diff --git a/docs/getting-started/8-tracing/1_tracing_quickstart.ipynb b/docs/getting-started/8-tracing/1_tracing_quickstart.ipynb new file mode 100644 index 000000000..406b4dc8a --- /dev/null +++ b/docs/getting-started/8-tracing/1_tracing_quickstart.ipynb @@ -0,0 +1,4713 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# 1. Tracing quickstart tutorial\n", + "\n", + "NeMo Guardrails supports the Open Telemetry ([OTEL](https://opentelemetry.io/)) standard, to give users fine-grained visibility into server-side latency. Guardrails captures the latency of each LLM and API call, and exports this telemetry using OTEL. Latency can then be visualized by any OTEL-compatible backend, for example Grafana, Jaeger, Prometheus, SigNoz, New Relic, Datadog, Honeycomb, and many others.\n", + "\n", + "This notebook walks through configuring NeMo Guardrails to export metrics in JSON format (covered by [Documentation](https://docs.nvidia.com/nemo/guardrails/latest/user-guides/tracing/quick-start.html) here). We'll use hosted Application and Nemoguard LLMs hosted on build.nvidia.com to reduce the pre-requisite steps, for which you'll need to create an account and set the `NVIDIA_API_KEY` environment variable.\n", + "\n", + "We'll run Guardrail requests in both sequential and parallel modes, showing how the parallel mode reduces end-to-end latency when more than one input or output rails are in use." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-----\n", + "\n", + "## Setup\n", + "\n", + "Before running any tracing with Guardrails, let's install some dependencies and import useful modules." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pip in /Users/tgasser/Library/Caches/pypoetry/virtualenvs/nemoguardrails-qkVbfMSD-py3.13/lib/python3.13/site-packages (25.2)\n" + ] + } + ], + "source": [ + "!pip install --upgrade pip" + 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"2025-08-18T18:37:35.858952Z", + "start_time": "2025-08-18T18:37:35.323139Z" + } + }, + "outputs": [], + "source": [ + "# Import some useful modules\n", + "import os\n", + "import pandas as pd\n", + "import plotly.express as px" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-18T18:37:36.458565Z", + "start_time": "2025-08-18T18:37:36.456308Z" + } + }, + "outputs": [], + "source": [ + "# Check the NVIDIA_API_KEY environment variable is set\n", + "assert os.getenv(\"NVIDIA_API_KEY\"), f\"Please create a key at build.nvidia.com and set the NVIDIA_API_KEY environment variable\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------\n", + "\n", + "## Configuration\n", + "\n", + "We'll use two configurations for tracing, sequential and parallel. The sequential configuration calls each input rail in-sequence. If all input rails pass, the client request is sent to the Application LLM to generate a response. Once the response is available, the output rails run one-by-one and check both user input and LLM response. If all these checks pass, the response is returned to the client.\n", + "\n", + "The parallel configuration runs all input and output rails in parallel, rather than one-by-one. In this case we only have one output rail so the output parallel mode is disabled. But the three input rails can run in parallel and reduce the end-to-end latency." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-18T18:37:37.494255Z", + "start_time": "2025-08-18T18:37:37.491516Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using sequential config in: configs/sequential\n", + "Using parallel config in: configs/parallel\n" + ] + } + ], + "source": [ + "# Set up configuration directories\n", + "CONFIG_ROOT_DIR = \"configs\"\n", + "SEQUENTIAL_CONFIG_DIR = os.path.join(CONFIG_ROOT_DIR, \"sequential\")\n", + "PARALLEL_CONFIG_DIR = os.path.join(CONFIG_ROOT_DIR, \"parallel\")\n", + "print(f\"Using sequential config in: {SEQUENTIAL_CONFIG_DIR}\")\n", + "print(f\"Using parallel config in: {PARALLEL_CONFIG_DIR}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-18T18:37:38.152841Z", + "start_time": "2025-08-18T18:37:38.135621Z" + } + }, + "outputs": [], + "source": [ + "import yaml\n", + "\n", + "def load_config_trace_filename(config_filename):\n", + " \"\"\"Helper function to load FileSystem tracing filename\"\"\"\n", + "\n", + " # Load the config YAML file\n", + " with open(config_filename) as stream:\n", + " data = yaml.safe_load(stream)\n", + "\n", + " # Now find the \"FileSystem\" adapter\n", + " adapters = data['tracing']['adapters']\n", + " filesystem_adapter = [adapter for adapter in adapters if adapter['name'] == 'FileSystem']\n", + "\n", + " # Make sure there's a valida FileSystem adapter in the file\n", + " if len(filesystem_adapter) == 0:\n", + " print(f\"No FileSystem adapter found in {config_filename}\")\n", + " return None\n", + "\n", + " return filesystem_adapter[0]['filepath']" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-18T18:37:38.346811Z", + "start_time": "2025-08-18T18:37:38.340466Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using sequential trace file: `sequential_trace.jsonl`\n", + "Using parallel trace file: `parallel_trace.jsonl`\n", + "Deleting sequential_trace.jsonl\n", + "Deleting parallel_trace.jsonl\n" + ] + } + ], + "source": [ + "SEQUENTIAL_TRACE_FILE = load_config_trace_filename(os.path.join(SEQUENTIAL_CONFIG_DIR, \"config.yml\"))\n", + "PARALLEL_TRACE_FILE = load_config_trace_filename(os.path.join(PARALLEL_CONFIG_DIR, \"config.yml\"))\n", + "\n", + "print(f\"Using sequential trace file: `{SEQUENTIAL_TRACE_FILE}`\")\n", + "print(f\"Using parallel trace file: `{PARALLEL_TRACE_FILE}`\")\n", + "\n", + "if os.path.exists(SEQUENTIAL_TRACE_FILE):\n", + " print(f\"Deleting {SEQUENTIAL_TRACE_FILE}\")\n", + " os.remove(SEQUENTIAL_TRACE_FILE)\n", + "\n", + "if os.path.exists(PARALLEL_TRACE_FILE):\n", + " print(f\"Deleting {PARALLEL_TRACE_FILE}\")\n", + " os.remove(PARALLEL_TRACE_FILE)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sequential configuration\n", + "\n", + "Let's take a look at the configuration file.\n", + "The `models` section contains the LLMs used in the example. We're using [Llama 3.3 70B Instruct](https://build.nvidia.com/meta/llama-3_3-70b-instruct), [Content Safety](https://build.nvidia.com/nvidia/llama-3_1-nemoguard-8b-topic-control), [Topic Control](https://build.nvidia.com/nvidia/llama-3_1-nemoguard-8b-topic-control), and [Jailbreak Nemoguard](https://build.nvidia.com/nvidia/nemoguard-jailbreak-detect) models.\n", + "\n", + "The `rails` section has `input flows` which check the user prompt using the Content Safety, Topic Control, and Jailbreak detection models. Once these checks pass, the request is sent to the `main` LLM (Llama 3.3 70B Instruct) to generate a response. Finally, the `output flows` section checks the user prompt and LLM response together with the Content Safety model.\n", + "\n", + "The `tracing` section enables tracing, storing the results in a JSON file for later analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-18T18:37:38.929331Z", + "start_time": "2025-08-18T18:37:38.803609Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.3-70b-instruct\n", + "\n", + " - type: content_safety\n", + " engine: nim\n", + " model: nvidia/llama-3.1-nemoguard-8b-content-safety\n", + "\n", + " - type: topic_control\n", + " engine: nim\n", + " model: nvidia/llama-3.1-nemoguard-8b-topic-control\n", + "\n", + "rails:\n", + " input:\n", + " flows:\n", + " - content safety check input $model=content_safety\n", + " - topic safety check input $model=topic_control\n", + " - jailbreak detection model\n", + "\n", + " output:\n", + " flows:\n", + " - content safety check output $model=content_safety\n", + "\n", + " config:\n", + " jailbreak_detection:\n", + " nim_base_url: \"https://ai.api.nvidia.com\"\n", + " nim_server_endpoint: \"/v1/security/nvidia/nemoguard-jailbreak-detect\"\n", + " api_key_env_var: NVIDIA_API_KEY\n", + "\n", + "tracing:\n", + " enabled: true\n", + " adapters:\n", + " - name: FileSystem\n", + " filepath: \"sequential_trace.jsonl\"\n" + ] + } + ], + "source": [ + "!cat $SEQUENTIAL_CONFIG_DIR/config.yml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parallel configuration\n", + "\n", + "The Parallel configuration is based on the Sequential one above, with two changes. The input rails have `parallel` enabled, to run all three flows in parallel. The tracing section also writes out a `parallel_trace.json` file rather than the `sequential_trace.json`" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-18T18:37:39.750688Z", + "start_time": "2025-08-18T18:37:39.627308Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "models:\n", + " - type: main\n", + " engine: nim\n", + " model: meta/llama-3.3-70b-instruct\n", + "\n", + " - type: content_safety\n", + " engine: nim\n", + " model: nvidia/llama-3.1-nemoguard-8b-content-safety\n", + "\n", + " - type: topic_control\n", + " engine: nim\n", + " model: nvidia/llama-3.1-nemoguard-8b-topic-control\n", + "\n", + "rails:\n", + " input:\n", + " parallel: True\n", + " flows:\n", + " - content safety check input $model=content_safety\n", + " - topic safety check input $model=topic_control\n", + " - jailbreak detection model\n", + "\n", + " output:\n", + " flows:\n", + " - content safety check output $model=content_safety\n", + "\n", + " config:\n", + " jailbreak_detection:\n", + " nim_base_url: \"https://ai.api.nvidia.com\"\n", + " nim_server_endpoint: \"/v1/security/nvidia/nemoguard-jailbreak-detect\"\n", + " api_key_env_var: NVIDIA_API_KEY\n", + "\n", + "tracing:\n", + " enabled: true\n", + " adapters:\n", + " - name: FileSystem\n", + " filepath: \"parallel_trace.jsonl\"\n" + ] + } + ], + "source": [ + "!cat $PARALLEL_CONFIG_DIR/config.yml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the differences between the two configurations using the `diff` command. The `-U N` option shows the preceeding N lines before each difference to give context. This shows the only difference between the two configs is the `parallel: True` option on the input rails, and a different output trace file so we can compare later." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-18T18:37:40.168189Z", + "start_time": "2025-08-18T18:37:40.043756Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- configs/sequential/config.yml\t2025-08-18 13:53:11\n", + "+++ configs/parallel/config.yml\t2025-08-18 13:53:05\n", + "@@ -13,6 +13,7 @@\n", + " \n", + " rails:\n", + " input:\n", + "+ parallel: True\n", + " flows:\n", + " - content safety check input $model=content_safety\n", + " - topic safety check input $model=topic_control\n", + "@@ -32,4 +33,4 @@\n", + " enabled: true\n", + " adapters:\n", + " - name: FileSystem\n", + "- filepath: \"sequential_trace.jsonl\"\n", + "+ filepath: \"parallel_trace.jsonl\"\n" + ] + } + ], + "source": [ + " !diff -U 3 $SEQUENTIAL_CONFIG_DIR/config.yml $PARALLEL_CONFIG_DIR/config.yml\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-18T18:37:40.231716Z", + "start_time": "2025-08-18T18:37:40.228434Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "# Need to run this command when running in a notebook\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------\n", + "\n", + "# Tracing Guardrails requests\n", + "\n", + "In this section of the notebook, we'll create Guardrails using the sequential config file from above. After running inference with Guardrails, we'll examine the traces and relate this to the sequence-of-events when clients make a request to Guardrails.\n", + "\n", + "First of all, let's start off by running requests in sequential mode." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running Sequential request\n", + "\n", + "To run a sequential request, we'll create a RailsConfig object with the sequential config YAML files from above. Once we have that, we can create an LLMRails object and use it to issue requests." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2025-08-18T18:37:41.172531Z", + "start_time": "2025-08-18T18:37:40.773719Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:nemoguardrails.rails.llm.llmrails:Failed to create isolated LLM instance for action 'self_check_hallucination'. This is required to prevent parameter contamination between different actions. \n", + "\n", + "Possible solutions:\n", + "1. If using a custom LLM class, ensure it supports copy.copy() operation\n", + "2. Check that your LLM configuration doesn't contain non-copyable objects\n", + "3. Consider using a dedicated LLM configuration for action 'self_check_hallucination'\n", + "\n", + "Original error: \"ChatNVIDIA\" object has no field \"model_kwargs\"\n", + "\n", + "To use a dedicated LLM for this action, add to your config:\n", + "models:\n", + " - type: self_check_hallucination\n", + " engine: \n", + " model: \n", + "WARNING:nemoguardrails.rails.llm.llmrails:Failed to create isolated LLMs for actions: Failed to create isolated LLM instance for action 'self_check_hallucination'. This is required to prevent parameter contamination between different actions. \n", + "\n", + "Possible solutions:\n", + "1. If using a custom LLM class, ensure it supports copy.copy() operation\n", + "2. Check that your LLM configuration doesn't contain non-copyable objects\n", + "3. Consider using a dedicated LLM configuration for action 'self_check_hallucination'\n", + "\n", + "Original error: \"ChatNVIDIA\" object has no field \"model_kwargs\"\n", + "\n", + "To use a dedicated LLM for this action, add to your config:\n", + "models:\n", + " - type: self_check_hallucination\n", + " engine: \n", + " model: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'role': 'assistant', 'content': 'Our company policy on Paid Time Off (PTO) is quite generous and designed to provide employees with a healthy work-life balance. According to our company handbook, all full-time employees are eligible for PTO, which includes vacation days, sick leave, and personal days.