diff --git a/.gitattributes b/.gitattributes index 66184824de8a278c140fc469f326fe8b2fd9ccd4..2abca71ea85c31a04bbb4a1b38489a6958d1290c 100644 --- a/.gitattributes +++ b/.gitattributes @@ -54,3 +54,5 @@ data/b2/video.mp4 filter=lfs diff=lfs merge=lfs -text probes/bridge-20261028/poster.jpg filter=lfs diff=lfs merge=lfs -text probes/bridge-20261028/terminal.jpg filter=lfs diff=lfs merge=lfs -text probes/bridge-20261028/video.mp4 filter=lfs diff=lfs merge=lfs -text +experiments/robotwin-native/standalone-1/combined.mp4 filter=lfs diff=lfs merge=lfs -text +experiments/robotwin-native/kinex-1/combined.mp4 filter=lfs diff=lfs merge=lfs -text diff --git a/app.js b/app.js index 296679c3aac4456059b249a22210eed0d1d0a168..0dd3ca62edde91a02704b073bc55748dbb1a12ae 100644 --- a/app.js +++ b/app.js @@ -14,7 +14,7 @@ const metrics=`
${[['Native steps',num(e.control_steps)],['T if(tab==='overview')target.innerHTML=`
${e.video?``:'
Recording is not available yet
'}${metrics}

${esc(d.cost_note||'API-equivalent cost is an estimate, not a subscription charge.')} ${esc(d.media_note||'')}

Observed behavior

${esc(d.observation||'Analysis pending.')}

Episode record ↗
`; else if(tab==='instruction')target.innerHTML=`

Task instruction

${esc(d.instruction)}
`; else if(tab==='artifacts')target.innerHTML=`

Recorded artifacts

${(d.artifacts||[]).map(a=>`${esc(a.name)} ${num(a.bytes)} bytes ↗`).join('')}
`; -else if(tab==='session'){const v=await load(e.path+'/session.json');target.innerHTML=`

${num(v.length)} messages · Download full session

${v.map(m=>`
${esc(m.role)} ${esc(m.name||'')}
${esc(m.text)}
${m.data?`
Recorded payload
${esc(JSON.stringify(m.data,null,2))}
`:''}
`).join('')||'
Standalone evaluation has no planning-agent session.
'}`;} +else if(tab==='session'){const v=await load(e.path+'/session.json');target.innerHTML=`

${num(v.length)} messages · Download full session

${v.map(m=>`
${esc(m.role)} ${esc(m.name||'')}
${esc(m.text)}
${(m.images||[]).map(x=>`Agent observation`).join('')}
${m.data?`
Recorded payload
${esc(JSON.stringify(m.data,null,2))}
`:''}
`).join('')||'
Standalone evaluation has no planning-agent session.
'}`;} else if(tab==='tools'){const v=await load(e.path+'/tool-calls.json');target.innerHTML=`

${num(v.length)} calls · Download tool calls

${v.map((m,i)=>`
${i+1}. ${esc(m.name)} ${esc(m.summary||'')}

Input

${esc(JSON.stringify(m.input,null,2))}

Output

${esc(typeof m.output==='string'?m.output:JSON.stringify(m.output,null,2))}
`).join('')||'
No agent tool calls
'}`;} else if(tab==='policy'){const v=await load(e.path+'/policy-calls.json');target.innerHTML=`

${num(v.length)} inferences · Download all input / output records

${v.map((m,i)=>`
${i+1}. ${esc(m.task)} ${m.inference_s?.toFixed(2)||'—'} s
${(m.images||[]).map(x=>`Policy input observation`).join('')}

Model output and submitted interface

${esc(JSON.stringify(m.output,null,2))}
`).join('')||'
No VLA inference recorded
'}`;}} function protocol(){$('#main').innerHTML=`
Experiment protocol

One task. Three control choices.

Execution

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${k}${v}

This fixed-rate device protocol differs from upstream VLA trajectory execution. Published StarVLA benchmark scores are not a direct comparison. RoboTwin 2.0-Plus is a separate suite and is not included here.

