi5qRH9gK7iqZRbUw3246miuB2OEu3XpVnkfC2cVFIEsrGj0oX0GboZc7mfyuRAUkwGvUe7Iy3sJkZw2WfTInYvSlX5/lxbSaTJBEHJXG8g7hpjv3bUlD5OPC8ulx2gzseQuwdxj6VkTtWdBmXO/YCYl+Zl8kaDcV+k55VGGnEjronYXKLtUWnguEtVfIJ56kIOwG8/k/qz+XjCrr4wpMuzvnJwmq5RBXt5TD5Nv7iFDsLJ6y8NGBrmfMaFi5EMuuVerT19ZgWGbU1rlo6y+95sMc5cg42ifffYZdt5551V55Z6v/t7IHL6UMFf18Pz4QIbc82jhgXfGqrLTBj0L2ocxyyDXbrkn028/6qRuSD1jl1zklWeyp8cignbs94l6N4cNeZ6hot16RK8TXht9JRvllRYDOSGzaQ+fK3vuM4ss0kciu9yhpu3kRWem9Z+F+kfe10/Ivn+fjueONmVLiLB06Dsd7PVFGazwk2N/X6LPyVka6VfkS4/JjAz6U349I4++zXNfnvxiNMofXKauX7kuyOJAH8o31uPcMQpkqW4+V+7xnXW+T6gkuUzzdDC46Vx/R6w60RHllcb4TskgIfHeUS4yiN71YZn0l4zG91J6V7kydo3zJ19lTvh12lSd5dXX2Tvk6tncP5ltBfRD2/cXxXuebtIreaijd5A8niwLOcqVuxm9o10jb+VL/1Fn/cv3nlMmocLeJwd5HXHEEY3wWNRRFnlnDNNG/p8VBp87C3O3aO76k5/n6Yv/1VVZ/J87h+P9UtbcE9n3K22cOy/pQvL3XrY75M7hsR6TnfpmYcDi2gEHHDDsuOOOLS0yoT+eUXZ5y4eOK1vuE83dhtoz7Q3aP+O/dKIX0iWzF73oRY08Kp/8LMZ5JqH78oxeq2Pmzr6O5OqzsWymyV895JX7UqXpHfLOAXyRvzohx+Te9+XYGN4bj3W+kw99znib+1QD/7uTub/nM3qonJlb+3fohfF4PK71n/upk8oLKxWlmYUVC4Op47sRJPt2hPgZpLfaaqsWlnr88ce3yckE6UoL3p4HP/jBbZIQknPMMce0AZ+R98Mf/vA/jIB4C6XjHd4GBtUDHvCANoGeeuqpLV2DuDwYdPI+/fTTW+gKEshY8Nztbne75i2y38N3JiteKt4wZY3h7ll3NgopY7gIp4oxZF/k8573vBbaybjjgTAJHXTQQcMtbnGL4b73vW8LnXnrW9/a/pa2vISrqoPVdWm++93vbt8zCExG/o9BSZ73uc992kqrch177LHtb5+fccYZ7UoQoUuzJi0GkTAn7+29995t4n7DG97QQu6EQDktz/+bbLJJM2Q+/OEPN7k9+tGPbgaGiVjIKvmamIWNkSOQkbSlwXhH4Ny/KW31lBfvgB+GPbJPPu985ztbXayim4gZzSCM1OfCsHh7GYnamXyFt9ILxpEVckZFrx9kZR/i1ltv3eQnzFg9QJ18R6cYK9PAyEAgZxk/DBrXmMz6niGk3JGVUFi/6aG72Bgi+oP24kmih2lnZb3lLW/Z2pluvOlNb2qE1TPe1YfolvROPvnk1vZ0kJyFZdFd4bjyZ7h7TlhXv7pOD8hBXvqMfHbYYYfmMSM77/OMq4c21n/VV1r0l4dN+oy7fffdt6Ujf8RcG6mv/PQnbci4kw59oK+8vyeddFKrp3Tk775YOkMfpKUPKBMZa3M/+hg9c7+sciuLvC268M4hO3vttVeTkT5FH7PoIi/9Qv3oILkiJPLSX+VFb4WnWzzynLIzpoUrM6YZ+dqUXBiWyu37HnRaO+ljFhn0Y+UUKu3vU045pfVzdbj73e9+Jr2ljzzfymyMkScCPL70np4YHy2+7LHHHm2hQ9/0GfmTu/Lrm2TBE+/n5je/+ar20G/0K+VSPs+qE5265jWvOdztbndr7URPb3KTm7SQXESDvNVPGvr6Qx7ykKafxg3y0xZCTZXR+EjuQgp9ph7GUfcARw/pvTni/ve/f9MxUQDax1ygD2pXenW/+92v6Yy8n/jEJ7bxSVvRd2mYP4BOCnNUT+W1OGU86wmHdy3eaU/jmLFgl112aXqu/hadjKP0AEk1HwmPNd7QG+OU/mjOEU4ZzyD5GYfJmq7RDwtj5jmyUk999fvf/37rS+qkjMZHc5U5QH3pOD3WXu94xzvaZ8hYFtHotvbSJ9RfW3nO+/e85z1XyZa8hN/TDbrIe6fe2oJcjEPqR++Mr3RB3bWZOtEreWkz86P86D/98E7GI6HcSJj+p476LNkA3TDG6Wv9POQ5aSgbuZI93fGe9jQfkbP+QP7aXNvRM23mfzIky6OOOqrZCtIj55A54x89kIdxRjSJcaaH5z75yU+2eokwQdTVS4i4sUp65OIKK7YFHVZG/VgfkIdxzRyvLORlfjF+sivIslBYiag9kIUVC5OEwZ+RY/Bl5NufwrAwMZncTEgMEOGBQm5iINrbY8IxwZp8Yvj1YARIjzHP2GaUMNoYOCYE3zGQpMFws8fBxMDwREBM8u7GYqi4p9J7JjHhUMpqIkZqGAQMIN+ZqBEcExoyGQ8ggxKhUQ+hMAwHdZAXY4hhpZybbrppm8gY0wwChpmJmrGcwzHkFQ8HY8Vk73tyYtBIX3rkYxL76Ec/2owvBoL9dtKeBQaI0DtGoUlV+aRp7wiyypBGLKS5zTbbtHSVAZljMNnTpy2SHwMknlBGEBmYpJE1e7YYPgxahqhJ+La3vW2bVLOSazJmXFndpSPayfNZNVZX6QlHpUfK4W9hqfYkkuV+++23KhSpByOGMaC8jIN4JejGne50pwXvBs31KtKY5r1Y7HtAuMmZjtMPhgbjmJGy/fbbN5LA283o8pv8tbM7QnlrkDhtRVcZtciwxQvtQVfJ+uCDD24Ehl7SI8azfmW/MIOa7h999NFNZn04mxV1ZWGAMahCXJQTyX32s5/d2oLBRO76DSOSgaxcFkK8pz31DwafvoaEaXP7QPWRQw45pJFS+kJODE3GorJ6BzFAbpX1wAMPbGTYMxZ6GL5IlGcQBd5cZEp5lC+6xyAGJI/uqCs5kKO+SjaMuN5TZ5xQJ3pA/+QlH/1d/ekrHUOElVP/oD+eM0ZpJ3qpD4/HpeiENOIN9Jx0GJjuxmWAv/SlL/2PMG2yUx/9Rz8xLtH5af1Y2vRCXZWNjmsXOq/t9EfeILJGMhADskeEfKYuyq7/e894aKxF+hj3njfm9DoXHWYsGzcZ13RVPegS8m+cMNbSb3pApxAbHjXjI50J9EFjrXTpoEUH+qLfICS54iihmZ6h30i6BThy9L46xyuNDCKi7jukP8ree5aUhz4Zj7Sh+im7scZYbRymG+RrXCIfz+YqKjI39pMBPU0UBlmSob5Mr5A1OicvZXr605/exgQ6jHAYZy2+ZBzS7voX2dEZYzm5Ro95b+Nl1DbGebqtX0gDUVLfILqFiNJx7aReFu6MKeZP8qYLxnv1Mz8hP/KhC+YHdUJuyc3z5jZjgLlTPcjXGAD+lpaFEeMBYscOGEeJkCE5GUMQbHOc+shPeaRnTFdv8lduYxZ9oCv0VbrG89gKxph+QYauWEiQBvvA+IV0WrwZQ7rmBWMLWZIReSKBxlB6orzyJmNjjHFcW2oPi7HG48yB6kXnx/vvC4WVhPJAFlYsTJ4GdEaMQZXhZUBN2I8B3cQW0maC8j2DyQq61VcDu8F8vM/HZO0Zk5mJw6Dv+ec///mNBPFwmdj97TuTvcmP4ciAs7LJ28EAUc6EzvmbAZhwKnkz4n1nQmAUgQnOJNaD4WPilZaymWRMVv29l+rjf3kkn4RcIlMMpZzayBBDJpHMhMIwGA4//PA2oSJw5MiAIEMyltYsr1qQUMyUyd8JdyIXRpPJ0ao9wysryzw1DA8r9d7xHJnGWEFavMNz63ntwSixWECOZM2A7ImbPJN3ygEMKLLUfgw5dUcoEWcTOCPFO9L0Xm8gBvTJ+xYWlMnkj6AgCYyUtXnQBhkzfBhjOeQECUHitDGDFTlDmhn1DCpGLyOS7LJHR7uTM10lW3pPT7xjZV8+Fkrsf6Ib9Ahxoa/ICCOLh8Dn/UEvDFneSu2COEkvIVtIDl1K+J8+ygBWl4Sa0w8GJiAk5MqolodVe+2IBGgv3kqkjHHI2KIr+rP+71ltgQTLTx/ynXblHVIPhp0Vf4asdKUnn4RI6gMIIiMSqUDYLWjEC6/u0bGAcasO+gqiq0zqlhBJ6RqT9Af/kwe90z48Iwn3pkuRW0B31Sdhr36MG+RGp/UH7Ule3u2Na31d3nRTuyiXnzFJ1S7xqvmh/xZT/FZvcojxLV/5S9c4qMxIlzYhQ3WUHpnTKeXxrDpoG32I/OixsVm5jH8+87w6JAzRe7xIdCXeZHocIqiv9vfe+i0fckEMLVopn76uHaWDJCDzyqXtEx7dj6F9KKjyqqcyKZ+2ow/ZHqGdlUt/MdZ61zzFs6WP8nqmjP245G/ypMfmBH231ymyR6zoI7nRA1497YeY0GMk1efSQD4tmvJ0kl30OPphfjFe9LJTd/XTXsoZr6s69iHo5EBHyc24QoeNMWSs3P1iynh+yuKYcuoPypOFUm0gH/3UOCYfZcq2BYSLTikr/dR+ZKW8PfRncpG3uhsHyMR4pD/28k/50o+kZ4wCOmJe4k1VJ+/nFFVzr3mC/lsYyPxqTEmUS3Qwc1T+Jy/1JGM6lMPSlFneCLV2UHfvqTO98k4iRoyP00LUC4WVgiKQhRWLTLjCqgz8mURiDGVSyN8BYzT7i/q0eiQMjbEdQ9EEwfA22TH2DOrJKwe0eIfxKb2EH8ZjsNBBMCaChSaDPj3lYaAxYPq9Gin3PAfOxChU3n6PVQxmE3YIUOQ4Tz3mRdLs24i3wSQZA65/FlE0ySLZjEFA7E3+MT56Q2WMcZmntY92067akeHks/xm2GSPTg+fewYRt4JMdkjsujgoYVwniwrZ6yV/RiZDhhHEG8DYY/gDkt7vK+plkb2RjBzeOuGsvLHesZrPwOHBinHJU0An9Y/IyP+MHQYlo5whHRJuMUK5fOe3z/Ur72t/acQYj2eVwWtBQ9q8qyAsElFlSMkrkM7YC9C3d+opn6TPwOOBRTLVB3ri1ctpntM+1ctiCGKcEPT++X5cyl49zyJR2fPXP7dY20/zUk4rn/6tXNow5GaehQ7twdh1WiaSl6ssEl7e12Wh/PNcT7jH/Y/sED2fR8e087j/5XqbxdqCPkhPiKwFQeTU4hPCoz/IZ9bhJtPS7fUoZehhYcK42svVPJUQ/XkwbSwjc1607PsMMQFtIt+URf9DzoyN8VLNap9puhRCuZAckp7vlAmZk9dSFs4W0pH87stqsYrX3OJEDkwynoyBgKo7kmzBgex9tpT9guqBbCLZ4734ymW+kh5voDkj9RmHg0+ra2Q8HgfAQri+aVzLvnqQrqgli2a8ohYtRUpkz26hsNJQWllYsTBZGUAZzgbbWSfIQT8ZIyjZy9CjH4RDGoS5MMAhB4r4MXExwrIy6jsGUDySsybG1T1mfjzhWHW3EsrYGqdn4huTyv7//M1IkYYV1niPvGtSNCGOvSpjIJ6zjIU+zxDUxUgVQmMF1qTdI6vEDAKTp3YxaXpe2zOsloOwaX8Tt1VuRkMO48heyHH9UndGDULl1FTltzo8beFiISxFJ6a1JSBR2pIRLj1y583JYRhWtS2y+NG2WUiZtSiQg2aEngolQ5CFguk/vB30nIwYZwhbHy6pDE77tbDDgOu9Fww+3kn9TxiXUMp4nafVVXtrEzoqP20kv+xBykFM6sWIRsRmyW1ae0iP3Lybw34QgGnG/jztSdYOChFCKCyQYT3Oe5yO/xMymzZbSp7zgD7Yj2bvlRBIHpx50090gj21wtoZuSItxvsz1wSJUNAWaVNto+9pi8XKuRAZSYSI8FjPCWum/3QbsZhl9C+U7iwYMywkGZtyGE48tyEafbr9PbALIffJ8pTnQKcsgJBb9ulnjjFexjO9GKYtbiyG/jnl8MPLPCZp847/09Ie/7bIw7uPGBpjREZYoBqnbUwWGmq+4CU0vtDdaTKeVT7/x1ag7+Pv6KaxxliRSALtTa8WItyLyUIb0x32TcZrfxvzLASyeZx7YPwUEWXRrVBYiSgCWVixMKgKY0PyrMzaQ8MjZ1A3kJu0M5kyPv2Y5OzDQEjsHUFWhP8gCQbtXPYOjCOTkz1wjC4he/ZcSNdEZoLO3iWTmYGc9yKr6fKKIe8Z/+cUN/khoIyjvmwpr+fzTqBOCWVxUA9PDIPERCa8TP1Nlowt5cnKqToxMuJtzCmQDH/hRlavGW3yMuEmTCxeyl6GIY3KL7SQATY+BTQnFSIZVooZ/9pKfXOyZtKMbPwmb/WxwqwtGNImbhOydtVuSAzPk3ryhpAfw2mcbo8Y5to6R8h7NqQ5sleGHEbhsCIhgEIKhY2NCbk607OE6GkLhiNDlVylT/5+4qmO7BOS13tPlMmzOYF0mleg1+N8n7b0jjajG/Y9KbO9iUKReSKEUwpno7/2W2k3ix85pTTp9bqYQ1PUzwEXvDY+t2cKOWLE5NAmnp3eOKMf2l4efmtDhpjP9Tce24SF+snJizFE41Umd/nxmsnPb/lZ1OG1VH570RBRHk1763rdCqSZNu/z8YP80w17JOlVDsehx9Lo0+nbh17Fa0a3sweUbhiX6KZ0/R19ia6nX0fePkOsRRYIUSNP6XpfH/Bu7zHqT6lN/aLPGT+id3nPZ7xvyqUfICL6pfSVz/PT0sjnxj/1Fw6IFDOu/d+XZ5qepi368s9qC4a/8ZwO8xDmcBxIObLgkVOX47HOJfDGuvEdfgiiUHOLKMJ4ebQtjtDHeOVT7pQtBIIOR3+jm+P+18s5UQlkrf1y8I25xPgQT72y6Bv0AyHUJhmHkkc/DlhgEYGhfuYkOm9PnPeRYP3TPlj91hgjbdEQCe3u2zanrEaPfR89Tp368XRcrswNqbvxWv8XqptoDeO/uUU96VkOqtNW8vFjvBrrQt8/Mg+mD+rjdNV4Ztww1vbjYWB+NJcZz80pZJCyx5tI/5VPX7N4pB9EbzP+yMf8at6hAwi6edTchDAi57azKJcxQ5hy5oq+D87qryl3byPQR+OaPmCxxtYN9gf5yofctKsIHO086z7KQmF9o0JYCysWVleF1PH82MxuArXKzLg16fveQG8SR7BMJCYLoXAGZ4c8CM9jTJhoPN+HbfJoChXxrB/hWg4MYDyZvBwaYMLOseiMbCSHUZ0wUMYyo5en0OTDGOaVYMybbBhMDDqTGjJqYmBImMyVh+FhL4tN+siMFXQTsP1TSCwDxyENysKQ9j7vgslNHZAKE5DDRmzUR7TVy8TpO+kyTHgmpMmLhBgicyYrq58mLJMnkqnuJmakgoGCtAopZRxFbryzJjen9pmUyc2qsYmO4W9CZuRIE7kkG+VBKBA3R8prF4Zq9q4wcqw2m3DtydPODrbQbkgSQ4IxyJiyJ6cPB1Y2cnEwhHQYl0iy8pO1+qiXMjiYwmEp9ElayIXTLLVFDANtw7BVNySNPjm4Q7gnHTSpCy9SBnKyH0o7k6f/5U82ZOJ/+om0KKc6aC8e9fG9aGSd/Wo5hdH/OY2RYUxf6ZY2lD5PC/mRJ6OTbMnO/kjyVkcGLeOYYUomvtfWdIDRR0+RY7otFFC5lNN+X2Wlh1b4c3VMZG5xAqFTRnkhAsrNS8EIo699uJ32lS4DlEy0C0IlfaSerBE8ngQ6T8etxvP28YYxvLLfi3zpuf5Pf9VLHoxafTOHRPmc0etgEQsX2lC/cIALfVUO4Yf0NtcHkJd+Ta95TxFaOi1EGLSpfmV8cciLPq/OGRdyRYQ0lM8ep4TEk4m+rDzqg1SST0+IGN3koPze90M39CP9SfvRa7+NH/SA3vvRZxxUhYzrE3RGWtoaCaArFk6U13glTTqSUEmkRZuD9KTtIBzylF8WoOiN9jH25YqMXG8jXXLwdxb+pE8eCJb602FtSm+kT/+M4WSV/ZF0SF7ep5vGPeOxtHtPX8Yl+kh3Qwr09Vzl0S96KAs98Zu+0y9ETVvnuok+KkW7Jcw6IZe77rprkw/iwdNrTKCrZIQ40F9jhHZC/owfnpGu9/RHZcj9vSBd4ZLmG+MTkqTOvPnkY+w0Pzj4iu7TD15jZTbWRo/lL1RaP9MW9Ew7Iiny0l89SxZ0giyVBVnSrtpN/0aSjQHanAz0X3okP94/CyHS14ezMGs+USbjp/nM83Sb3BJloH+ZO+Ud3UikAXkbRy1mxGuPVGffcNqErBBXMuoPU9Kv6LbyWRwjF59nwZSO0QNlUGefGzedhOuAHG1MR/UT44w29Z0DjKRrriB3c3muTCEf8lYGfcD/xiS6bA5RN+XU15TZAVrGcYdgGY/oq3FNusYCi4DmRXaDE5LH+z8LhZWCs/1rOTY8FQrLhKOOGoYddrCPcRiudKXJyp2B1IDvxwCNjGVFGgy6/SpdJmSroowxRkhWYschLvEO5S67ft/Y+L6y7JmKBwcYOuO8/a+cyqusWe3uv89n/V6U3P+Ugwb6O9hyL5pyWMnMQTq+S7iTd7IC6vMc3uBvdTDx504++far4GMZepfBx9NlX0YO/wmUX5rqqCzxEvSrydPSlK965u7MrFinrsqkvckg8u49tZ7rD9EI1M0qM6O29/zJsz/8JbKWv8+VYVpIlnL0bdaHJkVn+v+loZz9fp68F69Gvkudx17PeKr6vaP537spJ72W/7jsuUM0sosXIZ6I3JPW76OSJ9klvbRRdFi+s/bg8Mjljj/p0lH5Mh4Zyww/aXsuRDZ73sZtKb8c3jS+f1Aflo7Po2v9oUepR5/uuE+mD3kvcovXsn+v39/sGfnmbsp+0cK79FRfUmdyyB2gvf6P5S1dsiFb/TV3h47THperr0//f8aPfsyStmcY3LmTMJ6cPNfXNTr1xje+cdWpzqk3Qxn5YIT38k75+jtzk27vfR/LIP0vOkyus9oidUxb0KPc9zkt/D7javpW+nC2HPRe3JQl8kIG/CTku79bNXs6x3lKz7tZsOr7dD8+0hHP5rCuyGZ8GFmQ8drvhKMH6mSRw3uRwzx6TOeMj94Zt0/uUu7fj/zVz3f93aypX8aayDF18XzusEw6vUxnjUd0QtSJcQOBo3/0EJk0B2U/Lh2gqxYFkTrPqZ+FEmTdgk76J73v56d4IMdzb2yFhAP3c5L0fZ9tFdC3Y+bY3h7p/582Hsk799Vm3I1c6ZOy9OM4HHLIMOy++zCIvO+2ThYK6w1FIAsrmkAW1g8YATy9OcSgNvEX5gHPgtV0OsOzlINFeFAZe1bbl2vPX2H5wBvjrlZXcPDKJCScBweBHC8gFQrLDfMNL6cTUYWv00H6x4PHu5yDtHgReQJ5GXloEzLLo0p3efI2xjGmCGRhpaEIZGFFoQjkykBWV2cdTFIoTIOVeR4DIXj0xqo9MimcEnmc5j0urH/woAp7tX82V/Hw5gj/zamghcLahAgBYZ08icYNixZ00JaQXK0BvHbCPIWW89Dx4NmegUzmaqKNEUUgCysNRSALKwpFIAuFDRsJwbPXSMiXkMjx1S2FlRl1oM0S6piw3SKPhXWBhPHbD00H6Z/Fi2nXXwkPte/QOINE2l847ZTnjQlFIAsrDXWITqFQKJzFkXXE5SAL0kBA/BQ2HAgFFP5XKKwPZE9iDnRaCLyMSKOfQqGwflAEsrCikXsDbZjPaXqrs6opRMvGdAbSUlYqheRZ5XSAg8lqfHLmrDxt6s/pq1ZSx2E1VvuddufUPicGronhnoNn5Ovkt2krtvOU2aZ+8s7BL8CD5DCOaRdfLxW5ooQ8ySSHfDj578pXvvKZDjDakJH2dwKjEw7700vXV3nI2ap+2pZH0AEdOdQoVz0IN11TfVxp6PsH5OAL7eJnTUPe+v4uXR5XY45TJo0ZkfNyIFfMONXVWOaQlo2prZYCY7JTXskhJ3SuNOQwHX3PWL8c4+hKhjHeWKKOxr5p1yPlcJscorY2kOtr3Nucaz16sAd4Oo13ORhnuZHDvrS/g3HIox9rxn15deybQmF9YuMMFi9sNHB6o4MdHI29ujBAuwNOOibyeWGANwm54sOFvv0dkosZDU4v3HPPPduJlNPucVIm3zs4YHXQnzTqoAHHnL/qVa9qhuzqAKlwbL7rQFxx4Gh7x7T77dh9R6vnLr/VLa9J294Vh6w4zrw/vIMhuDHBCbYOhHCtxbi91jW0rcNtnFzoegHH0tMZn2tTe4lcQeGKCVd69Cc6bgxgpL33ve9terf33ns3vT7ssMOaXruGgSG5Ju1CjuTnKhVXDoG+pP3dHbfccB3CYx/72FV96KwKV0i4dskYsrpY2/2SbulvdMNhUhs7LEa53sZVTP0dx+B//cHVFH6vzbHQ9TKPf/zjm45Ma2fX1WgTi9PLjT4v85qrRchjmh3gOiXjkkPGCoUNDUUgCysaTm7cfffd2+b41QWPAE+AyWI8qS0Gq8b2WPAW9kfALwQHhdjUbz+R1dZZZUKc3AO2uiusDEmTVO5Ey0XVqwMro2TM0+suMXdhuffSHWS5X8uE2194vlQ4nENaJnWGBuRuv/G9bhs6hGFZIOgvMWcsrA8jUtvy8LpLD6F1Bxz95C1zYIW79dy9+NSnPrXdc7k6HuyVDB5Vd4cy5kQfOB32EY94RLvfkyHpTkcLPqt7YTf5uu8OGUccwQEg2t+1GMsNd/RJ2/2DZ2XQaXND+tjqwIKixcm1tWji8BdjgblnY1uYmQaRDdttt127j3Ts2fe/+dwYmCt71hYc2OWOXPMZ6Jva2QKrsVi/9L35fTmRhVJjPXtB+uZkC9GzdNj8lytKCoUNCRXCWljREObCuE2oCyPPZJDLmBmEiEm+N1gnFNO7JjRhQ0hg7mvynbC23Cfpecidhgnz47WBcQhi7pTLHU4Jkcn9gCZIz08LnQmUa6uttlpF+BIGpz5+q6MDBKbdWykc8fDDD28ElUdFuaUnb/JhxPpMWFd/L5l7u7w/7WACzyG+0pGnOpOZNBjY0uP1YBDJM3d45X5JchiH4PhcntJgxHvH38lbnoi9C7xzn2HaR/uqp//7ewhzQEvukJt2j1oQDxuQadJhNGo/oYUJAezl7zuf9XewJcTXjzr0OtfL1ufKLi/XH6QcX/rSl5rHC3EhJ3L2fsLbvDdur/yoa3TXT+4GS0hqX5ZpyD128lQ2v/1Pzk7dJGd/I0F9SCRdkkfuakuYoM/IMeXN3XTSpB++I3ftT57S9DcDWp9I3ft2Uga/o0PySJhpr2tkoV2kE51IiDkdnBaO5jmfR79TboskDDyE0sXeL3vZyxo5811C7Tzf30sJ6qG9IfcYpg+mXvLTv7PoRH7KmPsMcydrny79Uxfl6+9/G7el9/SZ/u5H6eWS+Nx1Oe7j6Vu5wy5jY9o4d/ops7r7nXtjtbHnlN9z8vNZ9Cl3jqYe/u/1Ocj9qdLWl/o281nuYVROep07DHPnZl8v6TvdN/rpPbIlQ2Uy5vTjhvzUIf3eszzGrokwtgn/T59UTvmnbouFWkrLM+N6kady9G2hLAmr7kPJo1s+V6fcLRnZ+Nxz0ku4fz8een6hU449a07Try1w5ICk/r5j/U15+nswyV9+uds440DufqUL3lEX9XUFRx8JE91PvcZ9VJ2NH+qaMTpbT/oxfFZovfxzmE6e0ZZbbLFFS0s6ImiOPfbY4YlPfGIjlRbR+nt0e7sCASQb7d/fXald1WvWOBM9eO1rX9vmZouv6kNm9C7jhrErY7bxQznHfVkZ1CfjX6GwElGaWVixMKmdeuqpbfC3omngF2JnEOZBecc73tEGeJ4TK3gmgVNOOaWF5xnoEan73//+bR8ExHPGUydU8wY3uMGwww47tBAooZX3ute9mtFnMn7Xu97VJgGDve9jRJj4hatZYbTHyWTqTiqTN+/c0Ucf3QZ9E6v3p0140pfm8ccf38ixi4/V5bTTThs233zz9pvH80EPetCw9dZb/8e+iS984QtNDianE044YRXBzkXMwmJNcsJaycykxsukfMpt9VW9F9o/NfYyutyZYaB+yqW+wgKtrJts/SY/Xi6w2iuMSNndz2VVmsHep6uNlJ8XiLfTpP/Od76zhS3f9KY3bW1pIjURb7bZZm0S95k6eJd8tS3vkp/sbfWctjnmmGNa/iZuab7gBS9oMuJ1syKsjXgwyEp78+giUfKQ/i677NLuH5OGumgzbSs89Q53uMOqC6tPPPHEVQa1tnXp9RlnnNHadJtttmmr4cJH6TKvkTSue93rtnYiG4YT79jd7na3JiNkk5yRa5+TwS1vecsmJ/ecaTsyp7P0c+edd17yXiJGFW+ketELxhXdk0+gbsoo5FP73/ve9275CHlVB32SQUrOjEd3sdELn3tXHyMPnmv6zLBC0BC2fh+yZ8mLjO573/s2HdIe+gHCzWvNQyjsm6yEBXpO3+v7IuMxRts819OAvUfSEu5GL3gEpCXETh3JX5nVXZnpDx2VhjqJJLDA0vcZekDfyIXeul5AKLv2I1965C47+WpLZVE3fZSxqD4M/Fx/ol6BtF2TQue8Szb6IVnxHmtPui0MfcsttzyT8al8yJKQSvXWj/U5bWxMMY55/u1vf3trN0YvYu++PQThiCOOaGmLRvAb+dLe8hJOq/zKQ88RcyG83gNjszLqE/oAGXuHPOSrv735zW9edQk7+T3pSU9qekV22kLZefOi/9re+KGP3va2t21l0m5O59Q2+qHPydez+pqxQDrqLk91kjZ987xnfK9f0Wd1X+gwqNTLWG4OSr30U3pOPuOxlP4qN9JEv+iBvme+I3t5G9vUnW6RifbWJsYZefrcuKTt6JL60hmyHC8oyV9bvu9972u/yUk/Uj59SHmNef42thlL73znO7dxQV+XH8JFz+iLsGH6Y/ygq94z5xr/PWtcMr7YMqLsdPu4445rhEi9/GROM34ol89EpmhLOn/66ae3fqYPyEcbGEeiT+nHxsrMM/6+xz3u0eZ1fe3kk09uY6M+SlfpuDTphPmK7MiA3Iw97AXXhtBRY5KFUmO3fIwNdFK92A/mVc+aA/r5WR2Mc8oposc1ONpCP1KGD37wg619yFDdlJPubb/99m2soBvKqMxkq6w3u9nNFhzLC4X1hQphLaxYZEWZIWaiMfAy+t/ylrc0Y+7JT35yG3CRJoO8SdZEbvIzmRnwGcZ96JA0GSEmRxOT/00SJh+TJwONwWzyMbEzhhmkWeU2kZjwrBoiB8rGUGa0PO95z2uTEaMDwVgIJlbvmsiyqm/iQrwYwcKyEB7pjmWC5JicPCMMj1yyXxMxMfGZLDORm9DUV114Lp797Ge3OswbiipPRhbSLg8/CA6Dz8THGIvByBghS/cAIoGMMrJ75Stf2WTbI4aNelsUYChrK0aUCZfBANJiuEubTEy02pchrp16D2WftoUHxqewKQYaWZvEGdraluH1tKc9reXpO/ICkztPmtVqzzAQDzjggGbc7Lrrrm1SZxxJn/7RF8YjIm9lmqHHOJU3uTMm5MkIQhLpG0OX8YpcWOTQbkKZYgyRr2fUlQHBIGWkKQ/jL7KjA6tzEIX+I20h1IwVujEOt6SX2pDBpG/EuEdoGWk8ChYptBudt79Sf7PfWPqMfGRC+2sDXgIez3GoaA6eETqtjvSZrjGu6L8+T4fpGBkzyslY2mTE4GSove1tb2u6spQwbrIje78Zf/TAXlAGIh1hkNrTZa8kAx/5MiZZENEm9EmbjNNUf0YzgqA96QBDkWFssYSsX/jCFzajVR8lM30AOVI/uhGPzliv6VYWuOKhoC90kQ4Zg/Q37TQuFz2VpnJ4j+y1I5JMr9TP2IGg7Lbbbp7k6AgAAQAASURBVE3+dF2djVGIhv5iDNKf9Q2/1eHII49s6WoP7T32nHgOydRvjRsPeMADmvzsR5WmtOiPsc2ChXrqn+qjPMaS17/+9W28JQftbKzQxzzLg0zO2s3imP6LBNIjREj/lS+CiDAgsn6U1XiifOYSumghMXq2GDyjXuouff1Z+/kZv28ec9ehOchz+k726qm/sUSfNMYoo/kAOaTb+px5R5uRF0KHBNFRC436iPKP9V/b+Zxu0y8LC8ZcizLyffrTn97S3mmnnVq97fc3jxqv5K1NLNSZb423CI6+oE8gj/okgpnwUPv5lCve0X322acthklf3tFjY7r+asxE9LS7ucu8rX4WSf2tTNP0CcGy79+CE92g3694xStaO0pbHfQp9TD+alfPyUc7qIc+og7Kzq6Qr3qmbMpJh0SP6CPGOf2drTHNI6pfWERRXnNzvLX6kb73zGc+sy1UyiuRCOnL8tL+5lf2g3F/1haYQmEloAhkYcWCEcCQTLiICcQKqImAgWFAN2AznExojAQToe9N/ggdkjeeeBh0jCW/E96X0FUTDmOAt40hy0BLWBv4LquXDC2rkIwEHhQTixV1Bpo9fQyfaSRNfUwmWdlWDs8yfhAdK5NWgU1iiFSPlNc76iWthPwwhkw6ZBKSbKK02m0iYnCYyBgoWcleChLyhLiY9HhrlFs5GGfagIHBYCMDE7VJ1qEJ9nuOjWEGssmWnPv29T9PHCMe2VJ2RoGVbBMtvWCwaCMTNOOmDxEjD9/7IUsXUfNk8Q5oIySG8aR82pfstDGdYbiRD1KsPJ7j8ZGn8vgMcUAw6ULuLWMEM5YYzwitNkj7hlAkrNlvxov2ZlhpQwYDHdRWVvd9nsuxGfLSY/xZwEBKtGv2Nq7uSYbKpD7SSTh3n1bCJRnF5MCQUlfP0S+GEGNRW0lLmRl7FicSkpZ28Tu6OkbCpxN+58e76bcJO2YsakMGK72iz3SCzvEc0RVG2XihYjGkP/mN3DDg9E9lkCbj02IPvdHveR3ornojfnRnLFd9Ud9I+f1NL5B2esfo1QeRY8arxRH5aQd9mO5YHBiHwZMh4zReVmXWZ5A772WvK10cyyF6qD3VEWE33tElhFb7WvTwt/T8ICdZFJJ32jNhwZEbedEV7yLXCJ/3exnrP/o2GeQ5Yd68g+pJp/wgGUg2XUMS9Ed56QP6HrJMdsaO5GGBA7lFeBj3+rW04uEyDuizQgYRGuWLfmYc9Vu6CKr06PJiV9H09VI29UJGjSP6TH/wGvmrD6JJj5VL+ZWNfuvvQmnJAsk1/ntfubSNctFvYwKSaFwyHvqNCJKTuahvd2ObRQqLLfRY2ubI/fffvz1PVuYYek43zGfaEmH1P70lR7qlfMYmc4j8Q7jpkXY03pp71Sd913ilrvoKeVsEkiZZqBPSbNFIX1ZWeZhH9B/6pIzmcNEgvT55Vr+Xjv5nHjDGW/zTN/SRhP9mrsw4Z9ylOxlvMu8Yv9gV+qf8yU09ldMiWsLf1UUavV0QZFHG74TZKysdoR/e8SNt78qnj5gwz+iDdJwsLOAVCisVFcJaWNGYRsB6IzQDtEnQRBTPn0GcZwHm9UiEHJks+smq358gj4T4+Nzk5rcB3+TeG3yrY9jnnX7vzrzvhRBD9o0xEBEwk5fJWHoMSOWcd29F9uyQi0mW8W4CzJ1x2ZdjwjfZMmIYHykPozaHGcyTV+oCPTFkyJjEGQ4MO+SNwTbt0JeEOGWhgCwQf4YCw1A6CU+NBzfkJcTJD0McEe8PUGJsqg8D1+TPcHFSL/LBAOQNWwjKQpb9nXvSYVwxCJPXmNDROV5fnk9GHnmEfK8tyF+fsmKO3DJ4tCf5Mc4ZudFRskTmESL9cbmO6KdbQo3tU0SmhUwi09qTUYy0yzuLRUu5piceBm3CsNPW9Dd7w7QBgxIZQFLVN/vPjBEx8LInsk+377u9jPrfPtcXjVUiIhA7smM89qGrS+kzi11LYhyQnwU3hEo+jHoLYuN66O/aelo4fl9Hv+U7j+x7EkqftCOdl0a/pzltEZA73UOqyKi/Ukl/8i4PHRKTclkkQ2D0lfRt5GHavKAP0jME0wISDyF9m+eah9S/r5eFtPEinXHT9/pU5pjoUsZZ45t+rX7eV2ehqcYYZFukh/GcrlhMof+Zi8hgfNWUNtV++Zy+6Te8bRZgInvIQpmyR/b9/JexlEwcQkNWfpOVCIQsyOV5ss+1QUHSy95N5DNjoQgPafAMZqF31jiC5OdKHs8od+6QXGzenPZ9v9c19YQsBiPD2SqDhM9zpVeQMvZzcy+n5G/xwYKNbTlINY+lProxX/1S2HBRHsjCisa8A6fB3KRmkM8Jn1aTGSeZxKcZQIEBvTfCrGT332dyMflajTdRW9FGZBg62SBvou7zWqz8azIxLDZJprzKJhSKgaTM2a+20KmA43KZ1BAke8FyAAFvXla7lcXEyNixSss7hWD4nPx5zbTHrAMpFpOXz3lkGHgMeSSKp8LK9Dwy9AzjiAdAWzMI/DBE+zDh/jALusDYJDMegVwBkzrFWGHMMcYZZcIf++sVZtWVkWsxgkwSjuc32U47CMP/vhMCi8wxJMkjh+v0BslSMI9+Msx4cYVX8cLJl/4j2NlvGuSAjpDyvkyz+mGPPC/dGHAMXORO+CIDS6giA1d/p9f0WZ4MLX1x7LUf16fPnxGLlPPaaUeEqe/H0QWfI/gMWx4XdVFWuhP9mdZms+rXgywZ4fLgEZKf/X+L3ZM37xgzfsc4YBFJf+Zp4q2JZ0bf1b8ie7/j8R/vH82YuSZjmMUG