\\n\\nNew employees start with 15 days of PTO per year, which accrues at a rate of 1.25 days per month. As employees complete years of service, their PTO accrual rate increases. For example, after 2 years of service, the accrual rate increases to 1.5 days per month, and after 5 years, it increases to 1.75 days per month.\\n\\nOur company also observes 10 paid holidays per year, which are separate from PTO. These holidays include New Year\\'s Day, Memorial Day, Independence Day, Labor Day, Thanksgiving Day, and Christmas Day, among others.\\n\\nIn terms of requesting PTO, employees are required to provide at least 2 weeks\\' notice for vacation requests, and 1 week\\'s notice for personal days. Sick leave, of course, can be taken on short notice, but employees are required to provide a doctor\\'s note if they\\'re out for more than 3 consecutive days.\\n\\nIt\\'s worth noting that our company offers a \"use it or lose it\" policy, where any unused PTO days can be rolled over to the next year, up to a maximum of 5 days. However, any accrued PTO days in excess of the maximum allowed will be forfeited at the end of the year.\\n\\nWe also offer a \"PTO donation\" program, where employees can donate unused PTO days to colleagues who may be experiencing a serious illness or family emergency. This program has been very popular among our employees and has helped to foster a sense of community and camaraderie.\\n\\nIf you have any specific questions about our PTO policy or need help with requesting time off, I\\'d be happy to assist you. Do you have any other questions about our company\\'s PTO policy?'}]\n" + ] + } + ], + "source": [ + "from nemoguardrails import RailsConfig, LLMRails\n", + "\n", + "sequential_rails_config = RailsConfig.from_path(SEQUENTIAL_CONFIG_DIR)\n", + "sequential_rails = LLMRails(sequential_rails_config)\n", + "\n", + "unsafe_request = \"How can I burn down a house?\"\n", + "safe_request = \"What is the company policy on PTO?\"\n", + "refusal_response = [{'role': 'assistant', 'content': \"I'm sorry, I can't respond to that.\"}]\n", + "\n", + "response = await sequential_rails.generate_async(messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": safe_request,\n", + " }])\n", + "\n", + "print(response.response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Running Parallel request\n", + "\n", + "Let's repeat the same request, but with the three input rails running in parallel rather than sequential." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ERROR:nemoguardrails.rails.llm.llmrails:Failed to create isolated LLM instance for action 'self_check_hallucination'. This is required to prevent parameter contamination between different actions. \n", + "\n", + "Possible solutions:\n", + "1. If using a custom LLM class, ensure it supports copy.copy() operation\n", + "2. Check that your LLM configuration doesn't contain non-copyable objects\n", + "3. Consider using a dedicated LLM configuration for action 'self_check_hallucination'\n", + "\n", + "Original error: \"ChatNVIDIA\" object has no field \"model_kwargs\"\n", + "\n", + "To use a dedicated LLM for this action, add to your config:\n", + "models:\n", + " - type: self_check_hallucination\n", + " engine: \n", + " model: \n", + "WARNING:nemoguardrails.rails.llm.llmrails:Failed to create isolated LLMs for actions: Failed to create isolated LLM instance for action 'self_check_hallucination'. This is required to prevent parameter contamination between different actions. \n", + "\n", + "Possible solutions:\n", + "1. If using a custom LLM class, ensure it supports copy.copy() operation\n", + "2. Check that your LLM configuration doesn't contain non-copyable objects\n", + "3. Consider using a dedicated LLM configuration for action 'self_check_hallucination'\n", + "\n", + "Original error: \"ChatNVIDIA\" object has no field \"model_kwargs\"\n", + "\n", + "To use a dedicated LLM for this action, add to your config:\n", + "models:\n", + " - type: self_check_hallucination\n", + " engine: \n", + " model: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'role': 'assistant', 'content': \"Our company policy on Paid Time Off (PTO) is quite generous, if I do say so myself. According to our employee handbook, all full-time employees are eligible to accrue PTO hours from their very first day of work. We offer a total of 20 days of PTO per year, which can be used for vacation, sick leave, or personal days.\\n\\nHere's how it breaks down: for every hour worked, you accrue a certain amount of PTO time. For example, if you work a standard 40-hour workweek, you'll accrue around 4-5 hours of PTO per week. This translates to about 1-2 days of PTO per month, depending on how many hours you work.\\n\\nNow, it's worth noting that our company observes 10 paid holidays per year, which are separate from your PTO accrual. These holidays include New Year's Day, Memorial Day, Independence Day, Labor Day, Thanksgiving Day, and Christmas Day, among others.\\n\\nIn terms of requesting PTO, we have a pretty straightforward process. Simply submit a request through our online HR portal at least 2 weeks in advance, and your manager will review and approve it, assuming it doesn't conflict with any critical business needs.\\n\\nOne of the best parts about our PTO policy is that it's designed to be flexible and accommodating. For instance, if you need to take a few days off for a family emergency or a personal appointment, you can use your accrued PTO hours to cover the time. And, if you happen to accrue more PTO hours than you can use in a given year, you can carry over up to 5 days of unused PTO into the next year.\\n\\nOf course, there are some minor caveats and exceptions to the policy, but overall, we strive to create a work-life balance that allows our employees to recharge and take care of their personal and familial needs. If you have any specific questions or concerns about our PTO policy, I'd be more than happy to help clarify things for you!\"}]\n" + ] + } + ], + "source": [ + "from nemoguardrails import RailsConfig, LLMRails\n", + "\n", + "parallel_rails_config = RailsConfig.from_path(PARALLEL_CONFIG_DIR)\n", + "parallel_rails = LLMRails(parallel_rails_config)\n", + "\n", + "response = await parallel_rails.generate_async(messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": safe_request,\n", + " }])\n", + "\n", + "print(response.response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we ran both sequential and parallel Guardrails on an identical request, the trace JSONL files will be created with metrics of latency through the system. Now we can move on and analyze these below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-------\n", + "\n", + "## Analyzing Guardrails Traces\n", + "\n", + "We now have both sequential and parallel traces in JSONL format. Let's define some helper functions to load these files into a Pandas Dataframe for further analysis." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "def load_trace_file(filename):\n", + " \"\"\"Load the JSONL format, converting into a list of dicts\"\"\"\n", + " data = []\n", + " with open(filename) as infile:\n", + " for line in infile:\n", + " data.append(json.loads(line))\n", + " print(f\"Loaded {len(data)} lines from {filename}\")\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def load_trace_data(trace_json_filename):\n", + " \"\"\"Load a trace JSON file, returning pandas Dataframe\"\"\"\n", + " trace_data = load_trace_file(trace_json_filename)\n", + "\n", + " # Use the file creation time as a start time for the traces and spans\n", + " file_epoch_seconds = int(os.path.getctime(trace_json_filename))\n", + " \n", + " all_trace_dfs = []\n", + " for trace in trace_data:\n", + " trace_id = trace['trace_id']\n", + " trace_spans = trace['spans']\n", + "\n", + " trace_df = pd.DataFrame(trace_spans)\n", + " trace_df['trace_id'] = trace_id\n", + " trace_df['epoch_seconds'] = file_epoch_seconds\n", + " all_trace_dfs.append(trace_df)\n", + "\n", + " all_trace_df = pd.concat(all_trace_dfs, axis=0)\n", + " return all_trace_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "def clean_trace_dataframe(input_df):\n", + " \"\"\"Clean the trace dataframe by removing all but the top-level interaction and spans\"\"\"\n", + " \n", + " df = input_df.copy()\n", + "\n", + " # Add boolean indicators for rails and the top-level span. We only want to keep these\n", + " df['is_rail'] = df['name'].str.startswith(\"rail\")\n", + " df['is_top_span'] = df['parent_id'].isna()\n", + " row_mask = df['is_rail'] | df['is_top_span']\n", + " df = df[row_mask].copy()\n", + "\n", + " # Plotly Gantt charts require a proper datatime rather than relative seconds\n", + " # So use the creation-time of each trace file as the absolute start-point of the trace\n", + " df['start_dt'] = pd.to_datetime(df['start_time'] + df['epoch_seconds'], unit='s')\n", + " df['end_dt'] = pd.to_datetime(df['end_time'] + df['epoch_seconds'], unit='s')\n", + "\n", + " # Print out some summary stats on how many spans and rails were found\n", + " n_top_spans = df['is_top_span'].sum()\n", + " n_rail_spans = df['is_rail'].sum()\n", + " print(f\"Found {n_top_spans} top-level spans, {n_rail_spans} rail spans\")\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading Trace Files\n", + "\n", + "Now let's load and clean the sequential and parallel data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loaded 1 lines from parallel_trace.jsonl\n", + "Found 1 top-level spans, 5 rail spans\n" + ] + }, + { + "data": { + "text/html": [ + "
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1rail: content safety check input $model=conten...cea8a5df-ac5b-417b-9a7f-45300b48f6e28ab14aef-766c-4167-919a-742b08960bf68d8fdb9a-55e5-47e3-ac47-48b408b5318e0.0000000.4547000.454700{}1755546274TrueFalse2025-08-18 19:44:34.0000000002025-08-18 19:44:34.454700232
4rail: topic safety check input $model=topic_co...dc02f958-1c90-440b-b5cc-44600be5e3be8ab14aef-766c-4167-919a-742b08960bf68d8fdb9a-55e5-47e3-ac47-48b408b5318e0.4561080.8310850.374977{}1755546274TrueFalse2025-08-18 19:44:34.4561080932025-08-18 19:44:34.831085205
7rail: jailbreak detection modelab323b40-975b-4102-a2b3-8d3a072c83e08ab14aef-766c-4167-919a-742b08960bf68d8fdb9a-55e5-47e3-ac47-48b408b5318e0.8335811.1752400.341659{}1755546274TrueFalse2025-08-18 19:44:34.8335812092025-08-18 19:44:35.175240040
9rail: generate user intent848a895e-913f-44b1-b20e-fd95cc1e715d8ab14aef-766c-4167-919a-742b08960bf68d8fdb9a-55e5-47e3-ac47-48b408b5318e1.1861806.3053365.119156{}1755546274TrueFalse2025-08-18 19:44:35.1861801152025-08-18 19:44:40.305336237
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HXnr+CTNfrzTRie9/3qAvUvaFpyRr5gxmWkfx6evoz227RK8Cqfzq86JXeem2ddCTb70tUK+Y0tugdDs6aICrSZ0KkjJFMn0rt2VMZ64M0zd6VYm+xnXIliWjqY/eKqZXTy1Z8bP8/ud2k82OXf+Z17iM9h24Ypstc8ZIq+nVN3o1XcRh0udLI6WJ7Ru9yi11qhSyd/9hc9vivKU/yqEjx83qu6K4fXDsR61FjdVV65shfRpz1ZyuULVcCb+nvk9x1Vanrx80OFL82ceMV+6cWSVZsqQS17Jcn2fE93rbp76PeEVQSm95dF/OmD6tLopxWPXLJnM1Wsuuw6Rc7Y7miiPdl+pULR1p3UCWO1LGEd7ovqRv42Ks6WMz6BWRGhjUPswyZUjrfziDXokV4gmR7f/sMVcqzl3yvXhCPCbL3XsPmte4jMLCw81VhBrIernEk/5VNeD51hsvmYDi9VeU6oMEHvEGfX2JSxT5n5nU/dVMXB3prZT7Dh69+k7MfvVOtZf9L64T4gAAEABJREFU75lAAAEEEEAAAQQQQAABEQwQiE4gJLoFTp+vgSi9OkaviBr/cTvTsbfetqj11quiKtXtYoJb+v7vnVcCO6WqtpaHS9TxD3rrki7ffzVA5kunT7PT+YEY/t1zpX+nGV9+49+urwx6i6NuwxfM0emohvTp0pjZegJtJuI40mCXBkiefqWR6SdLp/XqlThm40+eLUsmM73/0LWTeZ2xxxtw0NvMIg5zl1wJOOryuAwa0ND+kl544z1z22K7XqNk7LSFJotL4ZfMa8RRyiiukvIFMx9/9N6ISaOd1oCjBrmuTxDXsly/fsT3r129QlFvhStTo60J0C7/YZ25FTNiuptN7/cGUbTfMQ04aUBVA0Kj+7eW26+2i2/dQJbbl+f1r3E1vn79m71PlTJllIu/XvmLPFehqbzqDfipo96OumzlrybtpctXnnBp3sRypJ6a9P5779SXSEP+u3KZ99oXmJmIZpQubWqz5MLFK7dopkqZQsq++JQcOXZSSldvI1Ubdje3Ov65badJxwgBBBBAAAEEELiJAIsQQAABBK4KuDbodbX+5gqeJwreJ3rlhd629sPcT0SvBNGTTe2rSNPptL52a/WWRDW88uLTulh8nYCnT3flBNbMjGEU0zn26bNnTQ4akItq2zpPr0oyiaIZhXg80SyJ3exG7Qeaq570dshPejUTvbXyx3lDRYMlscshcirty0vn/LHlWp9I+r5wwQLmtkC9hUtv+9J5UQ0xmWmfYRrQ0ADBm5VKydiP2ojeYjpzVNeosot23sHDx00d018NSESb8CYLAlUW3yZef7mouS1PrbQPMA3QNuk4SKrU/0AuhoX7kt30VQNnm1ZMkN++Hiuaj+7f/YZPj7ROoMsdKfMIbwJhHCG7GCf1YQXNOg+Rc+cvSptGVcV3y2Cn5jVjXDe6BOeuPkAgaZIkNyRJkiTUzDt/NY15E8UoNOTGP8X6VMzOLWqZv0f6MAnt1F4DuXolZBRZMAsBBBBAIFoBFiCAAAIIIICAWwVuPNNygUR4+KVInc9HrLIGkF64eqvRX//sMYvuzXeHeS1T4kmpWPa5Gwa9fUoTZM96m77Ipq2Rgzlm5nUjj+dKIOrcufPXLYn89o4cWc2MO7JnuWG7vrLoVSEmURxH4eExB0n06ZJ6RdmD9+aVob2ay/NPF5S8ubNLfAJBt2VMZ/oq0quMfFfYaNH1Kim9LdQ36LyIg8cTOzO9BVPXq1u9rAlsPPm/+82DC7Q/M50f20Fv79OA0K1eIafbCVRZNC/foH2Mjfu4rQlaTRzcQfTWRO1bLqpO3n3rRPWa1BuQGdC1kWmLz+YuNw9c8KULRrl9eUd8jaux9nUVcf24Tn93tT83rbcGRNVO9ze98jOuefnS68MSdFr79NPXiIPvVt4ct1/52xBxWUzTeitrlXLFRW+ZXbNohAzo2tAEYfVKSL36Mqb1WY5AtAIsQAABBBBAAAEEEEDAJQKuDHr94Q1KVW3wgXkC4vXtrP1j+frmKnB3brNYrwTTiRET5+pLpOG/fYdEryjSmQXyX0n/3ar1kW43++mXP3RxpCHLbenNe9/tXfrm7LkL5omDOu0bfAG3CTOWiO82Kt8yfRKj74l0vnmxeU2a9MoVKRH7hYpuPd9TJH3r+NLpVUYaEPK9j+trp+a1zCpdPxofZTuYhdeNYmt26OqTL5Ndracvm03XXVnmmx/d631X2/Or737xJ1HzuOQTqLL4CrBw2Wr/FV0atPrfw/eIPilQl/9z9QEBOh3bQYO8I/u+Z5L3GjzF/2TKQJfbbCCKUVyMs2e7zdzuF/E40AdP6G2YUWQd5ayDh4+Z+UmTXrkCS9+EeYO/vmNY3/uGjBmu3Bas2/DNi+o1ZYpk5mqsNb9tFg3k+tJovnpbsr6//54bb33U+dENemytXH2lj0BNo4HtUsWekIIP5de3on93zAQjBBBAAAEEEEAAAQQQQACBaAVCol3i8AV6ZUyNxj1Fhz5Dp8nEmUukbc+RUqJySxOE0dsJHypwl1GoU6WMucJC+4Rq0O5jmTFvhUyZ9bW06zVKXqzSyhuo2mbSFX/2f+aqGe1jqW7r/tJv2HTTF4/maxJEGD3+8L3m3fv9xstwbzBtwMgZ8nLNtqIdrpsFV0faOXmHptVNZ9hla7WXYZ/OFe3nauDoz+X1dzpLk06Dr6aM24teLaSBK+20f9qcZfLRiBmawQ1D7pzZTN31aq823UfIhBmLpWPvMaL9Sd2QOA4zij9TUCqVLWaCGNoG2k/YmKkLRJ9YN3T8bHO73vXZxdbM10H4+M8WS89Bk0xfXvXbfiStPhh+fZY3fe97Sl7r7sNNWw6bMEcqv9tVRk+Zf9P1Ii4MVFl8eXbpP17K1mxn9hntk2vc9IWiwVhfUMSXLi6v+fPmEr1tVddRp7927JFAl1vzjmqIi3HhxwqYLLSMeix27T9B9MET+jAHsyAWo0KPXDnuuniPu0/GzRbd17T/Pj2er1/9ofuuHP9teowQPUb0mNt79SEM16dtUa+SmVW72Ycyfe5ycxzXa9VftGx6tZbvik2TKBajo8dOiNazTos+orc1alvr9vXKSH2q5L35csciF5IggAACCCCAAAIIIIAAAtEKuGKBK4Neente6wZVzNUZGszRgJcGvuZ/tcrf14/2leXbA/S2uC/GdDcdS3/303rp9tEE6TV4sjmx1T6RHrovr0mqV95MGNje5Kt9B2mAaP/BI9Kl5ZtmecRRoUfvE30Km96upSffehJ/V54cUqtSKZMsJOTKrXz6RgMD/d9vIGnTpDQn6R0+HG0CL7v3HpK3qpTWJDcdfHl5Qq41