Source and model identities ↗`} diff --git a/data.json b/data.json index 0ef02586ce700b3847a00db36bb5d2f888c34198..26b9a43130005746e779261ded1046bca7ee2354 100644 --- a/data.json +++ b/data.json @@ -26,7 +26,7 @@ { "id": "standalone", "label": "Standalone VLA", - "model": "Qwen3-VL-OFT \u00b7 40k" + "model": "Qwen3-VL-OFT · 40k" }, { "id": "kinex", @@ -36,7 +36,7 @@ { "id": "kinex_policy", "label": "Kinex + VLA", - "model": "GPT-6 Astra / high \u00b7 native run_policy" + "model": "GPT-6 Astra / high · native run_policy" } ], "episodes": [ @@ -44,13 +44,39 @@ "id": "standalone-1", "task": "1", "condition": "standalone", - "status": "running" + "status": "complete", + "success": false, + "valid": true, + "control_steps": 800, + "tool_calls": 0, + "vla_calls": 50, + "input_tokens": 0, + "output_tokens": 0, + "cached_input_tokens": 0, + "api_cost_usd": 0, + "path": "experiments/robotwin-native/standalone-1", + "video": "experiments/robotwin-native/standalone-1/combined.mp4", + "poster": "experiments/robotwin-native/standalone-1/poster.jpg", + "wall_time_s": 261.565797 }, { "id": "kinex-1", "task": "1", "condition": "kinex", - "status": "running" + "status": "complete", + "success": true, + "valid": true, + "control_steps": 621, + "tool_calls": 61, + "vla_calls": 0, + "input_tokens": 1774043, + "output_tokens": 8679, + "cached_input_tokens": 1715200, + "api_cost_usd": 2.73758, + "path": "experiments/robotwin-native/kinex-1", + "video": "experiments/robotwin-native/kinex-1/combined.mp4", + "poster": "experiments/robotwin-native/kinex-1/poster.jpg", + "wall_time_s": 635.897416 }, { "id": "kinex_policy-1", @@ -95,4 +121,4 @@ "status": "pending" } ] -} \ No newline at end of file +} diff --git a/experiments/robotwin-native/kinex-1/artifacts.json b/experiments/robotwin-native/kinex-1/artifacts.json new file mode 100644 index 0000000000000000000000000000000000000000..49a4a8d24af24d21704e562edbfe99ba3a87308f --- /dev/null +++ b/experiments/robotwin-native/kinex-1/artifacts.json @@ -0,0 +1,109 @@ +{ + "evidence": { + "complete": true, + "path": "evidence.tar.gz" + }, + "recording": { + "complete": true, + "enabled": true, + "encoder_queue_drops": 0, + "lossless": false, + "manifest": { + "bag_messages": 0, + "error": null, + "fps": 10.0, + "run_id": "7b75d3e8384a", + "schema": "rosbridge/recording/1", + "spectator_views": [ + "third_person", + "left_wrist", + "right_wrist" + ], + "state": "complete", + "task": "robotwin/handover-block-aloha-agilex", + "topics": [], + "type": "mp4", + "views": { + "left_wrist": { + "dropped": 0, + "error": null, + "received": 250, + "segments": [ + { + "file": "left_wrist.mp4", + "first_stamp_ns": 0, + "fps": 10.0, + "frames": 250, + "height": 720, + "last_stamp_ns": 24840000000, + "width": 960 + } + ] + }, + "right_wrist": { + "dropped": 0, + "error": null, + "received": 250, + "segments": [ + { + "file": "right_wrist.mp4", + "first_stamp_ns": 0, + "fps": 10.0, + "frames": 250, + "height": 720, + "last_stamp_ns": 24840000000, + "width": 960 + } + ] + }, + "third_person": { + "dropped": 0, + "error": null, + "received": 250, + "segments": [ + { + "file": "third_person.mp4", + "first_stamp_ns": 0, + "fps": 10.0, + "frames": 250, + "height": 720, + "last_stamp_ns": 24840000000, + "width": 960 + } + ] + } + } + }, + "video_available": true, + "videos": [ + { + "bytes": 2505135, + "channel": "spectator", + "fps": 10.0, + "frames": 250, + "path": "recording/third_person.mp4" + }, + { + "bytes": 1719860, + "channel": "spectator", + "fps": 10.0, + "frames": 250, + "path": "recording/left_wrist.mp4" + }, + { + "bytes": 1454029, + "channel": "spectator", + "fps": 10.0, + "frames": 250, + "path": "recording/right_wrist.mp4" + } + ] + }, + "schema": "rlebench/episode-artifacts/1", + "terminal_evidence": { + "available": false, + "complete": false, + "error": "FileNotFoundError: [Errno 2] No such file or directory: '/run/rlebench/private/episode_01/evidence/terminal.json'" + }, + "workspace_complete": true +} diff --git a/experiments/robotwin-native/kinex-1/combined.mp4 b/experiments/robotwin-native/kinex-1/combined.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..2b074df69ca374c196f3c55d54025a28c9f49e16 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/combined.mp4 @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2c086a9ec449338ab3056eff3315773b24fa89b3737c81a31337ba11a74ed81f +size 4426774 diff --git a/experiments/robotwin-native/kinex-1/detail.json b/experiments/robotwin-native/kinex-1/detail.json new file mode 100644 index 0000000000000000000000000000000000000000..0bced2e7d9f2da04ab45e2b8e463875e8f418059 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/detail.json @@ -0,0 +1,1375 @@ +{ + "instruction": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. 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0000000000000000000000000000000000000000..6bc0946439f4effbbe5d26adad190e24531987ce --- /dev/null +++ b/experiments/robotwin-native/kinex-1/evidence.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:66000d8bb81d51f2a4baecf42b7fcaaf237e23a1a544fe68e3c4e0f6073a9042 +size 5690824 diff --git a/experiments/robotwin-native/kinex-1/instruction.json b/experiments/robotwin-native/kinex-1/instruction.json new file mode 100644 index 0000000000000000000000000000000000000000..4e077823a28a46891c7e3cec8542328dbaa964e1 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/instruction.json @@ -0,0 +1,14 @@ +{ + "binding_paths": {}, + "bindings": {}, + "context": {}, + "instruction": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open", + "native_instruction": "", + "policy": "modified", + "prompt": "# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n", + "prompt_template": "\n# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n", + "schema": "rlebench/episode-instruction/3", + "source": "catalog", + "task_id": "robotwin/handover-block-aloha-agilex", + "template": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open" +} diff --git a/experiments/robotwin-native/kinex-1/media.json b/experiments/robotwin-native/kinex-1/media.json new file mode 100644 index 0000000000000000000000000000000000000000..e703a6366f1d7b98be5478959fad539170807eb0 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/media.json @@ -0,0 +1,101 @@ +{ + "native_frames": 250, + "published_frames": 250, + "pts_verified": true, + "source_start_pts": 0.2, + "published_start_pts": 0.0, + "pts_offset": -0.2, + "terminal_frame_retained": true, + "views": [ + "third_person", + "left_wrist", + "right_wrist" + ], + "fps": "10/1", + "native_recording": { + "schema": "rosbridge/recording/1", + "run_id": "7b75d3e8384a", + "task": "robotwin/handover-block-aloha-agilex", + "state": "complete", + "views": { + "third_person": { + "received": 250, + "dropped": 0, + "segments": [ + { + "file": "third_person.mp4", + "frames": 250, + "width": 960, + "height": 720, + "fps": 10.0, + "first_stamp_ns": 0, + 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"text": "", + "images": [], + "timestamp": "2026-10-28T21:25:43.964Z" + }, + { + "role": "user", + "name": null, + "text": "# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes\nin `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.", + "images": [], + "timestamp": "2026-10-28T21:25:43.964Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: ls\n{\"path\": \"/workspace\"}", + "images": [], + "timestamp": "2026-10-28T21:25:46.605Z" + }, + { + "role": "toolResult", + "name": "ls", + "text": ".kinex/\nharness.md\ninstruction.md", + "images": [], + "timestamp": "2026-10-28T21:25:46.614Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \"/workspace/harness.md\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:25:48.800Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n", + "images": [], + "timestamp": "2026-10-28T21:25:48.807Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: find\n{\"pattern\": \"**/*\", \"path\": \"/workspace/.kinex\", \"limit\": 150}", + "images": [], + "timestamp": "2026-10-28T21:25:51.841Z" + }, + { + "role": "toolResult", + "name": "find", + "text": "devices/\ndevices/head_camera/\ndevices/head_camera/profile.yaml\ndevices/left_arm/\ndevices/left_arm/arm.urdf\ndevices/left_arm/links.py\ndevices/left_arm/profile.yaml\ndevices/left_camera/\ndevices/left_camera/profile.yaml\ndevices/left_gripper/\ndevices/left_gripper/gripper.urdf\ndevices/left_gripper/links.py\ndevices/left_gripper/profile.yaml\ndevices/right_arm/\ndevices/right_arm/arm.urdf\ndevices/right_arm/links.py\ndevices/right_arm/profile.yaml\ndevices/right_camera/\ndevices/right_camera/profile.yaml\ndevices/right_gripper/\ndevices/right_gripper/gripper.urdf\ndevices/right_gripper/links.py\ndevices/right_gripper/profile.yaml\ndevices/world/\ndevices/world/profile.yaml\nkinex.json\nmemos/\npython/\npython/.pixi/\npython/pixi.lock\npython/pixi.toml\npython/ros/\npython/ros/build/\npython/ros/build/.built_by\npython/ros/build/COLCON_IGNORE\npython/ros/build/kinex_interfaces/\npython/ros/install/\npython/ros/install/.colcon_install_layout\npython/ros/install/COLCON_IGNORE\npython/ros/install/_local_setup_util_ps1.py\npython/ros/install/_local_setup_util_sh.py\npython/ros/install/kinex_interfaces/\npython/ros/install/kinex_interfaces/include/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_generator_c__visibility_control.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_generator_cpp__visibility_control.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_typesupport_fastrtps_c__visibility_control.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_typesupport_fastrtps_cpp__visibility_control.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_typesupport_introspection_c__visibility_control.h\npython/ros/install/kinex_interfaces/lib/\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_generator_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_generator_py.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_cpp.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_fastrtps_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_fastrtps_cpp.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_introspection_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_introspection_cpp.so\npython/ros/install/kinex_interfaces/lib/python3.12/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/__init__.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/__pycache__/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/__pycache__/__init__.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/_kinex_interfaces_s.ep.rosidl_typesupport_c.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/_kinex_interfaces_s.ep.rosidl_typesupport_fastrtps_c.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/_kinex_interfaces_s.ep.rosidl_typesupport_introspection_c.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/kinex_interfaces_s__rosidl_typesupport_c.so\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/kinex_interfaces_s__rosidl_typesupport_fastrtps_c.so\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/kinex_interfaces_s__rosidl_typesupport_introspection_c.so\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__init__.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/__init__.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/_json.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/_lookup_transform.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/_wait.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_json.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_json_s.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_lookup_transform.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_lookup_transform_s.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_wait.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_wait_s.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/PKG-INFO\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/SOURCES.txt\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/dependency_links.txt\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/top_level.txt\npython/ros/install/kinex_interfaces/share/\npython/ros/install/kinex_interfaces/share/ament_index/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/package_run_dependencies/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/package_run_dependencies/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/packages/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/packages/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/parent_prefix_path/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/parent_prefix_path/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/rosidl_interfaces/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/rosidl_interfaces/kinex_interfaces\npython/ros/install/kinex_interfaces/share/colcon-core/\npython/ros/install/kinex_interfaces/share/colcon-core/packages/\npython/ros/install/kinex_interfaces/share/colcon-core/packages/kinex_interfaces\npython/ros/install/kinex_interfaces/share/kinex_interfaces/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.bash\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.zsh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.bash\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.ps1\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.xml\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.zsh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.srv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.srv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.srv\npython/ros/install/local_setup.bash\npython/ros/install/local_setup.ps1\npython/ros/install/local_setup.sh\npython/ros/install/local_setup.zsh\npython/ros/install/setup.bash\npython/ros/install/setup.ps1\npython/ros/install/setup.sh\npython/ros/install/setup.zsh\npython/ros/log/\npython/ros/log/COLCON_IGNORE\npython/ros/log/build_2026-10-28_21-18-39/\npython/ros/log/build_2026-10-28_21-18-39/events.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/command.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/stderr.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/stdout.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/stdout_stderr.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/streams.log\npython/ros/log/build_2026-10-28_21-18-39/logger_all.log\npython/ros/log/latest\npython/ros/log/latest_build\npython/ros-ready.json\nskills/\ntools/\nworkspace.yaml\n\n[150 results limit reached. Use limit=300 for more, or refine pattern]", + "images": [], + "timestamp": "2026-10-28T21:25:51.880Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \".kinex/workspace.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/world/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_arm/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/right_arm/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_gripper/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/right_gripper/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/head_camera/profile.yaml\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:25:56.807Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "identity:\n id: robotwin-aloha-agilex\n name: RoboTwin ALOHA AgileX\n kind: arm\n model: ALOHA AgileX (dual ARX5)\n source: user\ndevices:\n source: user\nsafety:\n source: user\n max_joint_velocity: null\n max_cartesian_velocity: null\n keep_out_zones: []\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\n", + "images": [], + "timestamp": "2026-10-28T21:25:56.823Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "name: world\nruntime:\n domain_id: 83\n observation: time\n nodes: []\n ready_timeout_s: 30\noperations:\n time:\n description: Authoritative world clock\n transport: topic\n direction: output\n endpoint: /clock\n type: rosgraph_msgs/msg/Clock\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n wait:\n description: Advance or await duration_s of world time (finite and nonnegative).\n Zero is an observation barrier. Waits are sequential; one requested while another\n is pending is rejected. A terminal episode rejects further progression without\n exposing benchmark results.\n transport: service\n direction: input\n endpoint: /world/wait\n type: kinex_interfaces/srv/Wait\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 120\n stop:\n description: Cancel the pending wait.\n transport: service\n direction: input\n endpoint: /world/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n Kinex supplies frame transforms independently. This device exposes no task instructions\n or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n base:\n parent: null\n translation_m: null\n rotation_xyzw: null\n", + "images": [], + "timestamp": "2026-10-28T21:25:56.823Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "name: left_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /left_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /left_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /left_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n as left_arm/, and helpers.joint_model('left_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fl_joint1: 0.0\n fl_joint2: 0.0\n fl_joint3: 0.0\n fl_joint4: 0.0\n fl_joint5: 0.0\n fl_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - left_arm\nframes:\n base:\n translation_m:\n - -0.296999871731\n - -0.419500023127\n - 0.782000243664\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 0.714142563298\n - 0.700000389985\n parent: world:base\n", + "images": [], + "timestamp": "2026-10-28T21:25:56.823Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "name: right_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /right_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /right_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /right_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n as right_arm/, and helpers.joint_model('right_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fr_joint1: 0.0\n fr_joint2: 0.0\n fr_joint3: 0.0\n fr_joint4: 0.0\n fr_joint5: 0.0\n fr_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - right_arm\nframes:\n base:\n translation_m:\n - 0.306300014257\n - -0.418499916792\n - 0.78100001812\n rotation_xyzw:\n - -2.9802e-08\n - 5.9605e-08\n - 0.71063355368\n - 0.703562409721\n parent: world:base\n", + "images": [], + "timestamp": "2026-10-28T21:25:56.823Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "name: left_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured left_gripper, the native normalized gripper opening (0 closed,\n 1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n it.\n transport: topic\n direction: output\n endpoint: /left_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /left_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /left_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - left_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: left_arm:flange\n tcp:\n translation_m:\n - 0.1199999989\n - 7.305e-09\n - -7.152e-09\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 3.2247e-08\n - 0.999999998609\n parent: left_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: left_gripper\n open: 1.0\n closed: 0.0\n", + "images": [], + "timestamp": "2026-10-28T21:25:56.823Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "name: right_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured right_gripper, the native normalized gripper opening (0\n closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n from it.\n transport: topic\n direction: output\n endpoint: /right_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /right_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /right_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - right_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: right_arm:flange\n tcp:\n translation_m:\n - 0.119999992883\n - -9.757e-09\n - -7.153e-09\n rotation_xyzw:\n - 1.0e-12\n - 2.9802e-08\n - -6.4393e-08\n - 0.999999973161\n parent: right_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: right_gripper\n open: 1.0\n closed: 0.0\n", + "images": [], + "timestamp": "2026-10-28T21:25:56.823Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "name: head_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /head_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /head_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /head_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - -0.03200000152\n - -0.449999988079\n - 1.350000023842\n rotation_xyzw:\n - -0.948683301192\n - 0.0\n - 0.0\n - 0.316227769682\n parent: world:base\n", + "images": [], + "timestamp": "2026-10-28T21:25:56.823Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"python\": \"from kinex import runtime, finish\\nfinish({d:runtime.operations(d) for d in ['world','left_arm','left_gripper','head_camera']})\", \"description\": \"inspect operation contracts\"}", + "images": [], + "timestamp": "2026-10-28T21:26:00.106Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"\",\n \"result\": {\n \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120},\"stop\":{\"description\":\"Cancel the pending wait.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_arm\": {\"joint_state\":{\"description\":\"Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_arm/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Stop arm motion and hold the measured posture.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_arm/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_gripper\": {\"joint_state\":{\"description\":\"Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_gripper/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Hold the measured opening; fingers may settle briefly after the stop.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"head_camera\": {\"image\":{\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"depth\":{\"description\":\"16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/depth\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"camera_info\":{\"description\":\"Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/camera_info\",\"type\":\"sensor_msgs/msg/CameraInfo\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10}}\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:26:01.104Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: devices\n{\"calls\": [{\"deviceID\": \"world\", \"action\": \"load\"}, {\"deviceID\": \"left_arm\", \"action\": \"load\"}, {\"deviceID\": \"right_arm\", \"action\": \"load\"}, {\"deviceID\": \"left_gripper\", \"action\": \"load\"}, {\"deviceID\": \"right_gripper\", \"action\": \"load\"}, {\"deviceID\": \"head_camera\", \"action\": \"load\"}]}", + "images": [], + "timestamp": "2026-10-28T21:26:04.491Z" + }, + { + "role": "toolResult", + "name": "devices", + "text": "{\n \"results\": [\n {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:26:10.567Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:26:13.473Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n Return a stamped source-to-target transform using Kinex frame history.\n await helpers.transform_pose(io, pose, source, target, stamp=None)\n Express a pose given in one frame in another.\n await helpers.relative_pose(io, child, parent, timestamp=None)\n Express a frame-attached child pose relative to a frame-attached parent pose.\n Implementation: frames.py\n\nKinematics\n helpers.joint_model(device)\n Load a joint device's commanded joints, link FK and IK from its URDF.\n Implementation: kinematics.py\n\nMotion\n helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n Plan joint references and duration without device I/O.\n Implementation: motion.py\n\nPolicy\n await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n Infer once and submit the valid action prefix to the commanded devices.\n Implementation: policy.py\n\nImages\n await helpers.camera_frame(io, camera, timeout_s=None)\n The current image and, for an rgbd camera, its depth from the same acquisition.\n helpers.image_array(image)\n Native-order pixels of a sensor_msgs/Image as numpy for computation.\n helpers.save_image(image)\n Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n Implementation: images.py\n\nPerception\n await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n Locate a detected object in a stamped camera optical frame.\n Implementation: perception.py\n", + "images": [], + "timestamp": "2026-10-28T21:26:13.479Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py\", \"offset\": 1, \"limit\": 250}", + "images": [], + "timestamp": "2026-10-28T21:26:16.257Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n \"\"\"Load a joint device's model without device I/O.\n\n joints lists the commanded joints in URDF document order with limits and\n rate limits; names holds their names. fk(positions, link=None) returns the\n link pose [x, y, z, qx, qy, qz, qw] in /base. ik(pose, seed,\n link=None, **options) returns commanded positions nearest the seed, or\n raises ValueError when no verified solution is found. link defaults to the\n model's tip_link. Measured links are also on TF as /.\n\n Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n \"\"\"\n return JointModel(profile(device))\n", + "images": [], + "timestamp": "2026-10-28T21:26:16.262Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"python\": \"import asyncio\\nfrom kinex import runtime, communication, helpers, finish\\nasync def main():\\n async with communication() as io:\\n f=await helpers.camera_frame(io,'head_camera')\\n out={}\\n for d in ['left_arm','right_arm','left_gripper','right_gripper']:\\n async with io.subscribe(d+'.joint_state') as s: out[d]=await s.next()\\n out['schemas']={x:runtime.describe(x) for x in ['left_arm.joint_target','left_gripper.target','world.wait']}\\n finish(out,file=helpers.save_image(f['image']))\\nasyncio.run(main())\", \"description\": \"observe scene and schemas\"}", + "images": [], + "timestamp": "2026-10-28T21:26:20.658Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793222782.251334587] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n exec(compile(_request[\\\"source\\\"], \\\"\\\", \\\"exec\\\"), module.__dict__)\\n File \\\"\\\", line 11, in \\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"\\\", line 10, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}", + "images": [], + "timestamp": "2026-10-28T21:26:23.111Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py\", \"offset\": 1, \"limit\": 180}", + "images": [], + "timestamp": "2026-10-28T21:26:25.414Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n Both come from one acquisition (equal stamps); reading never advances world time.\n\n Example: image, depth = await helpers.camera_frame(io, \"camera\")\n \"\"\"\n return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n \"\"\"\n return array(image)\n\n\ndef save_image(image):\n \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n \"\"\"\n return save(image)\n", + "images": [], + "timestamp": "2026-10-28T21:26:25.418Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"python\": \"import asyncio\\nfrom kinex import runtime, communication, helpers, finish\\nasync def main():\\n async with communication() as io:\\n image,depth=await helpers.camera_frame(io,'head_camera')\\n out={}\\n for d in ['left_arm','right_arm','left_gripper','right_gripper']:\\n async with io.subscribe(d+'.joint_state') as s: out[d]=await s.next()\\n out['schemas']={x:runtime.describe(x) for x in ['left_arm.joint_target','left_gripper.target','world.wait']}\\n p=helpers.save_image(image)\\n finish(out,file=p)\\nasyncio.run(main())\", \"description\": \"observe scene and schemas\"}", + "images": [], + "timestamp": "2026-10-28T21:26:29.317Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793222790.779743893] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"left_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\n \"right_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\n \"left_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"left_gripper\"],\"position\":[0.986124409870668],\"velocity\":[0.018415418030186134],\"effort\":[]},\n \"right_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"right_gripper\"],\"position\":[0.9861242744055662],\"velocity\":[0.0184164128520272],\"effort\":[]},\n \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence\",\"points\":\"sequence\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence\",\"velocities\":\"sequence\",\"accelerations\":\"sequence\",\"effort\":\"sequence\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}},\n \"file\": \"/tmp/kinex-observation-ftia7zbo.png\"\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/01d7a694-1.png" + ], + "timestamp": "2026-10-28T21:26:31.346Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Segmenting the red prism**\nTool call: exec\n{\"python\": \"import asyncio,numpy as np\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n im,de=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im); z=helpers.image_array(de)*.001\\n masks={'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}\\n out={}\\n for k,m in masks.items():\\n v,u=np.where(m&(z>0)); zz=z[v,u]; p=np.stack([(u-159.5)*zz/358.6421814,(v-119.5)*zz/358.6421814,zz],axis=1)\\n tf=await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'})\\n out['tf']=tf\\n from scipy.spatial.transform import Rotation as R\\n # relative_pose returns pose\\n out[k]={'pixels':[int(u.min()),int(v.min()),int(u.max()),int(v.max())],'camera_range':[p.min(0).tolist(),p.max(0).tolist()],'median':np.median(p,axis=0).tolist()}\\n out['tcp']={d:await helpers.relative_pose(io,{'frame':d+'_gripper:tcp'},{'frame':'world:base'}) for d in ['left','right']}\\n finish(out)\\nasyncio.run(main())\", \"description\": \"locate block and pad surfaces\"}", + "images": [], + "timestamp": "2026-10-28T21:26:42.398Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793222803.632560113] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tf\": {\"pose\":[-0.03200000152,-0.449999988079,1.350000023842,0.9486832972651139,0,0,-0.3162277683730379],\"timestamp\":0},\n \"red\": {\"pixels\":[77,0,115,67],\"camera_range\":[[-0.16374677337382487,-0.2402380547197954,0.707],[-0.10597470674429707,-0.125013181174009,0.862]],\"median\":[-0.1355138980314043,-0.1914066542090244,0.776]},\n \"blue\": {\"pixels\":[199,55,244,86],\"camera_range\":[[0.09508362866543728,-0.15700467742024501,0.8140000000000001],[0.19417403643976383,-0.07631422464909199,0.873]],\"median\":[0.1445019651556246,-0.11359511544617211,0.84]},\n \"tcp\": {\"left\":{\"pose\":[-0.30032357671522697,-0.193827798419468,0.9419981020126762,-0.000006960933310190554,-0.00000395167326060435,0.7141423608015681,0.7000004917460364],\"timestamp\":0},\"right\":{\"pose\":[0.30523323124039986,-0.1928057309036048,0.9409978556569187,-0.000007012336903923293,-0.000003885552193758481,0.710633293626997,0.7035625927553112],\"timestamp\":0}}\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:26:44.722Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking possible tower view**\nTool call: read\n{\"path\": \".kinex/devices/left_camera/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_gripper/gripper.urdf\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/left_arm/arm.urdf\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:26:59.090Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "name: left_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /left_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /left_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /left_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n pixel-center depth from a raster twin; calibration and the optical-frame mount on\n the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - 0.07000001149\n - 0.031999994336\n - 0.065000047158\n rotation_xyzw:\n - -0.589367966393\n - 0.589367919412\n - -0.390698641271\n - 0.39069867358\n parent: left_arm:flange\n", + "images": [], + "timestamp": "2026-10-28T21:26:59.094Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "images": [], + "timestamp": "2026-10-28T21:26:59.094Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "images": [], + "timestamp": "2026-10-28T21:26:59.094Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Finding red surface coordinates**\nTool call: exec\n{\"python\": \"import asyncio,numpy as np\\nfrom scipy.spatial.transform import Rotation as R\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n im,de=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im).astype(float); z=helpers.image_array(de)*.001\\n tf=(await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'}))['pose']; out={}\\n for k,m in {'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}.items():\\n v,u=np.where(m&(z>0)); zz=z[v,u]; p=np.stack([(u-159.5)*zz/358.6421814,(v-119.5)*zz/358.6421814,zz],axis=1); w=R.from_quat(tf[3:]).apply(p)+tf[:3]\\n out[k]={'min':w.min(0),'max':w.max(0),'median':np.median(w,axis=0),'bottom':np.median(w[w[:,2].6: raise ValueError('IK branch jump')\\n q=nq; t=elapsed+duration*f; ns=round(t*1e9)\\n points.append({'positions':q.tolist(),'time_from_start':{'sec':ns//1000000000,'nanosec':ns%1000000000}})\\n elapsed+=duration; wp=targetp; wr=targetr\\n plans[side]=(model,q,elapsed,points)\\n for side,(model,q,t,points) in plans.items():\\n await io.publish(side+'_arm.joint_target',{'joint_names':model.names,'points':points})\\n for side,value in a.get('grips',{}).items():\\n await io.publish(side+'_gripper.target',{'data':float(value)})\\n wait=max([p[2] for p in plans.values()]+[float(a.get('wait',0))])\\n if wait: result['wait']=await io.request('world.wait',duration_s=wait+float(a.get('settle',.16)))\\n for side in ['left','right']:\\n s=await state(io,side+'_arm'); g=await state(io,side+'_gripper')\\n result[side]={'q':list(s.position),'grip':list(g.position),'tcp':(await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'}))['pose']}\\n if side in plans: result[side]['joint_error']=float(np.max(np.abs(np.array(s.position)-plans[side][1])))\\n paths=[]\\n if a.get('observe',True):\\n image,depth=await helpers.camera_frame(io,'head_camera'); paths=[helpers.save_image(image)]\\n finish(result,file=paths)\\n\\nasyncio.run(main())\\n\"}", + "images": [], + "timestamp": "2026-10-28T21:27:44.857Z" + }, + { + "role": "toolResult", + "name": "write", + "text": "Successfully wrote to /workspace/.kinex/tools/cartesian_move.py", + "images": [], + "timestamp": "2026-10-28T21:27:44.864Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"left\": [[-0.167, 0.045, 0.835, 0, 0, 0.70710678, 0.70710678]]}, \"seconds\": 2, \"grips\": {\"left\": 1}}, \"description\": \"approach block with left arm\"}", + "images": [], + "timestamp": "2026-10-28T21:27:47.996Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793222870.183791116] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 40, in script\\n runpy.run_path(file, run_name=\\\"__main__\\\")\\n File \\\"\\\", line 287, in run_path\\n File \\\"\\\", line 98, in _run_module_code\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 51, in \\n asyncio.run(main())\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 22, in main\\n local=model.fk(q); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0])\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n q = joint_target(positions, self.joints, check_limits=False)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n vector(value, len(joints), \\\"joint target\\\")\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}", + "images": [], + "timestamp": "2026-10-28T21:27:50.898Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: edit\n{\"path\": \".kinex/tools/cartesian_move.py\", \"edits\": [{\"oldText\": \"local=model.fk(q);\", \"newText\": \"local=model.fk(q.tolist());\"}, {\"oldText\": \"model.ik([*lp,*lr.as_quat()],q)\", \"newText\": \"model.ik([*lp,*lr.as_quat()],q.tolist())\"}]}", + "images": [], + "timestamp": "2026-10-28T21:27:55.238Z" + }, + { + "role": "toolResult", + "name": "edit", + "text": "Successfully replaced 2 block(s) in .kinex/tools/cartesian_move.py.", + "images": [], + "timestamp": "2026-10-28T21:27:55.247Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"left\": [[-0.167, 0.045, 0.835, 0, 0, 0.70710678, 0.70710678]]}, \"seconds\": 2, \"grips\": {\"left\": 1}}, \"description\": \"approach block with left arm\"}", + "images": [], + "timestamp": "2026-10-28T21:27:57.609Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793222879.778297666] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.16,\"waited_s\":2.16},\n \"left\": {\"q\":[-0.4095152020454407,2.26525616645813,0.7202089428901672,1.5543266534805298,-0.3688046932220459,0.013095114380121231],\"grip\":[1.0019286491654136],\"tcp\":[-0.17529561461992135,0.03572927260289499,0.8417034955027122,0.0007973011467065941,-0.001999688929063644,0.7090469286738864,0.7051580095930252],\"joint_error\":0.05355189650084757},\n \"right\": {\"q\":[-3.0567537834258474e-8,-0.000004077900030097226,0.000004151807388552697,1.029454779200023e-7,6.391106808223412e-7,-8.509506983500614e-7],\"grip\":[0.9861380918459459],\"tcp\":[0.30523325599148965,-0.19280648083225077,0.9410027440767832,-0.0000026235312808848334,-0.000008356054671572675,0.7106332329651421,0.7035626540179656]},\n \"file\": [\"/tmp/kinex-observation-2d4xk9x7.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/a8adce0d-1.png" + ], + "timestamp": "2026-10-28T21:28:09.087Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Waiting for next part**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"left\": [[-0.1665, 0.195, 0.835, 0, 0, 0.70710678, 0.70710678]]}, \"seconds\": 1.2, \"settle\": 0.28}, \"description\": \"insert left fingers around block\"}", + "images": [], + "timestamp": "2026-10-28T21:28:13.014Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793222895.152228323] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":3.64,\"waited_s\":1.48},\n \"left\": {\"q\":[-0.3148799538612366,2.548339366912842,1.4422779083251953,1.1141002178192139,-0.26099324226379395,0.009243687614798546],\"grip\":[1.0029944208535282],\"tcp\":[-0.16302799174504953,0.18793424064579894,0.8378929771294567,0.0013159567918552503,-0.0023886792458947675,0.7054448415417577,0.7087595770155819],\"joint_error\":0.035971618500195035},\n \"right\": {\"q\":[-3.054815778114062e-8,-0.000004089587491762359,0.000004202859599899966,1.0338320066693996e-7,6.390118301169423e-7,-8.509547910762194e-7],\"grip\":[0.9861380918459459],\"tcp\":[0.30523325596087636,-0.19280648459693756,0.9410027702409021,-0.00000260137554040625,-0.00000837856983353962,0.7106332330065999,0.7035626539759058]},\n \"file\": [\"/tmp/kinex-observation-vp29og6x.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/b833ee1b-1.png" + ], + "timestamp": "2026-10-28T21:28:21.891Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"grips\": {\"left\": 0}, \"wait\": 0.6, \"settle\": 0.08}, \"description\": \"grasp block with left gripper\"}", + "images": [], + "timestamp": "2026-10-28T21:28:25.461Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793222907.657551470] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":4.32,\"waited_s\":0.6800000000000002},\n \"left\": {\"q\":[-0.3116726279258728,2.5611274242401123,1.4739559888839722,1.093721866607666,-0.2568727135658264,0.01041070744395256],\"grip\":[0.7422830950130116],\"tcp\":[-0.16291990834412706,0.19365203773945056,0.8379081966183919,0.0014630621736401283,-0.0012951390698152862,0.7051584468060392,0.7090470696378064]},\n \"right\": {\"q\":[-3.0559299091237335e-8,-0.000004090014499524841,0.0000042047331589856185,1.0359769930801122e-7,6.390172870851529e-7,-8.509651934218709e-7],\"grip\":[0.9861380918459459],\"tcp\":[0.3052332559637977,-0.19280648473495687,0.9410027712441067,-0.0000026004973479138483,-0.000008379470005694451,0.7106332330007554,0.7035626539818014]},\n \"file\": [\"/tmp/kinex-observation-b4k5muhh.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/db1b4a52-1.png" + ], + "timestamp": "2026-10-28T21:28:31.315Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Positioning grippers around block**\n\n**Imaging block top**\nTool call: device\n{\"deviceID\": \"left_camera\", \"action\": \"load\"}", + "images": [], + "timestamp": "2026-10-28T21:28:47.425Z" + }, + { + "role": "toolResult", + "name": "device", + "text": "{\n \"ready\": true\n}", + "images": [], + "timestamp": "2026-10-28T21:28:49.352Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: device\n{\"deviceID\": \"left_camera\", \"action\": \"image\"}", + "images": [], + "timestamp": "2026-10-28T21:28:52.318Z" + }, + { + "role": "toolResult", + "name": "device", + "text": "{\n \"deviceID\": \"left_camera\",\n \"timestamp\": 4.32,\n \"frame_id\": \"left_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-n86hgu09.png\"\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/c1d037d5-1.png" + ], + "timestamp": "2026-10-28T21:28:52.525Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Planning right-hand block grasp**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"left\": [[-0.1665, 0.195, 0.955, 0, 0, 0.70710678, 0.70710678], [-0.045, 0.08, 0.955, 0, 0, 0.70710678, 0.70710678]]}, \"seconds\": 1.1, \"settle\": 0.24}, \"description\": \"lift block into handover position\"}", + "images": [], + "timestamp": "2026-10-28T21:29:04.382Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793222946.551821575] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.76,\"waited_s\":2.4399999999999995},\n \"left\": {\"q\":[-0.6309064626693726,2.1239354610443115,1.2338547706604004,0.8957475423812866,-0.5893312096595764,0.018119316548109055],\"grip\":[0.7441786920482463],\"tcp\":[-0.0523685806723061,0.08833117353535963,0.9531195122310145,0.00023793746021872822,0.002430648176331042,0.7080719927932259,0.7061360268086014],\"joint_error\":0.024221290566641795},\n \"right\": {\"q\":[-3.056838693282771e-8,-0.000004090662514499854,0.000004208202426525531,1.0363435620774908e-7,6.391171041286725e-7,-8.509500162290351e-7],\"grip\":[0.9861380918459459],\"tcp\":[0.30523325598005857,-0.19280648498184277,0.941002772994267,-0.000002599035845421926,-0.000008380945574451785,0.7106332329624366,0.7035626540204932]},\n \"file\": [\"/tmp/kinex-observation-3etlic6u.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/0bd66e5e-1.png" + ], + "timestamp": "2026-10-28T21:29:16.723Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Shifting red block forward**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"right\": [[0.1, 0.08, 1.035, 0, 0, 1, 0]]}, \"seconds\": 1.6, \"settle\": 0.24}, \"description\": \"position right gripper beside upper block\"}", + "images": [], + "timestamp": "2026-10-28T21:29:30.223Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793222972.262569144] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":8.6,\"waited_s\":1.8399999999999999},\n \"left\": {\"q\":[-0.6511423587799072,2.11344051361084,1.2211240530014038,0.9018359780311584,-0.6114063262939453,0.01861589215695858],\"grip\":[0.7490885907953436],\"tcp\":[-0.04701344964065174,0.08107771982913375,0.9547894699199675,0.0005889016793547773,0.000834883228444015,0.7086906013221871,0.705518665778875]},\n \"right\": {\"q\":[0.0732794925570488,2.1728670597076416,1.6520055532455444,0.6286025643348694,-1.3997211456298828,0.10128568857908249],\"grip\":[0.9882686869664626],\"tcp\":[0.10952570589101249,0.0737831238675754,1.036011892666211,0.001709156521781137,-0.003622655986114087,0.9995123184453645,0.030969023613326],\"joint_error\":0.10358034141029537},\n \"file\": [\"/tmp/kinex-observation-yer04ywa.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/797caa5e-1.png" + ], + "timestamp": "2026-10-28T21:29:40.366Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Adjusting camera angle**\nTool call: read\n{\"path\": \".kinex/devices/right_camera/profile.yaml\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:29:50.138Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "name: right_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /right_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /right_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /right_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with\n pixel-center depth from a raster twin; calibration and the optical-frame mount on\n the right_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - 0.069999999887\n - 0.031999976227\n - 0.064999980711\n rotation_xyzw:\n - -0.58936798274\n - 0.589367967612\n - -0.390698619841\n - 0.390698640561\n parent: right_arm:flange\n", + "images": [], + "timestamp": "2026-10-28T21:29:50.142Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: device\n{\"deviceID\": \"right_camera\", \"action\": \"load\"}", + "images": [], + "timestamp": "2026-10-28T21:29:52.504Z" + }, + { + "role": "toolResult", + "name": "device", + "text": "{\n \"ready\": true\n}", + "images": [], + "timestamp": "2026-10-28T21:29:54.030Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: device\n{\"deviceID\": \"right_camera\", \"action\": \"image\"}", + "images": [], + "timestamp": "2026-10-28T21:29:57.196Z" + }, + { + "role": "toolResult", + "name": "device", + "text": "{\n \"deviceID\": \"right_camera\",\n \"timestamp\": 8.6,\n \"frame_id\": \"right_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-hf7pp7dk.png\"\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/13f789a0-1.png" + ], + "timestamp": "2026-10-28T21:29:57.484Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Saving colored-mask helper**\nTool call: write\n{\"path\": \".kinex/tools/color_geometry.py\", \"content\": \"\\\"\\\"\\\"Measure red block / blue pad visible surface bounds from calibrated RGB-D.\\\"\\\"\\\"\\nimport asyncio\\nimport numpy as np\\nfrom scipy.spatial.transform import Rotation\\nfrom kinex import args,communication,helpers,finish\\nasync def main():\\n out={}; files=[]\\n async with communication() as io:\\n for cam in args().get('cameras',['head_camera']):\\n im,de=await helpers.camera_frame(io,cam); a=helpers.image_array(im).astype(float); z=helpers.image_array(de)*.001\\n async with io.subscribe(cam+'.camera_info') as s: info=await s.next()\\n fx,fy,cx,cy=info.k[0],info.k[4],info.k[2],info.k[5]\\n tf=(await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'},timestamp=im.header.stamp.sec+im.header.stamp.nanosec*1e-9))['pose']\\n out[cam]={}\\n for k,m in {'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}.items():\\n v,u=np.where(m&(z>0))\\n if not len(u): continue\\n zz=z[v,u]; p=np.stack([(u-cx)*zz/fx,(v-cy)*zz/fy,zz],axis=1); w=Rotation.from_quat(tf[3:]).apply(p)+tf[:3]\\n out[cam][k]={'min':w.min(0),'max':w.max(0),'median':np.median(w,axis=0),'low':np.median(w[w[:,2]w[:,2].max()-.008],axis=0)}\\n files.append(helpers.save_image(im))\\n finish(out,file=files)\\nasyncio.run(main())\\n\"}", + "images": [], + "timestamp": "2026-10-28T21:30:11.820Z" + }, + { + "role": "toolResult", + "name": "write", + "text": "Successfully wrote to .kinex/tools/color_geometry.py", + "images": [], + "timestamp": "2026-10-28T21:30:11.823Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/color_geometry.py\", \"args\": {\"cameras\": [\"right_camera\"]}, \"description\": \"measure suspended block for transfer\"}", + "images": [], + "timestamp": "2026-10-28T21:30:15.191Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223017.267591956] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"right_camera\": {\"red\":{\"min\":[-0.07704535908917484,0.05037363663380437,0.9359792452628558],\"max\":[-0.010132965239384423,0.11802476366946671,1.0577161411202283],\"median\":[-0.01790569017331503,0.08437329579285988,1.0223647101538353],\"low\":[-0.01968119294135831,0.11215254015326631,0.9426068395053633],\"high\":[-0.025401475412971358,0.08528556780882164,1.0568718285682772]}},\n \"file\": [\"/tmp/kinex-observation-wrxb6ic4.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/1953a6e9-1.png" + ], + "timestamp": "2026-10-28T21:30:17.881Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Aligning the right finger**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"right\": [[-0.044, 0.084, 1.025, 0, 0, 1, 0]]}, \"seconds\": 1.2, \"settle\": 0.32}, \"description\": \"insert right fingers for handover\"}", + "images": [], + "timestamp": "2026-10-28T21:30:27.842Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223029.805918588] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.120000000000001,\"waited_s\":1.5200000000000014},\n \"left\": {\"q\":[-0.5804733037948608,2.104207992553711,1.1955286264419556,0.9432793855667114,-0.6488001346588135,0.010548126883804798],\"grip\":[0.7495477158914913],\"tcp\":[-0.09106256375115962,0.09291270090328957,0.9573460230231323,-0.00019867867634500307,-0.014277350018817896,0.7457316671533772,0.6660933856508923]},\n \"right\": {\"q\":[0.28108417987823486,2.3755812644958496,2.0089495182037354,0.41051310300827026,-1.1974685192108154,0.03320124372839928],\"grip\":[1.0481817857785658],\"tcp\":[-0.00454658121577145,0.09928463248112668,1.0355422699375194,0.00233159746702242,-0.005923685166572289,0.9996083980399332,0.027249296786112258],\"joint_error\":0.07316711639015039},\n \"file\": [\"/tmp/kinex-observation-skgw42gu.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/5c5beb92-1.png" + ], + "timestamp": "2026-10-28T21:30:36.669Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Stopping after contact**\nTool call: device_stop\n{\"reason\": \"Unexpected handover contact displaced the left arm; hold both arms before adjusting.\"}", + "images": [], + "timestamp": "2026-10-28T21:30:41.248Z" + }, + { + "role": "toolResult", + "name": "device_stop", + "text": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped", + "images": [], + "timestamp": "2026-10-28T21:30:41.346Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Testing right wrist orientation**\n\n**Checking gripper-camera clearance**\nTool call: exec\n{\"file\": \".kinex/tools/color_geometry.py\", \"args\": {\"cameras\": [\"right_camera\", \"left_camera\"]}, \"description\": \"inspect contact geometry while held\"}", + "images": [], + "timestamp": "2026-10-28T21:31:01.056Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223063.023708069] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"right_camera\": {\"red\":{\"min\":[-0.1384726838486533,0.06276790815003651,0.962540244023994],\"max\":[-0.05922303365747568,0.13124786536745542,1.060490489585483],\"median\":[-0.06550222925211087,0.09469825304345947,1.0415251530870966],\"low\":[-0.11114994769135768,0.06490913732700883,0.9669796277877257],\"high\":[-0.07376581631004708,0.09461002694122791,1.0596298811854297]}},\n \"left_camera\": {\"red\":{\"min\":[-0.15688127335414823,0.12266262357040175,0.713505453118275],\"max\":[0.037058832820139165,0.40054043859581245,1.0031299882724507],\"median\":[-0.07028974145388796,0.40000044254322253,0.880343311680841],\"low\":[-0.12835342427817242,0.4000191366344823,0.718829186316962],\"high\":[-0.06946718738984804,0.4000030238401364,0.998369265895211]}},\n \"file\": [\"/tmp/kinex-observation-95p9oprm.png\",\"/tmp/kinex-observation-8vj3yjvv.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/86db988c-1.png", + "experiments/robotwin-native/kinex-1/observations/86db988c-2.png" + ], + "timestamp": "2026-10-28T21:31:03.947Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Retracting right grip**\n\n**Retracting right gripper**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"right\": [[0.15, 0.1, 1.05, 0, 0, 1, 0]]}, \"seconds\": 1, \"grips\": {\"right\": 1}, \"settle\": 0.24}, \"description\": \"withdraw right arm from contact\"}", + "images": [], + "timestamp": "2026-10-28T21:31:28.561Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223090.836931004] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":11.36,\"waited_s\":1.2399999999999984},\n \"left\": {\"q\":[-0.5804134607315063,2.1041758060455322,1.1955393552780151,0.9432682394981384,-0.6488250494003296,0.010563568212091923],\"grip\":[0.7530979514122009],\"tcp\":[-0.09109739092829977,0.09292400028290476,0.9573646424377735,-0.0001907213903724974,-0.014287166396507055,0.7457598088703344,0.6660616697989699]},\n \"right\": {\"q\":[0.008498204872012138,2.4111969470977783,1.9728046655654907,1.1586765050888062,-1.5211362838745117,0.7175896763801575],\"grip\":[1.0059774978594347],\"tcp\":[0.13843104276620039,0.10128114809864314,1.0534453784964337,0.005817304808264196,-0.001298347103139134,0.9999791132900113,-0.0024992485451372226],\"joint_error\":0.1610297677623429},\n \"file\": [\"/tmp/kinex-observation-cvsfv2kv.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/5ffc47a8-1.png" + ], + "timestamp": "2026-10-28T21:31:35.498Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Positioning left camera**\n\n**Positioning right gripper**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"left\": [[-0.09, 0.04, 0.97, -0.5, -0.5, 0.5, 0.5]]}, \"seconds\": 1.6, \"grips\": {\"left\": 0}, \"settle\": 0.32}, \"description\": \"orient block horizontally for clear handover\"}", + "images": [], + "timestamp": "2026-10-28T21:31:51.357Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223113.323128756] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.280000000000001,\"waited_s\":1.9200000000000017},\n \"left\": {\"q\":[-0.6108473539352417,1.9071658849716187,0.9704519510269165,0.9435008764266968,-0.5798410177230835,-1.5156503915786743],\"grip\":[0.18181818181818182],\"tcp\":[-0.09249703641535548,0.04354302206857258,0.9685886998113872,-0.4883985901176337,-0.49195225124892156,0.5129229388662707,0.506319917094927],\"joint_error\":0.03695103243016362},\n \"right\": {\"q\":[-0.024585271254181862,2.5281856060028076,2.199103832244873,1.4551665782928467,-1.5355974435806274,0.8628689646720886],\"grip\":[0.9977146685123444],\"tcp\":[0.15515497764595165,0.105221917700341,1.0726259319873463,0.0034350023240606736,-0.13122159004534528,0.991332287535084,0.005421324142934947]},\n \"file\": [\"/tmp/kinex-observation-ytn17zdz.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/5df3e384-1.png" + ], + "timestamp": "2026-10-28T21:32:01.749Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Stopping the grip fault**\nTool call: device_stop\n{\"reason\": \"Block slipped during reorientation; hold before recovering from table.\"}", + "images": [], + "timestamp": "2026-10-28T21:32:07.590Z" + }, + { + "role": "toolResult", + "name": "device_stop", + "text": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped", + "images": [], + "timestamp": "2026-10-28T21:32:07.658Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Measuring low grasp**\nTool call: exec\n{\"file\": \".kinex/tools/color_geometry.py\", \"args\": {\"cameras\": [\"head_camera\"]}, \"description\": \"locate block after slip\"}", + "images": [], + "timestamp": "2026-10-28T21:32:15.826Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223137.713473185] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"head_camera\": {\"red\":{\"min\":[-0.14741029664711341,-0.22615111740019297,0.7399306176168698],\"max\":[-0.04856247048801367,0.1220448308461407,1.051112871594798],\"median\":[-0.06579691841584068,0.06822012458681859,0.8542956355803348],\"low\":[-0.06418249593562522,0.06932628355036818,0.7452554129330493],\"high\":[-0.14378272558189698,-0.2207285243094647,1.0450464282700715]},\"blue\":{\"min\":[0.06308362714636938,0.1010429531080218,0.742188561370138],\"max\":[0.1623789745629382,0.1994037568596595,0.7463403147189581],\"median\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"low\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"high\":[0.11265531983118941,0.14487610727830103,0.7460899470953907]}},\n \"file\": [\"/tmp/kinex-observation-2g7v533u.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/4a6cdfeb-1.png" + ], + "timestamp": "2026-10-28T21:32:18.315Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Preparing the left block**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"left\": [[-0.09, -0.08, 0.87, 0, 0, 0.70710678, 0.70710678], [-0.09, -0.04, 0.79, 0, 0, 0.70710678, 0.70710678]]}, \"seconds\": 0.8, \"grips\": {\"left\": 1}, \"settle\": 0.24}, \"description\": \"reposition left arm for lower grasp\"}", + "images": [], + "timestamp": "2026-10-28T21:32:26.401Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223148.217007165] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":15.120000000000001,\"waited_s\":1.8399999999999999},\n \"left\": {\"q\":[-0.7753829956054688,2.486633777618408,0.6931433081626892,1.7631256580352783,-0.718991756439209,0.010876113548874855],\"grip\":[0.9957061274485155],\"tcp\":[-0.0874784424367745,-0.044104560377094204,0.7831832909467894,-0.0016927903651302151,0.018796832322172552,0.7041006341624548,0.7098493576298149],\"joint_error\":0.03182466435917464},\n \"right\": {\"q\":[-0.03216967731714249,2.560060977935791,2.2720913887023926,1.4922796487808228,-1.5310924053192139,0.8622229099273682],\"grip\":[0.9966695552522487],\"tcp\":[0.15903478459190437,0.10693136194152325,1.0789327091897247,0.005149923323030299,-0.17022306684924737,0.9853442948891231,0.009705994625001104]},\n \"file\": [\"/tmp/kinex-observation-6nj6l9eh.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/bf1286a6-1.png" + ], + "timestamp": "2026-10-28T21:32:36.193Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/color_geometry.py\", \"description\": \"refine block position for recovery\"}", + "images": [], + "timestamp": "2026-10-28T21:32:39.644Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223161.661026146] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"head_camera\": {\"red\":{\"min\":[-0.11061178078762514,-0.2457689362177062,0.7399306176168698],\"max\":[-0.03470604127310729,0.1427277026477306,0.9475643068469688],\"median\":[-0.06918190786485898,0.07990095152521826,0.8826486653824216],\"low\":[-0.06299468250458584,0.07044721886777061,0.7465854293892895],\"high\":[-0.06552924214969097,0.10698729422722852,0.9434633257962752]},\"blue\":{\"min\":[0.06308362714636938,0.1010429531080218,0.742188561370138],\"max\":[0.1623789745629382,0.1994037568596595,0.7463403147189581],\"median\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"low\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"high\":[0.11265531983118941,0.14487610727830103,0.7460899470953907]}},\n \"file\": [\"/tmp/kinex-observation-iop8tv52.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/af212c1a-1.png" + ], + "timestamp": "2026-10-28T21:32:42.258Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Estimating block center**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"left\": [[-0.068, 0.1, 0.785, 0, 0, 0.70710678, 0.70710678]]}, \"seconds\": 0.9, \"settle\": 0.24}, \"description\": \"grasp approach near block bottom\"}", + "images": [], + "timestamp": "2026-10-28T21:32:48.490Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223170.440709280] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":16.28,\"waited_s\":1.1600000000000001},\n \"left\": {\"q\":[-0.600791871547699,2.6094110012054443,1.232616662979126,1.3856762647628784,-0.5472012162208557,0.00989964697510004],\"grip\":[1.0027754415165295],\"tcp\":[-0.066016572510952,0.09042241913998889,0.7872048785646522,0.0005284683532509702,-0.0016981723383381042,0.7057319257426141,0.7084767363290981],\"joint_error\":0.03893985785541654},\n \"right\": {\"q\":[-0.03247563913464546,2.560077667236328,2.272132158279419,1.4922877550125122,-1.5310924053192139,0.86222243309021],\"grip\":[0.9964158291166479],\"tcp\":[0.15919550134342006,0.10697694676322184,1.078935974341295,0.005124039207660705,-0.1702399443739578,0.9853400206235566,0.009856435426625896]},\n \"file\": [\"/tmp/kinex-observation-56ho_eio.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/e84d68b9-1.png" + ], + "timestamp": "2026-10-28T21:32:55.061Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Aligning the block**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"grips\": {\"left\": 0}, \"wait\": 0.48, \"settle\": 0.08}, \"description\": \"secure lower left grasp\"}", + "images": [], + "timestamp": "2026-10-28T21:33:04.545Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223186.546428863] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":16.84,\"waited_s\":0.5599999999999987},\n \"left\": {\"q\":[-0.5934005379676819,2.619466781616211,1.265495777130127,1.362592101097107,-0.5382099747657776,0.01096574030816555],\"grip\":[0.7462007796222514],\"tcp\":[-0.06500761711904887,0.09769283767447573,0.7873715682345719,0.0009898267987188964,-0.0012068581027551903,0.7051936733802603,0.7090130088798676]},\n \"right\": {\"q\":[-0.03252800554037094,2.5600783824920654,2.2721314430236816,1.4922852516174316,-1.5310916900634766,0.8622233271598816],\"grip\":[0.9963579855181954],\"tcp\":[0.15922302830337134,0.10698503508441021,1.0789354564549725,0.005119894090041461,-0.17023770143448508,0.9853401691837524,0.009882443409605259]},\n \"file\": [\"/tmp/kinex-observation-5d7nijxy.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/3d9b5793-1.png" + ], + "timestamp": "2026-10-28T21:33:09.970Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"left\": [[-0.068, 0.08, 0.885, 0, 0, 0.70710678, 0.70710678]], \"right\": [[0.1, 0.08, 1.015, 0, 0, 1, 0]]}, \"seconds\": 1, \"settle\": 0.32}, \"description\": \"lift with larger clearance between grippers\"}", + "images": [], + "timestamp": "2026-10-28T21:33:16.582Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223198.774657728] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":18.16,\"waited_s\":1.3200000000000003},\n \"left\": {\"q\":[-0.6028794646263123,2.28385591506958,1.124043583869934,1.1707262992858887,-0.5630607008934021,0.016173673793673515],\"grip\":[0.7481350180777637],\"tcp\":[-0.07060291642799227,0.08306513196640691,0.8812370042212011,0.0009367501040187683,-0.000825354343784573,0.709443067756566,0.7047616440340682],\"joint_error\":0.017319022485110303},\n \"right\": {\"q\":[0.07760625332593918,2.268001079559326,1.7254252433776855,0.6678985953330994,-1.4511550664901733,0.11310850828886032],\"grip\":[0.9975609156218442],\"tcp\":[0.10518671059120635,0.08316107064569606,1.0163136369861834,0.0003802638280060978,-0.0063742821117829885,0.9999764069935545,0.0025316759748120754],\"joint_error\":0.01376317773439939},\n \"file\": [\"/tmp/kinex-observation-7wv0mr12.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/4f33bd50-1.png" + ], + "timestamp": "2026-10-28T21:33:25.131Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/color_geometry.py\", \"args\": {\"cameras\": [\"right_camera\"]}, \"description\": \"align upper handover grasp\"}", + "images": [], + "timestamp": "2026-10-28T21:33:29.664Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223211.784921453] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"right_camera\": {\"red\":{\"min\":[-0.12343917734098808,0.0605901090306426,0.8646018955545028],\"max\":[-0.03777323677106045,0.12671369821273332,1.036432952542873],\"median\":[-0.04040799159067776,0.09224365837616032,0.9880418815912079],\"low\":[-0.09459596192475068,0.06060752452967939,0.8692415165210006],\"high\":[-0.04925679433343136,0.09392724195870489,1.0344916769744117]}},\n \"file\": [\"/tmp/kinex-observation-evmbcelr.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/f9d2862d-1.png" + ], + "timestamp": "2026-10-28T21:33:32.566Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Positioning right-face block**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"right\": [[-0.07, 0.093, 1.005, 0, 0, 1, 0]]}, \"seconds\": 1, \"settle\": 0.32}, \"description\": \"reach upper block with right gripper\"}", + "images": [], + "timestamp": "2026-10-28T21:33:41.087Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223223.295463317] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":19.48,\"waited_s\":1.3200000000000003},\n \"left\": {\"q\":[-0.6064146161079407,2.2680065631866455,1.1165276765823364,1.1589206457138062,-0.564342737197876,0.01636873371899128],\"grip\":[0.7491666525602341],\"tcp\":[-0.06978309887078618,0.08097908591451436,0.8845432426730855,0.0005590646864575862,0.0009305853341717258,0.7085652278336617,0.7056444851075103]},\n \"right\": {\"q\":[0.374615341424942,2.502243757247925,2.131927251815796,0.397475004196167,-1.1541471481323242,0.024385878816246986],\"grip\":[1.0017903392965144],\"tcp\":[-0.06014010459146446,0.09413422683807998,1.0075094922363719,0.0009083332956943216,-0.002139926032686006,0.9999960431704957,0.0015840740357489504],\"joint_error\":0.02744833812895031},\n \"file\": [\"/tmp/kinex-observation-65ioy38v.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/b5d4c986-1.png" + ], + "timestamp": "2026-10-28T21:33:49.334Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"grips\": {\"right\": 0}, \"wait\": 0.48, \"settle\": 0.12}, \"description\": \"close right gripper on upper block\"}", + "images": [], + "timestamp": "2026-10-28T21:33:54.898Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223236.977331379] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.080000000000002,\"waited_s\":0.6000000000000014},\n \"left\": {\"q\":[-0.6069324612617493,2.2672977447509766,1.1154533624649048,1.1594316959381104,-0.5664319396018982,0.017290595918893814],\"grip\":[0.7638818540356376],\"tcp\":[-0.06995436989865308,0.08067682635973633,0.8846298546530436,0.0008844987711302622,0.001196786566270506,0.709116506111797,0.7050897574944884]},\n \"right\": {\"q\":[0.383068323135376,2.515486478805542,2.1559560298919678,0.3830629885196686,-1.1423680782318115,0.02483583614230156],\"grip\":[0.7542767009951852],\"tcp\":[-0.0654515172886107,0.09498601759678144,1.0070095627793196,0.0004312189457866284,-0.00020835919012824746,0.9999947648052887,0.003200156040570001]},\n \"file\": [\"/tmp/kinex-observation-06vm8zsn.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/e7cd2d3f-1.png" + ], + "timestamp": "2026-10-28T21:34:00.207Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"grips\": {\"left\": 1}, \"wait\": 0.36, \"settle\": 0.08}, \"description\": \"release left grip for transfer\"}", + "images": [], + "timestamp": "2026-10-28T21:34:03.292Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223245.217918798] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.52,\"waited_s\":0.4399999999999977},\n \"left\": {\"q\":[-0.6075312495231628,2.266892671585083,1.1154754161834717,1.15908682346344,-0.5642680525779724,0.015033591538667679],\"grip\":[0.9626180990175768],\"tcp\":[-0.06932564109333014,0.08054488784278918,0.8847849114380533,0.00010642963722792084,0.000344471082472518,0.708141468352586,0.7060704857249375]},\n \"right\": {\"q\":[0.384282648563385,2.518695592880249,2.158612012863159,0.38260990381240845,-1.1413120031356812,0.02465474046766758],\"grip\":[0.7497303905812177],\"tcp\":[-0.06626623117850927,0.09513037725465906,1.006063905169769,0.00026264212673088436,0.00017109248919661276,0.9999951122919751,0.003110809964219864]},\n \"file\": [\"/tmp/kinex-observation-fc7wmhrj.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/a51b5070-1.png" + ], + "timestamp": "2026-10-28T21:34:07.678Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"left\": [[-0.12, -0.09, 0.89, 0, 0, 0.70710678, 0.70710678]]}, \"seconds\": 0.9, \"settle\": 0.2}, \"description\": \"withdraw left arm after transfer\"}", + "images": [], + "timestamp": "2026-10-28T21:34:13.657Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223255.835021672] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":21.64,\"waited_s\":1.120000000000001},\n \"left\": {\"q\":[-0.7814388871192932,1.931073546409607,0.43255481123924255,1.5073320865631104,-0.7431031465530396,0.020603640004992485],\"grip\":[0.9946320246566426],\"tcp\":[-0.116480907981041,-0.07714561771582135,0.888037576896595,-0.00005574318770465079,0.0027014518750809666,0.7082489303319541,0.705957613270164],\"joint_error\":0.04895595821654197},\n \"right\": {\"q\":[0.38584333658218384,2.520796298980713,2.1592612266540527,0.38251370191574097,-1.1410601139068604,0.024593651294708252],\"grip\":[0.74832460148768],\"tcp\":[-0.06717741407694655,0.09484375843603249,1.0050543348354926,-0.00003133563748486725,0.0008554720467265697,0.9999966224054394,0.0024540504005274295]},\n \"file\": [\"/tmp/kinex-observation-s9tov1ew.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/f0765732-1.png" + ], + "timestamp": "2026-10-28T21:34:21.081Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Measuring head bottom**\nTool call: exec\n{\"file\": \".kinex/tools/color_geometry.py\", \"description\": \"measure block offset in right grasp\"}", + "images": [], + "timestamp": "2026-10-28T21:34:27.096Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223269.388614648] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"head_camera\": {\"red\":{\"min\":[-0.15821772589290822,-0.2987673906932839,0.8398380605979726],\"max\":[0.14645363087424962,0.06300409512308258,1.0602012979118725],\"median\":[-0.0769250015221273,0.06051332279327992,0.921461141233301],\"low\":[-0.07438347114092453,0.05827808110437249,0.8424585763634667],\"high\":[0.14441538763929712,-0.10189180637681439,1.055024763226523]},\"blue\":{\"min\":[0.06308362714636938,0.1010429531080218,0.742188561370138],\"max\":[0.16214894027171578,0.1994037568596595,0.7463403147189581],\"median\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"low\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"high\":[0.11265531983118941,0.14487610727830103,0.7460899470953907]}},\n \"file\": [\"/tmp/kinex-observation-u10oxgvz.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/c3226fa8-1.png" + ], + "timestamp": "2026-10-28T21:34:30.162Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Moving above the block**\nThe block is now supported by the right gripper, with the left arm withdrawn. I’m placing it at the pad’s center and will verify the release.\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"right\": [[0.12, 0.157, 1.015, 0, 0, 1, 0]]}, \"seconds\": 1.5, \"settle\": 0.32}, \"description\": \"carry block above blue pad\"}", + "images": [], + "timestamp": "2026-10-28T21:34:39.166Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223281.404363054] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":23.48,\"waited_s\":1.8399999999999999},\n \"left\": {\"q\":[-0.8029563426971436,1.903110384941101,0.38487792015075684,1.5342161655426025,-0.7633680105209351,0.021171852946281433],\"grip\":[0.9984204416925258],\"tcp\":[-0.12033357727388883,-0.08948404422128958,0.8902496235827575,0.000004593705238664185,-0.0005345615343604223,0.7076974864572219,0.7065153797936703]},\n \"right\": {\"q\":[0.05061691999435425,2.6250691413879395,2.372485637664795,0.37307676672935486,-1.4840843677520752,0.12098056077957153],\"grip\":[0.7465521422299473],\"tcp\":[0.11225161767899541,0.15427922863367133,1.016213647769426,0.0003283806839788719,0.00014140216044888562,0.9999977304223577,-0.002100314664298886],\"joint_error\":0.016504502293561263},\n \"file\": [\"/tmp/kinex-observation-2umqg4z9.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/bab02154-1.png" + ], + "timestamp": "2026-10-28T21:34:50.335Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Lowering block**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"moves\": {\"right\": [[0.12, 0.157, 0.916, 0, 0, 1, 0]]}, \"seconds\": 1, \"settle\": 0.32}, \"description\": \"lower block to pad\"}", + "images": [], + "timestamp": "2026-10-28T21:34:56.718Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223298.768252578] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":24.8,\"waited_s\":1.3200000000000003},\n \"left\": {\"q\":[-0.8042228817939758,1.902944564819336,0.3838373124599457,1.5342135429382324,-0.76336669921875,0.021171730011701584],\"grip\":[0.9993253485722976],\"tcp\":[-0.12008906515642134,-0.08988933922742132,0.8900465762383285,0.0000010772669426179937,-0.00009598160339580161,0.7072440127824126,0.7069695164360356]},\n \"right\": {\"q\":[0.04501097649335861,2.72459077835083,2.1980509757995605,0.6329107880592346,-1.4919719696044922,0.10310453176498413],\"grip\":[0.7487442723729394],\"tcp\":[0.11575673991909216,0.1556721693320849,0.9233802663062167,0.0001254167071825891,-0.0017099633021399826,0.9999923417559874,-0.003518057067655621],\"joint_error\":0.011100574249183759},\n \"file\": [\"/tmp/kinex-observation-cc3ukbqz.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/451da7eb-1.png" + ], + "timestamp": "2026-10-28T21:35:06.178Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/color_geometry.py\", \"description\": \"check block base against pad surface\"}", + "images": [], + "timestamp": "2026-10-28T21:35:09.822Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223311.719651992] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"head_camera\": {\"red\":{\"min\":[-0.16217849814359225,-0.30906706361326325,0.7456756236374391],\"max\":[0.16107405276545833,0.1959731213087743,0.9920060863424123],\"median\":[0.0997301809589289,0.12246768758288817,0.85327489402623],\"low\":[0.1336706950183812,0.14817067406106688,0.7460941018292808],\"high\":[-0.15934001440462964,-0.3056585705895022,0.9859815823860145]},\"blue\":{\"min\":[0.06308362714636938,0.1010429531080218,0.742188561370138],\"max\":[0.1623789745629382,0.1994037568596595,0.7463403147189581],\"median\":[0.1071707446695593,0.11953112527781917,0.7459859885183578],\"low\":[0.1071707446695593,0.11953112527781917,0.7459859885183578],\"high\":[0.1071707446695593,0.11953112527781917,0.7459859885183578]}},\n \"file\": [\"/tmp/kinex-observation-syl6cojd.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/0d7d46d8-1.png" + ], + "timestamp": "2026-10-28T21:35:12.226Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Settling the block upright**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_move.py\", \"args\": {\"grips\": {\"right\": 1}, \"wait\": 0.6, \"settle\": 0.2}, \"description\": \"release block with right gripper open\"}", + "images": [], + "timestamp": "2026-10-28T21:35:21.357Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793223323.319521528] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"episode ended during wait\",\n \"code\": \"operation_rejected\",\n \"details\": {\n \"operation\": \"world.wait\",\n \"type\": \"kinex_interfaces/srv/Wait\",\n \"response\": {\"success\":false,\"message\":\"episode ended during wait\",\"time_s\":24.84,\"waited_s\":0.03999999999999915}\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:35:23.860Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Inspecting measurements and image**\nTool call: devices\n{\"calls\": [{\"deviceID\": \"right_gripper\", \"action\": \"joint_state\"}, {\"deviceID\": \"head_camera\", \"action\": \"image\"}, {\"deviceID\": \"right_arm\", \"action\": \"joint_state\"}]}", + "images": [], + "timestamp": "2026-10-28T21:35:31.217Z" + }, + { + "role": "toolResult", + "name": "devices", + "text": "{\n \"results\": [\n {\"deviceID\":\"right_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.761039051142606],\"velocities\":[0.8033322339708154],\"joint_names\":[\"right_gripper\"],\"timestamp\":24.84}},\n {\"deviceID\":\"head_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":24.84,\"frame_id\":\"head_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-j_sgq_yb.png\"},\n {\"deviceID\":\"right_arm\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.04493730142712593,2.7255666255950928,2.1971681118011475,0.6346826553344727,-1.4920541048049927,0.10271850973367691],\"velocities\":[-0.0010401532053947449,0.030199257656931877,-0.004802028648555279,0.03646739572286606,-0.00745355011895299,0.00008076801896095276],\"joint_names\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"timestamp\":24.84}}\n ]\n}", + "images": [ + "experiments/robotwin-native/kinex-1/observations/245fef43-1.png" + ], + "timestamp": "2026-10-28T21:35:31.413Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Releasing block from pad**\n\n**Ending block-release attempt**\nTool call: write\n{\"path\": \".kinex/memos/handover-block.md\", \"content\": \"# RoboTwin ALOHA handover block\\n\\nPublic interfaces: read harness.md and refreshed profiles. Native period .04s; 800 frames =32s. All movement needs sequential world.wait. Load arms, grippers, head camera and right wrist camera. Normalized grip 0 closed,1 open. TCP +.12m flange x. World arm base transforms are calibrated; use TF and URDF IK.\\n\\nReusable exec scripts:\\n- `.kinex/tools/cartesian_move.py`: args moves={left/right:[world TCP poses xyz+xyzw]}, seconds per segment, grips, wait, settle, observe. Precomputes Cartesian interpolated IK then publishes JointTrajectory; returns measured TCP, q error and image. Kinex FK/IK requires list, not NumPy arrays.\\n- `.kinex/tools/color_geometry.py`: args cameras; red/blue visible world surface bounds from synchronized RGBD and stamped TF. Bounds include silhouette/depth mismatches and red highlights; inspect images, use interior pixels if precision required. Wrist depth is unreliable inside .1m.\\n\\nSuccessful transfer geometry observed: vertical red block ~.06x.06x.20m, table z .74, pad top .746. Detect current xy each episode. Head camera clips tall block top. Pad center from min/max xy, not perspective-biased pixel median.\\n\\nGrasp LOW with left TCP at table+.045 (not +.095) using yaw +90deg quaternion [0,0,.70710678,.70710678], approach +world y, fingers close along x. Approach from y-.15 then insert center. Close .5s, lift .10m. Keep vertical; rolling a central grip caused a slip.\\nRight approaches from +world x with yaw pi quaternion [0,0,1,0], closes along y. Target ~.03 below measured block top, ~.12-.13 above left TCP. Too little separation (~.07-.08) caused collision with left wrist/camera and displaced the block; stop before recovery. Lower left grasp fixed it. Right camera gives useful top and side geometry before insertion.\\nClose right .6s, open left .44s, withdraw left frontward ~.17m; verify block remains suspended with right (grip ~.75 for .06m block). Then translate above pad center, lower upright preserving grip offset, open right. In this run transfer and pad placement observed; release wait terminated at 24.84s after .04s, before requested .8s. Last measured right opening .761, velocity positive; full-open motion not verified. Native verdict remains owner-only. Never retry world waits after terminal or infer success merely from termination.\\n\"}", + "images": [], + "timestamp": "2026-10-28T21:36:07.936Z" + }, + { + "role": "toolResult", + "name": "write", + "text": "Successfully wrote to .kinex/memos/handover-block.md", + "images": [], + "timestamp": "2026-10-28T21:36:07.940Z" + }, + { + "role": "assistant", + "name": null, + "text": "Transferred the block to the right arm and placed it on the blue pad. Commanded the right gripper open, but the episode ended during release, so full opening wasn’t verified.", + "images": [], + "timestamp": "2026-10-28T21:36:12.040Z" + } +] diff --git a/experiments/robotwin-native/kinex-1/session.jsonl b/experiments/robotwin-native/kinex-1/session.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..43aa2afa6828109a4fd44c44f22d395a12c7dd27 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/session.jsonl @@ -0,0 +1,122 @@ +{"type":"session","version":3,"id":"01a18467-8ce0-753f-a7eb-20be90a95c04","timestamp":"2026-10-28T21:25:41.216Z","cwd":"/workspace"} +{"type":"model_change","id":"85835909","parentId":null,"timestamp":"2026-10-28T21:25:43.611Z","provider":"openai-codex","modelId":"gpt-6-astra"} +{"type":"thinking_level_change","id":"4fb78f5d","parentId":"85835909","timestamp":"2026-10-28T21:25:43.611Z","thinkingLevel":"high"} +{"type":"message","id":"14c17edb","parentId":"4fb78f5d","timestamp":"2026-10-28T21:25:43.964Z","message":{"role":"system","content":"","sections":{"preamble":"You are an expert coding assistant operating inside pi, a coding agent harness. You help users by reading files, executing commands, editing code, and writing new files.","tools":"\n- read: Read file contents\n- write: Create or overwrite files\n- edit: Make precise file edits with exact text replacement, including multiple disjoint edits in one call\n- bash: Execute bash commands (ls, grep, find, etc.)\n- grep: Search file contents for patterns (respects .gitignore)\n- find: Find files by glob pattern (respects .gitignore)\n- ls: List directory contents\n\nIn addition to the tools above, you may have access to other custom tools depending on the project.\n","rules":"\n- Use read to examine files instead of cat or sed.\n- Use write only for new files or complete rewrites.\n- Use edit for precise changes (edits[].oldText must match exactly)\n- When changing multiple separate locations in one file, use one edit call with multiple entries in edits[] instead of multiple edit calls\n- Each edits[].oldText is matched against the original file, not after earlier edits are applied. Do not emit overlapping or nested edits. Merge nearby changes into one edit.\n- Keep edits[].oldText as small as possible while still being unique in the file. Do not pad with large unchanged regions.\n- You can inspect PI_* environment variables for current model and session details.\n- Be concise in your responses\n- Show file paths clearly when working with files\n","docs":"\nPi documentation (read only when the user asks about pi itself, its SDK, extensions, themes, skills, or TUI):\n- Main documentation: /opt/kinex/node_modules/@earendil-works/pi-coding-agent/README.md\n- Additional docs: /opt/kinex/node_modules/@earendil-works/pi-coding-agent/docs\n- Examples: /opt/kinex/node_modules/@earendil-works/pi-coding-agent/examples (extensions, custom tools, SDK)\n- When reading pi docs or examples, resolve docs/... under Additional docs and examples/... under Examples, not the current working directory\n- When asked about: extensions (docs/extensions.md, examples/extensions/), themes (docs/themes.md), skills (docs/skills.md), prompt templates (docs/prompt-templates.md), TUI components (docs/tui.md), keybindings (docs/keybindings.md), SDK integrations (docs/sdk.md), custom providers (docs/custom-provider.md), adding models (docs/models.md), pi packages (docs/packages.md), environment variables (docs/environment-variables.md), MCP servers (docs/mcp.md), codemode scripts and non-LLM models such as classifiers and image models (docs/codemode.md)\n- When working on pi topics, read the docs and examples, and follow .md cross-references before implementing\n- Always read pi .md files completely and follow links to related docs (e.g., tui.md for TUI API details)\n","skills":"\nThe following skills provide specialized instructions for specific tasks.