/WjaHXjkYVEOOUzeaYvxwolyW9DRbvRAn82BYvREaLi+BtkH2Y9d+dwCCD0jHx4/iyaQ9l5MtoFyT7un0/90VLmUkd6So7ILoRf2ilDxCMYzrpw+0+8Rf6RN+emNEE91z1wknfFVVdrWnMjzmTpYCPEsnRYtoEyRhf6mTRY6BE6ZtYMwVgs6ys1L35Ny9dY3LWDmdOL+O+nra+bojMfqZG6aJzoG6db/E/7ZzzOLYd55OaecI+3kxdsr8sUCw5rcHzkt/xxYJIRYZAOCvN9++y04nhUK6xNFIAsrFpkUTGiZUPzO35ns/G8Szd4O+yB4DEzIuV/QxBbCZGL2vEkYCTKxIjsmLhM8Y9K+D+F5Jj5pmAR9L/xKOJD9EcJW7cHxnHdMiPYBmYwZ1QxLxo8V87E3IsZQXxd/Z2LJqvDYCE/5EWYTp/RNMJFTn3aMLR5T+2PIQ10ZIlaexwct+LEiTFZ+e18eQmpMZOpuHwtDhffOBKfu8cySqRBchjijwCSI6DAsGKtZee1XvFPWGB7q0Z9MGxllNRuB0c68QkhFTugb603aPHkxxMhBO7vbkrdHCJlQ3HhEpBVZWiRgNAvdYrz5TrtLiy4wwKz8IyBWisnZ1QoMUM8m7+gc3SBTOkSnhKH5nfu/6Jh8pZmTMMfXxmhzXkjPSQux8zedF0IbMjpNHpHlOMQtejJNzwKGkj1WeZ9hHEJPpvInN+Vl0PECMQYZeIxTni2Grrr7n/zGeckjhFA7ax+GsL6UsHL154nRJspBHx1KwXiVvr6I3I09A/04om3UQ3n1X4SfJ8d+W0azEHTknqHoeYTRGCGUU+ia8Dz7BO1v5CnyO3u/+nEq3qSeeIz7d0h/2lD+ZMrLQRbTIieyWKAO0Y/0mZ4IJd9ZERxkx8sp9JHO+ky70j/k3PggTWQSabGQYmzMXmQLRMbPRDj0dVoMdCB7aeUvT7ri3b7cvLvKZrEIAZK3MUdbjA9KSii8xQU6IZzVeEcXhBybDw4++OA2Hhn/tGnCqpVffdUHYTROIfR0QZ+ig7x/okwWIjaplzFBOVOv6B4Iw1VO46lyZp6Sj7FZWYy59Fg65hBt4FntY4yJt458yCZhpNIxnowjS+gzQmpe1EeEjdpfSabGUuO29PUp7WlcEl4snYyf/SFy0W06q248j/q89Pq+BsYHn1mk0c+Mc+rkN9moi/HYwomxXbnifUx/muUtjBfWXmILx+ZsCwX9PJP2Mv7SH3VLlEf0Dca6lzlJPp43V9AH8442nLVg53k6FVJOnv08AP28n4WylEc+iKqFQiQ142qhsBJRIayFFQsTqD1HVlcZoYwJRMUAbbI1MeZ4bAaB8BcEweZ7BqewGpMs48AkbKJEAoT92LTvgAaruSY/PwwkP1Yb7RGxoZ/xZBWVUWfSNLHmdDrEwQRsBdbqvdMJDzrooPY+48y7Ph/vRVJmxqcJlKGRk/sYFgxhhqTy8vD4HFHoV4O9xyPEIFJXhq1JNwZ8JiTyMxk5pMFBCepkxRkJNPn2hoZ35GUSR5yy50vZ4yFBDnNwQA6dQEwZLyZn+xwZwIidgzdcNSINIaPCPNWb8YJcMEBNxiZ+MmLYaU8Gpe+VRTsjyDnJlTzIKntflF9e6icsL8a5yVmZGGUIg7R5BxnNnkX+EVrhr8osrDXtwiPDgPZ3ri2xLwbRYDDRJYYWo8k+pxzGFM81YsjAYBDRN3qLODHg7C+ywOBdxqV0GHS83QwFeiM0KvVDwsnJincMOARGvvIhn5wwSy68JvR3DG3L80pf6RbipF/RI2XTvgxuaU67eiF7dRzARN+ji9qLYc1oZwRqN6v1DHxp61P6mwMovMuYZGSqK8Ld5+N/cnH4CyOa0a+9YtCpn7bhXdLWOWGUfjuIJScaI5h9uBww4hCD1Fdf6D2bdFMfy342e6e1k8NOyIbe03fp0gNjhnBapIVBr0/RGf0hBwUxkrWFNqTT8RRpT33EAgXdYvAzoi0yIaPyzTU6xi8/fYg2+dFnaUmTXunz5CdNbZKDlvQh/0/zdlgEEkapj3gXtKtDeLxrHDPm6Qf6jL6nD2kX8tEnjG/0n7FsHFMm/Q7BH+8L7XVJf0HCyYqhbDFK2aWjTdQvoagOrzKOKI/xUf/ItoE+TbqnbRyy5fAc4y8DXB2kIy/E0bygLR1wom2No3SXDomu0BeN7epLtmSkbbQpXVZv7TaGNjOmpF7C2KWNsGgH7anP+Zz+6Dfkql/lrl2y0DfVwUIRPUcQjU/y19fpmP5lLDEWmO/sDUYeydFc1I8B8TjLiyz1xdyfmbsHzQtk47Aouqbcxidjbw5iomsZS7RTDlMjK/prblZ+uq7O+qyxnS6orzLSEfVUdmXUlx3CRZ/Mtcql/MqlnTJ+es9nYz3WFhZ+sphrTNCXtRciqZ3I3rhDd7O4bA7X78hVvfyWhzJJh+5qQ3WVP91id2i/7G+kU8JMzSF9uXxPdvLhyVZG4522Y7P4Xp8yR+m3+qo5VTmVUZ5IdBY7zbs5ebZQWGk4279WJ+6pUFhLcBDmDjsMw7e/7Yj9yUb9wOCblbvsr8v/Gdj9nzvDEubUr4j2B5qYaHJnmnz6Qz8Y94zA3NkXr19/J5fP+jvicrKcdBkg0N9XF8SDkK6Xcvf/p7zZVzSePD1vgmEUyyPPpyyRSzb1564t9Zl2elw8KH58l5VR72cf4RjeYZybEE2yveGSlXjp5b6tHLwQ9PUcyyGHBeV77yJA2oVRwpBGDhEj5WMQpn3GXs1ehuN707Jny6TNY8rQSQhcT0Syz9a7JvTsT4vXNkZB7gXs926lDbyvzJ5JOzHQ6IvPEsaXVf5er9NeVu4ZbQyLhImSBTn1997Natvs7Un/6T1G2W80jXAwoJAbByL14YORtb6SQzf6MiAouRePfPo+Nq2cdFQ6jMfc1RqZpM3JKuFj0swdlOP7GgPPeD/t7//sr5q1D1g+PA/61/geUh5H7eZzOjJN33rv30L9W5kY6rwnSEMOcqFnFluQi/4gl2l9aFafmTV2AFki/ghN9gSOxxbp6Ae9Rzf3LCpTwtblGX3PPsZpcmWkI8RIQg6NyjgZr0x/iFLKLc8Q8r6NLQYovwUBpDJ9If2pX3QL8VGu8T2HPpe2/qccYz3LPa680hY5crJmgDQJpdcns8CSfWu97kHqlStS1CcHa+WuU/C5fuMz/U25st+Y7DNupa38P+006h65r9U40d9tLE06l+9yDUg/b/ah8r3eec9PZNV7/fo9sfqL+uZgqf5Areh77ueMh7PXp34PYY/xPOO5PtooMk8ZIPKEcZ8Z/w+8sxZKRKJkURL58wx9GJcr9kEWpJNXf6dkZNp7MnPKtfRz33C/oHfIIcOw++483fRjZjMXCusM5YEsrFjk1LQe40Maxv/nRLXxZ9MMmn5lb+y1MMH1h5wslgcY6E1Oix1skhMMx2ku9P8YJp94DqY9P5ZLTsCbhRDrpRxA4h2Gg59p341PMJzWnovVO/8zPoRB8XrwgsjTJM3At3LdvxcDdFaZ+3L1obR+cnrjtPcYV+ODTbLPZ6w/0/YPTXufDo5XmMe6wSCzom41nCHdnz6ZAzgWwuq0LTBueBHky4DiERkbz5E1z+M0xIiaB/EkBb1ME+I6Rk4hXQieGbfPYtB+0w5+yl7asZdnsQM1FtJzHmeeJh5gno0cQsRTM27b1elDPXjYhMkhSTkpebGxpYe8lXHa5wuhD8dXh4xFPbGahZzQGiBzZEQ2+jJPXNLK6bFjyHOa53CsP9P0LHfO8syN0+4XCnL6c1+vWboXcjAuYz+W9uOCdKbJeKG2GmPWHKCsxr3xPtRZ82b/PVn14cSzxhnPzLqfdVp/WkyfFppnyGTaQlL/3Pj7WX0IkbO4yBbQP/t5Z9bBcGP7YNaC2SxkAa1QWOkoAlkoFFY8GBRCz4QFCTtiDJjUc6/imhzkYQWbhw25Y5xKd6mEY20CgUTiHFghHFg46GInbS4X5CuEUFiWfOs0wOUHzwaiLvSSRw7REc7GYF0q6V8Mwg7tmXNwjhMelzv9hXQYedWv/BZ6PA5jnhfCZIVc2nfuIvixB3W5gdwIG5/mBeNtUh/P+C18E3mofrJxAKl2r6jtIqIBEEjzg3HYAk+1c+GsjAphLazYENY5b34onEWQa0kYnwxRq+l+r+lJtkLeeNgYuVaPeTvGpyeub/AYMU55f+b16C1nvoz9aSdlFtYcCYsVkil0j173YZbLCYTHPt54vteVjsvXflD6FE8borU6dRQuKy0eNcb8Uj08ywnhicoitHFN61VYmchWjdyBK9piTeed1UGFsBZWGopAFlYUikAWCoVCoVAo/BtFIAsrDRXCWtiokaP4rRTbX7XY3sLxu1l1FFa21BXHHLAi/+zvqpXpQqFQKBQKhcKGjLJmCyseC91Rt9h7ucPL8eo2xC8FQhsdfW7v2eogR3c7Qt0x8SvtPqdZ90wWCoVCoVAoFAqzUB7IwooG0uWYdHtdVudkspyGJo2FLoGeBietOR5+sVNVZ4G30/1V9k84vGKlwX4k5XKK4bo6TKNQKBQKhUKhsGGjPJCFFQsePJf3uiA6B0zkjijXGeSy3yD3gAk7dc8WCD9FBHP/nTuzeBbzXu7Ayj1b7sIK0XREudP3nFjYw4EX8si9XGNClvTlifT2d25Ng/RyCbk7oBL66uj4vqzge8865l9d+vuu8n0u9+7f83feRWi9714/3lXHki+VXBcKhUKhUCgUzpooD2RhheJf7fTHww47bPjkJz85nHDCCe3SaCcHfuADH2gEzrHprhdwKbWT+Fw58IlPfKK97bt73/ve7YoHQBKPPvroRkgRysc85jHtzi6XBJ966qntcnr5OKHwAQ94QDta/1vf+tZw/PHHt1MvH/awh626yF0ayKFL7OV/y1vecvjqV786fOQjH2mn8PncpeCOmZ9Zu/+7mF7+7vhzx9473vGOdi2Few4/9KEPtTq6ZHzbbbcdbnrTm7aj/k888cR2bP3nPve5Vse73OUuw33uc59WJ+X1PeLrZMCrX/3qwz3vec+Wz3HHHdf2YvKmundus802a+QRwX3Xu97VrshYXU9roVAoFAqFQuGsg/JAFlYsXFmAWAlD3XrrrdtVAkiPsFbEyOdPeMITGnlzN9jLX/7yRr523HHHRj4POeSQVZ5IZMwhNnvttdfwjW98o+1JzF1/7hT7+te/PjzoQQ9qBPBlL3tZ88rJB5FD3BA+adgTiZhtt9127d1XvepVLS/v8BhuscUWzdOnLPPseVR297LxqN7iFrdoHlP37iGE22+/fftfmXk2lcmlxl/+8pcbOfb885///OGd73xnI5rPec5z2j1yCKXyKcOhhx7avI6IdeqMLCO3Qlc32WST4R73uEddXFwoFAqFQqFQmAvlgSysUJyt7SG0N493URio0Mt3v/vd7YJtxE5YqDv7EDjeQ+QJKfL83nvv3cijC+jhyle+ciOXvIlIk5BYXkQkVNrImM95+lxUL4RU2t7jwYP3vve9jYwhX4iYS6yRRpfa8+D5nRBZZRIWOit01ecurrdHkufv9re/fQuV5UU96KCDGoGUjvIqozJc4xrXaJ+70P0mN7lJKxvv6SmnnNKIJu/ojW50o/b3da5zneFWt7rVcOyxxzavJtLLQ7nllls2Yi6kNfJV/zodtlAoFAqFQqEwD4pAFjYI8AAiZbx7N7/5zRuZgvvd736N/LzpTW9qhC/I94FnkDY/iFO/P9BnIVDjS6mzxxEZdBCP773vnStd6Uqr8kT+3v/+9zcSicDldNPFrv7wjDS9AzlsR1isC4uF0u62224tXaGxKaN0kUsk0l5MhFiYbl8n5FRIq72Z8pFH6lknrxYKhUKhUCgUVgfldiiseISIIUwOmhHKKZzVHkYeOV5A+/4+9alPrfIWIohIlQNqlgM5EEf4aEieEFV/O7TmyU9+ciNyQliVDea5NzLPhNCpo/qoB0+hOvKiCp8dQx0RW15XefJYKkv/vbBdMpqnLMrgnSKXhUKhUCgUCoVZKAJZWNFADIWiImrCLV3nYW+fg3Ts67MnUFjp5ptvPnzxi19s9z06lMaBNAglIFl+4hVE/LI/EWHqvZE+75/NuwjkrW9960bQXvjCF7YDcxzsc9ppp7XDaZSFB9DhNQ63UWZE0LvS7PPo4XPfh7Rd5SpXaSGtL33pS1va9l/yrkobCZReTmZVlpDWG9/4xk02yuQ7IbjCYYX7XupSl/qPfKRFtrliBCl3oI99o7y8hUKhUCgUCoXCNBSBLKxoOC2UV85hNcjS0572tBaK+bjHPW7Yf//9h8td7nLtoJ273vWu7aTUt7/97cMee+wxfPazn22kEolD6pAiJ5cKA3XFBRJm36TnnOz6+c9/vu0R9Axy5VAeP4gVgoiMImlPfOITG0GVh/2H9kwibltttVU75dXVGEig6zWcburEViGkDsmRR67LSEjuGWec0fYgCjW1f5In8alPfWojoHvuuWcjxMJjnbwaQitU1qE98nJgkJBeeT772c9uaR544IHt8JxrX/va7URZnlj1RrSVOWTU6bXqfMQRR7QyKgvivVxe20KhUCgUCoXCxoez/avi1QorCEcdNQw77DAM3/72MFzpShOihcSBfYCIDwLkMyemIpc+ixojYeA7XkOEK3clZu9gvI/+7z2D9jbm2eyZDOHzLA+ofBBKHj6evewp9L8yKaN0kFTE1HtJw+fZP5lwUaQu/yPGnvc3Aikf9XBgj2cQvJ133rkRZ9eT8FTKA5KG9HgmyUX4auqQesXzmM8RWwRWeC4SqR6Rc6FQKBQKhfWPQw4Zht13H4af/3wYLnrR9V2aQqEO0SmscCAyTlftgfD4GT8HSF2PkLYeiOAsjJ+dVh4Eawz7FO1XDOxfXCwdZDFXiYy/Q+j6qzVCkP1GAB3gk2f793zHGzmu07R6yb8vp72SfgqFQqFQKBQKhVmoENZCYYUDaRSC6xRYpNLdkfYuFgqFQqFQKBQK6xrlgSwUNhDc4AY3aIfcJDS3UCgUCoVCoVBY1ygCWViRcKXh73+/vkuxUiBM9bzDFa94rTN9+oc/rLcCFQqFQqFQWEf4y1/WdwkKhTOjCGRhReL617enb32XolAoFAqFQmH9YsZNYIXCekMRyMKKwg1vOAwvfvH6LkWhUCgUCoXCyoFF9Snn7hUK6wV1jUehUCgUCoVCoVAoFOZCncJaKBQKhUKhUCgUCoW5UASyUCgUCoVCoVAoFApzoQhkoVAoFAqFQqFQKBTmQhHIQqFQKBQKhUKhUCjMhSKQhUKhUCgUCoVCoVCYC0UgC4VCoVAoFAqFQqEwF4pAFgqFQqFQKBQKhUJhLhSBLBQKhUKhUCgUCoXCXCgCWSgUCoVCoVAoFAqFuVAEslAoFAqFQqFQKBQKc6EIZKFQKBQKhUKhUCgU5kIRyEKhUCgUCoVCoVAozIUikIVCoVAoFAqFQqFQmAtFIAuFQqFQKBQKhUKhMBeKQBYKhUKhUCgUCoVCYS4UgSwUCoVCoVAoFAqFwlwoAlkoFAqFQqFQKBQKhblQBLJQKBQKhUKhUCgUCnOhCGShUCgUCoVCoVAoFOZCEchCoVAoFAqFQqFQKMyFc8z3WKGw7vHnP/95+O53v9t+/9d/TdY6/vnPfw7/+te/hgtc4ALDpS51qeG85z3vcPazn30429nONne63v/hD384fOxjHxv+9re/Dde73vWGa13rWi2NpaRTKBQKhUKhUCic1XC2f7GmC4UViF//+tfDCSecMLz0pS8dvvjFLzZyd7Ob3WzYbLPNhm984xvDZz/72WHTTTcddtttt+EWt7jFcK5znWvRNP/xj38MJ5988vD4xz9+uMQlLjHc5CY3Gd7+9rcPz3zmM4d73etewznOUWsqhUKhUCgUCoXCLFQIa2HF4iIXuciwww47DFtttdXwu9/9bvjjH/843PnOd25kb9999x2ucpWrDEceeeSwxx57DJ/4xCead3Ix/Pa3vx1e//rXN0J6wxvecLjc5S43/OQnPxlOP/304S9/+cs6qVehUCgUCoVCobChotwthRWPC13oQqtCS895znO238JXb37zmw/ve9/7hq997WvD+9///uHGN75xC2kFjnXeRr+FuAqB9fdvfvOb4Vvf+lb7jscSOT33uc/d0vKuzxFR+SU01ns9OU1aPvN3Ql/97/153/V93g/y3bgM/ffjek37Lp+P0y8UCoVCoVAoFNYERSALGwRCghJxHfLVE6eAp5JH8Utf+lL7//KXv/yw+eabN4KIcH7ve99r73zmM58Zrnvd6zYv5wUveMHhpz/96XDKKacMv/zlL1vawluvc53rtH2SH/rQh5qnEum80Y1uNPzgBz9oXk9/b7HFFs2z+ZGPfKQ9o2z2VSK0v//974dTTz11+PnPf94In1Dbr3/96y3vi1/84sMd73jH4cpXvvIqYqls9mYK37X3U4juTW9600Zy//SnPw2f+tSnhi9/+cvt2Ste8YqtXhe+8IXbs+r8+c9/vpFspNs7d7jDHYbznOc866XNCoVCoVAoFAobHyqEtbBBEkkk8XOf+1wjdwjibW5zm1Uk681vfnMLa/3Vr37VSNrTn/704dBDD21kzvM5iAfpFLbqt0N1nvGMZwwvfOELm3fz29/+dksDKYTvfOc7wz777NP2Tu63337DU5/61JaucNqvfOUrw3Oe85z2c8lLXrKRyEc/+tFtr6U9lTyee+21V/vsBS94wXDssccOr3vd64YnPOEJw/7779/KqTyI4WMe85jhqKOOanV617veNTz0oQ8dPvjBD7Z6velNbxr23HPPRi7t31SGww47rNVLXtL7wx/+MFzpSlcaPvzhD7c6//Wvf13PLVYoFAqFQqFQ2JhQBLKwwQDx47k75JBDGrk76aSThtvf/vbDy1/+8kYgkcuvfvWr7dCdn/3sZ8Nd7nKXtmeS5/G1r31tI4U8cggij589kNtvv/1wvvOdrx2kg3jaV3mnO92peRUd0uM9BPMGN7hB8+zxJCJ7iCPv3zWucY1G3pC1S1/60sOWW2453PrWtx5+/OMfDwcffHAjcLyUf//735uX0vPPf/7zhwc96EHNa4gcfv/73x9+8YtfNHIpFJdX0s81r3nNRlwRZSRVvTynXsrIs/ia17ymfXfccce135e97GXbIUOPetSjWv0KhUKhUCgUCoXlRIWwFjYo8L4ha2eccUYjgrvuumsjkcJDEUwhnkgkzyMCiJwhcUJO7ZUUDhovZsJgeTOFqCJ4/kYIPes75I1XL/sckbZb3epWjeD5jRgiazyECKEQ2OyxVA75513puS7kYhe7WCOb/ucB9eNZ73qO91F9kEzP26eJWCoTzyMS7XoT3lT1ciKt53kyeSG/8IUvDFtvvXUr1/nPf/713WSFQqFQKBQKhY0IRSALGwwQLl4/XkUhofYsHnTQQe0AHJ5DBPJHP/pRI2SIoP2EiNwuu+zSvIb2O04DIsZj6Rm/kT5eSSGr9he6czJA8HLVh32GCK2QVbB3Uigsj+fjHve4VXdVOkE2mHZrjjSlIa1+P6d9lH78P61e97///VfV6zKXuUzba2kPpDBcYbJPetKThvvc5z7L3g6FQqFQKBQKhbMuikAWNijwtDn0xkEzr371q4ePf/zjbU/iAQcc0Agb4ucZxEooJ8LZEzVhrNOIaQ6acZjONtts064QAeksdDekvPIu4rjtttsOF73oRc+U9je/+c0F6yQPh/NIiycTQUSG/R9IO4cG8Xz66cF7al+lkFbhuA7qee5zn9s8rkJhC4VCoVAoFAqF5UDtgSxsEOg9d0ieA2mufe1rN6L1tre9bXjrW9/aPIk+EyKKiH30ox9tIaaIIxJnP+E0DyByJlQUURQOai9hwkuFi/L6zboKA2HNXkOkTfhoCKcQWnsmp707/sxJrFe4whVaGOyJJ57YPI7K6n9ezYS+Cqft66W86uZuS4cIOZTn8MMPb8TRlSW8ooVCoVAoFAqFwnKhPJCFFQvkEBHkNUSm/PhbqKfDaB75yEe2k0gdLPOyl72sXbnhsBv7ExFKeyWRQPsG7RW0X9J+SGlKC7lCDoWibrfddu1QHmTM6aq8nA7CsZcSeePhkxbiJszV3zyPvIT3ute9Gom1R/HJT35yC5lVJoTOPkZ7E3P6q79zoI4yKI9TVIWh3vve926EEIFUL95MpFd4roN4HAAkn9QLoUQ0d9ppp+G0005rZNUprte//vWHq171qo0EI6aFQqFQKBQKhcJy4Wz/muaSKRRWABCio48+uh1qg2QBsid88773vW8jZU46daBO9gIiUEihz72XE1QdKHO1q12tkS9XXCBuwlSd3rrjjjs2MmgPISKKpPIsInD2GfL0OelVPtJzAA7CidAhaT7jFXQ6rLsnpeXgG1du8CC+8pWvbN5IEE4qRPb4449vzyKg9nA+9rGPbWV64xvf2E6DRZLtn1RP5XAYjtBW5XPYjjx5PtVrk002aeGqyKk6Icj2hz7kIQ9pYbyzvKeFQqFQKBQKhcJSUQSysGLBY4dU5RRTiCfSnkGfeSaHzvjcVRtInfd455DMy13uco0QxuM3LS3v+JtnEPkS1upKjJzuKp2853/Py6tPS3gpryXPo0NtfK9sCTfNc96VRv9ZXx/EWfgpD6OfngDOqhfy6G9eVXVwH+X43UKhUCgUCoVCYU1RBLJQKBQKhUKhUCgUCnOhDtEpFAqFQqFQKBQKhcJcKAJZKBQKhUKhUCgUCoW5UASyUCgUCoVCoVAoFApzoQhkoVAoFAqFQqFQKBTmQhHIQqFQKBQKhUKhUCjMhSKQhUKhUCgUCoVCoVCYC0UgC4VCoVAoFAqFQqEwF4pAFgqFQqFQKBQKhUJhLhSBLBQKhUKhUCgUCoXCXCgCWSgUCoVCoVAoFAqFuVAEslAoFAqFQqFQKBQKc6EIZKFQKBQKhUKhUCgU5sI55nusUFh5+Ne//jX885//bL/PdrazDf/1X5P1EH/nO5/l/3wG+dzPQun37+S95LOUcqaMfX59+Zcj/b6cY6xOuisR02QGaf+F2nOp+fj5xz/+0fI717nOtWxpF/4TZPyHP/xhON/5zjec/exnn9rmf/vb34ZznvOc//H9LPTv0Y1znOMcq/pg2vfvf/97e7b/rjBfH0gf9J1nfKd9ChsndJWf/GQYLnWpYRg3M1X4zW8mz1z84sbj9VXKjQPk+fOfD8O5zjUMF77w+i5NoTAdRSALGyy+853vDIcccshwyUtecrjKVa4y3Pa2tx0ucpGLDL///e+HT33qU8Npp5027LzzzsOVr3zlZuD47HOf+9zwxS9+cbjHPe4x/M///M+C6f/1r39t77zrXe8afvzjHw/Xvva1h2222Wa4+tWvvqRy/ulPfxre+MY3Dre61a2Ga17zmquMMJ9/+MMfHk488cTh17/+9XDd6153uNvd7jZc9apXXbIhSxbHHnvs8IUvfGG42MUu1mQiDcazst/97ncfbn/72w8bOrTJCSecMJx00knDn//85yYrhIJxe9Ob3nS4wQ1uMJz//OdfYyLAKP7qV786vPzlLx+udrWrDQ9/+MOH85znPMtWj8KZQdbPfOYzm5xvd7vbnYmk6M+vf/3rh4997GPDfvvtN1zucpdbND3vfeMb3xg+8IEPNJL4u9/9ro0RW2+9dSOpdOfd73536zfnPve5hwtc4ALDXe961+HiF7/4RksiQ/jmrZ8x84Mf/ODwmte8Zthpp52GO9/5zv/xjHHruOOOa7J88pOfPNzwhjdc9nJvyECqfvWrYbjgBYfhohe1kDdskKA6H/7wMLzqVcPw/OcPwxWucObv//SnYTjggGG4xCWG4dGPni/Nf/xjIhvD6vnPvzjpDEn99a8n8rzYxZZGVP/4x3+TMuU8xwq2fn/5y2HYe+9huNvdhmHbbdd3aQqF6dhAh7NCwWTw8+H9739/M1oYN8gjo+cHP/jB8Pa3v70Rqj+aNYahkYwb3ehG7bnvfve7w9e//vVF00dKbnKTm7R33/e+9zXyh4wuFd/85jeHF7zgBS2NeDzgvOc977DZZpu1MjJ0r3Od6wxXutKVhtXBFa94xeEa17hGy0N5EWQ/DOb//u//Hv73f//3P7x2GyK0yc1udrPh29/+9vCJT3xiuOMd79iIMULw0Ic+dHjrW996JhmvLhjZF73oRRuxIbuFvLsL4Yc//OHwS9ZAYUHou7e5zW2Gy172su1//Vg/5ZXkHfzLX/4yfPrTn24LCPPA88i/dtxll10aOXzb2942nH766a0fIEYWdYwH22+/ffv8da973dzpb2jQJ77//e+vGg/nAdkh1xbdfvGLX0x9xvf6JPlpq8KZ8ZGPDMNjHjMML33phGRNw9/+Ngzf/e7k+/EQ7f/f/34Yvv/9iXdvfeG3vx2GN75xGKydXvKS/1nGL3xhUteb3Wx+Ukel9t9/GI4//j+/+/Ofh+Fb35rIpgcSi6AedNB/frcYvva1Ydhnn2F4+tONy8OKhalGPf/3f4dh003Xd2kKhdkoAlnYYJGQqQtd6ELNO5QwNV4pxG/8LEOHp2FeD1UMqEtd6lIt/Utc4hJzh88FDOEPfehDLZ13vOMdbcW+T195eAt5RXg/Ekq3FHjee9JRR0b4Jpts0n54Ne9///s37ywwnhnJvJ+8kyGVfvsfEfN9wtIY4n4YoAn5873vEiLo+3F609LsQ22l48d30vKTfBerK1ld+MIXHi54wQs20mxhYLfddmuyPP7441cZyQm/k4efpN2XO/n35U5ZeHK1fUJ/fead1DPPRTY9/P/Tn/502HfffYevfe1rq57p5R+ZTENkN26L/p3UoW+fcRtPK9s4n74Nx88nLXLq66A8/ldG706rv+9T5siqr0Pq5PelL33p4YEPfGDru8rDe2jRxWKQPn75y1++9SEYt+c0qIvoAbriR/p0hzdeHzziiCPago0+ol/f4ha3GI4++ujhJ2L0/g/KyetpsWmexZe+vyTsUxrj+kZ/IqPIppf/WMbT+ld0O+9F7n26ye/LX/7y8JKXvKQtaPi815+xLPO+98jN+DptTEpftOjVh7fmfelKPyHn+byvU8qS91Kn6H3fz1L/6FUvj3E/7/tCytKnOeu9vhx9W67u4tvNbz4Mm21mEXFCeGQhqSTnN5Xbd19e+H9/1v98/OPD8KIXTTxv4++m/fTwvzyJ2e/kP35moer57vTTJ+W7z30sGvwn2XvXu4Zhk02GoXdAj8ulDP3/5z3vMNzoRsNwtav95zvWd5/5zIls+vLd8pYTmfp+nN5iP9e4xjBst92EmCLEi9V7IXlEpuM08l0v56W2F6/sO94xDFttZWF46eUrFNYVVrATv1BYOkIUl0r01ha+9a1vNSKx9957D0972tOGU089ddh2223XWpjceI8Sg/lLX/pSI5D+500Q5urv3/zmNy0kl3HOcP7oRz/ajCjhfje/+c0b0ealYajd8573bB7Yz372sy00mHfzMpe5zPDJT36y1ZH3QZrCD//f//t/LX+eO/nJhweDFwi5/chHPtK8IQjvySef3MKMGXfCUh/wgAcMV7jCFZYsH2GODFIGbQifMvFSKh9v1q1vfeth8803b+WULyKhbIgiT5T6+l8b/exnP2uEhfyUEykVqofYbLHFFi38mcx4upGPG9/4xmfSOXm/9rWvbV4vJBQJQua/8pWvtBBq6Xnmlre85X+8C+rCu06+FgCU7Z3vfGcr8/3ud7/msUOQfvSjHzXZqfed7nSnJjdkQZuTv7bdbrvtGuGeBvloT+mQoYUSHl0ebUb15z//+ebtVVcec/rCqFY2Zf7tb3/bSN4ee+zRyEave+qJlGlPodn0S33UeauttmqeeW1Ol+Qlzetf//qN2B144IHNK49Q8qJn3yIvJP1VHnrDGz1tb6+y0sPDDjusERxys0DjefpARuqiDtLlpdfmPvd3ZMMrqX0WC1vXnsjmr371q/Y3WXhPuLV6PvKRj2zfaUN6de9737vpljp6lvz1Le/suOOOrQ+SByijUHf1uc997tP6ELnrR8YWf1tM0f50+Ywzzmhy9jz5WmTxrPJpV22h78qPh10Y76abbtq8+cqmPT2vHnRCn5gXZOl9OuVv7aS9pU8f3/ve9zZ9kLZneJ21A73TRj4zVpCVfqMtlNUiHJnsuuuubeywrYBu77DDDq0fnXLKKU0vyUNb3eEOd2h6wfuvr5ORNIU/e095EEt9SPtIc8stt2xlNQbKL2OK540dq7O/8yIXGQYR15/61IQICkbgZEeChFJ+9rOTzxG0d797GL7xjWE43/mG4Ta3mXgcP/nJYTjppGH4xCcmnjrdWJDK9a43IXSIqfR4L3/608l317/+hJwhMd/5ziTthH1e6EKTvO1TnLTXJI2f/Wzynrz7odf3v/vdMBxzzIQIj7uB7+XB+/j4x0/yBeTqy1+e/PhMiCqPGrIonS99aZKvz4WiBn/5yzB87GOTn09/elJnw4o6Ipvk6W9y++hHJ3W+zGWG4Ra3mJTbe8jhjW88DBe4wKRcynirW03qjpD5/4tfnJTNM7x88phnyrEI4N2vfGXiGda25KkO0uUp1p7a41rXGoYb3GDyjrIa4pXNs8JnkWPvKn+fN135wQ8mnusNNeS5cNZAqWehsJbAQGHIIQ6IC4NOiOXaDPWyWs5QZDgfeuihbc8YI5VRxDB7xjOe0YwphImR+Ja3vKURRHuYGJOIETLCqBTCyeg65phjVnltGfpChxmqjD37oxBLxqd3nvrUpzZDHOl4xSte0cgXssBQe+lLX9reld5BBx3UDGjeDfl7B7laSogd4w5Rsof0gAMOaEY0UoE8SJM3knHI+FNeJJ7RjBQ9//nPb8ap8GeyQnbU7c1vfnMzwJGo613ves1oBWkqK0LoOUa9H+n3XsrAIoY9d+Rm7yzDnSdLWCWZ3OUud2nvK5PyjD0cDGrGvPIwbpEfz2gv7eizN7zhDc2DhnSRPXkouzBmn/khc6Hc07x19FNbIDVIMAMaeeY1Zcwzyn1HRn60J0LEKH/xi188HHXUUasOLxqHDScEGIHX9uSP+PDCM8zJR5nkQ9/I8WUve1nz1vofCSBvbaDuoC0sBCCBiMKLXvSiM3n0e5Ddox71qKYTyBsiy8OpHZRfHwypjieNFx/56Q+9Qhp8Dr3nqofPEE95ICbKjiwpAz1805ve1OqMvCi3RQf/I43kSE+RSMTqhS98YfteGfURaUrH2KGv0dXsqUaMLNjoX9qFPLxH3voD2asjWSORkad2QU6RcfuyEST7T40b0tbO5IqE+c5nSwHvrj5pzNPGdF569NZ3dECbkgcvszb3nAUaBBOJA2PVZz7zmVULO3Tf2EVO3nnPe97TSKO6v/KVr2z50TF1R1y1i/6DfPtOPZ7+9Ke3vq796b4xiAz1N3s4taMfbWBPtb5rXFrT0OYzzhiGgw+eEJpnPWsYTjnl3+TNd7xOiAmigfDx6hkKP/OZCVG0dw+x8D2SiZR8+9vD8IpXDMNuuw3DYYdNwh6f85xheMtb/v398543DB/60ITYIKhPe9rk8x6vfOWErPDMzQr9VIa73vU/9w2qA28ZUsf7aBhMSOtTnjIMJ544DK973YRc+vu9753UDZk89dRhOPDAYXjDG/6dHjF//vOTH2QLiVRnhC0hq8iWz4WxqrOwVL99T17qTL7kd/LJk3KQcyBc+JBDJu+Q27OfPdlbuRjUC5mXnnfJhJe0b0vhse95z6RNlYvMTffHHjsMD3/4MBx11GRv43OfOwxHHjmpex+K62/PIsBXuUodRlRY2SgPZKGwFsB4QYqQBh4WBIShx6BHKhkta8MLKU3GOvKiDAxKhAcYSQ7TYYAnLIvhzshFFBmVjLccYsJwsz+MF4Oxet/73reRHUSDUc2YdcAMoxkZ4qV80IMe1EgHA1lZpOc7xiADkvGHcMoXiWI4S0t55LuU8GJlPvLII1t+jF3GIm+F/BAN/yNGH//4xxvBQXTk6zNePGQCEVMuBmcOIuIp9h3jWf3Iybu8JYgUss3bwxDlLUNEyTjkV9kQF6GRjHd19rcDnxiuvEzaiGwZqoig9upD5YTjMvhDXvxmdPfhtAxdRjSvDK+275ARJB2RZDDLl8dO+ZCkHsIZX/3qVzdZIBjyvte97jXstdderb2lTWcRIuVj7KsL2Wg35NW78k14ad8+nlWuww8/vBEzxjjyjODwgsmfka5e17rWtZpMQF/xmTR5D8lB2XLQjTbRbhZI6EDeG8PnCJLy0WGEBRHrT27uy+tHG9JxOuFv+SgPjxRo62mHXCEyZG5vJY+puutvfRtKB3kL9AOLD/SRF07deEfpYPqGRQDf0V99Wfta/LGQwesbbymPpsUIY413EEf9VHi3svKGK4893Po7vSMPJJNO+o4+0UELQ8i27xHO3rM8D7SpH/UlZ/2KfMhEm+gzFpy0LSKMSJMFndNvPCdiwnf0Qp/uPejS5YFH/tSNHuoDiT6QnrGXrLW7/KSpz5Kf8QohRSDpB32QDhkj/uprjNbmZO3/NTld1tqN4j/jGdp8GJ761Imn7A53GIZ73GPi+eNJQzB42ahWutMjHznx+oV45PAXxbnTnSaePKQGCdJMH/zghFjxYP7oRxMv15OeNPG88e4dccTEi9dDGZRr2tlUPIlvfvMwXPvaE2/aGIggkrTXXpOygSHs7W+fECBkWd323HMYdt114n3k9XM4jBDNF75wQigDXtIHP3gY7EBR9sc9blJ23jse24SGGsrUWR7kiZibtnbeeUIirXkozwMfOPHehv97Vx577DGRPxkht0jb/e+/MGH73vcmspUHmUkLsb385SeytUdUOyOOyqotEFWyu8tdJvVB4MnEjhIhv8i+snpeeoinhQTEOt7cQmGloghkobCWwCDj1bPijkgwShkpVtrt0WQsLTcYUAwqxiOjCFlBYpEqBjiSxvPwve99b9XeQ0AsHYTxsIc9rBFFBpn3Gf1CNpExxiiD3UmnvEGMLQZejG+r/4y2hGgiL7law3MQY408/ORkU0b2mIQsBIYpgvfEJz6x5YH8krUySB+JYkSqP0PVZwxxBixDk8GKWDKmUy6GJw9R75nqPYvqgxDx0CELvCAMUM/x8jLqpSN9JGzsrfJ82ghC9BnYPKOIJENbeoh4iMc0qJNQR147niPtpq4IiPahX/LRdvRsmmxDnBnHaUOGtmeRTmnRmXjicmoxMpr9wT7XjrN0kScXgeQFQl7IUF2lTX8QVLo5voJlmqevv4pDngtdXUOOPHjIrvwR5Ve96lWt3fUPJCQLK9lvqi8gJhYI9BH/kwMSqY3kpZ8gkD2UB0nxHe8Xz60DnXIg0CxE5mSobvQG+UMgI2P19Z3y6kMIkMURetqTmoTYJuTae/pW2qaXp75L15G4EEykV92RNvXux6alLnRZoBKuzJttIan33PZ1SlvmuiULA6IiyNp7yLE+nvynlcN72jQHkZFL9jCSj4URUQrSRJT1N4sTCZPVTxBlZRYOSz+VC5lP9Ia+jMSuDlRbsUX1U5ucOIrQ+Fzz6Jr+NiwgDX01Pe9/P/7u14C8i1ghIkJX/Y3keY437KY3nZBMnk/vu36DczdEbyLTYbj1rSc/08rOkygkk1cP8evLpg6IF9Egb30QhrIhVcjR/wVxtLJTq9Q7+fdIPdXF31R8tO7VymU6EQ4bmSiLz/uhyPuG0HEe5GA/pHaQhoN/ELdp+zt72Hcp0GLLLSfvQo5aEALMM3z3u09IpDyFp/LuIqnqg+ySvfUu7ZVDvdM1hcTySCLZ171ueR8LKx9FIAuFKejD2Mb3KPbG2CzjClFEUOw/Y6QFQrCsbPOEMfSnvT9P+ouVO2VH6EIiGY4IB+8GIslQTmgWMvO85z2vefOEsDFgn/KUpzSjVh14o4TQSYtxycDjLUREPRvPIeOLV4XHCYFmmMfYF8Y26yqM1akzw5OHZ88992yk9bnPfW7LW8iwtsrBGdmTmfBb5E8onRBHxiTjPGVQr1mnpiI6PCPICBkx9nNqrjzjccmBRmTTAzFFIhM+qZ7S1EbIlf1tuU+PMS0MuMfYY6ZdGM/CIIXmaVtgGHsfwQsxVq/xPkjlZWQjJCH2IYYMafKiN0hgvMTS1tbzthUSwMNG15T1wQ9+cPPCC+VmzK/Oftd5oA0RuX322ae1sXwRCUQWqUYIyCV653m6qU15ndRdOwl15oXjmfXstEUfn2tHiy6IpMNqhHIjYz35WewglhBisifj8fP5jo5Lr9cv+u6dWXtde6Rv8FAj0/7XT8nD58YJHrpcBbSU9jHu6RsI/CMe8YiWB6/sYqBfFkSMGzyDiKNFkZD1abLLNUVINbIqygFRDLHWJhZi9t9//0bKtfnuu+/eFhXosT4B/fhg0cmYZ0xBLumqdPWxeHuT91IQQrQY0bQeIlTVMDnN4ydiG6FDeuLMFiKJ2JieDOf+9r6wWM/w0CFxvIVve9ukLDxofucgF/lmG3bKaX3l6KMn3sl4RvvyCgXVtA960IRcBsphPePwwydlkLe6rA4p8rw1PmGiSB9vHyx2BUfymXZQDWIbwmn9lPwQxxDgfB5Sn7Q8Q7YCTZJm0vEc+eWgI+8gz/IKiU1a+buHd4Ttfu5zw/DiF/+bXBYKKxm1B7KwUSFGWH/y4eqcoMcQsseLZ4sxxPhIqJzQO8ah37Py583jZRH2iBwwlv22ym2V3N4mBktOKc3Jgt5lRM1Kf6E6531GWw5pQdoYzvYhKRPPAILAYGYseRaptfeI4eg0U4afMkgPkEaEM1eNMDgRN9444X7Im3qoLyOWp49nDlFDauTPs+D9nJ6Y0xjTNogKeSvXYqeG5iTY/JAtryBvGtKLePH+8D44jAWRkL6DN3gphbUhTQl3U39yYjDzRiSsTjvxVPiebOTte+F9OfwkHjrGJQ8dcqLNyQdxVlblUi8eHwSWd8vnueKD54pRi5zmgB76huBJ28E92kMdtKuyqiOjWagg4owkKae2Yiz78Z5n1HeawavMPIBCB6WnXXgFebN4sLM/1B4zcmOoa8ucotufxjsLjHhEV6ikv11bg1AiCUIJkbbeA+i39OmoumoDxn7aWr7p3zmldFr+0tU2ZJxFDDJCWMlWqKcwRbLU//QN++QQFs8hMNpXOkijdvduiP8YFkvoO3k+S4xauwPwN60NldXCAU8mvdPnQl6yaKGMdE1f0q/oD+jD+kz6sdBfUQHkqM/6nAx8ZxEDGZJWTvAN1F+++rx2QOaNCfoGPZGW/Yb0yXOiJugtjybdp7fx2PZ9MW0XnVAHepKTT8lfO9Lzvs3Gbeh/dUdoLXAJfVeXtK1xhZzoJ+JMlimXMVr6ucJJ+elMworpcdIMITU+6HPIJd0kg+yZdvCX93gh7UmVj/TIw35WdZp3TrGPz3UVmpsnyt/22/nhtULc8F0/PFjCF524KuwysF7jfXsU7elDMHKrClX0rP2FPGTCKJE5pM//wil513jd7FE0hQkp7YtvH6DwWYTw3207CY+110/o6dgLiGAJXaWm3bWtq4CUbb75xFv3kIdMfpCikDPhnNbHyMcPcqtOKZc6I47qICT1BS+YPENu1vekT57qkjSQ1Xghf/zjyeE+9k86kMYeUu+Ql88dzkPe73znJPy139+pHEi3kON+6ysC7BYvV7LkIB3Pvfa1E/nw4jpsyAKAcgtfFRZsjTFEOmX3v7r4Oz/eRdSFvBYKGwLKA1nYqJD74xgEPDsOQGAoCM9aajqMb2l530mYDEiQtlX2nOg3BvKBIDJgGDaIJ6ONwcQY4R3inWS4WC33vLIyaOXFiGHczUp/GhjajGDeCWF3PGxW9RmxCIt0HSbjcAshfVbqfWY/GmPJc4gCg1TdGf0McFBGJEcd+pAy+64YcMgKw41R95CHPKQRN2SM7F0AzzgVIoZ4MYjVV9kYjJ5jpKu7MjOOZ+1pA8YqQ5dximTwKCFcyMiTnvSkRlilIy8eRh4onyPvSBGCpl14gcmYwYyA8pIwMHketK1DRRAphqv6M7zjkUGmkUTeqnilp5EKxirCbb+Z+iIoPGEOuVEGxja5Jtx42vsICULLcEZelFVZEAz6Q3b0igx4ynxPf3jOeEd95mTbEJIePC0WC3hXGMbKahHg4Q9/eEvHYgL95YHxP0+kuiNLOeFSX9BmC3lkhF7a75mTbpVVP0hYM1kgWIgaPaLLvPPq6+AUMvK5d+kMUqZf6tP0mL6Nw329S9b0w3tIivd4uegx3VV+uqvvKYPn+32inkMi5/HqSR8Zoyv6BIKqr2gfekXGiLO0EFjPZG+nfotQa1dkR33Bd4ih75Aw+qfc+sejH/3o4eCDD259Wf0tANgTqc9qH3VGhBAyckIu5aedd9ppp9a29M++SX1D2RxA5X1y4DEm45Bp44I2i7c+sGih7Yw7FjiQPjpiUYKOKCuZWHChi8olX+1pfJB+7qdEDOm6KAjt6Vn7Fu2zdSq0cvII6rPGYoQOWbRYZaFD349X26KJsugLxmLkUJrqZ4GLR5m8EMjHPe5xra/QbwswxkukVNvRRYRe3nRPHsinsXkeICeIFrLwmtdMwhiFhSKOxx03DDvtNDkRVZPbN4jQUDf7A3viwvuGRIGw1H5aQJiEPkqTg/xRj5qkiYR5V7qu2fC9Kez2t/+3txGUDaHpyRLy5jAY5bVHc9y9kaz3vW8YdthhQiJ7SEeaSFY8cwibtHgrlRHRtQ8U0UP67A28852HYccdJ/VB1OzjdEiQ952i6uRZhNZBPAgjD6fw2cgTASNPspO+z5FV91b6329y4Q1Fjh1443uhq0J7U0deRuWPRzhgQjzhCRMCr7zKKU3EETneZpsJQXS3pTrx/trDaXEAwUfQ6QLirB66kiACBwJpT97HJz7xzGG4hcJKxtn+tboXHBUK6xkMLwa/kDETP1BnJIVxyKhjhDBi+jBKhgMCwFCaZfh6FtHJaaQMjBhP0g+xDMnq32PUZC8OA4lhLB/EjOEYb1ZIrf99l9BBq/GMKobQOP1ZZU34VfbQ9XeySU9eysKjwIDLYTP+ZyDK3wp/DG3PRmbACMs+wlw5Ee+JNLxHTt5LCJnvyEnaDC7fMRQZfj5j0PpMelb4yYycZ4W5grqQIcM1B6skTzJQFsjdmgxVz9MD8vQZGTG4vc8YpBNIWMJR4xnMHk5yoEPZp8eQRIrtc1uofeJNVla6Qg/krYx0yP8Mb+0+Sw+VQ3mix2RKzsqiXrkLz2fxjpEl/fEMuftuIT1X/9x/SJaeZ2zn5F7504kcDON5eYQgzLonMNDW3lFfskyYJPlGTvLxmXbUdn5rI99pIwsc3pMXmXtem9HryGNcL7JRdnJSfrLOQUSptz7uuehnn07KpX3Sh2fJUJ+Xj37gufT7HKZFt9OnlNvfZIhYO3zKIVDqrBx0VF0t4iB+FgC0R38no3TTZ9IOfsg6xEo6OchJubKPk+c5iyLqn7Yl19RFv1UGMpOm7+hXL5/IWL316+RHR+m9sioTXfRudCcHQnlOm3tP+sijOkpDejlVGIG06CZNepD+rk7+1qfUTfp0J/t6pStN3lb9R5reR6wtLtEb9Vd++euj0tA3fZaw7oQU51Al4+W8h30hFLxfCYH0kz2BiJcuYDgV7sijhrghOdbQImokRmikdKyTIE3ZK3nAARNvl5NBOYi9l3eliQhJE+HyroN2TDt9iKfvhGUqS7bVyssBPve97+TAm3HIJfJ46KGT/A2bvShcseE02N13//c9hvJwGiti+IhHTLyv/+eEXwXlUoac5Oodw5K01dlQi+gqWw4aUt6kg5jytCKwpgH19xmSiBT6ztSS9iAbJNvnSFvq4HkEWTm0xTh0VxmUH5QXUYx8xu3oR968oH7Lj7cx+Qny8YyDihBecuvO2SoUVjSKQBY2WPBEMQSspvPuML76C617MAZyvYTDEXgIeBUXMgIW2pfXHwqx3FiTPZDzpj3ekzXtf8SHV4UByUPIW4V4LkUW88ppbclzliznlUM+Y6zyZjF6eY/pj0WL1anXUtp3sWfXNP3F3lkOXVxMz9amTszTh+cpy1Lzm0fXEB3kcZdddmljWH+QEPJEx3je7G/NgTNLqd9iZVtbspllUiyUhr7l9GMRAzzaWXQQIs+jicQtVKZpMua5db2HBUbEUh9GuHlKeTbH+0wXOsRpKXq7ukjy8yTtWeQDgROS+ZKX/OcdjqubNiA7PGY9We2RkEwezTG55Fl0iuiTnzzZC4ksIVxCZZFhnr95r2learnnfW91012u98dphSgjs3V4TmFDQYWwFjZYCCtyWAXDKxdPzzoRMt4yq/8OVhCutxgWMhLWhgGxrtMe5zP+H+F2qiT5uiONd2Ba2ZZDTmurzrPSXYocgPHpUnkGrVA/4avzlHmp8lrqs2ua/mLvLEe7zCPfdZX3cqc/T5rTdA1BEvqJKApP5pVOWCRdy35XHlSLXjxwy61La0s2q/Mu7z8vaK6K4bk0vgt3zj2g85Y3fwuR9q6xi2ylKdxX9ElClZei92tzXJ6kP/+zvGzCQl3hgci9/vWT6yVmBUQstei8laPDhs8EXrJZnjJhs8I6ndyq6XgJeVBNzY997PzkcXXKPe97a9qUy6kK8bAWChsaygNZ2GAR1e1XnxcKM5vmHVjbRsGGjByqIhxMeNea3IW2oYPuJByVscsALd0prEnfsj/PwhfvIs9+TqT1nf2ACU/lfUOmxmG6GxMSkiukVAipxSrkb95Q0WkQdSLNHAgmFBqJzOFUGzKEWTpUhvfRtIaAODhnpQzRwmmVzXUi/najjX2c45DQQqGw4aIIZKFQmIm1Gaq7oaFkUVguLBTmOevKio0dayOkeJzuxiTHaSq0kqq30stXKBTWDEUgC4WNFH3PXpOJ2wqyAw2cOLe+DYDxaDWtPMu5P2WS3iTBXFo/Pnk13m2hh/HSjr/LvZyzvOS5cmZ8muhC5VnKHt012b+Yg3pm7S9eH0Rimgz6Z2fJQ136/YQbE6EoFAqFQmFdofZAFgqLAG9waIAT1tib9nLY/zGHrd/IjFPipOHUvWn2qhPbnAyHlzhBbylhSEKZlE0aTrFzypw8csiC0+icFOf0t3zuFDgn0OXCY7+915/816f/lrdMjhp/7nMn78prTE6ddudnbdnj8ouckFmwjSkn6+UZ5csJedqIDJTLO+riGfJ1VL5T/BYrL+KRq0qcvIpwODnTVQtO50T6XIXinksnODpR1nUVQn4RSvduujrBKZGuH3CiplMw+3BE+3KdFoncOBl4HriX0b2ZwvNcN7L11lu3/V3K5xqHt73tbS091xXYr7m6QJpdCQKujpjnVODVRU4MdvKpq07I1rUV5DyGE0IdkOIqiMjS8ze60Y2GXXfd9Uwn+Sb82D2gniFvbeGAmnkIe6FQKBQKhTOjZs9CYQ4PnEMKPv7xyfHeLkh2FPn/ne2wIBCWV796ctDBYx4zIXJjIEXPfvbkoAEXPzu5bl4ihhgdccSkbLvtNgx3uMO/3/3kJyf3UO211zDc617/JouedWy4I+Yd364eLjB22fOYHzh+/Oijh2HbbSdE+KCDJhdXI5vseukho+4WI5O1RSCRP/eSubQ5fEK+97735B4vPMCeGyf9uSAakESk00XYSLDP40VVX0fUL3bqnUNOkEeHnLgnzn2R7s5zRYGrF+xjO/DAAxtpcd/foYce2v5/7nOf2+7wQ1rc7WeP25FHHtnI0bOf/ex2hQAgTK4qcCel0ybnhT1xDgZxL51DoZDSeDcRSuV2jYTDV9YE0nOfHqwLsuXgGPJwgfttb3vbYQcXzU2BQ2acwmxfG88oYuhuT/+P9+qShTsTEVGnNntP+u63dN9koVAoFAqFpaEIZKGwCBAWBOq//3tyspxLjXm1AIHqQyZDRnoPndPseMam2d+5cNhF0o5k503sv8vvPu3+e4RU2ZBCFxT3+frshz+cEEnPcNRI44Y3nNwH5tLqU0+dEEd1G1+/6Cj3k0+eeE/veMeJh9MlzS5Fvv/9Jxc7+w65/vKXzyyLlDlymVan8XeLkfijjnKy4iRvsnQpswu0t9hiUjZH2SPULmNWdhdvf/WrPGf/vjzaRdbK7IJqJxki9bMubua5QvgQMZ48d9ltscUW7foXRNIJkccdd1wjL5tuumm7kxDp2X333Zvn0YE7yB1Cx+vFU4aMOt3Ws7mz0bUg0s79movBezxsvI7S+8QnPtGuhMg9lgiTn/vc5z7tEBIkVRnl3+eRe+585/P8rd6ezbUSrpPwbO8J9Jwf33s3dyv24bq5EzEhownTTbrTwkcdTsQ7mLrMCl+VhhOYc/AMmfIybrnllv8hx2984xvtYnjX95D7TW5yk3aQCq+vkzpnndxcKBQKhUJhOopAFgqLgD2KYPEi8tBd/vKTMEjeu899bnIaHhsUUbz2tf9NxL73vQlp8Z0LlPvw0HjuXB6MNPKw9Xas73k7XcosJNMJ/yIRedXY1AiVtHkIfTYmf8iR73kGlUNZ824um1Z2v91o4vfYVucRdVT8Xe4y8dwhbeQgFJeX1HsIGw+f8F71c58VDyVZIcbK7AQ+MkydPvOZyXP4iLSkI8z29NMnefIKcnopPy8pD6l8yeu2t52c5qesvK3HHDORz7e+NQxf/OIwvPCFk2PkgUcUiZSHNkMytY+8EGsEmodyIf7gBMy73vWuLXQTUeHh8uN/YZSI5CabbLLqgneXjCNMSKF7M5FPZAf5EmLquXjykLx3v/vdw/Wud712uflS4W47l6w/5jGPGU4++eRhp512auTpYx/7WCNKLlB3kqc7PJ2mi8Rus802zXP56U9/uoXBCrU98cQTGxn1+emnn97K69TKO97xju3v0047rV34LmwXIUVW5fHLX/6yfe5KHN5XxM59fi6hR8yEoXrfHYfy8aywU2GmvK1jj2bIKFktRqZzDU+IqRBhv6+bxu+g/Ahw7jBVB22BVCKe2qxQKBQKhcL82HjPBS8UlhHTPGVf+crE2/X+9w/Dhz40uSCZVyyeOBcxv/e9w/DKVw7DvvtOyFGA0CE3++8/DO9618Qj5sjz5INU2nP48pcPw0c/OrmY+VWvmrwXb5yLmt/xjmE4+OAJaesJKlL1ox8Ng6hI3k9Es6/LuD7T6idfRPB//ufMd3chp8rKO6kOiOnNbjYhwW984zA85zkTMvjWtw7DM54xIXfkgWy+7GXD8JrXTIgxTyYP4Ec+MqnT+943Cbl1t5lnTzhhGJ7whEk+CKr0/+/6tgakWbnsc5TfVa4yDCI2Uz9hqs985sTbKn9eSbL42tcmaSL1C0VlIhs8VUhgPGHIE+LIE+leUf/zHoYM8aD5Qdy8FyKE3Ah3te/OfaUIDZLp+xvf+MbD6kCZeDyFrx5//PGNoAkBtR/TXXf+fu1rX9vy4C1FDoXP8tR5ft99923E1feIlDBaf0vz+9//fqsbL6o07KlUb/tBX/ziF7f6bbXVVu3Sd+G6PLG+Rxpd3G4fJmJ9wgknNE+f+vveftFvfetbqw4kmobFznUbH0bE06kewnWRw3FayCVSmn2RSK3rHFxU76dQKBQKhcLSUB7IQmE1gYAgS1e84uT/N71pGF73ukmYJ08lUiVcFMG0TxGBAfYxsvSpT02IJdJzyimT/Y8hn29724R4CZnlMeO1Q9bufOcJgZMPcnXzm0/2UArr7O1uBA/husUtJgRViCnv27xgVyOD6sKz2oej8goiswgr4mcvIfhf+TbbbCITz+2994SsIXf+V4+ddpo8o3yHHz7xOJIXj6FnyMO9Zsq75ZbD4FwZJHEcypuQXFyEDHhJ+/NWpIlE5lkkk7dU3ZDel750Ye/jmIjw4J1yyimNOAmzRLAS/hkgNQhKTi7Nu0iTS+F5JRFMHkH7+Hj+ek/cUq8KcVeeQ31e9KIXNXLGY+oePSG1/udl85lQWc9+4QtfaN/z1H3gAx9o+zgRL97Qt7zlLc2j6l3eTOVEgnkTv/jFL7ayIaE8eg996EObV5E39j3vec/wute9brj1rW/dPHs8nne6051avqeeemrbC0pO/kdSEbnlvFNUfT7/+c8Pd7vb3aamm1DaXqbaDInVToVCoVAoFJaGIpCFwmqCPfr97w/DkUdOPGa8fsidn5zW6odN2/MBNivvXggmbLLJJNQTeNt8L2TTATw4Bc8jr6TQT/kgS7e85YQknfe8k5DPEEiE6vOfnxx6wzMpnTPOGIZttjmzJ3EWvM+jKq973vPMp8LKQ96Pf/yEJCLC/3cezCqixrPIy6eewmd5Mb0nUhDZFHbqRzqbbjqpu/cQZWGpvLi8nmQgZBXZy8mqyhYiiYD639/CVHl85Zk6IuzIotBVz4l65CXmhbRf0vP2Rs67D9PhK0iHPZCIijBWnizkKx41hATRFEKafYG8dcJEncCKjCE0TlC1vxKR8Twy6aAeexqFd+aQncXgfaGmyN/rX//65uFEDpE/pNX39vz5jRjyxAnlRAjt/eNh9SxS5wTYJz7xicMuu+zSiC5ym/2KE9n/c/j2t7/dPJ0hatIVToqM8shmz6O0ves3T+hEN/6rkcjlhnrylJLtNOKtvmQesqhNlCne5UKhUCgUCktDhbAWCjOAgLgGA6EbR9XhC/YHOs0U2eENvN/95vNosXHZ34hmIvn8zZsHyJRou5BEPzxx8kCCvK9s/YE0yGKgvMJrES9EUlQfD2T/zCxIC1G1P9DhNPYejm1yBC17J3kNOdCOPXZyCup++00I6wMfOCnvTW7yb6+q/KUl7HePPSaHEfG88tzyMPpOXT0njPWnP52QbJ/Lk1w8F/g7e0fttbQX1d7G5McLKww45BPZRlZvfethcEaLQ4cmcvz34S/TZfKv4Wtf+1rbM2g/JHLHo4Z42Wco/BMJhN/85jeNnAjtRGaQS15Lp30icJO2/utw6UtfuqWByAn35CGUDiJp/+FiZfq3Lp1tuOpVr9pOiEVSeRDlhcQhbPYp2iupPMqKwCF643sUESx7E1/xile0U2P32WefFo7ah5rKC2lG1pDfQF6I2GKH0fR1Wqhes7yv0971t0OEHKbDGzq+fxNcY0LmqTe58waTG9nMK+tCoVAoFAoTFIEsFGYAGdl998lePiTEIS1CJBEytqY9hkieMElhmIgT8iacErnhleShRMj8H+8hIoQQ8g5+4AMTLx0y+vWvT9IAp7J6lk0uTPUKV5h45qSHLCFZxx03+d8+y499bOJZ4+1DnKQl9NMexMc+dpK3Q2kQJu/aE5nyIsnKHO+eckk3V38E3lUndVE2afjNu8mjRx5+kD4eUXLyvTIJTZWPvZzIKWIqvNaZJ/JKJCHHm/ra1+kZcgVysP9RXpGjciqzQ3ak5VmhtYissjmhVR0RXGVA0EOikUly55H8zGd+NxxzzNFt7yAiFYRsOHBGiKZDZn70ox+1MM5jjjmmeeO23XbbFspqX2H24iGH9hwiLIcffngjhjx2n/3sZ9tdkrx1QkTtQbR/8FnPelbzHHrHXYv2SCKrDtgRnrkYseE93G677dr+PyRKSCryyDPofXsWnURqD6a9ivYx5mTWnLqK+PJ+Ipr777//sPnmm7c7Fnnt1CvPIZkOnVEPRNePtHlNkTHP+pF+QkTzrmdda5Jw2GmIzPOeNELuhP3au4nABurnACHlzR5HzwppFVqLzCPurj2Rr7JpK2V2RQjiK8TWs9IqFAqFQqGwOCqEtVCYAaGPiIkDa1x3wSPGy2h/Ho/YXe86IU5PfeqE4OAeiImDcZA2XkChmsiTd9316LyUPfec3CW53XYTosTuZU8LCbUnECFCMJEx/wtRlTaPn/2Bou6kceihk0N0kDz7Bd2TqGwIJJJlz590Pv3pCblUD6enOsBG2C2CLMQTyUTaHv7wCbnlTfQ/L11PIDmdEq57yCH/vjMSKeORRPh22WVyrQdCzDvI04rcInHCYe2FFJrq9Fl5+Zy3MofjyI9n0h5P3suEo5KR/ZXyR4R9rp7aQOivfBzIQ55PetLke2kJtSV/5BvpFHKrDW960wnZtADw+9//eHjVq141/Nd/nb2dEupQmn/X+RftEBnkkicxxAeRclANUmlf4UknndSICeKyxx57NE8fsvSGN7yhkTkHyQCS96QnPal563pvGfIlHDZ3Gtq7iNAJRx0fDDOGdJDFe9/73sNmm2226tAfnsiHPOQhw2te85p2tyKPmys5hNcK+0RqkSr1FYrKE4pIqZPP7I9EjuNt9A4ypvxCcP0kDHTHHXdsZA/Bli6yikgjZcjg9773vfasvZpCY+2V7K8FiRwctEOG3vEuz6+68XJqA4QcQXZwESCVyq49Ik/yQ76dCOtd3kl7NhFNHlplc1CQ0F8IqXffJQ9roVAoFAqFhXG2f1XcTqEwFQm7dJgNooXYIWCxexGknHbKdvW9z2z5QgaRNsQRfJ97GxEzBIdHzD48XkBbw0RB+tu7SCKvXDyYCBYvJPs2+/+8mz1+iJjnkg6Pm7ykxfPnOUQ015Ek1DPlkp9n1VV4qdNLkcieQPI8ylO5x6OGcklbuYSGkpv8eBR5ARFsRI88cp2HtHkNQ8hTHnc3CoV1ymyu7PAdeSpf0pC27+1/hBymI5QVlEee2kO5kXDv+Iyc5YP4Xvayf27eNl5FhCvXOsRr5hTPhJVO2vJsjezx9iGH9gQiSAgP4uhwGZ/b0yiEtA+PRJrccchb1u8tRLwQIfv1PBvi5g7JnmwuBHsthdX2JIgHDmHiDeXZRAyRM2RXnVwrktBPYZ0+Q8Ck4XPvKZsyIrnq53untKqb/JQZKfUuOfjtOXX0rvogpQgkQugwH+SSjM7c3/7VPIQ8pEJ6yYOc/fjb58rtdFdpgTKoN0Lan3grX2mQH5kLYfWZZ5VLG4Ro+5ysp5HaQqFQKBQK/4kikIXCIuh7yDQ7Pt/PexjL6ua/ttLvgWwJ7XTy6uoelLmYvPpn8j3SK5wX93FSKzLMizt2vo1HqzVpjzz397//rXkXEQqnq857AuqZ0/p3wVbn/VnpLSWtWe8spWzzPrs69V1TGU2r32J1nvbsvGkUCoVCoVCYjiKQhUJhvQNxfdrTJvsakVfk0V7PdWHT86jxVjkcZ7EL7AuFQqFQKBTO6igCWSgU1juEnwoz/dznJvs57ZUsLlcoFAqFQqGw8lAEslAorAjME/paKBQKhUKhUFi/qFNYC4XCikCRxkKhUCgUCoWVj7oHslAoFAqFQqFQKBQKc6EIZKFQKBQKhUKhUCgU5kIRyEKhUCgUCoVCoVAozIUikIVCoVAoFAqFQqFQmAtFIAuFQqFQKBQKhUKhMBeKQBYKhUKhUCgUCoVCYS4UgSwUCoVCoVAoFAqFwlwoAlkoFAqFQqFQKBQKhblQBLJQKBQKhUKhUCgUCnOhCGRho8S//vWv9nNWwz/+8Y+zZL0LE53/5z//eab/owt///vf23fLrRvSk/ZiWJ286bK0N1R9Vu6//e1vZ2qTwvoFnVob/aCwNJB/P1eN/z8romRQ2NBwjvVdgEJhdfHzn/98+NCHPjT85S9/GS560YsOm2222XCBC1xg+MIXvjC8973vbcbb//zP/ww3v/nN2/Of/exnh69//evDH//4x/bs//t//28429nONjN96Xrny1/+ckvrfOc7X3vvyle+8lzlMxF873vfG37yk58MN7nJTRbMa03AIPrSl740nHLKKcNVr3rV4Y53vOPMZ01Qn/vc54bPf/7zrU5nP/vZV5XLd+c4xzmGu971rsPFL37xmXX60Y9+NJx88snDH/7wh+Gc5zznsMkmmww3velNh//6r/8avva1rw2f+MQnWtqbbrpp+1lb9e6hLHSBrJXxile84nDLW96yEZBTTz11+NnPftbKd6UrXam14bnPfe6p5frpT386nHbaacMvfvGL9vzVr371pj/+J9/f//73TUbXuc51hhvc4AbDuc51riWVUzof/vCHW3lueMMbth9tAN/85jeHj3/848Of//zn4b//+79b+enzYm3/wx/+sNWdrknrspe9bKu/tqF32uLNb35z6yPbbbfdsBwg1y9+8YutLte61rWGLbbYYupzv/nNb4b3v//9TW73uMc9hgtf+MJzpU8X9eFvfetbwwMe8IDW95YD5KU/n+c852l9ZSm6abyhG9Gl6Ni0NqKDdPG1r33tcPvb33640Y1utNr94Lvf/e7wkY98pMmQrOUp/z4vcj7hhBNaP6Djt7jFLRYsl/77u9/9bjjvec87XP/61x+ufe1rN935wQ9+0PqLMZJ+r0m5VxLIha5+9atfHe55z3sOl7vc5dYovYzt+ivZaw/9zXhA/sY9fy8mO/PRJz/5ySZv8je2eE87X/Oa12xprg3567+nn3566wv6mrzldfnLX76NSemn47yV0/iuX+pLyuvnYhe7WNOXy1zmMmfSzTG8Q250TNrbbrttm0+MEZe61KXa3KMcs2T+jW98Y9UYGZkby69xjWu0ecg4sZi81F29P/OZz7Q5PnL3rn4gHWmvK71Xr+985zurZHCXu9xl1ZxQKKxklAeysMHCoHvAAQcMF7zgBZtRdf7zn78Z+S9/+cub0XXiiScO97vf/ZphZUJAGE1yb3/729sEthhMTt759Kc/PTzzmc9sn1360peeu3wMd3k94xnPGH71q18Na3MCYtS+7nWvGz760Y8u+Cw5ICcI5NOe9rTht7/9bTN4fEZ+xx133PDtb397wTQucpGLtDz33Xff4ZWvfGV7L0QUeSH3//3f/20G9roCQsD4Ofzww4dDDjmk1YlBQDcYN/REW/h8IcMOydLGz3nOc4Z3vetdzTChBwg1Q2WfffZpMmZkMDqWigtd6ELDFa5wheHAAw8cHvSgBw2f+tSnVn3H+NIeRx99dMuXcb8Q/vrXvw7vfOc7h/vf//5t0QRRYZCp29577z286U1vagYSgwkxYKgtFxiCP/7xj4dDDz20GaILlfHd73738Ja3vKUZ8UvVaf14Hg/nvGkiTS996UuHl73sZa1sSwGjWj/Rdm984xvb2LBQG5G9MUqbrolXgT7qY3TvUY96VFvkGIMePPShDx2OOOKIRXVH/2WAP+tZzxpOOumkln6M/ktc4hJN7h/84AebMbuxwFisj5gbLOIsB8jNuGAcJUdjH7kaF3fbbbc2Bi7W7sYsBO5JT3pSG5ONDXT0sY997LDXXnu1PrY2PFLGawuhFp7ogTyMb69//evbItMHPvCBqe95BrF93/ve18bUjJmev9e97tXGWPWZBfkgoUcdddRw7LHHtj6ivvoTGS7W1xH/X//618OTn/zkRibJ/E9/+lPr0/e9733b2KkNFqu7xSP96AlPeEIjtNLx+8EPfvDw3Oc+t+WxLvHLX/6yzd/6XUUsFDYUlAeysEGDEW8SNokxUK0wM7JMclYZ/c14ZVxbkWekIxTzTMqMKkbj1a52tVUeC7+XYgCfccYZzcBGWO9+97uvlVVNEyKPmLotBvmTlbogRjyHyHdAlgzMhd5HzKziM44PPvjg5h2xYq4tGEEm+Uc+8pHDJS95yWFdgQzUP2VniGUVl9GPSDISFlshJxOGFaKnHmTleaSTzNT9Kle5Svt+ddpS+te97nWbh4LBhBS86lWvamSbfvqOp0s7LLQKzcj42Mc+Njz96U9vpPExj3lMKxPwmqqvlXrGHJkwRleH8M4CeSgrwrEQfE83eEmXAnXfcccdm0GJPC0XGImIBAPx4Q9/eFsIWErb0SWkgS7R71ltRDe06f7779/I3EI6N4+stSm91L8siFkYS94IqoUz4xr9prcLlcsYtvXWWzfSyevNgxaySEcY5Ntss01LZ2PwPoJx/MY3vvHwhje8YVnSIxdkypig39KLzTffvMnWfHHve9+7ja10bJZHDeiGtvWeOet2t7td+zHOPPGJTxxue9vbNkK33O0gPX2TjtIvHmv5G5ce+MAHDi984Qtb+TOmBPQKcVbfr3zlKy3CwdwjSmP33XdvRI4c9I9p8H68heYP5fA33V5sTs7c41ky1Z7mde/d+c53bosDiKUxz5g4S+5JJwst17ve9VoEhWglUIfb3OY2TfbrQv/lQWfItMJXCxsSikAWNnhk0DUQ3+EOd2gTjInKoCw8i/dlvDdsXZQJgTU5MVqtjJqQGDLKaeX1+9//flsNZ4gKKWP8eYYxwXDm+eQ18o50GK68JlZeGZIMa5P1vGGBs8qZ31a7ERfkazEwnnjQhDPx5tz61rduROX4449vK8HqYqUZgUYelD2ho+osdEs+ZOR7YbcxOshGSC7jlmwYGCZ69ZzHEJ826ftsuY0BMmO88yLSMcYXstSHBc96j44+4hGPGA477LDm0XrKU57SZMSAJ6PFkFV7YY0777zzfxh6DCLeVrpEntrWKjeDD0EQgkaXEM2EPjPI6JYFD/VgVJK3932OeCHUPh/nB3RWu/Fg001paNPoGF3mnZYnw89vUC55KqO09VvvINI8kOSqHPqL8EsGrAUZxu+tbnWrmeHI0+RusUOImJBeBOrRj370ksldn5c0LVzRc+Wnr/ScUe5zclNPiw6IGn3XN8jIuLDlllu2frBY+X2vfbQF781WW221KgxTH9Qu5DYvPH+f+9yn6aD2Vz66py+Sue8yBqkDPdI2iCZvnvomdNNzjHa6xTBHLKSvvfVhhIi+qwOdM3ZZ6COHtJ805UWG3kVKEGLPS8M7iBrZ0j16oFzqrP/pD3e7293aGGGs945weu1g/KAr/bjP+ySEEeioCBH1oVvGWv3K9oX8v1D7+K4fR+mAz7T/PHONZ/o0+kgXYyiokzKTuzHH4o3+Rf6+0zfUh/dNGCbv+FLC61MGYzh50FPjxLR+nmfztx/9XXsZ17VZ9mMbF5VZ+2rrWcRydebkzOnKQi8RWP3bmHqzm92sjX+LjcP9PnHPkrvyx4spD3pBx+icsQjRpu90NeHs9NmYRpeFJPvcXKrPgLFMG9Fhem7RVr83DtMz72nrjWXBpnDWQIWwFjYaIBkM4ay+G5B5GqwsLhYOuNwwAfEAMfR22WWXFvZoEspkxRiwwrznnnsOb33rW5uB9LjHPa6F+3mGkcjYtgfJvqtMwogogw/ZePWrX91Cn1YnJMtEZRI04TPelVX4p/2R8+6/YCgJAWJ4CoNSD8YW44bxwbvGy8bwVVYTuzZRB6QH8TSZCt0hC9+puzAqoVBWZHk47YvxfoypxerlOcYlI8iPULLFwpqmgaHGMJAGo5hhrQ0CBMCqN6Oe0WGvHlKyUAhXQMa8uAgMuQkF7cu4mOGjzekE45icAsZ8DBvEhWFMxkKzrKwziMgG+ee95M1SNyFzL3rRi5qeqjNiEZ2jiz5n2B900EEtZI+BPQbjTVtatEGQ6G+MPMTkHe94R9M34eB0AZRFyLE6KSu9sJjC2EKqeU4ZcGTtb+UUZq3MZKe/zBvyJQ1eD3U3JtCxxcK1FwM58Hqou75KH8iAPgiBtphib5P+ID//C/vWJhYA1JFxuRjIB2E0ljA4yUi9jW/a5k53utOSCAP9ssiAbNA/JF56QhEZyIiDhQ3/I3N0nw5lz58xiW740Y+f//zntzrat4p8vvjFL27jnb7++Mc/vumj9+wJpQOI5rOf/ezm6dJ/yMZYwUAnS6SYLh9zzDGrvKt0hqwRU/1l1113bSG7xs6XvOQlw3777dfSor/GDeRS+1jU6sOV1V0f0S76KnIrTdEq9JEx73P6q07zQH80dmoX8lAPCxULeR/H0EfohTBG453FRAtzyktmD3nIQxpZQcLovv4JZE7e5gREzpzwmte85kxj1WJQX+MPWSLf+of+vhA8j6RrZ+1qT7S+RU/pq3bT/sYhcjE+6tdrC+YjOm28RswWA7lmTya566+ilYzL9oECudM18tFP6ZywXzISpmzONj7qy+RgjLKIZg7UJ9SfrmkT/YcH3NYCcy59NxYYv+k6vS0UNiQUgSxsNOi9TCYwRiZDxcr0ul7ZkzdiZSXYyiSDzETPwAGrkwxtxFaIEiLG4DGxmNQYiSZbK/877bRTm2RMiiYqaTEkPG9iNpmtzgouA8Okz1hjtMl7KfvCyJShxGhgjEmLRwU5krbvrcAqOwPQJG2izH5VBrcJWDied03Q2R/jHR4NoUhkwECe5yAVeTLEGJ75ec973tMM5KXoQIw2xqc0GAL2/fB0QIxbhEQb8qZoN3nNs9dPe5ETQsBrLoyVQZG8FwM9kg+PQU/4GTrqj6TZB8koEapFD+M5YNzwOMULZRGD8YkwM4oYp4xobULnEIzsc9Uu5DBtPyXDicwSQma/cfSSEbnDDjs0PeeJoHPamjGlbZAfaSMZ5KBe2j91U14eFzogPBDxYqgy2ufZI6lMiJYFDl4GfY5hx8hdkz2W+guS0Ou5fiRNXhBGLRkoN333m3GKrDCojRNLWQCyICVNZDwLHPoH+Sx17xRvCDloZ16TkCDEBVHUzttvv32TGWKvnkic39pSn1R/+iZkXRm0mXQRU58hPXQoCxkIqe/JwRioHrxUvmPIq4u8kDqklcFN94x3FgeNM/SFbOkM7ybjXMgtAx6htmCECCF/xt6QMPCbzJAve4dFPhhP1cViDC+e95RX+ZDpefqjsiPV9vX5bWxejICNoe/Qx7e97W3N2y+0NeHbysRjZbwlG8SY7ugv2kSfNq6qj/EEMZHeYlA3Y5o+boEJEdT+e+yxx6Lk1/ij3eiJetO/HEylfEceeeSqvkU3jA/0alY5lissF7GdtsA1a1yg/0g7uVtQyt5mY6zFKmOdudx44zOLhNpCneix+c+iQUJijQW8n/6mZ/RAH6LDxsVXvOIV7X9tJFRWqDA9pmsVwlrYkFAhrIWNEowMBgUDnSGwLoE88YwggFbITaImGiucDCoTDZiUGFMMGAa7Z3IqHEMgRqbfjG4TNYMhYUWMSQaSiWypMFEx1nj3GGP+N5EtNdRTGlZ9rbYKEzOBex9h4C1iBKo3jyuDhJGRPYWeVVcy8HmMjawKM5oQG+FJ8+459C552HuUUFAGAFKylMnZswiQ9ko6VrYtAgADhfGEjHiWseDgHcb9vPtkQf0QPWSGxxaBm6ec5Edf6AMjKGGYPmeckIM+kP27/cFBfmsLbZRwaeUn45xq6IeByJBFVP5/e/cWa+1V1Q188+mFySc3Jlx5YYzGxBiJ0cQbUcRAGlGO5VTaAi0FUaQcS6EUWkoLhZZwKLQgYIVQDqIctBJBLlBjJPHGCy+Ml2qMQeO1Z778no//m8nDs9Z61t5rv+/eb///ZGfvvdZzmHPMMcccpznmox/96OkdKqnay6uf84gwZQi/41nGg31JaZf7GYWeL81Qfyn7HCWMDn32flFQipp2zfcO4hUKtWd5jr+TLrdrLEXARA7wkihh2s5BIFKUtMN9QTlkFONziiUniH6EJ0YlXPv9MDLN/aRbrolYpx/4RbESUVtGOAcGwxS99lU+tYXDh1OKoYbW2c+XKtZJj/e593CapMoxw0/ki8HBAAPP0DftxHNJU07lS+OVip9kc64l7/CM/Xf28zKGGJt4OGn/riHv8Ic5PcoQNE8qLQPd881daY2M5PC/+aJICh4lh+Og8A57btFVFJIhkf2Na2DOiU55ByNctIkh8/a3v33i+zW8RcZ7BhqR9Zwk+EOmAINclgYjjGz0bONjjWFIMobRXn/Iccagz923Zh1wj/6KkOPpNdVM8THDneHN6NdXbTTmHGqik1lTyFKG06b2HMJw8gwyxZit2dbhenxiDpAB6MmIFBkHzhNjaT3RB3yqQJK+oZkfY4sHvQ/vkeXmJEdinBDGJVswPMNnDFY8lme7Zp91oyjOAhqBLC47ENwWYPtlsoCd9Dy5tYaVd1g4tYHiImoi0mhhprTyaG7ajzlGTxUloOhajKUTioAxLikOlBUpRgxIyhvl6Lh9s4Ba3HJEifdSSvZJ+aS0WADH0ueMD6l8IgY8spSTTW0c6coooEjyClPuRS8omhT/tQp+DA2Lu59Ez/ZFlHzP8HtUqoyh8bToGwtGNIMpRVnWjkeKSDA+KSAM8V0RKfdQ3qQWUhIpLON32m1MU2J/E9YYXvjA3MHD+im6wVu+NBa87iKpoiFSDSmM29KO9df3lP/QkCGSY3IO5Y33HIaq1D9GrjnJaKX0irxxBOyLGLaMa6nfjoiwH3lbMZClz/c1WvGxjApKK2ObIY7exynS493mpYI6jGvGP3kZRZYM07/0l2Luu+zvk87sb84aY7gtAprqm8lACC+ROZ7BiSUi4zmMPhEtDiTGlP5F3vm9ay6bG9rmekWMUlVTG7L/VnSMgToa78kUEY0jd0SE1soN15lzZAVDUBRLxHtNenKQoyQYcPiUoWEdSzXfe++9d6IhGsT5kH2l3hP6GyfPWps+6zrGNHnineTlGr5MISE05XRg4ItGJ5PF+JkP2hsjLe0+DXAOmIf4ZZ8jetAqezg5VI2lzB60xud4wjpLPumDyPCS04ezg8wja1wvSrlJFnh2fkZ0D2RxnlADsrhskPPQeP9SfEWkKHvh9lFILRCiL1HOLTBJoST0Ke7z/TE5OFx1TB5Xi6ZFh8ItdYVylpSYYF6MADxDSqlnUBIpjPok2kWps19IGygW0qV4zOew8FnIXD/2e06DFG/wQ0nUXwoUw8HfFIJNC/5YhGD+g972MjI8GH9Le4nGvo9AZ8Y3RYBRZlFnJGuHNklBmy/g8z6O/ZoXS8jZiaJG876NxTCWPsvfqcYq0iwqkPMxKaVSwrSTI2GeHpn7jd14aLSoSfaBJk3Wd6I8S+2k1EjlpPxQ/KMgj23chV3KCn7QR/0SRcYTxpH3XBRk3i/paXiUws4JwHmwTYGm6IpkcY7kyAN9pbx511hYZFtbU0TEOCzxhTYzFKUymo+Zk9IwGWKiDuaca0XKGQ/juGV/poyGOFbchz5khDRc0TmfzQthjLyzrQ8U7hSUWho//cIzvqPAMnYYvmRcqh3PeX0T74wwr8wx/CxKy7hmUHBSoYkIjGehB9qQYYwGBqeIq3HWHvKG02cT74WXjJF7tUl/7DPURnLMXmnyTnt8Tm6gO5mJrmgvxTPVO8MX83dqh+eL4N16660TXzK0E+mxx42RZ/+lsc79IktkDX5Eb7yZNP/5uIzzbF5UhiHheuOCrpFb5tE2J8L4DPPGeDDMfE7+42HtZuCEDxkpjCWpkina4z36ItpnPbQOkClL7V+Sd+B68st7NsnW8TP0ElHDT95tLvhbxNoYooH0YFHzJWfs+L+xloKMBtucMaOM117rjbXQcTbaYE5J450fozN/ZpwvkT/oGEPab86MyKcUApsbfr5jOJMlHCSManJnac5nP7P3cjLgMfTJ74y/cZvTvyjOEprCWlw2IIAVDxAR4Ake0+cUWNjHu2fxcY/FOilWIizAQJSqQlHm5Q4IegqlRVN0g9IVD7bfFChKKAON8k9B8B4RJ0obY9ffFjxKD0XEYkSR4SHmpZdWw1PP00rBoujZx5aUzxSrsJhJfZVipK1jKpZF0MKKPpQ592krBYDyRIHTF/sTLXRSepaq8YF3Wew9y+LrGdImY3CLSFFELIjeyfgwHhZg7dBXypaxo+SJEtmH47Oc5UhJYlAyTrQFDY0xhTPQfsoDGqKpd/KoW5AZnJRV76MIeK5UJLRWIGE8JsKCTcn12/UME+OGRjFQ7DllsEl5Mw7Sx4wDGhkfkQhpjRQmSg1FLryXQhX6wOjgoddH9BMJQsOcQaYf+puzC/FhgJ8YET5X7ERhG04Kihva+fF8bcET+kqRS+of3gLv8uNdvvO338aHwWAstYtib+y0gbEr2saJgq6ebxyNr77huVTY9C7jin6qGEJ4lKKGdowQ8wWdzFt7fmMUGk+8zVDOHlm/0Usf/OibIioMDamY5skoE/C4qq2iOqMyliNbKIRSnBmWUjLxi+eN1YiNpQIZMgkYBiKlSaP1o2hLHDbmgOehgT4y5PQXHYwbuhpT16E7uqCTyJvxw9uZb9qLthRvPIOvszfYOHFS4X209w78hT8ZHgpboVcKrGwCukuLFR3SZ7RlbEvzJKv0Fx3xvhRrNNdGEboYhWRiqs3qkzGOI0Ub0EB2g8gMeUTRxveiU+4hd7SfvMu8N7+1SyaGeaYd5pnPcwi855KD+N3/eMNnvre3OlVxyT9Glmu8R6qsFHvpoX7wufEk+xhrqQ6r/TIgRBQTIQ3wJuMylWLND+Nv7L0Pv5BZ2iQiL7KIr8aopvmGfsAhwOliXPEyw9Az8IZ70V2mQrZkeJ/sDkWKRFwVxOJQ4Hwk+9HV+EmNx1sMqxRawlfmF3ljvPUjRwkB54B9feQLORLgAfeYk/ptXvkfbaU9S8EVWcYLaCyjwnz2bLTQDvTA8/g6vI8O+FgfPds+bPs9nTWZNsWRY36iIYMaz5pPWS/JQnMilVHtT7Sf0xo5N/LJL/1xrzlsPsp6IVfdh+bWafOafErNAjLLfeFxdOR40U7fc0zg0WxzIF/wpLnpWmuqOYc/RcI9Q/QzcoIM1Sa8gjfHthfFWcKjvlX3RnFOESOJoCWwCWjKIoVy9LozSihEFAILkKIh/rdPZZNgtkBQ2vy4X8QiZ6UR9KIklKFxf6V7LBiUOcojhYkiFM+lhchCadHjtXedNjN0KC+8/RY+Xn6LYhZXbUxxCguMKJc2UDAo8j5nuKZsvWs9R6TSIodGKeiQKpwUBu9O6mnar62UJ+2nzHg2JWUpHcqz9Mu70TWFUEROtRntvEckkQJLsaEUROHyDgtvCuikoqiIWooNMTxCM4onGor4UCyNSeA6Sgy66AMl1IKvTz7Xzhjx6K1t+k/ZGNM8vcv1aO96bXI9ZYfCop9o4TNGPSVFv/SX8qs/3o8/VPTDY3gzfOY5eITCS2kw1sYm3+NdfUAzEHFBixzvMkcUQcaFZ+oL5Q1vULYpNIxmNKag+cw1FGz9MFZo53ufG58cN0FZxkvaRllFQ4o0fvBcig5eSoEYfTOu4XnvAsYFWqONZ+o/HogB7X/txz/mJWUe/5vflCtzBa/EaMhz/Y8mDEaKvsqVDHhjGjBevB9/oQnjVz+Tap7jCih1+prIB8MmjoUorhRkiqd2+8nxE55PGUVL8wVv+V4f0d5zvJcBGb5HR/PGu/RPnz3fnLOXLOdrejdeRJ9ERvQ3xwignz6ilfE3VngcjSih2oB3dh0NQ3n2rnE/NbqIDOozw57S6/1kis+9y5hpi/fof87i1X48zDjCNzkmRn9jaOEH8ov8Ifv0Uzv971p0w4uuNyfQKUekoLH+ai/jxP0Udu/Em/g50T+0Ni4pFOR6zzLW+oL26IWXyGROCM4z9+iPol5kAmV+HBf3kkf6hBYxDL0XLdDK5+hIHuTYpXHNibFr7ozPQG/PyN5cconc1x7zz33GH6+TH/hYn7WZEwAd8LlxJS+1yZ7QVCM3ZnjWDx4mi/Bo3pfjfDx/POt1PGLJfTmyKAW20DG8hi/xoHbheX33fAYnuhmbFCmK/NFn8ktmR2iubaF5Ci4Z6+z5xA/WaLI6e+WzH5Lxpl9kdegeI9j4o+e4BuorGZjzhF2rv2Q8eUnGe4/7tcN7kv6bzCFzPnLT2OojXkEP65I+4jV9MKbkN37UDzyChskEsm4y/I+zBaMoThs1IItzCwuc/TcigZQkC9CmvYpJs6GY8kiKKG4zIHOPRWHpudl/cpqewbR5fq6gz1OMZtP7KS0WTws2hfM4C9Ca9MFd9yc1Nn9vo5k+Mbooerzm6B5PL8dAIq3SfkTF9q1yeMi+zdu9VAGYkiRSlSIgx2nbSMNd9zAQtYWic5KD6zc9fxfPbePZXc/exRu77qe0Ud6ke5+ULzbRW7/8oO98Pu7D50tggOFrSiXZtG3v6iH6snQtzNs8jufS94ds10nk3dI7UlxqV/9TcMr8odAzgER4c8alKBvDSYR6n6NS5u846XqxxGdj35K+Pcp6/CT9nLG+z17ZfXhnFzIOa/seRzBaW9ePazydVK7MnwNr5PC2MVq6PrwNaSenhEg4g5WDoxHI4iyiBmRxbsFzp4Igrx1PvMjZJmU9h2LzjvOeUtJECC9XwZx01qSknQdYSO03USwihSh85kdEiOeX1z2VOM/q2OXQb17mfY3HYn9kzxBFc4zmnicwIPGMCAgD5jz24bxCpEqquDRZckdKqIhZiq+JoBqXFBA6TxDBF8VNNPQ8gAFpLmjzIzXyJkLPaSHt97yMW/HIQw3I4twiHr6kXvLabzKWcl28fYf0qBeHVR5EkqRkGSvpQVJRKXTnxRAuiuL8INFfKc0cVdJtK2uKoii2owZkURRnCknHhKQPF0VRnBaS6bBP6nVRFMUjGTUgi6KYkMJD8z0jid6ORR72wVgqfiyZnnft+6xNz9nU/pPgkHuBLjW20W7ffUqHbFMU9zgOzoPTYBstLyW2jeNpzI/LHeeJJ88CjrMH+ixiac0a5w+UL4pHOnqMR1EswOKgmItzwuYFHHyn2p2S70uVMQMFeFRwS6W7pM66V0VDFdnOgjJnr4WqhtrqWJKxEmP66zgKVWiVaF9bSMLia8+iIhT2q+q/ann2ZSpEojremmdFKVHRTjVK+3pU21NcwAKugp39nqr2qeIp7fUQqbQqOyqEo885wuVSpNfZu4tf8M1xDG77YY2v/nieCoNoZDzQDk0V21CYaDzu5TRhTtmLbN+ZcXTEhsqPjsBxPMpx6Y1PpD+rsOgd+E1VQ31UgdP803ef4WtVIlXzVIXVnujxSJclWqKfCqSqRpo3KkTmYHGVV+15PTR2GaepTqnSp/bg19HRo834GB+p4Jkqtodol3mCFql0upY/IxtzNM4oGz2LDJoXFrkUIHPQVQEv+9EuNshNVWhH512cZeivivNYYXQO19j3b/zRPM9RlTXVf1PpexNyHqh1DFQGVZjNfmPtMLdUr83ZuNrrKB9/rz3LdRfC4+RYzlodnTd+VD6NXNsEc126cs5oHI3DPMP6pEAfOeJcUP2lC/j/yU9+8lTFF8+rFOuIF9sriuKRiPPvVi+KU4BFlvLpPCxneFHCFFKgoCn9zaCy0X3XMxT3sbfGuYBKqiv+QpFzrpQztuYHEl8KUBAcKeCsP+XD50jJd+3fdij5/JmOJXBmFiWd4kqxRTvnrymVn6M6tsGirqDC7bfffnTbbbdNBUYY7gwMZc6dk5ZjSZw5xuA/BBgXFCPniimicanAEFLUA8/sC7RjpDl7zRl2aKYqLB5WqEj1YkqZwkX4+WLyorbhNWfNUQrBfKKochAcFxRCCp+z+PAMfuNscHyB436cS0rh48Dg/NEOZ7ExqrcddRGDFy09gxKJDz3bOYuqBudszUMiDoCkdG+COeJM069+9avfdfC4uahKLV42Tw6ZdIQm5CMn0T6IbLTf2fE8HECMd3P5jW9849HLXvayiS/WypvTgqNFyMZLIaeNk2qkjmNCB/PWGsRphpedPYq/dwG/GydHk6AvnjXvnSnJAGLwWd+2jZV3q+Z66623Ts6mOJrMN4Y1x0/WwxxLEqDdLv5dA33nsHj1q189HW9knvsM33BE4cFUot0ERq9nvOlNb5ocA+73w/h1v8/iULUemlf+J5fGtUDRrhwdUxSPVDQCWRQL4J20MDrbyuLizEEKD1ikfL50NuIIi6uIRLyizpeiwCsHb0Gl1DvXbDz4/FLAAsyTvSn6RLF2HhdFYlvENaDsMBoplo5VYOQlMpOzAEULdy32YIF2SDYFxeHlaIWWStKjJ8WdouVv43QooIVIzaWMfuARhhClzyHh+r6G/qPya9woQZRNVWz1x5llogMMIWNhTChGF3M3A14SVXXMTN7r7D9jPR7Wvi/0j+PHgeaMqRyULrroTDsHhTP0RBmSPsuxIRq+rQCXSDpe87zf+Z3fuRClR0s/nBdrHCL7QgVNzpanPe1pU5s39Vnhl/RpDvMiEaNDAu0o2pxLzkp0APva7AS05kwjU90jAm0M9EXUB986r/eBBx74joPsLzbwjKMUtPViI4XhVIhN1Jgz4aMf/eg0t/GjObRNRvmO7E1EV1/wu2c/9alPnRxJzgl+5zvfOX2+dHyM+9zvHEmGG2M0x2NkDvne+Kucag5bU3zPqPzEJz4xnds7npm8LzzLOuRMRfys39bRnOlqPSDndslrjiSRV3KU3JNxE5jH6MpYJoucKwucS+SH/zO/VOvFryeRVUVx3lEDsii2YFwkx4O9HQa8ViEbz/MDC6GUOZEBHuQcBM6gsnBRGkUtLXYMI+8WrXGfxZtxI9WGJzWH3ouIaBNlTNTKZxY3n+UIkxhFIjxSmnxOgbP4j/s7LPq87rzSDGWKKaVbu6SX7VISGX0Or3b9tdde+11pfRZt0bBdh5sn/erzn//8pCzo86jkMz5EOHNgOFDiHcau/w6u1n4IjUU6fEYpRQfecu1Q6ZWRIFKTQ7VDD2MkmoouFF0GCuif+6VGUkooMTnAWiTUGItiMpak1uaQ6zXwHBFfnvxnPvOZkwEpKpP+7AJe4lGn8L35zW/+jjQ3vylPlDqKXpRBXnXn4GmnapTo6rvwCyMTL+ApNGM4iGRQ5tyDZ9AfLSldaO0HH0tVpHT5HA/7H11idBk//IzPRUw4b/TBeOABiiIjyNisoaE2MpjxwvOe97wLSiHjT5RB/80/88xzd51tKe3Ns0Rg4kgK0NY79NO15koOmGfY4QtzxrzwPqngxhbtOKaSAof/zF1tMScp3I4pEqVFN8q/8XedSCKe1qcYlpExxkF0RB/xec6kWzr7UGoe/ubIwtf7OEz0B5/jT2fWSfEz9sdxuoyRRjRzVu8NN9wwOTlE2zjrGAjkHT7BS+gI+FKfY+iQLXgWn+mTtEU85B4yQ5/dY07G6EYLz9YnhoQx9Tka40HPJO/9j7ZkgffjfeOu/frvPTlwXoQMD5NFfsgDc5I8NO675B86ug7vhkbf+MY3jj784Q9P0W903/WMOcZ0UvzFuScKzDmHV/RJW60X5L9++Mz1nIz4zVjj98gi9EQffGxs8LZ7rDeMXQak7/CYd5Jp5of3eIa5us8RKWNaLFjHtBXvrdmjP57Xm99Ji7Xejt/N6Za/yS38Zd3FU/qNB/GovlsnOR20C0+Y9+iBhsaT7HEtGrhfWjz+iJPwUm9rKYo1aAprUeyABcpiYYFgEFgUE9lYC4oERYbiZwH+wz/8w8nTzqCjcFhgeII918LzJ3/yJ1PaqwVGChNv8bvf/e6jj3/845NSSXmnzHompYkCIOJHaWXAuJfRYTGi4HgeZc/ifc8990xtovSLbsxT7/zvXs+iyGszQ1AUiwd8GyyujAZ0soiOyjY6eibDyyJLWduWouZZFA2KtoV3rhxEqaHwgUUdbSlEUjJf/vKXT+9i4El7Mn4MvTvuuGOiPwNJBEWKKKUcndBDapcFHvTXM/WH4obGlEd7k171qldNbaMcSbvzXu9DX6lhUgopV1Jv3btPGpdrPY9CId2ZgvOlL31pdUofhUYU2G9K0ZxXKZ6MxOzrolCLIlEcpRRqe/YdaT/DDp/hP7yFR9BGvxnXaIcuUjw9w3z5tV/7taPf/M3fnO65//77J6NRuqyUUYoXBcpzjSO6mVcU0+xf/L3f+72jt7/97ZNBwfjH72tTCRnG5pdx8zxt1SaGmPE0r4y5+bBrz6yxoLibw1EwR+BLThF9Mi/xlznIeGH8iCDhwc985jNHz372syeDkMKIxlLpfGfeS5ljpOA9c55hp91+cgSRuSgyxyA1PgwtEWpAR/MFfaXeP//5z58M5vBy4DnGSEQFX6CR+a3ta6PQrsOfDAKODfTWv13yYQ20iWHGiSM1kYzTRnyE1oyw66+/flLM0Q2NjStlHd3w0mc/+9lp3yK+Q2tzR1TaM/Td93jXtfqtD55tXqAveYzHReiuu+66yYg0l8hZ+/3wJF4mB4yTbBL/myPmjHdxOpCFxlj0zrvxEVmEVmtobX4m08Vz8RbZaY4eYr8yo+Xxj3/85AhDZzKD8w8tOEz04x/+4R+ma8lLfGIeie4D+qEvQzd75TmmzFd8RsajW/gXr5KvcXpyEKDPmPK6xnhEV3PB3CbLZajsW0+A/LEm+JGeKzshzrRtMLdFWckqvABoZmzxrWd5Jr6wTYV8tNcU3734xS+e5CB5y3gnL6051gnrv3nbupbFeUENyKLYAUqrRVJ0xsJJmdl3Twfjxn0WO0odZYDHl+ItukIR89siy7Dj0U2VN15fSgRFl0JDAfEci65iBSJzFCqRFSmP/mccWFy13ULG683jzjPPaHIvj63UuKRFWjgpRPrquRQBbRFBoRit7TOFgeLA0zoafZQIRqy9aZQ3CsAuA9Ji7feSspQiCDGO/LbX0rMZL5Q+SgKlVjsYYxZySiIFx3NFINIG7TUOOVcU3CfqyMgU7aRsxniiNKM1ZZKSTgkTdWDUapsx0BZ0pyDMFflt/aYgUTIo0Qxx3nsKqv6sgbGi3KVgxibaRVlyjeI1jEWRWzyAZym6jP7wWRRj8yB7h9CLQYM/vc//FFPGur7gTYoixZeCSWHF01Le8Llr8Jk+JopmTPAt54Br/DCO9GkN9E3ESfsocAxVBoa24DvRIsos77+5sSsCiQ/1cRsttZmBYa6KEHk/JdO4abs+4z1OI8ojZZKiib7G2zX67D6pk3heFJFDyZigp/5TvH2OL8zXKPjayWhlXHD2oDOjhZE1ggwwrsZJfzzHvGQwrEkrB/KDPMu5iVdcccV0vzl3CAUYnczHHCqPrmSh9uIJY0hhT7QM3dAI3chUcsD8FIXmxGBooxVnDwMxc5nTg4zEByJh5IOx9nzGPJ6MPDDnyQ3vJCc4A/COcWO4XnnllZNMN0fwM57HCz4zL8haso8TgvG9yxkUg8gPecoYNtYMDXLrEFEqzzAv0QAfmp/6i4b4Ah20NZDKif6cTdokahs6W0fQC79mHfM5GhkbfIvvvAu/GCPjwKFj3VrrHDMW5q/9oXjO/cZxX3pwEtj/mXV9rWzVL2tJKkf7saYywDkyrr766kn+4UvPtW2FHCd7rAHGEc3REX+IJDOiybi1WzuK4iygBmRR7IAFkXJN4aOMME72rbZoQbaQ8DiKNDznOc+ZoiuUS8r+JqWLYkppcj/l0MKjPQwSxkrSXRh4lCMLmQX9Gc94xqQgU/IYNhZv91KUKAYiabyonpnqiRQkUU7GLSU3Jcq9Y5+9dxRt11MyR6XA54xW76GEUTx3pRxl7xGlbhcsxmig3RkfCqg+il5pS5QNxtFcYYkyMCoinoluflMoU4lTZNWi71mUH4p9oi/ZK0oBTlu2FalYAsVNqpzIiWhJUuREX9YoWtqAdnhrl3c/Bpz2GiNKUJwAjEV8hv/QRb8pR6NSOT5npBsaMDLRj3KEH0X9ovy6Jil42otvww/oxoFB6aYoooc2rXVieD5jgLJuzCmJ0uXsycXvPP3mBkNsTSoh+nj/tgI/eIBRo7+eae7as+V95qU+6TOahJ+MJYURTcxNxoEMA/8bi1SHzJhTXBlD6BEjH2+F9iJWxs+z9DXpk2P6Mp7FS+QKnjU/RM05StYo4d6V6A/ef/DBB6fnGF8K+SEKpngGWqOd36KNxsB70EZBE8aMMcWf6CYK5LvIRPT2nd+eg4c9I04VdEc/DjJzWZ/Mr0R+3Ycn46Dyt5RODkAOJTC30VIbs6XB2Gsb5wC5lfcbd2NqXuKltYY2WuBXcsAaJNqdeTJujTguyAd0YdihgfZrn/mDzmM6P/kqG0YUzdjjd3wTfkfXsTZA2he5SW7gz4yR+We+mB9rZGTkM0NVZFiasxR9xv++dDBGjDrPUGBoVyp7EP4ZqwRz1oiCynohN80pfWIwGm/XmJPeQe5wGKETnkIPPOK609hHXRSnhRqQRbFyAz9hb6HlPWWEiRDto9BaMNxPAWfgUS4otmNa3qjobQOFksc0i24Woxh9FirpNDzFvJoUHwuehVuBCilVFBLKaCIUvpOKZM8hpfu4iiBDxLspVmP0Q7sSxdFeP9v6qb28tBQMqVVLntlRuZ4jz0Zn6YLoQNkRPRj3v4w031WQIr9TmEYKE+Uh+7EOAUoERUtbGSAMbcYUvpHWKeVxF4wlxQ+Nk042xyblc9wjNOcz4xc+25dnY2SvUZK81/5Nqb+MBFHdXUWr5qDQKwwiaoOnOUU4UkTNzAtRyV1FSKIwppAL5W8JoRUeRa9Emxkt+H2MVozIuxk6quKKYklxZaCIdM/bxkjjgPIec9Xzt81DYzXfs+xeSi66MkbIMxFmRv4a54QxTPRR1DkFVIzRWv5cU6GXkSJyZRy113vSXkaM+UD5fsc73jHND6msIv4iaeOzdsGePY4z7zDnPHPpPvwnLVYKJtnGgGFARcbEuZDCNTke4yRGnvvILQa++fzrv/7rF8bcnOTsO4kBqd2cRBwtjENrEcPReHJ4io6RbZG95j36m0cKR5lbIutrYU3BH5En5BTDaZ8zaOMcMK8Y5trCIWv8GP9r6ZE1MxFUPOX+41SBlnGAh8g4EWhbRpLCm/Vdu/EWWTDuS57vryyK84IakEWxgLmBMQp8CwDvsjTSeN4tFEsb78eiBeM+NAu/BSZHCvjOIk3xdI+FaNNiYsG14EvBkjaTIgDu5VUFSoYFUQpNogwgGsGoo4jwLosIUdRiPFkIKQiik5v2RDEApP/woi4pxBZJC3qOSLAgb1KiKBTaQ+lb+j6V8kQ2RF0TkYnCTrmiAG6C9riPwSCVlGLHux6FxW90C+3T1m37W91nbxYPPAWXc4FCFGVxmyFKQeHlz3tGpF/o7jpRb31nBNnPR5nLnr6MOd5bMsiSesszz3hi6I7RADxBcRxTv8Y2uyYpuu6lKPpMu9BPBCDXJ5oikpGz5uY08D1FT7/NHeNu/OKkmNMi+83wMSPFc3Meq37j2aQ3b0LSlinAiYJSOhn8IPKEH0JLe7uWaJnqlWiBj6R7LkVW3Gte6l/mRubxtkrLvpdeaSyktkojZqCgeVJjXYMGopNkB6UdfXO+7JLx7zoOiHnhJUo3R5Z0why9oE94mhzz7k3zMc8lN5wbGv5kbJjz+BEtMj/NTQbDtqIkoXE+16bszxQhIiO1Ge0Z1Zkj5gJDE41FkKSH+nw8imhecGWJTvZ7kyPmcuTB0n14xNhKk+U8QlfbBjgT8ZpMj/AEJwLnBFk4vm/+t/mAlzetH75nGJsnnArJrPAd2pJB+E6fl+bDknxOGz1bNNVYSiVNer+9gHmWtsVIzv36TR6hBaM+fVyC6/GBHwa1OSSaHJ4gM8x165Zrjd/SujJv/7wQjuc41gnf4h/02OQ0W6JFMnCsV94/l9+bZOR45JP1BV+QK7ImZNtolzkR55L1Gg1Eo5eemTbhc7Q3tjUqi7OKVmEtigUQ2oQ9Jc7CIlJIUSDQLXKJ3lkMpU/5sSdm/gyLWZR3hgyPqwWFUk8RUZSAkky55mm3x5En3z2pHEppoGBQMi36lD+KlQXcBnzv9WwLE89xwICwiFFeE4VgAFAck1JFEWVcUgr1k+JAmbCvR5qrNFvXRaHM/j+puBRyC+Z8fyJlihGqL4oIeKYIgSiL9/ixyOo35YXyx0hQbGS+mKKJPV3erT2eY48YpZ3CxviWqodeaOTZ+mjsKCb+j4FOEUMPCie6UozsK6Mw8h7zQruP8ud7f4Pfnhn6izwzRPRLNEiE0FhRnLMXRnspSa6nkFAkcs6hdDn9cB7mPDXYtTleY0zv1UZt9R5VDdFP/0WT77777ikaNSKKniiBFC80tjfHfdqtf8bPM/VXO42x+zgZ0MhYKwakzdIDGbTuEwkxvq4V1UvxFHMDnfGiuaL/+myMKZqieIwNzgnfUai80zMZ1cZKP9HP+OIre5woqL7TJim9Isj40vzRt23Ht+ifdzLsUsHVPDE3GJJxDOU8Qk4V381pKXXXOxUxsQcZLfQdLRkf5q93SGHDp/bEMaoYGA4fNwelnHqWMcZPHCdohhbGleJOHpiPqX5JOdU/im1SBckcZ0+ij+8V7aD8u88zfY/3zWFySb99Ft7lGMAL+iKiZu6Zi1L5KN6+I0ccEzE3DswxMkcf5k4W73d9Knribc9kZGrHyOuZH4wgfYgzQ/uNhc/0kQMERPu0R/uMKblATmmH/uNTEUmGiFRB8gAP4W20xoN4S//1wf9onuJZ5Dx5Zd65j8MKn/vcGLnPfGJs4nX8LDvCmGd/GwM3x2XgYVke+pw24HVtyO9koHAYpOrsGC12jXFnjHAGaq+oPHiGaC/57V3uD53nxo95zDkSZ5p3uAedPYcMIz98j6aO9EBP8sHaw9k4jjV5as3Bz3gymQHGzDwl68hKPGiu+lw/vMNz7ZO1fnKISRn2DHPUuqjwkzE016wXI7/EceS5ZBKHh0gkPiIXtF+7yE9yw37OVDgOPIPzE2054tCTnMVHX/7yl6c5Ik3Ys/U5/IIvs77kt/dFZiWTyHjgC/3WDxkFZAH5kbXTNhgGpXeijc/xIPmGluhnbK2deEOfWpW1OIt41Lfq3iiK7wLlloLOWIyXP4sooe9v0TrThwLDqGGAjHCdRdFiZ3HJM1K4gFIZj7J3UEQZIJ5jL5MFRgROdMCiSzljXCaaaHGzAGZ/BaVtVPgoPhRHik8+p7RYHLPoW+h8L0WUUuF/RgaFnvKU8vXoABQmbRd9tfgzXsaFfikVjaHm2d5J6aWwUroosjkiQ3/RcNNCaeH1vnhlGbUWagu2sUI3dEIbz0ZzizvjjqJPQUVDhRuSFuZoEHumKFL6q9/aZ6yMgT6HBiI5OQZE+72bkqlfPM8Wf7Sm+FOeKI2uQ0/9j5edQsl5wEhjaIzGN6VCG+NocH2OVqDYGCPPTrEgxgTFhfK16Yy1KOrGnFIdXsFHjDzKC7rqs/4ba++nRLsG/bTV/Z7lXjyQ/V4UK+nOxhI/43e0iyHpOSIxqTZMYZXehT5429hpux8OEc+j9Ok7/kZjvMGQoLCih3bbE2a8GePbIiBgHF0TZdJcM96ek6M3jDNaUsK3nVeHXhT6KOTahh76mGgho8iYZ0+ueUnRJU8orwwd1/sfP2mHOSiKYUy1h8KJf/ANGjB0GMyMSXPP8/BdKsyKCOERNDKWDCHOEuPnGcYCvczVnGtoTngn2ng2PmfEcRYYB9HQeWo2fmcUU7oZ4gwmdEjVX/dpvzno3cbJNa4dHSJkI35UaMR89p0+GQ/tQJ/R4DRm6KC/+mCc0BEfmfuhm3e6X3/NJfTHe96Vo3XwOfmAvxijjG9jyjAQhdQHPI+fjSXaGx/j7P3onPTHGMr6gC45Psk7tYW8JaPJB5/5Ds211RzG/wodGQN760dnSGQems/PZ0xUTxvxK8NWX7RnHiVDB/KOrMWjnoVeZB86Zw/8WDCO8wHtGH2eP5fLxs81eDRnIbpX39CZfMZnaMMw8248hv7kMpk7yiK0RCNGuPnKuTbOw0R1zStzUD/CT8lkIJPxnXll3ETI584lz/Bu4+L94zPwGJlE1uMfY4mXyLPwC5q5Jtk/+mMNIk/MHf1loOMVcyxzktwjM5NG738y1bwh24wJ2vmNB7WPY878UyF46XzOorjUqAFZFCcEJWCfPRy7npX9Umv3Qronex+3pevM3+P74yxMSdmjcImy7HpG0u+SxjSPXPh8bQl2ioIfCtyaM7/GNnjPuBdtbMc+bdj0zPy9DRQpBhTFgAK5z1EwS9in3cZt0xisec8Sn42p3muf6drw+a52j3Mrf1PWGXuUQ0bULpovzaX5Z2Mq2642uQ4PpvrsUr9Dr333bSald5xTc34d/0+a3vidZ2Sf1a5+hH9yrXnKEPKdCO2uM193PX8tTdc+L2nMSwbNaBDsi5GOS/Jh3o5UyJ7zVHh07VxgYOSYB4bEceXBceTXSda0bWvL0rVzei7R0P+MKoYz596+Z1zO33dIepxURq6dkwGnH8cc45oB2whkcRZRA7Ioir1AgeeVF32xyHdxWw9GN+8zL/Rxld1HOkQ6cgh5