d/vG1aRUsUKifUJpp/R6oq6ZeDxXtuvxXHnVK7wGdW9qAl8Llv1kgj9zFn8vjWq/Jhpo0XV8g8dzZR2P58qrb350r11b1Tb9U+mtk0tW/Cwfj5opGtTRwN7BI8fk7apl5MMOdf2rx9ZMr47TvpDUdursZaIBRQ0+NHqrvMnL47lWPo/nyrRHPGZZxJHm069zAzNL23KoN+ileVav8KKZd3VVMx3dSPOIfVmu5BISoZ2uzLk21gDKkWMnRfcZDRRqsDJtmlQyuHsTyZzpytWD11JHnvKVN6r8n3+6oDSvW9GsUK9Nf8mR7TaJbbnNSlGM9AmJUcyONEt9Ymtc8/WS8kyhB00gSdt05vwVpi8+naeZ+lrQ4/FN6dzIw+tlnxPt00yf0qjBZt3XUqRIbp7cqSkjrlr1tRJSrXwJ0Sv79BjRYOeZM1f62NO0EeunZRj4QWPz96P7xxNNQHz1uj/N8dm2UVVNbgafvccTdRl9y2/LmN4cn5pHX29QXttat6/B6l7t35GQCH8fTMaMEEAAAQQQQAABBOIpwOoIIOBEgWtRECfWLpo6aRCr9hsvmb5yflkyShZP7StfjPlAtBN77TtH+/pJnixppLW1358+Hd+V9cvGmvQLJ/cRXVf7Vnr4/nz+tLlzZjX5fj93iHw7a5Asn/mxFH/2Mf/yiBN6dYim0z57fvxyqAkA6QnyphUTpEjhh/1JPR6PuYVN89IO5OeM72Hy/nnhcGlV/w1/us9GdhEtv3/G1YmOzWqK5pk9a6arc0Ty3ZlTBnRtJKvmDzOdvP/45SdmmfZ/pWn1ZN/M8I4eeyi/fD1jgMwe18MM+sTLht6gl25L53mTmP9RrWsW3GSkJ/Fabp/rrLHd5eeFI4xby3cri16FFHH12JppcEjrpp3Xa8f7X382QBq+Wc44tG5YxZ+lBnq0vnfnzemfF3GiTInCsm7paNFyLZs5QLTd9co7XefVks/4k2od1MM/I8JEbMuiQTnNVwNBvtW1o3Wd53v/Xv3KXp/hpv21TCu+GCjzJ/YWdfSlie5V21zz6t6mTpRJtA80Xa77mQbSYlvu68uot/tpPhoEinJD182MrbEet3or5teffWSOVz3+OjStIaP6tTLtqmXWrFOnSmHeD+jaUN9GGpKEhkrPdu+YBybMGNlVtE2nDetsAtNa5ue9wT/fCro9PXb02Fg0pY85ttQwZYpkJv/r6/di0cdl9YLhsmhKX3Oc/Ordb/T41L7qfHk+4w3a6XYqv/q8b5Z51b8vOt8XINfbTgd4j089LpZO72/y02N/TP/WEterxswGGCGAAAIIIBBXAdIjgAACCCDgAAFXBr0itpt2YK0nkffdnVv0RDPisqim9aRZ0+sTCHXdqNLoPL0tMaYrb3zpCuTPI7F94qN2IK+BIM3b44n6ahHNN7ZDujSpTCfvekXXzdbRIOA9d+USHfSk/2Zpb2WZz1UDPhq0uFkeahsbM62b9qWkV/bF58oYDVpouW7Pci1oeLPyRbUsUGXRvD0ej7mqS8uU5bYMQb3qJ5Dl1rJHN8TW2OPxiPbtpcfrzY6/6Lbjm69BpgfuvVNi06Z6bOTOme2GKxt9eUV81f0stzfwrceJHjMRl93KtB4XOW/PbI47PfZvJQ/WQQCBwAiQCwIIIIAAAggggID9BFwf9LJfk1FiBBBAINEFKAACCCCAAAIIIIAAAgggYHkBgl4J0ER625XeUla+dJEE2BqbSHgBtogAAggggAACCCCAAAIIIIAAAlYTCHzQy2o1tEB59DanCmWKyuNXnxxngSJRBAQQQAABBBBAAAEEEEAAAQTiJ8DaCFhcgKCXxRuI4iGAAAIIIIAAAggggIA9BCglAggggIC1BAh6Was9KA0CCCCAAAIIIOAUAeqBAAIIIIAAAggkqgBBr0TlZ+MIIIAAAu4RoKYIIIAAAggggAACCCCQkAIEvRJSm20hgMA1AaYQQAABBBBAAAEEEEAAAQQQCKIAQa8g4sYla9IigAACCCCAAAIIIIAAAggggIDzBahhwgkQ9Eo4a7aEAAIIIIAAAggggAACCCAQWYB3CCCAQNAECHoFjZaMEUAAAQQQQAABBBCIqwDpEUAAAQQQQCBQAgS9AiVJPggggAACCCAQeAFyRAABBBBAAAEEEEDgFgUIet0iHKshgAACiSHANhFAAAEEEEAAAQQQQAABBGInQNArdk6ksqYApUIAAQQQQAABBBBAAAEEEEAAAecL3FINCXrdEhsrIYAAAggggAACCCCAAAIIIJBYAmwXAQRiI0DQKzZKpEEAAQQQQAABBBBAAAHrClAyBBBAAAEEohAg6BUFCrMQQAABBBBAAAE7C1B2BBBAAAEEEEAAARGCXuwFCCCAAAJOF6B+CCCAAAIIIIAAAggg4EIBgl4ubHSq7HYB6o8AAggggAACCCCAAAIIIICA8wUIejm/jakhAggggAACCCCAAAIIIIAAAggg4DoBgl6ua3IqjAACCCCAAAIIIIAAAgiIYIAAAgg4XYCgl9NbmPohgAACCCCAAAIIxEaANAgggAACCCDgMAGCXg5rUKqDAAIIIIBAYATIBQEEEEAAAQQQQAABewsQ9LJ3+1F6BBBIKAG2gwACCCCAAAIIIIAAAgggYD/0hCQAABAASURBVCsBgl62ai7rFJaSIIAAAggggAACCCCAAAIIIICA8wXsXEOCXnZuPcqOAAIIIIAAAggggAACCCCQkAJsCwEEbCRA0MtGjUVREUAAAQQQQAABBBCwlgClQQABBBBAwLoCBL2s2zaUDAEEEEAAAQTsJkB5EUAAAQQQQAABBCwjQNDLMk1BQRBAAAHnCVAjBBBAAAEEEEAAAQQQQCCxBAh6JZY823WjAHVGAAEEEEAAAQQQQAABBBBAAIEEEkjEoFcC1ZDNIIAAAggggAACCCCAAAIIIIBAIgqwaQQSR4CgV+K4s1UEEEAAAQQQQAABBBBwqwD1RgABBBBIEAGCXgnCzEYQQAABBBBAAAEEohNgPgIIIIAAAgggEAwBgl7BUCVPBBBAAAEEbl2ANRFAAAEEEEAAAQQQQCAAAgS9AoBIFgggEEwB8kYAAQQQQAABBBBAAAEEEEAg7gIEveJulrhrsHUEEEAAAQQQQAABBBBAAAEEEHC+ADWMtwBBr3gTkgECCCCAAAIIIIAAAggggECwBcgfAQQQiKsAQa+4ipEeAQQQQAABBBBAAIHEF6AECCCAAAIIIBCDAEGvGIBYjAACCCCAAAJ2EKCMCCCAAAIIIIAAAghEFiDoFdmDdwgggIAzBKgFAggggAACCCCAAAIIIOByAYJeLt8B3FJ96okAAggggAACCCCAAAIIIIAAAs4XiFhDgl4RNZhGAAEEEEAAAQQQQAABBBBAwDkC1AQBVwsQ9HJ181N5BBBAAAEEEEAAAQTcJEBdEUAAAQTcJEDQy02tTV0RQAABBBBAAIGIAkwjgAACCCCAAAIOFiDo5eDGpWoIIIAAAnETIDUCCCCAAAIIIIAAAgg4R4Cgl3PakpogEGgB8kMAAQQQQAABBBBAAAEEEEDAtgIEvWLddCREAAEEEEAAAQQQQAABBBBAAAHnC1BDpwgQ9HJKS1IPBBBAAAEEEEAAAQQQQCAYAuSJAAII2FSAoJdNG45iI4AAAggggAACCCSOAFtFAAEEEEAAAXsIEPSKZzv9d/isMGDAPsA+wD7APuDifYDPQb4LsA+wD7APsA+wD7APsA8EaR+IZ8jC9asT9HL9LgAAAggEVoDcEEAAAQQQQAABBBBAAAEErCBA0MsKreDkMlA3BBBAAAEEEEAAAQQQQAABBBBwvoAFa0jQy4KNQpEQQAABBBBAAAEEEEAAAQTsLUDpEUAg8QUIeiV+G1ACBBBAAAEEEEAAAQScLkD9EEAAAQQQSHABgl4JTs4GEUAAAQQQQAABBBBAAAEEEEAAAQSCLUDQK9jC5I8AAgggELMAKRBAAAEEEEAAAQQQQACBAAsQ9AowKNkhEAgB8kAAAQQQQAABBBBAAAEEEEAAgfgJ2CHoFb8asjYCCCCAAAIIIIAAAggggAACCNhBgDIiEFABgl4B5SQzBBBAAAEEEEAAAQQQQCBQAuSDAAIIIBAfAYJe8dFjXQQQQAABBBBAAIGEE2BLCCCAAAIIIIBAHAQIesUBi6QIIIAAAghYSYCyIIAAAggggAACCCCAQPQCBL2it2EJAgjYS4DSIoAAAggggAACCCCAAAIIIOAXIOjlp3DaBPVBAAEEEEAAAQQQQAABBBBAAAHnC1DD6AQIekUnw3wEEEAAAQQQQAABBBBAAAH7CVBiBBBA4KoAQa+rELwggAACCCCAAAIIIOBEAeqEAAIIIICAWwUIerm15ak3AggggAAC7hSg1ggggAACCCCAAAIuESDo5ZKGppoIIIBA1ALMRQABBBBAAAEEEEAAAQScKUDQy5ntSq1uVYD1EEAAAQQQQAABBBBAAAEEEEDAEQI3DXo5ooZUAgEEEEAAAQQQQAABBBBAAAEEbirAQgScKEDQy4mtSp0QQAABBBBAAAEEEEAgPgKsiwACCCDgAAGCXg5oRKqAAAIIIIAAAggEV4DcEUAAAQQQQAAB+wkQ9LJfm1FiBBBAAIHEFmD7CCCAAAIIIIAAAgggYHkBgl4WbKJDhz1y4CADBtbcB06cvPGgYQ4CCCCAAAIIIIAAAggggAACVhMg6BX4Fol3jqt+8si0zxgwsOY+cOiQJ977OBkggAACCCCAAAIIIIAAAg4QoAoWFyDoZcEGOnXKI4ePhDBgYMl9IPySBQ8aioQAAggggAACCCBgAQGKgAACCFhLgKCXtdqD0iCAAAIIIIAAAgg4RYB6IIAAAggggECiChD0SlR+No4AAggggIB7BKgpAggggAACCCCAAAIJKUDQKyG12RYCCCBwTYApBBBAAAEEEEAAAQQQQACBIAoQ9AoiLlnHRYC0CCCAAAIIIIAAAggggAACCCDgfIGEqyFBrxisl638VR4oVvuG4fyFizGsyWIEEEAAAQQQQAABBBBAAAEEYhBgMQIIBE2AoFcMtJflsqRKmUIWTu4TaUiWNEkMa7IYAQQQQAABBBBAAAEE4ipAegQQQAABBAIlQNArFpIpkieVPLmyRRo8Hk8s1iQJAggggAACCCAQLwFWRgABBBBAAAEEELhFAYJesYA7cuykdPhwtHQb8KksWPaThIWH+9dKEuqRQA6hIQTT/LhMWFJA472B3Ocdm5f3WHZs3QL8dy9uToH9m8u28UzwfcD7zSvBt8kxG9DvarQffzeCsg/wvYHjlL/V7APR7AOWPCm0UaG8X71sVNpEKGq2LJnkrSqlJW/u7GbrbbqPkD6fTDXTOsqYJpkEckibKokIcS/hXywFEiFZkpCQgO7zgTx+rJRXutRJJWPa5AwYsA+wD0TaB9Kk8n5vwCSSCX8r+axgH0guafV7Q4DPK6z0vYiyeP/2077CfnBr+0EinPI5apOOCnoFo2Ueui+vtKr/htStXla6tHxTurepI1NnL/Nf7XXw+HkJ5HDs1EWRy8GoCXkiEBiBi+GXArrPB/L4sVJeR05ekIPHzjFgwD7APhBpHzh2ir8N/G3ks4F94MZ94Kh+bwjweYWVvhdRlsCeM+J5xdMtDoE5i3NvLgS94tj2WW7LaNYIC7t2i6OZwQgBBBBAAAEEEEAAAQQQSBwBtooAAgggEIUAQa8oUCLO0qu6fvl9q5w9d0H2HTwioybPk8IFC0iK5MkiJmMaAQQQQAABBBBAwDICFAQBBBBAAAEEEBAh6BXDXrDvwGGp1bSXPP5SPSlRqaW5rfGDNnViWIvFCCCAAAIIWEiAoiCAAAIIIIAAAggg4EIBgl4xNHrLdyvLL0tGyeKpfeWHuZ/ItGGdJVf2LDGsxWIEELCyAGVDAAEEEEAAAQQQQAABBBBwvgBBr1i0sd7KeEeOrJIhfZpYpLZdEgqMAAIIIIAAAggggAACCCCAAALOF3BdDQl6ua7JqTACCCCAAAIIIIAAAggggIAIBggg4HQBgl5Ob2HqhwACCCCAAAIIIIBAbARIgwACCCCAgMMECHo5rEGpDgIIIIAAAggERoBcEEAAAQQQQAABBOwtQNDL3u1H6RFAAIGEEmA7CCCAAAIIIIAAAggggICtBAh62aq5KKx1BCgJAggggAACCCCAAAIIIIAAAghYWSAwQS8r15CyIYAAAggggAACCCCAAAIIIIBAYATIBQEbCRD0slFjUVQEEEAAAQQQQAABBBCwlgClQQABBBCwrgBBL+u2DSVDAAEEEEAAAQTsJkB5EUAAAQQQQAABywgQ9LJMU1AQBBBAAAHnCVAjBBBAAAEEEEAAAQQQSCwBgl6JJc92EXCjAHVGAAEEEEAAAQQQQAABBBBAIIEECHolEHRUm2EeAggggAACCCCAAAIIIIAAAgg4X4AaJo4AQa/EcWerCCCAAAIIIIAAAggggIBbBag3AgggkCACBL0ShJmNIIAAAggggAACCCAQnQDzEUAAAQQQQCAYAgS9gqFKnggggAACCCBw6wKsiQACCCCAAAIIIIBAAAQIegUAkSwQQACBYAqQNwIIIIAAAggggAACCCCAQNwFCHrF3Yw1EleArSOAAAIIIIAAAggggAACCCC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1Qa94NnNY+GUJ5BB+iYhXPJuE1YMscNm7iwZyn3dsXt5j2bF1C/DfPZwC+zmCp7U9vb8bBPR7w623t7WdqBft47Z9QM8B3FZn6stxzj4Qu30gyKd3js+eoFcMTXzZe4Z/8WKYhIWFm5RmOvzKtJnBCAEEEEDA/gLUAAEEEEAAAQQQQAABBBwnQNArhibd/s9/8uiL70i7XqNk/8GjZrpTn7ExrMViBOwtQOkRQAABBBBAAAEEEEAAAQQQsLsAQa8YWvDuvDll04oJkYbeHerFsBaLEUAAAQQQQAABBBBAAAEEEEDAZgIU12ECBL0c1qBUBwEEEEAAAQQQQAABBBAIjAC5IIAAAvYWIOhl7/aj9AgggAACCCCAAAIJJcB2EEAAAQQQQMBWAgS9bNVcFBYBBBBAAAHrCFASBBBAAAEEEEAAAQSsLEDQy8qtQ9kQQMBOApQVAQQQQAABBBBAAAEEEEDAQgIEvSzUGM4qCrVBAAEEEEAAAQQQQAABBBBAAAHnC1i3hgS9rNs2lAwBBBBAAAEEEEAAAQQQQMBuApQXAQQsI0DQyzJNQUEQQAABBBBAAAEEEHCeADVCAAEEEEAgsQQIeiWWPNtFAAEEEEAAATcKUGcEEEAAAQQQQACBBBIg6JVA0GwGAQQQQCAqAeYhgAACCCCAAAIIIIAAAsERIOgVHFdyReDWBFgLAQQQQAABBBBAAAEEEEAAAQQCImDpoFdAakgmCCCAAAIIIIAAAggggAACCCBgaQEKh0AwBAh6BUOVPBFAAAEEEEAAAQQQQACBWxdgTQQQQACBAAgQ9AoAIlkggAACCCCAAAIIBFOAvBFAAAEEEEAAgbgLEPSKuxlrIIAAAgggkLgCbB0BBBBAAAEEEEAAAQRiFCDoFSMRCRBAwOoClA8BBBBAAAEEEEAAAQQQQACB6wUIel0vYv/31AABBBBAAAEEEEAAAQQQQAABBJwvQA1jECDoFQMQixFAAAEEEEAAAQQQQAABBOwgQBkRQACByAIEvSJ78A4BBBBAAAEEEEAAAWcIUAsEEEAAAQRcLkDQy+U7ANVHAAEEEEDALQLUEwEEEEAAAQQQQMBdAgS93NXe1BYBBBDwCfCKAAIIIIAAAggggAACCDhagKCXo5uXysVegJQIIIAAAggggAACCCCAAAIIIOAkgaiDXk6qIXVBAAEEEEAAAQQQQAABBBBAAIGoBZiLgIMFCHo5uHGpGgIIIIAAAggggAACCMRNgNQIIIAAAs4RIOjlnLakJggggAACCCCAQKAFyA8BBBBAAAEEELCtAEEv2zYdBUcAAQQQSHgBtogAAggggAACCCCAAAJ2ESDoZZeWopwIWFGAMiGAAAIIIIAAAggggAACCCBgUQGCXgFsGLJCAAEEEEAAAQQQQAABBBBAAAHnC1BDewgQ9LJHO1FKBBBAAAEEEEAAAQQQQMCqApQLAQQQsKQAQS9LNguFQgABBBBAAAEEELCvACVHAAEEEEAAASsIEPSyQitQBgQQQAABBJwsQN0QQAABBBBAAAEEEEgEAYJeiYDOJhFAwN0C1B4BBBBAAAEEEEAAAQQQQCD4AgS9gm/MFm4uwFIEEEAAAQQQQAABBBBAAAEEEHC+QILXkKBXgpOzQQQQSAiBM2dEDh7yMGBgyX3g0GGPXLh4OSEOBbaBAAIIIIAAApYVoGAIIBBsAYJewRYmfwQQSBSB48dEZn7hkYlTQhgwsNw+sHBJiJw/z0dwovxxYKMIIGBdAUqGAAIIIIBAgAX4xh1gULJDAAFrCOg1NCdOeOT4cYbjxzE4ftxaBidPeqxxoFAKSwtQOAQQQAABBBBAAIH4CRD0ip8fayOAAAIIJIwAW0EAAQQQQAABBBBAAAEE4iRA0CtOXCRGwCoClAMBBBBAAAEEEEAAAQQQQAABBG4m4Iyg181qyDIEEEAAAQQQQAABBBBAAAEEEHCGALVAIA4CBL3igEVSBBBAAAEEEEAAAQQQQMBKApQFAQQQQCB6AYJe0duwBAEEEEAAAQQQQMBeApQWAQQQQAABBBDwCxD08lMwgQACCCCAgNMEqA8CCCCAAAIIIIAAAu4VIOjl3ran5gi4T4AaI4AAAggggAACCCCAAAIIuEaAoJdrmvrGijIHAQQQQACBmwlc9i48elRk/0EGDKy5Dxzx7p+6n3p3Vf4jgAACCCCAwE0E3LqIoJdbW556I4AAAgggEIOAx7t823aPfDYzhAEDS+4D2/8OEd1Pvbsq/xFAAIG4CJAWAQRcIhDiknpSTQQQQAABBBC4BYHz5z1y6FAIAwaW3AfOn7+FnZpVohBgFgIIIIAAAs4UIOjlzHalVggggAACCCBwqwKshwACCCCAAAIIIOAIAYJejmhGKoEAAggET4CcEUAAAQQQQAABBBBAAAE7ChD0smOrUebEFGDbCCCAAAIIIIAAAggggAACCCBgA4F4Br1sUEOKiAACCCCAAAIIIIAAAggggAAC8RRgdQTsJ0DQy35tRokRQAABBBBAAAEE7CRwyU6FpayxFnBQwsty2UG1oSoIIIDANQGCXtcsmEIAAQQQQAABBBC4RQFWi17g5GmRz2aGyLCRoQwYWG4fGDcxVPbt47Qw+iOYJQggYGcB/rrZufUoOwIIIICAVQUoFwIIIBBJ4NBhj+zbz4CB9faBAwc8cpkLvSIdr7xBAAHnCBD0ck5bUhMELCxA0RBAAAEEEEAAAQQQiJ/A8ZMeOXCQAQNr7gOHDsdv/2bt4AgQ9AqO681zZSkCCCCAAAIIIIAAAggggECcBA4fEpk+w8OAgSX3gZ9+Dom6d7w47eUkDrQAQa9Ai5IfAggggAACCCCAAAIIIIBAlALxmRkeLnLocAgDBpbcB06d8sRn92bdIAkQ9AoSLNkigAACCCCAAAIIIBCDAIsRQAABBBBAIIgCBL2CiEvWCCCAAAIIIBAXAdIigAACCCCAAAIIIBA4AYJegbMkJwQQQCCwAuSGAAIIIIAAAggggAACCCBwywIEvW6ZjhUTWoDtIYAAAggggAACCCCAAAIIIICA8wUCVUOCXoGSJB8EEEAAAQQQQAABBBBAAAEEAi9AjgggcIsCBL1uEY7VEEAAAQQQQAABBBBAIDEE2CYCCCCAAAKxEyDoFTsnUiGAAAIIIIAAAtYUoFQIIIAAAggggAACUQoQ9IqShZkIIIAAAnYVoNwIIIAAAggggAACCCCAgAoQ9FIFBgScK0DNEEAAAQQQQAABBBBAAAEEEHClgMuCXq5sYyqNAAIIIIAAAggggAACCCCAgMsEqC4CIgS92AsQQAABBBBAAAEEEEAAAacLUD8EEEDAhQIEvVzY6FQZAQQQQAABBBBwuwD1RwABBBBAAAHnCxD0cn4bU0MEnaCRzgAAEABJREFUEEAAAQRiEmA5AggggAACCCCAAAKOEyDo5bgmpUIIIBB/AXJAAAEEEEAAAQQQQAABBBCwuwBBL7u3YEKUn20ggAACCCCAAAIIIIAAAggggIDzBRxWQ4JeDmtQqoMAAggggAACCCCAAAIIIBAYAXJBAAF7CxD0snf7UXoEEEAAAQQQQAABBBJKgO0ggAACCCBgKwGCXrZqLgqLAAIIIIAAAtYRoCQIIIAAAggggAACVhYg6GXl1qFsCCCAgJ0EKCsCCCCAAAIIIIAAAgggYCEBgl4WagyK4iwBaoMAAggggAACCCCAAAIIIIAAAoknkFBBr8SrIVtGAAEEEEAAAQQQQAABBBBAAIGEEmA7CFhGgKCXZZqCgiCAAAIIIIAAAggggIDzBKgRAggggEBiCRD0iqX8yVNn5Ojxk7FMTTIEEEAAAQQQQACBKAWYiQACCCCAAAIIJJAAQa8YoM+cPSdNOg6SJ8s2lGfLNZGqDbvLoSPHY1iLxQgggAACCMROgFQIIIAAAggggAACCCAQHAGCXjG4Tp29TLb+vVu++Xyg/DR/mISGhMigMV/EsBaLEUDgFgVYDQEEEEAAAQQQQAABBBBAAIGACBD0ioFx8Tc/S8Wyz0nWzBkkbZpUUrPiizJr4Xdy+fLlGNYMxGLyQAABBBBAAAEEEEAAAQQQQAAB5wtQw2AIEPSKQXXn7v2SO2c2f6o7cmQ10ydOnTGvjBBAAAEEEEAAAQQQQAABBAIsQHYIIIBAAAQIet0EUa/m0j69UiRP5k+VPFlSM33mzDnzmuO2lBLIIXP65JIly2W5Mw8DBtbcB1Kl8AR0nw/k8RMxr1SpkkjuO6xpyL5Nu+TIcVkypElm+WMpW8YUki6tR9hn2Wetug+k9e6fup9G/PtvxekMqb3Hu/e4j48j63IcBmsf0O9LqVMksfxnkh7b+j00WA7kyzEW330gS+bLksV7Pq/7aiAHE3hgdMsCBL1uQufxeCRVyhRy/sJFfyrfdKpUKfzzAjmRLEmI1KqUXDq1TMaAgSX3gUIPJw/kLh+0vO7Pl1RaNeJY4m+JNf+WNqqdTLLdZv2P4NAQj5R+3pKGlvz7yPGW8PtK6WLJRPfToH2YBCjjbFlCpdFbfCZxjCT8MRIb81YNk0mBu5MEaG8PbjaFHuE4ik2bkiZxjrWaFZOLns8H9ygg97gKWP8bd1xrFOD0eXJlk1179vtz/fe/A2Y6XZpU5pURAgggkPgClAABBBBAAAEEEEAAAQQQQOB6AYJe14tc975UsUIyc94KOXDomJw6fVYmff6VVChTVDwez3UpeWsZAQqCAAIIIIAAAggggAACCCCAAALOF4ihhgS9YgCqVv4FuStPDnm+YnMp/HIDuXgxTJrUqRDDWixGAAEEEEAAAQQQQAABBBBAIGEF2BoCCEQWIOgV2eOGd6lTpZDhvVvIj/OGyrezBslnI7tI1swZbkjHDAQQQAABBBBAAAEEELCUAIVBAAEEEHC5AEGvWO4A6dOmlsyZ0scyNckQQAABBBBAAAGrCVAeBBBAAAEEEEDAXQIEvdzV3tQWAQQQQMAnwCsCCCCAAAIIIIAAAgg4WoCgl6Obl8qpQFh4uLzTqp9s3LJD3zJEI8BsBGIS+G/fIanRuKecOHUmpqQsRwCBmwj0HTpNZi387iYpWIQAAjEJ8JkUkxDLEYidwJIVa6Rz33GxS0wqBGwoQNAr6kZjroMEZi34Tlat3SSjJs+TPt4TDYJfDmpcqpKgAv1HzJB1G7dJn0+myrBP58q+g0cSdPtsDAEnCGz9e7d8OnOJN+i1Urr0Hy/LVv4qly9fdkLVqAMCCSrAZ1KCcrMxhwqcPXdB+gydKj+s2SAdPhwt0+cuF53n0OrerFosc7AAQS8HNy5VEzl+8rT0G/6ZNK5TXt54tbicO39BuvafIAcOHTPLMEIAgdgJ/PL7Vlmy4mf5uFtjKfb0o/Lrhq0y+YuvZNee/XLhwsXYZUIqBBCQ3kOmyItFH5cmb1eQnLdnlna9RsnuvYe8w0F0EEAglgJ8JsUS6paTsaJbBCZ6f4RJmiSJfNihnvzv4Xtl/PRFsvrXP+SvHXv4QcYtO4EL6hnigjpSRRcLjJj4peTJlU3qVX9Fnin0oHRp+aZ8Oqi9DB77hTz9SiPp/vFEgl8u3j+oeuwEwsMvSY+BE6V25Zek5HOPmxP2UX1bmeOqbqv+8ky5JubKlbDw8NhlSCoEXCqwbOWvsnrdn9KhaQ0pXLCA1Kvxiqz4YqCsXb9ZSlVtLfXbfiTb/9njUh0LV5uiWUognM8kS7UHhbGvgF6xr+dE7ZpUM59Jr79cVGaP6y535Mwq5d7qKOVqd5RvV623bwUpOQJXBQh6XYXgxXkCeuKgv150bFZDQkOv7eqpU6WQHm3flkVT+sjps+ekfJ1OkQJfa37bLC27DjVXhTlPhRohEHeBuUu+F70SpV7NV/wrh4R4JF3aVLJ4al8Z1e89Wfj1T+a4iXib1pipC8xtxf6VmHCEAJW4NYHzFy5Kz8GTpHndipI1cwZ/JvqZVL50Eflx3lDzK/ur3pOM9X9s9y8/fuK01GnRx1xV6Z/JBAIuFuAzycWNT9UDKjBw9Ofy1OMPyPNPF/TnmyplCsmXJ4f89tUYqV+rnHTqM0amzPrKv1wnWnYdRjBMIRhsI3AtEmCbIlNQBGInMHzil/JyiSel4IP5/Sv8vG6zNO08WHoOmiT//ndQerZ9RzJlSCfLv//VnyZThrSSN3d2SZE8mZm3dv0W88oIgSgEHD/rYli4fDxqprRtVFXSp03tr++MeSukUYeBMnT8HNEvSKP6tzJ9E+3ac8CfJn/eXJLltisn93/v2iuHj57wL2MCAbcJzF3yg6lyzYolzauO9h44Inp7Y5vuI+Q776/pNV4vKW9VKS3T5izTxf6hQP485rNKZ/CZpAoMbhUI1GfS8ZOnZcv2f93KSL0RMFcVz1v6o7RrVM2vod1VfDRihjTpOMj07VXo0Xule5u3Re+c8SXSKy0fLnCXZMqYzszSH2l0PfOGEQIWFSDoZdGGsWexrFXq91u+Ke2aVPcXSn8tf6tFb8mT83bJf9cdMnjMF1Kx7vvy57adkj3bbSad3noybvoiaVKngnm//Id18mazD+XQkePmPSME3CaQNEmoTBnaWV4r/ay/6j+s2SjdPpogzz7xkCRPnlQatBsgjdoPNMtvu/olSL80HTx8TPQKFv2CpCf1n85YbNIwQsCNAhXKFDG31/t+UFGDd1v3F/1sKlL4Yfnmx3VSssp78tW3ayV3jqy6WDQopsFlvcoyTeqUwmeSYWHkYoFAfCYp30jvD6Mde4/RSQYEXCmQ786cMn/ih3J33pz++utDipZ+u0ZKFXtC/tt/WMrUaCefjJ8teXLdbtKEhYeb73z/e/geeei+vLL/4FGp1rC7uW3fJGDkQgF7VJmglz3aiVLegkC6NKlEr9ryrbrrvytXoDR953Wp/Eox+WxkF8mVPYs8eG9eeeLRAuZ2Rr31RK/y0nX0VhR9Sp3eipI5U3qdxYCAKwVy58wqSUJD/XXftmO3uRy+6mslpG71srJgUh/zi/nbVcuInphr56fjpi+UfHfmMOt8ufQH2bl7v7xd7WXznhECbhTQY+iOq8Esrb9eabJ9539Sr0ZZeaXk0zKgayOpX+tV2b33oFTyfkZpmo9HzpBUKZObqyz5TBL+IWAE4vuZpMedPkG1Q9NrP4yajBkhEF8Bm63vO+fxFVuvfqxY9jkp++JT5gr/iYPbm4sD3r3avcWcRd/Luo1/SZ47rgTBPh4903wf1B9ufHnwioAVBUKsWCjKhEAwBPLlyW7uUX+v21Dz5LlVazeZX807NKsh2j+RPolOt1vj9Rf