\nUse the read tool to load a skill's file when the task matches its description.\nWhen a skill file references a relative path, resolve it against the skill directory (parent of SKILL.md / dirname of the path) and use that absolute path in tool commands.\n\n\n \n btw\n Helps you use the /btw side-conversation workflow effectively. Use when you want to think in parallel, ask side questions without interrupting ongoing work, or inject a side thread back into the main agent.\n /opt/kinex/node_modules/pi-btw/skills/btw/SKILL.md\n \n\n","cwd":"\n/workspace\n","kinex":"\nYou are Kinex, a coding agent for robotics work. Use the coding tools normally and use Kinex tools for robot/device access. Direct camera reads include the image; no separate file read is needed.\n\nRuntime facts arrive in kinex_context messages; they are host state, not user requests, and the latest value of each section applies. Tools and skills written during a task become available after the current tool batch.\n\nAsk the user through the question tool.\n\n\n# Robotics\n\n## Workspace\n\nThe nearest `.kinex/` (here or an ancestor) holds the robot:\n- `workspace.yaml`: identity and safety limits.\n- `devices//`: `profile.yaml` plus node sources (`node.py`, URDF, ...).\n- `device/`, `tools/`, `skills/`: workspace guides, tools and skills; override builtins.\n- `lib/`: Python shared by exec, tools and nodes.\n- `kinex.json`, `python/`: config and the Kinex Python environment.\n- `recordings/`: recordings.\nWrite `profile.yaml` and `workspace.yaml` only with profile_write.\n\n## Devices\n\nA device is a profile plus the ROS topics and services its nodes serve. Its\ndescriptions are its contract; operation names come from the profile, not the type.\nOnly nodes touch SDKs, simulators and model services, and hold persistent resources.\nThe world device owns time and waits. Use the device tool to load, unload, read and command.\n\n## Python\n\nexec runs Kinex Python, separate from device and simulator environments; never patch sys.path.\nDiscover devices with `kinex.runtime` and return only what you need via `finish()`.\nROS resources a script creates end with it. Install missing ROS packages through setup.\n\n## Principles\n\nFollow profile units, limits and safety; never assume missing facts. Avoid unintended\ncollisions along the whole path. A sent command or arrival is not success; verify with\nmeasurements. On faults, unexpected contact or lost feedback, call device_stop first.\nWork efficiently: skip checks that add nothing, combine tool calls if necessary\n(e.g. perceive and return the results inside the same exec for motion),\nbut never at the cost of soundness and task completion.\n\n## Tools and the user\n\nTool descriptions define execution semantics; skills give procedures. Don't repeat\npermission prompts as questions. Ask only for missing facts, choices or physical\nfeedback.\n\n\n# Task-scoped setup\n\nOnboarding prepares an embodiment through an agent-authored DAG. Use setup_plan to\ninspect/save nodes and select only the current task's target nodes and prerequisites.\nMissing plans and incomplete profiles do not by themselves prevent task execution;\nexisting operation-specific validation and host permissions still apply. Global profile\ncompleteness is descriptive. Return to the task as soon as its required facts exist.\n\nReuse verified factory, model, simulator and calibration facts before measuring.\nKeep unknowns as ???. Split broad objectives into independently useful facts so a\npartial calibration can satisfy a narrower task. Optional work stays deferred.\nBind procedures to actual devices and frames; morphology does not imply an interface.\nThe frame graph and setup dependency DAG are different graphs. Plans live in\n`.kinex/setup/plan.json`, calibration attempts in `.kinex/calib//`.\n\nAvoid adding new tests unless necessary. Skip python tests during task execution or setup attempts.\n\nPrepare or repair devices with core's setup-device skill; keep existing working\nintegrations unless replacement is requested.\n\n"},"timestamp":1793222743961,"toolsAdded":[{"name":"read","description":"Read the contents of a file. Supports text files and images (jpg, png, gif, webp, bmp). Images are sent as attachments. For text files, output is truncated to 2000 lines or 50KB (whichever is hit first). Use offset/limit for large files. When you need the full file, continue with offset until complete.","parameters":{"type":"object","required":["path"],"properties":{"path":{"type":"string","description":"Path to the file to read (relative or absolute)"},"offset":{"type":"number","description":"Line number to start reading from (1-indexed)"},"limit":{"type":"number","description":"Maximum number of lines to read"}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"write","description":"Write content to a file. Creates the file if it doesn't exist, overwrites if it does. Automatically creates parent directories.","parameters":{"type":"object","required":["path","content"],"properties":{"path":{"type":"string","description":"Path to the file to write (relative or absolute)"},"content":{"type":"string","description":"Content to write to the file"}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"edit","description":"Edit a single file using exact text replacement. Every edits[].oldText must match a unique, non-overlapping region of the original file. If two changes affect the same block or nearby lines, merge them into one edit instead of emitting overlapping edits. Do not include large unchanged regions just to connect distant changes.","parameters":{"type":"object","required":["path","edits"],"properties":{"path":{"type":"string","description":"Path to the file to edit (relative or absolute)"},"edits":{"type":"array","items":{"type":"object","required":["oldText","newText"],"properties":{"oldText":{"type":"string","description":"Exact text for one targeted replacement. It must be unique in the original file and must not overlap with any other edits[].oldText in the same call."},"newText":{"type":"string","description":"Replacement text for this targeted edit."}}},"description":"One or more targeted replacements. Each edit is matched against the original file, not incrementally. Do not include overlapping or nested edits. If two changes touch the same block or nearby lines, merge them into one edit instead."}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"bash","description":"Execute a bash command in the current working directory. Returns stdout and stderr. Output is truncated to last 2000 lines or 50KB (whichever is hit first). If truncated, full output is saved to a temp file. Optionally provide a timeout in seconds.","parameters":{"type":"object","required":["command"],"properties":{"command":{"type":"string","description":"Shell command to execute"},"timeout":{"type":"number","description":"Timeout in seconds (optional, no default timeout)"}}},"constrainedSampling":{"type":"json_schema","strict":"prefer"}},{"name":"grep","description":"Search file contents for a pattern. Returns matching lines with file paths and line numbers. Respects .gitignore. Output is truncated to 100 matches or 50KB (whichever is hit first). Long lines are truncated to 500 chars.","parameters":{"type":"object","required":["pattern"],"properties":{"pattern":{"type":"string","description":"Search pattern (regex or literal string)"},"path":{"type":"string","description":"Directory or file to search (default: current directory)"},"glob":{"type":"string","description":"Filter files by glob pattern, e.g. '*.ts' or '**/*.spec.ts'"},"ignoreCase":{"type":"boolean","description":"Case-insensitive search (default: false)"},"literal":{"type":"boolean","description":"Treat pattern as literal string instead of regex (default: false)"},"context":{"type":"number","description":"Number of lines to show before and after each match (default: 0)"},"limit":{"type":"number","description":"Maximum number of matches to return (default: 100)"}}}},{"name":"find","description":"Search for files by glob pattern. Returns matching file paths relative to the search directory. Respects .gitignore. Output is truncated to 1000 results or 50KB (whichever is hit first).","parameters":{"type":"object","required":["pattern"],"properties":{"pattern":{"type":"string","description":"Glob pattern to match files, e.g. '*.ts', '**/*.json', or 'src/**/*.spec.ts'"},"path":{"type":"string","description":"Directory to search in (default: current directory)"},"limit":{"type":"number","description":"Maximum number of results (default: 1000)"}}}},{"name":"ls","description":"List directory contents. Returns entries sorted alphabetically, with '/' suffix for directories. Includes dotfiles. 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Use an existing run ID, a plan node, or a procedure name\nwith concrete device-role bindings. First use creates an isolated attempt. Use the\nprocedure descriptor returned by setup_plan full:true for roles and computations.\nCustom procedure code runs through existing host tools, not this offline registry.\n\nResults distinguish computation, quality and exact resolved fields.\nValid computed results that miss accuracy targets may still be accepted within\nthe user's accuracy/time budget. Do not repeat calibration solely to meet those\ntargets when the user accepts the result. If the user explicitly keeps a result\nthat exceeds a limit, honor it without repeated confirmation or forced recollection;\nwhen requested, persist its computed fields with profile_write from the archived\nsolver data even if failed quality leaves proposedChanges empty, using the\nprocedure's frame conventions, and record the errors, limits and user decision in\nevidence. Always preserve measured errors and the original quality assessment,\nnever mark a failed-quality node done, and never treat missing or invalid results\nas approximate estimates.\nApply accepted proposedChanges with core profile_write, preserving unrelated facts.\nProposed patches never clear calibration requests automatically. A pivot solve\nmeasures translation only; RGB-D color calibration does not measure depth facts.\nURDF introspection of the bound arm or target is offline and does not require\nactivation. Base verification uses explicit measured checks and tolerances,\nwithout controller tuning. Sensor bias averages calib_sample readings of a static,\nunloaded imu or force_torque target and proposes the current bias plus each axis's\nmean residual; specific-force accelerometers need gravity_m_s2, the expected\nstatic reading in the IMU frame.\n\nArchive manual/factory facts or external solver results as evidence for setup_plan\nrecord. Reuse valid evidence and stop as soon as the current task is supported.\n\nBase measurement files contain required_checks:[name,...] and checks:[{name,\nexpected,measured,reference,...}]. reference names the archived source or independent\nmeasurement. Numerical expected/measured values need explicit tolerance and units.\nOther assertions compare exact values. Optional timestamps must contain at least\ntwo distinct increasing sample times. Only required_checks determine coverage;\nmissing checks fail quality. Do not claim untested axes or operating 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file path, relative to invocation directory or absolute"},"gravity_m_s2":{"description":"expected static specific force in the IMU frame, e.g. [0, 0, 9.80665] when level with +z up","type":"array","prefixItems":[{"type":"number"},{"type":"number"},{"type":"number"}],"items":false,"minItems":3,"maxItems":3}},"required":["operation","samples"],"additionalProperties":false}],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"calib_sample","description":"Read one sample into an isolated .kinex/calib//samples.json. Start with a plan\nnode or procedure plus bindings; subsequent samples use the returned run ID. The\ndevice must be one of the bound participants. Inputs and participants cannot change\nwithin an attempt. Returns the new sample, run ID and count; camera attachments are\ncopied into immutable attempt artifacts, including both RGB and depth files for\nRGBD readings. Both copies must succeed before a sample is appended.\nUse note to identify the collection step. Samples\ncontain measured state and timestamps; this tool cannot command or load devices.\n\nOptional settle waits for measured velocities to remain below the threshold over\ndistinct samples. Unsupported or malformed feedback is an error. Cached readings\ndo not establish stability. A timeout after a reading saves that reading with\nsettled:false; cancellation saves nothing. Without settle, the sample makes no\nclaim of stationary motion. Settling supports joint devices whose joint states\nmeasure every commanded joint's velocity, and locomotion devices whose odometry\nmeasures their declared velocity axes; their runtime.observation must name the\nodometry operation.\nThis blocking sampler does not advance simulator time; arrange world waits\noutside the sampler when collecting simulation evidence.\n\nFor joint devices, optional frame names a URDF link; FK from that exact sample's\npositions stores kinematics with frame and pose in the device base alongside the\nmeasured data. Use it for TCP calibration; joint-device reads contain no link\nposes. FK and artifact capture still run after a settle timeout, subject to caller\ncancellation.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"deviceID":{"type":"string","description":"device to sample, e.g. arm"},"run":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"node":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"procedure":{"type":"string","minLength":1},"bindings":{"type":"object","propertyNames":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"additionalProperties":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"inputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"args":{"description":"arguments for the observation operation","type":"object","propertyNames":{"type":"string"},"additionalProperties":{}},"frame":{"description":"joint-device URDF link whose pose to compute through FK from this sample","type":"string","minLength":1},"settle":{"description":"wait for the device to stop moving before recording; omit to record immediately","type":"object","properties":{"velocity_threshold":{"default":0.005,"description":"max |joint velocity| that counts as settled","type":"number","minimum":0},"stable_reads":{"default":3,"description":"consecutive settled reads required","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"timeout_s":{"default":30,"type":"number","exclusiveMinimum":0}}},"note":{"description":"label stored with the sample, e.g. 'touch 3, rolled +45deg'","type":"string"}},"required":["deviceID"],"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"device","description":"Call a named device's declared operation with action and args. Inspect the compact operation catalog for names, topic/service types and payload contracts. Prefer exec with async communication helpers for streams and composed operations so intermediate data stays outside model context.\nload/unload accept no arguments; status observes readiness without starting or restarting resources. stop invokes the declared stopping behavior and cancels running scripts that commanded the device. Runtime definition changes require unload, validation and explicit reload. Never terminate borrowed services.\nImage observations return selected image attachments and acquisition timestamps. A sent command is not measured arrival. The world device owns time and wait semantics; reads never advance time. relative_pose uses tf2 and ROS frame IDs.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"deviceID":{"type":"string","minLength":1},"action":{"type":"string","minLength":1},"args":{"default":{},"type":"object","propertyNames":{"type":"string"},"additionalProperties":{}},"compact":{"default":false,"description":"single-line JSON output — use in polling/sampling loops","type":"boolean"}},"required":["deviceID","action"],"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"device_stop","description":"Stop the named device, or every loaded device with a stop operation when deviceID\nis omitted, and cancel running scripts that commanded a stopped device. Stops use\neach node's declared behavior and affected-device scope. No approval is required.\nResults report each device's acknowledgment or failure; inspect failures before resuming.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"deviceID":{"description":"device to stop; omit to stop every loaded device with a stop operation","type":"string"},"reason":{"description":"why the stop was triggered","type":"string"}},"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"devices","description":"Call devices concurrently with calls: [{deviceID, action, args?}]; action is an explicit declared operation. Returns {results: [...]} in input order, with deviceID, action, ok and\ndata/file or error per entry.\nImage observations may carry attachments; attached images follow call order. Permissions are\nchecked before dispatch. Sampling is not simultaneous and effects are not atomic.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"calls":{"minItems":1,"type":"array","items":{"type":"object","properties":{"deviceID":{"type":"string","minLength":1},"action":{"type":"string","minLength":1},"args":{"default":{},"type":"object","propertyNames":{"type":"string"},"additionalProperties":{}}},"required":["deviceID","action"],"additionalProperties":false}}},"required":["calls"],"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"exec","description":"Run Python source or a script in the workspace's Kinex Python. Device environments and the world clock never choose this interpreter. Python cannot load/unload devices or control recording; lifecycle remains host-controlled. One execution approval covers composed operations.\nDiscover interfaces on demand, without starting devices:\n from kinex import runtime, finish\n devices = runtime.devices()\n # runtime.operations(device_name) lists declared operations, even if unloaded.\n # runtime.describe(operation) resolves ROS payload schemas when needed.\n finish({\"devices\": [{\"name\": d[\"name\"], \"loaded\": d[\"loaded\"]} for d in devices]})\nUse actual operation names from these queries, not names inferred from device types. Catalogs remain in Python until you explicitly return selected fields. Load required devices through the host before communication.\nFor async protocol work, import communication, args, finish from kinex. For example, where args supplies a discovered command topic and its ROS payload:\n async def main():\n a = args()\n async with communication() as io:\n await io.publish(a[\"operation\"], a[\"payload\"])\n finish({\"sent\": True})\n # Execute with asyncio.run(main()). Sending is not measured arrival.\nUse io.subscribe(operation, depth=1) and await stream.next(timeout_s=...) for output topics (with or async with closes the stream early); await io.request(operation, **fields) for services. World wait operations own scheduling, one wait at a time. Observations and tf2 queries never advance world time. Bound work, inspect fresh measured feedback, and stop on terminal task status. Service rejections raise kinex.OperationError with operation, response and structured details; inspect the response and relevant feedback before deciding how to recover.\nImport helpers with from kinex import helpers. Read kinex/helpers/helpers.txt under the Python library directory in runtime context for available functions and implementation paths. Builtin Python tools are interfaces to these helpers; call them directly to share this invocation's communication and cleanup. Put workspace-specific helpers in .kinex/lib, which exec can import.\nFailure, timeout or cancellation stops every device this invocation commanded; success leaves their motion running. finish(value, file=paths) returns selected summaries as JSON, converting ROS messages and NumPy values, and images only through file paths, after leaving the communication scope. Do not print entire catalogs or raw streams unless requested.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"description":{"description":"Short lowercase description of the task, for display","type":"string"},"python":{"description":"Python source, including multiline scripts; provide exactly one of python or file","type":"string","minLength":1},"file":{"description":"Script path, relative to the working directory or absolute","type":"string","minLength":1},"args":{"default":{},"description":"Values available through kinex.args()","type":"object","propertyNames":{"type":"string"},"additionalProperties":{}},"argv":{"default":[],"description":"Script command-line arguments","type":"array","items":{"type":"string"}},"timeout_s":{"default":120,"type":"number","exclusiveMinimum":0,"maximum":2147483}},"additionalProperties":false,"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"locate_object_3d","description":"[Python tool] Locate an object in the camera optical frame (+X right, +Y down, +Z forward) using a detection or segmentation device with RGBD or explicit optical depth. Samples only inside a supplied mask; otherwise uses the central half of the box. Returns a representative point in metres, pixel, detection_score, depth_samples, and reference (visible_surface or box_region). Box estimates may include background. rotation_xyzw is null. No mount calibration is needed; convert frames with relative_pose after constructing a full local pose with an explicit orientation.\n\nDevice input camera: rgb | rgbd (checked against the selected device).\nDevice input model: model (checked against the selected device).","parameters":{"type":"object","additionalProperties":false,"properties":{"camera":{"type":"string","description":"rgb or rgbd camera with calibrated color intrinsics"},"model":{"type":"string","description":"model device whose Json detect takes an image path, prompt, min_score and max_results and returns results with box, score, label and an optional mask PNG path"},"prompt":{"type":"string","minLength":1,"description":"object description, e.g. red mug"},"depth":{"type":"number","exclusiveMinimum":0,"description":"optional optical Z in metres at the representative region pixel; otherwise use the camera's paired RGBD depth"},"min_score":{"type":"number","minimum":0,"maximum":1,"default":0.25,"description":"minimum detection score"},"instance":{"type":"integer","minimum":0,"default":0,"description":"zero-based index after filtering and sorting by descending score; ties keep model order"}},"required":["camera","model","prompt"]}},{"name":"profile_write","description":"Persist profile facts in the workspace:\n- robot: partial manifest; identity requires id, name and kind initially. identity.kind describes the whole robot's morphology and must be arm, mobile, quadruped, humanoid or other. Use other when no category fits; ??? is not accepted for this field.\n- device: partial profile; name is required, type and description on first write, plus frames.base for spatial devices. Use setup-device and the selected guide for new devices.\n- needed: {item,note?}; queue the reason without changing existing provenance.\n\nPatches deep-merge. To switch a strict variant, supply the complete top-level section and list it in replace (e.g. [\"model\"]). Arrays replace wholesale. null removes an optional section only when listed in replace. Changing device type replaces its profile. Invalid updates are not saved.\n\nOutstanding requests persist across reads and replacements. Clear a resolved request with top-level needs_calibration:null for profile: (or in access for access:); the store rejects clearing incomplete facts. Unknown required facts remain ???. Set source to the origin of the data; keep diagnostics in calibration evidence.\n\nNew devices use loaded:false; activation uses the device tool (action=\"load\") or /load, not profile edits. Profiles live at .kinex/devices//profile.yaml beside node sources, using the nearest .kinex/ in the current directory or its ancestors (the current directory's .kinex/ if none exists). Validation errors identify fields and the guide to consult.\n\nDevice description gives general identity, capabilities and limitations. Each operation requires its own description for payload conventions and behavior; both descriptions are required strings and may be empty. Typed templates guide setup, while actual operation names are device-specific.\n\nPhysical facts (model, limits, color, frames, etc.) are top-level. Every frame requires parent: device:frame or null for an independent root; there is no implicit parent. Root transform components are ignored. Non-root null components mean unknown, never identity. Mounts are frames.base.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"kind":{"type":"string","enum":["robot","device","needed"]},"replace":{"default":[],"description":"Supplied top-level sections to replace instead of merge; null removes optional sections","type":"array","items":{"type":"string"}},"data":{"type":"object","propertyNames":{"type":"string"},"additionalProperties":{},"description":"partial workspace manifest patch (identity required on first write) for kind=robot, partial device patch (must include name; type on first write) for kind=device, or {item, note?} for kind=needed"}},"required":["kind","data"],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"relative_pose","description":"[Python tool] Transform a frame-attached pose through tf2. Use device:frame references or ROS frame IDs and an optional world timestamp. Missing frame relationships and unavailable timestamps are errors.","parameters":{"type":"object","additionalProperties":false,"properties":{"child":{"type":"object","additionalProperties":false,"properties":{"frame":{"type":"string","description":"Qualified frame reference such as arm:base or arm:flange; ROS IDs such as arm/base also work."},"pose":{"type":"array","items":{"type":"number"},"minItems":7,"maxItems":7,"default":[0,0,0,0,0,0,1]}},"required":["frame"]},"parent":{"type":"object","additionalProperties":false,"properties":{"frame":{"type":"string","description":"Qualified frame reference such as arm:base or arm:flange; ROS IDs such as arm/base also work."},"pose":{"type":"array","items":{"type":"number"},"minItems":7,"maxItems":7,"default":[0,0,0,0,0,0,1]}},"required":["frame"]},"timestamp":{"type":"number","minimum":0}},"required":["child","parent"]}},{"name":"run_policy","description":"[Python tool] Infer once and submit the shared valid action prefix concurrently. Returns actions_submitted, duration_s and ik_failure. Use max_actions to cap the execution horizon for receding horizon control. IK rejection returns normally, including an empty prefix, with diagnostic ik_failure (device, zero-based action_index, error), or null. Joint-only submission is asynchronous. Chunks containing gripper or suction devices publish persistent targets paced by world waits before returning; the final target remains active.\n\nDevice input model: model (checked against the selected device).\nDevice input command[].device: joint | gripper | suction (checked against the selected device).","parameters":{"type":"object","additionalProperties":false,"properties":{"model":{"type":"string","description":"model device serving an \"infer\" action (obs -> action)"},"observation":{"type":"object","description":"Observation object matching the model's infer obs contract."},"inference_args":{"type":"object","description":"Additional infer arguments validated by the model operation; cannot override obs or task."},"command":{"type":"array","minItems":1,"description":"Devices in flat-vector split order; named action maps use device names. Cartesian chunks are converted through IK before any write.","items":{"type":"object","additionalProperties":false,"properties":{"device":{"type":"string","description":"joint","gripper or suction device":null},"space":{"type":"string","enum":["device","pose","pose_delta"],"default":"device","description":"device: absolute joint values in URDF document order; gripper and suction values in [-1, 1] release at -1 and grip at +1: grippers interpolate from open to closed, proportional suction takes the vacuum level (v + 1) / 2, binary suction switches on above 0. pose: xyz + quaternion xyzw (7). pose_delta: translation metres + axis-angle rotation radians (6), accumulated from the starting pose. Cartesian modes solve URDF IK and require a joint device with model.tip_link."},"frame":{"type":"string","description":"Cartesian reference frame, frozen at planning start; defaults to the device canonical frame. Rotation deltas left-compose in these axes."},"target_frame":{"type":"string","description":"Controlled frame, rigidly attached to the arm's tip_link; defaults to that link."}},"required":["device"]}},"task":{"type":"string","description":"task or goal handed to the model, e.g. a language instruction"},"world":{"type":"string","default":"world","description":"World device providing relative wait for end-effector action pacing."},"action_hz":{"type":"number","exclusiveMinimum":0,"default":10,"description":"actions replayed per second of the world clock"},"max_actions":{"type":"integer","minimum":1,"description":"Execution horizon: submit at most the first N inferred actions, discarding the remainder before validation and IK. Omit to use the whole chunk. Each call infers anew from the supplied observation. IK rejection may shorten this prefix further."}},"required":["model","observation","command"]}},{"name":"setup_plan","description":"Inspect or maintain the agent-authored embodiment DAG. This is task-scoped planning,\nnever a gate on device operations. Read with full:true for the complete graph and\navailable procedure descriptors; the default shows only the current task's gaps.\nUse the returned revision for save/select/record; conflicting edits must be reread.\n\nSave version:1 nodes with IDs, kind prepare/calibrate/verify, skill, concrete device\nrole bindings, dependsOn IDs, input facts and expected output facts. A profile fact\nuses kind:profile, device (omit for workspace manifest), and JSON pointer; optional\nvalue requires an exact match. Use narrow profile pointers for both inputs and\noutputs, tracking only facts relevant to the node's conclusion. Keep volatile\nloaded/meta fields and live poses out of persistent calibration dependencies and\noutputs. File facts use paths relative to .kinex. Select a\ntask with objective, target node IDs and optional direct facts. Only the targets'\ndependency closure is required now; unrelated work stays deferred. task:null\ndefers all calibration. An empty targets list can describe a task requiring no\ncalibration. Skill descriptors list their exact roles, requirements and coverage.\n\nRecord done only after verifying persisted outputs and archiving supporting\nevidence files (paths relative to .kinex). Existing factory/model/calibration facts\ncan satisfy a node without new measurements. Partial results may satisfy a narrower\ntask: split broad nodes into independently useful outputs. Never mark deferred work\ndone or clear incomplete profile requests. Verification receipts describe evidence,\nnot safety certification. Revisions and stale evidence never prevent unrelated\noperations. Replan only relevant gaps and return to the task as soon as satisfied.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","oneOf":[{"type":"object","properties":{"action":{"type":"string","const":"read"},"full":{"default":false,"type":"boolean"}},"required":["action"],"additionalProperties":false},{"type":"object","properties":{"action":{"type":"string","const":"save"},"revision":{"type":"integer","minimum":0,"maximum":9007199254740991},"plan":{"type":"object","properties":{"version":{"type":"number","const":1},"nodes":{"type":"array","items":{"type":"object","properties":{"id":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"title":{"type":"string","minLength":1},"kind":{"type":"string","enum":["prepare","calibrate","verify"]},"skill":{"type":"string","minLength":1},"bindings":{"default":{},"type":"object","propertyNames":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"additionalProperties":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"dependsOn":{"default":[],"type":"array","items":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"inputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"outputs":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"optional":{"default":false,"type":"boolean"}},"required":["id","title","kind","skill"],"additionalProperties":false}},"task":{"default":null,"anyOf":[{"type":"object","properties":{"objective":{"type":"string","minLength":1},"targets":{"default":[],"type":"array","items":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"facts":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}}},"required":["objective"],"additionalProperties":false},{"type":"null"}]}},"required":["version","nodes"],"additionalProperties":false}},"required":["action","revision","plan"],"additionalProperties":false},{"type":"object","properties":{"action":{"type":"string","const":"select"},"revision":{"type":"integer","minimum":0,"maximum":9007199254740991},"task":{"anyOf":[{"type":"object","properties":{"objective":{"type":"string","minLength":1},"targets":{"default":[],"type":"array","items":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"}},"facts":{"default":[],"type":"array","items":{"oneOf":[{"type":"object","properties":{"kind":{"type":"string","const":"profile"},"device":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"pointer":{"type":"string","pattern":"^\\/(?:[^~]|~[01])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}}},"required":["objective"],"additionalProperties":false},{"type":"null"}]}},"required":["action","revision","task"],"additionalProperties":false},{"type":"object","properties":{"action":{"type":"string","const":"record"},"revision":{"type":"integer","minimum":0,"maximum":9007199254740991},"node":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"outcome":{"type":"object","properties":{"status":{"type":"string","enum":["done","partial","failed","needs-input"]},"note":{"type":"string","minLength":1},"evidence":{"minItems":1,"type":"array","items":{"type":"string","minLength":1}}},"required":["status","note","evidence"],"additionalProperties":false}},"required":["action","revision","node","outcome"],"additionalProperties":false}],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"skill","description":"Loads a skill by name and returns its full instructions. Skills are listed in the\n block of the current context; load one when its description matches the\ncurrent task, then follow its instructions.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"name":{"type":"string","description":"name of the skill to load"}},"required":["name"],"~standard":{"vendor":"zod","version":1,"jsonSchema":{}}}},{"name":"question","description":"Ask the user for missing facts or choices.","parameters":{"type":"object","required":["questions"],"properties":{"questions":{"type":"array","items":{"type":"object","required":["header","question","options"],"properties":{"header":{"type":"string"},"question":{"type":"string"},"options":{"type":"array","items":{"type":"object","required":["label"],"properties":{"label":{"type":"string"},"description":{"type":"string"}}}},"multiple":{"type":"boolean"}}}}}}}]}} +{"type":"message","id":"dbbee242","parentId":"14c17edb","timestamp":"2026-10-28T21:25:43.964Z","message":{"role":"user","content":[{"type":"text","text":"# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes\nin `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place."}],"timestamp":1793222743960}} +{"type":"custom_message","customType":"kinex-context","content":"\ncore.guides:\nDevice guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-3106dcd2e6594338a2b1e037988e4bc4/config/device, /opt/kinex/packages/core/assets/builtin/device.\n\ncore.python:\n{\"provider\":\"ros-jazzy\",\"project\":\"/workspace/.kinex/python\",\"venv\":\"/workspace/.kinex/python/.pixi/envs/default\",\"executable\":\"/workspace/.kinex/python/.pixi/envs/default/bin/python3\",\"present\":true,\"library\":\"/opt/kinex/packages/core/assets/python\",\"workspaceLibrary\":\"/workspace/.kinex/lib\"}\n\ncore.recording:\n(none)\n\ncore.skills:\n\nLoad a skill with the skill tool when its description matches the task.\n - calibrate-camera-extrinsics: Measure a camera mount from robot poses and board observations when a task needs camera-to-robot coordinates.\n - calibrate-camera-intrinsics: Measure color-camera projection and distortion when the task needs calibrated image geometry; RGB-D depth facts remain separate.\n - calibrate-dual-arm: Measure the relative transform of two arms using their calibrated TCPs when a task needs cross-arm coordinates.\n - calibrate-fixed-arm-mount: Survey an arm base relative to its rigid parent, including a mobile base or moving torso attachment frame.\n - calibrate-gripper-grip: Establish a gripper's grip joint and its open and closed positions when a task needs grip commands or policy grip values.\n - calibrate-joint-range: Resolve joint limits from models or referenced measurements only when required; world geometry is separate.\n - calibrate-sensor-bias: Measure IMU gyro/accelerometer or force/torque bias from static, unloaded readings when a task needs bias-corrected measurements.\n - calibrate-tcp-offset: Measure the tool-tip translation in a gripper or suction tool's base by pivot touches when a task needs a TCP; orientation remains a separate fact.\n - calibrate-world-geometry: Record independently measured world bounds or surfaces only for tasks that need those spatial facts.\n - setup-device: Add a device or repair its ROS nodes — choose an instance or type guide, provision its runtime, fill a profile and verify the interface. [resolves: access:world,access:locomotion,access:joint,access:gripper,access:suction,access:rgb,access:rgbd,access:imu,access:force_torque,access:model,profile:model,access:other]\n - verify-base-interface: Establish missing base control and feedback conventions for a task, without requiring a full mobility calibration.\n - verify-base-motion: Measure selected base motion, expiry or stopping behavior when the current task needs evidence about those operations.\n - verify-base-posture: Verify selected height or attitude references and moving attachment frames for a base that exposes posture control.\n\n\nproblems:\n(none)\n","display":false,"details":{"core.guides":"Device guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-3106dcd2e6594338a2b1e037988e4bc4/config/device, /opt/kinex/packages/core/assets/builtin/device.","core.python":"{\"provider\":\"ros-jazzy\",\"project\":\"/workspace/.kinex/python\",\"venv\":\"/workspace/.kinex/python/.pixi/envs/default\",\"executable\":\"/workspace/.kinex/python/.pixi/envs/default/bin/python3\",\"present\":true,\"library\":\"/opt/kinex/packages/core/assets/python\",\"workspaceLibrary\":\"/workspace/.kinex/lib\"}","core.recording":"","core.skills":"\nLoad a skill with the skill tool when its description matches the task.\n - calibrate-camera-extrinsics: Measure a camera mount from robot poses and board observations when a task needs camera-to-robot coordinates.\n - calibrate-camera-intrinsics: Measure color-camera projection and distortion when the task needs calibrated image geometry; RGB-D depth facts remain separate.\n - calibrate-dual-arm: Measure the relative transform of two arms using their calibrated TCPs when a task needs cross-arm coordinates.\n - calibrate-fixed-arm-mount: Survey an arm base relative to its rigid parent, including a mobile base or moving torso attachment frame.\n - calibrate-gripper-grip: Establish a gripper's grip joint and its open and closed positions when a task needs grip commands or policy grip values.\n - calibrate-joint-range: Resolve joint limits from models or referenced measurements only when required; world geometry is separate.\n - calibrate-sensor-bias: Measure IMU gyro/accelerometer or force/torque bias from static, unloaded readings when a task needs bias-corrected measurements.\n - calibrate-tcp-offset: Measure the tool-tip translation in a gripper or suction tool's base by pivot touches when a task needs a TCP; orientation remains a separate fact.\n - calibrate-world-geometry: Record independently measured world bounds or surfaces only for tasks that need those spatial facts.\n - setup-device: Add a device or repair its ROS nodes — choose an instance or type guide, provision its runtime, fill a profile and verify the interface. [resolves: access:world,access:locomotion,access:joint,access:gripper,access:suction,access:rgb,access:rgbd,access:imu,access:force_torque,access:model,profile:model,access:other]\n - verify-base-interface: Establish missing base control and feedback conventions for a task, without requiring a full mobility calibration.\n - verify-base-motion: Measure selected base motion, expiry or stopping behavior when the current task needs evidence about those operations.\n - verify-base-posture: Verify selected height or attitude references and moving attachment frames for a base that exposes posture control.\n","problems":""},"id":"a2d6bda0","parentId":"dbbee242","timestamp":"2026-10-28T21:25:43.965Z"} +{"type":"message","id":"989a0c21","parentId":"a2d6bda0","timestamp":"2026-10-28T21:25:46.605Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_557120d31d354bdcb113ec14e5b73fe8|fc_0df8288370165955016ae2685a2e50819da406f073fdf4fb3a","name":"ls","arguments":{"path":"/workspace"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":11322,"output":19,"cacheRead":0,"cacheWrite":0,"reasoning":0,"totalTokens":11341,"cost":{"input":0.11322000000000002,"output":0.00095,"cacheRead":0,"cacheWrite":0,"total":0.11417000000000002}},"stopReason":"toolUse","timestamp":1793222743982,"responseId":"resp_0df8288370165955016ae268587954819d83985886a45f3fe7","rawStopReason":"completed","durationMs":2622,"thinkingLevel":"high"}} +{"type":"message","id":"3057c142","parentId":"989a0c21","timestamp":"2026-10-28T21:25:46.614Z","message":{"role":"toolResult","toolCallId":"call_557120d31d354bdcb113ec14e5b73fe8|fc_0df8288370165955016ae2685a2e50819da406f073fdf4fb3a","toolName":"ls","content":[{"type":"text","text":".kinex/\nharness.md\ninstruction.md"}],"isError":false,"durationMs":6,"timestamp":1793222746614}} +{"type":"message","id":"ef51bca7","parentId":"3057c142","timestamp":"2026-10-28T21:25:48.800Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_8340aa3e10fc46f0aa98cbada5362911|fc_0df8288370165955016ae2685c096c819d88323bf779a16f98","name":"read","arguments":{"path":"/workspace/harness.md","offset":null,"limit":null}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":226,"output":29,"cacheRead":11136,"cacheWrite":0,"reasoning":0,"totalTokens":11391,"cost":{"input":0.0022600000000000003,"output":0.0014500000000000001,"cacheRead":0.011136,"cacheWrite":0,"total":0.014846000000000002}},"stopReason":"toolUse","timestamp":1793222746660,"responseId":"resp_0df8288370165955016ae2685afeb0819d9123209017d49085","rawStopReason":"completed","durationMs":2139,"thinkingLevel":"high"}} +{"type":"message","id":"e5134a1e","parentId":"ef51bca7","timestamp":"2026-10-28T21:25:48.807Z","message":{"role":"toolResult","toolCallId":"call_8340aa3e10fc46f0aa98cbada5362911|fc_0df8288370165955016ae2685c096c819d88323bf779a16f98","toolName":"read","content":[{"type":"text","text":"Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n"}],"isError":false,"durationMs":5,"timestamp":1793222748807}} +{"type":"message","id":"762966bb","parentId":"e5134a1e","timestamp":"2026-10-28T21:25:51.841Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_aae6560e66c84c358b4fd70c7bb8d73d|fc_0df8288370165955016ae2685f34b8819d8019f73c6e473a17","name":"find","arguments":{"pattern":"**/*","path":"/workspace/.kinex","limit":150}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":11495,"output":30,"cacheRead":0,"cacheWrite":0,"reasoning":0,"totalTokens":11525,"cost":{"input":0.11495000000000001,"output":0.0015,"cacheRead":0,"cacheWrite":0,"total":0.11645000000000001}},"stopReason":"toolUse","timestamp":1793222748850,"responseId":"resp_0df8288370165955016ae2685d2598819dacb28fe9bbecf226","rawStopReason":"completed","durationMs":2991,"thinkingLevel":"high"}} 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results limit reached. Use limit=300 for more, or refine pattern]"}],"details":{"resultLimitReached":150},"isError":false,"durationMs":36,"timestamp":1793222751880}} +{"type":"message","id":"bd24ee21","parentId":"31a57a2f","timestamp":"2026-10-28T21:25:56.807Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_73a5e4b6bb4444bca2d098facaed338e|fc_0df8288370165955016ae26861d2a8819d88ec34b3281ae1ee","name":"read","arguments":{"path":".kinex/workspace.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_6625a9cb6f434c88b5bf2fff5cb3be8d|fc_0df8288370165955016ae26864b82c819db4dabc0cb6072c19","name":"read","arguments":{"path":".kinex/devices/world/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_ada7312797534f87a5c4cbbf16b557a1|fc_0df8288370165955016ae26864b82c819d9cad53b113c442d8","name":"read","arguments":{"path":".kinex/devices/left_arm/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_4f4713cdf22247a083c3322102fda0b3|fc_0df8288370165955016ae26864b82c819da747d21af5abb784","name":"read","arguments":{"path":".kinex/devices/right_arm/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_1424db23db3e45cca0546ad2f1550656|fc_0df8288370165955016ae26864b82c819d8f47a10a70595903","name":"read","arguments":{"path":".kinex/devices/left_gripper/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_91eef032bd0f4752bc461a87f7840538|fc_0df8288370165955016ae26864b82c819d8ff98e4e0e6674d9","name":"read","arguments":{"path":".kinex/devices/right_gripper/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_ebfb289d66754647b4e0f7ccfea8adcf|fc_0df8288370165955016ae26864b82c819d9e50eedd577fd641","name":"read","arguments":{"path":".kinex/devices/head_camera/profile.yaml","offset":null,"limit":null}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":2709,"output":223,"cacheRead":11392,"cacheWrite":0,"reasoning":0,"totalTokens":14324,"cost":{"input":0.027090000000000003,"output":0.01115,"cacheRead":0.011392,"cacheWrite":0,"total":0.049632}},"stopReason":"toolUse","timestamp":1793222751925,"responseId":"resp_0df8288370165955016ae2686072e4819d926384444a568a2d","rawStopReason":"completed","durationMs":4882,"thinkingLevel":"high"}} +{"type":"message","id":"d21d533c","parentId":"bd24ee21","timestamp":"2026-10-28T21:25:56.823Z","message":{"role":"toolResult","toolCallId":"call_73a5e4b6bb4444bca2d098facaed338e|fc_0df8288370165955016ae26861d2a8819d88ec34b3281ae1ee","toolName":"read","content":[{"type":"text","text":"identity:\n id: robotwin-aloha-agilex\n name: RoboTwin ALOHA AgileX\n kind: arm\n model: ALOHA AgileX (dual ARX5)\n source: user\ndevices:\n source: user\nsafety:\n source: user\n max_joint_velocity: null\n max_cartesian_velocity: null\n keep_out_zones: []\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\n"}],"isError":false,"durationMs":10,"timestamp":1793222756822}} +{"type":"message","id":"ef397517","parentId":"d21d533c","timestamp":"2026-10-28T21:25:56.823Z","message":{"role":"toolResult","toolCallId":"call_6625a9cb6f434c88b5bf2fff5cb3be8d|fc_0df8288370165955016ae26864b82c819db4dabc0cb6072c19","toolName":"read","content":[{"type":"text","text":"name: world\nruntime:\n domain_id: 83\n observation: time\n nodes: []\n ready_timeout_s: 30\noperations:\n time:\n description: Authoritative world clock\n transport: topic\n direction: output\n endpoint: /clock\n type: rosgraph_msgs/msg/Clock\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n wait:\n description: Advance or await duration_s of world time (finite and nonnegative).\n Zero is an observation barrier. Waits are sequential; one requested while another\n is pending is rejected. A terminal episode rejects further progression without\n exposing benchmark results.\n transport: service\n direction: input\n endpoint: /world/wait\n type: kinex_interfaces/srv/Wait\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 120\n stop:\n description: Cancel the pending wait.\n transport: service\n direction: input\n endpoint: /world/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n Kinex supplies frame transforms independently. This device exposes no task instructions\n or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n base:\n parent: null\n translation_m: null\n rotation_xyzw: null\n"}],"isError":false,"durationMs":8,"timestamp":1793222756823}} +{"type":"message","id":"f75054b3","parentId":"ef397517","timestamp":"2026-10-28T21:25:56.823Z","message":{"role":"toolResult","toolCallId":"call_ada7312797534f87a5c4cbbf16b557a1|fc_0df8288370165955016ae26864b82c819d9cad53b113c442d8","toolName":"read","content":[{"type":"text","text":"name: left_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /left_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /left_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /left_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n as left_arm/, and helpers.joint_model('left_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fl_joint1: 0.0\n fl_joint2: 0.0\n fl_joint3: 0.0\n fl_joint4: 0.0\n fl_joint5: 0.0\n fl_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - left_arm\nframes:\n base:\n translation_m:\n - -0.296999871731\n - -0.419500023127\n - 0.782000243664\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 0.714142563298\n - 0.700000389985\n parent: world:base\n"}],"isError":false,"durationMs":9,"timestamp":1793222756823}} +{"type":"message","id":"d75eac4d","parentId":"f75054b3","timestamp":"2026-10-28T21:25:56.823Z","message":{"role":"toolResult","toolCallId":"call_4f4713cdf22247a083c3322102fda0b3|fc_0df8288370165955016ae26864b82c819da747d21af5abb784","toolName":"read","content":[{"type":"text","text":"name: right_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /right_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /right_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /right_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n as right_arm/, and helpers.joint_model('right_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fr_joint1: 0.0\n fr_joint2: 0.0\n fr_joint3: 0.0\n fr_joint4: 0.0\n fr_joint5: 0.0\n fr_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - right_arm\nframes:\n base:\n translation_m:\n - 0.306300014257\n - -0.418499916792\n - 0.78100001812\n rotation_xyzw:\n - -2.9802e-08\n - 5.9605e-08\n - 0.71063355368\n - 0.703562409721\n parent: world:base\n"}],"isError":false,"durationMs":9,"timestamp":1793222756823}} +{"type":"message","id":"f106c879","parentId":"d75eac4d","timestamp":"2026-10-28T21:25:56.823Z","message":{"role":"toolResult","toolCallId":"call_1424db23db3e45cca0546ad2f1550656|fc_0df8288370165955016ae26864b82c819d8f47a10a70595903","toolName":"read","content":[{"type":"text","text":"name: left_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured left_gripper, the native normalized gripper opening (0 closed,\n 1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n it.\n transport: topic\n direction: output\n endpoint: /left_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /left_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /left_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - left_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: left_arm:flange\n tcp:\n translation_m:\n - 0.1199999989\n - 7.305e-09\n - -7.152e-09\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 3.2247e-08\n - 0.999999998609\n parent: left_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: left_gripper\n open: 1.0\n closed: 0.0\n"}],"isError":false,"durationMs":9,"timestamp":1793222756823}} +{"type":"message","id":"705ec8d2","parentId":"f106c879","timestamp":"2026-10-28T21:25:56.823Z","message":{"role":"toolResult","toolCallId":"call_91eef032bd0f4752bc461a87f7840538|fc_0df8288370165955016ae26864b82c819d8ff98e4e0e6674d9","toolName":"read","content":[{"type":"text","text":"name: right_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured right_gripper, the native normalized gripper opening (0\n closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n from it.\n transport: topic\n direction: output\n endpoint: /right_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /right_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /right_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - right_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: right_arm:flange\n tcp:\n translation_m:\n - 0.119999992883\n - -9.757e-09\n - -7.153e-09\n rotation_xyzw:\n - 1.0e-12\n - 2.9802e-08\n - -6.4393e-08\n - 0.999999973161\n parent: right_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: right_gripper\n open: 1.0\n closed: 0.0\n"}],"isError":false,"durationMs":10,"timestamp":1793222756823}} +{"type":"message","id":"17b8d0de","parentId":"705ec8d2","timestamp":"2026-10-28T21:25:56.823Z","message":{"role":"toolResult","toolCallId":"call_ebfb289d66754647b4e0f7ccfea8adcf|fc_0df8288370165955016ae26864b82c819d9e50eedd577fd641","toolName":"read","content":[{"type":"text","text":"name: head_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /head_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /head_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /head_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - -0.03200000152\n - -0.449999988079\n - 1.350000023842\n rotation_xyzw:\n - -0.948683301192\n - 0.0\n - 0.0\n - 0.316227769682\n parent: world:base\n"}],"isError":false,"durationMs":9,"timestamp":1793222756823}} +{"type":"message","id":"8e36d79e","parentId":"17b8d0de","timestamp":"2026-10-28T21:26:00.106Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_1f5da46d3fda4d33bd3e155df8df3148|fc_0df8288370165955016ae268671804819d9189b4948ddb0c5a","name":"exec","arguments":{"python":"from kinex import runtime, finish\nfinish({d:runtime.operations(d) for d in ['world','left_arm','left_gripper','head_camera']})","description":"inspect operation contracts"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":4585,"output":55,"cacheRead":14208,"cacheWrite":0,"reasoning":0,"totalTokens":18848,"cost":{"input":0.04585,"output":0.0027500000000000003,"cacheRead":0.014208,"cacheWrite":0,"total":0.062808}},"stopReason":"toolUse","timestamp":1793222756881,"responseId":"resp_0df8288370165955016ae2686535a8819d9eda728d2932f669","rawStopReason":"completed","durationMs":3225,"thinkingLevel":"high"}} +{"type":"message","id":"582be810","parentId":"8e36d79e","timestamp":"2026-10-28T21:26:01.104Z","message":{"role":"toolResult","toolCallId":"call_1f5da46d3fda4d33bd3e155df8df3148|fc_0df8288370165955016ae268671804819d9189b4948ddb0c5a","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"\",\n \"result\": {\n \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120},\"stop\":{\"description\":\"Cancel the pending wait.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_arm\": {\"joint_state\":{\"description\":\"Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_arm/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Stop arm motion and hold the measured posture.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_arm/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_gripper\": {\"joint_state\":{\"description\":\"Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_gripper/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Hold the measured opening; fingers may settle briefly after the stop.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"head_camera\": {\"image\":{\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"depth\":{\"description\":\"16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/depth\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"camera_info\":{\"description\":\"Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/camera_info\",\"type\":\"sensor_msgs/msg/CameraInfo\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10}}\n }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":994,"timestamp":1793222761104}} +{"type":"message","id":"01cb21cf","parentId":"582be810","timestamp":"2026-10-28T21:26:04.491Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_16eac218dcfb4206bf7a1f0d0fc0b989|fc_0df8288370165955016ae2686b9f5c819da3ad030b7dfae385","name":"devices","arguments":{"calls":[{"deviceID":"world","action":"load"},{"deviceID":"left_arm","action":"load"},{"deviceID":"right_arm","action":"load"},{"deviceID":"left_gripper","action":"load"},{"deviceID":"right_gripper","action":"load"},{"deviceID":"head_camera","action":"load"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1346,"output":78,"cacheRead":18688,"cacheWrite":0,"reasoning":0,"totalTokens":20112,"cost":{"input":0.013460000000000001,"output":0.0039000000000000003,"cacheRead":0.018688,"cacheWrite":0,"total":0.036048}},"stopReason":"toolUse","timestamp":1793222761132,"responseId":"resp_0df8288370165955016ae268697fcc819d84f0f626a315bed8","rawStopReason":"completed","durationMs":3358,"thinkingLevel":"high"}} +{"type":"message","id":"aabf9fd8","parentId":"01cb21cf","timestamp":"2026-10-28T21:26:10.567Z","message":{"role":"toolResult","toolCallId":"call_16eac218dcfb4206bf7a1f0d0fc0b989|fc_0df8288370165955016ae2686b9f5c819da3ad030b7dfae385","toolName":"devices","content":[{"type":"text","text":"{\n \"results\": [\n {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}"}],"details":{"kinex":{"title":"devices: 6 calls","isError":false}},"isError":false,"durationMs":6074,"timestamp":1793222770567}} +{"type":"message","id":"2ec51e42","parentId":"aabf9fd8","timestamp":"2026-10-28T21:26:13.473Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_e32dfeef88cd49a98044fc70a66a051a|fc_0df8288370165955016ae26875077c819daf1bff4228d0f538","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt","offset":null,"limit":null}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":296,"output":39,"cacheRead":19968,"cacheWrite":0,"reasoning":0,"totalTokens":20303,"cost":{"input":0.0029600000000000004,"output":0.0019500000000000001,"cacheRead":0.019968,"cacheWrite":0,"total":0.024878}},"stopReason":"toolUse","timestamp":1793222770608,"responseId":"resp_0df8288370165955016ae26872f7d4819d9b07b0c172239d6a","rawStopReason":"completed","durationMs":2865,"thinkingLevel":"high"}} +{"type":"message","id":"28197cb3","parentId":"2ec51e42","timestamp":"2026-10-28T21:26:13.479Z","message":{"role":"toolResult","toolCallId":"call_e32dfeef88cd49a98044fc70a66a051a|fc_0df8288370165955016ae26875077c819daf1bff4228d0f538","toolName":"read","content":[{"type":"text","text":"Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n Return a stamped source-to-target transform using Kinex frame history.\n await helpers.transform_pose(io, pose, source, target, stamp=None)\n Express a pose given in one frame in another.\n await helpers.relative_pose(io, child, parent, timestamp=None)\n Express a frame-attached child pose relative to a frame-attached parent pose.\n Implementation: frames.py\n\nKinematics\n helpers.joint_model(device)\n Load a joint device's commanded joints, link FK and IK from its URDF.\n Implementation: kinematics.py\n\nMotion\n helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n Plan joint references and duration without device I/O.\n Implementation: motion.py\n\nPolicy\n await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n Infer once and submit the valid action prefix to the commanded devices.\n Implementation: policy.py\n\nImages\n await helpers.camera_frame(io, camera, timeout_s=None)\n The current image and, for an rgbd camera, its depth from the same acquisition.\n helpers.image_array(image)\n Native-order pixels of a sensor_msgs/Image as numpy for computation.\n helpers.save_image(image)\n Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n Implementation: images.py\n\nPerception\n await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n Locate a detected object in a stamped camera optical frame.\n Implementation: perception.py\n"}],"isError":false,"durationMs":5,"timestamp":1793222773479}} +{"type":"message","id":"f1eb1ed0","parentId":"28197cb3","timestamp":"2026-10-28T21:26:16.257Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_594c0aeb37864a37a3508edeae992b74|fc_0df8288370165955016ae268779990819d9072a6bd2ad63c99","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py","offset":1,"limit":250}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":548,"output":42,"cacheRead":20224,"cacheWrite":0,"reasoning":0,"totalTokens":20814,"cost":{"input":0.0054800000000000005,"output":0.0021000000000000003,"cacheRead":0.020224,"cacheWrite":0,"total":0.027804}},"stopReason":"toolUse","timestamp":1793222773516,"responseId":"resp_0df8288370165955016ae26875ec50819d87f41e2cbce36d43","rawStopReason":"completed","durationMs":2741,"thinkingLevel":"high"}} +{"type":"message","id":"6fda1c44","parentId":"f1eb1ed0","timestamp":"2026-10-28T21:26:16.262Z","message":{"role":"toolResult","toolCallId":"call_594c0aeb37864a37a3508edeae992b74|fc_0df8288370165955016ae268779990819d9072a6bd2ad63c99","toolName":"read","content":[{"type":"text","text":"\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n \"\"\"Load a joint device's model without device I/O.\n\n joints lists the commanded joints in URDF document order with limits and\n rate limits; names holds their names. fk(positions, link=None) returns the\n link pose [x, y, z, qx, qy, qz, qw] in /base. ik(pose, seed,\n link=None, **options) returns commanded positions nearest the seed, or\n raises ValueError when no verified solution is found. link defaults to the\n model's tip_link. Measured links are also on TF as /.\n\n Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n \"\"\"\n return JointModel(profile(device))\n"}],"isError":false,"durationMs":4,"timestamp":1793222776262}} +{"type":"message","id":"199e64df","parentId":"6fda1c44","timestamp":"2026-10-28T21:26:20.658Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_e5af90cf3ab749f9a00db0884473c5c8|fc_0df8288370165955016ae2687a893c819dab6f8abbc44f9f18","name":"exec","arguments":{"python":"import asyncio\nfrom kinex import runtime, communication, helpers, finish\nasync def main():\n async with communication() as io:\n f=await helpers.camera_frame(io,'head_camera')\n out={}\n for d in ['left_arm','right_arm','left_gripper','right_gripper']:\n async with io.subscribe(d+'.joint_state') as s: out[d]=await s.next()\n out['schemas']={x:runtime.describe(x) for x in ['left_arm.joint_target','left_gripper.target','world.wait']}\n finish(out,file=helpers.save_image(f['image']))\nasyncio.run(main())","description":"observe scene and schemas"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":416,"output":164,"cacheRead":20608,"cacheWrite":0,"reasoning":0,"totalTokens":21188,"cost":{"input":0.0041600000000000005,"output":0.0082,"cacheRead":0.020607999999999998,"cacheWrite":0,"total":0.032968}},"stopReason":"toolUse","timestamp":1793222776299,"responseId":"resp_0df8288370165955016ae26878c620819d83f7bbc7a4b33eed","rawStopReason":"completed","durationMs":4358,"thinkingLevel":"high"}} +{"type":"message","id":"696bc35e","parentId":"199e64df","timestamp":"2026-10-28T21:26:23.111Z","message":{"role":"toolResult","toolCallId":"call_e5af90cf3ab749f9a00db0884473c5c8|fc_0df8288370165955016ae2687a893c819dab6f8abbc44f9f18","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793222782.251334587] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n exec(compile(_request[\\\"source\\\"], \\\"\\\", \\\"exec\\\"), module.__dict__)\\n File \\\"\\\", line 11, in \\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"\\\", line 10, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}"}],"details":{"kinex":{"title":"exec (Python)","isError":true}},"isError":true,"durationMs":2452,"timestamp":1793222783111}} +{"type":"message","id":"888980b4","parentId":"696bc35e","timestamp":"2026-10-28T21:26:25.414Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_764ced789f0d4188899433ec60bc7b1a|fc_0df8288370165955016ae26880fc78819d82a1b2e75454d578","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py","offset":1,"limit":180}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":592,"output":39,"cacheRead":20992,"cacheWrite":0,"reasoning":0,"totalTokens":21623,"cost":{"input":0.005920000000000001,"output":0.0019500000000000001,"cacheRead":0.020992,"cacheWrite":0,"total":0.028862}},"stopReason":"toolUse","timestamp":1793222783142,"responseId":"resp_0df8288370165955016ae2687f8af8819d81e2dfce1ef85fef","rawStopReason":"completed","durationMs":2272,"thinkingLevel":"high"}} +{"type":"message","id":"85aa9ef6","parentId":"888980b4","timestamp":"2026-10-28T21:26:25.418Z","message":{"role":"toolResult","toolCallId":"call_764ced789f0d4188899433ec60bc7b1a|fc_0df8288370165955016ae26880fc78819d82a1b2e75454d578","toolName":"read","content":[{"type":"text","text":"\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n Both come from one acquisition (equal stamps); reading never advances world time.\n\n Example: image, depth = await helpers.camera_frame(io, \"camera\")\n \"\"\"\n return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n \"\"\"\n return array(image)\n\n\ndef save_image(image):\n \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n \"\"\"\n return save(image)\n"}],"isError":false,"durationMs":4,"timestamp":1793222785418}} +{"type":"message","id":"e1574d32","parentId":"85aa9ef6","timestamp":"2026-10-28T21:26:29.317Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_1f72f2a5fef14e6c9770552b06e256ef|fc_0df8288370165955016ae2688345b4819daff7e571c13a4374","name":"exec","arguments":{"python":"import asyncio\nfrom kinex import runtime, communication, helpers, finish\nasync def main():\n async with communication() as io:\n image,depth=await helpers.camera_frame(io,'head_camera')\n out={}\n for d in ['left_arm','right_arm','left_gripper','right_gripper']:\n async with io.subscribe(d+'.joint_state') as s: out[d]=await s.next()\n out['schemas']={x:runtime.describe(x) for x in ['left_arm.joint_target','left_gripper.target','world.wait']}\n p=helpers.save_image(image)\n finish(out,file=p)\nasyncio.run(main())","description":"observe scene and schemas"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":351,"output":167,"cacheRead":21504,"cacheWrite":0,"reasoning":0,"totalTokens":22022,"cost":{"input":0.00351,"output":0.00835,"cacheRead":0.021504,"cacheWrite":0,"total":0.033364}},"stopReason":"toolUse","timestamp":1793222785452,"responseId":"resp_0df8288370165955016ae26881d63c819dbf736a6eeb815e4a","rawStopReason":"completed","durationMs":3864,"thinkingLevel":"high"}} +{"type":"message","id":"01d7a694","parentId":"e1574d32","timestamp":"2026-10-28T21:26:31.346Z","message":{"role":"toolResult","toolCallId":"call_1f72f2a5fef14e6c9770552b06e256ef|fc_0df8288370165955016ae2688345b4819daff7e571c13a4374","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793222790.779743893] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"left_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\n \"right_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\n \"left_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"left_gripper\"],\"position\":[0.986124409870668],\"velocity\":[0.018415418030186134],\"effort\":[]},\n \"right_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"right_gripper\"],\"position\":[0.9861242744055662],\"velocity\":[0.0184164128520272],\"effort\":[]},\n \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence\",\"points\":\"sequence\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence\",\"velocities\":\"sequence\",\"accelerations\":\"sequence\",\"effort\":\"sequence\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}},\n \"file\": \"/tmp/kinex-observation-ftia7zbo.png\"\n }\n}"},{"type":"image","mimeType":"image/png","data":"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+{"type":"message","id":"02612523","parentId":"0a37bbcd","timestamp":"2026-10-28T21:26:59.094Z","message":{"role":"toolResult","toolCallId":"call_a035a832a8bf43bfa0c7ce6196f9a693|fc_0df8288370165955016ae268a2ee70819dadfaf8ab685b02f8","toolName":"read","content":[{"type":"text","text":"name: left_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /left_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /left_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /left_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n pixel-center depth from a raster twin; calibration and the optical-frame mount on\n the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - 0.07000001149\n - 0.031999994336\n - 0.065000047158\n rotation_xyzw:\n - -0.589367966393\n - 0.589367919412\n - -0.390698641271\n - 0.39069867358\n parent: left_arm:flange\n"}],"isError":false,"durationMs":3,"timestamp":1793222819094}} 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asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,de=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im).astype(float); z=helpers.image_array(de)*.001\n tf=(await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'}))['pose']; out={}\n for k,m in {'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}.items():\n v,u=np.where(m&(z>0)); zz=z[v,u]; p=np.stack([(u-159.5)*zz/358.6421814,(v-119.5)*zz/358.6421814,zz],axis=1); w=R.from_quat(tf[3:]).apply(p)+tf[:3]\n out[k]={'min':w.min(0),'max':w.max(0),'median':np.median(w,axis=0),'bottom':np.median(w[w[:,2].6: raise ValueError('IK branch jump')\n q=nq; t=elapsed+duration*f; ns=round(t*1e9)\n points.append({'positions':q.tolist(),'time_from_start':{'sec':ns//1000000000,'nanosec':ns%1000000000}})\n elapsed+=duration; wp=targetp; wr=targetr\n plans[side]=(model,q,elapsed,points)\n for side,(model,q,t,points) in plans.items():\n await io.publish(side+'_arm.joint_target',{'joint_names':model.names,'points':points})\n for side,value in a.get('grips',{}).items():\n await io.publish(side+'_gripper.target',{'data':float(value)})\n wait=max([p[2] for p in plans.values()]+[float(a.get('wait',0))])\n if wait: result['wait']=await io.request('world.wait',duration_s=wait+float(a.get('settle',.16)))\n for side in ['left','right']:\n s=await state(io,side+'_arm'); g=await state(io,side+'_gripper')\n result[side]={'q':list(s.position),'grip':list(g.position),'tcp':(await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'}))['pose']}\n if side in plans: result[side]['joint_error']=float(np.max(np.abs(np.array(s.position)-plans[side][1])))\n paths=[]\n if a.get('observe',True):\n image,depth=await helpers.camera_frame(io,'head_camera'); paths=[helpers.save_image(image)]\n finish(result,file=paths)\n\nasyncio.run(main())\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":489,"output":1241,"cacheRead":26368,"cacheWrite":0,"reasoning":258,"totalTokens":28098,"cost":{"input":0.00489,"output":0.06205,"cacheRead":0.026368,"cacheWrite":0,"total":0.093308}},"stopReason":"toolUse","timestamp":1793222830395,"responseId":"resp_0df8288370165955016ae268aec93c819da5d9f539ad00f319","rawStopReason":"completed","durationMs":34462,"thinkingLevel":"high"}} +{"type":"message","id":"ed34bdb2","parentId":"cd6711d1","timestamp":"2026-10-28T21:27:44.864Z","message":{"role":"toolResult","toolCallId":"call_83515ff0f7274002a9175087a6022bdc|fc_0df8288370165955016ae268bf32bc819daeef75bb1c85047f","toolName":"write","content":[{"type":"text","text":"Successfully wrote to /workspace/.kinex/tools/cartesian_move.py"}],"isError":false,"durationMs":4,"timestamp":1793222864864}} 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+{"type":"message","id":"c76dfcac","parentId":"bc1338e6","timestamp":"2026-10-28T21:27:50.898Z","message":{"role":"toolResult","toolCallId":"call_0bd080577be548f4b8adb745b8436cb7|fc_0df8288370165955016ae268d2b184819da58990c63104de48","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793222870.183791116] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 40, in script\\n runpy.run_path(file, run_name=\\\"__main__\\\")\\n File \\\"\\\", line 287, in run_path\\n File \\\"\\\", line 98, in _run_module_code\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 51, in \\n asyncio.run(main())\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 22, in main\\n local=model.fk(q); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0])\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n q = joint_target(positions, self.joints, check_limits=False)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n vector(value, len(joints), \\\"joint target\\\")\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}"}],"details":{"kinex":{"title":"exec (Python)","isError":true}},"isError":true,"durationMs":2902,"timestamp":1793222870898}} +{"type":"message","id":"2a226851","parentId":"c76dfcac","timestamp":"2026-10-28T21:27:55.238Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_8a1beec4629348509212b3929faaae45|fc_0df8288370165955016ae268da030c819dbf655887159e5844","name":"edit","arguments":{"path":".kinex/tools/cartesian_move.py","edits":[{"oldText":"local=model.fk(q);","newText":"local=model.fk(q.tolist());"},{"oldText":"model.ik([*lp,*lr.as_quat()],q)","newText":"model.ik([*lp,*lr.as_quat()],q.tolist())"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":815,"output":87,"cacheRead":28032,"cacheWrite":0,"reasoning":0,"totalTokens":28934,"cost":{"input":0.008150000000000001,"output":0.004350000000000001,"cacheRead":0.028031999999999998,"cacheWrite":0,"total":0.040532}},"stopReason":"toolUse","timestamp":1793222870930,"responseId":"resp_0df8288370165955016ae268d75054819d8c26049c32f5b38f","rawStopReason":"completed","durationMs":4308,"thinkingLevel":"high"}} +{"type":"message","id":"5eef5859","parentId":"2a226851","timestamp":"2026-10-28T21:27:55.247Z","message":{"role":"toolResult","toolCallId":"call_8a1beec4629348509212b3929faaae45|fc_0df8288370165955016ae268da030c819dbf655887159e5844","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 2 block(s) in .kinex/tools/cartesian_move.py."}],"details":{"diff":" ...