aXhYcDKIcou07CpyVZwcDEgRNfLgpM6k8w7OC5kAIo8pQPRIRdJdZVw80vmiOLuoAVkURVEURVEURVGsQl0bRVEURVEURVEUxSrUgCyK4uDIfqVxj9wjKdlh3t+T9n+k5XFhf87SWZr73H/WxzD7jY7TzvDsodrxSOX9s4jsTzz0OKSYzSFwHubXSfcHHrJ/8zWmKIqLi5Z2KooNe4FUfLPXz6Z4lRJVq1M10Hc5qN3eFZUUVTRMxUnV4JaQM9RUxMt5c9nfkIXQ+XpLVe8u1h4UlTBVmNQeFRFVk1vbHu3XL4UQVOOkyKvGp6KkfWuKxmw7cuFi7D80fsZU1UOVPlXKG/um36o6qsSo/6oc2uu5BjnXTIEhlQ9BsRz7yXIW4L5Fi1Ktz15TVQyPW9QEbzonzXg4omFtO8Kz+BvNxmNitkH/VS7MmXr4XB8UhFCVc00BnYwHnlRFkwKKf3LouXlknHyuEqRKnyo5KkqiAiO6r32H6qiqiHrGpvm7hlYpkqRCo7G3h0nVUPJjTUEs1Ry1RZ/wn6qaKj3mzEPX6KvPVbA8a/vE0N/8yfmiqUJrb5vzJfGhapTo7HiRXfQki1Q+TeEsFTLJXGNvHnsHflJZdanoCjqmsrGxmB93cxxol/1p2uDZjhHa1Zdd88tao/2q6Z7WmHoXGZhq0oA3jyOX0dX81v+sFamsjVft4SOrrIfGR/9yTMf86KLj9MPYk4vmmDVz32JVnqH9WetUjDXP5muBaq6qzbreHPazTXa6B7/m7Ed0URxprSwqivOERiCLYgFRcJ0L5zwmhobFlsLqrDeLZI4ysGg6woHiuEuRoIQrB+5cPuXdLdwWGYuN8vyUkkvZZ8aiM7ScJ0bZWKsYaT8F0fmQDqdmdKniiIaO+nCeFSPyUoKCpl0f/vCHp9LoqTYZ5HBufX/jG984KcPGfW3/KanOcnPul5L6zjejaDpqwpl2lOd9oZz7Rz7ykenoD8bYcUHhozgqP79vxERRFed9MszWevvNFYVu8HkMYEoV/nDg+9Kh50tIqX9HOJh3io6Ydz73ji984QtHr3/966c+cfBQjJXNZ8CsRQw3fIqHjwsl+Z1l+tWvfnU6QgO/33rrrUdf/OIXV9E8BpejG9ApfQWKsnMic85fjk44ayDLzJl3vOMdRx/60IcmpdzY+8y5fCpLMuTIzDUwxvjGsQ7GCH0YJgwU5xcabwblJqUe3fGvM2Dx0KFgrPXlnnvumeTEcZHjKT760Y9OZ/+dNhhv5olzFs1pfHQcpxSHEFlqrTDHOc7wqrF21NIHPvCBC9cAQ41j4SQybH6+srN7yaTjZlXgJTLbOZcPPPDAd8jnnDuJb3IupOt3FbSJvEIX58Wiy3GdC0Vx1tEIZFEsIOecOWPPoccWLIrK0572tMlAoNSkUhzlxULsYORt0SrXWrx5MT2fZ9JZkgFv7UlSDE8Ki6PFzvll+ua8KwrhmjLtjGLKMoOJMpQzvEQG9Nfh1Im6XirwUouG8ogz1I3rE57whAtnMYpyUN4zpqJlaz3zokMMGeeG3XXXXZPC7znGlELhoOvjpLrxXGvvSY1v4+ggdv3aR2HUB3RIFHUtzAfjjqfwgMiK/jMkGNTOOrviiitWvV/UFE9STvFkxoQhYk7qj3dRjs1ZivF4tt0uoImoozNZT5IORyY4E5Zy7ngRUQ1KNYV6DbTX/HOkA17FO5l/5Iqqs2gwPyPxLMFYGKucu2l8zPuvfe1rk1H90pe+9MLZprvgGs8xtuC3/50zSPF3ILtD1rdFn3wnMmY+5miHk8IzjKv+OOPwJDA/zC80E70+TWg3enBu4TN9IA+Pc5RTnB3aDSLD5qVxEW3mSIkDEd2vuuqqoyuvvPI7zr09Cc3wApl03PThyBVziazivNU+vBIeIdPjcDNGm847nj/XXDWH9ZvsW+ssKYrzhhqQRbEBlDRpg5/4xCemaBUl08Jr0bHgSN+xcFr4ecKl961VUFyXxS+pjxayGKBSdKRbghQjihNFTASU8ckDziNPYaYE5Pwy6VBScyzmFi8KqH7sc07lcc7Pci6fFECGIgVxfJbDkx2SnUPbExWQmggWbff4TtQFXf0vFci1UpQSCcwB1jncmwKTg7HXttkYMhwZi5R7Z22BiBmlAU0pvGuhTZwKopc333zzd6QWGivGEiVCO/VR9C2RZn2gbLje+GoP2uQw9ZGOFDNjK6qDJ/AAGvrteqBceyeFB83wCF5w4Dc+FbnxnTZ7HoXS/WgtNZRyCdqpHT4Pvx0X45mLeNX/2rEvNo3veJYjGkofpRz60Wc0MX/QAk0pzvrFKKeEon/AgUMJZgxKcWa8r+Ur70cnEUjjgw9+7ud+buK3fedTFO9N/XdOnGipsWVUm/fGk6Hkf9dKfzZX9NPY6qsoC2cPOjH2RIXIMLIi55kaG7LH3IthbX4tnTW7rf0gi0FkzTtFetamg296pjnGQWBsOCViPGbvK9lh7NBF6iyZOqej/9EGD2hftiK4H13xDz7FBzkPk3yWxo0W3h1Zpk2ci+Yt+nEaJDpsnpuffptzmVt4Uj8yP9F4PAsRv5p7vmco6Uv6RY7ol+9E86wHZDvZBdq25tiH00inJNNEmRli+H6cl3iQbMWbxgxN8IS/9YuMxG9//dd/Pc1B40FmeQaaoCP+xsPWk7H97jeWaMWo38cgxtfOMpbJQOaTD3GwWfO1V0r5oemVdGLzzHwzB9ekuRfFWUJTWItiAwhzi7VFhafZgiddyY9Im6iSxc3iRbGw6O2jbFJW7O2yQElRlCbjfil4n/nMZ6aFlGHC+KLUMHie9axnTd5d6Wyf/vSnp7Q+yojF1/d33nnnpFyIhNjvIxpGGTlN6IvFl2I+Lu4xjBnXFCj9ocBqu5QhSj1DUDopA9RCeu+99x5dffXVU5qpBVy0yrUUXoqXlGKfU9ZuvPHGo2c84xkTjYzHWlAwKDnayzmgTdkTw6tOSdkHFEjt56GmcIyHulOUKAqMFO3HQ8aEUohO0lMpgknJkwpNAZWq+Nu//dsX0qookHjCe17ykpdMqVv6wViRKkUh9l70Qy9OA8qZ7/XLj/1H0rHREo+h81ve8paJ90Rjr7vuugu8gheloeGlGCvHjc7pGyVPCu0nP/nJyWBnYO0LPK6flGY/5sQYtfG3vj/3uc+d5qRr77jjjqPnPe95Ey2lvFEUXaNP6PzQQw9N1wVf//rXjx5++OFpv6homdS7taDw4kfjhJb41DgwKvY9y01ftZHSrK8MRHwUMCT++I//eHJYMB6MtZRRKZXmGYNGGqz3+t/fxoC8ecELXjClHX7qU5+a+irtGn8YX8aL9G3zyTM40PBinD37QHulALrXHGfcHhfaZkzJg5/5mZ+Z6DxmB/iePPQ+zhTzgzNrSS4YU7KDse8+6bH6ip9uueWWaX55FoNRVgV6fO5zn5u2LcgyMF+Mj/nL4fXggw9Oaco33XTT0ate9arpOcbN/+Y7I8U4MeaNI9rjj4997GMTT9ofON9DZ70xxvgHj953331Tv3z+rne9a5Kr2iEjhkzE0+braUcxl4C/yBlyjYyRuj9GhckU/beOoZeonrRQfIUGZJa1Sz/wK7mGpsaH7JD+b3zI69e+9rVHv/VbvzXJzMhsDgrvx8ccIvvIKdcydmUMoGfkn3lgXeU8OI2zZq17Iuj4gaMJ39g734JAxXlCDcii2AIpN9JMKdC8xRY/3noeU0aTBUfkjVKzVMRhCTGweB9FMhmifjNMLbAUBl5QBol3eT7l2zso9Lz4FCCLsoXIYsfDy5Byn7ZRoi1GlPWkk56mAqEdFJ4x5dX7KVOUm1e84hXTIq+t/qdUiyw+5znPmRRvShHoo+dRQhg3FFj9pxhR/NCJl/1JT3rStLgDQ3nflD6GnigkgyxRPB50DoN9U7rQnqGIV8Z0peyppCBRFiikFMtENhhReCuOAoaoNGjKjNQ87UiU2rMZNNJQGaMMPEra05/+9OkadGb8ogWDgmOD8mw89JNhTwFNGjFPPcWF44Mih5coa2jhtyiPSCxeQuukER4H6Kp/xt3fxj17+9YCTxkjirooB8OfMZoCF6CvKaiBpt7B+cMIlx3AWMdPxsE8E/lBvxgiKZTBYGA4GCtG11p4P6OBQY/PKbScAgzBfftqnPRVP9PXRAUBv3N0uE67RVuNfxwOjGL9lyZPhugL5wS+F11z//XXXz8ZZBRkdARGqWvNP2mHybg4zviTl9qNFgyek0C/yUn7GCnd+HsEGnBUieSIGovsm2dLBiT+5jww/vpm7jCsRfZFI/GO6KZ92+ggKmaeGlvziPMl48DgIcMYOXiG04ATgrwiiz1P5BG/obHMCvLOOOE96f7eof2MIoaUvuFB7SMDjGX6xUnJSHKN7605xt3+XY7Fi12kDK/qK4M7hY3maaUcB+RqCsXhP/3wN0OaQYh/s0dSsbGkvHM2MsI5TskiPIlf8yx9J1vRXt+tk/vAM9CQvLWWW9ONP/kqao//T8Oos16gnfUOD5Jt5ncNyOI8oSmsRbEFhDylnIJAubKgWPApAoqBWOAoiBSLtUjqE0PhhS984fS/xZEBSblhrFL6GBSUBp5dEbx4Zyn+lFVGBWU5SiSlhLJhEXYNA5SidJw9LvvSiGEbwyX988PYochS5ijWlFMLdfYI6gODEB0pmtpKudV+f6O3fuUIClGNVF+khHivZ+wT4YnSwFhj2PDqe4dnUrD3LbZhDLSZITkWYvA8yqJ+UB4pfNoq7ZbSY+wZ+sZPQSU8oF3aR7kf92zF2EMDvyltlCj0xSuKNvCke5ZoJUVKhJdihtaULgpW9lJmnydnRHiJwkvhZmj48ezw3Jq9sJuAz0U7vVvES9RFP3nd94nYo7HIMWUa0NocoWCCtupHUoX9ZtDrN4PCD/5xv0g94/6aa66ZeAhPAaPMe/Cs+9em7uaoDuMkoiUtzjv0VTvf/OY3r64UGf6c95URps3bEHpyHHEYiNwwgvUjzgPtyJj7zN8xtBINp+DiHUYVfst+733ASfHUpz512hPK8YEWmff7Av+aL3ibc8P44Kmkrhv7F73oRVNBGgacrA59M1/m8k+7RBJFdvG5vmfeeg96JO1Y3+MY8hx/47mMN97CM35bFzjrGM4ipIxzss7/ZLsxCE/iOfcxgBiX2s+AcA1eyX47nzOy0i+GbuS9dxpLctA4HQKjAbO28jaaiAyK4DG6tYujJoXotFGfs43CmIXG1lFyynha6zzHODLo8KLoLJmctNJrr712knvhR/zk/cbP7+PsFY+jlXxmCHNMSrfFb9akQyL0JVvNCXKHg4JcyhyVJWEdz/5z7Tmre56LRzYagSyKHeAxpZxLTaN08OorrmPBklrEIFhbrXOEhZSBl301oiQWTcaBxYwSSgGTFmShHfdMzhd4C7TUNEomrz8vOS9u9vfti332QWbvDyVHhHZsX5SuGCH6YJGkUMVAQgNKxvi+JU+sxZQHWjoZI8/77Ks6bkVKtOEc8DwpjpSe43jwKXs8yQxoCt7Yf/2KcUYJoCypUOmYCSmHIrOUCAoEQyipcXgj+1q3wTM9i/Ipsqb9itPgVYoJz3/2ti1hiZeMDUXsUEWP9IWCSMk1byjNlCSK8z5AD88xF/xQHvcxbPXV9RwZ6C5NVbqiMTvpPuCksnMmGRN0v//++yflj1K6755PfU0hGn1l6C/x5lIb9ZOjRjonvuKYmKd7boKUSAaJKC+nBseYCPRx0vjQWuSIAcmhwaASPTxulEW7KN0id+aQNsbwM2+0V7SWvBb529RmEUdjY6yMj/m5tE8SxmyK/L/Ufp+b4/gTrUVDzW+85R2M8W399l322zKQU0VYvxiO+uV7a89cLhxq31yOJ0GXfcYohbLwG9qL1K5xwo3txuvGlkNNRNLWDPKQjPA7a4W5lQjktuftC7KJ0Wr9kjGC106yX3cbrBN+bNPgwOB4SKXWFKRLmr6xONQ5o0VxaNSALIoVihBDg7Iirc9iJ9LgMwYfZXEsL87rPE+xmmO+QFsUGRue7W+pmpR4i6KIpAjepoUy/zNiKVeiXrzsMa5SGIMys+koCe3ltWeUJTKYdMxEtbbtM3zyk588KQ+UunnK3thebfNse1ey95CyRTlKIZhNfaSoZJ8ZRc3etl/6pV+6UJCItzhG2Da6GzPXMGwoLAw43t/s30x0IaCEe/amMaU4eg76p2LvXMHL/3iD8iBlVERGdEhqLo+0fVvGWtv0Q0rXLiMOTRivFCuKF0cEpd1+GgpIzvDcVYBm/J/RAozS0CqRZRCdovisNTDHd1AI3Z/jGDxbO9ccu7Gk1M7HalO/gkTxRDkovOZYUorn9+S52sgg87OpSrJ77ZGTZpln4dFUMs5exDV9TZ82tSfwTONjbBKdTyaCfWF4ifHCeeOaPG+JNvnMmJhT0qvxjr2TIrcZO+O+JsqTtnoeB4fIEv62R9P4h64iPWuPwfBMNGWgcP4wIh2loe+cbvYAcuZIGUUXn499zTyUdcDY10cy17XJYMhc2SRjl/qY5ybCqG1SWf0kpTrnoW7rmzXF1gRp5JyAxtGY6pfvyIjMx6WMi5xbyEBeY3TMC6tpA2PVvDe3PWufiB4+YQRaw6SUSmldOzfJO3JYVoK9kOYJ2cg5K7tCFDdHnohKLmUGjO+yprh/27EhKbzkBy2s59Yw7bYW4N25fEFXThARwl1G9hIfWUdFamUz2dLhnYmgxkC2pQBvc7xYV8iPojiLaAprUewAg46SThkTpUpkzb4Ygt+iE/AqUiTtfeLFHJFqdAyGVNtj2FlwoxBQgBgA9otZRJMepQgFxZNC4TeFiaJAIWaQeK40UGdRSltzD6VDNEoaHMOOgUGB0+75wkaps4+GUSUiat9cil5QsN/whjdMqVUiSEvKleigfYx+pIeJmlJ4tA89KAK8rGjCsFXkJ8ccUJikdHovBcEijS76SKnwm5Kp386YtOAyQCysIrXahLaUXf02Bjz+c9qjOdqnqIw2GVcHgYv06jvFwCJP8fDb8y326CZKuXTIvP6LeOg75YfyJDVS5IgiQ/mJIcEIodzrr7Fl6KOTaymcDBvPQgsGsnbot/FFg6QYpoAH+lHcRMgpfPpD+dNO78Rb6OkZaIsG7se36GrcQ2fP1n+RcJEoBS5cx5Hg2e5DN4at6Ip9WtIsl5TZVNNNZI7Sh19F/Tg1RAApS9qkoAY+pTzPjxiJ84OhgQacIHjNvfieMkeZpCziV4oonhvnhu/RHc94BgWOUp9D7vFBaOJ56OE3h4n26QujBf3NS46O+RwgI4wFPjHOjJNUsZUK5zttUkiEDNnWV7ROKrG+ep52eJ62cfSY2+a18WJgkEH6ajxFUXLuJjnkfn0TFeKowQvmCVnC2YM++NQ19uCJduFBDhHtloLMKCJbpCiav+TR0rh7RoxkMtJ4METNMenLDCO0Zyjor6wJewjt752n+KYIVw57x0/+N87mF0MHD+J3URztYVCiKxmINhxxxlhf9dFYeg8ai0LqI5ra+xZ6+h9dPM/v/B85qQ2eY47iA/LL+DMItMPYS4s3XmRR0lTtq9MWcw0fplrwWJjNnCUDZLaQc2SbdngeepIl+qFf5j0j2Xv0Dx1E7oy9+TlWgx5loL7rp/eJ2mqD+xlO/rdlwt+K/RjvtGE+NnjSGuZvvIZ+fsgtzxDpZ1AyxvRZH/U5BeTwumcYA+0hG8gC/JKK0GQkAxJNfIYmslD01TPIN+OR/qCN/9GP3CaTc5zIUvtT6ZzMM6/xorba84smWav9xtOeg3fNSxH1eVZAHKJobGzQE8/kGWQ/ByjeQQ9z13vxAxmJl0Rz9y26VRSXAo/6VnftFsVOUCIodIR7FD8LoAXRYkMpMZWkjlKgKEzz1MoUlaFIRImjUFh0x306lA3GhMXNYiqVldKUSKTnijRRpv1YZKWpWTAt/BY5C5IfyiPvv7Yq2EC5sY9kvqBS/LSLAkkZlbaW89oo35Q/Sv62Yw30z6JIGaPsAeNDHxiL2f9k8VcYBz0pqKKKDB7tRT/KO5pQkLSJgkuBtehSbv2fFEu0ouBa+FNkg9KSqMnYNgqBRTrHOjC6tIkCRomieFHQpH6iR/a7GiueeYb93DCdv4MS79rs56TwoJn2e14OuveulKuXlgaMDUo6GhlzyiRFxJi4Fg0p0niAwqrf6af3oUf2zOEDdJeGRXmh4DH8UrhBWz2HISZKYowZHlK5UoKfkprS+3gezfArRYhDwrsZREt7bLXHGOMHz0o6IfqhRQ7lNo6ituYQnp6nHeovpUp/jDsHi/a6n2OCoWCs8CpFlXGlzf5HC/eiOSPRe7UVj+i/d2mP+8w1zzJGinjoI/40t7yPEose5hIZsDQH9NkzYnAkFZ0Ca67jVam7FONNfcV/0j31VVuMhTExVr7TV2OqTcaDwaTPxiV7XPWTMs2hhG9EFPGW93NY5GgWsoBya3+uuaWd6KWf+EL7M9c4kPCsyq2eyUmytKeT8h4ZmPP6ZG0koh5+QEPjo1AQ+rtmyUgxDvhI2xka+obv0Ei/tc8cQw99Mfco5PppnP2Njmin/eaQ640DhwnDgVw1XuaT/vtbe8gm79b/GNTkh/lkXPCGvuZ4DXMZTb0HHchMRgqHQdLbtZ9s1B9yz3vM8RR/8T06mPNpb/pFjqBV+mWMvFu7vJcsY6AoDGZ/PjrNDUgyUJ+0Ab9ln2IyM8wrBqN3MlrxTo4ZGoEH8A2HErljHoW2aGQOopPPjbO5hK45RifZDeYHwxC9ZE0k3Z7sIheTgWNt015rkGfot7UhVY7JaO0xBzg/8Q25gi+kpi7xFpmSIlzmvOfGWWcsyURtwrM5Y5YcMa/0a15tNnMYTawheNN78+5kAcjWMd84UfGHKCQDGH/4bp9q7kVxKVEDsihWYGnPyabP/JzUg+gZFuks8LuutRgyrnhHKc/upaRQNiywFBzGCcXVIrgpJYsyQMkejQKfR9lY23aLJQWB4bGpiE/SutYU+aE4i/54nogOJCWQ4UOx8JsyZhE+ZDXCMaVwbWGJFP7JXsa5IpcqgvPxHas77tvGpT1aJ1FENvEg5ZLyQ8Ebz7C7GLQ9FPQLrdcUtnEdJZZiiLc4cJbamv2rkPTeefGLXHPIfWub6BdeGtOyt81hRoWIjegwIzdzmNwwzhRbUTaGCWX6pH049NhnXmW+GeNN59+O8+y4c27tXB7XhpO8Y1e/XMMwM4749CQH2F+KeTnScr4m7DtGoohSphl5HKKHnm/7tGXbs7Ku7rvGFsVZQFNYi2IFtu0bmn92iMVqrWEVMFgoD6rFMhjdz5sqesSgFE1I9dht71yq9jZGkNa2nWK+SzlfW5VyjG6KBFHOKbO86ZRcXnLtFp3i1T/0npF9xzRFcLZ9v4mex1VK5nu9DoFNPIjmojUiMCd918U2HAP034enRXlEZjYZj/Ox21Q18dCpadvoN75rDY2Nq6iLNFxzyjySuSAqRQn3vUjnoSIkhx77+bzaNr4jbU4yJvvM5eP2dW2/yEjRVhHDkzrQLsW8PLRclEWDdw/NY4eUr+nvvmtsUZwFNAJZFJcBGFXSoKTcSMGh3EvLYUBeDgtT9paIqEqHoqBLj5NKti2ttCiKdRDZEmnlpBFltodOpFE6aI8RKIqiKEbUgCyKywgikUnNuxwMxzn0LRXrjnM2XVEUu9OWpa5GhnSOFUVRFHPUgCyKoliB7M/ZtK/qvCNHVFyOjofTxLivcelczdPCpdo/epo49B7Ryxnz8b9YvHcx+G7b/tWiKM4GugeyKC4xLMaphmifn30bCpTM08ZE3lS4sy/Jniwpqn5vS/m0LzLV4FTuk9K6z97Dsw40UWFR1T/KhsIRqvrZDymVV1qv6n/2BNkbOqdXDm5WmVLlSsaTaocq8o37blznWdJnHXGxie6njVQbTDVL/ZRmuGnPJYU81XpT1AcP2Mc29s/+NpUO9dNev217OE8D3qsgkr5ps+iy6o141ndSKxVI0mcplVK05/uiRM1UZsX37lGl1LWqPp5Ge1UBxROKVak+qbhM+GfbXmN8qdonvgO8ZAznVZs3QT9VfDW3HSegQug+7Q4PqXwJ6EgmaHOODdkFY5RKtf4mr5aqfqo+akxco3jXvOJ0gIaqg5JVqpae1tl35riCY3hEW/FGjocgg1XPNLek76LrUiEassb4paqmMVdVU6Gh0+C1JZmH7xQNwwveaZ7kTOJNtMs+8vQxY0822jdpTm3bd5/jOvCdKrrkyCGR4z1U69UOfLCPM8s+f30ztuaSCqzzNdSYab9K38bWGBvrTTBHzBVrbuhFNqkSa66YNzVyi0cqethMUZwBWPQtmm95y1umSqqOAxiTA/xtcXQG2x133DEpEbuUfIqBanTOZVSan7FxuZ0vRcFAh49//OPT2YoW+9DF4k4hcd4bxXQTvSgbFArnSNr/RSFbOjeMwqZEPgPgUiVu4BNK0c033zyd66nt25Q+/XCN88jwFcMj59eNYFxS3CjxeOtSQDs5SPC4Y0JyPqof9Ha2HUOYwbWktLmOUug6x06g06ENkSjhKgI7546yykj1XmccOh+Rkr0N+FJf3/e+900HhsM+BjslmPH6tre9bRqzfYEmDDqyBq0YVYx3zzMH/L0L6K9gi/nmDMd3v/vdU7vmPOUsUWf4OXvTXuVN8oei7oxXZ61qz2kB7c2Xe++9d+qr4xNC+xzj4+xGf8/P6QzMH0aII2z8ZpB/7nOfm84/JGdPa/7gPcarsy+dN2sc8Z5iYsbOOb2MqG1wD1lIfqjanaON9MXB9XEqbHo/w9m7cn7ioeFYiw9+8IPTcSTJiNhnbI2NsxWvu+66xbnhGC3z1thzWuyqVJsq2uSJOe8eTpNbbrnl6JWvfOW0bjSJr3ik4vLSJovinMJCxTsvCkA5pIjwKAeMFkYAxY9irNrotkhionGe6W/XO0/ucjMg9YcnnPLE4FBkJ0oQBYTRR4F1ruImeqGT89UoAujknL25geJ/54lR3OYHdF9sPhHp0GbtEPXZNab6o18UfPdSsuagLHM0UCxFYy420NP4OUJC5EGEhSGW8vbmhAgyY8V1S0iUHV30wZgeOpLKOGAUqXbs0PYbb7xxOoNPVJpiLZJorm4DA8Z4yDTQTn9vMlaWkDPpjOO+SnbuJxcYE2SCIx9uuOGGSbH+y7/8y6MHH3zwwrEJ28DRwoDRB2fjMS5GZZpy7Xk+cyzI0rwKfKdNo8w7LYeTsxZvuummiTccUZIjVxgHIqave93rprNSNzlm0J3MQSNFvK6++uqju+66a6IH5x4nx2kYFSKkDq/n5MFrz33uc6djmRxrRP45B3KX8YrPFB9LpPzpT3/6RAuH23MmOFd0ExL9N/9Oo3+eT56Zw3NnxBqQ7+5VMZhRzwk1OiO0mYPQd8ZW1HyXgwm9jDVnrAit8505Kv1Yox966KFjtbUoLgc0hbUoFmCxYYgwQKKk+cxCYkGhfEhrkfrI6yvl0eKVc7oovLydvPRSXSj8m5Sn8ZywpOxRyK6//vop1dD/yutTcLybkrNm/0mu8bNpP0kUgaSjiRLkUG8KoIhVCmnoF0WPMgi+l1pokXVPSsfrd+hGWaNwUAy1m4LjemlkFGf98WzKmJQ+EQBtsrh710ifbbSj2FjUKXC/+7u/OxkiKtIam1/+5V/eqKDn/pFW28521EaKir7GsJFaqY9oQDHh4TdeImUiwP/4j/84pRtT8PIs9BHp4TBAB/TGU2vGdE6TXfQZ9yst9TFGmv4ZI6mG6EX50k59wJf4WDvNC30yPqLiaDGm0VLIjaPor2dJ98JDu9K9fOeZDDORrfe///1TpCipqc9//vOnd+2aR/N+j/00NnhWXxmYIjDOzfPcNembaCFyI1Xw8Y9//Hc83/iKfOwyBpfaNu8TI1Q7XUfhx8faqX1xduQ+fTJ3yQd8yBDb5CxZOjswbUF7xhEe32UgpA/6ig4MsS9+8YsXDFPP/dM//dNpfnMEjO/FP87UFMmVCslogOwvJlcdEs8gY5zri++MlfsYUgw87fUeUUT0ochrO5mjDduOWXJMybXXXjtFPTksGFKyCzgfvHONgyhyC8h3MojBjO+1h0HtbzRhrGWrgjaa92Sgvkm13DU33Cui5hxOEUgpp6MskIZ75ZVXrpKV+Z2x925t5Fgwnz0bOJuMhagnvojcT3tca30wFsbQ8/TPD7jPd641bn4z7pJ+Tk6Qn9YR8oUcHPnaOuIdrjffdjnK0i/XP+5xjzv68z//82l9xg+AT/yP3vh1Pgd30WycK5Fp5CC+i7NBX7wjaemRieana81P/URPzyJP8Im1Ev8k9b1pscV5wOUVjiiKA4LQ57G0iFoApdZ95jOfmRYS//MCR3F57WtfO3k8KXM8k/aTWci+9KUvTdeuBW8+z7LFzr3e5eev/uqvpkX+0HtsKNIUv6997WuTAsqTLR2N8WCRzX4Z/WHQigxZ+Bm4n/rUpyZF7bOf/ewUiaE4UHJEsnxOceTNZ5RYVHnKkzr16U9/eooMMjT0z7uk9FFIk06aRX4NLOYUKJEgKavGibL1ghe8YGPEah/oA++/VC9pVujGYy/ljpEvauN/42/ctEVK2O///u8fvec97zl6zWtec+HcSqmOaEchuvXWW6cIkHTU00zd2wY0ljL9kpe8ZOJpCr6ft771rVN6G/oxzEV3KTx4hKH00Y9+dOILhjt6U4LwAKMPv5o3xvyaa66ZIhuJ9GwDmjC28Q2FXmqyZ1MCOWlOqlhR9MxHqaaUX0qzthkfCusu4GPOFm1cOtpClJTSfFJQPBmQZA45ZF48/PDDU9ocPhph/pi/FGaK6dp9Y2hpTIypPn3hC1+YeEE0am2mguv1lxFGJmirz8xhzgf7accx8y6ywm99xDtkzPg9OWpMyAByIs+TWup7MobsYIBxLOAvKYZSH0UCGdpr5KznM5LMTzLQ8575zGeuPkMxjhdzOvtKGQeMB+sB48h4+DuG5e233z613Xw3nhxd5OEuOef7r3/961Pb7NubR0f9zwiO022f9sfYBRFNjje0YeiS42QVOaX9uU82jDRTBviLX/ziC85E8tZcMhbSsz3rne985wU6kZHeZ0257777prmo7ejgPXHWcsqZ+9ZSsmaftUCbOZvIaG30Pu/nlGAEM2KPE0HVNnOPnPijP/qj6T2cJ/hY3+JE8T6ZKtYL6wEZKcWZke0+MlV7jKe11nyVCs9x5vs10f+iOAuoAVkUG8AwsNhTjiymvMwWM0qXBYLXVHSLAcCjLWIifYmCY0GQovac5zxn73MKeUh5lEXSLMyUE8oIpfWQFTJjODAAKT5+RGEo7BRKSrv+UhApedqln/pI2XzqU586pXBdddVVk9JksbTwi8SILFB07JURjfPcRFMZYQwSkSUGKdqiJ+XbgsyA5rG/++67L3izd4GSis6UU22hvIjUiIAdwpsrkswLbuwt8PoqgugdDHvKASWekoFOFHAOBEoEZYxyiSf0ldKFPpTrX/mVX5meqd+X6qw99KFYoTnlJ5E6fCcSnMgjBYyHnKfc99KC3/Wud03jTKF0LyWSIoUGT3nKU6Z+UhB/8Rd/cXU6KR6nyDsInFMB3a644oqtez3X9lO0RfTAOPmh4Iu4iPitUdy0Jfct8ZU2nrSdoUFS+fww4im+eGWM0hgjjhKRPsblmpTmEeQKI43sIgfInWQFrAWjhsLOuPvKV74y8YZ5b+6RgaOyzhGjP96DbzjKEp0C4+NZ+Mp848DTZ3NGX9GAHGT8U7bNMfPeNZwODBfv3YVEXDm7OIQYnuSZ563tu34w2tGPvMEXooN4gzPOPMH/Ke7ic/3mKMLb99xzzyQ/9XGX88i78J45tBSFD2/vO/b2pnK4cdYw5skkac05DorDEs3Hfd8+JyvsodVfkTUGKLqL4JLrUs2NBb5lRGqbvnq+53gvunNW/MIv/MKUGpp3AuOMk4czCg+RL2vTtT2fs8m6zXjjUODMIpt+/ud//sI1+8KaqE/WLzyLZ8jvREutm3jKvOWYsEZykMZ5gE/xtv6YB/pmjX3CE55w9KxnPWtqM2M7hnpRnHXUgCyKDSDo/fAYWvBE4CjaFkwGQSpZUhgZWRZZXneKEe+yaJuFPylaa2GhsbBasKSBWYAo7jHADoWkUibtyCJokbcXSsEdC5qFX8SMscQQ8jllgcKmnRZPKV+33XbbZDgyKlxHgRQdoEz4cR06UTAYeCIAlISkSXo+xcV1FCTPpCQySPcBY4dCYlwYd4fY85k0L+2OAa/9itLw2FOapWvpi/FOYR9jJnKUPYdo5nsKJcU5lSk9y7Mvxf7U8BPjdUyzNs4URG2j4FJes7fPOLrWeGsz4zqGtbHGSxQ2z8T7rt2WeroE91DOYmwcYl9mjh+AJQV8DYxT0ilPq3hG2jmm2I1tD9BbZMP4mLe7CoIsvYfzg5OMISWybN6KQjPW1kI7yDzKsOi7ucA4IRPnPO19Itv4P4YTgzN9w0tkhPmTSsrawilnLpFReEx7zXN8RY5wCpDVnGD78Jp2kxMpSLMP9I2BxOhkANgXy1FCTt95551TOzlTGJP6CGQDXiYbyJT0cdc+utzHyBrpdVxkj3xSdjnrRLs3pXCP9Ixzw3jof9LdI0PIBWNBFku/ZlSJSooomsvaLpuBEep69zM4GVdJvfYMxqux9DtRxH3kNectY4x8ZkRqHz45boTPuGo/p6siWE984hMv0MtaZQ0jFzhS8BTnoTWSw0Mb0IBsRBfzQ5u0B221zTjQLaz7RXEeUAOyKBaQvTIiRlIReS6lJVo8LfaUA0pylDwLYfb+iLxYkC0S9tmIEOwLUU3GxUc+8pEpnesQEZg5sh+HZ5cX1DtF/yy8KZTAgJViJbJnT0f28SSN0//aRblBFwspjzaaMK7mEdMlxQctPSul5cH7s/dpH2Qxptwd4hD09HeutGkz2jCuRWwoSuM98zYFFAYpXxQK3nHKiDRPn++LQ53DRsmaF9+gsIlI6SMFiJK8aSzGNoi4iK5IQxSdYVhyvByHdymW2Tt5COgjw+UkMNaU7qRyb0oLPAk8d5wLwZLRyyDjqGFIHqcADdpS0I0vGcPoE0kXrdkHDGvFZCjRopmey9k25xnGoCJElGT71LYdXZJ9lsZNSjVnBBlFvjJ6tfWk/I+/8Bn+3PdZZAwDlNFojmRfHIORzMx+O0binA77yjXvIn/JouyZm2PJybDLIJL5YG3L/t+1e6vnjpj8P38/vpBdgkbSNTknGVj4m9zRjzgNN8mI8MG+EMH2XjJaNJCDY18nywhtlIHDMSy11tocxwDD0fyz5uFRDgXpvMbePWhgrfXZl7/85Qtr3phhYy7gxVZ1Lc4LakAWxQZQonlNLQ6iahQtKaUWOmk9lMik/FFwRJp4wBkFFAf7XHgs7YtgXPGe8zBuWiBSyAQsPBYh+3JyztdcSciRAttKiW+KuPAYi6r6XJvsx8izefspeBZ4e1YYBfYW5uw73nNpqbzIFACLoSitz+xTQi8LaQrv7FKCKZoWT9EPz8p5YGis3xZZ+ylPUh7fOFFudynZ86IrxtXerhQuCkRGlYtnUPCkRxHaZtT5nIKpv5RgfZbSpbqriItnGMslHknUi2JPafEs/IYX8Q2lDM3WFD8ZQamxT3OsHOoaKVpS83jFvSdR5F3QD4oaI5LCRdGXmhxe4GBgxK1RkubKKWgHPtt1lEoKRwVohEel4aYt4QUKf57FeMP75tXS8xkxxu4v/uIvJsNthOuNjz6a7yJxm45VmBc3Gttp/sk6SDs9y+fGP3yWZxgfBrp5x2DP9Wi0thhOgKacYsYNb6bIxza+SootWmsLWcE5wrjjVEo//WibzAyyhmKfiNrSnBn/Z4SIYlHazUPvIkuNZcZwvF603Pzc16Ae+Y2hG+faNswLVIF0UOm1+J4jTB/XZBeQyeTcUnTbs9GMvCG3yYgRZIJ9x9qdrQSb9hyPEe6x3+O74iQFMnBsz1LxmaXiXoAP8ZRsBntNpfLaAkFmxlGRAmrJWFnitTx319i613eeifbmKnnjPlHBJRmG783VbetL6EXeWpf9mHOcu2gl4qqf+pT2y1LyYw3nLKEPGEOOOWuo6CQdIn1xP6Mebcifk655RXHaqAFZFAuIwsl7qTgBJZpAt9HfAmG/iNRLKUoWc/s8pJ0yeCgQ/nePRYtSZXFQiIBCPS/y4HkWewsrr7XnWpSkg1LE7Q+BVPS04PlN4bA3iAI5j6xkHxujybM9V5v0Kem12ilNkGdbuo0oo88tehY4ezC1Sb8owpRaShzl0IKoIITUI6k5lGbpZxZoCrAzsyiS7pOqlvQeynpSOLXfb6mrnkOx1D70YcAo6kJ5FM1yxprnbFOI0YyyS2nJ2XYxjKTW2buCtnM6RXFDL+9VJl9fKYGKc2iTiAr6J/qKP/RVkQaFdChC3oumjED9dK1nU3r9T4nxQ/HQF+Pgfj/xyqOpfaeeNQcDW/SSQYsuUvhEW9HQ+NmHNY9+JYIapcvY6A8DCC8rXkHp1Bf9oizm0GzPFUmxt4eCh4fQJemsnmss3ed/48v4wpMUKY4T7+Bxdx26iMhLlXTvNqeHdmgzBYpyhzb6Zvx48UVvl+YsWms3PhA9NZZ+Sy+075ZjJsdn2M/EQaJfaGDs8b4IvGjJkpLqPlEUkRtp24onmSPapa/G3zVpJwVzPiaMEgZQjDPjkXam+Ah+My+Mr2JFfrQTHVxvDNDUj/kp/VL/zHGyAd9KSXTdnLbkAf5CY78puNota4Lzy94zjjDjaW5T/OdHk2SMGAT40ntE4OwjxJccZ95jLsd543p8Zowo045A8Fxt9l5joH9+XOuZ+MQ42GftXfhf3xRgIV/Nr1zrt3faW2YO6dc2eZHK0O7F8/jT/cYn53wuGc/6Q95pO7kRJ+IIPGgemD/6Y97gL89DV/3KsSGRiwxijjpjvcR7ZLLUWHD0hjmgHfqZqK4oIv6wJnjnfMzMp8hF/fX+uZHMSWKc0Jis8twUQkOnpGmSl+Y+mulvUtf9NpapkGsNTeqmbRGeL13Vd2SydcdeZzyf6Hve4zlolv/tG9W3pbEl//WJIaqYk77iRc5MdOUU8Rw871lo533WKWtNCgnNeQS9tEl79Zdhal5wBJqrdACGH+OQnKEbSLGWeWR/NVnL2Upm0wXsj/S5vvvOuGcc6QfWUWuvbCDyoxHJ4qziUd8qdxbFd8GiSqnjIaSopOw4hU6Kis8IfF5fCiPDMcc4UNItZNJl3Gd/ksXRQsNYEM3knQ5MQYuTQgEUS4oYBdVzLV5Jb/Qui5yFWcqZtFOLOuWLYj5WD/RMiz7FMMZlUoQSpVAgxTNcJwpFKbDAUwJFyhhQvrPwAaVDP0XNPJMho31ShXJUhoVQOp1F0LOjUFNwKTYgZYpCgbZJ8bEoi6BQvEVcKRvu0Q7XUc59xtDcVEiIIudahQss/PbRiIqIZFI+KaqKszAgRjpRKLxbn5L6CuiMVsZVOjEFn7IpbdD4eZf7pLF5B0NJ9A2dvc97XIdPGENoS4EQ1U4KMCWM4ouGFAr04qFmyPtsbCdFCh20U4QbfdHcuMSoTFGHQB/wDJ5En7F/iWwZO5/pizZ5r0iH/og4U4y83/gZOwaK9jMSpHNSjjKunqN/+uV9xoGyxsjhDKFQU+5EJNBnKSKA5niaIog2+pU24RtzyPygiI33a7s55PkprZ8ImHaYs9LQzUu8YDzxFEXQ+OAV9JGahsYKWiwVNopzxlgllRUfcxaZ65R4fMLwxtvzdqKL8cCn+jpvp2cZE3OCgmlctBOPMeTxG6Pa3HctHvM/PhOp0wb9Y8AzctFtLms8F98nqgLmqWdrLzpoC0XWOxQkGY8nyRh5j7/dh354lDEkikKe4I0Uf5HmyXiQTmhc8YN2kyHuxado4zpjpd/aYKwp3MbWZ8aPDNYvRlnkimeYY65De44/122K/uElRhYDyTxAX8YGno4jSKRpnvaYven6qZ3on6N4Mr7ej87ohiZoRW5yNBlDcwf/p4/mDl7j0MA/5v98P2JUNfRkPJqb3smwTPTXWJLBHH3kuHEY72eQeSe6W5PQUPvHAlfmDgcXHsXTHBnG0rhwlnCy4Alt1gZ9sx5ph++8Bz9wNOo7x6C+4BM09p1r8Ks1Ee+LFGpLaOtaz/cbj2kfOeR9eHJpbPGUsee0sIagPRq7R/vQl0zh8CArfIe3tBGfWhv0dc4j5op2kW1kP+epuUDOaD8e0BZ9isw0X/XJd/QIPI6P9Ec/coarZ5MD5jo5lAq76MjwNl9UZz309pWiOARqQBbFCRAlfH6OV7y9qYznGgs7b6OFhYF1CCS6cdL9cNm7tem8yE33RAEePxtTo/L32gIxY8GdgNfYwhwj4jj9TP8OXcU2/V9DP7xC+WGsKMAD7qdcMEQYN5R6ihuPeYrvzJ+BRpShuaF4qD2RY//Sp7xjW/+0y14f3njKWCqdUjr1UUSd8sb4tN9u15mLm9q0lPp50r6NfaDwAgV0F78Yj8zzca/mIfkNDfeZlzHuKKEM9+Pu+xrn8iELPI202ZdOa2jBCccAEn1KNO24cvW4/c6Zgfq1VkaLpmo3Y4JRt40mWV/QI3u+T8Izu+bHSedaopzzfen7tNNzjK35yYg77tguPfdQczV9nfczziFrxXysUkBpXPMY+qKPyWDquZDFWUTdGkVxAmzb+D96dS0gFg7pqIc8y/FQil325+17z7z/cyVp34VvqWCKBVlURfTzuAvpcfq3T//XPJ+SwhgWHRBVEx1IlUkRRV7tRLg3FRfZdEzEaVRwnfdpV/8SzaXgUYZ5+PWNoiv6on/2/VCIjntkyaGM5KXxiiJpXET919B003gckt/2fY4+MBrJmrVnGi7h0A6J8bnp0750WnOt/ov8iogdt/0nnU/j/Wufpd2Kca05imW+vsxxUt47JP8urRPBcd4hapiK3YfAxeirz8cI/oil6615osonWfOK4rTRCGRRFMVFAoPK/kAprhQHqU4UopRzP8+wlEhvzd4sKZFSCkWCDhVxL4qiKIri0qMGZFE8wrGpGl9xOsgeOhBFuBTnP54meM/tC8wRCeWroiiKori8UAOyKBZgWqQICAVfSthSqglFWbqe6xkD87O0lp7rnpz/JJUxKWcX25DwXtXlUjZe8Yj53rqTIEdPpMz/Eg1Dj5yziH7ouKkN0iGNS76X8po0Pc/wrF1jkf1DxgANcji867UvlWKlW+5bvAC/2OOXQ7A30QVN0AbseZrvl9EP6a7aMN9fu4lPc03OVPN59tf4TkRw19mYOS4CDfACmnjHtsPZs78n1Sj1Zy0vz3kkbTwJj+Qe8xJ/57npx2nMszgFcsRK9mkmLXnTO8f7xr2d417PnA2HTvjBs/QNvXN23Pj/LtqsRQogjW0NPH/XOIw8DuiAv3K8xMiToQGQE9t4/pBYOh5FO/VrlCfaqK2bxnEul/CZz1JNNTzot7T1HF8SmXgx+jpvo/HTvm1tBPJxSW6P/O4edMOH2fPpnuy/DY03zfGTQnu02XzPucTasWu+u0abc6bxiBQc0y/Pxs9k26HbXhTnFd0DWRQbFhYV86TjSctz2HvOswssvCqVKvCigpqqd6q0bVO0GQWppGlhzgKuZHyqnV4sqOqqwIkF0jEXCps4wmDTXo19QWGQqqlMutRNpdJVfh3pQwFRVdO+OQUDFBiyB20TDUM/z7XHyfEe7rPYq77nWRZ6/ZA+ubTYUwaccaiwC5q7nhLh2APPNJ6qMDpuQvW+fSB1861vfet0jMATn/jEjdehu3L9nm/s51Bd0lES2qAq5jY+VY0zFXOlw9pvyJBQ5dB+S/31uSI22w5u9yyFV1R0TWQUXezLQs9NY5KiLY600B9l7NcWyHGvvioYof2vfOUrpz2SIyh3KtPiEfyjwNCuvUGMeMco4C9KbH6rdHuSfYHbaIef8J8iSXgYDbxXmrIxWZrfZIK5p3ok2uWcS9VUXa9oiMJRDEeyCG95rnFVcMVxAWhhzMwzxZnG6r0nAWXcc1U5Hc+iNR5kotRk+yw3jQOjwTxTFEofHEugUqb5Sx6osqlqsGJRqoL6nOJ/9dVXf1c1zNMC/iMH0VY1TvxFBumrKqz4TpvJxlTf3CSXVBhWRVrVZkWkVO5U3dqYqjirKJN55RrFtFQkNa8uVno3mquQbK7maAs8q49pI5mFv7RRNW086ciNeRvxLXmr2qx5iz74QcVUtDTXVLBVbRZ/KB7nWAtjTg4dt7DTErQl/UildP0xz833bbIIj5LD5pm+xnHB0FZllSxSERkfm+Nk9SFrGBTFecbllTtVFAcC5Zkx6LeFiXI8nl3nt3LsFmTfW7S2GT6BRZkiTMFg/Nj75n8Rn4sJC64z5xhZFnRtobwdEow3CzADRklyCuJ4sHqKrjhHkeJtYd61FzDHFjA6GHqUz0TVjAEDTvl4Zf83eZ4p2t5HMaCsGucc4wEMJgbXcQz6HHlAOdwG7WZoOEduCQxZSpmo8Bo+peQywhwngI4UXYa1cXZUASNm1/jiQeesUaKuueaaScHMGXbzswznbVAwx1gqYb9PUktK4+MR5wI68zORqPE4GucGUuLQY1cBDQaOoziUx6f8UtKNpTFmEJ8G0IDRge4UaA4l9FMQxVlx6DpG4wKKLgeA8v3aydBiUIp0qMqrkAYeIVsUhtEP4+i3cTGf8D2DUyVf47UNORfUsQy7xilHC5BPFHP98aOyK95iYG17hnYyyBigHCbaaF56rj7isxgSxtTYmDe7eP6QEK0iL9CdscxoSARd+xlJDELXbMtGMO/xcQxRzyD7zBvyxrOME353JBAHhzm5VGn5tEAukG2cY44C4ZTIETMcUIzEZKCopIyXXL/kdHKNsXKPexOdwxecC97hGcYY3dCHfEHL41Rf3gZzyPEnxsp5oeYRvnI0x675zqi2ZnD+WpMdD2I95BAwlniRbMuZp0vncxbFIxU1IItiARZIHkxKD0WAEjUeXuy3IwkshpQDC+ma9FVnQvntuYwlhhtPbQzIpN5Z4OcKZ1KiKI0U7lSNpBAyfpIaxqvu3qQwus8C6B7X+59HmMJPqfAZ7/p4bEFSezwraa55tkV36fDsJRpayCkSlCUGjqhu7ov3nxLlOkrGrhRayoizFm+55ZapTwyFpBcxEJxVRgHYlnLpnWhFOWCoUYodUB7jltJLmYhyixaMGrT3Tu1O6qhxylgAPmB86fM47q4ZxyhGAEVV29F0PKydN5xxiyZraIyGohvGM0fHeH5SHH2+K30VH+Fp9+BPYybCRVHU1/CU/oenQjPvTypuaIOnXZM02m3tZ3xR4h9++OHJoBrnmXPhPF+70HdXiqZ2GmP91g9jIYNAdCVpd9pmLJLC6Mf4ju/NvFpKb1vqB97VPnTGW4w+fIRfOSyWnAWeqwKv6BzjirLqfso9Q4OTQ2SIDGJQ4hv9csZpKhb77b55VU59w1dJ1fYuc1lkWzRo5NtNcy1GBfoz8vygpzmGntvGIQ4kcsV4MJrISDTRP1HXHD9jLHzHOaQ/xsjcGh1rSWXGf/gr/RrhPgbc6ITYNW741px1QD3Dh8GLb40Zox3dcxbwNlqRoQx+/Ipn8Ks5rK3OH0yarGgXx4Jr0cg4zOVIZI57tMXP2NfMMX1NqukuoCte817rmffhJ2uQZ4sehibeZ6w3Gc5ZH8lOY+k8Q+uFfjHCjDVZEv5IpelUxjXf5m2frzmjs3EbzFv01T/zXRtEHp2Tu+S0GWFMrb+qfONr7RN9dVYpuhh7ctQ8cG3O0sy2j4yFNuuPn3GctE0f/aQi9TjnyEbGKj7vbrLivKEprEWxAdmDJE1LOhzllpKQ/TE8ktLFcpD1LngWpfIDH/jA5DGVrpdIAyXN+3JWpEWLR5WiRpEXXbBIU7IpKBa7F7zgBdO7RUJjRFm8Leiu8R3FyDscKm4Bu+GGG6ZFTvqb+3jdKQI5fNn3Fm4Kpt880A5Ltnh6lrQsSpZn/sZv/MbGw+BHUJL08YEHHpi88aItFnuLJoOSV5qysZaGFBp0YYxLBZMWxcjxrJe97GVby9sDRV1ESwSWgaQPFHWguFBs9VObPUvVVAoVhdDYSHUFUVDfUwh5sClcol483xQvUcykaIpsxACjQHuWvhg3kVnGrzHVfuMguuTHc8eD4HchZ+uBMdtnvx9DDu+gDT6VdslQQQfPoXQyBET1KNR4iuKLj+fHr+gzHmIQUBx3RT9jWNx///3TfSIZ5gTlyrOM8Voe8Sz88PnPf36KbjMM0FvkzG+RX5kA6GpuS7HE0ww185vSZ5xFUii05rqoxnGODkC3bZErxmBSG+f7YAGdGaBowgBlaGrHXNmc/4/P8LHUUD+UaQeYkwuyJjK2+rxmT9d4DT6mZOPxbfyVPY7mATnDQNF+4+OHDMI/HGlSp7XHXDQ+5sMoA81ZY+Q69NAP847jB4+B8dJnSroIq+f6YRzsOjYG/770pS+d5KyzTLUBH0ixXTPmrtFefX3Xu9519Dd/8zdTu7LHXV+ltXMIMXbwN7nLWRJHJIfes5/97Gn+k5OyB/CosSfbtS/p3SL95IO+miOMJjzPONw0Jtro2fjgne985+QQxRdxSnDweQfZbp4nWrytz2SW+a0t6C8Ka45prwgeAxxEoPGLe6yb6GNsjbmoITlonbFGea97GW0MwV1ZKeaXdpBH6GOdQltzdlvKPuA3zp7QDN/ce++9kyFqfcu+XUBr44KPjdcrXvGKC0cxyYbRRmuqz8wrY+R6zjFjZD1GK+t2dAj9tLZ4bxx3rvG7KM46GoEsii1gAFgUGRjSrhgWlDWLoEVgH+UeREIs2hRlC6cFxAJu8aCwSHXzDou8Rcd1DC0KDcOTcSI6ZcG3YNoLZe8JQ8N9r33tayell+LJg04pSWTPfhwGksWWYUg5sNC5V58826Jm8aVIfexjH5s+tyBTKN7ylrdMz7UwMyYohWtgAWZo6zfFneLnuQxiC7hIy76gmNnvRbmQIog2zk9cc24WI5uxYv8OhQ1dEm2h+Bhb6ZQiHOhBOWD8in5QeCjQb3/726eFn8FBCZR6afwoxOjGYARKmvTdxz72sdOYUoI9O5EXY0ZZoFgbLzSlqFAeszdnLbSVYo2n8uM5a0G5vPnmmydlmuJ20003TYYCGuNRfIRWnulv1/l/HilAN0YfRwGFds2eQ/QQPY6xgW99hkfwB6VsrYcer9uHKT37rrvuuuBoSWEjPGeMGRs5SoWC7zO01ycRCEYWo1N/zSsK8po2JKop9ZYCSblmhEsvniPFRzbxbAp5cEzYY5fiW9uAT7M3LY6N173udRMv4kPz3t60bcbGHAw3z/TD+CAL16Tzeb734CHjykDwLDLM/GE0kFfGx1xCXwo8g8N8Ia/MbUq66zg38IR9inD33XdPNDFuZCeZSQ4Yq/vuu291ynKce695zWsu7F30HPJurdMALzFW8RnZnEwPBq95xFAUmeMkRBPR7dtuu22at95ljG2VMN4MCu13H3mbLQfZR28vLL4RAeRIc9+avnq29cI84BBNdNQax0GR6CsDbw1/6DMeM18ZVsZC//CX+UUO6rNxZBiSldYsTkPjy8HDOct5Zc2RUYIe5CH5xUG4i8+shZnv6MkJikfIqF17LcmwzD+04DxgBL7xjW/8rm0I+ug6DjRjqe0ghZfMt8ZxDHAgMB7JTHxE/ltvybf3vve9E22No/nAyYUOeA/fc4K2anVxXlADsih2wAJlkSTYLWgMOsZWIgdr4X6e4ve9733TQmeh4vGlIFk0LdaUVj+pgseAYNTYq0IxoaBYLBl19oRRSj2TgkbZtbBZuMfqc/MKghbMVDq0yPLAjkUSGLNSeRg2FjcpnQxlz2QUWKgZoTGS1sC9DDcKJAWCQkHxQUNKyL7pO2ipz6JfikIYB7RZoxDrMyWY8slBgJ7+pjj5jpIdoIF9YAxHKU233377pChQ3ChY7jF2ojz6wQMer7dniy5qJwWCcSv1VkQlqZ4UN3sdE0WlfBkvCtg++6M8D5/gKcozZccPQ2atQuK92kYZ1T/GPocHJTgVTBOJ8juVg8ex806GDhqaM2ix1kgxhpwq+iAaQsGmcHI87KNUudaYUUYZBBRj80XkjXEVpTEZBvqRCKDx1G/jZewZm9dee+3kQKEgr+FTNKdUcgCIpIkqM8aPWzgEH5h32rjm/YwPTg/0o0iDvtizmKqfHDD7GEdkoPEXeSEv9tnHxoA1zxkI5BNaisZRtskChi3eRXO8wqllTowy0G8yBz+Rj4wyBqe+GjM0Nz6+N4cZSfrqul3RxwAtjDfeYURq77b066X73cs4sj4wzBjCHF3kB6ObkUiOh4YcCyKVjELvYigaYzLdNfoqckcecCKSKa7hoInsJqONZ4qJrWkj2WVeM2bIT9E2Mp9Ro93oqA1rgDc9k0FqrnGgyNIg6/GfCD+HgfaigcgjJ4L+MCr1x1jhE3yqz6Jwnum7XQakPqEP54J+4K8XvehFq4zPAH9x4LlHsTeOrJGWxoT81jbyn5zXD2PGAeV9+IwRaiz96K+MDrJQf6xP5l4cqGSD39Ze4xHHrnuK4jygBmRRrIDF2UJPMZRSRPDPq0XugoXVDyWF4SiqxRh7//vfPxlTlHLKACVcNCR7HMdzGqMMWZwtrmO5e4s0Q3GfqNM2xMgBC1/20Pnx9z7KFfDAoqOFWuRBJIQCcdy9H9qRNCtG2ppUvIwBJY5RYQwYb7zJ2pTjE0bk6IgoFJRpyitvMU8zUOzmYFi6JvunjJXqh6lE67lJ/UxqbtKl9qWJ6429/TzS+njhpW5SSNY+y3iioTESuXvb2942GSGM7TVFV4Dizau+dg/avA+UM/ShyFE8KcbGdlsRn/kzskeVQqZqoshNnAaU5bnRNPZLuxlaY9qp56Ctz5fGeQ7XMsSNgyq6SaE9Cdb2P0YwBRYd/TDGOKkY4iP24TF8xCHACYS3OKtyvMgu4Cn7JTneRJsYeww86Y3knKgmJZyBHBmI1ozNUQb6MY8yfknXzvxkgDBGs++WbFjr4PMM2QMMWsaAe9EsGSdrYZwZwJw4HBiMAwYg2Sf7gIzhjEsBITRgTDEsIbwb5wZE3oYOeJnM09fsRRelX2souw5/cnqRexwDZKD1TWaJ+csgWyNPs4fU/doj4pa0YWPKqJQVkcrADGqOMv97n0gqAzvycGl8dyE0Y+Bx1FhP0V1E2nzf9QzfM6StBdrFaZbji8Z7c9xKZLn3GhPykoOA43asZRCEjtlSkOwTBjKHoXHg2GI4Zo9oUZwH1IAsii3IQmDRFolg3FmgGAJLC/bc6BtBGUmqowXIAio1zt88yhafN7zhDVMEhhK1rZS9RZ8CYgFKYRbwLIvZpn4cp+9rrtum4OY5FkdFJSzqlCtK5Hzv3Pi8Ne9fumbb/by/lCaGgoWdgk3pYFCKOGzqx/gsKVlSAlN8SBRgadHP/jdjmzTP7M/a1v5gvidujRERJSWK2JKxtIk2olYiBr5j9OL37Hul0C/RZFR0gaLMUBGpHSuy7hrTPEsEghFM0bRPWARpKdq17XkUcimEvjdHjZG9riJaY9R8vDdKHX70Pn0ejUXPyV6pXbzpGvTjKEoEdh+ehl1K5K7iNYw06YQyFPwwgsmX0ekTpX1Nu8bvyR0GRvgiY7ftGaJUHG5Sgynp6GJ/ofmA50RptCcykAGGj0YZuK3Pxsx9jMccS2FeJ4ofx9EmmJ/kAkNMertMAUa49Pj5WZjb+mqstZs8YTCKRqav9p4zshgO+mItkUHBEGdg7yqYFXAUah+jk6OFAec5Oc92zXiaE2hrnjD2jCnnANnkuRyb6e+u55kbKcDGQGYwo7ssBlFO844DIoYwGZwiXWiBJ12T8d1XppvvjF7PwQeMY/QwfuRI7t/UB87YO++8c5rvtmkkG8f17tfe0Zka+EybrOmyDaxn1pM1GRfutRaSl8bQHJCpgHZr6V4Ulxo1IItiAwhwCkn2HUlDoThRDhJ9pKhYeFLB0bX2PCx5Pi3wFmxFWVKNzW+LnlRGyhhvLYj8UDDsa7EQJrISJcgCbV+mdKPsF7PYUQooBDzvPqPk+Z7iTDnz3CxOo4I8X7Dydxb1XD9+nx9tsFeTJ3Xe51Rt9W7PomhQaCnXvPw+QzvPCQ2lt4pKJAKxa4xSaTb9oAwmgjVXGkWHeNmNQ/YMeW/ST2MgzPs6Kt4ivPYK+ZzCxeBPVciMkx+KFaWCYUbJYEiKYPsdhWZ8rr/HaIv/8zfjdhON/a8Prh+fF/5Kezzbu7Vl6dgYyh0vPJ5JexjajA/jpT+e43vtwG+eI3KT91FEpa8xtuwTxXvaQLGkZC3t4Uu1UIo6+nOeUC5z7EB4JPMsRryUVClzIz2ipNpnimbhcf1IZVPfc8CYb9LoKMzerx9R5mUEeAfYh8b5wRgw1iJTeH7JIEk753MLHdB91z5KtMo4jteFr0aazZXi8Kx+2lMl8kqB1378jlaJojBm9AX/igrKqliKro7zKzyElpR2Br6/vUPa77Z0wUTbzHnpeonAMGQYKylQtEkGGp85XUd56N1SWEXCjBMZiA7ooY0MV+nkS3yP76Rdew+jhzzWxuzDw0spUqavMii2VT7lwNNX/SLXIUeFMDAYT/pqPERg/Y0vUgyN/BsN3rnsz9E6CkAxzJPCGtmpvdlLvQmMJGmatkaIoCUNlHGfNc5naJezPDdF313nOeaqfmaOWS/97T3mlbnN8Ur+ku/40lpovCN348iBzAGfoZN5R+4s8Za5lTa6h1NVH0U7PUfqrLTiOY+iN8eBtFdyyxhFzhgTEVVyIPTPnAsvorH+kP/4h0xEM2u270YZkf5lzpIhjH9psXjWu2OocjTo76GyiYriNNAqrEWxAIuOBcc+BUYHhdZiLfXUwkA5kZ5I8RK1cJ2F12JGsVKEJAtCwOizMDNuGJiUOYqq/UAUKYueBZYyowAGJdxvBogF2HulyVis7U/RFvdQ+rWN8iWiKYXIeyjC9oUwxCykPqcwuo5BaaGzcFr4LeaUDwYGJc/+DopVjnXIfjqpPtpN2bZoijJZCBUHoARoU9L/LML2sjG2vFeb0TGVRnPYvQUYDRUy0RbGL2NDJIDCtZRKZZHVB4qx92qD8UI//aAU6P+8UqTxYQxIVbM4G1uKB++7QhZo439t9xt9vYcyq50UVAYV5ZBBJwJM+fUdZcg4uRcdGScKLlAq3vOe90wKrDRARV18ZmzBPr8Y9xQkvJF380xT8EJjQL/QmBJDATMu6ERRCp9pjzYYZ/0VTfRcBR8odfO0Sv0yrviJck9xwgeiHTmnL4Uv9I9RyVDxDo4WCrD+64exk56moESOR6CI4gERggBPU9L1wbvwqXkjjdDz9YPyiL4MWIYoHkEvBpK+RmEN3MMIsJcz+xYp3Pa6inRpI0NBtEEk2Zhyauifa81HfzvuwrzCJ65zjfliDzN6czgkOmocfMbAMDbmrEhRDAjzBF3R2OdzuN8c5GDwzMxzhoxxMIeMI37IGZD4mvxAJ/RBS59rlwqR+kcp9j6FWEQ7zC2/RWzQ0/PxpfczHsYzGM1Zhgh+9n5RZX0z1sbfc8lBhjz+lCqLtktwn8ic38mQcK0iVuRIzlzVF5kZDIKcVaj/6ElGZF56hnlDTjNmRe84ArTTnHate9GBDNAP1+EVhk2gL+QtXqHIkx3mBUdHjqrA7/4WGTen8ADZsik9Fi8q8mNeaJd70ZVBiv9TVMrcMcb4KQXArCfmg3vNf04QNGNsaqs+eAa5zCj2LHT0GdnPaBKBNQfJjU0FyrTdeJlrqbSaY0fQLdk13kkWu3ZbWqv+kW34O/uJzWV8N9KbXDBPFKshD/CfNYNDg2OKQWe+6xd5ggbZ0iEl1Xjb1zxCe7XLOOJD88S8QA/rh2dw6qIR3k/Wi7mOh9ARDfwt4hyjlTzSHvdYA1IR2vvIBDxiXDgJyTnyzjpuzPGr+cnx4RrrhD7EGHYPvtaunImKb9HZPk7jLtNJW8itNenERXGx8ahvNUZeFN8F04ISmYiORXosYmGBzPlOud41BD3Fx4I0PxfS4mGhtDhSzlLCnnIeI8fi5zuLkPfl76TLgMU9BoT2MUS01XPGipdJl6QYMJwoX9qUKII2Z29j0mDzf/rlf21LxDR7Lv3vtz5TPnMcCEUoi53rc/akv/Vn3DeV8988K2l+Iw0pldq9lLqWqFuKD3mHtqY4isXZguxnngpqDNCBkkVJERGgDLsfPUOLVMAMLfydin0MGM9xb/ZPUX4TvXSN/npmxsH/SYPMmZ7pc/qTNMp8l32v/mcE+ls63Ejj8Gn6mf2aPh894MYHvSjJ2j1XSvSbk0CbKbCeyZDMkSPx7OM3hltK3Pud/qT94RHf67e2MFQo84z8cTzwSKIbu3gk7/N35sa8imnO6/Q5hwDFjzLL6Ehar+fkjEefe8c4r3wvdc3nHA45V9SzOYsol1J8Y0Qk2uv60Mrz0hffeZ/xH/ctj3TI/elniv2gTear7zwzPBp6Z376P+PPMEMjBjV5FBmD3uYHHtB+zgxKLKfXeGyD8U+UfoyAZh9YxipVUEPfJSSi7rrQMvwUWuWzJRmY8zW1IXvQxnlpnBnfFPhUwTV++ia11XwVmWP4jXvXXTtWEQ4PhOZpO5BF+CrVmLf1NRkB4xqA7qm6m+u0k+zIfDS3UkRF23ymr6GTtjHAGLEcH56p/ZwP2sxhwODipOSowrub2pj5Oad96JDP0F/fyeNtfcYr2ps54dna51nZPx/akLs+y3mmI4/HwIs88bzIP/xm/+1Slov3iBqSb+Z7ZG3kM9olsjqOU+gwj7Bmri2tj/nf99qL/+K0MM/dm8rqoA/4IfzrnaL2nAjaia/wKEcQB6Fx43wF/d3nOKaiuFioAVkUxYlAUeMxlTY2VnMtDk9j3vy1e6XOCpJezSjled+ngudZQ85XFI1MCvZ5BqVd1I7hmOJO5w34S0RLdEjatPmR/a6Uean+opMcCUnvP8/854gJRrioOAcZg0lkkgFDPojC+Vta5OVgeMSIJf9ELC+HKqUyFW699dapsBeHhj6KaopOytAwL40jh0jX1OKsogZkURQnQvZHnWfD4KzjPNM40Y547M+jkRIkep8o6HlH+pMKqOcRSVGWsqsfMgEYxAowMR5FglI0a1OK7XmBKJZInJT4HBPBwOCYkZaeLAP9HCsJn2ckeu1n25mp580hmEJjOZ5Gaj5+5Zgi6y+n/haXJ2pAFkVRFEVxbpECMqmeSwlPuuLlpIBHXRNZta+Okaiv2c98OfX1ckbSZ/Gr9FvbT2xnSZGrojgPqAFZFEVRFEVRFEVRrMLlkeNQFAdGCgqMZb9T1CLHG4xFK1IkYAnS93L9iHjHs8Hexnye5NNOJRsLduR/bdC/FF5JwYYUvNj0nBSy2dS3FAGxJ0kRhqVzH0+jf2NhloyTtK6Uk1+D9G1+XMM4bksFUQ6NFBvxrrW8MY7NWARq3tYUuvAbXYz3GvqEh+zJcs/aQ8wvFsID4QPtM0/RZG0fIbybc+o8Izxsfvjcu1Jo6Kxhaa6n8A5+2EQH/EAWZA7pq5/wE6SIzlhsJzLyUtAihX4yJuTNUv9SZEkfwwvavTblMwVj8H54K3IOTUJv758/E61EnBSlGQuenbTf3qff+PMkae551lgc7jiy87TSWPWRDNxUXG1ECiQl5dw9a9qftO4U0JuvbSnWdl5TvoviUKgBWRQLsGjYn6Act6MVVOlUYVShCcUYfK7st03uzvzKgdVLUFnN9fY6WMQChUUsykqdK8+v3LeN9am6epp9S9VCxSVU2lTZU5lzBTVUZJRO48gG+4g2LZQUJmXnHZ+BPuORBtKrLNaOcFBy3jU333zzlG512rDwe6dy+BRFle5SDVXZ/BRy2UZjNFIK3jlgaBGFxeeKwVAyVDkcKzqeFigz2uGYhRzwvQbS3Byf4SgLJfiV6J8rrfhAARL0cUanoxbWFBmhhDt2xZl4qgYqdX9WoG2OhzH+xoxxlzPYUqlyfoTJJgNUHxXvwC9olOqajgOxZ8kZiiomntVS+/qh4qU5rVKnOc6wwVOOeMih93PoJ5lkvx1aqQSpUujDDz88yT3Hk5AZnsEg8nxy0vEo+PRS0ML4ktmOFZES6HiIuTGl6qmjf8he/UplW/0jJ3YBXciWr3zlK9P/5lOqhTIqHAeERlITyTs0G2Es7rrrrunIEEdanBQ599bxNt7r+BvVWU9CQ8VcjCc552gRNCQ7FY6y1incc7GRY3IcqeKYF0eibOMxdFGp1nmT1iL9IK8dy7OLN81vRynhfbytoqtxTpq0tqCzdaEoHsk4/1UAiuIUQCGgHDEYYyRZOCkbFDCLrEINqsJtW1AtyJRZixCFyzlPfpTutrhZoPxNWWW8bTv8+ZB9o2ApvqDcOyOWcet39hHpF2Nlm8fWQssA1Teb/1WP0zf08VyKDeU6ZxXq48UABYFRTHGgWDGenvSkJ039U8GQErIrc1+bKYroZNzSN/87Q08RhItRDTXV+Zzn6Xy3TYd5L0GBDUofZdCZhugxP3je8SuerYKla7dFiHMECp7N8QKMDP+fpWgbY9sZcPp/1VVXTcomhV0lR06TROO2gYGlQqIz+hiLzvP0HFU88TLlNEdgOA/2rMJcV1SGA4Sxz1g0FyjUzq5TtZRDbA5jyznCqeXsQ7xOTplXzs3Dk6mG6Zn4gTzgILpUUSp91RaGsp/5HMfv5IEzJs1pZ1CS8T7DG9tkwshbziEk+/HW8573vIm3GM8cFmSM+bBJllsrOOXIzF3wzpyvug340HMdL7M0lvvA2FkL0JKhTd7hF+N/5513Ts6m8diTJTCo8cc+smpNu6xXzgBF622Ik4+cdmyQtUnl5DvuuGM6zmOX7Oco4hxyLd7gdCT/82ON5JzbB95pbMjP7horLhc0AlkUC7CAMoQsPm9+85unQ4J5nUXaKFQUrFe+8pVbUzzBIsoQGw9utwBSYihjnsHIynlY8ZLDeIah5yRlap4a5TNnSEHStra1Kd5y7/SbIuh5IqXKi1MSLKC7oqC+Z2BbpD3D/wwrEQARCkq8PlM80970gUI7eoIp7L6jDCVKSwGjlCa9LGlyOXtrE/RfRJiSp3+iLqILPjOGaO8Mv21RY+9lUDmzMGfjUeQYxsbllltumcYtUUlt115tz9l/Y/po+phKkLvGKEArpd09j0ec0prD6XeNjfcYY9cz9BmgKlVmLPChow700zhRvnNuGiNLn9AvUUtKNt6gUIriGtekK+qbH7QZeTNprknzo1wmLdwYonPS5Iy9/vo+VSTdz/Hgu6RcbqMbWonwMBBEoFKUghHlPEDVK8e09E00Z2yJLjKwRNWSoi7qYOxFQ/QVb/hcP8xb7R5TOL0rZ2uOKXTjOZ/6h8bjAed43Y97xvRg4+JZfmvn/JzTJT7I+aOejw7G2w9a4Wf9YxiPz9FO1zKcOV3wCaOHHJN1ISpLIY5Mo2w/5SlPmfqhP+HxzIFUed3F86MsG3lpPEMvqZpjRVXf5ezP8ay/pcg0fmRAiw7qo3v9ZDw2wZgzQmSiODMz6euMLJkjnA36OJ4/qC+uSaosfhEJS1p8Us1zNqB+hk+cr+mZjPJnPetZF8641f/Impw1qw3zNPLwnjbF+bkLSdPOmkDGWQvIUE6TT3/605MRxagC1+ScypxbaX0Uubdm6nfO1dQW12jLKL9zxmt4O+ctGyd9sKZ5rjalHz5HW22dZ0wke4jDR8TR/4xPEWEyz7jnTNKlTBTP9x7j6NnW/Bj82njDDTdMPKYveC4p7FnPkjqrz5nX5sd99903OQ/Iz4xV5GbWjZEnPC+8frlU1C0uL5Qri2ILLC7SkhhWH/rQhyZBz7uadMBdClFSA3OtBeHBBx+colsf+MAHpsU5Hkm/ea79WKhe/OIXT9EPi4kIqAXTgmVxY9jmDDDeVZ/zfDOKKIS7UvTmfbTgS3dkWK0xHsHCyJCIwqx9999//+T9ZWhQOvIci6rUN9ELi6m0XalIFknXi2SBg7SlQzrPjMEmSqs9on+8z7zs0qgosWv7lp/sCcye1W2wmI99owxQnqSD3nbbbZNTIM+kWDlYPsaOsaF0ULKNG/5BC2nMDvmmlPt+DShdIsIvf/nLJ+OPcsbxsI9Coa0UV9FGqVeiLknz/bEf+7EpXWs8XNt46Cc6aS+FWft59BmhOSQ7tKGUffCDH5yUNn33g37Gz5h5BrpI2fNeB2hTePEpXneNiLd0SgeB66NnUBy1Q1/xhXPtdqUMizCaC+4fKxr6LYrCqIxDYBMYRvpK0cSjI631XYpmDn6P8SwyhYeN60te8pLpXaINnDLmiX65j7NF1NJcc622ooH3iIhRWEWDGWyery2MYQqr5xszSqi/zaMXvvCFk5K8NuoXWeP6KOqbIiL6pr1JXX3iE5840YIir1/OHmSIkYf6kYiliLfx9EM2GV9jv2vsyA/Xm1P6xnihbIucm0eMAPyj72jAqMIjuS/RN+358R//8e96PnqiubnKkfCKV7xi4kPyZJfxCA52Z9ThrXHvcxwU1oSkzGon3o3z55prrpnahD4i2OjGGJcm7xrfkRecNGSj/pkHn/jEJyaDTbvJRTLUfMq+U3MIXeZjaC3AY8ZLmjra47Hj7kGPYUeOe79n+/+pT33qJBv0AT9o94c//OFpvqIXGap9xs98MFZkA2eDsXMP/ifTjCvDlOHnDETjqB/ajH8yn0X/PvnJT070wvucavP1zlwzz2PIc/zg2xjh1nM8Qx4YuyUs8YR2GTtt1z9zX7/MEeOkrfqULRy+F6HOWOq/e61f5Ka1g2xAS+NOv8AfZKd2uY9csB5eDscGFZcXypFFsQMWInucCH5pX7z1lPA1RpZrsn/O4isd1kJCoaCQjYsCpclib3G1uDDCKAmURul0FhQKnZRDCxml/KMf/ei0kHoWQ5WBdu+9917w4u+CNklVkp5EgWewrt1/ORZdyflkn/rUp6ZFmcE3Kt7ayjChEPqbAkcR0M93vOMdk4LMUBEt4ylGJwu+xdgiyoCjUPpbFGEtGOLeQZl773vfOxncFMdxL+quvqER5YaRRGHzA1FEpMT+5E/+5NRnCqAUN33TJwYXYwDvMPj975o18HyKmXt/9Vd/dVLI0RiPrE2Dch2nAiWEMsaIpKBThLWLYjKCIYDHGTn4SUSZ5xwNGKL6gAejoPtcdJNyS8m9++67J0MSnUXwtB1v6gcDmPLob4qzdlHs8Kz2xKDJ39lnTClFS/2eFzQagQfNC1FXhkHgHrznnZ5Bid+WTv3Nb35zUgD1ZylNWbv1P04ItNJPiiL+Z2wBJdffUqj1G/8ZA30U+RMJ9w7KvRRj8xzdzEWOA9Fv/OXaRPc5aPTD/mTtJCP2SVvM2Ds30Vwyxubc0pz3mfYxPBgADC/Gg7ahsb55nvH0DEq8Ocq40G708T+DEk9sgzHixGJ8mEv4Ar0YEZ6DrugpKoSeInPkHgWdk4mRiv6+25SiTB4xeBjkH/vYxyaDz3wyz3elo+Mthj2ZtGRwoJVxzHYG89+4kOXuI7NFpRhW3o1PGRvmmzFkkDO20BYv6JesCXzFqJLpgXdE9dyHN7RdRsWSLOCkEUUnz40LWu5K050jWRTey6gxpuSQPZYMHLJEv+PoQCPzjkGJP1xn3ho78pzRRP5zAnLCoQfaaBueJpP1X9ooRxC+035rHFkQRx5eikH9wAMPTAbo2C8yOxH39MOcR0Ppuf43l6wtu7ZVmCvajpbaYWysrehhDSMT8es999wzRait13iYjCCv8YE5bX7QI/THD77mSMYzHBLGi5z1PvMST5BB+GksXFUUZwk1IItiBXgYCX6LgcVuX6Fu0aKwS4mjgCs8Mk854qnlabXQ8k6LPFlQKS322lCqLFDaQBGhDFPiKBNJVWQoWOxEldYoCxZ9HlQLHcN2vk9ubd8snhY9HmEG5DytiJLGQLSoUvS03UJMMbWYxkjwHQWEcp0Kn5TLGOKUgrXtS1RUdOi6666b+kZ5ExFcayR7FwOBUs9LzpGQtEURR0YS3hAlpTyJihg33umk6aZQB0UuHvE1wGOUas/GB5Qv++0Yw/uOEeWUAsVYY1Az+ignlNTxWeiL92Log/7oK6XI9/gxBUjQwbV4lnKONzlCKIaUYmOLNyjd+Ez/KfAMQ0qcCChljxJovF1PYTYXPIsxQtHD43hr27ilomxSnsdr/c0AEbHTnm3e/KQ0Grul6zwrn3uneUcJRAe8i1/MK4ql4iwpvoPnzV9zFf0oj6Jf7vVM3zM00Ue0Neltxit708gMc4QTixGZ6N8ueD7DiryQ+cDoMg/wdfYyLoEBo1/eQbE1pvjDveauccQfZBqIwCXK4514LCnduyCyI/rkGdrKQMZLeAc/MNo5I0R6yBf0SoEgxga6Ss01T5fel0ghBxtDDG2lI3KY4M1tMM7aZByWUnHDE/kcTxtHRg6Zx/jGU3gbvbXPc6RGmlfGUh/QVr/MN7RHO3LRD1paH8gDcwWv440lpwqevPLKKy+kjaNTtkasBdnJuciwYaxKvbWdQ99kqhgbRrx2iKLpK74m94yh8WAQcbCJCJobHK94h1FmPUQPY6l9Oe6zAAAVt0lEQVTfUsOzfnEEWO/wFWPT/ZGj1gp8YO54Hxps4y98ZM7LvkhUEm0YhGsKJ3l2qlRnzSbrZZIYF/RlZFuTU0AuBdyMl/E2xhlLbWYkkxMMW3SQpmyM8A+auI48NT85ec5iheeiaAprUewAxYGSQiG0kNjjZ7GkWK0R7Ck+8v73v39aVETALI7ZPxeMSq/FJpUjKRkUSlGDGGbus4D7SaTPvZQLipHP18B7KAbufdvb3jbtH2MIbtpHtNQ3iol7KD2vfvWrLxhYY9/8n3bGM2yxjWIV5cs1FEWKqueepOCAe0UWpAJb+CnPlI21lUyzD4fRqSgHhSOR53xHWaCEJE3WuFJ2fX4SeL53UnxSEZZBhU6UOkrYPuX6KZ+U85tuummiLYcBI2TuxPBMCjrDVVQhe+3GcZiPSdL5jF0itpQjvIofE7HLvjqRCZEDhjBln4LFMEYzfaYculdE1zU33njj1HaOiW08mT1bjJoc5RDFnvLKWKXYacu2Y0fQivJuDuHBbengORIj7xn3dYruSpOLcRxFNPTLfI9R4jv0J1dEvLUjlWND99FQcf+2FNQRrjHXZRmgLyXfuKzZL01hN24cMQw0EXYRoIceemiKovh73A88f++a9kVxJrtEqBgvkX+5PxHfkQaUbgbCuJ95qT/jNgHjKc2Y0+P222+foroUeKmQm1LbjStHEWeGebgr5TXOh+xFHt8/tnM+pnE4zWmWasLGT9pjMig2ReQ5XNDR/EHL8N4+8A7OCoatv8d9fuYQI49M5djB39myMLadUWV8sm4lqu3/VHnNvksw38ho8y+00wZI8Z75vNlUrCf7RUVpGZv4OGOWPdW7gK/IIbzpeZw+1jfzwbPJ0sxD9EUnTiqZGIzr17/+9dOYjSBTrXvWCfMp67b2WK88a81+76K41GgEsii2gDJokaRwUzAo4JQlxRR4ioMs+kvKEg+lNB5eXMoXxSURDM8VLdwGigDjzqJtMY/ya7FK2fVxb1MUYBgVsE2wAIqc8fRSOKQaWvhHpWeT8kHhY2BRlF/1qldNikvAWBaJWlKewOJoMfauKDnpQwrgzDEqbmuVIoqPhdwiTWGUKji2adO4eba9cKI1jn4QJci46ZtUKhFS9E+Bnyj2vPTb9pbl+Zve7XnGnddcMSKRT31QvEMqWdJ4t7V/pBkwHEQmkobFqz9XUDg6FAQRUWDIbCtVv8uYw9sUMAY7xYsCjm6McAqdPaUMOg4VPCuaSyH0vTajoVRFBqQ0TqnOnBSb+mxsKJveywCb80YUzyi5m+hmfjNyKbL4d/4cMiEG4Sa6+E7khvLKUGe8rilKZS5I4QZzfUyt3Hb/3Djd9HyOoTg5Ygzs4h/KsigpvvMOBgDjwZgyKkVjIm+2vXvbuzLX7DMXsfPspeIoS/1mdBjzXXKOISNVkAGIFzgEyVVtN59i+C/RUfvNH/yLDnOjJQ6lnB24C8cxDDhY8IaxQ6NNjg3tl0Yp24ATI86bsa1rDHv84V34JZV2AwafrBgOLvOY8bgUyU4hpBx9BNqSvbRjm/KdNcWaGBpbP3dFiDcZjxxhIveieJwAORd2rWNjaU8wuSR9VyTbeuezOEX1kdyU7cApQWaT4/MCVWjJ+cGAZpySkcZzTYXoojgrqAFZFBtAGZCmJd1GlM6CZ1HgvZZ6Yg+EKI1Fwz4gC3YMicAiZX8OL7eFRYoOWMQoMlIdRUxSyTGLmr+z4GpD0lsoEZQYqUEWd4uXNL9EXCi8lLsUR2HcbUrxSUVM7+LpldbDULFPT1pXSp7rq8/mi7g+8MBS+qSnSteJR1gbGcwpIjEae0kRtJBScCgTFllAP8oGQ4DSQlHRd+mO2qHf2qG/9qEwzJeUC+/TN+/xXmOnmqR3ScPyjqQVUyo9dx5lMy4iq9ooZSt9o7BK56M8UaxFz3iU3WN8tJmBhKbu8Zn2e5d7036OCUrzvNx/nuOZxpIxjRYUF0oso4zSlkp/0qHQYqm6aPYwGWP8IqXS2FCoOCC8i9KSqqD2uBmLVA5Ee98n+ul92u+78GsUvUTLfJbzUqWv4UNGu74yXI2BvWE+9wzXUsQYkyL7qcbKKUHpF0FmZGoL/jAfRL+W9vkaD3tURcZE7JNqniqpSYPzm7PEO+dz1viJIjAkc8RJ+piiVe4Nb40R2nyGZvjf/0lD9R5jMVdgRxriERFLNEqGAV7JwfdJ58678gxjIjPCvUtOlczz0cBxL/oyzrelrqMX2UURlwptfMwnkRnGpdT+ZBfg96SX4vnsh2Xgob+9bIzO+VEQ+kKOJerNgeEa95of6VOM88gQxqxUSbLXHj/0ylEz45yO44C8InPRIfvSzQORIP0UOecw8t45OJCkFeI98y18Y3zJAn3LnJzzxDjm3jkeUj+OV8Y4tEzRGD/4Weq5dpoL+ommcb7lJ7xnzP1N1uEhNHG99Uz7tyFVbzdF97TBXGX8oCc5FQdN9nYaC8Y5upEp2ux55Lj1KWmpoUGirOSB7RScL2QrOYmPMu5jBdvx/zm/kzfWH9eYF54XupA9cSZsAr6b08CYoGl4FdB33Fpi/6bMCttV8JV5kDnrHuOFbtYX63PmPFkUx9SoCxTFWUVTWItiAQQ4hUAEhOJN6FtILBQWFkq9tFYLpH00PJ0WJ4rweFi7RZsSauH2TGmsAeVNShQFmZKa4hYWYwpJigxIEWLA2bfDMEkBC/tGpIza06QQAS8wRfB1r3vdFO2xYDKWLOaOC4mSlxRDCz9FgFJl0bbYUch4jS2uwFjmnfd8EbwxIqK9lFa/0UTfohDom0IR2qePaCcNE938Rk/PtU9Ie/XHwuw5FFKGtggkxVWUwBEM0nh5w9ESXd3jh0HlOfGyewYFidJBcbKg269CYfEuEVZ7S9BEG6WmJjVpHDdGCCVIvy3043fSk4yb1Ep99b2IEQNMlJDXH52lgzJy0ZtSwSihXBsTRiAFx/PHCpWURYYXBYOyRXGH7KNDF0YU/vN87dR29Bk94YwWfKOtPOZoin+MF8eDfuARfaQEM0TxL17QHwo5XqAAuY5xJp3a+2JIozXlR39FZowhYxFtpJyirWgghZERnnL4+qsgh/akKiHla6yeqa94RQoYemk/3tIXabx4YUwPo6QxskVQGQEird6LftpqTl1//fXT/4l4oRFazNOBRWrxvPabQ+Yg5dYYpIKmucbQAQqpd+AF44N2Io8pWpSoqv1d7qVs4gk8ah7gU+1j4IvocU5xypjHaGCOehe6S2FEM3QmIxgV2pTjdyjfkUH6mbMc0ccYown6xyGFTiJb2/aDiZCIOJlDSa3jzDBG45mGnCbGFe04bNDNc9EF3zFWtZ+sYoAE2dum3+Y7XuY0QS88hlcp6uhlXngWucKI5VwQDZIBQYbFkeKdDNwo78YlchtPG0+8y1nFMNQGzj7PiuNm5C2yj+wQKVfUDE2MVY5W4pQh14wHXjCuMS7NaXPd/NAXhpH2kz8+87dxMaae4XNRRryJBoxcPOm3d+dMWs4M/ETu6Le5jq54zxwAc4rsZ7iT7/aX4w9R9nnKrr5om+fiV0aNsVo6Ogg9yGfOBPyX8Uc7Mtd6YM4bF7ypCBnDyZzzGXqjv3XR+zhn8bs1B6+7xnM5Ns1z9DPu2mffLTqjpTWSYZaIJp4n/73TOJCBcRy96U1vmmQkg9Y8sdbMj3MyHnjU+8wva4xnpzCXPuJVtCEbyC7f+5+Mj3GOPniETPUOc85akD2f+iVLwZxAMzITrxt7/UITTtldBd+K4lLhUd+qm6MovgtJSbIoUx4oHyljPqbBACXbtRZ+i8y4KI/nX43e5XjEUx4952hZ3PxQPLJvKUcipBIp5dP3FOicnyWN0t/eH2WYscuodT8FaUxZy5ET8cJTKFzns3jH87n3UsYo3vPzJ9O3+T6t7OdJQQzXuNc7tCt9TRSMYqCPlLYckZA2eLf+6rvfOVvLPQxdCpJFfEwN1Y8okugyFrTRZu3VNn2lfHj2uAd1PHdznmqacUs7PCNpuBRWykKeg86UbNdSiHPmlzYxMhjgyrdTnoIxWoY3clZZ+hXvdNrAAMw5aWOqoP4nMp2z4EKf7LnL+EGu0W8KtnHwbrzlb0qyqAe64z200ZYUl8h+Sc9JerW24Vd8mTML0xfvdh8aaiu+oNQnyuR/7fc+nyft0niJAlGwRyNknLs5o43R4TenizaPZzEm2jXy2/gM0C5jm8gyY0k7Mu8yT32X6A+k+BNFWNvxBP5GA23Ide5LdDCVf9FL29DM52io7YkK5SzRUUagIeOBA0q6ddK/53Pd/zmTM/ypXWgwnl85h77gb22PDMiZjSO/J3qSip/onjmbqCoDhgEkhXSkNzrgO9eZR+gO5o12Rg4ncyLp+j7HI+ZZxjLn6oXf0l7XoRWDzrs4HBj34zm1vmNoLR2Vk6gXBV+GgL4ysLw3siBzbjy/NnM5/QT/j2Pq/8hGbUczc8T4kE/6hH5orn2uxSf+TrQsxcb8jff8jX7GA68xasjTpJDOxzs8PEblUxhpDtcZS04Lxug4/gxa7Tf+3oEH0Fu78HXmB1ol+hgeAZ8bT3NHO9EnztvwgfdkfR75MrJD++aFtLIOoCHaWdPmc1978q7xvrGqa+Z2CjPhGZ/hA203bp5h3OLMMa/R3nhl7dFHfTC/IwvmMrvHdxRnFTUgi+IyhGltUePh5c0fPfGXC1LBjvKyz7mXZwGUhxTkkBbYg6LXgZImCsWgkxq3TyGhyxmUVZFe81yU5yzOdcaDaBwjyJxdU8SkOHtg2HNW5IxamQa7jkEpiuLyQw3IorgMEU8wL2zOM7zcEC/5eTQiUuQhEeezqPCfRSS6gJ8ZIKXb/0fS60VmzqozIjyfrIvifIKjQmEvUTkpnBxgl+P6UhTFdtSALIqiKIqiKHZCaqfsD1kfqdJcR05RPPJQA7IoiqIoiqIoiqJYheYdFEVRFEVRFEVRFKtQA7IoiqIoiqIoiqJYhRqQRVEURVEURVEUxSrUgCyKoiiKoiiKoihWoQZkURRFURRFURRFsQo1IIuiKIqiKIqiKIpVqAFZFEVRFEVRFEVRrEINyKIoiqIoiqIoimIVakAWRVEURVEURVEUq1ADsiiKoiiKoiiKoliFGpBFURRFURRFURTFKtSALIqiKIqiKIqiKFahBmRRFEVRFEVRFEWxCjUgi6IoiqIoiqIoilWoAVkURVEURVEURVGsQg3IoiiKoiiKoiiKYhVqQBZFURRFURRFURSrUAOyKIqiKIqiKIqiWIUakEVRFEVRFEVRFMUq1IAsiqIoiqIoiqIoVqEGZFEURVEURVEURbEKNSCLoiiKoiiKoiiKVagBWRRFURRFURRFUaxCDciiKIqiKIqiKIpiFWpAFkVRFEVRFEVRFKtQA7IoiqIoiqIoiqJYhRqQRVEURVEURVEUxSrUgCyKoiiKoiiKoihW4XvXXVYURVEUjwx861vfOvrmN7959O///u/T///7v/979JjHPObo//7f/3v0qEc96lI3ryiKoiguKWpAFkVRFMWAf/3Xfz268cYbj/7+7/9+Mhgf+9jHHt16662TAVkURVEUj3Q86ltcrUVRFEVRHP33f//30Wc/+9nJePzRH/3RKRrpNyPye7+3PteiKIqi6GpYFEVRFN/GP/3TPx19+ctfPvrpn/7pox/+4R8++omf+Imj7/u+72vqalEURVF8Gy2iUxRFURTf3vv4z//8z0f/8i//cvS+973v6Kqrrjp605vedPRv//Zv03dFURRFUTSFtSiKoigmWA4VzPmv//qvo7/92789+tznPnf00EMPHV177bVHb3jDG7oHsiiKoiiawloURVEU/x/SVL/ne75n+vmpn/qpox/5kR+Z0le/8pWvHL3oRS+a/i+KoiiKRzqawloURVEUC3j0ox999JSnPGUqnpMjPYqiKIrikY4akEVRFEWxAQzHH/zBHzz6gR/4gUvdlKIoiqI4E2gKa1EURVF821iUrmof5BVXXHH0n//5n0d/9md/dvSMZzzj6DGPecylbl5RFEVRnAnUgCyKoiiKbxfR+bu/+7ujP/iDPzj6xje+cfRDP/RDRz/7sz979LjHPa5nQBZFURTFt9EqrEVRFEXxbQPyP/7jP6ajPP7nf/5nijp+//d//9H/+T/d7VEURVEUQQ3IoiiKoiiKoiiKYhXqVi2KoiiKoiiKoihWoQZkURRFURRFURRFsQo1IIuiKIqiKIqiKIpVqAFZFEVRFEVRFEVRrEINyKIoiqIoiqIoimIVakAWRVEURVEURVEUq1ADsiiKoiiKoiiKoliFGpBFURRFURRFURTFKtSALIqiKIqiKIqiKFahBmRRFEVRFEVRFEWxCjUgi6IoiqIoiqIoilWoAVkURVEURVEURVGsQg3IoiiKoiiKoiiKYhVqQBZFURRFURRFURSrUAOyKIqiKIqiKIqiWIUakEVRFEVRFEVRFMUq1IAsiqIoiqIoiqIoVqEGZFEURVEURVEURbEKNSCLoiiKoiiKoiiKVagBWRRFURRFURRFUaxCDciiKIqiKIqiKIpiFWpAFkVRFEVRFEVRFKtQA7IoiqIoiqIoiqJYhRqQRVEURVEURVEUxSrUgCyKoiiKoiiKoihWoQZkURRFURRFURRFsQo1IIuiKIqiKIqiKIpVqAFZFEVRFEVRFEVRrEINyKIoiqIoiqIoimIVakAWRVEURVEURVEUq1ADsiiKoiiKoiiKoliFGpBFURRFURRFURTFKtSALIqiKIqiKIqiKFahBmRRFEVRFEVRFEWxCjUgi6IoiqIoiqIoilWoAVkURVEURVEURVGsQg3IoiiKoiiKoiiKYhVqQBZFURRFURRFURSrUAOyKIqiKIqiKIqiWIUakEVRFEVRFEVRFMUq1IAsiqIoiqIoiqIoVqEGZFEURVEURVEURbEKNSCLoiiKoiiKoiiKVagBWRRFURRFURRFUaxCDciiKIqiKIqiKIpiFWpAFkVRFEVRFEVRFKtQA7IoiqIoiqIoiqJYhRqQRVEURVEURVEUxSrUgCyKoiiKoiiKoihWoQZkURRFURRFURRFsQo1IIuiKIqiKIqiKIpVqAFZFEVRFEVRFEVRrEINyKIoiqIoiqIoimIVakAWRVEURVEURVEUq1ADsiiKoiiKoiiKoliFGpBFURRFURRFURTFKtSALIqiKIqiKIqiKFahBmRRFEVRFEVRFEWxCjUgi6IoiqIoiqIoilWoAVkURVEURVEURVGsQg3IoiiKoiiKoiiKYhVqQBZFURRFURRFURSrUAOyKIqiKIqiKIqiWIUakEVRFEVRFEVRFMUq1IAsiqIoiqIoiqIoVqEGZFEURVEURVEURbEKNSCLoiiKoiiKoiiKVagBWRRFURRFURRFUaxCDciiKIqiKIqiKIriaA3+H2JIutFBIiwNAAAAAElFTkSuQmCC",\
"text/plain": \[\
""\
\]\
},\
"metadata": {},\
"output\_type": "display\_data"\
}\
\],\
"source": \[\
"import matplotlib.pyplot as plt\\n",\
"from PIL import ImageDraw\\n",\
"\\n",\
"for i, doc in enumerate(resp\_dict\[\\"context\\"\]\[:\]):\\n",\
" image\_by\_page = {}\\n",\
" print(f\\"Source {i + 1}:\\")\\n",\
" print(f\\" text: {json.dumps(clip\_text(doc.page\_content, threshold=350))}\\")\\n",\
" meta = DocMeta.model\_validate(doc.metadata\[\\"dl\_meta\\"\])\\n",\
"\\n",\
" # loading the full DoclingDocument from the document store:\\n",\
" dl\_doc = DoclingDocument.load\_from\_json(doc\_store.get(meta.origin.binary\_hash))\\n",\
"\\n",\
" for doc\_item in meta.doc\_items:\\n",\
" if doc\_item.prov:\\n",\
" prov = doc\_item.prov\[0\] # here we only consider the first provenence item\\n",\
" page\_no = prov.page\_no\\n",\
" if img := image\_by\_page.get(page\_no):\\n",\
" pass\\n",\
" else:\\n",\
" page = dl\_doc.pages\[prov.page\_no\]\\n",\
" print(f\\" page: {prov.page\_no}\\")\\n",\
" img = page.image.pil\_image\\n",\
" image\_by\_page\[page\_no\] = img\\n",\
" bbox = prov.bbox.to\_top\_left\_origin(page\_height=page.size.height)\\n",\
" bbox = bbox.normalized(page.size)\\n",\
" thickness = 2\\n",\
" padding = thickness + 2\\n",\
" bbox.l = round(bbox.l \* img.width - padding)\\n",\
" bbox.r = round(bbox.r \* img.width + padding)\\n",\
" bbox.t = round(bbox.t \* img.height - padding)\\n",\
" bbox.b = round(bbox.b \* img.height + padding)\\n",\
" draw = ImageDraw.Draw(img)\\n",\
" draw.rectangle(\\n",\
" xy=bbox.as\_tuple(),\\n",\
" outline=\\"blue\\",\\n",\
" width=thickness,\\n",\
" )\\n",\
" for p in image\_by\_page:\\n",\
" img = image\_by\_page\[p\]\\n",\
" plt.figure(figsize=\[15, 15\])\\n",\
" plt.imshow(img)\\n",\
" plt.axis(\\"off\\")\\n",\
" plt.show()"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {},\
"outputs": \[\],\
"source": \[\]\
}\
\],
"metadata": {
"kernelspec": {
"display\_name": ".venv",
"language": "python",
"name": "python3"
},
"language\_info": {
"codemirror\_mode": {
"name": "ipython",
"version": 3
},
"file\_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert\_exporter": "python",
"pygments\_lexer": "ipython3",
"version": "3.12.4"
}
},
"nbformat": 4,
"nbformat\_minor": 2
}
---
# Docling agent skill — improvement log - Docling
[Skip to content](https://docling-project.github.io/docling/examples/agent_skill/docling-document-intelligence/improvement-log/#docling-agent-skill-improvement-log)
Docling agent skill — improvement log
=====================================
Agents may append a short entry after running **evaluate → refine** on a document so similar files are faster to process next time. This file is optional and is not tracked by every user; it is meant for **local** learning.
Template (copy for each entry)
------------------------------
`### YYYY-MM-DD — - **Source type:** (e.g. scanned PDF / digital PDF / DOCX / URL) - **Issues (first run):** … - **Pipeline / flags that helped:** … - **Final evaluator status:** pass | warn | fail - **Notes:** …`
Entries
-------
_(None — add your own after running conversions.)_
Back to top
---
# Using the Docling agent skill - Docling
[Skip to content](https://docling-project.github.io/docling/examples/agent_skill/docling-document-intelligence/EXAMPLE/#using-the-docling-agent-skill)
Using the Docling agent skill
=============================
[Agent Skills](https://agentskills.io/specification)
are folders of instructions that AI coding agents (Cursor, Claude Code, GitHub Copilot, etc.) can load when relevant.
Where this bundle lives
-----------------------
* **Cursor (local):** `~/.cursor/skills/docling-document-intelligence/` (or copy this folder there).
* **Docling repository (docs + PRs):** `docs/examples/agent_skill/docling-document-intelligence/` in [github.com/docling-project/docling](https://github.com/docling-project/docling)
.
The two trees are kept in sync; use either source.
Install (copy into your agent's skills directory)
-------------------------------------------------
`# From a checkout of the Docling repo cp -r docs/examples/agent_skill/docling-document-intelligence ~/.cursor/skills/ # Or copy from another machine / archive into e.g. ~/.claude/skills/`
No extra config is required beyond installing Python dependencies (below).
Usage
-----
Open your agent-enabled IDE and ask, for example:
`Parse report.pdf and give me a structural outline`
`Convert https://arxiv.org/pdf/2408.09869 to markdown`
`Chunk invoice.pdf for RAG ingestion with 512 token chunks`
`Process scanned.pdf using the VLM pipeline`
The agent should read `SKILL.md`, match the task, and run the appropriate `docling` CLI command or Python API call.
Running the docling CLI directly
--------------------------------
`pip install docling docling-core # Basic conversion to Markdown docling report.pdf --output /tmp/ # JSON output docling report.pdf --to json --output /tmp/ # Custom OCR engine docling report.pdf --ocr-engine rapidocr --output /tmp/ # VLM pipeline docling scanned.pdf --pipeline vlm --output /tmp/ # VLM with specific model docling scanned.pdf --pipeline vlm --vlm-model granite_docling --output /tmp/ # Remote VLM services docling doc.pdf --pipeline vlm --enable-remote-services --output /tmp/`
Evaluate and refine
-------------------
`docling report.pdf --to json --output /tmp/ docling report.pdf --to md --output /tmp/ python3 scripts/docling-evaluate.py /tmp/report.json --markdown /tmp/report.md`
If the report shows `warn` or `fail`, follow `recommended_actions`, re-convert with `docling` using the suggested flags, and optionally append a note to `improvement-log.md` (see `SKILL.md` section 7).
What the skill covers
---------------------
| Task | How to ask |
| --- | --- |
| Parse PDF / DOCX / PPTX / HTML / image | "parse this file" |
| Convert to Markdown | "convert to markdown" |
| Export as structured JSON | "export as JSON" |
| Chunk for RAG | "chunk for RAG", "prepare for ingestion" |
| Analyze structure | "show me the headings and tables" |
| Use VLM pipeline | "use the VLM pipeline", "process scanned PDF" |
| Use remote inference | "use vLLM", "call the API pipeline" |
Further reading
---------------
* [Agent Skills specification](https://agentskills.io/specification)
* [Docling documentation](https://docling-project.github.io/docling/)
* [Docling CLI reference](https://docling-project.github.io/docling/reference/cli/)
* [Docling GitHub](https://github.com/docling-project/docling)
Back to top
---
# Docling Pipelines Reference - Docling
[Skip to content](https://docling-project.github.io/docling/examples/agent_skill/docling-document-intelligence/pipelines/#docling-pipelines-reference)
Docling Pipelines Reference
===========================
Docling has two pipeline families for PDFs: **standard** (parse + OCR + layout/tables) and **VLM** (page images through a vision-language model). The `docling` CLI exposes both via `--pipeline standard` (default) and `--pipeline vlm`. The right choice depends on document type, hardware, and latency budget.
* * *
Decision matrix
---------------
| Document type | Recommended pipeline | Reason |
| --- | --- | --- |
| Born-digital PDF (text selectable) | Standard | Fast, accurate, no GPU needed |
| Scanned PDF / image-only | Standard + OCR or VLM | Depends on quality |
| Complex layout (multi-column, dense tables) | VLM | Better structural understanding |
| Handwriting, formulas, figures with embedded text | VLM | Only viable option |
| Air-gapped / no GPU | Standard | Runs on CPU |
| Production scale, GPU server available | VLM (vLLM) | Best throughput |
| Apple Silicon / local dev | VLM (MLX) | MPS acceleration |
| Speed-critical, accuracy secondary | Standard, no tables | Fastest path |
* * *
Pipeline 1: Standard PDF Pipeline
---------------------------------
Uses deterministic PDF parsing (docling-parse) + optional neural OCR + neural table structure detection.