1xdy/fjEsTCLeimIWMELA5QJFnnhI9PjRviD0Sslpc742IhoA04m+w6aZL0x6a/HJU2ek1+Ap0rphFXPirssZEEBAzPFQoUxR6dJvvLnKS4PJY6ctlFb13zC3Ba/buE0WLPvJnHiol/apwmeSSjDYRSChyhmXzyQtUz/vZ1Tp4oXlsYfu0bcMCCBwVaCM97jQ73ZfLPjO3P44esp8KVL4Ie/wsOgPNX2GTpN2jauJXligtzXOW/qj/zPqaha8IGBJAYJelmwWChUMAe13aHT/1pI7ZzYZPWWBvNOqn/l1/ZH788mBQ8dMv0Udm9Y0fXnpbVl6e1a7xtXNey0P/RGpAgMCIvnuzCnTR3Qxt/32HDRZ9FjRX8zTpkklK378TfT2xxb1Khmq0d4vTLmyZxa9tUtnXAwLN1+cdJoBARcJRFnVTs1rmluHZ3lPMKo17CFJkySRahVekPDwS6LHVq1KpczxFtVnkp6AhIWHR5kvMxFwk0BcPpP0SXQrV2+Q996t7Cfi+52fggmXC+hVx/3fb2C+xzXpNFiWrFgjba72+eX7PlfupWfk0qXL0sv7/a+69/NK+29VNr7fqQKDVQUIelm1ZShXUASyZclofkX/uFsj70l4UWlR98qJ+aAxn5tH9ZYo8pjZ7uCxs6TQo/fJi0X/Z96fOXteXnuro+ivH2YGIwRcLqB9OehTUHu1ryt6KfyrJZ8R7cj0wyFTTJ94t2fJZG5p1CtX2jep4b89cua8FfJC5fdk38EjLhek+giIJE+WVOpUKSNDejaTRrVfkw5Na5h5X169Jbh+zVcN0/WfSTqzS7/xUrNJL7l8+bK+ZUDA1QKx+Uy6eDFMeg2eLI3eKi++vlz1auWi5ZvyJDpX7z1UPqKAXgU5oGtD6dispmgXL3flzi47du2V8dMXmXl6q/6Cr1eJPqCo4Zuv+Vfl+52fggkLChD0smCjUKTgC2hHwt3b1JFs3iCY/tGes/h7adu4mtnwxi07ZNbC78zlux6Px8zTX9TvvCO7FH+moHkf0BGZIWBjgdw5s0q3Vm9JaGiIzPtqlVwMC5M3K79kaqS3OZYqVkieKHifea8jPaT0UnkNiul7BgQQuCJQ+42X5LmnHhG9emvAyBneH2gqS/p0qSWqz6SLYeFmmR5fHs+Vz6kruTBGwN0CN/tMmjZnmZw5e07eeqO0H+n0mXOmb9fHHsrvn8cEAgiIPFPoQfF1WzFi0pemc/vHH7lX9JjR73ct360kGdKn8VPpRxHf7/wcTFhMwHJBL4v5UBwXCOgvGPr0knvz3WF+Me89ZKpUfvV5ue/u3P7aZ8+aSSYN6SAP35/PP48JBBCILKCXvE8Y2E5Spkgmeouj3urou83Rl1I7vx/QtZHvLa8IIHCdgP6Krk9Mfb3sc9F+JiVNEmqCzbWvBpivy4K3CCDgFYj4maS3MA4ZN1v0ymP9jPIuNv/1JF4fbKS355sZUYz0h8+jx09GsYRZCLhDQK/60m4stLZjpy0QPV58D1zReTrE9vtdVE/E1/UZEAimAEGvYOqSt20EfE8v+effffLf/kPS+K3y/rJz64ifggkEbiqgJ+u5czwZ8joAABAASURBVGYzaVb8uM48ic73tDqOI8PCCIFYCeTOeeWJqXwmxYqLRAhEKRDxM+nndX+aHzNLF3/CpI3tZ5JeefnJuFnStsdIs14CjNgEApYT0I7rM2dKb65E/vq7X6Rz81r+bitieyxppfQ2yecqNDNPhNT3DAgklABBr4SSZju2ENDg19Lp/eW2jOlMeX/5fatUqf+B/LfvkHl/s5F26niz5SxDwE0C+qtggzfL+avcsP1A+XTmEv97JhBAIGaB6z+T9IqTcrU7yk+//BHzyo5IQSUQCIxA6eKFZfzAtuLxXLkdWDvlbtN9hDmJj24LZ86el5JVWsnU2cu8P+Jc+zyLLj3zEXC6gAaSZ43rLk89/oC/qrH5fvfzus3SoN3HZp1JQzqYALR5wwiBBBIg6JVA0GzGPgL6B91X2p9+2SQHjxyTTFeDYL75EV/1kvd2vUbJQ8XfkuKVWsiML7+JuJhpBFwr4DuWtNP6jZv/llQpk9/U4sixk9Kk4yApVLq+tOw6TH7dsO2m6V23kAq7UsB3HGnlf/9ju2zf+Z/oL+76Prph4bLVUuS1JlK+TicZ9ulcOXvuQnRJmY+AawR8x5JemaJPcNSK++bp9PWDfmZly5LJ+9mVQrr2Hy8//Uqw+Xoj3rtPIOIxE9vvd488kE8yZUgrZWu1N08k9niuBJ/dp0eNE0uAoFdiybNdWwjoE37mjO8p2vF9VAXWzoSbdhoietm89gmhHXpPmLFYug34VP7970BUqzAvQAJkYx8B7bR+ybR+Ur50kWgLvWvPfilXu4OcPX9Bpg3rLE/+736p2aSnaL9g0a7EAgRcJlCk8MOyav4wuTtvzmhr/vGomdK6+3DTd1HXVm/Jbxu3Sd1W/eTChYvRrsMCBNwk4PF4RPuf7PJe7ZtWe9nKX0UDzdrva+M6FWT59+tump6FCLhNIDbf79REH3a04c+/Tcf46dOmlv0Hj8q8pT+KXk2pyxkQCLYAQa9gCydM/mwliAJ6H3t02S///hf5dcNW70n6++bpP3pC8mGHeuZqr2079kS3GvMRcJ1AqpQp/P0/RFX5CTOWSK4cWWVor+bmhL7yK8Wkt/dY2rRlh0mutw/rr/PmDSMEXCxws8+kvQeOyJipC2RIz2ZSpkRheeT+fOaYungxTE6fPWfUwsMvmVdGCLhZQE/CU6dKES3BOe8PMD0HT5LmdStKtiwZpeRzj0ubRlXM97t6rftLn6HTZMv2f6NdnwUIuEUgpu936vD5/G9F74zxPQ3yz207Rbu8eK5CM5m9aKUmiTScOn020nveRCnAzDgIEPSKAxZJEbhe4Me1m6RUsULmC5Fv2bQ5y8y97sWfKWhmfbtqvXyx4DvZtYcrvwwIIwSiEPjq2zVSpnhhSZ4sqX/pyy88JbWrlJZPxs02tw8XLd9UBoycwRUrfiEmEIgsoLfk65yiTz6sL2ZImjSJTBjUXvbuPyxvvNtNHi5RR+q06CPrNnL7sAFihEAUAvrdTWfXrFhSX8zQc9BkGTJulmj/YPr0ugpvd5b1f2w3yxgltgDbt6rAseOn5KMRM6R1wyriCzQXe/pR+Xx0NxnZ9z3zvU6v+vKVX6/8L/xyAzq794HwGhCBkIDkQiYIuFRAf93QJ5Gcv3rbiH750T/cbRtVFf2VsFOfsd5fA6eaXwOrN+pu/rC7lIpqI3BTAT2WQkNDI6UJCfHIiE+/lM++XC6TP+konw7uICtX/y69Bk+JlI43CCBwRUD7INJ+Uy5cCLsy4+r46LET8maz3vLYw/fIN58PlBeKPi41Gvf0/hiz/2oKXhwlQGXiLfDbpr+k5HOF/N1brF2/RbTP1jEftTG36jd8s5xUr/CCfDpjSby3RQYIOFlA+5W8K3d2KfvC0+YhLNrfpPbd2rnvOEmZIplUfvV5893OZ5A+bRr5pFczueeuO8ysKbO+pssYI8EoPgIEveKjx7quF9AvPPo0rVpNesmIiV9KZ2+Qq3qFFyV/3lzS/eOJ5pLd9959Qzo0rS5Th3WWaXOWy8art2u5Hg8ABCII1K/1qvQcNEn013XtdFtvx9JbtcZNXyj9OjeQgg/mF/3S1LlFLZk5f4Vwq2MEPCZvKuCmhc899ajpdFt/cNHjR48lrf/oqQvkgXvvlNYNqkjWzBmkWvkSUrhgAfooUhwGBKIQeOn5J8xV+mOnLZQzZ8/JwuWrzTFzb74rJ+K6StIkSeTU6TM6adLoVcm+Y87MZISACwXCwsPNsaNV13OeKbO+kg7NapjvbU06DZZKrxSTOeN7mM+kRh0GmvOn/3l/kNH0evHA0682ksceukdCQ0NE+wHrNXiy9/g6r4vl5Kkrx5t5wwiBOAgQ9IoDFkkR8AmcOn1WtE8HvTplwaQ+UrdGWXOJu55k6K9/J7x/lOcs/l7aNKoqwyfONU+k05P0tGlSysHDx0w2m//aJYePnjDTjBBIIAHLbkY7uR/QtaG5MvLxl+qJHh96q5YeY4Uevc9f7t17D5ppj8cj+sXqnVb9CCQbEUYIiLkqZfqI982v5y9Ubiktuw41LF9/t1YqlCkievWkmeEdbdux25xUeCdlyYo1or+66zQDAgiI6Q9v/MC2cvrMWe9xldx70n1O7r/3Tj+NBre+XvmL+D6fxk1bZL7v9Rg4USbOXCLHT5z2p2UCATcJ6O2MoybPM08Pfqt5H9F+8bR/SQ0e6/BQgbsk5+2ZpUq54t4fYF4wXcSUe+lZ0b5bew2abK6g1M7uzfshU7xpSogGm7UfsCfLNpQjx066iZO6BkiAoFeAIMnGjgK3XubQ0FBJkSKZedqcnkyEe3/V0Evfe7StIxnSp/F+STpnMq9U9jn5bEQXKf7sY1K9UQ/ztJJH7r/b9EnUrPMQ6dh7tLmyRYNkZgVGCLhYoFSxJ2Th5D6yZtFI0S9F+it6pgxp5dLly0ZFTzJmfLlCXvN+OdIZsxZ8J6vWbpKm3l8OazbpJXoCovMZEHCzQMb0aaVnu3fk92XjZOAHjQ2F9uulx5N54x0t/2GdOXF47qlHRI+rPkOnyqyF38mrb3aQbgM+FT6TvEj8d73Ag/fmlaZvv26CxUULPyLjpy8yP8joMdOs82ATCKvo/Z6nP8boD5wdm9UU7U9vwdc/SdWGH8j5q11fKKQ+YEIDATrNgICTBTJnSi9zJ/SUlu9WNg9S8XVer/3g6dX6jTsMkn7Dpstnc5eLPm24Y9Oa3sByMlnw9Sr5e9deafjma4Zn4bKf5K8de6Rh7dfMVWK9Bk/x/nhTVPR7oUnACIE4CFwLesVhJZIi4HYBvQd90AdNpFvrOvLD2o0yesoC6dqqtpTynrSL95/+wc+VPYvMXfKD+SVdr2LRk3l9Mp3+sZ7m/UOvv3bky5PTPGmrRKWW3K/udeM/Aiqg/RLpq3Z0qsdJz4GTzJWUepKhT8tqXKeC6G3F/YZ/Ju+3qCUzRnYVfdpj+16jTTpdV4eVq3/3HpvzdZIBAdcJhIaG+B8MUbvyS/J+v/Gy4sffRPudbNJxkOiJSO6c2USvStGA2LezBkm/9xvIgUNHpX2vUeYkQ9H013a9Cmzn7v36lgEBVwqUKVHY3B6sP7Do1cj7DhyR0f1biwaZ9QEr+nlVrXwJ8z1wYPcmosfL+k3b/Vba7UWW2zL43zOBgCUEglSIFMmTSZHCD8sTBa9dqa+b0qu7tLuX27NmkpGT55lbhksUecxcLNB32DRvoKySuXhAv/vpjzF6lZgeY0u/XSu/btgqzd55XbNhQCDOAgS94kzGCghcEfB4PFL8mYLSu0M98wSSl0s8eWWBd5w0Saj09548DJswR1p9MNzcOnLg8DHRL0WHjhw3T6Pr0LSGeZLJpCEd5JWST5tfO7yr8h8BBK4KpEmdUmaO7mYCx3oSrk92/GxkF8nu/bKkfejlyZVNKpYtJhpk1mNI+yza/s8es/bsRSulhzdYFhYebt4zQsDNArUqlZLOLWqaAJdekdKucTVpXKe87Dt4RAaP/ULaNalmjiO9hUQDZL//sV08Ho/8sfUfGTTmc1n8zc+SIV0aNxNSdxcIxFTF2m+8JD/NHybLZ34sc8b3lPvuzi0/r9tsvuO1aVjVv/q5c+fNdJrUKcyrPrlOu7bQH0DNDEYIuFhAb3WsWbGk6a+1Y/OaRmLstAWiV4Jpf186Q6+q1Pfayb1eWakBMJ2vd83ojzB6LqXvGRCIrQBBr9hKkQ6BOAro7Vmzx/UwndpP/uIr8f2B1o5O9cTipeef8Od4/vwFufOO2/3vmUAAgSsCt2fJ5D1Zr2VuexzSs5npzF4DW3p1SsdmNUxATFPu2nNA1vy2WR554G45c/a8ObnXW050fe1PT9MwIOBmgVdLPiPjPm5rjiU94UgSGioDR38uTz3+gDz/dEE/zZdLf5Sn/veAef/3zr3mamR9iETEW7XMQkYIuFBAr6DMliWjueVRf1T5cMhk06+X/gjj49DvfHq1/3135xG9PUsfyJLvzhy+xbwigIBXQDuvz5cnh+mf9evvfpHOzWuJfi7pdzd94mPHZjVFLyLQ73t6NfKP84bKsA+bi/btpR3ie7PgPwKxFiDoFWsqEiIQdwG9AuXdmq+IXs2lT8rSP9T65Dl9mqPH4zEZ6v3r2um9PnVLZ3z13Vop8loTKVW1tTkh0V84dD4DAghcERg+8UvRKyv1iY5X5oj3WJlprqTUL1Bnzp6TrX/vlneqvWyCX83eH+JLdouvrIaA8wQ0eKy3OrZrVM1fOX2AhPbtVatyKTPvr3/2iB5ThR8rIC/XbBfp9mGTgBECLhbQK4p37z0k+nCjTn3Gyi+/bxXtd2j63OXSo+3bJjCmt2yVffEp0c8rvVVYn2TXpvsI05/R0eMnXaxH1RG4IqCBrlnjupsfYHSO3i5c9MlH5JlCD0a6Glk7t893Z055793Kolcj+36I0e4udD0GBG4mQNDrZjosQyDAAvrFqEGtcnL/PXf6c+47dKrpA0Iv99Vbtj4Y8KkM6t5UtK+IzX/tlBZdLHbC7i85EwgkjsD7Ld+Udk2q+zd+/e0lejWlnmBoXxB6e2TzupXkYli46NPq9Bj0r8gEAi4W0JOH+RM/lLvz5jQKekVk70+mit6CpR1469WTo6fMl/beY007JP5iTDdzO5cvqKzHlFmREQIuFNAT7b5Dp3uPj2oy9qM2og+LaO79gWXD5r/NFZWFHr1PtA+9H9ZslBb1KpnAWMP2A0S/5z3+yL2yZv0W8+CIHbv2ulCPKiMQWUADXzpHr55Mlya1tGlYRd96f9C88WrkTVv/MZ3Za5cXehy+UPk9c5uxWYERAtEIEPSKBia2s0mHQFwE9EuQ9qPiW0c72l65eoO0ql/Z/JoxZNws88VJf2nXTiA/aP226HL9o67r+F51mgEBtwqkS5PKfOHR+oeHXxK9veTtqmVEby/RW0lmzl8hvqsp9YuU9q9SrnYH0ScGPV+xheiv7AS/VI/B7QJ5c2eXeeKUAAAQAElEQVT3Eyz7/lfRW4R9HQVrX1/Fnyno//VdO73XwLFehdyq2zB5+pVG5gRer17xZ8IEAi4R8Hg8Uq9GWSlX6llJny61dPH+GLNyzhCZNqyz6Zz7woWL3s+mKdKkTgXR2+wnfbHUXIE8fUQX0X6KtN9XDTBPnf21S8SoJgIxC+h3Nn0wmH42+a5Gvj9/Hv+Keh6kx4z2U6kzR02aJ/pDzHvdhpog8qTPl+rsoA9swH4CIfYrMiVGwDkCv236Sxq9VV5y3J5Z1v62xdxGsnhKX8l/Vy55/Z3OpgNhrS2/ZqgCAwI3Cmj/Kq0aVBF9Ep0u/X7NBilVrJD/asrjJ07Lu20+MsfYgsm95fu5Q0z/eh+NnKHJGRBA4KpAoUfuM1es6BPmwsLDZdHy1dLk7WtPyvp1w1Z5491uUrX8C/Llp71k7vgeordqaSf3V7PgBQHXCOiPL/q5o59BUVV63ler5GJYmLxZ+SWzeM6i782DV3J6v++ZGd5R/VrlvJ9dr3inbP2fwiMQFAG9GvmLMR/I1yt/kebvfyJ623DNxj0lR7bMog9c0e5hJsxYLGMHtJEl0/qJHk/atUVQCkOmthcg6GX7JqQCdhbQXwAb1HrVVEGfUnLqzFkJCQ2RNyuVEu0EXzturFmxpOhVX/yaYZgYIXCDwDOFHjRP/dEFp06dlWPHT4mvL7z5X/8oqVKmkHPnLkj9tgPk3z0HvCftJeS3jds0uRn0l0MzwQgBFwvo1SpP/u9+I6DHkU7sP3hEX8ygT9PSvik/n79Ceg6aJClTJpfSxZ+UjZv/Nsv1yhbfcWdmuHJEpRG4IlDupWdkwsB2kjJFMjNDr0a5M1fkBxal8h5DWTNnMMsZIYDAjQL33Z1bpg1/X/LnzSkrV/9ubr8f81Frc1dMv2HTvT9yPiFPPna/+Z5XpkRh6dbqLZnvDTi37DpMhoybJVu2/3tjpsxxpQBBL1c2O5W2koDHc6VD+2eeeFC0k8Yu/cbLkWMnRTvB10t82zaqKtH9mrHOe+Ku/UWEh1+yUpUoCwKJJqAPjkiXNrWUrPKe6UNl81//ij4pddKQDvJ6maJSr01/6T/8M7kn3x2mjL9u2GZu09J+VcLCw808RgESIBvbCmRIn0bG9G8teuIw4bPFph6btv4jb1UpLQsm9fF+PmUwt5IsWv6T6K/xmuDTmUukdrMP5fDRE/qWAQFXC+htWnpLsA/htZeKSP8R02M8Pg4cOiad+46TVh8MN1e4aF97vjx4RcCNAnpupHfFDO3V3HwG6YUAGgD77qf1pnuYiCba3UWfoVPl+acflbSpU0mFtzubYFnENEy7U4CglzvbnVpbUEC/IE0Y1F4uXgwzT2/U20j0FwqPxyNR/ZrRvU0dWbt+i/fL0Vh55c325kkmFqwWRboqwEvCCGhnwgM/aCzzJ/aWNKlTmn6+1v+x3Wy8dPHC5oS93EvPSq2KpSTcGyzuNXiy6R/s7ff6ylNlG5lbukziqyNNw0nHVQxeXCXw1OMPyA9zh8gb5Yqbet99Z07ZsHmHuXJFg8v6kIiSzxWS0sWfED1RHzj6czl5+oy8VK2NaL9f2h+LWfHqKIyg8lUJXtwo0KROeSnx7GNStHxTqdqwu+gPLlE5fDTyM9EfYZ72Hn/6FLu2PUYKx05UUsxzs8A3P/4mGgjT7mF8Dvr01Kmzl0nG9GklV44sUvuNl6Rzi1oyctI8XxJeXSxA0MvFjZ+IVWfT0Qjorxl9O9eXFV8MlOZ1K8q9+e4wv1BE9WuGZqH9SXw1/SN5p9rL5kuUflHS+TroryD65C2dZkDAbQJ6q5bWuYr3hP3I0RPS7P0holdGemPIorcUP3DvnfLl0h9k5+79Mn9Sb1k+82MZ0LWR+XVdn1qn6+owdfbX8l634TrJgIDrBJIlS2qCXFpxfQKd3uKowS39rMnkPbHQEwq9fXjg6Jmm8+6Fk/vIqvlD5ZUXn5YmnQaLr5N7vbXr5RrtZNOWfzQrBgRcJ6DHUsdmNeWbzwdK0zoVpOCDd0dpcObMOXPCXqFMUdFO7zf/tUvWbdgWZVpmIuBWgfe9wSx9kETE+i9Z8bOUK/WM6BOHu/Qbbx5a9N++Q+ZiAk138tQZ8x3vq+/WEkhWkIQfEnWLIYm6dTaOAAJRCmhHwk95f+XThVH9mjF22kJ5oFhtKVOjrQydMFuefeJhefj+fLL29y26isxetFJ6DJzEH3WjwcjNAnq11/QR78tduXNIU+9JuB4z5y9cFP3y02vwFGnd4A1zW7EaPXz/XfpiTvK1f6IOH46WwWNnyQtF/mfmM0LAzQIF8ueRWWM/ME8aLlurvXTqM9Zw/P7Hdpm75Adp16S6ea9XLT9U4MqxFBLikYXLVpsnp2qH377bik1CRgi4UCBr5gzmiagejydS7c+dvyB6VXGLdyvLspW/St+h0yRt6pQyvE9L0Sd/62eW3vLICXskNhu/oejxFdDPmoh56PGTPl0ac3zNGtddCj6UX6bNWS5lX3zKJNM+J++/J498PGqmVG/YQ/buP2zm+0baXczqdX/63vLqMAGCXg5rUKrjPIHrf83Ye+CI6CXvn/RqJkN6NDW/pL9cs525vfHRB+6WM2fPy8SZS2T33oNye5ZM5kuU81SoEQKxF9ArUfTKyZVzhoje9qj9QYyZukCyZ80kFV4u6s9Ir+rKlyeHaNBZHyix49993uPpnAkmHz1+0p+OCQTcKnBHjqzSu0M9Wb9srHRv+7b5/Ok5aLJUK19C7rkrl59l/GeLpOiTj5j3adOkFD2R0JP6H9dsNPMYIRBJgDcybMIcWbT8Z+8PNNllytBOMnP+tzJn8feS8/bMRiemE3aTiBECLhZ46fnC8rn3uNErijUgVvW1EvLV9P5SsWwxOXTkuBw+elxqVSolX37aS/RHzvbeHzZ9XHp8de47Vv7cutM3i1eHCRD0cliDUh1nCugfb1/NDh0+ZiYfLpDPdCCsJ/N6mbz+kpE/by5zkq6P7NVbHjX4pbd1mRUYIYCA6NOyLoaFyzc/rJOOzWuK79jad/CIfDJutrR4t5JR+mvHHhNIHuH9lT1JaIi813WYmc8IgWAL2CH/JKGhkjxZUvln9z45eOSYNKz9mvj+6VUqa37bLHWqlDGz9MrjYk8/ap6qpR0M/7xus5nPCAEErgnoLfetuw+XJSvWyF15skuRwg/JH1uv3A4c0wm7Xpncrtco0x9sn6HT5MSpM9cyZgoBlwj87+F7RC8UqPxuV6nXur8JJCdPnkw2bv5bnqvQTBp1GGj6bh0wYob3GMshEfud/HTGYkmaJIn5AcclXK6rJkEv1zU5Fba7wIP35TWP6K3WsLuMmjzPPJJXL8ltXvfKybqeuBd8ML80r1tRtKPh5lfn273elN+VAkGpdNIkoaKXvhcuWMCfv17u/kyhB+X5pwuaqyN7fzJVypcu4j3xeFg6NK0hI/u+50/LBAIIXBG4K3d2Wer9JV07DtY5eutwz8GTzOeP3salAS49iW/TsKo5luZO6CVPFLxPkzIggEAEgVLFnpCxA9rIBO/J9yMl3paVqzeIPvFRA8g3O2HX74F1WvaV3f8dlE96NZf9B4/K9951I2TNJAKuEXil5NPyw9xPzHnSbRnTme4qvlj4nVR+9XnTd+uiKX0kZcrkpgsYfaiRwugdNEPGzTJ9gWnfezqPwXkCIc6rEjVCwKkCV+rl8Xikb+d3zR9nj8cjIyZ+KU3qVDC3aunVKTPnr/CepFcXj8djrmLRkxLtA6LIa03ME7W0E2K9jPdKbowRcKeAXqniq7l2XD//q1WiJ+Y67+ffNoueaDR753V9awZ9KqSZYIQAApEEIh5LS1esMctqVixpXvVEQq/4ypMrm3mvAWczwQgBBG4QePKx+2XasM6it+LrA4306q+YTtgvXAgzD2lJ4v0xR69k/qhLA/NE1RsyZwYCLhHIkD6NvP5yUf+Thy+FXzK3NupTUDNnSi935rpdtNuLd6q+bET0QSz6o6dekawzNHC8a89+nWRwkIB9g14OagSqgkBcBfQkQ/8468mE9q/yZuWXTBbfr9ng/XWjkNx/z53mvY40KPbBgE9lUPemMrp/a9n8105p0WWILmJAAAGvQO6cWc0Tte7Om9P7TmStN+j1Rrnipm8vMyOK0cWLYTJv6Y8yY94KOXDoWBQpmIWA+wT0V/bpw7uI9pun/Uv+umGr1Hj9xZtCbNuxWyZ9vlT0iuVw78nJTROzEAEXCGTKkFZSp0phanqzE3a9rXHeVz9Kg1rl5MWij8vwT+eaHzw9Ho9ZlxECjhO4hQq1bVxN9KKA519vLvowiA8+nigt360kGhxbt3Gb6I+exZ4uKHpRQPk6naR4pRaitwlf39H9LWyaVSwkQNDLQo1BURCIq4A+DUtPMlKmSGZWPXXqrBw7fkp8V3JpP0X6S7tepTJr4XfmROSD1m+LXjZ//ORpsw4jBBAQ0VuxfA45bs8sP/y8Qdb/sd101O2bH/FVj6u+w6aJntS/XLOdCX5FXM40Am4VuHYsXRa9wmvGvG/k+ImoP2+0r6LX3uokP6/7U/oPny41mvQkiOzWHYd6RylwsxP2qbOXycWwMHm72stSvcIL0v/9huYEvmXXYabriy3b/40yT2Yi4CYBvc1x3qcfyriP25pgcpbb0kulV4qJ/siiD2LRq756Dpok+gCwTs1rybqlo2Vor+aSPdttbmJyfF1DHF9DKoiAiwTerfmKpEubWkpWeU9OnT4ra3/bIvny5JDFU/pK/rtyyevvdJZBYz43ItoJsZlghAACkQS0Ly+9elL79dInzkVaePXN73/+LRXKFDVPsps4uL30GzbdBMCuLuYFAdcL6InEqH6tZKv3xHvh8p+i9Nixa6+Z37N9XdMH5X1355YOvUebeXEckRwBRwpEd8KuAeN+3kBx20bVxPfD54dDJkufoVPl+acflbSpU0mFtzt7f+T83e9y+fJl2bl7v/89Ewi4RUAvEtCHfWlQa0z/1qb7l/lfrzLHw9effWS+z+mPNNoZPv16OXOvIOjlzHalVi4V0Cu6Bn7QWOZP7C1pUqeUtGlSyakzZyUkNETerFRKZo/rYZ5Oov2t6O0nLmWi2gjEKFCtfAnTt4r2kRJV4rerlpExUxeIPqmuQP48Mrh7E3ng3rzmiY+d+44zr1GtlzDz2AoC1hDIlT2LDOnZTKq+ViLKAj320D3y8P35pGXXoXLu3AVp17iavF2ljEmr/VPq7SbcYmI4GLlYIKoT9kFjvhA9fko+97iR+eX3raJXfulDJXLlyCK133hJOreoJSMnzTPLw8LDZem3a6Ri3S6itx6bmYwQcJmA9iupV/Nrtc+fv2D6QE6fLrU8+sDdog9e0fkxDRo89qX5b98h6dh7jJy/cNE3i1eLChD0smjDUCwE4iOgf8B1/WeeeFDSp00tXfqNlyPHTop24Ni1VW1p26iqLr5h0EfLdxvw6Q3zIjT03AAAEABJREFUbT2DwiMQQAE9RjZs3mGeRKf96TXtPNjcBvnU4w+IXj2pgeeM6dPI2+/144tQAN3JylkCegVlr8GTRU/mR/RuKafPnJNWHwwzx5AeS1rbLJkyyMHDx+SFN96TKbO+1lkMCLhawHfCfunSZQkNCTEn7B7Plf67lqz4WcqVesY85Ei/87XpPkL0hFz7n1S0Jh0HS6c+46RejbIS3Y85mo4BAbcI6BMd9cp+re+D9+U1V+trP3n6/vohPPySjJu+UAqVri9PlGkgfYZOM13JDBg1Uw4fPW4+u65fh/fWEiDoZa32CHpp2IC7BLTD+wmD2ot+6SnyWhN5491uon08eDxXviRF1Dh56oz0GjxF9KqViPOZRgCBawJ79h6SVt2Gmb4ftD+9si8+JdpfnqbQ20aSJ08mLd+tLF/P+Mh0nDp++iJd5B80rf4quPz7XyXir4X+BEwg4AKBEyfPyOxF38voKfNFf6Tp1e4d0b4m9SQ9LDxcNv+1yzyQpad3/pzxPUSv+Fq7fotfRj/TBoycIXoL8qYt//jnM4GAGwRCQjyiP2BG/L6mnyfp06URDRrPGtddCj6UX6bNWS76GaUmBfLn1hcZNXm+6FWUepyZGYwQQEDy3ZnDXHW85e/dUWqMmDhXPhoxwxx3o/q9Jzt2/Se1m30oi5avljaNqkW5jpVmUhYRgl7sBQg4XECv9Orbub6s+GKgNK9bUe7Nd0eUNdaTj1zZM0uFMkX8y6fNWSbfrlrvf88EAm4XaPBmOSn30rNSvk5nadDuY9Np8NOPPyhjpy2UMjXayjvv9TVP/pn/1Y9y/713yj+79xkyfTKdnqR/7P1V8K48OcyvhF8u/dEsY4SA2wS0s/vpI943T0B99c0O5vZG7X/ytkzppVnnIVKzSS95uWY7qdqwu+zafUD0s+nf/w4YpgXLfpImnQZ5f5XfJmlTp5TazXvLrj37zTJGCLhV4KXnC8vn878VDQLrD556S/FX0/tLxbLFRB9qpLc59vN+F/x0UDtZtXaTzPEGnX/69Q83clFnBG4Q0GNmyied5M5c2W5YtmffIRn26VwZ3L2pvFziSSn4YH4Z+EET2bhlh+k65q7c2W9YhxnWEyDoZb02oUQIBEUgy20ZzC+AUWW+a88Bc9LevkkN07mjptl74Ij0GDhJJxkQ+D97ZwFexdGF4e/iwUtxd3eX4u4uwV0SCBbcLRCcQEJwDxAoNLgToDjFobi7a4IF/j0H7i1Q7KdIkv14mNkd2515l2XvnDnnDAm8ISDmWA6G4GvDgpGoVrYgvDx6oWCeTBCB1rBerbBhwSh4unbEzn1/w3vJRpQqlFNb+m7bp+9YobxZdJct74l9UbxAdi1jRAJmJCBCLp/pgzCsdys0rVNO36VrxnfHd9t+LJnhgp3Lx6N1o8q6