\n 18 arm=side+'_arm'; model=helpers.joint_model(arm)\n 19 initial=await state(io,arm); q=np.array(initial.position)\n 20 tf=(await helpers.relative_pose(io,{'frame':arm+':base'},{'frame':'world:base'}))['pose']\n 21 baseR=Rotation.from_quat(tf[3:]); basep=np.array(tf[:3])\n-22 local=model.fk(q); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0])\n+22 local=model.fk(q.tolist()); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0])\n 23 points=[]; elapsed=0\n 24 for pose in poses:\n 25 targetp=np.array(pose[:3]); targetr=Rotation.from_quat(pose[3:]); sl=Slerp([0,1],Rotation.from_quat([wr.as_quat(),targetr.as_quat()]))\n 26 n=max(8,int(duration/.08))\n 27 for i in range(1,n+1):\n 28 f=i/n; r=sl([f])[0]; p=wp*(1-f)+targetp*f\n 29 lp=baseR.inv().apply(p-r.apply([.12,0,0])-basep); lr=baseR.inv()*r\n-30 nq=np.array(model.ik([*lp,*lr.as_quat()],q))\n+30 nq=np.array(model.ik([*lp,*lr.as_quat()],q.tolist()))\n 31 if np.max(np.abs(nq-q))>.6: raise ValueError('IK branch jump')\n 32 q=nq; t=elapsed+duration*f; ns=round(t*1e9)\n 33 points.append({'positions':q.tolist(),'time_from_start':{'sec':ns//1000000000,'nanosec':ns%1000000000}})\n 34 elapsed+=duration; wp=targetp; wr=targetr\n ...","patch":"--- .kinex/tools/cartesian_move.py\n+++ .kinex/tools/cartesian_move.py\n@@ -18,17 +18,17 @@\n arm=side+'_arm'; model=helpers.joint_model(arm)\n initial=await state(io,arm); q=np.array(initial.position)\n tf=(await helpers.relative_pose(io,{'frame':arm+':base'},{'frame':'world:base'}))['pose']\n baseR=Rotation.from_quat(tf[3:]); basep=np.array(tf[:3])\n- local=model.fk(q); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0])\n+ local=model.fk(q.tolist()); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0])\n points=[]; elapsed=0\n for pose in poses:\n targetp=np.array(pose[:3]); targetr=Rotation.from_quat(pose[3:]); sl=Slerp([0,1],Rotation.from_quat([wr.as_quat(),targetr.as_quat()]))\n n=max(8,int(duration/.08))\n for i in range(1,n+1):\n f=i/n; r=sl([f])[0]; p=wp*(1-f)+targetp*f\n lp=baseR.inv().apply(p-r.apply([.12,0,0])-basep); lr=baseR.inv()*r\n- nq=np.array(model.ik([*lp,*lr.as_quat()],q))\n+ nq=np.array(model.ik([*lp,*lr.as_quat()],q.tolist()))\n if np.max(np.abs(nq-q))>.6: raise ValueError('IK branch jump')\n q=nq; t=elapsed+duration*f; ns=round(t*1e9)\n points.append({'positions':q.tolist(),'time_from_start':{'sec':ns//1000000000,'nanosec':ns%1000000000}})\n elapsed+=duration; wp=targetp; wr=targetr\n","firstChangedLine":22},"isError":false,"durationMs":7,"timestamp":1793222875247}} +{"type":"message","id":"e3336eda","parentId":"5eef5859","timestamp":"2026-10-28T21:27:57.609Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_ef852ce2a3fa42c1b7603121b673065c|fc_0df8288370165955016ae268dcd920819dad51574c26d56fdd","name":"exec","arguments":{"file":".kinex/tools/cartesian_move.py","args":{"moves":{"left":[[-0.167,0.045,0.835,0,0,0.70710678,0.70710678]]},"seconds":2,"grips":{"left":1}},"description":"approach block with left arm"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":161,"output":81,"cacheRead":28800,"cacheWrite":0,"reasoning":0,"totalTokens":29042,"cost":{"input":0.00161,"output":0.00405,"cacheRead":0.0288,"cacheWrite":0,"total":0.03446}},"stopReason":"toolUse","timestamp":1793222875281,"responseId":"resp_0df8288370165955016ae268dba238819daa7933748fb91726","rawStopReason":"completed","durationMs":2328,"thinkingLevel":"high"}} +{"type":"message","id":"a8adce0d","parentId":"e3336eda","timestamp":"2026-10-28T21:28:09.087Z","message":{"role":"toolResult","toolCallId":"call_ef852ce2a3fa42c1b7603121b673065c|fc_0df8288370165955016ae268dcd920819dad51574c26d56fdd","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793222879.778297666] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.16,\"waited_s\":2.16},\n \"left\": {\"q\":[-0.4095152020454407,2.26525616645813,0.7202089428901672,1.5543266534805298,-0.3688046932220459,0.013095114380121231],\"grip\":[1.0019286491654136],\"tcp\":[-0.17529561461992135,0.03572927260289499,0.8417034955027122,0.0007973011467065941,-0.001999688929063644,0.7090469286738864,0.7051580095930252],\"joint_error\":0.05355189650084757},\n \"right\": {\"q\":[-3.0567537834258474e-8,-0.000004077900030097226,0.000004151807388552697,1.029454779200023e-7,6.391106808223412e-7,-8.509506983500614e-7],\"grip\":[0.9861380918459459],\"tcp\":[0.30523325599148965,-0.19280648083225077,0.9410027440767832,-0.0000026235312808848334,-0.000008356054671572675,0.7106332329651421,0.7035626540179656]},\n \"file\": [\"/tmp/kinex-observation-2d4xk9x7.png\"]\n 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ddUZZFeXh02KAWbMA/BaTYAQtpGQCZJHmWW2ufvnLl6tWrX3jwgV/7lV950w+/Mp8t0gziYTlOofCQYB8KvE7koVbIZF0DPy+Hddg0ZMYb7DuHkZJoViLxhGCdfYBWfaHpjjxckcq88YgN9GDMerDyGsHJPhYdXOT0AGEBCnCjdUHGGYei1SkBCBlSY3CT5vc1El/A0INZDV2FwRx6RuQZIIU39WjC6VN2U/wthja1wSH3G82mPCx4TCyB3ZlqmGoE4idRrgnhLKK1miRLs3To7NcffODJi09Z9M1y0MYi3dD46EqQyiCTk8T0vV01TWFN2zWdAXdz33VD16cmaZuuqsqyAJrpuhZMTXI3k4YM9wXL06nJBKOQQKhQuuJu4LJGWFXTNGleDsZ2Julsl6cFhak6cJ8NIHoREAo+Z4xHGIQkU9CvH+B/wGzVw2++4x0vef4Lbjpfgnc94biUBLi0B3ZMRuBu95tM+0RGeyBQVvkc3kEKiBq9c0CPwn2djeFliWwhGfTIOxMdagzxdjqO+J2pJn+PxbKL9CPGbeKJxx0EpE5NjlvIR9vUDXgr+wE4luFk1oZHeW6g30hFWqJrR8s5Ye2KScC3knv6i1zaQ0i9HkLsxjE5Xn47zTuEljV2gUcLwZWhSszq6PD+bz3Q9J1JMpSvnmmBhCSEtEVKtITvFZQ2eIjTpO3SssiyLM8z0EUpsoM7PcvzHIjXJT9oLz7JNv6bo2Eot2njggtacgh6TFaA2BEGf+ok60yerVbZrEoAIwmSXBbWA1V5oDZJ0Q4AoslMnuWrw3q1bopZmTSUq+KXU3uhGEqiVzTUuEbKMP8USZJI53Mxe08OZrSYwaqq83lTEPLPs5EJOFasTEyAgsLHjPwzerMG0jEYnHqCehllGvjd7KOs4vaH/9LU2Bz+4si6w/NPsMXgdxeGF6vb+W03H4FUVe5c/lbByuIYnrcYPIacP2Llzd1zDC/3kxXcOZxIr7u5iQuBIxELwFhGahcndz7zhc8/denS0PepycQMYdnLi+hsV1ZKDcCKcQ1SoNoBBCB6chNrc1MgYgJgx1meF3Dk4FGiPBnShsE25ftRlhWY3ShjUf4TnwQEBynhaZrOZ3OTArYkS5NVnuyU1YEFYGfXdxiJwtsi4BfMYAmgUHYOqMoMBYfHDcmwd7BfJKaH0JKXG5p8RYPx285/KQQhE8kqmVuLcGPE1MmeOW/x+cPBCjQrpyNgHYThcOJH3UQgvG6tJT6vTuE34y0n+yqyIATJSCPyaq1o/XyVkI9TIvgLshVHSqgTnM68yJBP52gMU0QzkD5q8GP3UuDLkwEyX4spxOG2tZT3ZrVDQkjuSczJ9CO94uMxdMrNrfeH8utokMgoSKHnzqVwKCbA7gdhGLQ10zRPbVc3tu/T2XxRHh3ga0AeG/mQkB5ZhQ6QVfgbuAwZFJzaHoGKyIqyNC3zYjafgfQVSwiiBX3HIlfDbikSCyQGaQdlUXQ9xqb6Hnza8DOQewpQKmLMkPiTD0m6s22uXO55jAnljTISLKWoFS0XRIQhG3GAiFSZlsMw5EV2ef9qkpm0z0GlcNCRcEs4y9x97qYZFRpU+jypq4WOrSiFiOIVDh9ktVXILCBy54z2pNoq7hwXfZJNrLiKM2K1La31A7Fi+C9t3QszcUBLpRlq7UVJ6ihFw4ksVk/I0QIfA6uHtJkUCRgQNh6Jp4Ye5FJEy+Q0BcmC0tPlsX/yrVstypj0s8mOS5cvF/Mfxbq0VqQWwD1M4FDKRvYXhnqEdlDgHnRQV0/RCSUKMxvyCdCDSbPDw8MX3HnPnefP7+5dbluAQMgkpSho2SBm6vXLCwg4QGoMfd+s06JMEF9ZzGZVNavKEv1JoHZT2Ih4K92FQkjqHSDVCgxjfCS4j02WFEWaZhgq7uq6YRowqC9naTqbd4f7QHsm7Y2BzG/0XYFCZjKH6mZeA6IcfiMVgDI16qOjpBvSJLMGEjaIiYATjBVUzmHmBF8BOmqGGlJSsNDuI5/f4xg284PQBrYBcsJRt9qNQnbaXSlPYW0TOXPAu9VuRE+8pNP6IbNT1Z3uQMwBoEu/F8+EH47mI/43AZqKg1JkUsyUYBcNFuyayqY95EsL9hlxe5zJ7iVbMPuK07qZCYlM8UKOUarXJSHkqFk4nBseqpH+rcRY4S2r1AoPwJXLSQ/As9EB7N1Ojg0phZlVGE0RAiOWmWR/JYoNXlYMukGsKE0Wy+zcuXP5t/K27bM8I5vTbR6HZfTeLHfnLO2GobfD1rzK8qKczbIc5BJJTm9RUJqqiAiiY4wqoZuMDlgtAHEkTu9NAY9ZlgUYvQDaBFBXCgpyfvquG+0DD2ZJmxV5NV8crddqizFbRW4gskBwVoAXG4bC5od7R0Nv8zx1GU5+cViG8UtyeQVSJ2OdNpChWmNyKgaaEiJeXYpoqPwZ9l27hFe9TXkcfnQjixmT5HmIOHBWF6LNDOa+yzlTikCQzKCpUNLKvfUX0nMoi2JzMvzS29I8P7hpiVrTosyXO3MA8XGiM+kQWBHAh4A5gCH7kF+Dyhe4AXvQBgNNGT8kq0KBZjxTJJNGdHjIrme0bj7IJUN2YKCH0LUwuYNsG8E4sJcWAyTq5m5rTQSRaS0dJxQrlibMq+ukMHT9UPfr2WJ+0y3nEfgAZRvIPEPZS6qzP0SmMwtBG7esytny5HZazol2EFRJ9R4o+96nO/nlJTlM5EpqA5U/QcuZi6EAVBNevZpV83S2f3CIuGtQp2+5+1nbT156qmvSHrRipHlWLzC4PZBTU5Q/ZzriEqQQ9T3qm2pnsaxOtN06zdJ+IPVe83nZNU5NdeqWcli7+ST1S+qEsL2ith8GxhwvVbvfKp3IL5zjdsw5lGHH4lcoQKjFRdKFIN0wHe/nEg7a2pUNjiVAZFMpEonSWJnypUKJd7cKQdE2DeQWl84Qd49z7ENEYIAU7r4z+dZN5+BzcHkg33X+Ry8CtGbtqh0E+s0G95VyiR2P0SFJ6GuYMGfwqhCvkihAioDZiObBqLd0g3QD0LxO8zxRWsIhOR0dryWSxEIUKdiWQDfr9mC2c+aOu+4EmzjPu66jNWCslHO8++UkLQoiNMPQV/P5bFbasuyhagqcROV+hA1hWDnWD+TORKXggcSgFXgkEX0hA6bCI23XmcQu5vOht03T9l3/6pe+/NFLuxc/ewXAd0d7ABthICTcGHOPqCCMzAb7CW3XtlCCx9rlyZMnbryxKrbq+igvC2HcXMtAasigGgVjECVLb07vutIGjJCni78zASsUo74FceqUFWB/Hy4hIQTofSx6/UH+KDebUxLDQwep3E5w4+RaLOpBTudmJqRvFB4i+2ScPEXO1SK7XSiIGZKMlgbcQg2mPIH/O7SJMSggpU789aoSQbj1HZMKIdHaL+9Ct67oVWjCeR2bSx6M3pknS3GKOLvBTRVLEUXBygT2JjLDC0PNWw9JPZHmbhio/NUAwV6sjoSOgzIpkz4p8wqKFfmCUHAo88yJBJJwsE5paqrFvJwt2r6Fk51KIO4L504WC1aqGnlzwAxo/hpMbKApgWpaJD/Rf8VFpKDMSp+XRYlXnb/h7D333Pnxr/71PE13jw4oI4KuAnOYEVjsgQWP92C4RJlJmqauytnZczfYrk+KIcVXBtUjzYD5klYHnjIqmEPannheOIrr5aBfUN4q4yQEtWqiTOsvTLQf3ZbjWXdkoDar235gLYZqvHdBC++Gh2qB6/aFnCY8We1V9y4qwyEYuFMeSKWQefD2leNsavd6WiPxRjZmnqQp1GHCTE+X6Ohi8I75hdm1x4TEglfdkOXr2J53/IvRqCwbngj/g+JwbpzeltUEH+k63iMV+MYmdkk4Wj882gpQjstPZd8ngCPMk3bosIJbi6VnMN9ONGd/f5LJTKFZnm5tb+ezWdO1TkUihIbSsf0dkBip1B69lnit0IvcNI21tihyQjIpq0zzIEj+tuhDzk7u3HHLbWXdV8tq3fYE50pskhd5keR103Rwps2zLC8KiEtD2iP4w1Kw8JNFZra3t6DwUpJ0PTAOyL+iHUIldAwgLBENphQ6Pbc8sc5T4HXj6b0k7x0vjVvKZHSte6L/Jljv4DO/60bApMmd7h2jG+KmzidkN4wtIFfZq/Hml7P13ARDtVAbIjMIhpsERwVTIyIr+DB8gcgwVxbnSF+VgU4o1OEM6nOiBDHvEYwGEcWCFOvSfmqvIuobaMaBXwE1ilpO+xIzdRODKXUZUF1zuIKNjr4iiNno21EBHpAfKaf/ZelyuVgulgNWHyNqJZ+zxJr0OxLbwYp7+BGdhO7gNEfZZ4ahKEvwn7lsPvbKcNY6ZvCbLEnXzaooqguXLt10181VOtSrA5tBlqIxCRBtYrMMnHCkICCP6ihCNbQkZtO+7bPUbM2XBQWrM3Q/k8cRK7wkgNnCskKGNXNIidYi1y8GWdpOUCu5F5g5U4QdxhtiSR7t2/g+itHHR2DBj76dOF292ngw4eYOvc1eAVRe7+lnxIMX1R0y3UDNEgIOZJWXtooK5d/RHgtPidiauvA6f/asS38+ekTM15XWMakjBGQ+learJi2cAVFA8BFAaz3AC02eGogCZ02zaiH3rrVSulboyKvGpFcbY2ZlmVPA1oMuPOSDdG+t2mEoF08AEgfL2xhDJR8hKpsVJ0+fGqwt0qzr8NkU1PWWMs9HN9iiqrq2/+YjF1/xpu8vmqPDIUnmC9McJEnadxhJwgTGEgwE4AVwq97abKA4szFJ09s8y0+fPFnOSmfkComkCSQvdVmBpTugpJZNGhiT8tqonRYvarjcx+2o8FqjGPR4R0kBs/Cmk4JltF0mNlEoKpw1p/de9C7xSzllMHy76Qjn+HO5ljPBUHVyVSOn7qI+DD2hwcuPZ9jR2HgEk3p1fIZzX7n319xxksMde8MxsssDYkZD9DPrwr49y2IorWySrEs6m5Qor5KkXC7rdd3UDdrF5D8xWH6aaiUwGUOt1SyfzxdQbQcL6zriZdciu3+wBjeSUE9wZURxtE3bdV0J8aaqrSEBAZDYELXqyrJEvxjQdpYhHtvbz8BSwP+ML9o03X//8z+78/k3zWeLC3Xb101vhwy0+D4lSCXxGryWihx2nVjXAB0zJrVbxSwxVVIMRVGJmQ2JDQloAcNTF5/qL9fGpOfvvhVeoYdq44oepnB+xy3kmM43fL/pcI8LiG2D2uiCFYr9BScE+ul4PEqJCKTIlICdVEvVoNXnSmIFp8FtyQYmvXwDEag4r7dvr+dQOixe6/Tb8TLotKCQcehXCVin5iDHL6O7MIjm+688h/Zga/xngJT4NMnKWbI6vLo6yldpb9IKPVrJqh1WR2V+UDeHFvIBgJxAPlMekricpHg85ySUZUm5Dq4UDjqeA11DlGn4BdVa+KBaLrI8B7+RTWazWdPU+/sH67QGR3ZRAL+gMtj6tSkAh75JrFQ35FVx5eKVB7/6xNbdz148/PD+3l7fDevmkOJcpBRIOj5Uu2IUGhQthVrig7VN3V3e3weK7YehpgJOXVIWmSmS1fBbv/tffu8df9TsHfz8L/yNX/m7vzJPd0zSSiFu94JaUk2aThGR6D1wPMXajYxcW0zuiRNG37GI4EgNHpPTxEO1EapPU96lY0Y8wWvCoQYE7Mlm8q4jVWfToQVvKPmUaA3vFAQHo0vkwuOo9DpGNX3b0P/M1Ot+TYekAy/vkP2Tf/7PP/elL61W667pIV8flMSszAqsdzNAXlEHRAT+KorbKTFIDKEo8sUCMMkcBufwCohplJxwIKgZKrqiAwk+X69rY5KKKm+ghwx6LGRpUVRnzlQQlT06onQFZnEa5wAGe0ZKAQJM4Flr2/3XP/7vzVDvrlar1brpesJ09iDnsVWCpPUJAIDzI5EZ2Hq9+tf/t//1P7z9f5u1dV2UkPCfQw0g26d92x2t9u+//4EiBVgQ2MmVKYYCuAGAvVyEb+y2GJm1U0povC03iORkLGP1Ek9ojuFOnVAQIuaywWcUH1MA0bEkn9y2XupO6iOKqDwBT79wdOXkAkw+fop58NQoP9OxjxTxGA5gfIl9JjQc+EW1djCaW5smmemHPs22/uL9H/zGAw/u7e/nIGLTNC9sMnRt39ihA7Mx3dnZqddNS9Xqeizhjr4F0qPh5ohFyPOcATQojonGSfEm9bXIc9CHwcHbIy4jLYqCi9WCVzdLUlDOoeCOMW3XJtau1ut50+DtcV4Z8Ynv4Vi8wHIAiTG0T199en140HUNlKCEwC9HpwneBSYsluTg5jSC9sIRDkWWP3HhqYuXLw+JmVXF0OErYMpzYsAyw+TE4uyNN+XzOQ4oSzpwzk+LRxfYj9djzPojX8nUYeWfyKs90nPV2ZFNrgy0eLxjWTp5W3Xvsar43Rz2+I/za58fyGTtFXiGh3PT60/4rpoBaxv4mHcYj2HKIz1+XMzdndkTngond0Ux++RHP/Lvf+M/ffuRh0GVBTGWFjm0FoF8AUjbtVkGEA4qTJFmWY5ddZQ4xEAy8AOiYk6yZfei7F2qNTtYWxYFIBYhlTCt63pWlXXbkhYOSboJVLSDYCvEsYB4traWYDNLOkpUQMUhLuVXdKQnXWLS+XxRFiVAvwQ+hI1FhJ6koI8oERBVpuDzweEBUnRWr8CZlhqT50WeF3UDFnXTtXme7ZzegcZIFhtj0bpPEsP1uXt5Ejd8kQRwi4B0Nl2hRuGcII6S3XebRHy4TyeudfcY2wLhPSfLggfDn/w2+FATsC4Xpcehhaf7ZIJDfpeHJ9gQgHGNa67nvhuOCcEukF/nAweK6kCo2P7rX/va/sE+0NjAfbyyPC3yHNw8YHu263qN0Ghdcgemmaq9CZ6TcgadNSA5EgQ+x+gUAKyHPsugMUuRFcYk67oB0TYMwBYg5gO3I4d2VYEPqe0buE4yLvQ6SUFeR72YrJ9D+koLVbAgCu21ZJgT7pvkJ0YtCNjIkPwImB9MTBy6Bo3r1OR9nzVN0/dN2+RZXmTFzs5puAqCU04pFfbpnfyRU2O0dNdD2zZcxwmSiPzb6mFOhY7dyCKoNo1A79PjBznBtiKhMmV1j8XZpvdFL/T00zaPaGTAXA85HaP2xI+dNoA2XnJd2sAm8zpktF6jo2TpIsmz9UGT51VuoBYklXGmus9Fli/mc2OSum0phiSIQoQxgYZMjiV0TZPdIBBrZx1Lb0TQV0kOZzmvSD/0BVa92Ts8qOuaS/9Ciy2M4ecZlOmBIVGJ2QBsExUHdABscEmn4I5isayShChDWRIApe8KQeARYIUBNMxPBKiGg54DCA0Ka6GZADlVWTabVad2tmEQXZsU4O46bjU2LVe8p9VGn15xo2y34x+waQOFVivbWhsk8Kab6btuvPKZiL2x/h9eHarQ/PJOLVFT5vUEMWKP02+PH4dik7Hl6dzC1/1KARuJVKlNF+jPprYDIozh73X3mh941Z133L5eH9RNnUHdDIuZC5nth3VdwzylBtN3EdMuNMyvIW1OMaAChaMhDoMOLuozyv536ouHt0JkJdx1wEz9NE2rolxUM5OmpKjneZ5nWd3UdQNy1G9XQVRKy1Q/IepHuDNqvFDYXcPZFD7VJU5xBBdLBVETQkSvYEQJaRniXDxfmI4K2JIiXyzLMye2ktZypBgDUQGEONZUJ83jqTUb57kn0bpHhnTojwkrSvtvdC6V11GJz20YVWycj7XlZ6gSBqrodR9KAism5J0Bm580HtD12zPHHBPSWLsWppSK+LjmBIwXYaTAYcsv3FpdslU8+557vvrNr4OHiiowto3kP0nTYIRYKd8zUqOKOqA6DaI7z/O2abkzsGwXlK7QvdJlQbpIFCCboIwWVNuBOs9Qct3WTbOum6YFHDVUyoH6cm4jk8DnVECPrlaYahCawFaUB8CXUuTIF5VfIEsANXafnE8ZjfRemAeMZwJUK8mgBECxWJ7ePokqNJcMEHCudsb6camYv0ewaNLS3pBw6cxo9a53e1yHhRwcm8+/tsB/5oeHW04+MSDCXF54BF12S3sMzus62cUE+kXbQiNV/Hg5uvHFxsxYbjFpYh2j/5MWACGhLi1mN9x0DlJsLfiwMgYQ0iZkzDpXqnSMjepRColK7ujQda2OJXIXMj3J1ItK4aHVG1kW4xhWxQp4TVM3OztbRCGBV8yNLFKqgImANuwsP3JfU1ha9GnObNNFMPXMuZNcAhGhtjCLAbjY6175qnKeJmhio89AWb9uz4w1uAhcoLeKUx8mQ1DuiOl5k6q2+fuAIDfkIdBXfiDXI3uvj42M+VHw7ah5CTJH8Jf4M/jWykKfxmxMmQ3HvMCY9hx/vaYe/l07vfVtvxuz2SZ5mvYQRL3h3GnA/Ga5NR12+EJpgRAJaHuNsEbIMBRSRW+WkAISCSInsYcK5tZrAJavJBJkbjugJdkVtCxoRqcZCP80OXPmNLi7Etu2oEtzADpMhHCvwgnJIsywvh2SKbIMrMselLpgPYIiXMRUCOZBieDSkpVYMCC97QAzkybzMjt39kSyXwNSDRq8jdD5bg2c0XSNFdzMZ5NQAEzgmY69uXNiTQturYpHnzgyvg6qnRzFM9vR4Ui0GQsSGIIjPRRTwQ7xlBmKa05GWmgJ+CiPpIlN9xa87gEef+I152dM/0EC55QZs/G5UeSABBLozDvbW8uyPOw7qFWTQQYs5tNbk8EkDElSpNUK4I2u4KvEkKQsFkaXsIkBlpHFqA9pt55qXXUTDgf5pHTXyweoiYxkaWUOSRRc9sbVnnUJSdwJnvFVZGCXRVlB/BZyLzDBaADyQzImdDZaA1jmQxoeIRIDhGlZFQYarVD2hWMXFEi2SW6zpLjl5juf9axnQ0YDFBiBti3BVPsflMF3DBk4v3FkNk9eYka/B/C+KVU8GNYmk2r8oX6fkdp/jVFd6/Px1wHfiC8DFg54veh0n6+oBqdzOHU+gNfA3ScjZ/3UL9crY2O3libX0dtGF+orJu6h4/VykvNkgAAB1P6Z7ZPz+VZR7b70pd/3vS956c5iKx26rMqgGpE1fb26dPGp3/3DP9g/PCRnMBTnAAwEVYpywhTwDHmWtQbCsJRg7IueCLJS1TPB0UhkVtYOf6f0XTxUgr8kLqmXZN0Y2Qnn/KXpqe2TP/36N+w2qy9/8a93L+9SFpMresR2MrRNo8GD1tCs27SY3X3XXXc/+/mzqgCvljFlDkXFLfQ6g/I6XTacKJZvedNPnr/lZlPk4Hx2LZOmSdTpRxsIUlN7IIafCU2Y6IajnRYBB6NQ0+TgA24yutXkyc9Awd6Ma4o+R/0uT2ybmDJ49gDFXZKygI/EKqO0NL/X46lU4CptiMZWrrPGxi88ZfhOzMgGOh/P+zGujmhaNn1OJmiW/MDrXn/zf/qve0f7v/Rzv/CqV732xLxK0i7J8yTLkr5Lytn73/XOd77/fU/v7hpr8jxr2hYaGqS2Q1sYYrtArkOep03bMZpSYs5gL7tSLr5bo3wghjRbyVR3j9KT5EQ2ln0FF5llakeKd4C0IVAfqqO2f9HLXvaP/vE/2D5/27/+F//Lh/7iPgjXpoXr7k6IKijBge29GSZdZIO1r3ndq3/+537uxlNnzbA2RQV8pLNJ12NiagoFL1uwyBDzISJBGNS11nCD/CK+FZdweCaHjR45utXxpDUt6qeCt9ETnfDfBP671ojjUU6JX4RStnVrhqKY+U8BXldAEjkRc46OLhAgaDiRagplnehxU1M/oVQHTw1+iD1ZU0zB33cDf4pez4XmA61BD2/DyDRDoST7tkmK9DnPvnt399Itt5wuZl3f9dliDup13Q42KUzy4z/+5t/6vd9/5JFH2q5f1SvqSAJgB67NAAQMue/zede1RYFdEaR/gzdXfSU0NzGxqqbUY/yUnV3ObeV3qyvoo6t5ESHns+pwXW93dvv06SzP1w2YBlAQmlKeEbAFjUXLKs9t2zeHR0edbc9unXnxi16wPSu7ri4LqEaMvrAELC+IFdmkHUBnhlthszxuJa78KX7aI0myQWP0vyjX1+TymU0Lqk6Ir9qMoJrWlqOvhK1Ed55kFM/A4p0g0Q1n8U1zaNC1nCVtM9RJWhQJ9VpJTdJzCfK0I5UPzgW9iJY6I7vomDFMtCI7TkIec2h7KbhAfzIaTESxx0jyaUWAaBg16iH/P/76P/tf/82/2Tp9FtBZSZo2vclMlmZciLVKz589s1wuD49WEDUFODS08yUklFNyWxTExFIo5YjImHEXDmepd76GZ4xgd7psmp4JbLIgIpVsYRxnCulQad+21axMcnP69KlTOzsHh3BKDUWtoTtxbmwGmf5tNxwkpsxNdnJr5+blDb/4Mz9z793PXuYVNaMFqUv1r70pVsDekJcj/Z0eP5Jj0a8jndafNcWyN7qi7cYNdg0qdeebjQGtCRF6HWrx+LnTe949Y+oVXEhpSljmtoAmeWlRpkWS1E237vFn7jFPphrobES36dCuVgAGzksuYUUlEennca7/xCiP++D6RLZbEj216udNJspm9ST8WgiI6K7rwUtQLP+Xf/pPyu08tQWg80F4Yg0KMyTDKknmN990w85yO0sA80DFJblzAgEzADeVlWW+3JqlJlvVtfM2ORIkJwMrx6w3oC4rMR4tnZUJH+hyPhlY7k2RIooPo3SEUFIOlViSO267501v/RnTN9lsBr3IgZVAP+IhMUWfNEk5mHqomze89rVlXt1w/my2s5O0rYEylD5oLfXM6GkD7ASucKomdUy/kQNl0xFCTMIlmwQSP1OLcyxAN0VepmSAzqK7Thlrrvs7L2bH21h7oXvoAQtlyprOFEVemrZZd12aFUVW5lg9y/RtNwBIvTBpVpRV1zftwf58PkuKQlUejNjVNJPcMG6vRqq7bFacgskKZabn3NE7X5cOLWeKG8+gaQdgomxWVUmKzgJM6eV7pBliK5Pvvfd7T/z9myH9MBlaKK+eQH+EYeigaBUAHjC9tn3ysSe/9IUvHR4cYgFLQUQw94t7kWiR6zRRNVVBAVPJQGYjWEpl+GYClC0Ivc66bt3b7cPVq37sNa9KfwTLauTg9WhbqFwAGOkB9OFZkWyXSY81D1tc5aZD6QqyWg1LOAoNDhpNKCveeROuy288ecKkX2iTZmmfCfWOu3OPClAdt1NCk+27OY5VLja508LMkPzq4WFhTVWARLV9M/QA67XDcLRaz2blbDZLcpMVedqnUEcGWXqWFUmVtd3Qrw9nRQ5WEHTQsbD8rkqvvf4Zjz6ICHKku4wtlGNiQsGTr2OaHYn4UBkWlEiGpCrZLwAe2hQrEtF+NUljX/OTb0oayCtgAd53yBmlsBtW1UjyeX20/vX/5V88ceGpfujYMexS/10ZiDGZxjM5KjjrKMThbsjm9V4YPoH6HaGTMk2GFn7I0OEMTmMEepcFrOYCTSTgM12SpR3WKoBsRiqC7MeiqvgS37xGcQW1Pt/9MbUTkuu8Y7gVAykT2eFeyqlPRjd7JuJpw9cbzp9QszWqipNF84sXL2ez1ZmTp7aWywLqRczAcVOvEfjTNaujcrGVAPooAwgBtKccoLhKltrUFtAWBPpqZXAh55RSUsvmd5tUpEfo6FgN3lC74HpW7ZlxR3W22/rMqhVvdBFmznxIkroHPRJzfUDrztOkoG5J1EwlB1JPsmqxuPX8TXlWtG3nqrIrpyb5o+QxqkLlhlcPyyt76nVJ+P5L7mFpkqIsyiJLWojnQoss6FjWcbgBcRp4BQaB2G5KwY3pFBNf8d0921UaneSv/DLCnRVHNs+EUK/LjjXH3Swe3vVc6JEP3+UGmx62Pc66cCS6aXiCAQKhsK7roWnXq9V8sTx78szWErwUxWxWlGXXNn3bN0dHULi8Kk2egdcaPSQqUJxmWBjZApoXO2ZBBB8qHcrDRssQqdmTfuyN7zz+XMg7mGfFDsSVM6VXX+shXulWmJYAo0inYZFKkLQ9WMWEtKTS4a6pbQpVAJIsP3/DaemgQMgMNRRWP7XqzJtO47eEQqNpkwLnQr2+ZxYBS9BPgQU9wPuWFIWpW2heQohl9mLoqUPG5Ari8PtGwEptiEarqdTsiR15jF624dzv+rDHfnucEa454HerM0y6XwPFWP7RSRTjUUx8iAScZLbvutV+ffHylbarlwfL2azaWi5nVZnn5WCboets2zZtU0Dz6RJTTLB3ElaG4kqr7Fy1fdv066FczH1VURIxztsxLiw0TVyRCX0N75P6LTwztiKP/SoS+4HJHEm66FppK8FLIFETF0yGP+jnGYYbzp6jZiZo47jejbJ8U9JWy+Ux8E+U5UD2BgRMabsG3OZ5VmByMrENbgfgYNtKtEpDI2mq6J9GryPwznAduFfxtGkzfrPok6Bv2TXl83fhsnpGx5i/PJPHbRafU6dF+20zSHNECvnJ7RNNm17d39/b2z9/400FFBC1ZVl0XZuleTmbAanWbd207brt2y6vKqjzAqmrKVRMHDqEHBG2NjFpnoBpvDKJrcoKALFOBPn1nizntUFPOo5Nj3QbL6LGEviYY4PWt+mB/gev7CoTQN6Uav8LXXI1fZucOX0aaylz1IgTb4OnPANx4zVZb/QK9cpIqPcCoLmhzXeG/UwpzQBSl6EePUUZoolmN94GKeQ9VaMpiv7WLQj0jAWvEbAjfqtg7abmxIxKix9zTIjBZ0L8zxiPfx0XTJPAhlHF7iE+8vmJ5U653P3GlfM3nisK6FINRYABSwNVXeCU1KTzcj4vu6N123dHB/tJMswXW+VsBr0NBwp7ggsEk9QgBoWF2mCPNEcrYAGkSUIVBwKH+MbuMtpxdoQTzsfMgttV16MwX4cxE83d8Qq23ZSqGrq76UfpTpIUxYnTJ4oyV3EeL8aiB15j/T1NiVrNQoz/d9kJiOrkI8MyABDsx45KUMadmySETaud1OXVuKY56rz30TQKKfu5vyYNu4WaKjnob/9MqDcY5/Fq84Zvx/aznoqJ0PSmwV0Hy5g8JeCJ/sgXZZXm+e3nb8EiDMAqc4AnQDnv0amzPEnmbbderfZ39xJzdbm9NVsu07Sw0HsSNGhpI5uh+YRVVC3UaGvqI/gZ4Jlc3IVHerzvIBj75BnXx+fiXXX84yaXZazkKIEcLLkIxGjzgW0MX57Ympcl5NNvHE1UF2azB0tJK/+5+JiEIIivYnY+0TCnNTMNhDxJsQM1D06pCylqPGF6dO7nSa040BJVWzMn8P3yRnR+rKQykaU2No6OPa5TpI9Xblq5HpP1d23KR4ht/yG0ps2K8syZU00DBY2w7DiUOd14qyKfFduz5XZ9dNSs63a1Xu5sp2UJdYeZKmVfuV5CEFFeJl3bHB6UszlEKej+2GHIi6nx4RltOBG8Ohs0jUi0xROx+bjmDPsJHNUPc2uvtyAJZModwm4kSZKs1i0oOJwipPdiaF8EBjkVt/Fc2BFnKIPlf1VhXDuM+2EokmToO8gfdLq9NwdUFvmEPrB5asaQOz9RYwSlgCWCZZXkNhup6LpZmR5ZyE8TlfofFbh7ZoHh67NyN83ERD7j+Ff1iCCR5jpHGZ+WQ5aqHUzf51UBDXCgssR1HFlSbS+qZJHU3cHV/cQczrcXWVVBVjeWXBNUGtta8FwAgcyHrrdYkiItSyBjdwKmpYnbNnKNhKMP9vw19eFIPX1GXDCUTo5iN1US9GwyYv/QzxWgEQWAhe++8+6T2ztPpE9wZvD0wkURff+JFFsPe7sHJ2qR54wQcJpB1Qzoc9bBDzkWfO2xMaXbT3GzkuvZVWNLQw4OTTkepDxkFNPSjoNJBs1LPJrwY97d6LUY6xPXl8T7XRzf3V09XwrDHNE5wrDDq+BdcpOatm3LzBSk3z7To8q3Tp/q1+sWq7zkVQXdPayFAKLkyFAAg0tEAbYBqr31bZPUfbZYcNo3na/bFATHiONe+xipXpt2WkQpx9wwUrM92EPd0aP/SOdE6k2gw0nfdXnX3Hffh/YP9uC063NWTZiMvhWteqDeqTwcbnJOn6IfK4Ny1k0Nv+dFMrS2tdDrAbqfjCYnknl+LNdaBoFhg5EtqjK0d3F1P9mHFzr/vIiiPBk5gT7Honleu5mAWNpIh5hYzesxfUNOMm3i+vO/a6VYidyIOMf3j74JHahQ4zgvi7TYrDNHF7sDqqjQUkHGepXO2r493NsviqIC/BZ6NQGuhGhqzY/Bp4LFA4YhaXuEAfWoV0Mtdf5cr+umOd+oJ4fTEN0qwhI4EfVMD9bZJqGqtE0tvF0OYMyjo8P14fpg/+gTn/rUO9/9vgtPXcJAEuTsjKxHJZQmsOVSB4+eG0Xm+GPvpfG1byBNH+Z9DRXoW6g0h4REtBU6kI5R5zb78dzuBxwLNKtIoOuSsSn8BSVpUQUY+qEABxqqItiKBmwJ0teoOhssvcYzSi+vnGwQXD7H5S1KcuqdjCDwtLNJkSYAPAmHOjZJotdS/0wcE7wsXPnjtuj423Ho5LsV4Jj3VmBzqmsdToFX8CAskyzVRqHMf761zNu6Pty9klfVYmsLsB/U/Rnb5mKCK6KIuZkt5R7Dhsz6vqvrHPDG1L8TY5WaQMZ8SP0zMS/umgmWrJnnZoG/8Vv6RpvuGs4tsdBu6G2XFeXVp/f/22/+3je+8ZXLV6/sXt574skLne1chRudA8ja5dRTw5MiDqSG4fF20lIBrCKwjdIU8HNQmQ/CBgNA5yCrLOXa6xMCRcFR9Si8Qyvsx8kcp09ms6QrsWsSkmZKtbKgTnQKZGqSjgx66JMKbA6oOwcHAbUizQW9ahIorAVuF/zQpUBh2iKfk/FAhzZJkxorYZukzaA3IkNkhD5YG9dlTzfM4nVKwu+G9jZQ/OQd3JIz6Gr6YcirtNkbraBTYFDSSu44JaI79DzQMhbx7kC5ybL5ctsOw+WLl2eLanliB2RsmkF1GiyoBj2gWXRIAWNAFJo0zwcs79S13XwxA2nsg5CoXZMqrsPI45WI3KrjaYo03hiZMDnjTiqGu9lZj8EQcD67IQFXXWtX/e/8zh+860/f88RTF7q+ITwjFLloMV1pErexWa0Qq9mMS0kqQeIh0EzAWU7ejaIom7rpu37d9gu6AJK9Mbto+sU3jCQaNE0g9GWxSYkIvH3UeJdJskbpCoZ2bdusS9J83g0NpEdDRXhIjFJ1UbHVBXD8JENyhbICyZAlWZ80uFGp7l9vE6hV1kGiBTjlIN6RzpNkVe0OyanTbbJXdwuTs1I51cdI29jHHGPbanJZrtcDNXXS5IXxrOuTYjKGzu7wrwvNTjB/V/bB134IyFkd1EELC3znJ05sr1bri49fOHH6BESDswSoXPpQ09TSAREs2pBZluZ5XhS9Hfp1U1Yl8he0mkC5kvF4X4gIJfKBBRS3IQgUe5uUlRNBnPyMKVCB95yNp1V+bHvoVliaZq97+2/+4Tve8QePfucxyOJBQWjAYqCizARHVTqw8LWRG0PrP+oCR7m+qrmgoEX8UjM0bOALHczqep0Mfds2mG7RAzEQ4NmbiM/QoKD5gfTKtG6bqlp+6zPJ//DPjz77+XVbryHD3FRQvRjG3VrwgtvEdAhfGUAbNrkBOVvALaBdVAHfwjBgr6D2zFhTBJ2WiQWgEdQVM71JKnotqFBtuqQtXnRj/h/fm9x9eiig4Bfq3A5hpjbCxM/HH9dpUoy/1t6Qawh8p8FF305yGTsiYPEWOKcDcTtFp0TFVM5MEa7/V8qLYkJs23UFdNHLiqTY391Ls4OdkyeyomCfM9WUibafK8gGaatAyqhx983huqpKMKq5GXyYb6iR4RsnWGEb48/Dk8ebOJr8ycn0Wi2c3ZkuhxbY5v/3G7/9J3/0zkcefSwBSzCBrExIBuJ0PGFkI61hZAU7xiWPU6yKbd0IvyE/SYk6BG4Mu3vQkxCIAwpotEPXGZtB/Sq4rfRPiAM8xx6OFeNOaRpbNQdPXtj6zOebev80dJDqDpNkjneEiIPOk3cpzsyTPPNEDi2LgEhcEM3chMC00Elc9CYD4qCCinqm62z+1QsHj37N3PFDXZFUKJnpGZzFMa0DXw8Zb5qMsWQO7nZ9OAV/2XWcybqwX3spK8ulwH1kkcopafnKHyJiA01aqqOkCdidCYQOCWiQtwTY+bZpL1+8tFguwDBOoeAoth7pISZCBcf9EKEAM4hk7kQOmcld33frejav2DfGFcOp7fZIv90oJDeH6a+NGznmK65Dx/YZMp4kyT73F5/+6F9+9MLFJ/JZNnRQG44SfaBBEWQWYAKmbkYmgnfDQBzNs7NV1k0o1pGsqNDUUxANYEDmrJv14cEhPD1N87wYmgaNEjlbck/8tMXvOjauZOKwfRuoSI25AqGppLCZLdfdMAPfh6lB3cX+6OgFkWg1kRaX1Ga0mrC1NLGYJgUiOrMWrC8o3gafAzBILODUQmi9AR9K0udlmg9bDz96+IbFvHeC183YmMaOp95Jd/Q1z5yeouNP3ewK2+QzUwdbIFTIG3/ifQSi1lEwaK/4q51QmF17uxGmF5ADVDEGu2mapy/v7l7d29reXs4XeQFQTfJjY2Ksmmup0Eb4BcSE5WleQKWLtoEml9h0ExtuZrh7oHGBAtxOHeP0aHnz0VQdc4vRPAeCnNQBrNSRJC99xctvvvHGr37jq2D4Q3ycKJBThqAfL9GaT8vXC8WfSoV2PVhft1b95pXn4IAy9LB2bVPv7l9t6gY0S2CCtq7bru+qoixnJfpyqQnoBrtgYw03KeSAQXxY62a112xvn9y+8EQ39Eco/dAxRammko6I/9NXrOFQ7EipFlBEDOW2OwdLlAXb3eD/BVQpgFpHfTqkD3571YLyZ1trC1fFbWLgNOfjBmvPsCSAnqrY2I54xnd3Wxln5HOheYdZY1WWE9/YJYUHdo7G/0Ep5qNT/8OB58CpVM2FSNqJZHwIta5nKC4Upxia9apZ1wMk3wFHhlApq+sYXMBG96RZIWSIWAhwaHSpFv1gu6ZbH62SrgOxlheYiCvvJUbTxHR4YmClXaTB9UynWwaPeAqWyZ2VZsnqMJmnd915R5ZD6m/HDb7ohdCmx/QgmiceuLgH2engS145ueRU+fCRviULF+AgHpEaUxYF9ATu+3U9HB6tQZGFnFBrMFA3gJ+hhrqwjLJwMeNo3sZwqPGUwoplVZHsNS/4wealz99NkqtJSsXu4f7G/49pp/x/h//Tz2AD4+IRobqf8Sv4lqA++tseOy91iW2RUut+SL799eWTV4+aejVAvjMBisQD4otL6ReYXOXQbprcHmPJ6i9xP4XSNZK1nmWMbuUXQjBRPlVLti5OftDcDD9h0UsGLVu9aNlqbVqolHcX31Z2F1dmoxJtJC6gNUnfd5BinGVZVZXN6sisTDWf5QXY4ZSuSqVQHStxYlP4D/azhxQaiExkJm3bzjYN9BorKf4kr8HJA6rIM3vJroMLHu9rmODQ8tr8TZoMve1bkyS33HIT1rHjgCupMQbqY0k2klgr7DzWISU9t2La+F6HfhjccAS3NoIlUmYPWZ51bbuq16v1+uhwheWwoLUSjRR7NZl13RSr9XwBxTZCmy2qrXFdhtwwtAeDGdr9W28soIFES0FeIC42a0eiitvLjPEw2sRnPquH5KQ0HETQWWKaJPmLT/aPXihuft6QQ+rVSLGa8IMcexxD6k4eOgCJ8rdc427PzEpzkxbeF3/DBH0XIxLSZZolJRk9U0LTgq4KykQ4F5EDv5F65xrEw19ZlnZDv7u7u7u3N5tV8/liXlV1vUrzfL5YFjkoQjxMZDY0JhLMVCFZNjYqzJgOT0KrB2Wwh1DWMCwXC3Z3sRBXwEyNWJig0g3dofQvzl81zq1xZ3EHEjhvOV8AfEK5o7CvQmIzhEOqWzJpcSF4VeVZs27sakIyFlUaKI2i5p9KOAO6riiKLMu6rj863F/VtR0sNO3NIAWKge7ooQUPRZLavrddC32DabtvgogFkOnI7qDt02d9n/bDTtWdO3XG2hU6lrnoCKVDjz2qTrr4RVD2CG8n32sxWD7jZh/BHbkFhOjRenHliZV5LrZc5hIxKLon3kkNZuT63UhjxwSEJ281Sc3Xg7qP5fMYKA5aVk78n6xZCm448ctyV0li79kKnaVuf7NCKExO9Ya3Xdeva2qK2bd9B51wM0ge7ut6GOxsvqCkVcylQJFN5V3AQIZBeOXEETI5XsT4hY2bJl0L+mJVYpkuwt9BXBFL1QdlBBWD91M2zrYZ+XMmmShLU6FICmsnyXx7BiWU6dZYJh37hiJ6EdGClNhP7Ap1alB3URtJBzgRbQnukcJP8ik6xNqEhiHfKE3zAv7Lsuzw8Gh3dxfahec55vFDT1FgHybJYZ6gQ0pmTJWmtm3Xw5CX0HMU/Iu+NMdoBwY0rE8QBz4E+/K9o3x7q8HgUC5cyblGQ+k7NZf6a3bIMK/brP5aeDz4TG2Sd+apCykkTQ7A4sAHMNZ13UKPiWuC3MZRPXfm8ai1a30T3GfiscE5Y+MJqRAIGJ194qNCEvbUS44q94+PHDl5O6WZBgEBYqtD07RHq3ViTDmryqqCqi5YXyuxad/1bdMA3MCkEGihHYz0zBPoEA8kmFCOUeMw6tNFFA2Dxiuaph2Guu/75WIOg8syEMuMHCNRrLaEm9YYFjK1eMHsugXgngnujsB9um5ebi2qrcv2Ckoh8vchMlwFfn3QhxwyeQ5RzqE3FmpxA62LsgNQqhyba6P3OMcJFN8zSX3o/NC0Xbff7u8dFEWWlwBBpPyHNINrgZjzHHv7go04QHC+s02bN00ONUkLaONErc+iJPvpQ76iyvC5zUzbDe1i6ySU0vUpT1GlZeeYOX5uJWdL97iaXg6DgQ9jIUYxXLhwZNL5uulLqPdEW0ix1+tw7W5gLSNti+8V8oiYzY1+iPymk280ZhkR2ZM8sEMO7ifwK7Fd65vcBj5nabLhnaKqQ8rEo1UnSIwn98PQQr1Su72zTY2DoF46fAySZ4BvG9hdWLEJ4sDQbUca0xIJc95OkOgJIwNUBLWx7RMoJABQa2zBnRuT1V1nkgTSGPOiLDEKBZSMOM0BBbiznJXiMLEYXlsO33miCicNs7Sr9vu/7wfuvvOOx5/6DsRdafbwS1KrfRyYOv7JG7GqCThimCfUgWCiIAcsy2bVHDp9J31VlogHAdnets16XacmrWtgW9gWAgh1QMGe5XlZwusD3gGl+dB2tujrvsVqhFggCepS9kkLO8GBL7miw3hjTfBrKNhQmCzpZm3SLrf7NK27vkAY5DW9CcGS6qRh1j0CJ2NEFe6gGmPt0Kbf+NypdX/UNS1wQ0apaB3qeux5f7b/YaNzxNFCaKA5luGv5H44GxVo5QOZOFgYqnMGiIkH0tW35OCgr/dm+Y95btUGVzflXj4Op4GfAq/Ps/M3ns+LfLVaNU2DIiEbBmxiAOAgSGkEF3M+QNdMwLJSZ0unh3Ok1ckrfqQdGmiV20FFcfB1mzxNc1t02PzTQLMQk6XFYJP9vYOh7xZb86KooLRAWYJezSlQqK7zNLmlOh5AowAYsb8HkjQ6OxRVcudtt33yr4quWQ9UrFOukh5ioGyAMYCsh0vPSX9SMPLNUGaYgg8eVYgxt11blcW67i5euoREiy4gA1YxUHieFWVR5NAsBVCtgH/O8zyfz+dQ0LuF6raoTvWZTYamh4rAaQYFNRGCjk1QOxTMOdftmnb7RXMCGDpgTGVWm6qw/c6sI0MI+qCyDexo1VkD03sVtRQuai9yesKcCcsyWSyYkBl71OXLD31i9+EL8/M71HPVZJNSdEKPHY0n8CMqieV9zCEPckA6vlzLW3X3KSNg0wTzXlQTomYR9m4ehn2YgEcRX/+Zj1pyLwFaPzVOH9TlBw9D33UAuC2LEvvyAbAmyyHJAbYd+W84E4WBQKiAAVpYMIHcipoUBDdlpEUDVCQFDDV04oVVS4akQ/0bDUTEEoJgr9K2Nvv7R223nxfpyRMnM8idgL0KVZGdk9ND05TS5fnrGAItg3Zq54ClppKuKBdnbzwF6gT2R0LVlBM5jEnath2GYTabZXgCx+JlCGQVQBAGKzZTp0OAH6bmaLW6fPnptoE2S30PrqwMNF/QOoApZgAipw6Jxpgiy+aLeZplhwcH/dCVZZVDcBhS0Ey97qGEf46VdsSjy2hX35nFT8J44wm/h3SjNLVDN8+7VZLubJUQK7Ik3icuZgts8/a1G905I3pIWE0A52APVvzR/uLxx5KbdwAlgoFSV4l+szjdaIWH+pdHHDvu4q1E9XLuECM+UAHUc4MXVMqF4k7inCG+r1V32DPg2CC9mf9iHVrBNnxoV3uew5lQ4+YIkjx7wC5BQLFZDtsYIK4AzzLW9oCjIZ3OUyp4I7AXtvsM0Ic2xQiJewXXTghIu8JM5oOjFjY6+q6o5R+irAEhD7kDWLrNpklRllkOrHl/b9/2u3mabp/YTooK7WRMbBywc4pTipxk1rPvF2g0HwSpyIsCIpbm9ptuTgbsWsKnE0wF8S0mWa9WbdvO54uyFCc8/kU8jtUZ7LNNkVbgSchnyqKqqgomBggHWBRmBzpgFTIt/GtWzbq+/85jjy2Wi3k5K4tyNp8BhsmYosj6xHaQ90fpJciEOF4TvqdWdd2L8uZGAkY9YBjStFzMiy4DNSiBHo52BpVJfZUApye7XR3oz1N2JnsKRPKShGa3g+VLqJFpam1mhyEfyqcevlK8sOKUG0YI860CCpw+IqJzSTt+SIJy2HB9AA3VznL1ubYKYudLKKi1ScdoSX8xEjA2dybfpiNaFUNSzmexasWwiPhKxDfR0AUQCLRHS1OY8KZrIZaL/XHBW5IDSlijAMnqcagGgHBhB3igYPFre90KEZnG5FVZdV3XtA3Ce8ilgXX2UFf1USmMlAIIDImkTcyqaa4+ecEOyWw+O3ViJ68wkxl6F2Ctc4sFnz2gx3ksnEPCzbGiUFQbsfxFtnPydFGWaKfAnBZQphNhR6kpimI2m/ddd3iw31blYr6kzqM5qg6Hq6MksSV4ogoKkuGbwOvN5zMKD3AsSsDrTvMGB0ABzmfokJiYK1cu13W9vb2dgZc6XywXNBllUay7HuqZCVNnjslI1akNqqUplYxOIc6L7e9smnZZnsyyvu320YsBTWi8A0nvznjDaAkZPVeUaOeMcEq1oXFjqAI+hdj7YLq+Lx94AHr15d2QF5TlonJAhO9MUVsooh2tKlpWhKQNx8CT5SdP4f9cjVB3ifLrBUqKzqkRdWU0f2JggwpNMlN6RDuIJCq4CtFBw3bBWnf7kUwSZw1SDXhc+iEv8hRjJB1gIbGlOGUEU31zV+w/rBTD2XPcQpcqeujeEhyI6boeFOQKyl920PNa9FkcCWwkp66JTCavLURY0lnWFx3o+GBVmsQsFvOdnS3o3gbRFgD+Y1MFmlenP4cWnfucQl+Qxdom2dA+deUTn/hEmkKAsmlbkEkoJclZbkxaVmULNmieZkm9XqV5AWEnY4o8r6qq73sIACGUSkQRyCKEx/VwB+iYIVRLLgOQq3AA6+yHpmnqdQMeB5nRNE3OnjqVQm9uiDwlWFEUZDzW65JUUeSbIE8p1Cwv67REF1R3tTVITmJT9GbV3fXy/NS5nfXD6FYEf0avIHJCDxIRC4lDTbUz9UaiCfuwOmIRjBXa3EPSNH13/9fLBtTLdmjzBmukQiUYcWazuehvSrGLWJlWSq6CrDuSCxGwmF8VVkZgZZglrMBgfcIM6ana+TWaCOEBir1wWROx7CCM5FV6lb/g9GinPWsajRQL5/9xljZD+wF81YOai2YelJHuhxzoBigNwOm4D3BTk2+Wk2IUSoK4BrhzGK6lXpnEBQZW+iw1BfLbruvgLUAJhYUBOJ9IeF5wnDlUzeGLMitnBmRF23brdXNld+/ylSt5ns9m1ekTO+lsgVnoZJqDeY26LFAPD1bcxrzQWQnUm5frS5f/T//n/8vHP/e5Vb2G0GvbZiaZ52lvTcsaNbCksoAGkQSNthbGQAAViqFlWYZYLlanqcEv25248QAnLnFzTlZMWDOnhWjbFr3KWOvEJFWV3XL+TAmwDewnkQPawUL3YjJm2MsBDKJpQaiCLZ2ppDyU0pHrXgoYpmlZGFvbo75tX3LXUxe+Y5LuJCU6OICr6M1saoebVu38iIICEy1yLlqu6QHe0DxNhnWyuO9jF5+8fPquU31X266HMEuWAfNyeR/xTh6PQQ1gAko6SjwJiHAzTkNEcqSrjvUOvpEomw5lFAP6YW8K7EcrzxQG1vBmGphTZwQgK5uLV8WTLwQkkJagOSeUYwBabtFliv4bQkkBGVHZLLBQUyiXGEyuKBvo0GK8rtdjeMulzggAxdWmWdt1IOUcXIIJmJ0ZXFttAMFIVE21MbO8WCzzoio7gC+3R4er9dEqzdL5fGs+ryAkkxcNoL66ORQAhI2cF6gl0qwCHqyDxjJlNVy9+K//r//PP//kpy9dvpTPCoIu9n2/f7SazWYELCMzGM14LGZAmixOPqRpQDEa0Y15PuBfX3WQoBNIS1mWQbPIxDR1vV6twJpomqZt86IoQZnOWgqqm6wsFnfecWdZZKTVACA157Z15JAhtQVYiQXIDdXycLmKFHkH/QlG3XNBHB4dsOk867O+LWbzW87P2yHD9SE8s6yaEp4CULmOYHtAIcQyHVX5Gw6Q/m+NaXb3+ye+c3DzCaw7YoYkBwOdQ3RYYVd5HKKby9hYpoaj0eq/XBle6tzyQTZweI+RZ//YBAolokO1F52HqEKTNyBAWwmO0uvP4oEhSeHTWemeYuh45R0zInqoFJ2mhnIiAEyP4Q3oE85cnDkxqbVU0FJxSR/sJhsPqrAoNRozJbDKh4UEcGstVI1BqxttYwy5WggokYOMfGZsnVPteVJHUKcm8ZPm6SyfJbYCfb8FiMnh/tWrl5vWptV8nuVZP/SXrjTr9aooqhvOnpmzw6i0UJ83Tcrq21/56//Pf/iPn/z0p56+dKkEUs8620Hmhh2aVQNpdSXoyRwY5jAQvGsGspfXHnQWBIaT+UCci6gdKB3fn2JFTdtAbknXZVnWtg0tIeZcQhm9JC+yHFRzcDJ1w6t/4IduvvV8MS/qdW/6HowZoGxJFaJwuwhiAL70mPkgyU2gCbGZTcRHZY+Yt0AdoaRvhqzrk7Nn29Q01s5BfwZXgtuBuoS8ZNkEBqBTP0PJ5CVwaLgZj3XHeBxWxGpmj3+7+6EXzuB32xcYiLCIO0CFGV2Dunq2JxVRsZ23bJq2gqROPXQ9rPGVk4jwKXdV+KTQQvfxVHBicW6vU5gZBs0oStGfxfnL1nL86ECVoK0J8AOSGNj0i2DUgLLo0duNxpVYOIT/ksu9Pae1czDLUMdW+a44SUgBgAuEeAHUxmxqjJrCxhzMkOUFKt5YwgulHWqbbDiJf4Oe6gBamFIEYIjCzGZQ2aVrV3XTABbUpllWZtXyxKLv20sXn24gMWp9dmfn5NnTxQ03Pnb/A//63/zbj37qE/t7+7OqyssS0n2gSkkGmdQQPOt6yMml8KSbQDZmibAHOxRZCda8AUWXlBvUk0F3RzUawkvrvnYstskyCHZ75wtkAruskMV80Q/DYnu23DmVQapt0fcNLgc1xMFHs+udC9eyt0UWgVRrcDRjkIqcCD5SCkSBDAfqn/U267dPLAezTmyNs4mtG91iM7H5jT61byPvipds4d5OQv0XwWtpYxPzyHfW1WzR1BYw32gHgHhAugyyvARvLZk3IzUe+aayudXAjxedE4drhOUNYc+nggGoF+TxKE8bh69gzOKFdrkKJDlJb9auZzX6jRgwX8cX8/6hPBZMHqQeDuBAhcBGgmKBZyiYSekrjx4Ub7T6FAeEw/tG9vJQUux6AA/ZtEUqg/INaZ6XlcUQLFyFSQTgNwVhw55F6lYgE0t2lLsrxykQhZjOsNZmP+/qGvq8dfW6XfVUERGahc2qJ/YPvvrIg+fP3fjBD9z38HceB4woGLRN3dREYeQWyvKciuaPNgSMUuJGiLSg9gkYCeHtQi518OGDGtIxH4Td6Xiwc4/AD8QS0K5OMLN/6Npqq0p60zWtBd8EyEyKueHm5wqFfn+KMwnnQQwyVKwwMs9RLrpSIkpgvfRDX0D2foP7s4r3YrxBVUPNON9I/Ryq0CPasDyd8OK17dJHvloeNENB2xyrKpJChv9KdInGrow2T7bupvpP8ECtlkbD9WI2pI8RvR+XHhc71by0Y0sQXpUkMBkIUcDXpfRqS5ffRE2z8BSOy4mLxQJ5pOCDgaRhLBQA28X5wyTwKDOgvhLmIiQsBExDoGw6MgCQgeJuTpK+MbMib/PE9HaeFm1rQAMGRzLogdIbCJMeshzIgxRmjKLidpT0Aq5AzKYpOa+IU6dFDvDaImuLvK6brq0taGh2e7ZIk6Zenrh0+crz7r1358z2448//v4/++DDjz56dLQqchC2VCHadgBRAj7GE+pTJj17pbkAeZEifku0JTkob0HcWpyVBb79PAenA28MccBA8UcsegChqCFNbJ7nUiZLIsy4QI6BBo4V1HJ4CkigwyRiX3NUZXh64AIEfPZdNzS2a5KhAdPXzPE6p8E5r4bak3p/Hgf84g2tYz5GWcNE5Qi16Nri9Hs+fPGf7O3ceaofuqSzPRhQ2MlcAGZecDuwpiTBsjPHYQ2n/Ut+bKMx01CYL0bO6dENRtfpOYmA2+4nKW9oczQiQzSloLKc+zxCYftR6wYjoUkMf6UAD4CU3WGAKhBgvEGtHIzteKiW86DRRd69J8ocK/DkOcfSZjKxnE5hjFlDjfLG1tkfv/uPD/ePXv7yl910440ntuaz5TxJ8sYmdd8mnS3BIITkJKc2Y2PjHtLC0R4iHDYJLwy6+rUmRg0V96qysnYOBQ06kKpds7e3PyTduVOn2u5EvXd4UK/Xdf/6H/ux3f2rX/6rLz76xGPdAE+ndlOS2+aVGO6+4uiX3LroQnfjxL41jG4kX50y5w0oGqCeZ+B8lswHUZd4NRGAheFfQIEUXQ26gMlgA2CBGriHAIsUMVMqr2S4kKqQtAk4sFhj8F62ASuK9t3QHAwmr1ObDukWBbBHu3FMluI7iSVPbCpGTmoT0hBsux4MtcODrb0nrT2R9rbLyc8zIEYuGIPXjjcni49cUeGngfm44ZjSKyZuuyFZK74Rcy4kT8C7j4CTQfmccaRXYRkDfkq8y/u3LPhlqRYHwJvt0EFqgSiKekA+w9gDNfWGpjxH59R3KhC4aoAqQBG88cyN737XO9/z3vdcunjlD975hye2T9552x2vfc1r77rj1htuuHH75M5iUdWrpE/6ddOgQUwYJqwZBV2hCjK98X1A3LrKafyH8hpNn4JzNcF08SyxgHWqllur1SozyayaP3rhydtzs7W1vbN16urh7s03nr9yde9TH//4Q48+3KxrVONRE9HReyVgBV9EM89gUsJs6PqKkFEk/gL6B5VDLtOgkyWQVSJSGm2HNIde7UlRFGUJaEpQwQDBhAFb9+48x8zWCU6HVVeYnxBzpYxr8V/bJIUoITivzbBodk6cJPZI5RGpilXolZrex7qh+GgjK5VNHUb7OskUGro8OfHQ/Ycvu3e2BvFLSY3OG+tUd3FEbaAjGcpxlGk2f+qQltc+rtuUlqGziIPkNcpn8HaoFHaRND4PI5scumSMON7IhSOpyg7DsLEaR9e2eV4i9Ffdxz/T06+zpoOCM2Izg78axTKsy5BVZTVLsqPL+1//ytefunRpQCPswpNPXrjw1Of/+ktbVbnc2rn99jt/4k1v+N5n3TM7sZzNqzWQcNp0bZpn87JCIw9K8BEimfqA0c6Qog7erhmGDrPU3RwmeZrN58uuh3Ltp0+dKG2yKCEcPD+oVsut5c7Jc28585G//NiD37p/9+rlvm+zIsMglwM/qXopUl8DyAPRkUgoriIHuz7JeSyED/YtmLigCZNfj+Ll1LYZ7u2M1BxCX3kCOQ+lQcxZ5toUBZ5d+Dlz8IDBpkNGAE/y+zF5k7eEA5VDCrI8rZJZOsvypEqzFjQHnDelHE65ZtWuUhLBOdDkLA6IxuhhOAKihqhRkrRPPFmbtBxAW4HInPPmOPrSd1GGMB8erOX90ZzpokmacUtcsC+QTOGhtHEv7hWahZdzUmHfcEcgYDF+HT5FVX7WvqV46jVWVPJRmcBoeD77YUh6LJMP0hIAlQpZKuav6M+OubAv1J3L+gDSFWEWkmEoIJsm6/r10cH+B973/g9+9L66bQsDwe0MEmtN13ZX6+bK3v5jjz3x2S985sRi57Y77nrTG3/8rjtvO33i1GK+WMzydYOmW1ODGgndVQsyfSXND/7LIGUCGyazt4tE+ADetmToEoh1l4DwTraKvCqKrbrZ2Tl55XD/qQtPF1f2Dsu917z+1S99+Ys/9fFPfPP+B9f1EfmBDNYv8BqNR7r5EBNYmtiyANeI9VW0QlBf4EUHdgnMDFrDijtbZxpLoAFrcYArGyLwFEZDVUOSImWttdeGowLUpU30cjEDqLgWtgrH3ZkNWdqYfp1nZZ6VgAEzPQailPCQveOAqU5dV8FT+dxlynijNQC2JkrUsUVlc5s2fZ88cP/WUVcjHCAjJ7uAsdhIHWvNcVwoVPldZDgoAhRI8AkycT8Gpm3wUyTw/Y0mPWGOdFCFRtcD8nuV7ctwTqEr/jRszM0vzH4YTqDgpBqvjnEIFkLBKBGEMwTuKx5wPFqFV5UiIYDvo2Wy4J9KoQrial1/8tOf+p0//KMrT+8WVYkJE1zBA0OXKaCQbNI17aX1pYu7l7/y9S8vy2Jr59Rtt93xY2/40Ttvv+3k9nKxtewgBdF0w2CGrgI0MbRrJLg14Ma6HuNPmPJDKC6yUimMMoDhNUBWT5qXcyAUk26f2Ln15psvXd69ePHS/u7e5VO7p06f/aHLVz7+iU8+8MD9dX0IuUQIV+YJ8UyNvKbAA6uqajtA+TpVCNJu8CDnthTt4belZuvEC1ypT6yABZ+ALYOxbwY8+6aQjlxVnSq/qUQFEG3NGTHgbueYHFbksCYpqv7w8Lnfs7tz9o6jRy8nw8z7jPz+cbtey8JRIGVCLIYEY5VO7dx2uAtXtvjgn+39w39Z3Xamo2ThiPDiMlyTivIUStrxHPHFRF8dG1tyS8wvf5wlPHY5+V8kigMAIOarjs1xOUlmwx61oVQA/UbMNBnfJl4v/MW5Oajau9hXBE9wDjAvg92ote8IWT/p4+C8STNIkctMVvd92qUHq/rP7/vgn737fZeeurDY2moxg59zbyn463P00hTqM5iu7ffb/sr+d77z+ONf+OJnlosTt91822te+9q7nnXb+fM3LE+c3FrOuhrq/hw1HTm9UbLBG4EjKi+g2RZKQKosTnUFIPiFtiqAOjG7qOtaY4bTp7a3l8v1qr585QqU1b1h//wtNz/5xOOf+OjHv/3Iw4eHB66GFmWEOcQk+a4Q3pTXzRrGgDgKAFckkFYFAEw80L0uAln7CoQ0JFHJVOUcC55Pdeh1JkGgr5HMp/LoDO1x4V9mElC0iHcjNlEb1kmyTtOX3r3/vu/sdbY0tlSg52hzbtziGz8N7d+EXakip8HNaTKI7jWXD+xjjx/ctFNgS7WOci5CIT5FzSNfk4oCu4/CoTFaebJwsb8iyL+KNPeph2vrIfidKAb5NyT0i4zVTuZAHqqKNnIfXm7VJ1qyCCOvOUChIHoCfl6SAx4SL9TudORgWYSSaaiADsiz2axEjKRp2i4zyeH68M/v+9Cf/NG7Ljz55HyJViiiR6jojONH5G3mGzHHx7Cxtauj+vDgqQsXL375G3+9Na+2tk7cfMttb3nzm557553VspihWDYmb7oO0uRNbqxpoe4WS2M2NsVnTuiXVpznCJaEty5zU2xVs9m5nRNbV/f3bjw8dfb0ubM33LB3+cpnP/NX3/zWNw8P9rrecgUndj4hPaYQ2ebeewNY5pAhjDXxsfgOuMS4sTowE8oLQl6DYTxxkGHKdJEWJtnZmqV9n9RQHWWk+2m1WS0FI5aoaheWdXWqGqFfMOGB6/oAHNl2tU3b6sSJRQed0xrsXUYwRpEDnuVPyr+RLoaeMCz17i+xrhgKBsoEctFBsiS6vtNm+fiDh9nz5hBnwQaYrhFJ/MioMKY8JPbfRoPapAqHp7k5HbvvNkhqj+IKJaXQvzLmXUK/2+3yPvRsNpHDChts8kqU3+frCc4NwXd0M4rT4I6nzCclo50pIyZ3zPlwsKDig+ac5iXUAMizYnW0XhTlldXee/70Xe985zuvXL6yWG51WEDdhZ3YAOe6caJTiLcArHJ8IWzaBy7Yvuv29tqrV/cfe/SJz33hM4tqefMtt/zY61939z133XDuhu3tnaoomnU/QKb+kIKmCFIN0vPAn0QpgBTipXA34liwhReA+AoY0jwxi63l9s5Wt25varvn3HXX3v7+s+6+67EnHv/URz/xwLe/vbe/BxWqUFzKDJFxi5FcNCAA9IQJSV3XgWhOU+qp4HaJFAPkHcoBJ6DnbDFb3Hj+5qSaYdqz67itElKYip0jSRt6XFYBqNDVYBDrmbQGKLMFyMkhK9PZsjt5os7M0NsZVMXIakBoUdzYQxihsxH+ABVYOA+TxxGVVgY3AP6O9jaXks5sUiKev0M/HV4OkWcgdWxWuNy7vG+GOTZ5sRhnICjWBLFN0JKyaCbE4PjLCSiVukDJT39x5PcKlKfoXBfjY8FJrDmn+nUKHO5vx15G1b5EQLuiazGMWJV3Uga2czz1hC5gAzmeEFkr7+zmuCHemkrMQ9b+vIK3xdhPkaX7B/t/9Ee//4EPfOjq7tUCctZbjmhANMWhtzmZwSVoes+5PB2ruwnEneK/xvZNd3V9ZXdv9xv3f31WFidPnbn91tvf/MYfv/22Wxbb83I+HzrI5zW5Ke1g8mKA9PgkzSGSitFWDwbF8RAjAzmVmmxrsZ0vMFBnkhNHRydPnLzz1uc+7+7nX3jyiQ/9+X1f+/pXr+7vQlcaYASQaMXZSLR5sZMF4bGwMwWE1nNKGBZFHAHn1GcZ3XBAdRDC6dv6R179+ptuvNFPP0vgSAir35S2y80UAFoO4X3SGd3aAcdGzzlBQsq02F+lJ052WT7v2yPI5rOVTSrCgCZJnaSACcdSSwtjGtxRfQLY6VLMdws/m1688pX0HcV67mZl7RZC6WqU50vomQSntuicS8uhHmyb9DuXnp4dDukSEx2xIAplkga0drzpquFZ0eQEVqAo0A4WEl2jFEzFFDdayyIr1amJAjTTNyBCcO793V1BZ1e8XWKS4uYOXAgOEEsZtwLFEr8Cdm6AXUe140jN8aNyyjRvEUnhFTvdYt85gCSXmE7cDSYv+3Xz9JWn3/u+97z3z9579eoeIBztwBk6kr7jDDrBS4uYwY3PkEx+uAwGtWv4C/HGCJNMu7q9vH948eLlhx58+DOf/dT21onbbr3t1a99zZ133Hb2hrPLne1ZtVjXdZ+k1XyWoWlKCckpdH3BUA8V10Qmh/0ZQKZ2CWYgDJDxN1/OykV54uQdN91069nz5x956MGPf/RjX/vG13f3dvuemymjJQ9vgNVFMK0S5xOa+WEIG31LKMpw2gjwjQ4tsCUKk7XDcGLr9A+/9lUnT5/GdhZCxIESGZJxvLU8ew2Yvfi3uUMO4GdNNc8Pmn6Z7VKaEzrQ1tCJmFtEI7bRlqlpACyd9L2tCCo6JF1m1tbOIK/IHAzgHwPjC0+mgl4Wqh5A7dJ9awt0uSPQGpsMDzZPzNUkaW3S1XB2c7TOBtPaBFq4uXET+MdZcqE17AhZNVJ3m32jrhwkBTqFZgND2EC1sT6upL5Sj9w3pPNB1WKlHfvzBMfhayP5vOlgyDxm5XJyvlm4GpD7EKhE84wBhF5bV88m37K/HnvtQPiuqArAOvedybOury9fuvSuP/2TD33ow7t7B0VVdezI4aERLsUl/3oFUXBdfHN0GqswhMMTsZ6AnKdvmjrDzJt+6Pb3m/29w6eeeurLX/vrrfn89Jkzt916+4/+6Gtvv/mmE+dOpOWJtsuWs+WQpNAiAYgVtEGQo2j99VgtLk3SrmsHa9frdYdo56GtMW5VVNvF7eVtN5y58dnPe97B7t599334r770uSeeeKxtoHUNYKOxypArnUPMEzV2DtjH9f5SKCyGPTGSpKl//qf+xste8sJSeoaRP8dvkrGx57yvDiqnorSK2F3zIk6WSnMztGVV5W2yX25tt/sXMBl8Ll2jTJbXkGicgIdvsFUClRMon6DHKEWWZ1ehzyhV9IayHl2RHlKd2sEWmVkbWwJnsh06WCpj1jiyzg4VbrJyMJU1Znni5J13PTLPT5YwYRC9V2CYjdatE3pjGhSXygTNUZBJAlThJe5UFzLcSNyqNPmEV82735ykMt9+8EGX9u4kKjlgqOWR0JiUWwo7PCgepV4LVWusv9F2fQ9lqMhRqVDeSp0OVBN2eRNHT2xagMGaYXXQzCRPXXrqv//RH9533317h0dFWfUd1kFFhRW2B8SG8UoJbiHknq0n70sQYLf2oTiHj4NqD/2wWq8wkQWjRjx5+AMaXCl0TkwXs8Xtt9/2Ez/55ptvvvn8uRvO3Hzj6e2dZt3XSQflNEC3xNp07NAyq6MjQFHXUJJufbS6ur+3s721tVh2WKiZsyuGrO7rS09f+u+/94ef+eLnrjx9sV6tOpC9UHcSnGc43rKEDnEGQl+26ajQJ0g7aLmQZ5A4kSZZXuWmes49z/5X//If33X7HYsMckqc6eoZspshvbFVvhEniJNKpYqTCqwD8x2AlrNuGI7Ww9C0s8Xwsc+ntj4qZpVdN2soL9jmeVrmBRQ66TMo0mDB8ICgdm66ockgNDfPctN2zTBUq1VRLZoS4IJ926YDpGUC2aa5raohh4bTpoHsytL2VVF0WOPUDr2pFjNru+Fwfs9z6uXprbMnoIREBvmkjM8R6gpVVFWdMcJnBf7VmMi9x8aLIk8dMRJI3TjOhOAko9Dg5p2poMa+p24qBIw6mpKLXNidcTZ6f4+RbMzUfEgLPoApxzo1UCCmgCiC513iH6M311mZTm/HeG+aF1nTtkPbbW9v52n2+KOP/uHv/85HPv6p3f09yICHcrRUBz4zWdq3ncHa5Wyce2cZrAoEnkLPkLeNveLNBGxMWhSwa/b39jAX36s0rjIAF//jrAqo0zcritNnz937whe88Y1vuunsDafPnrbYj2gBdSehsEZqTduu1utmwNLzVVms1uu2bWezSvIEDXQdhdQLKM23Wrf7+1e+8/gTv//7v/vpv/rs0IPnGUCUnDMMgwQyBUdZVrdt3zTQkgC81VD8ZF7Nn/eC5//yz77x4gX7E29544kTs63FAo1j1bPP+xGPcYsiGEcRsAPZ+oCh55BAj21nVnUyK9umHuo6PzEfjjqofgbZSeCGS+q+qLI+NfaoN/PMzvLcAl48rfs2Tbsy7Q+bqixsaudJDhZy06bGtF2fmHSYp6ZN5gWUPDKgXRvbrJPc9GWWtH2W5l2epqsuTbM+K+bzKrODrYreJgNkcLpWmH4CJASk9cFIq1Z7PXQUhVSuSFiThr5kgoCDrwIYixCwQLQ84Qg5Z6l58IFvQYrKNAGL29gB19WihjSsilnhT5RibjGBDupRiqWrnxK9hMNhQ142tG5PID2777Myu/nsDd96+OH/+L/9+09/+lMHB0dg93YdYLHQJbuoTt77wud86YtfhAptLeAZqS8dBlJwCjjvDn7tkQyAL2GUFbuv8MtxST08gIBBAq/dsjkPup4GalckXu5hgLQCk5fpyeWJl7zsxS9+xQ889557nnXPPWmatnWLpdMhIdEkpq3rarkosPt2Pwyr1Qr0lCwb+g5J0B4cHqzqo8yasly84w/e8cfvflfbrKm/I9iBqBRUZQW5GSCE02JWHO7uYq5jC+CQvvsX//M/fcNb3nzH6e39Vdb3R9VstqhmWQ4JdgEB+7VwGzkmYG5nR93boe2f84uoeXEODQzzdz3ou6lJO/gLlhFRoSyHoNADuCLQeQ/VQfCp0AylA+4IhkGWme6oJygpKVWUVDyYPDEDFP2y4svAjCvg+AhUQegoBBkgdbmc8Rka8+VIyOlenlGFWuoEASsPvaM1R5aRBA6IXaurIQFrOtJXqPsxjSs7BsVwhhVW2aeowku+Dpajeq5ELuaOWy4zIftxubEgBlZkESDfBicncxehX9CIwGMLnvHU7iyX83Lx8P0P/bv/8P/94uc+f1DXRTVDpw64caGpUlX+4i//zRc8765vfO0bpm66pOmg9Plie3sny5JLT19hFDtCi0lmAqLXJl3bQj40FpVyBEyDABGNVicW3GIUj0MjO0UG3V4Q9KH3gVqQUARjaNfNU0cXP/SXH/nWI4/983/2j5u6hdfpYAuLlB9WR0d114DpWxSE7CLtv8VSPqv1er1egQmSDM36iTvuvPnM6VOXnr6EXmhu1AoXpVnfDFVhvv8HXzLb3n7vn763rGZAFKm56eY7XvKyF20ni70rXZPs51mGyUQD+Ls28mFZl4l963UUjwdm96Xze/o7QKwek5tgEdEzbXLqnMC1A3L4lW8KxTshkJfi4JgkTAp1gAtEeyJ4ACSMK+VmMsxnNFogUr0WYgwJ5M5gDiOBODGoLtQq8miCQkUNUfmGobmoUZz6Ip8M6nIQxxtds8n43qPf3cM4m4A+dLAVktawgTGIRJUM8GOHhfaxmNiQF/qVSAKbD4FwQh0VO24pwvc6rYthCFekcNEAul+f5lBWuEyqIq36tn3gsQf+y3/5z3/1hc82TZsDCgqugGobQ5In6U++5S0/9qpX7x3uZlnaDk1Wpjefvf1f/fN/+azbby2283f8wXt++7d/62h1kJfVbbfe/sRjj66ber6YNeuma4e8KARtiWwb4WLcgBu3HRGnD5IJSyTguFKs8ZVBcELIxqR5ZvJZVv3KL/6Nu591Z15Clh/UMzBJi5HSIsuKqrx6eAC4660lxKMM7PO67Q6PDvf3sQJ7UaXg86ovPL374T//yOrgSIpyACoD9XhIQzBZWs3mW6fOYLJk2XXdbFadPXXmH/z9/+mOm2+Dpg6Q5VDmXF3edXQfHyoz2fuv/L4TJhc7O5Q65YQb52GS83+AjkV0mdQVleLT7Kwk9w9aD5xOxJRJHjrpY+7dnPwgA1FfNSypKMweJbSjuNquN/ZjJ44mwzHJbTxGJBgYw+5+sYCdvtb/HrMLNVxeFs6gxTLY0IUTCNgCi/OcRecE86AYVOvm3Vs/jOEIsqwpojSgS4U7XCnxS9uBswI89aLj12J+wmCHtu7Lcjhor/7lX3z4z+/70Le/9VDTDybPqRUS5acdHh289a0/+9JXvAKIp4GqH03T/MrfetuPv+7V5++446Mf+sCznv/cn/rpn/72Aw995KN//gu/9Muvf/Vr/h//9v/+rYceet3r3vilL/zVk088Dh3I0DNEPjPaClDsBjUVUsCx7R0XTHa5WRKO83lpBCWnfP3EJMut+a/+6q++5PtfVmRziBYhWnWQ8oFFXt1w48I8na1WDfsGgH6x5m5iZ8tZ23XNAVQHunp0+atfu//JCxfXbU3RI2lTAaOG1Isya7vmoG6OLu1SK4ZZNfvFn3rri1/43KoogcrzLINIHKQsGOjZ4BzGOjDiVtXhskdpuW53oroWuC6CpeXdJ03Hgw2LpVY4R4TcEXQrqnctHkJCbyMghy1tibAG6q67u7iHKAohcoGyxSOByNaSf+GQXqRUPId4R8J006HpzidLjYK8MXRLB3LwWRJLjpNuxSNOgFbMEZX212SdioiX6E9cmt8jPfl54oF0NZcZzsG9s9DfUaTQoUflKCru6a5HyxFa1Eo5F8bDp4D7ve9DH7jvQ/fd/9ADEOSHzh3YXhBqTyTrdX3TTbf90I/8cJHN9o4OLly4kKb59taZV7761d+4/1v/9v/1/37k4UerxfxHfvAHH/72t0qz+Kk3vuXr93/5oUe+9fd+9X/3lp9+y6//03+CPTgPF1vLu5793Lbp9nev7B/sQc5C22O1ChiIa+PiStI69cMxdEnX4hahiTHbW1t//3/8tde94fVZUkKKUAK9KThSznMI/qflfAF5WlgpAxqLIor71OmTV/cPH/zmly88danpjh595LEvfeGLR6sjqIVAcG7JAOayHehT/vRHP9rVdTP0s9ksz8oTJ7aWVZ5C+Tys3YGWA+rcZC+MKks5cREFOVywyPMpB7qVHKaprBkkUv7RVaBldJg38Jim1V5VxZJ0iURfczliEYm/hzAdoXP33SSZqV/DbmHBabJlmag2Iz1GR4Dz0Iq+0nI87ENdETzYvaiaLwkfcFAXPEX+1hK7dcjG6SNMOWMKxsqPmEMDpQEp9ptxTpDzWYoq5UwNqVgLn+cFaIZZauYmX7XNfffd9573vPeJJy4AkhbAP1SgCgDDq/X6hnM3/Po//Ed333XHRz/26Re96Htns3nX1H/zl/7WrXfc+p//02888O1vN4erpu0/9OH7jvb3fvqtv/DC5zz/T/7od/O0eMkrXnbf+z+wv78PKnJiXvkjr/yZX/y5G86eS6z9yw/e9+4PvP+xhx+F8pF2gArOmCUAjikoGKOayTuFiQU3m8pZme8sdn7hb/7iK17xA5WZU0cygHHjG5LLjepiQKskY8oyX9d100BB2r7v8swcPb3+66987Rtf+3pvm6eevPjAN+9fd6DuI+CXi+BKBj/4bPI0K7N8d//q6vDI5OCKTxfzcl5m2Sw1GfibADZClE/4R4ei07tWbR/9t1hJ7jP2ZqFHQeEiVPRytMuVo2OEPBJJ4y4OQ5I+rS3Yi6EBa6S2oTrZP0hdz9FFUcaZcTgidSJoxJJ08mOgI/Op15LQQr5BTpEnbK+9bpb3nnp5+Z0jnQo78At6x5Wdmi7VM9QbL8QevQcSq5lCIy/oGoB+Xm7z4xwDLthGNbgQfwMQnDyBJJ6kM50ZPvDn73v3O//0iceegIRz1D+pNF2aZeu63t45+Q/+7t9//gvv/fDHPvzgI4++4N57D44OUpPdductH3z/e7/z6KPtejWrqr//v//H3/rGV973vvc/7yUv/cAn/uJP3/f+N77xJ289f+t/e/vbr+5eNcbMZ9ULX/yisigff+zCMi9ecO8LPnjfh6mCbdH1z0mTbyfFgTXlsE7zqqXUaRwMQ3lcJBvnfUjs9mz5K2/7W6957eu259tdD3VeMbjbUxY6uZ5MMkCcE4gWa98i2ruFQs7DUX300LcfePCBhx5/4tG6bh5/+LGmq9sOCnsBTpPJhbufYHwaQm3z7WVWZGfuec6squDmaQpwqwGLMnv/+WDJMtDGGucTeRj79GbUuYZSjtd5BoJCIQpkLyZcoLxu2JqjrevqzMtAx8qBog/LTmz/TD9Y+cm72kV1EHNc1ChOzKebxY8JpLLLopg6PKg4OmOzLj5y+m+aLCWV3QHuE2/3CAFzXa/xigaqlKvtTnPk+UDX9vN56YJC7p31W7o2TFjAEvTDxObzJD9Kuvf86bvf+a53X7x4IYXKFSB1qRJymmX1up4vlm95y1uf88Jnf/YLn/j93//9F734pSe3tr5y4YkfeNWP3HTrXd/++jd2967aYfiVt/3qK1/52ge//pXXv+GN99x+12c/9pH1unn5D3z/pz5231e/8pWh6/ph2N7aOXfjze97z7s/8ZefompY0LOzWVXGvCBJfyKfvbevH+lWP7TcumLMF01ad0BPBLgXBCmzdpuanfnWT731p37oh37k9MkzXd9h2RD+j3chAU6yDOC8HZSP6vqmb9vDZr3au3r16sGFixe/8uUvPvTth5qhPTg46LoBortdn6E6LJwXpopqk2dFNqtmpk/e/KOvffMbf/zc9qm0TFKTN3lapVWSF9ADmVjOOAqg871kcblTqbf/xD0p+xHbAGcJlGiV5H5RsgSfqJiEVo8FAhDWCiC+zns+cOSq+0Tj1u4hy1eKhAxMbi1J0bEsNhAixnzkkG9HCCBHIWqYExQ9piQ1Pu9PihyCzrPtVI34HgKqdmLZ/SwOaX9n0lvdLWSVg1WNsqxkQCRROHFEJTIQxnXAnkMAVETMjl8BOVHK1WJ+XA+SKqkWyzIr1n3zp+/5k3f+ybueeupiXhbSnpjEr+nbvlzkP/7619373Od8+8H73/Ou9174zmOnXvOag8OjR554/OBwBdlV86KcFdYu77znOQ996xsf+chH/9Gv//rhlYu/9dtv/5mff+v3vOjef//v/t3u7tUsS4d+/ea3/I2TJ06d2D598uz5yxefunz5qWa9Ai9yVtijI1PbW3Lz3HT5PUf2Mydmy7KqbHcIAZ41vwi0LLJJDs7OnfnWW97yk69+1StvPHcTac5QFwBq0GA6CIJDGOWZwIs0XQP1Otthf73evfr03lN7X/3KVz77hc/u7x2tmyOIo7YNbSfsrIAtiDDXyuvQxpR5PlvMutXhC17yorM3nk9tntk8SfuyKADLwbNP0x0Sgi6JEhKFso/0zhKD06IPHJuwOW2cFhPSrqjcilRfiPcwV7QIO3E6ap3KytEjdJlT7jurKWCkt2ujUh7oYSvgPMAy4SEiz4WL+fyxePQZtO60SGLqn7U9EkrxsWnt/w1ylPSEhGYz/iQSeJpHu+s0CbLJIBF88hSaFHPogWi7viwrTsGVriN+WuVKsrShLmaeQvd4kx8eHr3v/e99z3v/7KmnL2UFVn4Vdwk905hsVpSrrv7M5z53/9e/9sA3H8yycrk4naTFlcuXe1tevvDUi172itbmz7772Xc/+1nv+O3//OKXv+SO2+/6zKc/1vfDc174nM//1ae+9vWvI2ElRb71nOe84MyZ5U/8xBt+6md//v3v/dPf/I3fONf3p9LiG4NtB+g79KLZ/JQZ7qvXXy7MGjzMfVWU0P2AawsDLqE3yVa1fPNP/sQb3/TG8zfe3HQN9E7tg8KA2PYeE7N6aHSOKJc6scO6Xl++9FS9v/ryl7/0yU9/endvNx2gap6L1SFgA6YO9HB2pHFebpplVVmaJC3LnVtuvHOx3E6Svl1DpNj0HTU3d5FtL8+CRQ40XufLCglJNjHrEPBjZjJKhyBfimsfKPzFFTmVetXOq+I8IZ6hCElqOhATPfS24RND6jbBP17YSbNNep/gLE3/1PciGE8IbZhS2ENiC5+tRqh5l5rsYw1mz8cETcjKi4u+KqWHPoESFfyZ4rwTenOg9AgmRNlRNLYOy5qix4S7ZvskF+fj5tZeZrB9nqbzrW0zmKbr3v/B977nPX/65JNPZTmWm6eEVfF9kMuza/tPfvxTXWPXq6PedlmRfuYzH2uP9p984qlvfvPbz77zjlf/6Ct/8sdeU/fmj37vt/7yLz7yi3/n79T1wZ+9592veu2rb73zji997DN12862Fk3TvPmNb9w6m//O77z99tvufvaz733okUfrpn19tnhy6P+63i+zpK6qm/Psz+fV5xblUTdkWVI3LfC8rGhaSl4zTdMWefYTb3nTa1/9yvNnb+n6Fsp0DQYdV8qpIEVZAWsFOGDostqA63t3dXD0+S9+6WOf/Pju5asmTzss/0iNkTDbF2YAChU4KA2m+KYmLcuqyKvDg8PX/vAPby2zfl1TMVys+MzNFhyOXYo5afcGYwr17praUS53m/InlBYm+1E0ZJaQIlkQ4sp9eqRnhCedDQ8MN53PfnHHRuqFQ5pK+J4o7DcVVd4La1UOlw6KPh/TdTw6Yj1BGw8+LBRpNzH9erNcDBdvv7haec6Zx4p34KCSEgXublPBq5BtS9xUyWwabQ/pO81iawubxXKZeH4jqTrJWh0OC+svF3lvjlb1u97zJ+9973suPn0JFh28PhSH56l2wOmmbc3QmSEty3wYoH7VN7/6zQe+fj/A4vPhD//4HZ/+7CeXy+Ujjz2+v7t7/tbbyrx65Jv3Hx3u33XHHafLnZ//2beu1s0TT1381Mc/9qzvec6X/+qrn/vUV//yQ5+1CWKUm/V7s7LJ0/xweMHpE2lRfHp78eUsaer1fF41dUOtyXtobdAaMIkbk5kXv+T7Xv/mHzt/9nxnW4BiJSmYBfyirssFil8DNQ2Grs9NUdt2/+rl9cHh5z/3ub/8+Ceu7O5Ch0ZUOiBa2vVQAKTMocwyxscdkBt7KkO3o8ViTj2KbrnlfAXIoxR6jaGe6uAVsmCBOua1KR1B8bvAAyojVXG8hQWMwba5yIvA68SfURMllc/kvuObjUxCJTICkze6deLEjqsi7u/j+gM4/AhfJKVAvcHsooNKUiol2k9IGOPhKRsxQtnmMTWJKI3eLXJxqy+V0RGtBl6gbOD4FLW64WVSLwaCt/QaWDoUynrnsO9cpUsvqokzuzrD0CsoM9ViXmTlarX+g//+ux/8wAcuXblcFAXSvUwriRE0txDwifMBaQgQtIHYpoGOVd0AquZsVnX98ODDjw52qJu6NNljDz/yW2//z4v54uDo6OBg9dDDDz/5uPnhV77y05/82PbrX3fD2fM/9L0ve/mLXvG5z3/2d3/vd4a2s1l+ucqrvStv29k50ebdzOQnls3uHhBMURxhz86mqVE3ABFrh+T7X/4Df/fXfvWGG2+q8gLhn8DFXDs412IZ8iKB+CDgDbUk7NDVR+v28KN/9ZkPf+Qvr+xeyctSQDcIPjJJWUFHkq7rMKeSWXKWZTC/ZTmbzxaLGVTSa+vbbr25yGdQ+7kD7VuJFkr0Y6iN8jsGxU3d3nDaKYcXRrvBhQz15mCXCXWuEPiGlwNUg4NKagI4MtxSjjZG7lKl9dF3bILzUN1fiSi/Up+Jixyr0orO96cJWxkUgkUKBFuov7sbqUGMhxv+IvcPu2roWfP8JwwIKxPY5yQH8+6UZQixivtKw9j16gQfSb8TEDIA3Xd94eF7AAZVFUO4VHFpwS+hN8XVe8nMLC+vXj34wz98x4f+/IP7+3tlVUF0FEvIYTkp7IqGDUoRmEkzCPtBSk4idePmoIx3BDeA76aEDIpsGIanLl4g4OHv/8Hvzt45O3f2zM6Jk08+9tj3ft/37V2+/GQ2W25tPeeuO6uyats+SbqtvYNfW54uOvM+274a3Gb10Nvdvd0ccgDQRiCAdGLbobv33hf+vb/3906dOlVijYChh2wFpzq5RHfhWtxdcRj6dqi7/uhzn/vipz716St7e+WsIg0b7V14NajUk+fQSU2qAhGEI03TqigXi8VyPp+V1eUrV07vnDt77gxWqASnGJTtRVST3yE+2UbF42OuLSJbGWHhLhUVSGvgisqIeMAlIEmKzvKhoDU9HWOKgRDV6Yx+ozNObKwjj2jBeOSY0wmAhrUvStREBmUGBMC2u6cKHkzwoBGCZDQI95MvDBeQzsgmnRS34lbQHcy5KVXAuYimqAApSct4SL6CRcgMEXuLKiKmC+OJmBbP7FkG4gvCIyETFBFnKgOcM6Cs5uXs6t7+f/1vb//Exz+2u7efQb8iLmqVQw1BA0mzoNY2EPCkapRKu3IvhJl9uD+o8IPzf2DwZsAWSFlq2qZdHTUHRyuTfadbd4/+6Xvv++CHoKfCzs5yPj9arYdk2MryV5elGZJ358NjQ//aVX325uelBw+a1LRYNqDHZuX9MNRtc+/zX/C2X/7bp0+drIoigWITkMBPFELBFFeNj5JyINWhKIYhWa1XxvZ1ndz/9W9fvPAk2Mxp5nPNQe/IiqIEoDO4dgnmCpMGxfGK7NSpk+D2y7Oh6xZ59Us/9TOndnYgt70DRaUzUHWGcYmigrkAREAs3p4Kt2QQeBgp0T5fVnanT27hhDzENVMDGdXcCmLgEBTkrlgxMQaOJkYU8q4K9qXzN3nQltEav1IB9JgFqaF0W39xECmZenEd9BmdplX+sG1LOKn+HZTnPUaFuCpZIynu4MqOXVKqLySXsu6mtO3A1+WpniUwFIfAIkzUEWyABo4oHHME5XMDDs5J0rAtMHqTtK6brUW1e/nK23/zNz/72U8fHh2lmMfLIDh8u67vr169ig1KAeQO2ifk0Aecm1/X2VYKWMBlXrnxCKCRqBc2FgAwObSHSNuuXTfry1d3sSodxBOazLzP9H98dNjPip2ue7BZtQ8/bPuuLMqmbVooxQ4B63VXP/uu5/ztX3nbHXfcUlVlPpuxce8aIdLulQAYx5DQIO7bpG3WQ7f66Mc//tAj3wbihvLzslI4SCh0lOdQhw09Yaz1pKbI852dHZOnrYFMKbNe/ezP/szzX3zv1nKbnP3g20ftUaEXJaDolbbRLtVFjtR2k1zy8ORA2ClNVjXUlexC1yRMOaKZl0Q2t88qMpEo83vZPyoEdRh6BU3RmnxlRNI/RNUDYsrn8qUCy1GSOTqmasZOWrjB5I7lb8CCHEhUna8DZcSLpEaFn3+WkamFzFgsoagHr4fiwW0oJaDMWgObmZtTYsSDEtcBCOGKXXAAQWtpAGKAEvmLqjps1v/lN9/+6U9+cv/g0GRQWE/wPHD0fV/jIyj+CWyCAqGh2zSAzjlRQia67AK5rQBz8TlQ5wdwFiwwEQWJ0zlka5vaqrJNu5/nf2zW5okLODvY5xo5UNv3d95219ve9rdvv+22xWxezGcQ8cbaOCheeOJ8o0eBTtlkaLu2qft5Vu6uj77xjfufeuopxOxjySvXqwxL0uV51gDAEpx5pILmeb69s52XZVujC7s7eOtPvfUVL37Jya2TaZJCG+QEAJ8OKTUhMLwHOlRatSRQ5LGJulUzxVCYKZ+W/EWd010PY2cDb1IylTdoNHrtNlfRiYRWPkp1CC/l5AZq6KGtYc2BKKyC7FY0bW8nu/cej2p6rMpGmeCaNHGhb06LYhZFgT4k2QSCoWaeCEXtsLuVrswc8hEHecXd1DZwDImFEAd4kyB1uqhKQCOB3apCyi65G1VdSOLs+2U129s/+vf/4T9+/rOf3ds/xLYr0HaA9D6ZQlDSobQiLpGrIKOXKHINOAejnlHvk/efilFJBGeTAnPqMQkZOjjBHgG1e4AwT5rmTU0WuwHyAPjUDWfPv+2Xf+XOO2+fzWblvMqygqx0ADyFCo5uGge8DmoTd0k2lEn3kfvue+hbDzRtkxUFkb1+rzwH6329XlNH78FCmYnFYrGYzY7qelXXw2Bf8Jx73/Djrz+7dQL6vWAaBRu8XBTev7XTS3i7e7fVxHZXbh5tJwqPDFIMtcdEza/+S9whWEsz6B8bCjk/HsUM9N7W9/ZkZ0Wax8vub+T9zpjsj6qWs1VkR9BtgW41qEM4itvNEvwXXhVZHiNaVhbtaLYVe3AfjWLLiq1J2q4nUCmElocJBZP+Rx6PMbix6jWodqwqDX3X59i0uqnrvCxzyBnC1CJ2jPFQ6Z55YZ68+ORv/sZ/+9zn/+rq7j51zuOyGWI7k+4Fjt8M0khFfFE0LNCy3LLrbef/nvL+iVzgM9DVBS+FbVCRj1C1HWBEyJAg9oOq4dAlJt1abL31537uuc97/nI+rxZVVVYA9WZ6pU0tXjsuZoKKNDSVgaPrhzKFOsYPPfTYUxcvpCkk2SNIEtcPwB5g/ycmWa9XWEwXviny/OTJE/P5vLP9/vqgXrff89yX/qv/6R+dOXeuAp7YusCeJADx7EQ7O3DFjjda9IEWHUE1xnFIQ3wfonNHdrNufunNTQXDFNzuOObiEgU1gwiMROuy3EYvFeT9SgPHeAaCvnrukbId+Y2JGzpkiIODEtYinDbP1yYnOp5nz4umXoH/OLiWU2lV+SruTqhiC3rhg1i3gWKoIIDFUQMN9nLsc0f5Nr7dDqvlDjMOFmDbNIdXd//b7/z2X3/5r/cPDzDpG6QSgi65fR6tMWjOxsBtueeA8HN6VefviCdKo4kCiJpeC69XQ8sSKFXV1HUH0GjQV7FZKa+Zd+XDsNKuGb7/dT/0vOc9ezFfQItFLLZOYLLAWy/pILRv+65v+76oirLK13v7g7V/+RcfefChB7ukx7LPFCKCwVI9mCIv+r5vAHENw8zSbPvEdt8Nl56+3A0AwHzRc773H/+Pv3b6hp0UgmdQB8T3B1QOKe9X4uVV1RnIptKxCbxC+3hEr46mMtyi7jO3dZxg8RKUI7B+jpwI80uiQFwKzayDR96KD+zuyUMpDQpQIXA01RfQr5IjYZ+nIfYPuvWx4APPpNJSPRfyNrdLZ4pAWu6qQMp6LhaQ5aTd7J7ITjnqBYIETH8UXiKcC8wjxoKhDbSl78ApTHHzFHYcOIf6vihKLn0mAV9tqrRdd3R00A7tuXM3Pv970sODq+t6ffXiLlSWaRvsHAYZc0DU6LQGgYW14nyqiZPlIwGg8sZH6Woj/5/TNMnYPjg8AHgjdSvDSpo0u07XEjhgOp/PXvCiF547faqsSG3hCpgICXfywHvT8Fooy962zWCrpukglJ2nDz7ynaeffpqqsdPZUu8TkoS5lwp4zoG/QH5vnq0OV51t66Z94T3P/2d/79duuuVEMSuwYXcGrf8gW1r5c/RbKzHntxPXXFD6sf/CpwhqCaxTVafIRk26FI7g30h9xdJfXhelpG4+QfAVrpGi69LsH6V8xY4dK5+lITL1wCLxxIo7m66EakTUk1H51WR9PXsRqC9H86kwFDW4COZLTU5ALhOyNrpgJJ9D6LmYCv40Jxz4bwbVwueU0O9uEj/HDceAMTa0bP1SFxGUrdw8Hl6yI+y+l0cy59amg1nMtoZ+eNPrX18DjAkC/Pt7h1f395uhe+iBBw/XqwtPPnnpwsWj/VUztE29bm0zDG0KiS9iTKGMl0YQ/LeAgFSJCLcYwQrzUrquRX0HuLF13ZTYLJtBe2xESvyUBCrWgcoyc8ONNxgDSGYK+4L57zRDryR6Ow+dN8AdZrPq4OoBSuT1wf4+lPujtAZZOMRUQaClhybZwERyaFhaZgAgWV/dPzCZueuWO//h3/3VW2+/Ka9mUE8HzXjqmakhBhJ78cFGgSZ5GITHGMb6s9pIm3TAwEiUfROqgb6drviXvHTnrSjC2LFfZ7V63jGybCNlJ/HLS7QIoDXHP524lxMg9umjvpMELKMUEDJ3c8Eyi7TpNXWEWOd4rAFRBZ6peKY3mHsytWFZAC+p8JOcHKS8qNp4jqwn1J/bDrC6SUHt8DzUEcUzF4lgFo9eIWgFaLGzd9N2/dC2rcW+uFAvuU8Wi62yWmZpfsdNNzdDBymxddu07bqr+67/1je/tWqa1eHexYtP713dWx+u6r7v6nUL4hqcyBAjpQ66GfRMotQfGhXrwBJiwr5fXBrJ1YXO8myohzkgTxhap3mjgpHht1CJCjKX664tur6CZghQUk+HSHzyJKFWINYGPdCWWztQ6a7rre1XR0dXLl9Gp47kseL2Bhc+3BNMcWg9DvZ5PlvMk8Fe2n26a7rnP+u5f/dvvu1Zz7ptttyCUmDOVUpB/HADeGnpdUMqNjMiiCmjeLyPjjPovCfY31rowD1Ei3RXPyOQRRNZSeNnkkLut3SiWba4/T0Ju6cKjpdMMnmBKNHCwyiZPrQWwrTNhO0EBEtCUhKV9HdvFVqk6uJoMjllUL16cJK/nQehE3uEIpzeOgpVL/ea1Na3bSG0w1IMFU78EfkSBZA4Jxw2Mk1313er9VHTQCMcqnoF4qVLQALhGKh8PDqg8zLL06orCnsCM+juue3O3gygOLa2gzhse9S1Xb3+6te/tru/t3fp6f29/dUKvu7atm3XSWI76nFkQTWFVqJShlL0AC7pDEUCDGIhhqFpoLkOolPAIGfB6xpt85uyQG7qtmtaO+9Jc+FNIXtG11rF8nhAvYvZvCMEeDp0q/UXP//FS5efZj8X8jhXpy5LMzDF+wGKbOTlyZ1T8+X8sSeeSNP07jvv/jt/65df9uJ7t7Z3EoBsgqzB4LlrUeZFjVt0dIvzbgIGy3bPhMY33ikBETlPi7dS9XfM4+I9GZm0xEZ89El+CWI5yhSK2IZ3+ExavlZ6iGjqdTFmpWMqEtY8WvmIRCOKpzOctDhKHioNehaVISJj0lauevtR0emIM8aGCpJaLjWNVekUfwo/h7Q1qtIufT1AdwMrzWRoLSBzc3hMY9ISMbA2nc1OdV3bAJ0BrJdAIFQeFUDMYPBSlD2FijNZAihIjIyQwpPnM0jvKWdt30PMpE/uuP22tMq7w1Xd9DY1XdsdHR19+pOfPFgfrtf1wd7B/u7V/cOjplmDljxAs+8shZwNqsMJRaq4cCwUoCvLGb0dAK1AdY3lsdtKUOS1bqCMBoh6fmPCcXrsmUBf264FtNl8jhBuaLeVZ2WPvbXXzSoroGiqp94Uwrw4pCHP0qosF4vlcmt+9XDvaLV/27mbfunnf+ElL3nhmbPn4D3Qo0Z95xxQ1u9QImZdWwpfhhgEwRUcD3c6tFrwYK+N9s0zPAJ/zYSEHqXRxoU1/IAcO1AJE+5gTUbcUQHMzGcL6g71LiDEN5CINT1L+qvpMKSmnpGYE2stltj6B4fWCHWfcLgBx4jAKepQhoyrMhrGj0K0C3wNSFusq0aaKG70NM06yw21uSUvdsnAPZuePnVyezYHCsGx4w16FphDt17VmADUYClyrC+DzW3QlUWLCaCRAWpRARALU5cQ7TPYoenLclFWYDG2aZeZ7I1v/DHq99P23eHR6mi9KrLk8e88ceVg/8rTTz/x+IWnL11aryAPAdgGIKuHFutP0xvB7k7zHhy65LxgYIdySVHyIBTCoV849stwBbcwJjMpdfjIsMQXnAlNiwcEqqV5UhZFZZIDUOlR+aU+w5S3AF1R0rwb7P561XbtqqvPnDj7N37mZ77vJS85c/oGqFAHvMN7Z4lZuhwDjXTkz9H7An1LcrCw1a7zOuiIxGIgcOgNifbHiNa1DIjcTWo3efnr95j/3v0WZzh4zElgUBpRkSa8OGG6bPxdoJC485RDXgGRNTuK9OCQGU0pymqk4U+BgB7LdHdbpYzzRJE9Dw5V9mdp2vcrxU5ClxbDwbQEBEWW513fUbU0wWbCfaqqPHP65E41zxFngK4nk1twBM0AD4ztTE+x4YzZO0Deq3V9BNXMW0xLbHuo2IjoJVLdScyD7IaGD93QDkCDIDlnVQGJp1mSFuXMZNtbp8uqzDL74nu/d23bZl3v7R4crQ9X63p/72pqkocfeXT38GDvypWnnrx4cPUqVKRtQUvo+gaNe/CZQskOaCgAXEfiYgNIdex2jJhJAujTpLL/FBoqQjfVDkpYW1uVFYBNsdaBTezu1f1bb7713Jlz+1d2sQ81b+K6bQ6PDsGFBZHhIYVs4GzIshu3T735J970/d//8ttuuaMs0X8KsFIqcc7pAewiCrY4D4ugC0WOlSmxwr4UGdZKq1aRg+0T7nU6Q4GpxzvKuS69k2l009DsnXhaEAqcZBDeBNQHZwtGolLBnfjjQCyFJO700pASGCSoIVHBk+V2OmzkLQd3s9BUcA5e/QL6dp7deaHNcDMxTqgWQR57n0friMlHsCmx0zTl1MCHs6qq2wYxx+BI6QewQJGcZmdOnjq9tUVPwEAMpCgQopF6zKLzFDPsMNMAuv5meTbPlrMZiT/Ifc9T6JMBPTVBW4RSASDChysH+0erFbiPAVMI2XuQ7971WQZ6MqUUoP8o6bojSEIYkiIvF3Mzm81vvOF8UeSveMnLu8wMQ7d/9WB1dHTUrnqbPPjNbx2sDp584skLT1w43DsEiu7qer02GcRrwdtnh/poNTRYml1hG0htJpQP+PmapobAcrNYLICfQJZDt+7qFon/sDk6d/rsU8vH2rYxGXityAvAjV9YCU8LU5xd7rz1LT/9/T/88nNnb84rhLxBzyTvuXa+F73b3IoDF4AYH+rOFDBQse0J5GS8W6apWWuNIgqntEx/9kbGMHGiBi1pmTKpxDsukQilyK53ukEkFAOW4/VTr0p4EmO8Dy8w06Pyt+vsPqFqLSzlB9fRI2AMLmdpCsrgT2GG5bUTjyQVIZ9ie9FjZxiRRAC4AgWPH431EE1q6hpUyiIv4CQLeXbzcn7zDTec2t6iHvOUB0uJovwaqKJATSVy/aNdQoKUaJs82phNiFWNM+rFYJNZRR6bE6dOE1ACWEMH1Nu0XddBb6+9vaO6bUkPbuqm7Rpjk9a2RZmmtuoxuWewZlWvTZGZLN/ZObG92K6hF0p7+6tubUGkg4CndPyrV/c//+nPHNbr/YOrF5682DWNTSGJv2upRDM3OXNRtKIoQP6atCyKrZ3tJdS4AcsZ7Oe2HYauKHJbZHfefffh0dHw0P0Hh4eQ3gTuQYr1MHY0z4vtne177777ta995db2VjUH3RebgmECCcsVH+r0YSPZSJD0D5nZoDhjmQ5e9CD9QG+aiQ0wScZipYX6nkYnTJnPodPWi0KHs46eFZiWDrjsHHLBBVY9i2OEgT7q/hLV2OE1BESl7xArss79xQ5qBwGLkyfoETxcKQfpI9wCVFAobLnzSGdwbI9nWsrTCyZZ7svBXQByBJb06IAYGEhfdN/h2alJihJcptQOpW27YeiqPD936vTpU6d2lksmWiRLnAPqlesnXqUpw/cY4iEbGkU86tYMk/AIeAbqQQAUQUqQp5QOhc3nM6oA0p/aBpdYmqNzDUDNwHe6vs+s7Yy5cnVvdXS0rteQlA8QEXAw9aavbGayrAfR2RVpPi/yzraJzWZnt37x538xy9NVe3SwfwhyssiqvDTQOAXCSKSmktsKE3iLowHakS63thbb2wk2AWa8D/CjrLft4dXDIi9e8tKXlIvqgfvvP9zba5p1Atm84Gcqi2zr5OLM1tl77rrnn/3Tfzjb2gb532E/MfF+elyZjokqmwdLdiD5AhZVlC+BQscsP1jpyU+jr0dYWwVvCF0ugUoY30Y02ACvtOl036nIf6gKxVraw1zJIAQ3avVXPy+aAdGoQpM7UA3osphI4pkMMXEUowlAZiMxfeySBIqEcjjKOKCwuwd2umvc3KA2O9g1wohEdIOWV1QVpjQkeV4mdthabt18000nl0uqIkPmCGcSToSmXKaDszv4FbyRhs4lMJ65dZiLrbt5wia9XOgRg+xJin2cYUxwFUIyM5tDnBe5z7LI16vFqqnbtsNakVQ0E4rXrSBM3QJpg8m8guocqLmvatuvbJcM8/lyNp+hz9g2HdSsojgQF+0zWdsNewe7TdvsbC2Lat42AM9ELQFTRkBbTobhIEvT5fYyNdkrvu8Vz7rrjo9/9FNPPvF4njVA47kpZ4vbzt7xlp98w0tf+oqdre11B2m+7HLACeFlCrif3k1wAAKkgMIofm5VjQ69WRRwIaYmtT+v5bhSH43s3UAFVgEk+muc+KRFdHArp8ZODy2ZkKPBIPSFTO4Tp28iJfcmGu8YDD22rVFcE5uRpFZ/QjSYqFfTmJFN0bYbNqjQLtdZD5AWNEtNT/UloXxkgQ2lwcBqmtYm6XK+OH1qZz6rFuW8hBrOKDSxBAb9HG0KjVhzDTqIwWqnP+dW8K6bch86Lkowa1ZguEwkYJTBLQa5/AhgGLoei6z2AGYssiKHQjwU+8KLoT5rAe7iLQTyQFIB5BZ0Lci91iYH7QowpL0F0U1xX877xV4LyDzaZg3denOoQl+vVo4ySH9IoXNKWxXV2XOnD48OsbJVmmbF614/39/f/8aXv3LxytOz+eyeu5/ztp/9hZtuPV3NT6zqzhR5iu12GQ5GHfoCAeN/oEnGygE54Vu5hm3gxxwLDK/PhSJUGXLhttKenyjVa2qDhRpt8OhYqE6ykFDM+qhe+JSEdVXfmshrLG6s0WakL1SLihgO5UPffsgaFhUIUT9fTmHEjQzrp/mYYyfBm0QOecVJtJns18nzbogDR5qMVrXyLM/ztOtnSVJVAMnPQXPG7q7LxeLkSShnAc1eAaeBHly8i3NHK6srbqvC5rmT666uiVdysAGaBxareeVPgh0gTezwEQSq5nYZ8C3WUcc0ZrFUUHAyfAoVYQhrc+stLFsFpn1v5yY9mW/tH+5fOTxq2/rg4LCazYhfkAWRmsymZoawx9WqOdg/rMoyzxGjijsEOQjY6oMdqvlsMNBtvsyKalbdcPpkYtKXfu9L89TeetP58+dvWlbF0boFtyCYAZjjQXQrIswn/OBMkjdLkjQwHgZv4SGSTtcSsGg4me5O8RzrjT8x9SrrenxoItn45ZTUEXY+Tck8CRGeybpvSdbpIZPhyw28FNn4x0V8auOb+N+YMmLUhZzhGoxS02LOLsYtp6beK+xKH9BSOrnWz1x9M0GJSh04Ap2HzFgA9GVQMgZw/xhKxMaWRTUrKpMm0M+y7ymZX7rEwn4lInHtZxQ3HEUW3AO9A5J/klpbIRrNafs6rBgRM2fnSnty7CuPDmzGd3JrKOIhUJXdYUJ5hREK6h4O6QKnt7ZP7py4dOVymkAT4qP1uizAxUZOPqDFxeJgf69pGsiDJEaglC1jwEhGQHU6K2eQg2VMWZRFlqdFMS+zqlpsQUXLtm/THDvPowMM6VcEnduwboP7ylf4A0TA8pyiyqzLuB075oPBMfnpRoVZGh24JVQUHu1CUatGbYS0ZekeZ46XxqIqasiRCUiKaTh421hWBk+LtQCfqehpTB7jKHFqRsKBKv0Les6Sn0SeFVyvxez4OFbBIYuJ9Vk1aNYcQH+GQk4ZFHNBusWTIFqDaTZ5ku5dvbqu6xtvvAHgi6wUM2TOx9hDCT+5KTRt0sGdsAMq1aqIfD7N1DBDiGKk6AkX8oWqPb7oGo1OkD3KyIEPMvCLJeAwSwwAr0ADb3eq+daNNxys1ruro76zSKnJumvqGqyMthu2t3dQVe+oQoukL2I9KIjspgMgw4ehs0WZz+fzAhN1TmxvlxUEzGDODaBSqZCy7McNK8gz4oPQWI4bTIoJm2NUtnVs+20UQ7ykWnfWp2sdb2qcDE4LpIdbso1SfMMR8HITJCy5yhtRITi+MIwbqXBcMLQgLziEiod5CGpex74vjmZLQrgdsEPyZos21ovGX4UT4PqtpAjkECkeLDE0/0a67UAOgDHshKpFzyi26gLwU1WWQ4MBYaleEbyJTErcsjQctyrHLb9HLD6GzyndQ7YDVcmEn1G5Je8V5lIA9kOyKSM/Kq8pPtQjfaQMD9dSR0MWwjx5np1bLk71JwFWirC0p/f2DhtoMVhk5dFqDfFwXkDCXlNZVSx0D9oKOIjNHDIHM5POy7xCFcdgXjQU2AE+I1iwUGNyZskEnJkC9FT6DzO21Kkiub2fg/6ZUFTjuO2YEwQmeLic0xpgaObR3T1PH5FwtMgT+31DsCSh2zE6TQiIb8lmY/D6sqUmGBqeEKvyI1nhSX3DaDiDAtMnsfTjaLKnHz3xigGBi9Dm/sAuFUPGRQfCd0AZI5Sz0+JdhstgbdPZumnW63XBCcNSuVNS+OLUwonFmJgDqbk9Eh5jFqVUS/WeiKNGMgAsF5bXopFwRVuVCEMqnttjQfVgoRm2H7F0JKjmiKgl55Ix6Q1nTrdD//TVfcSTdFTQRnpTwzwB4jy1YDlDZ0KArAxJP4ckxrRE0Qw4Kc49puozqFs64RvunKAvqHgSSPzmOTRk1vk6SsGMt4xaA5X8r/aSvk6JPPcXf8LsMLCXo70/0mPj/a/NnwkaniAST4s2WH7m+1q+HyP3tHM7VBQ14kU4Vyyq6eNJpSOYWwqFZpNF8fSt1FtsYGqRGxLfjpIZEGMsfelQMoNBRQhorM/q8w9domvT2TLLt5cziOJA3JWJTpMbK8ySpqOXTLnWA3cIR66RoYzeWIsk9aXnEkQFpLaAKIP0WhZoLu3f71AnfJ2A18g7ZVohmbjoF0XUGeA92KZL0+TkfAnVdZYIlsIKVUTI4i4D63u1biBlP4EkrRxIDKsIoGrDNSUp8ua1QefokNx0/cJe+mJAH5DWhc9+dvMV6rbKhJEfVZhvDNN3xKdIRgSxtzvGeXKi7KhDvxmNKVaFQzNm4lqhteOxR4n3nODpLEpGgahw7wSWlfretWkaQyXG5MgL4w1IzkNG2IALiIbXRJpQwJFC5Th+afgdJDCbt7ryLKKXoVBzQP10fwR1DFCBvSiL7e1ZgVgprknHJ3tkuS+FFA16Si9mLoqFAmQPbtBQIpkSvBenRyGgkmVv+BL+BkrP56dJHgp96wH5Ng5WI26M2FuS5LA6vmhDwKAxHcikyWIGzRYg5YhwyS5Q5oJqHPB1sUNWejhXQfmyCLpFxArJGBj8ReG7yWjeMFsh4YSu53FBwOn18vSvq64F1glnPrGuMzFCTyRj+2C01CMd3/Jc+5dX76WXOLx34FoKlOWY8yh+E1kKkXcnUjNw5QaA7vcFVVYNjjByN16eiehXeAyWeiNx7UjupATaMOxFVUbJzRTHTmxiqyw7sbNV5qBZKl025F6UUh8IDJ2OrSt2BfQvxDupoBzHfVllGSBsA54rKRmruL2qYygjVzcP76W5vRQKiDaJasAVqK1u7rAbDAhrtv4g+UMCbm5Iinl6rKtT7fyIHL2zyIAIAEZ+wd7xILvxtExrp+Eg/EvTJ2MrN5opRxqOrWw4aAvwhr52A2u6Jj415o5J9FZsFYqdPbmqXkM8VjCo21+TIU6oxmr9aR2x8wYoquGlmy7xF/pvNwyD0wlDbxvZY9iTV5n6RIZSq5ziPFLH3FXnC1V0r9fGbzz2mskg47Kf7hSv8gUfeNsAkxqlXA4hPY/j65rtyHeM9tY7JOYqKtohTnevrvFN6HzWXqXGq8TqmHH7S7TOpKSNjZQBx6/9e1O+UVFA1ofYwuIyHAuGCTs0WC13Ats011RURwxmw0mhOJwK816bmiLlMrh16PH2k8BHOAXqU1e6Wk+Pa3fKp6n68htUZm8DunzwUOsTwgE4XpBmtNlmUGOMj1AFyBPICnIeTmcKcm9P5ZZlWxYpe8gMZAJArxCxnaP6nrK1A7qeMiG0TSSCbEIP3LRPlLhA1oKoCcRsSIMIzzGm7jZWfNwkT6pRfMJobGGIM8ClMp5D+wE8mGTy8KQn1rv6Tex5gm0A5rko0GGBxK7ba7j3cYJkk3uTz2S5H3DbsdLoz9WFLPmSY+TV+IHX3KzXulkSoMiIUXMoOIq7yuaKF9WRbEjD/uHHLJN/sn7SRlcV1BAGjOIGLTIQRxP38JtHW5fQH9ixBPkB7TrOE8eXp8YOtGQQXsryWTmbVYVsxaDWpvtHhdSiKfFaorf2Azbt0BbuGMM5ZK4E1U7Qja7r66aB/GG/hycXT+7r9bKAhcWzp/y/G9hJuN9dlU9VEsALOYExRYumJmkkEoK+9pSpj5hJqEyPMQqtcAUwae27vFZcZiS1/UTplzxGR5pSXK8r3ntNYj3mKut1Oak4OhIfavO5I8qDn3hvncarYmDhDbVOqDma96RRS6gUQhLkjo4OzYWibxRkJ35xLOzuE6VYPaM6FVJFl0lX/Kl4nzxNtxZzqCPFKKaICvm5znc2EryegYRTFw4v+n2iVD/fxtVS6fseCne1nRALvhmvZcRG+GY+yUR9orUcYeAuAziwr1TyeijM1KLE4e9xx8lI4VMwvcgnRPyAuthgA+EyA7NKvGqTDEb7pH3IkOefU0TDWVXqkjCQqbXyN3IbIBCHek5itfm6qHWzahmN1ioDywlhVn2u90Zuxp1CpWylENHh+kFMEtyElklfcL/soHFiyBedxquHpJ4fUxJ3ZvDT7T/mnUsGoZM+PE+97QHuJ16ucDheTCTHx7yDyEDwyi4lyX/k2Z72G3J8gNzc/dC3LdSsohXV1UXj99NPm4pPb1xwzQQmCiF4K1kJS303pWzGSSneUAlruIjoVtengI0ryrKCQpYyxR7AMBpy/G7+zRWpBoqwHlU47vEcyT3GPVuUd0fC6ROvri6PRq5HN/FOkwoVP0r9ckwgY3wrv1kmYjdOsDquOvJfjclXjxJRxhCuOC5UMDG8kPMRxQrKQPVkdlvFO475LCV0ANGEyluu3DpOhOsH+d+U0hF4k0JfoT5nSoEbh+kUXQ5DDxn8JHu1xuTLC04aM8xpnGY0/iGYSuXdopmKZSv97wQmSTE1uUqZ9k9R/oJACaXNjqVz1ZmABivKqsp4CbQvMBak8eYK3yY+S4lLcVyGazHNA10xjGgLhNtutBVjfr5JyY40WnVTMz5lIjI5wSzU6vo5dzNMkZPROJSs8rtRbTUaUvj0YLuQMgauGVW9K3iUNqsmXQIjxc1a9ELzJgg9lwIeFQaPMA3sJLrcWuYpdCpDzhnPmbMUlJQKGLmoKNGI9RpHGtiok5R+iwEq7WDh6TaIZug8OiegFMeZUBungH1Kc4+svGCmfW1gZn0jxqXaZ2hBLWI3UveQvYfxfeL4YPaWJfajiSdF526FIQgX4hslG4TvOjXBmjoc7CpY9HGOv5qfYIx6v3spuYl0jz3oVsatSOwkEWzu9ejO4Xj97GvvwWhLbmRF/lYBK6ZVoBKmqmvN1PgiUzp+gFYoMaF/mrU6MuK0IM7emRXVcjajB/BfuKZOg4pVV31M5XTR5leWWSw5+JxJ2IdNIGAENXVaj3YOX3Wkm02qhX6XT7fZ0m8WWKcSRPCR3KDMf/T6AT/T34yxOFEoB1uZZ3leVmWaIzY+Ql0EN/ZabKDWTjw45CLhFPk3jMksTNqJbhiKq6mv+RRZcf/LmH+Gr0NsePq+Vpt6AVGP3l4LmQ3KuAxRdos06KVVFLTrRpcz/qWIPWjVtUEgaQPYrbob4SjkSb9Ck1DHRKVLL0P39I1pIYssO3FiWZWl0hVwpnjamM+7y8OPdIDX6R8yah9w3mwghJvIWkpUgJq00tFFjmDC1IRsWK1oL8YbWVF3fITeHcUMTRT3PYatyelxuqVCI2LiWJoVVQUVpAn2FoiwOPlm/LL8BLkm9DjGskrzIP+cjSIjvL0ioY3SdTKimDzDw4hbJ2RcTkjoKK88IAj7+E+isTlu6/mFiEWBmUuIWCpgXfMlmcYArZBCnAd7m9Cau9ePGJh2h0ypjbgroJKjmgbtblCPxgJUkD03n82g0RPrKHqN/WREK6G6L49vHSydK/8lN93AIfGDAatMA1iSWv2O2femiXFzMj3fI8igFBEUopq4LOKc7hGKoGMqUfzCaZW6yz0vB/lUCbBRQP13BHEJM495jNvBKoQx+UxnGgSFj9zddG2n+OIJaG7gPVWn+7byASMYz/nEV1qY8FSMzjMTWHFfYVYrvNfQO9QbaRaqBs9ubc2mpELHKKqv0/2Dqr8svyE9CeKeDBgWj1M0z+otnRjyI+QFzEF0OYXbLZxQkZsF6ESaZVvLOZZ3krhC5F92yRDKHDxG6kwONtjP8QvwGMnvDD7nFspcUGsImTLHAPTVsszCeIIxjBTPSQmhXzS+NuDo4SXeTAtscv1C4+1C4yQ6pSTBHKvmQcqh50yjHa/nLSDHMDM9GL6d1JInmkSP9ZPgHBV3DnAwEQcJZJ6bvKmnuVkKRx7fK+GyDH4YFDVXque0rq/e4jpEfyjKg5DvtMI42sWO5dJV2IZg4JKIPHShwQmiCG7MtWzw+ZLM4L5SV5PWS03LaCNWZUklV6HGgIfzhiw+0M5GE6FL60SE4UXueNReW4JCVACWhIZI0OWEMC7KE+5fPtxqHHSaouFY4I5uoBiZEnmKc7vNNlIM+bo4GjxCBSlqI0AglcSHEjlY8h1+lK5tfPWUBsGvukl3dYB1z7C1rqtVeD0NngbiBXI2drwZvEybjsBPKjLu7AhiseGw7i+fTq6cyPS1dpjq66K38FErfU445ABqFsBWN/lZY+2HVRWSmlgUMXAGqjcK/vXncfcTzJGwiemxuRlPl0u814OXDQOlFaGroBjhQVhGeeemhFH4MnFKoz4jcEBPcE6CcjZNjZWWGfnsHTWaSY8URyLdiRSNsVCKy1dM8ulJ/INSOL0g9HJEKxLyQqFYwjwSRGpw72XqayHxIrUbRzvcMZjJjU+fe4yObHxRAv07TXfkGd9Uqd7M4xX+Ycqjf+wx9vCNyUzzxOPEpuG0hut6jfhxWhfc8Hw9f45+Y4yGytOR05kHyO+CmtJP0IgAvT1F0OK33JULvNquNS5rnm5DiWvTFcZMZjOo1YZ7jGKTUaG6yGs+xgY4r5zb6sE5vnixmgA9WRRGw84Ha7J6FPWOV0bi3WGKrOcNceaCI/wNFYq8veVXLv5RXiTMyJWyHoyK9tJWsx5K682wj1FOWf5YPd8J91hijTi+05pD0T5mLU5Ms+LjWivpTaMsINYTNcFOPcRpV2oMkRqsxhCP/NivJyJmEzewWisJZ0IGpVXe8CYR41MOxMnTIobMW0Nm0j088F3zm/AVGmErNw/VO/+ZS5FQeSY011jYfQrkIPoY9d8bMuzHSdfIiivWEmub04fcM5gr4amUTcPVOMayGYNoQwvdTtZYehoCoUGQRMlueW/Zk1xth88KPW2xpRWpWqEaHr2N4wTuDk5M+tC6ZEoSs/Cqq0stZtJNM6izg6pylNkbbt9J4vHygyc5Si5Rq+QH6BZRcyZ3k9AOHu32gPUFbYk26ABafk1i8Cb0VuW+CVQPP26rrmbcAvlhwyXWL6g5sJPoYz4X3NtNL5e+UOMLTY1QB/a1rr3OJDrLeJV8skrg83VIIBWwkUCRySfnmq/m6C60RzlxYns5X/h7RhQ7USQhEL8hecvoRG6Ly1DluNPrq8HZwTZNs1qvqHA8tVKb2gY6ZUusAvEfulUei08307GuO/1WHm3lvhDFAitsiEzVC4BN4AKQB3ZSw75jUD0QRa43WTwrGlkDbt/4TiGh9TL9dppXej0hkJX6Apo6Xm8/j27NhXu60kO+IfoGhnMcQ9QJlqPpFmYYfWOdPLATXIpykugTDfqd1KJj6uW3ntwH/EAdmOBJk94uchNRiBUwcxzvE+cOUi+2GWK+rnZlMAeCOQCcHlWlDJIPnKB0rU7yLFssZlSfTTbktB7ih65eXk9YuH4TmufEq+EQmxaoFzuMsx0ePU5dReXkJDU0bjuJqYfTYwx2fjz0sRRyROw6/lGZd26ijJURvOnKUDbZ3/AX9pBxJwvn4m14jCrj9kY0o54/+2XyJaLpsW5zyBp4OvawDxxpDJtgNiFDdFTLIs8Pw3OZcGha0tG9/NqN5b0TtIqLx5OQ+CGGe0k0H1fWz3vZw2h1eLPxNMcfiLwJhb+PQIaLMnlP9pUzHktHpqgCMkQNwWFJQedQjKkn0MJSQr8D5PqZxiwkWJ48z3a2tkvADxjIZ/Rmoys34+++YcDT8xJqP055Vu9JOwsrS66BerEUpFNMRy78ILEZjUjaia4sVvD4CVUusgPGe0qUsJgj0CugHEVJSlRJhcWovZiQkxZQ3tLkfWpHG3JMxRs1BLUMwStJird8zN4K4WtO5jqVhvZJ/EgpZiiPIDNAVCc5c0QTwQ6ZYomjSHGwJBMLMfXOydS8mGOnTGvBI9nrf2YIk/Km8GQEZ0evHYcJgw94moXveVr2rQIgvgQtMalmu6cX8rB71gpY6MhnQxKCPgRtALciFCCnbQl9eBUzk5jsxIQGN9U/cgkuwYPShyIWvK9EhgxJgt26rltIM5KB4TvpO0TT54iNgzGScugjbQo2HccP9IKIiBAnlNKBlM/eQOuyrChKyM5FtkG8yP+nCTjcLY4WNmrtU+PySnQ0x2H6oqSIOqwAFBp3uSr+BYP7B8qMuGGolSSLX7f4rOhjfzq9lQNsy4R+6j5WjHzK9R0MbMLLlHgC9/ShdSN/YnhHZ0zgCYG8DJ9ES6dUDGWMq8FH6r2bVcczg+QmtUBqprjTF0IaUAHiiY9mQuYNo0gDeaG1Gu6yd/DO0LcvTWfzWQYYIKl9R4N0W0L8JkrjnSKHQFCPmS0/MjDusNAqFMfBJEEmcwfMJ2TaiOvTc2Aqug6tywx1WrWJETWNfZCuyeL9G/Dwo72C7wI8rizLasZGrNseccUyYV+adwViUD6YINQp9VFveic6ObgWsGyt/xLPUPLWP0M0fW+60/wqG8zrIPIs+ALyk6UpBIHm/U4Y+y03wL02xPWmRHJ8IxNOQ6T4b1Ro9XeTMnjCiT7prIvuHGly6nTpCzvBBkTGuJ4grC4Fe4GVVS45MgwDFnbH6jNefWP3Bix0mqU7J7ZnReXET8DbXIabG2QMTp16k8lXHX2MZdmhwgbXx/Hyy4cyNQpb+5Pcv1DWDnqIUw48ok+hbSGUIgiUSD2cST2aLBUVZPHVtKHZKub3MfVqO4C9s4GyP6LeDSqmUIDfRBOKuxILRJi4nNiKQjKlWXKK4iCULKTpCZbfU3KsWXHmu/q0TLEHPPeDXlKJHfqyoAIDzBMUS/JebW+p428jB6d7nZDoxwaFF/iGT9GTyQ5EHZiNvJej1fC/hexgk1M9yMHxCk2kRIqMdUUj5aaTadGY9J+kXAreOcBc3S3nfiQ+PCSgQnvoJpzkvT9wVZaky2rOSZJuIb1vVwp3BK+t03Fo6wU7ms6NxIp2lbu7QJpgCz0f4ARxoWnlL/J3+m3qbgs6I/Q9sX0PXWFIj4C0HnAYQBMFJxVd4NtdOq3T6Qg3TBdAlDG/jy2kWAsNxzim2GNUZ62PTI5EPYN7eCUGqum2zPLoS05zdaUP1EFde6QIvahh1PBNRzigdCFNLt/W1eeUlBwDZQQhcw0c65qLxsImPiaL1QolTJ1Pj4+mz/gZYa/QdSXNjzzbsf98w4gjVVyvoZbQoyEEfMgJWXVnbGgI1bPgX5akzP2o2DguKOlGKIMgnVCEBsNC6M0p9nvq5E6ZFz0XP3aRV8EjjPbhFD9TWmGIqdAmCfNLdz2AJYF6oSsKKxUxzevJCp7LrIa4LjYgI3neddDNDQs4Qj2gLIOGSdqqc8sjKPxxdxxnOhOrK4q8nM3gnp5U40VzRTgD3SB42tQRWT3+XSOVSuYVI5RYVEiqYav1l1ZTnjla/bdqjhVILKU/uuKaTva63hPkKB0gOayDpqlAw4FU9I6YsZ/TxUan1dT4PCZRhUE0apJwJCorQFkrm9mkG2lIusdf4CyimAY3caopOyiQ364ZwYCFh4VLYKvNUOq5WwK95NQHgWIJyikBheyKstxebkmWgCbXoKKBjM7pEBvfWs24GWs7XgjbpO26pmmgRzaGxVyHLGW2upNHc+hACuHDbWLbDloEU+iVqjryVtfMNNDTRHEkBuINBFAI8jwvoSoVcIQQRjaaGwVUdRzc63MOyzaJYfS32jCr8tB+GKiFKvWFAsoF7kWzp31pAuxBI4XBbN6/5xZoQlvwwwmUW6YrGgB4HdKciqUFyrBTHfUtaFWj+FIYoY7OdlNmJzY1T4hwYU/B+kd/y9hgCRN81VOnFyDmR7HPKtIuHGOZuJ/TatGiHfohgXJJYYIKn+KmDNyzgLb1RCKIPjzLnjq5A8mG3qcWPMvdcWzDjMfnpuoYtcb1iGi7bl0D3Eqx0ikG5gbjFXHVVMNtV0+EPqSUUmmiiUYGInhC36VYIywsTAqSHNuDok4+EQKLXzfS7R31RmZZJId4YvRXLIe8XOSYHzRF5ASPQDORmFE8MrwUM1WwuzOrneNXiGZbzbqeO7G0od1s36dZOkq8j+WlZhA+33TikmCCIoeAieSY+1yw487gOWZzqg/JcgzNoFjgjEa34XYhoxrn+ATeMPyHyBdmsocSbwM2J4uw0uEEQXtRL96893FIkiLN51UlqR0oo0dZANERhTaCH4PpcOAeFgfsTSHVfRggT7Dv5QozkZQkCbpjee/cMY5NBY+XPUdeWuFZmrjwuhBGqBEJdH2WZwV28ZY8g+M6msQxleArz3sCKuVXGGFjR1PPrzEMTdu2TWt7Xw1bcQd6DlgigmAhC4wbLEJTUvCbQL1LvtDtqTGP8dyb1iIw0FEId9izDWogj+Nm7rZBYV1cTIUbHq9qtELB5PojkNmREh1+P02AoxQEvUiTyn+8slFaU1h9iDau7P6IziUzB7WnAUtHSsWrkH3Ig/FfMN7crBF6i7GTO1t5nmHhWLwIjyAGEqsfk7ZAjOZR5CWuF9n+rjQsNVVAIH8o+fmO5F4JRZrI3vGjhEzV9ncG3MT0hx/QuPS0YcIBhHwnqTeY6SkmoHSEaB4jhu2AKr7QvLf/ZA7pFewAnvqm7nvxF3ifoNOMqd+5dt8zBVOcHDJDdL2OKNYVT3M4a/4kMn9ak5oyKTPAdEcqZnjTac96TJXqipisDIu2yCZRt/NgmfHt4oK5sW9sQ/meYKzB79MDF/Dz9I5wZ7mwHCwoskJwZMn1ITjEAS2TnHRKgCLgSqJkMrbvl4t5mlDlZ6KVYL/JpnO6i193VkP9BGxWnsVYFQsTJAl4UMH0xSeihufsCj+zfr69bPLhxzGLETCMG67giyOrZGR0BXgPECiQJQTpQjnCJJW5HSreseUjQFXGoI8gm5O2FtdrcZFv4fda8aIO5l2HjivneQ7nWgOwA7cf7pfeDug5dsTm3XpxDnM40o32zDC0TWP7oapKUxQs0QJIt/gd6G38qri5HEvKQM66rWCdDIzkrKsCKW/ubMyg8g/xceXci0G5Ac8IyHuqpGw8aHW2s2biKQNPAXn4mcVylqzABx3KIj6IanJqQUhllsjjZU0ChQ/LUmZXxEBoVMTDVmInbBcZvlHsJfBUB03um6ZuahC/mAQrLu/RXpGwiQY5TCupep5MyA/HaJNQuIRqGk6OgfT6PM9pu2vPWwRgDD0yik5x0E69ix6kr1CKqtLH3BkS+gYCBl6NbdzUfcfGkkh0NSdsN8HPzOyDGfM0HBkiwps3CBzcfu3QwK7CNA15CdEbnDTx8edgKtQnirH5pVZxfzu97AKKCGMMU5TOe8GJJRurQWMxLPqQC746ViIR3w0yK3y7wLAT/A07fjDIS63BhaKC2j/uRlTYHRRDyvKHS9PEnD5zsiyKvvcgM9/mLNRfRddyAMnNlOSWgSbAKf6g78PLQHnJet0RZFKlgwkHddg9eXlhpj4Lw91Zi6nY3FAjinnDWMj7wWOOAqDMGbARXRPdQ2AToaMnGl14qeK047I/MuARJ0eWTXgVf0OflEAXOchaBCMi6eOzPlRytduDDCMgAH74yjLdvHG8jwLjxdBcGiKaqTtddBG1aGpTjlYpcsqrl3PwMqum2veNEyYiQJaIv493wsYYdaAihfyeuYhjl9HU+hHH4nNiB+AQue8nZeJQn3h3TrR4ah4AC02nO1jzYlZuLZdA1Aw99kliotcRa/I0rPK26JGq8oXicmzLyfMdpyQLrW3qDjEbMikB+1QgThmEbISAb8SiZ2JN/KxcR/KFi6DCNIECDfkcnGg5oYrElBknfI7UBaXORfZJ/DZqirmKCs0HBQ9liSLx6wIyvquWN6hYiSDWLd73YNnkJmHainpVvaL0FlJkl9QClCGMS5fRbYi3aa4yMRWOVAVu5rr/uM+csuC1h0krwNNYpNz6yQuHNLpY/vYYKzVyfQQ8Tl/va0MT40E7CHMJ/YUssSLa8tYa/MlB5GK2DBfoMcmJne0yLxArrQjG3VTq8ynXGHVglNEG+tHUrOlZYJjfQA1Bfckqd2fPmoMtpCZYTYSb1omttnEtNUTA6TN+BRz1Ys49Zwj6u6sHin4ytYB+qhxDdSeoJ6vrRr7xkbRiLcRrz5QigluDMHM+qUK+Yh+gujNDkpl4o/lV/CfU/wLlJrKPFPWoK/2LKY3c3T5QyJWixr/S69HUUfd2thgTjc4ODW1l8E0tvBPo1wXZGt8icirps9zLaL1X7bF45gCKiv0J8BdPuvSCOqHQEaTgjCxUTMvzHCak74bBLqpZNZ/R5Mg4Ri84Cg4IhTgB7S2ESNpMcigoUodP98q6t1uUGJ4s+e4G5CpbizKl/V4x55AJFx1My8xAJZSCGRQZwXTKQBcJXiRYZc1wFA2MfOU6OTeW4SO3lvpJJSowWlK0VGecR8DsKcEi2WwhysHV//MrPeru7dbFR3/kE3bHiFyUWXT6fLAUjn9NbKloAlj4hgSc+LcJbFaVWh9NY8R1XOKPX7JgRcbvHR7xLpgQxQw3DQsLuEUC9DpGXlQKiAxF6Qrucc62sICFxoQ7SExKTJGn5244XRWlSpUTPTV2oYnWxWur7u/UuFFoU67xg49v6VPr/FR48p0yVbw2rQNiI34ZeZiiXREugN/KGCblijdZmqcG06xZaZ+U53h9KP6VUhuH97VbSWJk/IvnSKOXUTue+awAEMKpdSU3hJ/7KfPpjn4fBZtEMjzZs4Uj0VEoZQwESrnXqrwPGAqoysD0K7qMULuBfCcIBp21RL0CgxWy8awnmP7N6xQ+LPJX6Wfr8KOeoXhoYfMcPTg1af5TN+tDD3FAWQnWhMWJ7h7mX9cC7gZndkAopbVDbpLO2uViubW1lfR26KlsjZ8YpRs4iaHhGT5bNtaIIotHLQ3tOlgNFHCYIhTBGhWX9mIxmDM3EV7xHUn+qd/HoWT/HJo0p3oQ6FKs33BNR9xd73CauZEF5FdW8xSeMqd5BCOOw1yOAcvsUxFGVs11yV89RJeA5H2S2uHqTGtmzVSE2jnWqKeP2lTOExw2InR837FT1/JTe51VtfxIn5Pv1ex4FR9ds9rFnDjY5ZSAjE2aaWk6kRs0jtpeq0hKoP+75fJtG8NmGO78Yejruu46KMRBjc9EdpGhD40cONLE74yVXBCSY6FajgG0xmCHIs/P33A2s6azvUiNUWTEx06cisbPiifQY80n4arK4Yokj4aNMwM2644iAN3OGDMRt1CBMjqhRykdVylgvnYmlhPCigZUI9K/90hvUtxWNuVI8VGP5e8cYlZW2uU6hxr4MXlwjsm67LkIFiYGjS+zKrYxX43541rIyFuRNAkivjI7kqbICqjkQsk+U7pH3CnWT5hW2fXeV8F1LQbkAhLpKgvOHEuE8RHzirFyPUHHm5hDzJkD/dN97KooBd4W9rRYC72B2pYRWlhtQRQZ4VTu3gLVSiDyix+nSW5s30IOWnf+zNksTfuuV09R0xBxUee45KVyQQiBcvDHgars31iRtbWQQYqeLDZwRnZbcIcpURosUQR92xDYcqWB9LxHWhzHjjByRKp03HxKD1NzEjmJ+U3AfkMNItCwJKztdjjF4UNZ7PcyxWS8bRnk4rOC7QSq8BV+U6VRs65BU+280M580huJ2ATbD6zh+xWVd7LGDqQG+LJPclMXwY8UCs+L+S1IP2PsISNnqQuHs7CTEWEFu3Zq5WNYRMibpiSN/mlTeC8w80b4Lv0kjrXJZwDCadn747x+ki3oysjA/SDSAHkqkEBHqwLPyfO8KIq6bbM029pe9uS6DELkrhQtvwEDkEZzR2Yys22adCECd3I0G/IjoMCyLBmyrOtahiHLLtowXTJVTCFBIk8keOOROhssrFkSHU6Wed9VZKJqTzWv7VQyD/NjpTmEauvUKo+FgtM13TTLBvVRVaFjVdyTgFouyKKJcCC9KdBhPJyWKmii/gbXgKMbohIAzMCit0q9Eu7FFhS4xIF6mX6DBueBzTq5Ol59CGeRQQ6YaIWhqU3KrN3wefA0pXVtuIMW7UKXEerS0zP9GLjM9A+uxk+gu1EP+g4PyiYSK0eiOuDbwuxCvsy2bQddOAXdDOf2Nm9tn1l77szpqppZyK3D2WdjVJDdbkJRqwwG6bacr70cBnwdnUZvx7+SVjqMfJNTazEx5c7UmBCJk5qPnl1PLqPziEthGTBCboRWZTgaRZHaL+BmwCPTaHqYDYtqIZmS+iLPjVwmlHumBIQ8u/b5gWSxK6SnwHPIM0x97LhioVivlADD5OxIiM+2m/NUcJ85TYwqKvZdb6guKld+EVs4rC4klB+sWhBFYw3Diaah6wgYQp3sHEGY0QrLRG9gzKITaeVb2L/aCSPOFi/reMtoKavIN9yKPGhgbZi83XoSDTcxCWRX+QiRHlxohVxdxBxzk9obz57Z2d72tplrFKYtNvebfzm947Sqq+p76GkLtCz9OTtJnW6k50zffsMRSdzRzUeneuybo2DFqZSukeZ5QSW1NocKlTeSaDIKnI6H4OuJajnj1fOwAI9kqQcJUnSpsSnoqm7XsNacQKIylKeCwAJV58RqQpJNCUaKvJFoqjwJElVgbkCpESRLKVGMZCDrVqr8F55re9O1NukFJ8CuVGzFN1o0VYw3nHjRrlhfIc0RNrqgzXxxahNARBRVb4boRM5DtYgbVzgct8iqYMT2Gop6wA7oC0CbNgT7ZwLWOCXm0EDB8HOKM8hK1IC5n/RcO+RnT57cmi0hK5gy7EgRZwrUL630IKdAucVwooDO9xM8ksB6tXgfQhwbBib1McYYEK2QBpM18rJFinR4hXsLb406UnJiXJwoBivEir7o38JZf+5t4jCzLjU4eg8nVSe+GBvJcs/R+apbKSHw6BPY4qDrAmjMYpUMnFiUiqyi0RtDlEF4jntnD5V0BhDLYlkYcAdgfywPkvaVFmySZcQgZArhEqrOM1rQCbCN8986ZZLyNNquEdNPJkTtLMsfRMJBL7ps7OnZ5fOVw2KsJ6qlkH9kqGOGPbpUpUkKjGxo2ga70otnS8kpzWFgMjnfwQfxNDQyn88WaZ4h9aJjmiPv5Lz0qDcvKpTtOSF+tfHgyNzLIpEWoQ3FpSF09WYfeQ5iDceJ5JiIJpiw08zkX9aoiHqxii4m1yFsAwpoSSQlWr/gSXpKvUofXOHiIJO63QYn5/QT9QeSaCLlqqgK7wDyih4k7h4PlHbl40GsYpU/nk804sjNyTVapNo8KUd0hUKhkZj1mgOxD0yJwjtQBVFKk2D3plIf3c/BmgVRBKResPugPEUwD8xerEcij1KD1HzRclyfgJVpjU4fC2hlAU2S7lj5dOlQZPd2rDyLhqxll/zRKRkakyOmEvyYY0UYmGHxJLun2wnhi14K3NZaDIUENZa1ky/FAQb2fqB9gzGkOALj+nYE99Ps2P90DTV78nKWZ2K4we+pAfeew12F93HzrGGAxyv5wZMnt1qscm26lz6PkVj6AtRY+6Fer7u207TmgMOQ90uufg71wj/gmhKfE8pz1IUcIpL1JGi8xkJVRC81elcFegiOP4APTGdMaVCXM5z9uqgV5PHCW4Do7bDikZ/9cJJMPDPj2aJ/xqJk0+Ru9JsEi62+cHjFiXtHN0R2Bu/V9R1Q7yYXLZRG8UaHCyWQdEnBR2Wx3AWuYJ6hpoWKODHKkVIYhj6Bv2K2va/lHUzYCNcUacD+ll5TAPOpQ0ivP8nfWkfWPOrRQ4GeAW+VfaRkpGCVnP8mTdO8KPK8kL6eoxvIDwzdPFaAKtJSv0+H9L0GFRTwi6ZOOXXpHyiBxoWP+A8aqo336HpkrXeU06z2PSlpXNRG1yWTcBNLG/B4ouJN1McxTJEcIUQD1ytMFHNvwa1mzOgpLnqG1UghwRkYuuqpGR9ed4jncTzJ4XIcg/twPCUolsyzIBe6IW/Uml04N5DdXh9ybNdVllEXa42PgN8Z1MfCCo1QgSGxA1gxMJNZCiV1XMqIL+YoyxCihcSWwFiyAUkMTDk+xVyHWuJDBSbBehJQk3Q0rdo6iudnwyfXIQ3VqcSSPIcDKZRjbA21SxXQOGatpt5XmbmRMSbfxExN9w2L1Q230vKLp3jq3wJaKvA/KujvuLb3XzObwld1JVdpQaXunRsFyTl0XOnSx7JrFcxd7ROd2xJ2V3VBXQlvhbzbMVXeWqiJIQeSe02YzN4uSRTNRVtPUZj3e18TWakYssJUqVzIY67U06I4l7hLHE4YzVoP4AluES46ZgVL+/rUGIBYuTASTjSUleVVUZWjYxXe39E/jqxW34E6nLQQ86pETDRVFipg1U2DDIb3UsQiY0qItPx4D4znOOAJEWsmaAthNQgyidQbmHsyWRvSWqYeN8n6ab95pWqDXu9jvO5+egqC0DUsW4YVswF/g30bffqK83qwOYv1C7U16hz+XL9SyUwGbCBrD6E1aublPdRkMTWLlGWVBpU1DQGSk6Md7IO9rpKmn3NeYBYFMllWYACBRBc1JliLCKkxLWv8/Mg8aoHqTglu4O67QbSrJXPmBiM0NukBnFeFRo24qV18j/paghacZZCH5HoV8nx6w2/qBfUbYOktImP2iMR8Z1O5RuG3HXhcqBYMkohXssdbV/87ObANsjD4laeczQEy2KhLIHhuC1CewSkwfv1NT4418s0iQcZzbS4Qa9H+NUKmTRsMQkZMsEwi4kJ2ZCYeKeb8kXoo6jXbBA4PkkBNJUzrFY1YuydG+FkvLHnZfZ9G5ZUO5sSLC4ScQGgUSsTouwlqJcjk8YEOw1GTQImPDp3t6k/ZKEz9Hj6ODjYLfN3MNr4zZ4hYdvcEXEDdk1x6JDS8tcw9MxDlhtqvNQYbfNMlKg4UclyJQQa5QfpVgHFijVI2i70W6Kg3qLEtpp2kfRMkAE8P9CQxNOM5irTS4xZh+kzvo3XrnqZIvUURWILXd3hfRmD0TMa8rleGBxvVK7x8J6fPEY2ARWNBwfKycWI/a1En/zC10UO8pGA1G/YataDyGU4ylqDipKyoXhvxYsecbLR/8A9ozh0Ee1kNlw0iDGdDVoL1DDR0uMbsX/uaJvlroPJ5BT/C9UavIBzS70qv7QdKCodVLOTtoOeQzdaJF2Lhrwu4wUal3oJgL3EDW5rhnK2Ca+sWU/tZKxWDHVLQtRyqL5C9gnzXt4P6twPEHVRYNdj0fmwbSyvEIx1r4CMzk39jTCkJGtCcwfYFtSS42ZgCN5kX/grX0Esv68ZZnXiQr5egdEXXY0H2M6u61LiNs1ox1wxoACuLBvl7QbKWzqQN+SE9Bo3lIQW/pgyJyz2ozenjQmqCRAfFSBXoZQFqwPEwPS2M4gK3lR9YcL/RZI/4gfUhOuVBEAEaC/1I3m1azVjrDjbDhsWc6j3hWToi4KRcTiDhwyqLrnsnuPQAgtZDAXdcayhehssKClVqoKxs4CcLNttUmdzRgD19OGslMIncjR1ccJS76tOAp1xCG03PgOGOzonXx7X/wg7fwNVcL24qNBmjRAOmOPGUmJK9QAqZsB/AxBAdAz/2RP3CHr3INxE9YshA4YUl515wMgtCrF6u0HXTE+ZBvw755B7h1HuHttM0zFKQ00B8Fhd9oiZUG0ScN4ehK4Zejw8h/Jgp21BBY5f5BlRVLGDjeiIBCfl7+mXddEwqByMyHivvelrlKzUotlF6O7R9lxWFM0wkIQsupd5ITrZE8niKQUV+76ntxjtMBSIDTu8A4NiYE4vZQk1iAMEH8lfew6Wjj4alF3WKRaupVDaqcgygKz7Li7ygUnV+bkcv5a8ffRhzZlEbFAj6eu42ybs2SSJHxTwDGP3C8gwUkePQoHt9IWSVxKsfI5lHDMunfQW0h9pcH76hM7K9t5kNDyp9hoPBg0zgWO6pd6OlxQFzHFHBuvk8lCGqilM4gcmo/Nr0NGqnwoRECnWkQIumQUxGqyYPr2tM3E1w4mQAKaWOwRp6DDxD0OqgpWqoVPnZOpXXQmeGKETt/lGWfzjkeP2VoqJgJEp0OqPXn89fUKW4YUiTHjaJeLPYA+JXOTAu9OQ4vSkaX5AAIh94ZRJBV6CFYI+FIs0z3StyE/U+w0PT2LEn6X/Hh8Po+E8UfNV5iFCxAspJCg5WoH3CxeW422hMiHQ3KdALegm0WoHWOtAOD7gblq+3HTuStCMKM5OgSBjnqEkaFOaZEwCbqNfHH8dvjjmwnOfqg/yTczQxQ4amI4oybSAc96/yRYR6vR5WjOpQyr83t48jZZ5SboYQVlYJYKGByNTeShFe2LNuXddFl2FcidgpzLmFZAZ0WLixT0s0XYDZ1VFQ86iS2MY+ILd6AlpRhgWlztA2QyE8frqCgF4fdkarCE50RWoSMQ7CSwIWTeTJJmrb5C2bVuC9K889fJqGA9454kHudn67TdnSzGaxP4qzAmBZC64dBfUCW3D3D1DCV2IS0e2lSgL1P8eKDwNkpyXYHj0jsK3ftJb6yxQVeVNYsLiCDy72i5MtiQmbKA6DAb7UR8SeRdNU9tVYPU28tFF3CE0yZ+FNLYW63K1GcKJfTvdrAMAI+GvwqUaHBGd4a531NZ45xfGYcULHkm4YfL8h9HugCs2Q1uA9NIDBjSogR2fMUsRfHRt3otrrkqwo3RcgxbRjpAs9mhdfTbl7vDwi4FvjoW6I8vHt0gR6LFDQSJ+3SVZGn/v1mFTcRcEMWNjUcTwHV+Tqun9xY5nxbQRKpUUJhIiBfLIiAV8IQGWCARFhuKgw71CqfYVKHvzQwz0wwRR9qNiEBVcOmS9KYBmujDCu4uDGw0xcdDtcJynP691qUj2TCDCIeKlQpyINo2ZipL+OrIVggsOtfqxybI/1XAheeXRFhIPAz8Qd5QUWY88D9IEQGaqKqOkw1WjsVL551J5nRnaTtoApTD9B8t5s412iIy2hUkMwetoTdFOlNXFNIdH21CMUv9QxArWhgwOz27i+AWTVFGVZgGMgBolPqNEbNoNermAS/aJoPhNtnWhw4e9jOyW4vU8H8KOOAqUOGw+V+KzN8zaDuofOn6AH6yEWtGouUYF3uCwubVL0WdAZqsiuEFJIUAH/DiSgO9dSD6yoyLYEf6VYQ1xB3fl/7Gj2eUtNVZT1CYzh1G4wbB0Kn3+NOUFwL7UqoQvXczBC2ZA1j0ldwcZyhTK4VzvvV5Q36CySSfRPHqCaHebbXEMWRNTr+7mK+HUroqjBcyxBnIgJ4ReJ14hA8MJk1Yx5808WivbomLw88MvTeoQ0kOokqPuVRVmgJL7eTFD9sPADu+kcxbWiAU/zAS90R4JdrbXnlT5YEEfo1N84xXleVJVNswxTBFrobK75qNd+cQN5a1nXROL5JYY9WGhSoRQN90eSl6f4oFKm/WLi7vR5Ua5CmLBvSbQJ+YKi4eCIrC1PXfyxVMferBbFh1Ih9A3Dl3OVdDQwQ10mPzOtpCazmJXpZ8mbDzR7HgsrIF+mD45/ktJkQAJHPVR5xsYvEiqE1Gok9DIewwcmdGnaGz3mnfhUJJkb6cwwcRMtKeM7u5C/RMp1PxBy1RZFwf1R3ANDPnHM4oar6X687t3g+Jy6NAgBXiNi7K7zZaWCc0ORw3ov8tmiyMFfmOdJbayFCScQLT/aKenChgMJg5uH4sOYJZ1Z+IF8B5q9yovodkMxs9UKFX5mEiw6lkqlAA1xUBz9+rPu+QhqnnEMnSkt0IZEJEwyZ5HknN8XXhb+FN2D39GRM3oFB1A4GB4ThUy9Tuqw485H4lI6yLeoBgy9kYK0ZPfugoqesLgYJcc4Of+yI/6kZYufFP6IGRqUtGb0nPNzUlHciXs421vfTVEDf+ty4WQWyS+KsY2CvFZOqQ5HFdxwQhEfHZuoTUWTwk/ULHr92rMOHniAjlBlwiYeZ49js3BAvAEjdjQbeZFDUhtC9ERyEOcThuJG7iBQVkHujKnKigC4+n0jyFyYFDAxQD1+KpvPpdQwfdgXhYlUD/8T63TH0rTsGYKTxasbj1VJa/fSemUmhFo8BSJ+A7HrbosZmLARh/9/aVe65jiuWy3ZrupO3v8t8zdT3iTlw34AgnbNjZI7XbYlihuAAxAL+ag2EsqxltsAEQJpemgL6uZVukzF4zg5Jhz40IIQVDFaBTyy8/zZ5diSg7a5NFcF8nkz5PpH3kp489kkm/+3qdV8LnU5n8laxZf7bHcgw2bg34lXHHT6UgflLYJHaoFZTZszk2vBH+WhdqPBxjTvy2UlCCeZD0G2xZm7vrvkL6czqee2/CXzGB1NeQ9RKXcZZ8mD+jmDHUJV4zibB3uQufk6MeHmWn6FVvx1CU8jyaabAGMONPz5RertnPqIm0qcds+UwzGcnOsGAmgISc20x8lUzL7QMZTl43CgUJBWc0dOOzDc2e5XAcsJU6Smk9izLRNaFV7+VB1lkpwyG5ahjaPnGDpev65XspeKZ5+OIU+wdTahkDwhHyl5+fhNAs7wa2XC0z7ATRPPUlSF/L+FNVqGBzp4Ij9WnVcok1DeHztAwlMt/gUh7ijXynB8c1RbphAJJfbfzxqf3MR+JbVjyn1PDeazQSd7NnRUNXY/XB9WfQqSC2SeceRiF+JjOaNZ9cqQOqImHsMJAw9lPb+hHtKtp9O+rgyhpeT9+OZ8jOaCS7wyy22+XfCU981W41RNr8fzwYn24HTYj/PBgOUL7nhn4qsP5QK43gOVOf76UgdytcgkbAA8p+lm2/NfqrxzQVm6/O6GSs0T9QS9v3KLlVF5gn/I+KtFKG1oUcNVjiDRjVLsUKZHGmEHKdfhhTkru1rEbgxKJ7mk2Q3D5TaMrsXlOk3L0syrjMwNw3WeYcem/toxXfVF91aHY+rZFQ1YBzQ8kT3V5EeG0uI/I86BHjKtlAqsSvRw/okNOGQ1oGCG9x3AD5DbPSpSwq2+HT5tcPYW2PbHkzMe8aZy0JQSd/F3/B9dzymcwqMR1qQuZGq+fn1/M9SHbfqedbt1Bev22PzFRL4fX5OXIaMZ/Pv9dBVp0P0sEz6emQwPCDffbGdDVanAz9iKauN8BmwvogzjcspU5wMlbHAbPIkpFnbAhipZSDKtx7qyL7fZnYRQ/LCvoJVlnLs6Cea2XW6YzKaN4LdwOUONiSmMf1LYooM5ny0KhX8lKmCTRC56Yqs2HlxwUqSDHO+Uu40mJ5N28sHBc8bgxmBgSP2mtO7wL/u2PZ5Ev5wqtFmDcG4tgQ8Z4CUJJtTJCY/PlBD28vX1JYo6qJ+la9mQVMTVHKfOrhF1409lOqrhAelmYO8zaDh/Q3kNiBX+CJ5P5DypPgKIFo0ETbvVJZHiAOfuZaO3gNp+y8tPDfiWD6KasTSyYDF70t26qqRfML4pg2N8nX3IPs32lIgJ8x7hwQzyVWYhDIye8lEaUndzP+ACPcETNNoOJWoiuSKKDCQ/ktSOquhI54TOg4EJLqJVOFFAvzQGnMrwRt5Vbv/IwTCw+xumVreoSaXjxXH8bAUtbgXwvsI3gy3lHeLrLBZMnpErke/1cr6UM3Wb9wZbDp0P4PV7Ki5tfJTVDRmDztD9UrhKUib73tgEmHuDbQ5LlmbST+1VPrlxUGwR4Xq8JHlezitw2ElaodB6wG0NLWSm9UEKLbJmUdde4eAbZqHx9Gxh/JFAsv0DurYLqTx1Ta8rA4AaF2Zn/ZAFzexlYYSE15oMlv8z0oVcQ+puZKpx+N8lh5nldJzJPee0rhdSha11oBZjvz6v5jb/a/NBzHOAC/6Jko+yb71ll9dSlkUEBXOu7/HiZnhmIqeLEvPMLs4ELqJQDHQSxEnp+WRUoyQerBfYj3q19D/Ii+Y3h5DgsJGQa32upp8ZR0MLGHXrrWKt5qUJPs8ULeZAIwJwhiczFtFvDt6qOM8VchHhmS2FLcM7Zl9LJJPEwZqQTLkdhQbw2GIBSVLzkwN3K/aBHB6eT5OQSCN4zhuxHZqiLCuaT9tZhbrqZZIrm/yIz4R9xtODQq7GPP1V9DdJcILQ/WJHt2zdQdC3NJxWBwduK2Q3UNKjbdPzCrWH1ggImzBbVzvGniJFfpe4mGo1I3XQm4jP95LxF1erSI0fW+Scwc1wJ2+R5OwbpymOxrAXeoPhOOee2cdLZRdU57Q8Okb8Dt6NhME/OSxeYoBxzTWUrIalBIj042b7Vu1FxjDcAnsiN/XziSMxqMyt+C0gh0A0dUo4AwYwGjHKZVjOmoDy3gr9Zbp6WBnhhZHVxNEhwvOE9zCLgl7nE2mxItqTOwa3qLvM0xL6D8p8FzoHditCDAVHCcQbKHZ26ei4hxVaW+Q2u/JF5mEV7DLneboBhMxAols6zOebxa9a+QplpI63trY3i92cGyZckb6ZNjcTvPUbd8KB5jJAUb5a81ekPqc3eKFazHmh/n9Jw9G2BdDJA26T0AOA2FcAuwtLwzUL8y7IaY9Q0j3ip/30gYK0r6fn8eAjxhCy4/mSX0i98d3URjjHwXForHi5ph7FgIMWBfTvBHAtSgUFCr6O00614z27q5cu00Pf9OLh34NyYgXX8KmMrkaQYPnFRazi1zSRg+BR3evEFdZefn4t7NXtbGW2kZ01s+LMwTKMqPB1L0nAcXOR24rSNJCRcnC3tNa//+fVnF7kmXgPgcb7vWE9aIcURVme2XO2fc27V4zFYa72NKb5ZSmzWhqPfekiTcQQ/KzixSeV4kzJXPKU6DUN6tejlM6p8fSr2c8Qr/s+fdccfbSTnyvT81eypVO8b7Au52+kTu4HbdvzWceueluRxnnxBcAuTMD5l2Rjdt3aH/O6FrlH7+dMJ8erVJm7vDJ0FAAtxxRmhLwXiEuKhEgKCMrhIiEK2L03gNn3zmAD0d+nIHwy/DenQr88fyrwd/ZUnosRJqTn/AiNnFlMiBJgIfGWdQ1cYsTjeo/kRfSCevZmL3M6zoRRRqUcQJMu7o/sL32+0MkwlX3XeH9OukZlH4RXHO3BYLzVbEZvsbT1KukDBZTHN034hHG4Zp1yIwZjZNvbOksBs22T0D6b/WgOPDuQq2sRswt6aABIDgJOxJ9U+smMeJ1R7qGkyWEARSmguRybXuZ4FQ12ywGaRaxDUJmYnaXQsUD9+QSOV8fHjS9MH4Kbx1mY4unxu0L3BjVLi8E4QdwjlwknO9v5Y29MH1TUrfMf9QFzB0MDpM8KYoHKQXgXJhJE2gjGaMX2owYIeNEDgDa2HQ9OZk12E+YcEkhrI18mZTlSrwYrpnUHOMqoCuGs44PYMJxNG/qoiktma0CAWGORaIMLnsipJxT6Dg0U3GZihakSpWqgQMCJ5suFDY7bN6MQBwrSNqUdtFrGCH19XQHAlTmrhIu4dlkXik64UKiN5/LGNTL7AHbXhH/MYZnf3KXfXc0yNaQ0eCCVJjJgKwrd/CoyRoZbV9B8TPlvkbC100lceooMi6bgZj2urXKo1L/mED3BN5wazYCclEn+U1jzrml3iIbZgduz1h0avZinrby3nkSk7sQTad91eDq3GVa7gIaz+509wKSkV/NvFB92nOl8OOsIOFEqDW11jiUTcIy45TxZirsdP3NlMxVn8UK1eu5UT9FAaeS+rps5vTdYaTpM84jzZeF0sES9brVLJgb35cubWU83hwFWSfzpwnuqKK5LBfeMPxWqPkZ0VoSv/Nqz0MRoFdYM3FCAHOhMBjpRcGkpHzWTWqiDpLAKRwZ9ouxfOMr3Ybu/BNpj+Y9OXDBGW8+XC4euU/ZrrnWYxnuIfTtwSnMNi+rTkzfbCJxrKgHwQ3Ar6PQAKuJ5u5iOVFVLNVoujzPktXEal+fMHCnfEAEbQ4P9PK4FDKzDnfG2Zh8fx+P5eDweOsAhpX9+UeLsEMxmk6Uph7n3JHq5jKD3O5pNnlWVuVr/MYsn/DsxUb+B1eNQMuGOYL4TfrJWmFu07Uf793Bla7NxZnf6IfDSMPrEiBdO4aHD0SybZr6Io6kAMDUcYJjO9hAtWc3ylHAChnXdL2dOJcqpK1lOyd44lEoODnkvg05bP/Gb2Zj7GDCItEJQDU5mzoP8ZWVxHbRYYWdkH1oij7NzbuybJZU2fC5kGO5xHJzjOA4pgWtR/dHlgWm/j6ZGUDtc+7G/npTPRe0Uuv6RIcmv+k6gXj90kDRaYo7kzIcRk5xFlNgudaWRZc4GMP8Yjb97en5Df1zpzRa53Mxmux/yUx1MdEbgm8C2D33k3DjhKzJCT8wQ4/KaGTLXT6j1o+3naKDQb0C42lVlBuabhb/wIl4vFybQ47jTGSQ5ActZw8G6AFfbiwpd3ZVYS4Pj+4krS/aO/C3jvn0He9Gtc5NXChVQsNd+bFQCUsu2wiRgyj+cc8pKyUJtdFrCpO6fciGgDdNW2sfLMYPPp4cKefaj8LV7Tx4DMlTdmdzuLpJUbcRQeiucqaf6f8FM4zDsP7/+jWT2Mdl7++Pl8f5MxrGv3Ok49SdwnrwDQWuARbETIefIWk2FxuDoNx9ZWlfjXEnnC1ZrKxq6F5xOAZoin0Hh1StVP95elxPlFHUpLUxlfbMkRbOSXhbk3qD44hxRWbGCEgdOzqgsSEPnv8KtKiN9ejhnwgaODFmuorck7272N3RjkmdhQ7/oieeVzgRKAlQQbOtsG2m/dBKgwrBsl9gz7y6X1O7VSWlNL3R0NrX5wJbpW3/nltgO9he9zI2Vs4f4BSwm72ihAM7+xik4L7sjVH4Rv16lHW6bMjMjQ1O+EjwvvGN80EjaoHd284n1Vyy95OfkUSqgcb78+UOo6+d243q0Cx2fSgJ72W7hPFw9LEqnKg+ZXmYi8147mkNbl7NFpZ0S0a5pTYzCIFA2i1qq8MZZr9j6jp4cKI9dRtGJILv7a7RdglGJY2aPkHHR4/3xPZHZtr8eD7ZdGfE2c5n1jWHGbdSpC5wiR7KKuyNafiwkUNGJ4o8PQqQSxmcxO+yG4KuFCQY/h27m7RC3tfK5fvkRRIBY5SJbglzSjvPdIlquWaxDt027RwbWpk+D+UtyNBmwmgmrG8HVJ03tw0Gw39+UjPp2v7M/n5TjihRKllbPmUSdxAhftPdPXQ98lDrW3D8gUVUuAN1I7IiPQ77Im9vAat4cpgkLt9PYJeNxrqgIYyO+InmSRker6WVMM9g77FKQFNyFJwUNPvTc2ndDwDNDaImKEb2NaEqMp2SaFIdnM6Knm6KsyzvR9YuB/utHdFXrD9GbBCyHxxsMVzCsfec/xvc6FTKZpSUXfFYMTb+2M8dZVRlszBbGnXDmV0Ga9q215hswFVmrkxEvlOhHKXVNTV+u17//9ff7+wsBt7ejiloX2uEyKuHpySC8aXSOSXsSIuTN3UlBhg7NIEf1uvGvwhDn3x+aZ0pCbdXgr0K4+ERyxkL1zPGYQt9rukrKVtJeadj/uCmfr9ftduPKoykZeX7YxU6SSyoBpBtxoGxmVPZ5Xs+clEvVpz41btkceSW7K6Hct1dDRch6uh7Y1DdkWgVRg7mtpBV8li/q7E+YA/7tvpRqvNRtVZ0Z2uxD2YVyGPD4UkfLQ8diqyfuhs9b0JokkNJjLfI9XNav6xdFjEZrYeYZ2sq64+wqW7O7sRBgkEwSJUK1XKst3FaBnoLTJH83nyWqtboZEVvMvPgLpzU5KLE7wCJDq8H6kszu9bEOfpBvyf1OxgZ1/YGu5r2agLG9033nQ1WKwDOK1+eSKCsFjsI86yDDPyEnZJut0/Dl74i4h7fwJNBVky7hN13qfpyZvACITRG4pbZX3mjTm/Uam3p4ozpvAXbDXdiVs67z7tY6rBOtTcjxi9SVKBjRb+G6Z5JJJkwpC3sBcFkezqnmYtc3zcDBu/7hAXvC9EgW3aDkdq2kjA9zjkYjk4PS5FrFODjudArWnpsDo71NPI6ZJ9Bx38peaN4Nn6BLoCicZ5Aa3rPOwIEsz6QL61SP5/P+eIjTDA0ApwSTyxcRlfBKmlFN48Syl5w3tKAROuU2wRhh6kNJD781TuhpCT85Y/0SaC+/JMQJzy9MLrcba+CJb9p7T+BjfL5QOrtJ6IHfnLKh8H+VvULm96DijijqQMxbNjK8Z4xtbpVwNCDYWb2vPSxOlc4zJ294vl7qnu1UIRPjzlLDSmUXsuBH6iaRZzgbKdA9KJ1HeQ4vyfyH8IGOPCmPq4bwi6jGKmOi4pfUCKQPb69loVglEudrkLnfwOGEOXTX2gg+hvI3r0eeAFvmfd/ut9v2enliEZih0jbsZM0L7I2Hr4p2gOfgwpnF7eCgMlvbBemXtD/qEGTmQnFwkflBLOZmGpFTZglx8ZTsuzcW5jfCzIQYLEFhFJFCeKYyd12X50ZuiRbY5i3lhNT2Ci9n4F9rKo9IO9d5Ao8DaQu4Oy/NvzotcG02KuzE5eik4DBXhzjxLllZDm90ae22YxyJwrJI6VLM0OAF4kTvXprjKrlOp57JUPJJRkLV5LTSulIxRapToifxOzK8ISzGKxmCAJWV2bftue+Xy3Xh2Cwt/+dK8SrxwLoisFoZQITZfHD3iE3k7lzH/nw87ve7FZWDCUA/V8DP+fCqyCmjY0kfxBRMNbt6e7Yv/SQje3qq8Q8Cl61/4Qj9bt8i6IAOOreZt5SkXycg+w/hmNHcbokBMako356C1Y5qiFV/rFit0FwSXeuunsyI9+mYTU8ASmGtmzjQv6Sq07EtL3KcvV41t/eicNoLmW6nnRJqhUCqykHI065v5etkxY4MQKg/eLLXBN0l/655/qsRisfx2q/X7+/v6+WqA/cYrCjTHYJIKZPqAz/2Y7tevshlmP25+ByB5oAhtPFqtSmAm7CLsWHMKKJQCSOh/3jcJSdzcTh2vlBRLoaSRdhFXmlOK8BhCxdNFgEAGa9w6sAXB/tHPBG0Ef4n/0r4pumIF/a/1S863t6I6A/QupXry7xfMjsQkwqVq/wO0GmcZMH7BX1zy7AmxCF/gAOwR/gH6Vt/ZEdr8ClXSJUHdvLJfR6n09cX7eODXaWpmrFsD9oY276x47SUdnABEspxc+zVKL4uivO9RZUJ5JH0QDvYUiDt9E1DukvSuj9U6cbn5EP9D4axr9d2HI8LFTMjt1F1g6OUOkno5cpUMMCoUoLr6W/wSpWn0+u5vTibkf8+/jGuLTAQ3x1u09CPnGyDz37n1OucJEGxugSIuXtQ/R9fPZxuSDYD3/JtHhwcEdcGVVBEt81GnHafeg9YoGiUtHc6da/VcrKrSlEZjj5qPDqNogkSSW15773ELM6eWNe4BISGjpt4ljt3CvF/sQ8POzowASicIBMXbQ8KkacgeSk7oX2y8pdrcYHyGU7fqToR4Z3gZ5BPsu3oWT4o7uZpJgsyuaqm5Tq2/X67r8tyOf83B0aW1MMDQgTrHqc3eEl9ApnLddnZGS1blbrLKKrsO1s4WXOKGXwRfBa3Z1zutHKj10X+Ob1BUxBTnyVuIW6f4DQQEJ6qLb8jzmvlHxh8muwANO+VOxyp72ZvAZwY8Zb64BTwlfko0L8+6ttzYJsOeZlu3KArDxkJG58GQbSaEJFnIbdDkF/AwChPrYrVMHWoRDn+rm68pNNKdmjnE26rZXQqxdlP4NunJlQqSE47/vV6EgzM8x41v4aroeFYhioE3Myi4NMZoZ6BcPkyBgIY6Ce/Hcd++/lZlvW//v7lEAxfL0T9SF3iLqpsSkS7ivl9d3cc7RmsajPE9JdlmuUqD1yA+Dju9/vz+cwhUXpfnitli9AexHXg/YLfmXrZ8gzJrt5dnR6G0jd5jwDeTATvkL+dhKbteousyMjKumugye5VKiPHtwK5+sxhrkA9Q49zdPFzN+ic1QY79QNiy/uioBbAv1mizobcfG8qDdf/4vhTzj3PyMtYh68Y6cbPl0JrK4XHq8U7UmpQXq5UO8bmDTlcVgBQB8Cli4yyKgqNMwVs8QbrYDT1Hzi8KKmInNuP4+fnn5/bz3Hs5ovkHjjqH6I7n+P7hfeA2I9jcT1ZkpvNI8uCfTrMa/pL9N2dQ16v1/3+YGuhxNYPAhjawWH7zBQA61QmB78agPJmY9hOKiTU3FasSi2xNyAifcBmxiaH5mrXZv3vvyvORdKU0liUhUyNuFMQvFcSjCamOgyPKaEifDxkZicFsVn4PC1vXj8fZ55h68+xc+JhK3od0S+a047Q6XOjqgDSGnTNQRYHil8v6vogBXaDy9n482rqHozeDfsoe28oxXlD6CuMDWH8Dk+w2Hzut/v9dieo39GaC+6q0Hp1HG6SKM1eZUzQsEB0WgkDnNHTsDQF6e12e76eeiw/B4rjhQdb9o95G/CvGvTbDKhlMfB2aSMgVEckH92Ix3fkJ0JWjTpA89L5TAzGB6AGbGX8IwjdaTYWNr9YwtZskeFXqF/vzAJRnRurMzo35h80kMEa4KwyGZXF21owfN5H1uQPSdbo1+sJzleJqHhLkhxmmE2fvZT1zEZeZh65DxS71XzrlnYdRxbD55BeilFHf5SU+Ubl8H673++Ph9i6zHxelsz9GAIX6ed9UwgNnNgDGxKHDhyWuwtp6RZyen6Q+FVXGR22D1VLVuHQnWOib4CHrPovZy6UsnKUs03oyO+tLatgllPFfxAE/+pyD/gWCA6ytuEZDojGDmSKnDqw1UeaNzS8A+1iejCK+SlhqYGWS4fSEKxfyyxgpj6ZjmAMGiqvkXUleLltmrc0Buebx3Redhh+qYOH5yUGf3ApO3z9+rpcI+owskaB36P1OUjFNqSrB+bd60MpE2vA1S9JtFhnMBQbJZJt2263nzudEtkoG341g5GSn6TQQi84E0gp/aHvt9fPzz/b9jKMCwSK4+72WuqUnbjbIT5xs8v1qrYr2DIfVMoiOBBGjNPQPvy7C8k4YdK8pcotuUpi4MdSWjb6eLzjGw0IMWDXqAb8YtourFriAazjVVfW6vOW/wK3mjnx547naS2SpVg4Ut/YDZgqS6rdVWkH/TeFM4tJdhcs4RwOai7KPeu6Xr+u5+vVSpWPoD64PNuPQ/zYfxo8mUBEhBLAWEDeF4c2r3EiHd1er/s/P4/Hw1IDIW43DcURKv4rA4Sv66zDBGeCzLceDJ5/bnd+GWcdSytX/n+kmtgrviV8Zq58hQPN76Wm6fGpp+3zA1G/l3hjgqPCJdOW6i9toihhXf+QrWbsmXtQR5rcz+E5qUUbVfDymGLHwjYO9XcEv0pm9nXkMy2vzf2v8+Diz3LXuYdsRNPE/bY79p0q1IJ3UWx1QBpkv0UVbJC7+BFd63uIVLp+FMqMZBNqx0IDgXcTiwQKlPj5538f9/t8omwOEKpKcRaMByl4K43DqWepNz1fr5/bTaL8mndXCRzzgtLJ5t14IN+1nol9YvhbN7dv9Jr+cm6C2M6blT3yjk90r/osFqPnPca1zI+llQ/jCtY4ULkP0Gk5jjvypgVqTyfE9qcsu/4NCJM1N+gtZhDvpkNnFcFohegcDSt4sk5Znhxh6S9yWMTfTccMkxX9JWdOkX2J3wRWrKr3xAjAARpfnxUCGyMMXMM2LQKw7Ah/i3R1JwhL3sfiMRoV6pNXk78QGDMRsDce8zCcBaAQzwrctu23n9vr+bS5SWe8bjsPUFZaLpdKWraPL+uVwLNXb/ItlV09M2+IIEzhUsMbxhfnYI6xGGua909KwHBz/+o68gSNuk7mVtseNHp+fOGLrvhZrbP6WLJ4lSGmKkruemHY1p9P2lvmr7F9s+zLOI5fxUKVCt/1FBzaqDaxkbVri+Wzl6lSrkiaPiINAzXwlAB9ZZ6qxGBaunMvp5HkYoOghiNGKk3EORQ8Jzcd7GR2u9+oHFQedQKeulm1x8eJIDTURhj3hisWuUFph8r83u/Pu4BnWP8QrG9kSIZbOfSUbQBcoyz8CpTL+JTXdqCnRRYNd85wdN1R046/vQCZsk38NxK7znxvv57Qbiuipr1LZgoc9iDJkUfKF5D737f9lKcFrGu0ae+N2x3pIvHLdRiAaPp1kZ7sTPAU72acxNrFPuj37IbLdbMi0CndHipMtfn2ek7iWNaMD0hcpvjXpMyV4zUNbGD3frIDi0HLVyfcmS0Xoa8ineVTMAP7uVjGj7HWa4pHCIRFF/lnPyVmEDcFkFNiFnYL9MicC8D/Ts8XFwpZIALm8kneYB/iG7zFnWM6MfoeF0+ofni4i0D8dCVv4lnzPXOpGzjVgCmka4kTKrX4/lKfSvnSbIWW7d+3h95ittVY1YF5jl0t/RbH3LzhffYQW4r7pIhf2w7z9fKz6YPA53495IhiTGppGEMb4yTT5LAgjhDJ28idngrjGsk15lrCImWO0KOV/kfhR+vyskCQMm3lSyt+sN0fd0lnjzYcd+mO5AEmdZ0biYoCMBUHMbxcvuMQrj0pOYAOMvQOs2ye3cAcEO/MgxfAU+YtFYFsr8zcf6+chqrQb9FPMnP+ZTTQwJuP/QoXHtzs9cdQPxu9BD0gzWaslQdzNFHuB4qnrJGZFOo7PQLosp+sI+DLp7GvJE6bTiS/k3KQs1PI3UvYo6WxsXFGL+xkkuotUUCTpHORdnxq3R8DpjcthPcpBDzA6bL4lEVzpXploIF4Upxi7qbWxfD+er4e98cuZKVnr7FsCFQ5ticdVQFG9pAQO7pFJCOCnbxhns8NNRD7+eOpK9jd/ZA87Gvk6s2X77hE6alR9zsbqHUiJZMrO3Sp8IkZDaseOACh/N7Z1VMskML0YTAMAwdbRp0lI/hCneKaZ+7KULTBEl10/AeIP6X0mYSS1I9+nJ37HsKPolw5clCKmLGSyp6egGdxhsrCHu5XOPQf7nTnQrpjJc8CKgoApxtpDUatDKe8nm3YobHtYJ2jlatlLqvkQi8iLW73lVXH+eN4vp6P11ODsEbHxIBT5FIHjhw4JzY1WS9Qzifhjk9JN+nqCAwzmRIml7G96FVMkGWcBNEBCdv0Lhi39wwWuroZNO+dEXi6c9b5eL5jVuTHNpjfk2Aabp7TLi7icA5XZG5FsTpLgbtzxeoQ39hecZsM133zjgnbNrcxVz7A0OXjNgUPHuLDI/a+AgmVrtxFWNVD6oBT3I/KQTSBDfZfy6zHKqoSWMP9nKflua3CwyRdfqlOvNSUCM8IMOZnRxI/BFAy3ykdFXloadWJ2MoqpUwHorU0R44qi4CXAHm4ZYGcyZ/myGaDgXkFeR2N1hXOk6OyXoz+5LnhHa6+kyOg9xNLbNlh3tCR+v6MBRIzaR7zhxqp6TMwkON4aDG5BiDcnONPniusFv6G8cj5pMdppQKdjZpQ/RiUHs2ZHpW+tn/BBOr37utER0ei/cr81RKTdnudkcOIR4nf70B+AVQYp0tS1oOvSLTl74a8j/i/QRzGrJvvqXFJKzTF+UPCYca8YKprYwYB1BJ5d9x+7veHhDXLDLp/Ex4JSzywmDQAtMR6FNnBaT32/UWxmuLvluKRkQ+kH2QWzDgCgMSSHJj4ZPHLUctpE0Mivm47wJ2xfjKyObk0vc50k5sd/26azg00EH3gl5l+Zgdf0lqNtB/ugYjN8UJAat2LKOxGa1Dpq5GELnRVLHi+04ETR3VZ/aqcGiQALNqvnP7GZis8CBiBRjiYmWun/7EUPp20RFZNbcV7L6yzbnKy7PaH5PqzA2X04mwnUSlaOZ9DGyd6295SjVnPkiRvXxR/zblqgEGYO8i+H/f77XQc33/+UAoa9krUhAgyEQtFLHJwvMrcjHagxyGA+a+NkxRpyjy4ZfjbVA9Yh7I9tPPmG8pqAyW9cn8WWOhPlx/VleujatwTZANGQQ7NO4HsIEZeHqhvyv4Q/fmFyROthdV2L1Fv9nB0JVDCCWWnBFrNEwfuuqlrIjdF44QD2GYewDyzvEUKFDQrKdorunIbrE9RNsUstkMEgQN/Snqf/6sHLejQKEjaLsmwrLZUdwgBvSu658fisEXjJ9/aErnpUVRqdUANLCtTRk/SO4pYut/ulJuZbHtRH8lEPtVGMlYAbDkzBk+1J5+p/hK5v9B81W3YigWNpiwr6LPgBe/oPjZf6fFv4x2Ynw5p8EYgd/wIy9aUmR/u9dOfgkQwD56egWc5NGCXyQT5nfEG7Bo24kdjqhv1tjaY3sJpSgZxeIefJcmmg6YsZDgprvJyCaGJwTucBpQkgXMJppUesPyU8t1wypUoFZeiLPgiR9IHeXRsG5UUsuLy+nZ7OtKAjK6ESl1kE1qpsgnCFCRGAKZyjzwYeVytf7hNhBGwQYr+s+ow06A86ZtJV5tI7t3jcV9Ox9cX1aNQqSkpdfi8jRMKxWzFY0nJc7qnGCaK49o51wEw0556g4Npe25RzjZofe1yDtsghhi21N8K019d6B73+Vp6yYgujKqKta+yf9wYi6Cr7UAnfFN3xjG3Nhf/xePYHI6oK27SCnWMWSIEi5DOh8gJjcuzvBbdjj8D021RveZ8ZudnT+I/hg+0m2uB3UVCeKMoiIGP2q0m9EIW646MczWPwM2KsI+sWFJjihzImFHARKSYisN5JpdLwKEJpIfjVG+E5fD95/ZD8c9idTbb/k468Ho67XpOBgDMGJClIJSNum37kw/BJbvhIFTahGHG4TDxpv1mJjZmJ+f1fL3I0VkMy5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YRf0MIFn82jnejw2Vh7PxZn1sOPH/QK6w3ZsvxGQuJSsktacCNxFN0O44RL3yGwDGD7QzQCaCHVKohe+RQGQpAQlalQ6rPbV345Be/esfdd5956tS8bcGQHpL5rJnooUMOe+Xe/l1K7+Rqr57PZ3PEH1DqPi2qMssBqmPs0DetGUCoA4EgWw3WZHk+9Ka3EOXsuw4IUalqUk0mk7bth8HUTaNVeo1KrzLDyen0w7P9Q4fWsslKvb9ft23Ttkli/+0f/edm3r7hpp88tHZo0EmmsywDWT80bQn6Rjc6hUh6goZpkkIgCMVPcJUE+8lgOm8bg6g4krZ4VCKhJ8X4wnE44ZAI3yZI38DzznJHzZKCjMtUagGwZPqVrPjA+37l8qtOfPM737rj+3fUdQv2iUL0E4KEvNoPUso9FWENmWvQTTLGpFq3XaeVyvI8VcnlRy7v1XD6qVNgOeMLRKfKX/eqV93w/GsnZWU6VMoI1PB+pgiwi+CS9PgIlQam0aAe//6dSIAc5AiWAv5E8CP2m8dicxk7CV0ZGELQenQKI8m6yC0LbidnBONr+HiNeCtIlvAamwGRocK86TZpwQQRVoO3+QQQojcmLQpr6nbe7tbzne2d3fnMNjXRokE/d3+/vfOHdz7+2KkLFy7s7O9YlaQ6S5tmvrOXWLM3JDWgdWyVl4MCakAwYUouMgdZEdBKhjCBFCF1gCqD0rzAzJSdA+XLpw/oAgPwDDB306HS+vqyOLK7f1eS7JteZ0VRVToZ9us6SVWZVUcuP/Sql770Oc99XpEXLBuUWlldsbZ/8ZVXFevrRZ5P1tenedl3SW+NNeDpgXcdyMOndR0lOLc1bBsne5elHEZkEGXvlzpGKlCbMH7lkbn7uiMeQMwlShVaJUW5Ozd/+ucf/NxXv/j0mfNDonKdY7IA0JQeh0qswBkEvG6WIaiDaQADE6SHCWHaNv/sH/13aZ7+m//4n3b3drXOBmMn0yovq3/wy79281t+ciUrdZb2BK2UxDnOjcskNcMVSbdb02beR3U07gILfjdG6tn5O8vcD/c/kRwUbIbWggzljmWCPMDwGfoERULCIYmUSqCL+CuSOUcOk485CIiNE4F8v0h4eMebDCb/FYCwQoS2y9J+3tq/+PDffPf2717c2Zq1LRI+eLgp4qFsmloDGIm+6xtruqZW1hZZVul0UKYBdylLhyEtyyS1CtiNJLaG7yA2llIMWa5JDqKdNyQpMDMJFBDutFYMN1FSBQHk5JAlCrC1qVXZI4lqpiv1+XOdUtNE5SbrtAYB1LZ1Vz/55FNnz13Mvvo1ioABEpoceWPLMltdO3ztyRPv/ts3v/qFL9JpaYmCHXzO2WiseX0E2n3IHXgkTEeSVhycU+AODuBd6oj4kjivTe46HqIPjEiMGui7dADTwmKeKbX2cDX5b3/l75249upvfPtbt33ntqbtNMSHQRU72AZDs0g/ou+CGh1j3Y5mQezCualke3s71/oH9/7YGlPXNQCkrAVzWudZql/w4udOslwXIJ3pKlE4x0cUEMOUemiBYyT8SsqO25M//CGRQ5SliZMTfsdjkebTY+Lywnx1mTnmXuFzRk7lmM/DPcXuyz8tgV0Eg0OIEQFUl9ItSHEHK3FmUCQDw3ZFS/f3IYmfKj23nTamLPJ/9W9+76u33ba9u41xCwXxKGQhIDs0Z1PA7UK1iOmNMV1qh2mRlyrtmnre9nlVJtWkB8Sswfiu33hlTB9kIkUORtJHPBlJK8wNkUtGKA36Ori7YHMrPTXm7P5+qZJpkQ062217IGvSHaBXqM7CXY3KM8BJHDoDqPqrT578f/6z3776+ImWYMIEh1iMVPqQaWy3efHsT1JYNIummTDN47eScOnRd5jR4tUwyJW+D8xHStX2dlBlpnult+rmQx/6i8985Ytnz5xDxyW1hnH7LDfR3lFgisOLKx9YcinTwwuwk3WTZum111zdtN358+ezDFQ67F+avv2n3vxP/uFvTXQFUpqyrpzA5YINmduIH5YKOUjVQ7reDh3kyWJMG0M0pDyQV1pqycRucPBLnO4bZf8PDEiPrhOQEqP7OYkXglXOCGAakua7TxUtoYnwMb8Po40bu8F8QczBuZ2+/PChb33/R9/70Y8uXLyAli2qO7Qp8cDJoQLflVwSNnoxA2sVoHZ0qmprc5UAQmgYeixqcZoTTWV+QeYHKguoDoAfmBCBwW9kOLp/CtiTlCDKAALqbKaHfVAKpppMbNerLM2ToUEYNIa5MXPEKGPU6ODVp8DIPWRBhsGeevqpBx5/+Morr0wBqW8oOE38P5LYARId/EWxlwLpEQvgsHr3eRa4/oIqMqlC9sCHTkc0E+EKUXJTbixVGuIUkIFLDlXVr733ly+78srbbv/27d/7btt2WZYNNjVYzMb7TTI/qHRfq4S1exokQ14URVm84bWvufWbtwFwUg1ZmU/KalpVN776lYNJGgh6ZJxX4FK9YJGG5H3QZkIeuVIsnaQZWVlcECOVXlwPE9mlPtEY4j+caRolSp1QdrB3B5gZsdGy9LGDIYoDQGyxcHgji4zARY7vQzxPBOS8cb7gz/tti9blnQm2oP1NPafgR9Kka+zu9s7FrS0oVyPpiAwmmItlDbBRCNGggAfz2GLxB3pmyO3gUSdJnuW0dMj7IxoKylbc7lH8k2w6azAcjcFKiv9yVDMIQqLeYT6viyJPswwqCNHeAzpKErCEMa5N8Hx29eEawLBgfePiyADQme7N8MSpp/f29jY2j6BLiekrefYyOB7MZjYsOBNGnOf33bOEcJdHL1ctsuwPwzg+Mkqt+EQf5U3JNoS0Aeb5yDFO0n5lkv/c297+tjff/J//9I+/8NUvnTlzHoqK4OmIg6EqgfjKeVZeAcIp6ERDjWGuJ+XkJ1/7mh/86N6uaVWaHD106Pjlx37mp9/6she/cHUyQRCYM+l99CACGgYjwwkurIVEsqFPDMDAENbmw3O5RVe+xlIlSFCpuQRDeAfX395j9TzrsP8QomxSQFDkOgYkeuc2cpoCKDrSqpSTZEkVx/29EmB2pjJhXqUzNcLlw7ec1UmuZLieF2Ykd1SqO5Wcu3AhSQZwqyAWHe295yL/NN4EJoDE0NvaDGkF1hAJuyzLNKhZzEvhC6KXWldFCdUteQ6CmzQyx5jAW+sh8ML/Ej7DEwIlh60dqqosyhLyJQ3WSEOeCdAFJB/Z4RsEdB7kAq2VXephGJq+T3V6/uI5jKAZrZNs0FhL6WLDnPoQCRA+PGJ2X+DsLIbgi5G89zo0sgUFR0oBnTjNE5BZEgYeNKXIPXjVT3YMMChYEBqKwaxRid3IJr/5S79++PLjt99525133Fk3bZ7liKCG4GIU7XUKktUzyGmIe/zyL/7CDc974f/6P/3Pg5mlebZaThOtO9NXEObIFJRnRgTCyDLO2gdgjNtOtxPOmYL/WAV1blDiCGqeakLZmvfOobAUvN3i2NcVszuhzdzPYkBwaEjnkrEj8lrOA3IMTR/CgE3A7goWxr96k0JaCRQBDBcPaTymFvdEIkkWmfditXzwfC7e8uLCxvBnhBAnuamqguBNsHBcPG2Nr0GmZBAzLRnVQDhQYQY4iq5NDTCBTjGxZyFToxRUtEC5aVkWRZGhxxVgyZg6gMJU8FkTwO2jOEC1mVhj266b1bW1hjTjMECGC+JcGIEtVFKkiWlrXVZYlA/hHBYowLbOYrXGyXtWPxCjSXUzn23v7Je60rAEozNIJ/lTDzkLJyBFLIr+FISmI12vkd1pOneAxcDiOalwShziBDOf411eNkhjmzWmk550TfqSO2A4vL5rOtMXqX73295281vf8td/8Ref+cqXzp+7gGIqNVjITFFGphGwP8AgH6yFknytq0n1ohc+P8uLlbXp0E2GzuZF3nXDpCwg8IkZZEKjUgjaYTkDsTpkLxlubKhAuspRnbEJyOKV9YmB0uTCWbyk0UlSsub28XHyweCTHPlkRJ8PInFtNAsRKSzDgXGiPHwk8IdUmNJoJ1OUbCvPmlyp6SqyvcQSSD6vDJ1UEUfvINwesyuZlytO2FfwLSaE8ALNB2yYtv28muRXnTypdJpmEKp1z0vygbhXxgRhA0gP2GHQ1N7Amrz3gaBBJzrP8tXVFUpUkCjAWpMApfNiixaNgRb4Mniy+JcSX03TNHVtoPScE5Gob2Ens6JsrK0AoQHRF3KvhYnvN86zFSw9VSrPsqGo7vjBj3WhyqrIywJaZ+C6KN5DzjDIFVyPK7Txnjw9KMoD4ZXQSXvacW4RC1xfKxcqYQbWRYHE3JYEUFO0+dLy9oTjooUpWRDIl+CsAOKjTPVUT//hb/3W1c97/je+/Y0f/uDO7a3dtut6QLLAC0IDqDLwGZNU5RQKuPL4iZe+9KVluTEtjS0nREwZlP4S3gOEBkYXYU89iMMdMePdIp1C2URshjOopO+7umnBB1694jL4vCHR4ywTGdGMLRYB248o3n1X2O4Oo8JQUcmeTiXx90YnEPaaMoBuDcSEQZw6P4bQoN7fYgVIH3B6OljcwYImgeaOMjK6gu6W1c2hhQdWk8JBDCv13nTz6Ikzu5nWJssMYIipeY+PlvueGNw/h+/NtiTCpAYzMU1nJ3PTTarJdGWSZRnAlQ2qRrEroc6dUOykMZ1ZzrRODwrKU08nVZ7nbdtQsjjLADM4DHalyPu5avpBlYAB09BgQgS+PQJH5MsJOUgPp5Tqh748cvTwlSesSfICotv8dOyOORRP2AbeRm7TJIPWLoIhfCZnIXuLQ5hVgUAVfz+kFYPfyz4VCwynb4LfFzgf5Y6QBpAuwAxR0vcQZM/0r33g13/2F37xT37/Dz795c888cSpvd0dMlXRlU11prFjCTw1FPTr/L0//wuXnTxZZSsqaQarlU7ZxQb0BYQhKSWB1g2i9TyVshmAzOgpmZ+Y6BzaRaFtZNLBZgCHTuDs3K5RZwHPe8ISiqhf8jDd1X8mZmnsGiNYXXwsKDTqKRPHsPgUQgk900KAfgnNTZYRPad0/lgmyyWJW/inY8y4vwi7a1SHRaceSJlDc6BBE52UFVi2c5MUxaTtOiJ+jgm5swHLlkgJ8zFuSXBCg7EZuJRZNal2Wqg4W5lOLWYUqUKMiMwlGrhcgbSR6KVFOg0dVo5tsdK2UGo+FEUB5amArU+H3lRFec21Vz/5yMP7TzwJyWed9pmG3BeFz53Bs2SboOwFom4DeF1DVpXFSmWabkCpQOYoufscJ3cVVuQVhQNn+RtgklJPSh4cpaZQhgoVrVz0KBQz+HCsk0N0juHePqPsLgAK0bc68BltvKrOQA+BTO42pmv/p9/5py980Ys/9IkPfftb32mg65Vamay8/g1vPHbsKj20TVfP6mZnbz9T2Ztu/mld5AqYuUi0iw5gDJPcMAo2wj7i77RvKBOcUUAtMkLei3Ifzj5OkjTT8A/JVkLfO7dNlkrTF+QGxsZmFPc7AFKzxMvkyJcTGcH+Ddu4CL2QjCeJQN47ADuCdcR/834sr9v7seyVxX/zROI8eF9wGBRyovpU2aKdzV7yyp+44vhlDz68Yy32HvNOgiNT7gHmOm4R/6cKej+ZNF3ZWJ+trqyXk0zrvocSUSdKXZMGlyx0EXEm+iAa2ClG08zb+uRfIVMrKJTJWtMPakhLffyqk0/e98BqVeg0zatqu4UCGm5YwXKHUc4ymI0OPnAnwJiwIxV8sjUQysp8IsljZDnp4/BJkhiEU8HN0SSViDRNDJlzNRV+e1T4w+i/XpsIzebcb0+6oo+IxBb6BiRgHKOkBTDzLMunb3nnzbvz2dCo2XyuS33N5Vf8zj//p5N8LTU19DC06bzeH4yebE4xsm9QQbL0QJ0u+wVI1ea0iFi7B52GJ2MypA6MFpCuvH+RYhWXiP40RmDxi6I00VfEN8Lb3pySWNfI4xJd57y17JE87rsS5soAPi48EfQhL74AOAnSQcQOwJIVwKHA3tHTOxCHb5NoUpvmmUr2dldXVp1By6EcgqI6PhSXInuBMMRaTzY3h+k0zcBJpRgya1Zh0Iq2lY7i/WfIDYZwCPadowySX6SLB6GLlqQ2LfJqb79+7WtvtH39zW98s2haq6GqFA1H1r4cl8QkNth7GGGmu/W2M01fTIo0NUlfJ103GAOWIKgAqF1yzqxnQAqZszZwp8H8IQMh/h8fWwlvuCMILMvuQuJD2ot6QproUqv7r0f0LSJdtL8hH4krMp1J6zpNyp/9hZ/52b/z1qYZsqTX2SSp8sFY1QGYTiu9mpeQRkoBQDdoQMR7m84/WLS6wK7j1S8HTPDnqUXlkIms6ch8lQ/nVY43XMVf4yjQ8pd0mIMOX1TintOinJLQn9J4XtDGS+4bL046wxJ870XDklvIlRPB+IBZmiht0q5r6qqYnD71VNd23BQTTWXnvAVV6dxgsG3TIemUAWx9WYDlDV4NxadFK0ksUfWuGrhMrole6B3qtbFr+ot1Q8JqIpeUolzpsLuzu765YZrmpc+/7kef+0KS53tKk/lljClLyFS1TQsIXcop8N2tGrC57GAgmp1lRTYF9QIxV+zAiKjDkLjApWEynJMK3DjApdNEd4+DLLTQGis0cBIUqSLvOr6Sp2ip5USKasFaFJ8OrhzHKciWzTPcUz0kbZ8M0AAz6Vy6ZkjBVca6X+pRhxcx1KTZLckHMEb39bXuPhcz4kfJZtKJh5QWOsQoexdVWPxsLiDkzeyR1SMYPPpV9Nphm9YbluNdX/rGIi/F4mqZkHK3WxAtl17wgS+OlnEtijO52UhIbbV56J4H793a3jGQhu0Je+UMQcqRg1b0SRqi8s6YJNPFZNplGcUhsZ8od1TygTUK8gum9vzrHFOnZglwS1UvFN/1O0T7TeHgtfX1rmnveuDxYn1zr2t3dGpNr8Dgg7YbEI7GthIEHCEYJlf/Au4ai5JT3bX9T7z85XleAq4jzxMomsO2I2lKoRcM0/v9owxGaLw8iqT7cIWLOXO40n89eEvUdpGkXBIOMPal/B/4zAL7e0CT134R/TgFw5cKCG54gVAk2oP6rDRXSZUnJeBIoW+PGpIMWlaCU+RxFmOcvg/ELviaXnksgbC4YAGfYsQ7LCvEQqU+jK8lOZz2YKz0hbMhr+G3Ywm7LAKql75GRoErgg0/eDdWfG50ycXiJymLOXe2bJFkEaRKGSi+AYMSIFXYeEnrFIgY8M01+K7wfz7dhYqWujfjfVxmmA5S66yarhioO3NZBCjN8x0TuHUWML5TSNz3yNmd1BQb0rKZpjZLRZG3bQf9JZy65yQfXoF0e5YDzu8bX721O//cSZpfqLtJAZkQ8G9TBb1v8V4aGobwFqFcgFXmCPEf7JDn+WWXXwYMPkBVTtK2BjDS4BUb8O1hX0CBZqAlDKBGwHZA294dho9aSudLOIakhqK3XFeRgO5X6JL7ELTLe0ZEE2R9jMxargMibIF0t/hHjAoymDHj2FMCYEz3damfQlMVmUAZ+fuxQbiMf+ko3V+j9qkUxAqb59KjCxQsFfpSYzXcLf66tISXvw5m3chhXvzWolHhN+LSWt2Xtyxcj2NgUl7QZimYTmE60ETFBEgT0i5DkpmkG5I8f9ErXnX40Mb+/kUATqSUUAnpbooXU8tlO4DxPECetiqrChgSc4Ogt0PZIKtgAOtyAhx/c9Ml/GLhT2nSdSaHFCPkeSeVtpDFBEOAKJvC2d59txCLTraai9+77z5zaNNu77U4DcDaLi2KVMHinQVAogYUioXoM9SuIrJ00MpecXjzUFkm0B8mSXKVFTlZzIMqdJ612xefPnehyItpuVquAiY0tT303BpJ9yVC1mlStlWYCFyqyOWAHT8lWHvlELoj2Ky8/AK/SoF+EG16eRu+6rS00hxJoiWxySOcRLmeIKSW2a0OfRJ4xzt3o7WN3oHcsl+ZBDIHQ3zZsy5yVGQOLX7Ff2bZPl1a9S79olzAmJGldI31diRK3NSF0WV9/sB/C1naapvisJRM5xyx78EtSmb9XtutruZJc+HIoc0nnsygsz4neDD3g34pkSIWIVBbC4BDbG5uaK2Hvsd0Iri/YX0cnQKMKxVrO/tTWWOx8l7lGZgCZhigaXiarkxBNACyGtncN4uAAjfmdKYnC9hNiJDuz+brh9arXM/n9dPGdKaHwgYaiYIvp7QAjKVTkAvM2Eq1ne3TrE1TNe9s30JrOJ3lWg95mg3Z9qmz//t/+Def+eKXjhza+N3/6794wcteVOgKJ0VIdcnBuXCggTqlf+Wg9igSoacjwks5Wa0Irw0lJGygjsgqplWX2feI3MiQjXxmpw9DV9nwtq+gdMsUDovXmBFdCWSkaMQ2WprgmoWigvEPfGWIQotHPtDCFUg2mYZd/uFn84pdgyV/WlDikU0+thD4Y+MFxNIkPIITpd5slsfjfw3iFozbdDp58pFHf/+P/tPZ8+egvAS4LkkBCqkKXRjbbm1vN007m88wfstAixATdqEsRNAlm5sbmcbhOjDzhoEonO8NiSL2HKlUwKikwwb/m+vrWZ7bYWibpjdmfVKpRM3nc2yAmGeQHiTrGmcsMP1QnpNBDTi5ybSmP3Xm3PbFLa1UD4UNGvOUHA6jjhMMHcHSB29NAGzQ9v/+D/79H//pBwHS14PYyCGIjs5hkhqYslRv7e1tX9xIlZpWk64F01OTPndlcyENHIUpBMLHbQKsCcGhEEUzPQlERZAvdMoLcAtyiP0GFOYSTeOYMiYtenOB7yJb3mt9iR2Q/OwDLiMKjNzdA9gjuqm7rtQ3ITPqJJ1T0YKBL2UYLzzVGE1x0OdGa6QNPEhOqAVFOvqI15wH3TEGVB+0GLmhbJXFPCyvMCiTGNs3STJ56O4ff+mrX9s9fw79YOoVm+Jcr3RQw87Odt91GdaI8QgtqiP1K0bFS/7uZDKJIpQp8AQOoQJgXZZqq8zO7t4wDKsrK3mWQXK47/M8Rze1s8NQZPCCeJVKS+w/3HVdluUMF3FNGPy/MvpF7+RZlqp0RyVlqnudJnlBVoDPbziYAeCGSAwBlM8m4HKjpq7rhoJcWaatAtyY7cjnN/W8UdhuYuPwoURjfz5eCEUIPFdIduJmDx6W4ctlrTVd1/eYskogS85/N+ASwPf7rod2A2UFECcZrlmwAlkDj98OpLzU5wtdfTwcUNjzwkEdfXOZrTcmzvh3bx2MX0JACMEUa+Dw0M/EzIHNhN4bDrrSsxENSz34gy3nZ3GxsAb+WTohcscjGgr3dRZdqq1eW/nUxz72yU98dOvChXnT6EwDCivTpuspdwD528FWkyrkd/xtRcaHTCkICBHqOH5mHNYBNUnAMEptbmxURUF1Z9bYPM+xLwdEgzgiTDDBZGi6hoCyDLCW6FHXKT9OPcE/5KMXZVk3bVZNsAcltP5w+6NSnAPEeCZSa4Ql6yHx23Yt54TQU02hW54F+ZWqedNB5/S8SHUxObQBVgrkqAewohk970VX/F+6nAseU78grJKH8Dy5HtSmj4bMDf6chqGpG0CAFyXV2Y/Q7yO6ktCC2Gs+2KkjZes5OSCOBdZ4zHsi+vWMXCANw5HbzLceuRuwBsHAUQAoIGHdoz3jChbEnTdEZbERvXXpK0kP/pk/J8Sb864WPxIp58CkUmosC52DKgPKHGxy+syZL3z9G9vbW0qnkLVPVN5n0OUtA+cQ5mXARBwZDPB1WbSdcBun4lI5aowsIt/f0BqjdTapqgzKbtnfA0sVdazvn4RdDvFblGlNdQnMj4MJuA+Ir4F3yoz3lYWXNQNMCcCIM8w65MpFXr6AMOK8A9TsTh7Bu1C0jNkvrGoe0pTgHJDH6vCJ+rq/8bWvWpuuwQiQHtoIQMsfEEByl0ZmZzh7A9MM4IWPjsM+yQpA1mEwecJ2OJgKQ9LWzYCKOMtyhoyJMHDsfQWduGhGLjMvY0kTJSAXhZBwpgMfjolLUNyIQRZcbncmC8sEIIcf2uikyOLrQLdXyNGR2x19fanXHivq0VWfla69hFly6Ws5vr3EXXxiCYWiStRVx66A4Y2ki7Cep+s7qvjLoBYn69hRdLVPYtwG1oARPBJ+Qy2K0SkXuPKdpYETNFRo101d2JwqGfIsq6qqa1sL5OzndVLaiSbegS9d5jkFunnYsoNyS6BxSBqTSw58COzh4kniww72xF9gC5cviAElGLRJBUzQsAKr41FjQlEnlDTr7NDRDW2axMCwpSSBYjyxw+RWSNMVQ7rogkMtEFjFEJPzIoOfgM/Vh6MdO6Ak7LpusENRDTlG1D0OZRk5LCdKscSFD6hnbe49GyL23DvOfSzqwhCMd39knoe5hhBbiD46XGqxskONvGsEVFz8VuxbxPDUxY/7Gzwr2+MgEeMjUsFQGgU2lt1ehi54AjUM+d1YWSmqSWU6wEYB8gIkPFV+Ug+NyTDUdd120EPWywjx5NTbAkGbZAbT6GtuXRBaAlE1GzjFPCDLtF1Lircoi24+x+4QpDwJ9wzTbyd5Bla8wzwImN7C83mUI1bDabgFJHjcrCsFES7+qsvbokRycG4udUDJhU1YfWNMlHVeduSlPnn8SuhJoAaLbjM23WMeFUXbWE7pmg/xi8qwGPofWDWUeHlDP3Hn5YLQxpimrhMFBZWIqxAE581lFmSjk48MT64mcH8JdQURKnbB9RsRrGe8RRobDqDVSP0KfbNoYAKmxG0jXyJy4embwvgeFQP9H3qN/GF5mUtl5J7djcZ7IQz48SKezWXFqZB0S5PjRw5PJpP92V4/9AmA2yGAArTSNgR5JJWLQRQujuVmBxGV0eRcCa1zPMdvEm/hEoFhIHxkYaow3CtN9MbaGiVmySDvTA8tXixY187YptkrZFpHlSL++gxJwr5KEDMrCn8cbk7lgtmH5azS9MOGX2R2+5ZG8OAMQLTDdFJd/9zrYIofGBEpuNcQuPdeM2pa/B90pgXbAnPhDr4YjiAgazkb7CPVQxhmFWI7MKrTmL5pwB0v2MUIBBejbw+Q5xzEZgvMYzAWtYnotjR2zZ79a8Frjn5afEfsTMYpLHbAxMEtDUFFS7yEWbx0acu+tMT2frYG9PKbLErE5Ys96BaedPwHCCqQ7NT12vrmuQtncYSVS0XSQB2xT7JMw5E28oxwqiATa0xO7qswbkUFH7dcSLHBLCkXNozhblDQBxaq1gDexJGlTdtm2FOemzfEHeQimcYF2nDfLKOLhCAXMiKnNcW2cVURzWnhcDGHteSpUb0E9O8ypp9OJ8ePX5EWWQIx6DxpOjP0EE9AXwM+4Tr+8WRMCRRlJl88O6xH4snwQxSnkgFaDIl3bQv9u3AyuXdJXXgguRRBOJ4MYyLpCzROyqtCr5PFKLnl2vXA10Gfcyew3DH2wakBTOiu7/KiiFBHXjOS/AkBEH+V2LmXt4n+IsTBgucuNmv0++jnWO6Pvi75/9mHqcevhYCFF84wc6Ed+ualr7nxZS+84YknHyZdgeKZhtkjSEPOAUSF7IBEvD9cpYMGJ5ij7P1yyYtrC+469DsOQ1+bNLSDRzla7HoDfRgwz3zhwnbX9xsba9ySx0kCp6dGm0U2OwAwCXelqU2HaFsWh7Bo4aJvIOGuo/58/KCIX8EyrDSpJsWhzVVlU5iK3Ju+7bClbt92PdZocO0hd3Fx5Bfk3XLSZ00sHJRhJPI5XA6JejDFoVtIpMXGUIrg5ixeyE2uRog6gm18RRQ6O9hKi/oH0srZ8Ino9BIu6fhzwlgIVC3XGGtTRGINMCKtN3AmOQaloXGAZzyRoZaruYQRunShMhY9NkJGqx+9LvX0wQJ/xtelDP+xyxIJCqgoIW8ouf76q4u8nO/PcxyfghEjPk44SA3ln2DTOl/Wl7n5YBZMyuN2kXDuGFTmgl6kfhdOFEEnP/szjHHF98gJh6FkWbG5uVFVJV6w90060K+UGyCMTIQlYzdDaAePK6I4lmhMwQ2wCPeJ38J8d5JYDY1+NPRTZpgXlkBRizggHhgsU06OHDl0YmUync/rqlG96bsWxuZKC5iGlwQlH2a2xWrCObeuCkEq3iG0dBqZHCgljTG5zeFoYmoIqjWiDzp6kSiGzgq269u+68An51naTqL51r94mhn0L4OoXlhbuMkiAS+ksf5rXgA2h7Cn7XsAq0O8ATo2qAKj01KLLkwwZDU1un2smN2ORN9Zbn5LMN144tkl1r/sjs/y83LF8nYhhkdjwuhBwWa+6uQ12Ms9PXnixLHjV6xNVpNUmQ7KigY9NPVs/+mzPz71hKHNREYDXBJW6oSTTCF/gwEqPQxQR+jdOZpmNOq867YnKGb6JIxSguEaMOoBgIRUioTYS/iyjyr5JwwVENRjDPpcTieT173+tffc9ePzF7ZwVAtPpyOLlriJO0GkgOi0xuo03dxYe/nLXzmZrA4JzPWjW0EwrIOxTe0wVMPw02+9+ZorTxw6fMQ0tkbFBbMpsDiSCixE4TTewlsNIrnqdY+nEdFbDd/xtYlyq8QvmH6zmhTjqNZl/HLnzopusP0A2BFEkIiZ7Gy/Iq4VBaTrHNqpFJpBF7lwSMlHjnFKcg3C2ojflAJJZKTCZ3ktWTPfzybrfddzKXeKwJa6oThEXhX8SK5VAy5sAbckL7skLeQnb6tl4et485eXGS7j5EVNvlTNHoQjpY8eJCk8LhZoM7MDhIiuOXmyyMuN9UP/0+/+7itf9coVSJWopLeJLk3X6pXVv/iD//B/+1/+FUSDoaiOKA3qlhBixTGbHPLGVlsNeRa0XSn0Kpuw+WCLX7jvUYVt17EGlwqYXA4ZzgtTOXQX3zpPJD7ZEHCdG2F0fWuG//l3/+W//l/+1y9/69vQ2A46zANTYFUjcjFGqtDTxjkuGYSkfupNN/299//qiauuTmwNTWesSvoOhAf0oEnSKgOPV6thv4E4MiTRYTUZoCFdhQVzryvR8mErbDA5anAVxN+4xH1w0kmccRyHpRhZOgCIc4EClqoHhrtA8L9tYc4rr5ibxo3wIcFjosY7+MLhz4g2ceLJOydRuCsQ6JgqDzZGx3+A/rTKdsb2uc6BXYfEdh1BeFOcDUJdSHWRO89CgLPpim6RS1hITn6VCTn/JEt285lgHpd4BRdPvBXmwy/blUvFt5zOSrNC5UnSzfZmKxurh4vk+c97wUa+kazopOsgsJGn4Htm6XUvvkGn+ZB0TdPaoU8Bo1ti4InmJEGlb93AEG2a9ymbNnqYMVNssFtFCTrnmpgWuHc8Ti2jjDGZzxQPwq+EJq0eOkIerBkS25gXvuB6lafrhw6buoGujNgYl24Jw7igVyy0rdXKDL1pMU29sbZ546tuPLxxBMpxigouhBM1eRpFr5Kmx4Q5SD6V2FQDnitUOFNrU/cTt7Ny4BL8gYabM7+OUG3ysCwKRQc7W2QM+AOiMO2Q8TROcd6LUQ9+AyWhBcXbtpBr4Oyes14Eg8lGiHQRM9imbcxgCgSExVgYbn8+8m6Xk94iJS79ADCwzVOdp0m9u7O1mmwUVQm41TQD+xH75yXQ+jSFBAD15s+zBKbRpQmA5l3HySWcc9Ar/tAIjPLMr/i7B+nh5UmzZ1rMkiv7FuS626tf/5Y3veP2bz98/8Mamnsb6MxgB9gHlZqk00kyXSlzxEIjthnySb3BWSTk7mnobgY6Da/btm0IC8kyfSHdImfPLS44w7REDqwwuM7pNwcxxOUjGcML+8UjpAmNexTc+tjll0+nq307h5ELeOI43TgFoN6QQpwkT3PESqZ59t5feN8LX3DDdGUVFmGoQzJ2LqYqBUiTaZieRMFsCyhxb7pzs8VMWBn0aP7/JBLVmdBOcPnHY94xpscu+lgK4rRDFBFCYcFjBkXXAN5aP4oopgXqi0AxK1dQSSchewzgP2wAC5Mez5rs2VSVCgPgy53t6LdLU+myCl/XqSDrgPb6NC0mVdrU86aZV9UUZh1Dw0SjEC5Hmw/gIWjSB3xrmy7t+yTNk5z6YmKFnbcunn1UaZEPw69SUB18xaVfP/BDwtaSWnrJ5/3HyBnWuc6S+fDf/3f/1OzP9CaYKtCL1zEQzidJTG2nk7Ku91OYJUmFtUwR6BcBCpO626RZWlWFnRlqCBrIjtXJkgSJew4/AXuMuY6ewPE3JWm8kqNCBx6BZDrb9bO9i698xSuSNK9Np20H5421+ClUD2HnQ60Hrfut+WtufGU5Xbv88mMrqxWQAWwMCAJiFdeliWwWJ48o8R0cU1kb72NTwXBlSRaiK8zD8aO5Zx9ostQSQcziwQ2Ww8g/mghONLhDXrg0CREx45sT9sJ8F4QTlHlU4gSg1zZN0xLnJC1Ql7ydo0x2MZdFlEaBI3ewQHgqzXpjMpUUkzKZlH0NQ9yTIann+1leZlUO9Wu+/wG0KbZDh6kLSB72s92tCUzInHC723D1uBfJs30tZVTpvz+Tll70wBf9/ujPB6vf8AMFcTCW1QIn62kFFgpHLpBkbWK7JLXty2989bveevOF2W6aZYi3RIcVZt8BtNekaZHl0LYSxg2a+Xz/+9//AbUP9Ufj2ka7Fs+hiYTrUOemH+EXlvv9rIpdy2iMS1F+ioppgH3RY1XVZFJM12648XnPu/76tCjB5s3I+IIB8+AuKzSEtU46eDdR1QDpaciFu5barkDMRxMiyJPfyBhdLzu2uXX5jzNnRbwrTpHNV6xq6HjuhdRlEVLDDgCn7kBO0g6QeHBp8tABnv5GTj9NBo5OYZF7mc4X6Ac/AbFrKA7LNAy4CvG35L/idRDMCR8ka9q2rACmCu+kqq7nhYb+LPs7O2mdT9fX0FQmVC0MDkHUTAetFVQyna6oYZjvzsqqSAtojAQHT5kntn7iRf/XGdjSzIg088EPvPTn8feGsZyLFuCNJR8psQhIgEA9OBRY5+DhdSnFhmzyu7/7L3DeJjZeQRdvgMljOJpooEhhads+PbTx13/xp9///l3QDx0HWPpG8ISF8r3VgwHJXMB2W9gRibfwOmSMNyBPDiStE2MQ5ShyZNednd6aIrVJnidg+WOsGLtcMZ6waW0C03wSXac5TAZhWImPRYVNdfwZFKOQiK4ey+37Ig+LaKcDctAVw/WpmIkmJqYYCY58TccujpWtMU0zbzuohAqJcd8AG+LroQIRo4DUlBu9AMni43VLoyJ2hlFuE5wbke16GdbYiw0nqpaU2cqNTZYN5FbZzs5e3Q2bq2vQ+1vDULVhsAW0KctM3+1t70yn07zIwSrCTocQzoOAJNZ6YfvMEkatmXbWVNClEDPJELJ0jvEI2yntBPnUS5XrkicatUwSX1/y5dGWXfLFPBGvRzrD3iWm7eaqGreMFPIkSZEnecnt/MHGBjWlMq/3oIcW5FsyKL6/+sQ1EPpSrdMMC0U62NEjPExcXRPcQRFOiet6FsokyefmNBIsKc8L23YAdCwraMrWAjwUvCVyD6GNMXIo9usBJCUwLkgxxmOFtbIKO5iwHQpAMqkMVXh9u1jcGcla0s8oNBXDJJfFVLzKwxluBlxh36DX7w45AV4bh80kwcR8NVqDIBa35zEQJLjpvelzQNw5JMnI4120CP8PvNA5UUBcadPUF02vUz1dmU5gjlYJQVVE2wPScrD1zi4M66mKrMhUChg+MBGo7gQwSTgBYLD1vLbGTqcTEOSwN2hljeoHxvatkJ1SQy7Fe/CGHeBpPxvH+4CtENePKZJ+5RqExQPwbRywfaMxSQ8ltPABxPTCFykjSGYvrR/gD61OJqsb08m02J3t4EecjBeVQMnoJ8HgMpYj80/BbfRaVyzZaR2aywXjDqFJHQ/OTCHAQSOdoOcF1iqCyQWFx5zYxOQUdaWXna6lreJkYBSfjUh0qUUWoTOWQfP89bxlFynqIf546O4dLLZw/4XwQqhr8izsLy1XOk7P05C0YFFwkDmszDU3WTCe1bMC7l/qxcJFZXlVgf5sTT3fm82bjbWVomim05WizLMi39vZTfNc51nT1PO9nQIrcvJcY2JfI+AUHrPICmqcoBLV9n23P1tZX8WDD1TI5DTWCVF8JnC734tow/yeL8vqLfd4xRfHhoD/k1AOsVEXX9yVpIwxug4yidEp9I39rI9Qz8LheizJSTHitXtxfzpZTdQZYIoF5AZfenEPgn28QOWxueMUZCQyWfGgdE5TlWc5FBsAhNrtANTNQ3SOa//A6+dSSNcCgAJCAlIcqdwQHfdiw1cEuHCXO3q/9vB5T8pLnm4EDR0CuCI4viOQpDTwmTUFStwfY+xNRfsXLOfY/ZXrHp2XuCXWWQ0ZjbBY9nqWamch+eQAm+m0yBUEBIDxzp6/MGtaq5J5PZvP5razRVlCCMD0OitWV1dt19azfShko24NAB0DODxEVxFcj9Nrs2oy7dtutr1j5zXgHADhTk426iXWBePdXsaOz/ha0O2xQyb+XxyUFxlLds+TQZipIFBo5FfFLbL9D8TAAkjkaTpaIkRuq2TW33jj609cdRWOQTjo1Jawr/9pYXCmu6eQk/G/0VQAGA+vM+ii0VvEEsGIMzAcoMIQkKJkouGCMYqOuA4av+TEHo8YdMcZD5PxrBYxlB8YGkZ/+tVd8sTZrHCBvWAE0Wv0Vc/py644toWX0KD0z9g5CdrDqaPwX/eu+8UvNszijG+42JB8+aOPBMvIAMCOHNOqUkrt7s3aru/6bm1ldZJXMOoGhh0D5xXTKTjjXdc0DY7RM+1sP6kqCK9pABzBgBDbKUWFlwRT18mgykrbvqv3dpVKJ+trGNpJEP8jqpOX7Kl4nhFgS9pDl5Bcsd1zyR0RQiRYXRGXR8qETzX+1eddqbkO/79svCSCN8RA3DiqP3nseJro3nYLi4z4/uAndbrPO5DBjxTq16kcOTYBY0AZMCRWVkFzL+wxDceUqhTErlQ90kzyVqy39sPWiF/c3wPXin2OntTDj0c1AAdsAJuzUlT6GcxsE0f53di2GvH6ksKkwMO+iVz4Ax2MiGqNfJSgIBnrhGY05skXanWl/XLgYUvqlJ9nwEA2pKqsJnv78zQZrrz88kMbG2mmbNdDM/4sheAqTI0odVWWa6v1zn7XNF3ddPMaQPTTlWJaKcATptDNjAOd4BrAsHOqjoW55qaezay10+lKkruYNk+14gDPohsjaGbEw7G4WtqKffxaZPuFgxuJxhjRKf8nkhDeAnNz59m8F5NQRz3KuAEMpmQSfeTIJhf1j325+IdImAQ2WQwtOP83OHCheD+8qMwCoPcwGJzGKWBeCS6DYxmAk90wtXgHwy/Med7rc+mfMPFE2LcCgTTyABd/vES+EC8vBKNC9C/Btj1GLfhmboyLX2BYz8jSHhGCXO+COT8uhIijheGCBAsl/ExAE8u2dd5bXPasy3dANHVEgsvyPJ3PuizTL77hhsnaihoS03cwLEO6JO5Vra9UdiWBIhrTtO1sf6+p5+sbG1AUk2cWekRQryJC9iJIF8IlWa5KwmR2e3UBeVQM0mqs8BbjCqKF+mlmi1uz+FSX4OGxO+0uK1SHl62CsR1dyrGFoo7cDfFipnKunzNR6Q9+0mc4UvYjetvrzl559CgAVnl23wFlqrEikkw12hLp8o54mO3MEMMCNA601IChXZTR9l6GqEUb3Xd8V7yg6w3Av/ujFNUphJbkvx508WBzU+2lWBT+SyF473jhqSoAgUPpBWWzOHeLdxRz6sJOHPAalv34TA5crNdDZF1mh0ldG2RgfqZFdn22nuJyIs9SC6ry8Obh6UpFXfyhu+8lrpsmSZmnST6BFqe6qesLZ84dPnYUla0eDGDnELzufEKRBYdf0qydNxj7SqqqYruanmwkMiT4+xL8eaCwXvqOc1YY9INvL0HbLhxpnOx0mW6G69En3FtcV45pI8i2hgNFnwg+AjGBdOiS1VXwX+jL7MnRAPeFBxj7bQdthedX95acxcOWAQpVnIWoUujGgxoYaxTZYHBMyEatA5AsNA8MsiQYAFihCIXKYJlDLAy0kMIR1NiTgLuJUCrZ2b08ZhZQVQDIdnNKqG4axqGi8MEp3NDlF3UBDT+2EOSFLliIt0qMge7yOscI8TBYkeq7hEe14Mo5oRvBq+QPwmWRj+++G2VP3VAohKcGg3lE50uWNb78Ep6EsTaV1sbM63maw6gt2/WaivsPevm2oyotVyDrVM/ri2fO2WSYTFeqSQnIO8oXYs8lZ/oDWUPgBI4tt9Cb23YtdOqeTCYIGQXXC/E9Hqwjni0SjAsHEQXzF17hChxdoaYzjoVD6e34nEe6l99jbULynm3ipUbfAB2euSaRAIVYrdm3xqY2qwBlDP21mGW9Bzi60Ej/ShfTI/5xYWwpx8Y0Lzn8P5X4AloyyygFyM4qJEuHnMpIIYwh9yEmIHEowWQIwwegqtz0WufgI9DsIjTYe0AEAXYZh1HjtBYckNwDFAO9cQvYqi6BwIqGYL5Kkg5btuUwBQ4y6iD9qPelsmowvS7TuuuS1LR10xW6adICcN9dOuhBw/Vo1viCM31AoW4QXIJ3BXaMkdGCQJZQG1seIiFN/XFTHJKz5JtLrjIi+mVGtssaZG1v08SuVNOqgCFdiX5W3EuT5hFnp6tV+HYzn9ez/dn+/urGBqSL6XmJeoUnBKXV0B4YZCQACWwFLUG6vmvraVWAh4y+GDqKJJpdltRH6EfKaKmlMFZY3j70U+2Jc73ZxjJJWs7Bkxtp5BDVHilqXiUlUzA+BOYoRBHSYWipQmfWWTPN8nbW7lysf3TPfQZaxi+1kRbRowsucoiNi3+lvnchWjagMd4Ds0GxGhHKB8GRAeAdZo/wkjQldOynihTRkvAB/g4a3CZln+hSPa1bpYsjSdIAE4JrBZdv1WAB/whpKQMMCxVZwK4YDcWcY58DzRSosZMh6TsQcqm1ZpJlNPa8g5X2OlGd7fNWt90cdG2pk1ld7CTDiWN9d67fr/oScyIHW25LWHeMlvIukjMO5UbHX1wQB5GLzYY/zxaVw0Ai9fAsbeuICjKongGwOoWIl30r0LEk9wA+p34U5co0y4u9vb29CxdW1taKqkwAH41eILWm8L1I3FVc4EHlZZkXZTLYrq4B9YWuGQhpzG6M9+HAlwx9iQiBcHpHo3cP3BpxDGK3xgZkBHvyOFs3l55GAlOTqMQoA+WG3XyveeD+R3f3Lvz4rnvv+N6d9z/6eNO2DO8SCFSp1JeaFj57uUyUi6EMLu7M6CuacUTca03XNFDPAwUqyrYGTFccPqRh6B4bnzHN+ofk5+dAK1a2K6vnXTdZyX781fQf/A97d/6gbpomy7MhyYYhBUkGBAxtgBComSN6pCEvBEC8aDCjq1i4EDcwtLtzjdhyGN0JzUNQ5KikBQMHLOtmUH3SFS++fOV/+5A9udqkPRZcwWUZhBIQnMtCVqNj560VatrDYpb5MiIkFU19YCiYCzmNmhEsHumzeW8sO7PE2rzMKd4S6GL0Bc8V1LWQ1+PfSZQZIH2Y5RsbG3UONU296avpVIFFBkEG1+qJRjy7HAs3RYOiWCa7tGjbHutbbZFlYFpDuAWrVwMQT4R23N7EjzYs+Zc1rh8u8qycSvEJ7y4HM1fSgwMVuL9QSBfocbDdkGYqq3q7Zz7+8U9/9JOff+rc0ztbWy30eKP2FyFWs8zMGC+SsISSlvx03YBRCCrXB66Yf6F+CAem9aaDqgromQvKL8uoRWkoZVy2EcGWEfNGCLCoTJfW27Onzq3e/v2s3t5IVN61dWILkMSqTewUuY9pLRlRvcddgnWMPW7hfQJmY8nWoNGGTpIhx/v2yZAp1Q7QNl5r1Zlh7d5ze2cetle8uC2S0kC7ZJpdIQL8IkQdr0FublCX3CkkoE9YNccEN+L2SG1QV1ImPKeCn4HalptjQmXwv3AlmI7DZiptjstzkv4SITUPseHsQlDBWKwG/fNpwjxMOk4NICv3hyEpykrn2QB9kgALDDU51G6Gx4JAToPvgN2UBqWh7h0ykUPSNzkMsIRZI4iRoEoJac7Eg8JHItaJGl9tG4P8l72CAhfnPJJosekc7ilmFVNUGmI3vRmULlcKu9/+xw/+l49/+mP3P3C/0TbpE41zvZ0ei3E+S1h4/BbR5aVowdnOPnfEAwedswyAHIoIpBloN2R5MHFd29p4T6Sg5J94yhHRJoiEdLtRtgMTOMmbdtCJrQfsJ6tUP6R7WCqsUd+GYi5O9gT4IWwbkobBHUULH+dBon62aJiB8zwoQ0k5pXqtTFHs52Z65umLh25aqw3cAwpFoCI/EtbMjv7Jlu1gAJFxQn1cfLqUvcJPIp/Gg2MBAwMhTe7OdaBqXWaCx2cQLAG0X92AbwwciwYIzpcbcT23UWCWQEeYzVKazEUMDsaQgWmyiUrns1miVTVZmUwrqGiHtueWWpMTKWCHRxbEKsGeLnBmFPqBUh5joGYsLXMH53JdBCgkHvmGztgRJgMtSfYrXbYvYwUu1eGCr7vEFedEJIOJKVEMhXc2sXmRDHv7//b3/9OHP/zxxx5/3KYGG69Cvg7+jFOBly1h8Uc/PkGONWeH21tyDADz5eeegaEen5QwDhhSadcZYAuo6840jjtMoUlduF28jiVrcjsFF0w0yNzBzOZ9srFRndqrrdn3fsCgCo7AQ02TMJ/gJ9c3z52a+xV7D/BzeDQTR7GpdUAC/6+BjC100FCDuvce284MwFI0bLNHeVBZSMB/jpvaLYQUuFhJloSNiIdldQytjmt3iLeorQ+C2KgnfvJsXpfmaPeua+zumpvKTwe68uYA8y2Pq+buLa7u1JdywW01dH4CJxsbi+zv7rR1vbaxrrHeGB7NYIogQCDCGmlqF04JgmwB4A1SZZre2L6oCmBjqnCEWh/uohgUssD+u4yRH2vLpk18Bos6VkCCIviXjF3GHrf7aUm7JGW1UR/8y7/+9Gc/+/jjj8JIB5imDcsC3SxCnItnt+TcRoa/z/hE+E1eSZT9xao5Mgch6pFl81nddi32M2yTtoEOBGDzSAJckgOQ8LnIgseN1mWpZ8NzX1v/xAuzJ0/tJyrHMY2gJ11u14Xv5a4uv9ezofIB/x9MM8Q6tcbmp8/Ypy9ur5Z6ZXXFQPkmcLpnSxm2JGJdMGwcYtP5HW6yOM95PiBKxOnw4N8wlcOfQeHRjCvwFv0MrGcKWQU/bWFzhKT3A759hccIt+nKNcKLx2ASXxArB3nhEvv0Bsyw7TqImqQwwHZ/f39ldW1lZZoVWaJxdggOsHSNzFyLQQqMOR4ZBgM2NBSaqw76e86tsaur0yTLoXpxGEL1ovdQHErZWfoSVHQJA3rBQh7Z0EvZLBZ6scbEpq3lxvbW9tnzF1ShqVkcto8ExcA9qqjczG2a2HVxD3ks4UGWDVv1cWg3Xg3753AvW9hryOZ0F7cvYi8HqGIYug7Q7EUBDZxxDrC3Ppbv1pJUF1QeDmnXZWXeza49vq7SIulRtUrZzEIQMaf880F4+DHaafx3/mVwmWTwRtpk7Wu3Nk/8g8krn5eVeYUJZYh4uZwzrhXFlKus4a60Pnfv3AsKGUDWjSfLgWmy1EKKXj746DNR3nCFOU94dbSCBOwjjoLFpxmz4pJjABPaHYHbDOnySr3rlS23FnTveX/P+TAhNEErhO7BSpVlBd2C5vsXd7en65srqysKNC03Oqem/6LYJnjysOEwSgcumao0y/Je9bN5MyR1mqaTlRUuQgaL1CGTaTaltJsFnURBGreD40DYQa9l7DqSkOzhI7F0pq9yuz6FbBn0p4EGjiiMSe7hbDQYQTgMUBUkW12JWy4ranDITWHzsdPLdBDFrph7UUbO5/XubH9vtleVBTXVaLtWD2kGTkpKffEOctRiTeBBWCQue933mVHTyh45nIHmSzMAZ0AqyU9CiqERRDD8Do1UlvcVYaMoiOQDH4O4VgotM1W3NZ9cOL2rn59NJlObDjodAHsANg8/lxv8EoSy00TO53V8i/84ABV/e5FALmGLy/OCxBnEgHCeFrapip9y8Wf+XZKt5Gf+mfo/i8RoqLrwHi8/oGNex7rCtQzhrkDSrnMC9wCGC0A9cZ43dbu9u2MHu7a2lhU5cR1alX7kdYBJ8VHBwDteM3YdKCAjAb1Ozf7OrkqGCUyph4u4PLprzCge32VpBf+GWmyPOBp5e6MAht8/qR+F/SugWWRRIMnYw0c3rIae7zpReZ7bpuE+w2h65CiAIKOD073FElBxLDH6YlLx4Hp2LX3Wl9o/c+dxijXUbbu/v9d2LQzy1YBl7Zp5P89arate6TxzopfcahFZl+LJx2mE0QTeDghrNcyT6RRyPCqpoB0vXnFhqHYUxccAAoa1AuBrqYUZ+Nb9rlyPIWg0poY27bInnsythRhXnmUlNAylbp3YHiWg1EIY1h2pc2ZEfTVvOxuKB75iccJ2RYB8eD8HG7xCeMh1Bb7UayzJl2kYyNFBS2LfL58fhaM/3mB2fYZHWtc9oDvQA9QYxeO6vqvrGqboWmi6eXH74u7u7uEjh8uy9PAQp33Z/o2WE8w6dCVcxMsOMM5rf14DD3Q9zq2HMI0bf83XdC0oBC145oh3JHCx96sDqCOK5EjlHfGwT1pBfgO6qq5vrMMkJBzv1/c4hYy+y/OGkKwyGF/Q9z1KaGroy6uNqZ/JNYL1SGMz0r0Yu3JdRPre7OzO2q7JdJoDr8If6rYtun7oB9PuTVanRY5IHk7ax00zpGB1ETL/8Nhka0hMMzeTYqJtUrmV+QYfzmYOTwE/0D2koS3qFYT3EnI3spu7cgBF0PVJ2iVq2Lq415rNeWt0CXwCs15A4fmMLBc90AOIgHsMrghuowXICQydIgNPHj5b3jGjjmlKkBYwmzG9tlCCG4TSuCjU6fBxqw//+fBDhprVj3wLxIl2K/u6vi85KVL3ZNE9WY35Z3HdIYIKRNxN0zRbO1vTyaSaTHZme3vz2drKSjWZTqpJOFTfSyzwcpAV0F7OkSQeNaB5ae1ZUQzW1g20861gPCyTMcK6sFEZzfgUWnecFQrPNFJ0l0o+yWq+ILhJq0OYDVoUTcoqVTArlBys3s31oyAbPXSqUuhRhS1guA2dPLOwIjXaZNdRGYUaTht1IVQw1TG0DH2etra2jbFZBiFoCEtALUtGo7vBF7a2b1royI7do3jsaRQglE+MoW/vPCFiBUrDbWn1sL5mIQ+IE2DAyoMrUE+8sem78KvjSi/ivJ0e5NlITyhvjie6HTr16F2H9lub5U3fpgZKnt1OC/J234t9BbYGnJhgR51DgBJ84H0MkjsRTJwTSMvpZ7CQWdQa+876lY3cu9HzLapFQb2Zx+AHz0bM7/AeL7f7F1Pk3Ek6PuX2Q6O7uMg0UmdnurYz87ptm24zSbM8h9pYwFHuQG+9okDVGfwPX8dK1yMUCE6ppLkHskkoWa6YwMl0ZnU/KJjbZ7pJCS226bpoSCE9UG9VytG7EFrIVbjjWUpu4T16Qm5V5b9CfhO5DwMk/9rkda9/89Un//zJM0+D3U8D53FArkopi8YOKuF2yHaANk40CtyZx3LeGBfZUqVEHAyh/2CfAFM3UH1NQeitrS2AoUMHdprwCaIN+o9nGppeIT11TZdYlRc59a2kOocFfzj+Gc031PUA6+v1dDBtmXUwcGvoE2iIT2ujFO4Q/+ulP4lM1+8h3MizbjDH3P6PoFWQahxs3yWrX/3u2SfOrK+ctF3fmz5NofNxiDcLNo4m3ggRKccoQL8hasOaAoaTW5NEPgXKFIHgX+AzWdEPAWnT9R1FJqLScS+keJkLcpNFdDCGEvTwESLuP+As2XjqozOkHU/7naMASZQHkv+4z1FdSddDpjDPi65rm66DnlsapbYZuqYZBluWBUJiwWVzOTMfmcE+4a7HG5oN4Zx9FpbuSz0lBmjelQ/WzmZz2/dVVWXYhJ2whFTWwEgSRqNwStabI96Vcb8E2grhG/lio9N1jqCe7rZVeX79iRPf/O63SABZa2gAGu0R8KjgUmQZRWM4vY6lijkyui0AWpIczA2IbdLh06RfIrUsh0Y5dV1nadZ37S74FyAYcpzmAh3m6ZPQCwmcXorVUE6vH0zSY4Ewdv6BfB1+OObe0DiHDSQoYtOw5ZnViV1fKVRSD0OO4EeiOAzZYdIYz4oZWKAMxlAm70wml3oN7gecQjCUie5nu+n5cya/boKOiE0GHMm2HAIg3JCFn/g4kcRT9IaI/iOPzln0UkvG2djRdsEZ932nqaembyeyCPIPBBZZtT42hM6BzXzgljQoA4a9tmXbmVRXcIWd8S99legRoqgv/kbdvVQKnRB1plEVQF+1oizbpgHcB7bhxIolfi6a3ileOAQoCp/h82HPFzRy8BDx9hYjn3QCeVnYLOuGoamhknE6qVDzZEmRQoIOcZrYV4i0DYsC55E7CyYaK+uEpCi7DaauXy08X2r7XhWTo5ddhmDjHm6Ee81QaXCu0Edz4GO+nWszQfoS0qmguVHKYCXm5qHNnZ3doW0o+IcoHAQZp2nbtvO67hpoTwaYKNxwH4jW0OMst8bmWhewn5DRY3MY0VoAtqCxbBb+47yjcfhKOkd0c1hZWWb9lFwXqEAaJtCDhaHtVNpCOwoP58Qi8TPBaYmxnY9AXBRZvF7tuRiG02AAGbBpkprMrG09uVu+/jBaeAHdEDk6o2igYwBvjEefhBb3egB3E9oPOVRETPlS3oTKUn//AKbAsVRWqRZGq2OGNZroHPRQZAG6qKkzVpgHB5xyIRI4Mr/rPV///z6CFZpuCtZd5GbZHxpmOluMuE5gS4oi15ATyrA9LXiG1IMbJ/qRwUs1EL4zr1PIvnCWboGL7PoWvDtQPGAMWgWER8WppETTLIPGDXAMMFx7vrujknRjbRWcTuhLkQEh91BYCnFgwtUTmMQ97igp5Q3XYLaKnI5zAoD1QN/2w8bGuk7TFkMtLr8NY0symG8CW0OTdKiMiS6JtrTzIqA0U8OcPGwXnuf5/u4epKm63hqwk7MiMwZmeJ5v2q7roUNZnkOgOQcbhy+o0rIsaJ4g4K4AIYMdJ7GEGyDtBlopwWawfhTtrUTDxVGy2wcF08TmwzxfN72BsW/tMMGqIt85ahxYFhBStAB4KlIMVg8unnyFsg/l5Qk8BERIjJ08/thuYwBCk4Fl7QHqIUMbWDoSF06AL/P6sZEnhgowp+mThZI3w28QuA6ZXmmw0Nn3qte6Q/OVCdrH/d2tF56cqY7uzN0SMYiFzIO9UIXVjEWMDmklRsqNHO5oU50e8c+DZ0K7AgokGapJNUlV23QwW1pDm/h5V5seEsU4pAPJGswxmKnDkWOOHmO+2FXVMD6GAXJKZ1lv2/1ZDSY5NHsC3YcduxwSHZ+Drgb/TfVgh4s7OwbmjdmN1bVqdS0pJ8r2iQEh4vUVKyA0ndxQbo+OXw6WJr2NZpGGGugcBoqurQMDB8mMKjbLoO5SJcn+bJZlWQFWCUOnaIPRvGdBRTYwaIMsq+sGe5rbFtqFzsiuw6AIBJ1XphO4K7ZNRmeYH2e6MoEBMUrVfUdvFlCXRLUISN1sfVH+HXaBRVbQJ+Fp3ToJBsYzTbUqkq55/o3D5mWb9WwPEABJNijo0CSYwitTmd71jQ69e+wrWBYGPbi56EkgNS9cutbYRx9Z2TdmAk+S+WwBj2eMGtqwx+R9MP7JL9YlFOjAyFER09h8+kZAhbzVFmcn3PXYizXgnnfQUgE6UhEbjz4obXD5HzfAHG8KjxdmSom8i5tzx/zsoQkB/OeVfmBcEekXVQ/0CdLfMBReqZ7y2EPSdZ2TCpAOouALVIgZ8lzI/XLjVp2n7YdxOpgbEGqRl8YOTVsbUOv4XFAnRx3VvUPBFoSbqw79upqmfvrM2f6p04cOHT58eDPJMliJK0hm+gJTE7IR9Bein0iaeUEd/ET6gIHxUVk2XTucZZChoTA+xcPTNO1xB7qu3Z/tl0U5mU7KouDnxWp/hw+CQaQGaooxo4v0BKAu7K1Pl8J+kvAivwO3j3JSQHpVVa6trm1vbSmtu458sExXpSpyqPrC2h2YxMZLhB2GCkN3fI4ovRIQ/iMlkwaYFK+SfrftZnbykmu2Tz++n9hNn2UM6iNQ40gHeCyHZ2CCqglqJrUGDxcHZ0mdY4/FNpl+/bbtsxf1sVXstp/jPA2GXxHFSDDAgi3tKTLkSTl/igU7wHrYLDZGHjvJJGMxlHmO+NI5skpB+JaQ6dQnReiBONyyoCklFg+i0MEspjIFhg+z5ytwk16WyZ0nmRK2OEKChNthP2EgPtbLFkEMPfRuQGAKxsNc/Nl9HzgVZ9+KbWaP3WU5yJwmaZNjQ3nYX+wNZGHcLWIYRC9gyuI47CpUSU2qSZZlTV3vbF/c2dleW11ZXZkUWanyHAbz9XBobkCCDOY4go7iIOjdUVSKvDwqpVJJYS2O4OQhudRZHYP7pkXPv1QKYnsX68lkUlVVDhGnrKyq/b09wDxmwGNwdVS8GEinKd1pqSvQvXjOlPoFQgZu5rhWmqR5kU+n03pen3r6aa3TjfWNAmb2qJXpSgEBPygPTlVG8ZoQCoH2F4ZqxZ3ZLyprfezUg9kR92rnsxVdXHU8t0OVqtyCDxw05KI1LJQwB0YF9/o3JTDdsze9gmkPNjQczP6F3dmZU2V/Yq2gpvXuAw4Pz/9xgBdxri7KwD/yD+SrwdjRHpAMQBIub+dUyfhFlotTMG6hMiYFFNp3Giu08WF9h//IzGWYZ7guER89hyUfmJMrAnwY8BuBw53W9pzkkZ9eAweHwLMzBl3J+E0hZgRzWegWBHCnVC0Dd31tYWiEwm05w/ia4IYxOJAzrriTBfrBYLdzryknCJ3YwQMWogUFQV7kRZEbM2mbbjZrdnb3lErKotzYWC+KQokoKl2ZLs1LdQlYf5wKiyeHYWiGvqrrJEm/d+cP/uoTfwV1P4imsKYD9ZfByOVc54NNqqq0BoBlxvTzum7aZm11TU9017Yw5xKxqBm0g2e9QOGADNpyA4oLAqSUHsPH4q455IE4KP9sPu/aRg1QH7a2sgZz69NhdW0F1opoQ+qw4xiS6aFr28FYkLEUumcAMWdAGAfmqp9x/g4YLvPBrK7nQzL1iQ3hZwaa4f+M48OeVomgibKdNuagBg0uGkTajwjf4EBiiBBtPT0rkk1je9uDJ4x9Rzi643WYm3waFuBMTXfFYDJgPANfQU9TjEpyl//HcxubBiJUEuiGJ4JzKifuhuu3QsiGBTx2qjLPoy5msQx9xQwe4mjOTWAWjgwsNqb5ZqhjDJoK1AeJLwaXBu/Lwf680R1LIFTWmOrigDS/HVjZ4ZT830CeYWDW4YKJ3Xi/uXOMyNeTuwe6WmdFpdOs6MFK6rDT/flkSMqyqKpCYyt0QG7jPkNDFxw8h5WR6AFQVHmA9l5A41lawRSp9Au33vqRj3z6m7fdNm9a+BtyFQiMDGobAFOhNdlmWabzLMsLSLOB2kdjFu4LyhjUY991HvWBxdxgMFOo0UfEgSFRAcO34K8Q3q/ruu/7Is+LouihiXeSpVlRVEevvCKfVJC6Uym4/rHrg2XeYLmrji/Ndj+a5kjOmrUYhfvhodNcZzbJV6f5oHociCbgPOxMyiTFiGZHvRYUZtnckUW+oLCxiSLgNjB/zUCRdf7IYzD9uqnbIs1AV4jSV+k6k+fPKZhwA5YvoUk7SRTyvKIYVyjNZa4c6eNgtcY/kAJFtwhC3K4jS8yu4WHVUjwgzEYK9Tps6AcEhw9Mu4d28Xw5Ydq5vpGfHSJyGCWiKl+AH1EpE6U6EdiMBSMUnwKljLOm/NnhpelNjsM4e0WacgFjQ3EVqjCT7fDdVwKve/we0pLpIXhF9ZqAKAKMCRT92d50fV/Pm/lsBqGxLJ9OV7M86wCzZLb256Y3VakneaGhSSsUxcLj9EOfmLLQSTX9xue+/Gd//pE77rhjdz6rplWa4YA4mBwLtk/TtAWgJrjYG8wBneYKAgUGFTLMLoLYFQzsBacXWz0GfePMMIhLZzli3WDp0N0ZRvI1IGi6djabt02js/zwkcOJGiA4jSWNb3jD60+cOJ5nGpMjcDYYTBGAQbZRKC+f2L7nRsxpOmRW5ZAIxM319hD1bda5SlanWZJcHJIS5nl7NnPhjmWG9Ijk3bnx03rClsw8uFMlKnAfVca2/UP3r1zc21dmyKsUamYAAMTM5DM7FGeIeUKkEvwbrts3QfGBh6kgZbzwEMk68HEEaIP+COhoDZ7iqH1R9G1ZZODfx0fNSPn6cLMXUrKWR3ZiJQYI3LqYIg9MhRW/FogS2ArbgPZ9jwaDRk+D33d2EfdH5md1z8OhaAHO4ro4x6MuMQBvcnoR5Q/2o/JdVDwgxmdahRqgvqU4sNI/EZhiML1vMGUJ476sqZv66dNPQd/TPM/LIktTY83F7dmuSquinE4Knei214lO1lbyJC8++6mP/fmffOSOO36038zyEjxgxNVDCGqvrlM09QfM5FGKm54xVWlR5mRcwXi7HPD4sG96YZ9hvGDetg0spyzp3Nq2ASfCmLaFJDAB6EAgad02TapUlmUQvZoW115zolI5jEzBPnYh+h04BDkGTAumMEr1gGw3CAfB9lmES6e7Q9eVZCjLfjodEtUlSYlRZSoadTT4DDj+EFfwtOCM2JCoF/8OQiMiZyVtoza/+KWzT53PTh7WsImQV0QLQahKR2R+JEQIG0eWq7cKSeUglSiDQc3gRi1h3OgJwkU8afOfKNCEBn7AFi5eIDxxpJMhjQTmplS5jM6N+ZfZzCl5Sh0s4sdGOg979ULXd1KKRHOA3UNAGQRmvNPsIa8UdwyGS9C9lMIb62H6qtt3VOWpsTBeGcejYxdzDOn4YDvZ7BQrYwOQGZhQ9PgtZ3JDy0RdDEOuVDJZWWmbbn9/bzab7e/u5DqfTKebK2vWmv15vbW/hxa1mU6LcuXYJ//z33z085/64Q/u2q/3ycpoW4SyULtOwChDthazNpjwIRKCuN0AgzEctWMEAEzlAAFmCBe8MAMHbu3+3j4wGsri1dVVKlgkcxdie5D1xYy0ysoihxKoNL388KFCZ2AUwfBuNppdGguyeK5bgiN45/2TtWR6Y1KD0xjZWQJh3Xdw2G2/Nm11MrPpOhqoJqaRuGVPiP+F/wjCl0eNPbWd9klYT+DFXboAPBjwJ9rt7eTimeSaywoQMOTSof8TlUo4hnUCI84S+1xhTNzkCUu0hQjpCj0pbsN2xzig7BQg1UqBHS26iC9JRwejF70H6GKA1Ug0ginyEXwtHnX1CdkgKQM89woTNqyUI1WEc3ffJuuua6DKnwIAIZgro37Mq6xuPSCLj03kMRyeFLFNYJ0Cxffz+azrACoCIxTBeSRPksIEBBhgr5FdZUI9in0nV4ePl928QalJVlaT6UbfzfZ3ty5ubW9vZfuAq+it7Qyk9VRv7DQvVjegILU8dNXVJ594/OGtnb2+gTVgUTdrAA3Lo3JocJz9yVPOjZILmCsyYEXnOafcXJ81ivlleV6jqRzkOm4jIyspp+mqgtl1ppukqpyCx9+1ONNMzAthBwcz8QSwFJ16uHyPUuOmh65ApDgQUp60ydAk1ugW8tCqAiUMZjZ0sQqmUpQhXTSn2VGOu+nT9/BNQnqw06uQ6F0wisY/DJlKhswcOXfaVK8oBjgSeEshpNILC+4k4UqqFkPJCyrVEQjh1QQuOrD4AssFk5QCrsHXdSKJ88a4fVIDB6kV317sCWhHDmI589mvSMSjg+fvELvBhgnxRPnEXDTNkSSnJxEzMgxt0xHKMA5r+yZ5kTB2N2TIwNjAcb8TOQ/GqLJYWS3293e1tZkuzKD7wQy9zbG5WZ4X0CcSYVWuoQ9FFwG6xRVA3LWIILvwVx4y4XYGbKg8X93c1OUEamvredsCar7voElqkWdb57e+u/0DlWeves3Lb9h9zne//Z1HHn/86TNPdW2vNNQPNG2DwblBATwM1Qfejap1EGvJ5IChZgz/uldkg5BfMGCbFpGgZ4w6kTxyb4bRLHJfcQIw2OfUYwyioLyZzsF1skwP6ZBy2yOSGRCzQFAnwF8SQM4ZaD3nygahYd2QWlUWyjZNlpbQvI8K+njMTER/gRAZgOXf9+rUOzRCefimoAP3jBeOMVtQ0KZvWHvgoRb7hCiAho5r9hYYI3SE8avzQR8XaGEACWCAKYQcOu04nuPBq/FzBr7y147clWhjogrO5Wt1ahiw0MF6ds5oxMA+sO7deMdS4kLh1qFXMBYe+m+R3zRYGEEKahnMBR+w93o3KGF/bfaLefSIP0qnGtE+wE2FUF46bKxU27O9P/8vH3nhC264/OihQxubK+uTtclkdwZ586bbR/AThCQpv0wJEHDteqjwErrNIc2ZH4j0WfdTZWJRFqneMNNJ3TQt/H9hDIwSQOyk2lhbq+t6SPSrX/eaG17yojtvv+Phxx/d398xfQ3tTmESBRibpGbZC/DHA1vEvTstSCJCK7PJwGdOz47oMC+sOUfiwtScc3LjiIjq6bksVDs5uCkyPEbRo1Ah4lmL0MvSBYBU3w9Ql5L0AHViOY4VTTwfJa3nfWazYlKDH9650oUlSo6J3u2CUHPOnIup2glvTAANpNMcWs05RIwKMtmjDw1t0qxm0GYgfF2Sa3DFQpLSgw6igJF0DqliBARZb7mzW+RtLLCdeAIRjIqL7aJNCXuz5A/hihDdQDHinV76WIBmycYW7rqB3cKF5TbjjV0rTSq+p1YEeLR+/g5lzNGoCNfw94ljDeR/u6aMvNuIlWAjBDK0QMxd0nef/NgnPvHJj/3VX//l2spkbe3Qtdde//abf+qG575gfbVKs3yv7pIkbfsWaus0NHjjYSk8eRsT40iGAOcnDYb1Bq4rgDtm3CAN0OEiK3I7mWKwBNJP+7OZac1lRy5bWVl9+ty5C1uTrJje+Pobn3fxhu99747Hn3x8trfTtS0aqg4/6BpZerr1aT1opWs8YMclsp0Vi2gNDq4gT/s0J0QDfMs/DwXwWhzLf6GVD05XY6y1G//LBAvhA8QZhKIZrEknq4S7Zwm+IKUEsNaZOflSe+jo2mz/KWNd13EiEAnKcZpWkqozcaWNxeZI/L7zhCNCp5+0Gur5UHzpi7OHn8pfdDUESgnpRJUhgry8oxtN6x5VE0T6H08Jtgn7RkD2HuphQsf0Rfb17/hAHhkRmOBG7pDSjR2I0TYsuSyhhImBaaBGQHAyzJh5zOenpHocXdO5X3Rf1EJY3EJpTwfotUPXgjB0sFvaEF+3H4XZaKpNBERz1Ok9Y5xsCgoVe1FDN+qts/Xt37rj3NNP60LP9nafOn3m3vvv//o3v3x447Krr736be+4+fprTm6sbhRFWa2U9YycKSR8MxjbaTtgzjVDssbIOTWHNQhvQis7pJCxjiRJh3RIoOg0y4phsFVZTiZd29Z1PUknx44dBZqHPNWKMeo1r3n1c7eee9ed3z/11FN1MzMw2EcnOvNmM9Ekg75d5I9BlFwj4ZgByzvyHNWzWxNksriQyTGdS64I0ocfeghTt/AQVP0FUS70ySkZTEdCOw5iAnnYSzRIBEIMlqYoCccWrm6s2W77Im9efPLCqUebRFXKuti+wPezPgoLi93HMZA4QvvFhKfEexDytEmm7SRVZnt3OPVkfe1lZY7hIfAYWORz9MRzQsxjl3q5w/H9sxhjHaBZQckGW3H51V2tIxf4BSd59FouGOiNjOV1yGAFBvaIM/F9oYoXr+oxdg5lxh9CDBq+CTklMT2S/xruEW7lO9M4zLpYAMW34CxQ7FiQC22e1aZNPvOZz999zw9VTsY8QAyNsbO92e7OI488+ch3vnvbSlWtbxy6+urr3v6utz3/6qvXqhJqWFWelVVnWyhLNKkm8CVaSjD52jev76Fih6NDnuHkmvFUsacPlCv2XTepirXpylXHjp/f2nrqzJm97d3V9cOHNje3t7bvvOPOx598omlmw6CKEpqTkTFGMRKuKHaimBrfeV1NgQPwbAkGIPw0ysZZA9fBSVPhbFz+HHfOQoqeGiNwDJ7OHGttvCLi2huan8MEzKGRYO2SdEd5A3G1xAztoEx2+NAElg07xmVVrkEk1wGOJrhTOSi+6fucek1E4QqfTw5TaBLaDMrBMAyF+CLT3cq5U7PsJ4pEdVzECNGOuFjB7c5ShedjLGPucX4sFriiOwadRkUc1m3hYhgq5KDZYmVLl7UZvS3Nd3l2whShewEQL5zWgg8stW5sQvuHCG6sox4pRhmiRUU/EFCFWiEMjHjLYUm1dSRyJPuK7cQ+praHgK3WRZEpPXz0Y3/z8Y9+6vyF89jqAmJVoKXIQgQll/Rtt9V057e2H33s8dtv//rqdOOqEydef9Mbrz15/Iorr9g4srk2XWta3ZsehAFYoVA0RQFpRCERuJihO6EvpHc8WfDgbaFnvkqGfDJJ19fXj1x+2ZUnrjp16tTTp8/urK6ure8fPnzk1NOnf3D7d588fQpqEiA+hJuH2DoJtKWmhpS6oPYXGBGEcl3IL2FHBF9bgmkzrAd0cUf3f4QBxLJnCr8nMAyJ3V0Af3I60ZtxcNcEcE18jFDIhAkxF9p0H6TIGcY8gEugZCXP28NruxpmHkJ3SOiEBNoceCnUyRHumrUo4jTRW4Y6S0UCiwJCDLqCKQ1MAjQ4S+E7UKigkg66cIFrnIEMpBtotb+7o+w0hbHyXJrCSUvBVwsRbyfwAgH6PFNUlkWey6ChZNSy/IgJ2F9FAIcc+3nSZvUgGSnKxzuTQTiUnAuwVMwgQBtOeIg+VJKLJE+JGAPvwCjaRlHlEPfCjDPVuEUSKo5cSVmx9DfXAgpYuMcuJ6t5am324b/58Cc+/qnTZ57WmTYdlLF5kYM+AiMVICMMCJOhnrf7+2fOnD939733TKtybX3z2quveetb3nTD9c/dOLJaVJPGJkWW1k1DFWTQaQogWlmicPYiGNasiHzxbzAK4YWVydTDAVc8LYrnXX/9FceOnX76zIWLW/Vsftmx4yevvPKO733voYfu39ndhdgmBhBgl2JrhMqxUxw64os02efUGQYHACxBLTkIRuwDtZwwQYOayjJgQGGmq0kFOXkcNpYqqK/0I+2dq0bhoEENkGoCWCOMgwEJgCMlYNoAJZzJr3ZKech1MfTpxuFUFxum3VPJRCe70MMYuLfAwBNC4vGzPqGj1B5ebUK5fzS+apyEVFBvLXTKZpiTh/cTFklTDK20qeoUaNo8TW0OXeoH064+9Xi1Z9V6kUHzcIDcwbQ0Fl8xhHnRrnRKz5uSzuaKRyMBNkZnGNjHDYwwmbH34o2NEImmNAcCKjUCKpcoe7Ga+A24dQy74vt4HEdgg1Hj8aDbvRsj+NtZP3RJ8juMtVDOKjtghlLLWOHGKjkIP1feQe1vhg5ot6ymKk//y5//+cc/+vFTT50qysJA4+Uo6+Ax3NjRBngbVgUgfihgadu2rdsLF3eefPTJ79x+23SycvLk1e+4+earTp44efz4ZG21qLK+hYwB9I9vu9o0mc6KogT94QZjA16bPFbXIossU7JvQbdpa5t2sObw5ubGxvrF7Z1zZ8+vbu1fdvjyq6+/er69842vfu3HDzx0cXsbroPFBZQlJogExYR7i6lgqCoHkDGMR+kBrZxCVwDKiXpAFcN6vN0FggtZH5G3Q1HoQ+urWZob27p0L4IovR/kzxK33UKghlrnpH3bdE0D4h9bArjIB59WqpOqyorWmO6stXtJ0umkgl1XO5h3zRIo9rR2KGAOeKKhUkVByYexMGJeJXNU5jAhSam5HQp4UJ6lkqKMqHC0ksFe0DXG1TuL/GwhYLZjVWsH2yR5YrJZrU0KZ5fxrGZBZw5VLp3vEIkZhYgl4Fm8yICBviswZhsCFZ772dwV/e1kHZdEdUgr2i8ugn148LAI4nIvU2+iyr6UzsxwlTbOJxWMFWoZnNiOTAb/NmancFg6YWhjCREHxhzBjVSuc7MozgNqPFU91qyul0Vnkj/70J9/+MN/c/78eZ3l0M8xFC8L+eArx9wt3UwJjs5obMDf9cP29tbW9vZd995dar2+vnH1Nde882fe9dwrr6hWysnaRppVSZbNmra3Q1lCtwuMdvFORxE/OgM3BgpmmpRF38HYrrIoD21urE9Xdo9CjVCRq+3d7Wuuv+bUqadv/eKt999/7/5s1nY4kg8qkHGRrlW9LwhRiW1aqHmAYkM9FFhKKfwXemoubfTpsAzZ3trh0MaxQ5cdBVAgRzDZjAtIdEmmZM0CtjPph75tWmhuDLg3clD8HcHbSHUy6HxNT9NsyFYLtd+DyDC9hQYdVGjRQD8fTN6l6cyaFVTO0AcEZqDBMVusVC+UslnSQghZtQl0BchwXmGDrAFciTZ3kaYNzlbDKROw1NyqLEn16qHy+uefWdFH86GGDAU6A44LnGaUqtKzR3hL/FXSo0v2ekgRTu+Dzg0sGVyYLAjRkUaP8JSExaKRWWPA1ljjO6eYMu8E5PDlCu4uoqeA5E/5XxlV8t+NIC3BSQIf2OOngmcccbvgY2GzUPtI/xloC5XY3KSTqiirybyef/BP/uizn/7s2XPniqKiB3HhP79M1x9Ugti9CCZYDz01cgunvoydN/3+7OmzZ8/d+f3vTarVkydOvOWtb33+c687fuz4yvpKlQ1NU1udUeYeRIDOqM8JtnT0tZ3OaQFtDE0EsV4qW8nyPutTnW6sqbW1tb35bL6ze+L4dVdeedVdd/3oW1/71tnz5y9uXWgamD5BOpaMHIyNc+0Y9s4AjCT1OiyKAqdJ4SaxK4XwblcEB+hrmBSfaNP+7Xf89HSaN80cNwFd7rjfij8NrqMF/ceyykthWBg0PgloQ9AJma4qPSmbv/dP9Mt+qs2hRW6f4vhviGU2Kp2kuTI9sKhKswrWbFOV1clgVJcByLowFkpYoHmj7aZ5CZlz264YkFtGZQP0WjF5Anar6nrV94fz3ORF3bV7UKGVwYRha3azPr3++VnX7U8r2D7oHeRRZeHhxM8RuwrLL9qNiLX8j9Rwx5gUsCujl8hyRWhnQaNUjmaxIdz4y34kOP/rbAOqSKOWdcKnFr4jm7nBXR2ZVyP7mv9xeQtvR0M9YW+yIk8B/e9Ty86YIGPcTdwTjB3VCSKui/ulGz1My+LsuYt/9ud/+oUvfG57eycvSqy/c0gZzmPiCqhjzEISzD9TAM7Q9RG6AxfJUuwZBXR37tyZp8+c/tE9P1qdTtfXN5/z3Oe8/a1vO3HdyY3VlarKy6zs+8zCIOA0AzGA6gyuCKzEmBC8gwYVCFsO5YGFLpJKY2GQVqqYlEOmrz5xXVGtbh46dPb8udu/ddtjjz6ys7sLCDNaKzIz7RV2p8UgFs4oxSawGGDrsMGYGz7qcI7gV4O21BBBPHn1817+qpcXqoAJaxQ848mSC5TJdACUQ32KzGBTzGE7289ND8MXxNagrc5g+uJIlr7uRd18f219JW+g5xf40TrVzZBo20B0UOe1SarMKmPrtuqVgR4MpS4LU7f5vMmqbMhUb9OiG/K1Asz/KTBtAoONFTRHb3qtBtMOWVnoLK1AsWdNNvR1m3WqWFupbJJXMACKAl3i1MPPSxI3/gMR90YfHQe9AO4GBiC0CY0apo5eCwlep9C4daQLXywaQuNIG+29euSRh9EYkh9zhT6j6NK4jYyvUYzNLV6nC59iQamF3oiAjgVYqpg3Fm9ZUPpeJ/PoAlK/cGq2QOU739/9wz/8oy9+4QsXt7fyooQmHNjyhu/tZUhwy5n7hWz1ktaD9F0wn3u7crxxSJIG+mYiYhENljTN1tdWjx49vLp+6OqTV7/jlp8+dvT4dFJO11aTQRdZDp21oC8IQbg0kj6WOoVEACp8M7TzZt7MYCJD11VVVeZlO/RDp5p+ePjRu554+NGvfOXr997/wP7uNkgo8IphgANRE5T1YkE/QH+hoDDDJjtQX+SiVyrLoS0ylupqaBVt1GWHjv76+3/5jW96w2WTTZBOAnwVzoKhmi5YiWXJk2qiUATv7++3TU1ed451Tl658BwSGN1LY5YUDA4EFyojUB2MBqah7/A7+M/QrhsSE2g440xgMFNwNjxiS0FMDVYVGMNCSCga3/iQBhxhlvw4MwwKsTBED2+UBfgMeQ4pCcSZsUUYtbmJ+gMJD9P/d8londjGDsJg6NpuPq+NBSUcEuTyVoExY2WODdLKsgSkoKu/cxUB3N3Cl/1wAJmg4A8//BAEBtDC9Wty9UhhBFR825A/EH1VYm7ETWLljggYrB0FbewZhR96fHHHcI7fsEUyJCGgfZQajm4curC185cf+ovPfvYz2zs7oFJ6w6BEik7hBkSWfQAyi1R7KFYYi0ovNcjInuNEGOz8QzhpBaWz5DDAOIJifX2yuXHkxIkTN735zdecPHHZoSMbG9VkMjG2HBQ4/9Q91FVBwv/D4G9r2w6a0imlOmO3d7ZWJivrKysmsWVeTKfrakgu7Gw99OgjD9z/4Bc+/+l77vthNwM4B1QK95D+JRgWMAfM9y2SRDVNC0PSAHsMgIphUOura5BpGcx0ZcWa5Njlx372Xe945ctfduWVxytVghCRDplEYgYpjB1bdTadTHCp/f7+rOta0tnEwFztQWY4Z/xdnJIw/1TM4KIv/kBwH32TbD4HDG4DfRrMQfv5E7SDjo6dbe9pUHRmpZZG5LZAqz9ZJ+hY0xFH7CAGVhuzXEAphPfCoukna8y8njdNy5bg6LviO5Ls6X8acfJFUTh0ncP1hvY0spQf4pWQP3jwwQcxahCxkWBgkQSLOHT0Gith5CIcEaLTvjPczQ+eEE92Ufsm4+y2U5sYPemsTnRRQuZjZ2/7937v33/tq1/f2dmBsFAP1QoaJoSAfMb8EvkRaGSGPQpyUhQe88lLgeJKj5gOhiSZzWaErPCxcOcd4HfdXHStdVVVG2urK+uHr73m6pvf9lMnT16zvr52eO2QzieQZ4U8C8Abof1r03VdVze1NQB1TpTab+aJGVamU6VVrtKygAFus97u7e3tbO3v1bPPfOZTX//GrRfPnzdQloRTl5B7FXwY3BOtdd1Cp1I8W8ZJQ9va1Bzd2Hj7z74zayevfc2Nl21Ojx85pnNd5jzeHTmRvdvIgHJv2cFmOptOp2maNl0HWFHTEzonz3JoW+t1uKgo42qd0FkhBMjZvnNHTxl2R8shMOxTI84aY5XmFeDgYnUBTMqKhVdEAwYd6jg8WUS2oaVXxAWh6C0yT1z1aiCTEJgCoYwgPKjTEmifEcX7YQHy60rBwKqirBAIiMiZqHGkF3qcGYSQMKSRMD0eoTF8Vph3ODS99l20o4iXxJE6f9ZzQI+AW+4MKQ5TPE2kLj0F8NS5obfQRkbbtktsf+7s+Q/+6R9/81vf2tvbz/IC8r0o3MH+nKxtbK7vXLwAyi1JoVgP0PbQBYSUP3ZphfgSh9ZxKTJm4JoKsBhJAagIhfUFVPVT42Q2EYXgARMQSA87CdSz+Wx/355++uFH7vv2bV9Zm25ec/W1f+stN11z8urnXX3NocPHoLcztNBooDMaNMfCVrepBsGb5fNmDh7mkEKyt+lB3PXQLDtN+zLr3/3uW/b2d7761a9AMWaWurLqASsdWL+tr6wdPnzowoXz+/v7tJl11+ps+M1f/Y3Xv/oNWhW92l1bW1caECrICu5pfI5EWn0elYdEQn+mzBakuFkP+qjrONbjKwK4aI/+QLeVVO+A1o45A0nxxCeheqSdqAJ43POBu71nZKYuB6TgG4pYprgQJxwDoDBolhHbc6I0PGa4FATRMuoV6ZvyBXIR27PE+0R300D/Lpc1CX/zksrByUmBQCCe7eugqNnMY34fTyQY5X0oBx/8Vrwx8AXqWlAE1FhcIEVGQYRglnPyGUEHA84NAsRflxS5alP7je98+wtf+MI990CWResMWm0h+RnTlXn1Gx/4wEqV/cF/+I+DnUM3NtPkCrpZ9RBCm1sDA3ixwAIRiDzRi403VwHAmVQO5ru67UzrHgoxeH98MTc/PfbAdXFuRP9jCcJ81u3vnHnq6TPfu+eHL3/By/7+b/xKmk1Wp4AuhJgeQFCGHmLd80ll8gzmngF7gH3caaVrk9TNrIPC4/267Zt5q233hhtf+eB99z06f4SglkNisTcsIKRwokVy5PDhf/6P//Hv//6/+8EDD8Kwi6GbFNMXPP+GV7zoRRjompVDBpOlSuzCTVWKbvMDefqjFTVw2LvHAGQQgR9BYsv4dVyk4EcecSvYoC35FqLo23d58XaA69AjuNNnQ6Mghop4YOTU+odxWo5ZeyFaJ+8TWJTPWQQuRh8PxRSOqDGaBQwMLgyjeMZLEUwsls2g994OeRo1yvaP7qVYeGVoEDh/zxsskkCDUBoFqEd8HE6cZw1Riwaff3Wx7tEXxxqZHBz4BlTwop/XZ7m6/Yd3fPTjH3/gwQeHHrCThKxKs6w3ZnVl45fe99433XTT/ffeg5gHaElzxRXH3/9Lv7yysba2sfrlz3324x//XNu3xtrJZKWd70FEChsoU5YV4RbuxHC6GDXkBF8Twm6hS4gM7PmzDsvGMg7sGgmAf3DcE7W5vn7L29565IrLy0kJA2QwwoH94nqorTT27NmzTT2vSjCeoaIC+gWbtu1n8z3MfigN02HKWVvf/8gjCaycahZgsehdUwNKEB55kZvEnt7eS4vMzJvp+sqrXvKq97z73dVqVeRppircHqy88urNuUmSEEO80rHVMAxt21JbD5FI5QCDp6BY4whSFw1jwl/dDo5CEZ6FgpezwGLLXyr6W8Ryvsmrswv8rULM2d8s5ByddSjCPQ4Q6Z4jbljFEHrMrcMUFgS4yRXy1xYKGxAtQXh4tmODue874dAduOEo/IaoQDHSRQSfw5oECDO6oZdl3vTw4R9qhgjIxVCY6qIaHungKcA1DkdbFBq8gVsGukhXaTbk9itf+/Jf/dVHHn7oEQ1kmAx9B8XmuIAyzW75mXf+xI2vnmysb2/tQAK/H2555y0/ffPNl112+PSZJ5W1/+d/+A/vvvvhu+/5wQd+/TevuvLE//6v/7fd3b3jV57c2ToP85haA3OGnYlA5QQw5gtkIfbvwviVm+zmg2uOvEOkgm02ssN722qdHd089Hff+56XvOKlqysbML0FLW1sKwyiZjJdyfIcYBttP5mAlQqtt6hmAsxrCB6VeTWbb5+9ePrRRx+79Uu3njtzhotWXG8X0iwkOPdne5/94hfOnj1TVMV0Mi1WVn/tPb94/Pjlqu9UYk2OmEnkfzeyytke6Img4SGoUXAwJPMB8UfliD48ER5fzBEcwZkC8CgkQaX2dUEH7+dKP9zJE98ZwiMxFpjXhzoWVKWvdSBaXJL9FTqdbhsmOESqOMQGDnrRECCdQdCcSi8j1lmqxsUblLVRCaRsIkidMBuoVBzvhIj7AHxdzr28hLHy92ni8Kazc2imAE6jh2gH1SRGdprHbgclTChAKPwC3J7VeaUBAzB8+87v/OVf/tXDjzxaTQBqZ7oe/WmFdZjNLW9/10tf+FJti26/me/t9q2pJmtvu+XmoU/+5T//H58+c+rVr7vp3uc8AAmafPqil7384vmz83b+/l/7tZt+6qb/z//r//30qdM6TXrb5eU07ZUBUB40pMIxwn7UKbatgC43wQHGFInv5+kPAM4OulkO0Czq6OEj7/+7v/K6v/WGzfVDYFYlCRgB+H/QUhe6JwK8dFJNYdy2Ss0A8eX5vO6NOXr88Pr6ysUzF54888Sjpx575L5Hvvud23f3dzsD8A8XnkFcMrYZQ/CIms3nH//UZ5re6ElhbbKa5oc31sskbRNlQASpgk7cnVZgVWElBiTsop3nMDhMXS7HvkiNYgxm0Dpeky7Jvcaoxfg6IXa1wLmDtE0X+YrnHAcVI74W6fbwP18w6O7sGzr6cq/4aRcQluR+oRrmNqzjD48eQ9gBg7Vd20FoUOPgyFD4NmokRssAdeaCBaILx2i//RsSk+WYXgSgCIrreNMlsDBx6vvpB7PZZ3ZIqIM7aqDbH1CkSfIqKbtk6ytf/+pf//VHHn3k0aIs0aw11HnMDKbv+hMnr3rTW193+VXXPvXoU1WZA/M1yW//s39y2ZXH/sff/hePPPxIY+df/fJXfvDd7168cPFdP/vuq664+o7bv2fa5I03/eSTjzx24dx52Js82VxZ/+3f/p3jx6698wd33H//fT++++5Tp56EHlS8POhWjnE/yGqym48M7oar+WgGwaSgE/qRw4f/7vv/7utvuunI5lEIEyrgXmM6ajdJshX6PCfJdAITjPu+AweoH/bndaKGvm7uv/+B27/3/afPndm+uPXIAw92pul7iAaDgcAjeVlMk0qFFnkMPR92t3eSVB+x69A7U2cAgkAy5iPh+oPI+eHGBv4NIcN9CodeENaFEBr32XPF/lLhOvcr8J27HBdwCe/RL0A6JElMemMOCUg+xSscObO8FKY2r30XHtl/myu2OHbkM6ihXt1XzwVNPFq8Jw38IwKzYF6NDMzGK3CxgtjB7ruurmvXxc1xIWH7fCzL/YDVBd7/jZzfSD2OQx0hrzrS1dSnDs1NpQBojyorrksUtEFtStFBguI9KDVMOgOFCvt1d+s3vvg3f/ORR594Ii8LoGnsjILdu207dMevvOrXf+VXjx674tOf/NQ11153xBx96PFH3/iWNz/3hdd+8A/++MzpM0rrPCl/4zc/8OMf3XXbbbe9+tWvtX3z47t++LZb3n7FieNf+OLn2qYtyqKe1e/5lQ9ce831XZu89U0/+fab3/LBP/zjj3/i6b7rMq2nbb1ndQvR4aQCQxoqF0JrDrIENesJbkuUpUfWD7/3ve99w+tvOnbZMWwTjy0bGNQCWBbo6GcB3GSHpCjLVKn92R73V8ySer/++re/9cgjj517+uy5s+dPPf7EAIU02MAEZ3B4qAoXTWCT9wLa39qrT16zsb5elmWSpuuQWIQ+VQpAZVSvJw4qIvdwhDLg4RF1NEHDdw3z/VAJEBYqC0NeNQSMhazwqBnJcDITEH0vsLKLh4WVC45QQno4zhSUGml8YQY4vUdDXqNmQnAL6nEhze4gg+Q6F7Fc7vkAXNmzRMC+T45iBBNxN0HH4Vxi0LZNqtPpZAqBGuZAWdkXdgCHgPl2spJ7xy9pBvj0ghebIvpH/eZ7k2IszgXQAo14aeqyvKhAXD/NpjFZmrVq9rnPf+bjH/vY6TNP59jxGEaQYq9TQMDO+yuvuuq/+cBvvfClN3z4bz7+xVtv/fv/zUuPbBw9d+78C2540Vpe/vjOe2Ck0ND+2m/8tzf95Jufevyxm978lude97xvf+eb9z3wwJvf+baHHrz3ju/dPiQQMZoWqy/7iVc89uB9/9/f+4Prr73m0Pra93549/58BrCTpvmd1SO3K/up2U7Wt8+x+d1aWZhs6HAKmC+nvvMIUjEqSY+uH37/+3/5DT/5k5cdvhw8bNu7+ikGzDlwArijbd0lmer7tq/7edvt7u3t7F+8eObiPffec/bp08baxx97BCKT+EWIcFM/QByDQnqYrBscfpIdO3L0N3/z/VdcfryE2FZS5GVV5u0MxahoJSK2f3zgI0eJzWUcwpRlDrOBDIVRAfAEqEsuZIXp42jghziUMNECt8XsFQHkRvpo8Xscnw4YIimWwrqDZe2Mf/ddmYxygFNOIxPvOaeCLKto3KnTZHGAzPGii4OwBUpaFIvho7jB4j5I3kLLLmlr6JFYVmWRFwozG8xDVLtJ6PQBCruwXJazeX4/g7zid+IdiuGQwbkmhH1vem5g5Tr1CD+c9jFklLjXobIphJdhtFHfdV/+8uc/+tGPnj57FkDOGNdxch9a7B/eOPKOt9x89OTRL3zlK9/4+jfPnzlXbZYPnL774vkzk42X3H/qvovzLQMfL172ip+49567v/zFr/zqP/itnd3zH/ovf/bam17/6te86muf/eITT5w2sNTml37p3dO16mu3/rhthzt/eJc1ze7+rNK5SRLQvnbYPLy+ubf1K6uHNkz+75L5dpb2vem6DkkWvX1MSJkUBn8eWt183y/90hvf+MaNzSMdNsmC9sko4LELFeSjKAeb5xkGuaH9ertf7833H3/68b2d+fnTZ753x+3nL1zY397Zn+3rLMfrDAh0h9OlXWVthqEsDfi7TNn03Te/43lXnFApQNyVAQRii0gPRz1cOcpuiwcSeWuWPD5BWiSVqeQVhj+wHYckBBIBRqtwf1NXLcWTnMVxc6LJHblv2yUsdmfqxhwR+G3Bww4h2eTZvNw8IG6g5B5MJFuddR9ZAyGyKa2B0JBA+pfeFA6BNIDoFAAl6NrWlQuK53EmvTMs2B8h88VYW9fzrmtzhGfxyAEH8kd4LEpVxqfKRTsDaGxULWyJtKmp9SFgDO3QW0OTu5wzzAt1NQXOVidwBMB4DPSMGbL1cnJu/+zHP/Lhz3/uc2fOninKCpgESskJKIvj2Mr0eS++oVgtvvfd795x251PPXFqZXUyTVcPrU3r2u6cba49+rxXvOw1u7tbb735rSsbk7/8s0++5vWve8nzX3bP3T84+/TZn33PLzz58EOf/MxnDczITfJscvmJkysrG7/z27/znl/5ta9+8fO/93v/rkiSa4x5vKz6TNf1PH9q9lvrhw4Vky+kSWeUnUMFT1EUWIsLXNQ1HY27vGzt8C++571/63WvX5luYA6JJ8dhCRD33YNdgL60oM/2tveLPJvN61nbbm1v723PHrzvwe9+9zvnLpyHHcIGJkoD7CLTqppWpuuhkRUhUmhwOdbmQ2fqoqyq7LIj6xmGzg3qajDXoa8rtwd1JO9b1C0YnjGXEK1waIe63vvol7e5sCqaGkm7ah/GLgWbyxEovbClcgSZEAwUyC0qMY98W/+mWmoxjqhV+AIBQxeK+bg5OTV8D0RPKs1Vtwnj2VsU8tOea2NTnXLCWOOA9a9+lo+rEj7Aleb/wsgbCGl19Rz6roQRJdjoP4O8YIpADvHMzjwZxZiXxho9aI4fiQqGiN8oUxWvLnAvJYmhIYpOs1InrUmLMkvz7Z3tv/nwhz7/2S+cv3AhKwuM1wL6l4tesMtFYtVdd/3gvvvvtZ2Zz2bQ0CTVP7jn7uecPNoP3Vdv/fxbbn7dBz7wa8nQzZvmS5/77L33/Pg3/v4HkrT+0H/5s5980xtf8Yob7/vR7Xt7+3lV2nn9zlvecf01Jx996JGHH3742muuOXv+vKm7v5tN0tb80f5uafpVlVxbTR/T+e+fPnX60HpWllVR7Xf7XrKAgQSFcOrwocPv++X3vfrVNx7aPAJsa4xpDekj9BixmzINFsXKofl8Bs1BhrRruraZz/b2H7j7x9/61rfOb28D0EqnPQx/w0ojLFUDYFnfc/iKWAcrYIB7MTWskyydlh0MI8KOgFBtS1zH6jeeYuVJRjhxUUAjWIuIOcHgvJtB4+EXntGcHxXIcsxa3pkMOImYzw5kRLfySBMLq3vE09IL8DEzgTQLyC9vacoWRAKKGOko+RxR7GCJU0BDj3poiWjcSLqFDy81HkYamfcX+ABarrCV3KVaV0MBFRD+nj7ttZhmE+aDa2ElIG5uqAu4QVCYag1adNhshaDy4mF9p3hAgQGKVpVpbrMir8qnzz31ob/4y1tv/eLu7l5RQnEv8oVLW/iObTZpmna+P4dr5yDM9mZ7H/qT/zjNs+29HWWTf/uv/38ve8lLO2tPnT57z133XPuc645efuyB+++fzefPveG5a5O8nncvf/nLz54/d+/dd7/ida89f/r0f/qPf7K3v5MX2d7erE/6T7b9VeVKMp+9rlxRafbD/a3PzIbHJ8WkyIssn0FNBZQBUekPTnvKNlbW3/e+977mNa9ZXz2M2ArAkEJpGZU/kNjCX8sKwunG9l1ndF7t1vW8me3O977y1a9859sAEU1zYMC+a93ITnZMoCIK6/EovYWTiUDzwpzDFLqLvuplL19bnxYwSRgaHgghLBIL3p8TiO9lPBNRGSDbuq7HfjQkONwAUyjScAwiYzRRoMbnfR1ixPlcMeN5B5HS0Qf45jETKNZp6pm4gjriu8b1PIgj4IwDQod8IgenoCwsCbg4UCVx0N5B5Sau0P+wa6jtgatQD6uMjY5li/V1U85giB8lGSBS3aZYnhp74+MrLWCunEvjhjhQDT3YefQMYA9DJ2Hs0xeJe9F5GmMiWNreNlbV3ZDp0489/ucf+pPbvvXt3f39FMgRVA3be2jyY481hD8wOfBIDYrl9G2zX7dYGG3uufeB+x98BKvzk8yqve3t799++52333n40NGynBw/fOQ9v/jz5y7ufPHTnz551ZVXn7yy3958wxvfeN/99//oru9DYkTps3m61+y/o5i8TOV1b5pJdbbKViZlqrJ9mNnSYrIOAmDJkGR5vray9ovv/cU3vvmm1ckmDGEa4K9UTO9MDgzsWQu1Jio1qmv6JC8rm6TNfL63P/vCpz93+3du29rZgWwZaGvyV3EsIwWgsTmk74eJQ6E1Fi1NsiLL08IMw9Fjl5V5BYVKwFbULy46PyJX3/JHHOlC5EpSZwyDdREnnjsFXW3huP1s52CdL9OvhDjm6m/n47oPyyircC+lYS2dZuXoWdxGLY1bkwtAM259GN1LER9fcpYkt2l30XJu9eoiVfEDSbeT8EsQH4FSlR6KaF2VSMRTC6/xfkWQNkbKCWYi1DOQXwo+MDuX8Ta4eNmS+1H3cP6SU6fQ5yZNVdt3mQYQiSsiC2Ev/DSLZ7DcCcOYDU3bfP1Lt976ta/ed+/9TdsA96JKZwgFJk4wAWzkpAay3fDR+DQMIrgU4K5VDxQ/ZDq3Knn0scefOPUkBpHUn/zhH372U5+46vix591ww7333HP4imO5tseuveb915587PHH/vv/y293fZumatL1P59mL8wn36jnLx6SzbXNjWk179q6qWezeZ5BYwqacq1UWuTFO3/mXW95y5s3Vo9gY1XsnSU1BgaQMfeLY7KhrQyEovK0aNvadPNvfO0bd95xx/7+XlEV+BcuEoRRoBhHHubUZ5uz+qhFdKazlel0bW2d9jc13dGjm3maQ84dZyMtCZmO0FCeppw16mlasEc8C8xXApBYAmBoRwOMkY1RIbsC/+XkI33SuCgmeo0UsLSVQ65YLfh1vPGLhq+fbhlPXRDwfJ/7FalR59iO/IIwMj6ICYcZ6Ej3+kq+ZZ20xrp3KVfHu0HgKO+wYITEDk0L1UjQojs0cvI8L7c72iG/OPwkN12ksApkKyC1zNcKYWq23DisA7Z2b0qd9739yq23fuFLX3j0icfarlOYIIHwC/Y+pEp633KPC87k8bpWBFS7RuqZqkAz0MBwH5jVC6IKir23t7Z2dnYfvPexW7/6DZhpXebfvf17J6888dznPef0U08pLH3KjH1pMXmuTT/ftncre5kZ1ifl3mxe93VXg9mCne06bBJp1yaTN77pzbe87a3Xnbj+4u5OD2EHLC7HcG00ZRn3KAHToofFDP3ebJa0862tnbvu+uHZc+dhriEiz7x6SLUuy7Iqq65uDWpUwM5hQRWUEOZ5kefW2KZttFLHLjt+8uTVoJBTKJB28tprkgVycZCbZeQifUhfpeAhwCHS6sIf2GuA5QNULfkQN/3vIPoUMD5W9LEyEk6j5FZpTQ+eHYQ+FuKTlJevdeTq5MC0ASrJ73uQGopKhnHIpLW3RUSnKn8J8qlcjypRshorWP9o8vF8vDDem4h/xj3oBwv5BmghE1kwXkwvuS1pFLoZWLTQ0QE7J6NOR3sKm8XzWDt3f9+qCpugZXm2Mi3rrv7SZz7/6U9++tSpp3Duo9OuwG7AyXv7ezUG4LB3HLbk5mNxmE2xOWwU8O+usIhhDgS1tr0FRDCWKWLrImMee+SJpx4//Y1vfQs6PHbtoLRR6b3dfJZWd9k6HexjzZA+fXae6SGDscBdD3W8oA8HNc0nt9zyzre+/earrrruwvaWhdaU2iYQ7HG+ZuxEYBPYIekGm4IbP9RDPfvExz/68CMPGWtSnYPdghKRYJI5KDaua0G+gb2lSSgVjJaYQHlRW6cmOXb8+Ht+7ucPHVorCw1AWjSfcYSbp7wAI/JesG/0vsBX4r8OxyBUdyiTpYpPJ6/pz7GuHCkAN6wWWB5NKjeNKoYni3jvwkuyjQqkHGSB+9yoKknIK8+rIs8VPupFQIKC2Efswgdl3V60MtlcTnKi7AEilKPQ4OGc/C6H+kb3ych44veB2SQyKt6EUKzrC4sJeYNgRqgvGxAyRqM67GArXUhol+d6FwqAhVnAAyZ9333sYx/70pe+/MSTj4PeQdvTBTYxDGBx9gd6Lhwn8KH6YPuEwlGehOiHHfq3XS8VD1LHqZuQwoSOLtjnw0LLd1tUObidqdpR+Q8B5Jhmuf5O1xRdvd/D+BYAPBroUj6oZGUyufkd77jlXe+88vhJRGuA74BAdgA4cP6B3QuKYKFXnCZN3eiiAht33l+4sH3fvffVszmYG8SlAVeE3WpS3TWAn0aLBAIMaaqqolypKp1l0OZnGJ578vqff9ctV115pEpzxIxEeP9xrIWFvBuZ7SlkiUUXqm6kvJSKg23LoKBCNyNPrPLFvfjAzqT0OE9LZAyExwssvviqi6mnRFixkZMcEr2RUIm6vIK3wbThH4W7xRH9oCPiEUoYQnU9/WmyWswr/PXAO7wiMU3GezWRN7P4sPIpXDQ8TLdw4s0ikEPUki0Rerx49wO13oIWg+jZEUSBZsnjJG0chuQyRu5+9HTQV9Equ7JSZqvVR/7iQ5/59KeefOo0fAga+vCzu0AL/AC4H0ycMl1HrZvGSF53FxGsjF0N3i2qTQ46CBKZMLJeYzUm9JSm4BPmYAa1UxY8gbk3FqfxGZjDlNzyrr/95jf/9MkrrrVJ3/WdTqEnhm8VgPeTOUQgT+iZkmd1jZXJ2uap+tznPndu6wINj6cxn76OW2sNpSC2nzdzmsoF5qlKYPQScG86N03XtNdddc0tb37TFccPrRRVojTibnk3GAUnSrV5VawHo5eT0iMC8IM+IngHXY931Ks/8NJhCq8XlmLXoxtBdJ6H38E71DGTda675LJXZDxGmikhDAUhNZwBLFvShI4BjlHdThM+TTQdlno7vh+KMN/Oj8Wy6DUAGtvVh8UWsIhJjWupArX6Qmj/pWVFHz5hh9oUGu1ewkcRy/c/g+mD8RhKzNIcWihOt4MuCGcnxoSyk0S4HSjxT/NhbXPjj//wD7/8xS89cfppA31+eTRsmD+HE2VxJILPWGCc00POI6m7sEjhQi3QQqjdDCYCpGSg41vTtgRk46YTEEXiLDR2S0VkeZrUdfvCG176lrfcfNWxK1QKGhwDZrBKGhQnADw+G4G9hDJdN21Z5MlgO0SDnDl9tpnDY9KkWXJ/iabzIldKNTX03EESh2l41aSqinJQeq9pmq694sgVf+dvv+O6665anaxT2ArM0tAS0Uc12CoMyRAurgm749j8AO6J0MWBlJwKJ+QQgHFSjeX+Bx2Ms+F9hByzGRaRtMGSXIKlFBBmviyVgiXRs7q8mB8IHwoWRa9iz8vItTxGVlCOdCGCEyRUO2tvbMDPzaiYlqh3Z48pGAkQl/W8wo6U+WS5Q5JthUHuZIf3L6AgHK/HgehxjYU4BQhsQr0bmD5csO8uiyoZiAyqYdzg0PFRW9XYLsvT3poP/uEff+TDHzl75mwHVTsQNKY9pZpDmkgIGAYqsvGgECl/fdJaCilvRIX9EJhX4bx5Dwvx5mme570xNUIjKUSPxpIT544X+ZhUeuzyy973q+87dORoWVaQsDHaKJgt7PD9InBFQRMMaGGnFbTAwdmeZ8Pw1x//xH0PPWCA6KmHGz9SCkUdGdBB30NgD9dOLammKytJMuzVs9ne/pWXX/Vz73zn85573frq4UFRDbN8PL9hIlEpDPSQBI5jtuP2TQe5omPsEfwH8+KGhoALU9rbAOFiov9cdKOIT/mjwnMVCxK8m4QaQG/QO2HvrGMaGuyhk87QdaFlBi8FmzySDn5X2UgIFoAvzwvpK5pW1beEDGcedrvh4wKCYsd3GG23h7/FlqXbIwRy+JLhuKWCuAZPFDQdJKYJ88y9yJxsBqgX9I9AV8bbV7w9IGeNactyeusXvvK9O38wZPn6xhGlhnY+723fISQCxQjOcEB9i30jfEgzpNM8n/pIuCx1CVTDxrRwD/wfHcSGYsV1XQO2uQdMshfMHFETOokAd53p3vhTb77y+FUwX8x2TQM1Ut795kJ9D6BFlk4VYryxTRnUTuSTztrd+ey+Bx7aurhFHr8nJNxVgB2rNG2gQSlKxlRPJ9Xa2nrbm93Z7ryurzh82d+55W0veOFzNlcPJ1kC41vikx67thRZWLC2RrwRoZEDKSx5WxxI+AO3/oXwIQ15GjdwZQZDEK+8TtQVKy4eXBbzCYa28v846J83X6NYNeG4REiP24cL18I5PQGmFOCcogTCKw7/2N4apIAgQ9M7gNJELUiC67psjyNjKJxlOMdIujihAaNVwooiuR1dFAUmdUXykU00nMj+tZjQ56byWB0rgnv4dAgZyuu6ueE511133bVHLzvczbqt3Yu7e7P7Hrj3/PbO3vb2xfMXd7Z25vuz1qLq6ZoOIsUDtT6Gm0EDGioJxspNtm+4ho/DdlG6Lrjg0l4hpnalEXZe1yFU6BRU0LqYQSSONkatTleuvuYkwKyggKhVMDILF4YtdKlgMCAX8QW+HoSo+hQ63uQ2HfqmPXf2/PlzZwZlM2xk7MJFCE/n/BD0CqGWtEWR55MysWZn/8Luzv6Jy0/+3C3vePnLXnjssuNcTENj+xYMYPZchV4LMzE8F4w0W0RxYRdDSaDkdgrFsLMPXGgU5OOpzSzjciVBuhodbjwmtLi0lZyNKBSN4Fyv/wZGXVOJI09kdXOtResUIajQygxIcMeizseg2lah3/33/OoOdNLdVgAvZFCoMsA8rRClj45l2RWW8LL4ShAz4oZQRiqS6d7MEosX4DdsxoYAqTDAhR82zwC865Ut9Vvzz6pSmMq1u7PbtHWn0rIsZrvtMNi19aMbh/TVJ070Q1/PwKvr2rbt2xrCM90Pf3DX+e0Le1vbs715M6+brmuauusbNOKtTnM+e2yPyyg4N1xv1Knck563vCnJAj4u9hGj+SBe9+J3eNaysM3gTlBcoLMWGrG3g83BN8BaH+BeF5zxNEBkDPHnwXZ9n2YAmtrb2+u67tvf+fbFrZ0UQuHhw9C+J9U5Otvz+dxaWxZFCVGraVlNnzx9qmnr44eveO/P/Z1Xv+KlRy4/hnrfJjgNKjwrN38KtBD8QGYAB6kd2WuBPT2PSpUoXUOf12estec32AFlobibh8twkoAXR+0zUw1TWoIrM5qtFbVxjdW8dAhFQUWIf6O4FENSRe6ICdMN6/Eh02CUjSzHZXjJsKrQq1XaOmjpAAwhy8CmY5ACuyzhg05uLb78isaH4w8mROmwSozXEo3kCpdilY01vlhAZ1xpDeJFoYVxoqEBUIZt3Lg2kUa3A0IEjG3VNM3ebL9p5lqlhc5xBiOMgsb8/9AbuCy0Vi31pJpCrDXPtdLPv/65SaYGM/Sd7Wy/vbO7t7Pz4P33Gz1sX7jw9FNnd7d3Z03dzGdtMwf7G/gE4k8UBnRTFJCqOKfEXQ343LBCwFoLGp4HrUeHx66w23iifWMNYkugUoym+FLkDeHN2CsH6309ah+hOdjdCmPabdN2bT+vodCi67CpHYUTYK+gMw6OL1V1U/d9T1VGWZHP2/b01rn9ve2Tl5/4+Vve/vKX3HDZ0SugXxYEUjSWNLpwSXTa3gtmlQPlMBhREEPz1DN6u7EtPiIpH6dxhimwsIGaab+j6OXIr49aM4cMh4AzeLPJq2j3CXeCzr0ZYvZyWUxZfCsC8azWpQPtn5EpX8S4ZD2B/0cqaKnfBevhrDOIGeG4Y/fXA5Bpy9wVSYkSTiGXja4Kt9SRtx9dmusTMfZM3d6pYTfEa1Ls5JQhtJB6YsrHoWK3ejbvGoAKTaqJNaCIsK4FMVfQ9hRH3kGnUm6imwFT9YMyQGY9rDADpjfF5uTYkWPPufoEckJiuqFu271m3s6be+65e2e22+7tX7i40+5sbddNXTdNXXemoYAnzhHlOdR8tGTt2wS808LD4DxoiqALnsSQJiBNAmp2XtcwP4nH8jmhHYxnn6NinKkxfa6hNQaMTbAwc2V3tosFD7idbAODqZznBaSasW6ABm0U0MxAFbmyQ3fy+FXvfue7XvnKlx8/dhKH4BJkmq2k4Cs4kJRIqnJBBTboA3CIpwzX6jW0to+NNGdnC3aXejpo5tBPi+a48yj0MBM2Ui2S7MUNIxpkGh372Z6U3UzTIWLssPMcHBGo57ByKuxzv/rgN1G7cwd8X7sYIuaMBLIeAkvFzjpmTrTCdsT8F+Hkh1Mahx9I0IioTvhbOExHkwN35BAxyuUv6jcN/xDKHsFzGLXCzCV+0ZmOLNsRvWyn08nRw5vTcgLDLxGqVXdt0zbGdk3dtR20sW8a0ErYtQDHoIHjAIY61uvRRA50rq3KYGgOGA5lUaki0XlRTVbVWnLVFcdTrXIYdWVs0837bm9nv+maRx58aL+rnzp16sxTZ/Z2diFt2nRD3xoYJEOYCPDhceIlDLlHhsCD8DFID8dAViOAMWSb3NAnJn1KFHMMWsD+efLhMJ1OgGOTtN7d703b91DTC6BISEOw740VgQrr/2E6WZZlaE3AQc1nzWWrR372Z971hte89uSxq9MM5HqPwHIPaxSHG7dWcRkVjESAuMKRDvxhn6SO2WT0U9BPMkTtTWOmd/8+RiyV6qHjmg90Rg72AprZvy9pdGwdCMdEaFa18E0HOWLrKPL2PC8H/zogJIIB6uIrHhTsYX8u3BvBFd0K+IGQDbBDODRRE5hSfyL+PwsudYxmWfzBcy9jPFkYxy8PNPfwKzSOUQdjL0WjLFS0s79LtYQEP2LcyaBTdXTj0Ob6usa5mFTcXmZZVRRqdR1sPwUzqVuIQIPZiZkp25uu7fsZ2JdtU7cd1OrBqHVCMkF5JUytUtAaDgEYOc4rIYu9B4M+K6ZlmQxrK5up1i9+/gvmfQs9aJPUDmZvtrc3m//4R/fMuuapp568cP7ibHevrpu267u67rsOpg6Q2UyxUuhjwiNKqDAIOQbKTaBcw6eb6Lx5CJSLsmCdVmKHNEuzBHAiILzqpm5qM/QwIsxAjxHwqhH26Hrc9V3bEooDR4qmNfj+XVXkt9z8ttfd+Oorj10FTS8pFgG+FvS78jCiEZXweFGcAkg1Q1C9Gbp5iZaUC2Sy7LXkj9LQ84+Ok8swHE21G26WrpAzPu0yUhxxsY+vAhqtMr79ED0zPRlsLM2qdGM58ZORQAr8Kb86WokP9bGjwPJxIU8t/VteAxwinCNSrGe82DdY/DWsM4ipxZvhpwi3CEis4MtIj8DbZqAkSD0iHcPsrCQv8izTAHWkFnYOk0I0XBT50UMbGysrCOUFmIdcEYK+oSg5SZIiUwmM1SWjJBnUZEiSQ5swv4MKFsFxxC+C8h9s2/fb23swPaqFQBKCh0ETdlDDBX3b2waUOdkBXQ34KJ3qIq90pvNqsnEoufrKE50xTTPDQbrQGWc+mxtrH37ooe29ndNPnb544cLO1m7dwIibvq+pighiXNiSK1NagT+bAfICxtri35y+Zag89jbAaHlaVaXOcqV0Xe/3xuhCZ0O+urp+9NBlk6qc76UN5YFREswR3YF9pxDBgqbn6traC294yU+/+c2XX3YkwxF/qHoRL+09pqgFeCB3wJ8AoCvDfg6sDMf0IgljeLYOsDTvggnnu7Ez3pnjEYIun2VI1nu3cTKWrWZaaRywEO4wJ3v9bRfuKZBSC88X8VVgXz84YiwEqOGls8Slm0r4I0U11OJJJS7as7EQDdJgFma3KKfmSgP0Vrgjx5INdKYRAUDJgsYGsSDOszxH3AAzJHX9BiBi3xdZcXTz8CYOwiLESjZAXRGvApdHmA3qgM7gevJMcUg7gChhOi8Up3mYDH4MkBbTag1arvctd9ylARB26IyZNzUmdbEFHAxBgJ+6HoJkCbplUKLUJVCqCVowybOyKKqVCmBM15w80VsDqSucgVr3bd+0P77n7ieePr19drtu9ndQXQ+9yfK0aduu73NbKJzowNLPOUVkceU611l25Mixvf094Exri0xbKD9ou64/edW111x7/byZdzs7pCfJicBaBWcf6rQqyxc853m/+Su/fOTopoYxmdyN2tlr8rAkZI9FN7SxKsBmZvpw1BLOORCO52BvNAYPbQmTSVdYvBm8Rho6HwaYjQhfXFkoRFYagT+DahtFemL/EF4eJOFdBtagrgjGXzri4VAahFehP4qXVGrejhBete8WPe5OielwHAmAACF60NDmWVw7aGcKFvj8WmRb8ScpEpcijh/m3GLRlIjYiCsTtCc0Q8W0VpoiqBAUEGB/oOkSNouhzom5zq44dtnh9VWKYlDPN+myyJAcvqhRW/AhxErYneC9ovZtMIohyUyS5Cn22wG1TDbEitKH1jfAssDCAWwqCJq9HwxMxKzrra2dHizXDsRiWXTQcR5vCM+RQCWgSqrJtCjLrjNVb9ON5Norr2xs37XwbLP5/nyvrut5tVImWnfYcMMHhLAagbu1wcnlqTHDZGWCwCzohoueJ9Xppn1rVKb/1mtel2r9wIP37+/tQiQ+sdBIDPvkqVxlRTmZTl78vBf88nves7Y6gZEN1kAj/uUgJq/heCPBMsiyooB5s57YHMmP+HHRH/U22bI0satrIPIYS3+hMwgtrxFmMjJVo3WPeTWGbiyPzSwmwfyL2m7g2BxfBRD4fSmbMts4KG/QoqNt8mVInPJ1CswFttz0NLcQ7t3tT4ZNWpcVWIjfedtc2M7Rzvk7YgEnxFBoMoNYY1grfwdGT2N7GJAwiKGhlLkFWD8GcjA5PCmqjc211el0YzpFEBVqXR+Zd1eNOjDgmj0G3fUuZBeJyzhZmPqCThx9gu0AOPgKcHQqC6ZxIyrB7qtwfSwuL1SmVLVWTVd02TTtgM6RsQkEw9F0T9GdmM2brkeuhAqNAWz7ZKgb6FENSGmdZCv5xgqAHCHPSJgNgDfz0n1Emlry50WWqmw6mfZNA0MYcMQ2Jq2zPINMr9K6nE7e8MabXvrKV3zvW99+5NEH9/fnBPnQpV7bXF+r1l78/Jf+6nt/brJSoSUMC2Jyj3QHU6CLRbKHpjPoNECjt8XxLoF6MNnSefupJoEmWCGHs/OZRq8/BHhT0q+H/LvRrAv6d8xIovUe/kWSSrhB7CknsWwiGxo5B2cTXcLud+9IXK6cvhAu7IPvsh2lMOM5yhcn5qg0DVvTeEuEi3pdo6XxZvjvi6Kc0fIFuhtlFQA5/PbI56KnwO4+rFk4v8oxUwUtMoCNYJGHD60fPrxZYmCaYkASkD42tDzUdEETkBPJZaZkV0N6iSpAmDZgDQTEQo1GH6RmFSLMiMQDbAbBWhgYDymjIUkNsbbWaa5zThUiyVcZ/ArZMuziSyuBMYKDhYEISQqSChUljRzH3lSUQkcJgyNyswzyRTR5W6eZhSaS0MjKdxm2pk9SlVeZ0tC6IR2SzWrtp9/0lm9/d/XH9/x4d3cvz9TKZOXqK65+5y1vfs5zXnh0Y2Xe4Rw2CAexoxFarQln1xctoOjHgFUGSb5YWTk2FYrUce/ojPy1DuA2aTAKfRnqFcP0TcmSB2hNn3oNaj5c2K9k9FUVERTdi7+LXgNwcPQJn85auMAowiRMjfHKJSQs8m7d5HEV5+3AhPYqyiUZqVTdicT4MlKALNkk3iYq4QIi8zvv8mpuxrVbh9bQ8p98ZpUkRZnnGjKnxibT6erq6nR1OqmgFBYhCfgYXp+Oi6rCe7ETIfwK8nVBw/MvZOEucLo3ZUKyMAzVo08RYAGXZTsDjq+XGV60k47AvuywARmmdvDiED+1Q56oYZU2Nk1nTbMzm8PwQAwJOW1ocZNyciSGBNAa0A0ngzpEfAe7CmImTmd5VU763qxOp928hkpgraYrKze/422v+luveeLhRybV5DUvvGFj49jll622SdaaQRc5zUKiIQiS+Ghdobic/D3Xa5aSXn6bF4hVWpUjHpXYgVEe9YBX7JBLZhvD+yi1I4ZMxzFdyUYyE7vI/DL3ucT4JrLwNglHxMKMUxd3cnuwyDERbx308M7ZDB8MAznBPYRANA565f61lNNJff6C1y+8+iiHtihHPKIb2RgbPjhF5J8rzJPA9lRJprOiLCAVwfNzMeljhqPHjlY6TQxEg3HcCCG0uAGuiwZ6/IuMPoh4iRBZoEjRcfW7MhKJQbpHJxnyDSR+QvoWBR6wTtspCzHkeLwTGd8QQA9oJhdM8PYk/NkYZU2RZ4fWV3f29sCyJmwVjujGKcs49dNAs762hSorhKYh92KCWCmVZ3mqdQeec7qxsTGZTLtmjunZYpKXx49e/sZXvma6tp6a7XmjoPVlCvNTQZng/AUY1YQGgJhJ5tjW0SN5XtijioJhIlYkTHDeSBE6juk2crwcm8X1ESxzw24yDYbi4SAkhf4P70cwZXmcIW6lntkAXng5VUw9nii/5yPgC7Fkesng3UhT+N7m4bby0eKmMC6aRfuKNiKi/TAp6OmaytUQghAKYxdsGf59/AbrjHDg2JEj+Dge4cnOyECZzDwviwKQCP6GNkmLDDI3rSl0BlqZcpKseyV0ZUQeXHkttkC4VzzLInJ1Qqm1dBY8Rj6+BqaoXOYWP2p60zYN5ZZCw0R4NBdVcNLGW3z+uNnYxEIKcCIg1TwcXlnrBrsP1wSV2/RGpZ1uGohXQHAe6haqsiBoGsg5Zy1gNUgPWErssFHACLq1HKzurEiLyXSykmk73+tIhmewS76qFftaOqp2VBgAvz6zirnXjNv6hm108IKIXJ1F9F/1ikyZmNuW8PoiHUZWtY8HxVb5AcGvJdalkp4gfYKmJhM4kD6zTMceECMT1wrWqXw/agQ49hW9isQmaFhcB7OuOeWCCgNaF4btEbsVy6gFI4O3l4uQAQgsxKewfoT+BvQOVBqhPHMSABBiXZMkAKVaW51WZengHGzXLcx0cwmuhXhGKDAgN8FVJHj7WNIdy3NhZ4WndpMDqOUAdY201kAEDnrFQkOagIcFnqSUDGjQUNxMcUYmH94+7KnutmSAXFRq02oVA/iEAE8TgIP0oHDrujW9mdeNtehvB2AINuUi4LeFEsC8LLIyB6xomlR5muOwb3w26OxMbfgpikmlto55vfDj4BOH/Vz+BNxlykV53RobyPTF5S6u+Jj0dAR9xh7IgsZYvNDSmHlUBhCOcsS2sdRezmjD+DdfsxHfN/pF3sK7X4tSI/764j4d5Kx6G8dNgAVf2KdLGLZI1Bejb5avkVfgY4herMD/yAceh7RZ6uMQAMIVUBsd4gACTyiYdtv2TZOsTLncLvSER6ISjoEff020Jm9GKs6bIx4bHJqhhD5Dzu8N4XoxnNfdi/YLSwVxEhHU11IEiIQp+bzIdsBTGBFj5e2WF8oA/Apoy+ASIA6Ik3A2EmGyplmeFGrW9bO2S1QCs3BA9sLXEABFrZ90makyg85bOs+Kqhp6WxTQxML2vYVgIQgL35SJbozLheuAPBKI5XBmIraB5Q9gqPN38VMLxCcswEj+j9h17MJ4OOEBbB/nnGj9zg9f/Ea4mn+I6H5L9JGwJ6J9iF6eEQ/Mt0Ufk++I/s9899Aeidcr2lgt4TynqoRBTRyE42MBPec0oGu0eikE88iuj4UxUzP0hT7AQ0dFSH2w0RsOX6LKOa11Z6CctQBgIM4Sk+wrLSn6TXSzGRlG/O4iVbhmRiP0mk+QRYl1BqFjHFxB9QWqXhA00OWcIggS809NWVJwR1Aaui0VU2viOnNqbQnWjzgvXALmq7Is2yiq1Sn0YedB64DGArnbtjCHCfQi5N+tytPJtCp1mWijddJ3Bpie2rS4yjh/4xB49pqLoIkh3sF/RcwZvKhRgYhwhaqEEa2Js1iibJf4mwuU4p0lYbS5pXGwVXq8l7jSJV+BYsLRx+3hRAfFxL1cFMa/EVmrrDocYjPS906hSAEW3jqI5zw9j+qtmIe5GMirTzYXAnJHXsgbShEENUhtPnbMLoacVDAsAm+Qz017wXhB12QxUSur06IqqHrBMThdQgwf9fJo7NyEDywxqOKa3kjKcH9/0dDUqyQcu2is7RFJzYP8gs/PgQxx6JhnJ95lHyCkArwV74e0c1zM2bLcmALza8QveZIUOUwzxh1BRHQy2IqSdsDo4KLDhGNEnaUa5hPSnCgWBsE5JN8mhFIja9I9j3szhbIwaAUAzSu9NTrKBQRnZiFqLKn8Ury1AAQOB+lT1CwAxye4ZPGR2o0/GrGQ8+ID0icO0A3iWy6byKLL2xsHzRNDShrtwRLrO1BZvBsilD1KY4vkFhvSgWo4Fh2PCh95l04bHtDqCqJ0kAd2AFbPDL4a0jEwoqkch1FLXxxIC2ON1lZXoKeEsArc6kcCJTqXJe95KiMRx2Bg4eiP/DZvbcvdwqeAWBGiJZF7XQRCEAl7AuIouUFEJEWEk+RmERAGVazEmbnsqrvEEsUYsNE9/pkGonACVkFBtcYWnWCEU7MibmmBuWI0vHnkn6M8FnB+2G6QIU6eaAiGFRqhWhH9HJh6XXgJ4l94U1pT0Tjf4Cx7c1N4gM6SF4d+6cWEWG6klRZ1aLhgssxMGMWZXZR2LIEoiCmIMSbPOJq6bKn+eWMTQ/K1A04wUsEDgwk672wlyq1FESIpfMJG8DWcssG2smLJdDLe/eQ2Fy5KiBSGMwmwUdPKSoVVb1Lz+mtFKv2gFwd5Y5Ejzz7aWRlZCS4ZRZ9deBsaPYNW8zPUwtdHWULPw+z5sNnhHkCqBdxzkdaK6vXcIbnn9iYWl5cGRc0fBYgpuSU99tMGDew5GHYYJ8rEk7FFxUl0Z9JLGvJ8JXTSCvEFwb0LbEMsKYLTo7OLrbVYNwuFcyBtI1fwGDEBQI6uylZaBPuSe76Y0ZG/eUNSRVCIsO1sUgcJF+wPeavozkxY7rBDbcYYrOikwdhmX7Zw+gM0csHMYmKonbRTdwAiCFIljrv71ILcfSpQYAsaMphubIF7sKiDLjuHwvcA7kV7MYXJRmTWj28R77t0jiIzOqYP30GONRpFc8TWxJI38gyQtXC/ATsF/48oT2F8uJo2H2MI3EUELHDkXrdEHlTs1YReid7QEdZZzETOtmFtzdIGy2Zx/je1AiAhwZzt5wuJkJ8TYfSwwaVIVQqF/2WBxrNj6iUL8eTAlST+usFaCp+X5nl4buHzHvTiyA9VWfua/uisOFDsEgyBKCIRG2uAeIkkeAfeipEwlaa59/0jNbsYOQp74Z6e9t7DpMPlF00InwL3941ED60S+w5DtIjG8GLfoRQHWZHG4qnpPPmNrkNHHtvzvho0RbATBLGC6xV50742zn3T/cUmQ55ma2urJZQEQ0NTz54Hx9SE3Ivs4bFBJI7NXXbcbtwvFQdEYB0jRWuh6U/fYW8qHm4on1+U3I3+u7BMVrmxanLJKwYRMa9FQa+RsopuH0DMHEiAnHALUxci4yV0LZUM634MPemIuOCZChyAprWWiBlpquAeiqcJho6j7vFrQStHJqKEgy04R454sGER4EuZCAWGe0GzC0t0YSEL2R0RvVbkVY4v5XmQXTAn8fxVl81EE2fkttfdM5gNvIexxhU7tKC8xL2phZMxxMPsAlND7BBW8TawsPz8zYSEQB5HJQdBLNGQhL5CU079cXnrlVUIpI9LnWc8QFQY7XI/R+ZveGCXs5cW4tIXH0RkJDk7F9eKuTUoUQbDAIcWwURWjzgMO+pYLxzg6L7OOWHdtHA4QSp72y3YqIsJmJEcojfdIA48PJwrDFKGN3Z8BQFmCnUcfquDXs6zvJpMstwBcmK2Fc8hROeyx/OXDo8aTOnwWKMvCJ4YfRAhSIyFOlBlR3nUJTpx9CeucIyUTBIvPfoWx4247YTcg4PXwyKUZwKHhr9BinhpPb6INAlddtSFT+gYIb2X9hTGpACj8BLpgy60s0zPjx6QO2DSUkIjEvqjAN3Kxl6U1LJNUwICAYcExTThz36MXgnyZUwX4xV6jeVTfl4OyQ8wO1H0GHpZwIQEsJyFyeBQl9EmjRblrOGQ+Qv3iMPN4xevINDOmNZF4xi3S77daRwyCwJPyEDRxzX4z67lKirfHOaiV3lesBcQq6PRw176V7Ej8QPKZY4+e9BID4jKAwbUzUwZfz+W56Pbx8buwmeWFhgsvBz2zTXzW1J2sSSezi+BRT7InPQsOn4v+rvcOR5QTEVS+AmqZndzYSR9E5pHGO0xS7iMLGSBIQ/sPiXIbyFEw9dBOx7r+zV0iWO5Ej28+J7wq+XmecUV7778Wdq6MgzjeQw3Fw0FuErX9l3bUM891xnT2d6hIVscXJLQ3RgW5LNq6iDTzjGYXPgBpkR0yAErIndDYjMWCEao4uDlUEgxz/NqUuUa2+tGnukyq+DZv2I4Riw75Z+jzhGxYclosNFGhDuMMBziU3FHgiUssjh1MBEEFZbijCwoBuIpIgu6ngOMB2y8pzlpuom/Ljb1FFb+SFDwqn2PjhBlk4kb94jeyuOVjT/grC+c+wGd09hUDYuMouIeKkzNMVKVbh7eXF1ZDcM+fYWEMGQilbdoJZHTGlsK/hoexzA2wumBAunDowHUuW2tYeSzkL1jfzwOx4ogn9/05YJfyL/o3ZDpXvbiHDKvaTHJ4MPsontplFV01wiuDD485UKhZ2U1ybNcJMTiRLkw3ccLcyTmuXNpwEpccXSKka043i+Cc2INhnzcWGwdYH+OT2zMV2PZosZqJrIEyftOUwgXjS+2bD0urLBE9C3wKiV9FoxM94PnBjKt3ItHn4Hr6oKJBLMT1+EmnqPBh2HNwVulL/F0wpB/D0twp+fWA2X0WKM3rSpiLyhvCDOmQnO/ZyH/x0o+PLWHUpKtGD+BoB1ENaIrSS1LMJhFulfut1uS309/OzdYchlMSCw0fMXFYEdiZSQiPbHIGZ9hNyM970gjtGkOglweqrsIKRMq8IT+727HlqjBaEGL/mTYmij0JT+zJP90oDvrM3JUxA5zjLEEh52Dg16xZFimccfGz8HXil5+b6GQwGp0OuM43Mg2EuTCnL08oTJik3DDxY+Gt4k98F+akNW5IXjsn8WLWaoWXJYpXJSW6jKHwjcePZn/1yYwR6AqizLLk0FhWgu7DYSoIAt174yNNMoiRUjh5sWEG8q9ZH/DIw4JDDRqG0OADV8UScIt5t4R80bGb6CwMalGPy5xLiPbWSAFgvL1c3rdR/1OS7AMJ/FpGYvZzzA1k7wY5N4sz2M55RYkiTTe6EU+Gjltyy3W0ceXXCQAZOlhME5CfRPkgS9EE4KvNWaFg1JV43zlsPh40XO46ZYKkfshuE8kITT2ojfP11+MK49hH/4QBMmNpIEkegBQ5Dm1msCTXRoPE0QWbFsmIq+RyP4GBl60vuQFfN0azllQUBZMHQ41BvHF5EYhsuP9WHIesmKAyI6C3FEB0hIVwJgNgIO1LQw34Fna3h48OKzp7xXiQ7GSGfd4itWnf67YQxQkvPCKTT5ybwLNEDTH1ydLy9k/h0hjwz9pqsuqyqDN5UhfR9/16apIlcsHdl9fQrfenPZyatnxyU/zW04A45SOuCfP0u5Z45dI2hykZ+Np2fiSciKyz/x6sCtVFNhxh+DDcCNjQ7r2IWccElqi1fSilvHk50/apx+QgXH+htF976kHy4SEHhXl3lHtd3g55xOrkTiOs+BMSIsBv6RVluu8LHOd6QhxKgSN3KBFVe69hgUrQd5dImillA5bZHDwF6Vh5LIXqMRtm7C8oujvmCXHlqR/pCXEN+LbSDuEDR59ySO2sAMAVWgG1yGyvATrcvQByo25VmHRlPQFXWJdMhcVjoV+DM8c5Jd40IAYdSxzEFORx+ZqP1D7YjPo+LrLN1Fo5WAdLuxktL2jdgRJDB2QtLucmuSH4/3z+y1FiED4Lnn0SOyMrX23WnTUxA3AJsD5I9yJGKYMiV1fcIkXX9g+gwZCgwbGt/xDStHsHEcKwkC1zfpqWZQcZxKyb0n2wdfnLtVq/pNBzvgWdvHoOPlENHvHmqat+w6411dQLFjJguddNu6AE1gyc2C0Wjn9dayN6Tcpu0cbLxQvx668eQ195IlanLZwEThv5lFbQxjcCmMLsT87NUMStO+5lf/jpZZ4cNlD1SN3nKU6nuEXtsdlJeNniz+BsUjvEGD9MjV+WNjJRS28wOOLhxAe4qB0XrJolDtN7oZu02a6cQrPZApE0aJnqvyPLeCRCyK/5FwDd+ApzP3CjwmL0L0RSSzvAfsjxvo3BKvCILwgYrhgVlyCfoDmoDors3JaVphI8hL2mXcjXNpvssjnxFvl3MgFWcYPiE0uqEmmn0PkzZblZpqgZxc1io2SWB8f8BqeXSAuOP3DAve6ogcao4YhTCxFllZy0A6YSkXsDmVkULHxBFxn/Dk1H+KSUukHUGAYA+KUgh9+HD+gAO8sWNgji138wbnionjdu2lS3kWXEY+75G8HHIAnymCqqpHNwFce8bL8M1OaAK97NSH+c/BafKjjQKPEXZIt54i7Q3JLlgpBVZI0aeVD81elA4D9ZFSSGghhQVtZNgf5gjIvyZ8fVFLobG0FRhy5Eu0QEuADX8ZAwelf6gONRZ0LP/tKWOFVkOfbGyg0ciOIIkk1vkFIDCyeScx1i/s/FsFjB2kkkbyFENc/uuvgm5RC4CsQnI3+yHOOXfrMaTKcrAOsS1xBcxW8nbFsYUHTB09GMPVodxiqGUqWIiPGMX8Qf+GG3OrbIcKFZHLoq2f5uqQ2lQ8V/flSKYPw8kAMb4O7QAn/WYbkRF5amP7+Xp7G5Y0vzeKcrV0q+NkdSrWlOajcLpLaMLO9u2Cqeg3MZaoc2oTphALG4KuMJUukqeo7OKKiQHubySLS20FCLtpD0oZYEm5wLODrVsK7MiaBYSsIO3fQuTnMAAzlAW7v5EY5lDjn4zhv5GTdM6vexZjcYvbRfybUFYYnwCmTZFgiIyKOEtaPU+tI0/LAWs8FpHo5ZejsUu6RskhC466RtC+BdkQAxZ9ZfH7SwuSItz/VhX0S1M177NswIs7HPb/DIMhAQnDyR26qeIBghIwOYkRGAa2cLCxq4Zs0sI5DeiMHM+Li6JglqCTKMy3rpyquGlY66pIbGX2iTAu51vkJvueTtDCiA3YfAgAx9sSSy8eeW4HVqK38oAa7ub5WZPmYRX3IgN6NfxgH4aMvxy73siiJ+AT8sYfGkjgJzVvOsR++yI1kryJkmv/sR2ywsPMkFqV74vVGvy6qMl4BYzJokiJ7tNwf0jMi3RTKqbGCEHo3Dzk07XCZJU70UwkPZ9REZOAAO8YtxFU5cX7BHYVjKB+Zdh9nHJ+7OC8iWBasEcY6eHRIuF6oLFfKTU7zNpQ88SiMMCb3S8eoD/A+RUDf/9fzi0jrsb3KU/jEocVnOFJ5i7cTfPGMvnHk+bqPCknmUqXOZKaeGV75yqIK/Hx8KiIeRJMFPDDXj6ZyBgjMIhxsmecrk5La2vnB5GIzIsrw638Gn2Yk+JaOfHNr7gEr2XDYeWSTjww88RNOBia9500Mxk7zBHIZ5hkbZ5GN7J8pMotYcwW71w8lY53r+st5vUzTnJQdUmUJLczChYicWdans535jW95PR9MY7EBoZEuzzUnyBY/NeJM3bh4riV3pxbon+9HKPcRHEeocb4TtRzGFtTQiAsnqPDVfWpbHvEywRdj0A+kmINgy/AS9Sfu5dnEuSZxTaZ/jqWdLpxvGhSN+6q/j5AOUhHLFYw8Q28cuXsAibjSXfojd50d1bDHLp1XZyEElflmMeELDNqDX6Ba0A6HDm+WRUEtm1zTWWc3+ltFTvHC44gNYp4XfDG2Sx0Sgsiw7/u6nnddx83n/FaGoMDyFzUD0qFHH/bJhW8Dut2VuMlEi4gjeRCGZ+3xncLtiV259RFNdmbDF4Oy3qzkgC2fBLJ6WGq4arwJnsC9AA8m78IWM2dRG2IeOY4z6Rg3LujT62T3Tdk7IxJadGeZyPFnrhKrocTFNVoaEsgwitiab4y5zKMOdzuga0x0mkst62QRBSK+4lNzgjiFbeDb6Yzt6nCp2F6MbBpesw/sh3IpgZpa/sJSR5xo5xpXexjneBleX7qvOknH96C2slI0CJ2OZmCm9draKmRfMfsk1kD3EaewdJsPfhAhDWE30gWypXV0HbRSlvKC1au/gL9jkLz8R6hOwjm6NNDJmd3MuuOIRcil0h2che1zedxPLtAgYkvJ9KWAMdvMrNuE9ctqEoaSwpAGb7rTXzhKsUjEB9C1wH8JkU0yFy4P8FIO1fuD8bk2D+Hyxcz0nEHBjowbqi4NutJnfclnJ2kKsNbUwEQIb9Y6WopZ2Lf7fyb6GBPL0hJ/dSkL1j2Gg0lwyXy8t57bXCegwMOjJLO8fmS/hH8Y9+R9krFIDA+DNKNULwlYjoZ1iiqMepAKmCcogA/sjTf3F2wOy+GeNFEb62u5zrBOD8acuDbHy7hXhuqkdI2f3z/1qAgoKkFg/rB927QNVBoR/4kRDBKwEDGxx2XxkmxilIGpbNDfFZIxZD+TyPP0LcMMMbgAiSTGfzg8GJV0QrgYLg4jk3GQApdjkrznnaQt5TJg7NIfbUrkdAirxLdDiZSAZy6xeDpUNFh4wKpEffkZm4IEI6Hvq65Fp8DA4/ztyEhy0TUQvFgIZrEXCvv8Xl/HpoWUHZHtNYKNLZKLtLo5yJvIQ1tugfNj+DzqMn53xLy8JH/xygdchpbGa/KiQyxubIhCzI+Ssq4p3cgqcuJWtuPz+S8CEoAG9kvkD7kDBHZNkqQqSxrRQN7jaDdHGne01VKgeJNq6ZP74r+gy4ek69p6XltjhA4bR2XGN3Jhl8jFg7wZdsO16Hky2ICeiJsSLCxr9Abz+wgs4l1d4F6KV1F/ksg+YHYH7oWiZW6rz8kAIa5lZyQ+EM7Kj57VPaH/P/Z3BsCHwyhJ7vLhYvTMu3wQY+U38rrGdLawz04TO9uDSjuHBKoXwNNJ04KTSaM9XLhfeJO2bFwxNFZKlzCzlWORuEyZ9pGiiMurBEK2zRl240cdPXwsBuM1LF5cXorCVH6UKRYXpgb4EGwcEcAXFBRZ9ALIhyeawjQv7GfhaMY7yOxiFylqLTTafT91j00Yb98CEExaIZGxKhcafZgJzQ5DB64vjC8J3cEd1QarJ9o8F7hho2KJAQqDGjpLeCYKOFFjMacJlmerXZEZZ9N9HSeSBrSDznJgX/Z2Q1AQz8xZC2DXUhSdiSWQS/h9ifcxXo/wQIO5S+uGWc19R6Udkny8COctdLuzNEEsiZ/e4LMV1ps7K4fcQDUCU+ChMUoPmgVjAYs7OWbecK1n8ZJ2ceiVkIyxlpL1XRqcOVgqmJH7vLC0yDoW6wxf5b8sQZqOvbqRXHTBCC7bSntyqYJtH6+Dvu3x19zG1P09A+4w3owIawbOVcN0pYIAI0QmoeOpDwSwoojNHj8KYLQRC78Gsy1IVl8AjO9YY7q2Nah7IxSFK704IGLMOxoihOKL3hftjUlhbAKoDjha61ANwrQT3w0yUBAybRFUluQFgMMDDcaAAcdoUPmIvTLdjRY2RQ5oC03GxEe8z8f7Lhh5GMAFbZsabiHlafguC03HvsvtoLCFo998N1O3EBdoJ3OG7TYKoIEFn/Yqp/lMI0stIuzws1SbC6bxUlktN2v8YqaJuttBQtR15nDCyAWcYviKd/oWS/bDy3Pv6L7e/Bp/zamdIHmxcBrROgomJ/kwy5KHYQ72LXg4uQ2BI/SBXYLMK0kaBDLYpCxKJ7zwgFhVi7mPizIqmPLxAwsVLJ7fHYLgOWtt2zY9hJ0xi+vcSH4QF3oZm3a4c/Li40ZTPntMw+Mx8yYNl+GZE5OSggEcXqDu9QBJEVUSzgaMBbZ9h4LW75MPlQS7aUTDC5pxfLwBw9LbvgVrpXcC5hKk54zGxYs6G1S8L/Sch9tRt7gAE3OS1xkrtjdt0iSDzRC3d8n9dMuU8nJs2i3a9Qc8VeL/49REmCMUVGX4aMAJxscQS4bFIKn448KvI28jZvVhmRucpbpntNG4iZuzc8Vyvf1No3aSBDpyOFOKv8wG62A319bWplMsDRPYAL+o0Hp0GaUsbtboYSMDxmsXlSD3tl1HziH3H1WLQxkWuqFyuEXeKzoV99lg1MTQU/HnS7zQlyKsY5EXWQ69wfy9yA4SxjuKd4vq14JuFIHPhU2IVhE9xGhNLlnB36YxFD1yrzclZB5zwcCU2exoBV4uxpmLYDLS/xCazW5ItDXMPjBcpkV1kWUZLFSIdK/yluzBaHP4j4tvik8OSz472kombm6mHLpPuM1l1nbqV1zLpaGETcC7sHBonvmkiSNrHxcWx0EKSKPrTmMbc6eXSQv6gAiz8fhJiTGgHhgzRiF8laikN7bQ6XRlwjEw4SoFJ8j/u0CFIxBOfOelj8QmEeretmmaEIOSF/UWyEKQXuiJhdsFUTx2MmKU7QGcK3tl4JUIL5UXeVFAK2ZH85E08PtBDSjDkBexrsjOFxJ8bP4fZMm5b+ItMGrl5DCbY34sWjTHwouRRVCKU+iLL0oeYcbIZ8wIuCIgYj6Ogo4CdAc16QDzP+ItWfY0sR3g/nuwLI1Of1iwqultr7phaTjWGvtjyWAWZ4I5xD4mD7dnooh/LNzEenwYXyq3pZ2WnYk+DBobvmd5huA8OEQKzUjPMYw1jm0GugL2xHIeMkWLcGamXV2ZlnkpWEAGAcY7OJrxEg40+nT0zdhDQ40Pr75tG24W73QBrUnmfd0ORUQoDEAWrmNelo6JiPiFv/vrjiIcwpBk3VsA/xIazgk48TjehcVMHbVulx6u5MjRTgnzP7bjhM8SQl88bI6GVQoa8yavlNb+L16Vck57TH3iGNleI0ZFXBn2c6CSRn4MQaE+4oKGB43GWFLRK54yOjvnWfjEynLrmWlD0lsw8+ITZTXgIzdgTuJcaJEOHhHCEgfXxfHEyfqSb1HtKI2c8FRx0FIod45IUyTFYOYv9GmmLJlrbos8GOYcOMkA7J6BSKKqcQefx/C0KgtsmBasPpH+pT1xRyUqpuNzdNvHP4f0lLCqeUACkSLEPyBF6s/HIQYCafhjkTGMcSmJ/zXs6P+/sy9dbxzZsRQX2Vl3qdt3ut//GfvHTFfaWjgflgMcIIKSZ1iVmbZEBiMQgX1r/jW28DT8AQEFsOra1nX9kHbnn1rmgUlIEVnxEvGNuuqbBKaicD86QZBYrYKgw5Kv/rFWryXVBkqG82Ks1lCitLHDHxqTppJqjfAEalzqtchHXp1+Lc05l0MpVtYorxOUIlhQsefhoNHbDe9OefERomiBegURYlVBPcELKn3u16h7FLpba5jNN5NP3GBidhFT+ugs+7Y9Pz6ej+fN5WhyScNi6dwzPoTVa38+n0JVL9IBOwjMv//jX3//29/NM5txlREOEinDMwdAxN2AiYKohPGUTmGeUoT5I0qXJY8I1gZXYinTEc4PxHQrwH1DYAhECYaRMO/kiMXMRQrKaQdAy6r38OMCjFyRmMrELJxn3c8mu2OG2cbaqmhRJTFyCEhEe43Ci8eskhux4Cor2ajJECoXCfqplUCkb6l3itZzF9OLmB7fqHBrHYf2jEGJX/yTqnSVqdl8E3DnsYfV+ZmKIxUaOGl87l4nHMZG9GTdhP3ZyxLy9QBC6qOqNgNHmF9w5Zsz8r7tNz0t3o2s9p2oNNDaoorNfz+e0t8h0sUfz8d12//49an+Ybhgo3BsSGgISSQsYuoEuLsMVRcV9wZJUTS437XVZkQgVJicWpdeGoxHMyumFvtey/KwgjMIRVqRbF83T930CKQSB5GR4hAo6JYmYjEgMjZykO1ZqMiHIqazsByXcU7ABdRCTE/SyXB3ZfkBe8Tq+GjfQ9P2SfSpnC7KlTn5VcuqJVF4mOyo+hII5m1LKyiG5Rx8utEiM9lbnH8qqf8CMKfvqRoiOdNyoo0SvJp4HlhsuxSY0zjB9SaVdtzL5BvB54s9MO5vkIocloCqvXIvz/vj849fH+Ia8cyYGvaehSdLLa2M7tEXuRU0YoXi41yEe+DAp4VZibSpCjkyfFhkmEK2ArlCKNhtY05x7Nzj1z6NMrPk1FO7x7qs148PcRoVZdNNV66qBD/Ts6tZBA1/S6jo7EwXkthvCvMyRzdmbTac2cFtQO8mxs6Mz8NZhGvLYpEbqdj7uUsVtGBws6lzdAvz+5xFZb18FPJf/rnz+CJNOJQvzHI9Hyvpcp6++FMiV5Yf4nB1gsAJB+tQYxCTx8evqv7iPVZ2jcR9fFuhHRrQcNi9TLEArelyaD6weHc1lkZagt0vgr3W5l6Tf+LwsaiUqfHhrQJsTsg/zhUfYZCSI+Mc1JqKgGuj5M7WeJyqwfSX5a1xnOrhKAVEon1LMQBT4rDT8m3frru2z86iQ6wf5CI18NTokaBwvJx5DHv37JA0Ns2UN/YZ30X0rGZsE0VIKNdnCTRZOQEFumIN6Zoz6r0t61WyE0zdL6cfIhhhdM4Sr8iiFMGlWMHNWTvpo54CTJcq2tL+XPL8EPYmEpPsiW1MKDJ0M8piTlz6pvF+zHzpsZb2dHBBprjqRxCf8LZdr9It8JAoI9FW7B6N9i3OL3+l4uh+SLl2STSS3Lbj+Z///vPPf/5TK3XE28rpBO8KGxkBB5DNWIemxoSzTU3jZq7SCUuYMOyW0NcpNapCnDZyHpk3uZ3RutF2Nak0Iy4Ijf1yHNu2esQV0QXsBW0UDrq4UFQFPKPsI3IWCbIiYV1KBu7iF90FtEOmMjDM8jFjpLabdBWIVUvz+jE2N5GY0BWF0wRRpuAw0Oh6gaW/JJwWThImUjE5FavbucGMhU6khoXsdlicPlKgS5f5hG8pgtUGpSkRa8FPM+HYoMBLqGIVLa9dcyZte7gu14/9svx6Ph9ublSUUM0WRIqS/qU06aLJDJZEen8u+7L+8cevddki0kotIQPTU7pLSpgLUDTNGbTweiRJhaQDxFUqigcjqr/Tq/+Pq7Lg4SpaaD4T8pdE53+IFcd69iY/mJqXzAQrzJciN+L94yQoJ8gGNaJCaafloX7uZsEosQNGYkMNiWZvME5GdJuPbFEoyFbVwv1KYCWB2t8+OAYUm8VUwoXQ9R53aQyGQVaksTXNWzrUkxpFUDtRTzGVFezFvsHZjYMcTX9J4qIp0OZALDlD3VGqSYp/sk15Mzsj/CMnu1YdYbleHpJ6JzRQM08P7UYKX0NEe5i1YpXmZvrf87Jty//6889f10/bfuTK6UCp/meYMZmy7ZNw6Wdcc/pvku8ZmVbHlbMBmfd9Xdf7XdoLwqIbm0r8LtTNF+h4etUD0FlgKzgKfUDo33Xfr+I32qLaEItmYRPyX0z71T6Jfc9LD04AE8JMnWxMZ4hNKR+Q1agZpRAx6/uhZMWlbkNUoUa420TyQ2TliDPbVWXYN5HFPEESvJUk8MLl2bdf18ssl+qD0wbUnZoc/vAmQSqShWu0CGQPIrrgi915PovX4Pc6hfbet4NZuqrvqTokGc/XTa76ZMAkpDizO0hXOAvOXaV6tOyamVKkOk41A1yssPuybbf7fd8/P399YibPGkrsdSCh2tguAJC0NnlrSXrCEfN0enwbhw27cj2uhyTTSMKdJqJ7hRECemxTmqZ/iMWzOB37nMRmDxAHE4U4IHEy149dhWd+MM2ZgTdWOBPtmkJ/CAxvGBlSSvNkxiQLmRo8KsHBnLV6GqMF4iiYUR/Hsv4MhnJCcEqjPJoRJC8/5AEPWaIgcvFE19EJeASWp4MYQdCW5a72d6c7b/QoDZVm2icXK2ixQXezmxTDVAr5J6Th9TVpZlGl/2UQvKJcwMv3Ee4OZBnOCzQnkcwhXYTs4iZfPbenNOM+VikmE4KoJfQvx3PXZnL//o9/fXx+oFdhLMirwFQruiJqalO0SHwGNRiaCnCkkDAdzr7cDkS63RF7RUDFm6Py+Zt9cJARtyJO40JrJaY1ggJy67oukir4oc0Q0ngV4xfB2D3ZVrTaS+VDCkksz8rqTLWbNpb+rsZ+828KZVOMcYOWiq6S2yhpz7ZxqGkp9QmDhjoO8yFNmySMY+ZdtAqZ8SqRLCxsD0Uz7QsrunB7XC6Ph/aqcgNXCcfK9dG2tCs4aAe1f2nF3Kyv3YHQpalr3cXlFowTCx6DOVJDYgJQCWmf97CF3ZlevyZliUdC0ja76XWrNFRr2zbNzCOt1TZA84GXf/3tz8/t+Y+//RHVCY1kQmoS0WqVurUWqCm8qCg1jrQDYSWCxt6kEYftRiscq+yXI88gGGVASblOE7VPiG3XznMKhGL2vOYbSQCDHd9CdSFgEJnRKjY2/9rDlktoJrMtEn2rxTBI+33dQG/NM9b/LIzOykRKioW6bc1Trd07ZBuNwVqBnOD7UbHREdhrbWhSrw1p4RwaBy3eJPcoEYECbbZenrHUMGzM9s3XUSGewZPpZAYzdTOKG03U6vn0giVRMDtDVDLWK7YuIetGYrx0OA8d/2KMkqDIMneJ+sO2zQhKHbnMbpTD/T6UXNNwXM8sipv2ixZg+/Xr17Jsqk3IbYKw7ij2xVr1UJHPM1EbRy6D7mKayYCxEa3nTBMa5SsViKRZWYaNhGkiGFqpCRLLKCS66V31daMNAZQGszSYWSWt68fHJl08q0jlS1Utg/iYoJJJdM2zwLoIJIFhl0AVAn4vL1VKjfO5IceOTIizStCF5VoohdW6iyJdxjyNrxpWeAx9KewshvQAJ8pvUJq+S30+W9X85W1pnlf/ecKjwJ5k0hkOle3xg+j0QOiVEvoLGWU6Bk0Hq9hXPm9IWAekWYRnIg9ojIik6D70xKDlQxsLKLImtdpG/IjV7th22UkLjIwa1xKGuV5+/Vq3fVXIW6/Q5GnW5ya2PaRzig+agopiipugWRYQd4hL+nbXnDjCFsSC4E2tcJ+veTBQzjHgJ4YvKGsqDVqdDeFC6M9Ix46ak1tsocnO0aA4dohp/niVUFv0zyCpsN6Ik+723nTta0kqselYuYL7Q1pXWpUMwyDDdm/vQGoJFAopzeclSqyJhGYsgP96EhIFvSfn9AqpOHPpzAJbHUNsQrqqKoT+Sbt4Y1AujWvVERF0Ch1eWMY1i13Fm7c7by/iGO3shBjEJ6tcMttseBAwykqI7TyGOe5sGvmU47BsyC4Vi4S9eailbLj4gf/r3//5988PtUPLLh6e/OXEzbOUmEacvn1i3mvhCZ3vEY/WYjciFEVZO+h5eWrna5/7ls7sVmeAIxlagbZ57L6wXyqTH+y0yNwStiGar+XrM/YmHH50GRdFFnR+WpAuscAledVvI+ZXqckz+HwWphS3b9jRAyNgrtN2sVYjZ9NVy8MWwqFPRDWCwi/0LVmvpi4cpfyg/zSg0J7QGRnlJp8+wvWsAE2cimV2uCZw/cnliujJ3SHm+rJ1E2ph0/mos0obsNA0Qj+dlDrkDiWqD2nH4ifEJKn9n//42/axP24P1Zmkzl0alp3AQiOVj6WJSyMr76Djfv76WVZnNReFkNXqOD2lUcNiC3U7uY+kpwB4Ab6cQ3ynyTcag9Qj7vtyYXh+ME8Y+3adXl2esL+Hp2vNGaZPxrQ8jQz1fkKqj/YdrkDqbB3pbUGC0v4OcS0tixXW1JLVVqCekDMsqMV8rc2SYC9KMSbpHSzlE52wpPVVSZZ8llYSULKqb+JWeU4398AvaeZvAhDpdg38nVM7klLUFkj4SczGVMhiSjuYsbr4Ws9W/gL829b1KcV3tJDvou1F1Uy9/3H9XCSQMjs7JOwxLwe0m6QlR9tt0fOl1FXMxewSnSH2RC/BUeRSrKC/oga4lq2PMC52PROMGzdMRhq9T8x2JR5QbzOPcmKGRwEVnOJIOYpDW9Ua2qM2J5pIwVkvWlH1gr4KH8+ELC8RD3seRziHLtTWHvE8Ts4BBEFfzTryBFK4e0Pe4AjHdPJz5AkIB6Ca0WI9h3esosDQAgCNvktBT4tsY5/wQeXV2ygUz3jKHAFwnKATVYeiC3NYFg0VAtEmmXYyvPQv0aQGqYcxEb+6ddqqWGpbaaHDSmLXXdRa+USk6tGca9vsOZTOcaQEnliz8pDmIlmMHtKEB/iYL/7xuN++H/c73UUa5ovrpR4x34ohJAifp5lzl4RBS1oQt5svrRXKg6yr+fooduVhKQ0u9f3hbH63jhwl3ktcWGck3nUJoN2vCM0u2ibmiSIFIC6RhmL2BT93hmNuqfQafXAs2ThRGBFIUfhFvD+azslw1hc1/LLJEXODiJ7VaWLQ56Nir2/kEKN2jLuaRdBeCkaNN7oRnNY63N6qASCW2yGVFJ8L7TDp5yswlX4ZxBUTEcWj9NysLtbDaOMuL32KRsxRpEVTL5KHFkvRInqs9jgRIoqfMwghBPQAhNpfJej7/R3kq2/C+UV5Q8n8yponClVHbK/MrKNp3KnYrq7X62LF9FLUdJGqSG7StElSICeN7+aTWN7rAPXJKlXSIcAZWddNKvssUpVSfFiWA5y+/ozgt8m3uPY2tTBQx1H2cBZWq8xhWThbTCsTg7Ton2RfSnjgiAgFBkQgWa5FSiYV025S5sXDmF7VjzyF8rANTTSL0690p53OHrrhBuTWJCYNk/MjSuO9W4DZGLdtE6npuTy8Qa02N9MECEC/D+KKau2ioAWeVN83Gcv5QzVH8Bgk9BYEOJ7H7Vu3p0bKVRJXQc18xtZNulmM0qhala8G4VyBIAFhmvuKIhLFsd6sw5bAbP3WIqlnynNzUeNnNaFxeSVZ0aOEz6tk/Elt4PV+X26L9kbwKoWwUDFjwRaEOJVKk3Bfsz3X+VI2vFqovbxpLovh7kkV1rBC7AhakYMKeztM4ResaytUCqV4xeYspxMbanCOCR6xNWfWpzNmMKrEDGOCGRSmiq8QnfnmLJxO9RVzqtznaMbq6OgnP0udSLQdEWd29RpcJPL4sOZmja24rF9CQQcouBFDc1AsZLojrh/OSBshBIgfnW94d4L8pnbCrXgQSk/MpDvwuAF731bXal3Si6FlDImZ1Dp17jLtfCFokPxuwrNmq7N7JXN6mFXMaxqcQLZdhTEVecPjZ45j9ZQUD0vSSCgthZEtLV1O5iV0vdvdvOEFiHvUY2HbJ85Gl68JZxyC6WrxYotWPQuATLiMZtmC4Da6JpmKw8hLvZUvp9cRo+M0dafPDPKDlFbJSImR4gc4y78EYnUMHV/p7wkqSmn1TCR4DkZhL6s2j7PmwOt63I6HVOQAMexZAqb1sPMrlJfAxyiEqYGbAWFPsiX2ErwOFbpIZYkafEz4Q6ojxj/CYSoqj7tBbJfYiQ1vL7daOdsmlQwyz6ac8lQoFXtvz4eovm7rIc/qyQa/8MD5Pbw8mi2+jTPc5ArrMXYcohxZvMVzjeoIxV6MmQXbCg0he4rzAWjriUAF6hzq/6lhE4NLOuvHVYt4ZChXDtqCHaY+Noms9iZPHlDEwXnT64CE4WcnarpYL6WJVzF+6dI7ITEeDbFgwofjicbAxn98KIqiaNvOJKrUMXSYm8vwWC7boZUllHYn7vCONfaL44dw/xQAQKUNaghHQSO4AeIQtnT6UpJvl6ogql6Hfy+sJW3PWrxSv7iD68yGhh1ARFoQLqm2qOm+yjE67PM5472qmInfSO0KoS+VY1mhSE6WPACvTZMDz8YZDrik8UOiJLd1k4ByMXRkwpyXpJamLprpRZOMyRjv1RYBEWY3CsiCo2wks/wUgZik/XtXZDyujnQJ2qspZQMdzsVWFFfT5l1L4bvqi2iEiSB4IfQLhhvGfz7WqFgwikPdMEKfE2WA9TFJakNhlsNCYiu0yoWhM324CQ2UD8U2ItQFF3a87KYwwdlcSWNdGcGC4xW9S6ennrpty4SYWTRa/iC3bFqidTkuj+OxHsdDTNyh7sxVmtdXEYtJnUIvUYeCFR5CAK+UffKwDTLFx9kgCGhKjqIENaeoKV4ZotM5xig8knsiyXL1PMULHBfgqUihzuGt7V3CjaQYtVrJ6Nv9dll+f3+JQbfSP3ceWaSeZC4k0DhWq+kTsLVu28fn564yC4OJZScckbIYXtp0Ax+P+7c01rkX6yAgGhrB4ZBiwpDtUngKFeSvPhnwmOzJ8wnzMWthDETv+DSEMjHxjceeDhy9TjMQTPoDp+u/ygcICmq8rHNVtzlqgJdHwrtXciCYVSazl2lvv+dxPy5iWvOpdvbZWGIXh4gzt5fFceHYT6U3ETwI7N1mbjT9y08eOtN7uoKfTXaJ5KpbBAgkGOApMIXtp0FgJwa8FJzJJt23VEyUmfUJ2XZbt0O6FhtDM6Mtp7rCuole5El90iehKdw4VxC7rdasJAy7aMZthAPpuDtBLKmxOPyiOyZ97W6lvGHiBFtSmGjqlQpcaC/djhPn75QH4ku7H7/1Mzs9IS4d46E+P5ySVCy9ojzxbac8k1l5sUTuFWPnSSxaHi+z1LWna5qw50zmGyzDvm6mMYWb+IAl8MAOH54f2gc3slHQczKj+mmxCFiQ0UWMrhKxcVVLaU66wC3EKA8KVNu9YV6jykEc4ldy60/Exikgq5JQcaGuJgps90MMjmFBV2LjUnexR2tJdrgvyGaoaYMW3xNBswVykbfvshUWGgEe1jslwodPz0gbOkVP4vfaIVVzWix7ObuFn4yXV7XfgB3GXQNxHz8l4ZWOCwUDD8jVcD0itnJjAm/jEab28V2elNhC0POKAQ0njn3kEs4jMCT4fcOZuhy8sfWkJzpU613EMEa6tXOvKTxZ8aEJ6/Wqn+f8ij2QvoQ5wuiTiwmJvfTOrqym+Tly2qn0YdbN8oVVJJxgb7FhlOMe+0g1i6vxuxE+FpfKmp16+nhST3PfHx8S8a7WaW1yanNBFhK5fpMP0K5i7r67aj/xZKbkPOb2bEGf1ZhKn8DFxS2uNNlIzgLNYCg4W8/Ba3GcUXq4jYvcBx/hLSvMx3l+xGbgiyoosxjNEmPeVMJhkkH0qU0XVkVEObQStl6w305gb251xnuTdkSmWmQjQA7zIbhmTVidrR7WQ/LwJB4ixiq0Jh6Kl87tP9T4AjeyehplBkzaFDeHtuQ2QDDQSVgFfNWVYm3vMz/qjNe0EXNyhLvgVqXw72gHK3bAthGnNcJs//jLdZGGEssqfbC+v74ez28rnaM5DNZhcIokYLz0RuPY2mTFfJBFpqpUaXQGsuxaMR1hYdGnL4tnBhNm8sj1vvLqwq5rC2ca7CREeXrWm3cmljBVjc8yoGEq6GJbfDoSc9jTuySXPGJnYwjaxcdHRCkGaS2EbafJHpCbpKOxyLZE/0djiS2N35U2QqUkgxkkMhGkQXhmoAyuaLGf+uO2bx+m9OYBmcK0TLhd9PYpoSWxhI6IpzWHDn3KWSZj1nsTeVvEQBJNZnzKMK/L1ZR4cc88PAvCzF3EEuJsRKJBSl9mJrNkD1c7/FOO45ksH3ymukYQwJR3Kk6qbZ+GivdUv94y8NX5b5WDF1tqZNScpCmw372LfP3EQcLL5PcyEvFUjk4I4TCiFxJdgV85Lc4FNukq3EgMK56ZH+9Oa/Nr57sC9PHoh0v95HpIGrD4ZMxN6nKAm5lyvQHLfsFmS2t0pE35WQ0uBgYVm3epMLlfzd4cdD5vr9KTl/8zFlxjXhhoTW5P0a/K9YBGLTgWQH8BKTr9XX5OQgLMdXdFtaWZVQGSaZS1Av4yZmVNUiY+MZK5jWwxzHQJcr5nxKE6b0zkgKxhurRpNxZKxjq5x/tRGetLpbZVVDmF5EgzGUZnd4IKFaCToJmMjtTauJkRg6Jz4bGZ0mT8Nzel+MOa+amiS+2+NHGgjFJ6iswRK1fCJYqk0LsVu3wrwnM27/PklwG8OI7JcPPzQlwZa52yqqNIStJp5PdVkvSvUmQjcaoynn659UrVXysO+Opueqphz4w/zwjSy6F7rHqdS+BwOJ2ySB+Cvr0BgzvQTP+NGuJt/v0jszRf1pqz7dZPt9sEPiNApq+nVLdH2UNiSB6NTU9FGok7YyEZjxfir+znAX3azQOvwhCIQ+LWXfWG6cVeS0OApNr4J2VWDZmJbM2z0+AyaUh8UmnFy+iJbXLZ1t1DVyM5oYkuTSIuBN3CQhh7B1hR/CTOJkKh1B0j3vq7dKZG+oqzrphGSUElUM05YeVhwWHEyiqRkuL1iDoyoEHz3cg3acA2OgDmi6O7wwD0/LdUvs8tyemfkIx5xBB1yaZBiyKZQiXEJTYL2A5bEyxPFVLQvAZBji29PLTwxyz4bGCwp1cxSmW+BqiyVfuRpDeXbv315QgOwTYH/vrZYmiixY0bL/BNHskYKNQ4XuFhrLwWHp3CN9orsxCYCi2NC3qpzVTiIy2+sK3iRsrJwkUFYuHDF5UPcnnUK83JF64TuUJkBcbIVulFYgxuEk7sa3U07WLsXGz1G2qtVsJ8q6hqdVE9TkEaPjlLTXjWaDg+Aq7qipnNeK9NxF+YhhGsa7q1HPLSTEt9US/x2mAzMuiZiTZ9cx5tUoia/b1oGuJOLTwmk57qFhZEwf7iCj8K8ZzvXRVTi5c3Y1CkdExE/XE6YhnnMHsCfTTDrjGUYXZNLGLzC1SzAoemlB9Tg56QG0KlZRmcMKcLoLSi4mO1v1cNYwwmnkblOJVsZzMcx+Z6/BwxlNm7nePEQ8R/v7+/NA04KIxNckbE52d27OdYajVZPU6tMKEdVKXPAOeUOrwGG4e9xKZpoRvUdGfkNIP61VGwmE1A2mbXHHu5VHGJSSpy4gimQbmzP6rzaJvF/Yp6fYPPHTCowix7FIKI1ZrDCZFaHqJNKKj6uBYlLjs6QrArgIUbP1xHBD+UlfJPIdo3rMNNfe5Jv/utZ7Cdf1RJazthvUki053yUBUoSSaBqJp+4HywKKr+tkZKqdjKC63AR6sSirPj++0uiYTH0+rm8driF67xQKJSSgmN4IXWYF8a55UQQUnCkjqbYRpJlGhgrAQUjl/UhrMJwvZldxUnPYlfDMEcvMoIlR2fQDH35YRDFN9ZGTLRHaZLizS0iu3SHsurNdQZschdZ+KtOAJpPTqbAp7nogXTnUZZ0nxtLERFJy1JrUXi09meRUFSol7MoM9UBtPC2SmWOJx9gLvizMhbf36NO39yuR0xvGUhrKYoQpMdWUuYlww2Hsh6jDJWvi6ztE2EHo9/60syXyCMn+b41WDXRz2USaC9lnINtkpZmYUm8vZycWMp7ySF6SRX1hxkhpAkZVGYVQgy+D06ygb6eg3ioIQAG0nwkCBHjnwiMRf/D9TKoiW3BypHSWBhHXlPTAloYUin5VdgKGJ3Rn0jszVmAv7JiBHjFGfHmVF5znTs5dpI3epMWIPlCHJyKRRxBZeEVol+mTEjP1an2Ol49RoPmau/wPSQ43KPGdhWXk4Oo3TemhBpPpc9RoHTQyQfmHLKWV/KNgR+J8834hyp2s6rKwU4k55vN7FdRYoBYoLHPn8Dd59kW3TRwP6IwWqX0AXLH8LNqPFUYZ/wwotJes4C5dOniL4DmbooOt/pGdReqWET0xYzFPuoULMQXUJeMEcN9ebMqRG+nstUMHQS9/rR0S+DJyfmjDAf3yevTQc2UXnuIgMFfQWtTYVwSYiPKnAUEJlMZmBuZPA72wYiOF7A4UzgnTzVL+2VIn+FRhAjNmmc4EOxPXoqJZnBTNO6J4V91zlA70GvDhu64LW/LUOV0oVFmsX9rrkmT2O/STK7lJ6QZX5ltyaDS3RBQIm4x1THs44KzciRM2JYtAXEZfzXE+WLm33i4STDTlegii1r3JfCw/ttFSYzHjLEUNDoOJv+u9LoKoUY55/360uOM9E0m9H/7TVNwpiuUf9WGUozTA6h9WHlkn+fohJdyOIRaMpolfPrL56qTySFnWEyPp8R2gaKejomlkarCrFKpVcqS9j5HQDnh5+EbovG30P46sQroGn80Qu8u5WfPYF5Rt5cMpjarr5vUoMyciiikxLBiO0cE0AxC8Ea3bchPd2uEqohNmfq0hKPVZvL5AQi58jbpKSAWySoXFaQ5QBhZB3FlE8O+iQsqB23jjtzME9k7tmNuvR9225ehY/XMCuRX37Mjlcke/x078cZ92baGJfleO2SKLdq1RaVOKERP5+SfUzcFUpxEZlnLy58lOeTj6Q4OFj5O8l/DYDK5fmuYIBS/cWawq/WArDRdbh9gnCwfnQ8JUt7frhIMvRmVs570SzLBTMi2/o/a5J1O+S3m1Sw+5IyjsH1M0mqLm+GvZR0UA51VDdf10V6+e5XuUE5Jz9ymtkSizZFWfuDwvfL3t5WiGd2EvjLycsGkh6Htnmc6hLfXDM6V/YVQq9Y9CRzmC0fLi+V+RYgg1x6UBrC8toUR1yMn05ZO5QzfBjAPQiFxf8XPlOPqtPpH34bKzHBS1P/+zl5CW7W5k13cESv3/52fN/iWhYlsmDUSuq8omT5xBHqxwwZ9xLHLinsDqtZZox9E0WD1fgBTSjiPtvFLv4USuTD+/3++/fv2/2uzTjCCDe/ehpLj/tnZcatV5J2I424rx68gjiNOVtp3yRR06SF7ArkDovJW31dRBcGyXkMtOAlke/9hTD6Eo3LqWgSRYa2h0as3Wfp1iJo+2pniwjNgcwshU/UuaaKU78YZR/yJ9MdRVqXYJPHohlUFgRWfKOXavjhSR9vDVftmk5iIglmLOsbEgu95bVp22xaVn0EgRUhnuZQ/ruTKTPOrtq+eVRNMrjBwzUjbDLt01UCYZoaH7nIoVTzeD5/f319f98QY1Cxl5XpZk2N4z6Rsx1A6nfYPEtB/L0T0oBCDW2dnAukHQC02Z+UJ4gXVDqFGb3eOztjEDGIR5w9Vi35b9B58vXJ0Bw6YD2jjav9WH1NZPH4SB/NfwkxbObJGrCqti6pJT4m6wkSum27SER3iZlHU2J71Pep5figEW35Ai9LYtRWGT+f49tkwuNw08cDHYKp1uOvtjrrOgF9uEVF8Ua6BGLl0IpCS2/L5N6Gven7iweNGiHSoYcMi2hwu9++vn5LfrlpGNxT5yUy+FqJznrsJ2R7kQo1LEEKa6wbSVTou1V7wNWdI+ql/i3Na7R81Ph8shd2kgc1ar4Qlk8bpJl7dUJ6Mt4LAW84OE75bEOi5IZWEmUhPwWI6SJSzEevNKRkoAEDia8nusOUOI2fxeuLbL5tktIs/nw3MSL78FJC9+ZvTXGAUvNGMWMoqpg/DuJXXn2lvHt1o7AVEAxqXJDe4W4Pif1ziToiihz2GZ7mNoQMDI5IQZtG1GEAHp8rOZNVlpBFax361+/f97vYTqqYn8smBeTsEE0eEDVAG5FJFWKuaQzd17WgSmLLUEiZkvZkWuyqqEL8eDn08TiP234pfQrLc05j20cVn07I+Cixnn9Q9s1qrEsFSZEK0PSknv/hA/sUeK2GLCt2M5UGuqDyQnCst7bDXA+c6sLqGJQ25R4nV4/3S1IXqxhp3A/lEEK2Qd6vv0xO9wnR6gN40ozqxPon9PhiRiA5VYxYPr2AA/gdUAB8bj6bWFOOn4PQ8bQig5Pnh+oSrxQWmh9u9TaCUptJO/ogZc6Fdw1iPoFXvcxwJe0RCZNOknL8yxkHHI6zg6J8Sr/524JWv2G/kwWw+yCid5a6e9H4W7KyUMNgtjL8mK2p+Og6qnFaZQpcjdrFunmzTijTS8UB92r4p2y12aKZl10GSb2DK1h2iIdEH0CbJi9vE2s2zBkx7TSNpkecK6YyvMPKNpkZBk6Qfgwjykgq7SNgOQXm7GnlBuwyu4hSmq5xYtK6LM/H4+u3hT27aM7PRLh9QU3a/HHy+NGLOWsXX9HtWtl+pBzNdgSbitrnklmhdZ5ZciudTuNTumFc7GAZaOwUv4wIxIJj0cvfXiRnFCmhAlTr1pkFKwNT+NZhOikM8aLBAF16bmeWfq7R+Qm/sv0Rpnu0//X7Ni1174umpHEejoytwAJDjZgPp3KR/dqjA3HS3uAwrbFja7wwwUk0dSqy46++w4j/k4tcIawthPndQylXaQRE2cVs6kNoTMajUgRigQqmQrPXZJ7jW0xX38YGcHhiOyfrenfZoXluq9icvaxpHJYIja3KXCJOv0Sb9g6DbpgpsWkGOKw306qqLBU3d5mI8rvg+KTAITpGTqsGG3CXSadMDHWu25zyJwsL962xYA6TVnjCPv8SDNTERvva+5jHQaJ9L6sfJlLki3nkCL5r5xp4KOWmr66UXVD92plZ00hqtkkTOvIsxzrnst/UQNNTI6pUPdKBsxPj086EvahwoABQwzTc71E9BA+awKb5savEJ2flMj7owcUyNmVg+ymo87L9dVbjFyn7SfcLog87fCZjBSlFFzLpYyRVnSxFUeeXuTCRfTXsAE/SSsygMj0XIeQdcWmtJWn3vU3Jm0u12Tc9wtmwZCqH999fx/f56BEMXBZwBPuVorN+b3iLUrxCIBPVW9ab+QBHvFaQujxxffqNRo0Tzq/7TRMpLtBacHgTReC23B6PxxLZZaSHTV7akTPOXuRtXH50Dauq29LInU8rEitqsGalGdnBBYBVJH56nBZYR8ZR+vfaw1ost1EMq4k8dYaVNPO2JV9WJPfJShMDKfIbdfWpJEqFbcRS+jzzFibT5jdVh6Yk6FtVOjFseEiLYmAtZj07/GQkV7+Rt+fmHHUY60PMGWVnPigFPxHbzQV1um70DiXLC1qkWgEcPksiw2WKnb1Lco+Z9FzC4qV6UCnaOQxzi3B8xL2nFXhSGmqyiAmPZRlnwLkzF52925xh1+UqvOt2dzkwvE7h2Ghb3jFtNq836wiVzNErJbIRibvU0j8lUgCRB/wnBWQN0JKemSUVgjb6ov2cxbFjGQUhkqSgN4LhZzumkLzfHuL41TJoYQLvmNAgSUc2ZJwhAenY1u16lS6+tpToweGHq0w5JJClyf1ufNYwPUNfEhBwzCoUzhY/OXM4RCPqMjjn8AsCTsApAhSqgp88D1iovKxCCix8XhW36GxVpoKBxSTs+g5CcGdcOhqSWyZ3Tq/ZeR+vE1gb9kiNvl07e0l318x49crBmU03keYcqIGPb95I4khlcjn/2JeiYw3UfyCOSFcqdS5r2tIiltjluR7WZhmnAA/uF+tApRUQyvIiXC2jgHuDtr7IoII6yvP5UOH5YdUqvc44H8+Z1pR0C2eMD27AZ/+QVoIiOcNiJ4q8nSa61ahUqv81AlB7HYuBvHPfxOBBJK2y9bjb/jnJ7phG3fKTK7ShEbqTe31mtlGRo5JSh1k11ETv1kqKt0sOPI4KU1OfS4uX9qY8s7z6ESg/u1IJevWMTnETIg7Pi/pUyLj6ysxucGrhpCdvs4nwprOMWIaNX5pAvrxtxebsHIsOJ69vrCYeorkZEQCVREPOKrV5UkysrgCmN/YSRy8uI2nB5pKyL6Kp04FenyEGbhSwSeY9rkDH3vbt1+enYKxgr5iPQ76CzDhkGLCqBkFATFf3WzEODGewSQbJfU7Pa/VrFLM4v6aKVcPr+7FoaGeLCmhTKD7TC22ZJA5U0aLYU1gCxNmsOV9SLgwEmrxzPXRneGo6diJaLsV+qnVC6aqlM3U2u/bWMgpl5YHgYklk7lCf6jPnB2C2oI6THWuqy7nv9IlzctxyJ8pewCaDpVFmUWrceX1G1kQGnjNbaMA/7L6RMKuveGjSb5QC6IjIfDgXxMsvYaCYjytwvz4/r9cPSTN7PtTfjWAM6027vCqbFdHzEnN1u2mbc16c3poBtmfrL9CmjxJ+KJ/4syHKt76dKT+beOCQb3pIFVDwiR12a1+0bah5AgDEsPzamI7TOnCGoI2k8FiiCBv3uOVo/jCSv/IbySl12XOwZfh5yhqLhIUuy+OxLBKIY2m2bgOq6xvHq3bO00M/ap8EpRMppgSW/lj8GG8PY4MGNEgGFkmH0k7WqiIlQDDlt5SoHELdaxWk4NqT1q4PLTc5aqVT+YbGJEWrzMrOyXW/fn5+Lssi/a+8B4f/yfJ9oc+jkRezGOe9t5uw7tk6Q9M/UxjaVaUphsxg/KrSROL7Ml36OESGVvNmpaMkaqLqjRqmJqUnxfFjh1/lrJo0mycyXutpC0Xycm0KCiT4m9kVCwNNQM4AGHTuLM9gCu+Mzmd8OfQVngBgb3xetIYHFaUvwwzvgtBGFGK8hzj/bHL4Zob90wUmha8Dlyp4PqDVAn6u8mfVTOg8IsfFitqlp7hHh3CLhr7RdUH05fPxvH1/P4UqRkrIWe5RySYLRXTceFvNuq6aLCjWC+t/ld8nVw+TXSY/5yjiXnto52hqR9wXE5v2MxSef1+xsFwz41iHJVlbxhYlvmu+crdWWGgFHBKW4KGHO9Rj7czGAGkTMqgZPeZ3kvgShSDNr09WrvEEM3Hq3CGV3crfhitZTmjkOJJHPBM4rKn+mcbNpvXpBSzBwgmf4i3pz2epsfuVJwt4YUIrsCAAD9B0e6VWMXhexOAc5WVkqbvQK1RkOF3iKXEqsETq2vMbFXN8Ao2kpAqeuxHD+RgTo4IkNWkn0A9FQs3bQM3rls2aM6NVoDuoZCx4pSV25Tv0il+SbRWnF6yMJBe+QvjZbWen+EQC7W/o8qLtifUv0hrLETrske2uzQ44Cq7FNuVOwrJgiZwmyZwxfXgUI4hMVfF8hMgPhMzkwBxscuRLDYcX7VKLJpIx9xFxGOtgRu9HJmfe5eHTiN/iPBrvITmChp0cBVZE/PRaYffoXyTp71aV0m3ww+z1n7QLxXC8mfiDCWlMoliGbFO9GA2x3+a9bIyZqvxC3weZ1kLk14+rdsFNgOo+1jDbJrHBgGbFcSxWPEQNNjBSrtsbplDBVGEyPsGR4jNujxHok6k/9jV1qpOyejTGfqlejsQgWj0kCMU2Gjnny0gDhtNGCxTNOUVhyYNXH7IpHdGkkXRsfyCaFhLlKLhQgCHxYYqVTpW5xfCWd6CEeWgNEDlZsZiBvk+VEP8tVRqE/MGllxBwIEtEFEc7W/9VgmWhGmxPLaerne/84PmUXkePB7yyOUCekPx1CGHNKXsGFfZbJ6zNQK/VKmYtxSO7EXoeuQH9B5WqLL5UHb/wlYRI4ACZRCn36fGaSLU4vX6i5lVHPR77AWPC1NHF6Sixk1HGO2diyQxM5us9jQHETVG0KNfguly0DoiPz6cfwWAgusFgXkHxVLRe2lxt4RLgIwG2fnygI9Yz2GXbFqk44C0+OnMg1Imdf3t61WkdLwaM4HFZb/ic/M62R/i3ZXgEu4sm8/aRWK5utxpiixvrNS6sJpSBRdqGS/CNpfuKBh8MNc4upc21VImoTmeR4aI6x0qJYJFow+2Jx1hzkBy38E+dAj/C/vnXI9ejH2fEZZaUrMl36OhNEmQxtSrXH1NdKkpk5zj/1v1W8NTYcmpmT8qnKebERKf0kQtb+Ub+gHTlgexQ8orC3j+RF/hi2KQkP7te3VgXMMuPPX26+rnGM0P50PqPVKWsKswoTLmh8aTpaW754/GQZgvi+3VFqnREmjyLOO66pDH3cpH+ZNIHBCvzLMGU6JqPgfxXUl1Y+S5KDBOU2w/j2tjQQsR5QLcTsRn+tXbDPAaoPn5mGp98zJxFmaTYnTVguMs4/gHhshPBsrYBBiRHgoNCEy53cSgJhcMUg8Yo0L0XYXzQIYjhMjN/Wq/T9XJdr+uy3G432XqiI2ObvbbaqbXVaXkG15rOhd+aQYup1iAnT35nbbt8xgDrZScNe6Im7WXuR2cKbGel7FmAT7bp+/YtSb/kXEPpu+RrnaENoOroYWU1RC7SsGevPAnsqNy27knUN5BgZzdbnTHMpHRcsI2BwNN6c95YRZ9he40qyfpmA1QK+/upOC096zR3YfjG+5qlTcsRvqz0RJot00ACaOo5fAtOLJdBKG+oEsOpCEpxG04lKzDbREM4SGlq268fHxJvK5Y8iiaiyjL1vRC15xvMkVu01EGoKAJJEqxXEd4/oeCJf4ZIatN5apGVs8MRwVm4hU9wOZWLsN/b97fZpDJw0b5v7If3vRHkTDUmlcWKOFnCr/VYMBoa6NAOuU3VTmLYnKM7SpKFEtnD53FCo+ieOax8bXORu/6Si021qsquBSq9GMorAdM0VYl8hsM/4mAxPLbybB1V0Z3eV8mkSVjFfEBgHI1AXeYfmFHwLJ/uC0nFr8Jw0ukhnvB9v358fnxI1prEDFg1oB6Tb1dmaJQjCk2jHFL/l3+dUKuf091+vYyUyGlKVXgUTBrva3hBD7NI7nskcRFSMSf6YbsMm1F+o9I4iFP1vUs4MzWcSDiwFQzKSmZFis6DI2YLYb3Cew+J1kL1lRHB3gKy/DJlzfT3D7S3QaCuohJ/OOdhL+aqAPGG3YUZ9YbJ3o39OKrKsQAAEBFJREFU/bg524BgzHd82scsAnO9z2XsCBEcX0fIcDanc9qzYBg6plb14ePjQ6USETv9q4nhkN8BT8XbA5M3vJz2W2APasWceCbuKQceS2uVl84DSfIHO7b3x/37Jux3dMMUtDqRF+I+FpVMo9OKdYLAjr2erkDVyXyiKbV4aWdJUxAM1mJ/zrEhZmVV3rLYH6rF9GPpdD0l6u/3fw6KMzE+YdlBaXIFWkaSFaFByVomn+5FV0TSGxMSEgyDSOtnoYU45ggN5mT8kjm43VcSE39zu19h36bDrvE/H9eP6yp1l4rxjuyfJ3ZzH/YFXafM1VrrMAia1/4bRv1/vZAV7NQP3QndaxdBcUQEvRs4f1AXJ2Goj99//UbJq8Zdc/hUYlLoat+zWK5xPquoc9er+H7d7IxiLgWOmKK/Re3NpTJ7kbEaw4eNxQ2uoecgpOQE4A7IoTmLGebPSOL0NDSmTBL2eJ66dA9Y+qfKfWu3R5qY2DwyajCWh1xwWhhPCkfGh3RrUmcX9DslrvNGzTB2Lh2HSZLNX6/iY476a9EAPC55WZfr9SrM5r4c1pgiBL4xGTJFcfAkpjqJr8Mra+CXW/MIADQAjzXxGr+TkLytpG128KM87szgCuqGNUDvllYpX7+/vqVebNKBGayBYu2bjiA44lrwdl0tmUYOZeq+hrKhAYMVQqgQ7L3fVXguzbynmU9xWENA4JmUU1mDuiN0YVzo8IODpalSdFd11s6x/IRsx1lwa58GbwwRC1mbIL3owXWGF5F+krMgHsue9rnwn4JYJ/tQlSufohps+XCJk6tSQXvfMrJPmh4yeDSFWPrNCh+mhZm2XK/EV9qNxpyIqPWHRoAUaHYyPMXeN5drSfsk+2f0e8QXWfhAb3lK0sLX19fxkPgQTjvgvkypBtPbM11fKXWpEKGBRMZ+xfjsJsTCGftCwUy1upWmMeKQTkNipiukEIQ5vEY/TnGznl+JPT9ME6FIpUHEpYbs9GqtFztgQcteB2O0iiWj2EMTnOUkQ4BqkR82SMngyn05k2DO9saPS1T+r2cdK6a4zcWPTIdV0+JsyRbdIi5Pa6/E96Po27g2F00ZnIVCUPuRXKsvj46+Fzx9t//vjxOEKBlPi3RCdGTsbNwn5kQbsjyfj6/vr7uUNRnePEhlqZgxz8wbIvrOKypYSI0Hx81kUlj1nRRdtMGMYi9Fa9Q1DUuaHqLJd7nq5Yci8XS2s5vNbzl/zF9e62kN6ZL+j3a400jZGlnKP1gnuShw1OU09wr7rOanKHOCeeiMnRxWoO8JAl38mSw7TejVOIWkRshvP7huV2B/OWCJ9RLM4OWHL5fbTXSyxg7GtnTENSf28OE4MEuhCSR7DHn9B1x2uJzRho9YKnJwwIWBooh+obhSejKSbqVR6NeX1AtAA4RIwyuUZiDl+KFTDJxQNcUo/w2NLutNctFvj0gR90Bi7+ToDZyzd57s0+t9n5wG5Vi8wIkSXz7uYuRkMoqnUx9VNa1hR4aDbRqH1d0YnqhZc53F0k9TfIlQaa3I46com1285heB2XkxKnRiNN0U3pLT1yxlCScyqSl+ZtO6CA5rk+qe9HnS8nrQJoa1z989H+hHvHh6sfixmBEr473nOlz9wnqN3u/3//nrr8fjaSRNQ54sr6MkHZGiEDoYxyo6zQi5EZWppWCKlZuMZ6O7Lwlo8rl1o/BISa6qM6njmisqxGoignlpz9dXY3CR/xo4+Q55h29HKfCMENJ9TvKycV8cWAoiw4/GEYPNljcVEWo4iDXwio8He5cKXPyXmb9oOWPVtPAq3scZGV5x6fjA4xPxzbGW5fpxPTT4VxqXNtkxbHyNi3d85ZkUclYSGnri4XTl9P27iwQQ9QcaigSdpkDLHkkU1/P5/Ov3X7fbTVllmD1DdgnbOWHcxCNVswCRWuRlUPdNmyFixKgzH4dN52jlr81jFHU/QqQ/ceOkZOHHhG6c5m+cgZWCy8uy2o+zE2efFL7B8f2dNpztBAjbosEbxbg0TIUlFz+OHCx36hatFYd1NyIkd0zKnM2zS5oRiRm++cQOWvgZbB1FLlWF6RDjBdTffe2rtfSAV5xG4nzy+nZiDo0knLz7bNcKMazkYxhtmkpgAJT2ohRsMbtKRWnnh/ebNPo1wu/fOuGaZebWFzNlso/QfcsZsthSpVommwqJ8xr2WaEg90dSLygWlvxdc0BitFNRpqhDxGERTIuy6Ox3qW9KeZZUzrmIQ/OZBRd6pu5ZdnL0nqO55iuYhWeoW3ne33IuORa5krzB7nlMoWvkwGP9gPyWoqnZYpXrmjw1C03AVc/LbGfz4AmfkDLx8osYPatNCwpe1yrLW8afm6gKO0qBSqdm/I//OwZC11WA34oOnJKtq69UvhwfU5autEqRlH2t1d6cqhN/2kuJAOpZcE7xUkq3I7msjzjfCjITQc5qqlJMLiFfP1QvTsWLsvTYl9iLCmqcBRZWBwoGBeHkXUX4GvAvb6Ma/jp9lAtGB8kWW+TUhvNF84cfAAij0PTC/5TRJHHWevFirOBFqYiKvjntM5b1UvYcronuwfE7ZsgR39J1X5aLFoA6pl0TYquJPpStyqIo/umUT8+mU7Je6OO6tOB0biWwPVWr8C5lOQ5xupDyWnasvFDZzv0uDFjxzZobuvjjVG+cJ8Sj4YjCnawYaJrcvm9XjXqDSySLC/p/mlVkwc02MrivzwKuEtskP7R8tuzbE7Y7s4AlES7ggJ404agR7cD61OkLMZhvTvm63xrlvQKLVfPlyO7Y7hl58lI8lh9uyaB8SPO1xCb5m6LNceKOZ6tVrHlBIindW4eytJr0R7yYQznvB9VjfS36NRIQfSU0XFeLNFm2qcXioDA2MUeQT6dgE4Ya7DZSDsoiKE6or6rIwEnX/MSF+RQylru2j2N3UWhI/ejdqUH6H8/j9mXsl03jg1Ox/57uqniVhwFFsRfrQKcNFyAPOv93dA3JzVOLcPTTBBZD1+Asezd8lYM8lBDXTet7P5PtXghV+c0rYa9f57wEjDSJU31rtG5O4ns6GJ25LH5XdmuYckpzthmR/WIcuNBFZkqTORR5qnaacc80ddQ4ebRP61IJcxdnygAT2dVPjvksLzcpwJw6gykIQOU6QOPBHQZTn9r5VZlvsvwET+yTxdFEDvAuBd+PZZWihSfppmEY0h37llZl32kMbG/G3KeSU/ZMi3mxLcMTF5B15G83yhgIjIwSytkjASGAGyF0Fcur26oRwPyGeWxWw5/iBvlEnFFX/1MnZAmk4Rc+Y5h2vLOiiX/p0VfRo/1M8wNfY5KZ4CtzeYELdmKz/Ie6HZgmsL+6OWOacJ8Rjhm2TdElnFg8w89wKyw5ek5xAnJ6dWluQI9v635sUhIq9986Thkqk/u2C9KT1/Dh51PWChYPxKZvXtzg+2ZbgEpV8v4dmfGYLh4osiJq7z8ej68vTVqQbiwt0gkvoxOZAeNxBlnM9PrU4SOy2txaJMTtJFnSGo9zp9c5L40vIiXKN44hSoEDXXOdbHxyeJLPXdYaSG2dQ5nhuKUh0QeJaRQunujYBOw1/1HaCxIDuojnKMyOpHGaxwsmFkY5PGrCGwhPPTzTZecZiXq//TVMT8hsWZdSr4UW6ChaMdnPPe9wk3P143Xdjk3b9HAKR6QD2HEei+HCTZWbNWTfY2vt6AX3nnj0KyNhiJb9sk1YpSqlBrjbeQipahzLtJObdCE5xG90GnMTP0cjrAr5alGB0Gt+EI+7Uj2EhOWqzh0vCJa/NO+eKp4lpnksXjxRbeJ7KIweUVh64RT259SmQXF2sjGXQTouTxUGbCKKhm0k9pJ8SlNo8o4RSgn7CFXTbK35YmtM54VygJvOgRyjtK4CDYzFpv4xrJOPfYnHRZ+1CJzs/CnuT3U0COdhP5GAOXt3dS2jciJurvRacFhPYFTqg22Icu2S4+WPKS5RkfvSm2HpzII6cQEPplkwptoZESFGdkg8xrYfj8t9u4gZDtJMYp7Pxd9joU6lvYqfCCV7xZbj0mQ1oevYIQmnLKd7ZJEIVhDUOtjC0ZgWKXrvhG4xw8UfN/mklTj2rMpgM+o+a/05aKHWCC7WjakQOleRqLPl9L+DfXDoBU21oJiGPSOlwzki8C4ln8oLocNKzJq1cIwZ1P5TiRFYXQEfcV/JkO/1sAKXqJp+FWUYEenbJFEKBlJLpBB/SihZEX7REb1ocmvC1kQiImYeR0Zgyaf0j/jTV2UcbHafSLcpDhPxLCIZOTogcOBj4tax8h4s3Q4nr+mpLhv1A39+H8/teVvWj02SkyxYLhN//NGnNNG9qa8M1MY6+tleWQgW8aDEYN4mnp1boSL8Cj1+j6c0LPaF+r+GmCGbRnIlQcKhEelQVOk3RVOifulzQZOrGD5IfH4QLw8xshpCc9ccGdMiqHX0mz85QEBdWRWarqqEhJluGzvVaFhq4rMYoMXzpu3ZE7981AJ7B5v6zD0ekuHGiyWgWslYlC9h/pj5VXZIWmxkhO40HSDsupT8ABkce5Er8X+5vBa1gTBFOjkv7V38W6eE11ZETOnfN3CV9g7iYGF+NtB3wtUsKYY7SR7wIWzvsr9hisKAesAAw5dmBQke7SGjvYHWdf9j//6f4/Py2C7rcVwvntNrL9Hu4I48UpnmeDwkCFrLwPseGEEFtBJkwQGyiE2gNty3iRKq/Uo9fcmPcNRC6VMzlLiz2Jhzoc+YoIEuMsdCzrTjjBDe2DTbM9aLC76C8PsCk03Z20k4Dh+Xk3YGvizjonbNkAHodMMOFxQTKJNVmtVF5iNCD/PYKTE96uNoteF9frhRgkUmNF+9Wiu3ZZWwNfOZhGSWaAETYqRGFODgJyuwrYHxs+NNoIjNinUjQqIYouwUBXelZtbMnmE7t3lmmwIqtGQwyjkYSQyWyiJDHlfiV5q+aiuEPledfHAIBT4ma2AmmjIn9jA9JsmPkrr5/MNfQqTDafrzcmzrcl3Wv77u68fn9t/746//8/j89fj8x37cL5ti8nKRrkPSTcezw4/n5XF53J93kb2UMplxyXckijPX9E8dwLmM8ArglTSH8PxFcJRVztRlXR4eJi/4C/Jr3kY55xqxqqRMY34j8srJnWnLgCsKXytX9xgbMCaT+d0fogiT/jv5S8uh6zdP3UPgJsb0sUw/98QnQ2vlmYZDEomyCVWS/rUmTehazOokjyqCSV8MFYn8ZGVpKd1iR85j3ZxCyrxM75Ve9SFEK8dQ4JhPSZBrWbDutMEv62WTCioKV+czi8lVBuYUT23RDs9w66k4ptTiuS1SkWnVbICEjCI1obIBUGalfdi1FR3Rfxz5wDvkyTk5ctwBFXesQBfCh6zStxF6lpzQZRWlDOprklDE/MUbmWMa/kMdeQpYrLlmPCVnA7qqV0ewHddlYgWJkcY67ACpZJ4ES6KAnZI5j7NvfcOs7mdQLbUaL1oS6LIvl9vlfv3jsl3++t/3/wsLIFYOMu+5OgAAAABJRU5ErkJggg=="}],"details":{"kinex":{"title":"exec 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The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /right_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /right_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. 