### CLI usage
`# Default (standard pipeline, OCR + tables enabled) docling report.pdf --output /tmp/ # Custom OCR engine docling report.pdf --ocr-engine tesserocr --output /tmp/ # Disable OCR or tables docling report.pdf --no-ocr --output /tmp/ docling report.pdf --no-tables --output /tmp/`
### Python API
`from docling.document_converter import DocumentConverter, PdfFormatOption from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import PdfPipelineOptions # Minimal — library defaults (standard PDF pipeline) converter = DocumentConverter() # Explicit PdfPipelineOptions (docling 2.81+): use InputFormat.PDF + PdfFormatOption. # Do not use format_options={"pdf": opts}; that raises AttributeError on pipeline options. opts = PdfPipelineOptions( do_ocr=True, # False = skip OCR entirely do_table_structure=True, # False = skip table detection (faster) ) converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption(pipeline_options=opts), } )`
### OCR engine options
All engines are plug-and-play via the CLI `--ocr-engine` flag or the Python `ocr_options` parameter. Default is EasyOCR.
#### CLI flags
| Engine | CLI flag | Notes |
| --- | --- | --- |
| EasyOCR | `--ocr-engine easyocr` (default) | No extra pip beyond docling defaults |
| RapidOCR | `--ocr-engine rapidocr` | Lightweight; see Docling notes on read-only FS |
| Tesseract (Python) | `--ocr-engine tesserocr` | Needs `pip install tesserocr` and system Tesseract |
| Tesseract (CLI) | `--ocr-engine tesseract` | Shells out to `tesseract` binary |
| macOS Vision | `--ocr-engine ocrmac` | macOS only |
#### Python API
`# EasyOCR (default — no extra install needed) from docling.datamodel.pipeline_options import PdfPipelineOptions opts = PdfPipelineOptions(do_ocr=True) # uses EasyOCR by default # Tesseract (requires system Tesseract + pip install tesserocr — see Docling install docs) from docling.datamodel.pipeline_options import TesseractOcrOptions opts = PdfPipelineOptions(do_ocr=True, ocr_options=TesseractOcrOptions()) # RapidOCR (lightweight, no C deps) from docling.datamodel.pipeline_options import RapidOcrOptions opts = PdfPipelineOptions(do_ocr=True, ocr_options=RapidOcrOptions()) # macOS native OCR from docling.datamodel.pipeline_options import OcrMacOptions opts = PdfPipelineOptions(do_ocr=True, ocr_options=OcrMacOptions())`
* * *
Pipeline 2: VLM Pipeline — local inference
------------------------------------------
Processes each page as an image through a vision-language model. Replaces the standard layout detection + OCR stack entirely.
### CLI usage
`# Default VLM model (granite_docling) docling report.pdf --pipeline vlm --output /tmp/ # Specific model docling report.pdf --pipeline vlm --vlm-model smoldocling --output /tmp/`
### Python API
`from docling.document_converter import DocumentConverter, PdfFormatOption from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import VlmPipelineOptions from docling.datamodel import vlm_model_specs from docling.pipeline.vlm_pipeline import VlmPipeline pipeline_options = VlmPipelineOptions( vlm_options=vlm_model_specs.GRANITEDOCLING_TRANSFORMERS, generate_page_images=True, ) converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption( pipeline_cls=VlmPipeline, pipeline_options=pipeline_options, ) } )`
### Available model presets
| CLI `--vlm-model` | Python preset (`vlm_model_specs`) | Backend | Device | Notes |
| --- | --- | --- | --- | --- |
| `granite_docling` | `GRANITEDOCLING_TRANSFORMERS` | HF Transformers | CPU/GPU | Default |
| `smoldocling` | `SMOLDOCLING_TRANSFORMERS` | HF Transformers | CPU/GPU | Lighter |
| (Python API only) | `GRANITEDOCLING_VLLM` | vLLM | GPU | Fast batch |
| (Python API only) | `GRANITEDOCLING_MLX` | MLX | Apple MPS | M-series Macs |
### Hybrid mode: PDF text + VLM for images/tables
Set `force_backend_text=True` (Python API only) to use deterministic text extraction for normal text regions while routing images and tables through the VLM. Reduces hallucination risk on text-heavy pages.
`pipeline_options = VlmPipelineOptions( vlm_options=vlm_model_specs.GRANITEDOCLING_TRANSFORMERS, force_backend_text=True, # <-- hybrid mode generate_page_images=True, )`
* * *
Pipeline 3: VLM Pipeline — remote API
-------------------------------------
Sends page images to any OpenAI-compatible endpoint. Works with vLLM, LM Studio, Ollama, or a hosted model API.
This is available via the CLI with `--pipeline vlm --enable-remote-services`, but endpoint URL, model name, and API key configuration require the Python API.
### CLI usage (basic)
`docling report.pdf --pipeline vlm --enable-remote-services --output /tmp/`
### Python API (full configuration)
`from docling.document_converter import DocumentConverter, PdfFormatOption from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import VlmPipelineOptions from docling.datamodel.pipeline_options_vlm_model import ApiVlmOptions, ResponseFormat from docling.pipeline.vlm_pipeline import VlmPipeline vlm_opts = ApiVlmOptions( url="http://localhost:8000/v1/chat/completions", params=dict( model="ibm-granite/granite-docling-258M", max_tokens=4096, ), headers={"Authorization": "Bearer YOUR_KEY"}, # omit if not needed prompt="Convert this page to docling.", response_format=ResponseFormat.DOCTAGS, timeout=120, scale=2.0, ) pipeline_options = VlmPipelineOptions( vlm_options=vlm_opts, generate_page_images=True, enable_remote_services=True, # required — gates any HTTP call ) converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption( pipeline_cls=VlmPipeline, pipeline_options=pipeline_options, ) } )`
**`enable_remote_services=True` is mandatory** for API pipelines. Docling blocks outbound HTTP by default as a safety measure.
### Common API targets
| Server | Default URL | Notes |
| --- | --- | --- |
| vLLM | `http://localhost:8000/v1/chat/completions` | Best throughput |
| LM Studio | `http://localhost:1234/v1/chat/completions` | Local dev |
| Ollama | `http://localhost:11434/v1/chat/completions` | Model: `ibm/granite-docling:258m` |
| OpenAI-compatible cloud | Provider URL | Set Authorization header |
* * *
VLM install requirements
------------------------
Local inference requires PyTorch + Transformers:
`pip install docling[vlm] # or manually: pip install torch transformers accelerate`
MLX (Apple Silicon only):
`pip install mlx mlx-lm`
vLLM backend (server-side):
`pip install vllm vllm serve ibm-granite/granite-docling-258M`
Back to top
---
# Docling Document Intelligence Skill - Docling
[Skip to content](https://docling-project.github.io/docling/examples/agent_skill/docling-document-intelligence/SKILL/#docling-document-intelligence-skill)
Docling Document Intelligence Skill
===================================
Use this skill to parse, convert, chunk, and analyze documents with Docling. It handles both local file paths and URLs, and outputs either Markdown or structured JSON (`DoclingDocument`).
Conversion uses the **`docling` CLI** (installed with `pip install docling`). The Python API is used only for features the CLI does not expose (chunking, VLM remote-API endpoint configuration, hybrid `force_backend_text` mode).
Scope
-----
| Task | Covered |
| --- | --- |
| Parse PDF / DOCX / PPTX / HTML / image | ✅ |
| Convert to Markdown | ✅ |
| Export as DoclingDocument JSON | ✅ |
| Chunk for RAG (hybrid: heading + token) | ✅ (Python API) |
| Analyze structure (headings, tables, figures) | ✅ (Python API) |
| OCR for scanned PDFs | ✅ (auto-enabled) |
| Multi-source batch conversion | ✅ |
Step-by-Step Instructions
-------------------------
### 1\. Resolve the input
Determine whether the user supplied a **local path** or a **URL**. The `docling` CLI accepts both directly.
`docling path/to/file.pdf docling https://example.com/a.pdf`
### 2\. Choose a pipeline
Docling has two pipeline families. Pick based on document type and hardware.
| Pipeline | CLI flag | Best for | Key tradeoff |
| --- | --- | --- | --- |
| **Standard** (default) | `--pipeline standard` | Born-digital PDFs, speed | No GPU needed; OCR for scanned pages |
| **VLM** | `--pipeline vlm` | Complex layouts, handwriting, formulas | Needs GPU; slower |
See [pipelines.md](https://docling-project.github.io/docling/examples/agent_skill/docling-document-intelligence/pipelines/)
for the full decision matrix, OCR engine table (EasyOCR, RapidOCR, Tesseract, macOS), and VLM model presets.
### 3\. Convert the document
#### CLI (preferred for straightforward conversions)
`# Markdown (default output) docling report.pdf --output /tmp/ # JSON (structured, lossless) docling report.pdf --to json --output /tmp/ # VLM pipeline docling report.pdf --pipeline vlm --output /tmp/ # VLM with specific model docling report.pdf --pipeline vlm --vlm-model granite_docling --output /tmp/ # Custom OCR engine docling report.pdf --ocr-engine tesserocr --output /tmp/ # Disable OCR or tables for speed docling report.pdf --no-ocr --output /tmp/ docling report.pdf --no-tables --output /tmp/ # Remote VLM services docling report.pdf --pipeline vlm --enable-remote-services --output /tmp/`
The CLI writes output files to the `--output` directory, named after the input file (e.g. `report.pdf` → `report.md` or `report.json`).
**CLI reference:** [https://docling-project.github.io/docling/reference/cli/](https://docling-project.github.io/docling/reference/cli/)
#### Python API (for advanced features)
Use the Python API when you need features the CLI does not expose: chunking, VLM remote-API endpoint configuration, or hybrid `force_backend_text` mode.
**Docling 2.81+ API note:** `DocumentConverter(format_options=...)` expects `dict[InputFormat, FormatOption]` (e.g. `InputFormat.PDF` → `PdfFormatOption`). Using string keys like `{"pdf": PdfPipelineOptions(...)}` fails at runtime with `AttributeError: 'PdfPipelineOptions' object has no attribute 'backend'`.
**Standard pipeline (default):**
`from docling.document_converter import DocumentConverter, PdfFormatOption from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import PdfPipelineOptions converter = DocumentConverter() result = converter.convert("report.pdf") converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption( pipeline_options=PdfPipelineOptions(do_ocr=True, do_table_structure=True), ), } ) result = converter.convert("report.pdf")`
**VLM pipeline — local (GraniteDocling via HF Transformers):**
`from docling.document_converter import DocumentConverter, PdfFormatOption from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import VlmPipelineOptions from docling.datamodel import vlm_model_specs from docling.pipeline.vlm_pipeline import VlmPipeline pipeline_options = VlmPipelineOptions( vlm_options=vlm_model_specs.GRANITEDOCLING_TRANSFORMERS, generate_page_images=True, ) converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption( pipeline_cls=VlmPipeline, pipeline_options=pipeline_options, ) } ) result = converter.convert("report.pdf")`
**VLM pipeline — remote API (vLLM / LM Studio / Ollama):**
This is only available via the Python API; the CLI does not expose endpoint URL, model name, or API key configuration.
`from docling.document_converter import DocumentConverter, PdfFormatOption from docling.datamodel.base_models import InputFormat from docling.datamodel.pipeline_options import VlmPipelineOptions from docling.datamodel.pipeline_options_vlm_model import ApiVlmOptions, ResponseFormat from docling.pipeline.vlm_pipeline import VlmPipeline vlm_opts = ApiVlmOptions( url="http://localhost:8000/v1/chat/completions", params=dict(model="ibm-granite/granite-docling-258M", max_tokens=4096), prompt="Convert this page to docling.", response_format=ResponseFormat.DOCTAGS, timeout=120, ) pipeline_options = VlmPipelineOptions( vlm_options=vlm_opts, generate_page_images=True, enable_remote_services=True, # required — gates all outbound HTTP ) converter = DocumentConverter( format_options={ InputFormat.PDF: PdfFormatOption( pipeline_cls=VlmPipeline, pipeline_options=pipeline_options, ) } ) result = converter.convert("report.pdf")`
**Hybrid mode (force\_backend\_text) — Python API only:**
Uses deterministic PDF text extraction for text regions while routing images and tables through the VLM. Reduces hallucination on text-heavy pages.
`pipeline_options = VlmPipelineOptions( vlm_options=vlm_model_specs.GRANITEDOCLING_TRANSFORMERS, force_backend_text=True, generate_page_images=True, )`
`result.document` is a `DoclingDocument` object in all cases.
### 4\. Choose output format
**Markdown** (default, human-readable):
`docling report.pdf --to md --output /tmp/`
Or via Python: `result.document.export_to_markdown()`
**JSON / DoclingDocument** (structured, lossless):
`docling report.pdf --to json --output /tmp/`
Or via Python: `result.document.export_to_dict()`
> If the user does not specify a format, ask: "Should I output Markdown or structured JSON (DoclingDocument)?"
### 5\. Chunk for RAG (hybrid strategy)
Chunking is only available via the Python API.
Default: **hybrid chunker** — splits first by heading hierarchy, then subdivides oversized sections by token count. This preserves semantic boundaries while respecting model context limits.
The tokenizer API changed in docling-core 2.8.0. Pass a `BaseTokenizer` object, not a raw string:
**HuggingFace tokenizer (default):**
`from docling.chunking import HybridChunker from docling_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer tokenizer = HuggingFaceTokenizer.from_pretrained( model_name="sentence-transformers/all-MiniLM-L6-v2", max_tokens=512, ) chunker = HybridChunker(tokenizer=tokenizer, merge_peers=True) chunks = list(chunker.chunk(result.document)) for chunk in chunks: embed_text = chunker.contextualize(chunk) print(chunk.meta.headings) # heading breadcrumb list print(chunk.meta.origin.page_no) # source page number`
**OpenAI tokenizer (for OpenAI embedding models):**
`import tiktoken from docling_core.transforms.chunker.tokenizer.openai import OpenAITokenizer tokenizer = OpenAITokenizer( tokenizer=tiktoken.encoding_for_model("text-embedding-3-small"), max_tokens=8192, ) # Requires: pip install 'docling-core[chunking-openai]'`
For chunking strategies and tokenizer details, see the Docling documentation on chunking and `HybridChunker`.
### 6\. Analyze document structure
Use the `DoclingDocument` object directly to inspect structure:
`doc = result.document for item, level in doc.iterate_items(): if hasattr(item, 'label') and item.label.name == 'SECTION_HEADER': print(f"{'#' * level} {item.text}") for table in doc.tables: print(table.export_to_dataframe()) # pandas DataFrame print(table.export_to_markdown()) for picture in doc.pictures: print(picture.caption_text(doc)) # caption if present`
For the full API surface, see Docling's structure and table export docs.
### 7\. Evaluate output and iterate (required for "best effort" conversions)
After **every** conversion where the user cares about fidelity (not quick previews), run the bundled evaluator on the JSON export, then refine the pipeline if needed. This is how the agent **checks its work** and **improves the run** without guessing.
**Step A — Produce JSON and optional Markdown**
`docling "" --to json --output /tmp/ docling "" --to md --output /tmp/`
**Step B — Evaluate**
`python3 scripts/docling-evaluate.py /tmp/.json --markdown /tmp/.md`
If the user expects tables (invoices, spreadsheets in PDF), add `--expect-tables`. Tighten gates with `--fail-on-warn` in CI-style checks.
The script prints a JSON report to stdout: `status` (`pass` | `warn` | `fail`), `metrics`, `issues`, and `recommended_actions` (concrete `docling` CLI flags to try next).
**Step C — Refinement loop (max 3 attempts unless the user says otherwise)**
1. If `status` is `warn` or `fail`, apply **one** primary change from `recommended_actions` (e.g. switch `--pipeline vlm`, change `--ocr-engine`, ensure tables are enabled).
2. Re-convert with `docling`, re-run `scripts/docling-evaluate.py`.
3. Stop when `status` is `pass`, or after 3 iterations — then summarize what worked and any remaining issues for the user.
**Step D — Self-improvement log (skill memory)**
After a successful pass **or** after the final iteration, append one entry to [improvement-log.md](https://docling-project.github.io/docling/examples/agent_skill/docling-document-intelligence/improvement-log/)
in this skill directory:
* Source type (e.g. scanned PDF, digital PDF, DOCX)
* First-run problems (from `issues`)
* Pipeline + flags that fixed or best mitigated them
* Final `status` and one line of subjective quality notes
This log is optional for the user to git-ignore; it is for **local** learning so future runs on similar documents start closer to the right pipeline.
### 8\. Agent quality checklist (manual, if script unavailable)
If `scripts/docling-evaluate.py` cannot run, still verify:
| Check | Action if bad |
| --- | --- |
| Page count matches source (roughly) | Re-run; try `--pipeline vlm` if layout is complex |
| Markdown is not near-empty | Enable OCR / VLM |
| Tables missing when visually obvious | Remove `--no-tables`; try `--pipeline vlm` |
| `\ufffd` replacement characters | Different `--ocr-engine` or `--pipeline vlm` |
| Same line repeated many times | `--pipeline vlm` or hybrid `force_backend_text` (Python API) |
Common Edge Cases
-----------------
| Situation | Handling |
| --- | --- |
| Scanned / image-only PDF | Standard pipeline with OCR, or `--pipeline vlm` for best quality |
| Password-protected PDF | `--pdf-password PASSWORD`; will raise `ConversionError` if wrong |
| Very large document (500+ pages) | Standard pipeline with `--no-tables` for speed |
| Complex layout / multi-column | `--pipeline vlm`; standard may misorder reading flow |
| Handwriting or formulas | `--pipeline vlm` only — standard OCR will not handle these |
| URL behind auth | Pre-download to temp file; pass local path |
| Tables with merged cells | `table.export_to_markdown()` handles spans; VLM often more accurate |
| Non-UTF-8 encoding | Docling normalises internally; no special handling needed |
| VLM hallucinating text | `force_backend_text=True` via Python API for hybrid mode |
| VLM API call blocked | `--enable-remote-services` (CLI) or `enable_remote_services=True` (Python) |
| Apple Silicon | `--vlm-model granite_docling` with MLX backend, or `GRANITEDOCLING_MLX` preset (Python API) |
Pipeline reference
------------------
Full decision matrix, all OCR engine options, VLM model presets, and API server configuration: [pipelines.md](https://docling-project.github.io/docling/examples/agent_skill/docling-document-intelligence/pipelines/)
Output conventions
------------------
* Always report the number of pages and conversion status.
* When evaluation is in scope, report evaluator `status`, top `issues`, and which refinement attempt produced the final output.
* For Markdown output: wrap in a fenced code block only if the user will copy/paste it; otherwise render directly.
* For JSON output: pretty-print with `indent=2` unless the user specifies otherwise.
* For chunks: report total chunk count, min/max/avg token counts.
* For structure analysis: summarise heading tree + table count + figure count before going into detail.
Dependencies
------------
`pip install docling docling-core # For OpenAI tokenizer support: pip install 'docling-core[chunking-openai]'`
The `docling` CLI is included with the `docling` package — no separate install needed.
Check installed versions (prefer distribution metadata — `docling` may not set `__version__`):
`from importlib.metadata import version print(version("docling"), version("docling-core"))`
Back to top
---
# Metaxy - Docling
[Skip to content](https://docling-project.github.io/docling/integrations/metaxy/#__skip)
Metaxy
======
Docling can be used with [Metaxy](https://docs.metaxy.io/latest/)
, a framework that perfects the art of doing nothing.
By combining Docling-based document processing with Metaxy, teams can save compute cost while accelerating experimentation for multimodal and HPC-oriented workloads.
[](https://docs.metaxy.io/latest/)
* 📖 [Metaxy docs](https://docs.metaxy.io/latest/)
* 📝 [Metaxy announcement](https://anam.ai/blog/metaxy)
* 📝 [Metaxy + Dagster + Slurm + multimodal walkthrough](https://georgheiler.com/2026/02/22/metaxy-dagster-slurm-multimodal/)
* 💻 [Docling + Metaxy example in dagster-slurm](https://github.com/ascii-supply-networks/dagster-slurm/blob/main/examples/projects/dagster-slurm-example-hpc-workload/dagster_slurm_example_hpc_workload/ray/process_documents_docling_metaxy.py)
* 🎞 [Slide deck](https://dagster-slurm.geoheil.com/slides-multimodal/index.html#/18?clicks=1)
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---
# Docling
[Skip to content](https://docling-project.github.io/docling/_generated/hybrid_chunking#__skip)
404 - Not found
===============
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---
# Docling
[Skip to content](https://docling-project.github.io/docling/examples/advanced_chunking_and_serialization/#__skip)
404 - Not found
===============
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---
# Docling
[Skip to content](https://docling-project.github.io/docling/_generated/pictures_description#__skip)
404 - Not found
===============
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---
# Docling
[Skip to content](https://docling-project.github.io/docling/_generated/rag_llamaindex/#__skip)
404 - Not found
===============
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---
# Docling
[Skip to content](https://docling-project.github.io/docling/_generated/minimal/#__skip)
404 - Not found
===============
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---
# Docling
[Skip to content](https://docling-project.github.io/docling/integrations/.actor/#__skip)
404 - Not found
===============
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---
# Docling
[Skip to content](https://docling-project.github.io/docling/_generated/line_based_chunking#__skip)
404 - Not found
===============
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---
![\\"Open](\\"https://colab.research.google.com/assets/colab-badge.svg\\"){kind=icon}
"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"# RAG with LangChain"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"| Step | Tech | Execution | \\n",\
"| --- | --- | --- |\\n",\
"| Embedding | Hugging Face / Sentence Transformers | 💻 Local |\\n",\
"| Vector store | Milvus | 💻 Local |\\n",\
"| Gen AI | Hugging Face Inference API | 🌐 Remote | "\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"This example leverages the\\n",\
"\[LangChain Docling integration\](../../integrations/langchain/), along with a Milvus\\n",\
"vector store, as well as sentence-transformers embeddings.\\n",\
"\\n",\
"The presented \`DoclingLoader\` component enables you to:\\n",\
"- use various document types in your LLM applications with ease and speed, and\\n",\
"- leverage Docling's rich format for advanced, document-native grounding.\\n",\
"\\n",\
"\`DoclingLoader\` supports two different export modes:\\n",\
"- \`ExportType.MARKDOWN\`: if you want to capture each input document as a separate\\n",\
" LangChain document, or\\n",\
"- \`ExportType.DOC\_CHUNKS\` (default): if you want to have each input document chunked and\\n",\
" to then capture each individual chunk as a separate LangChain document downstream.\\n",\
"\\n",\
"The example allows exploring both modes via parameter \`EXPORT\_TYPE\`; depending on the\\n",\
"value set, the example pipeline is then set up accordingly."\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Setup"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"- 👉 For best conversion speed, use GPU acceleration whenever available; e.g. if running on Colab, use GPU-enabled runtime.\\n",\
"- Notebook uses HuggingFace's Inference API; for increased LLM quota, token can be provided via env var \`HF\_TOKEN\`.\\n",\
"- Requirements can be installed as shown below (\`--no-warn-conflicts\` meant for Colab's pre-populated Python env; feel free to remove for stricter usage):"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 1,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Note: you may need to restart the kernel to use updated packages.\\n"\
\]\
}\
\],\
"source": \[\
"%pip install -q --progress-bar off --no-warn-conflicts langchain-docling langchain-core langchain-huggingface langchain\_milvus langchain python-dotenv"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 2,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"import os\\n",\
"from pathlib import Path\\n",\
"from tempfile import mkdtemp\\n",\
"\\n",\
"from dotenv import load\_dotenv\\n",\
"from langchain\_core.prompts import PromptTemplate\\n",\
"from langchain\_docling.loader import ExportType\\n",\
"\\n",\
"\\n",\
"def \_get\_env\_from\_colab\_or\_os(key):\\n",\
" try:\\n",\
" from google.colab import userdata\\n",\
"\\n",\
" try:\\n",\
" return userdata.get(key)\\n",\
" except userdata.SecretNotFoundError:\\n",\
" pass\\n",\
" except ImportError:\\n",\
" pass\\n",\
" return os.getenv(key)\\n",\
"\\n",\
"\\n",\
"load\_dotenv()\\n",\
"\\n",\
"# https://github.com/huggingface/transformers/issues/5486:\\n",\
"os.environ\[\\"TOKENIZERS\_PARALLELISM\\"\] = \\"false\\"\\n",\
"\\n",\
"HF\_TOKEN = \_get\_env\_from\_colab\_or\_os(\\"HF\_TOKEN\\")\\n",\
"FILE\_PATH = \[\\"https://arxiv.org/pdf/2408.09869\\"\] # Docling Technical Report\\n",\
"EMBED\_MODEL\_ID = \\"sentence-transformers/all-MiniLM-L6-v2\\"\\n",\
"GEN\_MODEL\_ID = \\"mistralai/Mixtral-8x7B-Instruct-v0.1\\"\\n",\
"EXPORT\_TYPE = ExportType.DOC\_CHUNKS\\n",\
"QUESTION = \\"Which are the main AI models in Docling?\\"\\n",\
"PROMPT = PromptTemplate.from\_template(\\n",\
" \\"Context information is below.\\\\n---------------------\\\\n{context}\\\\n---------------------\\\\nGiven the context information and not prior knowledge, answer the query.\\\\nQuery: {input}\\\\nAnswer:\\\\n\\",\\n",\
")\\n",\
"TOP\_K = 3\\n",\
"MILVUS\_URI = str(Path(mkdtemp()) / \\"docling.db\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Document loading\\n",\
"\\n",\
"Now we can instantiate our loader and load documents."\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 3,\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"Token indices sequence length is longer than the specified maximum sequence length for this model (1041 > 512). Running this sequence through the model will result in indexing errors\\n"\
\]\
}\
\],\
"source": \[\
"from docling\_core.transforms.chunker.tokenizer.huggingface import HuggingFaceTokenizer\\n",\
"from langchain\_docling import DoclingLoader\\n",\
"from transformers import AutoTokenizer\\n",\
"\\n",\
"from docling.chunking import HybridChunker\\n",\
"\\n",\
"tokenizer = HuggingFaceTokenizer(\\n",\
" tokenizer=AutoTokenizer.from\_pretrained(EMBED\_MODEL\_ID)\\n",\
")\\n",\
"\\n",\
"loader = DoclingLoader(\\n",\
" file\_path=FILE\_PATH,\\n",\
" export\_type=EXPORT\_TYPE,\\n",\
" chunker=HybridChunker(tokenizer=tokenizer),\\n",\
")\\n",\
"\\n",\
"docs = loader.load()"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"> Note: a message saying \`\\"Token indices sequence length is longer than the specified\\n",\
"maximum sequence length...\\"\` can be ignored in this case — details\\n",\
"\[here\](https://github.com/docling-project/docling-core/issues/119#issuecomment-2577418826)."\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Determining the splits:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 4,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"if EXPORT\_TYPE == ExportType.DOC\_CHUNKS:\\n",\
" splits = docs\\n",\
"elif EXPORT\_TYPE == ExportType.MARKDOWN:\\n",\
" from langchain\_text\_splitters import MarkdownHeaderTextSplitter\\n",\
"\\n",\
" splitter = MarkdownHeaderTextSplitter(\\n",\
" headers\_to\_split\_on=\[\\n",\
" (\\"#\\", \\"Header\_1\\"),\\n",\
" (\\"##\\", \\"Header\_2\\"),\\n",\
" (\\"###\\", \\"Header\_3\\"),\\n",\
" \],\\n",\
" )\\n",\
" splits = \[split for doc in docs for split in splitter.split\_text(doc.page\_content)\]\\n",\
"else:\\n",\
" raise ValueError(f\\"Unexpected export type: {EXPORT\_TYPE}\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"Inspecting some sample splits:"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 5,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"- d.page\_content='arXiv:2408.09869v5 \[cs.CL\] 9 Dec 2024'\\n",\
"- d.page\_content='Docling Technical Report\\\\nVersion 1.0\\\\nChristoph Auer Maksym Lysak Ahmed Nassar Michele Dolfi Nikolaos Livathinos Panos Vagenas Cesar Berrospi Ramis Matteo Omenetti Fabian Lindlbauer Kasper Dinkla Lokesh Mishra Yusik Kim Shubham Gupta Rafael Teixeira de Lima Valery Weber Lucas Morin Ingmar Meijer Viktor Kuropiatnyk Peter W. J. Staar\\\\nAI4K Group, IBM Research R¨uschlikon, Switzerland'\\n",\
"- d.page\_content='Abstract\\\\nThis technical report introduces Docling , an easy to use, self-contained, MITlicensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayNet) and table structure recognition (TableFormer), and runs efficiently on commodity hardware in a small resource budget. The code interface allows for easy extensibility and addition of new features and models.'\\n",\
"...\\n"\
\]\
}\
\],\
"source": \[\
"for d in splits\[:3\]:\\n",\
" print(f\\"- {d.page\_content=}\\")\\n",\
"print(\\"...\\")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## Ingestion"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 6,\
"metadata": {},\
"outputs": \[\],\
"source": \[\
"import json\\n",\
"from pathlib import Path\\n",\
"from tempfile import mkdtemp\\n",\
"\\n",\
"from langchain\_huggingface.embeddings import HuggingFaceEmbeddings\\n",\
"from langchain\_milvus import Milvus\\n",\
"\\n",\
"embedding = HuggingFaceEmbeddings(model\_name=EMBED\_MODEL\_ID)\\n",\
"\\n",\
"\\n",\
"milvus\_uri = str(Path(mkdtemp()) / \\"docling.db\\") # or set as needed\\n",\
"vectorstore = Milvus.from\_documents(\\n",\
" documents=splits,\\n",\
" embedding=embedding,\\n",\
" collection\_name=\\"docling\_demo\\",\\n",\
" connection\_args={\\"uri\\": milvus\_uri},\\n",\
" index\_params={\\"index\_type\\": \\"FLAT\\"},\\n",\
" drop\_old=True,\\n",\
")"\
\]\
},\
{\
"cell\_type": "markdown",\
"metadata": {},\
"source": \[\
"## RAG"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 7,\
"metadata": {},\
"outputs": \[\
{\
"name": "stderr",\
"output\_type": "stream",\
"text": \[\
"Note: Environment variable\`HF\_TOKEN\` is set and is the current active token independently from the token you've just configured.\\n"\
\]\
}\
\],\
"source": \[\
"from langchain.chains import create\_retrieval\_chain\\n",\
"from langchain.chains.combine\_documents import create\_stuff\_documents\_chain\\n",\
"from langchain\_huggingface import HuggingFaceEndpoint\\n",\
"\\n",\
"retriever = vectorstore.as\_retriever(search\_kwargs={\\"k\\": TOP\_K})\\n",\
"llm = HuggingFaceEndpoint(\\n",\
" repo\_id=GEN\_MODEL\_ID,\\n",\
" huggingfacehub\_api\_token=HF\_TOKEN,\\n",\
" task=\\"text-generation\\",\\n",\
")\\n",\
"\\n",\
"\\n",\
"def clip\_text(text, threshold=100):\\n",\
" return f\\"{text\[:threshold\]}...\\" if len(text) > threshold else text"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": 8,\
"metadata": {},\
"outputs": \[\
{\
"name": "stdout",\
"output\_type": "stream",\
"text": \[\
"Question:\\n",\
"Which are the main AI models in Docling?\\n",\
"\\n",\
"Answer:\\n",\
"Docling initially releases two AI models, a layout analysis model and TableFormer. The layout analysis model is an accurate object-detector for page elements, and TableFormer is a state-of-the-art tab...\\n",\
"\\n",\
"Source 1:\\n",\
" text: \\"3.2 AI models\\\\nAs part of Docling, we initially release two highly capable AI models to the open-source community, which have been developed and published recently by our team. The first model is a layout analysis model, an accurate object-detector for page elements \[13\]. The second model is TableFormer \[12, 9\], a state-of-the-art table structure re...\\"\\n",\
" dl\_meta: {'schema\_name': 'docling\_core.transforms.chunker.DocMeta', 'version': '1.0.0', 'doc\_items': \[{'self\_ref': '#/texts/50', 'parent': {'$ref': '#/body'}, 'children': \[\], 'label': 'text', 'prov': \[{'page\_no': 3, 'bbox': {'l': 108.0, 't': 405.1419982910156, 'r': 504.00299072265625, 'b': 330.7799987792969, 'coord\_origin': 'BOTTOMLEFT'}, 'charspan': \[0, 608\]}\]}\], 'headings': \['3.2 AI models'\], 'origin': {'mimetype': 'application/pdf', 'binary\_hash': 11465328351749295394, 'filename': '2408.09869v5.pdf'}}\\n",\
" source: https://arxiv.org/pdf/2408.09869\\n",\
"\\n",\
"Source 2:\\n",\
" text: \\"3 Processing pipeline\\\\nDocling implements a linear pipeline of operations, which execute sequentially on each given document (see Fig. 1). Each document is first parsed by a PDF backend, which retrieves the programmatic text tokens, consisting of string content and its coordinates on the page, and also renders a bitmap image of each page to support ...\\"\\n",\
" dl\_meta: {'schema\_name': 'docling\_core.transforms.chunker.DocMeta', 'version': '1.0.0', 'doc\_items': \[{'self\_ref': '#/texts/26', 'parent': {'$ref': '#/body'}, 'children': \[\], 'label': 'text', 'prov': \[{'page\_no': 2, 'bbox': {'l': 108.0, 't': 273.01800537109375, 'r': 504.00299072265625, 'b': 176.83799743652344, 'coord\_origin': 'BOTTOMLEFT'}, 'charspan': \[0, 796\]}\]}\], 'headings': \['3 Processing pipeline'\], 'origin': {'mimetype': 'application/pdf', 'binary\_hash': 11465328351749295394, 'filename': '2408.09869v5.pdf'}}\\n",\
" source: https://arxiv.org/pdf/2408.09869\\n",\
"\\n",\
"Source 3:\\n",\
" text: \\"6 Future work and contributions\\\\nDocling is designed to allow easy extension of the model library and pipelines. In the future, we plan to extend Docling with several more models, such as a figure-classifier model, an equationrecognition model, a code-recognition model and more. This will help improve the quality of conversion for specific types of ...\\"\\n",\
" dl\_meta: {'schema\_name': 'docling\_core.transforms.chunker.DocMeta', 'version': '1.0.0', 'doc\_items': \[{'self\_ref': '#/texts/76', 'parent': {'$ref': '#/body'}, 'children': \[\], 'label': 'text', 'prov': \[{'page\_no': 5, 'bbox': {'l': 108.0, 't': 322.468994140625, 'r': 504.00299072265625, 'b': 259.0169982910156, 'coord\_origin': 'BOTTOMLEFT'}, 'charspan': \[0, 543\]}\]}, {'self\_ref': '#/texts/77', 'parent': {'$ref': '#/body'}, 'children': \[\], 'label': 'text', 'prov': \[{'page\_no': 5, 'bbox': {'l': 108.0, 't': 251.6540069580078, 'r': 504.00299072265625, 'b': 198.99200439453125, 'coord\_origin': 'BOTTOMLEFT'}, 'charspan': \[0, 402\]}\]}\], 'headings': \['6 Future work and contributions'\], 'origin': {'mimetype': 'application/pdf', 'binary\_hash': 11465328351749295394, 'filename': '2408.09869v5.pdf'}}\\n",\
" source: https://arxiv.org/pdf/2408.09869\\n"\
\]\
}\
\],\
"source": \[\
"question\_answer\_chain = create\_stuff\_documents\_chain(llm, PROMPT)\\n",\
"rag\_chain = create\_retrieval\_chain(retriever, question\_answer\_chain)\\n",\
"resp\_dict = rag\_chain.invoke({\\"input\\": QUESTION})\\n",\
"\\n",\
"clipped\_answer = clip\_text(resp\_dict\[\\"answer\\"\], threshold=200)\\n",\
"print(f\\"Question:\\\\n{resp\_dict\['input'\]}\\\\n\\\\nAnswer:\\\\n{clipped\_answer}\\")\\n",\
"for i, doc in enumerate(resp\_dict\[\\"context\\"\]):\\n",\
" print()\\n",\
" print(f\\"Source {i + 1}:\\")\\n",\
" print(f\\" text: {json.dumps(clip\_text(doc.page\_content, threshold=350))}\\")\\n",\
" for key in doc.metadata:\\n",\
" if key != \\"pk\\":\\n",\
" val = doc.metadata.get(key)\\n",\
" clipped\_val = clip\_text(val) if isinstance(val, str) else val\\n",\
" print(f\\" {key}: {clipped\_val}\\")"\
\]\
},\
{\
"cell\_type": "code",\
"execution\_count": null,\
"metadata": {},\
"outputs": \[\],\
"source": \[\]\
}\
\],
"metadata": {
"kernelspec": {
"display\_name": ".venv",
"language": "python",
"name": "python3"
},
"language\_info": {
"codemirror\_mode": {
"name": "ipython",
"version": 3
},
"file\_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert\_exporter": "python",
"pygments\_lexer": "ipython3",
"version": "3.12.8"
}
},
"nbformat": 4,
"nbformat\_minor": 2
}
---
# Unknown
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{\
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"
"\
\],\
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"![](\\"{pic.image.uri!s}\\")