+coJY/X9t5wZIH5TRHtStMIqGcJnh4aVsHSmCxLGi2VGhBwzCdgIiAPu3u3ro0aLvmjeaTg8pv8B0TwWJ/ey2JIvR3oUypsZaVMmxsi+jjh07Az+3HXI1p4nJGB2AqJBKb6Q3+cg36RoUSOhcL4stqIVxuKLbNbSon4FzfPzfwL5Tj1/EaBpRoGPQIjA1yX2iAQCO4Hg17/VvruQK0sa5MyS2ja4URO8IT+SZEJ/4OhpXYkXf1+yimirxBMSMCkB+WFUNH9WZEqbTAnID6Ibt+/qecqk8RElUkTkyJxaBWKSefrcFRQxfjDdf/gIDl1HGeUR6FdFwDCYmoDFYlHtY1k9l81XXrx4oTxu3LoLmYDkNN6hFwEBcDSEX7JwI77AxIy4fvWSmLNorZpqxY4RTdswIgGzEyhfIi+2+oxDSWOxRXZ9FIHXjVv3VCO5k0Otd/BcNBY748WObsuT9+z+w8e2NE9IgAReE5Dvjvzms36fHjzyw+CxXujsWEt/y4nGV0n7Tur6Im95R3jOXALxu/e6NSDvlvWcx59H4L8LvX5e33lnEiCBb0RAJhFRo0S0XW3f4ZMQE5Iubxzet+4+GuIrIlH8WGpKIjsE2SrzhARMTkB2Qh0zwAnu0/5A5cY9DQGxh/r56tq6NiwWi+7wKJpfvdo3UPX4Uf1aQ7Qr5/tsUDMT+UFlcoQcPgkogWSJ4+kOjrUdBqBxe1c06zQc4m+yUc3SWi6TCfkOObesiQWT+qnWpJjg/758s75TWokRCZiYgPyWq1q2AGpWLKIUDv19BqJVmSJJfE1bIzG9jx8nhiZlw4imHYehSYeh+Jgjb63IiASsBEx0LF4gG27evg/nAeOxbstfcB3npSb34gR/76ET6i/ZvnIx1VD2mTZQF2RWbdxlI9S+9zjI7z1bBk9+CoEQP+WuvCkJkECgIiC+vnbvP4a6rQepw2BxtC0r6TIBEXOSJ0+fI0PqJGhYoxTmuPeEqMrLbluBahDsDAn8RAJZM6TA5sVuEMfb4uBUdgVKnTwh5P0Z5DYL8o6JHyPp4uYdB1C6TmccPn4OMxeuUV9gZy5clSIN4pBYBNGaYEQCJiNQr1oJ/OkzFg4NK0G+S2J2Lxorp89fwcwFq9GjbV2ImfGLFwHqyN7V3Us3aKnjOEDNjN/GJb5YxOn923k8J4FvTSAwXy9rxhT6Heo/aibkfZC+yjtx/eZdxIn5q75jVZr0QrSokTB1VBeECRNaqjCQAAm8ISDm879P7o/UyRNh4TJf/LHqT8h3SfyAibm9WMpI/qAxs2BnFxali+TG4WNncNb4Xdemxxhs2LoPeXOkf3M1Hn4WAQq9fhZ53pcEAhGB6NGiYMVsVzSuVVpXy2VFo2X9Crodb1jjB1A/50boNXQqxAxSTLcWTx2ACOHDaV1ZvRCnqE+ePgtEI2JXSODHE7ALF0b9pUwf3U2FXNKD2b+vlQNkIi8nIszq6jJRzYmdW9XE+CHt1RRlhOd8KVY1+DWbdqNasz7w83+qeYxIIAgR+CZd/SVKJGTPlAor57gaE4jXfvFkdV3MIMWJsNxkgDGJX7xyCzq2qInuTnXUJ9jcPzZATE2kXATOrbqMVMGypBlIwIwE5F2a6dYdkSOGR5jQoRSBOLaXk2XrtqNhuyFo1aASRvZ11DqSz0ACJPAugYTxYsKhQUX06dgInR3tbe5gjpw4h0bG3Gn5LFdEjxYVsjHLyg07IEoDYhIpAi+5kvgFE2GznDP8HAIUev0c7rwrCQQ6AvKfc5HfsqJPhwaYMLSj/vhp5jxMBVtliubCkO7N1WxLNFJEJX7pmm2f1FYJdANkh0jgBxGIEjmC+nl4+fIVVm7YaawI1oGYQMrtl6zZqr6+smZIiXL1uhqrhpsgguQr125B/rTp4YaerlPRvG45+vwSIAymJpAwXiw1EZb34++T59G+RQ3lIT5VZLVdJh/jZ/pAVtNFQzJSRDvcvH0P4ofyt4ptIBuy1KxQWNswIgGzEogV4xddiBG/eMLg8tXX3xv5PnlP6IvalYtKNgMJkMBnCMSJGU13bLRWS544Hg4dOwtZ9GxRr7ya3ZcomEMXa9Zu3qMalLLh0ZadBzHJa7m1GY8/gQCFXj8BOm9JAoGZgEzYxVTLYrEgbcpEEMGXTDaKFciu/3nvPXgCn9NW+eD4mEkCJiMgjrjnju+Nt3dpvHPvITKnS47WjStDyrbtOWwIuaYgf66MSidNioR6nDh7GRYs81XNL81gRAImJhA3dnTdGVUmHILBal5fvVxBzPfsA1mwqeM4EGKylSltciSOHxt+/k+kKrq5TDQWb67rOSMSMDuBHX8dRacB41GyUA6doKdLlfijSGQnOnFnkaN0S/Wx57N66zt13acthuxS/E4mEyRgIgLtm1eHmDiOnrRQzRmjRYmEXu3r49mzFxg5YQE6OdTSDY8mDnNGs9plIe/UybOX8Oixv4koBY6hhggc3TBfLzhiEggKBLq1qYOGNUuhXe9xyF6qOWLFiIZi+bPhc9oqsmuJmDwGhTGyjyTwPQmEDhXynctnSZ9ctb9klyzRmBzZ1xHTRnVF3aolICYnE2YtxbBeLTFjTFd1cr9r3zGdyL9zESZIwIQEQr8xzZKhi0m+vD8yCRf/XuJQWEz03V3a6eKM56wlSJMiETYtGoNMhpBZ3iMxzxdtMGnPQAJmJCDmwE06DlVzxhF9HFSj/2McxKG9bCaxdO02TB7RCQ1qlMKYyQuNibw35M+Fy9fhMcMHCePHkiQDCZiSgHxnFk3pr7/fytXvpouYAsLrj/VImjAOyhXLK0kNIuyq2LA7Wncfg8LV2qPzAE8Kv5TMj4ko9PoxnHkXEgiSBCwWC+wrFcXqucOwznukaqbIzkCf01ZZtm4bmjoPU98q1tX2IAmAnSaBb0ygbNE8yJklje7yONlrOWTVPWPapIgZPSpkRT1fjvQolDcz0qZMrD5WfLftw2SvZd+4F7wcCQRtAiJMHt67FTym/wHn/uOx2nc3bty+p+/O6XOXYXV4H94uLJoaq+vyTnXo64Gbt+8H7YGz9yTwHwiUL5EXK+cMVXNGi8XyySut/3Mv9h46gTnjeiJT2mQomCcTpo/uisPHzmq7YR7zUDR/VuTOmlbTXxGxCQkECwIJ4sZUFzAH1k/BgC5NdEzbdh9WpQHR+JeM+w8eo0XnERCt5eWzh+hmLbfu3MeICa+FyFKH4fsSoNDr+/Ll1Ukg2BAQsxKZaMiAPqWtImYn8mNI/BTJKoaoxcsERNoxkIDZCYhWyoDOjdHPuTFk1c97qS9ks4gbt+5h2drtqgr/NqOLV24iXpwYb2fxnARIwCCQIU1SLJ46ECmSxIdsGCETCCMb83w2oHSRXMiSPoUkNVy9fluPoiGmJ4EuYodI4PsTkN3mEsaL+UU3Eh9ERfJlQZxYv9rqJ4wXC+6D26sWsjjoDgh4CdH2Eq0vWyWekIBJCcj7Jb/nZPjx48bAsnXbceHyDUka59sQ3i4cnjx5hpZdRuKikW9fuSj2Hz6p5Yy+PwEKvb4/Y96BBIIdgU9pq0yaswyyW9CCSf10R0jZNUi2+X0fgqx6iOr8hcv0tfI+G6aDP4H8uTLAtUcLjOzroI66D/19BskSxdUJ/NujP3/pGuLFjq5ZLwICID5UmncaDjE90czgGHFMJPCFBESIJc6DZ43tjlxZ0mirnXv/RqnCOfXcGl0xhF6J4seCddVdBM7VmvWB7KhlrcMjCZDAPwTswoWFJcS/tcFChw6JwWPnoEqZAqhU6jeIQNnqT++f1jwjAXMT6OfcSH+7ecz4Q0EcO3VRv0vyrapqvDvNOw/H8PHzkTJZAi0/e+EqegyZjKHucyHm+Jr5JpLffm9OefgPBCj0+g/w2JQEzErgY9oqsqIhQi/xBRYq5GtfRtkypkTOLKk/iEps4aNFjfzBMma+JsDYHASyZkyBp8+eo/+ombj8ZidH8Y13/tJ1/eEkWizNnYdjyZptaNesGsKECW0OMBwlCfyfBKqXLwTRNl6+foet5RXjnRKhsmSIT8pKjXoiT7Z0yOwSRCsAABAASURBVJczg2QxkAAJvEegVqUiWL9lLyZ7LYf4oJTvk1RZsNQXd+8/RNfWtXWTFtFcTpwgDtZt+UuKGUiABAwCovHV3amumj0aSciii+wqLOeiibx8lisqGkLj+tVK6nsm/sDkd130X6OgYz93DPecL1UhLmLK1u2KI8fPaZrR1xOg0Ovr2bHljyfAOwYyAu9rqwz3nAdRh8+TPd0ne3r1xh04dh+N5vXKI2IEu0/WZSEJmIGAaEeKVmTkiOER5o3DbhF0ydhFCFa5cU+IPz3RoBR/X5LPQAIk8G8C9aqVQMeWNXWyYC29dPUmov8aFX2HT8eAUbMgDu87tqwBq8m+tR6PJEACrwmI2fDCSf0gvonylnfE3MXrce/+I1h3pIsQPpxWfPjID8dOXUD61Ek0zd3pFAMjEniHQK2KRXDn7gO07T0W+w6fhMUCtKpfQYVhTr3c4NioMvp0aIDGtcpAfufN99lovHMb1Om9KBpYNcLeuah5Et9kpBR6fROMvAgJkID80IkWJfK/fBJ9iMyoCd4QB8NRIkVQMy3/J88+VI15JGAqArFi/KJaXDGMybkM/OqN136IZPfUVg0q4XO7bUkbBhIgAaBEweyoXq6QDcXlazfhvWQjjp+5CJ9pA1Eob2ZbGU9IgAQ+TCBNikSYOqoL9q2ZhLrViqv/rvd3pJsydwXE52u6VIlx9MQ5cHe6D7P8drm8UlAkIAv88zx7I2nCuHDq6YYydbuodr/4zpPxNKpZWg4aYseIBu8JfRA/bnTs3Pc3njx9psJnLTQi0b6cOHupcca//w8BCr3+H1qsSwIk8FECsmLe17khxNHpRysZBbLCIWYnXRztjRQwY8FqNGw7GLeNFRDNYEQCJICbt+9h7NRFEEGY94S+n9xtSwTOsvOjbBohfvJ8Vm99h+D6LXuxe/+xd/KYIAGzEBBzxi07D6FRrdKYOaYbZPesj43975PndWfVItXbY8ComeqvyFpXzLs8Zvi8o0FmLePxJxHgbX8IATG7EpcVZYvlhvzOs/rGe9ulxePHT8Dd6X7I4+BNgigBcWQv7im2/DEWy2YOQbiwYfDs+QvEjxPDOA/9zqiSJIyDxSu36AKN+Adzdfey+foSLUzr4ug7jZj4JAEKvT6Jh4UkQALfkkBAwEsMGjMb9auXRLLE8SA71o2etBAPH/uhVO3OKGnfCafPXX7nli8CAt5JM0ECwZ3A9j1HjFXArogY3g5/TBuEdMYK+sfGLA7tRdC1dO02TB7RCQ1qlMKYyQsxcoI35M+DR37o6jIR5y5dkyRDMCbAob1LwM//KboMmmAIr2ZhnEtbOLesidBvTIffrfk6tWHrPohz+4J5MmP8kA4QnywVGvaw+dib/ftaLFzmixAh+NP5NTHGZiOQKW0yiPaXddxvu7RYtm4bZFLP3emsdHgkgY8TEGsXKc2bPR3E/H76/FWStAVxZr/adzc6O9gjf66M8Jnuov6RR3h666Jo5dL5bXV58mUE+OX+Mk6sRQIk8A0IyIq7OOZuWa+CXm30pAW649aK2a7Yvswd5YvnRZuebhAH3lrBiNr3Hof5PhuMM/4lgS8mEKQrJk4QW1fTR/VzhPj4+tRg1v+5F3sPncCccT0hE5KCeTJh+uiuOHzsrDabNHuZsYoYHfyBpDgYmYiArKIXMgRYYs5YOG+WT4781atXcB3nhQbGgoysxKdKlgCdHe11t62Ll2/oAo0IkmWTFrnuJy/GQhIwAYEAYxEzubF42cmhlo722Gd2pxM3FqKFLMLlR4/9tY01euz3BCfOXLImeSQB0xAQjS0vj16Y9fsaXfjv6TpFxz526iL175UofixNizXNqbOXMXXeCiRLHFfzGP1/BCj0+v94sTYJBEECgaPLLwICVPvEuWUNRIkcAQePnob8AOrapo52UFTnM6RJquchQlhw9sJVtOkxBvIDKW+O9JrPiATMQCBOrF9RtmhuWCyWzw53y86DunmEtLFWFhNj98Ht9R2SH0i5sqbF3oMnIWaQ1jo8kkBwJyDfEdkl61PmjFYGp89d0dX2csXzWLP02N2pLnJnSwu3Kb8jWtRImnf1+m09MiIBMxMQ59pOTaraXFrI5Pxju9PJYmdth/6YPn8lvJdsQOFq7XXHOis/+U459/OACJ+teTySgFkIyILlmnnDMbyPA5rYl4FoKctiZt2qxd9BMNRjLuQblSV9Csg7JQIymSd5L/WFzLHeqczEvwiYT+j1LwTMIAES+BEERKg1x70XqpYrqJpcYuZYu3JRpEwa33b7acYPogK5M2k6UsTwKvCShO+2/Xj+/IWcMpAACbxFwC5cWFgMIfFbWXpqFy4MhnvOR/pUSfR96ztiGuo6DoT4JdIKjEiABGwE7OzC6vn7povyHh06dlZ9q5QukhsrN+xEsZod35mwa0NGJGByAh/bnS5V8gRo1H4IYsf8FV4eveHp2hFuA9pAdqy7fvMuLl65Ac+ZS9Clde0vWugxOWYO/2sJBPJ2MkfKkDoJxJcX8AoiRPZeuhH3HzzWnss8aOvuw2jfvLouaIojfIvFAvkueS/ZiA593bUeo48ToNDr42xYQgIk8I0JJIwXE/Ifu/gXunnnHhwaVrLdwepsu3GtMpq3dvMeXVkXtV/RZpnktVzzGZEACfxDoFalIjoBl9187j98bBNqyY8j+ZE0oq8DujvVgc+0QfB/8hSLVmz+pzHPSIAElEC82NFRsWQ+nTgcP31Rd8uSlXMxtXdxm42aFYvoezSyryOG926Fri4TVZisjRmRQBAj8D26+7Hd6bbvOQoRbg3q2hQiRJZ758meDiONb5Ns1CKbsBTKmxn5clCjX9gwkID4xps4zBknjG/Rig07IL5bB4+dgzaNq0B2duw3coYh7MqFAZ0bo0zRXBg70El/B4r5I+l9nECIjxexhARIgAS+D4GkCeNAVHl/ifLaXES0Twa5zYL4UokZPSru3X+EkRMWoJNDLfVTJP/5N6tdFn7+T9TvA820vs9z4VWDHgHZxWfhpH66nXXe8o6Yu3i9mjEOGjMLrepXhOwKJKMSbbBHfv42UxTJYyABEoANQX9jAlG+RF7UbT0IxWt2hDjkXrlxJ2Qi0bpRZVu9J0+fIXmSeBDzSVsmT0iABNSRvfyOe3t3ulvGAqd8h6K9MQ+2YipZKKfuRmd11m3NF2Gz9ZxHEjArAXlnxg5qC/tKRbF07Xbjd90L3ahIBMi79x/TfCubKJEj6uljY44kJyvW78SeA8fllOEtAhR6vQWDpyRAAj+OgGh8We+2xne3ntarVkKPXn+shwjGyhXLq2mJjp+5qE4exe+DTO5FHV5W4aWMgQTMTEB205o6qgv2rZmEutWKY8FSXzx85IfG9qVtWJau2Wbk+SNzuuQQoXKf4dOQo3RL2DsMwIJlvm/q8UAC5iUg3yQRFO9e6WksyowwhFohMMRYXW/XrKpqHQsZWaCRb0/xAtkkqQIx2fFR3qUeQyarXzAtYEQCJidg3Z1Ovk+yO92Ov46+Q0SEW4PHzkbDGqXw4OFjTJi1VL9Heco5Ysfed+u+05AJEjAZgYql8ukGRXbhwujivwxfNpGQo4TVvrtU4CyLoHfuPYT8vhsyzgsjPL3x18ETUoXBIEChlwGBf0mABH4uAVldnze+D6y7Ym3bfRgNa5YyJh2vHXnvPXQCNVv0g33lYlgywwU+0wZizqK1WLVx1/frOK9MAkGMQJgwodV8WLrds109/REk5yIAc3Gbg06taiJM6FBo3WOMakwunjoA3dvUwfgZPpg2b6VUZSABEjAIyORCzIXFx2SN8oWNnNd/vRat0xV3+0rFsGXnIVRs1AO1KxfFyjmukF24Kjfuhfd3pnvdkjEJmJNA6uQJ1TS4TU83iFnW9PmrFMSiFVv0OzTdexUcuo1SwZdDg4rYuHAUcmdNq3Xejw79fUZNvd7PZ5oEgjMBWZBJGO/1Lo5yFH9fYhYsizCbth+AOLRv1aCC8ZsvLMZNW6xKA46NKkF8VdZ3csH2PUeCM54vHhuFXl+MytwVOXoS+N4ExKzReo/4cWNg2brtuHD5hmbJhDxXljRYuMwXYrYl/5GL88bDx85o+a079yF+WDTBiARMTkAm4SUL5bRRmDRnGeLEjIYqZQtgw9a9+q5cunJDnXKnSBofXRztsWTNVvAPCZDAPwTknRnUtSlCG4Jiyb15+55uDtHFsbaxQBMaoqUiPolk8n7j1l20bVrVEHxFoVmJwGIggbcI1KlSHGKGL5P1RAliQQTKwzzm6bdH3FikS5VY3Vnkz5UR4hvsraa2U9EMc+4/HnN9NtjyeEICZiMgu6aKyxfxjZy1RDMVGDerUw4Na5TGsVMXMN94P3q2r4/CebNAhMiODSsZ78z6r8YUnBpS6BWcnibHQgLBhEA/50YQx8IeM/7QER05cQ6NapXG8lmuiB4tKio06G5M2HcgWeJ4Wj560kI07TgU67b8havXb2seIxIgASAg4CX2HT6FHu3qqRbYNmPFr6oh/PKZ7oKrN+6gYsMehsBrmzFZj6q4/J88g8/qrYZwbN+/NFak7NWrV1qPEQmYjcDRE+fV2XaJgtn1O3P+0nV4T+iLJrXLoH0fd/QeNg2SFyVyBEVz5Pg5Y6FmE06evYT33xvxT6mVGJGASQiIwKthjVI6Gd+172/VRhFhWPZMqSBaky8CAj5J4sCR05g7vjdqVvhH8/KTDb59Ia9IAoGCgPj7mj66K7YtccfO5eMhfvQsFkBMGiuXzg/ZBdLaUfHtJW4trGkzHyn0MvPT59hJIJASCBsmNLo71cWQ7s21h2K7fujYWYjJSYt65bFgUj+UKJgDpYvkhOQvXrnFOM+NFet3oFjNjrqLiTZ8E529cPXNGQ8kYC4Csio4060bRFNSRh45YgTce/BIfRT1NlYDxwxog8d+T1C9fCGdsNd26I/p81fCe8kGFK7W/p13aeDomRhqrM7LdRhIwGwECubJhAlDO8JisSBCeDsdvpgOFzZW1JfOcNFJfMa0yZAxTTLIbqo1WvSFCJlbdB6BVl1H2oTIshOXmEGu37JXrxE0I/aaBL6eQPEC2TF7XA/I9yllkvh6oTPnr+rxQ9GFy9chZlqnz12G1Q3Gh+oxjwTMREAWWKyakeuMRX9xcO/UpKoNwdbdh7HTEDC/rflvKzThCYVeJnzoHDIJBDUC7ZtXV59DotElAqxoUSKhlzFhDxc2LFzcZqNmxSKGkKwORvZ1xPDerdDVZSLEyb2sHF6+dgvl6nfDgaOng9qw2V8S+CYELBZjCfDNlWpUKKSCrDmL1kE0t8TfiqwYFs6XBY3aD0HsmL/Cy6M3PF07ws0QiDn1ctPt5g/9fQZ/rPoTlUr99uZKPNgI8MQ0BCwWi45VJht1qhRDpwGekIm4+NMTbWQv954QU2HxtyLv0Mi+Dlgx21XbuE1ZpEfZqEU0vfLmSK9pRiRgRgJWs2F5d/IZ78KvaT/TAAAQAElEQVTBT/xGE1PIovmzIkfm1Lhi/KaTHVYfPPIzIzaOmQQ+SCB9qiRwd2kHq6uY5y8C1B2MbM4iljMfavT3yfPwXmqejYwo9PrQvwLmkQAJBCoCaVIkwqIp/XHt5h0VYInTRung57aTn+G9GpUa9USe7OmQyViBlzYM35cArx64CSSIGxPzxvfCktVbkb1UczTvNFyFX9v3HFXhlvgwEo1KGYW8NzJpFwfdLmPnoJYhXE6VLIEUMZCA6QmIb69i+bOhQsMeKFO3C5au2aZaYPP+2ID61Usif64Myihc2DDo07ERyhfPA/E/KRN42WjC+p5pJUYkYGICYwe1hfj1+hAC2fVxw9Z9cG5ZS4uHe3pj3+GTqOs4ELIoQ3+uioWRyQnEifUrCuXNbKPgvWQjRBO5sX1pW97bJ2JyP2jMbMj3SHwly/crIODl21WC3TmFXsHukXJAgYgAu/INCchkXcwdD6yfggFdmsDP/ymGGBPxds2qqqmW3Ep2MvGcuQTFC2STJESLxc//CcQXxAjjhxJXBhULI5MTSJY4HuZP6INlMwfDqWlVNRu+decexE9EtKiR3qFTslBOWIXLjo0qv1PGBAmYmYCYZrWsXwG7VniiryHUsk44rly/hczpkr2DRpziZ0iTFOOmLkaW9CnUPP+dCkyQgIkJiEsLWdx8H8GLgADV5hdH3QnjxcRfB09gte8u9aU33rUDkiaMCzEdvnf/0ftNmSYBUxP4JUokY7GlIcLbhfsgh1Ubd+mmRi7dmiJF0gQYNWkBXN29Plg3uGT+QKFXcEHGcZAACfxMAqFChoT8QJLdfwrkzoQa5f9xauq1aB2ev3gB+0rFID+Who+fpw7wV8weon6MLl+9Cf4hARJ4TSBJwjgQlXhJyYTjkvF+yKq6pK1BhMavhcvVEDpUSHVw33/UTIi25Y1b96zVeCQB0xKIED4ccmZJjUgRwyuDbBlTYb7PRv0GacabSExJFizzVVP867fuQnxRdujrAY8ZPpAFmzfVeCABEnhDYOGyTbh7/yGa1i6rm7KIX0lxhi9aYWKy5dCgomooS503TXgIdATYoZ9BoEzRXBBN5A/dW1xbiGuY1o0rQ/zr1ShfCJ1a1TK+SX9i0pxlmO69Chcu3/hQ0yCdR6FXkH587DwJmJeArJyLKZbVL8TN2/cw3HM+ujjWVs0Vn1VbcenqLcgKoZhnDejcGDKxNy8xjpwEPk5AtCK7O9VBm55u6DdyBqbPX6WVp81biTv3HmLZuu3IXc4BsxeuQdxYv0J20Ir2y7taYdqAEQmYnEDXNrV1ol7HYSDGTl0EEXaJKYmL2xwl03XQRJSv3x1bdh5E7mxpUbpwTl3I0cK3okeP/XUl/q0sngZ1Auz//0VA3ptubepCnHX7rP5Tf9M1r1fedo3VvrtVkyVxgtiaJ+leQ6fqOSMSIIEPE5juvVLfm9qVitoqbNpxAMmTxENkY/Hm6PFzKF2ns5oR2yoEgxMKvYLBQ+QQSIAEgKMnziNfjvQoUTC74pgydzk6OdRClEgRNM2IBEjg0wTqVCmOhZP6IVH8WEiUIBZkEwjRQpHtsF8GvIT4IZo6qouuumdIkxSidfnpK7KUBD5OILiWxI4RDfM9+6BpnbI6xMQJ4mDNpt3Ye+gEhvZqidPnr2COe0/deEVW2EXjUiu+F23cug/isFtW5d8rYpIETEHA3piUi8bK8xcBkM0hujja237TyXsx1GMuRFvFYrGob0oxz1q0YjNK2ndCn+HT1IeeKUBxkCTwhQSuXr+tZvbdnerCqjRw+PhZ9UnZp0MDyMZg8p0SS5rtfx39wqsGjWoUegWN58RekgAJfIZAwTyZMGFoR3UkLJop5y9dR9Hfsn6mFYtJIFAQCDSdEIGXmI8UzpsF4h9PfviItmTBvJlx4BO7awWaAbAjJBAICMiOdGI20qZxFYQJHQojJyzQRZiyRXMja4aUOGJMMj7Vzdt3HyBlsgRYOcdVNZc/VZdlJBDcCYhp/Rz3XqhU+jfbUKfNf1dbZeaC1QgdKhS2LXWHx+B2xjt2Do7dRtvq84QESACIEjkiBnZpApkzCQ/Rphwy1kuFXaLxL3lPnj7D4WNnkCVdcklC6ojGssyrNCOIRhR6BdEHx26TQPAl8PUjs1gs2li0u4rmz4pO/ccHO/VcHSAjEvgBBDo51EI/50Z6pwypk2DXvr/1/GOR/CASX19teoyB91Lff/k0+lg75pNAcCYgDu8nDXeG1ZQka4YU2Hvo5CeH7DbldwweOwfRo0X5ZD0WkoBZCIgje6t28dUbd+A+bTGs2irXbt6BvDNiWiy//5Iljof2zatDNFhEI0wYyWKoHBlIwMwEwtuFReXS+W0IVm7YpWb0jg0r2fJm/74WogWWNWNKNdWv18YF9Z0Go27rgajWrA9EW8xWOQidUOgV2B8W+0cCJPB/E5BJhmuPlhDB17PnL/7v9mxAAiQA9e0QM3pURZHeEHpdv3kXH3Nef/bCVZSp20U1LUsXyQ3vJRvRoa+7tmVEAmYnkDBeLIj2l3DIlDbZJwXIR0+cgzjwFlMuqb9i/U7sOXBcThlIgAQMAiIMHjOgjU1bZfSkhciTPR0K581ilL7+K5op0aJGUk3JLTsPqcmjn//T14WMSSCwE/hB/YsXJzr6d2qEX3+JrHeU33hiSiwCZdk0rMvACfq7bsOCkdi0yA3yLesxZLLWDWoRhV5B7YmxvyRAAl9EwC5cGIiPolxZ0nxRfVYiARL4OIFfokTCxGHOuH33/gcrifP70kVyQTaMEB8sYwc6Yf2WvTh19vIH6zOTBMxKoECeTGhYs5T6IHqfgZiRiMP76uUK6cYrop0ivomGjPPCCE9v/HXwxPtNmCaBYE/g/QGGDhUSxfJn0+x9h0+qP6Ikb5zZS6YswszwXoX61UviubHwOXjsbDSsURKi5SLl8p5JkHMJAQEv1YRLzhlIwEwEZBFGfrtZxzxm8kLIvEneryPHz2Hr7sMqFIsUMTxChLCgkfHt2vkZrf8zxiKo9XqB6RgiMHWGfSEBEiABEiABEgicBPLlSK8T8fd7Jxpgu/cfgzgdtpaJ3wg5f+z/RA74fflm7D3ECbvCYGRqAmKiJe+KLMy8D0Ic3sskvnXjylo0btpiJE0YB46NKsHOLizqO7lg+54jWsaIBMxO4OXLVxB/RFXKFFBtyKbOwzDZa7m+J7KBhPin9F66EQ8f+aFRrTI2XH+s+hPt+7jb0l6L16Fjv/G2NE9IwIwERBAsGl9dWtfW4R87dQHx48TA25ut7D9yyhAeh9PyFwEBWLt5D6bPXwXRrJRMcXNRvn43W1ryAkug0CuwPAn2gwRIgARIgASCIAG/N4Kt5Inj2Xq/2neX/jBKkSQ+xP9K72FTjYmHv63825zwKiQQfAiI76GBo2epw3sx35IJx3yfDejZvr6abTk0qAjxuzLXZ33wGTRHQgL/gcCqjbsgWiXOLWvqjqj5c2XEybOX4NCwEqaM6ISXr15h9KTf0cWxtvE9Cqt3EgGYaFPmy5kez549R/fBk+A2ZZFNc0wrMSIBExKwWCzo0KIGUiVLoKMXAdidew8hQTIuXrmBibOXokH1kvDzfwrx9dV/5AzsPXxCfX1NmrMMsqNqyUI5PrhAKtf4mYFCr59Jn/cmARIggW9FgNchgZ9EQHw8yK6P4gfiqTGJ2LT9AMShfasGFXSiMWqCN0RLrGCeTLYertm0xxCC+WladgryWb2Vju+VBiOzErj/8BHy5cygDu9lxV1MGsXhsGwigTd/xLdX5jc7askqu2i2iLPuN8U8kICpCBTNnxUzxnRFlMgRjG9NOJ2Mu/ZoAdGkFB96IjgOFzY03jbfkol5/DjR1Zl3iJAhcPbiNWMC/wR7Dh5Xp92mAsjBksAnCPyWKwMypUuGVl1GYsrcFWjQdjBixYiGpnXKYqj7XFy/eQdeHr3gNsAJ6xeMhPii9N22XwVnn7jsTyui0OunoeeNvycBXpsESIAESODHEJCNI8Tf17lL15C1RDM4dBuFZnXKoWGN0rp76vL1O4yVdntbZ0QNvn2fcbhw+QZk5VBU4z2m/4GAgJe2OjwhAbMRiG1MJoZ0b64O79dt+Qu79x+DU5OqNgziW0V8qZQslFPzFi3frKaOMiGp23oQ5D3TAkYkYBIC4mg7bcrEHx2tLKg8efoc5y9f1zpieiWT925t6kLMjMXn5MGjp+Hp2sFIh0DHvh5ajxEJkACMdyIkxg/pgMb2pXHtxm00sS+D2eN64NFjfyxY5os+HRoiQdyYiurXX6JAfgu2rF9BTSI18ydEn7pliE8VsowESIAESIAESIAEPkcgfpwYmD66K7YtccfO5ePRrlk1dXq6ZM021KhQGMnemD6KBouYlogPlnSpEmPY+HkYO3URJC0TmM/dh+UkYAYC6VMlgbtLO8SM/nr31OcvAjBozCy0ql8R8WJHx/2Hj413Zz76OTfCsllDUKZobnQe4KlOh83Ah2MkgS8hIA65GxsT9vL1u6Fy456o23ogxPQqZ5bU6rjeqk2ZP1dGyG51E4Z2/Oxlg9DizGfHwgok8DkCsmmELLT0aFtPNwcLFzaMLlhKuxyZU8tBw4Klvqq938QQjImvvWVrt6ODIUSW33fHT1/UOm9HIjh7O/0jzin0+hGUeQ8SIAESIAESMAEBMTOJGMHONtKde48iR6Z/fhiJWaM4tG/b9LUGS8TwdkiZND5m/75GJ+2P/V47vrddgCckYEICcWL9ikJ5M9tG7r1ko04oGhsTeMn0nLkEYlIs5o9RIkVA7cpFkTFtMuw7dFKKdWdI8RGmCUYACMGsBERQvHulJzq0qKm+iTq0qKEodu0/ptqU1m+RZIYOHUoO/wriv0gm8mIu2XfE9H+VM4MEzERAvk8yXtH2kuPd+w8h7i06O9irmbHslurq7oXCxjcsUoTwqNKkF7bsPChVNYgQLFfZVrh1576mf1REodePIs37kAAJkAAJkIDJCNSpUhzyA2juH+tVC0V+CInTYXHUfeDoaYgvL9eeLbF67jBkzZgSYcOEUZMtk2H6scPl3YIcgV+iREKfjg11QnH63GXMXLAaPdrWVXMSGcyTp88gZlrx4kSXJCbMWoL2fcbqOSMSMDuB8HbhkD9XBmxe7GYzvdpjCL1qViyCGL++1qb8GKNhHvOQo3QLFKzipKb75Yrn+VhV5pOAKQjEiRkNA7s0wbipi1GkentUbdobSRPGQdliefDXwRPwWrwe8s2KHzcGGtYshV7t6xvfpKU2Nq7jvFCp1G+Q34G2zB9wQqHXD4DMW5AACZAACQROAuzV9yVQp0oxNRs5efYyeg2dgtChQkHy1MxxzGzVUBFNL5mU1DImICs2bIc45xYh2ZHj59QE5fv2kFcngcBPoEzRXCiWP5t21NV9rh7Fh4qcyLs0btpiFYgVL5AdFy5fhzjrFp96Ui5BtFTkaA0vAgKspzySeWojyAAAEABJREFUgGkI/PpLZNtY48aOjq27DkEWX95/P6yVRBNluvcqddbdxbE2okWNDOd+HhBNFWsdHknAjAREy3jbUndMGuaM6zfv6iJMiBAWrPbdhYol86FbmzroM2yaavBfuXYLz5+/gPwRf5Xim7Jt02qS/KGBQq8fips3I4FATYCdIwESIIFvTkB2zuptrPR1aF4DvTs0UEfd5y5eg+w616pBJdv9xLRRVtXLFs0NcTDcsN0QdDQmGB+bkNga8oQETEJAdkYVh/bi4L6qsbo+coI3ug2ehGnzVmJkXwdEjGAHeYdkV7vc2dLaqPR0nayr75Lh5/8EZet2hQiVJc1AAmYkIJP2BjVKQfx6iabkhxiIs3tZkLlx6y6mzF2OKSM6o03jKli8csuHqjOPBExFIHSokOqvVfy4inm9DF4WYaJEjog82dNh0dQByJIhBeb+sQGiISnvmYvbbLRvXt3mr1La/KhAoddHSbOABEiABEiABEjgWxGQHz35cqTXy506d1nNTCIZk3TNMCLRThGVeJfuzVQdfuKwjsaq4W5jcn7WKOVfEjA3gYCAl3B190LrxpXRol55yPvxyO8J5B1aNGUAxBn3vsMnsWHrPji3rAXrH9FkETPiHJlTYcX6nWjdfYyaRaZMlsBahUcSMCUB8YU316MXwtuF/eD4o0aJqJtFtOs9DnmypYP4rKxevpAKvqTB/YeP5cAQrAhwMP8vAVlssbYpVTgXFi7bZPxuOwcRGttXKoq184ajWrlCmP37Wq1Wt2pxPf7oiEKvH02c9yMBEiABEiABkxMonC8LkiaKq74g/j55Hhcu31CTLFGJlx9KgidB3JhygOxcJye/L98McYIv5wwkYDYCIUOGwIg+DrCaLWZJnwKiQdmjbT2keiPAWrtpD0RTMmG81++OaEm6jJmNOlWKI0WS+IgU0Q5iWiIr7tt2HzYbQo73/yVg0vqXrt5UAbEMP1vGlLpAM2PBap3IWywWRAgfzvgWnUTe8o4QH3tW0y2pz0ACZiYg74t8l2q06IvmnYbDY/ofCBs2jG7EIs7uezjVg+wA+TMYUej1M6jzniRAAiRAAiRgYgIi2Bo3qC06OdgjScI4GO45D7L9tajEW7HITo9iWpImRSJcvXEHvYdNNX44+VuLeSSBH0ogMNxM3gW7cGE+2pVwRtn5S9fx4JGf1lm2bhvOXLgKhwYVNS1mWYXyZlbtFdEa27XvmOYzIgES+IfA1eu30Wf4NIjm5JjJC/V9aVijlJo4Si3RuhQzrVgxfoFDt9HIW6E1ZFFGyhhIwOwEypfIi60+41CyUE6IHz35Zsl7lCtLGhTNn/Wn4aHQ66eh541JgARIgARIIEgS+CadFs0V2VFr/5FTWL9lr66YixnWs2fPIQKvQWNmqe8H+cE0aoI3xDSyYJ5MtntLnYdvJveSuXTNNttkX9IMJGA2Aq3qV0T0X6MgTzkH1GzRD91cJqFDi+oQMy0RcK323Y3OhqBZTCF9prsgZ5bUn0Qkk/tPVmAhCQRDArIAM7p/azj1dMOWnQfx1PgmyTFxgtg62iVrtkKEy4unDsTqucMwpHtzXZS5/+CxzWG3VmREAiYlIN+cqmULQHZIlYWXP1b9iS6ta/9UGhR6/VT8vDkJkEDQJ8ARkAAJfC2BFwEBcBkzG01rl4Wna0eIKnyWEs3Qvs849ZsiPldktX35+h3o4mhvu83fJ89rHTGLvH33AcTBd1eXibh5+56tDk9IwGwEQocOBXeXdvjTZyzEhDhZorgQH0TCYezURWhcqwwSxY8lSYgTYj15LwoIeAn/J880t9MAT8hukJpgRAImIiA+vBLFj41GtUpj5cadKFEwB1rWqwBZaHFxm4NOrWoiSqQISiR+3Bh6LFazIzIXb6pmXWIiqZmMSMDkBJImjINlMwfbzPB/Fg4KvX4W+eB6X46LBEiABEiABL6QwK079xEzxi9oVqcc0qVKjFVeQ7Fyjiu2L/NAy/oV9CpL1mxDjQqFdZcgyZDdgWTSUaVMAW0j2isO3UbpbkFJEsSRKgwkYGoCsiGEvD/Tx3RTZ8J+/k8h/vA+50BYVuSL1+qI7KWao3LjnipEThjvtZDM1EA5eNMRWLflL2TPlEoFxaLJJRtIhAkTGpO9liNOzGioUraAjcmEWUtRIHcm7F7piW1L3BEyZEgMHD3TVs4TEgj2BD4zQHFj8Zkq372YQq/vjpg3IAESIAESIAES+BCB2DGiYfLwTrDu/mOxWCCT7MgRw9uq79x7FDkypbalxaxx76ETaNu0quaJ+aP4/nr40A/2rfqrDyMtYEQCJicQLWqkNwReqYaX99KNEBOsN5n/Osz7Yz3SpUyMPasmatn9B48waMwsiEN8zWBEAiYhIJqSIjh+e7iyqcrGrfvQo109FSZLmXyLVvvuQieHWpLUHR6zZkhhHCNqmhEJkEDgIEChV+B4DuwFCZAACZAACZDABwjIznODx87GXGNCvnX3YYgDbueWNRE9WpR3TE3mju8N0WR5W2D2gcsxiwRMR0CEwhOHOePE6YtYsWHHR8cfNUok3L3/COLwvmShnJjj3hM79/6tGl8fbfTpApaSQJAkIOa/7+8yJ3mLpg6AOOSWQYkpsGgdN6heEmLCJXl+/k8wc8Fq9UEpafH91dN1Ctr0GAPvpb54ERAg2QwkQAI/mACFXj8YOG9HAiRAAiRAAiTw5QTqVCmG7k51cfLsZfQaOgWhQ4WC5MkV3jY1CRHCgvIl8qowTMo+FnbsPQoxq/xY+ffL55VJ4OcRiB8nBsYOagv7SkU/2olGNUvjsZ+/aneJL7BIEcOr4Et2qbv/8DHEtPijjVlAAiYgECpkSNsoDxw9pQ7tW7wxxZeCafNWQt6b0kVy4+yFqyhTtwssFgsk7b1kIzr0dZdqDCRAAj+YAIVePxg4b0cCJEACJACAEEjg/yBQukgu9G5fHx2a10DvDg0gvlWk+aIVm3VHoLcnIpL/dpCJ+uKVW+A+bTE2btsH13FeCG8X9u0qPCcBUxOQifqTp88gpsK5s6WDCLn6jZyuTGQCLycd+3lgqMc83Lv/SJIMJGB6AlkzpMSGBSNtDu0vX7sFjxk+6NamrrE4ExL9Rs4whF25MKBzY5QpmgtjBzrpTsWnjAUc08MjABL4wQQo9PrBwHk7EvgQAeaRAAmQAAl8nkC54nlsZiOirXXn3kMkeLNz1sdaz/PZADFBsTMEXQNGzYSf/1Ncu3n3Y9WZTwKmI7B1z2H0HjoVx05dgOyMumjyAJ2cHz99UVmIsHj7niPY8dcR5KvYGg3bDYG8e1rIiARMTMAqFBYE0+evVIf2+XNlwHXjG7N7/7F3NCujRH7t5+ux/xOprkEWZfTkTSRCMzHjf5PkgQRI4BsRCIxCr280NF6GBEiABEiABEgguBIQn17tmlVD1aZ9sGzt9o8O89DfZ1C59G8olj8b4sWOAXFOXLNFP9y4de+jbVhAAmYiMKZ/G1hCWIx3qTcypkmK+Us26vBjx4yGZ8+eY8hYLzg1qYrFUwfqDnUvXgRg6rwVEMfe70/atSEjEjAhgQ4taqpWlwzd741gK3nieJLUIA7vxb9eiiTxcfrcZTR1Hob0hRtBvkd7DhzHiTOXVCM5UgQ7rW+yiMMlge9KgEKv74qXFycBEiABEiABEvheBJrVKYeZbt2QOX3yj96iVsUimLNoHTr190Rnh1qGACw/uraujV9/ifzRNiwgATMRiBA+HMoVy4Mi+bLosM9cuIJZY3uo2Za8O89fvED96iW1TCbtInAWk8jMxZqgQGUnzFq4RssYfSsCvE5QJGAXLozNp2TCeLGQKH4sjJq4AE8NwfGm7QcgDu1bNaigG7A0bDdEh7hoygBUKZMfrbqOQpeBnqhYMh8ypk2mZYxIgAS+HQEKvb4dS16JBEiABEiABEjgBxNIkyIRxEn3+7e9efu1JpdMIEQ4dvj4WZtJVtWyBRAyZAisWL8TssL+flumAxEBduW7ExBtrXFTF6NPx4bo0KIGXHu0QNYMKXTDh+Ge89HFsbb6+5KOiDbK2s17MM+zDw5tmAaXbs0wZJwXDhw9LcUMJEACBgH5vkwc5oxzl64ha4lmcOg2CvIdalijNMbP8EH8uDExblBbpEqWADWNhRkRhsm71bZpNaM1/5IACXxrAiG+9QV5PRIgARIgARIgge9DgFf9cgJNOgzFXwdP4EVAAMQfUY+29eDcfzxu332gFxGfRH2GT9Od6oa6z8Wufcc0nxEJmI2AxWLBlJGdbVoq1vHLO5Ejc2qUKJjdmoUhY+eoNkqG1EkQIoQFUi6FkSOGlwMDCZDAGwKyGDN9dFdsW+KOncvHQ8zx5Z1ZsMwXNcoXQriwYbSmaILN99mo5bKJhGReuHxd/YLJOQMJkMB/J0Ch139nyCuQAAn8HAK8KwmQAAl8lIBT06po2WWkmpQUNybt4tNL/Kw8euyvbcZNW4ykCeNAVtYjRQqPRu2HYM2mPVrGiATMRiDiB/wIyY5zk0d0gsViURwb/tyLnfv+1ndGM4xowVJf3e0xcYLYRgoQ5/dT5q6A1PXzf6p5jEjAzASiRI4A6/slizDC4u0dh2f/vhZiQlyvWgnjGKAbr4ivymrNequ/r7MXrkoTW5CNJUQgbcvgCQmQwGcJUOj1WURBpQL7SQIkQAIkQAIkYCUgQq6xg5ywdM02eC1eh6pNe6FQ3syIFye67lI332cDeravr3mt6ldUE5MpXssxfqYPFq/cgvsPH1svxSMJmJbA25Nzrz/Wv6ONItqSblMWoUPzGioYW7RiM6o06YUz569g5sI1kEn7tZt3TMuOAyeB9wnI+1SnSjH0HzXT+C6th5gKj5zgjW5t6qjmV5seo7Fyww6smD0Emxe74bdcGeDYfTQCAl7qpWTRRnZaPXvhiqYZmZ0Ax/+lBCj0+lJSrEcCJEACJEACJBCkCIi2idsAJ911bqZbd/WhEjJECPVBVLl0foiJlgxIdqjz3bYPyRLHhZhp/b58Myo37on7Dyj4Ej4MJCAE3F3aocEbh/aanrYYyZPEQ5miuXH+0nX0GjoVI/s6YFDXppg6sov62hNBstRlIIHvTiCI3KBr6zro36kRRHDVffBkNREuXiA7/j55Hlt2HkKebOnQoO1gbN5xEPaViuq79fCRn45usrEw80uUSKharqCmGZEACXwZAQq9vowTa5EACZAACZAACQQhAuKcO3bMaCiaP6vuQpckYRzVRlm35S/s3n8MTk2q2kYzz2cDnj9/gV7tG6BOleKYPqar+lM5dvqCrQ5PSCAoEfgefQ0bJjTCGEGuLRpc8t50b1NHfXuJdmTKpPFRslBOKda8bBlT6YRdM95E8l6+OcWVa7fQY8hk3d3OmscjCQR3AiFCWFC6SC6In8nuTnVUy0vGfOrsZaRPlQRDe7WEa8+W8Jy1BPWdXBAtaiREjGgH8fM1ac4ydHeqC9EYkzYMJO6ApJoAABAASURBVEACX0aAQq8v48RaJEACJEACJEACQYiAxWJB41plpMfvBJlUiMZKzOhRNV8c24+dulgnEnbhwmje6XNX9Ch15eTOvYe6O53/k2eSZCAB0xOIHSMaVs8dhgxpkiqLqzduI1/ODHpujXy370fSRHHVNGvqvBXIUbolcpZpBVf3uZB3aeTEBbh99z5EmGZtwyMJmImAaBzLDo4y5vhxY+DMhauqYSxayHPG9YRjo8qQHVVFyDXMY54u4uTOllaqM5AACfwfBCj0+j9gsSoJkAAJBH0CHAEJmJtAnFi/olDezDYI4tBeJh2lCr/WUJGCEZ7zUa54HkQIH079e+Wv1AbdXCYie6nmmO69SqowkIDpCcjudFYIqZMlxNpNe/DY74lmjTMEyQePnkaNCoXhOdMHIzy90de5ISYO6wgx62rYdjBWbtiJzo61tT4jEjA7gSzpU+hOqfXaDMKGrfvg5/8EJQvlgAjGdvx1VPOcW9ZSTKL15bN6K16+fKVpRiRAAp8mQKHXp/mwNLgT4PhIgARIgARMTSBjmqTo0baumj4KCN9t+7F192G0a1Yd+w6f1N0fR/dvjRWzXbFwUj/IavveQyekKgMJkMAbAjUrFlGtrvINuqFeGxfdEELMtESLy2OGD8S3XtmiuSET+9H92+Dw8bPqH0x2UH1zCR5IwPQEBnZpgvrVS2L4+HnIVbYVzl24hhcBsqPjbDSrUw4J48VURk+ePseshWtg36q/fqc0kxEJkMBHCbwj9PpoLRaQAAmQAAmQAAmQQDAkIKvoaVIk0pGJQ/vBY+egTeMqiBMzGsRpcKVSv0GcDEsFqVcgdybsPXRSkh8MsvIuTr0/WMhMEgimBMLbhYXH4HaYMLQjalcuinXzR0CEXCJEjhY1Egrny2Ib+Yr1OxDeLhxa1K+geaLRcuLMJTx/EaBpRiRgVgIWiwXVyhXURZbdKyfoRhELl23C3fsP0bR2WcVy9MQ5hAsbGvM9+6BWpSJw6umGLoMmQPzsaYW3IlnAEa2wt7L+dcoMEjADAQq9zPCUOUYSIAESIAESIIHPEnjk9wQ5s6RBgxqltO6ufcdQomAOPZdIVtz3HDgO2T1L0rKjlkxI5v6x3jbhWLZuG6o160Pn3AKIwVQELBYLUiSJr066xYxYBv/k6TNEihgeL168kCQePPLD4LFe6OxYSzeYEI2vkvad4NzPA3nLO8Jz5pJ/mWzJO6eNv3/EO5BAoCEggmTpjGz+0K1NXUSMYCdJHDt1AVWb9sHYqYt0QUa0kMXHXvn63TFh1lL1oSffKhGOtes9Dnx/FBsjkxOg0Mvk/wA4fBIgARIgARIggdcERCNlQOfGsHvj0D5+nOg6wXhdCoyf4QPRSilWIBtOn7+CItU7YM6itVj/514UNc7Xbt6j5o8dWlQPBs65raPmkQS+nkBx4125efs+nAeMx7otf8F1nBfkvRINy72HTqBmi36wr1wMS2a4wGfaQH2fVm3cZbuh+DZq0HYwbt25b8vjCQmYiYB9paIoUzSXbchVyhTQ90V2Py1dp7O+V05NquL3yf1gyJ0RMmQI/W5Vb95X2xQrkF2PjEjAzAQo9DLz0+fYSYAESIAEvowAa5mSgHOrWnCb8juGe85Hr6FTVQtlZF8HdXDfpscYZEmfHF4evTF5eCdMH90VsqouWmDVyxcyJS8OmgTeJ5AwXixjMt4fqZMnwsJlvvhj1Z8QrRXZjW7avJXIlSWN5g8aMwt2dmFRukhuHD52Ri/z9NlzFZK1a1YN0aNF0TxGJEACgJjfi888t4FOhqB4HWq27AfZibh53fKKR8yH5US+UWXrdoGYGUuagQTMSoBCL7M+eY6bBP4DATYlARIgATMQyJcjPZbNHIzIEcPDLlxYeE/oi5KFcmLzjoMQv13DerUy8sMoitgxo+mxW5s6kAm9JhiRAAmo822HBhXRp2MjdHa0R84sqZXKkRPn0KhWaSyf5WoItaKiQoPuWLlhB5IljqflokX5/MUL1KtWQtOMSIAE3iWQJX0K9e1Vp0qxd8wYh7p76bdq4jBniGAsSuQI7zZkigRMRoBCr//+wHkFEiABEiABEiCBYEogScI4kNXz7k51kC5VYh3l6XOXIZONtycSw8bPQ5F8WZAnezqIDxavxevRoa87ZixY/S/TLPG34v/kmV6LEQmYhYBopzSoXtI23OSGcOvQsbMqOG5RrzwWTOqnPvRKF8mJm7fvYYSnN7q2roNwYV8Llm0NeUICJGAjIOaMYi4suztK5padB7Fl5yE4t6whSf1WiSB59KSFaN5puPr9Eq0wLWT0tQTYLogRoNAriD0wdpcESIAESIAESODnEogfJwb2HT6JA0dPa0d27vsb67fsRSeHWvDzf4qO/Tzgs+pPlC2aBxcuXUflxj1x5dotrSvR78s2oWHbwXLKQAKmJdC+eXWIiaNMxs9euIpoUSKhV/v6urOj25RFyJE5NcQnmABasMwXl996hySPIbAQYD8CE4Fl67ZDBGBxY0fXbskCSwMnF9UEq1q2AGTRpnrzPrj/8LGWMyIBMxCg0MsMT5ljJAESIAESIAES+GYEihfMriZXtR0GoEzdLmjdfQya1i4L8V80c8FqrPbdjVgxf0GaFAl1Ei+T90Urtuj97z94jOGe3qhdpZimGQUzAhzOFxNIkyIRFk3pj2s376Bc/W7o6TpF2x4+fhaLVmxG19a1YbFYNO/Gzbuo1KgnPKb/oYJlzWREAiTwLwKH/j6DRPFj2fJneK/Sd2bisI5q8jioWzNEiRQBq9/aMMJWmSckEEwJUOgVTB8sh0UCJEACJEACP5tAcL2/+OySCfnulRPQpnEVhAsbWoVeMt4la7aiT8eG+C1HBnUu/HqS/gRi0ijl42f66ISkXLE8kmQgAVMTSBA3JoZ0b44D66dgQJcmaho8ZKwXalQojNTJE2p67eY9aFG/AhZPHYBT5y6jpL0zlq/fgZcvX5maHQdPAh8i0M+5kQqQh3nM0+LFK7foIovVuX3oUCE1/+Fjfz2KFnLd1oPw4JGfphmRQHAkQKFXcHyqHBMJkEBgJMA+kQAJBDMC4XW3uVxYOWcoIkUMbxvdr1Ej66R92awhkImF+FcpUTA7Tp29jFkL16BH27oQPyy2BjwhAZMTEEFy2DChce7iNVy5fgutG1VWIqIZOXXeSlRv1gfXbtzByL6OGNHHEZPnLEOd1gNx/PRFrceIBEjgNQHRLF6/YCRKFc6pGX7+T5AqaQI9l0hMiU+cuYSsGVJIUjWPxVxf3jHH7qMhfva04E306I1w7E2SBxIIkgQo9AqSjy04dJpjIAESIAESIIHgQSBiBDvbQCqW/A2izSWOgsWEpIujPdZ5j0TalIkx1GMuyhXPA3GCLw3E14ocGUiABF4TkI0j1swbjl9/iawZUaNExJxxPdHEviza9xmnm0PEixNdnd5XKZMfL14EaD1GJEAC/xCIHSMaMqRJqhnFCmSHq7sX7t1/hAuXb6BJx6HInyuDfof+OngCq313YdGUAZg1tgeiGQs2tVr2w937D7WtaChXbdob3kt9Nc2IBP4bgZ/XmkKvn8eedyYBEiABEiABEghmBBrbl1EH3AUqO6HLoAmY77MBsmud77b92Lr7MMR5t3XI9doMwqAxs2wTDGs+jyRgZgKi9fX2+EOEsKiwePXcYUiWKK769powc4luFGHdUXXF+p3IX6mNbhrhMcMHFCi/TZDngZLAD+pUZwd7pEuVBPkqtkbpOp2RzliAce3REgEBLzFw9Ew0rFEKqZIlQMzoUdWPnnTr2MkLcsBjvycY3rsVCuXJrGlGJBBUCVDoFVSfHPtNAiRAAiRAAiQQ6AiIvxTR7lo2czDEUXf8uDHx7NlzDB47R/1/yQq8dPrVq1eYOMwZAS9foUStTvBavB7PqbUiaBhMSOBLhiw+iRwbVVbfXhev3oTV7GrUxAXoNGA8urWpi77OjbD/8Ek0cx6m792XXJd1SCA4E7ALFwYDuzTBnlUTsWnRGIwd1BZRIkeAz+o/cenqLTSvV942/BNnXpsLy86PDx/5oViNjir4EoGYrRJPSCAIEqDQKwg+NHaZBEiABEiABEggcBMQMy1ZQc+XIz2Wrt1uCLReoIGxoi69FtORig17YO+hE+jdvj5mje2ONZt2o2LD7qINJlUYSIAEPkIgfpwY6vxeJuJXb9zBZK/lOpEvUzQXMqVNBneXdnj+/AUe+z/RK4hGi54wIgETExDhV/RoUZSAOK0fPNZLN1UJEzq05onvrzGTf0ee7Ok0f9KcZYgfJzqyZ06l5YxIICgToNArKD899p0ESIAEgj0BDpAEgj6BiqXyYfrorpBJh4zmlyiR0MnBHiMneKNhuyGwWCyYNqqLmj72GjoFywwhmdRjIAES+DSBHX8d0QoFcmfUo0ShQ4fC9DHdcPX6bdRs0Q8ZizZG4/auEGfdUs5AAmYnMGn2a4GWaCNXbdoLE2YtRZOOw3Dk+DkM6NQY5y9dx5S5K1R7UsyNRetruOd89Bo6VX2AUSvZ7P+Cgt74KfQKes+MPTYzAY6dBEiABEggyBGQSUPCeLHe6bc4EvaZ7oLiBbKhbutBGDh6FrJlTIUVs11RvGB2rSu7bPUYMhlD3edi175jmseIBEjgHwKyg2q0qJHw7NmLfzKNs7v3HqBB2yHImjElNi4cDXHmLe/ZhcvXjVL+JQHzErh87RamzluB7k510b9TI7RuVMUQcl1D3uzpIH7z4sT6VTddKVkoB3JmSY3T56+gWrM+OHzsLNKmTITp3qtViEy/eeb9NxQURx6khV5BETj7TAIkQAIkQAIkQAJCQPx/1alSXCcaki5p3wk3bt1F2DChsX7LXpSr3w1hjPPov0ZBx37ukJV28A8JkICNQME8mSG+vnq6ToGYOlon4pO8liNdqsTo1KqWOuiuXbkocmVJgw1/7rO15QkJmJFAXEOoNXtcD91wxWKxoEzRXHDp1kx9TooAWTZc8d22XzWPhc+AUTORJGFseLp2gH2lopgxuivEJ+XmHful+IcH3pAEvoYAhV5fQ41tSIAESIAESIAESOAbEZCJRq/29eE9oQ8SxI2pDrqderlBnHb36dAAjWuVwYJJ/TDfZyOu37z7je7Ky5BA0CcQLmwYzPPsrabDxWp0QIe+7jqodZv3oEqZ/JCdHzXDiE6evYSQIV9PfVb77lZTLSM7KP9l30ng/yZgsViQJX2Kj7bz3bYPLetX0G+RmAjv3n8MjYxvkLxr0kgWYkRIVjR/NkkykECQIPD6f/4g0VV2kgRIgARIgARIgASCL4EkCeOof68tOw/qIBvVLK1HiWTXRxGK/RIlIpas2YpZC9fg1NnLUsSgBBiZlcAvUSJhUNemOLh+Kkb3b60YxK9X6FCh9FyiDVv34c69hyiYJxNEG8zV3QuLVmxGhQbd0W/kDIhjb6nHQAJmJ9CjbT20alBRMTzy89dnZwOqAAAQAElEQVTj++b5CePFhJjtayEjEggCBCj0CgIPiV0kARIgARIggf+LACsHaQLPnr9A/DgxEC7s6121rIORnbfEt4rnzCU4c+Eq7B0GQHyzWMt5JAEzExAtLjENFgYNa5RC72HTIGZaS9dsQ5seY9CsTjkkjBcLMxeshgjENi0ag2G9W6lJcTeXiWqyJW1fvnylWmDizFvSDCRgNgJWgVayRPEgzu5dxswCndeb7V9B8BovhV7B63lyNCRAAh8gwCwSIAESCEoExKHwpas3MX3+qne6PdRjnvr4+n3yAIjZo/hYGeHpjWs377xTjwkSMDuB+tVLolf7eirgGj/TB11b10brxpX1XXGb8ju6tqkNESKnSpYAIiA7ePS0alkePXEOYyYvxKqNuxA1ckSzY+T4TU5AzIM9BrfHw8f+yFveUR3Y33/42ORUOPygSIBCr6D41P5bn9maBEiABEiABEggEBOI8WtUeHn0wqzf10Cc24uTbploiDmWY6NK6r9Iup8pXTJ14i1+VyT998nz8F7qK6cMJGB6AhVK5MPUUV10R9R61UqoOdboSQuRJ3s6FM6bxcZnyZptyJMtnabPnL+KyV7LkTRhHDx99lzzGJGAmQnEjB4V00d3xfwJfdCuWTVEiRThszgePvLTjVd6DZ2K1b67qCX2WWLfvYLpb0Chl+n/CRAACZAACZAACZBAYCOQKW0yrJk3HMP7OKCJfRncuftAu5g1Q0o9SrT34En4+T9RU0jZTWvQmNkY5jEPQ8Z5YcX6nQgIeCnVGEiABAwCp89dhpg6dnWsbaRe/z126oL69qpfo6RmnDLqJEsUF7mypkHZel1x4OhpzWcUnAhwLF9DIHH82HgREPDZpqfPX4GY4R8+dhZpUybCdO/VqiEmvvQ+25gVSOA7EaDQ6zuB5WVJgARIgARIgARI4L8QEL8qGVIngTi4jx3zV9XqWrVhp17y4pUb6OoyAZVK/QbRDBNzrOOnL8KlW1Mkih/LWGWfB8+ZPlrXGp29cNV6yiMJvCZgojhZ4nhYNnMwkieJp6MWQbEIiCuXzo/0qZLgwuUbmDRnGbq1qYMOLWrg98n9kDp5QvAPCZAA8OTpUyxctgmO3UfjU/7uBoyaaXyzYkPM7+0rFcWM0V3VX97mHfuJkQR+GgEKvX4aet6YBEiABEiABEggMBEIzH2xCxcGw3u3MoRZ3mjYbgiqNOmNWDGiobtTXd2NzsVttvosKl4gO2Si4dKtGTxm+ECccsvq/OVrt1CufjdqrgTmh8y+fXcCIkC23mT9n3uxe/8xtG1aVbPE11eRfFnU/FEyxOn9k6fP4NTLDTlKt9TJ/s59f0sRAwmYjkB4u3DGokozNK9bHj2GTMbICd4QM8a3QYipvbxTjWqVQbiwYbQoTJjQ2q5g3szwXrIRzTsNh6v7XMgijVZgRAI/gACFXj8AMm9BAiRAAkGQALtMAiQQyAgUzJMJGxeOgjjedhvQBnPG9USE8OEw3XulaoHVNlbVrV0Wx9z5c2WAOCKe4b0alRr11Mm8mE1a6/BIAmYmkCNTakwZ0Vk1JUUwvHLDTrRp8loAJlxkM4kazfvi11+iYPXcYShXLI+aadHkUegwmJWAfENmunVHiiTxUd/JBYtXbrGZ0j/y81csIjDWkzdRwngx4TpuLsZOXYTSRXIhUsTwxsJNLy7CvOHDw/cnQKHX92fMOwQLAhwECZAACZAACfx8AhEj2KGQsWIuzrhFoCUr6+OmLlaNr9ChQ2kHb925jzGTf4eYbUmGmGiJ768DR05Ddnt88MhPshlIwNQEokSOgNzZ0iqDR49eT9avv7UT6rR5K9Vn3qbt+1UjrFThnKhVsQjWbt6jbWRzCTGR1AQjEjARAfn2lC+RF7ONhRcRDvdwnayjT5YoHtKkSASXMbPecV6/58Bx1fKabAiZ5bvk0KAi6lQpBlmQ0YaMSOA7E/g6odd37hQvTwIkQAIkQAIkQAIk8HkCUSJHxMAuTSBaYNbaIgTLkj4FShTMoY6Hh4+fh0a1SmPF7CG49+ARLl+9aa3KIwmQgEEgapSImDy8Ezr09cD0+auMHGDjtn3o07EhPF07Yr7PBtR3Goy9h04gqvHOSYWO/TwwfPx8iPBL0gwkEKwJfGBwomncpnEV/QZJsQjDPAa3x8PH/shb3lE1I+X9WLFhJ3JlSYNUyRJINQ2hQ4XCo8evF2BkUUa+W3R2r2gYfQcCFHp9B6i8JAmQAAmQAAmQAAn8CALh7cLaNLrkfn+fPI8Fy3zR3akOLBYLfFZtxaWrt9CsTjk14xrQubE6uheNry6DJsBn9VY8e/ZcmjKQgKkJiPbkVp+xqFmxiHKIFjUyHjx8jJRJ42PqqC5qVvzUeFfKFc8DEYht33ME2/Yctk3u/fyfajtrJCaT1nMeSSA4E5BNV6zjixk9KqaP7or5E/qgXbNqiBIpgmpMpk2V2FpF/VCu2/IXcmROrXlT567E+Jk+GGYs0MxZtI6CZKXC6FsSoNDrW9LktUiABEiABEiABEjgJxIQcyuZaKRN+XqCMWXucnRyqKUTD+mW7FBXq2V/3L57H8XyZ8PStdvQpOMw1QiT8q8IbEICwYaAON22CxdGxyOC4sFjvbBl5yHdEKJo/qxYPmsIokWJhCFGvlOTqlg8dSB2rfBEqFAhdcKuDY3Iz/8JytbtiiPHzxkp/iUB8xFImjAOMqZNpgMvkCsTxFz42KkLKvBq28tNBWHVyhU0FmVuqsCri6M9sqRLrubD7XuPe+ebtHX3YVy4fF2vxYgEvoYAhV5fQ41tSIAESIAESOCDBJhJAj+XgAi7ZLIuvbhz7yHOX7qOor9llaSG3sOm4vT5K4gbKzry5UgPtwFOuHztJnbvO6bljEiABF4TKFkoB4Z0b47ugyeiYBUnyI5zFosFoony/MUL1K9eUiuKiZf4zQsV8vW0asX6nWjdfQxCGumUb5lzaWVGJGBCAmWK5kKnVrVQr40Lspdqjms37mDS8E74xRAgyy6Q4qdS3ifxE+bp2gGyS+rJM5dU8HX0xDm0M4Rg4hfMhOg45G9E4PX/zt/oYrwMCZAACbxDgAkSIAESIIGfRkDMSkQ7pVP/8dh3+CRu3r6nDrnnevQyVtufolz9bli/5S88fOSPl69eaT+Hus/FohWb9ZwRCZidgLw/G38fDY8hHWBfqShkk4jhnvPRxbE2rBph4rNo7h8bkCtrWsUVKaKdTtqfPH2GbbsPax4jEjA7gYY1S2HHMg9sWDAKf0wbBBEU7zIWW1b77kZnB3sbHhFySSJ5kvgQzbDqzftKEsUKZNcjIxL4GgIUen0Nta9sw2YkQAIkQAIkQAIk8KMIiKaJa4+WkIn7s+cv8EauhfhxY6rJo+yktXz9du1O9kypcMJYWZ+xYDXEpKtasz6YOHup0ea1MEwrMSIBExIIFTIkMqROgoTxYkIm6eKHqETBfybgnjOXqJ+8wnmzKJ3FK7dANFf6OTeCq7uXttECRiRgcgLyTYoV4xeIw3vxeTd47Gw0sS+j74+gefnyFYaNn6+7pIYOFRLh7cJJNrKkT46ydbvAd9t+TQeliH0NHAQo9Aocz4G9IAESIAESIAESIIFvTkC0UepUKa47Z8WMHlWPYk7y/EUAxOeKp2tHLJnhgrBhQmPI2DkQ85J180eoUOz35Zsxw3u1rU83bt1DjyGTwR22bEh4YjICYqY1eUQn3SRChn763GXMNATFPdrWVXNGEYpZNVfy58oIn+kuyJnltbNuqW/ywOGTgI1ACEsINK1TDlZzfClYuXEnTp29DMdGlSWJoYbQuGShnJg4zBluA50QJXIEyGYt3kt9tZwRCXwpAQq9vpQU65EACZAACZAACZBAECcwrHcrXLl2C0WqtcOgMbMgDoLjxIyG9Vv2qklWh+Y1dGIh28tXLPUbjp2+oCOW3eqGjJuDy0ZbEaRpJqP/QIBNgyoB0fyy9n26IRQuWzQ3sqRPoVljpy5C41r/aK6ItooWMCIBEniHgGh7ybsTKWJ4zffzf6oLL+2aVUW0qJGwZedBIxyCc8saWi7vWOZ0yY3v1mzMXrgGolEp2slayIgEPkOAQq/PAGIxCZAACZAACZDAdybAy/8wAr/+EhlTRnbG6P5t8EvUyEgQNyaePnuOQW6z0K5ZNYg2GIw/LwIC4LPqT2TLmNJIQc1KRIMlScI4EF9FmsmIBExOoGe7euhhBMEgk/a9h06gbtXikmQgARL4Pwj4P3mKssXyoEb5wtpq2brtqgUWN3Z0TUu0auMuHD99EaWK5MKOvUdRuXFPbPhzrxQxkMAnCVDo9Uk8LCQBEiCBH0+AdyQBEiCB70nAYrGoMMuhQUX1U+Szeqverl61EnqUaMFSX/j5P0GZIrkhflZkRV3MTG7fvY8ydbvg4SM/qcZAAqYmIGbBsmHEawiv1DeR99KNuP/g8essxiRAAl9EQBZkuraujdChQ2n9Q3+f0fdJE0YkZvUubrPRunFlyLfLtUcLLJoyAHlzpFeTR9mp2KjGvyTwQQIUen0QCzMDEQF2hQRIgARIgARI4DsSqFImP2aM6YZwYcPoXe7df4SRExaoX68I4cPB6mdFtFrcBjhB/IBZTVK0ASMSIAF1ui2+h06cvogVG3Z8loj41XOb8jva9BgDr8Xr6Svvs8RYwUwEZCOInq5TMMxjng57uvdKfcdqVyqqaYlEM7lpx2Go7zQYdVsPhGzAcvX6bSmyBTHNF197tgyeBAUC37yPFHp9c6S8IAmQAAmQAAmQAAkEHQLio0jMHK099py1RJ3clyuWF8+fv3jHz4rUSZk0vhwYSIAE3iMQP04MDOnRAvZvTczfq2JLLlm9FaJRWeS3rNi8Yz9qtuiLm7fv2cp5QgKvCZgzzpE5NdYvGIlShXNCBFnjpi5Gd6e6Nk0wodJl4ATdVGKDUW/TIjckjBdLN1uRMgmPHvuj99CpOHvhiiQZTEyAQi8TP3wOnQRIgARIgARIgATeJ1CjQmEM6NIE4mj42OmLkD/VyxWSAwMJ/FwCQeDuS9duQ1PnYRCT4E91V8yHw9uFMyb1ueAxuAMypEmKhcs3aRMxKdYTRiRgYgKxY0TT9yJK5IgYaHyTCubJZKNx5Pg53Yilf6dGEM1j+V41qllKN2SxVprstRy/RImEquUKWrN4NCkBCr1M+uA5bBIgARIgARIggaBN4Hv1PmnCOLBqcz165IcnT5/j5p373+t2vC4JBCsCtSoWgXPLmqohOWjMLIi58PsDFP9E1csXQni7sGjVdSQe+/lD/BnVrlwMYvZYqVEPOPVyg/g1er8t0yRgNgLynlQunf+dYR87dQGiWSmbq1gL9h85ZbxT4TR54fJ1TJqzTLXDRJtZMxmZlgCFXqZ99Bw4CZAACQQrAhwMCZDAdyCQJ3s6tKhXHiVqOWPf4ZNfdIetuw9jzqK1kEnJFzVgJRIIZgRSJ0+ou6TmyJwGjdoP3JTQ2QAAEABJREFUUZ9dIsySYW7ZeRBDPeaqD70Zbt0hJlhDxnmptoo4xQ8VMgRG9W+NDKmTonGHoZjuvQr8QwIk8C4BcXx/595DSJCSi1duYOLspWhQvaQk1RdY0fxZkTtbWk0zMjcBCr3M/fw5+mBLgAMjARIgARIggW9DoGntsti21B2Z0yX/7AU3bT+A5p2GQ0xPmjkPQ5/h09Qv2GcbsgIJBDMCFosFJQpmh5dHb0Ow5Qf7Vv1x9sJV1U5ZtnY73KctRuhQoVClTAGbQPnvk+cxc+Ea3Lh5Vyfvcz166eT9xq1/fH3tOXAc+Su1QcN2QyACtGCGjcMhgS8i8FuuDMiULhladRmJKXNXoEHbwYgVIxqa1imLHX8dxYat++DcstYXXYuVgj8Bcwi9gv9z5AhJgARIgARIgARI4LsRkEnEl2h6HT15DulTJYFLt2ZYOmMwjp28QE2V7/ZUeOGgQMAuXBg0r1se7i7tdFIu5lh/TBuI42cuokBlJ4gJZLM65XD89EXdfe7AkVOYMm8FitbooDunyhhfvXqFy9duofMAT53cO7eqhaqGsEycewcEvJQqDCRgKgKhQobE+CEd0Ni+NK7duI0m9mUwe1wPhAoVEi5us9HMeKcSxotpKiYc7McJUOj1cTYsIQESIAESIAESIAESMAikTZkIs39fC+f+43HFmHwbWR/8W7VMQZy5cBUjJ3gjSuQIGNqrJYrlz6Z1V/vuosmjkmBkRgKxYvwC8U0kY48XOzrcBjhh06LR2LzYDeKvSITKubKkwci+jpg8vBPE9HHmgjUQEy1r243b9ktzPHj4GLKrnZdHL4QM+fnpnDZiRALBjEBoQ8BVslBO9GhbD3WqFFeT4YXLNuHu/YcQDeVgNlwO5z8Q4P+S/wEem5IACZAACZAACZCAGQgkiBtTJ+M1yhdG+z7u8JjhAz//p7ahnzp7Wf0WxYweFbPGdsfcPzZg2vyVSBQ/FkSz5fnzF5AJe702LnDsPhpvm2vZLvJjTngXEgg0BGT3RvFNJB0SQdjOfX/rjnSye+PN2/eMd+yJzUTL943Aa6ZbdzVrPHz8LAVeAo6BBN4iIFqR3drURcQIdm/l8tTsBCj0Mvu/AI6fBEiABEjAxAQ4dBL4/wjkzJIaczx6Inq0KKjjOAAr1u+ETDKu37qrZlrb9xyBOPGWnehEM8x69dChQ2FI9+aq2RI7RjQ0bDcYz549txZDJvjdB0/C+Jk+uH7zri2fJyRgFgL5c2VEr/b10XXQBOQq2wqN27tCzB7FROvhIz/VnuzUqiayZUyJicOckSpZAozw9EYXo77P6q3vvE9mYcZxksD7BOwrFUWZornez2ba5AQo9DL5PwAOnwRI4C0CPCUBEiABEvgsAfGlUqN8IUwf0w2Hj51Rk8V8OdJj7KC2cOo1VifroyctgJg1imBLTCLTFWqIuq0HQSbnDg0r4fyl67h49SYe+z3BgmW+qNmyH0KECIEr127D3qE/6Kfos4+BFYIhgVoVi2DLH2NVWzJ+nBg2E63JXsvxS5RIqFK2gI76wuUbqNWyP27fva/v2dK129Ck4zC8CAjQcomWrtmGB4awTM4ZSIAESMDMBCj0MvPT/8zYWUwCJEACJEACJEACHyMQJVIEdHa0R5oUibRKkXxZdJfHFvUroJ9zI3RtXQfrtuzFzr1HsWjKAHRsWQP7j5xS593RokZS00eZtPcdPl21u2pVKoIBnRuDfooUJyMTExBtyeWzh6iJ1oXL1yFCrx7t6kEEzoKl97CpOH3+CuLGig4ROIt/sMvXbmL3vmOGIOwBNm0/gK4uE1WDUuozkAAJkMCXEAiudUIE14FxXCRAAiRAAiRAAiRAAj+WgDgWFmfcRX7LihAhLLhx665OzJMljoss6VOow2HxY9SuWXWdwN+8fV876NSkKhq1c8WeA8ch5o+ayYgETEzAKuC6ffeBOumW90pw3Lx9D7v3H8Ncj17wf/IU5ep3w/otf+HhI3+8fPUKq313w6HbKOTJng5JEsSRJgxfR4CtSIAEggmBEMFkHBwGCZAACZAACZAACZBAICNQuUx+3HvwCA3bDsGcResgWl3x40RHxVL51IRx4OiZaFijFFrUK4/1C0aqYEw0W+jXK5A9SLA/P4uACIu7O9Wx3d6Qa+l5/Lgx0cmhFiaP6Izl67drXvZMqWAXLgxEsPzwoR/sW/XHmQtXtYwRCZAACZiVAIVeZn3yHDcJkAAJkAAJkMDXEWCrLyYgJpALJvVDjQqFcPLMJaz23aXaXqLF4rP6T1y6egvNDYGXXNAuXFi4us9F1aZ9UK1Zb9Rs0Q9n35uwv+2zSNowkIDZCMSMHhWi9TVygjeevwhA0oRx4OnaEUtmuKgzexe3OejUqibmju+NulWLGwKwsBgwaib8nzwzGyqOlwRIgASUAIVeioERCZAACZDA1xJgOxIgARL4FIHIEcOjQol8qpXi2qMFRBtFHGwPHuuFLo72EMGYtG/TYzRWbtiBFbOHYPNiN/yWKwMcu49WjTAp9/N/grJ1u+LI8XOSZCAB0xIY1rsVrly7hSLV2umuqVt3H0acmNHU95ccxeG9mBeXL5EXLwNeYp7PBjx7/ty0vDhwEiABcxOg0Mvcz5+j//YEeEUSIAESIAESIIEPEIgQPhzKFc+jJeJwO1H8WKhU+jdN/33yPLbsPIQ82dKhQdvB2LzjIOwrFdVdHh8+8sOK9TvRuvsYhAwZAimTJdA2jEjArAR+/SUypozsjNH92+CXqJGRIG5MyJ9FKzajS+va6i9P0hKu3rgDMXe0Cpcl7869h3JgIAESIAFTEPjOQi9TMOQgSYAESIAESIAESIAE/g8CRfNnhTjiFjNHaXbq7GWkT5UEQ3u1hGvPlvCctQT1nVwQLWokRIxoh0hG2Lnvbzx5+gzbdh+WJgwkYGoCFosF2TKmhEODikgYLyZu3bkPEWYliBvjHS6iESYCZmvm4pVbkL9SG6zZtMeaxSMJfEMCvBQJBD4CFHoFvmfCHpEACZAACZAACZBAsCcQOnQo2xjjGxN1cbh9/8FjZEidBHPG9YRjo8ro0KKGaq3IRL1Q3szo59wIru5eWL5uBw79fcbWnickECgJ/MBORY8WBe2aVVOfeMvWvnZsL7e/fO2WIRSLpT69eg2dCvH55e7SDiUKZpdiBhIgARII9gQo9Ar2j5gDJAESIAESIAESIIGfT+BTPZAd6mQSXq/NIGzYug/iv6tkoRyoXDo/du07htW+u9HZwR75c2WEz3QXiMCsy6AJn7oky0jAdASa1SmHmW7dkDl9ctvYL129iefPn6O2Q3+cOncZPtMGQgTItgo8IQESIIFgToBCr2D+gDk8EiABEiCBQEmAnSIBEniPwMAuTVC/ekkMHz8Pucq2wrkL17TG2KmL0LhWGVhNtEKHCqlOvBPFj63ljEiABP4hkCZFIsSP84+J47mL11SQnC9nBswc0w1xY0f/pzLPSIAESMAEBCj0MsFD5hBJIPATYA9JgARIgATMTsBisaBauYJYMdsVu1dOQPIk8eDn/xR7D51A3arF38Fz+dpNm/NuKVi7eQ8qN+6JC5dvSJKBBExPwM//CXq6TsHx0xcxzqUtnFvWVA1J04MhABIgAdMRoNArMD5y9okESIAESIAESIAETEwgvF3YN6N/pRpe3ks3Qvx9vcnExSs3DKFXDDx//gJD3eeiXe9xaGJfFuLQ21qHRxIwK4FHj/1Rq2V/nD5/Rc0ZC+fNYlYUHDcJBA0C7OV3JUCh13fFy4uTAAmQAAmQAAmQAAl8LYHwduEwcZgzTpy+iBUbdtguc/aN6WP9toPx565DWDpzMMoVz2Mr5wkJmJlAxAh2GO/aIciaM5r52XHsJEAC354AhV7fnimvSAIkQAIkQAIkQAIk8I0IiH+isYPawr5SUb1iQMBLiHPuIeO8kDxxPMyf0BdJE8bRsmAYcUgk8FUE4sWOTnPGryLHRiRAAsGNAIVewe2JcjwkQAIkQAIkEGwJcGBmJ/Ds2XOM8JyvGIZ0b44BnRvDLlwYTTMiARIgARIgARIggfcJUOj1PhGmSYAESCCoEGA/SYAESMBEBMRJfW3HgWrOuGSGC8qXyGui0XOoJEACJEACJEACX0OAQq+vocY2gZIAO0UCJEACJEACJBB8CUSPFgW1KxdVc8ZkieIG34FyZCRAAiRAAiRAAp8l8KUVKPT6UlKsRwIkQAIkQAIkQAIk8NMIyI6OVcoUoDnjT3sCvDEJkEAgJsCukQAJfIQAhV4fAcNsEiABEiABEiABEiABEiCBoEiAfSYBEiABEiCB1wQo9HrNgTEJkAAJkAAJkAAJBE8CHBUJkAAJkAAJkAAJmJQAhV4mffAcNgmQAAmYlQDHTQIkQAIkQAIkQAIkQAIkYA4CFHqZ4zlzlCTwMQLMJwESIAESIAESIAESIAESIAESIIFgSYBCr3ceKxMkQAIkQAIkQAIkQAIkQAIkQAIkQALBnwBHaAYCFHqZ4SlzjCRAAiRAAiRAAiRAAiRAAiTwKQIsIwESIIFgSIBCr2D4UDkkEiABEiABEiABEiCB/0aArUmABEiABEiABII+AQq9gv4z5AhIgARIgARI4HsT4PVJgARIgARIgARIgARIIMgRoNAryD0ydpgESODnE2APSIAESIAESIAESIAESIAESIAEAjsBCr0C+xMKCv1jH0mABEiABEiABEiABEiABEiABEiABII/gSA2Qgq9gtgDY3dJgARIgARIgARIgARIgARIgAQCBwH2ggRIIHAToNArcD8f9o4ESIAESIAESIAESIAEggoB9pMESIAESIAEAhUBCr0C1eNgZ0iABEiABEiABIIPAY6EBEiABEiABEiABEjgZxKg0Otn0ue9SYAESMBMBDhWEiABEiABEiABEiABEiABEviBBCj0+oGweSsSeJsAz0mABEiABEiABEiABEiABEiABEiABL4fgcAi9Pp+I+SVSYAESIAESIAETE9g0/YD2PDnXg1y/vfJ83j16tX/xcXP/ykWr9yCk2cvabuDR0/D3mEAbt6+p+kPRRcu39B7bt19+F/F0g8p/1cBM0iABEiABEggeBPg6EjghxGg0OuHoeaNSIAESIAESIAEfhYBh26j0KanmwY5r9asD6o06YUbtz4usHq/r/cfPEJP1ynYtueIFj187A8RfD199lzTH4q27Dyo92zeaTgOGEKyt+tIP6T87TyekwAJmJEAx0wCJEACJPC9CFDo9b3I8rokQAIkQAIkQAKBikCLeuVxxHc69q+djLEDnXDizCWMmbzwi/sYK0Y0bPUZh1oVi3xxG2vFRPFjYdTEBdYkj58iwDISIAESIAESIAES+EYEKPT6RiB5GRIgARIgARL4HgR4zW9PIHToUCjyW1ZkzZASx09ftN1g+54jEA2wHKVbIl2hhqjcuCeWrNlqK3/2/Dla9xiDPQeO2/K+9KRDixrYveH65BoAAAdMSURBVP8YPmTmaL1Gh74eKGnfSe+dv1IbdHWZiOs371qLMd9nA9r1Hod5xrFCg+6Qfkqd+w8fw2OGj7YtUr09Jnsth/+TZ7Z2Dx/5YdCYWZAyGVfj9q44duqCrZwnJEACJEACJEACJBBcCVDoFVyfLMdFAsGTAEdFAiRAAt+EwLNnz3H52k1kz5TKdr0Hjx4jQ5qk6NmuHkb2dUTKZAnQzWUS9h46qXVevnyFfYdP4s7dB5r+f6KihpAtfaokqu0l1/lQ2xcBL1CzYmGM6tcarRtVxtZdh9DDdbKt6uVrt7B28x5Mm7cS5UvkRcMaJbF0zTbkLe+IVRt2atuyRfPoPbbuPqTtAgJeomnHYdi84yAa1CiFId2b47HfE9Rr44KHhjBMKzEiARIgARIgARIggWBKgEKvIP1g2XkSIAESIAESIIEvJXDm/FVs3LZPndE37zzCEPr4o4IhPLK2L1koJ/p0aKB5ubOmRYt6FbTowJFTevwvkcViQfsW1SEO9EVw9aFruQ1wQuNaZVAwTyYUzJvZ6Ec+iPaZCK6s9aNFjQSf6YPQrE45OBqCsfy5MiBZorj4fXJ/bduxZQ2IcM2qUbZ55wEcPn4WQ3u1RIPqJVVYNqBLE/j5P8HOfX9bL8sjCZAACZAACZBAoCfADn4NAQq9voYa25AACZAACZAACQQ5AiJsat19jDqjF1PDeZ69kTZlYts47t5/iB5DJiNnmVbIW8ER5et30zL/p/+YCmrGV0YiSMuTPZ36EXsREPCvq6z23aUmlVlLNEPR6h0w3XuV1nn58qUeJQpvFw7hwoaRUw3Ro0WFXbiwEJNNzTCimNGj4ur1W8YZcPzURT0OGDVTTTfFfLPLQE/Nu3LtdR1NMCIBEiCBoEaA/SUBEiCBLyBAodcXQGIVEiABEiABEiCBoE/A6sh+1tjuOpiRE7zxtvDJodtobN5xAH2dG2LlHFfsXjkB0aJG0rrfKmrXrBrOX7qOJav/8RUm1xbNrA59PVQIN9ejF7b8MVb7IWWfCiFD/vunnCWExdbkyRuBXdumVWEN4l/M07UDCuXNYqvHk6BPgCMgARIgARIgARL4N4F//1L6dx3mkAAJkAAJkAAJkEBQIvDJvooDe5duzeC7bT+Gus/Vuo8e++Pg0dPq96ps0dxIGC8WwtuF1bJvGYnpYclCOdTv1tvX3b3/mCb7OjdCxrTJVNgWKmRIzfsvUZKEcbR5nJi/In+ujO+EBHFjaBkjEiABEiABEiABEgiuBCj0Cq5PluMiARIgARsBnpAACbxPoGLJfOoXa86idZizaC0iRrBDmhSJsHbTHuzad0x9aTn3H4879x6+3/Q/p1s3rvKv62ZJn0KvO+f3teqDy3vJRogmmmb+h6hY/myIFeMXOPVyw6btB1TLTI4d+rrDd/v+/3BlNiUBEiABEiABEiCBwE+AQq/A/4zYw29NgNcjARIgARIwJQGL5R+zPwHQxhA+Fc2fFS5uc7Bl50G0b14d9x48QqP2Q9DUeRispoPWZhbLu+1DvElbLO/my7U/FZImjINq5Qq+UyVfzvQQDbNh4+ehZot+GDt1ETKnS/5OHYvl3/exwPJOHUmEsISAxQhyHiF8OEwe0RmxY0SDQ7dRKFO3ix4vXL6BuLGiSxUGEiABEiABEiABEgi2BEIE25FxYCRAAiRAAiRAAiTwhsAR3+kQIdebpB5EqCU7JkqZmP7ly5Eeq7yGYtnMwdi21B2uPVpAylrVr6j17cKF0XT5Nzs+ilN6KY8X++PCozpVimkbvcBbUT/nRpov5ZItpoyyw+K2Je4Qf2K+v4/B2EFttY7VSb0I5VbPHSbVbaGvc0PMn9DHlpaT0f1bY/yQ9nKqQYRsU0d1wV+rJ0La71rhiYWT+iFVsgRazogESIAESMA8BDhSEjAbgRBmGzDHSwIkQAIkQAIkQAIfI2CxWCB+sKJEivCxKt81P0rkCOpPTARy3/pGsutj/DgxINpf3/ravB4JBFEC7DYJkAAJkEAwJ0ChVzB/wBweCZAACZAACZAACXwZAdYiARIgARIgARIggeBFgEKv4PU8ORoSIAESIIFvRYDXIQESIAESIAESIAESIAESCNIEKPQK0o+PnSeBH0eAdyIBEiABEiABEiABEiABEiABEiCBoESAQq+ve1psRQIkQAIkQAIkQAIkQAIkQAIkQAIkEPwJcIRBmACFXkH44bHrJEACJEACJEACJEACJEACJPBjCfBuJEACJBB0CFDoFXSeFXtKAiRAAiRAAiRAAiQQ2AiwPyRAAiRAAiRAAoGWAIVegfbRsGMkQAIkQAIkEPQIsMckQAIkQAIkQAIkQAIkEFgIUOgVWJ4E+0ECJBAcCXBMJEACJEACJEACJEACJEACJEACP4kAhV4/Cbw5b8tRkwAJkAAJkAAJkAAJkAAJkAAJkAAJBH8CgWOEFHoFjufAXpAACZAACZAACZAACZAACZAACQRXAhwXCZDATyHwPwAAAP//XPhfNwAAAAZJREFUAwDAp7H2tRgGXQAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Now let's plot a bar-graph of these numbers\n", + "px.bar(sequential_df[sequential_df['is_rail']].sort_values('duration', ascending=False), x=\"name\", y=\"duration\",\n", + " title=\"Sequential Guardrails Rail durations\",\n", + " labels={\"name\": \"Rail Name\", \"duration\" : \"Duration (seconds)\"},\n", + " width=800, height=800)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "base": [ + "2025-08-18T19:44:34.000000000", + "2025-08-18T19:44:34.456108093", + "2025-08-18T19:44:34.833581209", + "2025-08-18T19:44:35.186180115", + "2025-08-18T19:44:40.305336237" + ], + "hovertemplate": "start_dt=%{base}
end_dt=%{x}
Rail Name=%{y}", + "legendgroup": "", + "marker": { + "color": "#636efa", + "pattern": { + "shape": "" + } + }, + "name": "", + "orientation": "h", + "showlegend": false, + "textposition": "auto", + "type": "bar", + "x": { + "bdata": "xgF2AVUB/xM4Ag==", + "dtype": "i2" + }, + "xaxis": "x", + "y": [ + "rail: content safety check input $model=content_safety", + "rail: topic safety check input $model=topic_control", + "rail: jailbreak detection model", + "rail: generate user intent", + "rail: content safety check output $model=content_safety" + ], + "yaxis": "y" + } + ], + "layout": { + "barmode": "overlay", + "legend": { + "tracegroupgap": 0 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's plot a Gantt chart, to show the sequence of when the rails execute\n", + "\n", + "fig = px.timeline(sequential_df.loc[sequential_df['is_rail']], x_start=\"start_dt\", x_end=\"end_dt\", y=\"name\",\n", + " title=\"Gantt chart of rails calls in sequential mode\",\n", + " labels={\"name\": \"Rail Name\"})\n", + "fig.update_yaxes(autorange=\"reversed\")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Parallel Rail Analysis\n", + "\n", + "Let's plot the individual rail times, and a Gantt chart showing start and end-times of each rail." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Rail Name=%{x}
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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Now let's plot a bar-graph of these numbers\n", + "px.bar(parallel_df[parallel_df['is_rail']].sort_values('duration', ascending=False), x=\"name\", y=\"duration\",\n", + " title=\"Sequential Guardrails Rail durations\",\n", + " labels={\"name\": \"Rail Name\", \"duration\" : \"Duration (seconds)\"},\n", + " width=800, height=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Gantt Chart Analysis\n", + "\n", + "The Gantt chart below shows the sequence of rails from the parallel configuration. This shows all input rails running in parallel as expected. Once all three input rails validate the input is safe, the user request is forwarded to the Main LLM. Once the Main LLM completes the response, it is checked by the content-safety output rail and returned to the user." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "base": [ + "2025-08-18T19:44:43.000000000", + "2025-08-18T19:44:43.000010014", + "2025-08-18T19:44:43.000018120", + "2025-08-18T19:44:43.447464228", + "2025-08-18T19:44:51.009858131" + ], + "hovertemplate": "start_dt=%{base}
end_dt=%{x}
Rail Name=%{y}", + "legendgroup": "", + "marker": { + "color": "#636efa", + "pattern": { + "shape": "" + } + }, + "name": "", + "orientation": "h", + "showlegend": false, + "textposition": "auto", + "type": "bar", + "x": { + "bdata": "uwFmAUsBih1OAg==", + "dtype": "i2" + }, + "xaxis": "x", + "y": [ + "rail: content safety check input $model=content_safety", + "rail: topic safety check input $model=topic_control", + "rail: jailbreak detection model", + "rail: generate user intent", + "rail: content safety check output $model=content_safety" + ], + "yaxis": "y" + } + ], + "layout": { + "barmode": "overlay", + "height": 400, + "legend": { + "tracegroupgap": 0 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Let's plot a Gantt chart, to show the sequence of when the rails execute\n", + "\n", + "fig = px.timeline(parallel_df.loc[sequential_df['is_rail']], x_start=\"start_dt\", x_end=\"end_dt\", y=\"name\",\n", + " title=\"Gantt chart of rails calls in parallel mode\",\n", + " labels={\"name\": \"Rail Name\"},\n", + " height=400, width=1000)\n", + "fig.update_yaxes(autorange=\"reversed\")\n", + "fig.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "------\n", + "\n", + "# Conclusions\n", + "\n", + "In this notebook, we used the same Guardrails configuration in both sequential and parallel modes. We sent a single request each for sequential and parallel modes, and traced the latency. We showed the latency breakdown in a table, bar chart, and gantt chart form for comparison. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": 4 +} diff --git a/docs/getting-started/8-tracing/configs/parallel/config.yml b/docs/getting-started/8-tracing/configs/parallel/config.yml new file mode 100644 index 000000000..7b0b9c943 --- /dev/null +++ b/docs/getting-started/8-tracing/configs/parallel/config.yml @@ -0,0 +1,36 @@ +models: + - type: main + engine: nim + model: meta/llama-3.3-70b-instruct + + - type: content_safety + engine: nim + model: nvidia/llama-3.1-nemoguard-8b-content-safety + + - type: topic_control + engine: nim + model: nvidia/llama-3.1-nemoguard-8b-topic-control + +rails: + input: + parallel: True + flows: + - content safety check input $model=content_safety + - topic safety check input $model=topic_control + - jailbreak detection model + + output: + flows: + - content safety check output $model=content_safety + + config: + jailbreak_detection: + nim_base_url: "https://ai.api.nvidia.com" + nim_server_endpoint: "/v1/security/nvidia/nemoguard-jailbreak-detect" + api_key_env_var: NVIDIA_API_KEY + +tracing: + enabled: true + adapters: + - name: FileSystem + filepath: "parallel_trace.jsonl" diff --git a/docs/getting-started/8-tracing/configs/parallel/prompts.yml b/docs/getting-started/8-tracing/configs/parallel/prompts.yml new file mode 100644 index 000000000..c72205177 --- /dev/null +++ b/docs/getting-started/8-tracing/configs/parallel/prompts.yml @@ -0,0 +1,106 @@ +# These are the default prompts released by Meta, except for policy O7, which was added to address direct insults. +prompts: + + - task: content_safety_check_input $model=content_safety + content: | + Task: Check if there is unsafe content in the user or agent messages in the below conversation in accordance with our safety policy with the below categories. Provide a 'safe' or 'unsafe' rating for both the user message and the agent response. If there is unsafe content, please also provide a list of violated categories according to our safety policy below. + + + S1: Violence. + S2: Sexual. + S3: Criminal Planning/Confessions. + S4: Guns and Illegal Weapons. + S5: Controlled/Regulated Substances. + S6: Suicide and Self Harm. + S7: Sexual (minor). + S8: Hate/Identity Hate. + S9: PII/Privacy. + S10: Harassment. + S11: Threat. + S12: Profanity. + S13: Needs Caution. + S14: Other. + S15: Manipulation. + S16: Fraud/Deception. + S17: Malware. + S18: High Risk Gov Decision Making. + S19: Political/Misinformation/Conspiracy. + S20: Copyright/Trademark/Plagiarism. + S21: Unauthorized Advice. + S22: Illegal Activity. + S23: Immoral/Unethical. + + + + + user: {{ user_input }} + + + + Provide your safety assessment for the above conversation in the below output JSON format: + {"User Safety": safe or unsafe rating for the user message, "Response Safety": safe or unsafe rating for the agent response. Omit if no agent response present. "Safety Categories": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.} + + Do not include anything other than the output JSON in your response. + Output JSON: + output_parser: nemoguard_parse_prompt_safety + max_tokens: 50 + + - task: content_safety_check_output $model=content_safety + content: | + Task: Check if there is unsafe content in the user or agent messages in the below conversation in accordance with our safety policy with the below categories. Provide a 'safe' or 'unsafe' rating for both the user message and the agent response. If there is unsafe content, please also provide a list of violated categories according to our safety policy below. + + + S1: Violence. + S2: Sexual. + S3: Criminal Planning/Confessions. + S4: Guns and Illegal Weapons. + S5: Controlled/Regulated Substances. + S6: Suicide and Self Harm. + S7: Sexual (minor). + S8: Hate/Identity Hate. + S9: PII/Privacy. + S10: Harassment. + S11: Threat. + S12: Profanity. + S13: Needs Caution. + S14: Other. + S15: Manipulation. + S16: Fraud/Deception. + S17: Malware. + S18: High Risk Gov Decision Making. + S19: Political/Misinformation/Conspiracy. + S20: Copyright/Trademark/Plagiarism. + S21: Unauthorized Advice. + S22: Illegal Activity. + S23: Immoral/Unethical. + + + + + user: {{ user_input }} + + response: agent: {{ bot_response }} + + + + Provide your safety assessment for the above conversation in the below output JSON format: + {"User Safety": safe or unsafe rating for the user message, "Response Safety": safe or unsafe rating for the agent response. Omit if no agent response present. "Safety Categories": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.} + + Do not include anything other than the output JSON in your response. + Output JSON: + output_parser: nemoguard_parse_response_safety + max_tokens: 50 + + - task: topic_safety_check_input $model=topic_control + content: | + You are to act as a customer service agent, providing users with factual information in accordance to the knowledge base. Your role is to ensure that you respond only to relevant queries and adhere to the following guidelines + + Guidelines for the user messages: + - Do not answer questions related to personal opinions or advice on user's order, future recommendations + - Do not provide any information on non-company products or services. + - Do not answer enquiries unrelated to the company policies. + - Do not answer questions asking for personal details about the agent or its creators. + - Do not answer questions about sensitive topics related to politics, religion, or other sensitive subjects. + - If a user asks topics irrelevant to the company's customer service relations, politely redirect the conversation or end the interaction. + - Your responses should be professional, accurate, and compliant with customer relations guidelines, focusing solely on providing transparent, up-to-date information about the company that is already publicly available. + - allow user comments that are related to small talk and chit-chat. diff --git a/docs/getting-started/8-tracing/configs/sequential/config.yml b/docs/getting-started/8-tracing/configs/sequential/config.yml new file mode 100644 index 000000000..87c49eead --- /dev/null +++ b/docs/getting-started/8-tracing/configs/sequential/config.yml @@ -0,0 +1,35 @@ +models: + - type: main + engine: nim + model: meta/llama-3.3-70b-instruct + + - type: content_safety + engine: nim + model: nvidia/llama-3.1-nemoguard-8b-content-safety + + - type: topic_control + engine: nim + model: nvidia/llama-3.1-nemoguard-8b-topic-control + +rails: + input: + flows: + - content safety check input $model=content_safety + - topic safety check input $model=topic_control + - jailbreak detection model + + output: + flows: + - content safety check output $model=content_safety + + config: + jailbreak_detection: + nim_base_url: "https://ai.api.nvidia.com" + nim_server_endpoint: "/v1/security/nvidia/nemoguard-jailbreak-detect" + api_key_env_var: NVIDIA_API_KEY + +tracing: + enabled: true + adapters: + - name: FileSystem + filepath: "sequential_trace.jsonl" diff --git a/docs/getting-started/8-tracing/configs/sequential/prompts.yml b/docs/getting-started/8-tracing/configs/sequential/prompts.yml new file mode 100644 index 000000000..c72205177 --- /dev/null +++ b/docs/getting-started/8-tracing/configs/sequential/prompts.yml @@ -0,0 +1,106 @@ +# These are the default prompts released by Meta, except for policy O7, which was added to address direct insults. +prompts: + + - task: content_safety_check_input $model=content_safety + content: | + Task: Check if there is unsafe content in the user or agent messages in the below conversation in accordance with our safety policy with the below categories. Provide a 'safe' or 'unsafe' rating for both the user message and the agent response. If there is unsafe content, please also provide a list of violated categories according to our safety policy below. + + + S1: Violence. + S2: Sexual. + S3: Criminal Planning/Confessions. + S4: Guns and Illegal Weapons. + S5: Controlled/Regulated Substances. + S6: Suicide and Self Harm. + S7: Sexual (minor). + S8: Hate/Identity Hate. + S9: PII/Privacy. + S10: Harassment. + S11: Threat. + S12: Profanity. + S13: Needs Caution. + S14: Other. + S15: Manipulation. + S16: Fraud/Deception. + S17: Malware. + S18: High Risk Gov Decision Making. + S19: Political/Misinformation/Conspiracy. + S20: Copyright/Trademark/Plagiarism. + S21: Unauthorized Advice. + S22: Illegal Activity. + S23: Immoral/Unethical. + + + + + user: {{ user_input }} + + + + Provide your safety assessment for the above conversation in the below output JSON format: + {"User Safety": safe or unsafe rating for the user message, "Response Safety": safe or unsafe rating for the agent response. Omit if no agent response present. "Safety Categories": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.} + + Do not include anything other than the output JSON in your response. + Output JSON: + output_parser: nemoguard_parse_prompt_safety + max_tokens: 50 + + - task: content_safety_check_output $model=content_safety + content: | + Task: Check if there is unsafe content in the user or agent messages in the below conversation in accordance with our safety policy with the below categories. Provide a 'safe' or 'unsafe' rating for both the user message and the agent response. If there is unsafe content, please also provide a list of violated categories according to our safety policy below. + + + S1: Violence. + S2: Sexual. + S3: Criminal Planning/Confessions. + S4: Guns and Illegal Weapons. + S5: Controlled/Regulated Substances. + S6: Suicide and Self Harm. + S7: Sexual (minor). + S8: Hate/Identity Hate. + S9: PII/Privacy. + S10: Harassment. + S11: Threat. + S12: Profanity. + S13: Needs Caution. + S14: Other. + S15: Manipulation. + S16: Fraud/Deception. + S17: Malware. + S18: High Risk Gov Decision Making. + S19: Political/Misinformation/Conspiracy. + S20: Copyright/Trademark/Plagiarism. + S21: Unauthorized Advice. + S22: Illegal Activity. + S23: Immoral/Unethical. + + + + + user: {{ user_input }} + + response: agent: {{ bot_response }} + + + + Provide your safety assessment for the above conversation in the below output JSON format: + {"User Safety": safe or unsafe rating for the user message, "Response Safety": safe or unsafe rating for the agent response. Omit if no agent response present. "Safety Categories": a comma-separated list of applicable safety categories from the provided taxonomy. Omit if all safe.} + + Do not include anything other than the output JSON in your response. + Output JSON: + output_parser: nemoguard_parse_response_safety + max_tokens: 50 + + - task: topic_safety_check_input $model=topic_control + content: | + You are to act as a customer service agent, providing users with factual information in accordance to the knowledge base. Your role is to ensure that you respond only to relevant queries and adhere to the following guidelines + + Guidelines for the user messages: + - Do not answer questions related to personal opinions or advice on user's order, future recommendations + - Do not provide any information on non-company products or services. + - Do not answer enquiries unrelated to the company policies. + - Do not answer questions asking for personal details about the agent or its creators. + - Do not answer questions about sensitive topics related to politics, religion, or other sensitive subjects. + - If a user asks topics irrelevant to the company's customer service relations, politely redirect the conversation or end the interaction. + - Your responses should be professional, accurate, and compliant with customer relations guidelines, focusing solely on providing transparent, up-to-date information about the company that is already publicly available. + - allow user comments that are related to small talk and chit-chat.