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"}],"details":{"kinex":{"title":"image on right_camera"}},"isError":false,"durationMs":213,"timestamp":1793222997484}} +{"type":"message","id":"bca1e978","parentId":"13f789a0","timestamp":"2026-10-28T21:30:11.820Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Saving colored-mask helper**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0df8288370165955016ae2695a61b8819dbc8b0d83052fb30b\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Saving colored-mask 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red block / blue pad visible surface bounds from calibrated RGB-D.\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation\nfrom kinex import args,communication,helpers,finish\nasync def main():\n out={}; files=[]\n async with communication() as io:\n for cam in args().get('cameras',['head_camera']):\n im,de=await helpers.camera_frame(io,cam); a=helpers.image_array(im).astype(float); z=helpers.image_array(de)*.001\n async with io.subscribe(cam+'.camera_info') as s: info=await s.next()\n fx,fy,cx,cy=info.k[0],info.k[4],info.k[2],info.k[5]\n tf=(await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'},timestamp=im.header.stamp.sec+im.header.stamp.nanosec*1e-9))['pose']\n out[cam]={}\n for k,m in {'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}.items():\n v,u=np.where(m&(z>0))\n if not len(u): continue\n zz=z[v,u]; p=np.stack([(u-cx)*zz/fx,(v-cy)*zz/fy,zz],axis=1); w=Rotation.from_quat(tf[3:]).apply(p)+tf[:3]\n out[cam][k]={'min':w.min(0),'max':w.max(0),'median':np.median(w,axis=0),'low':np.median(w[w[:,2]w[:,2].max()-.008],axis=0)}\n files.append(helpers.save_image(im))\n finish(out,file=files)\nasyncio.run(main())\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":313,"output":555,"cacheRead":33408,"cacheWrite":0,"reasoning":52,"totalTokens":34276,"cost":{"input":0.0031300000000000004,"output":0.02775,"cacheRead":0.033408,"cacheWrite":0,"total":0.064288}},"stopReason":"toolUse","timestamp":1793222997514,"responseId":"resp_0df8288370165955016ae26955f9e8819d8c27928dacd2543a","rawStopReason":"completed","durationMs":14305,"thinkingLevel":"high"}} +{"type":"message","id":"29a3cd49","parentId":"bca1e978","timestamp":"2026-10-28T21:30:11.823Z","message":{"role":"toolResult","toolCallId":"call_2a4098686c3d47a28c6a9f6357ff86a9|fc_0df8288370165955016ae2695ca4f8819d8273fa4384997686","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/tools/color_geometry.py"}],"isError":false,"durationMs":3,"timestamp":1793223011823}} +{"type":"message","id":"66cfe9b8","parentId":"29a3cd49","timestamp":"2026-10-28T21:30:15.191Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_392303a6dc4e4ae19df80bdcb6b5dc8a|fc_0df8288370165955016ae269664b28819d967fff24cbfc385f","name":"exec","arguments":{"file":".kinex/tools/color_geometry.py","args":{"cameras":["right_camera"]},"description":"measure suspended block for transfer"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":120,"output":40,"cacheRead":34176,"cacheWrite":0,"reasoning":0,"totalTokens":34336,"cost":{"input":0.0012000000000000001,"output":0.002,"cacheRead":0.034176,"cacheWrite":0,"total":0.037376}},"stopReason":"toolUse","timestamp":1793223011857,"responseId":"resp_0df8288370165955016ae269644da8819d880825da25c4a531","rawStopReason":"completed","durationMs":3334,"thinkingLevel":"high"}} +{"type":"message","id":"1953a6e9","parentId":"66cfe9b8","timestamp":"2026-10-28T21:30:17.881Z","message":{"role":"toolResult","toolCallId":"call_392303a6dc4e4ae19df80bdcb6b5dc8a|fc_0df8288370165955016ae269664b28819d967fff24cbfc385f","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793223017.267591956] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"right_camera\": {\"red\":{\"min\":[-0.07704535908917484,0.05037363663380437,0.9359792452628558],\"max\":[-0.010132965239384423,0.11802476366946671,1.0577161411202283],\"median\":[-0.01790569017331503,0.08437329579285988,1.0223647101538353],\"low\":[-0.01968119294135831,0.11215254015326631,0.9426068395053633],\"high\":[-0.025401475412971358,0.08528556780882164,1.0568718285682772]}},\n \"file\": [\"/tmp/kinex-observation-wrxb6ic4.png\"]\n 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"}],"details":{"kinex":{"title":"exec 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{\"red\":{\"min\":[-0.1384726838486533,0.06276790815003651,0.962540244023994],\"max\":[-0.05922303365747568,0.13124786536745542,1.060490489585483],\"median\":[-0.06550222925211087,0.09469825304345947,1.0415251530870966],\"low\":[-0.11114994769135768,0.06490913732700883,0.9669796277877257],\"high\":[-0.07376581631004708,0.09461002694122791,1.0596298811854297]}},\n \"left_camera\": {\"red\":{\"min\":[-0.15688127335414823,0.12266262357040175,0.713505453118275],\"max\":[0.037058832820139165,0.40054043859581245,1.0031299882724507],\"median\":[-0.07028974145388796,0.40000044254322253,0.880343311680841],\"low\":[-0.12835342427817242,0.4000191366344823,0.718829186316962],\"high\":[-0.06946718738984804,0.4000030238401364,0.998369265895211]}},\n \"file\": [\"/tmp/kinex-observation-95p9oprm.png\",\"/tmp/kinex-observation-8vj3yjvv.png\"]\n 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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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nuhYzaRgs3BSQE4eIn5zG7fIlXnAL8gGGsMNk8Aziwtb0u60yXnTNjwm/681UirYzNQmUVrjMgNzOk+bA4VjmA/HZN5uyTtf1/uuL/cfOdP/30I4umq1XOMVVAless1/l8TxuVFRni85hY0H/hXUgeKTbUWQL3D4dGEH/Kq9KFS2Wfl67JEgesl/gZz1pwdXhvQqJ1pZuaPNUfMgZd0tfTE+Lj+0qquMEoSu+2fNr8qbiu21S8ovc7ZQiSlyTGbLJK13uJ2cZoGEuzYJ+IEyw0R8KTI6Q8rsphj6ADPhhh4lYIWE485QEWzsdpxZ290i7v6jmapKRDhuSlahCtYQz+0VmWAdAYMer4E/8JGo48qYzv9zPgMdC96Ms0JLRlAWvvnM1drlTb9YvZQQfRoOARR4Au5B+oWVfACue5UFn9oiF9wvtZ4YypjTtXqX82nXzvhttHdWm1m2dFjxa0yrJxofXVq5kxOstQfiNqB9cWEP8wywDD5yUPq+FPRHw3rmB6uqNE9PkHcR/kDhHcLDmxTLdRzSHodLS0WRuipV52akRkpzwPAb6cmuo8tXgswyuI2PhuOLj0kX83HBi/KmKKg6jzUGYPrkkFUHrUCZk8fFfcOjnnDAwKimM6tfAU3lG/mgXpdQnxBv+QwEDE4Qr1+pAp+F2hpIdVI0+tq7g+MUOGGQ8fksAqhMKRgiUpl8Tza0y+UfQOjYdkH6Qe8OlLYpMJwxeOt8RkwZBuAYhTm2V5XYz29vePrffjQtmqVq7hXYVsEgR9T9a/ubGuCYYPzunArhlyXRQQw0N/l+6aJrPmyXH1j9em//zETS+79ExhTGHVTNmpdpMsu3buadtbXZdgc8Ej+CYehY/RpwBjZ3StD6Gv2IZ4iqMQ9RsrNkTujdRw4mkJBi+rMQku0us24q7SfS7wEyJm4QXQ0BcSBZDQjd3SkYpqhvg40UnEOOJF9FscTepZDzJWoqv9+JOEjnRQQkVIAgf+U0lCqdvLL0iK3ZUzBdNMgwoNyU3SV32oARTE3FIQzTNRP1wf0AoLMWC6gSWkpuvg5S8UBpoPuqGJG7wxIOZQBiGJCr6BCVjOkNMPRCm+jwapf0ZYVPwFsukCN/YxAKR3yKnrte06U9VFezD/8te/ce7q1dnutd3FweSFL9ocl3OtNyABUDdtZ8v8QWu+vZi11w5AOHMaJa9fWZQ5ZlQakOWuyGtrbAmCunxmQ/+P1fH/22T6ykuX+92ttSxTZbVYzLMLZ21vR2NMg0t9vyLRKO5ikHiE1heeNXFoAzkkuxPpgJLKAjeLJ8groPh1yhxKbju8XURBL7laaYeX6ZUdLMm7aL9E1dT5Ay63S5KyzI4YaBvxn4G5lb6CK5697hKwJS0L/32/qCteUT+Wljm6Orx5IZlG/B5tdnANJval0/qZB74HaiGgezxVBAmcrosUIXIKTL0JLD2qOunCRO+EPwBxIdKhDYCvfhDB6mIVTyjdJOkwlxkEcJZnOSClILlUZ9a6DDCInHQabRqx66SDYW5ZMPjxrMIXM2tt1zfjfPTUxaf/p3/5/7lwbatTbpoXJssPDvaKXPuc6YwSFYtCtW03m81759q29cmeWZ7na9PpeDTK8xwS6kyfZ3ldVcBQjTFZbuusbLrSmqmzLzP67+Ru4xvfvnrqpPoPfzg+OXFVOO+EvA6bfB1fnFw5/6cQvHKpafpyE312R8QSCyFN/CPZmrCl4e9lHW3414CpDEbOEi49h3HcTn4uvIzhsKHfQH5TEGpq1MfF8q5jVm3CigUlY+B2Hg5qaQ5sn6fbE8VtpOA0aJsQAbwgaRE1MJVpa1SRFbXKrep7zPoFDoNyKoaqvKALzpIkWS88hpmy2OD4q+DppHaISDNf4FMAg9VEayH20O8LsS3+nydg+oCyqUDqWpuXqu26ZjZftIusyKuyOjKZuLzS1ramD+LFL6BQlvF3tpZJqmeWJgzqd5Z/4Stff/CpJ5rFoqyrtqyzojC9aVtKSwe8pDGQcT0e1crpDhRv45VCVMOtbdq2N/AmJgnDiDHdHAdlwDfdWmdgnbJHJ6NP59nP3/nCt104d0q1bnREuR45AW85xJgZ2yGWVSiZIZnMqzA8jLj2ZE6LkyxON0tvvyosYWBZgrJFhgsfFEGzlJmaqrXBq+WVkXhKvbd1IE8jOURTOBxwLRiGV/miAOAE/ihLhKyODxXaKlOP33gOXWeIbmW0IhtpMslyeE8alXc18Vh9pvRQZMeVS4As4fALFduvIMQi0eXvrMvLupiuj4wy2tZSZ4QCDVE/Z/OSs8zIMA9eH69kss7jZSlnpni3cTTPpPnl7XYvw9mOYxXe3xPLN6Ajib0CWJrA38c58AdThm6WFR14epRpu539vS9+877tnZ2+7wEE1Zqf+rFX33L7bcfHR6gKg85ZREM6h3I5lanARK14tjDRH4Q61hPI1Xh9uvHY2WfmTUPOmKZrXNsh0pnLUliDN0cXGpX84Kojgo92XZvpuixLo7Xpe+tsZzCfCXWEMPVM68V+dy3LP1IULz9x4lazyOpRltkiyzWkvSMB481DEABz3IPApLiXV3W56kRwHZIvipcUuTfcgoWVpARxzgPZCjCQtOPYtsEXFvegQgrCBo/HgEQ3nUsqpBKkprAcg8AKJxCVRXjf+lR7ztwP5T78GILHPSazkeuHfH7RkpMsQxJMXIkEqp2kXQWyZeYomYpXLGJkiyqDiPUKBl/qfBAJQZ6aI8tCUQFVH0yvi/UbTkCAEhGAUYkPiFu6TY6OGTqIIWtBmhfeMhnqP/xAKpHhgWwx8SeIiTBTzyRC/DlISVIbnHichYwqGE/mnDG9dllelKa1bffd7z74vYce3Nq6BnM0vVaZ7fo/+9znRvX4b/zyr9x5++3gOSrAIaR6soiZNfI5oKOIbAonDppx45qRztR4+vBjj4FaDpmYdPLBJ02zhUpE+ERUj8dIXbxHJDKVc3med33Xtq1D1doaS25uig+HKg0A+XCqcKrI9FbWPl3rH9/bPnb6Bldntu+zrKAyK/StJP6b+BdSJZacejHT3x8sHiYQf1SKB3aP33PpESYDmGQTaxnhGu+3l+4QuAmTH4FLh5HaMH5kKME443fRNiKWE46JwzXHHRIQfqZiPK0O+Cm/iZSbFWVRFCXU66G4RFgurvnhRTC+ohZIkiOwRL8Iwh8vVU06D+wv9cZYqB7geUWkpoFxkpJy4LXAlDOmxx4USSj3oiz8hkh6T6VRM6At96WYgJgDZ0nRJAOzIpDocHiCs4dfqQwW58PHZfL3wVuYJauANRiuAlOOMntgvvalrz59+ex8v5m3C3AVZUXTaNMbo2yms729g//zP/323/5bf/OOM7e7ttOwEMEoRvkjHR9RsSTTx6j1zcce/P6lS5exIgsEgsUYSSbAWYJfjJnN513bkeTjiizEg/O8XyzKolTO9SB+XYlln/zs49mgiiygJ2Rm1tvtHz52rIfKO5Txn8WqVkjJ0sNCjmnhxvG1lTxz4OexmpUKEzzU/GVfkQiklufF8SzBYKIoibJZXCn2yqV1D6Jp6XVlcexIkAbOwPQK6VhY4AkJlmiZC3JJqmLdwwt2GDjP01rglsBxlbEOVLCcA4w0aa82B54e/IO0YD4kymOLJMxUG8iBjw1HQaTYxiPj68cJe4WrckhBGJONmSITRoE1XjI4Oggh8lQe03sEj4luyrgpS1iwgWHvNZ7BARGTTOlQ/pIyef+R8FSGEJJ2tjdZnunJyM4Xn/qLT1+4cmnRztEIVV3fE0kA7AnFZZHr5mD+v/yb/+3v/zd/58Yb75xMcq9GeVYXTRwxSAi3ZWU2UkrNdudFVRdlgboc+MxiFjUdLHSk4XgX1jgsVRfvZEFXMEWezxcLiCSBoOZADy9ZAkbAdbcqN7nem5384SOqOcima3AlslyO5eM6+/GGQxw1RslDqfiaMNySCh2eXYVclBiWirphouEy2jGuGPNfOQg5rFjqIhyldLHTolfesgqyF/8hAiZKDsc1aOZewKGw9ao5691+YMT0sVRVnsNsc9b9k9VLDqqo6SD02jC3mKIcv5Ecc+meGpBNfHegwEZqE6TBhQPQlZUDlNJi4S3JPAbS3MN5vVLrL8uWxugdWfLEDFL59QoDIZ2l+DQSsrgDLSYdKCwApsaFarpPfOovHnn8sd70Tqu+66uqyrO8Mz1wXapKZ7Sxvc713vbuV75277vvPjnZPKXaFtFOcsCJH4cZoAE7WLWzl//Ea2+75abd3S0qvQdqcNey+DUYLEKtu++6LAMNKi+4DlQwCojc27YtiiLPi77vMihrJzhlDNygqQZ2XdHo4plP3DP6R6baxJWn6L2QAp4v828JOfPFYkmF6SqFiWfxgco4eJTio6gUTvh6+osEN/sHyiMbWEIgpSUVIFI7ESyYJagmB9nPJSlUKJYl2DvHRFmj9x6WxHTzN+dqkN5BFFkHazBBHqZnUYhkrzQMQGYDqosL4uknBj94b1KhllJE/Cc9qFDywRbCw+DFbxKyDxqF39L4Ssh06dOhP3wo2Aev5MaHALEGvmmHWQV1aZr+D/7zR+7//v1d11tjO9NnWp84fryuagehGTgCeErBR9Rb43T2xa989QW333n89DFwWlmDfm0OzPitjwMjFHThMjAlmva222753kMPosvJ9gZM3uDgwSBRZgyQe99D3UMKQVECCmrFmTfaUCaQgmDgSo864f2ngFOZ5wZYbbY+yg/OXtSLmdKbsD6gTQbrXawfuV7D3uBcEtCUOIepcIweEpkeHwI10oOV7EFE6xOjYtVTbnZClOlJ8ReyTh0kLxMXSkrQaHi1ksGqoXMQawezs4hA1UuWHT0Mw4y4A8gXHKy2HGhgKyRXhVIQFcvhCRWuvfCccLv0SPtYDJdk5tCO4KoJX45v0JMDjgIN3gLSWuHF5tyP/lqRVCEeufL9wbv6R3iI2EBnnIZSj8VH//Sj3/ze/Qf7uxBUcU7nEPa5eOVSXVYb62tU5CKQTJaposwPdq899oMfvO7HXpXlEyZXSn8Q8sc/L2ClAT6Z5dmZG2/OQe9S1kJFZ5yQBStbQb5gnlegvoHtShVjSZsDqg6eeTqOmORgwFtNrhUkcrJK8W8gYCgybeC9sje6bxY7l7P2RF7U3ppNWI04gLhCfi4CozSY2tJvK/cn7sDgfB26h/EAMjB++MVw1PWSP5y9U6gb+9Ij3t/mRS0FQjI21qPAxq3wtajZF03YcWuw6Go4RUwKQEam78FgQNQriW8PyfNOgJiOLCFoK+a9tGhyNVfAn+ID0kphK+8l7xIqq4Z84AGXea4voSQd/rkfQfS5xF+WnzWMh4kJ+LKl8DLgPykK1e3ufu/b397eulICMhGlJbq7IFWgB1k4Go3BEI3xT6Crrm+fuHC+7ftRhTRGCy31zrCHkN6Xg8RrMPugM0dP3lDXI7eYqyzvdVavTTOdFUUBhyXPQaluGjee8DuZ7o0FT7hWfW+gCg+o2iRZANgJvN8BX8gQK0IOPPwuYrVwzqBDOFUp1W9tcQ1tECEEoZMyRng4ItGuXE2xbcMchFW7STcWdXbF7RMl09cSX/H9YDJFZUFIbq6/HJxk+DfW68WELO/qJu+PD01njFH3/0tYfXRWKwu10Lu+twJXzYAEUll606tO5XlBFeCZwQzpbeUfg4WVOCeRwBFDp6u+HtlsLLvG3CrRmMTdyCzEfOB0hVdQY0pmUYc6hNAP4+GDe15Hl6ZzkugpwonFHk6T5VCJ/tN/9Vmr3Obm0XbRQGwGPoSMd1CPtDaLxXzRTqfjsoKqjuT+ML3J8/p7D/5gvr8YrSmoah7YQ9hdyCJAYHOPlVyhhLPOyrFSVVm4oiim04lW2elRBcCMpqX9AiSVzuxoTK0HIPWXTluW9V03m8+sAU0ny7P5fG6sydGnb50taCOgZrfOwTaGDIUCf2rX51pVmau0yS+cBc25gArSLIQT/4IAIqf/Xu8V0s0CGctFX9K001sunYwk7CDs5mDjDzBPUU+QVdPCGQq6eKT6xF5VA2xTrFXpHezwVWOQe2LzjGB2CugI6+zYrEOCu31yMw9H2jjL9e7CXQfLJxaKSXRZeItNTJV9sYar1jzLPAEHn3niDfOXprCx672GZvBK9XdZAZEXCINBWlvJ17XKdVZOH7j3W9/7wcO964u8qNZKY+28mfdtZwyk8ZDtaky/u7c3Go9LoAdwTQO0Q6n5bHb52tbRkyfYiZVkhtCCamUh2TefFBgb6ve3dj7ykY//5V/+Fbk1tbaLpkGDFy0ZZGqkADgID4Gbi+o4a8x/WJuuGWOLIm/b1vSm63udgWrNCE7sEEApVOQrJq0RI626Lcr93nWPPqLK2julPBuVAdvraENLGxOJ5FkVwkQFXhImYpOXfKFL90hWOQRMAvg5fsJZNn5qwxGm4tEJikwOCpE2mjOBETHiipM8uPwYOMo0FABO7pwaJPLZh01PwBtWLuNgDVexgcEmJhsqLsYlkRJ48IXDKTUE9ZPL5J+HFVML41t27qVDlL6SwaGg1PgS9nc0GS36ZjQa9RlgI0zPzmdodpOrtm1sb4oiN71pFgtdV7nO2w7IGwjbtpcvX3nenc+DFgjWgqocFRin+r6FBjSZq0ze7H3qM5+795v3PfDtBy5dvtIsFgh6RicguJrZEcKWSfCwom7sJTrUAAWkGMjWbDwe16gRzBeLg9mM8yhycChy4IMd/3Cm8wLyDReZPpqp2Uc/vPabv5XfcEQh1SeL41c23SnxW/phKtj4IK84eQGJMdzo5ZO3/FX/7qq9lkKHr1lRa86PTdjxg6ckryCD0sIF7CNEr0+IN1PzKK98QM4L2cwieEsngqEbAky6mnpjiU+Z2BuuF1qFmGQCLxUjTimY91pSL/10oSYWP1mI4OV4kvimnIfkekG7jj70AUIrdWcOVQ7xzkomFEQBcs9K54WGHPe1I5OmaxeLJivytmmdsV3X1XkJpmNVdE0zWywcoKCB9MB7BA4nffXSRWO6vKwZSQK4Dq1adEpnRaX6Zj778j2f/fRfffH+hx+9vH15Pp/VBSToQ3MsdI1gMAmBFdQaRyBGyaIj9yaRNDT9oQAFYTYzPZ1Oq6JctG0P3ix2eaIGXYAkxmcUGQACqt5N8/zCU08eu7pb3bipyxzGGZnp4SbJ0hqG8aW7mOyB+HuFBhSORSKY6aPVEiVYJvJfSQx86gY6UJIb9Swzc8OD71Vr4oNZlgMgj4sgiKFgbUd6et/3cAocILTiQWXYdgwwC3Cnv8nKaNjykH3lZ9CzwP8q3o8rtGqmsslCqgtIFVqI9PD8Z6Xe5UcOuEWS3JLeZPDVIYNedSS9qmTB2nT5aDzvutnBwagoiwoBckVZ6LzruqLAfmJocY1Go42NI03XbF3dptwUQupcvnKlN7YaFdTmCAzS1ipV2Lbt28XBYvvf/95//tw991y6um3mrS6zsihB8kJoGfVkNfC/IEGLtUaHM8hKomBsFaZBaqOqjDAMW1TltCz29vYhiyHTQLx5UZawLwYam1mtcuvyA2U7lc1Nd7C7VXU3lWPEgUJJj5SBPgf9GU/iwNRZWn6+2WFXJUpassHXt7DihcEqTv8czCI9RCvNOxWKI646/P5izAMDdQnfZEKIW8XRIWcxb8z7sFkbiv5vb7+HrCJyefnAgqz54jcl8CqvhcBpyTFWodG3Sm8OJ7x62dOFQnFbrCKpQ3T44W5e12E9IED556Ffek6bD5cBkKFQubbKHtnc3N/bW7SN7tosy8qyKqsiL4oqy0kLxRQ9MDWbvUXfd4BGLgrbtcbaq9e2u3mj1o5gpCnDTopWT6u9q9t//PGPfu6ezz72yJN7e/t5VWQFuJdD5JDTJz2RejUtJGbEsj8Bsc8+cIgw4desM+ggwSQKVdcVhbuYsLFdXpar0mVzBWiQse177No3u3DuZPlqwvh6XM2Sz/n6ZBwU1sHCexQw/YGjHO7iUC7yu89B/svvSwEVBfnSeFYO/FkPefrytjWmuoAQhp6K3s8sGxYGzClnn8QqHlKSpUoxpody9gdBvTAKKNCwKWolPBe4c24Ki/H8lUpGYgCIRy7F8oIKnQJnhouwbEQ9B2J7rg6VwWtZqV51iXNqsbj1jttuOH78maefoolZYxozbxsKw+iiKvOi7Pu22W/2D+B/bdPWVWUgotBblX//occ6g24mwEWUoAvb2cHW9kc++tE/+IM/PHv+PISPygyhGj4qoUJkwgOhQ7VhAeuLV2PfYKih5SMftJvYgheCYcCMsqyqSucgf7jt2swA46nzbDQaZcUoPzgY525a18bYiVPq/DlIAs4y3Hu5KbI4wrMvckBQHUY5SyzhurdOvxhMwMOsIgFLPPQ+YrDiX+nWVsOvy8IAIemHUhqw9hAjapbAjDHFl+4SPkVPP+Z48D2llKWmrCF5jree7ynhbdHDR6BZR15xpH4B5l3h7b3OmlPo2BUrtmaAZ5QrlbBq8bvYtUTTWSl4Y03c1DwORyos5mFKtc8q1J296dSND1QPdm2D6wlzgkBR20EuXrOYLxZ9C7lBkKWLvqvF/MBpaMxdVnm72O/aPWVvUF2pqkzlar9Z/PZ/+u0/+ujHLl64WI5qhENyFgXkPXEIVjRhpSQC30U2bH2mc3J0+UtdDqHekFgel9SnBIPsLcvCNKZru74zC+V2FrM1Vfz1uXmNahSUoNYbStmnnoaq8QUBcWK75cN2Odm+1R8fambKqoXCkB3EKoZfC9SbyMxwHIaHLbp3VgWQAyUOyG6liawH2ih/k8O97NhHDDRe4J35A84XbxD16zD3OGne/ZhOJGaf2Kqej5C0R2XMAvyuz5GEDzE8wgQPo2EeCtaFTlz0wQW1kqzTKQ7GHX9Z0p6fNbw8+H1JefNfCfFhDc2+y+zo8c2qLrq2yaBBemRjGAbOirzsXNebvm2gbafpbNN01FIbIjpaXbl06cab74RUp9zsXdn+33/733/s439++eLlejwCa7frQ0IbNoYnbhlTUv2cQ3obNy3kP6xZNG1VlXU15bJzoU2aYNOhEXeeZQBHwX6RSmXGqH3dfT/Xr3nyXK3dLIe0sc0nH1MHB2pacyfy6G0U+a1SrV42fCSFHfpK2DOdf6lmXvcwDN4LoAj599JXVlCvRFmFZy5z9vQVSVpSjiLMTMRTrGA7yRBlJDMNXPthxcJqA7El479hDMEDxpaPhcRxW0ZbYmkUy4S9zGc04wcEOV1P6X32Kw75UioIBtGvw/YzaTcbrvRfy7ARSWewuW4ZqhRyfQHSmgCqBZhHSBuD1AH4zynbte3u9tZsscjy7N/+H//nr/7KTp5Xs1Z9/vP3/NXnP3v1ytVyVJPF62OE7J0iXB4JViZl5MJUTo+QuPg4iP1QUPfY8ZrKdHLiKt3Ki25uw0wnFIu2A/65LJu201ibtquKb9nFezaPnLl6ZV7noz7b/8JXtp/eOro5RSC4FKFLi7ha7ZXIjMOsxuFbkX3KcMjh32JxGsXWkpBajn4JwhMicYVOf8hAtf8RnypkKVoqCHeDiMHy7QSqg/6UDrzlpobDGzBnG3ZdCX1Kl5R2VOVAKTQY+giR/cMmm3xVvopn48RLd1zRFOK52btSri5L9+WrpXXnlzVeDEe/UE6/5AUvOX3si/v7OwyyQwCFPwdQAAHygZBOetMvFvPLly5e297ue8BZ9sbe8/nt+775zfFoYk1mnZvN94uyJLQjq0hEu+TYgD6Doi+Gp0YLwD8sh8UZbL5oCXAZqlHEhReKDODUlBvHAWS/dsQpQFTkBSQLW5spV7l8lpePr2/etnW1BcNY9ztXs2tXjb05Bwbl4Q6JbppS0oqlDTs4ANkP2L+sEi7eXllVOtmn4WuFy3v4pYFyvUSs0rV2+F3CS2iv/lfC0OZZRs5Dgb9MYA2x3+2S8uyPbihTGf+LsVGy0+kPQhQEhyAHVIOdBYkwAObhXGQe+nW5a/JCl1hqA3v1T/C/6/La5MOBGXydxz5HWbHyAwHVgzBKq7W7+cabHn/6SUoPMMZ0XVcWJYZVqfRPZiyUf1w0i8tXrm5t7+zt7ePF4MSfm+agnVfFQVVVVVEA+pYwGlx3yOcecOIsa6mBvCmNvm86A3W2YBMw8AuyejQe0wVlkesCQkOEsgXdySiA/YRyQsmxhNBEnkFwW4GKZfVo+rFsftd445Zmz1rVuX6+szU1GQhog3r9j64VxdWVTFEgSuNeyX1L/SuDIzcku8Gnwj7jX4LO6aHNS3J6KPVE8+NEPjvRZHepV3A8QkQqzyqw2E1NizNIs16hTww+ln8Fmg6GMaVaE2IEW8F3nda6Qu6CQxzK6cNFMQqJDMJI4uErWOrqb8frE6l4fdMk5IIsyfAhkcq8x9TRGgYAUApnXFdM12+44SQA5TAfCNqaQC6uL/0EHcWAgH1bE3uwP+t7SgyCJD4sbJBXJRRYgWQFxkVyERKREEZgSUz/omxqFMV9DxZ1VZV5Dun+oUxY13aL+Xw0HhcQzjXAagAFjcq3d18nq4PuSSidibVm86xQmeu7FsprOXW1yC9vrN9weWcBorkvdq4Vdc75Y8EGk0SY0Ntq+lq17OKXlG74rXinw5XJ6xqxtJ8rnjjkJQPTUD5nyVjWvuTqdYbDsVHUiVLtPqkw4BVld0hzT+93TQckJhiflnpI5HSDfCcXZtcCkL6qwMnmaWRZQRXgqKBf4ppxCTf+4HB35tIdxa/PSQdfYeYe8nuIlR1WLt8rGVppTJc/snkkyzJSe/Ms5/QjT8IIaUJAsbOLxZyDwz1oL4z0AIgOVLfznqnYYoAqgDG/RC0ZFGUMSBR53rUQlKoqsMCzDBKSAElSlmRyQ8263lA1MKqVFVIaOU99qA/G4n+5GpdFpZ3qeqtsr606W+cAEDO26J079xQXmsZGaMxUhluSilHOAFk2fFbFIVcYOInkG2zM6mMTdcfhV1ZdKVk0ViM77OLh9w4X/QOWxD2lYiZDyklCpr3/ONBlCPd7HNX17BL/pXjjgZ2ejI5qSwJqsDO9f+M6xCJ+eiKIrvRkrnKQQSlPKPz6K3zdT+OxkQfo8C2WT0zHCTBmBVUaJ5MJea+gbDqSEHAm9NFTvi2wWgwFyhWlcupVCRm8qCmTDKcTRxouJOUWBQAb+65rFo1SajIel1VpjK1HNa02ApwpxogoAMyAo1YuoLxjVmPbdWxNU1kY8RL77CPIRQu1oydj+GLbZEp1aBNbDf1cjj72EDroCAQomxQMTtdStubQ5Blsx7NtaaK3rr5vaFbyoyn1KTENhxmfmIhfnfzu26jQuVo9IzoSXEAqLP3KG6ZPFipn+FOMyt99+HUaV2hLFKup+nrkRLDW2rZp+65DL2jS4HKJZQbvOE+XVOilVY9D8anDz03Oips8N7eWeA4PSxbcSJ656m6gCLs7XvTiM6dv2t2+Fh31IVqDxRYovwcfiL9TNWNKCcfITSAdr5GFGvcMpCtVUW1s+Az7HlwhedZ2Btsp6DyDcDwjcrARBFfHwl/gDpAIDBV2pE3kdTOpw1HhAEg/r4qxnoK6vTc/aNp2gdK/ydQJm1/4y3tuOTtTN02jHbk670zatc8m1FbssNSz0sUXinps9DyQ4YHv/whPlMUtB+QZY2We7J7NIhjyL1TZkNFKjWWVWSDCQYfceumpK077gDMI+S7FvO9T15tWtRU4TAqFVbpShchbD4kAQ5fnygemeQ/PRofLrofnRuzJSybrx3EfYjKFsv8G4EuqN7fccGOWF1HLSxpicd2nPMuPHNmo6oqJ2QcFyW0lPUksfEH8wn6jWj6aTiZ1BSjNDN3EdVnlgAoAfkmClVL0+eBAghOq39zOhRZyWBJ1IIepBgWO1vamqeuqrkYV5FqQngxQ3o1Mb1+82J8/Z5zhlODnsjEyk2aw7PKX61mz171DKGfy3Lc/ShIhla7zgGQsh8BX3OGzYGWFK8n6d1J9MsIrfQc139lJdBjzZqwwDeJV15/pipzVaGug/xW0NSQiWsvrMik8az7pkg2kYF5KWSw0tGd9Da2RgaH7bFs7GN+KB3o/AlwJ6lAHmYD5xuamLiEqLuJ1zK5CRWIQtiVwOFFZniVfwjeSrnAgvzGEnPtosEW4RV7X1WgE1fChQDW7rjnUy7fRuqrrCPuQtCoNlCUaJnKwzkBDiboYj0bkl7bAL/Jeq051O1fOWajvsdK4lUbQj8J8n9MrMX7DW8sieJiws+oWQbVcfU6XKULWXPBaqZbCbYk6Bi+0pwAaQIs24PhJMCfuiKyRzQ8eTN7Tb0hzkGPmKaZGdvxXaGAAz+p6sJYOmYtglv70UllZ36hTCsEhKV5356+Txx3uuSxRr/NafZk/lPwvgBzhrt2iKvPJuN5rFx4I5a9CpoullaGXQYlpekSgtD+DYGgowy+MnyB84H4GaRjjwq7EV9djNyaSvlBo1qoiq6qK4gR8ixAuIr38kOMVFWGtegjyWxD+4/H+3m4NTnLgQ43WlVP9latllausgPQpQIBQhVBBXc/CKsXFwy1bqYcvb8lAx1t+MRF6AFdioaW65JK6u5yYfHjyBb+YeR92vNh4Qa8GljnB0lmeelng8qXSBPPXeI4iICo+DOgBdhSqIJRALL61Ym1SZuDVFob8IcSyLyEMsTI5c3h20nTC65BXYhccYo6utEwGQxb3uv6TlsQI3iS46TwDLbJcdfZFZ+742rHje7s71M0kQMtplymADzVTQJyCWqxcBgooYa2EIRqfQ+PwUX1CLIfipqDOEnRTZ3VVxu8oBywUqyj6go1ck8VXIUx0e7U8PdQUqOlZprPRqLZNi/sK1a1NlnfKVdaac89gPU0GgC3xhMNW+FmZ6GHa53O7cBUjD9DpFZVk4s0GSMali1bqEy72wPY3OezlPRuUiwuNOX3lDZ/Fz8kBEaURVDBfW56BAb6daPyU5AHgebC0MJvYq4Df8t8gRj0RU6cNo7quzzNovjE0HOQaw0/QQAuECon0ff/oZOGut4rDbxzyGoDjrs8Fwh9LVjtnXbISBEtrXdt3Lte333zm8Sef6Cx4mEKcLJRCA6pFasyg7BQrPTb21wnP4DpnBOQg/gt2KcOxCFBJp4WUMIutvfG7uMHGYjaCAf0W3GPeRRhsqqjfex6TFi+EXzINhaibtlufTir0a3c4LGvtTOvOqdEPHwb7vwRHPNb0Ss0/CdB9Trtz2H5dx6a8/hfT14obJHcWYAvPpldwG+H+SxRm9xxHSComlQENsV1PpuCeDA9n0eqpkxVubwnFm/kOTPSOoaKoZjkDfnkxlhRtrgcIbyNEsAeMxqF3isp+ccjuDtjZyhI5qZYQGM51vHHPvtzLjxGRMc8DYwoup3bYem1y7NimQnDF8mhDBD8vyo0jG1e3r1iP+vC+39CcJABx2YmN9aQpg0XWXUjRU0Hg8wa7Hgv31EfAYeZblQi9PInSiRMa67YAx1g0iwIUB1TMckjOsNYc5Nlxm+987rPrF/fKWzbRESBs4AhMTFnCih141mMWaHhlZovYnaH3MWxZ4jb1adGybLQcgxBX4RoxrdTLmPyrlxnNMh4reAaQhlHTjXVmoZQCBgt9h5XEwkiWLSyKb+HHn+KtLfgZiaEOvpTaC8urSKfNA+N7KAVlNCcbHv6KZWUP+fg5yNXrvLmEcL/+F5MD55VkTsjzH4jgnccPWywfqdenY9Q6GqGr8UlCCZw7B0Xk8hyS/ru21VRtEi8G/NOgwbGfgU9sFzUTeYhym2LxrizLqyo/evQo9IUArAi4FkMqj+hexvMJN0hNWBgzYk36pmmLrCiQu8+hY5pey/JLVy7Y3X2lTigAAEh6fW7SMhn+AEx76Jasenl4UHoDMZSBKSuahQ0w/sF5wX9G4n82lU+L54S9X2UsBNbPSrFYN0CsgxSmQSbTSFIjg7rr+0h6eUzRf2qzhOUv07EtzyGqGtyLOPTtc4y37QuqKy6NU6lh4TNRz342DWeV7ZRqwpLxJxcs/3kdFVoKW0+uJGITlT5W3vYBcVgya/WoHrXzORldXhryHREpiaxRQ19RuRZ4L0wzwiZvEgcXaq4MCXvouvaRJ3xZZ5umKfKC1O6oPkiwVVhiUc89hkHRXDdQ2QsqSORVrhZYvhgDzT34shbq0kX1olu5GFDIskzW1Zuez4J8FLLmR1KWV+wdi8MAUQh76YELgTXzDzT8vB4X2hH7DUy0FQ9HwAtJUXHhIMEbrP/FAxmohwcTToQvGhj1KT5LKxYqYUOJFA3eSeJjmCSY570xDsooyRO7zMfkwrExz0nLnFCHnXpyi0I4Wr2DJRdF7aTScOjrkAuGlLgaEbJ09UqHiqTeqKkEXsybgtWWiWZywmbY/EUvfukt99334O41QhQH+qDdAdYICcKwPtRXMjRKJcoi1QlTmUh/vk78OxXGIfMbY3Lsj3auBbuV+UNQJITGxT8I7ZGQmVcKemgF3o0mddlkUHeP28OoDoo7Gbd9SdWZmqFPjWC0g4UMrOiwmQQbUsi9Q/ft0FdA6vrnepWW1jZGXQfghCCk4ptMgljigPuYRRAR+5wS01eTkzKUMgoOBkbcefMVD5EveiO0Hv87Oikttd5cXqBD1mToGWdLGvpCRzSVrF4RVjk2Pg9rFmfJ3m0KSrJSkFwafoP/i2QGec8V+Ugr2cDSoTlkrodOe/XLawh4PW4ce6PizUPwDWpfZeDRyfIzN978yBOPd11HX+f4L+08kS75KghdabjbMuJRoepN0oAjZL4OfNR4Z77rwKWMxy2mzPoqZ17+St2Z5YlvkETH0gczPI+C0eTq+LEbmt2DdZWVkD7VW6WMMoW1+089MWo70ZltUIYhVZdEu6PrEufANBCbNngddgrIs8f1ccFP6s8a/+BsS2lJhr7w3C8Oe4ha8PkTQAabCPp5eE7hV1YT54XcaG4qHgtHEoemLG2WwIL7Bp+yBh8lAF4zrMQ0PJ/XcxGkIEX0lWLCIp2i6CKPnV+9XnTYAwQKG7C9uQUJFduvSLUDz8shNvBqUN7hm3/Yh6tJddUhWyklRJQsDioKK/oQhI91fVbXx44fLeuyBwJOWsd7IA3AIXIAZRQUqqGbUk+TAkGRHDWS0IjDZLGnS25cRj+ZlybXByNemPI+09hCicoAIGYMCXKbHDIiF1le7C36etaebJFXIaXPnaucWnv0e1CTBNtLYk2vpUEOrM3hBq3UgMLElrS1wzY/ajp4QQnMNHO9sh0U380KEpEUfUFVh5Rm324Y+wNCQysMEtAF8APTnYF6OTUbskEgzM7FfUJ/yoyLpdAfzuZVURQVJukgjcd+4mz8Rkr2mFsamTG9NXA4nuuZDh40+YL8cyihKKpPp7r0dRTZRCXBb9IJAZ7ne69FHs1WfAByXMc0/f/L67qiW74XtL6hmSlmyMYQI8GReVvVdUVZlWU913OsY+YRwuR49GG7PM+OHT16sL8PfVhwMzGsz32XA//1oMgVQxc7F+ObcWz8TxDg3nmVqt4Ez2IgQAgJ+sAG1OUAr5temGx0bfttBzu3X7vSmRa4jnVdrm5QxYVPfvqmS011EtIY40wxNCxW8/p0KFWC9CVM0UQ9TnxO6aKA280o16vRxHWF1i3U7sJZZK5zWQmV0x2AbzqT1aVxpnQ5NOaFevZQqrPIOKAORbsh8F1ArX0DJfdba3XTqcIsem1z2zeupOIJed5roxuV2a4rNBQfhCawPVSAziBdNLXCQoopE1dEXGCNG0g8ybDob9ziZSo+LBnW7z9peaHmYdz2Ve6IJfHrO7mwgkbmVVpBOhgsqG+QBKZG7KSa/Eg0LFHyz4KDud4r+Zw6VkvIofQPR1wPzx5XB2K/nX3p8178pa9+de/adqKhceMUFm1lUY7qUVVWWs8oBBUM7iVfFbF+sepenReDFj4Weoev8XxImL4+8OArZ7Heu4Ilc0fSUjvXvNIUr9i60LVdgU+w0LZFd6XeP3c+v3RJnToDj8WWSyCHvekVVmeJC4VaRYdlroXJpaFgicMIupxcKlLmR6iUbue9rYsyU0BokAwCxUyoAqhT2mgo72sN9HjMMiyNb7WD9mJgzpP+DfV2wVrAhhlVZtWYeCwUUslta3XW9rORKXrbZ3lhRqVrTLbnipPHuvlW09SmKrCwMB1qGjx0w4mWUaKiILcnNDuHg6+vPB9+2skSxkxS74c7BLgtipj5HUK3Hq09NVW00BJzWbcK6Y8UB5aD/RHod3Cd2O8VMuvwOEc8RuEnVYTzrT3Ti8M8RU0xRJG6Tht9fPPYE08/SRIVyzeznlvkUKdKQQEEwDaXZYlheu7SiS2UBMscji5BxsXtWzaXvMoUfGOeCwXCFd7aFcB21qJjG2HXHtlr69bmVpf4eLCBHfike2P2rl7adGdI9Mm6kavXOWm/KEg95VnpeyS2VtqECRgToC5Gta4fT8ZPfFv96m/O7vv2rG0WRVk5Bf8HbR/ytOh6rMGrLZXcxVtRE1CKieTa9d6C7JXqEK1CAQJoSo9FnmkcndYQH1eqsRpUsJeczP/l77pbj3bjrMgU+HAJFRPXltLMZBBBYMa8z+yQ9Qu7HjijjKgLXodJbhrqONAbqQWTGoRLfUV9cJr0H/ZFU88QaXt7ObaqN9JhI3+W1+HmcapdXod5RcsSSXjguaSPmD1FwiIx6XrTTidHTp06nhXgyif3Sbwt4IUBBItOLD0ejwBcCcAPjNmgGZxT4Cfkxwva88sVPFoenhNkcEzv9Ooyn2yP7RjgELzuLPB0vL+E1sakRDDhKvBbWTja5AHCU987XTljnn5a2dfQt5l3B5xueNRK36Rw7AstQsDDvNon8MAsy+JJirdFp59TtlWLnYOLlze+8e2q2Z3AUDuopI90WCtXJWcWSBrTMLluWDTxAisNaNY42KAKRDWsV3Y9V71R1Q8uXbnwaHfjq9s1XaJTGWKDvnw63gVb4sizFNmYSE469BWHIgLJ8WDznSAWnOcIvPVsMijx/j6Sdx8SU+U6iSiEBwseo27XBXJEfWmlASt1s5VMOlw8sGUHv4hPQ+jIe35kumkQBcviA44Qlqc4fvx4kReta6hbcziTmKaXUSqSMSZ4Cw3EjSjGYPOcu8XGsXgJHimZdHFGrAvbJInoS8TG0uYMGN0ALo84IagQRCwKiNdUFuQUQ7TwfFioQ28XTz8BOf4dFEYDcggYIGFWC5GSvBNOrafgyHEkfSN9+aYTVFGK9iOaWsg8nDZGNX3eF67f6QuzcFXZ21rZBbRUR56jVAOtJcDm1ZlqUZPIHLibEM3LU6Qq2VQHnzhniVkrOTmVNXSTgUMLW6fwTbizyrXLc1uqyZXtvY0j07LMoekytHqH21CTFLH2cY9JjQv9cViZltmXgSFKARroVrr3gzCGjInMIN6LK5UKEUzih2EbAe3nfczLAgtES9AB4wzYB3c4AV/XSZn+5p7tJkL6h+vdSh076p5hfSNQ7tDBINZKZaZvnTWj8Wgxn5HDEaJCaPkQNAJK4ZSF0tn6xka9vdV22NeX/8dsWhqIvFJyyAGGIbi1DC2u6O0sV1Pk4ch+OeFf3HtwhHKBEecKLNaHcW/aaCDq3sH/6yd+qDqIWVCrBwJs+8bgzAJo0D7mRc7aRGvkgfk4T+I19boBjQ5uTBUtwPrwsAPSOrCofmZUqdu5naxtrF252Fq7QPcpMFdOXPU7a1UB4pfHEJPSvW2HnCrWsI2lN/F3cT6UgzMM+9tal5tu9OTZpqpGSDQ9l7EDECotm6egAHkNGScYr6KSZUk0+LBwjOzdJ8u2UQAyU1hZlGuhOugNQNLIBx79OOht4U5bYfjASMFTK2GVocDPoDvhioEeVkf2ut9aYWCEvw/7StB1vf95oCktcQ4hSOBnAdpx8fLnv+Dr3/zWtatbEK5AL0jC+zCSVBR510KmQWSBiDfAwKPvQydmEJ/m34pOZfk/lfiwAt1Gh0/qDAtzZm8WGb9w5vDQO1XkRWd7ZcBbnnHEkqt7dVqNVbb4y8+YC5fyG44qKEFLOY5k5LHiIgKoQtTyg1HKSTYcVP2oVwRjC/4zCNEG885SVyDqn0ozgA/yujT7uy/88e3XvsR86pJV+gjzFjB3PbKEwRcswsSBOMyAD2Wcw4EIfxo/L1LAmt6WZ5+o95r5qMzHZQmZg6CE9sEsImqI5cj8ZpFyhXmhYgDXtysHL0nD6IUsCuQsWlkwh8wyoCDYYsQl5QHyy79kcA8JaCUBr0TeLX2Tf0iukMxj6SvSeBAegYQwpbMubZK6ajEFLpq2AMsZHNs8hu2y/VS8MYcbDWE6SNDPsrqCBnGoW4HDCJMMfTUG0YEykbaeBpaie6kVEIJ1Mm4wyIEVpRykRUylx8njVdelc13lrG/KxXok0nA2LfTOpfM7V2cbJ46qvoNWd8F1TwwpYEp5Qmwi+7nEpiBBCic11bxt5sOSLKKVMZCNZzMHDdOpliId2cypbq/Te7PuthtKlU2U7UEBZn4oFFCvoKarfB0aHmhw8U3nfwEwmjaNUp/9nHv8QvHqFxZ1pnWB2S02wxgvkHGYo8COBQkcIsarwmbxTzHg4aEUf4P80LnKga3mhjNSht8IxOn/kFo6jgP5efSLDxdENDf70V9x81PxIj/1RsbyB+nAV7d3FbS8+uEss/E3yLkztlzfOHbiJLgQMKEPy9/4xEDU+PK8qMrS1FBPEjoYaUNtpjCEoMqqhFa/QXsbHptn9XLEfTgUWezdYHH+wrNBqEBoHutcWZVtXxhnS9+whSYLFGNcl+u+78qrT2fz4x0aXYz9CpFgVjEj9phnEDIavSsw7KQEe0ZGI3eQnEyYnYXGCaQA4AQyldlc2cKYsi5OnzyiXE+iSOTHDk69cK4ECvW2ZXqa/CEjjEf8w/Eygh8MKiplmdpbjK6e3ateYLMMKpmA+KdqDhCbNf7/BIALhMoJhFxbX6qyMXgmB3+IiBouHIwHsJV5pqFFj1+/ML+gjC87ev0D0GoRHo3kOML5/i8i4LCpYazLBu3A4l35kkHK9MrERbS0dBKxGxxdwIFt75r9jbVJPapnBweo44HvJYZdUesD5XkyrutRnuu2M9g0FvdY6RG0EcXasQEokgyXfxnq1fKEk81Jupk3e8JVYdxp4VyPCkUVWild5jlUvsVy0mPUWsEKSh3ZTmujdb9otIKoizU9mG7MezztpDa8fxjL4USBWeasLLw9aw3SxxM6xIVA8oE+DVhGBc26FiZznVpft5netWoTdNeg0YSdW8mP2TMolvEQDXZJLdM0REC1OZX3+faW7VWlegvKFuZ/ozsAUMOAlu2h5gIU68ZZhMgf173Bd4dqZaQ+qS+EIUjBK/5BjpvnqigKaE3Xe+YkFtnTcmBlK4V00NREfSu6WPoPfrTXsE5QmOZzK5116E2Tcx1/GTwoSGzPRbFDKOxS35hXPf9Fd5y5BSwPEXZl1DvsM3DGuh7fcMPp0yeOr0/GVVVWJdAvuDtMD0QfTLsYM1p17q7DmgYxiSh1WBWQEA56x5d2oL9BspHYLR04nKk2XqirBOZob9esrZ45q13PSnc0z0MoLojfWGQoNEyU3v7gw8KCaN4TEYxm8ZUQeqeXMRaq/wB22SmoItK2nRtPCdm5Mik5kAE9k8lvRVcA79MU/w/fjguqcPN5vTPoC3fugslz3aOpSyoCVP1Fs526p5dlVUApf7ClqGwwAmzh/z5IPNAZBemuPOTLNf94XCgk8RVKUidUHN5ZZgJxQ8I1S0rus4WRlhY/fjM861Bj8L/kzvEl7nwYDfs/A1fHMpHKZfbFd77ohz/84WzelGWGab+8HNS7iPy0ZVXfcvutR44eq8ra9hYS8MFXZJ65eBZ8RHjCgi4xMNXToQ8hn8/yClXzPCWHjvDhGKA6YC1U21IlRCPoWLFZqsHBqppMtUb1jz6qimoEpXYwhy3IVFqTQIS83d6DLke/7EpKvQ9LALVkA2i0aGz2mSq7xcRWbrKZ67zXbuHMCO3TpaOS5F0Mbzjc4KhZS2jPUNuEncpb240f+976bmMmAMUuBEiVnFWgqgH1Ysf2ECUkoiUi55v5R8tHJGM+DM6QUAfqucgauqy3WIZQKiEigXIwEz58Hvwz7Kke3nluBHwdIEfA+h8CTvoR78nenOvTRErcQeUDr4HLjMuz1778leeuXPrqN77etn1ZYi9dDIpjQgD2HTKu68xmtflrv/43XnL78zePTHU9mdTFg9/73j/5f/2zx8+epQbcA4Vpies+q5GQ+q5iodJwqLz7WTgoAVUC6XAQ7LV9dwR9uBwGDddY15f6mNFbH/8T/Xd/c3pynKPcJDe6H5N3SXkshK9CEQwusoGXpjSQmtHTFIry0dvc1KggtwKUEVKV7TKtcoBt5xDajck9Qin2Ec+ltQuD9iYiR2lXngGBj+Obau2aPht/9p7F+Qv17aehIZaF2D7wP1EShaL5FKvz80IqCdw0Nd6Wf5cYaQGcPswmBnB7kWWgty/pyJwOk1iM0nTBTfVTHCAd4T/f3GwVznrF4IcjjL1l/Hz+C14pmcgi9IeRRjxlg8QsQB2AU8qqN7/+DcaYe7/59b7rshy6p+DHBus8Qz1n59qfe/8HX/2qV2xM17ONUQepM+WDjzzYNgedAcSlGFCykctrIF+Ro6SV8kRUWNY6DP0ERF1kCDx0ZaX6RQ99D6sRhYTC3bBEAKQGjPPi4vlnTu3uNMeKEgK/GDxNNAPPYph8Aj5mgD8YzCDMJF616lIsHIKAGXQZAqyxqFxTZUfWCq1qrNnFgdElVrh8bqQKFzwJ/hSvyofUyajASa9tnWm1v1dfOVecOWpMaRBPzopUNF6EAcYkmNg7K8+xtFTl2Ad6sxhp3HMM7udZ3/sShLHOBg99WTmmW3FiNLgLvRzmL3LExBMwqI+CnXggnRjYyqw6z+yXZpHo98k6L18wGPqKLx16WfSWcaZakYHx0+d6Wo/f8ea31dPxd7797e3tLQ3l7i2gfiCkqjNV3HrLHa/7iddN8rVZt7e4dm1/x33tS1/7s7/4zIWrW8yRI1oykVLXS/UXq8XX+lUcHoqk/DRLZyomj5FKszHauLK167q+gDxlRiEQm6UKi51TXaZsZw52t6v+iM7yssjMQHTImBebBVLnj4OSSt/yKqfvSYWY7UNs1aSNy5r85LSYj4tKZR3BYzBJOrkhOfbcdcxjHsqyAByo0Y5UVJoeeuALl7Wlq7Yv7eevGFvAV2JuP6jHUlYO1e8V7614CSt0sF4D53BMcg66DujRWdaBU09cSWvoqygubwPcF5OkTOFxLwN7JCQzrFJaD/cEJsqjvv5XhNBPlKmUqqWBJK+NExrElZLr+VfCS2YO4m65m2Tlm1//hle/9JVf+vKXH3rska5bdLN+VILH+fTmib/11z64Ph6du3LlD/74D54+/4Rt9WM/fObcM2exTSimxCy1zzkMs5r+umzRRQqV76cV5OE9qshjcEd3d3f29/fLnHMYwlaTIgGxLij9Dl8zV6/W1fOKHMx9Cz2KufJpwJsIRp9sUUzSHUgVlnrD6Q6OubDjEbAAGvNiMlKZWuzP2iLLerdpIRXBL53ns0EfH67fkICXXwOxrfFvADtzWRVIYHC9yX/4FGRBWKegOlxZcrIUNMyKRZKGxsOP/EqJWZqQLORFtVxsiIflltAOFtJacq8VbJT78/jCTPHhejkbKYBaVkpauftLFwSxPmAEidI0/EKyhUOpRu5AWeZscEM5NBo34zG1UghqgO+uq3z96PGfe+97r+xca9u9e7/2/Ze+4o5JPT155Pj4hqN7ly/+f//Df7jvW/dduHLRtM50PcDySHuTYI0YBQzMdbD/6cWJhcckLHbNK9jB8ewrmwIWwkG6YFFmu7s7Xd9Nx9OsN5xQFvgDhlYLDXhi4FaXL5ZQtK9wfS/dUyxxw0hlcCQYv15FxXd8z5LVp5kXWkQwkekg1IASu5zSdd438+bOV9ijJ266cNZgzT1sPZUQ5+HUm3I/McrEeyTUOO09JgFY1jZ98ch3xwvVjE3h+txijXSc6GHi/Ud/RekqK3MNlkv+RX16MiyHGmeC5Q0OYVXSlUEMCFSNYQntws3nTd+PRmBoqZy8DgwcZVNFr6S3waBDmp+7HulKCRSMslTfSx8jR3voiodIklfMqCswSNHeGK363Nljk0m9ceyGu8+4qdW96vr+7MOP/s//7j9842tfv3plu8+hYKUuFBQDxPwGYTauYM8rZFP8bYUaFE1i73Am2g0UzdWUNCSCF1qPx/WF8/tkYhrAA7IN421CrtrfQmkO1168UGRgCoHolnkXIjGBjm9QNX3dn0AFabsIvwMp206JLexbBs1awUQDv7nuXXlgqy279urnb3/ymU7pKSQbgOIoMpAjQ1xq1hmZ1PKx9twvPcaO1oPSZ/HNRqnP3KN/Y2ty18nOGAwPDs/OdYKdS4d1GIE4RFgnau/SZ9plOcSTLB0v0d2BcseDIRj2Ie4G1tZxADcXfhlv5BYu1yWkpmGTLmPyUU1QePoq6x7Lw15eARmsS/RbsSIDmyvoU4m/hCOaiZN3yWyOAk4odp7VUMcxanUPRf0QTa+M6nXVl6rMphvf+Ms/+52P/PED939368pVBV5pKAtB6eIeexvS2K7nEZfqIGeVSCcz2wORv4hYLgeOQoQS06R6Y11W5Ntb213Xl6NRXuQT8GTpAiERuSdg+KJyC6sKpzbPPu1ao6fgqXOQqMQeokB7PkcwLqKfUgBtBa8KuXyXVeolhoxrTIUsrIa1zrICAjemL/v56XFx2y2VgXzdhXNd3G/4B9tJQFqSdMylUVQ/y5VLHgxRF98kOxvmXkAwUW0f2EvPtM8/UVijsYyAcEomZ2pg/i3NeAh2OIR6h3x7yf4jVCCYwT10pbtOOCOmV3BMmnYlJhWGRqIok0G9Qcg3RmGUM4uWFO4SoMJSgaEtkBVbUl9IwBEMQQx0rJe0a/rmwGyMgTlx7RL1Dr/uY6R0+ASUAd+FN4sM0jMhYSerRp//1Cc++md/+q1v33+wd+DyzEIUuIVrCTvhj49f26U4cNClxbCiohoL5Qg7cam/HZpkDgUvgkiAEmDdyiK7ePlC14JGUJQl2G+7PbXrIMaGahRFGlxbqCOtnt1zT/bUlfXbj+XQqjzMXOZc+EGGUA7VIWaAs6jjJryjnrPGXUCuEG7Fqh3+gGQZmIRzbdfMO7vb9tP1NtPbxm1iEkSyVj6ABqEy7I8Zko3kfouf0qocHgWNf3L2twNsWF4olTX6/A97/YoKUrSM0pi3Is9YOHDpo8XbcbNCm4+0quZh/GV4cOOLUCPc3yMNpAnxReIsac+Vzjf5FXLrnIJ0diqLxiUJnOq7DlTQvh9BVofXUjC7A7aM61fEUrbS4RkMr6iFLBFq6t8VUb2Vw2YlYGm5RUQgcZPxKgT5CK48pfUoK1RZXdtvHn7i7Hw+K+qsnwNy0oshj6v2Yzx0K2RuUlRafV6nDw1RwGBAvR7DCEA/sIuAfZKjFCLA+3u78/m8KkdliQEC51SzjTVl8cj7RxM7tEbXWXH18oXp7vbCHIF+wYj6kIlS8Dun2EjtiDCD7EjhKNWqZKkVFfqolkEInkKhOSqfYpTr+nahTGvUNM8q4yrlGohhay9zWBUIZhrjVoS8HfwS1ObUlor04/yoKMsDSuJARS07+cFjRecqbecYN/TyIxXzKYZCuKGG+804gnQMh6jTq+1rPBtZVlWlg+h+L2tV+KzGaKvIYdLh9vF7GYBD0ZQGwcL55ao7eVF20FwAlTtjJuO6zEtCWGM5cdg7ygyNsffAzaUOwrCSgbmbhmniKFbqrLE2WLJuQzYW5UhUhPmGtmttub33gTe/+c///GNnzz3ZQZKKwSRTLKWNmcMwYA9p5z1b8qQtWQ+JghkLxMYxUo4R0RPJUqybg5VsoaeHdbOma9vZ3t5ukVPbcO781IHbA6OAKb4A8tmdhnrvXWe2tpy9s4feWtQbllDYlDUXK8mH9fFtD8J245anIopdJwR29LWtKWeHUCHeBw3JDXg1JN66rjF9n7mmyDsNRXDJcb68obJxfEgY9OUXI/VGcJqf+cBS137Vw9kzKuuVyS6ed72bVUqX2JFSzD5tsiGCPcMRhsoRURomK7bqJQzz4fvw3SzPy7JEbRBAHdymiyMegV35fY6WaETFJsccbCtMgrUGcGTSxqRJEovOshxAaVnWdP1s3ijl1tbXoOgY1V7DqkQEGYHuB6hy+paLYQDMJpJcvVXbyv/F4IaPwqddGw81XqJMjrNHMobZGmcX7cFobf0Fz7/zvu9/t+37rMgBxIunk+oyUTIniEiDpShidZlUExwOHmvKYHUekHhQXoK/yvhNCk1xGVz4X9dDHzarXVHki2a+v7ff9y0gcosS2/RgrX9dGGcOMj02ILB85h7cCtfE9ioH4bd9KYNad4jWE3yQS34xhtAzV6+WxqF7nkKigNX1GIbyhdqpTqK3UwA0QwuLhQfg65AY4trejCbz6QR0OuiFSgQvni5iyAmzFTLQh6fcKuHMPwaWMEXKcwTQLdps868+s3d2K3v+aXABgq4S5Esybzrrqxw08splTxuPNBoFyZlcSd2e7AFX2UPOjF8An78tMqSGDgguTxmPdLhngdlVVEp+mNjA40TuQPWkIGErB5pfzBvQurWqRzUj4MlPjhE5jFxFtTGUAow5FQOjctnXLOKGjBPzfws8nFialUQljXQKUjjVmn6Ul2duuRkglQaKiDpsitNAlVmHyfRc94JURQTEB9U12Y8U8Ml/++IsXLQdPaDAD6jbAPfOoTx3Y4uiaNpGmX7/4KDvO3JnUQooJLJkOit05fSYa5SicsDrBw6tQkPpVJOpg9ms7CDt0BhMnfc5cXRC0gKfPFrv2WK1TGYKROR+4KJe8KCmIm3hUKGEaBk+zqEFjClrZ1UBSgbX+glulCDqScQNLNJYXAh/YolZKZa9HzBGWNWAXSsshu729or9y21+2nYdlOhFGz1wo+TsHdKv+TAZy9P2UA058uXLgnkXb4uUAmXbfKyHKjAcalHTl5FK4Tj5FWIWUiD1ElqLPUGpciKoLQBKqeYLHtCuhcLboF5bMx6NqhKCXTrLIYWaHongXu7Q5zmMf9KSFAvnbMj+hG166NoODG9xcfDKIv20zf7JoyePHDl6bXfHmJ5SDlliEU6WToo3YikDlmtsyTZkQj5zI2J2HYLozsHhaKhkAKnUuDD4A0tzOeWKvFjYxdXda+An55QZ+CJJb5C/ed7lo0zNF1pjanzkSGxM4vPy7cv5bN5XgPSBbubsDSfrNjmwJGykBc/BHNnWNtw/Cs7o1EtL17L0pcAIVMZyWaPy0qppNS2yuTGdb/tCsjR9Pg9QnvAQYgn+NionC5vHyzwUlfjigQZVoS/M+PyTunwVqI7cFivV+qSjamjSpo7hQw5cGly7Hr0H5wI7DTFkSNViBvvjb5120/M13g10WklHBAQcLqIfUhaGbtlJXh3dgcu7Yb2jXJcO3GuzGdR/GY9GBaYMoLcL8yCpwwVnJwcBm3CmYZULMYrkxEQjPY504MHyunYyV7wmq+s618VbX/+Gj37qk8+cf9r00CoMimPh02NZYPyJGcXAiEgDp4UIxfL8MkS5kWHbtFD5CCt/YOoTAjWQf2TgWi6qg9mBtWZrfws6smO1LmDMkHTGeanEHvIi7yYjNwexRg5E6fYxZEkrt5jt9abrbd62XQWHAx/L+e/eVb9k6/K2U/qgPLFBR42GjMCp8JHjlBmuVgO3QAc5MKxa28laWRZ6biDMQTYDJ234L4uTvaILxABmIMtUekrmCzJ5gtj8gjIg0D35wuW2KMoOQuQuh9YPyZNSTXxZD3wupJna5IyJXGUbytWjc4DN69KAgTiuS8INYsFc3S5xxDFKZkiiwXbizrhYMw073aA1xwXKAjeks4nxytL1tmnaORYCGtU1aKrkycDKoRAE4yqH9CAhNnn1h1NOAjlJXdfBUspfvXkTb4KhBqBHSJXPpuXzb7v1C1+t2qzv+gacP2SsojyhrHoUyRBDpPoY0CUwB1QAQj1kYIyMc6qkAZU9wBsAmEYogpmj+wkb/KgCA0NOqavbWwcH+/RETEIFn1VUU4A0gC6xUVaWu5HxEeCBewflhqmsanb31OxA67HqO+iopDNX5OCZJb4jrJeQ7i84XOLDHGp8Phco8q1wRNlyDllKMPY8d+Ost9P9Ji/ycgoVIyGravWmBo1goODH9Jw4NMIQB7hVtqzFqqB3u1xlnTXq4YcmrepBj4fihqn783qOqMGZOqzcXKquhAVNOMJApQnjpFbEviWIXPzhXbydTkaZtZrbnRHJQxdwUgP5Os9B2KhmGmbeQroSpLtFOYGXg+8vxFGASOB21qlF20Fs2doeUIETaoJOMokJNwpYwYmShC5qFMj+FWIrMiQT/hVWMJ+KSMVCpnImyNrkphtOjsfjvf19Vn7RQQNpuIAwxt4AQc2AOox4mhDTUpUQuW3bDpKbFADmseMhMFQwtEyX51lVV2vTqTW2WSyMBThu17YHs4N2Me+RFWYFho+o4AruJUsx3+IQ8qeQ97VjbDwCznIuv+BtG0Q/ObdQarp1rVM6N6bElFPwL0FwgAoTBAImtSztkRXXzQvqeLaDoyLyWmlieeqly0kyWwPNFPL6oH/hayabJ47Ozl6AXpvCXA1mlD/1osoEs8JAD1HzFCeNnQAeD6qXCImsAjtz6jN/2T9yTr/wJpfFwhXp4+NrtdA8HAi4fIt0QeVlYUbenUMUvFpmhUqsS0Evr0VzzUP6tCDjl7D7uOM0iEhf1pgW+/3BOctFlxLuZcxWjac7HjE2IWKrMsvystR93zdNZ2w/mYywsFyhcki0sdBUAAPQXLk0KY7r6yZFpyhHIUNHSbF6EqEc9CVhsyLGsqB2pHZSr03X1tWlS+gbCNYC5hxaCyYAykEwKcHrS+NDrACYtghwBC+pdrCgwKRgNfNcWQDNOWt2ru0s5gsLN8NuzdATxELthwKyydldhdwUnVtgNcOaUIk9eBxE/Xujt7PsjEsMwXi0nWqgNn9x9Utfz/6eHRemMLbPtMucNrAH5LlA/yynuaKNLjP/iWknwU82iyOX9KohBROi11oIMq86OCiKYY22VW1fdtuVC2f3rdr01VDIj56Y4EsWIOfChv3386ZryBkWRpqJ+0QNDYSIA2Nke888+WR754lRDQsbQvJDskvYVPpBtMiST5e5hvhkNSsQJQLJlqOzLnTlRDkIPiIhial7EzSTimFtV8DJQ52PqwAIdxgZxSjtwUlrHcgczK3FaIiH4rPvlEt+BxpDXZKWlCrr0YL3um26BpLh7GQyRhwDMoVcY84FR5UTCRvh836JVq2RxGL6CUrna+B+WW9V0at3//zPfeLzn33q6Sd070t+4rJmWda0rTH9qK6VVr3p2W/MjiFU7xzQJTI1EKG8sllu+r4o8qZprl271vd9DmAMii1jQmhdYgkXELnUgIO4UJ5l2KsJqKrrW/ROl7S/uXNjZ0utQJNJKjv6SVu3keuD3Su6a3qXo7MSdolI3temRKqhmDhXl/YLHIg2deSKeB99GvlhPFthc4RpqbOszFzb2cwVN51UvamV2tdqwmEeBnoGGrYhuCbcObFRakpYFCjyuiD3pndQ6j0KEBbdDjJHVd6Nrzzdu9eAqADrybsk+SExeiwPWzxMvrxnVOaukzIv104cVflrYm8T/aTGtrhyVVlWju5Q5i/jHQFjO2z9EUmFUiB0VtWlzrN5swB4FvaMDzYbSJEiL+H8BVcgVxPG7SS9DcsGsu6A2B08/bODOYvdTE8mo7osHWLesJwbkj+qS1Sl3/f79afOa9yx/gT968t9CevXKyU8ZWic3e/vjo5vvu61r/n8l7/YLlosvA3qK2YyFSYzbdvYqlyfrKObGnpPGmNIv0WDnrC8YtGDja5cDa9RUfYkTzDLCEsi4RdFUgOK3SwroShX1bbt1vZW1/cba+u4spg4mGfXlF5Akx87CInSrDun2lzXxuWLfdePAHUJqgM2BIDVQ0U6NDOBpH9flicclOjbEcxbiEmJYBSGyfCgeqgOtXDIOnVw4uiadmOnFlTsBrd0hKgsbIkBY4DehXhMsQcfFAAAW5fNO6RM6CSjcmzCIlg3bESLAqGATqR0Q1XjeoxjAQHt9rbnkFcKdjh4YeL4E5idcHonXEyoKQMyTfzXw/VYWrnlF4nFXEGxW1l4XeKekuNFI4dgDwhhXxNfOVChk8Bh6gyCMnHYw1YrsP2ssV3XoAoPLB5yUAC+prXpyd3qJ85DzwIFghvfF9cmVRmpGwPU1vZuZ3cXZVE+GY1AESVvbI4gYVTxuTmGN7aiAsc/pAcvcpB00ZC8na4K7TqjDhanNk7koFID/oFhjxCyBWdVluez2ayHhpXq1ImTx47ecPb8WbiKurn7FQqaPYEJiTKJVwFSEmUsea49xaIlgmoLeeQrfDlnyU45mM2mkynNpCj1KHe3729b7QjILmmHFV9QoV2us9bYkrVH31vcAxhRxcGGucC5RekPug1vutfYhC8m6HCRmBPtN/DseP7oR6bLZpaN1ruyHLXdnoaKs8qqtTyDTmXWVTrrnCsh9SCwW2d11oC2wR2ojLWjLGucmyCMFy16W8M1CJzEHOAJdUa2bgrpPrDOrVVVrkxhcnD4dPm1a/XMFbY3SoMiQ05Z4qAppa3kTPypKLsmaTlcvqx8L8EchlfBNaiKQVM4PjcSIuqdIuF3v/5gjmW54egkIrFYMqE94aW6fwoRCqKAwMAb1ZUxUJOT5AkJCpIVIeLg/ZGx7DCWa2A9iZgPH36iJwCIQHPAru9b280XC+10XmRlUa6vT6sSyrKgzEYXHHGrpeQCAJDgueVZRH1azIObg2FmRwHs/vjxU5sbm/v7e8HNh1kyYMSOR6Ou70HuWnvpyqVpXVmo3A890yCd3hlwWQV/AREtowtRPnMcRyTt+xxCqo2I/uW8LIs8K5xyi/lCQXF5sJERIQPbkNliMm835tiv3BOLLz3ORyLTGmrKQp07qMuHnJkSczCDj9gL4WvEEeUbDXwusbwZ+RwGzp70fMeG3YIZoF1gSOqPusy1xs6VanI1cabM1Bw95D0GnFpnpzAy3REEDmoRgqSYOVtYleV6odwcwXAHmLJYKN1ZV2emt6AQdzi9uVN1pjuAtGBjUmNLpRaQwKDyRlltp3sHuSsM3JvAnkvlWVe8hEodiWlIguEEDrHLK+4m6ymJHcAeht7YWeUXS4sy8W4ZY3Nrc+y0AgRM3SQwRutNCMqGSMQxnkutoSBnXbumoe1NPdEekpP4HyIMK0DWvE4QWmygtof4FKo3ARRizWLRdF0LR7woppNxkRXYZg7xvsGHQsAWfCT0YSTHEnd9Z6aGpwy6T4JstAak/Aga11x8/KkPf/TDxkLOhsXuYe1ikZWlBmsU7U9s1ewccJbHnnqK6mVMJ1NrTduQ2awKwCxQTX/sYMReOF2WhSFUI5ka2JYJ/We+uCk0ScwdRM4PkFP0ZVGMJ+OD2QH3Vi2KqtSvNrro2gX0/+Fip+yEJZuPAnjUvaRrnOmgobZvuuB3HCUa8jZIuqd0YKoVHg5TonkJaZNCFdLzHf/2HkV2biEKW23oMlNNNdXdwQFUgDQlfi93qsy1NW6U5611rsgPenOEAF4Iqq4zqBSEjkY30q4F4wE4b6e1zVSTZcaadaeqTPfoqgfxm2WLTDdgFcPEOudqpyudtWubG7fc/lRmpwU4ExCPIEe99BKpMeIf6YFJPDHLVLsaC8I0HCz04ElAj4sF2yiuf2L2h5FEXLMXwmh9ARYaiqnisxB3TglH0jsWArXAKFCrpbJTaJ/4eE+IQvmMlyCDidmxUE7SL8JE4YhDxyI4zqFYsQIYU9v2TdM4q3a2d4oir+t6bW2CPY0QbMh5bIBlXjQtdC3Dat3gKArJbwxrJBSihVbQrnfOPP3oI//bv/2de+9/YG9xoLEtA1yc6YODA6irXpXU5wTUaZ1nFfR5wFxd03VdVRV1Xfd9D09U2iIGM2w1hYLKsrQNebmAYCtgfOAmIEtbaagosJjvt23bNE3XQ3+9sizX19fHoxFmJIBrvLdm/cqOs533I4iTQjwLiQXYMBTHasnqRasGQ9mBbWLdh/AzmOvBqqTCEFH1YyN5lWQRjpLoUpWSHFN9dZ1N+v6X/uvy+a83i61NlRvnFr1V0OcbrIEx+BkyiBHn2hmz71RVj/oyU1aNssw2bdPNO6v3UesB9b8oIDDWXRur2tTT3Xo0ratxNcJCwr3KinWlR13bG+h/aPKqKCvTNgs1v3rbHUXfzXVdATCb9ZAkzCFlsch3kB8uF9A8ZFmEj0r6RSJdLMGuGAw1QIBEP0f4T9waIzdwlnH7wAamkkLkadVY+c1bPizo2PfOUQTXQb1iiEexGg48gZENQltnWRjSSLwPamh7xHQ71sXh9OKgC2XAGIYmg6Zvm3Z2MN/a3iqKYjye1FW5NpmCbg88GRrR7c5m89lsvr+/Pl1bn06qsqjyAqkFstyNNY3pyxzAFvd+6b7/+OE/+ta37t+b74+n43pUz+dz0xudZ2UFziQI52C3MKBPRAoCkUAiGKrONsZ7oP9YqnfQPPOi6GfzEhNAKWKfKw2BJGwFapVrFwAmB5c+5nnT140xkKqC6UdQOnPRTA720aecrhpzc1ZgMDjjuvkCtEhQln2klDdNUGwCUowmNaUXx8Pqy5rGsrMUPRfSIepmsv+375EB3U6dHrX6ZbeqK0fHG4Xpu4kbFaO87dxorDsLdDcqM73fHFkrW+vKvoMNLyvolqFUPuvXtDNtCzg/4LqwglUGEc18UndlNdLVRqZsjl6WuizH46rrtdMGACxGGwPNLPqqLLJxnbvcLjBtMxzslH6jNpfaJwNCZR+s/2OooSaUl3zgRSf55rwZxP+XGNBlJrBSNwc4FDg4XG7QC81aJzFpzBmPBW/IdAi8wvtDOuz74b3AqDmGCgLs6fd6HlVTibAlcuLyGniHl4zVciyngMaw2OYEEyNqSKPIu64zXbfTXHNKX8mv1aO6qsq8KEGmNfO+7fK8vHYwu7S9rZVaG42OHVmb1iPorqLNuM6K9ZNf/Oynf/cPPvyVb3zjYGe3GJelAWW2rqqDvqckZ/DzQRwYC3/7lF5q+wA1L7GHHfnb2s5kFRrFlIfIAWpcAWRowOfA3AC9YMftkjawtr5GtIoBObgPyWRQEeDcAQiEXHht22K5edB9hlF9v9+Y669Nb0bz/cb04MHh1CBeTC9xmcGHAAqzmoByi95B8thTtWL5yHisJRo8ijM+DWhZgKtQd8bURXb6CGDnrSuVBgwpgczALMEKQGscmMwyaEZGVhk4myboHBnV5KpDPq6yosyNqbIcwmxl3o4qqLdsrO6cWfTkQUDPILilnXVQzK4sWhI0dJipJSNrxVHMDoucrSIbodYK7rUiDrT0Da/Rou8TZAA2SsLe46IOWOCOKx6eCHjYcSiCAjPq0AZGFGS4U+LW5nAuoRiwKnpRFlmhzdyAqwBHg20jCE6EXtbQ84/DaKiMCkEMEigNH3r4SLQsmBxCsAXU2Twvi9JUfd93Xdu1pu/7dqfBuhagVKP3uMi0HlfVuC4Wi8WVnZ0r17aLLF+fjMqRvvWWW+7500/+m//07x56+IcHB/vGmGYO/TfQKAUdBNwDmFGA9cABihgwat5F4EPM5IzKMmqMFhxaAcmMSQu4QODkJx5EM8T6iR5ZbaDvEeMnyTsNuJoe/DhYJrbWegsVpER2Eh+lvk+YuKB6rRfKkAeO0HIi8ys5BIOz5mUvA6fpWMcrg/AVoiVaY4IL+80D/gVoNtB6evoMsKOwiT0asRTDtSqHZjFAUYWhcARi5MIELeMFkR36fFDwMmQlLHxZuiK3gB3QRmcAfcb1h98pVgTwVE4p8wkESD2hCUu0bGWhwsQAlUIwfU8SaPz0EDIOq4ayFyE9PfhiWBkIqlHwdg1wlKLLc+AeDo4W4mgBuoOMAIv/e2QVSQTS0lh35rqFzvWt6RaL3pkMvY1Kqb41JCSwywZqjdB4BlvDYZ4TNnn3oRfv7ZGaPaX9cBJxZDQhcosKJR3MIs/KMi+rvOxMB12qeoBpdn3bQygIUSdlmdd1NanrUTXan8+2toGMm2b2ne8+uHvx6sba0dtvv/3cM+e3t7babtEjEsNBf/ccW/zYooRYNKNQYsyK/DlobQADBNhGURQGugHF9u+MNMT1KsuyaRqaJoEiKTMWrPQso/xULKER4fjETbQCnGZeNeVkUmE6nTFUPTS4iFl/Jn5SKncAWf22JLywT7jwLghhdnEuXoQI8HqL/wfoc3pkhR81TnZZx+RF4ybc+AatDpqgHhTBm4oxAw+OYQNMmmy8ngze9PofAMxyxL9pWgviOaHyiR+053cY3ucm0FyFhFgyAt7FBNIITKS9gUtppVJ73Q/87sJJ7QGRx32IcWAssyg52DdaEVHd4V29Ok7r5FxBFpciDujJjJyU1LCPk/AAlGQX1ljXQ2KbsWVWkTLZAlYJtEvKhANxmKFrzPehwq5DlFQMa4YYRQ5C0bqzSuY9qwFXEJiO1w+8OVpW4EceQZIAOHA7EMmLru26vpkv5rOZ0zbPdFmUk+n0xhtO7c8PtreuHczntm5f9KKXnNrduuP2Wy8/c+ncpXPnzp/vu17luiwrKiHkbRW0JCPwOv6gXAVAOOe5NYB2CUVjAy3nID+Lpmk4JMNeY4rvWGhZFzecPmFnAkpEYHpN186bWV+WHZxY9DFwZ2AWgd6TD9Y19A+et4DDAtekD8n763xXAgyURsCrlz8eHcuVbmJ2ahJN93IgOfIrDjHpb+xWFgZnBPr4YXuxH7UvUaQyPJ80Dja/qEEspHlQAVUXGrJSwCFSuR8NZ3N43RJhX1hRBpK0wyrxx4l+HIRh0nA0DXuv1KvFBOKfQfYiFIPcjQguCSPgmSd0LNRm6aHwsA5YLqhziVkR9DxknUCq1GSHzVoKqUBFzEJXefbvf+9D25cvvemnf/qmm246Ot0YT9eKUb6YdVZBak83m2PnZwRp5UVWoEuYkhtQRAdHCcAivMwJpgnDhiKbCckNGKvGMw7snKDEOq+g5gP61jH92HRmMZ/P5gez+f5svpi37WQyHU/G9ckTV7eu9a3JtJusre/s7Uwn07UTR8qyunJ1a29/F7QRCBqxS8CnuHr9SlAwbSjKXsRIB3itd9mR4ztkXJHCLYoZOEgV9nn/pIx7JwPi/dj3YNu+KYsM0DIY/8HCIchYEdGPbRThplWhxzqDaI2G5B/QqMHAxgrXgSwFTD3SfhSjFLplaSyIlndD9Ev2iolwUwfaJvuB1eBh0y5+Fj2Qc4MSB68PTcYzz3f0dgMJFTBPEGnDngMPSI26fMzMiUno/n3ky+zdQBkYCfj6inAka/GrVHUHbyXEJ+IhAfoT4EgBGye+Nux05AcYNVSvmCOQIyAgPa8iXZq6smAMBkRta7rja6PFfveVz33xwqWLX/j8PWVeTNeP3HrrrT/zvnefOX3jxvrmGnh/s3lnqlFpXNaZPremrqo8w+IM7Enw8VK5bN7lDVEQqm0Ta2ixjArHgJOk6EYegAKpTrkqi3I0qqfrk2axvj+b7x8cdF2/2NrSkIA00nrDuTYr8nptvLF5dOPoydOnTl25svX97z7wzLnzXdcWcC5AVScfgbf+/BGJRgsoPeg+gAiTT64IjBEypNEgx2UUTa4RU4ppTxC7spSe5eUNEwNDR60yHfTLxoRbrgiNPYFZdhXg1oaaZhNlLjhTH2ypXpd5ZRA95gUgU6+HG7ON4pVYXnrpr/IHI3iuecaBJjy4VZzNZRodKMNRgIUBxM7J7OAMKXEYwiclOKgtFOpH0BAmYEKKCanGof5CCMXIiSRYiFgZjH6BOlGUwh1ISJT+WbZIJZVK4hw+U/4ag0OefSTDioyMVzxRahIaHrik6Zg5B537qKRCCAkGpgAHE3pWYT2nTBeFPnLk6O/99r85f+GZedOWZTY3i2sH++fPnb/vW/dORvV07ehtt555+zveeur0qeObRzY2Njc2JpO8mDeonXNfdNsvsAEK1BqLRhrgz9EuB9ggwPvDZvCoQ+EBsSbkZOPZ+O2EX4qshtyeejSeTNHn1cxnM2P7o5tHN48fv3z58vlLl9qFW5+Ox2VZFHVZVbfcduWRh3+wDVJ60UNKoccuSQbvAwkM1bUGa99gKayAMPOjBOeqP3yAL/D8iGAq6C/g8cZT4JEt6KaGpaqaWWFUpQEKQXtTKFVn2cwaU1flHFBYO1DCrTaj6qCz6xaVKHoiK8aJoMA4Q3oiQ93z9Ph5ULkvCeDr40Y/RWA9gcOhUAy4jpRDB0pj8o5eEB6AaD7sNYLQL5AdmYiDAekbczBV9C6uTKeXyFEPe2XhgH2KCg3nMBoUwqucarAprUbJMnh3VeSHAzXoWRSPYdNgyHnoHAQGGd0XghVFItAPP/wIiXZgRSFShHsGPZvR1ofKiVU9Lacf/8Sff+hDv/PYo48URYF5RKhkUxl18vHprK7By1SWxdrG0dtuu+0973zHmdvPrE3X19bH2GIROqk33SLLslE1ghAKGcVYcS5EoUA0hYo1aFmKMIjcmphoHk4Vcxxo7IeKNXr8OtPP57NmPrdKVVXVzBZnnzl7+eoVcII50y7Moul3dq+ee+bs97/7wKUrVwy0ycTKcsBtYAAh+4rVARQggHx0Fmxdpcq8IIQqhNeLUmu9v79PTn6/a6hi+AH7XA9pDtMCoGjKs5N19d9e2T6yvT0FlLNuqtEkg2iYKsetqo7/5Ks2impvc+I217Ojm83pW8dHT21MK8jip7GCuimUZxwHNvmKsjhya28sesw5/4nS3+ec4X3jwfLVjeWLixdKYSY0WqG0E21GChNAei/HIwNIHuFL4VOK9tDYFOc78o+gQbEnhYIuuAHoT4GN8xw6KH7eKIjcSvCGVDhGozsZjLSone37tmkAmwtFqCI2NlyZqg9e5QzjFiZZcBdBJpHvtA5oCa8DcgFRACTgOnXWTkt99dLFr3z204//8DFAOiF0EdeQvayUqK+s6tq+db117srWzhNPPPHVr315Y21tMt249cyt73n3z5w6eXy6trZxZGNalYu2NTkUxLRc8xphDRiAJTwwhmCgmCQ2LvCqvWc9McqDH8UYJyub3NURdHLr8gLgUO1o0vYmU25jbX3z+NHLV6+cO3/+2s5erpvRRB0/emRz8/iRo0fOXbjw6IPf39vd4dp8vAUEGff6JjN+TrYl/6aHsoC6gc62ssOS8YyyRNcV3Y+q58d9SpBN6O3W+UGfHbz29W5zurU2raoy09WedsVotLG+dnx9XJ44MlP5qB45V+Ub0xNrE0iKgM7BRKK++g5RMuOwQxAqSd4Oh1K4cT0uJ7B0f6zoU2HV+BMnNMCktlnIc/OIHc9wo+BOjBRJEYGAJbJcPFknUiroA5J0JF17Oe9DRs5C/XNwr0KGdi5Ly0X7PtQ/iBQsAR/y7UNQ0YzDB7bjAEDBKyO83gOlYcW45d+ev6Ap8+BDPyDwbaBvz15BG8uVBlxBYbYv7P7e7/3upz/32e1r21Ccmr2q5Aph2ZIKKMJ8AWPQFrrHZzqv6nJcl3U1PXPrre94+9tuve3MkY2NjbX1vMrG9agzalSOegRXYSQHvKtoogBxkGMMJkdAQzoRHGpOnSaEFUh2jacMtUHQiY9JRwApO5jNLl+6tL19bdE1aM0CZG//4ODKhXP3fuMbTz9z9mB/z1DBIORopM6FukKkNs/ncyhAV0IUmlKsCIYJ2TFNa7CmKXG5CFeLiQPR2Su5fFlX61X1P/zCz62dOuFcVQEqzQF1Qnf5bDQeV9WorIu8HNVZUZRVNh0pUAdYoFEOZRCtqeZLerXX6MQPSZYx/DtUCwNmK0DveCbDKhJep/ABVq+JeNOEbxfc0JFThvdJDKYvlMA+yOS8Lnr4K3IoL+lEmU5/S/RZYPSeZYO/szAUJAHLN+WDBiDNWGkGzlvXNU1DkQ4Poky/7nXw4HsRIw9VFZklokGRA0ieynUHgDyW0wALsCoqFCvm/LlLf/iHH7nni1/Y2bmGpeXZVOMTHco18LMo3MY+VqhtR/gYBdxur+123ezylSsPfP/+SV1PptNjx07ceMON73nXO2+88fT6aFpPplldNos+LwooXoVLC/KKIYoUgCHjkj5kW4g0i9iSQIo2YjE4WKr9SCwqs7aqipOnT65vrM+bRbNYzOaLTOVnbjwzu+Xm0zedPnfuwte/8pWz585D7bsMBgEk7r0hOCtEHWMRDwBnIz4ZfX+wpEWWV1V1MJ+R/hBUAzw1vgiC70Mc6r5B6BcT5ibFaHTs6Mb0mO17qA2H6OZMATizKutyNK6qqqxBA8xAe88BFML5Vix9IwF7ihC1xKMOK89KcjWf2VTg6EE9PCm0hmTum+oMdMvkaC79IiljiUqinOFy6Gmr4cHLsxdPfQN/G9vcYGr1ztrc5ArAXJh4yU23Bv5gMdlluLiMQ/kj6f/AQmdcxCZeu6LZtJf3UZwKVhqol3LvtNXYLg1do34gMHwQVKCK9W3bPvXUk3/4Rx++/zsPbG1tFWUZ9EaKF7LJ4C2quAu+niUrJejr5sAvrojt+4Ou39s7OH/h4kMPPvjlL39hNKqm0yNnbr3tne9+15nTp6aTyWQyGk3Gpge7ujPQeSwHfRFNTUj0hlFT10aaXPT2kWc9ULB48RrhJ1lejEb52lquNomDqZ2da5evXO67fn1947ab78iKOq/0hWcu/OChh65duwbim6oTMMcDZQCqz2VZ1zYWTSkyucnK6hBWxXa+h4xiUn2wp1khR2Oby3Uhkgu43Y+/8lWjUV30HRRUpMo+ADEtimpUjEaQrFSWqixNlmEsjr2pPs7j3bvBdvK0EfKTlySGp97g2PXSTWh6KbmG8qfMx2MI3wt7cXEcgjjWK3VOvrW4UfSix2crmmpEQUaXqKf8VEVNKVEa2XRojDWuh8gC1U0OGK2UvJiQCH3E4mHpgpXdFan/c7QgOIXX27RhVQS5yv3xgKJIw9DRnEUmJTpjzSsHCU66KvNZO/uLT3zini/c88QTTzXzBXTrIXbK0TPxFJoQS5gIhGYm5qdGmgQtJbMv1Ausc4uma5ru2s7BufMXvvmte6f1aDLZuPnMze/+2ffcdOLkiaNH144dKWo9zbOmgbQWyjEAR23XoAYEMOXg6CMVHqWMp9jQODHmvIKmoTNdl1VZgC8qzzIoDDIZ7Wxf21g/orQ6duro88/cfvF528973vMuX7rw3W8/cOHSZWMhiIsVQbGml4XyzjCD+WI0qhnC5Q1jKIRQlCBQCdYXqg4NlEKvE6KuYZzOjx89+qI7bx8VNWhBJRi3LlM5eO8hsamo6wwTLaFGIZTvAmys8AkRc42H2qOMEr+RkLIrStgMfCqpNIkSlu4aSChirSWlSrtXCteYWi8DwitcvGkR4eS8Kf9039Q8DscjJIMuITR5QcWSD2D/A/AygWCIvE9YE8kiDZZKlolOPftMOOxeRNdKtFsSczhMUYwx3MmTcETAQqkXqL7NqgScy7bv1ke1Me6Tf/apT/z5J89fOg9ZtAXosZhgLa2T8JMaRMd2iAmz8iH+sABUMsJrV5wrQEBqAGO03c6i297Ze+b8uXu/9Y1xWY9Ho6OnTtx+6+13v+0dp24+tTaerK2Nx/XYGN05teg67dqygPQjqBcHPjZASlHZKQiG+ZJXYXuj2uiDopCJjhmFa9P1zfUjZV7szfaP6aPHNo4eOX7splOnrmxfu+XM7Reeeeb+b99/6crltm8hwRASnaAqZZZlXdf1BjCkFDCjdYI/KUMHfCTAYiBPGKNilB6DrSEA0F9XJTTMARtdmbZ760/8+C03ni7HY2OpZSZemoHjm3vaoD+ZJxS8TR4KJo8on+gQD1uWfWlQfpVMJH1xEIocXiNJefkV3bkRpCUsYT7J4fAvjULolt4RCy9RWT+l05Sg4isoanxkBS9CtRxOCxwaCMyALPZxYnyxp1pIy8Nfgr/w1Z4KfBmroLgEpTD5cvyipF1/LzbFvv/970PtdVKKrauqCuRvrv/o//q//vzPPnXu/PmyKqkiHuE6gkskrkcohSSyOti9FDFGVGs2oNdCFniMabBdTZRMSiYl1jjb9i14j10+XhttbqxP147cduut73r33TfdcGpjbX2yPl0bjRetwwRdBMlRJWvALGKTIjx7HPjHlmyp/AD3hdaqMx2V+LOmG48nKi/293e1Bd9D3/XzppsdzC9cuvTEk49cvXL5we8+ePbcM/P5vGuhIWtvwPHemw59zdQvCjxwVB4IUy/h4fVojKVzXNf1JCm5LrRVVQ2UqYq6yvJTR0/87V/84OmNjYqiN+jGxiRoraFoB6TPIeAcvQtYFJ53RVQeol+iJRzOUHg7yK5UXAs9LtDjsg0cZJd/K/0h5Koo8pwq1uIPKWvFAQ6z8BVNokXABOqkOAkzlsbygNJEPmX44U9kqmtTDxTspYHrzzG09F7D19JbUsGGTibtfD7v+x4zLkABFI3nlqg4qGpRz4+mIuxqnuvvfff7AEHEdmmd7dZGo4NZ86d/+ud/8qcf397ezorCAVjaf1tiMln5jhuMcpShVjQWPlY4Sgh/xYULotC7RxlaSPLad+XET0wPpjgeBIBooDJq0addTUb1xsbR2++44y1v+ukzt916bHPz6MZmPs5G9agHDHHRtC00+4Neita7jTNAk/M58TkLeDawGKUBUQy+ft223e7Bzqis18ZrAGmEWrG667vFrLm2vff0+Seeevrsl79wz+NPPtosOuxsBA1lEFueVSXUdqLVp0KUykKVrPFk3LStLrTpTI9taQA4rbNm0U7q6oYzN912+qbj68d+9g0/Od6oc4RbgV2DVWh5H8FcKKrxBGxgdOPF4yUWTajHwYKMioegsAEBp4c8PUqpXEyI3b8Rwc5LBJ/o1dF5w8p90NuHARVpPTP6P60Nq/3tBqb+kJLS0UYuJFXzQCTeQYHEy6ARgmJET8/Afpc2hrwi+VOZrp/NZh3WPyMHLPupk9hzsqIeURfhijhdjF9muf7WA/fnGDosM6h5tbt/7UO///t/9enPXt3arkGR5oLtgX1GizsN7/mDEfSBgKKBj5BHJBYIm4ghBMTmqYwusICazxdt2yIZAI6aiI6tMqxqrVVWjav1yXi6Nj158vRNN5+5++6333TjqSPrxyBbuCpd78q86BRkgpRFBf0TTA+FzrDUdVkAhcybRQvJQwh2gGwMu7O7N180IzCKRwUUhIUNNL3p277rTdMsdvfmu9tXv/TlL3zvoYd/+Nhji/lB27Ulln2mnsOYzE+VZ3WR5V3XHz9xvBqN+q492J8f7O8BaBmxg6VTv/ZLv/iyV7z0xIlT0yYri76BoiSk11D5T3aDwV9FWY/HRVXhefbwCjpigSlKwRvE7woRGQl4aNaR8Fyi4UgdyS/LQn+ZeKRIDQQsWcvQIzsYkU9Z9YqD8rQWD6J4ZvT8SzoaiGvhzEpGQKoZN8vwyBHJQQYTSzlPOo6Eng1ELluo4gJdrChNzeuxy0wv+jljZwUurkAEnOl7v/OAa824zCf16PzVK7/3u7/99a9/7drOLkYt2McuvH6E1skAIx1NmJjpxonvUXHjkVBJutQNCATM1we/BZO0P4ZIwAfQua+nMnB8hAKXRCUA0gh9U0AskJbVVb42naxtHL39ttvf8s53nLnp9OnNE8W0LIoiV6PJ+rjI87ZZYHgZ6K1vu9liZoyFZmIdlrBR7trONav0+tq0YLw1hhasAnXZdM1i0Xbu4GBnvjjY223u/cbX/vQTf7q3vw8aMe44WONUeRAxlZkunLG/8et/86477xrn2e9/5ONfuu8bi8UCki6z7ObNU//0H/39zcnx3HQFcNMcJmJVVkCye3DHkDGdIUi0rCoq2MNH2DvE/NkNdq2n5mTt08BpNETZcEv1ueBrTUWcPMSSDgSOInn0wOyWvyVSMPldWrzYG87bQYHgtDDuBW+KOnZYBYn4kiQiX1EA4WfsyGJpjFX+CbUkeGIgjuEMggNC0KWxkGLTNCh4mOPKx4oJ8EpLAmasXugdn2WFybpj69O8zj/9l5/5sz//kyd/+NT+/j7WW2Mvsk9UQowACH4OI3EeDkEiHOiBVDiCnksgDkps89FZb0SEEAQ5kKBmGTIL77aNQCJcQ2sMwV+9qCf/PQYhPbyYrscNhhv3nb16dffile0nn3zyy1/50nRUTyYbZ26//c1vfsOtd9xej8sbj59en2xWZe1yh+UBOgK4QcUP9NUpZSfjUZZD+Su0MCAbDBEgsKVt3zdYzWphOqtMVWaT0Wg62WgWTQGwZQJCeybmuz2MJuXR9fUbjx5T8+7Wm27++v3fadsOqvM4/QvvfBsY8iXU8oHkI4xUQyVGDP6ypzcYsLQuHinpDxj/Rt6zZTqR7yy5n5j/RnxDQkLR3TuII6VyaJCAs+oJw3vTb4Nwj3dLCa7iPaFcJCLpjuqSY796EIJBDDTc9ApfTyZoq76ZBKKA4D2oGgi9brxrK0FDRvZHyxn97gHkzr6YDCsfxeKf3OU7XTO5WYmG7n8BL/QtN9yy7so//JMP/d7vf+ips0/nGiITASxKfRVI/yUAhLJ2NF4rimxvb5edTRDSQWpGO5e74IbVzQLDAHCIFxDsfPA6s1vS9aiNKkQ965o1ee9c99KAC+LyogHUA6qfcwNepCCYSN/2O113bW//4qUL37n/Xq2rW26+41d/7Vde96rXjibj3pmuBXsUO5BBE5mmBRQ0Ck5QmlF1wJJ2WFAXuyK1W1evtH2PuVpubbrRjNTpm29+0ctfdN/X9jBgxCsf4H8IpMvMovn2N+7furY7u7T9/aeebNomg5r4dVUWL331K4vJGgAyFIQBsAaK0XlJGor0FYV18phCr9ElhsySNjsQGKl8E1IgJS8vw/nN6wRufb2k4NeVBEmfMe0tEVWcnyfHoEmLY50GpmQVqRUvAd3A19A6kJfxQqxyV9NDkIZJpkAFUsrqZAzeYEGXB5WqxV6NoMx7bzYE0FH8ltidwUfRCwwH3upikas//O3f+fBH/uD82XPYfZSrggfxDdIDaykDeWpT5eXf+wd/f1Jk/+L//a87SAWAYr4GPGB9XY1ygBGB+MKkM5C4WK4YMmCxTaHPCeCu0x4WRKI+FOdDFy7QjXJ9PyfnK/RQ8gU0/VyoMm3gxmzzUIkqLueFnjRw80ItPtu37fqoeuubf/KlL7zrxIljALNeNG3bYL4t6hq97Ztm0bRZnpVFMRqP63qE0R1IxaZqfl3b5mWuseVKhS0XbNtcuHjxBw98F/aXhoQ5DBKyq7Wr6tFffe3e+Re/CI5o143XpnVeQxUO1Z84vjYC/7ICZ3amXFZgARmoERuVWHmwvH0U9zUl7bjx+E/U76RYCO4Mn2LFYkQ4agfnMY2JLJNEfEg8u4lAFLJVlpvwkktgcTz1xvJeoW5IQhD8Orw8c8Tb+ggyvs0Mh4NJyeWeCfnBhJr3tEhUxwKcj5A4kspY8eIAtMy2xB9o+hK4MMJMZfs2sfAeVLg0Xc7P07b4P/7dv/3kR/702tXtDCO9Ycr8iw9za6jA3ddV9e53v/fHX/Oa/Z2dUT1SZg6wTtsXZXXnzbd98Gffd+LGM5euXn3kBw/d87nPL+YHvbFNZybT6bgqW0j5x8o9EG7hRDzu64UeGjbj2W8NkAYo1sONvwjiiUQeij/HocpmyKzwUbFm0gWoTp11dlpO3//B973hzW88ffImayzgnzvA0PkG30Bj4+na7u7u7sEu0W3jGmpMCPUUOizHZUxeVGUJ8X6jymeeeOLzX/jyt+77+ny2R8U5Y025YMljbYOyrEzmdq/uzRezsi4WbZ/nUE+1XF+HiqtZ1jYNAOAwsRKysZI2dFKo8uEHFwjtbfRAy3Ib4tz4tfGkEiRPetLDSUrvMRACAyjE8lVRq12lTseAUjRk43ENZ47Bcd4TG32xqXRVh81E6sk+0hnaDYRvMupo6MzyqxiOpU+SosMFlX9ZGAU+HblRQLmLZY8IEAwdQP17PKJx4eVqEBrW69CipnTEYrHELz714Y9uX90p6gLUXxREvlQDQyUJKZ1lWe3K97z7Z9/6presrW2cffoZqp3mEMbwy7/8N+5+y1tuvPnk9v7uzbfdcvfPvHM6mnziLz65s3X1LW96489/8IPf/vZ3P/axjzQHs+nGeqbN/u6uVVBdnlNoUVxjqioJLgYrK63AKqSe99g4wR+Z2L/Mg22ZhgVQBNtzc6jLWtdvrB15z8++7z3vfvftd97llJ23M8i5jeYOZt4jWnq6tm6wbE6W513fgb8a+w9ajO2P6lJZu7s1v3T1yhNPPv7IIw8/9ODDzXzGcBARavWhWfB+FDVAqEpnp9PxaDJCTzXE58bl6Mdf+7p8Uo7rI83BQW4t1MAFhwNH0QPAm08I5n2FLN/or5Kkm4rg4JNKjouIDMvjJ9wPQ7qN516Am1bgnAVRSZsw1avDmZWULP1fgXaoZwp3hhpQ4CEwD5KuqS0si/VJapPSLi6G11M5QBK6C/OEqFyqxpKEMr3Rfzv1JMSC6PS5LvKcSrLEjH42FYIg4i4S0dku7IpQaQIa6+xd2yvLvAcwd3gA/8dqg4Ugat92b33z2979zruPbB6fTNcQRJBj2yT1t//O3339638iU8Xv/O7vf/VrX7nzBS/+a3/tF173ylf/+ac+2Tv3qte89g2vfv03739wfz5/z/ve+/Z3vuOez33+s5/59Hy+8K1GCmx3Bohf4mdkckP/OS7UyNNCgoxqEMc/JN3GCh6+xwP+YaxdX9v8uZ//ubff/c5Tp8700Bymx6bPuBGgjYDbCgMGuu1aBw3dIW5UlAVEePsOVsAY29sK6zY//shj3//BQ489/ujFsxe3r20ZKheNvkmKMwRVgvYXuwDD+DKl/8d/8N+8+DWvqIpxYc1iDuWhj69PTh490R4Y6HoB5xWolJI60fyGMscAWUf2CkCzAivKcK+WKIRleCOkYxGF+xx2afSKEEqg2sPcVPL48VLHkyJ05kgcK23KhHQ8QUV2EC1W7kxNKqGGutlJ7HdZtjspcSP3GAxfKMYDy5RO0eARPs0usKNQPtVnI2JRdayskopwzxMC0FOaJFQ9xoOiY9ucdJXSGYUKheECjh4WTdvlNZQpx/LG3m3tS0Oh4mpdb2+86ZZf/Zu/cuTk8Scef3pjc31xMDe2q4r853/hr//0T/3UQw/94MMf+ejFC+d2rm2dfeqZbrb76EOP7e3s/PRPvfHHf/x1Dz368KM/eFDZ7NWved1LXvSSD//nP6AIORSI6boP/uLPvfa1r98/2H3y7FNPP/HUpYvnHnroISjDhXIY2pP4Qxo72bIIiSydl5/1Syxn5duLGGOPTI68730/+653vuOmW26zGmjR9uCPALc5dk3GKoFAtJjK6wwCZaA/A/rAu7ZXCozlvuuuLdofPP7IY48+duHSM7vbO/sHu8b14PcCd5uvjoiDIGHs4a/gMoamxGvrr3zpS2+78Za2AZrsxllVQsl41bkcWgKV7PrydXm5awudZagnDM0OkbNUWVWGNGlZIiccxATJQcvG7ZzlYfKHeHCgD31J4cJuxVRHl9xBGpwr7dN4I8FZUN3EKUPNEx96XWYrWhjWoY6ApJ/kudGnK5X+CGkgeRfaJ620aYVxEJ/NZa6wkqHHn62aL7MWeJ94OnTrFa2l4hSS4Ue3bRyTYLjQFQrA82FrsRFN4KIgmZU6tnn8n/zjf/Sil7zis3/1mb3Z7PY77sAAZrG+ufG6N76xGtef/NRfXLp8eXd334CyXXz3ew82s/16lL/sVS+749Y7/+N//E/3fuubb37zm37ida/66pe+ePaZp6EpTlkC7KGsXvGaV91y86kbbnp5s3jdZLr+jW987Z/+3/8pghMN9FsBOgOvgQZUKnAi9L97yFbgbxFRBao412GG7r7myNrm3e+6+81ve9PpG25tu6aoCmuyvud+JSTlSA5DwWpw79u2awGwmOXzxaJpmrZvtTJN31/Z2b5y6eKlK5e3rl0xbbe3fTDHxijcpwZVaA4MUJyLVCtugqHq0Wg6XpsePdmbrDU9ZmhBdwZwDWiDyagccg9uEZLhmPqYO2wHwUpLVZUlETCi3NjzmMRfBrgGb00ShQxt5ECX7BX0KmRyAIUyHKUmJ37F3g7RoL0uzSZUEdNciGeB5sElmCBZwzPD4O1aNvGd/2P4XEo7lN9IIk2yhp/oSuC9emyPxJZyPo9RmPj4OUDYGWriu9ylwxBSFUgMzpfuesiUD+GoKJy87RuL3ZHX2u8sBlHx1wJRGWRmhJaCLPSM65S1J06e+B/+8T9+9ete+6EP/e5H/+SjH/jFX7R9d/n8BWvtz7znZ5531+1/8gd/+PgTjx/s76nMjsr6X/2v/4s7aH/rH//36+tHfvJNb7x66ZkvfflzzWL+htf/5B033vKhhz50MJsXWd5Cq/vuA7/4wRfe9YJ7v/TF+x+oZ/P2zJmbH/nBwyAGUSdV3fwd4+lP6PFHi+5h55pucUSriS4uQ3suVhJCYwHig1Swk/6w1q1PNt7znne/9S0/fefzXthaSHjIddFD8UaqZc+yDfgCga+d7dpOOWy80Hb7e/vNopnNF3t7uwez2dXLl79///0PP/qDtoMocN+53pkSUFrY6xULtYdzREY49/LOi2pUj8bj1730xVXNzNJlGkAkFN6mDlde5+dODZ7lRy5MBE3okAIao9JxYxecPNQCYRtUVfqLaV0c+GiEeAkosJQSpZu6tgMx8yZI8gj38hQXu7Qk32SPjP9JpfAxxYsHxeCrkKSRxFW0tPWFkU/3C/J42cuVEJb4JWQzBS4UFQ2Weky/YX3CeqNfmas905sSLu7Jl93+UEwPJgUaJhu7aRR52WMQrOlQvgLWrODaa5RswmkHINfB9rPdbWfu+PVf/zt33nXXn3/4j//oIx+7cPFCWU16ndUba9a6elw3e7N20bWLplm0Splf/KW/vr42+dYD353P5v/V3/pbx8cnHvjON59+5txb3/62l77yJb//sQ/f88UvOgMirodqleMff+Mbr21vf+xP/+zRJ56o8qKHPsN6NptjxxQYWD6brY02KttOu/buevI6XT54dPPTzcGibaEiD2UC5cCDSBRAfR4EufTWbq5t/uz7f/btd7/99OZppwxUTIHWZi1a2NTSNxQAgKo+lMbb9aYeVb01PYjefr44uLZzdf+gfezBhx944Nu7O/sH8wXW+DVKQe0vRIgB9fc9aCtCbY2qLHRmG416YzePb2ZWu9ZUeQGNnyhJn1zrnnkui67o1OTUQ9TAMBcyANeSVxK9iIcblBnwn5LFkBwXPvqesKMvOu3kmkpk7xFfZWkKmoj3T4YVPBZsI2CsgGBDca440wHyeTjV5/JiiSYUcUmAkm58DEt8EKMyaRyO49o+7AY4H0W1nmN9w2Aae+cya1cENABFUPjyIvcT7xBLCerBYF6slXHRes86AAuU6ztuvf2//4e/edddL77vm1//iy98/uLF84g7zLt2PlvsL+YHBaS2Q8M+qPaeuel47Y0/9vq7Tt30zz75p6pwx288tXH0yH/60O/ubW+/8EUvff1P/tTHPvbx/f09rNYBIdl3vvPuWunpZO0Xf/6Xzl29/P37v/u97z/QLGajUaF7Y4vamIUzNmvaF1bFj9eTO0Foqysnj4+3XdP1o1HdtrAA2FQBojxQmwAHD3bvdONdP/OuN73xp87cdHuWu1Fdz+cNOH6hJYz334e0ZZRLWA2bO28CoKNpD1ooHtJ27Xe/ff83771vb7aXOXD4Qa4i1bzDi1FsQDaSX0D4B1tTgM+pBm23zFXmTH/6htP1aAyotb5XVIYag97e7cTBcL+XAmYUweXkEwPuFsDP4hBGWSmhrPwB1hjj/qzibeH6HCqoq8Eb3mYV/pehzp3GqTx8Pl4UxS7TLlAv2f/UOpNfBRS4Fi6ddCQuvJGavyu1bP/Q4CVGgzeSiX+PETgDfUEubgwdswbtDQ+IU0CTBAQwJTU7UhUe3gCgQQfVhRkaQlcl0xHIluDf8e0t2H0NNXYxqRXcNn5F4cFlUbz5rW87efz0wc7eX3zyL37w0IOd7SqbuabR1rYtnNem6UuX33nHi07ffGvu3N/8lV9+5Wtf+fFPfvyJJ3743ve997U/9spP/+WfP33umRMnT7zpzW/46Ec/cv/99/cGPM6gtxv9mte9/oUvfvHOzu4b3/SGxriffN1rf/O3fktb9xPjSd029zjbO2gLq7R+eTFat+Zz0/qRcXn5ysX9/QOaEJdi93WiaW3a3hzdOPqe977nbe94+6033aEzV1c1+pwJH+IbPvlmG1SAjpIXu64roFlgZns3axbXrm131jz43Ye+dd83t3eu5WUOORAOSuHHYBdIM060CukZlIWmtapHVQFNt0rX90qbm288NRqVBhAqHbRgCsBHdn4sn5WgzHo12vvFBikKfgD+AAzS9OgzjgsuyUPplRmobiyB0pfnMPGMeh7j0Y7sVlzhEQt0G71z6K5CGieXAXVmxt+5GFP08CSk6UhVHoCVBhPx77mVACwPCUxn5MVs6pMPpoD/ol9WH/Xxqjugc7A/FGfZCTw63xVe4IzMwQMS9j5h2BHJEh5PMGipuWCkBh05UI+CNwH5nlPZ5//qrx5/+NHd3d1nnj7fI8RfWdO20MYyzyCb708++tHXvvgVd9/95je97Se7WV9O3Wf+4hN//CefbG12510vOXXi9k9/+l80i9nf/Qe/eceZ533uM5+5sn01L4tmMc+tfsObfvr5z7/tgW996+v3fu3E0RPHThx7/NFHdKGLPnuJUe3xY2bvMjS5KgC7f2Q6+lzffMF2zRxELckfCEFzAzHXdVBG2cF79tjG0fd/4APveMdbbrr5zs71Os/bDqHO7B7hyDZTPIG10K0PorV3o7XKWbXo+v35QWu7B77z7a9+6Wvb29tFWSCzxGiW5eguNMvAQupspVEkGWkMdqgoKihqX26Ox7vzmVZuvL4B6jLUyMGGZjI3hssv80n1oAb+6TutUV8QONcxriBJTjLwJF+XLEyId1HViQE1+l+u64QOYuHwSxKhNfALxwxdDhSFemwUdcNWHph7if9jHun9AqsksOZpBieyJJP015VD9P16lu7JCC0/3FDIefm6yBBYdWLSxyJu4Uq5JjLWB66Tvlu6SITVfHc2/nYcK50IVRhveAB4gM4VdBzqtc4vXrx0+eJl/CzLy7ICg83+9m//xz/6ow+XkLWzuLbb/vN/9c9/5b/61VNHT166ePmb3/r6vfd+89rOtbe+7e0337Dx+7//7x774cNr69MbztywO7t2ZP3o3e/4mbZpv/jlL2xd3brzJc9TSn/hC5//0pe/urNzTWN3IgcdyuyHrLVbV1rTnzbdq6ZH+8nk26Z9uK7HVemapsU1apoFLStqXpZwkMa6I+ubH/zgB972jrfddPKWziyKvFRGdRbtXsSukZuEjEASasTze2vapsvyHBMJd/Z3r832Dr5z3/1f/+q9Ozs7eQmoRmhRFMq0oB9pAXFcaHRGCiuZbLT+eV6sb6zXo5HrzcXtrc70t9x4y5HJmu1sZ6BzHxWvJ8gMEtgQcyUVamEZezNY5twMj3Tk88E3A55SCpixB38Fma1WGcNpink6q8Qya5OHuIoDxNNXPSS3D20E49WQcHUieZ/15YYeA78g0rjgJtqpvuDBQOIr6Y0CJiSRi/GdQKApVEPo6aFxa/yWuBUVbEG8fYw+hEekCrzIJ/GMhbh/gafaYU4MW8lM0ZgyCHwEuCBU7cO6HGrvYLY/m+UA0y0WbXf27Pl/+T/9c+j76aB9/aJp16ajzGVPPHr23q9/Y2tr6+533H3zqZtm+7O3vOMtmcqPnzyV/St79unzL7/rhaPJ6OUvf9XREzduXb7y5a996WBvFyHV9qKdT233S5ONV+blWmsXmet251snADYM/csMgpKNLcsCOhT2AL3G8u1uY7Lx3ve8761vefNNp2+1qq/KEupjwMnlWgEof0NxEfIZcsa269tqVGqdH+zPmrbRynztK1+975vf3NrewlKYDpof+trPWaahowP4tLl4CN0HqBcXtyzLjY31cQ3mbmbMItO33nzbb/zqr2WTanYAKYQlldGPoMtgjKUuUH/EQv1E7jASgt6+sJSkFO994k53WJ8dhR3W5BOELQ7MdRMV5Mkd0DsZwlziPSSSBmARj8kP0bezBl2AYl94kLn8FKc345IGziZpZ9XIdNQgBKRxYJuH1RG/B+IWwm/Zpk5M6BTlHc3m0IssKPSsYQc5HJxgWOgpRKEoQgbtc6D2cNQxwq3FUWALmO4TRll0XQe10fBFoXNDzQQgSYEmF6v3QDAWmr8iTBcTjQDQZJVtAfuXaV3WRdv1n7/ns1/9WumUKsv8y1/+4rnLz9xw+vQrXvnK287c/omP/9m3v/Wdm265+Zabbr71pltPHT85u7a7N9vd2rp837fuU5muRqNyvnjfaPz6uTnQ9g8r9ba9/mWT6RdcvguFnLG/G44Q6utii1QH1XDM8c2j73v/B972trfecPoWq4F6jXVd32JLZawGS5zfZ3GRSq3znGi663pITmrswcFCmW520D/5xJOXLp7nr1GnXzJXMS+UILCA3gATGivx+gznqqrX19fzooZ8oxwKgNx46qZf/7W/deLEMd0pMLAVoChj+Ue/nxH643l/oGHfSgDRXr4+ltzvVHdOvUhoKlBXy0HAJ/5C/0l7coWeLBU4LqoW6rREFGRQeqOjm/UdH7kjXCquv/e2+dwMypJNqD59xXd0SpxerK38dGlx0ttJTV/MP3oSpH8hbk8U5rQNEhaCOlWqwXPDQMyLDwUXueIF6b/D2gqCgJkR4CpC8w3mlJA8SHWbABjUdazAeNINAoJyp4LNQQ1OYZHRVKFnYC9bbZ1etJA7Czewem67hx565IcPPf6lL3wFRFNRHhwcPPzoo5cuXLzrJc+/+cTpPNMXrlx65JGHKPxTGntrWd2RF/fV6r5SbXeLF2vI+Rl1i0uLFgvQZ9DKGavDQe+mHCa/uX7k/T/7gbe97S0333x7Z9sa+hhknemgOQw2WmQF2iPMvMrKniwDkr8bZdN5e+DMzKn+C1/6/A+feKyD9uWlMNNiaAOENnR8j1BGWuO6Hq2tr03XJgcHs75vszw/sb75a7/yS7eeOjWqKg0NQLEikL+eDkaANEe8rcCZyZ4/IWshFBULwlaqeZI4ydAMvSA8rHagIgbPiJAEQo1LZQF/lz341I1VaObsywojQuXH+w39V5hk8Inowff1DQ/Rz1NmFTQWJddJEGsAtdCh9UVffDL5KhEr4MZ8hVQBovtoiSZjYCnNW2CcaOIqRBrmlDxmmWTZ0bqFqgxDb0IIgkVFGjTBom0acl9BNMhaqKrI4cigu3ta9nyXto26FIQsNH8ht8KDLHwsUgs716leQ8o/NEZowd2d6eyhhx956LFHal3nhe6gZh2+n2WNbR/vu4+6Ync62XNmzdn91n7t4OrOyY2+6aDwMlTgAFXBGgPgKafXxtP3vu99P/2mN5w6cbNxAPDMVN6bzvY9F/KJPmeP96A/fHjNdF1d1rkurbIbG9MvfOHLD9z/wP7efl6W5PUK+iHXsAHYBsDUCHAQ8JJ5lm1srFfVePfa7qKdNU1/5sYzf/2XfvGuFz5/VIyLCvQXbqhKexJSbFiREwHcKMyihI56Ii06f+w/FBaqvAeZmwTm8Ul18dOhpbtKXQ35FD7+xmpMaPRHUebkvHkhzBQckkbwXdaPfRYeZeVBDneOOacheWDVYAYwCxUzmAb5hmFWiWxeJYb9xZGlhQekNwyUHVmbKLgbvY88B/ZjyLHQaQHllSskUNUewg4yV2Y4ffRhhqeyAk2rQ72yNRQlb4EcegOVn8PqHKKChDy+6IxnTy4fI0qDg+aNXjwgpBlSNwqIemFBAzAN0TzsLTiQIX4LbihMglVtlT9uVN61WuvdLP9jN+tHtd7bpyIg7IUB5AM4gDcn6+967/ve8667b77xzMKCOVDkBWT+oZ0sVFEvfEmHRqqkdjXkW65Gk8WitU0/PXrk0YceOfv0k0ilIVlCBALQBKUuLbSIxAGdc5PJeDIZL+bzxWKm8/z08WO/8gsffN2rXrk+PmLAA2ZRfaJkSRbp8cRzMWtBS8tQAJFWF38mJSUTN1NQ/GCOPj81FCSX4ceESaQvWm8wr1AL57pMng690RvPvKfeRLb7Oqr8NFmGlD6lbjiDmnFCBfTChyeQeJHUMN1nSKUpdHSYSin1gSF6Kr4G4jkI87CB7BcOEo5YS7xXMJ/JtRUAW+jS4DPJNBxObTBsBhoGVY60eeagi3yZF1VdITPwLRvDmON5YrSWQF8PFBEWdT7vmb8ZEwzADEMYM9cs0Q5rWviCsw76S+EfgKvCGjRYdgAcPs52SCLeUIeM3MJaV9Xl+9//gTe+6U0nT9xotMFyNnqxAKQUQ1No5F7WENAxniW2ftsTJ081i3Z/tlNW5Sf+8jMPPPRQA1n7JdShDOvA1S+wRizEsTsS72SKWmuna2tHNjf73mzv7fWdOTk9+ss/974fe/XLN9eOoV+/I70JOgOjCsCqnaz2KHxP4jQGcwybuaANzP2C0zxRqeDJsr3ovMPOG1wNjek64ES8EyrdS29fUEWhHoAyyPaEjAl4kbRahiSl8I6v3ZBkDCaX+qJArCMwIIoPXMwgjoX2XLJA4l6BFIM5ENiAFMkBQR/mQr/5QFkqi3mdwx6lZbKDrhzIwL+T6MOEx4KEA+/ToB58mHUH3qVlF0UsuRmW3e+AVRr76gAxQEpUyNcTY+XzL8Ddw+0ZPC+6OQOARpTGxAEyDNlb2tS4jKOUTPaUaUAZRSJVhcO3FjqNKFW8+K6Xvuntbz+6fjQrQX3tje36Rpi6IVIQ4B6hdi2jgqGAHFZUt51xnRmt10899vilixexxDQVPQjI9FhnFHrPQIsqkuigx4zq0bFjR7M8u3z1Sm/N+nj6y+9/71t/+ifXN05DGbwe6l2Sc4HOR+JDFgsb7ScBlKfcwAzq5MJu+Samg68Eo0tuCQXvgdUk3laxT/TmQPoKtADwJvD8o+7ktd/I3YMjgQ3N4RkJhjt3D47nXQwx5G1x3kJq1w6oM2YZKGlgxE9FoDVCh6UVIqRiXMToRkuPixS+131JkBSLr1XDl1ezTMJSfZkBuhhSGdlUXIkggb+CRqecARy+czaGL1EpD7U+/E4lSWSB8wV2lG6X12lojGwkhnPGd8aSd3Dnru8ga19pAF9Tk8SUMceGTn7HdA5FfI4fPfZzv/D+I5ub43qUA6YCrF4PEEBZ69UQDiF5/AZFLyB/EN3veVEibnJmu7Y3xYULz8CCZHnfg7HtwxnsOIKUQ6fapsG6PTC2vMiquirzwnTd9tWtK1e316bjn//l97/zve+YVJvIElvw9EGxSYj4+SVbDTEIModVGT7coO1T0wlMBhboYLaKYqMeJkivOmGpLyhIABYwk6rENYb7DLlxQLiR+zp6msMOe8RV6BA+hIcEiEXMRE4OSrAYyLFAhVvFOqS26mGxLu15fuTyocGbWCNv3wnDxSv4IWIVrXIqi8RYE6ETRYGwxELDACIiOY41/JsEjKlFJNWMYvvU71sU9IFPSfJh2tIWa8NQLJO7j3mzKeJmlg+ZtEskacYrRGCDp+MtoWhiQ0GQHBqJzmYzyqqHFo+Dp8rfKJoD5Tt0N+/e8oG3veCFL6lUVmKTQAxb+0pDiXTxWbVIuhT/xRoj0Fx3d3e3qJQr1Gw+K/LsU5/81DPnz+kcjHNuMiymmufFqK6h/jugQWECUDV6MrGmh4SOrWZnZ6fIq1e88CXvetfdk3INXfIq16WuXW9jtbtUIfQPCA4s74dmPokqFpWb9gECBiilR8iTcjAXkX4IqegXw6vtw02KPFakKoFE6KEYJ7Zej4coBLpSMgjvhSUbpgqHvG0/Y9EgHONi5Jz3Vqokg9TXNjBsNd9eANeQv4DTgUUJySDqOZhmP0sHQ4oKiXHl4EhheY6lGRJDOlY0ENxB6O3xGPESeBckpZehAO3FgkbTdYkSiMHgukBfrqIswINlLSUjh4uGrCO5QZyiuGkktShgwn0i3YYTDCxuMV/M5jNMd0Xwv/d2LMFb/cNQC3Muq+ry5ltvKYsc6ulBkhH7pahjYPgO6uCkLENEiStw4DOqquja1lkora6d6rve6eL8+St7O/thN4QAAIKv6wpyjDtsE4Eq8XQy0Vl2bXtvNp87pSaT6ctf8tLf+od//+bjJ5SBjChyYzCINlBkXOJU+Uu2i83YvChAwHP2QihHOdxfb6+E0+hXgBL0hISJxqvIXRc7y/aHhSh6j7EJtLi9Pi8PSXoyovrl5aocYBrwESvhVWjvf1lFqGKp5AcqTjmsGmgj/jh63Jd/KlVClhZdFM7MSJb8YYyBYF4r0lWWFPEEqJaOV/wT5xTy1bDZRgGF/6EjiFgXdsXLY4j/cvNTIAbwQkNLRQAbJYgCaaoJON9AIMcI5IDqQowyPNtLF1IAAMkN6RBtA+MpULhQaTt/hMTSyo3ktzOtNjeOzGezsh7XRQGV4Lg0QkAKBoMhADGiIAafpwP3FTy6KPq+JTwXOmoQdclQdH4wwMNL6F2xWCx6AxiXPM/LAvoTzWaLruuyPJuOpy9+4Yt/6zd+49QtN4Fb3Rly+AsJEgp2hPoJA6MjOkEIJ1FgYeoSVH25tH4ZxMKKQyJJEVx+wR7yvNtfkXKBIPapzkEPLW0ag/XAvHwOLiFSF9NmHcmdvO0WL4/SeZVRyFrFSv4gr5JMw+FbibIWAjzeRReUdxnN8e7ISBQsDH0RlWUB6P338R5hC5j1iJEJbSVMfJn/eFaMu1CUZQHpqMHF4LnWICslUgLm8EN2e++wkIsnQ1KtUVejYnAhfDFwGdIh9BqJiGEkQ+alicp93EagpKqqmqYlACxnJcv2Ksn6hXXkzmdZUS7mC6VzNR1HpE9YQ5ExSNhJ+gGYNUiwB36nsmwyHjvj2gYgIkTCPkIRKzNAWVqoKQnVsNqmBQUgy+uyGo3H0FjUzouyygv1vDue9w9+47++6wXPy7MK026BnYbDgH0pook3RPAKqDNV2NPQlQHKAJQlVK7lqGg0C5dJYLC+3HaR0MZyTwTDjXslfAWUrYQLhqljmCUS4JvetR0cNHHUUZAn9qcnBj+oePKDh0neOy5HosItUXSCZ9TBNcuOoXgy0zRgocoPLTUoa+7FV5yov590uonhsOM8/CPmKoId8kGhZKDU0VVRFrWpiCpW7exgn9kxnWdZ8da3/UynZ8889fRsf7aYz5u2wZbfmYaSz6CfUmNbcCFi4BTCuGh65zlAkbAGhWRncl1Il+HPhRZHCnNWVtX+3h7jh723MiRMyWPgt5rYKgC1u7bburp1w8kTCBZBzzDWmPQsOBab9O4YLA2L4dDMub43Ogfq1SprWhChTtnOtAiYlD4e7AuagbDNsmyxWFAUG7xW46qzvesgcVordectt/+Dv/vrz7vtllLXNofAN/RSobi2xV7k5HCULCZyiijL/HmE5a0qaFxMq+35cNAZhdo21LDi0aZMW5kHF9PehCc4/CtyM0mf4cAb+kywGcFAr4qZQFJD8CdYmH4DvIg4j/E1+MTHPnxgKTlagxs5aXwnIe7ULhe3Dk3gQ7KXryxOtVljFIrhjSEeSsJYKNtyQSINc3lXYdgIviTMKPLP6gwkBMbrBqq4YD94YOiHP0nFL/+1X1yoXbUwqtfQswu6783mXWOMeeyHP2y6bgHJCzMg7tn8YP9gdjDDgD5EdR1azpTMFDI+gh2LaiqC5DwjYvwTJkog/AYSA0k7DK1feFPFSpO+5gNLhIuCCpHnzj9z5x23lnnetO36emWd0ZTYhx7XgHkOnb25PSoezSxXk+lUKdu1DXRd6PoGqoo0AP7kfQIJBOCwHOpplGVJaavYmQiSkJt502KKpS7z287c9vf+zq+//MUvqsuxAk4CKwPoaAQuYwyH6sAGE3XAZYU1h58ARA6zJTy8QVyZWkRSqCYnFJLLIdbFSRs++4xXWhLD0PYm+RuApxT+QQ6+AgJAxzSK/SA1By7kqHgvz11EgP1Jl9qBTNMLH6dzVf43WZGdVpoeMeh+sEI7Tz3zIormiSX4HzivmpxwTGoxKkje4ZAbKp4lSF7WUYn6EeqhgLJFN6vUs8OcKCVYkIgu3KItRsVobYKblkHxcagW4/revuxlL+qNs12P5UwB/9W3dj6bzxaLa/u7Xds8+oPvQ8FV0x3Am/ODvf3Z3v58seg7sAFt22HjMSpzAZBhdJuQJghNhiilTimIY0U257mejPfw0Sdu71zvurzQ13avNn1Xtu0Yjil3VmPh7QvfBbAfMJweEn4xHqPHkwl4DZre2p6SmWazeds16+vTuh6BgKaeqkDqEHx1DnpS9T20VqF6OjCLTNdFfcvpG//2r/6NF7/wLp2PXAkQSz4tBLD0Bq+MpCXnyLNiz7pgulDikkpecWcWedoFpXvncziyPpJEWR7wkrWhOFCYOkul9k34IIy8a5PnuoD25KzkhlMnySUYG6l4XeHvFM+K1lpUsZIOYAIuuuIO0tpceg2I27sefFBzMOlULA6yAgfGLMEWfGsxwUb90QwMLLGIAjNKLh+sjddUvXNNpDQNOCE3kvDP0qowFSihHWjKgEqag2MWyCnLc9sAC0fiyvqmp2bSo7ocT+pjx47pInvFi18AaGSow4r2nnVda+YtIgnbmTP24vlLxai+cuni1tZ2b7rdvd2rV7b2d3a7voMnYA4jUhZ40CHrDcQFlpjBY4dN/jQkngWeiOVWSp0r0x7sz0DkQo0YrHGD7ACLpfPCeYbJ8hSuclYXeVXXI6RSSmxCPw/0Gz04aNbXjm1uHrl4sE9PVw5ahHd9v7e/Z53LobMVtY2B9RlPRrffcNtf+/kPvuoVLx+Nj+gCUJlYICXGn0Jcb3iyIqyCqcJTECrtaGIEvHvYcAmNlSdUCHF+ofiFuPrweeLLYoTxGvotzyErIy8KKKCNmRBej6FUgAQuQfw2mNYpOQ2tWAbv4198uIN451h7KrqD/+2Ql5O6+dA/NvS5Cq+1jNwKDT2x5KV14cUry3jII/dSW9glHqTFUnuYMZUOSgwlSOWEH8dJSBtLiGati3KknIVGtl5SaVIprYHKxlVZe0ASlr8CiapUb/LM6i5zGkUVzhmRw1k+KtfqYjJdOw3hZXPXnc87cmS9M2a2P9OZWvR9O4eU3t62SqtmNu+t2bp4ZXt///KFC9f29poGuubuXNtbzOcgPcC01T3AKtDw5V6HkJDUm/Kb37jvZ9/zHp2v60y3bVOVJao73vHvYVigNEMFG9O0LaYr1ih7wb8Ko8daLkUFHXf7nd3Tp07e9by72vlsa+vaommg4glcBqEUbOUK6wdFKMt8PJreePKG973vXS998QvWp0ctBqPBbxXUCDromFOdnJ9kI6OHNkaZuPeGz1g51PALNOEtI2+gYZVTKssaj4Z3HQ+/n4Y5w8HivuTOAmSVU+85lZoyjxJPkGwmMzhnq8Swf0rQU6IXKOiY8TJpziZRHu2NgtQIWBE4FodfOPSSCQwcsSvchIkBAEFQwpY5gMZSU/pwO//ggTaglh2JrH6RUOc0QD7EPFLJ5hOuiYyteNNP/hQUZ8KE2M5hiZiubebzazs7nTUZVCsHi7Nt27Zrus5AQzNroTMDuHIhyR56ZWPZDgLLAuChhL62WTHKcw3GNHiMIGNxUteTalpXZWcA+5SdKLI8e9XLXo09ijpAR2a6mTcHB3PwKml3MNtrFt1Tjz85h4Zj8970V69cvXTp8v7uflt2m+sbQNNFAaTYdaDqcidIXBX0ajvrckhadosFFONZm2yMJ2sQ9UVwEvjiIQYHBGmN2R0f7Lc7r3r5q7M8e+yxRy9dvNg1IKbBNimgtaKzpqiqelxP1qc3Hr3xPe+6+8de9+oTR07qEpGgoMiTAk0hTfIDc40xebxkHXN/0r3mTblOGFVjmzBWjliWMPJsxhPGbijvBfWx4/RC/9yBASxaG1KIBIp5orTh+jdcB5+xWZ6uZKUteS/hkIn6tpfasUIfvmjLoiXt5yOTD4KAjnSo0lVJiTOMYUDHJCVxYWVjCE9PkdZ8/De5P5M7DQ7httTbh7c9UZUT2o1nIBr4fpS0HOzo9Dq130Tv5Y/gcPrdadcsetNhlj6VrIAnA7YRK0faIgPvIxR/4cliFg5QDPXq6/reQaE6PZ/PwPJFrwu0Gum6tgUYD/bRbUCxhYRCVM11Vo7q3tqqKIoqcx1kImP34cyavmtaDUFpZG0ZFAzJoIeY7bsugyb3ZtEs2gVUa67K+tRNp5XVdV3XFejFEFv2L6RhzJOwtmma+WJej0drGxtFUSqI8aDijnVGUDG289nsmfOXLl/Z6ttWa5jX9x747mOP/3D76tXFYg5NZBDFPd6YHts4dtPxGz74gfe/4KXPO7V5g8Ka7FyolVAmXPFV+MAF9ch0wbDXFOHCTGOIV0ElLeowPFTBVoi1ANelDaIIUNu2AE1BVx5C3L13Ksj5lUdLPipEixJoPxcw9ZgZYSN6YyW4DGUUIBiDkRFhLB1fBfaxoTKAqQbsQTjx+VK5dpKQJX8S6AWBuJC/hncSXUEAKAS5DtlckoPFxYA4Rzz2OguWvEgQjtMSnauC7g7GJMYpDbTyCbw+uBfCjclV4qV+d7DnckgMogAURRyp7RhqxvQP5ggGbY+eyfKGRQexOVRytcqwHTLmeZMfBX1IKPTA3jYH88XeAaYHOte1/axZzGezvrcdyHbMIkCHOoDotYNMBwYBQzHdvrfNYrFoFlBseVJVRQVHvigmYwBFYa0HsHUJ4AL37/qqyPKqBJVgMtZKL+bYIh2LmEADYWuatmsWi9nsYHtre7FoyrzqnevsfHd//+tf/MoPH3+k76CO7HQ8OXnDydtuvuMX3/v+G248vr65yVFmXCYK8mLvMQYrR1SK57cDAg7HhRK6UZ8A5EZRlOlpXmVKpQcsHGQ6B5giSlhubJfMBCwyCCUFH25hDhTU6G0lyypkVXMwLEHLrsh/984iOkVUnx5KM2IlOwaJirkR/ZL2PgBjSM41JGshxQU1rpgkq6p+DQ4n4KFRkKAruBYatdD15lPSDSbJQoivBDKJ9hYWae7A++sjNgH9Fg1k7+okb3ezt5uVuYKa5J47EkwfaZl7C3D6i4wPBN8bGs/YIQze5FghXkfBnLDCIjkR6BnEKt4bmqVARwTSQnpn2nkLIClnG2x1TZ289/f3rdMNuMWhWdHBfAYiK8+rqtI6q+tyMhqDH42Au3hSszxHDaMdj0fjUd0Zi6W/1KKBfEO2Wpzu+n53fx+KXZb53s61ra0t04FpTMWv8tx99/4Hrl66qkfl6RMn7n7Pu04fO3liPIK8iYK7kFEDURIxvlizYKxiC4PMYMdE3Dt0o2dZBdpEDelZwSAcHKbo3JFHISTOoDCHqmEd5VTSeQqlMAaKexpsJ9VzmerEeRMBvqg1y1PhUWRiiJRAHB9BbwYNmitRepd7CrZiGcx6O5dnSAhYxTGn5reHDoRTJ4llJc8KYV+pGS1J/vRW9BbQLoqYAEnxHim5+ytXNbi/6Lq+60FjpW7YoZe9XDtWral5UK6bvX1wYZGfysPoiK8CYBD8wGC+8v8FN0hewdsZPvK4xaDjSC3KN7ohjwshBrDODxIrRD+oGS/k5IPNjZ3H8lFVg3Ont23XX7m2vei6g2bRtm0OcisbjyYZuqzJPssUuKYooaSuq2vbO73px+Mx0LA/GEEZgRZHHVSKbNtmdjDruwatBSxdhxyvrOyNp0+dnB6vR0XXW9X2qlCq0AZB7bHBF5YISE04dtkKZNgK2w1ltyqKsh7V2DVHVq1b2vF4woWy6oErlD8EHRjRdSw9M5KAGdZ1yDNWnDdh2HmlOXGQs8LvOVPAsgo1WEAH2cAmLZrbg0icV6K8ckoyVxMY4JpctHETLTpIyQQedV11Q0DNvaIhcKpcjXTVTZgVISI4ALtTM2fVanrXgPzQQnXUFpsloPeEFbHkOOFvkE4DAUeqsUW5ZrE7lmDzOHVuVOst/iUORnKYvukdA9QCJnRB5757lJguzBssWcWCmVALDpt89co4iEKiWQ1avbKm0w5AFFmpTxw/NlssFpcWyDoA69SZvlQQsAUNQumiLNYn0+1dakTu6tFItS2ql1iyj8UvjCQv8rVismjaDnLua2hYY2rsqgVHK8vKSV2srU031zdKrC0CvrIRFsfhxp1ewPlcsRT2H1vpDHZMHn4oDJpDEWlQngm8MSStdM3lWfW/0fYiei4c9ZSLB4hwws6HD4l5AEsbnVoDS+MLZgGfat9V2weC+Ih4zTY0Jh+CF4UAp6CrBjRgpjS0Elrx0snj0+Xi/CTBf+T3BtI7hnIGt/YfJ7sYH+YPs78XA0DEnYaxP6q9wUch+ZCTzjX2GaEcdEqzZEUkptfC8QUBQ0mvIhPRl7KIz4u7J6YjdzWyeA/kCVPz7i/uzuUr5DErd1gECStc4oLiMnSEefTchA9ByEslcQ8JSadOXdnamTVzSG3uOwX9FwiXptCjBh6h2Xx25MhmPqrU7l7fdxhtZsQmKQoKihkUUHFOuc7ZsoCuoqQVQzvv0fjkxvp4DFWxwduOrgBWcJD/UKNXfx6TpUrPeQwDhMxdL7tgj8gTN2Tdy7uwSoaEbNvY8UDYovRJiNCIkhbJEL0rKn5lqXdtYstLSDEZZT74KbiUPJqCELimT8gBSmcWQ9a8WIzSibdLo1Qu3j9ZIOknTxdtqAIfEjWKFw+d0Uk+MD9BeOJ5l5dqq4jFlZ6FVEVKsKVc7x7ximgTRecWkgIVweJyx0S9HrqMbwWewyr00uwG1Evg2UGQkEYp1CfiErQpRJNYyBYbFEE4CxzTfl3Rq5bGQUCs4+kalfXNp05yxQgsPIkgBMhq2tnbu3T1ilWurqrZYo4lnHNgDQgagTtw/SBtwLNugFh1lpd1Vuseiv0AUW1sTKejSalyiKsaivuhOkcGv8fOBO0ibPYKK0sqcf6sB4bLnmfwh6elM+SCy/eHdlV0lYEJEQ3iJT0yOTCJbrfK+l1q7CfM2tQojjKT6SygtzGZi4CpQyLyoWDP+6TtLAbLJgYoioiw45E5vjhg9CT3SE5ookt4WS0JSAjYJQGcKEuD5ZC7QdpkLEq1xDGYGcmjIdJToirAzjzqs+BDbaSmGayM5J8IP6FOBLE7GTvm5Q3uKOGnE0xEbrr/qjd74kdxI4RKE67E44ZxDgCQ9H3fdK1XE8Sahk3x4ZIgViAho6gIQ2nBpQxnWBf6xtMnt/b2dvfnAMBSpm+hhBWZCcb0LWUK6Qw8RgA+VaaFIhtlXlbVaAIJ9HkJSfRgZ6BRTUoRVPBC93uoJxl2K6ZVLnHYZCcDgp4uBx0dqBea/QbnZQjapRScUN2yPh1EsA/Zshjwo0ncQ6wrhaVcGjGHbH1jl4B6COY8O1OC7hvFdjilXlPmlNoBr4haq1xK6VWL8TehZku/s/O0usKWX5K/UctOaDqpxDy4T6S7NINpqJfwA2Iua7Lm3uMQRiauiU+h+ZDrFBBMiPxns4ymLrMdoQki3DdI4GCJBpYgKgTKB4nTEuk9GD+xiuYhSok4lFFJzgDX0vd927UYbUqMfkH5/I9oR49eMl/y2TvNwGRyRm2M10bVCJKrUCRBeAsPU9O1Td9BMBuwJ73K8lJnYAoXWT3GXoJFoYyBYDSBekCz51gtV3FenhNuqZBPK6RZOEWRR0Pn0aKsKmj+IHRnoXZJz0l6gJI3+LFUdIRkMBtiPK5YUcmnXoaocERBDmDS0YWbkD7PkC5DA418N4Bp50rRfukoCMlQwyU5FisFDrTYSE5eKGJPKgveAeShKSDUJb+u0hPFJoRZSGe210AP16NTXkoTFPvIaFI8gL5SuOiEItYvQls8FwgEw2PHaCJ49aiUdyjUDnlwgI0jGcV9z8BLE5If/IPpjCYUHGfqh7FayAztnvQvsYXByUJ37EFGtoCsoOnJ+ERSmsUjzRIqYlmDCY98JKBMn9ajvPBdPMnlBJ/WVQU6NzRlAfO/LDLoYIlgDOcsBiQzdA6waofEH1hrYKnCfZhQdLLPotmBSDRG04ZK5JVVwU2+paAQk472LS/eiuhq7PWHbToxFM5cLSgxLDbJSY8pc+HwrQrY8meBN9GuBNUokhYeuBKmUdD5o3t7V/KSaBRJ6z4VPsUuCaqgd8gtEOsRyZ/8WtZuBSWv5nbizcPpVoqRwbfCWKV1QtqPh0Ynezp4RXB64nQPbBEKbEC9OszeRVgelFkn5owpA5ivQ/2Bh6bNcH7eBR1sMKEtL40rJO+LAy5/SxeCbml6A1BNqPMo5Ll82kBdlaqqxOMEhSV0LAnlOXyiPC0y6Owl2Mrk/CdlEVqe+P6gDE/gw0paAqQJsTeOBzbQMqLuKOecJJQzo8eeFvBoKAiCXvGUB4haF3G69Heii3oNmMK/XLuZbBFAvHo4VCho738N6M5DNM+gdodH81p7F2hAidGPom2rEhA1hAAVRuUhvMHbaEOtI1jOUcMOGiGX4B06znVyolbNxivPoQnrKuWa/xbJIkIy+jEKXXoJwcXfZ2kRitnQFeLp4p7x2UHR4GwnTCn1DIKy2hDRmudZqDEKxGyK4O+IyplsaEqz4Vkt6RCHbM0QsibiEvF9n9hhnG06SMbtjaHUdT+zRPiHvPWBPp0+1jOAACD16TwShYY5wVQn2d/Dl97FAJO/cwy6+fPDUZaQW7t0mAZMOsGf+wgjIxjA9KVSQtJXKG4VPYvDpQ4z9ZE/tnqRfsELCCgObFEne6J58k20usHxDzeVfwmvZ6IGxK0C7dHYRWVNVdWEziGIDzUv51Jk/suhFxLjrtI8v8ivmQEzm8A2ctz4PlkOt1KCihuGsqxpfdVwIgKPomLZ5KQXK+JPyQpm550Vwlv/bN1X+a4hJpCMNwRhBWgKewmxPoI99KDtIGomYLMECRzyoNLZL8Fx4/vDYQZ/grgBnZ7Q3VGma/nygV3XLRaLru+JdUd5FZpaC4bIsiHFRPAHvk8ei4og7eQI0U4hG4Gq1ARuw8a7sF6lR46vZqYYwL0iaOpHmG7LYCn55akXyFcqJQNkbnJSg6XG0Rcxed8sBoB4WIYO8C/Ydjx+VRyd9CX1F7mLHn0ipEowbMSFLJOo8WPb9VBZoqoy3AlQZ3jbB+myZEOk0OeEQgKfSDVq/EY02XW8PHXc++8cbg6L1fYcbmBM8DGOu5HGhMSdgp8vHu/lVU4h0Zz8vnpszEUoVCFbNJIFCEmzaH/hcIrQs0CU7JJLKH9b+jhZxnTQATGyYl3pbwBqmhZyHTrocbyMXIge5/S5yY2DgAn3HhxFl8pDPvbBg0lttwYFydMIaiTyeCOxRARUSYUn7b1kVwzyQQ5ayHz9AbtL9LnkB49MtpPnGpxEP+CRgxQSrCMZZxEY+SpAn+c8dH2i9ket2c9AdgxhSRUXmqC8tu37DKfmW9jwAkrype/48FGy8GHiYlsxbkhPJfmDGAGpe2v5Xc+NJJs5TH1OjqV0jS8zk1VKp39URKZxXUaPDlva2PA80i7kaVr98giUBL4XBggZgfA77/dhweyUSMX0Bxbr8OJUm/QmTUTbOWvmi0Xbdb6e94p4erJiooK4WBLxOYM45Bvp7xwb8ZKMar4y3lNO1yM9PdeVYN4hdXmWGSF9NET8hyoKBXsIU3CwmCXV8k6NisFkhDovpz1UP0h57vsOUz5BefYExV/2fUDTrVrBeuUBHqysKKgXVyk55VSJGRGwmBvo1QVvASbHxNdyZLoehFWSh8SjzgMR8KkVr9WYDF/vyjOdwxXj1WL2eq+4H9y6nCVCDE8NmLB818vu8H6kT8H/U3ubkt7RLQ1hpGjMyaKWdO2qiaVBs8RDk0TohD8innP+mzolNSB+ocYypt4FDTFdQCK56NPwrI2lS+KtHp6/RB9NO0jEwqNRWUhN7BVGgvBD0zEIqZtCYMVxCGShl71IvEC95BmUkxzWqQzUG1c1tI1MGY5TUMysbZu24W4SCdlLnAMtIYVmxbSDvBi06xKc1x8psRBSoAqm3bYttTinNEZGJ8qwhu9wLMDP/n1JnOE0XJeodPwZ98RrtH6Yqe4nfkphk2gJUqYNl3NFHDGuaXQZkKrHxCpU8+TJNNrIyMR4pIzn27Nw8agqaPgxGBrvVCzPs6ThRU6N//DyrNSzw+1i4IAeB7pW10LqL+aUpV8Vpy3K7XAQA5Wn0n8lR04ERsIL3QCUEBQKv1SDym/yLLHNJyggCacOvubzRRG0D2X08wyaG4mTIQS8fHkFNjjjwjgGuhz0dWqg4gHUPxpEoFnZ8STqj7jo3+BnwGJuNefibVzeX/Fm+NU5SMDuu57tfJTGSTglIBM8aQfWORStYZA+isHNG5iR6fjo5deqI7mkUC3zBmnUCBkqD/9yhr/gOyIvTLqVkvGscoMfTj7JUfGwUuj+B39AWzNfgiq40pajI4fd+vDX0AINswO1ErBQDVTcipFGPwV2ZK3UBEQIJaLyD/H5Cbm1PBMGV/uTI5xig240USFmSR/MPnbvDKecRL19N/C89GnrgNeUbqsVSytWLSjJQ5SDXFmowA7FE7C9c7LRSwsjmJFQi9LnXhcGEbWqoUKcgj0cjgpDZRBi93ZirCUhZDEr1J4zJ+wjig5/SgZJD8uv4Fvz+ov0nNFODmX6IedbrHOi/l7vi6GQoqdU9k8fcl/PYPnaoLqGRwntwb+DgXdI7qBFK2TXVjnR/5JXIqdW4DzIPuB0c1D2YsB4MEyxRgzTGco8cW0S3lru4hbXJ9m5oDGSQzBi4MTpZoRLOJxBJ4vNO8TDhaVAMgblD4Z6Ma2JnaiDNRkoCmIiwZu+bKOExbZQEQGsX8KfMhseGharHpnw/njPVV+LynVykuN0h+4acZHfsADGClm/JEuFI2DYAC0dctgMrHBKbht9yFlNlkEwJrGNy6YafyRaZKwcjnA9MfPhtz1hchGlQ4Wqp9yEr8SHDzcv4bCe+TEbAyRWlBxDcn8uhLxquYVemYCPUHU2YK01UPKKq0nLZy17nb3ixK4LbrPFC8cPimUO/aEX6qa8SwidSv4nnhfHIjY+gIYi8ghr66Rf9ScTXwgr1N7VTNUmfIW6Za1K+JrCA/ieLHaGCy2vMGCM+Nx93t+glwT9JpShWr2L4cmiB/gABhjVWGK3wWkhbiu9HVKJiGtNQVOBoFyyJeV+ycUNK8KN+Hz6aniJDRWalhChS9xqcO3wNRBoPP6V9EEHhEWvx8x4s14MT/42VICWrhtsvRwOUy9CewH+5rEfwRw5BBQakFheC0smt2pl5MsaKKYFflKOcwTLLNTkDIQ3nDG1R4AzilAj0YVxuFHpSfVBBB5sWuAwbqk/k7FuPt+BIL4e4RHDR5SEbDk5gjV+DzgHNRkxVpiXT3dYZqRLos4re5FUeJCcjRXnFZJsoY4JJm9BVqMfPRWBC40YxCodskliZKETgliXwYkTgiC+oqsoGBbRyI/6r/+eR3JcT0YMNRM/CtpIWim3TBmRpcrz+lwesfrTqGYE38MKmRO2iqUWRfdWbPkSOa44B8u8JmEVgvKhrx8UnfTEGzuksISWQ01uesiyJLI3OncohQQK1gHGgNHOq788nI8nGQstvUEhdZktCsT7Ys8u/5934voyykuPCGMSD4gb4i+H2WNlIwaQ0y8+YBvTbEMpAq71gpk/1PEMq6sg5E2C2YIWl6B8Uga9vCzDAxiVf3o0pEFikrQfu/f5DsXO4XxW6MFeOko8W6LFRL17iSkMw5SoM8f1ip1EqeRQzLlbtn08kHV5/+QDSNFwEb3gwf3p4g1yoOSGrH4NPkomuuJrUWsNhRIooxNKMgUMhjxw+O913G8irhyuEEQfDjpnZiESK7j501/TNV7NrtIzIQYaqJcOBJSt7QEmFAsKRNNywMuHjwPVGXMAIXBNpmQOOD2EgnI/4Li7q5QylgMiCLg0ZNZugfywLCQm2DD0JyDzQ2uYePsQ+COWL9K+5Prw1grn9uC1tLR+3KH4eVCvJewZCtpDQy4hMr0ONQi9CmpMhXIavkvtRtF90nfiDmuV5imIrY56NGFkIEeW8pLCwQsIlnTe0qkQ7y54m8cJkivB66lhi9MA8Up+mK5wyoJSe2DFaV/yHYfLeDkiVhOxo1BPBroL8bYss+1Vw/SaDNtkvrdg8k2PxoUsYUi6iX5f3qXIO1ZN9XovCQQlZYKeSvh67vGZOob4dAxcJKn6BPl8kOOCBjAHHwDtA79ZUM4DxF52mxRquXcF8XL7MuhCgaZ7gseYmhJhhdCoKqb0Ie6K94Ci0em4+YKBPR2nFCIrg21N5YXPDE6Zucg6AjUkKkviBMoOSH5TOAdwaTsHeoEIugfgi7CjJQOQ+qsoA+IzLj3gNAQmORruazeGiaaCakl19urEspvaDc7//6+0L11yJseRVEjK7Jl9//fcX2s7lTpiDZfDAYLKLNvork9KRQQPEIcTBMH0h+xxYgGh9Sx6+kjBJBUSWArNJp+X+8jdQ5Pxp4E+uOGQSebR2sCg3yKK0GJiY8SJVWa88KAWouypMc8ycDKW5Yt6ynaFh8QY6J30BhHi4rEa7JF2VLCibGNOdrADDN6H8zNCbiOC3awuqUi0myxSOH80c8nXly72pJ+PwlHsmKKIUMkwezawjR452IU9My0CQMtAxowJx9TcQj9jw6AtUiCiCwncEcXjtZhNiO+Uzi6jx0D7skBk7gkqnjvlRYRr0E+wUd+k5jWLPMQYbqBnv4WgnFwNiPLLMjVRddX0Z/mKAc4lBaDO6dobJqyo0RDWeQSiuxYIaaZTcZKNSCRdYmKz8kNTqiaJAUIag1AaZoTFHX/V04nynaadq+gyg/7JJKvUna/Hj4ToYgP5AkMw44oZ1lSF3RfHUcSgqMRJ1kzZMPm+ugzjOBHbssB7q1FUzDNoWUm3B0WI8kTKJZVUyh5NgxadVofJx4MKLsGR5QVy8fIaq018hTXcn8NtRAiO+8zVPSBpC2M7QVSeFhD+SK7Xn2kkSJzN/Gip6xJFLnGOui8pIUSchr2Of1thrTotGmMH5MVUQi5uYejFiY/7X1XywlnI3tmqr6KV/MmoCfTwR23brsrv27M72u+sEuL9sDt1ORxFpcXOzWSCZN7qxGKlGv6rJtIttIJhRf0tzLyFAQu3yfY2WaUcUnyFwgJ3dUzDhdM0KwqwfAWWEFwPdxJXkk2KfSEQ456enIEvJI2WbEmXMCkN4i2brFtbinGjgauZV36ddlTfLJGPBKTlhtaUmq/XU5GzH+kaw1fWD3KRUg/+ENIckuMe+0MM4AbTeGSWCkMaRRsTS3Fm/0TarxJJEfzhvampc2L1vazF8Ry80KjMLDs2DqOXtHJmpkRyxe1cY01aMaygC+JG9DFceuPYrVotyJ9Iz9CSJoOwlC4xRy108/ciziVPLC1cS0gNGksmwJaLgs0M95U75E3dTSuoM4gr1FS+OvXVE9GqykP45kwjk9Yh5DT1irpyHu/b7a2JC3zbqrc9iTD3LMGEb82VRDpYre223z6gn3ESdDJm9ofztpYTVTZlshVCQ1lErFqn51PS/flmI+4RpocZoei38ogXikKKibF58COlEnmqPEk4tckziERaJjjWTOw1majCZ9HZLubegFjGh45h5i+ZBXIBqg1dwH1+0TP3naQl+AibFT7ojWLZGBmT9bC2jItcn5RyQFtAbBc2ytUJ3VqbQTt9StcDgHlWmKwl7JkkgkqTXAI56ucuPrZQFG/oDF5jNp5yyqFO1bysxQ1Mi/REU3+8cjUj+uBIKVvO6rWt3pFuYkAHdOXcL6RXrpvkr0/tUBKkAprRRKj0PfvtA4HfakN6bAmGg3KJJ4TFayYdsk1fo50XxG1e2WvuEMB5fy5ybiThAtT0/eL5yzgile8QsphEEDYsmXDUpvhMzxSBp8KU0/F0XZoc/rF6BDdWo8he005XoeeZeb2cZJ8neGkA17HpyCkdKJP+Z88BuVaWh2sgCwdspW3MY4+qnkqfMiGe3GGhlJdlJIAbMj8LBShCqHW0k8CScWlSFzqfNrUMW/U2ck35VKpsTLUv4Mj5ajjKjUmH5cBK3SCKmt4vORs1ISFWYPAPwE4lSadIuzs417koB/qElnlxDS9ZFiQjqB0tAggZUSSxvQyuOD3rQPGEnbIWRfn7GfCRWQ7dXPTRT2OP2M4gMqL5MLSyWPjzY2f8GJFteLA4uSz/ljhKaOzKFl0fG4lipE8ejsvnwMdlEtgeA+UbgxaDTdkU+LFAzYjmaCYwvtMMGJwaiQQIr3nWtZDNnBdBxi1W9C0Joz3POJ7qNNjIK7W9IVY9GOXn1EMAM90IdAB3qVSxrcE+EKi0vG56bTXmEMKqKoPXbXOfz6IBWQawuyDysLhJz/gxvRuZnCH9wVwuTxbDX5UsFpZR4yVdek0bVp9xq11H204LE1Si7iM08XrczMeXSzhIRak+OjpgAIEXuTJBs+sEa543gpCjnrTugChRRe5AatPghli6SC5jaS/I+RkXtlY8MB/YtQ3hX37ymker41oNy5cb8SnfewrB+ZTjxIFrhWarJFTmcshmBq4a+H2YgP7Ss1gxPSXNhuRPzzU5cEEOSjFN3VQxvTIBVGaThYcS6QEG3b02kq2FxSq79H5UUSg48XHVzKRTBuTyAc2HzSfXE1eIg44QbgX5Dh9siGfQ5zzkuZ/Lz01Om7hTdoNUZEwmdh6GH8Ge8tz1iM+jtSrg8KhV35fTcGQ7v0XauU1IBvBS7NC2cQ6ykKytenQFmqzElpwHhstfxrh4vP5wERPFD2VGHWowJ6sEkTdGoDWug4Ri4TEyXqvnv9QN/SVwcmZl99hwL5ottN/1OE87/aCsT+0Ad9zL4GZmLo5AoewE1tlY0S63RiGETYylAtmgK2nbb3cHkW2OM1/ThKjZ1gr1iPly/sD6iiNnuEQcDGuLRnbaUyazoZrsnCd4Np7vt5xAI1vqw7cGxGtHr8UcOJc0jTqS1dkPOUis7GFzNBI0EwqDallNwIK5RlWWE+IGFOACpqfhQysXzi+H7dn16f1IUV8f4WAS+kLLmSTwrWiuJHNhRarGLHRxS30Q49G9kutSpII1UbYMpe9G+vUaZ4Gt2kNPV/j55x9ks+KlRNKYfivOXu+IAgPGuylttFj9rd0F7GyOEjTQvsjasW00z4ME9+p4+dnheIKfbPXMlUWXBvAk1UbfE9VGopynCDCW2cJphFZpwJj8g4b6Qrib6XjP1MHbZzXcmQDMtUERUxQxmeZajkzO3vdIIa+ybgpCDgHRIFSVMOgtKCrM5yI4qdIJ7odCNGJ7shZ5DsGCXRIr0KgAhIVqa/Gu47Ui42nlD+WEGiLWBLt8qCZvFd9WWIUmJI44NBRRQylDmOhggYjrjBbHAhXTlBk2YJRsV5CjFeicEebewU5NaqHeRqhOecU3Mv1KEWef1GTi5hH07BG5Dhw2MjyVb6Yfjmyqh+xwBUJZzAAFrQGJZ2wi+nrLzFdjvbPTC+5R8qga8ugci6uAkMZ0yOuQwDiNRo+VKHMop0CoNki6k3VvQpA1QJY8Fsv4R+OfIy7cO0rZNxoBGpgrwoahSBvd8yNdxjWfRb/D5R9Omx6aU1+mOVKmo20b2qi12ZTwbzBrLEZqrpPjSCI6pq6+ptKzlPp3O8JwKLNA36qzWf15B1V8ZeXoxfqm+65bUf1nOvGaYugjI20DmQxcWks5OofNnFyWmAoH0hK8tp78Ni3KDjnr5nJ/5Qd6CvGyoB9iYwAzMgJE9mjo1kuL8c46V7Qid94KJeRxzTwfmwbUgeznvVFmEZk+SARehG2EJ9lLA/5DUHnDB9VhiWXwt4s0mU+lrWW9thSkWCqpJXqrjF8+gMG6eo9aLttwi1KR43YMzaAhlld/uxj1EyDjfN48ahMq7MNZWtYTLaXOjtIsJ1aoGVYpGaRAaiZCBduCEuyh2AtN1OOU9bYE4mLijIsyHk5QyZG3vccFv7CaW6Ya+S2Iovv89DiE8F35q5xX508XJ5Wa+vIJlFMp/uHJuUymJNpKdyrkGmJxPMNemsqwM+7vcljM67zRlgN7k6FiBvrEZHRCiP46caBtCLXbRLX47s5FnY5TMRy9YebXj4Mso5KYc2uZKrHkqatENpFoLrLapAVtsglIJgXbinC/wenKp3FNPsj9De35BZdN5nFX/3ydl1OWkSJZdurWcD+NxYRJqauulitHQvzIt6qPlxywtIPTdAZGbsbVGF13RJD5bVa72alUG7C9x3G/ymYjTbNWPVcZy8mhvFtQzQpik+OUHevJqaRGgcLjw+eSts3IhBkrPNQaGnnP/i2duutsWTxe8Mznu13J9ZgRHxj4KVa1mOZ4SkphcfU4AjpuYe9CvLiuA2clbWWJ9DR5aZkMpg7OEMVUarVtKD5RA/326VpkuFnFEtHAJKnsMJlaZvqCZUmgaX22HBWePi3NqHS32DhIFU0smxd/0hdx35Iz2fHbecufbUulXG6xpvkA1TmA0hBRxhL08zQ2ChX1IFJxXsUmXxL54iGblm0bYqoOEJemWudUFqB/6iBfdTWXr+63jIXYjgh6twLuXjV9z/f3tyZNCKeXIVrPB8LLy+zMyBo4jrtV+reZBMXx6p6t03dvB7ERGzYIWFY0x8EHfbkxcIBfmDCL1Lbiix74/7waCoW3m7Uepi6ok9TRGkdbGv0pdabeVZ65RwhDfWLbvSpyOFyZcVkJqIi5FhW8s1e7KjetILNM0pTwIJWy+3fuulXfzG8JxQ+v1KJIiBQrxPu0xtDvoew8n8i9VXHFUck4f2wtOhfTyz0p3gJCL+f5czk0AE78iJmNIOQGYepcpDWtpPvc+gancYiGacQ+QlTV1a/7xNQMe16TUik4pM19p0XbUmGeHX0Uxvkkk+Mqwa9cXugQwBs6f5mCrOqD4zyXZ9oi99Aa8mgv2ty5/k7wJcovVU2dJKxdeqgjVHmkusDb8eD60+/ouTXczO/Sk0GMaD4nZkqzFGPiFuDFi+O1GadYA8pNf5fDuqhIY2A7LY2sGrv8wsC6taXLT+Ueu1OvkN14TKTl+/t6PR4/srMyF29TnbrFbzyfXVqcshUDpG+mpMHAypDhWUXROiuXAFXPSKRJhrAEdbSdeQWwxx7bIg05f+LWXbxde0ZdHy5P/m2iyd1ff9xXRlrA+9TUxwZ/LmUP8w7f6XS5nprUropBTGCmRoeTvKxsxZ+2pVZRnGz001NFSBKQLjchehfYlTwd7c3LMH2By5WS63b50KDJL4Qr8NQ1jh39CwZYZHiZECb9CWzHyWnYD+HCitxavh5bW0HpJdKKt3EKgTquvkdaRsEOSbV4Rtn6L8eYq4PRjivkJaalhzMpWqSLpbby5F8qmZH+z/sc6cRiQ4U8CTkrDlIW3vhooHExFSy6R0yltmEcdIegbR6mUFdp2nAWwsA55ctryyRvqHwpoD2QDF4s6WJIl86740S80OlL6jI7hUospy3FHEgD52+ysPx4+zaGfIgtJv6eJab4UYuATH7i8ijzHimw+/3r+z/fssSSy8phfbHDaC1hJdrIS2yAAyxlEFpxsUTmURhcnu/ywir6lEY0zsIq0AQIQ+RED9eSRTIsBVkHX1/35z8/jx+JcrUD7hohCeEvPV4tACKulaySUsGSgGF+i11PuSMql9yZHOtQ9sHbbGA1syDXVXeFf7alv4rYMj/awPeefKO817U55+oqDFTraQYhQdxl9i/iKFZvC45WKQFzhSsbFdKwlOVNUfcq25b+IeIoBz02NL4Sk1b8MkbeODL2Pcc8BOLL9SSRFDzfbl/fEjXJuoNxfu6vaaTM70dXXpMYA5tzjBhpVKoOM1+DJYjrMulIK0W4PpFwaSjmxXkEYMbj+EOSkVfd1BreTF7PMEgF1hTnmcMz5otcBdJdE3KWvLeAsuso4UmwwxUN1tzgl50vBGNG70wBlrW0QrklCV+slq7Ydl9kGO+kb0ZOdm0OLm5q0WgwmeLa3HGdbxEo0dbmOolMhj5E8WZTRsGkWCWBDbBsKRJNkIPZTOg8Not3FXOjuNl7Mch9KYC+aKpX2evbkBRODIjCmvooBfUqUkm2Tg7+huZXLLC5aY66shkVFWQeGIFWvbrPKUO2APJUhv10bJ8+EBHiYdI5YD/oAlrQ1T3G9nOIJoVnpohCbP0ThGqT1jXah+0uL3CmszEpdsCpPgLhpv7GizLToUnTU00JlFlIhepL/fF9Bd3pkllLIJomdk/RNgtsWcjSaeYHjXyAJAP6dV3k8bGeJ9ltfuHfuW8LUabfveGYf1MWtKVQ+Jll04Jt1vfGxBO61WYN7SzkHFtRn2ys5yaxx664WdJ8OJqas21Er+9X+xhSVQJNIsjOiy5AsC27m9BJzN3Xl/nNnu/X5SUJBos/a/WEln7nPdlobHBL2mTHpzby+9tY/y00W60JLZmz8sG9cPlhY8angK2z/smAdEHKU0d/udYlqzQK+zcqgq7Iu/pvwi1P1sYGcvWhGV0MWCIrK3TJ5rDgyeVEcFNGVFygq3yzVVxCu2kxZ9RjJZjFYhPjAHOyJQ4g6bVDPc8yLeezzBBFAXfXQnEi8NJdvzsc3tq62SkX8Rm5ShThvRymNlnxUFjWxQgKgjQUpeMlh65Kt/jX/et6XMU38fiRlBmSpWwXfbbqJZLM6yFLVvqL4ghJM8jWj3aiIz7bhoML7H4xVz1YHad0l+Hgy3Qr6PFZO9C4cwDkE2dvrdVoUlv0QZQa36yrJBOLhv9NZbRkp1P36JucTuiCYO+5JCNFH/1TetHLleTPkt5GU0NWSXLw4T11BXFZz0WZr6bhCEsi5SmTC1LsXhbxXfniZIcOGQS6SOACngfJwjMhOoOS9H8iFbsmvi1dix1ZhVRdAdbBxPpApUw7zMtRMcbulOOajptEaj0RzFPICdgeGTtTY8LPa7lyZKex5vewjkXagDJlC1uJCI5QNk2MV0Rn+XkCp/iMgeATsdNlZuoPEhKwMFYpJvSLP6ZSutbuoc98F1SdQDNRoIR0fYAXNSrIROBupmmyIjvFVBieWns85aiASGBXfGI7V73ra7I/daa8zEfJYn6KY5dEkwaer7fU1ijSEb/TbzGuHWwVv+CAseOxHPhlbVuOrrdN+VG0o4cVs2eH0c6MsRi7mt90O7lFlcUYl8ckHE2OWnusQ06wKKYhOfVlnSCPa4jkqSkxTENQUop4XeVPtjK4+LZkgTWq8iyxyppDyB30fntYz+jdb4z16QpETU346MeeSp7frf7A5b1W8arz9xCuGTEEoLvUhIrM1FkDYC4uhDJctt7oiSNyY020JUMtdMOK7U3/KA1+o3SjGD/HGYEhYXttcUGiJr/UUHhYX2n7knhy7uLIMiXr1kIOJLbIWqU+sb22OSHpUQP7W1WVO/DNi/0EVxJC5IyTYcVxuWt4RZIgRbQeZ18VabAegrRzoQiFEEvAh7ke9MFcFpINL5fGD+DAKQ5Kg2s1HPVqObEsR039/WojG6OWv31SAC3K5K919gZ4PaHuWssqcxU1hUYrga72t+OcrGBVI+XT1y0pDl82wzxFJ4/dQs6I0rJMTbbpoAc3UNXdpqw/yKWBQJGiveQBCP6j1myvBUf7H13w9JABSy3GfAzuMtUWByhxWZCsYOA/McYed7R3M1MhxcBbxW4Vm4cpDw0dlIvzGU0D0rkMlRw6JPQr0HO6nlPJxFwg1Nnlotsxngiqr2CT7UITuMtvNnclDuNpHrFfyhrnScMLk7EDjklAF2o5UUgxU/6FUYb5AlCoBdAY0smtpJhGcv3cnsBLoUbsGIl0a3Z/To50wCkcgLl0ck+7CrpYP1NGZM8UG0tHbeUQTYp5VXLj8v9fW1amTQUl5QRb5ddjJJUSGNlWLFVYrCjIR4bsT9y64Of8MQ/3xCEOLiE8T5gUSKQLoTSYCSU4VsOuOIjQeYoOFV2wou+jdC99py44Nk8HQvuLWP0beSaxycZMe+OJkFPBQZS1+rBzY9Wl3IkB8HAwTH/LQnckDs7JABpXcWnDoidHKoABj8ahOPkumxQvk+BWL7v1ZbwIZSHsMC2737oex/f3t53NK79612OyxhZg44H9c3tKo4aMnjgsyDJjxwYoTqDQwXlOCwon+YlZVGvZ7tDKaEvbVHYM16o8WKZKYN5aA1cdTECtJVlwvlr159I0PnGR/XzEuLxFZlkRPpZmfooI2bymsdzbfscLTWm3ctoUKCN1loI/bT/vt/YceOp2wsy4Fqy9eTq/ZLb/4FOuiMXTjB1ZgPDelGq6Hf4EJeuzLpdB2u/v7+8vjZokvdOOA+dimN/IYq8U+PxjriqRLfbTFCxJMlcPa9G82juuS2lf/eT9ytBQjgivg5o7xNoxaCv91yM7NSF8xIT6MwgyoGHmye1gXIh9fN4riUjUp41cPpWUBQLSqXW7a701EHcJzQ9ybQpOKzuRFkNUlJb/ucaL1HIXo8bFblWLOLFGXcC/lbGnPvBd8x1R38u8p14VCv9+wb2yFufzK2vT/Uu2xUpwX9wMNhv2QPQFjWTI9uT4Y288uCnZyo/HRWp7tzTdorSyphlV8V3h3yUuqZClJoJlWx6nHBc7Wr/H030APc2lzgc8xy/kdWkCR2D1YaNOuHBKAjDJx7Q0GTswmRRByssmWHpHlFJ9geZJt195suCnrTwuRumvwH5b4PjDXdMVQfVUweRza8iWpYKn1LG2McW8h4i8L7kdNt3zH1iTVdZCZUNHMmbg+LpDetdjBCejXk8p/swJ5cijpS9DvxB2AAuS+L/1urSSbhQbRVt3a4Xjeb9REe0nTNlTRF/iee2BNVCxKBkkj9VoUJQODgmLHt+6+xlQJToCo3peLPc1z9tK/7NDKIu2c5+dfOPlypUeZtzhXh0KmcJ5BANls8yPA9Aea5aV896RPtjgilzzzBg8HU/fTpjcsarj+nNPxmsgQTt7u13P911CsV7pbS6IYk3kOya0my52EQSBfVap2xhlt+BdHFdLOPMeFLeVz367Yrah3eObMAxhNxFSlK8MKmWNRwmV2UZzwgdVY23T/DijYBdFJWmpjCMLRvrQPhKESfs4Al6GGDcTlOjbWiLHPsnUt/sbS/tKGR5Yd1hDsb3skxBjqrWR95aMmNyF+1KjNx9Cj383Ja1uVwmrIC9aTMnsSe18B0lo7NgpSXEGGfE3c69HRsmGwuvVTo4n2nMW6HixqoEG+2OAIyRC27W8EdKbp5NNOiDVWzmvEcT8NygsLDeKLBsaAEdCfutyHDRJS/2d9mYhyRD2NxriTFIW8su6vTyLlbm6PlefXmyI1xB7MExrE0vG3jFwfG55jtkqbyRFO97n+Xw81PxC3adk4ANR60FsI/JBwM3PJhyj6QY0m3eIoON2g79B8g9i3rdAbCI0l9i6cp+7Sq2X/cBgyibv/kKuMc98HpF2vitE3rperuchOVEZXpAqGIpo7S76vzYrXnHpFafV17fkE6/81+0G1DRKZeNsptyjikYSkkZOoadosGqhPWORxSQoL2dUP7gzBzZ1QLU5W0xSA+bSwPAeAG4795JRh5vN0bEY5Q9bcCwmpa5TwBWK/ul56+KUalFe8CPrqq9m00yNUIR366vFvu7LctgC0yjb0+lYaukKbBWvUUB/xe5LfONG0DGnKFh6kPoqIepk0MPNYo1uMVOZQ7LO0NqKN5kl0+tqAih/Tp0nrIJVJJM7Nb6SW0zF5/z9Hzu0lsV22NPnv4dZqyw5ZTf5xSzTmmfaXftDz0Aw0Y1wIlLuY6jb4pPf8ot+4wKL1dq1svY2YsGgx9jYoUFMMw/QpJntLDTYgiDvvF7v6/Wt8QXQ0bGtSOnz0gycHhS0caaCVI3FbU/HmTGclYtA1E9OZZsA/Nni7kr4d4/PrL05WJBXcNhfIZFDun6kEPoD6dZqqolI/rEdPz4e/ntGWbg+4U1wZN+5egQAp1khRRHLqLJ6/fX99R+R3nqYbWdIq2PBiEzVbq5GWSBG2LizqAsafOXGNytJU7jUUqW3G4feHhoxHoOKryoCV4CBMPUFB0XSryi1BNnEkChlJ0q1NTSb/B/H+/J6vrQZyAOqtLMEpkie60XVHhACpDt9PnFChj/Jyseg5wQYWUXBs/Nr+etOhNs4DtfxBzWwmdfreKkw3PsKU3LEL3agyoT1u2y4aW1fDfceU+B50qWhySXRg2Vn/7prGr3CeWgT1QQZIp5eEG+CzK0MF7M3jLFHWWUc75rcwkrvAtRRCLXrAw9EC1K1VAPUoEam7HDLg11g25N72Lfs8EHRT5yNRnNtVKgvKBOcl/f1ot4phKjDDOtOJtUURLqiypuxcoxOmjxtwGWiTXJCA4CcxtQf9gjCvr2izUib0/jfXBj75cdZej+646IxQsO3WGBqcOnbRxTnbg2eC0o4tLqvGu9Cty8F8uL/pEaKffeVZhVe2eWL/JK/k5PwIkkvMUdI9YdDqLyuIpHOj8WqZnpJZkby2cY72+ytPFHvch6qg0e9MDnBarpPTcHEZkTeS/eJuNi9yay9bI/1GGvLOmJ67F2j8ThaOrsMoUttVpgjxzBewHzFt8DySttGWPh7ecShg/lzondNpIuHrL77kQW3Fqr7QI6/Funx/K/zvL49U1TOMPoxjH9oniKm90t3zDn3jo2N/aTlRkMSMJ3dmhhuvn9pbliX3hWlFGYtP3N9K36tATJoQtvUbGX1gSRjQNlhsS177SyfHdU7wI1lI0K3Tdd48a23xaCxVEWcehw8+AlmDE2uXa0YnCYXSMvkhzorLV65IcxceZLT47hK1Ct3s4XROEoo3UqYlV63y8KhlB9h6kbVDqyAy3L90HuS4YbKa9Ywrn5FANxO7lyrp9QRcIm2IoWX1Cxwr7FqhMgt9xFbqjfRgmkoqR13ckTWA7lGMCc7LgQ16x6jltC7P+qgeH93mTLW1s6CU28XoILhV34VBG2y4tjj7CNmPvsIYbXTVnv2Ky65fUnAvGg5fG+aI9Fymm8kNksLVAlQ2pylI0OGBgDZvawTYJTeFKynO7Lkz5uWcL/fTjkdk2osQzIBy6jAQvE7fjnjCJIyqy5iHz1yS1K2QYCutAaLInsbPtvhUYSHrlin16LGh6t71nXiKSl1FNRYDgbesLpewyJr0EZOshXpjcz8m8Z0pEAPJdHKcjM2vFzvdz/WyH5njB0tTvwIgLoeIDnKdZ0CjV696umY6OOhz68nhSSQqiiZTWhUmxDW+GhuIdFpW0YjLlzlvgyPJHecr2N/oekkZ8HozoF87hnRSkLzfCD0F0kVLjuNzvN4HZdvP/AF/nCetaJnVVTYCMch2iegz6fMy4XEPf/JmmF0eHmLzLNhWfWcTwrnjlbN5FQsWGkpvBk5PKkV3unA4/4Ue/doRkXJTmNx4SVHUcemudKxRRo+koE0Zx5vqdJ719SwN0q320rp1iM1VnmKydkuM1MU4LEoHyqba3NUKbMSdeLFumfuK+C+83xvUNrVO/MJaizU+7hPGFNLLAHZ6iFHn3TT0eehVVj0d3BYB9pIEcEp7HXT6eV4POxBjb1xmiMShbHzzjpVWh3c7JiUb4xFiQRoxNnRubgiq94rsrW4wxlfdG1ZMHdtYH9zoW2QXBNXOK/5YMYQr6YTrGTWDS6YQ46MVusrM1+amtAxVqaZEmj+ae0tIiWP2/32rYmdKSivQcbFrLkoFvS06OPdlTq3l0h/hiqQ0txjYwlJkD7PiqoabcDHyQu05FRjYOiVT/L8WdYhcYyKHSz8QpIy5WAXZcoC9Tj/VvrIopEt90b/bJ4lqwlx9Bl6YKu7BVnH12np+SxwDauS8y6akeG6N2N/036YFh9AA9I9aNVU+tariyrXmwg14vLkwE1EUpVCBttVFHzkTpV/LJ4mtjF49sTjo+rcQzcy9LKfVDZIeFp2sETtRZExhqNkQy1ufC+966xg/WXVkwFETHqt/2pgcK+RO/UbG95CmIkU+LPZ6KUP9Wi5/iyNCnui43VCGOzC1scD0g7njl4+mggB7Vjx5UftMNrb7W3L+BzdEmtdhG5y3JMJamVHwmHrjUP7dSY0ULbRcHOThCuVyWIveTVyrqk6MMf2bd+lQfRteLqMJJYt4/xGEFDrTyocmur4oemOJb6uhgMsUKHCSSJKVgRV5ok17rfb/dbNV9P/OwNSrLE33diiQJ/6Ejvgx6T9ZWubHYysMzwkgvl4VPhmhJp1Le1be7gsHDtBSsVmYZvm9zgsTemMXpJ17ZU3WxGww71YKwwkZG20inRCdUJmdlhOM84IdryYQBiFAu0RQcoD61UXEf5gjNdr8xiUSocEbDsr6lsGuwQstbKzBBYNl938S9LrH7KdkIwCEuX4UBeTWPulBl2Wjp4/P4/H67lMK4OnBlKkiq8SEhhEP5DXamvOly5vr8VCN+ZbGSCcyMW5hekrjjMKfCgTCAqapOcKa8/Dlc//2oHNu+svMc4NdnjerjGhx+9TbUdeoTiDiYs1amVadEDMNziZ3eVyPl/P2/t+j808FTs0F4QFXTnzA4gd9h6YidsxSS/sABmhUm3hJgZ0+IW5gYVj5MDO3LWYLcWVjThdYHoJ7C/xAVoqDj0bSfVpuhOj3Ohp9+wB4rzf75/H4+cpecIT8ZSJ8MYZPKgFUpoWsCEH77h6bgqeJlodjDfFFns4ugWjkRv2cGdXssj8I08HjZBJmwQX2i2D4+b8w/LXziNaOv+blJGTzvV886sglDlXU80EM7AIHzNZAppmuj9KHM0GuNY9U7byaPnAhpWJI48BeD39EPLovgdl6JJx5fucOjZVddK3NI3sGGZbBn/duJoPStOL/SkcTeN3lzntbwEfxSpvLFC1vkw7RDUJ0dwCo3Mx0ZnnZAQv5Xo8Hz8//1jSiWqv2cOvvqeMmVu1VXqhjLmuONDI/VjRwmVFtXYz7nxm8pxGM2sPr1V9bp12bsdOI9lJY7h8u2OYf9ycd1Fe6cuh1Tqt2GNq9Kou6dkFtiH8ok4vBs2NgIKaNXcCD0g56EvC5RmJi7V0Hbal4Xa52SjHJMTXyYNZTJjdSmQynZMWK5og8ByaLepInx0xk9k3HLt6o8rSMXnj+hJir2QeIgofTOximwF89VctcNZAKx/uTE11WDoqp7G/X4+fH1nbo1qHbevkXm9UiEUoKE7X/RKsY/7JGZ1kj5ebBKiGK9zYi+WOswhWs1n+8P9pOL6mUHwh6Kx0fH9Nk8z21oIF+nRvjOoZSwTOWGy359bwJ/rO/mnG4bVYGgVMuVrT8kPtr00qMPYLa/h0+n2+5VA8N/suwrHeZQlTnZGZxxnhnk2mqjRk2xoY0Vd9UWRHx/piuC2TSk2Gyfazt78Gvzn/9Qh+asPijG5c7ihKopuohKhjwbUUy56eVwHPjwetCFeypiPut8jszjlxOrQku+3IAzomEXQ0cg6uWMCz/9zQC8rlN495w4OFW3kW+3WatLlicXSr63dZNDqIR7vbWvLxuwQ77vgQw1denEyFs0DMkaBBq8WzJlPmL2MrpjJaB/mVW7qZzW29LXmdepBarnKiHDva6l2QUta2I0k/xz4NI4n6gnvo5SLtE8wNZ5NRC087M3tjzaTRMUOlJhSM8vFfOJ8dllwVQufRZgYHh+lbIhe7XmJ+H3KsEhs9V0wU68vYYqHnqPjtjHk78Z3eXYZikdgIt95ezi9KARfPuspSej5wd8qvTO00OePABUuV0Yduq8pqzS9pQWZ+QslVW3vdg/yZWVPNWNzef4gWmaS9WNUJirj3H1ArxdcnyaoMxB0t6+e2fTrzD8u2M1st1rMkLIK2LA6d+GeBd3Mnuj3t104fr0+OnI1j3CC9uZGrPx84AWf01WKzSLBYFJqZvCXGCQaoIqm+/EAGVv556hUxC1Wl+wmlCHmHQi+zbeMm3tHuXb+y+a32NzWEscfvdm+heOmUa5lftu3RhfzFnsX+2O7u2484x84H6Flwc9NNc/OODXhd72eLzJYFBFxhP+vFnBvB0NayN0de0fFoto0wTzOmU1KdTnmQmS9OqRMkRv/pHHETGc7TJHQfBPNy0tBCtJvUNIoSHxY99mFtKjGqj3OaizYhozNOGgql4AS/7ybTYyqIsiy+7OqPm34ss4VS7hSxeRzd2NOZlhp59XhqtpRSWXDAOqqEfcrFsxYfG9mqYhF28UhYqo2AuZ8k6MgICDSNYjhBAxuDvxig45RDhTXN82sq390s2/f5m9uiDWD4oJUqTJ3eoSQK7Iyn7noWi9zSkQI3TUS4kHghz8RJWBYyGr+G/wo7+Vs/PIcVbX0JycSxUR58Kdf1bUKLAAZ79vBYrgSV0jaHGOvqVJmw7rV2Iyztm2WpXAqFdHEUIllOHqm0eWjGjnlIaOspUvqhy0ibLqRl4iBL9f2/ns+0OPAUM6nqZMcH20Y4mS1AVUierh7ZmUaFSk2DOpZdWT1M/k4ElqWaVKx7GpAvUR1Xvls9inTtYKAcLctmsgLZtWvQn8vE6PM1oZG6TBIBd8IDlpsO2CvuD8HUYUvK5J3tRcum1facWTjd+x0HyhDhCwXzO/g8jbiVC5aMFy0NracKIC/txfI9RASB/16WQ/fYrS0tYW6SMA0cwec0M9Bk13FHRUTHjIRwwtNEqBIxVGuGwYZNlW1dUK2557C87DRzEKwbjx4/cpCohbNWjouvVemxyfBo18hC7IItdBZVerv5bsGPdEWfUmIrlZpg4mzvIhDMkWtYDIFcg3UWk2BbBbkM+3AYVCW3RYuUflSQMmCgFeDSYkad09TH+Iluq/EeHVB22V+Y0tGjTgqHu0vdEcyZWFHA71sShusaUWutFzkuAFA4gQ+ecXgIkgMJgHSkezmez+fjef+S/1m4daVK/7qhQoGxuRhKqLHC8uJ5xpo/zz6YjpBLfdpnrxFITXN19olZnSalSou3bick6LOMR/1FBk2I8/jpmYRyesOSWYeDUE0r2EqzyGc7aW1tw+KjaVzojspG/Kj3k2EmcOmcVHvmAq5IzvInRl01YgU7VUsLqMF7WfkEBLyr5YmmBPbvUQm7FmStNW9wPnuedlQNezU4lnIhV2nE9ZBdR+f7/Tyf6TMhM0FlZHw5tzsgb5nsGN+cEbfAylnW+Z6P9+v5vj+/v7/1lFk9uyDYqsV3bGn3YU5Mlilky3bpxXw85gKU4RfM7/+Q8atehggQob98+W7Vw5KRw6phkjXrhBW69/n++fmxndlDh1f70QFrzNfTl4ECDnNVUPY7vlkbU8bYbJ9TvADV2HLR+90GhEpzPdXr15ItIj9RXSLySoflp98EbXyLrJk/0Vr9by4S/3BW2tQUB4JjKZ7fcbQUEMVlJoLAQVDSDUPfpYarpDG76n4tGBYvFt4YAlSwR2hLgxJZ2QntXLlE/30+ZQfyt6QPEBlGvcnt23nNcKOv2/b33M/u1M2EATG+01JnWs/EMPZPsdqLJzvveShlI3l/Khqok1/bsE5RNqTD4SeCc/wc6F7wptWm0VfmfP7oDCJijUs3GxtTLTKc84lby7S6Lm6b5pLIjWGXQrP50fFCxXm8871flpCG56dCV2zDjkeq6zzfvkYQyhAzmtKtnI6G5YqBnwDa+lsyjp28c73d5CGagzDbRbK5eg4Szd0JVPfI3NOPIiiK19eJX6/H46GzMx+hNlFac9yV5m2BC/cgbS7tUGsLSB2vN8ZpW/0+TKMBR/SuZuTQn7EiHg9VlWA/vZ4avYC/03a5IysNJDYNF/HOBjgbat9VQYvngWMMoAObeunkILs5zQQ4BqA/XEZhuLJ/lqugPrjxXbMMQ65G9g6LyC0pzFQhY3WgL7OF2aXdykOxy0lIaHZ7H8FKdNbE1fKeEVyZzBHK9cB8iWzXrxq+FjaAZiwBDmopSyx1C1Y4XQjL0X+5YVk8F0/JIHCeiqWDIdC4X/HRONdH1dPzEUi6mNM9aF95JKlKGCiC2LMmzIGjraDs0i7Jm/N8nG9JKV2bi3dXrWoRNmUracVz8vP1Jjk33PzS3BI2vve8I/SJhYgOBY1XULB2s5msBHaJI0C/1J5jcXUMFtHoohhVF5Eo2Z+6Z+dTR+KFhS7kN64ND0YJiUsK+P1DslvFho6I/5mj4ErJWK0Vf5bcCC/4klAf6eM91yRP7chYtAG9DHLuEwPpvix7is/11Bwg4pwmWLHPmcqKdQPsYuwxx8zFRMkDFhntV6tTvWlxF12vZhMYcTVPcrRKYb/zg5mQGcXrVW1kt+0OcYBgjBcsYWcaybRKtpJ3k0STZn77ngCo1PTR8QBN9F4EBafz7KeaeC+blooMrEqNasBnqxYWP8hO2JM0aVWwb66/QeVMtn0XWGqPk+rbAAOegbGlh0FU2aBZSPjuIPS5lhECaC5a9cVE9tYMks8p9tqcQiFo7OOXcT9y0+F5iAPyomlA9LKYEKJkZ+SRbDAGvizMCtbm8jzWlqklIighEvFMl94gEvHT+C9/M77V84E7MpgU3Clbf9/vdy6b4362pgInAvLMxPBhRliNODjc3U8aazVaU2BzOcFybYV9hNGaWGSmDhyW5t5Zk2yWNhSFWb/VtvUR6eavmWP8UtrHYTnLpvJVU/HBwtD2gwPB60FU0x6neIar9uNMHq9O/rGoSI+SjTOoaBO1Y/TPJc0/nT7sfCgmibgHhNg5TL6UwJZ94aGpEkZbZXDKCkH6h+zTwsz0NNbagZGV2zVTwlW9c8KVXIptpkFLbhc5JEOir7CHMm0WRn6ZT2Q+czBDJAv2u+qf1Au9TuOXa/+1YZRAAgMVYl+wblI+pqPEdZ1a9QjxVD38bP/sBXRKG1Asc/g2DpXrxpImo778th5N155fScidiaTrGNIYz6aXfL0GabE6BtrLXgRB6nCrwvZYaFaq7cil0r+6BaOpjyM4Og/p4y7aG7oDVOOugwEHFbu5ynG59BPz4jRZD7xCC3CNKtQpjAN1a/hKP4lOjEWoPCdgxbCyiCfZUsX8Yv7DBe+JUH1+sQThZ5Fa7KQOaVqN7OfEIfHIxh8MvDA0xTFf89BPdGGUEj8W9mny8sEAYV/KMh4uOAPbBgUQSIRHWVm2no/kGP4gnRLb9MpDvlEtHPA0ziE5ZXB4ZsG/z01KlS0yrD4t8DaCBDdWtg5C6f+xAhinoO/esZb7LjLZiGJw+g8erE1T+ndYOtinPMbVPKCOWhpGLaPvGuCPbXJLX0wfpn3+JTI1vMQL8LR10DArY8dIPcV2smGKQgMpcc/uvppskTMrydNHZk0Z7slF9vZpcyG8FvvaIpKNG8Fos13GPytLDS0//wLqt8pyA7y6EV6eyq3f+RT0Axdcpdy/56mLZIZCOaRkFwLACseGlUg8x5asdCeSAtC9OvM48nNVRQyPAzgYkPYcmWSH/8U1Qa0koTc7f9Fq9Dw3BLA5rfiVWnyg2UU98nNWjDp+6SwU2t0MayYL4hL8bDvoaDtJT61UXQtNEP0jQ8M88lncV1P0JIqM4PVZBte5xHoLDLno6V+kAQFsOnXzWXp2pE1IP12kz6rUryIcGoNB3jLT7v1ZrHsH6zloPiFD3zgLc9OQpRyanmguv/dv0V2DakuVKINvyxmRuyM3ZzItBt0TGYEutigFYEVKFqyICaP9bWcA6YnE2vy/6PLSesdfDfGtfBiyirm9z8P90KyqPBe2dIz6mamMTXQ/MHK06e+A0qZhX6/3M05hFro7+Qg0pAO8skqDw1GrZhIQn4baX50ODZE8PRdXIaOvbi4JcevzzeE7ob1C8x3BbDnsetXlT02+uTZHv2WW9JL+vteSOyCGVi6PE6zKqrL1y5LXVF8UgXAVKjvbOe7PHgorFvjXayKrn297u93A0+E8W6Gd/96D83qJfk3kxOE1toKleXxeL40PhVo8/rpSELW48lq0To5qbHqmCDFLP6UOPfIkgh+x3dLXXieGjJmMzznucEvVJ7JQWQf/eTxlAem4ZnBddtjXLXtiFgxd5eQ4Idrdzxb8DGOf7NibvmOWNSq7dOPju4W5eOCaVrCtCmI0lAXs9MyhcfSjj+sOXNeW2orKsrNnLZF6vZXUyFUwW8ZYGKuHJ8bsjcxxNsMJ44PoX5czKCslS++m35VZDUkPRV1+LTXunvWhiSRk0Xy8I5uKn0T/KVNZGCEI+kZcCrfTxtXCYTbEZywRhyzUyijMIGGD/kpH9rgHULChbagmlvSJjfVegjcez6ekboumbSATL3CRUvc+xlbGxIbhjOTF7uoKrRgsgdJCbZ/eBI/BuqIePAAAB4JJREFUY7mVW7DoKOSEVVnXGvCjs6rbm9igktqZGj42AnrLiUbLQNHEbEwO62fDx3voqJossGS16kgQoJPehXQhcoUnvr22BcOvng3QSyPwYAknEm37GM62CwX4ZcV9VlpH29LKx0bY0SVYCUhzmaiNXh1WoVKbAlUES6buY2dwvlZc27Hmnhq74VuJxLIxtcALoCw7qev9eEjW58t5mschQrDATA1UIKSVEQ99875nlpTSqIw3mSY+NVawxGhOHF33rWQTN0CtkL0NKNaR5UxMzffwvsjwR8y91RXhINjjXDdd9ObRVmhvUAYUdIieTfMC9wZjjVrLMnEYThzhgECkrGctzuehMcdC+mGdTSDkd9ADazrRIapFUZhNqbFVBGM3BkBFTeNg+dUP4SlRyeaX1XTzVzkrka7tdGYB7D5eCQAyazWNWMiTx9YmUL28pXPXI6K1Fld09EN/WnYkW+Xq+r2/zrfxH7GIl/R6vh6aecMQe+Q+8BW8aCQbTIRfczB/sVABnmXgwtPGpn2BpkQ6F+5Ip7QSu23aWGcobHoZspWpCCWLyHLz2GRZertqOLDlXDT1iAN4iTIhoG3tP0vGon86E5RtV33eVhk7SM249NKTCBNGkJC2ODa/53Hdzo22KQ2tDpFHOBrwtfGCEYDjioGxq6AGKwdNgJisLImRlhUlC7HgKKc6tqyxM+a+XzFF6E46Q9HKmypQuqzjWnlg1dFyzxfxPLWicr3vVNLHnR3CyhW4WPfXDwf51c1b5/W8H+chuzVtDkZ+qNf7/Xg9X6fkzQn3nqd6RZgORhtHJbWurx8uvgqcKCkfrO8n2JRf0nb1m1Qb7bYppoFli0kOz1JXyjRK8fUq3gCHlha06ztTwwcBhIBug3eBcqCPAx9BzFrXm+DycGIk+DWOeeAple3OfdpLiFZBoWg5dA6+Uo0BHp0vM4f80NJszZpC1aPkj1NTYOlGJbfqa4LOBSNQDhu9moG1z+ggrtRbvFfIhm62vKv8wiOQZqJu39WRSLzI1AXM8hyc+l2iG12wUpJDqFEI3LZ+WRLA2+V6v7yvT8vK6y3xo7os8vnyfmc2zyjb19RgbhIy1GjmROSNJALIxQ44BA3DE8UTpXKYUElo+Ko1ecYFdrJwkVp7aLo6YoGAQULAzVGnQOGiw7o8k4AY2fpK5Fp2ImJawipSXtAC+lAdWcX4jbvsOr3QortQoiZLAYlYRnSWbDz28uQHQE/YAI1sVlYFAuthVfpqnxCZm62S3w44E2NoetEj6VEfyoUthWK85KhSGF8SqO2pTIZ11M4JrDBohrSq2ogpJkqIdHHNzHMjytBn7tYUFf1PD7jwX6hAGl4LMosVCaHR+325XcWben99/SMG5ee8fIs1Ni/rmw6EXPOhFjpEixzhE1cnpmI+8nFSHBfpg7JAiYVLqjAqKfYWiQ6p5OC2rqeJ2ev40ss4AHuQnhwWKHTsnMTWc5EO8QdK5rvkgqy1XNwpX/x0GbTZQYMubEgKuXgQFmaLz9gtYxDSUri+wuZ02kGfAOLRifUsK1x9+JH0NYWlUIWkjcLKGWw0C2gjfRyX9/F294p8f6l9ybASfzipluJxwqzGDZZzHElg30vNiN4NQEGrKXAHlbj/0ASh6PzgCUD1TvzV1ZIDaXIYKOYKDovxda+K9wRjFafiaHz183097v91f/zP67+Py00m07fzIbuujvfleJ+HrnSrSAPlGeJxW51BzcF3DG6r+keiL/WEmcp5h8oBgaHhItSjIE6SIi+07hzymbzH+cKz5PjNM3b0MLLmb4NaXeUu34oBSzBltLjo9NKi/A1NE0LtGCvCjyiIPwUSCcqc85102PGKAWEdVbAPdYE2DohyeZ/H+3I7rpqczzeBB8sYsnkXY5RIO4GNNua8SDSEjqLG4cgbwjaBK3zzkXlOOFzLq/Hu6Z2rNMpUu3iVXpKwTuOGXFuSMo/sV4Gkjmg5AYpQyeE5wkjhHRXft0wU5VYkvIxYR5vbO/nT9ICm8UwyYWRuL0out0NCNo282qFwLWWyL12l9OfidHTAq9Cr5+V6O76O2//5+edy/6/jf9/P//mf47//1z9f3/85X5f7+/q+6M4r+f/leB83sciGaiSNu3ZLImhSVuWWnyDokDc0qyJ1SfLpYUyUJO16Eb+FwmjhdYVQ1lhxz8WYO//ShAypw4xc3itjOQiHMd41t9Ul58hm8tNO3YkFaVH9GcYiLFekAfGj10AHMZzhCNH3jfYOZJUR0nUSPpW0U94gmHoDUd6GegQ3QWajrO+mRWYceTcyt2qTNMb9uB4aciLNM4eTBuJILdf7cbtcX5fr+ZTNoakonJEMA0DhYfOulBvoRPJcHd+3Q6JgJdmFQnMzpljyMKEyPR3oLBYKkVTbnKIyD5QHj5vAP+nR+3WchyxeetitOwiL1btA8xtfSLB+6Jey+hxRbZ5FyDdGKRDUsoUP9Ya6gpCtz9PDYDNqWMT3KR4iISf43m2781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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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CamF9b96YxWThNxdaZ8OB74A/u3I6u6JNgexpVx3dJlgi+R8c3EWtGwT3j7Wb3lRsmo6pl+Sn2ukh/DLIkWqR9qJd9MCCr7GQ6serODHlLTKPkF9s9mLjJ7UTrgr8omho+qz707eMxfo9YEcCmnxlMTj2IbURHBcxnFTZ0xzfCZ5ue7FdHLrG1ra4TEjLt2P1mcnRDG37JMrhIlOxpimHYbsLCRLk58wUiIIqbVs/ASeyf/XaOOBLL1hUqjai+hLwcdQN1UTgL4PumujE+6lgL9lsdVG4+AzbVnsnGeCP6u9J3dnFtYA0vcbdzqmmI13NWaP1sLsM5pque19p0zKsl6+VXwCuKnkk6xSK8bMhL4FzGkgjbc7EDltK/Y2ofMntYTgGTDo6S4jnvrngINNIAtQRzKcG4BpVABeeNu3FMZLHaEZQJ78KJnuY4tFtEJ8p6rD56TR/V6dvRPZ91+Iaqvexy+TC1/zWaJi9MvLEcGTGwz6NVdfvcMwvPE910e20xUppwTbEUhbYJbmr0ojVGKS2VKGYJiMza3sNbXUvSQtlERZy4cx54NaByC9iFamuq4XILz7Vfd1OuY8RPojK5QdrwWdPuxEB6fnNQeDLo4K6ALFzgNRp3F/cdG6Q1/zxzYPxGduJSVLDGuIz71ZnJPuPGTYe9Ucgl9S/i3pJRcSxw/IszgnRDxt0T7VW2mYPq0iUo/wErLWCRvC6xoD2WCCxBFALqXN12Zgly1oLjMywHcm1cpN17cgNd2A+DWswXwc+TJzC0l61+xr1i/NnsaYEhhWxv9/J0CtXOuLRoPzgo4HRXRY4bz/dZEhcbLavz8qqIn3P/dn4ruGpqn+AIdTE6yUyStEVp3cWWJtnzBo7BDwcO9GrAAYWOUBYBBZUKSySwFJu/pRNMbHu4MLZbKrtVmBxk0tJe3nMX+SKhUDhA1Nt5ubDLApdh/7vnOQHFn+IrZQzjsF0p0zVPveckEDCtStgFQxPNrbiu7uF+97zPney5Az+Lh6vieUCxaaNDdp8Gk4QIKMh4GrnEgvEky41q20pIWxDvCcJrg4FCOEFxBmFNP+f4of3oFRHul++tDl0UdAhOTH5SeEiG+aK7HInNmMvUVSBSvczku8Hzc28I8BVxerX9Mkg95wqKTDhe6+VuEpAQ/IXgv5US6rF1IrRalEyR91xuH32ah1OD5cZ3CBSm0OJA+ZxMtKoBOqEVWuO5rWgs8IBPTENeZskjpMmEBXJLQEI9cGE3k1iRz2SRiVxggXIsq4wcJNbBTAtMDny/07zaa80eHGUUZReuJIXNQSS2/E7osBc7Xrb0PJPAbV13lUuhs9ads/aW1M90V8zTcUyyvkbBfRv8//7FQ46RHY73O+DTWyN0liUraxNYU6pWd0OGFADMjnZ70b6tl7QOFSMapiN8YiGBP4ciU/9e7szIkPVZr8zsRZwDu0UY9wn0EtUs2po4+ywec7DiEL8FeAmqNlRYtB4vhl8cv3XoHlTHF+s9YVEAXMJxFgCvQOAPnzmB6ixs9hrqkSi4k4rZ3M6p1d+8O5XbhrEM4a6OARsLkEDwE7ydlkkq5cpqRugSsPstAoFnyq7q0m70wNv4XvMsjy0f1r075Aa2qo4Y7WErjGEveQvIxnOZbsKssXhifaa1Z3CADUKTiwMQQcHlBoGzXdj4o7mlMSIUS3DCemc9E85eLWG7NQgPXmfgtnvYt5H17pTqgA/izsP9wtIVXOjDzZWzfhlR4Aar6xaKy5PJ0UNCK0iWBVbqbow1304JIYWTvX7XreC3UUcxsILcChYqNfbOEO5Y8G4vf0lgBPEMucx/FDRegbXzaG/AHaSRWeq0a39nP2aH6uENZG9S+wHQXuxr2pGnIHZiEAqDfUlYNSXBRC2MqGW+Ipq6BGQUo6RqMG/ZJiihtCPBhkI7KO6EDAPrRzgNUmjgDHaztFK1BJFiRNaCwpyPxtnRw0mWIcfGjU8gGLEcDIzWvQEg6SBWkhf1ltQd6eNiWugJJ5rggkgc4myjwEfNg6YQEz4JWcLCszjBg0RnaYXtBDePyDAQ3ceScYMcpCF6ZnycgF9wOBLH6ZBDgnamkjTJ8jxNEvQXBK8rLhJCLIU95MbhEhIdnwo7K2iDzkPi9hXbfs6Gsl92HsHLCxjXsJ8Tp7LwP7iPWgPISlolQqdYSeoZv5VO7C4IO+VpkhNpPD7HkoIwDAOCTRFn/EZ8Ee/veZZXFZm0tXcmYRxUTf+DC3fZ6qoouhWInyExuEv9fTxXca4e69gLL0rU74nVz33kO2eOD9qzdocTxpCshalbLYxSr8p2ITAQj3hatcUgszfVsOftZoB6YQymt1CcQJnPxn6OTmZM3hClMJWR2phKiKNCVK28ajSu6jrJUrh1VdKwrJSO3tzOEWkZDqDM1h567yDbzwz2gfk8AY0nuEus+HSai6UP0ET8krgZpnsB9In3aXX8Dog9JlrTmLapm6YF2DbhRS7MQ6Bhm94vEbILiRFxZkhjsOAppmoaUarGKA2wW4Hvo8LQ1dV5kpAPi/NvO4Nm0lJ0VTuntDgoOLabgj8Ur/ZE4cBjLMwDcnhlAAtNS/BCO+cgGxWP00Wi1TqFwq/LfuvHf8NbMfAiT/beT+IJIDyzG0+Ihsef0WWcnitxY647sZ07DmzsKNGF67Qsfh45153MRgw+HxMlmtAyaUWZCpGNprvI/kvkoDSv4GvCmwHAEfdIE+9iyY0AXebKGEB/MpCkmbrKpNqIPWMWTbOSyaapTAli2+P+xG6p6OWZhhFqgvl82rScoDuHwFCQgSEflSty7PYsny587rl28MIgaF3TAMmS5CWVPHYWSKv2OgPKbQaAxCNbT8q2baqqxppr0p2RphsQXQTf57RnK36Zeu8psyct4/Ue3Fhhihz8kt37TrlwS+GrXZffh7E0KxElEDBU88dV/0xqRePjn3M7jQ+UPSd6qQ4jYKGJwNqZbFRDYw/sjVVyRw9jt+psT/bkmL2z+KDXyS07jO9gn23jMu4tmJkWD9HOj837gFs68AHdVG2bivmRQ7umnSAuTCJANAPsHFICz11SgJhD1UxoaRPUYvROQLrePdQC2hnoWBXAWgFJSwDgM4DUGWwrn5/jFyOeqzgHmK+We227JMssJ29Men2M71vEXowYlCNX1LdJq0FIR1CSkXwJeMtbnrz+0PjneelIarRVpG0OXN00gKBpmQ1QMADIgTCzwTIrh70OEe2+KBsi2l08GDkoIUImFMFR9m/Li/cGD6dOB/UdAV6VSqwTxW87LifDDzYWEzHTQT0hGjf/ocfuOwJv4J07X8SfdH2AXqPr3YqrDNYnxaLpNkbvrx28T/zKHfkfCS/2VsGasB8oh6aZGNWW4tB4dF7JGsr70UQJSQ+WAdFiWU3RrSH5ivnA6Uc0jOGcxpgcszWVkGmaJ3oEt29ah7nl7t9fCPa60SxY5Z3BOfqwPyu3tyl0/eyLSBVzfNy9kmcNVG8J4pbsWzLY6aQAHUjS2SLRSl8pynNn7MfMi4HWNUrgCsEjnW+RnlYB0wBhjSkRsa/Lr7Jnab1taa0jRnkD89kNm/p9FHZOjJszsCT8VnYdQB2GOLCb20hhYwLT72a2rZdRb58DDVMa+5HP2FIONHRh53H73cHHPN10c+46GA9gsqjz8FhiD8UFIvXeb6wgRIWUhRDtdFUakeFZULPbHSxz73IO47a7twy4pW5T/pGMhRTTRCMECNtfw0HmaA47+rFFJ/a6D1K0zdbrBWZ5TUu0c8KrcKnuP0B8atBOIN7lgDHdXFhy8+I+zLwc0BZD6JQ9Ew6QxY2fWufQBxzspi4LYVKlldaaGcP+Bix7pDdblgf1tdfuu8drsO86DC+N30fschf2jwzO+OUHuULgo/HX/WH1dwybmN6F3FPSebZXsBkXj7yefYnNVPdB8u4YM51q1K51Ey9S/N3QEc+kx34CXF9plNErI4HqsXbCM/AZzkj7KR6xlusf5pVJ9Nkgg05Ag4YYUtsi6mh8QV/NCaH4OF3cjo5VMPl9GMbX0aRdvJTnGdJlPtIaFiJOHeJ8IfzAt2ZvY0RHd+VsLBSB20WUWGJnoWlqY1qltUhTnSRolFhfbrSKjt2437gd0xlGyOhhfw9tmc7OX0b2XIF3qgbP22FPDdu0r5HG0+dje8se1qfwfTb8wDlDGkuHMsNXznDlscOg4fE7sXm3giZGXuJ00p+cYbLloqcjmWOK1ADNLrVM85ERspaAihKQUYN4D+nDfUIjbziCv3bfLDhMlaxlRhYS5VWEl3K7pBsHDOQSCgc6JzhvqdWbe7wzSoywgTcuazsB9IE1idiBv6rHHvo/hxhO/BXmoSipFYBtx+Ef8snB0YCXum5NmwmR6JRtmm7ioEvZ7DP0JT/zAe6z/y+GNKLpgVlOlqq+y2TswP06bqLOEe++LuNfIsQ4mxvQYmOjvxPU7d5qCE+Bb/no9kFLDXK7M7Awrr51MPQ6LuJiPbSu10aa542SOXZb8LfycSi3dTpqtP3Zas4uV7HjQ6cIUwJNHrCmCFxavRfpMeceBUWe6t65F9LGwz3cJPC0rN70dFxG0Y8Ddll0onEsrpscHYYKGrSGdgYukGvbjdhUE8uGMMwM056C34uxHJsqRaKcycyIGdgHXZAO+QsvmZLOv/H3bIMQquHAHYbFYHxSxH76evLyF3DK1RI5xhnH0AQNDLivF8Q/xM9mD3LXdX3a7ouussyF/7LlWrqGduVBY8bmTeMcMjEsti5/gfilffak5+D4CTm36BKfgmPvIyUAcUDaCGZQ8vsGsU7Oy6GtsZ+h0T873mY8/S16eaYV9yrJ+d2GhYEZPtGwSepz8igs4XxbURIuSyTzZFzXNbTC0VAkEKWVuY5cKD4wfSZOtmRcOE43CA5A+5ftMeQzdLtIQkxtXEZKVp+igv5oTQaPIeHF7z4o3MIJvYEM2DiDw4i/vQDHX0I5w3pOb1MP6b9ss3D92S9LB5B88Oh6IOnvVAoxGbeQQoAdmOL4umfRA2qB+wFy2d3AEZs/vJuUIm3FHH5WElInrTrL7h42OJ+U/gT0ABXZ9MX0P6jjcudPXw6Yi1ncnjRi3Ff2XsnaATYbNnAjoBYNfdKcR93dxxuUPrgtQZmuZCNrW0Tp6wGs/xqtaQVKuSVCzEujnC87mND+wb1yKJ8F/Qhy1iCFBBLuBvze8eFtoth9RS/Vs4G7ulj/5/j8CLKCPW9wEf4+xxItI5K2fVHTzQnhK7pc3Qp0s2RXd9nB4Dn9F3A48wo1H52pBOJJrjVb9EjvLfLUF81ynOcWXpRyLSnlG7RGaKmC9QY+vBPhRHp1btBwDL8vMyW4IzoWvQM7Jg6uX9TEBY9A/8Su9hmtllc0LKoW5XVIpVVdx+pZRxu3xBfgoX2KNP1m9WjiDZQAYntr2kQvx2h9ZlgYr80WVUoZpVslkgRTwDjzuaDuSv9KnzQ5ZAP7FohhHveRfRc2hOzCcmniLx1wNV8ktfdMv+79Y/XODiNKCo/2VBDUgyLYqzohPTgSy/6crtBytpNPNJa6QfJKRDOi7p2uj6MTBNFWt/K2Q1Hdwdp+Xf4cKvU2SWbSRFa8wZ9DUeHTMnRw30NkDnO7fMhyCvPlnNespIRPdTwCbvrjX52gZfenJfvEDJB0mK9E67qGZA6Xexixhi7/d9li1PXKGQiep9o3IoHMPIKhujba9DZaj+drbRS0g6Wf2UsPvlNM4WHzwvCTruvEf8cv7qoqvb4NS57YmY/h0yKn8VJWv/xJ8f35WofV8VupP7iOJeCTlzsn9iRwP/Le91r7IUVqj9BJ3ipR1tUkSRb4KZjB3cVja8+0J3pRKx8CogZsGuosS5+UZLLpBNQ8v2p4v5AAFWRK9+U63MIbDO4NYu2FWXodIeslYcTiBj19sUo+7JMYcpgaplN1l5bEpJVJQC1aY9dHCw4Wscj+BNicmXiEzI6yM0MpY6E4z3HcbjKQR/il/pCYkq4wFftC7sDOt4xewInVV84Ch/Vu2NiqDqKcD7D/yJA8Fz7sM1MuT4Im3B/0IPn1B9Dh0U5ydt1mPKOFVUf4miQf++goI1EiXOcF+0PuXQh6dC3qtpYm+9in145crbfOb6M162sMOm/hJTAbt91MXEj6H0hdzoyYU1oidf60gsHDbHotcHjsfsiWsXnK7aqcXtQOd2zoplz7f4YmLTLS+99y+o9GJ9hHPDugJxhsdrSCxMwBXcte5ZQFS/hulnzEjEOxDrUKGqTeeAvbrvBCaA3JI/YRAy/Umzd7ZvgSeujExOmXKNY+IqTKPv+8SL138Ib7nBQeMPQzX3C/gTt3ZbBP7BRcL5egR1VuNvunG6pjzgcXgvB1l13loJPj1WE7ju6aRiYqW7Riqt+86sqVl59UBtutM4Hdf9uLmV+fZkfR1xKDzOC36Vv1+3P87v63LS6XZaxd1HFxltbyL9gLuEw1Ey7ianqscMakBl2ah2o/gz+byybnrAvrHxAvu3vN/+arNPzgI5sjLDHkeFd1kyStdq2qhw+WRc5UFLsvEkb6y5RVFpi3F/ckbx9tKfzQOzlSzy6S8qPJjnmNMzZ8vWfncb4zLMt1sTPtoQZc1WiviCpqIeAYpYR9AJqPCxXa+/J8997AbcSWmmYDZrOa68PrqxsNiEgFSFkDngYuWvlkdHJD/A++Vk8aUSvTKoSOD/vZulT3s7aGhGj8JvEI8aWiKy6amfPnsc3Xvz5W6OOhGzbkbhisF6eVqLM2WIS0JMfXsYfgj2A4dz41yz7FAyZ3ZO6SyGsYlj23qeuqrnzRY0exGZwoN1T0iWIcuL8wcarwsvt057ljG8cvMKgCB8Ui/nqpZh4fEd36gDxjg75W0Xr1h5wujmGyQrQwnk5RqhWyaMUoYxSU8Okol/0Ce5dq4o1Q2gA4h5gfPTxXakQ+rCGnTQcqsGPrxAlQ0c8Sm55pQDNpfSF42Nh+5rnp2Z2crhrHCn87FtASbrAsprF8mmL1mu+YgWiW4GKyO+LOHrZsEP3QWsm6+9wBu41RsksVCUKdAkx2pzC1zUFsRcOJNxX/yrSmKiupVJbmNr8r6PwRUwpvz0ElQhiJp9LGsAr76bldwufkehE6U5QdseRJHcXe0mqsbnLMnuC854F1t1s7xcyBFdqEOpZH0qV2FqdC7GVgfy2AtkI0Lz5x31cnSQDX52J8cGVTabwXtDvg07ePzdCx6Dt6H/k6GyMTIxMJKUUCWhR6Vsms+gEW3Hng8HGRDDZWJDs36LzfRRpNfFN3hUxc2ND70m01BBuxvQH8/WOjIRoUdxPSjnGSKnZCdM1e76nwyrljvFzGYVFjVRTKyDRLQ2oH5x58PB1eAH1kPRfrcPiLSJRzhGr3ZO+rPlXuv/TLdHj3bdjV7AcLukQNrJ1Mjiu4hjgETasXZvb2LPVv6QuHPIEWKFhCN4a4pPlCYRrQflADyrMJyHHE02DvZ/ZXoRmOfJTVxF+uRaybRLIqwk4wp6d1DfzSkRld+cEJ/iJ2Cx/f0k/2UR3DOYODhaNvwnj6cVqu7Y+B5R6d8FbnsPLAtykPloun3g6P59iRbDNEZzClxavqramqEmJCSYKmWSdoEr9XANKGa3kcuOOQ71yzD6vdn/DYl37/+xH0jYUoqsQvHnoOZSxYnAZ/sldnmI7VJWKm+FrvrFe9vX7D/woy3zdHo7lv0aLFlJp4PvaZLFhHUAaSLNcofOslzq9hG5iFTOlvhmZmCbs1spGyTTUbVnyH/if7S8HuogQi7lS2Dm+EsMoDfJ2dMvjt/uQ9dPRfx00loXQ0PYAJ90+AdnQXMo2eW1TLooaDeqtb9SAt3Kn0Q2tMXVdYT5lgZDhOtF56WALuGR1Dpw5/fUHi3W8YfPXdjYJ+7wnIkzqfX88hgx8qmrmOtI7ZgVd5mC4eTDvLV/he4ho2txmRiIfF4P6Th9mPajoBVG4JYdsMFXxX5zL8d/QOEZ8PJ2CONDqepTR55qi7t0Idvnkxx5JVvqh7dBXecEtvEfC12HcQvViw6Jc49SICTjcjTB1E34r0L3c+rqqPXw/zHgcK4raNz6K0K8K0ad8qqCOYvZLjHQttayAN24g0VD7yUxmByE5DLD91F+8V7pzqH8Z19Eghj06N77P8tp3PB3QBjzgYrJCOms5sV7pgWNTwxBlbxBr2kseapxmkaXWC2OkBBIAVntp5ncijg/JbCT0ZW9AZt3M7VUN9m2HwdyoqdAgeEiB4EIvHQi4CZkNroQsHWpmy19nnWL4s4YdIuVtyk04UKD5/CTJFX0tmn5hlySj8h1gDxCRmLxy6EdOl0jNsAY4M7RN/fJpltNqhinpg5zPqFRifhnCEasFVbucpcnp72R0ckJ1c6KGDbeTeCHqfDxgGff2PM6D+s+JPO5dH4sayPnIfh23Y0YAu+HK9j7qXOg+EH7r7Ezg2y8xxjCyoEr33dvpRmo9qR1OEidPFAnOq3z6s1S40+7WxkIcGcwQ8Js/gC3f27j6TtOSkQcPhwkLZQwPRL/udue/AZPTmHfYxzEccZrXr++ZSRSOx6xmKu6abYhZtVUe6S8fagyjonong2VoB1nXTNACUCYr08hd369gPIw3e/6JF84XJhW7FnjK4pS54/4D1y7WVruDf5yaxIdl/NvcoUeH3/oPz4LJcEvVEcdgj8H8yGlUK5KOfFC+BB1elo9L1I+Lg3oaflJZtjvnWKHs94q/f2Vy075+Fu/y4WNWZPThWe3ucnpSTztoxXsiNJ3eJ7Iyjz+65tEN5SMDqVknhnqwohTbK34imLZh1DpSEVRuFnAJ/uROXQ7vSSzJYoxaS1luJoGAwSE0K1D4E2JHAwV7vOOrN27CHuEY49Mjo58DH+LZcIpotLbl8KXBdgXiJYpwDuyoI0k7DqXjgwcYInsboYjYjHYugz3r7YiEOisIa1yIbjQATlDEdrlcOqqiduiX7bSehE5LlRZutiWIC79x4Q5imkfEBh5u537Qve7X/P3D0Aaru62uRih2fZPzf1k/s/3eMiU7xig+GkRAIOmRuBcqzYNaecgO6P0loQ10APZ6ery6y4R8gXACshvYX1qp2aXCdinOvKUb1jDYJs23bummpRqKz03pkQgRMitYgQ7wIwTvAv2PvVECKH+RAPg+Zvybf7PxvJ3it/ypACMdXRRdb/7+zN2IFyw7VcR7y/njewiSnG7/dSUFz5SaUN7q9v603O2gzaSmq1qRZJiDTAh1Ojr9QInYHoacjgQccta7awAiRS3GubdXd7xByIVoEfu8lW/fmaSC7d4CoL5J6B8Vg/xhMCbMdKqJnL72HcRF8+o3J06HxoHJiM5BRW42e66p/LVWDuxqKH5BMXXaBc7YgA7C1hO5ZoEC1rajrGmA92M0dPFHEnbuz6rQCImDZAIoZWsa92WG3gGqkZS7UYW9PPCP978Nkug3dryOPTu2pS0zmhXyMiGZjry8DCHYB4IFBxa1cOud4/35gb4xvOzIe/NIm2UW6Wnimz2/vHNglJVGN0NgthRCr3EOWaS99LZovgmeEpIq1rdlNx6IshZzAnrXdifY9eCixk1J6Qbrd55z+V1yWdL07Xg/qSFl7shnK4zT+NFqZwDZjruqfgWVJ1JklfOQ7UFB3FcTQIgJmanD8Ho7ofB4gYfRIIGMCPGEO3Vg9d2KHWwFuJ1AjiaYBZ1YoMOtEp+wvydsrRehOKbErdjn3vu1L3+zHeH69vLXRIbTpnK1rjV7Xk8qbKXH+Wn97+7ePMs6YukVXELXZjJtlg0Ve6jBOaQLkxSiH7EOqIk0SsZJInVhwHEe9btn5Nu8K4Y6U6WS4NMKUxtTZWJTgz7Jn+MwPPpgB2Xsheu18Hx7dm6/QeN7aJo47xKexzExmtHm9ONaW+W+yNzYkqE5H6nA+e3VCfafsHxOnuJP3HgUwNtaz0DkDKop9jH87vL9WutVN2yB3kEMieChlnilyCM+CePZSNSohL1XMp9lFF+GF3mdNB6Q3X4iultf7cZmeHGmLHVRem+rCIE7ogcT/OqNbtt0s3dH1KLPshoGcENejNQ7neRUi0iR4HNO+CEuj6eeT2u5qAMchhFjsNnm2VsnNmjxN0UCDQOxL3fDw3gxjZSrEqdo8bau5qiDpGjO/OoPhk+/DNz2O3HlSn9VH4tTPVVe0xEp1J7TH7kVXhQR2uxA8S5110RR9I4U+I0IOw2NbzgrpftNRqK3H4j4yad0WcwS8nKlx8WFtbETbsSgN+5kQ/U98GKdtTdM2Sdva3KzoKjcdoELDVypmnx0P1nIVaJnk4U/qL3yHeiN+7Cfdrj+pJORBims1u/GDPoTkkpnryUakSLRB8XmYlYneMa5S0l8sX9P242Rm1/DRmyZwODRgmgox+sRPbNz2jsm3j0vjGswFn8cA/+trF3EMidg3jHImhMhHddtmbQ1xYAp8OvJwvUXY8LiToltt1lnCnteayclwYm/22J3iJQ83DhnKgPEK3RmgowrrsenP9Nqu8D7gcHsIoHkMO8Z53SL6dtp8TfAPmL1WtQsFwX0u3NEarcvCiQPbkYntbxuk77Jot3Oi3BRWatY20BYqAV+0r2DtMiQuoJfR5RIX9gXtIn5ViA/3BYk/p8ex7D5zMRw7obEWwrXusNDLXfZc0/a7NqQC4U6nDp62ZbIrKmNeTfsAi5yE+44p5vEUxsYODa+u4aPdmcgnB44eqEARaqt4kelmwRHhdMCuqhleBjkDNdjAdVUp4E4jchKx8A6cEBseXy5LxlzL89Tuvo0suN44OkZFUKE92/UKrZ2/zjpZHyBNMVbf2/9ITSK+jtxWOmFqtVlqckRtWChhw087j8SyII8T8s4JDEC8WNzXt44GN7znGW7zYrGZ1kpRZyd/GWdrfkO7ifEe8Mh3CdGkpk7aBJN1WWtsZrZ4FfpCA+0NefjLJR+zB3MPa9+S6e2ISMT0hGf42H0aZUF1NLehRzCu7pkiK9y2k80a9XXNISc1/PkDbJL96J+iTTNvzWR0cH1TyHHX30X+OLvZvarYl8D0JAfvDlcYoRpstpSORiZJwI1ZX1wwf9m6uzwW/HnQgGPCGaVnY1qpqQ1RMB8dYUH2N48mBA5qZ9Jby2inJrCzbU9vMnGo60SoEJWUcWYLFRAnjmo+veHp2yWRMxM6Y6Ofj8ZA2Y4o7OFDkMFcxdufHOKoP70HBqqUbNyED0eS7OXR7Xs8EWm4AfxK+sJiedF0wt2YDbzf+va/W0LGwzdxJ0ck1NPiupfH26L3Y3TElr2TNuz8yHIdOuymcTpk6Kfnkp0tIftd48U+p3CvAMW8pqM9SSOSRDbQLHe0utYINQOtOrI9vQT2rsyBJMj+BBBvRNToLEmkaQCZssvG3K/LUni8wOyDBvSVa//W9G2SQCO1ctGWNURisqyV0DIV1QDs503dFlUr6oroi0oviLSwqBL5H9qRUFWFaCUJmPGmqdsKWgM32KkQCBA7FprGkrTQAOOXJGlG7k9KanEJ0DhMWjb4Bok1hMSVLWrzPWj2od6OEtmdXQQjU7oB4AAusjrdGGMtMJjsnkCRfQPDsqqg32/2RMyTjZ1Y1kZassADizl0RkeZ5J+Ec3zuI6PeyOzj09NT85bpgVycRvxuELkqyNig5niXmVei3BpHLq1ISQ9IZ53hxIX+LKKAblCQD1pkKyvefiPtlxtPUfuyGGc+WFfxNvNM0iS6gTVWwqAI7tty/SnpoJpwb4gzG9xb+HC3JQB4I7Dta5GPmzbJkrIthKgldP0DOoOqaVPrpoZO3Ylsq2Ik8zaBekyRQAtQGDESfdM2OgFNVIkGPEHAgoCAhdZthuGVusE+caaV2oiqmRvTVIVuKwTaaKoC0PPRk+cWJ/QyteMmGGen99j+DEi/DvqXq3V8svr6vtf1ZOwP09oWyjm9jWiY6WtcK+R7yOc3wOcwpCj6yfTW0FqFH5Fnr79numdzVhydu4/ZwB0Ow6zM7dmO2cYh+HpmWjzhjCz5lIVZGBT7YQ68XRsGEp8b6Bv3J1Oce1IuvIVnnyhmpEjHYyPlQsgcqvC9dRmFsbq4g3z+euuEW93il+Kd8GYeA757r3hn8Nl2+z+YrXZbOV9w8CDQ35h1gN0Ws11tkrGaCLHAXuNtbapWqoZwMnWTCWgu35jSNDJFeHOSvyYxUtQK+hpX0oimWRiRQGIwJBVCW/pW1JAlKmtsyVpnralVo9dkInQ2K6uNoj16eFGeq8qxGaXQlcJla1ha7Ugo+w2Fkq0abYHwL3xwsRQ69zpVF7F7Wq0MQCZxkEQ365G2xS0nZrbbz9CTZ33RrOgOFYVlYaT9Nnd47iD17nO/yEDtk+ugR4q/YIxZZe9E6i7zb+1jV0dXxz7S8FBLabxFu9uz7HZM+YG/EFDDoqLQWWFz+6cxLovpApBpM8qUECmusS0qYgP08eH9573DzUmjRCcO2k4kbSIDOlIu/AyQ5W31TtTjcF6Z+8QCYHGyt8QrjSlFk43Gp54Vv/Rfbj38mCiKTZUkUo6MyIRJjKmNqSVY5EbKEhFRM0ALAYAbKM5SwMHG3laQaETiY1pcC+eRQvJFEIQEJRAEXIzUsjV3HEn/n3+YXb0+zyAUi4IWpxCzqbgO5Av0XYTKIW5gGnKjtXPuRruIib74M8figkiBqUcRDGUqBuEV+B7sC7Yg4EPo1Cs9rWnBjRUEiuca+/RGGhp8j/AuyKsiGzSSvd3R91MmgwLkz6GfSONxUWD6BxMGrbfE77Y4P2TQHOhQb5BLhjlDutd0HA92fHAFYVYGeDlGM453AkIVdExAQCSjwUrUFbQrxHxHO2vWIuJGQN8g9f4t3vIbEzlAAudpCjMGJaYYs/LeNT/DfPSeeL1jycVbeaqU86A6joGE1oIJC1KnqIyeb7/88oGHHsuL7ZGQ621ZCZMKWeCN1lwxXCUNtf+LDgqk2RHarYJuddmABU3MDbQJz0MMVOaDjJUKLO/Rs+3OWy+r694tRlAIbRAPzKY00k5xkR6kEGYVBfUKqQX3EsSTLnwERbL7IWZiJqBDQ7PmkJ7TiS91V5UH+bqNpjoptnBcKJGjw+cDGfOnLiPjJRPQka78Cxu7Z7IgFgpQdmsbWoDWZSSkfOOlLeNZtok2GR5Dw+OT1qNhPhZ+vtM/A1UF/dKOsMWIACbj9RidFedOfwYPjKmbNtOJkSZxzcpgM3II0Y4W4qiUq9C8NoGuIJspS5RMdFXVGrXCFlRQV5tqL3Hpmz5xwjPJYDCGOA+0cYK0YOfZ9cvfOi9QqfamQuztZc2eSVUNzSfmRpUIOJGhoQBtofDBJRoQOC7UGDAdP1WYgkhN5yVCzxkD3VdxIWvgb9gxhmA8hdBIveAT09KkutLt+NzG+bUDU53nbd1oZZKWPF5U9+KXo5NRG0xPSuaxrqyhCt7h3T7g5KVaBJO0YAnDCoQEwkiW901Jp+mF2LLPN2IFWTbF5SLrgfuvsf/wh6/sCW+2Y1zaTcCn8zivQRSjjgkLDsgjIktkVYG/AmyEuqqqNEkVGj74AOwlGVwRXa3Pqbh8DF5tdSjRfLN3VH6SAEwdxetwV4KBBk7laKtYO4iUOWgpDcxIG5Ng6baTP+70oJvTY21Rr1Ww2KLHah0UA0OLBlkCB2lMUzdF3daQEWH1aULgw42B7tLw6l6R5IvF9B7yEvs6nFAbgcNQICsbKYr2fFlP147NTxetKVy5G3jC3UxCBTStEYGi8gWAcjqnhYTWmfb5HrURT7SzIsD+kAmkfZi2qpIf/ij9ws+loH0rnaSYg4buKWjhbSmZGI6nE7afrYMLNhgkIfM85IEjdnENkAV2QUq0ahuCkuf1z13hFdtclChEMUobl7ZP7KrAF5dKyebx7R9dD9sAJ3PdGS3hcIhmmmoKOKCMRTVViyQFWsly2nrKpDn83LZNBZBC3qnghxBUTHdnci54JuLZhC2XCMXGfa8GZwaRToObQzKTxz3TOVHQOQb1g3WNwZOknY4bqVJhCgO6hI/odmfd9wTGz2itvZj3ljM1ndemHUlVV2LR1BNAkq+rpk2hDSLGPF1PLueotbuPdBj21J6tQDSMdTioaDhcC4z3AN/I0mZn884Prbzn1o0vn61EM0FVBGIpQ52mWyEqMtDdriCCtLyvd3T8lp7ttBI4hxRyUTfp8dfF5mKxLmWSTYTUELuCsQLjIR8VOHUbA25s7/xn93SeO4xQoZ8xPO0CMqynFKPwQFM4aRqo07dajbX7lvjKgsih10MREXEZtkGGm5u9jSP4WMPh95RX6CNNdfAwfR3aC0nyDhIx5aMRkK5GvHL643c9VuUplbZNUxTFOEtFmgpNoK18k7jkm/C0LiQtEztR8nz3x1gFc0FhMUy9fjZwz+qqFlC1LZKV1cpnLYhmEFHGu7J4uT8Tm/DHR3uhc5qUe3Wzt7tTlwa4GoZmvTri3sh3uLYj7cTCwmP8IByQHzUaUjZTA/Nw8e5tu9irTWvm112mhFqBpzrvfDcE5jQXJ1eZGdB9sN+d3DPLh6iIOyoByvoD30lfOiXedysm1SGjsWoDdGpEMO+mqaFno9XF46m2fjuS0iCEI9HKFZ0lMtlabe401HEopMTCzvt7iJnzxtFBSOQLMVrruL2QBB5Q+/cVxEExdldG+Lt0z64HxX7Ja0+svU4hNAWqJnaCEnnO7sPkDhllCtRG2BNS78wXSVGMp1N0GimvG3lh6zLdeBtIL3KXkezSTyKuxUqPvTlv9W3lGmS1TSUW5Wyxkk9lYxYQ1QTSIEXahT7obwwG4sZqpd12jlOQnQTU26BqUhlTYDWSEOKx735v5dM/efjwvaCgNLWAIn+cM9dJL3gXwuNsmJo72H2WqYsBu0gqZh1iOSRq7kbops0asUjTo8cm0kjsTZwYAWCLbNtaO9Dxi79zJxl/wGy1kNqmtRI789HZEzviVmwFjFPkqu1xp0BWCb2QguJ8dFgFRERwQTKV1c6NpzfPhfenYTd4WxMO+1ZrSKiC5G73hS9R4kGkMD9hPmwsufcwVG4wym5/veiDMRhzMXTedQ75kfFR8ruTp4iqNcloTPIcZCmlyPjKVo4WZcU0Ja6gJwgz4nb2ZnVd51qlWMZNL03GnKtU9BKSxH00Sm9rOmuTu8V8ipb/PDidgrLu/reuq9oAHETbGKwEHk9GZVsldVMYoZ2rKBVtA45XIOYE7TaFBf/aiNpI9FebBsSLyRD+uYbkP5Ggc7aBtmbt+VatCrFz8sR3Hnno+ttuX19drVvMvhYihVlA+y749+2A3ftZZdmtM+1IN9FRjyTiLEYCBgyYwdgJQqqkyselEKUw66EMIdr0HTPKMfSgOXZ+6Hh8/EfSXUaANNB4Sjfq7KmmkCNVN6KWKXYO4zdDYQz4y95YQwJBIsFqBltLFBhOLDa5HNrPNg5Jt1IJUKMTbSoHtuPmlgIdUdNbuw3ttiKQgBbiCMMQWRfhhV7yy5LTvEbWP6FHw0zLjJUiSkOAWQQ/TNUkeRZOIHnrMtztuzuvE6bKQkIQVkTDdwS0K7Xe3ZvlSiQ60SnaJENcx0/qkjcfRpBlakYsUlyKn3PmSghrJKIpzDifam20al458UqdZ1VRJqI1kHokilZUQIohabPGvg0a8prEAfTfjEQDJGLIXoRswxSSJuC5cyEmTbuOT/zbv/rbj33wIwfvu1cW89rAHQJ0R8hEdnpanKTCMj74v9ZM8Ivni8VwCsCxXpYmSUl9XJDPOfxxjLGX1r3MyOIioktIJlbxgQpVbUzz2nGlW1E1TZIoUKRxxkjShnabIJd9yZK93OEzMIwI/6K9DB43JLbyYcwdSwQTs9qkhRwy1LFYwl201TrPdSNrm1YkQczwUV2cCr0P8UZBJhbF7R/2q86dHfMPV6C5Bo5loxLIbM2TrGvzc3e1lRhwvq3rtaaLatGz20C2jhzlk6RtNre2lBEH11cdnXWriGIfTu8FnH4dCWFf1s2+YNfDR03bVm2bpqJR1d5m8eTDj27t7phZsVuVp2ez+h/85rQqwVbPdAMpR6ibYsY/qNrQkBr6yaYJCI1zdSPrqlUaUJO0SqUqygJc33Wr8lQJmSVJnerTZT2uqrtWR7/3Z3/021deviaVXhnjLm5NWzOr3Gf6+XWxjoeg4fnPnbxzigu426ErMfEZAFXUYG5m7fr6SKqybWpsf+mN90Cxnc7oS7ZVFFtx0VHPeT2v9GqEkqppa/PGi8kcdJWyTRTkgoANbAncXuw96TYDzO3HsNt7vfKCjsBQCrxaEVHv0N5BY4NamzKfKN9HDLmY7yPy7fpsyu4kcU/9/lr0PtlWkdQJ/jH2Rst+DrNjBWoL7cJgN5o2G49EyoZH0ReyJ72z2me1Oj8XWRmkHGsE5qyxiePG5mZrTJKNmrqeF8UkH8FZdetgjTxhch0iGITh7aKxE1O3r+2dmOx6LHzDLgBZLhez6vnnnn/mhRf2NrdL2c73ZhDiTHJ1eK0QCYhcSAIGr5sUgvJ6qTDWNPgJahV6JTV1m09HbdkaWZe1KYuiMeCFBy5WI1CMkOMDa41s8tZk0+Tf//4f3Hfn3TfcesOB8XrdQFdLMCai7c/AI2j4VgIz5BFbE2DX0KPJoCEGAsYoBc7uNlV1uzqi3KcKjf7QoRjvHTIruiDl0aYKIS3LUuwVTgUgvHQ4bPYyDmxRiUPfefD0ydMHLzsga8BJwAIjSuJyXmAZTON+XSodRNODSXAX0pw7n/laNWjlTSRsiy8CtHzvYJ9TFAwusjnhMfl7CXwxbuhhnTOQJZfA4cPoB7f80eUwzRZhUmDmvcxGY5HqyOtqoz5xLwYgWEvNtsEKvjAgguEKAUSgMTqBNsjKmLqqtUqquqkykyD0gnXGWkx2ZpLZR8Vcmf3gv3ciJXznqRyFr6naOkllVckHHvz+qTNvbs9nEpMSWp0C/zAiMaqGnA4Ib0AOP8Z7iBFZ5EN09SqUZpASkKpFWcIJuBFklo2yBPIhilookySqlRrhHMxoOjFKzhfFt37w8FzW9958SzJaESqBCIn3AISRI5YH20H0Yn7dnF4d52k7PYQWoZKpnhajvNWyRsKtMCwE/jO3l/2G6IdYmSBjik7M8q1bkOsMwqp/kM0hVLm9rU69KS5fA8dRDRo+zikKW/vIfnS3qxguOUgC7+eNZjoYN9vRbW9rDF2zBu8wjetF+ChcdTMmeCqooorgGy7CBt7n8Jma9lcWLvJ4dE4LcHPEjCfHz8lwFZACIVvTCGnSfGTDP7Bv3bPYQ6ywRXWQ6Bc1bsJvgLmhTK2mrkFEQHE1xNIbkLjQhCjTaVW3i6qZ5BnkAbY1rii6JcPjfGpDNMG+U2nHMcjsA99nA5h1K9o8TbI0f/ixH7xy8vWqroROBfynkiw3VG9mMjRwQNNjVIQ05hQLFBcAsIY2JbnQ6XMbHYLklhxXBMtHjVQgkYWo6mJ7b1GV5Te+892jBw7ceN1q4r1tfEWYxHM7xwstxpK899gmn1PABSv7jGpMq5McZG610EpXkDIJIWInOAbEWUwEfTLyxOA/IYXAMk3DbgXNIkFFLnW9evKV3fyO9VaXiC0FrAoCwugEtR3IumKI32mJtjkEfWavGQLM6lxM7miotTItaysclLdl4h2zbyF+nXT0v0iFvpijI4BjDwL7ji0ViZEeBTv711METIBOU2U0xN6pcbanXneyV5xJ2DrqpcJsIEQ0FaTWiTFtWZbgwcKEOEi3a5sa0rYk5Ou0ldJ5Y8TWokyUnKTgP8KcvkiFCtBLnfSkzt7vUC+fZIjPqkwnP3j00edfem5nPlOJxtG4rCfK4yWmDPmDyIKw5TelTBDmMOQ52XnCBCaib9AxEjsPpBuiQtEqTJ00TSITzFMWtanKtp5tLP70b77xj3/zitW1laam5g2MPHlpoGOskaQlCR2g6chUAfEGKKjQ5k3WTSXqRSH25oVJkoNSpAZ80S2VK3i/TV949nZt75OQmcO1S+F4DG0VKD00Utbt6MUXt+Z1mUC1CGCs2AbJgfV4pcnnmPDHev1giJI7ZMwv4S83RM+wZBqAJnHaSVrEjwjaCRPFKLAh4AW1Ly5D2y3D308CR78yLZrt6MGzPQkQ6AkQWZqgGou9mihcZH0fISuadGTyqnvBG37ES5TSbdsgNC+GBVG2gvevrqHM1HZBwH0uZN02ZStH+VSnYm9ne308hjydtsGOobbGvLOPo/XgenUgdLssmNGrs0Q3Qpze3d3Y3W0FdHMG3RZK0gl70BEq3S+Atdkn2IBlaBEf/G0eb8glrjn26fSIUlYC+1a2oIhAs+C3zp89eeLswfUDjampTpUSM8iVbJGLUKtiUJbOOckhz+2/mP9EbwsIMqqsoTtXVlW3/5g4fGS0t7sjTCHBf2oJmGLYjj4Gt9ESqralkYwGw8Uy3laLojYvv5juFHurrVZJ1mqIwLDGu9w2cON3MVv8lsVy/UQsE7+d94hixQNCj9AwsQij41dnGfBRnC7IOdjAEEmIYLqGIHUufPC37X+7bLcHW9GBxoKPqTUiy3LwUWGra8yAsXhUTB+lntqWdDEiTvLYxuK9HtM0QKgAqSsVAEy24HI1SMA2KYQcCRhXSEC51kVbFpVZOXhIptrs7KkkM8W8BWUbbGNnNTFStYXZPSXMTzvpwVJWTakarUejE6dOz4uirsq6QelIdONxwq1uHj0lNPTw4GpRD1THJt3kkG81Hg9MD0hFaQDkOE9FXZ44e+p29Q4jKmxQjK5+xJOhOBAmOajYyer9AZGU8ckP9l0pzwmT4TYrtZ1mt1914vXjm6I5hKEuVygJWhVlEXH3VWfvseZTwbcwaDOzA+5JRRGmFNPvPFi/vqFvuwxZsVgmMgc3qpc9DBOi47Nxj2S3GxLm0clesqIm36LtQ9Pb9T2zmIC7OUHBBMOTDeftS2CfYzyo+HBL2M2Fnwb6C+iK3LMArtAmkPAIvY3xdIXaPsuitM5p0pvt/3Q47ZFe2iIfRALJlmKCpKVELgL7hRgNajCmbVWSbm9uv/r8C+945+0H1g7M6iZPlNJpnmiQk5CgD4aoB+gggulvPVun5FRcEqvw7CS7/4EHz5w/Oy8WAqwxi8DEZ8azKh6dJS0voIx2doN9ZvQZlPWFkZHHidxgSqJaMVmbfvOh773v3nvMbE+OJwrSO4DTkQmMj6JkEHQce2sgym7x1o9faeIvrYRVlKpu0sV8RYwuO5ag9xdCw65tm81siJ3Pg5giQzRsv3L4WUF6Cj80rMdMpajP75Zb57S5LAM+jlli4Ad0Lhvb/AC7Uu13LCNw/uTuBUyFDpcHjxYID60gsIU6Tyeexg0WrnNZh3kwTv3PPpXybQngpW8Ymb49UzFofFZiJDrPxuCvYofvVeF9zVZxhviIJ15LwB4ZjSpMbKYaDQEkCmwaiMI0DSXZuHY2foIAsXMyHt1x112TST4vZlVVFkWxMp0qretWAKpDXbWo9lJhO0EiMhveOxl98Ts20KFTILvZJKkuygVFgynu4l2sgXTxH3gHO2MOgMtlKw9MZrwCFtmNLa8vBqREa2NMMa+UkW+eevNAAqlBI6PqptZovGAKNW5qmjoqi7SP8EKLFcKxmSb4KciXMLpqqtqIRdNMp3PMm8AcbftCPo2OV2EMOqJpD7ga4IETOrtPus9b2YL/U1Z696QYvSuFYmjLCqkUO9CwgnphzMUe9lrFemz0rEFFoGcbRz8FuQrRcowlxV8Hbb4j43kFYX+Yfw8beMl4w+/RhqMifEq5ICtN5tNJdAm0lAEvFKstp3NJ8KLebH/An8hN64qGKJnDPsyqneBiaaBNlA3CeVMTy+GQK4hmbbqqwFo2WZLKlenO7mx3b7dp2slopNMsTWWS5mY+AxoGry+iLYYMfLsRwe2tTVOBW1k1TZWpTKJTtKwnEiK32HADavrQ8A2oFo4TYdWM214OO4VcwyGT01F3b/pJHUO+QWjVwQizTAtUdyV1bZp/9s//P//oV//BoSRJdZqkaQtMBplFhXYZZJCgYy0kKlpmb28b5ST5AJL3jYPYVWm9spogB4C63xgDkIoBg8csNgX563mEks4f1MOZL01Y17EdYQtQJ/lLx6VKx9V8R2t6neCLpH+x6rcF+J6o/VDgyG4sPgixVCIvP+JLSMlRUN4g625toFXIOtq6hxb26m3svB9K5LiYkDB7KOMZyzIQg74In2id51m3axMKWBce8haclblum4MQ88ozdq+wEAXWJYPQEBjetXimyOdMojHmC05argLAIuV5Dq38EM+mFa2WamVlOhmPdmZ7u7t7e4v5dDyejsdNPsqFqedzh7QMt0cEEJpWyFSGdCtRZavTpNBVXTeVGeWpaMzll1wqpSqrGfjVgKatGmh9Q+ghJZUumjC+BszadV9GThKiqqauMGsP038dxVh8NLS9siw7e+LEmTfffPjZ596jb1o9dCA/tJrKZG9nAaX/ugLfAcTLWwm8QC2355wTxJaH0NLaXLi2bpSRGpCCJFb/Zj0pyl+Qi+LOt0OkG10SDhYEa6Ss61q/9EJSybmSUHAN4XYqIZZdxFYIVIBD0xPqEpd4FIi4kPiNPo9fEB/kWjeQotUJYHAxbP0MdBrpowBlaq0q7oUeVlL2GRVT3mMmErTa4L5EtHq8LcR1fFmCvwycTFioyWK8PmAUKBZdV4ChAo5maENvTU1WRmwXGX31oDnjE6lC2BY5AKCNSoBoG8DfFgB6ArhqFv8eDXGtRuNRVTdpAsgvW3uz1elUjrNEJCBeGwgm0y5XRlZVZaDwoFnJdarHW2+e/k/3f+fUufOyNfPz52dlMdXpRlmADmA0hLZtMNJlu0NlPEKWddewS8EufMRJ19IAsgYAcKFvuPVEThzI/sj07t7e8ddem+3s/tvf+73H3nHD9ddec+zYkZuvvmKyejCfjA6MMiFGYGxA80Qqj2JuNVZP7X2tlntbhYmksYAMsVTmoxUp56FmuVvo0Re8XYM2ImPv9LLVi36apBMPlgSRTBe1PvzVr2y8fn501VpCMFdgAmMpmFeYaXuhgmIvts9a5lyLlmPIg9WlEUcpPY6DAWEoIYtOZkttl5uAf9gzQER5JAXkzX9PCRyG2RkK8yiBEphIobK0rmpJ9YBMe2rAawzk64xdCuvySBFRL9lapqkgYQ/9eIiq4zQ4hxDqFpVcJSiMncyFs5TWKSZmNQhGTGBq5NvySc1CiHGeZHrUNvX2zs7O3ky0rVZrItF13egkBcC1qq6aMhMK8tMTszoeG6H+xf/6u//hj/90a2enha8BJEQ04AW/4tprbr31VsArRhceacZeNbKw5C6flKbQJv8tm/cAXUhBdLwvQm64whrvK0L9HNnTc88+d/L0adM0erb77QdPfft73wXgdGGyfHrD9dd86pOfuOeOuw5dctjUmP7pMil95kagvQFfh/0QmncKVZTNOFlAKqWcoA1gdeagqHlPb3Rw44/bhuS4pspvusijcxjvsHeSDCSqVGZrS26cqK9cM02bNBDxAgkSN9y1rhbA3+jWL/T0+U7xRUiF2ZdU2AuzkwmTAvOLMG3WVYT0TPGgQlPPQ2z8DUpSiCQNOrEumoK5PR9ez6VGEVw3UQg2WYOqQFujQw1MQEjWkL4btGQnb212MyNkeBEoTcLkfqRZqnv1vlvbJCewO5eG5mW1lCpJ0rZtyqLQOrF5Er4VQ1ztBJqKknmWHhkfSqEhJWTKj0a50FAgn+g8z6eiqUsg1YVIp9/8ypf/3Zd+/8xbZyENCnB+UOZDIKd+4fnnE5Vce/11oxFo7C0CSBHEGkvopv4D6EiyzVr44jinJVqn+JIUPnM+PwrntkJoAGisEfMStib2+NjZ2X72uedOvXmyritIdCFKQL29EmZvsfH4E1vPv/rqb//Gb3z8Ix+Z6rGVZEEJDxmMPJ2CYkmOtNFmkbLQKhmVGaSFZUbVotWIUcK16NiWjozqruuEkUGwBSMrQnB9xXn82kLXh958Ze++u7MFxNHQLnPl3vZ0G8Ih7u+rkrhQZfQ6oLYzxXdY9+5Ti/8R4yHoh4nqgfsMLWT9WTcKWO7Ay+yNljmxLp6GuZXt1HdSTjBBwyiAPgYiDnoHEAaozSB5rUfKB4UAdz8QsI/2WrYAVdENJGnw8hC7tSxQjHe3OwsTr0ySRGvwvbZNWxQLiDYolWZZAi4OCxFlI2SOa8KRqomYgmWICcpQ1WjdywAdgOm3euXowarcEXl6+aVXXn7Zlec3z5dFAbA2GIWmkFgr5I+eeealV1696Ybrjx45vLp+IE1SqVRRFXVd1pQph/SpFKj3gImMb+vzvEl3SNIUzDnrV8WmURjiQltAtE0DzQ50MpZSZWmrdVMUbV09/+zzr73++mKxMKYFqFOcL99UHpS5XLdlvXl+49EnHv/QfT9mxlmwx137Ym+Z8X3tPI7eswvXpU2WiDzVVa6bosmxKAP4ilUcfEVmX2kMjiKWZRFtSHo8yWEqBfCqCNcEU4xp6zdONglUvGPhFkDF0xy7TI2LVTOHSKv3PQ6pk3zS9x4ETzN6FkGSEvBtV7wPPMBVNZgWmwbbgO4gsPtF2MFeuXeoNP5B6IeEXQHbD2xLzB3xr4aJqW0NyVJg95JkdcSKRRfOTWVxwGyISCtVlFVVYdI/h6l0dIwKJFnaNlBEji70D+skSdM0bdqmqBZGmFSnUNTtfNJu7qKkViD/kpKNDBQ2RQF0LK2wuQNGqny+vXvLLbf+T//y//unf/pH3/jWd5547PHtvR10OAKQIuiQbbO3u/3oDx7J02z1wNoEot9phdXBtUqaqqzrVoOPi7ir1FB6r6HAASCtainFaDKSUjVVtVgUUookTTDpG8WHllUBlDKeTrSUtwh12Si77pOfPHP8tYeeeeb86bPVYkZN5/1soxuCkmCANTSYojff22vgZaE5gkfEdkVALm3Lzw5TuLwaBgFh1Ri5MGmqkpW2gOQaK6nt9vUVnA5xkd0rJmcS1CynwJZB+YpwxSwJavWNHm+TGFW2rXnuhXSnLCCQyLqscO9ckFRB/F5EYvMy4u8nM7tZ6Z5G6IIKqx2d8tK9f2zVgvppG8ko0AGdd2JQAl8ka+ro65aSJYIeV22TZqnIEubxhysgBQrr+zDbkfedc+wl/AWWGwWOpBC7u3tlVXFGEexuuzcxQwTdrYjPhECEYEwmaZImCTgzymLRNDW1ioK8Di+/SZWC5+B2hC4eaC3hfkh1Gmfj09YmvCXMlQcFTKMHV/7Gb/3qL//Gr//Jv//33/nudx9++Pub53ebutWQToieNSEXZdFubO6iBJnmeTkdl/W8rSvs9AMjgVo/qczMjoRkjFJytphHvqSFF6RwNDXo3vPZrEmUGuUHN8vLd2bVeHTm/EbZ1Ii77oof6IVdsS+27wSHQpqqomlyEPKiqhqYRldryRLGGFVZ6rWpl+jEoiwcrffqW35sdOjY4fnxV5saKjfdhuTFIU75p8hXCNMEOqdfeXpKZ+cZ1jbcfw+zYtp5q775FfP08eSmSxvRgOcUxqAiYGuvjwemHRhKTBCdT7ymsERaLiWa0D3LWkMhOtO5uEvDqMpZkxJNEtgbfVC7t6tacCEMOTygrNfgK1MiS+05wOYpjo6iF71QTvSGfI0ghBsocJGYC9XUoGAu5nNOvZ6ASdyi8kybHLwRQgp0ioF4R98sqJ5CiqJYVHWFZqcEWI4ssb5f/youVKgTgNTH9HEENuznwHjPmV19YDVSy0yPql2AUv/iF3/1i7/8m3/wb/7t1x64//EnHtve3iqLqmklNAZqW12WuqyUEONU77YGVJEaq7Fw9IQfYV/S9pe21jrYF95FxzpUA5fJNFRftZUx2caiGu3utqrN86Tc2taA8B6yvOJiWlgeSQWzRr3y6puiEk2Ohg1yBApKkczoxGF4FqCLUoNjvjHNnmqnqrnjqrNvvgq4QAQXC5ke1jPh5Xg7BHjhF9gaCkzNouETfAgtiUbjMPi00EurhBlJWW3sVKfeqK89CLYTmI7O9uWeXRcDcwXny7b54HEBSuE2/8AloWdiqJvl8zusS5OrCBDvsfS0J4Evnnr5tgbeCz9hSaDUqZJZ5k9rTKMkFKxDpLSuaqAiyGfwdQneUwU1Q5AcLVSiBIroopwvFkUF6T1RLIKe6kGKed8oT73U3yIB81eVeNhP0iTPcg1b03L+lpI9qAIZtgRQoyncju3FyQifxYsm2lBNU0MrAJQX852yavZ+5hd+6vO//LNf+/Lf/u23vv2Nb35j48xZENdg/oPMSqZjMAwwxxtyEVswUN2augYT+BcUY1TgzbZdQtBQsK5Xv62RCurGgANrpM4LrabjS1cPVXkGELaokjhh59sd4ZKhKtHCvKuqLHZ3d/NsWtV1AoVT5FvweRhuPnwZUM+TrBREHOZlbXR7yeG0bsdClgTtREjuzofJN6n3b3EZYjiFc4mNf6O3W1hp7oqLcEfDJQ16zXRST86fnUmZSmWxOBxenZe0Nhz/9sQW2w4XumxZciV+EEfHqf9lJCiCfu/CAeh0QGZkfRN/j1RKX/drMY8JhRdC1JAVS0fV1AjBC75YhKoipxXVvVHVAfyHZi3+jNSrdF2WTdNURVlWZVmBEA7BPj/9uB3RxLViqm1NWVUtgKpDwlqWZgnavk3TzOfQsz5Bcs6SDDOxbXqY5WVa1lWNhQ7g3G+hHB31CJvAFmqAvN6A/NCHuKxQBiQuSAwGywsKVBfi0z/1+U9/9gv/+//uv/u3X/rdVOuqbdCxN12bTHfPvyUPZ5QnlaKcdN7ySNBD1i4abjxZB3PW4GcyGaD4SWdZlpXCpCrTqsh08q4bbrzusstOvnEcyq2p97zTWrwaCKY+zoYC4DBTSVANfO68V55ZZ3Wb8+RCqtHuR3tcpDqZmerAqpImM6Y0UNhICCMU/6jwDzY/AYOLoruspYa9tQPJtmvO6YA4QoKfQGsL/AEBwjBqCBsR7fydjR1pRuT5g1EECrHkQAwcs7uWeo8GNr/diLHTqes8cg8KGj5jhDakoBrZgKI38Ny+TW3NGcggBE4EEujvl0pJa9diEypEVZSJTWw2UHdDijKIGwzgtvO9+c7eTpKlo9GkritEf0HQfETNh7CTBtAJpFkQmOCyAp+zdSX3NBmMdtrQniWnGhUhUJoTCPjqJGlNO5/NDEBSZQgOmFLqFfppIGcDfDY1lCvRFIMswgwQjNR5FwrV2nXUftdkNjjdqHGPpD6DphFlWTf11oGjB6+/+cYjl11+8sQJWZq2qTJUE3IpZv6lXNEqtRpyiX/LPCohvx1LE8ERRaibEMQyctKaU+c31l5+5crDR068cRyVGsK0A1GMRVEg9uHm0JQJAPG1zPI0h6oHNPBJhUENlrY9VnQQ0wiJHDbbAJPSkHCAj8hcpotZM1lt0jQp65mUK0pgo26hpayNGBkxxWYsUkrq3gCtUqDjGZExMOaybadKQpIMCFsMApMchjbAsqKSECkyY1KAN5cLKXK07CdK19gnQtWVPnlSzYRaT4C6MXsOei4EYqL38PZQMLYHleYhYu4SmbdMmOsn/ikwJW8ZWelqRa07jd3CO+uQuTUtQJTScv5dCRi9H2j3gtxUeQZLQ+F15JsAvVRZQ1dlSdGWL730/A8f/eHZc2euufra62+4cfXAeppoCzohwA5EyPPSUi7ayYikS44uH6zwMtgaEeRYxVQrhCnGnIwkASuXOjWXZUEKIdAqAG45LtZiiiW0lq0VuLjAbdPWkGXl8jqi5QkY8B75w2Z6ulCuZzQUp7DdUMAjsL01/+IXfuXrX//6uTOn8aq6nM1W1laOHjp6AqomQD2m8XvvjW9PHExzG5OlWgOcaUr/lhJiZAj77LKnWkC/Hydbs3Il0dCZx2VK0yxSBpItzlIS+WTTNuWN118vta4aaMYCrgKlRC0h0RA4GV5A8HUMcsjFVr1yTd+pdpoJtde0C2F2MRM5BXgh9AoiDVF7QY2KddNCc6jGAh0gnG5Tj6TYQDmbkbYsAYSTrkqpZLMFwf0WPhfAv5TYbaEUaYbNg6tSJKrWOxtQjQbQY2CdkN8obrnj5sTJY4Z/fzFqaUfiLr2EtzVymxedf6BDumyOfe8e9j5pfdCtFUAb/m6HLcKFKBb0lkKvrP0KukTWhIkPb6TVuY1zjz/+6JNPPr61sWOkOb9x/sWXXrzx5puvv+668WRMTLyGns2irquyLDDYi13dqGjB8Umv99DLkLaJyHWwwzFlEr7RCvYzdpYykCbSAPXiAV4rIDIMr07yCbjU2kZBflVNRUpA4Sh/0LMapZv7DE/rJ2eYIFRHYUWwH6fVTsETtpjtjtfyd9x8w8OPPLSYLyDGNkpMku4k2ZEDB2ZvvkEtPG0pgnfZODkXLaxNi4KfQOhiZJhw2qxBiRt2K8vKstkrtldb6KpL/l9UtV1NufMgUFi+gS7bepyNRiopFwuVYbgloTBxAspKuaiLcryyJiHVuKE78E3mFgYoqzHqGLRNbdKpErNFCsU3JRr5tTG5klAjhCbPilS1BFxryr3WTTuRqoRWgzAf4I3A9giFkmVjtJIFNEmD/GptRKLFAuJq7QENZvYMzcPSmMyITIqxVHp64NDBa46rVpq6gQ5Kzr1tScm2sow87ax+c5Ach6JESygk/nqJkW8RPS2Bs2jaPndHb1GD5ZCtB3Z/26avyyuVbToGH68/IE+5aqoFpMQqJV99+aWHv//wyy+/NJtDFIT8MLt7p86cPf3sM09ffc01l1522draGiUlYPYhyF7i8b5gkIbtZtn7nrH/G35IJYkkgElJJmFZlgVCQ0MsGut7ITMpUWmapYvZDAwwJauiyFLoNGtLcLAGPDRIt1Nmh4MdoF0lBVm/rArZl1E5CgYCBxeAaDdmxSWXXbd64PDW9g7yT3PmzJlRPr760DoCFKOf1mV9xjTsOTaBG4OYBWUZSlpQrHCYN4xAaYifyZNn3zrRtC9vnJspDdFkcFIDmWhYdAz5oBvCi/22bYpWPnP8lanUIodKrAR6lcmmLKvFnhEmn0zuvPnWPB81AMMQIO2DNLNNjgUkudV7n/uCuObOqpmrcjaZrlVGVk0tq1pmIwjDt01mmoVKwPPXNqmpatBFVC0UBhrbEnKU4YVLaPitRFNLrdpWtokGjQNilFmWQNpkkaSqFeOy1PA6bSOTfDSaa7Ej5+aq68aTrNLglgG+jpgFvCkS8wRHW3wfIrx4h1EnkZlTvovGW1e7azDevwfXOD39UY4QwSINXrXfARV42IQvicuJ6O6mBQC1RVUXbS2al1584Tv3f+vs6bNFsfBeRcwgI/kGL3Dg0KFbb7v9sksuAz0aRS9Rmo8Oc0wA5/ckMYJwpiCGAPY+SdIsy8ajUZ6PEth7lOMA/iSdpsRr8zwHz1aaQMWMgmpYQOnXKs/SDhQGKwiwVEmRLpdqQsqB1Z4jErbJAGTNovqZpnuz2Wy+d91Vl//3/91//8d/8qd7e7Ms0XVTp9no0OFDi/mitla3bRdA2o0tznXJExrbGIL0cxNonV2uNRFqQaqBWJpaLxd7aVoD3LTYLQAJpDVAqx5DwwDpAodNoAe3BudfY7JxBkB/VirYtokYmlb5aHpw/ej/+//8fzhw4PD2fE8DMVivbtiidnuBi7iu20S0s6Kd12o90fOqXrQjaUSWACBnrUe5UnnSFqB3QYQclBXVlE0CXScSZWTaFI3R7TgBW6QyepToGpBE0nFSJHo8XslTLSEzHQAYoFJStrJuyxraH6SpGOlxWat8bZxnqgE5j+lzTjn2xdJ+0S16kZOH++ZydJ1Vna+GfneRBZwjSr6C1pE1+lxtDLUL0BCuD2UsYfuDwx/T+t/WgUYvzARRL/XMcgG0uqqKeVljZs/uYvuFF59//IknTp97a1EX1olsGzfCFSnQXiJVsr2z8+QTj1e3lkeOHCEBTinEoRg4lDVZPY0QOkFlRoggRGKUaSohuwpIWYNfF4v4AQ8OHFpp01Za66osCV5baV1URVPXK+PpKAePEtXPe09reOMQ8/WIAuQ4Jxedk88cHydkHoCKD+6Ass1kNtL5Rz/4kT/7879aWQVhmhqhk2Q+m8N7gNe7ETJFo8CaADaf0NXcUpZZbYoWoEUEaisO9wcyKfFkrQDhPdE7eb5YVNSCJcuRKAGWA6a3QQy61qADAuBnwQIDIyPTKlGAyAvOO/BiYU4dMOWqbudb27ff9o52IrbLeVUT8SO6kx2j29YuwqSlAo6Qa522bVKNRmlG7AwS4ABsDvzSUoyAwRFKZAL+L8w0wfo/KacZFoo2xgDCppQigx5ZUgOnTsZZnmV0rvWow/yosZJjLK2WQk4TBZBYuO3JhWbNEXIAclLx+Xju99i9/Hc8OI/obClspEleIqu4RaEjXh4UB07Z7xCPhKYcb+cwLSRX4dEuKnBsuohRXVV7W3vGiDTTp06eeOThJ1568cUzG+eKskCVuib/p5BGJ3qUpYlKlYK4h86mQpiXX36hbeuDBw4hnaAFTP4hhpVjdTT0MmkImmpYF4jhiiSRkGyVpiAZMJHDnQyboSwXUGoH/mxoTCeVLKuiNe14PE3TtCwrwGS1pmSkUPkH20QTHFlL4wsE7MRvN6pAEea6KKrG1OPcNG3x9W99qzVNkqTQHY/8743Jc2xULUUKKWMparnAgBAix4a5vbcoyXPIdRImG6VzUczme1k2IscbmqtJURYgVBtTAy5nMHSR3xLGuoVMoAQ2bxkCc66AyBRGxTFtTCFah9BJVplqurYy216spglSLqZpcTersywJ4QiDRDDVGh3LSpsEuwSSr4SiczarwwGsWwhsl9Pugu7YjoxaGoEMNUqmaaay1ECDI2UbFtscZ/syeIXGGgbb9cXOAcEHsRB7z9LdR7oOHp1KjGUnhbI9IANM3QG3obcSYyqLki9DPmv0FBsTfjsEDPLWqc21USmYvhCRaJoSEnQXFPY5/vwbTz7x+IsvvzDb3T791lu7u3ttBaV3BrM4APC1atfX1q+5/pr19QPT8TTVmDovzamTJ6XAWn/XyNXRDicWZM/guwFhhY0qQFhlkOmcgifWiKquYT2xfEKlErM2KyUq0zb5eDQvFnVbp2maJ6B8QWo4lns7NYpScmxxly92JdlLchfRfZwXmolg7p1wCHUGpGRdm6ZaOXbof/jn//yr3/wmZXhBLRWWmCfIc9KVFXSTU9tUBKZHBC9aW4fbF5wAEpQMtTKZjPJsNp+DYwnxcNq6QtUDmhJUEJEGm59DSAXz3Kn5LuXZwuordBuRCokhLQXexapKdHLp0aNgkqRSgZIe9n/k8yEZDLnrBH5lpalTyjFpzjuNHNSAVdotobofnULkIMYwxQDPSBOdQaBBhjwe3Nqk+jisKUupNrLsekz4SHOX6ILzeV/x2/mYpV0wZjAY1sWwQdtiWAQAQ7nGHCdt9EzmEEVit8dXeTsEDHoaHuCyhHtgRQIMaF4uRF1vnt975tmnn3v26dfeePXN11+f7e01EBUFnVmDawURqqBOr93d233q6acPTteuvu7aY5decmR9HUDtWrG5sTFZmSKfhTgkiTavsJLhB1mQaabA/QxvCqpfnuWgolGpkK2Hxei0bGSzt7dDvRfSTK+PDmVYlCuNyNMMyzKBZkKPC+br92mipA24lBNKN3EOaC6kQyjAOsqBc2A98Nr66l/95V9//Wv3b25tYq81UEwxMKPSBOoqEEVEUCGW3xEwAyFnlAAJ7HeYmVYTcUzHY2nMXlGQWqAwmxqImDyGTg8nDuWtdZedYkF7CFfE5Xe4D4XEpDao6cmz7OCB1XE6aghzl7noo0IFR5KYVearrN3G8w3FnMOa3G4ds8WZoS5fNHY0SQibQbRZ4aU2i8WTaXDvWm25U9kUG78+S9sXsXWIb6jAiFh7+GhJ2LhL6th2pK7KqrT1ZNxdhgPvZP3trwpgHfPbUqG9ZEH3EIjcRVXU1WI2a6R+47XXH3ns+6+98vKrL78C3XooEEAd413hJQQKIEVKmySpTTubzX70zNMvPv/Cze+46dixSw+uH2iVWsxmLZRfg1x1gVZbVYeTIlNwQ2oDUBmQawHJR2meQjQFlGdIhKKOpABPYba2N6uyats20elovF7XFdjJSPMgeyXCmvmVcfskOL5taSTkjFkK9sgDFBa2kaMQQPLLC4FwiE4po6EcY+/c3rm3zlO9PcRZ0SE8GY9Qa6DaabrayhAkV2xyRraE91e6RSC8PrwPiLksy0zbLuD1bZoqAuuSt9jld6GjnrUddEpdiK44uHfsZ0/PBFmPRY6Y5gEdeiiXlNBTLESsJ0EiUQrbYbKq2zsWGDIIWMLCJOBMnsAQRI3LlPWRHrwIHG9gLZDxSwUnTtR2sEJi8RoohYkypzFwGpR/19DR0HVec8a+OKCL1thng0sLdsWST+y/dlrwb2LtF03A2OmGfFdN3VRNvTvbq0twouxs7j730ksPf/97zz3/zM72ppIIyEpWrC0MtkOwzmd8JbCdgZpVXZVPPPnkNH/x+ptvOHbpscPr65RVnWjoIsIzpaklFXbDBRIF2ZuleQbqM6HS2VbICD+xu7ezu7uLKO1wJmjMaGG2TY1lvT7ByLtwg+85+KNQk0crAWxSq8pTcTiHxGRyjUIUtiIaawlrqL1a/MrP/fID9z/41rlTZQXxkFxnk8lEKVFCvwibI+Kyg7xqTumBAduEsFF9GIRWk35IE0jOKUowUiDk27aj8chmXLkXC64aH4lzW8NVVIe5sK8G9YxK58AP17IcHE/oufJhJCs5vNfOoji5AAJWNNE1fpLDxg22qB9JiMm6NE1HC27zYt+cGIdBhn0a+GAEccWJ0DOtAP/AYHp4EvIQPV3AOx2fFybXrmv4pEe9yw9OvZ4j2jssIWA+0AZUZQw6ooVWt/P5bF4WEOlV4o3XTj76g+8+89wLx197ZT5fVOCsAmg1ixrjmimCm9EuipVy3jIyMlEyqcrq6eeee/21N2686frDhw+PR2NIsUBNmKogyPoFtAlMPAJ/T5qA9MWkSUrABI04hWDg+XPnGsisAo8uOLnQrQsRpDRNwfVhtTOv96J9ZkssvU8Q0y3Jz4Tk66rreSZlTMI0sTaaiwqCArwRKUbjdHps/c677378ySc3NzeSJBmPoYEf4GkRqB2lZ7hKweDPdsBIGLLBdm2NoT6j3monnkEK9sp0YsRY1m2lQUwleYqIYVRF6DanCzJTFhCZqQQESC9jYUpQxoLEboTO0yOHjx44dCTJckq9dq1BnCPLsP+drg+mOtQGOQvOS/jgburrp0EH5qHwUI0Y9HRWUBxu5LRhppN7A5JlxIQkO0Z7kQqwHw3z4+04vPwtXA1HB6l/0IrufG4/s5qiW7Oh+/sLalM2JTYcQlOwarZ3t4t5tZgXz7zy9GOPPPbKiy+cOn0aeg5BLBEsT58HH9w8eCfb0dH+Y92GLpEJ9jtm14m16eoV1161tr6+Os4hSAZprdj5XYP1u7q6ura+PpmMJpPp6srKeDSB0nYstUnSdDafnT/31ng8ybMcEoywJHh1de3o0SMr4wnkTroAiJU2NFuuy2hwSuE+hDQuOEAJt4I3ZE72rGC6lc9XxLgigBpIlebp2mQllfpXf/1Xn3jqiTSDLG2CH7GFTawoNiypiwmDMUw5Z62pmhqg9jCq5AmYIkyEQKuTtIb4UXHZ0WPXX3ttUVaLxXxvvlhgfQhp+xJAAWH4FFLLR/na2irwzXyUpwlmPKgUYzpGi3ZjfvNtNx295Ni1112bKt1C90iE3qB91tVzndFKjnFEQYG8miDeuaRzSqxjQ96L4Deh17m9ip9AGk5mgR3hiJhBIFd+RNEX9xk7k6dcxOPrOun6d+b/DCnt7kDyKYpFWZbI5smXHxGwc9v1NApW9uyMC8J7G8jEYvRMEKj4MEiZbJoKMh3L2Wy+ubf7o6eeevyxx157+eXNzfMFVupZYCebYUIN9Kx0omHY7OAmzLrdhCj/wF2FqE7zvd3nnn12dTy96brrRuDTQvgvkAQyzaA8HyqKsnwEWRmpBnHUVkBozWJzYz6bUb4zZEdiXsSRI0dXppPpZALmKHFo1xQE/Ejoryaac6/r0zYAEqBmgHuR4PUEbFfHLxN5f7AmphUI5aOlURs7O1ddeenHP/mRl46/vCigax5Z3cYDfIdbWbREGkQNHmkIcYOKAElIhIPrKJwqKACVErzzyJabRrZZKz567z3XX39zioVfwFvbFuqVEgi/GNMWJYKbtNjCFyIz6WQ8zpIkg57MgKcF3TKE1KNRs2hMlq6sTaWBqg8LjMM2GJsCZ6Hb5GswQBRA+2DJCCa7+jrJvtxw2Pg+590iYodqxiXGqbHKN/cIO3oMynJvwEuk5xI/Vu/6feTzwFfkULTKvc+07VzorosLLjs3cpoUeeT7/YGZ7FWihP59oLhWVQ2pqpVZ7JXb27MnfvTYM08/c/L1Eztb22VZwQ0hY56V6Vt3MPOvOTPYORjgI2qbGVQeTBcsIZFD7ZQ7P3r22Suuvfzg2sFJljXgdwTnBaK06zwfTcaT8WRijCjmi9353vb2Nsh/wFNJivkizTLdqsNHjkzHk8l4ggzC8XakWwLc9dyOVwkG6oVYnXU5e9wfD78XVVhwtyIW90L/NPCWY9YU1KNW25tbVx67+sorrnrltVd8WnWYFqv+gfeICj4p31oJmSjI1gZeIFSeptRskRKHbFKPHT9myAnRltU9d96VZUldVRAylyLNcoqjQrBKp0bKCcSlsXMMxOYRX0QYAiyCEBUgt6iiNWJvD6JtyDuqCth0ktiqPL9z/GvbHc67npMfRDbQIwpYGXacs8HnYEMTwfkNHbeJZJalazoVwAmErwbmOmdA1XCsIOiZDN+DK6yR3A1BN2sgdIA/Bo9OmTN3YfmXQbiYYCjEevLbO0DphIyq7rPxqIsqSVIoroeoT9o2TdFWpm7Pnz93bnfr2aefee65Z988fuL06VNVU3k8RbeNnO3rNiZBJnplgMsspzyQ+ycoBpgPL8qieP65l8f56KYbr11dO4A1RshPMPmI6oB393a2N7d2drbRuwwOXwggwJd6dW1lbWVlPBlBvB9haHESrTsosCvXvMQTsE2xgPxDysv21OsR+Bj1uj5GNu/AxZwyMBcxGIaVScbIU+c3P/GpT37vsQePH3+FoHEtz3KGBkZsMTILyqcyUJ+cAC1hPbNUejweQwwKT6JcNKoZsV1W8SclVDYa6dWxzsYq01JnUIyHwbJWqAZyYL3jGMBDYBgSonGQjIpikmYGuwpRVgZ8i6ow1RK5TE+XEGEpznWRcJDhwfViE6rQOYlV45BOaw1hh7/ps7n3MedIOfdam+DeKU87VPreMyXDrx7x06Np+hw8S962XiTYfoEmewBX4etlHwbdHecZXHAQZ+EZqIHbeE2i+17xpBDPglUnuBOLv+rx1nUCCYDQ7VMni6JoyhoCRotma2f3hReefvnll069duKt0ycXizlCIKCd6liVl1BB4sUvxX8NfI0CNlDeCgIMdg8C4BoILM0f++EPp+PpnXfeAam62GFXgbOqPn3m7FtnzlDwU6mkbqCPVqLTyWR8YO3ggbX1yXjkXEExWhmOjqcS2aQ2a6o4v7OjVdTtQWRRj6YYDTTc16eRgZctxUiXMyq0EpN8tCjK66+7aX394FunT5NRaD0+SIAIk2vzFdoavAlYm6G0EaNRXlZQNe1VFmIVYU2dfwqqn8paNSZNE8j3cNkhBCcONftUAYKPg3oGDKcR7LLNoyLa1BpEL6ZyZVBKnQQHqMt7IiPAyk8LRh2mOZh21BxOQHo8QJFgqVpQxGNIgOhwzc5pr1hRGyxuydczhPBdQ6n+XZnPORLsXB/3Z4TnRtTk7ryPIr1c4YfFVdAcoOOHHlLMIyhMh7ztervZ6D1IYIdGT/ej9gUK0A9FK+qqns1mTdMWi3Jne3b8tddeevHV11567dTJE9vbOzBnrSL3rMWk8q5Zn+YfD7CfNOawcAK/o6pXXHWoxoN8PKVm27MHHnzoksNHPvbRj1w5Heu6fmtn5/RbbzVVnSWpRvgXTCeEioUD6wcOrR8Y5WME3SfEHTwQMM7uDJ+lExQFzBvBoC+avgR5a1/G1TC4mJafememeZ4FRX6A5sfytMg4bOu9uvrlX/zFh7/3/e+cPVtDQgSwCTKooXUwuJGn5DzzaerY6Bj4dqKBp2I6KjUkh0wrV4EY8jnRuQyAZAl4eqDIgwJLoEGTFYpaHDBB06ZpOtKQPU6uPfQVWtkApI9o2Biuy0P6klNdSCgRoF2wXbnThy0x1mQpQB+AxJa2xXJt7n0iduAci4y6Yjwfn+3q7y3Yb4HIIqt4kM4YiTISczYQ0757V/au8U/YN8pkDXzSokNip1Ne+JnRZb37uCeDqtZyewZmsK7qBFICYGdhX2yIPDdNXcpyc3H21JnjZ06cbstqc2sHOuhqjRXwPpLh91EwJkNMsSd+2SR0P3YyHa5VoONhZ8BGvHXm3B//xz89duDQJz71yUuvvHxtNG5G4I/Bzl2g/FGWMijZYK7BbkWsH4gRg7sI03Ip+855b70nkFCpkaSs7uzyv2wdkiVhHmdyjtcQj2rqJoMse6Q0Znwhmm7bFKVaS991152PPvHDrZ0tAX4lUE2NMdPRGNuZQhEmIAm1JdUwk2CtMYEWkzcslg41oSG+SUg/NjaGwwXcChK/EPyDQdrUKNK7LRHmyPIASocYGpZQ2C6OVgyTwx5J21JpbJd2dhnTC73UYlISTDL8D6J89jFUW0M/UbjBl0bYfhDO0mUOsIEoqunsd9IPlgV196W1yIvGFWEmlYdUXObo6o/Mne9DBvQhQ8r2CgJ7nb4qwewDanYZDSDJs7ZuMHsJ3FLYMBbYdbm3eOappzfPbn3k/fclGZTC1bVIMsigoCwlW+IeuhSFwlh+xL/td1Ac0vdJoizwqq2Ksjr55pl/+6Xf+6Pf//ebG29liTx28ODadGo0aN6NMOe3t156+eWnn376jTfe2JnttQICwkmWigQSA0kvdbEka0nYQl8SvBD4teLXpm5QBmVICeO5zySG4G2hCgCsVjA5EWs2XkO4pKnK8vzm7Bc/+/M33XAj1aZCyMa65XIqQsrz0drqig3GIp0SRyE4DKjy8VA+FIEjj7gLDpJ+AFXRaZaAFgzGLZi3YMZa6iUATPjO0TPUpkGSJ+R5ohIPxI8RIJDi2MAJIwu2u6+XdH2CdaRnqdDvcm9koJ6PXdqxSJkidYA0XCHeMMgLVHNcyC6oc5aC7Y15TkivnzCTystFqBcrjHj2Pzq349yif5r/E57gK5AjT1qkuzsQtt6D+4OHP10nVlPWkBsEmDhgHZVFuSgKacxjjz7+o6d+dOc77nrlleNPPf/s3mznfR/4oBbZidMnTr3xhsBQB2avOioNemn38b0P2GSyGfHgG8hAbNwY1I+qrYTUVfvai6986c0T11x19S/+0i9ffeVlZ89t7c73oP8CBELE6ydee+jh7x07csk97/6x6667MU/1KB9XZQVbEhUZehiVFgWr15q+WK7gnM22ihC3FY/5OnFkhe8cQadHycTGp2wFkTcEYUqgpVrdqCx73/vufeKpx+qqRmioVmoFSR3YpMeYdjwa6SSBjFQcA0DOYDZjorCDIEH/MU9MpwCS3E5Y35HaKiuPNkR7n8SLq6qm73y6VrDtKZkKU014+lcg176rqCfW+FUensjvgeDxsrAhCHKmNZaYxKX2oItZC8gZgDJo7O7pceYVF2nd0aGYtSo4p2F+w76cXvKluUjhTkVV6PsgXyq7Kkxrt6QwVnF8oQ38ggRsaiEROhix5IyCdB9QnutmtjsXqjnx5ps/evLJd9x4y1iJ7z7y4KlTJ66/5uZPfuJnbrjm6i//9V/9xxNvYMqeBK8qw7DYFyqg92WgdvZXDBeGHhrArCJTvZJazs0Lz7/0f/u//1/fc9fdv/Dzv3jsyLGz58/PFjMl5dEjR7e3d5544olHHnn4gx/+8Xvee+9Vl1+RJhmCursboj1LqVYgc5F0XUNTJ5ddHb/FnvQ+SouvbB0iVQNYXuPpmEBCIPmJQs42Ikqw8RCJrppaTKc33XyzhOpWwKnWaWr5A5FiC9Vd6HSCW1EQh/wLJSoJPvnBexAcq4PqJSLXDADqRmmekocciRREqnXi2rJqW+UT8qniJCaesEmeLUYaQY2NSLSzhnG+WzAyOW2xnYpqCjorbLSJFGrneKbRWgr27jxucA8V4keU7F9oeF9y8rnwceGMSO7Ftg9GqHLyYw1VSvgfuS86+iEaKklg29uyxOoZ0YqqhB8Qi7heVOX3H3vENG2aqP/4F3/y6mvHj60f+Uf/9B+/8+Z3jRJx9swpmhfvFA3+8VjUMieh/2FAEMfxh/BSgWc7/gP6ctVUUhU7i/vvf+DxJ5/4iU9/6l133L42HkMrwfX1eVmdPvPW1ubGX//1Vx566KGf//mfu/uOe0bjDHIeWPNhm+rsMfRsgoZX30KfJrbPyPGKg3EYuVCNDPEqiBAQ8AVtSKvoQq4YAjCX5bgaGwGBUSxtJ2WRQkrom2jaArM77NMRIldKyHOum4bMVDdDnY1GTBO3OTjSgHox3crmMNq0ZHYZeYQC7JZhHUoso2KC1ymn3tETu5A6dT7eO9MhdJo1Z/txHy89nWYDn08wm9RQE0HmrecrJK2LvrnZNU3ZCJjSEEd2O5bsMqJkwsT76ji5e7a1nz5OulQFyGU2U4UxPedk7owqelFrNSAUPyVyIJo5OElqcKPUYIpA/S4gqxv52vETJ984cdtNN9z//e+/cOL1+Wz3n/7O//a9t7776huu/Of/r//h2eefqaECxt4Yt6MFTLMfRvTMZjX82qF0pnJ2P3PKn/M54a0BKr1VanNj6/d/7w/+fDT6xV/4xZtvveWVlzdG09F0Zbq1sZkm2XQ8Xp+s5CMw9KBwD4oUbY0vNljzxQoOOyRg1pMZbquWojeg3Glw9ZUSk5m8d9tqc86icNIbfFGikalRbUUQAqQ9+qaotj7COcihdMNBXzZVVc1m8wMH1rCAySo4vkgLb2Nb/1jwSClrcu8hz6f6M5RmNisZpbLFieWiMHR49AUEHTOXRspBLMI7B2oJSndPDfUhoc6k+pNJdYe3p0w+ilSjCz1iWybIVDtAF87qOdaWUeUy/++S+kIuxwffrn9LP3VuJslk8c2/2Mu4N2E9qKL7BrGMC20zsZAQABUJ70DJg3Vdz4tid2/nqSeeHIns2KFjp06e3J0vLr38ive879618eTLf/4XX7v/W2+d39AakNNd+q+DZfOGE1X7xLk7Tl8e8Ep3xDIPRDF3tj3TTjA0XKpajHvMd/b+xb/8H1em05/57Od/5nOfPbCy9q3i20rpX/rCz954002UdoS4iUgTwehFIC5LArYMODzFArd3F9qmUsHsocPY1Wf4HA/HCqzPGLqUGbOqx2VVv/H6a4vFDEoaqYcjw1IJyExWPGrMMFfr65ODBw4Q3rbtcxUZYbb7MambwIvxodR+DbzXDVXv27vDBkL24CsZeHaUU09ZzkQX+S0Sq72/HUEHsUe7o5tkFdU0BLXA80c7i9aGR/WDe4DYwe3c5XHlzu9cgedTSfZE91TvF49CNxc4+tYxVVMpAO8bDMuw0fsEg4Gb0twkooFkqmJWgeospEvfb4qymi9mb7x+fOPcmeuvueZrD3zj5ImTpii/8IWfv/ySK1cOr3/la19/4bkXsxzCjCQuCN0QbDkgBtg6PgW9P8hIkY7s9WHCjkyE+C62XUdjtJE6T0ZaFfPF7//Bl773nW9//gtfeNed73zvPfdddvkxQPYBfCUEXneV+a5QMEps9ro6EbavZHDld7CnUYJjIBzR4QGHy6UpWZBhn5eGPjGS8KvTkWiKf/W/fOlb999flAUAzthkiGA52q4L9H4uRIgZUpYUKPOD7XrLyKw2h0WMaAxVOgEwLFAqMGaIS+QUVyRsBba6gnQZPNxMWxlgW5Z1fD+RJho2mVcHfR6jJ8oABRz0fmbCdtxQjPY9RyPq9fUMncGYaJOwbeQtcifzB8zbruoXdHOe2tERzeGDDgvYz+3jJo6i64AGRGXVbMqWHUNC3rJs6KMqVZ7nEoPACAFTFYsF+XXeePNEYuQll1z24kuvzmezK6++9r4PffSSK47+7pf+5cM/+D76fQCTHXxqqOghbgeCRGJokQHBdWrxcAzhJzYwJ+uimLI/uxdqsl/T/oXiVejbopQsi/kbb548t7Xxgfffd+UVl2LbYQjXCGPBLymMYQcX8NmdLKOkKmsVh7F53YGCwE0DNnAG+D7U+sQOG+NfdnTYW7SVCVQ4l7vFH//hn33jG/e/9urrlBYbh87D4UYFrJCKpSoAzS4BoIcNCVfX+rt9lgNIYEDyIdxRW+8FsJQusSzc3nnsHNR18JrHtGA/4t5aJkJZ0Cg4iXri1cV+ol7fVrV0STU+Jd6HW6zgxfwTn4XGd7IZUMWZSc/G7Xa+Xebw+AGC6Xzsb8h4fE/L3Y8EoztRoZZdOP/MKEV0/1v4xnomASybRJiihD5EEAGty6KuijpLszOnz556/fVrL7304Ue/d/yNV01b/PznfuaaK6/41ne++Vdf/uq5828laQa7BBvgerZqnSWoUUIxgEOTcFyZs0/vrvKrEhTXeMg2x2homoK5jX39wM1jhDhy4PCv//pv/uqv/sKB1YOLYpGnaQO1zGDWW9HrxatrbRSsUPwLrGObJersQrtZscsBIOCA8Z8mCdaFWbKnebD1hiiiodk39BTRbTVfzJvZfLGxvY35hLbFgQcIZomDwbNDk4M6M1dNfL5uqIOwdb04/9gcHBKw/K1t7izWORG1eA8TGpsEIEjJ1RaiwrZ4cY6HvouX+SW8WIw9a16exVLbXceiWv56B8pD4KEQF4QgNqaa0D6x8Z9gg/OsVr9LeKLVcmdX37Z3Ks2A5bv0bsPHUoGM4CQk4ZYYvD01p3Njr7m4aqQWewVBi5O2mS/msLNVc/z1l4Uxp2db93/3oc3t7auuuOquu+49eujgSz964aWXXgLTF1t0s0JQ+Ncn8iDiDdKVq5v1hkVvNpllG2/S3hR2bFEySmA5EVoZVr6qqtXV9d/5b//JT/7Ux9fHBxd1qdO8bgXlCJDwJc7iMzVYyWMQgE2cdOXmFeIc0CcVWyhNxmOoLUBHtJfeJPSw02INbgYo66+b+RzgkLN8dWWKpAXczeFFOrJnRRb+n2DuuIE4ZhL0En8FzQeWdrYEuUrUyKDkwu7qrINNSXFdRqwDjkBtnTT1xfROUFjO2/NID+29+M1cXmREv0HqYtgLqRehgnG7W7p03N545ZZlSDFOc3GEtpxQlx+Mq+3ziAG92qvmLGd16JKlRj5T1yl/LRFlI1I5XxSQM98K6IxQQjfKc2+de+3FVw4fPLR1/q2zp89I0f7ET/3EFe+4+mtf+8svf/k/7e3sjieTpiopE9ZtIVvr6FyduDBQl4BmmFObLzi1gWI6mJ0xYdN6YogVnOfgXjZiXi4OHzz8X//j3/nUp35ybeWQ1jKVGqUuvBk2KEa/M0bhyPilvAgX2rUEjLLXdxFlY7NgmaCHp1mm0gSS1WzbIcoyBnYIoeWqFkqXdZWmyeEjB8+cPlfXi7HUBw5MoUDY0Z4DtXJlPC4+4SYKbV5vVRBxeq4dXPH4XGpPj2nPQL6JhsiwAz8hyrOqDK/Bd06hSI5afdhlLnZqCEJ1vdd/OoGOEKq2v/Mwlo08Bxlsr6IPCTIH88AwLQfqv1GFplF2/MumF4oNRuz+G63jjQrX0rfeXO4YybJLwxcIG8dIdV62QmaOUgiO4CrIu0PYn0zs+Qni51oWX1c15BW3omiL1159qVrMZ+Xukz/6UbFYrKwcuO+977vm2OX/6mv/4pnnn1dal2WJmEdcGcFNBQUntqDFDwzA2Xy4sR824uoCp5iOB48lQeHMeFvGaJW0TT0vyisuveyf/u9+58c//NHV8fooSyuAugUsaKczW7eVtS+tievLTO0zfNPizozZ5qhwTpOkaZ4DFLoNgKN/yz5Cy2QtUUWzeeZ8muatSOazBaZ3A0T6KBvlWbZjn2tfCUsLvQrAeRXT5jw8hVfRASvHJqZ4MCuopEQjIoWGbgrqBTG8h626HQJrDKTh0xKtY6mTpOi0c4jiuFxlp8U5MWrzsK3+yxYp9Hyy/U2984qHo5n+bK0AoF4SvYx6rXx1tpixepHLPwnf0pkuhzqygmOSYGl1Ad+PcYE+HcXardPMuuZhtLcjzdipS9TLi1UycgWiR7rkPo2kGdX82Bqx1gBWS900xaLeKxabm6c3trefefaF9bXJa8dfb0x73/vee/Xll/2bf/WvvvngtxZFkWSZ64Lp3smmA7n8BZck5Cwup2XZupPIy+AKINmkxbPcm5JQ4wJ7SqvFfCGEuum6m/6b3/knH/rA+9NsnOSQ7FIUFeJDuj7ELkPSpk/a1sSuttW2qLH+nDBs/LltQeoqLbEHuclzyDRGvgBtDYFDgZO7lVoWdfnaU6996xvfefrpp7/4xV+5+667EiEnK+PFXiFzcLNl+cjJXmoxFTxn8S6zO8VhfDhZbDVqMrDt8mJkAn6DXZ8mrZCAtjuZZHBPAFTxXjW+K6zGGtBuYo+w08qhzxn2vCAC9iTn2bQ3ogL8Ml8tJoG9XuFlN6NoSjfxxi/0zPGNIkOwzQpentTv0ryoKSd/hx4pLKFkS0ghw85vzOXmQGdfRs72zvO8mmJ1f1gULDKD2mCrRXCXW6zWDKkJlv0nIgXMuqatoVxUJ0Vd7O5u1Iv2lWde2NrePL95RggzXRkfO3psc2f+8PcfPXXqdJrlVVUyOEerOiMvpPaXQedDYRzMHqtSMBqOJqKjsIYSNeu9cydRnBmXXstZMZdC3fuee/7Rf/1f3XzrzXk2TUbQUG++mBMpMjhnBw1rTWHQoZ2MQRnszErn47HbSglZzBARHrwGQBvj8Qg+hJxHiE9hbxOjE1PMywe/+c2vf+sbp14/tbW7ef8DD113/U2XHDmaKlmX+Xy299H7PvRXf/U3J+Rx/+q8YM6Tp/3IDcmvnCeP4Luz5VWWFCGggAZvliZ5nuNWISkNNQSuJ4znUHAbB1LmoeIszXnCpFIHrDq09OS9xTyyE6WDcHXcjjeSt8zoDW9DL4I1VIhpYCs62AYOe8n7gS3/71mUcQDsIizinvDoXhOIf8ndOLZl9yI/NQ6eGXtmqhaK2NlduQXg2UrvruSwgLapQohiDi38PPcYJ9lTz/3w2WeeKYuF1CrL8qKcnT575t/8L//zE489iu3JCDLGq10+hGCr5vl7uGQk511h0x8G09Oag8IU/+uhBzDxVxrZbm/vrK6s/uznPvf5z//8zdddNV47iLW4YjEvCBSW9iv5nKnkyBcukIfchkXRE8WVKvs62JR6USxm8zmG73SajkaQY2yqFiI6om3qsh2tjBopzm2c/5Pf/w/3P/jA5s5WU9StqR789jc+eO+PHb38I2mTi2TU6HLRzNfWD5C57IpsHPfvWw82Wce+O440iF2bksHoVycAtQtlT0Wp0wzSVsCbBRh08D1WGnkCdnwqGK1Bow4bxn7h8pJdTwbmfvJFEH7bWfL2LxO0cfdVeDR7tusvKTn1cuKN4kFdIX9RBHqhU4bOIo2vc45X1aPxXYRbK/gfyUvXSIllpAHxI4iq/UeJpyWiFEUFwZWqgP92t8+N08mrr7+ysbsFqw/emDbR+WMPP9rU9WKxwH7QZUu1tV0GF+wDa5Z0ZKtL1WVj6O9ar0/H/afcCT68bqSsGrO+vv6f//pvfvKnfuKKK67NBCD2A2x1WRdV2bQ1NC8mtA00aj318uIEhmXM18J+Cm0QMIcRYm5QJA8dDhWAVJV1W4tWJUku06apmu888I1v3P+tp3/4HPYrg4K+Ruj5fPbgg9+79c67Ll29ZCbkKB8phVhURLK2bMvzvXiPhPngqpSdQh8qZdWRkGQLJFpX88WC5JjA8kNuoJBKgT0zAnsP2nLHkxORTHiuP5tl2XUdQtGvHniAxUC8A9p5xVzGtq2csp5zry4OxKLoGDIaxb7HRRAyHdxfw8TKftfHSjrNcFS56AQr9PRKKiyqjy/3dw+bvWN4W39TaxJTlpCjW0J3ybouldCPPvHoc88+i/3BKIMHCABcuBXUwUA+IBS+QQ4geVAdy7adV20rKfsKnnvxv/reP/Zy3rix+8tPhjWBKOM6hQbW9Sgd/Rf/m9/6+c9/9tjRS5Ishe2G0qUGNPUSGvsgbTjvFYfKQeuXOWP6tpENStVNgZ2BtIJeRFLruga1uSqL1pgsGbfCnN/c+rN//6ePPPrQWxsbTVnLBDQUMHCEaox48MEH77jrnZ/5+GdU0uRp/od/9IdvbZxFO8BHyAcsf0ZxXtm1U+pVXJf64LY9lPCYqioSlb300nHoT0ToOWCzu/4x3i3sF4F7pLwji2vDka3jVauewtphNT711d3Mxbm9ALd/eyXZ6xEe9coJYPtXH0MDjhhjgJvAw5tsaK33PToydtkd+2o1fRCojpUu2LVEH51uoFkRL2PgL9GhZ7cDXKZfIleyg4Usq6oUxe7ZrVE6OnPq1MbWJqAXQeMR118DulTDQyATiIDaSOY6IHPXajiEwVmmgNWwuMni9AQnse0IecA1tiRQH0ZIMN0YUReLLM1/67/8h7/1D35tZXIAEgchmZto0jTgebZRltCfhUgYzWDSIZ0xFxQ1Mtr9UhljCiwCSpWcTlahRrctTA0wj1LqSZ6cO7/11a985bsPfefUiTM7s90kVdhWnkQsYE2leb4o5t9/5NG77n735ZceVWm+qMz27q4nzVB/2REvQQLzpI5oG/kNQrQMXqY0NW2zs7cL+XRtNRIZlvFgFnS0Df30RyzS/sDlKufG0f5kXLjja4nCUow1eLnL7+ZezAeovG8t7BwWMKWn8si3iI++Z9sTmP1lyE3akapRNvJFHC6rJrqDi0J1VW1HBejjhbRFLWsJkdCu/637GuHmjsAwF9pUIlEH1te33nhjZbLynSceeOyxx5u6plaOWIcqMAsPIMhJ9/TIvzYsyWjYhcc66mj3bdlPzqflhsvgd0LPMTIWySqqaoDpuubKq/6rf/jbn/7Zn5qkK/P5XCcpJJhCH/emrKBs1rVGcz1Bnd/Z9XtBxgSdKynmMaDA2jbdEuMxYPsmjWlG2agum0ynC7F4/cSJ3//933/8scd3d7ZhLTREXKuiUhBbIusVmiUaKR595JHnP/i+S9Z/fLI6PnL4SJbCWCsDipOnY+eKiWfJKb9WeHoo85h0ac83Tbtb7e3t7mVpLqB9edPULWJYOv+cv3+wQCO+4OnIE1eEczGwl7o07K/ylBYexEnWimR3jxAFcHQe059fIrZTBojCL1081LCh+i8Qn9aZ/YErBtOrIvbKPrJMLjKPuL8ZtQ70tAN52RAiC9gMki8bBFYjyVRoUe3t1bvzyXTlsccfP3XmjE3Txd3j2/IBCji1AnEWLZ/Ezj9hlXqR9njWnXnTe7V4ajFqohUEdbHTwm/99m998jOfWF9d26vKlekUpS+UDNTYzpNcZwTyYxVoa/TiR/hGaHpCvBSL1IKjlxJR8KEtGLyjvDVtuSjLqqiwF+mOmZXniy//1V889NBD586eg1SNfNSaan1l7cChAy+/8FILN6dIAfiQtEpmuzuPPvnD2297V5OqX/rsLzz9+NMnXnvNh3TIzIjClmyC2MR1p9uZwFBKAfjPTVsuCurYMp6MESHF5X6QKebME14+wvSkoNdyOeiXLd7dzF3q3WpMHee+ZRtfYiu/n1bKNi6/NT93aF/L4R3WOzPykg64mTvfDDmifbKN/TnmXrEgINA/pmq7FBMXS0NftKaU5O5Alzi7/QpKxIVuRCPqtjp27PCX/+bLz/3oGQA0tfDiIHip0BSaImECk7sFS4LhkxvU+0DDIZHZmcSxquGYgUsq4kX89K7YP0EDbiN2A/nlX//ipz/5k1rkrZDT8QgCpBATasuqxLQN0Pxdjr4Hw7EgGxRRohehjR0UaRdpdGiv8Ml4NDKtmW1uiTSdrmRbZze++pWvPfbEYy+/+up8vgvQt9Bd0KyvH/rZz3y2qOevv/p63dSQzYIocNihEwLF333gwffc/q5333ufWhG33X77Q48/NDtzWijIQebzFdMv59TeUiQR55MO0Y7CtvdVWe3NZkmaUus4hAnGulO3l/gOcEDWgVx8/UEU3XUKrC3MGNZaOx/GAjASvEuu8HvBzX+wLO2g2AcDqcoyph3v6d1X7g64vjrC17Xw6J3FXVvsMUyL9gg57il9ndzOJ7SYTaADngN1GFCkh2Izdv2pR2C1W4zy9JFHHz1//pzvZQ273FUtIdA5VIe71I249I5NinOt+KC88zDSHnKeFBYbcV+xLTZgChioWE60/tnPfe6nPvvJdDxORnnbAspMURdVXdYN+Gp8Z0Br9Trq9bQMhO4EMinpoeGg11vxYg3wdKIsyroVZWnOb229+OorX/rSl7769a8+98KLs925EroCcGVz+aWX/Wef+/mbb7r59InT0+lqolMssYVqdIJc1Gm2tbn50GOPbG9vL+r6cz/9s7e+43Z4dt1p2uLrTLyXwJK1twmDz8q2vKbGwojB0NTY8xwdAVjbmaYIamebleGO8XF1py6zKQ8+aG+0BokUDbKzozpCla2hHCoA8gTQo4NwU17a5C/weyS4+Ez3cnZBMPWHvvZ3GrqLlTTdzxnMQ/hs2RDsia7fQXQlLzmHlA4qG4ifP3j3iIO6gn4hjx5c/ZO/+IvnX3ihagBX0TWAxSlEGxKLhNHZHadg93SOEBN2wQFKegtVgb25WmqZWN4HPhjA0JgXs/vuuffzP/Nzh6eH29rk0wzwqzEfgxKcCZHREqQDs3J1V5Ywoa4ATXnI1IMe4XiBQCRaFEpKgFWSAFuE/qZlXUxWx48+9YOvf/2bZ8+e2tnY2d2bSQ3VVk3TJKm+6oqrvvBTP3vjtde++OJr29s7l15+ydZmfvb0GWwLRxJNIcKneOC737v33e89dvDI2pG1O++664kfPrpxbiOUJpLe7mKqTt8KfNLX+ntMXIrIQv8hoFvo+WrjvYhfAZECjA3jTDTBc9UBMQ2pIY6t9ywii7mLoQcW7I8Wr5uE0EkhtE8PdO6lOZet3pKJ9DlXf7T/ljG9pGOaSVsh9vbMXsZr8Nn9sv+OHsnhZzuI7Hxm2DnOXINkddKiwT3Ms3qiI5AZqaqWM6hKFKJuxHj1u9995NTp00ASCOvpa3BJ4cTG2RHXZm5D+4DhWXKGGmFW2LJspxDSH5Zsz/JyabwYxDTSlI05fODoBz/24UsuP7S+ug45RrCPAeaCMp1Ja/a4Vo6IXbIEmgALcClD+pVSOs3zBEFwHEAPVdki/nliU+hpjiZtlrT6jeOvn3/rfFEUUHoHPccqocxll1zykY9/eGtjp5mqsq0//LEP/8ynP/P5n/6ZQ4cOWPhLtwuk0ns7O4/+8Mnt+XxnvvjC5z9/6ztuI7w7q+CTe5ArusE369QZApF0c4UKFHqZpdjd3SvLEpMPtQJ3ATZgxl6qdvdYSeAlYyhXcIoyT270+jPbpEwEsk8CWolXsNyjWGm3y5/zTTs4Tw9V0K7WghOZpwyfxTSs/pnOzmPPjyR2LFmXS/EI7YZPWmCAjgeysTpVMtY4euZtZF0igLc3ksLL9TVS9yI2mVYIVYhSJPJPf+/3Hn3ysaJcUHmqK2lHyqDCd4RW8nHL3o25RhC9q/udZbkSPDFmBVHaDde3PKPB5DBCMZeLAnoi/vRPf/ae995Tm0QqnWYZoGgiPZZQaASSFbRo73OzdQp2l1R1vSgKKDmUCtoRTMbU0h7bD1uAO5tdTH0fDRZm1aUWcnfr/Lm33oKukFBybEv1oaBE6tWVgxvnts7P9954+c3p6vqpkyeeeOTRyy69+pprbsyhJ5OFcIJkRpRb999//1NPPVnNy7X10T33vGd1fR0TfhHjyosQ1yuKTx1KXbvhldQZVFLkSummabd2ts6dP19B4ABbFCrsD5po8G6Q3CblIiBJBwvIWsCMgsMRm23ICKwT3NGnp16EMHCEHHosxzovVz8DYQQ4Bos75gplh7d+55Dxvu5SsYUV9EPhNDREUkMfdNX8oJu4ybDV04NHNLDA7GJXByHd2aB9nyfFpBUg8JD1qxWxKoT6m2986/y5txLwZDbeh0/Zh8EP5DuIdBhfJ0WebYuuRuMgxck0plYf3EZmyn3wSVPPh1vecdtPffbTB9cPjUdjmWKFbVOD39mlVoF1W1UAKgKZJpUlZsqFRoRNILlEp1maj0bYPAE+D+jPLm0ESg7bBtqoFqWSqmmqf/0Hf/Dt7z3QtHWWpwqba2NyZlNX9UsvvPjM408fPHiwberVI4fOnN8+furUA995YJTlV15+RZpCPIk2vpZCZ+l8tvPQo4/szor5ovmlz3/hxhtvMcYkOslQ4hPhOMRXniMNn1OrRLTMzWKx2Nzc2Nra3NraBCRgyOnRWZYlWQL6nlZZMjaE+eDbzVkC9huIQbPFh/dlOWeGhUaiOj9mOHtBGtdYOHEbRGZ3y/CNGAQRlVKHxhGRDOpsr86dJP+tuxeDMRLbof2DO0JiATT0NK+kMxth2U3tqT6pxUtZe1A7O2d3hEs4uXqHFM0o6ZlA9F/61//rcy89R8CpPkLo1Tpf+O66fIVb9kmXDdtra346gpllYSPcFLkYSjiVdHdy0xVFeWC69su//EtXXHbZNB+PRilts6ZpQe4CqVqXW93UZV0VeFRliWjV9kiSJIcyPviTJqkwBoS26/zr3LywGE0D1AvkWzdJIsu6ff7ZZ06cfLNpm2KxmM8XANrs5qMRzWy+q7LEGP3UY4+cPXniyMFDe7PZu+5857VXXLE6XcFwOtXTg3upqOr7v/3tl198Ulbt4SNr991733g6NQLStqCDOUJPUHGYJWNC7cBkuLou92Z7G1vnz2+e39raLMqirgGnHo35JE3Tuqln8zkksTTmHbfcKFNRG8eiWKdXu5WY7zkSv45qw54LqRRMLDvQDtsb0ROu215OPe6RE/uRuatIU0A0CK+I8T3MLYuBugXT2e1dYoq9YUM/92V3GJijqWgKuxS+j0MtDMEShavatO9JDX+oOIkrFbHLzOMaheEC5Ogzbzz/l1/9yvmtLSGhYQp2FaNCWbcMVAyAYHc9YKthhtZ9F5Y35+0rQiQIQqb38pRnaKSoyvLTn/npu+55T56MR9loPJmmiYagdF0h6A3P0MCO2EDVJIOhkRYJD62T0WiU5zl5d8gwsJCNeA4oMKi5oSSv0wyArmrRPPS9hzd3Ng1q2gj3AdXFxFkAc6OpTr115rGHvteWiyxLP/zxD9z5ntve/4n76r3Zu+5412WXXOJ6h0PeCPa0ktsb57/7vR+c3dg8v1n+F1/8B7fdcmtZgHJOSjJg7WMdr23tgukr4/G4bdoKYLvh5YgfJRq6pVDD3hxCR2KxKBaLhVAqzbK1g+u6tjchXzzjtX4fOE92JHy7vglSkrx/ARNk+La0biZLrsHxwIzQ2GnW39nOSvDQfdFX7qJ4sw1Qi1xCO70rLnBiLIa7t9iXTC/u/txFb38nhRSK2mKux/hgYGhMsVAvPPD9N199A/VlK2GRZ5PTERafYKIriCHZFrnBlPIaFPMV8ZeNpyFobORDJQM4gEF6Ix4LAI00WsvatEeOXfrBD314de1gkiXpOE2zFHZzhQBe1FcHSdFjfxNsMrXdIRck9fjJ0nSU5xASQ0gOoPGqogJa6iFE4o4wAIpFVc6at85s3XvXXTddd2OSJPBQ1/ITO80TwIWclcXx4ycWwtx6y82XH7lE68lbr55eX1nb2tyaLQogHMDZIy0a+qc0Tf31b37jsR88vLezMzmg3/3ud09X1iqI4+My4QaGhoDUFcXA8OqmznNoRJbn2QgUiTxJUhpHPoJkk7qpi6IAJwUWaSfKHDhwAH6FDywgppvgIG7JnHGi1+Itswiza7mCkSoyGnzDZJeLSnAi1hJylk+c2BHved+aJhjhzkESkJ+9Fd49gn7Z+SMi35aXl13ZYN4GnXF9pT+Et3v0rAE2PDt0V2s9dGrvcMFE9W/+4I92iz3wBjUVESeFnqlpEHmGMLeJeQ6Zg5dJ46BUez9jmKdoOdgl5DrykQFrhcIBxRK1KHcXn/zEJ6+79urV0Xg0ynWiq7oqsBoZqBSbkdmb4Z5LlFVBqfaITHeKhdogMEC5t6AiFyUkV1tEZ8J8MDWStE6SeTnf2duZzXa2duef/NgnLj12SVWWbd0m2PTL7TvE81PyzbNv3v/At55//oXzZ85N9XhRVtNDx46fOrm5vTVdW4UmgZAhDcjvCVYjbm+cf/Dh729uz6q2/Y1f/c9uufmWEotJWK0PaFpUGWsgSiQBpAxsJIeopFWK5b5aJbu7e9vbO3uzGXYkSeB8JceTKcjnllLqYF293cJc/lzuemd0kLoOAx6eiUD/TVUB47NeQ1QHkINSZM42pkHFxE4+NVMm6uJRAb5tgp+OACjZph2MbNgB2UxjwU7uE80gJfm7XPD0YfswCkVf9LGU8N1eIt9Qj28BkwzXRiwK/k3eOHnaqoWYsONxRazyDIEpiFORo8yLdLgYG14wB3p3ZihDOrJXnP1E1g5VBkUXkTPTGqVmUdbHjl76wY9/cOXANEMparMgYTfAfnK4czbmARsdYL2gBJLGZHHlsFs1at1g9i7KcgHFvToHnTpHaHU4Ha8yo1FelMXuzk5dFqIRbSZXDq296653njxzarY700lqQXAIF1GoBBxp9UsvvtDW1Z13vXOSTt9zzzufeOqF51547uglx2685sbHHnvk3Lm3GtFIAMCB+qi2FQ99//sffO/7jh5dP3zp4Q984L4nnnoCuZnMUgj/IGyez4u1jXNROpGmSZ2QVQPw3WDtowWt2tZkaaoguC0wDpZCw0QJ4hf7MwB4SfCOcbPQ59EzJKygvtlcd5SvGFB2fkceZPDlyXaBPUlaRsA3AEPHQlx5SC/RrtmaL1PsWpgdj3OUpybcKC54yOXpVewBUWA6kj10JgcS7BBm+Ci6/5KhRXEa2yzSdk0iVs0H2vEd4xIkBSiLJRkvdDrFVMgMJtbgs0Qs/hbZNi2k6rXeXsKW0bwywYMs2vQoSsl1S0zRKSI+Nzj0rSF+KiCnG1HX1ac//Zlbbr1pnI6zNCN/slSo6IIuajVnb2KBl6iEyBK15yOPKdmAAF0C+RqAhr+YL6RSk+k0y6Clm+1sQpZx3RTtom6aRGih0qop58UiG+Uf/9BHX339+OM/eJxqi2nredNBJcnu7m4NNdLpVVdf9dLLJ5/80ROjfPTZz336lsuunGj5p1/+TwJSlGsSh0ole9ub33/isRtueceRyw7ffvsdV159zWsvvZhnowoYDnIv16aMFAqiBHg19ChqneRZjt71yga+bLoMzmpr0hSKl3WSVAswiXzlYZBqPNk33l2OQlioyX5iq6Q9lpI93VloASHPSnjgtXGA2VVZUxtiCkZA3CgJ7m3GGaJ04JgkesQq96EORpIugSKUqy5xNQ8Q4YA2vR+We6/AKfqAJwy7ySYKppwLGhxLEemNE7QqoUARQpsQPkKoDevyIEBJBKOjyLBDcvB5OU4NtmGKjk7tJLULCzIAY3KuOuHpNpJHk6OXa5pmOl279Y53ihL9SyCZQLEHRQ67sFG3Mbe41n1VosSlzprQbSwFHy3gJOM7pSm4slZWVw4cPDCdTGE/+lHg6NI0nUzGYBIniXWx1vPZ7naaj+649bbLLrtEQDYSTqdXPqm/tpCvvXz8haefWc0m5bxqi/ITn/mEbNSrJ95YPXQIu7lQQQawHHK0ffe733nq2cdfee31n/nkZz/5E59UGXjmEPwG7+igoPxyg4xFzcKTCQkz8txaEiZnRttCVAoi7aKswBvPnLlOHQvhDx90Dc5RFhciDZgpqaHUIWjhvlyQ6Yo+q4Oh1KNObZGNIHsO7bOyKhdFVZQNJOG7GJYv8udGdZeOJPvTO4HLrSh+FHI4fQDMfWkupPhGjp0oTtodW3he71P2lXu8owTqOX8xoByUrScTpEice7ISW+jujRhuKJBprbCqxgIpuRx4G7TEin86QHiCQuRsWnTW2upaShd0ngUoP0DxayW0fxlKUcZYVVEU7//wB26+5fpRnjfClFWJ0RRBTmZbLkgFy7b1JQwUsiPTlNpTe5sPLmxNkuh8NBoJMx7lNcSKSmolYV35CI8gpZjPFptbm74CSwKYQVFU+fvf84HzGzsbW1+d7+xRrXFwqqP3aXuxt7F53qhmOs7fedc7Dx09dO71jb964OutAV8UMTgyxeHsJN3eOPfIwz+4+aY7tg+fe89tdz/z3g888oPvV3U5SseQHQlZoi0qlJhYAp3onDKKb4UdbSTiVBFahaVpQlTORyOVoEcd8autHeQ4pUVHCCmLBFXrxZSVk2HrMYbRUQzdqX4zR4HO2IYKQh8FAqp8XnUALWMBbY1zD9vDRsQ3NBsXO7hTDOVXyOuOzuhe6LK1lgljW1kacxHX4+ki1IBulDqcFmrD8DObMoCqSM8lbKV3hKiA6w5yhuQheB5Em6V5lmZa6dl8vrOzQ2ZheKodOJANGi8Gmu25KlUbswcZSv5Q7GkbrAXHVojsmFfahqsoHQrfoapqKczd9/zY+voKKY1F1eoEOt9jSSOVH1iHGm1pwvDPMQ24hZTmkNRLJkFtTJPWGYAMqxpBRSjgHPIHpZkvir3dPRIX4EBSaaWMqZudnZ1JOv3Yhz564uTrT/zgMYU10ggW567F9MzXXz/1+I9euPrSS2951/vmW+fl4ZXtc+c3NzZvku0bUm5Ca+8EEHVowhvz6A8evefH7jl6cP097747P3zgwB9+6cHvfnN3e88iTKJbw/kHCf/EFglRTyZ0JYB7zCG7WsT/RItUq7aq54uCZhL9F0GF9F4gt7L98h6vzfLa/t4GDWo2ziWv3/cVhZzkGPkHU9YJ0bpp50XRSpFlsAN9Olaonuv0SBE91sBu3dd/lyHRRa/sH+l9Y6E0iiVyB0085jJLjy4Beo7gSjutK4fcBq79VXDtxhp34MKJVXFB7onJZES/SSXG41GCLs2qrmaLOZTyITCyd0cj1hLscgdTDhUw2OdWUR2OFcDeHne7Br1QrA7VeSmtzxj5cVXVx45ccsvtt0KqcNNQUpEvRYi93YTsDrdUKTi6EpROLlBNHAUEEaV0Q79sal1EKCaOJMB6rEAFN0YkWtfohIfwscxaVYm2LUUxXRndffftJ9547eypc9al51cCHEtid75z/wPf/LnPf3prd/LVr3ytmC/0uXPZbPPHsrWzAMFTiwYSSNHqFlKr2c7WD3/41O3vuOPyS44dOLDyD//Bb919x53f/u43H3/i0XpRU2NcL1NIWlGmQ8efTNsTnUzQACrNVqXOWkDzAh01QTTJeId1vFjd5GOHQBjVf8aOm97mDGA8/rQ4U94zSk8jkTyHHyG8V5TCiDzLMKeSbs8joOGZIiqBCtis9sOuxbnkuAjq8/xr2YR5GmOzwUbM5Wb0IgMauO0OC4aWLSnjS4CEYvkjlXsDYyaTN0OsNrpJi22x0lQnKYRPJ6PxZDxemUzWVlbAmeRK5MuyXBQFJCrXlY0QMgwb27GEGcpsKzg0SGdqkX1LYOpk3H7sYx+/4qrLtcTNh7eEUBe2K2ItyVy+GASrwaKyCiZOobfVqRynqspisYCINnIKPx4odW7bRVHN55B/ZbeC7eUBaoROAJOjactiXt1z170f/MBHsklum5VY0WdzyZROT5w489gjL03L6bH1I88+81J2Zvt6lU+q9malDqc5pElhxz+o+zOmLJrvPPDdR594bHt378DaNM/zn/7Zz/2f/o//l8//zC+sHzxINYCUgEygHeDvsY0EITIGOgc1H9Ea8PaSTGWpaOt33n5rmgDjQ8h7LFYLPYNDiDXYvkw7dDFVTxZReqHfhVGuZO8Me1f2RLvDewnX4UDbD0DXII8VEllB+fd8eD/6umgXdE+V5UcvW/qibuQq73rerYu/GV3hHRLcmef/ip7glHckLhvLkVLmeU6pNlG+HLDzbDqdrkxXtAaHVpJCzi35iCi6QKHAUAZEEDYh4cNnSweXBvEwz1NCc0DkQG0rtJbXXn9NalQCQVnKBAIxSh1PfCYjlT0S5aM/pPK5kd514q13uoOrLgtlt5R61TRtilnSyPfB4ESytcVTwNqM2Z0VbZV+6IMfufW2W60/yHmwcSSA6jMrZo8/+fg3HvrO2be2tordzay5vhTX6+RumV06XYPqzAa0EikM9o1SW5tv/ei5Z89unM90KhMpmjJR+nf+yX/zq1/8zw9feikGAGwNL+RepeCUo4QlpFz8n+LFAIQJPGtt/cjlV12WJ1kFhczIgOLsCO4fYoAbDts7mEJ2ipxwCdElv+P9n8i244mCPrVjwBEVCTaX2oN5I0rWTQsJNVjjHaUaxMRnhijqwtSyxI4ON/YG7kChQs+MGBakjKOE4sjlB1sHKtljMxPick4lCpZpQulxWZLR5sZckCC1PZUlaSL1BB0/EDSeLxawX5G5J4mGKmKIwYIo11YGAgyecaaXi70RWk0QxRY9g7rmWfxNkLRSqbUjh0QjGwCKAWAnol8Szl6Gu+xum3UIERXcqTZjzOaWgYpBUwRKeNOAj8viS6N7VoosyRONVbXAykSBlUiw81MFai8mYGVgUDRlM8ty+a7bbn3jtdfPnnnL4VrA/5Brkag8TTY3zv6nL/9FmiSHm/bObPW20eR7snxcNe3qwWwxLxYLB8Ihynqudfbyiy9vbm6srkyVEMVssVNVoqm/+Mu/fPzka3/1F38OZRj4lk3djCDpqkH6J/0CXlUJmWaJzlNRi3Ej7vmxu44euyTRGaJgQ+jZJlR50ev+sp6ZIJF5ECgIS/dr2IqejOh/r+h2wCScljvQ36BDza5SjZJN7GgpJIYNa7E+iTUk7Pml7NF50D6qsUOrYrquezXPpi5GgnK7tCOEGTsYgNP0zoLoHPwLc6KVBEhkO+mW1dkJdQ1jMT0gQbMT4i6UVOgYMNIJ/vH5dwncNm/bdJSPJ9N6PptZPacEUMyibmxwz9acYxZ+08Dv4Nx1/9l3tSzCMnryBttZhUjLTTfflI+zuq6TLKOIdA3g1dSFLNo9zv2FqVegDcDy85RPh4GF/jH0/WCvAjgHNVkwrxPIuDaNakCupWBKGAGJk6CyojkBiZ0wg5CHlKQr973nfa+9/sb99z8A5QIQZLY+d1ObVgNE/LwoVVndnagrF83xI2vf2ZntSZEv5onWFUTqAXG9KsvpdO3GG278tV/74tFLj4zHEyXVYrYwot6r56M6v+na6w4dPnzqzTcxjQomC/IlgWvChIIkx6lIs3RlbW3t0KGRym+99aajlxw5cvQI6SyeKOyke0utu326hYRBSA8l4kTeU06oTix0LOrohx72ovVEWCe70yDxEdifFf7YPM/w7JgM3BFzBf5LRGIRzlz3m4s+OH0HoIKOlTt4DXfs9c+wrd1sjQhThl3wIFI7wE2VpunaysqiKMhV4qInFqAG72q1TgrVSCEA3TxNi7KYzefz2QwRbsEnjb5eA49HUax912lqHwAKYVhP73km/yrqK2gfKrF+cNWUJXadgL6h1JTbIkHE246sB2yEjflZrAIpeLEMpIU0LTQ0Qr8u5lo3BpCbGzMv5qmA8gBtVIM6eYKVD/Vsbh1aFcjlJMlrRJxeVNVkdXTLbe94+dVXX3/5NXB6WwAtmiAo/YVkTNE+V7brq+NdwCLSKgWxDiIFXBStUuLosWO/+IVfuPuu91151ZHV1fW2NUWz2NrZBmDnbHS63fzUT/zkww99/8ypU4uiSnALE8agku1kMsX1koumTur6A++777rrr1+ZrCrVjCero/GYcApclemQW6e/tfi88i3NxTJ34CxJVu5Iss7e7Z1PMOLecRF/hyuLvMQ5tJirygzecelHPTs+/oynZvbvE+dD+ZjZ8tcdOJYnfbCYMI0E0+YgZu7HRg4R27bIsTJsXot9HOeLRZpCiTxQE8lP10Dek0FkPGMOc6KT1enKOB9v72wDEDylwuH52MsTqmZIqKYaDGb+KuBmc3LSG2dIgUYZc/DAAQnZ0OBZ9WXJHcZF2Q7g3CqgNXmaJDKBXm2WM6BmQdjZ41FuhJnN56Nxjv52rCGuW5OopmgQeAPmJEuzeVU2qIlD3cSoLcpKGp2lgDkmpcrSrCgWqq1l27z7jnftbO386Zmzi72FTXcBv7JKUujbqKQo6uZN2eZSP7W9XWejNMug4Sh0pTNtKi89ctmvffFX7n73e7J8dTIdI223VVUCUB4kPZu2Ljd255decvnagfX56TNNix4sG/Mze3uzJNfCNFrIyy694robrj+4cjhNIYNyNJ2kadpCrbSdWFbA661RL7oYHfjdZTteOhc9xpZ94NETNXFmhrzhvhoggOAU60lNV1fBKshRE7M/oQKEqpBzGF7ImtznWJaWGJSRizOmzcXh4/VvNXTKkPuPekGGebU9Kqg420I/oJ0qEpXoVpqNra2VyTTLM8zPp0Qs9H+ibmuFuXMjuB/txtBaTafTxWxeQakAMM2qaWAIDdQSgfWYQXFe06CVa8cc8nNcJNbekWpx1tcPZGnW2PQvizFJKM7MZw8bbQG1vxUwoRTK6yg4zVxiYJCvrExHk/HGxvm2AYyruoaKBZmC5NTaZBA3VkJDtso4yxclQJQopTKRCRT9IoFgG/ZS1elopFpTzQupRh//8Y+/9PIrDz34XSUTbG8D/jCwJgqAtkmTpFDm63V1PstqKTKhGmyboJW+4drrfvNXf+O999wjoJEv2KrFYgE3KUEDANgBqF4w53d33nfffS+88tzZ02c8ByULSCdqMl2dzWaJFB/78AeuvOKyPF3RkAet8zxrqqZoFq4PAlUTORuVbVdfPBj2EqktmCeCZfzoBHfxsuB0ipCPoginc5OF3Uq+fDv4yJ3l4p/dbtfk7PRShiCHIGB5Ibqig4e7+1K6Q2hLEjUvQqhGdw5qiSuzMBe6VXdk1sIkW5jCDDYJ0p1gE3ac28VmYmlA4YDZ2t7ZyRbJeDyGLiIY1IlYA6tkdm9sW79CRzyVaWxKNF/MoUa2KqFStzVQnm5KqsWLlBZnu7Lbe481gCqORjkW65eoEoN/CKsyIJ2ZOVxAnxRGTldW0wxLAvEVKcUKQmT4yLZt92Z76GZTdVM0TU3956EpWbGYZClZ7kIB+WmA0qkhQ2g01lqkqRGmNA32SUF/G3mMhGxLZSar43fdfderr77y1punAbsDu6fNFwuAdwfvKbjO3tAiNXlTlbvQ+6KcjKc/9ZlP33nne95xy006GVXNAroxl2UBEnghjRmNxwCUA171EmoaVHv5sUs1KBpWMKGLUpmm3d3eqpr22JGjxy49CrVJQLqjJNVCmrKAtjIs59GBFnnDlvk1Ow1LcA2ojSnuGCVkK6GREgs7Wz8khbaQp3bCvD486A0oi3nS1ZLDQAIX8NE9pzSz6kWmTYQBm8DSw17taNid6gNOQ5yGHS0NHX1NZehgtnD3PKbhLHmGd/pLBWCxaLxarBjioxAqt/UIVhWGlPeyrZHSALe1bpo8z7Is929NVOUZoxfCtIZ+F2gNbHI8HhdFQTWokFaA5AJtiswEu05TnkZIobIOC+tswkYQUgvTJkkKkLGQxusR652zhHIScMW11qvrqxApIZwtQK7DOjdUVo2BSCmkQ+vE9Y8CQC2jxWw+rxbl6jQDv53l/lDuoKRYm463Z0WpKzQukha8BEY0QPDQDBL5WgqiHhzj99717nMnz/71X/9lMS9xY6PTDhJZwN2tlF6UBYS2oH+3OHL00M999gvvvufHDq4fU2lSVHO0DjCLu2woQ2Nvtru6tlZX9d7ezs58duLk62+efDPVWUsZqjazVY1H+dXXXLN28ODaZHr00ssSCSBYYEQg+hdEgMndYePt9OpEG6ENBs8QplRqiwjh0p9JTeKkS/IV3aOWjyo0tkBZY6IiuIdIcYh16UhIRa6ziJ74viZlDUEXemoDHM4/ZhnPgIBc3uvXau4X6b/aX3sPprF/WKQ/R4GqSOXonYGhEm3QZUNpsy5N3WCRJinTWM3eYBskCC1ion5Tt4UpMBUeJZWPCQ4G4pycxnpscDlMp9Msz4v5LEnS+WKRJBbwyeqAoWbBFjZ588m78ih1oIJNPFtdXVESXGL0MJ/YhoVNMkuzJIV0bvJySGP25uVsPhdGUJIwkCDY4XBLAs2Cv8AlXa+sjvMUPOs2Gwxv3oB2qldXxvMSUaYhwipkltS1NBVEwDT4RHHfNc18d56Z/FMf+YlXX3vp8cef1FJDkplz8rsCA1XVlUzkTdfc8IVf+KXb77j1yLFLi7Jo0UmPukADioaptUqrsl5dWc1Ucm62++ap829tnHr5xZdfeeEl8KBbJw6SGdQipZ/6zCeuuubaSTISiUkFgBXoTEHLRCqlBCxriKMCSKWCLucWuR5Sjl3qBOWi2Gx3C8jkFfUQMY4tV4tc5cJ1kEOOuSSY2WJFpJX5LvrkgE/Yhnb7CFSf4L6y6nlEXlYTxiIwhPgPZ4ouOTEEgX6fb3oNFsIcJKaL+XzoFOaSZ0K4e253uHbQMf6kC3Fhep9tuYO1u/4ZWBBDhJmMslzphGAd0VFkgJAx9GIJKmSyOWJlAplr6FgXBo+GPA+9Cj0NMaozGWPdD+Y/20wta2rZu2F4iKQDoTdBjldZAARUURZ5Nva4aQiWSiWOAC4JeQ0Q8gG3FET/q2Znb1ZVzXgyznOoIE7AHd+iaS7KqmhAOrV1VWVZmusEg9K0rYMmBqCzUk7ydAYNfrFBGUDcwJyY2s5kmuRNW82Lmcnk5OD03fe+542TJ8+dOpsmCZzl4DVqANlW4/Ho1ltv++3f+u1jl146ySfgWve9HdHCE22bZxlWOMCzynnx1rnN3cXO5tbGUz98amO2W5smMSm8O9gFOh+Px9PRZDoW0N5UjNMxuSKbqgUcTWyGure3WywKUlJcQQpSLAsrEkqtTYNylRI2FOvYPcXnXdsK2y4ObksM03oZVJIk0DQ5zYAi4TYgt8nP4tECSQnobHcLcxiyPfiZgW/YXGunTjAyNow+4gIMp7EFyljaisGKkuEy4bd9LMnm3O9E/7sfvG3zafUKSp8gisb8K4ytwSfJ+qFDs9nuzi6EM5FxWrcwpVUGuzQi10DKLCwVeDz5x1SrMjxSwHyzMtwWAPEsVlw9r0/js9rZ7t5isRgvRotFkSa5CwnCXQkMAGGb7Qitm4usX9OOxqPJBDyx6NTGiBFuuwbKJgHdYjqZYNI0blgo8MOr4fm2IhozvV2hFPRkSrIE5HzbLgSQaCOkGuVThKaYmya57+73nj1z/m++/OViBq2J3IXgAZSt+cxPfvqjH/34ddde20pNTVylgZpP05o80zJRTYmhO0hakDt7u28cP3H63Nlysfvo9x55/fXjZV0CxLwzgBOt8jSfJKu5AjyC0TjLUvC9Q5ETDrFcLHZ2tmd7M7RrQKkmowprolxuDrn8gUWTEYShGoy2IuoAYiZ4nEm/qZyxi1lfLeID2FACpMFV1Wg8Ho1GBP1tDVlqHRTJPxL6Xstk6nPPE+x/t5Z1aOxhS4vFxduiZkCJ7xHUgJP8Qkev9xe73fL0Tkbk4SUifhQEsy/WBEVXwDKRgxENHHXTjTeMJ6NRDqUL5P2zZosvfHHpGXYeeHjVW1mWjELynRRQvEqAqVhO7CqK3YW83pXw+OiXBsNG21tbW9tbZVUtCmibQrCxBPiE8F9ORLi0a3qs1nptbXV1bQWKCl0jYARzgioIsIkV8JQ8g/RD3BBQ6xP0CpfRZW0/SnoRErH+1Hg0TjPI8VAa2hRCc+DJihR6Z3tPNdmnP/7Jm2+5pW5qW5vlullfcuyyu++9+9ixS1EmAYwImQqIDQK9FBaLshbYVkKp3cXemydOnDl7/syZky8889LxV47XZQXFYYT6Y1GjUtG2t916k0hgVakrEmBeohVRluXu9s5iNscsaOBR0ZboOGQtRbDMU4TDabAEAus9CYGBqNYKZ6sVu5lHSwGTZ4QAmKKqdIUkdl576IrRwTIEO2Ba7BLXM5i5ep1X3sQGXcBQ5vS6lIbCtHQ+34czDH217JIhSB7vgSeDkt+jPyKflRqaHjggBMo5Ver9n7hPInRDKKt1WjOzpy0iux0VL953s2aJmKxNKOwBfxgl6/q6Am/S0MMJxtYYkSYpZIAgW0qTTEm9t7s3m8/rttrb2SsRjRGiUCyPzoWhnPgldB5wX4+yNKcKJOIqmIYF1oGQAnCk0sSHI3y9hQ+tWBBOyvQgNqZkjcVMSqlRlmEvuQT6JDa1NDLPcmPahSjXD07uvPNdBw4dQm85shYoNpCf+tQnLr/sCp2pwlRlUdRV0TZ1CZGvqoHyDChsNk2bJWmepouyevPM6ddPvHLi+IkfPfVUUVUa4CaBcMBUAJNeZoDonq0eWCEgzhphdFE9AcSP7e1tBMeC9EPL5Lzr1lFVWDuHiO0ADRGAD5snW0xMzNOE4DZUTSQpBLkBC5OaTkQOYUdNgZ1yQAC+fX0Vni1/9blfAyThZYnDMA1bncNPi5iSWZ2tu80gzQ1IcP6Eizz6Y2f093dQx7vPjttKMVoGj4xSyS9+9gvf/upDP3joexbS3XM+v62Japyq00JWEwaKO0vDTWQI/BDBQJDGQQc7CY2NQmk7aQ0hmb0ZNKQWrRyNQKwolfzwhz+89qprTF0v5otFUUzGqLa5ysdWEsaEoXo7W10MvT9BNtuCYd9eBRs3QnPjulpLJwoVW69K+L6irljOfkKIbGgjo98PJRNFdxGgE3IoRSsyUG6zeVnMF9mH3//jL7348oP3P6BwfNLow4cvueya66TMQI63GL8Fbz+0/0SMu6QBmIEEkshasbF55rnHn/j617958tSJvcWsqsA29hYpNZhJdZKPR0pUa5OxFokUEDECq3g0kkosFovZbEZwQpFNGXJ34kgJwSzYds/WyW21SzJeIb4oNbBaa3d6QYDADn7VMdkNd6utjUGMEILR9Sg80U6nhFaPrsVpIfoVdTkPtxOkl9vrcgkZ2GzJ6E7D1DJ4VlyeFTvR+x91VJtgdPi8Md/218fDeWtP7iDsjcFDB/hUSvsMih0oJfLbb7iJErD84nma7bw6SWIq1Az7PhSL2MtogRDTJnW1OiSASQhjxMcF6wiWHXsXgVBC+EVdFsVb5zdne4A+V5VgWILzCfAl2roGVw1WFtqDsIrRKwPg6dSEDQQbtPnELMg0VUKsT1dHqHCy9YkNEFaipJXKUi1awKDSSiKiag0dD7IM1AcjsOoYGESSZqlSxaJM88m73nnHFVddQQ2PTdPe+c7bDx5cG2WZUKLFWhEh5BzqLxcg6MCOMIlQ03G+vbX1l3/5lT/84z9+7oUXzm1sFGjMe6lDmQxCikRpaZTWo2Q6yccjnSQNFGw3RVnN5oud3R3Q4V1pntdHrbwLYIVBCsfiGJ5DS+hb4GAyI9VaWRHeIOS+q8dkrkyMkxNSJZVek3oT4j5sP3l3TUiiZF4VvocHdiGPXODh9nSQVUHb2F+ULncUD1cEdjjI0C26t2J6gVcWgnWx/PDqkr/ckU+oEQZQu6dOvPrt7z5AyhLIFMu8BvNcrcprQVydpy/CoyQpjUioo9FIK11i5MZeTxWMiF5NGi/2lQfnjZIqy/O2bdBBAsz5oYe/f/TYkauuvmJ1d2U6mUBJhIHdCY8HAU4mrfWkUo4WmXH4JCgPxsIpZbSsy3plnGcaHmoj15ZvUrGUTzCwGhtNn4IqpWReYkAYzNemha5nSdakZQl+QLwS7PzJeFyUVVFWH773Y2+d2dra3D53biMbpZdcia0kcoDmaivY4DXEjLBLK9UDVaJKZGra10+efPDB7548+Sb0HIcUUid1yFUDIVCdplmWj0RT3HL7nePJZAIpN0ltRLEoFhuFEC0UQnhUjCAI7L9Eas6+JAhbu2I2qGRDSy7iyzYoKBCUtsFcWt63Qvg3dl4Rb5A4oADwE2Be/JpYgHop4crkOImTK8V1z6Kb9BVWsY9IvgicDApMUalKOJuT1kVEiTtXDx5OGeqPLkTqhwbuuFvYpOSWgtweEMLf+IPff+311ynQ4hxQgwMJ+BYeyZXqvIIehT+AFdXUaQaVq7YAHyfKtuQFIBrE3ZNylEN8yEtvG2dC3TVRuixmX//615557rkTJ07NZwvw+CDrp4ZGDkOSOL0tG3SgAliTXDdV3e4u9or5fJRmObroyAnjWbPznvC9arHCyHMNSY0K2hqC3ztNKFMScl/QNAC8A6zfAu8ZQN5CO9FPfPQTN998i870OE+uuvKK6WiSjwDhlSBHsIUv7lpIdYX49spI7Z4//82//fLZsyfB/44FldQflNLI0J0m81E2XplkeXboyLHrbrjuwNoBrVNA30CQrfne7mx3rwK47ACi4H+KIM8DubjoklerAB3AVf5z8WyNUbcJ4m3oUks8BDyMGaBNMDIZfJo8wdJyyDiLg6ugNsfL3pZ5p3xEynMZyTdrpAQPqLWDVOWk2VJPVP+S4bOiee6Y0x7/oHsV14j3u3+YRzfnRHrJ3/7Nt+a7M4RnxGzH/fxpThdmYp1mmZy37ntwjU7GE48R6/dC6NMB1qByAHqelxPQjAS6xBqU8+fOffNrX1dKHj128KrLrgTojKbJ0hEYpw3m5YB2CS8DKM1YfUy1xYDN0UJClpTi0Nra6ngMHUlcIjFOBp8n73MJ1iKanW3d1lmWlhhcpcp5ykwifx72OoQbYtIVjKYU5erByS3vuOG1117NVLp28OBoMpFGlyB3YTDEHQCNepQ1xqSJGq9O/sP/+Bff+f5D82qhMVyEtUwJ9lKqKGiQ5lkyzrXUqhXvuevOSy+9ZG11DbxJOJQ8z6qymM/n2MAN1HJreHmNyOld3pS0qWesS1VgxM6HySCpWKqU/9f/6RqnGIpC9gdpax7yiTtUmJlIubieqzidG5cA1Xjm4wrGcWQ2G3fzqMd47AjipNHN/2Aaiv21K8E8xp+/z0WEm3jJZWRn9zI/e9d1x09rFt+BYHuNSU5vntNKYKSkW3jcTcn2KZBOA2SIlOBYBjhJZCWj8YjqUbl/yO8DIlqlNVYsYUdj8gs7RwvZY9iqVxaL2df+9mvP/ujpX/z8L77zrjvrus6yxsaw8VTQHSTCgoOUA4UcKweh8uHg6goEjZSuamg+5AxA5xOwO9W6c6zu7HBDkDKh2DDRstV6Y3uraZo0SUdgeQK8IwRkAeC2os2H6ESgIQslf+pTP90Wab6arR06kKQJBKIq9GNjuZWSMh+NoMlDLlfy6Z//2V987Ztfn81mUK4EZZzgQE7SpERNm/DcszzTKhNNe/PtNx88cvjI4aPTyRSSVKQSEKtzEXsWGXXqcajLt62/udLrKDd4vAKlOUQcJjYdlUb7ydKO9eBbx5dN2O66Oe0m8Hq+r933jMHByJLjHfCD/JiDrtTf+rJDJn5U5u3lNEe0vNw+3u/oGzH2Z58lEtkISw9fx+vHwrgAm5SkqCpTg2aIMC8eozF6fnB08BIHj/EeMu8gBAowcBnEfoMSZm0EWOBiUVDzbDKDaTfRw5wrhAQQihApWqWqsjr+6uv/07/6nz//Cz//rrvumowA+qc2UmPzIJB82HTTxpWUASDkujp0YD1PoTYB8/gIo5Jcpx6hkwmHsNzoaHeyCDckYCwfPXhoDlWLVZJlZAhDehu4lE1ZIRoJduM10J6mTpPJRz7+8XNbp9bHK+C1buoS6h1r5FXQ9CTRet7UmTDzveLR7z16/q1zaZ5TyxYJ+Vi4+7FDilYJfKWSelHcfvsdN1x7/ZWXX7W6sqKAidhtDboqNk+lSXBND5zayoSsd1GGn7rbxu4P9g/bBDZFcsnWs508EF9QGZlAKwxC02Udk6JdiAEpgvvxOjGFjDCewDo6DJKT3JcILoyosb9dy58zfGbn0/5g4hJNPCfCku8WGQ8+nieJxyaE0qJtE+ri6XI++pZ20J59EpW7X/BAIuFD5rQCPQ8cV2CbWSwMi1Flu4FomxxP1IsJUK7YzI2M/rF9OUESw233dnZ/93d/9+qvfv3Xfu2LN1xzvdJJbRqtZT7K4Sz0obc14NZlaXLgwJqCuJGN5YJVabGr2MTHK+xcd65uwrYSQZW7bUZZBplepppDgKeeTvIMqiA06BqqLLHzW1ND4wclVQHx3d21lSkWOYGbHagRI2dQqJ2YuqyB/mrxJ3/6H145/gpUf2G4i3AnNFQjwawl2PwlHedN3Rw6ePCdd95+1eU3rB88AA3Bna6HjcjB72uhoZ1MZQQRrEwWjGFsutP9JvJgdc+2sSS+aSk9iKwjUHix+avTtwgUImhfLvsSR4toTFZJtkTsAb15nk+cDcY3qIwWdPgY/tLBg8Tarbt1P6G5T5pLHrS/Ut1RkUPjlCWXxQqxlTLWCsKm8Sapq4qS2hnobrdsgvmZQh955tig9vaYUEUeZgDRgAp4vwTU96VuoIs81M2k6WwG8Oj8UcE+oz1DGwn8xJhpr6Spm1dffOWf/bP/x6c+/Zlbb3vHZUcuHx9I22bR1FKDmZhOR+n6eJKlqW2PpqBVkk8r4JqRc66wf+Jh0CxpTOoFdzkEr5okSZu2XsxnVVlMp1NEmQPnMIpW2JcEK2QE5JNAormCur8WMqKhYgqqDaFIo2ybZArxO/PM888eP/EmVGGhKQ+prZBTA8YFOMnyNBlBK8Zc6fff997LLr/y8NFDk/GI+l2RNGxKQPnBmLXTS51aHNTnYKt6SzOmCxelHZLITpUjE8TGgJkIQSUfmmAkOnoiCv00SwkX0Df+dfweHITQgdERsHeFU0a2zygKVDZASqaz0T1gCKd5Vh5gP/NINTEr6nzEbj4sH/ulxfZ6HkGy6mXvyvBDN+OSG8+DpG1d0oi7I6BSh9JKGWcY1D0wk4CDCcUHhlvRT+v17QY7/YGFZjMxIZUXme5sNrP9x+wQ3at6ZkwLydyMUKI7ykRmqqL4D3/yR6O/zK684prrbr3l6GTtC5//2aOXXdJUJWIuSyiKx3va8Km93N3Fr24H3sHvASuCaTmpkhXeejoeLaoqxQal8/l8e6tO8wzkv0VbA86E1ZroDoS3gLaE4BtHcHwtJaaDtarJk1wlRvzhn/zxc889Xy4WaQ7IAZiLDD2+iSFCK/JRVlVVauS9P/7j115/w5FDR0d5Ch3ecEAI5WPxrtnS2QgfR3BlpMn8j04GUA0RE0Zcq+M7zUuoSARDOmeaEBS73730bCjlhMyX1Cvw3CjGJHkkYLswQQ4Hob9ErVwmsAz7u+M6Cv9Euze6UXhD67eKn3chq3Xolv6jJTTlL+A+tHBB33DwmoOFqEoiqJz+Bb7veh+e19YegNHVCpGNMoog8SeABl21WEsI2fYYqUgo9ZG7TCgmGtQ3F9yAaIpryUdgbnW1yPOREWJvZ++F5597/c03fvNXfuXKy4+MJis7WxuQcuVUCe4t9+vn+zQ6rmwL4nyWp1MwHL/2wCRIYNM8S/IUUiSTtFoUVQmwQdhDDPsoAJ5Oagw0WGsISxGbgyKBQb6HkZBTLSEPI1NGvvzCq2+dP58AnCCo/5C0iAZhW0N5tla6mJd1Vd3z/vffcfsdV15y5crKVKcQAIeQFaJ/QKfPEtLO4BWoCB81fgnsghfZsPqxOLPCzrInfgo7+yRdaicfNKJ439lKI8iT0TZDJhTseVgJyLF3cDw+yR5RxzNE58OsdKc7RNS7Pw2E3Srj07y9zk7sn9IRe10KNUt5wz5G9xJ9O/6sc+euhPa+QvsmYUDMQ+kVJqUTL8SdzOkIeAZCOzgivBN5HasWNrTl+q5S1KbUImQxkDN02Kto2SxL9nvH3w4FMJIuOKvtDwpKcSEkm6U7uztSqSzPjhw4dPcH3qPXj+xtbMG1IPuwGgH934ObIdB0eCdHwm4CLSU7fz8RAILO1Y1pUpWINBvlgBmygDxk8L6nKoOwLoKQgHuYoGjgA5DJ2IQ4Uaqarq0oA76mP/vzPztx4k1wSqNvGhNHwDtmLUEt6rY2Ql93zQ0f+tiHj65fPl6F9sgQHbPuG5uwbCu/kWtZhBMPlsGUqcjFEHLtIOJEzTDsV8wsstTrZSJLAOLTah1RdvNYdZyFmG23rpBEYr1qQNt8JwV/eLxSQ8TAkqlF76sejfTv0BWtsWy+QJTI86f+k9x1b7smkYn+jmNmOD/ELRcUzHqLo2MZedL1biqfi8fHTlU3aQqESol4Ps2DpsAFOewwAaHRgbq7lqNhQrzJbCOCaObBJsC0vvEoS6U4t3EeoICgPFZ/9EMfuuWaa8xsIQFwSkEGts0HtLoy22tez4gUKae+B7g2HF0IR4b0YDTGsT62RhedyLNUKighAFUWEi1t8xdEb0JoLoD2FVB+P4K8ltXpChTXAyqXPnHq5JtnTls6wS2epWmWZ3UFBjMq3Obgytonf+Ijh9YPTiZ5libUdZIGR2Uc6BrspkHwMIIDMrGvR6qsbbtqO6Tx9fL9eYN14UFXvBfbsQwrvCG3FJlHtOO5k9sydHLZWG5BjXBdWoJ/geXbPs5tZu8nuxZqvxo3UqVj6eRJznmHWaRHDkjKWNEdGqWzvrpm7bCm0D+je6pc4nuzBhDVpPk7eHxBn7fBUqXCF0F9Jj1UJklCGchuXt3TPV06iCTaLdSL2JOYk8HOZSxAbkkqUAb5BEV8dm+lydr6AYjIKHnk0NGP/eRPjMbrhLcJidIGcHwYPrl9el+BCMvIoF1oGE4e9JeGcGMo799oKTD/OQc0LATTRfgOCORkWdY29bwoTGsS6Ldg6qpB+zYr5/OqrRbzZmtzsywWCgHrVaMSyFvT1JYRaF/IldH03vffd+jSS6bT9VE+kuDCg4ooakSOmAHolUB5DdSIrbJppDQFCO8P47ZRGfzfz4snScepY/sZqYvgR11Zi0MYQcEKw8U2cRAPJ17AN7hrWEd8HzHybPq83T/WxzFsXPLf9wnjdCQuJ5sllwwaooPfRVkby/sI9+8R6eAxLMjSp3thEjEINjWxXuH4E7B+BWvQsf5d9pxP/+KpV+4BPpFdijRJfEFC0Ia9Mkaz6R1/9F6csH2XM/5KXmRjbgakIUu5U9WjPPsn//if3HDtja3ZO3jo2JVXXZHIBMKsUAeBIVD3jkGlYzPDFGVn7PpN5JBUnZIU2DD3KAClIMwJML9MaIR811kK2CPoKwcPkzJZmkklZ3t7bVuD0U4NKoExtaKp53s7u7t7RGa4CgoNQg2gKJCF0mSJvO++++6447bLjlz+/yvty5okua7zcq+q7p6eGQwxALEQAAkuEjdQJERSoAiCCwBSgGnBooIiKYqyLYesCL/pyb/O4Qg/+81vdoTDj2TQAgRMT1dVZjq+s5+bWT0DuUD21JLLzXvv2c/5zna36TpJ1UBgnFrIcJIMOyA4ZAqoEEWZI4jOHkhGBNOp3McsTFaWuTUr9zZGyZEA4nPvGxLUHGe37cQ8IORgwLRh1zGBcnuLDJ4sTnBNW9U1HVUc1L4urM/S3g2NAZe7v45y6zFprbhAvtZS+JphehMP0KtkH8rqcFY8ae6bWfzED+YqIn3k/AnqTshqrPg4wvU1vdCkcbwu1cBR6r0W1qqfi+v1xcplwP7Ym8NpM5OwW8AxSZb7HdN+Pc71H7z46Xfffas+NNN8bPoBUSYG04AxoIMM2o6DFhcsrFBZzKHF39qM6zDdSFEnOXRjSkLZAZar//CqavtB2i9R79Wha8dhOByO3PXi4f66qaEkz4fjdttfXt7a9htUEpFobprmGlCSYEJD13725S/80ddfeer+Jy9un283PdxSnGeOkixqF+SGkBZ5a+ymaVvUDCNNGw5/R05QrYSlqAT5FALSZHSuHsNvfHHvfOLqs7JlVZIJdAy1KxJEouQO0dPCRjUdwToo6eSW9Y5rm39FzZxXxPSqkvsIb/BqEhP/lC73CFdWUnXDNU6csaqLZ3aWDEw7S/qlcNmmyFqTu0F/LkqKq0TPXd8yVoZ+6QeY1WTeR71zWCVpyBGKXzL9AgsGWYrNXFebrvv1r36xHfqpaaepJfBHsHlquAuWRC0Zbf6MAwb/lKsoYjt5Y2u3fVc2hDlYPNxR112FdIymQgPHe7uzummuHgKUglp4w1t8do5+F9jQFHSHi61rNvXmahjPLy7v3LuLqPJ+X9f1Hrh2mKWu6z/zwmd+8L3v3v3EE3fv3R2GVqAfUZ1BeaKs0xoXVI3fQBo6wgYA4pAhXamaoTgNWK4WSNigWfWUiN+BCwrhM9TcRWYPqn1Law7FzWeBz718qA65acZDE3OTkRRd7G+vVQ65mos0yZW35qlZ7vo56qur5LZqJ+dbJK+W79LSeP0YAv4x/FmmQgQKSzswhkbKM9HgmzKRGZXKvo5VJwEAQelQLgbMYEpO0GNLvsUrA91MpYbDD5pgdOJVzd/SYrnHBJp3d4dxfOnFl7/+zW/MEwxGwoeuD/vjHoULqNqViohiGAXDjTuB72LZpkLQ8j5Oo4gZFUSc66cSj2PPOLptm9sXF787vt/0TXVEvS96O4HXEM5rVaO4gpD3fv9Pv//G177x0dXDump+97vfcm/Uqm822+2nPvn8G2+88eTTT9+5/cRGY8JUPk0FmGFdXMOIhT00dtROVyi9NArxlGOSxwDcY5+F2v8WNGZJGyJNapzFyJOmKPPBx6P5qQk1SQ7kAJHj4EXFJtMaMxffr2vi1FbnBnqYGSYwSD0ePN/kBlpa/X6dl9z4cl04PIppOVGRS9ddqobx5GBBiI6oBV34XyfgTcLcwukxl1l3izqPxfFBEISm/LhipzlukUJ1B9gJJJv92YNlJB0M2WGKbCZo6uft5lc//6uLs4uq6kAdIzT/I+QSKFi13zQfweBd8EIhXMb+4+e1Das6n/ElYiQEpkP9GL3/lqeNoTvhcara4+Wts7qpfvfb37c9yv26ukWC536P4UKQEQrlYT5M09e//rWvf/UP/ut/+W//6//876uPrnbnu+c+9fybP3z7uaefvH3nid12NwOkXRpNHg/MpGgsNRUjC0aRC2EeCwEcHCrU5CINjB8stAOPuR3RVeyuP9aKDJaI6RRilhsu8X4QWaywlnSulBRy72KEx2nK4NcTwasjzRrXMidbd+ya/rzytoq2a1rnLIetWGdFlBXCO6vyjxsXWkmHNtNQfi+P190Tzo9GZVYco2kn1AjY8pDgECs+IzGbX9q+ARZMOCBayNE44fw+Yz7Kxm1J/UnpjwcrhNMDzwWem8N33vjel//oi22D1EJAwEm8MepFLpdCw57ypdRsPNAksBuWcT7YCCS3EPxMCHjHtdDaGrYE2QPUVPUnnniibqoP/vnDruv7ob5+fzxcH1BeA/O4Hnrg8vzf3//T5e3z9372b4Zbzf/47//zE0/du3fviftPPrnphg730vRgNJSAbSmg9mp2GCyU9ysQX7IE89gxwRtCcrWUZK1jQ6xyMC5gJEkAQOJVYLQ/Zlys7UM9DgWK2llQjYx2T3DRkBAMu0/9sESKa42+4UCub/HFAkaOnwSCLDyx4+A0TpTJh4XkgHLHLrRz5SwfT2cuPtaP8aE836hQ4m9hoIX7ixDn9Njgr3KonKJOmUnPKgUdnMWeWZUGkYrxhkou3jTWxiXnmjaHYKPkFtTNxebyzR/98PzsVgtUDtiBhNXcoQVTtecqI/VUFeFss4Cy8bKcPnZA04jNMBc9AKm+wLrWtKWS4zPDEbdC10I7IPl4cXb20dWDzdnudtV88P4HB9Qt4YxblxfPP/fsM0/d77ddN9fnl5fP//hLbfUherpMVdOTTSk9cpFxRa1S4O0yBogRks+44jz20FpI/Eahttl+S0IL84SmKZSDLYiHiSOakSuw2e7uDdF0X0Vp/So6A/kmgCR2bGuKOVFjSnKUkeldBElKKzPs5ZtU5ko1Jxdh7t3IIfG1C61cekXEfwzqffSrFPCPOpj/jeqID4ucqQwlK9ETm5NIk44JGuQvon+MbqMNPpMxa8l7iopkJcQ6AC3fcwvUPEUiGODJRp5Qc7i+fufP3v7c5z+7AUB0UwEIDqju8zxfoxJHEGrCMxe8wTeC3VDs33ikqNT2vdAzYdnAKaQBFFcFl5eVC0NPZgyaeQCgTnfdHDZnZ/t//oAwOxEFv3Xr1vnZtmmQVjnOY9Meq2lDKg0XZzWEI08GAoXItJhAt6varrqU4j0WdYlXPZRlhP1gWaRil7H3zw6hNeVlsPwb0bhNJYlpXpqNQ1Ka/3LdIWxheBePqKbcc9O5vuuHjbuwoliQveGi0RiyP+kiY2Nd6EVjM4PJF2p3MOjsVarA+WO6z6MOOH2eS/z6/+/AuUbnDSQI8lAE5jUlcrhWaqROxqn0EAkuanN4pKnV+RSRwJYX04ntLyHfJATozXio51t3nnjlm69uurMebQQ5dQFSGM3ur9FeyJ8xr2miT3V1R59k9pXQYJOGgFwISrmH8qxhWzk5qxzyVhw4TBb4t4G1Os3bLUA4hqH76Oqjw9UeQqirN2cUH0YsFxKK8y7hE0QiC2QXsp2PSHXWRmNqF7h/ElOP9nSoRybqY+1WMzKygalM0rdtLHjwWVFjKcyY9v1UWQ33h1xbzeE8FaZVI8ZxPJJHjcz3duoZfxpg8mgwIBk6UZNO9KseljD4Rd1FtdC8maVYH4fVVJF4kdLHG31uS7JffPEIOiyPCNrBo4qaw4jkr29yCltI6NRQkAoX9OJqlEyPChIrOsrCNxjgdDFqIGJ3lFhNmhiTvqKLiqihlihz282vvf7q51/+9MXlLYJbQmFtPaPxD0VopHZCb6dvg9fMyTili6zl2ZkRTCNCvhHQrwYqOi8dox5RN8zQNOf0JHRHSYGsqrMtALIOu/31/iHHnwjoR5QYgvvC1uZgjqaywDck+HkBAtdIq6oBlFkjS5x8eZQ94baueqykutl81eHx3cCwB1DlW1JA5HnUGy/y2Nmt3UXlKo+rJs0FAGKWm4rkUkLMgo7d0VLS2Q6pZVeKuE2e/scE76iLddDreIKCDUkTa2SsXmxFzIjJIQUJPYZUDW7OuJsedUI+1GZaqzVvGIB42NMXMGWRSknc3bHa9Xc9MCTGKq6V0pgcb0I0pTqJZBScLLlUObSY1mfHK5mPc/3U3ae++93vPH3/qbZFsiBr49Cb9/sZiZM59L/GeYI2tWS0abZI7gp7QUAVbSWGDhUz6axkTWcUIta6OY1bNxjMVGRQVXAqt03dN5uub9GYgaZlAnItyjwABCaompEbRk3fBKEIWZYjZJyTukLwn1nLLhR8gdEJt4jKpjycx4/FYRfTbEQ4eUiK3gQlS6pVqVvSjtD/qDhMg0m8IdAF7gjwTX4scoBphM4zPEw8apqRx7skZXvWNneRmKsVt1XM11LcmVhDWWyHk9So3KVUGB5fFCsvCcvjUkbn+6bs0XAV5Ori+ckDkRI3AhEUaUvsVwxH2nH+NMYimfFTiVI6IikowcUiyB60Hadqvnfrznv/6s++9rVXzs/PmQCAWgMkKoAPW+6uB7Yi5/b5lf2gapMyzyiBgu3MI0X1EPqSsvvU19XEdFQfwiSI88TKSRESo/p7VjGh/KMIiNq+oqYSCow1QtBmJbIlDUWUbiqGapFZRnlYdVvB43tsoXVLIz9upcw9npKcjEB0AcVVZ2cZ686cncJFVqEUWS+9GFe66zoyGQZ2V+HajIEq2ZgKNCmsWhpkUPI7mLKDRScxFzJBDf9WXtIdNduMLtaSZ4TxzlY4+Q2UsngtT37sy4VrLs4J0mjFzSqOGT8EGKmgFkocykULfr1Io6TxoErOsB2pX5HDeLHbOJXYBl4KMErqG7aQY3jL3foQjGlQzHD31u0//+m7b/74zfv3n5lhC+OiAiULZdHwAGSUxbDdnnP9gLV7puLstnOVgzYCNRxGtap1hQ8Sl7dFxv8LL0/vVXbhPARfcXyI0bOPR+oOTFhSJjUNDDYBDfmilulI7LLvu24aBurhhqaKXMNgfac0auzPWph8vBskMGfUziEr4RQq+7g80DBGSR8TBFJOEQcS0BbRM2+owCxJfesWSPdqKa1DFolJuaM80W5aOaIoz14dQIOI3UzsmokSIRKx7gX3qyYeXtLK+vLapU98eMyXKUnrJ/tapUIB18N5KpqqRh3C8tRgSLhcIkhkFKxKCyQqOFFVsU7za44rHwg5vWirttpRm8/kEjPeBKhuJEjkO7fvvPfTf/3un7390osvE+geYDa4ip1xoSNWcTmVXgTtGWkxcSBui0C6xtyR/kXlu5JEEe1D1WKDolVaLiIlgrM6wjsQ40DpbzVQlIX6Aupk6QMFZUJ4jo2vXCzVIeuu3taoagIBTyMh7/U8gGkar1HvgdyOYqxFGl/UqERJU9sKgpcjQFSTxabGdrcd0B2KKieJtUKlR8SNQ24F2JkZuUUmltvoS51eM+bCsEN6fqWawFRhIoEDGJO16zJuGYNOgn8Wam7lfv8Cgv7Yr0cq3OavXDlN7AlaBWCvSy0DO7AWB/u1ABLTiN9IODaNQndgMCaZhsXg0KsJ06VaQirmFteZZBuRoQjMmafuPvnvf/Ob773xnafvf2pu5hHt+3AyJSdSPjD3OzRPWVbl3WjUCnUJDGax4z6SMJfcu5SU51ho+yhunLV23aQOG28Z5nwMkDcZXghdSxEiGrlRYGx6YjcWdSZETk0Ke56Psp62mXu0FAVOCFnvyMGc4DQ67g9UKWUTw6cFp4WKK3HsC1qotLSSTFJmNTWyNZum3W23fduj5zKQSfAslmgZxyuqvui6ASnW5V82+/MKCZUxXoiTvCXaaxkKIG3hOTGpTPqBYG7ZQ/O8cbBOHYH67DFwI94QFlHpWaJFWWyEj/k6zRfW1HaeDDuAo33wQlMCi/pJ1pkxthTacJGjBYol+59jFYIrqpo7u/CbasIQ8kA4JxeZ7hpIwBja+rknn/2Hv//777z2zU88cf/6sJ8O03Qg/B2KRkB5tsY8iYbXn9YIN2WSpci/DpySnZEqSaW5vuiqyiVN2i7DDvwsIW2zOhHa/Co/sxYTeHZ0O4RtuK/2yA4NbXnsemGk65sp3B68hzcuu5u6pt9u54dX1+O0LzhY6YJjZZgCWi310LBka8mDZ5uCVKmmgeUNdwGqEbkZM2ekxzv41RWHw7Cg7QGjjaP6i7NWGZVXi1m+zSxtUfRHpU4y/4nJADjbUGp9sxoOKmduWSQ7CHir7Q/FgYni5scjx8JdHWa6epwLzKfjJwyZzASctcHgxDLDWGwjwo/Q7y1TcjkwI9UggtHtnpN6wNxxgZH5OqBoj/OxnqovvPj5v/sPf/flV750fn7vIbeoJZC8hvIBjscD92cx6g2R0WK3+PoXWmPQF12gcfII1agjg9c62a9OdNTm4neOyWb2oxEYqbliYSjMGPzQYl4CoqJvKV4FjFrgxLKdgkwS0b/LKi56hFygyhqltTow9YHaRG53Wza2U8jCCiF4aHwV7QWrV9VQh0o9kdAM6cytgSH46Mpq2cVMO6sf5KJFspoLAtZ7hZ3qoi8qg3w9UatmmQTuxezXEE8zkfI8j6gpj04vteiJv7FA1mKDwHKj23qFCNd3xfL7BVXWH0u7dhfNYjvSMDvuu+28SSwfngl/Idgoqpt2XNA1TjHgdAOeCCsHEUKZphH9CrCZqASeauX6Yfjq5778n/7hPz730nMXu1tzBRhXpDUA0hkjPOAFz7PVV5hnYsEOLYcoQqBmfq5kr32SSfSS21m8MsnyFcpbKtQxChZyi4PIFKkr2YeGm0/YPyhOUE0QR8DD3zTHtt2TycpoW1qY55kovotDqkLURNWmZrgr3LNBg3Ig3R72e3SrUmeZn5NJRdvi+LNJ1TcBX5NfrAd8P33JDXQ4WdodG+rGC6YAo/l43pfeWG4tGbGi24ez404uKHrt5ULcPhKmYuBU4RU+SAWsrkjaAqvvPASdXi5RHoPyH+O1NONkO01V1UlNizk+s2LKrmPO5qcgZeSrnGlD6lCM/0YRZXyDY04Ux8VVwDJQcoSWg4CYGr71yjf/8z/+4+W982beUJ5zdaS8f6Tr0vFoaT8C+jyMMfjIYnKC7nEbi6Uxac2rPQF7m1Gi0BLibdR7495zV56fGt4F0rU0CVXz1EfPVhVPNrnT2ZEuQSKVJyjvh6cQcoqK46kA2JfR97uj7vGX4hs2u1ure3kxGwDrne12+7Z5eH3NQPBpU0r4S/5ptJ+owtOp9QsM67qn9hRczSLVycJSrRGwLIN5EIP9e+M29TywIuMqZsssIgDzMpxXXjs64dU9mAQym/iSambmnqUNrgjOJB4K0WkWabLvzB+zftJSi3ady7ab353GBKg0amMjIXadvUAl0zxsAVzMLWrtmhoRoDjn8uFYVijpoHCWcfv7/li3Ezp1NjOK1et7l/d+9etf/vitd566vd1PHRqLNPPhgF5kZFdBhwZeDuttouSlZ8zTEllJ+NeVOqIs0jJ7wkNV0DzSnBeKsQjyUPsiJOKs3BGsLQ3UWIcngtuVBHlGqdcfQ7goFUS0dTXAcJBmu4R1Xaxr3lPJLRRCvNJyjvZ82wLKE05qyvcKe0YJX3GimWc4/obJKFpSyrCCn5kmnyoWc07A6ks1k7gwgTMWHNImJcjp4qi6WGmdkmg0+aBiWEQYqsJO0KOj8SzdzSH/TGBL2WmREuHK/FLLj6MMY1iZlTX5vZy55VfC1hC3Ic9EsnosKEepi9jZVDQP7mtZqdI73phGttHCjIsrpR+G27cud7vdw+uHH3z4fj03ZxdnL774wr/9zd9+5Utf3u1ufXR13Z43w9CNB/Tg5HI4AtxAo0FreWLT4P1Q09yog9ZWNEplVRqhAqLfrsQq3Vfp2XjRSNP8T2fYbuq6x7VYIduZJhnVdtfAr62MBUT91QAkCETSUrk/42ClcLeGbPXOkXvYZ0m6Cd7vdjMM1EqdI8+x3J2KEMjbw2jvQb2UCD/TOItjbkDn1CBKjua764MZ0wtizYcbhx3F0MrLsLYKMcSvaNcEWZe+NP5PDlz1ZnM6CafhYYDI0TZtWlul0x/TAlSMsHW8lJqRpOOvK67HE2PMhJ3uEK6G6OFxPA6SCmdNDCQ0yInx2+2G++Uq3jIeQ2NtKGlzz4PNXGAa1AOEEpvarmogBOq+fvqpJ//6b375mec/++xzz9y5d29TbZBZt+maeUafLpJ30hV+AnaEBDZ4xcNmTyKInkkMD9kTQZnTMA1DZzD1ksXru9SvJDn6cYPplg45DiGiWUy96Xwu3sww8T7JWhcQ0mE1x4C7nDGUDeauOx6phQqnW9LisF9YR6UjzZXMynxMCcCYqba5gxgGhJ5Sm/UFDCzJ9qvY0vKzOBAJ6kjTOx3j0AdjNhldPMSWwiytbUxTc4zaMnmsNqCfdZ0j410eEi8dfhE+YzxXo0hUbcHUzC/NDaCdbepg2dlUzPiF/pwF9I0u7XLomrmQoIMY1I6XJZpEXmC42W7qBoithnol3FRNX/VR2yiUer1eEJ+A0QQTs30IsNXjr375N9/+5ncuhye2Qz0f5mMHqPeubVG6M42myAIplspxPCspKvmRVQX9SKZXt6ALB5YQvIPR8du78tiug9fXiL/4zwklJp65OpBeAbmXyZePo84r3LObAC7LE9TG92/RRIWhDbquHcPLSu3j0/ISe+VfVCuUztiZRMX6DCJpc6aKme8Pa4ybnIFcWuFr77MrpBv0XQd0ifwukW8pREu9Ombd+HSrgVDbRVINuBO+OWF8d69Z4iFSZQKWCiTZoeE2hMexwwijnRweqlBLV4RrKb/D1/mQ4o/ONCxAmJcMHmr30vgjNYBHnr3lBrFiwe9X712vBTqaGshvMOqq6oXnP//KN169e+f+drutQKIw81pWAigRmP0ncJkdOEdAW7Mzs/MIcHTi5BkyJcDOU/MS8mxArEjTBj2iINm0Sq9Bf/ZrxsUoQuCOySNjyOXGkkAuYTDOLCQns6ZbpUTXcnXbtiacGoT9yJuIji0cSY5RP75zokm5uaCD8uyivzGFcdH9RqGbl5LJvCE6Pks3YSpYbjpz7uedqG1uFuUDviF9IU0iJCu9eFsKoirPQXLRLTepDTKh/FtZnuoeol7zbqeUdoAKo4WV5JRltPmgIFuMa3HzxVjWqPdRL8vsBwGDE5tk4+AZ8P6RVV83HKqkOBO7X7jYgDRn9+ToZeWilL8eVLlqnvthqNt2mqthaL/1jW/cuby1wbygicHcaqSTDyUaxd48HBjsqpj5/FcwreJ6BgkqAskwYABtheL8nlNHQosC46pBE/UgSDHJQevI67DK1+OL+q1CDCsLsIw1e750DRVvNhCe2xnxe1Bxj1Ab9Gqr7HQoFDnfIvJyMY4CEFAWdXQiZ4NFXTNT8vQT8adq3YFmL+cHTs5beRbZW+zPsMSmwNsKF+RSNTZ3fqQPkbKVnrpIAg2U6dlrcYMIX1dMOFsvIQF9olShiIsg3k0p/WRE5glbE2nRu7oYwmkJbMeVvxefkXbH86sPIH/H47jdbqll5tFtfXI+67NF1435eGKwEro3hF4H3RjMa+jONhef/uLLTd9XSEJqWYNjErQp5NCi7srSRRC4DfPQ4INRvSJE5A1WBlu/77thswHkqkBMGgFb+UsyzYwf5yWJbNvXYykPzC6T8ZL2bH4EOsfdbaY8i/Uork/5z5ZOPCsQyuguyU1WSCBTGzUiTqXZoNcpHMDElWe+hChLJnSekPuoJonPuD6xMnGa0BDupY7kUkYZsqmMlZAzkgqeY6gtTqA+e9Q4T0jhxVdz8YN/rzUDpbhWeEejz5icZ3NvSVqWAUaxavYGcZpazfC7soI2qgXVLgxg3yWLV1Cg4mlJPqlOSd0JZYqx8BKynKa5p8Y/5OgIy62Zrj4BcfeaPUkZVkf0zttvhs3QD9TFC40xv/+d117+zEt3Li77vsdmojRdcZxZVSDAZLAllXzNWWAPaAEvicX5j7G3iqIo8qga9EPbAl6D+gaaf9EK7sLsBuU8zrVz/pXFWH+FugRiS9TIl+fILx31GVktcYNJ0m7ewOpgg/mjFNh1XOnBpMxliUk5JWWFUziCdp2oVshWPMnpjqJ0cFzAwNn1GqKiS2s7j6POZIGR6k6W2Eyp30i8XGg1maRDyKbYv3nSZ0mQXvsxSOFsVaTT5W4nvUgRMog+c0RurjhJEAFYU6ftQUrlurjt4h5pbVe+zBdRjk9MGf2BU25DVY8AIkagnkWGJgLlCcghAWHE/CVdi+Fg6qbebre73RYbq6puXTzxtde+dff2HeQAoHhkZMoyRweXviDj+SjdhqI9lcyakBlrDxw0YjkKUoI8SGjDtdlstpuuRfl71prLhBrPUIyaYI4DSmrkYueUqpz+M89Ssm+rYMlisW+QFhQbH5cklTIVwUdBU0i1IYzgal4u4YDhcCq2NdPMYd8K+ZQUIr09j49KwpgiFXXB498oNGW6sZxSsvGlRfzheOiOyHUzfuU6ggo598pFEywqRevbetZdqYFCXiD2WhYLdIOnodgGqnqLseb2lPhD2Z/RQDiFfnFBt19ol37VYuxr5F4OzfWpoNeJBFYSws/ThI6+ovTFNi8zeYoJXpAzWeIamxcEomacDsfDcRxR1L3ZUN+Tpmvmn77z9kvPPXux2WHDjZP14BRioKGQlKJyXwsVWhri+jMK4+DuAauxCmq/1m53OxSpmsx1N1U8PCxzmEU3g9KNTwwoLIyeD640AoTAvVzWX1eVVKFf1twKPq7zXbItU39wABnINZozULNIkDC8XNYqGQXVjARLzdAs2l+G8M1rlR9FaFgaKHGFnmoT3rlHRTr5A2l90e2JPVkPrx9CD2o23iKp0CY9X6LUGOO8ljlQeJXcnGtMslN5PeVnKaADEJ6yANsbwk0tUQ2FHyiSxaNy1KlQS4NRshjnig4d9sKaWlBeCWLWdjNqoqk+xvyNSsYYLyei66mC8WsUIk8lWi2Ct3Vdb7c7avbRdd3w5N17f/jKV3abs75uJ+wfdn8Kb3D5krKUdDJYLbdksKC8MQJrzI9XipOP/CvaBQ0b60scZtk3cDlfy4j88p/FGWufa6o9iuJXaTF1GJfHTAWOWvYmyq2eGokqPrYEahoqJGrnblZKJs/ZDMhqaqbBeu0ooVpzLYcAdyBlMxqY5EnIh8qgIOSl/IsGrVkejEPPUchxmq6uH851vRmQ26dPWdDiqkn7GLyz1vHmTR5AWVWXDOpksszsPiFu7s745AnzB6ZHntBOk5KOiTcy5I/PnNs9pYmm9y7c2Y966QaiaqQQ+SDPMLWO5GiFVTQI4j4RsFvFSbmBMYAKrgp2z2F/uH3nzma77boe8uf66s3vvv7U/fsXZ+cVpVVzvxXJXRCHpoRZrErR3cDkJ0EHYLjMVW5IZN0xhg1TxCJyWt/bb4ZNBzf4TXNTruhNAtbCHWWkb4WlUkQM0J/adWTlajYFXl/OvlBNAHJHjPXXMANuXSdUxaSZm6mZYKeg/zg58CAHqQsZ+b8YAXNiT6ZyTanRtptI0SkZusaJNEMmbewA+mPL0NQ1WmlQ25fx+vphA4fi4AnqJT99xEvs9GrlWAnKrZUBqSp18vrRbNKv9Cq6tomK3eYiCURYwtTmtVZnnaRnmskSz1oO/KYJWHzJSmdn6iFrljOlXh0PMKHiwVJkRxg6URc39snV0zB94braD0N/cX7edu1m6PbjeO/u/S+++rXt5qwbEIANLYg4qDCZc48rhPUOTscxdB4rVrhpr6cL6LOaL4XKjJDzHFNjJONJGGPiwsWsLm3a+SThh6uHi0HHAvUiFOf50kmQWz+BoIv4qOKOMrs5DjHu/sUuoTlqaeEQwySPM6B8qrnrWoQbpvGw31egZ815QRKYZl0FA4r/4+4taq5zdD0UlBcUrGvRoCW6JO1N4/jw4cOKctFNjJsUCNUfahOLGNP8DPk1sg03NNyRkBc1JbP7/2WhXAfLoGfJhyobxR+u1M1FARkNbkjBulLLeLnZxy5RXMajcAm0F7UY+ziOm+1mHFG7F3YaY3mKg8pYptEXHYDlOV5fk9mFP3fu3BmGoarmw/Vxf/3gz3/xNy88++zZZuBRtyghFAh/6i/AuhwcqExSYUu6Fi2KpG4jeQjDsZC9b44pyXxDBlgHv2fMwSwmZlVLW/9sIRaPtWRlyGhcnQvsF7YxxdUQJdo9VCKNVtbVMxxMiOguj/cvNULbB5SkYnOnUhTqCfSqDbI193tkvKt2JZ7lpApgcMC8oNiV+sHdBNPtYmXacRjqrqNjp2kiOYxeiimeWwjiMt5UuCaqQg+1JQjaqrPCRAGq5mhF5poWk+fWcQOi0aJ/jd0ouwMZS061UXLQnFKWUZa9q8OxAuXQYRMz3SmDRJoe+fgHbsMT0nelOZCsqt3IgCmp8oD8nljU4+HYtt3l5W2scY11fu65l/749T/ZnZ1v+66GLED0bCb8JOSOUwMu0jIpw8EEdGCUJRaGz4NkTHvWvG4WzlVkdzqhupmJZAsWQ58nCbvcJzHDyusAomhOUlssRtE5k2xM9UBh5GbTePi3eHKX1sVAfT/5mqtW6cnZkmUk0X9MEYqx0FD4sD8cD3uKwJNBJyo73dXclqwYGiBV8Pur/RIeUWIwoglwMpo4qKfp+nBNud60OovuZMFCiHmzmV/NPKIFtcVlTNrA6qKmw5cHG99UQRuzJiM/dd+e0IcURXDMklEiUtHaqkm30B5ckoYxyK6nkk7SiMZx3J3tKPxA/SxpeZhPdz30Z2m54xMpfbdBvuyWxPHIVLi4uNV07XyYr64e3jo/+/Vf/uUn7z95NmyHoT9QBBzVEe4xAQ0T+Uv7rORoTWsZPKM8RfbGN4nQOwNEdb00QE5i0Wjg1FKuTOv82P3Y6QZKYMaYQmsSIQuPeYhsMuyTrNfpgD1BRgVLMW5Nek5f+Yjktt7ySOJuFjlHrIBh8aTno8RwvWCCZhbVl1RIyAHhwNJlsZSvBCCLaEwDnpCBDXGb9tDUpJfJI6lzuwjNRxW9nP5KLaocGonrFglt5Te6hG4vd3SV7D1Eg/O2EX0qFJcl9j+OlIDJSqcA7q3kYy9cZHnEi9vhFhOkE2TgcSachfoIHCGqpyLinNFyGjooQzSoLYrqcI5PmCggLRrbte/7890ZubqOVVs/+8wLX3716y2gahqpQjUTl5aVu9qTm1TEVEhl0/XRkIvardEYiXvW2+5JHQ/A/wVhJqxgdDiu+RaVIEMMIi96cfySesW3xKVHsZbXrRdJQVnX6+3gKBFsAk6eEL8vckvlPU+HkGKoqOIFIU8GgIXmgVKtGcNX+hLzBbXxmgJrmGCIIJQCkS8TY0EjO2VCTQA0ADLWkONt7u6ECWAaz/okzXle17Kz1wRz1FzM3F45PxLh+vX99lkBTJo3eINEVphNNHB0SfZ/MM3XLruQxyqMdXjkxJrnpmv7tmNBSnaNgm5r4T5vGybyukFlwtXVFepLoZ1S1ww4NCFq+q4b+r6aq+urcRjqn//sZ3fvXG7bfiYbjHv4sf+ZsXKOwNpA0cI6oqcsK79N3vZgaZk2p/U0slM5wtSszUiQZIufjQ2v26Jufoc8rawPszuIso+ojtoSboPdOiedWTHOhUhXKdQVh3UydvUo/eKCie7MXUyCn0GzfJWZEZkCpKQekEKHirD9nvUInhzgmJFTUxrrKEfyrav+pjQut3K0zzCEx3jYXyMhoQNisRk10VklTxWJWp5p9qwIuqObs67lWkuYcjaNPGVJRagl2zRn+aW1MLP1pG4mQ7XpoCJQJEqNpIOgJEw9s6d86qdfDACBXOimGQ+HFiAQLbrgERHAZmPETnICoaCfesxTJBhyDQbMfq87Dpoz9Uw41nVzcXHr4uLWg8PD62n/h5955Zvf+vZms+tRStgCkUfa11FGHqmXKHWSeNIjxp82rX/w/Wk5HOR7RgmyUa8d7jlnkVCypnLjkoQj18ykmK1Nvh5R8TVLPC2qD2010mkqgmpsvkE9xyNLIU39jtOZd75igudKfztOJA2zxBrBf6pHBiApvxgWj0scvcusXp+5KBcg55IWnwdhY1oLD1WNuiURDYeA1brI0xWOK1Dbrz6epWnks5jmPoTpVuffrd9okOiWChLBk+bs/mvjY84GMqaacPBBLX8LEmx1Gwrn8mgBlRMC7HlgxHbKfxIgb1rsYRi6DiUHh+MRavM8HY9TMyISME1TD0krBS4kfqeLWxfnty8ePrx68ODBvYtbv/nFX+0GQj9r2RBlWS1lLaykzRMl+OnEJitv1UItnTeeOOkaNPqSdQziUy5fnpBl57qgs4oEKOnspAMk3IoI11Kgbjw0WwFRwOq8pABEkN+Ra0uI09c97J1YNaaNF6LpYHvP9cQosghqH1NKG2AaR0C3bwYYXyNFPoPTIi5ZVnzNTLUPHhBENGskJAxTAkKemImp8Fh5wWZ/inBodIrp5YIP1/7VEM9iXfO2sJSaFO7Py5svESxhDz+YzUS+I21kYgnlFj4uHzMzFLtDdzweb52fH0FNrg7xnFJvkfZ4GPeHPTBoum4PbRd2UVVVG2qKx4Yw594ibXKz3e+PaGx7ffWNP/3Osy99qm06gpGmvjk0SXQ6qZcMHBuSJVd5jj54wY+jeZMq7CF+oU90wGZTVu+MVPXt1VeyOIWWQiy0UE3NjR1tagZqZDchOdidKYcnKvL5ClzARY/5GBNSqZnXWMsq8x7N9w457wFR0Vdd4XDS7FgTG0ICoHRV6pcM/ti0rcQfYl+eqJzIVeMwihgtV9FgqlgOc7zDzGhbMO8YF6axSopIlHdJe0+R6dIBpRVHNonxzLDiqtwvSz0WY1q+YtVF8IVRUjmjzSgVCzwi+xcTrboKGW7UwZ5pm+OB6lQoE4pw23FJyjvHdmxqtMklfzXKdIdhoCADkqIpLk/uymre7pA42VTjR9dX9y6f/Mnb795/8qnNduj6ZgbMBkN/S9BIUNodfuuk8rrUcuVz2BmWQkRpz+gqwglaFgnTK9UnXMfrcx8PKUzTjIupMR/prCC6quBOMX9a5u5qkx4tOFoZh1Y5eG6umbO62WJggOfCUMvTFSMSb0wvEq0ipRnpnNj0iaJAUMAWam9QTc6YpYwvIOgCjK6lF+NpUk5S1CFRkdY81WjhCIWcSJiqMixpQ21pSdpLTsfaI8OZtIMCZ3vItJqlTmdVjzk3ZJk6oVcLKEjxtuEIP0n1Xr+/fLBeu76VZuolS32eJMfQlaWF8CBIF9i3+8NRLUKJLgnOW1MfxwMh3cDKFeOnRcSY+/fxmqH/LV3o4uz8cDh88OCjady/9d6bX/jCZ4ehb2DXoLlWbKnIKByGGh/+rrBYm0FV/3US8loyBwM8bN/DxWdM+bQO69THVBIj9D7dJSt2qs8RX7YLVBZxUpmFtZfsR4YXqTdyIlcgU28kv4CFRnU3pGsuRIdzLkJoh7Mj+gBX5lwf0stdqJJBndgGooW2TJQ62HB7JCuBEYU8KAIpYzLch+ve0Lpxoio9lsMyCxLV0Q7HCUWHXyvatYP48ic7MvyTNQH6PuRp6BLH12IhT+7YyOlXFfMYkooH8mQogIpKZC6146YAURrVdTegpQ0UaMXFkhjOOB5BfuRnIq8lorXidZzni4vzpmkPSLnkTiDT7uxirMcHDz/86OH+hWdffPXb3759cQcXqJv5iKx5CQVRrIkcV4IF4wX6xYMXtONMspgvXeAZSSLIuvIGll7dU1BxSIha3C0rnaFfge/2tDFcc4Y3zlTJAMTnkjNe2QMkqtIGmJrojD6VmqUSOCSnBMsi7hrhByawLKqTdMsVSZOvIfUqXobg1imFKuBNaSmfhxdUXQBxyrJ6rqxYdGmEMFHKNu/HCSFAhj11GEyJDhs85CzpIQsKLp5r6ZdfzqnKxPDkgdSyNrOmt4W5S/67bJDH4/PUmzWjDb2IZSGXi+mY+7x7Lgfr/nMHzzNlVnDPIZZlqEcjbflwPJLfuZsqGLbkdTp2XXf14AoNaY84ZK6qTb+5uLj1/gcfPHyw322HH73+xosvPN8OTdM3wJ0m/7UCIuMKBMvo/cmSBbNurdva+wZMMz/PlEyEjvCk2/uWc0wUNyAdx6bQS6IG6S7LSLqZw+h5zJdGdclojlqQiDfYR+u7QLVsBYRcTIlJkxA9SdNVMBr9Utees89Njpfa7fJqjJks8yfCUZz8JCTQPBbJMyoiOYbM2Cos+/lUuUiwA+VfLpThJncoFj96oZk6eHisdQAKbThSqHExW6/lo2SdrkiTCccu61Zjd8nyYsku06sFs6Zc0jiogObDzDzwCeOBsW7dysh1B9dU98v6MTeVZQTEpnn//febptlutjxLwPInxYYXvu976nVyIJS+ZrMdHnz44Xg49P3m2Sef+8GPfnjv3t2WfuJoLGlfWEppL8hdRcrnUn+WfrThLh49pQZyOiX1VuiNeuPek9p7yZV2Gk2BGJm1glO7SNMZS84aflGzbs4DlT1qFLzuSA+PLSOyXpfhF8vbCK6ylDWth3rqVvDCRJ4oHzQHUAKu1uFeDVZXS0RLlniq3lG6/DJGHRsznBcZ7T29EG23Fim6UpOsdcmKvBPQ0h13ymZbge657Xd2rLk2XnOivkY3WWIL4fB2tx0T6ERq/Rdrsb5ApmWs0+KS2gNfyD+c3gilOWcvKcqUpDfmUqILcZUgMmBM1eGpoAazE6Dfhp5igFBJyd80HQ7H25e3jkcGl7BWMs1ms/3owdU01Rfn3U/eefsPvvh5wqCUBnkaNOIeA+q78t6fywdZ1RjLD/EbQooFzjNPgatY2sjbYv/LK62KN//ZsgH1XwcDEfHONXnqqzURbLrmqrq1GAT9iWUAOVVWHGPrl0gZHstf4shCTqWRS6bfUnBbrI02D+BLFVjH96S46XTwJHHJLCantfgDmIVLbDx7MJR2I5fmO0yQtPlpAgedA5+yCIwCnoGqKZbNEiyH4eMrh4UjqdqdFFQvhAMWPuWVyy7kfgiEr2w4+9VTXlT+E7lRL0t3qUAqdgA6aeFoMjsRaFUUAbY8dUNgODvbsSYMawfYO+g8ttkCJP3DBx/WfXP//jNvv/NuMwGiverRn5YArlBrOo5IS5Ky2GCW5t2yoCFNOVxY/65B1txpC60ubTdFGMPicmFX2/pbss4K6yjnWIPucrpkS6b0VweRXyxhuFK254vDzSTL8n5tfCnDI+/FxaEslVQVS2I8zk8UAy5/fR0cbTButVTHQAfBeoLGTBZUCyWbtBXFLMwO8dXZJje1Zjq40BPTopIlkTil1XtzwihJQkxklAAAGThJREFUZe5oSjEvzYpfcSvGbC0XhvmAhQYcw33r61I+jjdzuCmQaS7bwD2k81s1hdYXkB0II8lEEQJuSykQlACIp8WMwErGxBwOezC1HmtxHI/swdoMw61bl1doijl3TfWzd959+ok7223bDlguag/PjURAvex5zsNdyL+Qc7ictrivFNUJUd+hR17BopDMZ1IkSNhuaSCulCUnX3ICx/SnIA2846mL25vyN4qNHnMydNH8/4XJtM5hVm/l7rClB8dg2iytJ8qEzOPKYWq/BZN6agVI/CIPlLcjWbFi+UKCt/M0oT+WoJREEyYIfR2sWCZcdWw5nzbTFfM40QFETWHmALCbemqmEQQ8NjOanpNMXiXXQpouE3ZKSB7VaNZErV95xYhiAZavXcx/NPGMogn0QxJMBXu0Riql2G9kaLTklG5IbaFWoIrtCtSf9vp6v9vtjiO6nzBjPT+/QBH//qrpmk8//+nv/uCNfui3fTcP7TySJ5bW7zCNCDVLZEU3/3LblU+/nFffFmqA1X1PynNBsiW7PiGT4hkyk1GZCwJKE5XDMFiBVoWZv7S2xeGaS+VBsQJPGQ3mPktPfHLo/ovvLbl6YBjCqpeyXsHVVzS7IrXLEOjDbXKFZbpCalRsVyHb2BAgqNUae2Bsfv2S7pkNvM21q9ppXrxApVMeaDds7hHYG1JQyDGmRGy8QM9YUwL9CTJlqVacdGmnZ0n9ikw/nBi/CrOa3NmLU9Qrq2YaJPCBm0lPVdNx/3U4nK1+k537I5onNMPQXz284o7b44hUyq7vjvvxcD0OffOXf/HeM8998myzral6iZCAsbvRnQx29Ri9nvYMWfBmbdFDJV5Xmh+moq7cpDyHDKv1bVO47V0Fi1pgoOHwtV61JDaiX9p6bi1ZWgSdY0aMnZA90tmCkFPEIxEAxqM8Tqcsz8/KuGX8a66l24NriaRxHlaUW8/sSOa4BDeyth8tOGYdgZsS/yJ5WE3YS+OElsnRLbvOlzzm7Wljs91XVaRi16tNw+HmGWSMW7sh62H3xes0y4/PKOS31Lm1ifNCYTedY41c85f8r/ErSRkiNwPai05T17bHEWGYM7QOvBbEM6k/xuQ2M1DI2rY9Ho9N3UKDnuAjRM/oat7vr6a2+uSzL7z62mtV1TdVtZ8nABKQr4PwZQlyKWhI5YQVXC8YIWnbJplKc9NKrYVlY+pZniozr0RlguEY/WhBUi9qrQMlxW+1rrqk3lTj6EIwMfwgItWgMl5xs5c0qMWB6Zum70qYDzpNg2XN5BDqCX9MwWy5Ebfk3T4qQKbKXxwRLY7MCWE5MnXBn6Ku+8AKQlTRZZxp2pWSX6bdNX8xHhmW9+FAD9+3raBZ3Dh8MRwKwZK6IPMzBba5oFaJ+kgYIWk9Qa4mWPsFn9cdrVkT0pSnM3ncDz23sSRjmLkjrkgA0RC2LAavqcV7U9fb3bYf+gdXV/v94Ww7/Ozdnz55927ftTP5CgSfDrKXg4GiPOsOyEtT0EWWAGEn5K9qjK3tOk9NXTk+v5ZfmTYfwHyL1Kj4Tv+wKSi5ChGXMWjIYupIezNbh/WiyQLnQ75ewH34oAsLzloMKYco7mE5+DHLYJG0tRAWkVPE+EhUQIuXT6FMgVxDt5yeps16vIKlGLHxPWXcSqBJQ66U31olWmlf+qglv5VQx6thANhwMfknXiu7KXxVEmyUNcHXYJOzFLqFKqhqQ+kE8ZVVFapGmVhL5FrVlE1OspernAjf6khZme2BfkJeR91MlDjXD8N+fxiPh6brXnrh5dde/9Nh21NqdVNRFw0qFCQ8ZCrWX6pDcWDrH9XgCILJ54j8bdRmepG+/wiLN4nZmCnjE33iJRvQqFHioWGbiws8YFspBy7X4l/8itxv6aHKz5jfhqa4Cj1mLW5tqKYVWnkX/ZStAs5+t0Jx34NxQBZoiWnbEexCuo9bAuop11+9Zlf5pPKan5rUkHvCV+AGcwdyVnOTSidv4SsrGm8sCTuprKRTHpW9s/Zrvu76XQJ/ID0Z9Ap8QAYLoBga1dkDmZJNhb5rJ+q0gH6CHH3q+sMBperH/bQbup///C+efub+brcbhk1TwTPBUhchZUaEMp9VUqbKx1mVHPJPWETCmOeaGCsYVLoq1NdHzV9hjkpF43LGFv9o7KJcqViW4Cr90kxYXH31juuuPm/pG59lzcwKB/C+o6aI8uRavbacBrumetJceZL2ZjHZKPjo6Bu+YPguXoJtDkGHp6AwKmPVWSoH2v98SCGXKz21v8rWqPoKgzWegbsfj8frw15QoVwZX2zQYJ74UIR/n17VQpGzC+WnC11fT1zD7mKpfYGRAhWwRhYELGGOtKuzFaqVZEFTL4zNZlPVNUS0ovoQfPNUb+rPfO4Lf/wnf9pWA+W0S9kg51cfUO6bwZhPKbH2bvVJAoFwysQArFiI36U4T6Vsi+koZFamzRNs1fJu07ZSUG/rxbgceagdDUp6ZFORCKN1Xk7NSV118fRrD2CDwa+U2E5VbAF3Mm9ZS/cI2RbRxAnqdNzv5YCKLJL0HGKRUq69l7UoaaTQgQ1E97LaCbPMv9oqwb+Q+UeaMrUhREpR6Sn9lp9z5WHSM/FfVbhO+w78XcklNYc0iIRThYrqjM8ubTxxh74nXbcHK+IKFQL1J4tiP47VWNWUUiUzTS9OWOvbrq6ai6H5xXvv3T0/3wykzY5AqCf9GaXDqjj7wy7KesvZXrJVG66E6oHa06NDN7SD+qbSy8WuKZGhfTLXBuSeh6QV2ju+REhiUhmyIsPnrODnxViIdmVlN2sSN+SdxKkMkRfxQiOIQC0uEospL1CqQmyzmkBzobxcOr60l2osLq5xEE2BsdhbngobYebsGs+r5YNOo/WpecSkMfXMqIpDrTvA7mFCJl9bGrjmBZ22hHmoK1sub7Xit/Vr+lXVe7A4XsQ2vIBQQ6n4jnc9Y1lM8C0fqrranp8NwzDP+Mg9x5q23W42EL7EzF9+6fN//NqfbHbD0LfUnXsej8i4BEwd4ezEcMP8KLWnaDKt2106yrItDRTj7Qbi13DVEl0EeltTqqLoC5PjbmT+NhhtNnXpSSzWq7nm8WaBYRhu3NodQ52Eb9A8GwvF+FQAvdSE83sFDNNygKTjr6yJZfuYn0gktbmdvMuLKIOF6DplATqvk+zoOJvhAvqoJ9hY1O1nLYxZVp2FEYYvNbJUITv4cDgikBq5ktPkTa8lm/b5Wz7x2iycvmiy+pcHmNVeUd4VsKwkYY0/NlW92QyXF7c+/PAjwL4S0CzQCWEPI8bbA9i5OtsMv/nFL1587qnLy9tVXWMmjoRRRxANsrv5rieXITxQoK1glMhIebEpFr1BojV8V0IccoebJ3zt13DTfO80f1m9Myo000ktSaXivGmMHOKbvESF4ZBGmpOUCwXvdDKM3i6zsMjAOeNhBZqkdC7Lw9k4LNXNXmE2w42i/I02ngVlQ/pa/knNvWBslBqQcb3KIrw3brLgmVBeKneiXiIHCuYbHykE46qbXNWpOKo0xOL+N3yxRqJLU3ypKJGd21GV0LTfH8gnTUc1MGP3+/08zbcvL0dqAkoZrCMHXaFO1/PYTJ966dM//OGP6qZ/eHV1QN87JGOChFFdrLP/KEaWlLUiz9CHTpUXBOZCjYu5R5mIuwgJHklyRVNJOogV21u0w0aT88VY9w9rxUtHMJuyrxlQoqm4ThKH8e1Eq8qJ+6WvO2UeFlyteKWvyt0Tyc4vXai40GWAH6xKfOhjYpuyMEGD19g8cgq0w4vA5QlhxVdEoWquFmEkwF2ta7KJzag2CXlybRrqTNSxxiDx5aAa2fMJYxbDsI51bCnkHCydhZq08ilcQEdxQuAuvjb+rI9QpNpYJ0+xsaYZ1UigN8pua7uO2jJU+Ev9bNhY4pxVrtiWtlhjdefy9t/++pfDgFSaua1rgAfP0xGBX0+WDcu4wqvWnmaJtcgfOc0TnucTpu+y8H2ZiBbybk6/itOKBOQ8SmSkWT4wGF+DYla7TrpEYrrW68qq1TQcteg2HZnaSopyDgKVZ7p9oS0RfGiFR2ShGqxMkuZC09MQBB0gnqOzfG1m5XZCuux1Fh3Nxp6pkN86YytifZmE63BT5cdGc+V0qVC3YLiElSYUWyyi9MuJKuc3xDjXDil+WB5zgluvTWfR2IAeswbFsj3DCAh7JtS2Gzb99cN913WH41H7BcIjjerOatwMm+999/XPfOFzD6e6b9uh6Q5zdbiG8avp6TI9KurWnyBuFttudoouPIm7ukXeJlX8ArUrSOq0+0/aEZoFGOlhEfAP7JuCnOalDUQV0hpJ9oLH6VlS2sv7LD16cLqc3g0+D9kvHJF3VtjPKkfyLcxDcjFCJfXixLIdGNpEyNcFM3StVd7AhrFbm/Mu6imqY0ojF24+ju2kBQwFhRWMa0WDCt8UZFHna0S6mG9gavhRYaG6YD7oPwV9eZw4EFLkLvHuK8t06nXDT6eOI1Y1TR0U47YbqXoTYSQI2AMSpPfVdrflqn3GcqaU46ppq74bdtvz19/8wSeeeHqz3VZNPR8oaDQeuM4h7z9ltDLNyylZ03eUPqz4qmOwKwpTy3UtS8cSkHJChT82J+ckyXZTNClykyhyMz+XrenggVbtsAyCLE6LkiBqZl5s/DivIgK3somCWBX0UsSBQ1JiCGutMlf90WNwwoZySK6IrhQXYzAlaaAjgVc7OiEZnBZRpkr7iKqScNIJeSn4J0PYl9wtCAIJeXIPXT/H56yc8OKuxspXX843TzZwKr9Z11Vdbig2GL0AjT9N89ADaBIYlCPSJHe7neZyoFUKY+4Q2lR7uD72VfP917/3yhf/8Azu6MP+4fX19fXheCC1SL15FnrOzxGD2AXdnDT9GGIP3nJDCPATVp7z5vTWfOt8y5Ml3/k4XTKD86XeVYYIKt8HF1u6n0vkhYrEF04AzWuTsuZdz/yyONTeBolyMpp34jsBSBRHNFuwopObX0j0kMKFhxchTlJWj0U/05jd5+y7Iayv/VCaVjX9L52Rxr0wxQo/viwRA5v7ead2QKLBZBOtcGsOiNDw5O/KiJgozJyQZxQmlcMaqgbKRLG6AAAj68FF8J51Czj+BsnP9HyMhsId7GDvNO12c/H6j964vLg1Hce26xBcOlB7IxRuBZwy4a/FlC5mxCs80yw59ydgcSk5sr1SXMLfGlNY7NClKbpc3zyvYXwulVfqDMBhyP2H8lP6G4SSCjmOgWfNL6FhF+68mxTlRZfLeaUd4EJ4KJPQt7paNwecWUks7i4tF1sHLNK+fVnm8fHTzMLAh7UouPC7hRkhJ2H5SjtlVu3AQ/FmxkTdQbdYSnvlHwVtETnFigkR/AHZG7eqpJyatrWfk0cqswAdsUp/nw9nXQpELMopcKENKZYyNCBCkBWtjVDHGe2nWhDtpm3a/Xjou+Ynb7711a98iZpAV4zE6F4Jeny+l/klwrKWUkPaaFjmtvYwi3o34HIoWK0BDfnJgG3W7ShzYabPJ/TaHG5cGHaJNch1VY9RF7Xsy6YiGlZHj7EWj0T52qSEiMV48povDoulhmmxT2ikZr5J5w2ueg+bdTGHemOFytG8b2nP7KiifBVe31jdxUrYPO+pbSkfYPSRQ3NZtuUHYnr0+pBVWP45SWtNy4pXZ6eP4jPHlZCccOgVllyaZmKhKqXxZZmc6pTikf71zeyS7rAmWcyEiS4csByCZAMNtlWzR/rUdCSoSsBYHY91BVT3w/FY9wDyvnf3E9996we3L293Y913/XEGNMdM1YKO58Oz4vOdQgH58X2oVgXoATluEdr1DZRnQfSKeohxpQi2IbbLUs9ZJ5dk6s2ryYKyMYKxk13TAZQBoaVmbqhKmPpmxN1cMuVTzqfc4MyUKCthPrUBnDkLJ13UFqoZ7L1MVysnsw4UmJmwLSJdKvD2MVpf0Dxz5LgaFVc4Mww/K95vZUL0N1sr09nqIuErGtd2mH0QZCgndTN1FCROWoCum6EnvNEr3HJxxiOtunzaUn8UHU6B7u3XjlEj0d8RaFhdfUQLnFn6dKM1CYs+ANtVzXHc/8V7f/7tV7/aQRqPxxlB8CNFnoRqrI9JQRGrT12qtDy3KqzhbkFoi4GNROX3iy8eL1xnff7ltmu8dCXpeAFH5UPMAtsrjyTfAaXS3G9BN8UksaXArANDzZTqB7r0jnL4xIyqPmhXcGzPeGFtjcQaVpxBEVA2AFOYnFmSPJX+Y+D6rmQEpZeVI99yHOyVOBYraFnqOjBPXthTwcLyQ23yz/38xSm6bcLGyBLfMWsdTVR+53W1xkhpeOH0xbLUH4fSo0A6ea7MEFMNQgn4DxgcnLlMATFsgD280CAY8lJUm80GWjSouvnEE/e///YPN23/4OqAskH4seED4w68bF85umr4u+xIEX7TzIfQFpixvKnFIMleJ15nnun54+SyFu4FBppOoTG+cBfbaOnKmqGuMq2874noom53xx3nDoD1XE/w8BOUY6R8azuw5HXiK11ZU8npT6u8zt9dAfVxT+vavBmQCvjKZCg7XkhRq+2kniUmfqt3Nqic9BM7UFy7X8LyJp9G5ELZio7p3DHdPbwKJTzvCDsmZK8GjprQjWzPMJWuBFDkTsVXOWteDyo/JhDv3DovDXfNUjB3Ee9QQJh0m+3u4dUVIWbMA2K6EMbc8RHYlJsNYQs2u93ZPE3v/OQnzz/3qbrp+wGZ4NNxPBwQ+IUPJzovbHJimY7uI4YDztMuj27KCzVAFvFLApj05xW1ODy0bySX7BIj0Z2X2pjkEoWld6E0OdQVJQwwgg7T5dyTpKeprkooKJMCHQs5CK1LL81S+1uXtoIhYr24yl2yHEDcOP50Cqujot/0h3S9UtXNNob0AmDQb/tJMhr1pXh/eT+EHWmGcUARXWNHHkHxR63XdNp8laWGZbhIeXbCsD3tW7LolHw9RG6KsY/HdndYiBUCzEM6pU35Pk+BMM8ywWDHqmqmuXvw4MO263b9FjAFBDF1PBzYKY2qPfJFUwP16mx3/taP37o8O6sOMGkO4xEFDmhHBSs6BUx0GYOjJnCyZPQEL7V5uqCxU2KJYJ9JdCZNiXP/zDbSG0ewD/vLpk9uaGkXmeNFS0uz7oJfLh6bVdH12nJucCMo3Mo/hZyMEegzilevXnU4u7pmJB/tPJsANb74g3iitOWab+lT+hxrup5o6S47v6nPJ4frZTmsToMgoK2kVbeC39cgFJKhmt+EQYYqlQIcspJ/mBMuiLqMHKT0Ik8OpQ4B3ErTDPoojdNLdTnbl7rTcgWMSBCxL31K43MFldAGZNwiNlMRpQ2lRKgLwJHdsNnOBJHTd6gKboEgKZ2vuKtCizbqUz1P/+43f/3KK1/Z7w9tA3FyjVIsZHFxKZM4KVxIKeNeph7JjtJtFp0u5Ldi7blpCXmMsAu1LCcESBAEsZXhGV8r51ZOaWump0SPilt/oZFs8FNJQ7C1S+vgi2iZp0rK0B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4f+/okG6XIgBvsTzZbci7zrHr/O97xrje/9bNf+cIdd999330PzBeNyPJcKQLhRaIq2rylTIPNAEaRacdCCmievMyVVEBTsXBDzB8H5EWh+15r3TQtdHXb5VlmyoKUc5GXxbfuve/EqVPnnn0wy6RRkiF9QTh4LEe0tcEtdqvt7RymIW1MHlLU3sCx5rTjXB/OS9Z5lZ5IhUSkihJoSPg92HbhNDYG5/35AQkP4z4DLRRY2ofyIl/VpyH9U/iNTOgqSD4L4gouCKtZS0/Julgoh4ud4IVcvcqEBnBqtL6xDeQRYWicqxJzbxzUibnrzDzsH8JCXLFfKoMalgrIziCI7WoEc2OZgSyrBGMsaOPYWHJcuJzFTHPEsas0XOGIPJIHs6dy8LOBfetwjTbqogjpqopxlQsEcnol1Hox+ciN73vP297zmS989vZv33nvA99p6kapQgH1SeuRYht4H5I8Wbh5/0j4DxEs+qgHo5qMBaQQbdtIpfq2LUcVAFhdVwDjgX1u+84IMRqVRotxNZlOx3kmGyAuhO51RthgG3aOVYRNpjpHIhI6fsvpBxylPAIru2CbE0KBQEJq0m/VgLfSfYv/5VNHQsCxVpCC7oZjpeAPj1wpR4CBiB0PRBo0lrJLKjWQQ6Qtlw6KYvqW7RGgD3a2PX2igyNnwuoM+i/LlNGil6KcTnvIcJmxCZdy6emd0BX3vsIYcnCU3AiVA+eHMA5zmIVseZQFpyu9Wwekt/NM/VLauLMPciX8FWebUvHpFz6yDz0UwW9fuEp45HAH9keqiqy55jabbQ4bAAcCG7wF0IwQetEvRC9HsvrJd970tuvf+um/+eQXvv61HzxxGCkgVSghGfxpYaUef8A0QWdO1jXxH6Fv27bNc0Ao4bD0jAk1fd+PRyMjRFEAm9X1uu26sizXJlM9nsDk1vpN17360NkHVF70oslgO1BkjfjQEUycj04EXiRZrdvO/rIxEmhsn7yMudvhzYLt4knGxfYGLw/atZZPsgdL2jqOdibyJcrXDb/jz7TszHlG9k+7LFLY7YnSwXxQVNpg46rh3h2rs7B1OZWYkSPVFFtDFgjJmAWRybwZTYwUpc+Np882EIkrGDs2UoL2sMf6/cuBXjI9YieqKnLdIevs4jBDTZbYKG7BrCgnK9XVsyxvQyycLY3Hbktie7v1ildn+Jix0Hfn9kg9Ry/Wn7MS38l9g8C0s6qFygtldNfpWkqzu5p+4qM/dfFlV9zx7TvvuPOu5587hp3Isp6Ahw7PEgE0liI2IUJqY2Gy73vYxpQHNqaTRmZ5kUmxWCzKsuo6uNzAb8De1jXyO7rv+ul08qprX5ULtT2bEQESpjksjF91q6+sJhj4bT6GbhG7ePzcIvhDWjlKFXv6dPs/2IulR3UbYo0Ld5CjOe/GxGdb0oA+Fb/s9cYnTTjZc02qAcMjBRkfSTq2BgM+MN0xiDveZassoqqHWFMEeRRlHNyfFMQyWabyjTVY1LT47crnWrKoV2g5pyzYYEmOJyupobVvdV+yrcLyw2aZ3LJYZWPpYTn/HNlHw8329xMl2N1zO5hLUK1DGb1C6PvNiDMFK6KKIRceCwYZYPFEXICPAvFflLnp+qbTnWjFm1796je97vVf/PJXv3HHrbfdcft80cBIAVLEw0bCRd3vNp3iQLSBxci4wYG2GAYVWapt26qqsNfaKCnrrmOFTKpeZXm2Z9ful7z4xUaprulQTuRq/byZl5J2JNeCYgpmgid2LVRORTNSGxVs2Aj4myx9LC1iuhsQXbQ7g7Vxyx1uOTph5Dvz3/YmbOgxVpzDS3ub0Mmw6OQxnMQys9929zO6b4vG8+KDAUsxBsAuXvCkXfldcODdLVEBYo44h9G6zAohZlKN3dlcpjfccHznA/My/jXlfss3VL+Hyp6mKBDbyPOu77kQK5gkcUieDbIVptRgeX0OJnaKvTsa5RkitRtH+BIxnVzMHxDZqBENxDYUe4OxvIswNPblgQgdHZ5lVT4mwJM2RWve/aY3v/Wtb/+Lv/jTT3/x5ieeeNpQDaDQsFaWNUSQpVZ2gC3ZPHOQVSrKQogj6/rOGJ2rrNNtVY37XjYNFLVATRos5VypD7zrnft27dK9Viqnqi9UcbKiCw8fm2AOVxjriogL/TYqOE0QKgTkiLcrmDxh8RNCt2sWxG0cC7AbEAOz4r0bZJLN6dIRQaumksldiN/kug6/oTHKymkMG94Krmf8iJGu8Za800QkiK21ZSvtrIYd6NyEoGxICW5OrlQHdJSZTEul8t40qNpM7JVlBo59v8ErSG63bmBdzyNKjaUsyNw0XQdUkKvKtv5TupshxxBtXOxQRYXa0WX9GVNmjTd1qIXT1OqK5FRiTseZm2QnE9vLxNKGF5SrQzNINCMRL4ZrrBtdd62u8tHP/eTH9x88dMudt33723cfP34c6jFTHWrwqMrOeU98ck+9VkQzg1BFH0OPUfTbAW3HWGgugK6NmEwmbd0g/0CLte+ssy675HKtZNf0gFrCgmYjGG67ZbfIePfRFtb1YQ1C6bujci3yrKgEKgm5tAMlTKTIYksq5v6IYxNMpN99e5jDsASnOeyES/vbErBQdh3xeSzK/eNFEQYPVI6DUH65E/FD6++c3aDIPa8GkRE+8ZA11vxWVzlDnM3PSNARaNfZtRxksDXyyOX3spR5L1HJn7HvRKvoA0QDd3foLyxRebQISWqQHR2VlaPJRICkekGQIKo9TdIUXrcyDiE20dzWxohc2ikXGbZC25a+R2dNSC2+3+TksSEQH+OpNrnDmMcTiMWSyBAWrmRviLawR9WdyESupM6AkqrnTfPW1732+p94/WduvvmOb99x1z33zHbmWVYYFDIzTG5F5iMiLd5ajoQBQs40leVZ03ei13//Ix/79sMP3Xb3XTLLuHBaCzkZja+49OICNyFzdKSgugMKEQ490+gBo4W07n9E2BAW2oi8HOWTjTF8eioEdtmkNA8Wy2mv2AmZ5m3xAazX0bl/+sjVoEr6AOSzK7DiZTkjvGKnCC+qwrBdGAJO3a1NdB8+RhM9Aj0GQVIjgzSqIo0qSu0VLRov2d9IYFnh6PoRUIwSb5WFKtXG3vVmWspMNYgMyQ41n6H7wI9k4EFKyfMw4+JtZbpEdrHXsstUtTYuVNGaMVmJQNnTmjthRutBj2NjFewp+7VymxeKa2OWC6BZjVBZavoOfBAP9bE7GPOoPVkw+sIVfE0u91ZIHGWP1vVl3zKYAl6Hu3Yf/H2VUUyJosVI1Wqdfez9N950441//Zd/+def+/QTTz6NshOFWn24ze6UDsJk8UPU3AHmKhOwFLIoMpVlTd1IraeTSbOo1bg4euyY7jtc05hqPKoX9XtueOdZB8/OlVJlW+E7ULykv13+not87eLaSKpbQ5eado9F/RlEr3XT9ZmR+frBs4jagBexa+Cwmt6qDalcL4xiAnZaMpBhgrxzUE+3nAm12vMQufheAW7hyMpwBUhBzvszuIIJemzIEe6pExMU7TMtjmu0EftZ9gFThJdrDuLXM9Avr4/LCqfpFw7Yu48hz3WulMgy2Qg9Vofe/f7DBy5ST36nJo7TvuFDTM6phRy/Hx9ACE3kf7wWsrRL5bGTjV3TAwdHKl/0fVlylsG1zQjV3kzhtnbTdlyhGmUWUrG8hK7hkjUunqfP2IC0oXaXUhj6JH6jXIHBEGceUDReMLO1YzmVu4ekC+K8IecpGYfQsr1+fCDHEQlvIu48UyKTCFqjx0K7TxS/+Tv/p8uveunf/O2nb7311hMnNpFTzoquR3UPUZSNv7CA425DVuYauJ4QCW3L7u7myVPlqPxvn7u5ndeZyvpeFyOUdJ594OD1N7xj48C5udTTvpc5euL4TY1TNdxAiu+aWhN55qELWsaAEKE6c9E2rRJ9LmQpTAcTxqZKADBdIqIlp8yqpziGsIqxPcMHzonCA+Ef9i7seaIMFJ3CpqC9EHaPG4qIWDRTSwL3mUuVeeIYwuQil8u1DPJfdeUA3vKwH1vR5i4aXEJLSXwtqhBFDpi+gaSkatr5Vrl5yUXjp76LZldRRJXbt/i2KbGmPZ0D7DeWhE+wbDsqKpusraHnw7jMtuambSmERgdaynPJs6T7hFtDsiKtwR0ojMMu3szhKkjrI3N0yXuOiUHhvBnHU2kjDPuQ/I9tH8NP5GNrzmCIdQHfFpuU0jtWdG/sjdGC2soFZEptptteh2CUyuRCmc70i513veft77rxPX/xX//05i/ffOcdd8zruigzagoXL7y1NPkH9dwIXrdChjlXpclUcc1Lrv72nd/KskyqbFRVG9OND73vpkMXHpKyE9ogIdH2WvVk/DgJFzsZsX1in13FysEpEuxiURZCy5wf11XHxOCTkNYNp2TY77IqTlhi4OPE+Kiwt+l3YrZ074es2OAaYdMSbnRawIWTvIeYXiIkBnwFbHQ2//hRPt8abUHkxF2fArcHuWUJllgeorroVZuPxfjic059RewIsWb1RngyJzWHrBvnV62hEClqzkvxk7At3QlhqoJOAk+7p/Y9kY0a/oiIxEOr0nA9U5ftVmfNocg4SRPH7sdqUKqHMgdKsGdPYs0W9xyfMyaTCHkfiTETu2/eyouuZf00b04SjBnqOjPozUN+yEf/7k+9+8Mf+YN/93t//sk/f/65F6CKlYQqpitRm7Og+ykH5Fqp0DJ0XVNWo/W1jY9+7KNHj5xQTz9x7gUXXHHxxW954+uvfsVV+8e7ZddpRdKDNht9O6K0r9uMkOZOVs+buFYC2ggLvlwWwEIHD9q+HOoqkQb+UkSwMUHF2apEr8b2qqV8R5WJI8QE4W6dz+Vbyy0FFVis2PzssoHt6NAlPNN1CCnG6LG83IvQQVFgOZiDjlEtlQ3WZ7DuBISwzpjQZS42dq0/L2UhNEyuFOIU1K9foaUn9nfmqwvjMB631BIZGmPit65DdhBNK+IkebQf0Y7H8iIK4geRH8II1ttJgNPhCu7Q5MSWK10iMEkxBR/MWfbBkk7uy6t5/js2PUXkc0dfT1O4XjBZBU5xHrR5FEAw74hWrOdrv/Hbv3PuRed/4Qs333rrLXXd5UVugMJAcxwr3r0cYCaGcwBl2pusrfuPfOJDb37TW6571bX1fLsqR7lStdTT8YboEC7A8Rl17woLFtFnTEsrf/fkEiw3dPdjBo4IJ1EMEXUOdKZZ4r1oQZfM1IEfl75isg3XjO8nuo34KxbdmJQ4h3fi84ftdiZqzJCJM5reeWp0B8uV79Djj1c9GQH2rE5CJzQhzK51I1RJm8+lv8EhiRSylz8e2+ALQIOV4qr1yZhydruNKXOfR1vaFt1FRCvLULew8jbi7kOZsTmSQAECHib8jF9RgNDHDVddL95m33YhFJwGM8hvR0Rxxv0bJLGlDp80iCxwJ48pvmUt9TyTBkFc0Yos/8mPfuwnP/qxf/cv/p9/ffOnn3rq2RapdRgxFPQl19QxnnW5jcmVkpWYTNde/443iW5nVJWj6SHuNixahMTQmsVkDhLkDB5LgREpBj8hjR+Fx044jmwglS99HODFYSPOtNuDzUh2JVq2M5xnmbd9ziHJ9EX/BkvJ/pKcL+Xe5OpLZ0v+it6MEjVLn9GiDnH8wSaPvVl7MoOwczmZFFJWWsydaecklS1IXFp9T9WsASwo0hv6CJQx63KzHgJt5xwphWqAvEf3xlQHxfS8bKuFS8ZlWEk9vksyBmK2zqG7sSXny70dLuP/8Zo7aPSgZIY8b7NX8TbJEFgK5SqDx4vEjpWIbBM4C05moiDXvl2IYiSk+rXf/Z0XXf7ir3/za7fc8s1jx08t2oWALWPtdQr1KVTjBpNB7Frbe8H5F4h8TG27u75D+1R04EGACcGzQDCnF57h42Rv0mWIbETqLev/DPZkrEiTaEa0MAl9hvMFMRn/HXqFDAvNYlE6NP2XnjWxiJYjbaddjeh+znzYIHu29Hz+6quydakZRNd0v0pBDSLXJ0CuStHIrACA1y950DGO9myS3CuUgfjwHTG8Ia2FbGAUQqEolVEVG3UBR8TSKXVvA69Y3ITCo/2IvQO/9OGzFJ63UjQ4KRss5YENz6optcmja6U2n+0lO0D4iAH5LW1uKsMcoXPEyxtDshB1bXopi+xd777hXTfd9Gd/9Kc3f+1vH/zOfVsnTrYANtuu1yrPqwyBMD5dKcX73/X2XVkhFq3IC2HYWNYUGA410Ml6JJZhah8NXssGMQsp6tyRL30Yr8Eq42iZEwZvrDCbY+E+UM4rlfZpXsnnZz5+aPue/vBVwigRUvGp3O8RdbkPw0li7CFRCurrOyXLvbtbAgpVhHn0oAF2Bfrowp7IYiPA/9RLeWCMa+BW0krVWU74IqQ8Wck4YIPr/nrGFXD8kHiZEV/bCLB9sqEJFhd8BNmf8O2Zd9mbxOGnLZ2PwD4ciiMblmZzhPscXMr9Exk79qaTtqB436LGKbSFaghgU1XzsZ/56Pt/6uP//vf+5X//7J8ffeFE07Zlnl1yyVUvveaas/edlRWiN11L+Kq3Xv/Gtcm6yHKGzeC03G8+cs5Xe3anM4MSPgptW61o5aMUotDLJxlo4PTUQVn9eApwYE0NXhGEYIk/z2B1p9aBf7Afh42HZzWnv2H3vOb0VJiox8hYjWO9aLwtZC4O7j/0gFKmR36pAQQSULqWDoeHzL2g6euuq7tVreguLZFl888M1B4xbU6GdcGRUvSbMbUxfdfnRa4XDdrKEDcHDbBi1xJjabAarng2ekAfK4pPERXAuCh93JolNr5WEkNEssEcwQtoKqAvoP1cpoBZDb0S0FyOntyjR5JThu228ob/9DhYzxpORKDSgHt0y0w0uutni3Ex/Y1/8lvnXXjBJz/7KaH1oUOHfvWXf+Hcg+fmBJKhxtwZ5oz0Wo2RXoDfawWvA+sMaCZek2UVOSRjC8lfsVD0zYEGDsdENoc7fbI67kQr7N5VDJ/wwBnE8LIJELPKkjxJ66BPfyfpo6Wm3Opb8AetNCP4a5Hidb8amKyZEh2avZJ7BfLSQi0IV9n0utViDUgs5JeMEI1UI0Plf8a0NJEFQ1nQAl71Ek1DeyWnBNkpjVhQVwgtZCtFBfw6qK2heR85rt6ZXsi2UX0vcjQMzlCwgkwJMpPWV/TJs2VuXV4Uq2qdTR3xcLQgie0RRbqCt2Ez0csqKMg/T8khJy4QIe67tkWRnosJWpQMUVamyrLIixL1wDb0nETqzkQJsfBPfiOzBT6rLKRuG13XTdVmH/7oh9930w0FGjuPRNV1iw7xoxKl16IB8AP73hEsnScKJc+00g6OFsLjdBO3IvrXU9lSJWrEwFG93/BKsYmYmIsrDfZVjPQjmXeoFSOf3BtiK49eNoHPIBxWWCbLj3J6J3Fw9dhG9KYjMv3ARKkyo15YdV4IlY/XMzFvd/JcbTcCTecUWnJ30KX9QoiF0SNEKxEMZXOaQ0KISxkzF8ge7xaAHiDriF6EYkHNkwnV1eF3TL5SRTYa5bkqhGhr3TeyLNBOAKqdhjd5crDME3xMHwqi54mywP74xBJdSSXE3+GT1VGN5dVlpzxoRau+gIHpurZhqJOLRUe9T9FMp+/7UpuCeJixrg6a6RVMdD/eCA1tsZ03bXvcBUHMvbLzPINQFULUdWEK2piFaNA0D6zbU/tqQFXpNLpFTomtejdcyvLkwJsPGtV1mrIrPNB5y4orinLy49jH8vBJu3zR6vIu0AyLIQUPka2nN3qHOs382Ax8BkM6OfxHa3c+9MfR/eEOh493mpu3akp3RiuTV+DNfqe++/77Njd3wEGtaVFylv3gMzfrkSyqqjNGdiarMtXrft6oshJStPgagQeUyLTIuGoUSUTZKyhnkStg5HMpW6PLLEP7Ddm2uigKWVZdLqZCnciyC885p5pMrr7yqr0HD07HI4GOECYnJRybjoOnD8uYxoViL8f9Fn05KI14mfyHLmYezpJwcAxxZxXCwE2a7te2XYsm6nakZJyXCmeU1GWuKErqfsW62J41uOOpZkrQ1zEexaM9easd13HAGa6MjESJx7FZaD69zVGrgW8YoBexSxyRXVjPJHiQLrI3e6Mkk8pSBg4pSNffCUKJap8J/Bm2KDFlYxs7ZiMvCM/ArgObavDLGXjxzE7yymstWSZnMg3OwMCDw9y9AmKns0w3jfnGN77xxA9+2C2a1uhF04q265o2m67VO1swsHuwMxrAFtS7uIfL2qEqDX32pekQxERbKyoBIiOYyuRoS3qNXlcdoWA1wQRpC1Wedxo2nRSygsFdbs7mG+Pxhz/4/nPPOx9WOc9OxGgXmzOOcgMrbTzHLctrnLiQqTsXAi7xGSI8nIdpWO3nezVZbdZ1GB7Wth0XHkRfTEnF4eEM/czwyuETY+SnmwGW1EqFJ/PjQ5e2OOyyb4Ae2g95eAkVIjGmS4GJeOTlQJalWjBZyliWLd3cjyT2OA9I7UdZ12sa0YGm8SHMZaffkqVmNCiPewENyhJCGchyDvk0Sm8lAy+/zsTAK49fvroTK14EOrl9RisgWuLT3mosbDCRtROmKNXO9vybX/76w48+3IpeiQzh5h69KWmwQEfdtUhc8kAsLuRCWQ8KzC2lQ+PyHEbkHgkGFNbQ8g73UPFlyWjJwGSL4c74LFM72zvb2zt7dm389Ec+fOGFl+DMmUQ9I+uThMJWCqZ465yiGKrjBIG1rNxdzVlIpETzmB0qCbRHI5ZtqRBr4J6H5cZ3Gm4nNc0MnRztE1AdlKO3e4YCJBuBX3qleM8VB4TCi6j2w9vm7n0CxLjiCXcXq/yvEGBLGdgfttrTOM1fNvBknAZue0zdQjkhQCm496aVWS6ocdfw1XaiyEknL7HrSvLmDwYfJc7XGV8/pgn9Y75O51SsPiw9auVd+JQozN5eFFJ35tOf+tThZ587tbMJiw6jcrich3IhNCHcnsyXwnGNi53CbEeUe9Xi6kYR5OJeEM7us8THddzMBfRCsCfLYExubW1u7cxM16+vT3/xp/7u2YfOyUdo3yjg1EXRp9OaMvEOyRUC0cFfg+YZckuY6MGf+aZOLnBlNPq/tR0xL3N7wIPE9B5Y2S9fYCSXWOJyPwlFXJZwXyEXl8W5M6B/JANHa+Lap3mhY9+NbmrQgXNVeGgFPcsfK/4ShOPQhM5tBhHzdMjzplmY4nQvxk4j80gJSKtMSHW71RlefTVl+LX7P445f2TsKbnWjzj0R35oj7A2FWaui3Ltzm9+9clnn9nc2aHYEsYjcRkJCnKNVkjO0rUtIN4GvlzbgOAD2hu1XRJt5bc/kpHI9k+LLcCNcJcHXKpppZItjG3Mrt3e2bnruw/fcOCs3IzIEidvKIRS4mxZvJqnN+ccSwVl4F2e5ToW3/neaWT2Nig31LPBHE/xYW884Atd8dSgC29kn5novmFQooVC31NJkMdoe/ZPARWxmkmplmWuLVW0VhbNpo7wMKhPXAYjpv7satMmfOy9Dh+HiA9bZm/+1xlEyAND0/pa2R9Fs65cEXZe26jcBbFdUxdrY8d3vZJJA7+lH/8InfB/+Mv87+deK8bJ6RBSVGs/eOjhB7/3vVNb29SgBdQJM9C9gt8WQ/YdH9iSeltKOWwmFtlssfvJ31iBluO6P3R7IRVtjPjGN2+95uKLLrz8MmiQwmHzB08b8OfB2TvNqsVuLg8Hd1H4CCVqT227HJBhTFyLSJ1taRGFhvnE/hZChjJWa/Yz1+fdeMETXxY1WHQF7q4b1FfCDpEeS3jIur+91sBDUrcdeodNKF4mfAXaPlMqQ8U1gmf0t+/gYk8cOiUv821sUCy9N9Th7taD7rPnzbnT+FAArdo0VhD0k2Psqq073fdVWYBvlVXgDmIa3bEXz8uvmFBi6TOQasOnXHmeyBn5kRx4Ojs5PmD4/rLtTdfjIjEjfvj00ye2TvZGN3Xd9Wg4isaFtg4hxB5tt5qB1TTE/a703ZLb9YGRkIXwPf1IvdO4Taplz/J6Pnv8yacOXHrxVEFgW1kfUXDIDqVQFl/PkKyZNZ+9/ep32LO28yIRHKCX64XhmdkZg+5x3Nf8lAnr60eszLTkTHiPHUnAAnw8u9FuZor75o9++QQV3TANN3KPYmWsX/1eaIlO38gsSSnyPC+LAv0uXZ7KyoQB+Zz+7/TDpfs9jc2a08T4M7mE9k1XSMmUYbSB6UOosb5ujO6RroBBfnq7OCbJYeOJJaX9P/paZtoVIm8ZpLbqJOFP86OMCFBP3y8AlDTjRx55pG6bul50LYDPRA2hcUSM14qsz/SqS9N5VkADg8aIbiV+MmuNWjOTGt0JobpnTp7QPUC6BPELEja1f5ZcrzhUnS5elOiLsH587RCLooY+9hfb2MMN57PlQKzA3XkcVmGgKIcCzQ4nE7bmyDUACEISV80IsjqkptUwFntdy79d13ctIDPOG3Ya3k1Xd3dod5i6W+pej0aVwqyEpKfxaejtzB7KGV7JF0NLM3yN69yGnU7tcnCcEI+GmEPXo2GYxp3jfXFqZ76zMwtfHozBtM3Als0h/2c0nu2MD/i//2Up8X/kvCuOdUvjlB2qw6e7Hnvowe35DhKXbev0TJizav+gaBbPxQptweJXuIBvexbaSYSKJTeLPZwntAaL0pjcaIiaZpTl5N7778sFRmPSoCDfwCF92iDHk0/sHUSLZ0nU7qm17ckAAEV0mI7b1k1dNzWyuSzsXTbJNrvkDCW952onGd/sAl6WJJz3by1VgkpEmVw5sL5dkADgRjtLJSW/INnSJwx6imja3rHV5Gn5Nv0SVXtwfXfTNU3T0PRQB/lM5+yeicwSe3kZXrGsXaw5nCczU5ZsUTYnIuMnokx3iDV8qIfYbGcujJ6MK2jjKPoY3aivAUxvx5smzkKKHBz/5VWLMdBQCbho5WvpJHEQZHCtwdoNlRaiCEWGmF/dyQXRrO3L5dmDLc3YtYuVa5xCjx2n5TscCPToniNx4ov6LPOzAwxcWKfLrCzLkSoLg+lbPhLjaZORjtH2pIOkY7+X33PeTyqGCUlhi+d6khbW4XJd8YNlOQDiuvaGYcprwNvHnQ0FVe8iLigS4hisGDEel91Hr5AIWtpNF4Yg7dQP+TY9UXAKHRqUpVPbdaptC1lSbmFZ3S/v7unggEvPE+zqkFUD+D25Kdu0jVnXSiBv//hop2v1hvVRSuVF1sJodJ6NlFs786osyqJAxsITmLc8uN3toK7QP4S32OJKfS/+ElM//op7rqGgPCMvJ0es8leCix6bi9GLnljvbF310qu/9JW/3d7apjKDMEvMnK6s3XZmjYRZLCZW8jD9Y21zd0J3n3YEXQAMs0VFm9Yb2bTtq69+mZG9oPnU0XgcCl071RgZrxFhWWaytbieD+0u+YVyK8+wCgxXywtytizl+MIib1moWDpY6IVvXpm+gh1NqbWOemEYdyOxYRc55L3WudYoNvBEFbbS7WbEv5jSQs6vN5CWNiPpHUFL4ZeLKEHrtm2JKYpIOsVpl5UPtvT2ysOS3y2jrixmcBaaf9kHCgky31vUJbht1QoOBYmInXlTN/36pEKCisPX1pIxq8Xh0Kp2ByTJuHg1Uu498yqs4s0f6zUwM8PWcxcqiyUyqEfpzjvnnCMvHF+YmlhTJSPYzmRJLFWKhj1d1itODiQKJFXjDmLA5IZQFk0H2HNgX9uZXBpZZLrvCPZAgF7mVOOKV63t565vaThm79QySriHyYNEAoOhAPq2mYnIZKOou1sViwyLn9gJ/QhnGZaCMB6Y3i1W6Li4khNB767racANJeT9y+MrrShx3EwczHp7ibwiwrP1xNHAXC8NwAJ92zYcoPbXi669Ut/E5z8zNw8t/yUG9pqXQ3FW91L0wflXMIswPxrr3redaFuMWqQW+BT05NXHP9uzuqryoiBCsdosXcTl1zJOc2lcuXuSFfHRITOsXIEVH6Vved5Ykev3Peu42hZ12xiakslzz7/gge9+l9ReSIuvFh20FAPtwe9Hj7TsBvFv1mZzOjNoHccZPJzQHoxsihZVJk8dfW4+m43XCoy8pFazAAIgEcjwO/oCbWJyxRW7FAHuB7cY46gj/zDx8liLBtix76c1yI25o31r0YhHe4r9iiD6B3LfMhNNTmiNRrA4mgblB+haZcW3bRE3LFribkaDhwywQzc2LDnAxsC7riuh+VcR+dAjG976ir9WaTd+P2VgZOocy4YIostpMwqUuqlnGPGGjyQQfPbsxLuEVnG0JIWcLVpR17vW16ls0rU2TcZ9u/VjJR4ewdPQsi2dyqrhhIj/odfSFxM31foM4bM8gwcGTHEvqpwYGPhisTN/zete953vPHBya4uCRGjTm+pgezqX+Yx3Jwj28PTxLIjT7rrPV1joLwdOeaNY5mY5uis++djh79zzwPruve9++w1lMUZ8Fn4qBS8yqCqmX7RozHys8bSFh6tNg6FbExvXttskH+SAxaHNXqzE42uE+HIkHXzetae+1L6brzuBlWXkPGijUZ7rY9g+zGdlAvV3xbksasZp5QHjnm4Zkqv6AeQ2kNfrgqYE/2j7+DSLmjz16Q5IygkRU8bG0/8c89LPQFswHPMMQWgoYerhy7NC4jyJ40hMccQIBbG5PRuh0K3AiFYubEr6qCY7Fc7hfkseMvpG9GZasbX8tAM+jc99mshCKm9dlSr1NhOVgrBrWqlRC0MLWYm6fvub3vLgd76HMIiSEqYrm5U+TWspK9yRB3b4NqOOvu3FQzbItmBj5CUvHuENMNSKWiNyEyzg4whDAmFct43Q+vCTT209/3xbyP/fv//9RvSvfuW1u6ZTVJ13yCvt3rcxzsq+NyrPdd8JzSgtp91P04rodGwdbMuANYhLDMOD+5jwspUTih0sGUSb55Q2B6RNBAYZUhDrGno7MeCjJB7ibQot51wpsYzMez6nFw2RTF/98pASrk+BLw0GXsaR+cOHfzgxHwBuA0W1/C03HJjqkKm80ofPfdKdyh10h7spMqWloFi5nfFnl5LGTlj/hBr5UUM1j4U1dduqTI2r8XhUKWEUozVJmTubbVXoPPgWS8u1cj0HLuvKryQ6zRFU5DlFH8W6yJhedLkpxhNRL154/Adf+vYdjzz+hKSBsEXTLESeZerYc8889MhjF7zoomo8ZhLj5BHhdMkwQSdQSoUyBoSDpb7EjOdIuBI7TjBSdoUxgXY6GWtGdLzi2Sh0t3XbwhlHSQT6h3dar0/G3/veo09/73tt183aOhuNJypvczkejYqsoDOoajS9/PKLfuFDH9h30fmXnHuxyMfUQ7VPZiIv2YF+7b2vbDtyOLvejVZyNqXP00aKzTZeX542HQyuVWKVG2f0fbOo2w5FXSH/Fpnj8T6n6OGEbPjmXRP4cJM22hOHGpJfo5i/e5Zwi0hN4H9FUVWjikuUV9NwZG6FBwyGy9KRfPZArpguKnm+IgpZSW4DU04akvjWGxPAiWdodY1UF/cfsDsTZeeY6xlTzmtCKbVOKdUbszOb5Spfw2uqpOjaNq9K0sYuarI0pyxitpWG7iqB+CMLmPyZB5cY2hDxzWAxat0BcrZQ//P/8n//xi23zebzedf2TWMB+DRtqu9brU1eFOPx5KILz59M15TEs8Pe67B06BFpB3SQUYO2MGjsj8amrhTHMTCYn9WBNx7ZleMhNFa985QrZTItpqXqjLzkyhd/76GHZZa/cOT5p554sm4b1guYdJplNFLegh8yldEoaiGr6oLzLvznv/Nb177qdfloRGzlG2+tiFasoOmwYmxHOOnoHf3AzvYVhaciM3p4rQRl5U9BDLywKfe4ZirKzPpdjnzvcJPh1/hW3Hf4zk9DcyHikCTBIrHvGLgcjUZoyRLdzOAGVrxWaOal9YiqkXI43DCUUYlJJOTR8g4XBNbNMyXm8xk6KdoiuKgTr0sNs86O8+xsBJAKUlmenzi5uTXb2dPsXptMqqKCldP1qixwFKti/4TxCsdKYFjOtvRwp22O9WMydsrh9DvH9Hqj1XjvZz/7qS9/85bDzz1DSEkeG8sQelcmqk1n9M5s9sILR4TWZVFked7N5/2iRgJnVGo3V95Houyasxvmbp4fxtWno+rTY0LA5HwIIeoVojSmEvLatrm41le888Y/+pf/SmlT7d/XatNQOwtQFI+Cow1j1c1IQ90LPWsee+jhz379a1e+4pXTDo0CSGu5PuypEk64d7Co1jhe/iT+O7Jx+G6GwjvaNLdKyfc4AeDmVMn0QulsB/YCBp7SkKOHUvxHUEoAj67uOcKzgy1wMQaGxhdP/4zTB6uPW3Gj/Mr7XnfwfQn96ZSuL4PkDrhkXlMdKfDxfpiFH1lgBSwPwIsuZDPHNP5YlUV54uTxLCuLomzrZlSNxpNRNRpR9q2DJwnDjVF+A5jhaXKwq9OFZ7JUhkc6TK89XUR6ER6RCvV5FsBMP//Cs8eOn6BUGfsI/E0XXaH5tDQdC74yMAxaVzRLrtq9KyuLGoXqsFLDniV3xGxFnIURViy87aF2qruzs2hjqFyOHUFpjtT5VYvNiw5srK9Pt5EVwj3qBh9izrgN0tg4KX5T6DUhc5kr0S3M337+q7/2C7+wvn+CXluZDU0ur1+CcYmytolrvyprMEgaRlaO039xHvo0L5c0tplL42qwBhI4VWOJ9JGr5JFjszjJ6S83TAWvvLHYIbMGVjDuU/TImaMzZ1I2UY7C0qrJkSmjln8OqUFQMDqIhjhBwFOFB6XU+AOnLtwwOCyojVTTstpRlg4mp7Vp2rZBhyhx8Oz9h84+u227nZ2d+YmTeZlPRqOqqErTYWXLgsSqpkhvtJ1Df9V/FLslKxdXDg+I1ydwbJwStKsfB5+xOlLX7c7Tzz5jjM7yAovjAiEWxGJbRCBKR0+A1ZPI7Pd5WbZt2yulhczQ20Y5JHmInbgIF65uPRBhxzaTuYvMIvs1fONGm7ZrcZMKXSzFqDq5tpEd29m/sfvl1173xZtvHp97Nsrb0eSdm60l1e28TQJNRHDyvCrqpu57apBnJDWMX7J6YiM53YYVlBbzQ4Sss9AWT8/WhEuRS1H+JuEBd24OnxuXATq95loR9onRbxEhrNIQpz/P4FpWIVuTwd62nyGcevhDF3zpxMuMngr72ELUIgdQlSGpRLEOPwYoKbpE5OjZ1PVA58fpWDLdgAVgF5grHNgo0kZz5w7LBeRTCyPWxmuveOnLUGiNs0Ktb89mdQ0AaVmOppPxdDKG+dH3KOlgmnN9jVJVma5FMJCS5/sxXrEtFyCiTvfGGB0DEldZQY0k7SO7UZ3WDeObscawnZIIwYdWdTobVfWJUz6SrKjcnBI4iBjzFbu249o0RJBRIyI703IgVlEjHY4L2jZH9JU8zzPU/XZG91lv2sycKsV4/1kXXXWNuf1OIEwgc127Ngz8tmLRhreZj9C0lqZh67buMTsRu+pRxH6pV619zGmxMHVld56Lg8D1LefY2LCjrFy2153AMoE/bQyxs5NyeTyZDNMsrHfnY9/DB1i5/afTieH5/EQFH2IaeLKhQtg316UncxB1fzFvtp4mJXVG02PVi5BYxHn4n81h0Myftmsw9KUaU6FCR9GUAMwmzyvqgAAT0dkLlkQt/XNMtUCDhGIyGnNMuxcobZ+uTctRtYNY0Hxez2fznc2tcm26tjaa2KcpCkyXoXounovn5brzjgali4EwnLqN0QF+kZZkra8H4FZHkQntEQSU3O5VLvbv359lmUXJ0+OwreLW052Lro12ExKJ1X42zzHpu+Aytdl8luc5ysFpm202siw4kBANHUBCxzeyiOqaLMyJVr5vW9TNrI8x7UEtmmYxv/qSc/ftWhtPRs3WjulQ2E9f5UlnlkOZNvlBYSwQ17RdZ51fYGBtSjmhGKa/BN/uZzcFNo2CGc4n9hg86rOL3ykDFsbg2tvh1l921b2GtSWYkUPJi2Z4AZkyAKDJqAkKf8USbMwxQ5/d+gGn4aRUQ3pOXvWPe3QnZmygkvgiav3hJc0Z0sqnQfWe5pUHUUyPi1qjts3zLC8L7slr8XGuS4QHT7o0PBQsJ4u9FLUsQiYaN0bQWjdtg4ZjmW/hK4oi371rfW003trZ3p7tbO2AnRfT6WRtYzod0bUy9BtoGrdKfjJy1LXL0lDwIZ1HEwbiDZcjpDedORoqh3zI10kF9y6EXK4uvOhCxHI14KKMMfXEyWew+tjCho2UWZkXbd+NRiOY0BoZYoMYvuksxIDhv57mnDoK4x8id5xHrrB+YyCNlONqJBUMep1nR0bVTt0eOvc8UdezMice9AU9VipYmgq2nLWbkIRoWjtuKY5EDBqDpeV+/GYwjwrAdZTuDaw2xC7R8RaBTbS7xrJhXBDBYGB7aCXhb9tUjqUZuoaRZHLQr0D9MT6Iogrs7+FchoMJZAxRIKftC5VjcA2zjafZINGDFuAVHQaVwjCohGZO75h6VFKkZsO6UBwrig4sLd3wTCtsyYFv7gUHd+Rg1BAvjRGibhoU6AuxWNSuzYYTZRyTtgB4L2I4yUI3aqHOoTY7y7Miz6HF0XG8opApxWaiZ8irfE+1u6yKk1tbfd1t7cxObm/v3bVr3959Fp2bFSALnlhhu9uGMIqjquiR/RMOFi7ZKP9JdFB0Gv+njSvxRMG2OHj2oSzPZd8izYPMjztFqORzX7KxADBQWVRVVmx2nRIKBghmyrkYt51LxqgYG1PwkVDGKQz2jy+K9BO6MGbQPJTZy7XZ19b7d+9p23q6Pj1SLxRN9wAWmFbSChvnb9q7RkWKh7Kz9tTScGG6H0hrAzOrHV47K5i787WimHR9mxeFJOupE1lmSPFiQoUSnaRWIQ2xcYG5fVqLvBQqx9XyHmkXPBBaAYoOea+s4P5+PKWiVUBYyTLTGZmPstcNMDV9JtFfQijVd5nMEOz30i8mEs9osT8ceaan7aQQ6/+EjGSqzj1GluNAhKL4sczjcC9Lhvfy9+0dS6SRgJci8lNKVSXmFS7qOaZGWExOWAQfdvbLYGHTUVjL92JgI6fv+6ZpshypCbK3V8BohRDT6bTIikXdbm7vzGbzshpvb21lmRpPpuBeNgR6ZJXzqnLDqLyJtATW8W6LOeMCuYOc5vYDPCNshfONe+DN9LRQZZ7XNSdjveIhi8DWq1sX040SEGVZKlg0ZbGzY6RClweatEAdldz5SbHw2hL6wxrnHGVw6EirTIGApTaq/IfKoM9hk0pVVZOTrTS17Ixay/KZmcFThIqTukeDLvt0yEtRxxmDnGphaVNm1GaYYKA0hN6rJBcj8EG7lH7pCTqNVFVuVC7VUdUoVU4L02mFIRWFkG3f9NC3lch1jnr5TiulsxKzSUSLlgMSM7C1BtOavsVXqHZKl6UQpgQ+JUMdUo8uvQuqvFbaLKQup5NiUhVbi+J43Zx9Vt2f0s2krwryUFiQB4mYjpIOtBGzsPfCI4MsIqJIezi31mlpa9y5UmZX/e07aaX0OYzApc7dGWznCKeVA3NHeSKAeLKM8HeYw0aBDNd70smUMIzeuTfs/LqYFsdjmfwlcsdGAE3u+iSyP3e6uypHZdO327Od/WftXVubtE2LSRbstxFcURYiNyU2ZNFI7l1is9YDXo0KTle8vKXso83pU7lp1M6vCwa1FqIsCridHXVRtOW2dsfCvnLaVhsGS1XjCiW4xkzyvGmxGn3fozyG0PZARGGWgi1lF8IUObqiottb18HL7SnajBFpUJSYWIkaBPg+KFQQZEfSlbXuJ9Nx3+lXXffaiy950Q8fe3RG8FgEs9q2aZqyQu4ddi2qTyASejSfgHDM8wLUgEbVuKKQ8H2CweyMW1ukQVHjCMkAm0ubvGkXxYZ6/gHxd39961v3NE2zyPJSyJEQ6GaLcJ7p7dgJnCmTYhtuiWwMGqNatJkwhas8bnAwdtxIMZcYsZwZfIru2Eb00nRgDVBwA8x3W12zv/pX/7W7cO8877Le4+iGQWAfLBlyZnywE+nR5MjoyJSy4ryzjbla0e+QOWQCJQLjdFwQny/yCk/7ym3sinL9i7qu6wW2HGmhIXQ0Bsby/bKAwacM6XPZFFK2Ms/hKy7qRZ4DzMA0c6Yb12JtMn3xZRfjVD1YJTQbSKOJckRIzF5gqhe54DSUlQ6ghQqrEAyoQSDDcn5IF0UbyNFEW9Pj8vIInwt1bHu7rhsg1Rl+EbDKNO3IyVTKAVEiWOu67/O2K/IiW9/Qx452gMgqqGWE92AFF4ymdvAZ5GaVMmVp0TGdVgWk1WI278gkbhr0YcUYFsbHOSwNjKlcatXOt05dftEFzz73rDxxCp3ZTFagOrucz+dUqRtWgQq5AUhhM2o8nYgqU3NAFHEphx1hyCcWBHwu+7aF3cvFWLxo8FOzvlX18e2nn9q4427ZbJ8Nm7xdCDMi0p0JM7E7ojRa0sPfmiLL6DcniUW2OF50NBpdCVE637sVpqDDecsXQswF2Fe2pnro2LFnftidt7fLs1IbuAEDhgxV6bTPHm2dHOWjbvR0jDSheutASlGsJ+XeaKyjTeWQAoOrwyNaEpX7ozh5xfGDGgCZd+g2jlmUoEuKkbrQeYjGcnSZ4AcsO51CckE+IDAY9EdKG8OPVaZ7cG+vMQMkx8xLrh2P7mTwCDwqkybSIdTxI90GW/UkQFUwrQA5ssM2B9ayiyy7pfVJI/98fmPoRU0l0Rybkr1I9Ajdmrpan3SzHSZt9B3n8CtxKWY604QcPoPrdAN3d1a3Z+/ZA7um66TQ4xIIu6IsAJ5EKURGnV5d/Bo1JBw9B4dArpKvi2eqRlmRd7pfiPmibaGxYHWDtzOlOmPavpsXou13tMjOueB8ddtto0yd6gwmFeo+U6ooCttfBehOWOZkhxOWE/fdlx36++dFRjVJMGihpzXqaVWvTddikSWGYXMpi7NlwAsosO/ktjC6EZjIVG51el3quYEjroSoCW2HsQkULqEMmcw5m8k96jgy6WSixtg2juVhqQEiIG7qbCNkvCOF6KToMHZCmLzo8656+pnZ63+iVAq2JMEYGYLjqSnuGUnvx5rXe14+uOhAGc5ptCZaJPNjV3pIs9Q6rpOk1VgPJwed3hpd9UpVsUu+5G2njTJdQ/lgl6KwLcVjkUhQPB/wiVovka1v+5Vx/Rpexoj5YtH3HUOlWf2G24iBY+5PjRl9KOL5sTx+3yGKF4VogGltGCmNDGzfSyT0j+K+jd7CgOkBG1lJ4E60Ejn57dw9TagCngC6riBa71eBUWy2QY2jbIN2EL3qZLeYZ3ptpPvFfDapqqIcYXwCLkZ4xrhVkaUOW3/H3fM7WNwYe0nV1zCAp2vTom23trdxyR7JIUmRQoUeGJNSl9Pp+v59Z7V126sMwTdu0cglKuwJK0X5Z/jhXHJWTYq27hZ1L0aTZmur0EqWXB4sZKFMb5558tnnjhzet77rnPPPl9WEBp5aO4Ze2N+uE7oxm41Y37X3yHNbWs+o3I0kPgxp5gcEMDTeZ2XAk1KDcHVqkPCcZMylziHrfFa/Pb1bSik700htlFaPfq/d2sqn6zJHqBCOim/AFWOyo9jl8pRyDvQBp84QphDlWWa9EA04jcNGVY2dQJuOosStBn4csqe7t2D5Lx8cu4d4CvjAfdu3KKqxbXIceyfxH6m0pJmYPuPik69cN8zqC4hnMggx9hgKhubxpA3tgjC0oS7iAQpR0SQSsj7PzMNx/BkJC8zRIfXfjaoyNA+wOpIzD17q+LSRNTytLcKYgL6fGZGp8vjx5547crRdLAqoJ0QH2npRFk/e/Z3v1D0vF4WdwB50xxSRcmxihWAjlem7qc5f2Nyq1teBKNc96M4lnSjyxa4HwOhLhZnWEqP5XQyvgJ4Dr0qxe2Oj13p7Z4cRcG3d5GV2V9vpP/2vu4uqNf0iz7ao8cx8NuOG7xzUICSYBVmjYYYWRVG0bTdfzP6Xf/0vzj/vnEXXoiASc9SkapudVs9OHn/g4UeM1P/in/2zQxecn9F2k5vh4GfgEw1Xada+6DUnr72m/dyRLWEOkHEGxWA1WFRcENH9oBXwYO/j3hshRUAqWtv/G50JqWWnTfnUD6ojp7YvWi+UKKlYhL11ts2cecvptZQio0aurC1h+RCN2AYdnGAJ5Mc0Fs8ZHFBo5Kpwl+msh8ETujCsZvwBKnppZYIIwJE5kn/of0t1CiHi7IwE69EBBRAZeoQs5PtzbUMpsQFTzCDlixghPx9mThWF7xGRPKQPkEBriSIrKL96eg3sbs39btv3Al9ME2RheLfAfpRFhoYmiN8yzHtQp+3zNxRJpqpRltE9YGldWVV/+Cd/8qUvf23z1ElEbxnOZFRRZOWomM1nVAcD6DZlfFXbIkaFWi4EpbBMHLSQSnZ51mZqz66NvtcLQpJrSdTvIM+2jQnrMEprOkxr2AaOexkFVJxEOXaGSt5O5wXmAFlLr+8LNX5K6Mc++Um9WABDQmIVNYNVibwsoWFdDyxygbh0lK7e1F0u1VdvuY1sTyC9KQKXUUxL9N18tqiLQs1hQSNo3ZOMUg40CwGPRxNajVU3v+TcsZB7vCsZKaqIVwM/DNyqJZZwbBcdFjdPlcCRkRStRXbb3eb5E6MrLkV/1yxHJJVrLv1QAhvkIJKL0uOrMsIUjMR4ZTxZ5PM6uWqlvjvpMpTaE7qddax7hUYKvnNjdNUV34wTK2nZdHSrOaHre/Qu8NUx/peAc6NURmLrQZ5RyyU71YYcKag+MC8V2dFQRlkUBSxq7jQTxkPh6dmnBqm19agcEaAvgtssCydLzKHpqq3AYEYAOgBMhOpFADRna+MxJgECL+CXcdCeP3g9PAtQZ+g48OzzLzz26OPb29ttU6N1d8e4QlnXRrUUAXaQQMaHlyV6AXs/x3aikrIk6NXG+pTG7bXctMVflgclocmre4XmgTw5BQrZ7jBUO5iGrXSXEhCmKkueBAZ4NsLjbT2bkYuLwbZK9zR6s6j7hvLGFUcobNm2BdJxjWKVZVnT1v0C4TEbOCU2RjCGpoqrrCrXNvB1iiST7LGF4YBealNJ06LbQXnw4G4haoobSxpyzAwQnpRVstuMqMRhQJ7L4R6Lz7EcZdxHxqicAmPzxXhxqpugy3qOwBrokkmHEpmhDUhU3DdEUNiTWqR4F32WaLfAJS4OGmnxYRSG/Kyug1CJ4TFDXbUcIkr1cTi7Nf7zrmvgJlEAJgoGWj52OD6FUASNkacQjsXikfZFXhNijl5I3CPsScazFGVVVqOKadrlIC0GljoIEIQBRh1mblkQ3Er1ay3+0NWQ7EHX9cdbDaTMoY2JWE7NZtKIUUGuDII9fJKQ8gtTrigOhOWo5Lxpf+9f/969377nxPbJTOW+OhKcTLhlrpl2g4solsRYU94nqunSvVa56ns9Ho+0Fh3mifrYPgsM+iNBbto/FEF8odNICvCwQo+9Y+wqywuaHkL9H/t+PB4JKatRRVGTPlMZklVkdS/mC0/mcCxwg75am6ob6aEQ0GrqLENE2jecyamlIw8ZylFKRlOG4ULzOnCqmn0r0WdiAZ3UT8a9EttGTLVpKYzMOfMVxqLTWku7PghixGQd9IuwHiwpwt5k6LDflSc3RV6gmb1rGmNRcSSm+eSR+bqikNG5xA436jtoRSYR84or1vdKPO3aGP/h2k2jPN4lcgeDPlcV/aePvuw551wUw2yZ3qU7xs7UY+uYys3ZonRtK4mqeJoj5vF0HQKMmGWZoRMxRE5Q//hXY5oBBbewqBlyElUVal8Gd+xYNzI7KSJjeTcEGKxosFYahU/JhF/0uoThgoSpTeu7PLsjU1ya7l8J3VVyNKqqE5snW3AhiguYw8fjcZll8/mc+46ym+QDztausOUNlA0yxdp4lJUVuaywm9leZWAGFwfaAkyGGlAcjq0Y7vUPPNx8IaWcTsauY6+L5BAYk8LaEgFtIXWDngw1kM9UHUFijSiP2j56vePakvHNUBMeV5vISQaCuFt4NkfOemBYVFaUBY03pLi36GsI7iy3rpQlQp31jW7UuFK9yW3jzji+n5iZoc27NcPDmwkRpFHjxBwXgf8QBDEKy3vy2ELIPXVtsgLM53Y99isDgUc28NA6dbKdQ43OfAhGqMMeOl3qH9V5x9Yo89qbprV05F37bGdE64mWD3LE3X3UO91/CwwMMUrAAxrlkwRt7SOE+kG7S8wwrtU71azDsqeBZyB7IPUJAESpI5Le3KCDbM6+7yDsOV6m+7L0XbBXWc4R57o8szU1rcEZIsxpJT8pOVrBDJVN4CKd09yLaHVspQIXxkOxAKtbnX3eeSovZGuhzaBmumOJ0ZzoAQfUkXOgOG5PmWB7XaWAIZ2OJ1kBs5YnA7kGCXY7Qt1mVFvvum2QwaCQextVFc/Qajt03nb9A1xqhEqXRIcvllXVNvXGZNy2GIkAm5JmmFmwSWQy8vMTc9oZhzZAy80YQqk8F1qxlMkAu81NBXsU/cAYSaZy5W+XhFqmTdHmes/uLstmPRCiHHzG4KyEMzyNepHqrNClY5xBuOJ9GZu/FFRr+rb8wffy7cVCtZnKKmpYQva2BfhGMCZ3PefADimQQ5xQX8CxkTtsHddIA5O8c36AE5O2vb6TmG7xpZCoOUN0m5IbobBxWXkSPp6hxH5NYhiGkzg5hWhswwerTOJoAXtifqot13xIcpiJ/pDitSgrA3wBqtVZgJMMd+helMdpDSrUukIwHeeCGAKgym9T9IvbNb4vP0CEy5Zd4MwWbLHmDWvl0kj2P6K/BgGkblwqjKLwcFW3b04lk60xGl956SWoEOAiA34YUkTc6J8f2UfqPcDQlxZKBZxGWVWkDEnd2vG/li4ob2zrFnzRGWVz0DKLcRlYxkpWZYUgKlV0sdBkn5nb2Vk9rHWeF33TdPVis22UEUCwsUGACyVRzWjAQSTinSL0JEU4LbI7OQbb95mqxqM9+dpaZ7pMyFxJ6k7LtESt4QhqhRPorFC9FBPK2QJxxfVrLp6cpid5RRzrLpnZLnEQbjRmfpn+mRnTdHLjy18++dgz1UX7xuicmiO6ZtOvtnzRe7DWoLHOj+MiB+y2JEdyjSanw4GyOQvPHqxpB4AQb9pFjxGgubz8tMNqcAvutC4RDV4bQLjiCB9+zymDiTAFhT9D5YI7ikem+KCooKAcDDwJ3xUBKuhbBdHSwzzwGQpb7M7X5GZ5mMI5qjjYm1HrcxKLS15IKMm1zMuQL+6XGcZkWaBpsoeWSh0bW7YiuOKiblXb5EWpuBcnGzscAHOh0qbtykocPHBgOp221ODKmp+onidvgyF/5Pc6Wo+GPnNgTopxBaOXa/EjXDXJIwAkk9r6sih2ZrO+p44kGtq7axp0pyP5iTAVvHrb2tDNDPa4PfyL+Ejfl+Pxyc1NRUa19W6oozn7iOz1RDBmW1FmbT7nFlrENYUz6FAFMlIml/r6179u756NfDIydWPvyKHI6UHJac/LTPVFgcZa1LQ4A8YGIGYHOAUFu7E9npndgGTLyW5tHfnEdRQxcRpnRVOXEZg+lVDz46fU00/rc9d1WRIoJg0shZiDm4riwIY2vuJzTWl63rmYiX9pt8UJJFdMy1bMoO+Hg5SLFgadzKWGo5dWeoWHdGlGGIU0u9uvUnDdSQO3XUtBF5/sjpU4d5d0pyQrmxGTsJzzMq9Qd6gUFb4SyteJd9ZbBMTnni8IDqMnXtcgXk1K2zVqHTrmLp7jZSA5jL7ZLQVdWf8GnFgIOiyJKQb99x2yTU3bK6mmIzu3yeIlmIWptQURnKl7deicc7d3NlvU81KAwIaYUNgG8AMN/gU6Jarr8BWB3KWTbWGOGvBqULLaprRsYI8M0c3NTankdDrpWqAsiyJvmrLrOgSVmsaazTiSsHiO5MiY972I8adSajqeYrIp5aXZTWc4IGSMzVEx+IEar3HUjXLpFDbjIgcbVuGtVMBIwB/ft+/cl7385aUa9TsLV9Xo+wL49llaF+O+VYtmjvYhBMqxBorlOF9awDEtl6G1eY0Bhw6YJdhm8d/uReeEhG3yrjz62GJ8zXpGaSBUN/pDHNvHVUrBjorQGqFtCDVIsbFML10iXnMMnsSguChvVeEOkO2y4T6QjDaPbJEoz0ziERUc1Eslbl6UvPKubSFMh8rXKnFK/lp2oki1hUCABSk/gaAlVSxQB5mo8BKaOJNS0ZTkHqNTFbovcdlD6J8SvhHtw5B9bY9bG3fmGJa19v1jkxuadNGwTgOHIE5untrc2s6rosN96mk5Iqb0KTnkc4SQpSq6kydf88bXXXvz1U8ffqI+cSpTxbgagcaBvmeQWde09fZsJ4PNBvtKglJc6EKKqqootWJ6FNxwFMBaYsisSsPtFGG9ZHnbtmWOubJUumCZgtFsfd9vbW2jiNo2yeDmJi4u4eMylOdhidB0fYbGseMKdSDZqMjrtnZ4MyuFNeDWNjbB/UNN3xvZGZNT5gsugI/7cHp/1/rGhz/44Ve9/JpqhEaiJBMoYUYAPQsK1yKTfWWaclrPG5mps6ghiIu1ej9xBU17ahjEtwa/Rz8jZLJw8UtOVqHEyeTPPLNpigMtsOOEVMMj+7NZfZsaw0vwmeg3quhCVJ8aGca4+phh3W6wbo+J0VvVzgkFv/QFB4fdt53T4swEW5jLHaZs568VGRrUx0Amk1wOYHzn/WpNphwHTa2IQmlRCWw8yA5YBUJCxY/i5DGizV3X0pyzskd02lYIuOr/aO/idXBm08DvdQwc2NfheyihPhTWxJX0W9d1pza36qZdL8u26U9tzop1FBEg0eC7p4MuyaEUWvT1ZZdcmqvy3W9/+yuufdW5Bw5lCFGZqsybXuQq+/NP/dXNf/u3jEjrtS5UgSwUuBctilwzOsvPVJ9AuUi6eU7EFOjIkSFu3MBPLag7B+H3eHA1tjPP8/FkzMII1fEW6GqtXtadvjDdlk507ctfdNnv/O4/3r1v37ga/+F//qPb7r23RHjRNAiD2bnytqMIN9YSuiqyi8656Dd/7Zc29uyvqqLIMzJHqPwp0xlV6lJb7yngMYzsVRJNiclPw+kowsP5Z1PLl71a7t2vnnkSHUMA10ImKQ4QrYxLxQ3YV7Kuf8fzkHEmtKMigAWbum8f+c7oxKJZR7KyaKlXPVqsh/CVt9GXUzgurxcRNMa1kcrkOVPc3T+Je1mN55V2eEzPvXH4DDPQupbAmjYzHbOBv0WWA66uefXMd2gA+MBuQ+PTUVAWYRI7k9XZVUWeV2XFoWZKT7PxbEE+vkNF2wHOgVqboqjrGqUsGdKqaRRtKfHrAmlhBG7q+EbTd4PsCra/hTqEXCH6NHT9eDypyn4yGVPDqajBm7NzemBSIMGhNLquyouz9u966ctffMNb33b2xm4hdFWO276e983aeGNez7745a8smqbrWrgGMHfhsqKate9zShO0lONxDWeBiIIxj3iBGnMeiPzMXhuAxrXOEAmiRvCEqaRORhQ5Q9+rKJ4ZzTSwnhyh4pBFEea9N77nNz/+sUOXXSbmMzmqeiF3T9fWJqNZ3SzqWnZorFcAmyHaBuUf+Sib7Nnziquu/Cf/6LfExkjMDEWeEJay9MT9VvCE8B4oP8dhGCq3QA9/ljdcR4W8zaztT6npSy7efPbpLaP3ilBCyGUAVhNENqcPxnI5i4+7+p/xn0ypriOZfXE2WBsB6Fgt1m+55+izR/rpAfxJ8Hhfne0buzk8gB+pOiikT1+cC+hdsDlJjNl/4sYbzrN2N5eWUUMAAK2QI7rg0DqOQyPhxjvtTK2B2rdLl1MRDWXRPNbMhZ84bYEmdUYpGIPoDG4tZ5RHcZIQdMbf85gEYrmWij8pBoPkNUK3rIhOYws4Ay/o3Xg+ouPeCPvP9xp5Su7yzkritxkJSYWyHWF/zbioEKclK5du2vk1OJkSfbbYnP3ku971ne99/5LLrl6frqlSmj7vACfNpBLF7o1zDuyfTMboLoDCG1JnQEgiQ5bn2XQ6AVhaG+jYOG7p6lFg1TivMM+zUVUiXg1LFoYM4BkZ+g11XT8Zj5OCqZAG8qRBuwvlgCqi884+sH7WWbO63Wx0s330Ex/58PzkyV1lpatKjSsQd98pIcsSUP9yUlRlnhXl2mS3yHJ9CrgXkYm6BWDTjf+EvUB5GPjwPCXCZn1sRxbeOEo7INXe5U29ZzS+4NyqJ2wlplEgGxMRV/B2XEFfIAg36xsvx8+BXj0pRyNq7IuQRchsS2Hak1vtC8/Vlx7cTRkTvCLnMjHVbJ42Yr40ZpbMTA3KLa5ISoxxXpq0OfmqOA+NIUMegYVgXO3kw3LxWrjAldN57qbzruso2cflvfYWWMQiNSBp1IbKKEMAt7YsGByZK8L6OPSvjw1QlbgQVTWCys1ztMShsBVjszjcHiV5kqiEz/V65qU56/aPKCyY7J2LQoS/w0fQHzRgEi1dgCXuO90qgL+rouyBTmShHOwhhSm6spXyF3/u71XTcjIqCOAhet1mpSpNJnpR5KPLr7h6tnOyUihmNEKWXdsDZwkOL3P1xNOHtxEuIrPYJ88pU8QoabZoUG8oDOcE/R7BVKM2+zn5og4e5/YmpQTflbvv+jLLTSe6PBtJIet6UoyKshqPSiBUstwIZMEnOfAYCF1IYIWpwkkvTs2MEggsZrLvJUFHscwIQJHfDsiKARwFSphmQtt0qOUOlt1Yxd7oeWPmWbu+QTXAYo2l6JKzZKnUqpdACKxLuXOEN7mZjdOxiZFvLQKRa6GRcJNttnV8Z5TvM0DQOE3miCW1mmM9mVxgsNSgfvaELeTX9rVNI1ena586fCEU2wEwR4NPPQcvCyXffI4v4Ap1rJQxOdI/IHOnvFz/M6h2SgqxICYjGuqC2BcczDMUedwPD1XgrxuhM62QYargGWZEKJZgT9eUgMSJ51P+4SaNBK83SlO79HsAtg3krLU9rLMHxxKWUilzXQF8tl3XhIcBJmEgKmUuMpX3rVgfT4uqghdHNhhOgmhttX3s2NWvfPkf/Jv/tRiVGXrDtPUCveDyyagr1O59u//oP/zB//s//WeFIofG9ll2GR9eYlyTRQdBMrk7DvEtxbqoxG+xmNddR2WYobzDo8fCCtr2yMRpmWyUqZt6BAdHmrruMlVUI2TAEKnkUcESJTECxiD8aniHrq8eoF2kLu04O+ek4NHtpBVJQrlA/MXmmWxJNH4FxoO7CQndjqusF7k0cymqFH3lwRAR44W3vVhnyWpLRt2OsyAIhre037BygKNTGm1M1h77AVqbzGtdAlZ5ujqDcB+Oj5PcT3QN7F+ek8Xas0sfE1zE/q6KOFCUl17RRx6PxFht3splbo/Fiu/TO/SBO9NjKoL1Txj2S9gjAxOQuZdno+RFUSJ+Veheow84OYyMCuRmgVKqogTIIS9QwY+GslUZNdJxUbb0Ln1ZvJ9I7HJFlqUtjURZXxs9Tvg3Dts7T8sme206R6icBpGITpvtuhkXyFqCON1kQEtGKDUXk3LUAUEF2eSwyNDPKFXdmeeI7VBoo8zzfNQhEizHciS28098+OOf/JuvPHTyu6iPCgG1EIhgZEck/qm7DZn61KQKLRaMEbPZbDwal9RjMArAuFuxzpvVAtzxRCkxrxdSZBU4U9VaK+qewT274cDS7NMeXZRgtCMmQRXUnC3jpl4ReIv/g5Pt86QI7WqB/fUxcAsM7aE5+wWC76bPMm6mQQ8VnjRtvhF0rCfYGPQ4MGgZkdHTL/xTeEkd2XJaqLnu9bNPiVmz6LumNDxlfmjshki7x0nF5puXEpFBDdsDqourUF3EJTok9MCOK43smVzPRz9z0aVHQ3+iJfXmYAqehy3kzJ8SDAxfBV6Og4dRZBGsyz1AKKYKYDOZzhmNU1q0GC1lRxcS/hBtYvIC3cG7Fn0bhZiOx9PJlCAsUZ32yqAV1/yl/q7P/3KDN8+knuf5r8h+tlVOrrs2NUnkBA59TLVzQBFxlFj3ZmE6nYkKaG2aMOk2jO+QSgUQzuEUuO15r6HVMKIMihRFs1ATVI5TEBazbrarPH/Rhec9/OjDtouKCysHjz3qsmb5OUF0ga+KPJuMxyUpOydfAiiScRz8+CwcM+S3IKHKrIAG73vT9TQ2DUNeEQxG9g94OMTbrc1KRUZkQKXYSVszwp6mq4UnYCZF+Pquz9ENiKPmNrNL8xxovpYQRdXlgLxRxoFTvp59hqZ0UM5hnGjkBKb9mIHromANhLCwRYVxGJrr99tG7P3854/+g9/efWiDQvBISbu2xy5wEKWDQtAouq47Ir5LKs/KYUdTNXhkfscK3qaLl43NRH3RfbDyc1Ri+fh03QGiXm7+lbdNg2eD12FPwugrAsCRw0algmVJ3ItgiQZor11wLJ1ywpQdxQwRNIzIcjUB647GI4R5rPbwdRq2ebzPdzH32rHijnutExwnhL2uDQwc4ye9JHWgNDaeycHAC7pU66IsGSjGwH0jVY3kSjcp4cdasIqlbbtSARlM1EHeAsobOLFItRRUKcbl+LLu27aann3eeeegVIASRQz/8C6GZeEkBOPVsX27NybP8rUpT4imWDQV7jnj1mlHB7mx/8DZhUdtpFzUC2FEVU4IZedVGjq8IC6OwDYo0ZZ9OOCl91EcTsqhVXECt26oQkP4nRpyMTAfAhFM0ou+ASbaLLr1sVZirtUGJWKttjxduY23m3zbeM/DA3VML7ocWdTS1vSHGBKaDPVSqPbESXnsme78DfSjtslZV35sSW/YKNLZuStv0bEfFVgi/0/FJhZJMHiq0z1msBB9xCMyrKJvezS269McpZZ57I09UmV507QYcuVJnfP7dh4HDDmVZVC9gNbDYuz6dr6YEciWuzWhSmUxn7doBw+z+eCBc8siX19bZ2JzKZC0kM/dqStpSnJFHnTloRyu+RwzcBRQDGzslsiHKjiGTr/SeAldFngAW4ZkaVFIlTfayLZHmQ03Eue2JNHEbed8kjfh4Mt+4338hmsMtUQzoYOHDgKk0dHAJ8eZkvvCxW5QXKZl7R8eLMpFYDCD2KtgfRljIX00hus8WdCozhTwotFIAKRc5rpFNovTy3QLWnc1eaxSoc8+Cp4po2Cp2xct2d3xMkL2lD/yDW6oN7uQveot5M6oXovGaPSAKttytJe4iWq8nGV5+l4NyftLdTcuUk3FZg7EQthrCEdvpdOcGBxcSSOz7uAzT9XXvWyqUQNLU1RPF09y6+lDzMl9RVEzVm6owSLKY0UcJURYiwY3L5U+DGgNosMVXcf6PhEfIZzmBkNbvifQFH+DotAKPcFsjS+GRCsggOlIDPWmoBXNAUFGcF7PmqZBGobblmuzs7WjTZ8XhRDm4DmHlBKT6Ro6bEE5h82Lk9HMcw7hbHO9nkA990ZJowSelTb8cawcZ4Pt8BEc2LZN13V5gdZ6NibD5UMYBwQpqqRaUEerEfqZxkFFW2cUbScfYOnHme0UB6DDlVHwmkdFpsx4XNWbNdVqxAk2rscMsIHYTnMDUwg8ZisX7HyTqHkmOwVMz/aFpC2JpB7DuSlckfUtp0BRMmJTl7wydhAl9aTUUhZlrqoxTSl185BCzzPHKR7nRnWUjjzxDiacS6g8qgoGqlbqbKy1rOsiVzVQQtBXEonlAVnGXMKfRO19vdc3/ALrXt++UPHA5SB/8JWMouXrjzwm4YhjEEZbUj/e1N6OnPOVCDF/JxG1EUyVKIkWqaeigIhqnMCLL+UOjq8JMCwVua66aIx4sfBxrhqwCoyA5oSi0YhCE/sSapqTYhmXMgJ9UiLpC2AC7pVedV1TBYxBcqjMDFh63nbdnl0bF1x4QZHlk8mEud0OAbfdhJcnj9pma9RQy6Ock6xvHHm2ga7Y4116am+aeoHRtV3T1NVolGUIBXE1Aj0llyBwbAQ22AK9NXSB4Cq3XLR1A759QrzX0T/2YpSPB/uR4q2VKMbV9JTYJNwihQe5dUVqOcfgAhfEsB4nsW5QDs7PdNaFWxJb8mXbRGam63RdZ0UpihwVB66FXRSRUWAo28qEphNJIUbUmTBKni8BjROcAvtAoRyBo+pOY8OurXtTLvJ8vYaSRve/ZLGS5UteHsMRlemyb0P/WLOJX75dSdSF1DrNAtmj3hx+sqxNjWpV103ARSKc3A2FAQPuiWzqpcAXaowRzaK51tRKyQ0mHBLk0Bx39OOWDXSe0WxJfxnf+mIoANIftp2PMQoV+LAwER33wSPut1JQzNkqLo2+qYsF4nuuB7fY2dra2t7qumbP7t3nn3/+uCinkynh+F3KzmqskC6zu+4BVr5OIak0igHP1hu28axIK0cqO/zh2cs2DxA6L8ocri8I1JvsQ1ud4hxNbxrCe1sCjrLLAcIX/+kYi9tRo+YVNpVYbC2uf+V1hw6endM8CntvgfODUe2FbGi3xtniWIb5hYlhafS7LWR2OHvUbFAMSZPfy9UbMdM7Cxz0TBG9XnSdblvKizjuCCGHJeJL3XZ7367+jA0RqjkzVSkuvGaye+9B4jLO/SSohNO8Ez6I18TT9rDY0HAkgyoWbeUT/q9FIYyeGfX1r/ZPPM+TML1h6waXxxwxdGFd/MPqiaW14EQ+RaRhnALfbt9OniR808kLa/xbxBK1JIvTmKuuFX/iHDa+QRvvpMkJXIxOsoBiiUCd8OCdnOvY4UbqFr0oOEncd+3m9kxrPRqPd+3afeDs/aOymk539aYFIDZUT9uER1xq5c3hCGo1wFx5sKStOeJCRz/JPk7B8DWSboHk4LHGRnI0L1yKzPeUJU/F1uOT60tbrA38OUoqRRaVb0vlyj3CrriQXAjICQwLFLIvR/LQgQPffSTvCagaYtoW5jzUbwR4jf1lR0rB17ekxWvBJO5NXPofWXaOu3G076bAXYITV8A6z6KHgam7luetWRnoYozB2hy0RHKEalv181xzm04CE7dST0eLq85+/LmnpDb7JEG5ojtYwiQnpHqaT4YvH+tyLMl/snmlCym7k5vdM4friw5MSnSup8rC6OuJhI6vaQsC/bYMr+uugzA7VxXRgI247XIINDtLIgl4WxQbtaosdA5tmZJclDfykjVxJhhmw8UMDe6TS56paoNcJ4U8AdXBUKsN0yHtTzV0VNxw/MRxpbLReDyZVIcOHVqbTtfXd9WLBacWYrfco6uD6OCAz8rwVVA1A7/XxW9S1zGNNvi37GQ92DYUnHPcG3sV/u7YkaI+b0CloAtUlWcAKFECmCdeOunnpHLEZVg69soZT4H6WZ0V5d49u4OCZSFDUYNhHXrCi/7fIcGQJudYt2uh4fBADFR3/fQoQkDBRpr/wONjXejEn5r7JBGsmaIxbIMQ+/NUCi7Bj3Mugzty1iwbATThnZI1ss9RhiWbuhrvligbVo0wiI+ExsfDaLQbSRvx0VLk2X/dWiwcr5JOazkCdwFnhCPyrBufeO6Y6DZ0hp57jJTxcxnTk6dIaJ/3H/BwtCAu0GhbpsUPZr2uJT/Ynd5+X9NcGzTZISBP5DSs3H//fR8TQYwQgFuQHgwNAjwjHIOIM8plGPYal5ZhfOFipyG8uxJ7du06dOjg7l0bWVZQH1lS5daJ89cP0jwOJg841v8/dnwj/JVD/SSL7sVbOrHJ9ldHXUFR2LZVoQjURRt9iIobHFKCAF9qOqTH0RPc1jKFgAoLRHsS/0K0zgctKFSgu3w02rdrDZ1Zve3GkRbX28qPmfEyx0el4qgWfljTP5Veto8mYeVotgKYTqm672ioH0Y6M/icO2Yz1CqqYWc0p9IKKI8GcQ0CVLs9QoDINyqMc7eBmP1judYrVGpsq9CNKUbN/t0VmuBDPRghF0bnRpQOg8HGiDVPbFfRcHauffOLEGF/8YPa+th3MvrFFjlbuaIlYndgvezY0bqe92rq24nFFk0IpizFsGwnfxe1TCuBXCQZ5E62KjdnC11mVwWTbbWgt8uZWJhKbUzyDCmswc3ZlqBCa3T0pyXpsJco7lVe/VIREvU6Q84ADYG01juzHUx+zNTZ+88+++xDG2vrKstQ8U015YNOImmKernW10KvIna2Zpg/JJ5+GPRsTMmsJGPiIuHUoYICRciRwkwskIgIqVaSY1lKtNRbJFdyQtO9oqBlFOJxIWWUS1JXbbtr9LlWZqNc27NrvSiLuka6xdnZdiGi2YWptAuIMj5fVEEe3YQFqjvNSySEEKvEdBWwBA7repp/yg33XXdg2wEnrB53C6gXdd20XL/vhx4izUtOHnd+sd+KUgrujrnlA+WBKf8G1LnGaOJzLhjlpdLtTMgK7hnOuU23UBhRSEmmn6kkNW7VqEbugHLDKlZKNcaUGMViUzMAODvbQ0vR0EIpYFNEYzAtSSk1w9RSMQGaxcx6MzPdxrFjZSuztUpK3XBqJV5459655YicVwcHCDhtF2h0QpcFBnkuGkXibLLFW+VDZLH0CZ/zXlNoWLvylsA7fusjIzx8384zkzJHK3PsgYVS0JgO8svZw+FuOFQEUDeL7a3truvW19bPOXTOBedesGfXLtsMzXWQS+o0vGHhwmtO+VIpBrnwLnIWKhg850a6OPi8iUfoBK7tvUhTOHkUOY3MRY469j7CEsa9PPgTglvYoiuq70Kxa4FCWmsaWwfUacg4JEQf8+MATJqTLmq0ykZVUcxk1hk718dxcXQLfhhrLJcc90apSbvlNlZlS4KsXmQ0BeW7O911DYbz9pWSPeYZaUHNZXnSsovTO5noJR4AZnCRYE1LhakkaCsNow5n9j2D/CrarCR3MrVtutBM2FsTmcjKrCx2Oj0xZpEZDGdSclvrHNMGRSvkdk+sSw+tFaY38EJgcjLKiCFPJfQK7rI3ZkxMS42vMJEPM9+1KZVo+U3C1HTaTJQ8Qfh8UYsd2bf1otAKg08LTtmkpnvAEDiejLOyg9g0RymsNLQgC+p1SkUORLqM7Ui2MfWEHdcHULFVA9pPP4u+6OHPQxSou3uU67YoSIC01RgzKgt0+88RU6USSGN0XS/mi0VdL1ATqvJqVB0859ChQ+dMxmOLE3aAEp7fw2Qdmi1FbQZc54ZB3Mrxa1RwZDEcnn0SlyhkkVy4Ab+5Xpn2u9TONgooxoEhN4Hc61KWBFgB7AdmceUGkD3CodnpA079ei/AgzxsobQFXVMX+K2Ts2tffM2BvftOnDgRF174UoyhRB7Uk6dWjN3M6OWEu+3NgWBGWfZNrXuRwaQHSilTRUcoSZJI1sWi/Du30KJu7c7EsO1xaOCoabWQmroeAaoV0Y0NILBUdipBQ3AI8i9RzYdcdG3UJC9n80UxVWpbKtWiwNlUyNAitgbPOAPLF0ouSIuq3hS53PaoZDh1JlPUU7o3VS63tCnReF8stCm4/i03rRRFb9akbJSotSiUqG3O1ExU1u7addZFlzyJPjBotti7WFuIX0WKIBFQidxPONj9E/iY+CSDJ4IhZmGJlgIdwUYMIDO3IWiXhxZi1s4Ld5UKnCRmbzyQI1doYogmdQVabUCckNDDhJyum89nTde4sTrq4hddcvbBA7t3b8CDYnSPjXkkpkhIpkVEH+WKLHrSQZ1d/79Vr+D6JTSeAEtdlMoOakLPrdxloRNPx0WKEv0b0lzU2Q9xvA71zISvjLjKe9F28Zw1zKYjFS3SuGqhZ+08z/uz9x343mOPhbgZO8T+aklkKVHCcTqCLTc/B8Rj3im/G1oIQmWOxg9853udMQ25ALLtVVG0hCTnklMLc0C+y1BRAnIKUR15VHSCjBggH5y6CcgZ3xUnSrKQwrN4TequDROhUM3f/3l1xavmu7K51tPOyHmb58XMyKxAnQOiExzy7wHm7YTMRrJroGm7thNNX9NFS0CBlKmqrG7kosZTUaeorKxMWSFF1bR1MdJlUXQ1KrCAX6hGGBCDs2xecdUkyzpqmuq71QVXNWGN6Lm8w5z2g7Tejm8JZuNYtKmZoUpDNzRq+PLKN8QTQvDDNUuySNKguGOREs5kjSkaII6ulJ2U6OGkMoXWN0WRUTcGFHYu5vPted/V6ATcm6LILr7kkoMHDm7s2mBQtEXp2xbP1nQObpsP90UGaMS8Xg+vYlriDO91xuiCNILpLWjLBT169BmMgUMK0sU2gsvr0xkx0wYjFhRNbmOmQL7UIsPxVtD6AcNgq3ycGcLVdF3X7LR1latde3b5FKk9R+x8B8GSuL5DJcw7ZodiRTAo29HKdift0a8vowGD3ZprIUOOsk+hWbXB36GBQtzXgmEOrh2hG+ZHiD1rNlDQKZItUVbfyxgrU9j3Qgay3DsRb3+VOLW5UYhmgRHiWZZNgMCW3VxXkwxzvXslK4HipvlCrI1amU1b3RRabLeFkBjXVIq6gZYvlK6MlJ0eo0mJyhYGo5sq0QGgjsnrqlC5MONModNIJ8S8Ubt256PJWtY3UtTEcxFrBrkZRzlcqGDVy5lRvuGqR4TYFECGVkS2dXm8Kun5Uzypk9vsgiEcHZwlpwUTN8qZe1bYIHIAOQ7DuSxAEZhFhnZNk/F4trPTdU2W5Ts722WWXXTZpfv3nTVdmygMHLLz3bj7vLXB4r31nOK6mfqmdC5y5YoWvMnMBkgc6XJVMYH5nDU8sC7C6YXJCPNsK6W89h/sWqRDg8Kj/kFWilZIwES2jE1/xpBUhx/BNyHRerTcJXiumpR5PaOJa2427CqKWEoIB2aOrIsBgtjxM4cYYeaSs9p03WScL2b1Yl7LDVGWVduipRENKONRw66wh1DQqsyphQhejAmhcxVwqGkUFkfIXJ2GC0FzPJsj6umj2I5ebqxDprKml6qUk43MmHYNFTFor0U+u8p6matCi4wmCMK4m06UlKU2XaGkzPVa1ilJE5+lKkwuTcEYulKM2NMeoRWC5kei1JnIso4FGoorjapGZZl1udmh7JGrSol2NEpv+6dwTxnrPxeCdVToTxGB9Kj7I54Q17IQOp+hX9p3+kJKwDymD7LYFs57Hk3R2Lz5nBlAyzqRt70uZMdZ7rKo1tc30JG8a4Qx26e2srKYz+dSygsuvvjsAwfX1tdUJmkugbtzlydzj2aLv9xUCXZ6LQwyqF8LgQ5Vvg6rFFm6ITHqlFWw3KI4oA+NUSAKcgiobFshF3yduH1C9D3eR9tdnxJOfdcjB0Gdy2IfmyHNNvwQ90CnqQOQoUIUZb4z2+kWqMb/8tdueeC7D9aNQ7as4OAVO8vdxT2huVDVgIN90wW71wSphBRWeY02PWgZVKKRLXY4k2jDTu28XSgzyyDsM2w0ecLBpwdzwcYmPYogCEFi3PP73Iq7WyI0HqVLtS40hcqiYyimZURRGhpRw5Pf+C5kjs7vIkNJoMtmExiTsvZ03hxUA17E8RjWw6cOwH7j5jPzzVGkPLJaqCMrJU+43NvWMrgygoiVljgsMF0UsPfaNnFE7Zc5A89ZN40Za9zjMebEyLt2vp9Xz7ZUk1iCxshal9THvAd2p9PDwLnnl110xZ5dk6dfeH57Z3synSol9+3eNZ/Pn3z6cC/M2nhc1/MXveiyyy65ZGP3bvSpY8T7cJRR8six9e61KY8Ycfo30r6uYDcC0A+a5gT5F8WjwgGMuEJnTMnFvdyinidNpG2zQtjKygEq/vFlXTQ0RfejqsSA8qbNK/TdjJIvzsNxTZOpOT2yTSqT66P85Hzn81+4+fHHfnDs5Pbhw88+f/SID+ElkPdU95rTKOREbzsEiHeG2fulfAFvhtrZnrVtrXWP5vWZMlkhOo3qAlu1HxLaxoiyLLlKkdEnVrqSNrYpLloa2zzauQ88lsvLGqIh4l1Uu9B4Hf9ELvQIIDJXuFkbNu6V44owuMU5W+sOku9uNraRHCbH1RhLF0K1GTfXD8DjUy3ijk7lRgO5CQwD7g3skjaoirkmkp8J/yM6RzWGfHudDan6mGPKw8OXLV/hsgB0iYi+FaAjlvaZYnllM6HyXaPJJ951Q782/uI3brvzvnsXi8XO9s5Z+88qinLfvrWu7V58xYsvPO/8yXjctcikcTDAZozcBey8vMFtRuw5bLjhJ/aGSJVzC4OmNKv5N3ozRFIoFl+NRznsyfjUA1533qw/J0U0ehTn4976tgNFIvxu5k2D4bo05p2YJGBzOZODGkzCZmjZ796YdI36zKc/89Vv3H742WfmDeY6dAD24/RxOihJDi7tpPW1/WgYT6CR4g35JJLWrEB13zV1LWm4BOBwLPiJ4ew8w5iKeBwIzQ2l+yDFSAoUytymqYPp46XJwG9E1ArpDwISpY/osWIsA6JAWZT25h2w73LHXNaeYZ6CHZHpSJjqcLzvLXn2TeSWRtLGM5udj+PTBV4yuJuN/glArcTpDx8w9C1k9OzjEGxESbSs0aO2bbj1tw938XERst7yR7weDh0Qt3+ILXArM+03HBAv/9/+/H/7ym2f/59+53fed9O7r7r8is9/5UuHnz7ctG1Z5ONqtHbWxsb6FAVJRY50SkIJiYBykGH7JrOaRTLH3q/j3yg67P5xatixfZJSc9wbYsrx8mJWS56PRpXdfz83yeWk3VlciauTPEgfYvG7+Xbda1OUmTYS7dFp/xZtb6SqlM6Uhiq23r51E9C8SkiToVNcluV/8Gd/+sm/+ewTTz7d6pZZCAjUgBRIVisNxEXiyN1vTCBh2EV4MYwDnhQkT2cW7WIxW4yrCuN9CXVpq3cZaOav4cuqE8xb2Da3NN4eiFQweUSENnYTLbi5u01oe9qwsxj4ZLE5GCu2aAsd3NYVMAw0n+fJSEW6I+nlTVLLwXHzyFCj6Th91RWcm+tNNi89fO2uh895fyvJg5DPwZ0bZdHmi8WiIR62MDi/CCkmPeUmnmXF0K80Bja4X4YnUfVv3jTbTzz1zD/83X/yype85Hd//dd/4eMfv/3+79733fu7vn/xi188nUyn07UcowZsPWVqjNs7c2opJlE7dzOJXUWlClHsNc7ROvU7NDAT7nXRMYd8IjBXNUKa0c4N8xqdA0Ku1sG+TYYngh1tr+HxdVsndh5/5IdqWl5xwaVSiqZtEI1XaBWNTBt6PNaTvCiyAsrUxR+ckSZk2/+Xv/z057/21R/88IfoGEU6GW4nRVkSQlnFxd5MtETqbL2ERF2M2C28NRqNRkHRYmYE8HjY1B5OQG9KGyhwOGOn2lw22kUG7EerEpdyeQO8dg24P5r0wKEefnmzB59z/bMleWZSD9EPwcMltTzgrtgKS/1XGUfrg5vrFpC9I8e/jtu9HosMavuMg6iitzM9A0c3la4Z3wohF1FTWmDup2sNEQsplm+OgLyEcOEaGg+oUV6zsgPBwOZHn8lySs0i5IPffegTv/YP/v7P/cIH3vnON7/hdbffeefm5sld6+tUEycRiEB40oaSbOw8FIQN5UQIJC/VG3lwRQgDhy947kusjZjS+TBuK24Z2PQIXaFZVz+QCtGKW2MAIZwcYaciK7u8N2397PPHvnDzzbfecufano13vfWGV7/mFeu7dhuUbWCKYgtIZqVFMe90VgAPwSPL7HZLzCKTunj+6LFnnz9CTdcY30tgMEKzDR40sf4Hznmsmn20l3zt2IbwZEuBfIGuhLKr5zNYEkK0RqNjDorYO+Tq3ThYPgG1F3PWWvAy4hkzYUMjagm05AU2Tx7FREqKfeEkCGYBxmVP4x6Uzh3s+GhmbqR8w7WSGLC7JYdciclMRnzsRV2wnNntiRSwk4WxaWNpwxFhpHrtJjhz2QUUVwjk6OadOUPwLIXuQmkNlHuUUPAVcyTVGNqhk1zqlH453gF7t5hOCK87k+28Wczr//Vf/t6f/Mmf/PN//n+77o2vO3Xk6IOPfh/YyV0bshyjH01IUVrDwFswyZInFQuuqTNbzlFSJzaQHdsFjLC3rr0L79vTxXAoYzRw20XJnrCn8sEqe9MROCDcich0t7PYPPzE4c98/rN3ffuuUyeON8+Y4y8cP3L8hRvfecPuvfswW0RmXa9rUSupMGen7abIkyvMVHbBWdQ8laO19YkmNDgu1EENO84IY3gHcavoUZLdtXab01Ah9OTMQ45f8bN1COs3TVuzYcHgVxd7cj5M6McSSVYHZ0n0bHhFqi6tqwk+PKkL6JkW49RYoEiJ4aY5RmFRM3C41Vbx+By272sSk66z1V2od2iC+VmXXnl5cl6yh737uGwuR2p/pc7ntz1YMg65D73l5F68eWF3NoDVEy8wrgsJt+q0IF4ALWMelTJaoVUSQlrWjkgewMmavAd4ti/EqMiz0ahs2vaZp575R7/xG6+59hW/+mv/6Cdee+3j3//hiSMn1nf3GFFf5nDVKcHifQ9/+WCTuciV67jhihSYg8PuOXt5ENAaxKuSBfN2MGFwqfEVeju5yPOSzrapLGt4A1ers0zpQp3a2fzc5z592y23PfX004t5rfI86/onn3rqs5/53Lw5/vY3v+PKSy/TANOLpkGfTWXE9tZOV+Zr46pQGa8BMRxmlK+vjzISDegiZHLC+HKKGPqTFyDNRKYprUAQjoKC1vQbF8w/XoCu7+eL+Ww+zxT6dVP7+gw9Y9G3nUmRWpDYGQu0RQlsJrEKQnDIQdq9FIkK0kLFiGVi57JRqBvYW+qmpKl5NCqtWfNaW9QpSQQenGRxZ4uWY8i9iVpe0mhiuII2WGsLwl3b5ZU52cGpIhkTXS+RobEREkwULEBMxxBspE7iNizL/r87m8NW04cW1BGcdgqZZtS6jKHpUAoWXoNG1WgKNZ+31K1fSszROn7y5Be/+PVb77jrQze9+2d+5qfPOfvCJ55/tuvbjfUNk6kCOT52oV2Felg/z73EsLZEIqpSiJ3T8FwBoGjfDxwcB60CxMtXfmQ09zDGW/qorY83k1mDNZUy6zBET586sfXJz/z1V77y5ZMnTqKQX2VCI35flubosSOf/swXHn348Mc++tFrX/uKzJhMFlRkCam6s2iavts9GZcKfQJs9a8qd+/ai9/Z1OhtO1vHNuiPILh9lJ0WHTqfpTvrjcIV1OVSI3j1fb+Yz7sebeW5dT5HzqWUyDy7ocS+r4KbxmWRn4GTo6BGQmW+PaeFcPm2P4PAfkiuOIXCrWTZkbegjsiAJSeZVC7HGINascrMR85WSe9l+1WmR9gUifPyWGQhOUv8NSwLDJ69P1kwJGOi9sONI3mWXjqkUbhADV0UfT4u5uEYBO1cO6+mIz/JUJs2f2dK9arPgBFGO18eFUsHovCIr4RpWmQEQpgDO6l3dmZ/8Vef+fTnPv8bv/Fbb3/z9ZnIDr9wrBqPsrU1KD0rXe1kFx80DpqXBElcHRjbjV7iew0cIMpLEsp/KYaHUI8LEku2GJqbSdhoAo0roq6jPJ2oN32tR6OqUYsnn37qk3/5ybvu+taJU5s0NYKJE938ykroXtWz9oEHHtypZ1qa1193bd0CSJArtAejMb9mq9G7xqJAitMUQul2rmQ2qkbUtxXlAE7XIi7pY408MZSdHJt3Dj4n01bYW14xB7glCc95X5W1bbtABSDMZkrDZgWayxf1olFZBiCKw41gBWxbbUvNvHS0zs4c8pwRe0FxgX1MXJ6dw7YEL9STJYHSHUo+9F70/pYLCyzJKefwxow1TMhGBCSjLyUeijd2qPoQ4QjUdNCoeqqdjr4WSGzZFHd7E/Fd4OH0OL5807ZN01CvsSRbkpSFLvOwPSCK5MWWCBsTrAs7ikUhdQedjILupm3nJMt5TbnIr2tR42qMbrr+xPFT/5f/+X/+xM/+3D33P3DJiy6Uvd48flJIND9E6otngJO9SLhCyqaCdx3gKnK6Qgg0VbE+oRHAHK6Fhrfy2IkOq0XyG4Fh6kgUjFNPZ2xjgJAIG5BlxTjb3Dzx6b/+q3/7r/7NHbfdubMzo+HGgH8Ec5LwxVkm2r5+/JHH/uN/+P3Pf+krXd8K3S0WdaclcJo5TOIFcWoPwSe6pn/nDe++5PzzGdXoDRCPieANgYa3bcYoLsgxLjaVWKTTbVuwBEMsKPiTqwzzjID1lbP5bGt7i6Y9ArvO49H4nAgFYOATnckZrtgBxtBwotuXb/p75Lt16BbXwcwMe3/ZYhPvtHu0TSBSthB4C3ApFLq5dbWxYHtUDHR3FDt0G4L57t5fsluMu0MfIIn8JW/NMk3SkMq2pfptztYEmeHlv73d2Ojzx3mvI1w13ABim22NF0VSwzNYe3ClUeU0WiD8dC0j/8E+Wt/3SDI3Td92CJdqDe+RRjZpTKwyaEOjaBAbHrwzdTcHAQj9ve99/+d/+Zc/+N4bf/mXf3n3+kZX1/moIiHKFn/APqX5ImuoxW01Bg8R1/36bLBXA7xEGvXSLYxSlD2yX93znDXOiVny8rrDdUPRoCHUCLaiPXni6F/9xSdvu/2WE5unaIami+7YJhmu/paGwRaFMn33wyd++Ed/9CftYvHOt75BqEldz1UjMqpxrxt0my5KTAiojShH40svuvirt97KU2GsgiVbH4kqV9OA0TMYvmUHkXY9mjYTcVMwnWyKDBkIEisO4l1T386maZoWZWEwmAn6RPVh+XRtrWvRhEBJMaoq9PflRaTRgMk0dNcXzNUOMi0yCoeVROKTRlU7TF62J4D7mksexcBoH4xBlSHKXDFS2EWDLSO4zivhG8ESj9xt909qqzonalWwS7KVHJBkIerlSlkRh8+pbMA2KogQjWzaB9DJUNsmrJfEsoi16qbpqcDLBr+G3oCDmaxy7V3sK4A1guB34GRvuJAzSEMijEbXK368ljoYGoO2dIDUUX6V0XYqU/NmURbFpz772a/eetv/+Z/9s9e9/icm40nTLajfXdQD1k5s8qWCXgz6HQt2V6J8U/BG5P4ywhEFrIQxI7gy4IGqzEs7kw3PZssK7Ahs3oQewMhxlZ3a3PzyV75017e/9dgjP9ja3pQISMGq9MmZkMngu1MiV4XJdNfpp5968k/++E+3tk69+8b37Z5MNndmpu7Gk1GOeJ5c1J3qO6H7qlmsrY0ZAot7ASidSiOoow0q48nUt34aR2hhFlArOQ7OkzYmdwetfeqmhuqgbqF8VxSRVEVRUiQDZ8qzYjIeb+zaOPzU0zz1pijztSk1BuUBJIy1cpvhSYKWLSw39yglWKhP27goe2TkpfEI15/c7ZedRG3tZOpTi6bcHTnBOZcpsc4NtJCypAebeJeb/ccVLOMeRqalQ2GuZ+CCAHq0Opu8RWnQ3Y0LQnzXK49FD0y9wlSOPwmhYe8GBixO9A0bVkjUaohEu8O8+PGBZrcVUVMrvgLjXlG1TZoNI+dobgj1rJMSo3TJEZFKAu/eizFA0XXTdX/vve9//eteu3///r6m+DLUr2dSW5vvhyqElGPwe5yZFPUld1VHqfvrDDXu/cXPwsUG2ujpZFKWACtQDyQXkPELgeldSEhOq3I22/qrv/qLW2+7/dnnn5Md2lnDJ3LYT7/x0Ur6tAlGo0hhnjvy/H//87/a6ep3vvVt5x06fzZf1IsdIc2oGBmRN/iC3pJmsr5R5mXX1JgVbpCLdWJE13UDQ7dyPXq7jlN8thkhJX2I621FFMRog+wA1w4FUuMKQsyiomlV1agoimcPP7O5ublnz16BLnZqOinhhNNAevR+drbZAKMQmcFWXWGmRATJCh4mvT3QHlZtesaOXekQPYXvhranmKycI19uOwinyVFGa/lAseMMd1AkP8L1l14mVqORHTH8zfMADSag/zuOt93ifBgs4lBeAqvqXM9qez1rj5CggrC16QanfKI1HCw+q1VPdGxCODx3iHunctT+Q7NBe4FmPn6Sty9TpmvD2sbgeR7rrJum3r1r909+5Cd//ld+Ye+es+Zbc8ySdQzra/LjmSgeGxkintaLspLefytKDg1YmLOqcFpoiBaMzLZt0G2+gPvHRc1+Bdkq7ptWYMBIr5Q5dvT4n/63P70DoJRt6kHGyCGwxng8amnoZhCPUT9Nlj7U8hfcuLmz81d/8alHHnjkE7/wM1dffOFsp9/emnWTflpNigwF1G2z1dazjfXJfHaSLmHnpaGwUamuw9TPXvc0Gx0Fc5o63aC0jlonM+USv2HQnFIZmtGTbLX9I6lYgPJDBMKGKq6Kstja3Dxx4gS3j0cQrlgbTdZ5aDO3E4VcsMZjEnfimp8keh8MpNhm4y84pORQCLjYg/VuOcRuZ555f7jvaB5AyIig5NIJjtA0zxmMkSMYGcBOm8YWnAhc4dp+DRGB8d1GN2+p0lUUOtXhys3iyHvK+KHnsOM3e33eGAcwip3roVaK19AHtYmKo654PkLkGTw5FyAfohONwqhB8njBGzTHjLqN0um4K+WoGm1vb6+vrX34Ix/51V/9B/vPOrC5uUlD4FAw5ip7wzwUW9jnU4QWbeEM4yg75Hk7Wt2Ue6nXFJlhLj/R90JJFC1Q8bTV89b1pRAXzbqfjovtne2vfOmLt9x66+Pf/8F8PudSaR6kZoQ559B5L7vysm9+667FvMb0W+YcasvGhm4k8MjGkqJZ1Pfcefdsvvilv/eJa15yzbxv+x7ehsyytmtG+drll7/0sqsfPHXqWD1b4JtZQVfE1mJSbYZGRTQpoi3LokTBELV9p72PAwA0s4q7tLAnS/xL9eIMzCH93EmhNrc2lRLVeAybUKIt0xuve+1klJuuk9WImy5w9M/7sJHdFgVrBkrNgsdiwJS3riPorNs1X/MYoE72WxqNxmiOaVPDbSurCo9AIs/zbMjS27/d7q/IJnlFn7Axvby6jvR1UGPpy7EMepjhTJxXteZqPEsxHnTk4G8B5u36DdsjKC4sOwe2j689DHD7dXX3GnLUaevtcMux9OE30L+/R99Bng3r7VWaaZTlEryNMr2un06nv/brv/7hj3xkY/fek9tbdggHxZnBDyGmGaaCRtzqI8p87SiYwrrX31LMvU5fk/rteGl4JuBoVFZV5bO8vAvUOUlT2kSWRVlv15/8zF998UtfeeHo0QwtUpGhJWA/7vHgwfN+8Vd+0bSz2++5T3czlROHUKU5qmNZWVlWiawn2ZtCf+fBB/8///E//fTf/5nXXvNyiYSBzqqxzKtZU19zxUt/6x+e/ftl9tmbP7uzOTNmUVQlj7u050ejuMy24yf+0MZQX25TFKUPpLE416gjJmeM2lwUCFwpqGh65rqpN7e3jDEkGojbjahGVTkpFWYVWcQWIjY9CjP4Ubh9pI8/EAs7jWtnzznX0hrGnvo8hQ0HcDnhyzPfqHNAaIRAkDCXQcpkjYgLuQB+dLcXIUONkxJESKsMMz4m4t4IChk5AG4s5vI9kw2Iao/omUIhRGA45467ChPvZXPfZfcpJ7ytag/A0bDm4RYGloSbPGjLrQcg6+iu3YPYzirUlbLvKqIeRu6z9cVzMYu8WFCy8Zd/5Vc+/nc+Ph2vNYsZ5Y2yzgDDl7SkCykja9QO/xcsIcuiflmjtQ31GqR+EfRn+idRgkw1dJf13hDjoVaAUqNsiKCkqt88duyvP/nJb9zyjVOntvO8MApaCNqVXPoLL77443/np1521dW333abkHKyPhpVYxQeIbGKnkvW3I+S684sktCBQj/84IP//t/+++Znf/aN1702z5XGFLFiXI4Xi+astX3/6Nd+czSafOazf33yxKmuk0WOWD1ZlXhMGqqLJvIIGvdgyiK3ZV72omTWcYGekjTNkJ9L9MhCS7WoFzaySIMOuT9lqTKYA02OzupS9UpLoWmEKOkQC6mK8Jlu7aMogG2J5MITIRIQ9Fqq4Yb84PSCzUywTWhj7EzZc/RLyVCy7uMzDu/tS3sjYFCqxLwFGVLU0k+8XFJ5gXFtY/4kP+VGDnPv8eB/U6WBLeV01wvjmpyDbxcG+BAOhNoMg2vDTCUm7HL4+siljg4JSj+JkFlAY8g9Ra5EOM5BqHI7XJvUDtpiFUXXwf3N8qxuEHr57d/6xx/72EfK0bTu0NxOiUzTCB6X5o3MZ99F0o4OTlVvkCEBPL5kwtkEAJvESI6whS+QRBHGjCeTssgZS0YXBRZJqZKRNrOtnTvuuv0bt37z+48+tqhnZYGuMcy9iLKq/KKLL/ngT77vovPPH01Gzz57JK/G17761e94+xsW8/YrX/rqbd+6s2lpWKab7eRnGtuaFoEOx6bvHnnkoT/8j7+/tXnyne94x7Qqtna2dq+vFeNK5WZjtPtXfvkfjUbj//7JP906ObeBTWdsEhiL1BQZF9xMG2AuEofWzudGqZlqaoYlwmUneY8sEUtIwE6IdXlymjVleK4SGkf1fd8VEmdmrejLw71UQuW824OBFeSNuTgG5CW/Q2glh6Tdq33xANcjW6PC0ONDOdjqjAho5Mpfo2hxqIUY2JMhUC6cqHHua+qmx2ztFWxq0joOd+ARXyjlV2XZire5RnT6wKA4fkRmJ5tc8FRv12bJYvFqO7EyBiVZ1pgPdx6ll0N1KKBFwGxQcJ/CaKAmzgxLqf7pP/1nH//oT6qs3Fns5MBsKXi+rirfhZjjls6hCNfjJpcZ2N/IoNFU8Dyl7Nq+61pLdsCVCcxxqVDxS9KiB1wN6U+RK12L2WJ79qW//eLXvv61Z468gI6o6NFBsWtsjZaZuOyyyz/60Y+Pdo0m+eixHz7yvR8+fMPbr7/6sqv6zuw5Z98bXnvdU88+/dThZzQmfiEZzqkdewZuu0R9YmC3ZNnjjz32x3/4n0+ePPbem24859xDTTvP0SJBzetOZvLjf+dnVFn85V/+2anjp2AFBDluTTBvqTEwi3FL/PDYBTA2XBgap+gsWkrvUcU9EDh2YArnoChx0PdtL9CNsV60W2I2riorZKGHEU9nB8452zZXPCTUxECO8PcJ/Vmr15Knb7bKzOs6ZXLTLh6zxeVK6BWPMF42iBe6WKiNHfpgjncVo0RQwn7G+qkhWBxXBi1bCskHKY7SafbEF/UB4aiemleRTDp6n2OMfCCn+iImZOm9hPRKqueXUW/JzcahiyVZgo7b1DrU+pOqbposy+aLRabUb/3j3/mZT3yi7fp5VwstGxrYYUPNLkXk483Ry4cl4tTFagsiQPZse0nnCRJugZUSLxhSnEWBlnMtZg7IjKEdmcr7nc3tux+8/dZb7/jhY09sbm3mRY4ELgYi2HaKWZZfdcWVH/3AB7a7xc1/+rmf+fjfOXni1JWXvfzqF190x2133/vwg6947csuP/+KtcnaDW97287Wzh133jlb1Gju4dq/uDyHyWRGk0KUzOUzzz77X/7rnz36yOM//3M/f9El5+WqzbNqXE26sqtG1c994hczkf/ZX/3R9uZ2kVfCyA4FovYJmVsJR82yguZahECQ4OmVVJoH58BKXf43jphb68BWmeM8dKs7OzPE6yjmBycEOUPoa6unXMzfhUu885jEcVmt+zSpjWG5+AarmlijMfzZQ5959hLrJWoQz7tsJ65yVJLeyTiMgb4+w7lJ3nAO0iXiaryi4bnsrMfDNE5j7YdPY1Xv30xwvqnHap/W42DIjkEjYl4z3+g3zWCFZmqp1z7MGMev6KIemJ5WV9Et5NrIMof84Fyr0WZez4ui+NVf//WPfeTDi7rRinoDEhyAJ/UMcBoudjUcxh2FQVzYz3k9iQVjf+XVtDYNJ7GYTEmKoyUIp5EkAXvAwqoYF9XWzqmvffOrX//m1w4fPkxWA6oWwrrQfaxvrL3q2ldszXY+9cUvL45vP/vc4WMnT15w+QUvHD1+/yMPnzi6dc+td9+f3Tcd73rJK1/7yL0PHNh/cHN7e3PzhMP9hRmEoMdc5ZgAYozMT5048aUvf/mF48d+7Vd+/Y0/8apFt8hUhXmdRu/aNf2Hv/6b5ST/s//2JydeOFHkeVWWXcszu9n9crFQtodJzzNMmteOswMMDOGIF+GciRE8WsjyPLePhbefGV0VxaKvG8Ku2UpGVnAwtXxNrLOoHSmFUPIKireeLEsNj62LQtCB7OyXHJIC2Q3QFsQIEzHPguHkNjv5aX/5IRlHSmA1Llp4rFLMwR5JnhDbsDQyDRM7FR7fhHfYPVbFWdnAx+G0FGHlqBZb0Ziwy8LWjzuOsNtDf90951LgamVJh90kdwYYm5oKcYSQddMYI0aj0a/+2q//7M98YvfePZtbOzQAXvbUNJADKhG3+sFbjm8jzKRdxbDM9qYiI8dpE5vhCNW+bduiuJS0CuoNMoURMBKBGdOhFEl1shN6Pjv1mb/5669+5WvHT57gQUh9i1q2IFzpSefb88999uZivK51d/1rrl/fc+DZ546sjab3PPrt4y88++rXvWVjmj/00Hff/uYbmhOnjh15/hM/9dPfefCBL3z1C/N548ZoWeuwKKv5YkH925DBVbKqm+bB+x74vd/71237S699zcsmk8L0Wd2JuqsL1f38J35u71n7v/6VL9x/z72zeaNypTBPHSlfth7JyDDoS06wDV9vB42EolDqF88MzAdQ+CSS7E7XEdMoAqlnRZH3NH+WVo8KxCmswoKNekJ74JDP6gUtahNpFk5O6WpmNhtl5Wk7rrgn4RFSFvADnH6EMQQEKCXqKFcNB4Tz4dwTjyaBuFWOxHigmhAmcSEFWgbjaX3oI6dBq+g88cGhyiRRv+kfkfnsoJrRd213B9uXk0/LsonjFIP7cKVy9pTJncciObIeEhx1CFrYel4KYhFGr2kbpLC6rizK3/3d373ppveU48nW9mYmcxT9A5IY9K1VuS5yFf3PZZCGbBt1ShhKUIsdizwPUzdoCsaDtgjtjIoLY3SJDqUiG8myyo4fPXX7LV+/59v3fe97359t72RlwaE0blnicQtM3F3XH3nhqJDH9+5eLyq9b11t7J9unnr+se8/tnffwevf+EY9O3Fg3/7JennL7Xe8/g0/IfrFY0881rX9qMppKLJFCAGa2aNFK0Aw6JqFy5Wjke66Rx596N/8u3976tRPv+Nt12fjcVEq04tF34m+fe+73veBmz74l3/2X755xy333XvvYt5yPJww5DQdkdovI2kXxBrYrCyKtm1t+b6d/EKwDm/aeHQq9XzMpBzRcqHDcp432pQFCh241oM0nTeG2cmJWpBz85Co7M032wnzB+lAmtbi4F2Mg45bf2KmkWZYkJfJXqTDVO4xWwGIFOu7YduoJowp09dILMU3g+RP/3Evd6STKl7IRWG3AIkKLlxqk8dpH+/IppEBrx7pPwLMQgwrN68Qg78RloT8tL7S0MeNzGh7z+7UETRkSTqltgknVnNp9GLWYtxF35dF+Q9+9Vc/+qEPF+VotpiPISOzXgN9wNYC5W6SknDXaSMMN1q+QYvySs335HMfRqQ5MTSsjf0m6BJCcLZ5lusevYDHfX786M5nPvW5++759rPPPbtY4OY1ML9IgGUwKGzFDdMd6WsKhPXdyROb99//4NbxU5dc+qLzDpx34XkXvvxVr95zYO/f/MUX3vDWtz58//dm2+15F136ja989QdPPZ2X+Wuvu67Ks3sfuP/YCyfaFi7GbL6Deg9qauC6HQpMMGnaxx597Pf/wx/sbG6+7V3v3LU2geFCnY77RZ1V1Uc+/tMf/tBPf/ozf/n1W7/+4IP3bW5tgQfRcgWL0fqqBteAUUlRFiUDoamKw1Xpx+FKbmoOewAN1YpSFVVm0COWkdNZUQC8SZFp7vUcvC6iXEpEWQ+foW6W2CjYwVYgX83apQ7xa20tHzpy2ooMBkqsWnK00VmfvyIRQOXuaEBg9TlqVcgGjLpxRpaqPdeyWhWp0+q+Fsejo3BbODyiQU+XASppY+qOOJ1RuJSfdYzHrQUhXgH3JqArTXPhhGS03AnTDuZOrmDlNHrml8TVJluqyHXXInMI7i1+8Zd+6aMf+YjJspluxlWVm6xFoJcgBRzujDpcxeCruHAwclVcGbQb5BbcSY+uHWT5YIEU02lF0gBTuoyGAUa4MBxdZP3x50988rOf+dZdd588fhxTFLj7FF2Dcypd1zeo4MV4Qj9vjfE/xpgHHnrw+z/4/pPPPj2Zjt7yjutlKe+/99bXvfG6btZ89+H73/bOdx5+4om7H7h7+9T2i86/+K1vettlV1zw+U9/4fNf/sJzzz3f9YQ5pR2iaD253ISnhdHa66effvKP/viPd+qdN77pTedfeF6ZlV2tJUEnMynLMv/gT37sre98zxc/+8k7777jgQfv2wLeAyF2yEeMtbVpFxTNaLOoF7bxI60r1wPbMQT0PiE9QTMa4TE1rnZNNjYQHyBqZJuX2nF4R9UjNDyx4FRkzXNUO3rRanonN4A8aDS8DWh5+g4/7K8RSASVHc4cci6hrfiwL5a4rgVUEgoeEH4SnBY+sJa+Ighx+n3rzlKkzBvGHljlk9/+IPu586mtPRLwyhSKc3alq+xCLBqvDu2EE8UVWwKWO3lMqBUQQbWnPB1417kYfiKKyYuq7IwpZP7bv/0777nxXaPxGrqZd7CEOo3SQ2506bk31Lk6DLQf0O27zNlevq4um4MUvqm/T/lH98U3CYosywoNqPpOG3TJ2JnNWK+3fSOU6uv+W3fdffTYC20973pkhrkJv+9Tpo1umlb3GNfos4J8H47+zeb21gMPfvf4iWMHz79gPJlcdcmlopWPP/bINde89JKLLvyzP/7T5448v2/fvrfe+Gbdbh/Zfu5V17/hyacOHzlytGsBDmeNl8EBhsaxw1UM6nL7Xhx5/ugf/OEf33PPAz/1C3/v2pe/bFSNyXDUvak7s8h0Ns7yt7/3vW+78UOf+m//+dN/+6mjzx1VHaY4oTesG9jLfeqRfQlgVKy4gxr7yb30ZFo0shVN86pXXLNrY6JyjOvMhMxK6vJvDewodOzJgtcmFfkhMMZ0OiwMkBg74AoQw1SqJOua2KS2V5ltYUBXYSKgvKUnW6IktgW4b2+qerz754gmHCCZqmOXdqCTU6aORYNl17AmEX4zsSLCA7n/Qs8th1TxN8lKmIanwuNwLSnTiS3RCRN4auIH+1uJkFBOWCC3YES+eWJzurb+2//sd2+68V3r4/VO6FxmPKy57TqX26We8SH4HKzoEM+ylDw0912Mf0X0ML5ZNlGqcpQXBaVGZSZyRIlISnVtKxDDKopq/JrXv/aSKy+95Wtfe/A733366acw9JTCMoiUIwna0nh22N8+I8c0w+X9cBuyvOmax37wxBPPPnfphZdcccGVjz323MOPPv6um278zv0PPf70D7LMvOXt11986XkP3fXIw5+9eTqZPvnk4bbvR12Xl5OWSgB5oAGpfCCr8I+hCnsjFvPZXbd9a7Go+1/82Ve+9JXjquIpYlKYeTNftM28a3et7f/YT39CTaq/ufmTz/zwmVKoVuTAlVP0R2tNY18pLUHxI++toAwYgg49vCT6b8E1yHR24LyLr37xVWvFBAlimHNcfDnoWhFog3lwVaDElY45VzLOvloBQjemV22nN3jjd6FdqUbDgjVIzjNggI14OjVH5twA0MGEvACANme6lAgsvExv8fTfWDPa3FzER1Hwxp3VatfQonvYKIhUcbRQVCvq6+QoaMiVG/HNnXaSWsLKJv3HIvaYnvPzDx361d/5jbe/5YY9a3vqvs2louIB2VFXDoZGx3X6nnGHWaPUfnZsHJBI/HvwOXwNhqMQZjnuBMKmaaay0bjamm2jTYkwXdOpRZPnxXS88Za3vu3qV77mm1/5wj33fns2WyC4aVQH4JYpMo7MRRWmvFZRkQiDWNvF4uknHv/WfbeXk/wn3vQTpsnufei+xaI5cPY5F1xxSbm+Z//efY+W5X0P3H9qVldGXF+OnlfZIxqTowQxFSdFiEghGjR60GZlWXR1d9/99/3+//c//OZv/sPLLrqyHI2MoOBcTUGxpj1eP7c2nXz4gx/L8vJzn//0M48/0S8aRiu3qL8D0tyd2eQFOnhQZxyeB4pK3950eZHleVUV430H97z7XW+/+PzzJ5NpkeUSAXzC9NlaiDiU6mM9Lgvv+NcdkSxaImRjay7+LHGCItcy/BmMLn8G+2LzLCgw/3lUmuzhGREhDTZXrGh74RJCobgoVPp7GvRBwwEG2mPD7NF2vkT88ihvnwpgO9Ii0AjUgYBMZP4zXjYSFEnIKoFvJZLD3a9P09hwhBb5//TP/+mhC19UFJXOqCq4RRAIc+vspBaq0Hfj5x3OygHhfd1ghOngOwnB5QgQH7VYD56YR8zCKvVSgEvhpJ5Mp/uEOXVqs9MYNql6JCUwC7koztl/zntv+sCBs8++7757n3zyya7peVC9gFoiy8WhztMtdaso81zq2WJx3z33XnTxBfUL81e86mB9zWs2j596/Rtfu/vAxubxE+ddcuGlW1sP331/rvVIicsWph0V6zvNViEa1vpUhy8yxbN97EgCIWTBFYDFqROb3YVtYUqNDFeLtjZG5yrrtN6u5yrL33fjB7QRX/vq3z7+8Pe3tjYFNTki+cDdeXDbWovxqBqX1YmdTaUlWnOI1uTinI19b37n20d6/NKXXTnJsz2T9Yx6QVLHODtOniOjpxHwbh+st+O1nKX2+HiHto8NuqHRN3BYQ1I3tWrj87omZzGu0h8TDFzmV884iSUqncnvJINnUYeT9pIrMGH0i8vDORSvb/3p2Dgc5r+YaHo/BsVdgLPBqBilUYkRw9mVjdzdZQU8MLRt3DJSiPYIZsn8qssvrca7sgyt0qqsoE9RhEZhFQs49pErW0oU6VrnoYV34975tgmBvZkV4zQ9yIfB/h0VRLMUo+YbqPaqcsy963tJAVkCMPWm71qZifW16Vuuf9vVL331N778+W/dc+dsa1sSWJ564lv5ttKEBECao15l3un28cef+Er2VTVtDh688OILzz2wb/93b3vgsYcf3VnMTz7/wqt3tp8uiueb7dF436RUHz1r/RGZf2Nrs/Y9QNwyAyiGUiCdVcXll13+iZ/5OxdcfIEwuobHbntEIeUgTIbwv9je2a7y5s0/8WYj5fr63m/dfsvWqW24guTUI0hGYlwoOa1Gb7n+DX/zpS9v7+wgaGv0WBV/76Mff/EVr6xU0cqTa9UGHjanWZokWjOne5O8TOIDOkvIEm9UF7uc9/B84FFIpJVStkyNvfgV3EiW14E9rVy3oJPTFf95TnHBOcdXIjYrHBV63RhpZ8/ScQbYHhhzhudY/xy+fiGyucNzDhtU0lscYhSio03ghuq+55N/mhgnv+K1wu+MjmU5ku8+cGg82qXQnQ0zvlDpz0WCtsTI/eKiYI5fA9TZs6+PS1nN69Mdob7IYer9TdiaDTswikPdaN5svSMFIOf2vMgBTKAwFx9M8Srkt5A62r9nz03v/8DB8w/de/e3v//9x9q6yxRyJ6E121LgwVEDdD5CRcI8/MjDzx9/7kXnn3/Viy8/+8CBetY+/YMf/PCJI5tHj0pVTqaTi7tmnyperYoXRHZ8vs04XzsnlTuA2qyFWV/feOk1L/2pv/tT551z7mQ8xTRcysXBohGC+vgCBM/tOGaLue67V7386gMHz+oXzW23f7PGRGt772yi09hYdfC8A+PpaL6Yd73oa/PSl7/i5S97BWZCybkSI1nAKQbY1XrnDjcVB2CG3BSrSd9oYoleXBQj/HQecuwuppdIEqx+70N9kr2z0ObOHhV1s/E34wpxnbAn2eErTEV0ZZv1SG37OBAQoz8TL9fdzEBJB1axKxEp+KjtTwTHtF4+Vp+iFaaD74+9Rlw4DCewgBkb2j4NB0cb5k2kyLXAhfL1yVrRwWmsBbDH5PVae5lagzpr2fGv/eErGPxN+fYsPqnmirspLOPw/M52sBtnG4/yVHhqKeVcfpsV6vu9e9ZVLp974dj2bCaMRGg1E2j/RoqDMEELpeTrX/eGl7z0VV//8ufvuOO248dOUk8d6hPEyO1AlwHdyfdNHTBQyH/s2PFjR4+e3N7eOLjntddcX7fb33/k8brIvqnFvp3t16uxbrq7KvNoXhwb5bpGnSLtie1/2UtInf17zr7pve959bWve9HF5+dllcm8a1oUXQAE0nMWgtv0ETVrjFcznVks1Gxx/vkXPPrIwcPPPMk5IhLiWJ48y2aL+fcfe3JUVmVe9EK85tWv/NC7bxyNR02jMzHmukShhRI9KioohR6pitVWSMQlQXNGne2CkRXLQN8qNHVzrW4d6I1IYXjv00M/XQpmpcxIFWXiOMLQoVyW4laBIdRl27KE2/XBoqROwbPE6V+h0U5Q1m6x3D9RACr4CCEEyBtIPXao9tOWqlprxrYRtqZ7mglb0rv27SRXZR2kHOD3rDDUUI9Dg2iWyFnOqMo3Zt0Qx/J9dFx8ixH65AZwAQrnC0KHa+twQCYF2C3OSjhBe5/kkjBieH06Huf5ZDw5a+/uw88feea553bmi7KqiiLXQD1DKRvdtujakk2L8bvefeM55513793ffuihh2Y7M4I/ANjA9ZLRJtBUbKcCkOZQqMzSWj3++A//6D/9ly8c+NpkfXpyZ0dqs69t31aOzs3HDxXt/XnRVqXqs0pI4MVo1lmnTatbodRF51984zvf/crXvvzAvvOoGbHsdYcmXtTYlLWQbRsAyWfH89aL+sSxreOnTswXm3v37jr6QgWnlYLNPt6idf/Nb95+amdLSbW2vnbwrL27dm20s1blSqLMIyfDh8Wk78NMK0gLbCXpaXIBEefYIjb3TkrSthbXlcck3JnyR+Q3+Tf4/4rMS9em09WIDI3awBTxLTtLlpU/hzLC26Ge2OmKxOjwvHwGvl0B6079ba/aE18hHgwTf9+OZeYK/+FlgzJL+n4kV4+lr6/T85YkLWiem0z3LXogUtU6miCiBZVvVBe3bE6CzhbBQS11XHkwd5g1eZ4roBdIWkc2uL0hghFLslztU7uWL07mMURBjaHCUC8ljBmV5aUXXnDBoXOeOvzsk888U+saDeLIyUXBTi8wpECb8bi49lWvvvqqV95z16333Pvthx95uOvaIi955jKnwH2MI66t4aRnJuH9z7abx2ePK6navt3XN29fW9veafpd+Vz32/Na9w26B9DwQXpoxPxUXlx4zoUf+eCHXnLN1fvOOtD0fZ6VVBTFBdcQj2wMMw4JI8HozaauT544dWxz69jxE5untk48/0Keq9ZgmikBMOAzZPDs0cpD4pY62Xdn7VnbtTZWucqqXOUFztQBuwJkPfsYoTVbFL4Z0utKrRwsUPu3c0T4ZF7wLn3VYrYc1aXs72iVO3KGiqWI8QIrJpAHVw4VuJd4hujG0KALy8mJjbf6Ec0ZYuvxd5afcFB76MIHUahtuB6wFMlfZz1nn99hO5iPLLQugD/D46en8wC8MP6U7Zhc98hbMCa7R6s7mo8ZOlbFU+x8nMqiOaifLHUMB+4C5AwHj6cl0LBofoc9W8cn2KuKm4x6qyjqzsHeWNt3mVHFGCgIAqepHsamHlXVZZdcsnffvqcPHz526oRNFIDCVWY6IU3bNl2vq3Jy/fVvvfrlr/nKFz596+23bm9tUWkAPF7rDrgi2/CDVwiQRPZOUXBfSbGvLG87ud1NR+e05ljbtirrUWdBpbn4DoBiUuUXn3fxRz/84Ze89Mrdu/b1Qo+rKjMZOsLbhrdcV4fez7jjDk0Ce0xYaOp52wu902wfOf7CI49+/8RsqwGs0oZpqC0eCuDzsrjonEMXXHRxUY3WqtHll1+qZFaOKoGOXH3T1HAqGCXG4SWWnl4Fn177xmGRVe8M6dJWP66g/eBCetYYGLKslGjMZfSeO9o1VnSy3EEiLQ+n9qs17aWvJ/SO7grBEfPBaV4D/9074wlg1PHR0qJF5/VcbUdSKEypddaLNX6dcbRkPEc3umQA2OWKZRRDKaFI8A6CQi23a/H+oVW1of+cD4h73oaFgK9RvivLsqpCt0QMqu3R+dKO/GRPkb6NShRuXBB7ok6C04BOqNz1tTEmm3JTKML3ainqrlOm21ibXnnZZUeOnTgMLPSMGrQKIwpqQIEUWN1uGznau7Hrne9+/74Dh+67966HH36oa5siq6JWZsH/slEN58e7TtNo+vf9us7G5Z6u29nZOb4779qe7RdURxHuOivKyy66/H3vvenKl1y5Z+9ZKstLNN4RHVreucIPpLkpfUv2CAZw1HUzX9TzZqdvjhx95vFHHr/vnnuef/45Luq0ESh2EzHtQfa9vPaal137qldOp5Myy7qqyNFxOdMaw82ZzNxCOy5aEXtO6SySmnEM2b7jP7U6LhwU2HdoKsfeb6y1nMVDuBRX7eTZIjqJj9Uw8wSQsvNJXZgoMLEM4tc7uoNG81E90SqhFEeVE4M5mesYVigpJEogXmn8DFgOdEHDuDF+z9qY3rtxCtljFs7g4li9HEfXjJB517VaoGCza9HiwueKPQMz1yV6mDqtw1YWounaxWKOxKSQ4wlnK0tum0rZYxrZYHFaoGWMFilKxn9bghj07SAU+HhUTjBVnFxljkdzExNC4Peog5fnHNq/Z8/Gs889+8xzzzVdJ2kCABQteYxtUwtjykxc95rrXnzVy775xZtv/9Ytp05toeEXrIMkweR9ijgSQ9NUTU852xNK/k3W1fkIw+MIn4ZGeUAvF1ddfuWHP/DBF11x4Z49+2WGgBOhULnXH0UFqYeqkKLr0Umsaer5fKdtm/lscWxr8+lnfvjMU4cf/u7Dzz17uO0An6Qudna6IHdoxbAqJXft21ciIDCGsm1N2/eCmngJAd1r/fwYURD1F0tZK6o+iIHRQzUyQGO4dVruahc5jAMEcnoA7Sfjt9PI8IBBLEFQwC+U1DqeivFUZqCU/XlSvRju50y2hQMZsxCx3BI9S3Sry6UNEX7ULi7s2ywviwKd3fo+iQ4GiRFbRik4JSTZk8hNYtAImNB9S/W3fiCi/Z5tSBo0b9SonWrfJGp0F4vFbL7QfV+NqlGFF/VD7Xg8EutzO5yp72mOL7ooDoIAfFH7gBgFpKYVjSN2nf5sGz43hZDmVmuBAozsoosuPPvA2U8/8+xzLxxBSS2FrTKhdN819Qy92bNyz9r6u9793gPnHrz3vrsfeug7XdvmGTCPvNWu8RgPdQUIuWkXVjA6eb7TiYWQ5XzGWGXE600/HU+vvOLqj3zkJ8877+Cu3ftEJjOCa9BME27YRaF8KgOEJYXQYN/U9axeLLZ3nnzm2SefeeLo8y88/fhTLxw72qNpHqovotgqLGmMsiqK3PQb69PpZGx60ciubxHYhKlCjfhhgCBGxjZMPE8zZPAiZRvj3+Ry6xafpYiCuE6+2nyBt86ZxCLmClH+AYFb9WtTW6sNRHeZoPCtKnbsE7JRtrpe2idJMkTL51zt7UecFF/fg0s8ctALu1QoRDp4oKVtsJnQQuhiziyzPB0xvruhKErA3X5naOyVC3ajIwdizrZFrJu+4UIfQxPa+cF8nJTQJ1mmRiP0i1lbm47HEx5Q6pUPrDsiKUbnInqMCiEstRsPmlZ5EslOx+MRNY4Fp3KSyUXBrRh3viwSaVpPRtXlF1906Kz9Tz/7zJFjx3nkdyaLvpdQU33di64q89df94ZXvOq1X/3bv/nGbd84cfwkgu85Wjf7rvMYrdr3s/msbVse/xaFWaSWGLCApcG0Cn1g71k3vOPtr3vtGy560XlKlRlYjMMC6Hpho3m2VbuGGUwQ5b7XjdazZnH0+ecfuuvuHz735Mnt7c0Tm3XbZAKYbnQs4K2nInHqBp1VWVXkam3PBrJi1OCuQIyfxjUQPzDOjyZpUNUhb641gEORoPUv6V0nn+xoRrsFKztbeCvaxyus6vbbNoz2OFM1iaVyAMtjE5c5KZwlscS9sLct+JEsExgEY0JRkaXpYQcOFwzyVmVgx0jRpYBkFzOIFsS/ndzcaWRFuGmuvFN2KmWjMbrIQ0SG4m1JQqTCLWJwZ13xuzm0O8+ws9BUO6vPB6+C52sx/K72iFvAyGw8KsqqHI9GPKLWdlGlGbYofEcQFWFlGiRQuG4Snh+Hy5LLbDKuuLDMZ9Zt/C7A5x1wjtWORM/nXevj6dol+/bue/KZwzs7OwBySXRTJ4Nat7rLtFofTd71npv2Hjx4/733PPTd7ywWszwr2cHGfSM4DLXpov8OOBPiI+BKadQ5hw594KabXvmaV5176KK8UJ02HZVABbiqq+5QKDQiSxxDMNTWyZPHTxx59pln7/rWnY89+v2T26fqvuPB9qRsM+5g68idihYASulf/tKrN8Zoi95lquUOphR9d+1xmNm5/btrmp6YYw6SGOD4EXw4VCGF47kQ1FNkRMCRmk5cvvhtT/+Oe8khdOGrKMyVWpVe+Xv0lbfzIxwfWYZwcLLIpvU2byw2Yio7jQoelhOECuNhUsm5rf5rwYJJpICP11r7mxt3S2ow6l1mZ2iEG04XMmksG79s9szl9HKEnRk8GxlUzKzx3AX7tnsymmxC4617lMKORhVmw1MMCYYuyuds6lMKUWCsJkYJWtfXXSC6TUv446LctT4dEaLT1tb5KIbXH24aqA3qseMAxxjUuXv3Rjkqnjty5NnnnjVCFWXJXjVEVN8t9CxT5nWvu+6Vr3zNt+/4xh133fHY9x/vug4pH0oJcZ7WDZmy9+c4mWdjy0P7Dnzg/e+77rrX7T/rUFaizAZdqd3IPaupKDtOfeS4lVQhynwxO/XCyefv/NbtD9734JHnX5jNdxA8RAwdmonbynAml9UrwTHQOFooc/DQwdLQs+So0rL2kxXxhApTmDjOX+Ib8KCfhAqDWolUp49oha7LETV6d41+WrUf5hLIJf1tlukewokgsTYJ5dzgxCeNDN1lE9tWjuIdNy6GXpYFbCPoiJOYriJAZLjbyOaINHXiba4yERI+ijjcGdspLBI3ZxNnttUdIyUCID+cNiTpnV0TBb2dH+GCRt6hAZNS71KafGzvg6BFnNe12V2GVHjzi/qhVJgMokrie0wbpgYpVLGn+87M5vPt2Yx7shfoqoGxuOzTuniz71hpb18bU+b5rvX1UVFwFAFDyAPM3voBDD6OTBy3zQAxIGXV912Zq/MPnn3Wrj3PHz22ubUpVIZRBTQtCAljgxmq09H0rW99x5Uvu/ZLn//MHXfctrW5g+m6Kusw8g8ZV/YRBi6ikaqoynfecMPLr3nZnvWzgAABTAPcS7RkRyT7fBlMW6jQQgjVNPWpYzu333rH177xtc3NzUwo9KyGHoGhkFGrPswldeWlFP/ArFBZqlz05x86WOY5RCJywlxdCAERusNh8Io1W6xf4HjSCkHrrPrwkKt1iEOI9mBvfTsL3PO4rUeGhbKU+TytPrXxWOpWH9T/0peHqjDFaA1dxOHLLHPYkomXHBBX/8VuxeAyp8meD94dFkdF6pp7dLj2feltpU61ZVbfntVbHGGDQnEGkj7clZK9NacIaXoQ4tGwrRF14cnU0A8EV+b0NHeLhAfpar+5XhWZ5BYvpVRZlkpm1agcjUeKmjl7yGVUVxWABlVZjZB/opNyJXqkf/1iRqaFPRNNViF0Gpn+uO22L7Ps4L69e9fG2/PFqZ1ZjSZbaB+I4FfbzNq6LIsDu/e+7wMfOnjo4N3fvuvRRx9tmw5snAvRtVzLkewYvOD8nP3nXfXSq87ad1BQ0bQvFnHBvsSjZl8iN4C1nNo+9uUvf/mWb37zxPHjRVWyZSNpGFVRFtT4FSYWlfFCWZVVVVaVVFnbtHv3H1jb2KNyGt6tOCEFKAzGoBY5l2Ha1lnUFcG6hAE9mxqa3kIeJuAtwmml0el6UISwQGL5xZQf07GrFqBejRxd80GsgY+3xCaRp+g9XVc9RJE7STjcgOhLz7n6lJ7bUq926PIOTjP4INK8/u+wDlZUOrcLH1CDo0xwn1CvqX30LTpdsHW9EPVixoqEaBeFycFzUG0sa23slNxXaqYDBxmrj+mzYGQEIQjkgMIHQtTYFuhU9mCbLE4mk6ZBN7aiKEajSkrRIs8U2Nfeg/PGkNCUcjoduzizna7m9t/HBkLDM5cSI/MgtPWh3iwd6jEw8cToLFd7dq1vbKwdPb65NdtB9geNdTLT62bRdmo+HVVvfOObr3zJK7/5xc/ffudtJ0+eoo45CvU+biKfXV6KD1zzkiv3HNhTrY1oeDYxrG9UYluT238h5DLUBrWyr3T/7OGj995737HjR4uyooIqikmiJQoandeLmvbBtrNDEC7PMRis63atb7zjhhumu9aLUclqExYkhXOpbA12qY31eQR6TH1uBkfitfrwVPJ+7OdGz50anz6TGuuqOBcSBUxdpQr1dk+YIswAPo1Kjee7Oy/aslIoKqQX1Vwt+axneiXAT//vmb6d1NjxKdKqixQoY1EgiRpnaDS1K3fy3ZVJxLfhGdmua6yFrfsc7pwbu1PhT0BIRk4vj+qwZf2UDslIRaAXCjosgFuJduxAQyJ4tOTjauYsG41QC7iYL1wvnrAWUVUK9OxkOqkK1FRwPxXqc+Wf0goeH1BJ1p0qCdhfx2wCgkW71tAIy/YaE9wO7N2llDi1s43HxTw3oKh79AhA6vXgvgM3vf/DZ51z8O5vf+vRRx6t5zU3BuHRbXYLtRxPRht79ozUWLed82ocRbBsilAOyPGoLldFl9dbJ058+Ys3P/nEYwjo0fgvDj5lMitKdr/Rp5JFVI5efrlUmI69Z7rxtre97aILLtiYriFzRdZGUeSYaUaOMzvBlop9berAofQrlgRbbTgl8QddfVjgcB+AsHToor2+ufyqSKS35G3omVol+wKW9OrJBBGzOsFiHVwvyznOL1zB1pB342hYYneED5OyolQ7J8cNbsc+gxM96YVWpIKcqOAmO5aBlz3x5CLhi1Hf7sFOBusQIPhgjIbgEDc8sk1EMfiPjoGni1kCVCJAQAcMSaAZGtSEwN6lQQO3ooBzrKDHqclzMM4s7pijYKgcLLNiOh6j/zEjHAdWmSs6HsB2bLTI9sOEL9o0DV3L1tXbBDIlh4QQe3avr03Gx09stlqLIut1T7YoTQzvF1VVXf/6N1919Su/9qWb77jj1uPHjwuUN2QGHec42I2g59p0ij7OwGZQ7DrUadnl8711aeAv/NSs04efOPrIw490bSORoEbUnxcBEao8b5uWImckUDM1GY+LatTqTpr+9a979RWXXHRg9z4ls94gD8GPDZeS5gw7MzOaMu9iSXHfQ0fJSTxnSLL8l2slaZOgaaiFeS1KVS+7vZ4v7BZncM+p83OcWfabuGSOOvqNbt9BloKPSMuuKZblj1gm84idhiLNC16/cKsZKtKqA5N+5ePHv3irh01g7oJEDWd8SZE/V9xkb4Xxf7oX+kKHK4QWnK7fJ8dyyLfiGTdcRmJz8XQbpL+B/2CYlK8vYpsONUKLBfShp3JXEWp7yFI3410ba1VRorFi9Fz2BlPFlj6cNRq4XIBcb9jOsSTkGeB0nzi+yLOz9u5e1M32bA6hhFGLSmmAlru2zfJi/8ae97z3/Wefc85993z7e997aGdrhg5bxKuCGlNv7NrIaUqgn4TuHAMHMoUJD2O4yNDhVZr2xKkTX/jSF597/rkeWA7cLRkZmFRYliW7JOy0FkW+vr4+XVvbaRb1vL3ogvNfctVLztp9NvxvRLxISROHIWgF1EcoMbEqxfeQTAIl8XLywkXJoIHhPKAiZ7faC9OLw1G2m9+QpDj05wQ08lo5VcZalbyC8PlqPr7hedIrtxXegdfOJmbC8Oje4I5ZLQijKIc9iA+skmVO8QRhF8c42dBIggnRNT0KNKN+0QAXMw/7ayxtU3qCJLzgDg0RRDAwlRvTEUQIhGVEyJu9X24vkMMJJmCDa8HHZ3SVXWSyUsCpQygVVyAcFtxpp9gdettZVwA6aLMxmUzHE9uOZukBVj6WL6zgoC8mFEL1Qs/Hz2vj+NbioUwiYF5io5ysjasTWzuzRU1WtswlGsphQmenJ1Xxxte/8SUvfdWdt3393vvufuz7jzR1ywjH0aTavWeD21NxCxEbd/Z1nvZPtJ4ri1JnwnT1Lbfc/p3vPrhoamqj5V0gCYROXlD5F/DPZZFPx5PpeDSrF03dHTzrwI3vvGH/2Qcm00nftaZHY1quvAqmbdBoDvAfhc4tq7p50SsNNh9NihWyAxMmhORSjzaQaQeyrSAzp38YeUXRZ9uVekCWpzNYI8Svr+jwkSqn3Oy/miOXNnG8pEmDHPd/ngatxWuVKtRAtEn4IPJZo7UOoYDUNo/lJg96szX0S0swfEXrmRjn3uYgm4YY2MsrBGnxH4f5KFICT4sg2aHLYSJLrR3rYjeOtqCpCf7p0xlMeNxKlx+q7boqL3dtrHN2OInEWzCRA/0ldx9SSdC9HU0BaigIFG+z864dtt9lJWhMXlkVZxV7Tm1vb82366ZDnjIDyACVBk0ts3ytqt785hte/prXfelvPnnLrd+sFwsp5dr6Lol5RVHj8kHFFjUwMaIvypHKM5R51Nn3H33iyJEjPCCCezUIidLCsizRWK+tYTljgkKVZflmPZ/PFwd2H7jp3e+67LJLd6/twjkpT0+laTQkhScGc8slp28s/zpetgQZden25Qa+cMTHF/yyh9BM2Ae3nvEjU4W6qzO1J4qdWEaHUe9birH5YUxneDkN6Ggsdm4dJ9Odh600VFLI3ZGc4x95yr7Vc+L6u1FjyXN7xgxexpJH60cOrzbW4+NXqUzWXX70mVfugwyUo/C4QCISv8FHJpvXmtBuoYBRRp8LmRuBgnkgOREPDQXiYQR68CNcMgeOqMug0FkSqylKqPvN6PWu3dNxUcRt1fwihv+8J++CYNbbpCu2bdM0NQMMk5BKBEpwEyt4vDr+BV5Zio0NWKybW7Ojx493XZ8RTLvvRdsiZi5FdtbG7g++/yN79+x74LsPHnnuuVe/8pUTgLQJHOpcMV/swX1Huq4vRuXadLro6p3Z1q1fv+3xxx/rdYtogR3cjl65PHGzbZu264pMVVShtegxU3VjtPbWN7/h4osv2LO+m5wDaglAla9AWhLIg6L+vZ825ECRMbXZvYkg9EvW2oCMPRs5X9MfZgMq3CXJm1Hx+g740BrPAewZ8+hp1aCH7MRqwsvkVA75OR6So/duwER6O5GZ4lWll+ee6yxZJbdk29sm4XsPwg72dOhgnXrAwQ3mWALXk/pQVvCEVwa/h7IuUvrJrUD020gPN0enFhA4NRg3R3EvKWEmBVfqkGb+gxlFaoGOgadG7iUgyRHH22Yc1CTRSDGqRmtrU4snsmFsFrHDYEMkKoPfC4Aksa/jXtuOKKqBjR47WEIuxEUzvpTK95+1Z8/ujaPHTh4/dZI2ApMwNfJIfd/Nqyp781ve9qrXXPfNW+445+LzR6Ox5RiBiisUUXK+iVvjoGmOAfwLs5MyI8RTh585euwYckKIA1gcoYtnoAc9IyilyhrdzOfNNC+vf/PrrrnmpeedcyGa/FE1k+8LLY3GkFUUJ2HoOZQx5v7GAj+C6sWm5GmTJEuWLS9ait9wLpb/3C9ygPW6OIUdkQHJ7+R+MH6XjNyB+R0fHCYF8iWT8eQ+Hmr4nrxf5kfABBc3YZJgoA3CcB4g5jWaa14fVV+4FWbHOHgbURTR+c5LMorHZZAtqzvEbhNz4PQedHJh/sVFTrU00MA8JBGwZjLSWJ4VReG7VwIWCRBwVLnr+CHFzdFyUza27VCuOhqPgKAMLYlY8nFTfnnW/r1FXjAcgp7H0p0LDSWrHqtWDrC1Xds0qEReeuzYMrIbb/eFNDcjtyzaV+BUeUbFibt3Hzt2YmtnS6te0SRghr+VZXYg2/3mN73OCLM2nXJthkB1Ps1DIZZkAka9ZFZURQFn2jSL2c6xoy/U9YJqFDRCC3aGA6LPTYsCzhKyEr1gFzVExrWvufYV177s/HMuFgr46r6DDLXODVqdSAA8CPgR5Ki3tVJD2NW7J/7ToJ1VwjjOxh7SXXD9PLrUM3pkKfG0Dxt5Rg2Vm0bqqC4YyGkaNjEIHB7fs6kzBwbB8rhyWFigXFiD4HslTnOqmtNFCOUdg+NchxNLu5ZPve0bkBapmEzEKM8VsFa0bcxi9y8k0YYrHz9hcDASe4mQWOS7cjdIzAshLqa0s7NJyVwE8UVduPzmhkW085W7vq+bpuvRGjIvQKkxYJsUCbAfu9amSB35soYYNLEkisJ9M1KCWgW0BBaLdye2z2zBlEuFRCaZpSZu4uAxDkb340Kdc2DPznxy4uTJrdmcHGNEeqFopcbdci8n4ic3y436lQnVdU1Ptf4jlOwWaHzT6vvuuvfIc88BI0mBPIa8IMtblnAgOuSxyqpa272+qOvmVHvl5Ve99U03XHTBhVmO9gqou8FmU3NBorAsA2wrzwtGubrVSuNWaVt9F5lmnvMDzDzJBspZFef3ByTuHAFdFXrLO3lg55VyisHiOodTCGI9mtBqGvEJmx7tv//IamSr+0TcDXL59h2HMH/HjxwGIfk6DXoWO9Qy0AmzMHU7sZ3crIvoOn4mvvLQ+w0bw5E5LjOjNKykOrVkG4av8EAD5zpYvQKjQ6n8jUZzCsSPOxaiFEAk+zmkcry179p8R/eqpOiEqdumwUAWgO7LsrLq1wHfraMoZJnl08kUQSN/Wt/3KAF5J8/DrA4/EwyM8Uk+MJpSY6K8Axwg0T3OnmWry5ge7j+C1Otr4/W16YnNneMnTnQak99QZYWOy8CXZBh6SkaKq522zQqIpSaTyWQ6aVvAuPpWHTl64tjx47Z7Fg+hFxIIUwAka2lMVVXj0UgKMZ/PXnTR+R/8wHvOPe+c8agQpu9bzG1DFwMKFWHWTJZXI4ye4biMdzRCZHSlFebga8Eei9yeZYqJqSZlsygrO+BKxyPk/jDhUCsV/iiowvjuVsR8ovedLzX4xG2bV14i2FaOSNxTB8Mkpvy4I1q8Rva3JSXoLk1NkZDJs7AlisUSgiaUP3mnObnjoF4jK5RQoNzoIUWyrHjF2mkg+0CwBIq3q0+VDPaasm3deAjGQLsCQ39vDo7CSR2JvIJYADKJNk5lVU4nEypISMbeEbXpjfVJlec+w2HvPzJJkuBzRKHUNBPdqig7pVPfw25RcJAC4Vql6+FrCQ0xWpWyaZwZyTO1d/fGxtp4c2fn2AnEtyRq9GguNtsZvp8Q3ynUZF7lxfr6BoqKkRbqmx4TUgAC4Xna7BxycKFD3gsw5rJo+nbrxOzsvQc+/KGPXHThhaOyxJAz8DqAMjwXm2uDq9GoGo2o+JFRMXYL40par+WGNBiFWU8XDfZF7BHl+vOm1qvdInck/42iGBhdLvIcjyNOohkDWk0d9kiFnYakQ7LYeKB37GClbTXlMmBjeNwgfJXeCB3gDXZrmoOPOVvj4uVA6MAuWZJWS/VgtjjJIybs22dIb8XLNHxU1JqGyqAQB+b+LEb0srP1cJz9iKSir6zgJH+RlaZSo6aWUhV5AU+tLNq24SEfuEMyOoUShcjXJpXCBB96PEZenDnNYDvaUq6WqyygAB3cMixU5AYNNsE9vMPN+vPyDrEOsdUVTdcTdkXu3bWxa7p27OTJk9tb3mAiZcq3judCj6C8QmZIqQINnBfUbbIzBmFt6qzjUmgo8CgypeazOZ42U61uurY798A5733f+y+96OI96xuAYbtKS6d7wfUlKWvAVENs2WJRfTSHAYbWrogWwbXetE8XEm0xbXgGClZmMLeDgGAojiUJeP6ES2HPAgjQsixDw7P4hzzjlsRWYWqFpn+EKEl4x0QOZCIqgv4MgfAlG915AEsyZBmemVyRm4GTFiU2VloZlMNbSbjMmp5fVIBV+jxcbCAlKzM0gyJG5k3NrQ51LwexoDnb5GHaBuM0VzNxJUP8neIWeTYucpHvMhqTqVHAgDY93EUBLyqmNdO8OmvvHvR5tSiadKPDJvKuxuKG6vKpeVfoU+u/Gh0YP7DvD8wWfALycoFrb91whwfmZTYLdN8XeXZg/77RaHRyc7PtMLcZJfoyz9BE2p4ZYSopqqKokQbCdASj9XxRl7KajEaLrW0E5TMsUZZns50dLXSW5brtu7o/a9eet73xTS++5KLd6xuYrYDRqraJpwO+iLIsx+NJAdgW7Tp7YQHkYCcw24F/YUi2Z23+wfObAjn6FJtfEbfyPp/KvG/J0U+i5GYsfddJI6jsgvJvBF+x3BsoL7FUT/OyimAFb8cR7NXnkPHBqZYKRmeQEh7KPWBka7IPXODV9+paVVqVbOfN8ReoYIEHnC3fs4+eMxiZ+mR5AJo/IHoCftPL3RB9CsTPZaiujMElTzX6QkHVIdOogNg1BhEua74T1VuHnk5HkWXUOUzQVQdwkwUmgHSM8bJ0A6+42LO+Xo0q31fJt1EMCiA4JIm0QZURqV9XaxEy0gG6E2nXqP57tfSP4YS+sNFvJR+thWg0ugJNJ6M8U0dPnEQ1AvizIyhypgqlu76uF1nG3j6cVTSUNmi5fdVVL3nq8JN3333nbHtO4gv9D5q+xXqiJkrsWdv3kqte8ZrrXvv/r+xbf2bL0rpqX6rqfc/p06dP9+nrdPc01x4YBmcMIgMDM6iEyChg8JYQiYEgxhgDfvSKfuEP8Kv+I8YPJkgMMAb1A2IEUaLi4MBMz0yf81bVvpjn/nvWWrvOoWb6vO9btWvvtZ713K8PSfYOVpZIbeZljf2OqJccY/u9jB31mnCZgSf7kwAB6wVeRM0zKzix1JPFYu6PBxF4y1KXpyyBLSZtvmIdlDTG51lgu/Xu6ZMnX/+wW9cHD18aee+S7pP6fF5TC5Mphw30EqXke1ifkCR/d4yNDc9bSonSRXnZf0qoKtYKXpNqwVGZ6RlusgZpq85SWSaagXZYbVxsoq6fyWYrlOwSBoancJkOBeePWYW2Rs+WUrtaj0oa5SxxYKM4CvgIt9a576qCUg0fIS33XZgWana3LNSdXNKuaNrx+dKt3c3NjWAq+JrcTKnkp+2C40ZU4eitf+Iztcdza5RtmPgVXuauiwhV3N3t3KyWnPC0uuPh8MZrr/ZD95UPPrhclsPxZrc7z9SMujve3uOwN/O3gbsHXMh1v98P3/e939ft97/1m7/51Q/+6Hw+nS6nsR/J1z8ODx6+8p3f+omf+sm/du/hbb+wYcW8QYIWbCMsh+P+3r17e9ZoXJpmxAiTS1M5ORwt4FLhqRa7pC7LU5zRRx5Oit25h1YrfvQtSgVjPkLhd55tcrqc5ydUO3k87G+ONxygdq3cIlPdH6O8r1Q+hddEdGdLVOOrkKSZcWehnCK8Jpy92RZ82wneEnQtrcMfT0em4ye4v5l3zw31XnPKaeqGGRqNzprJEIwMpxBRSsN0DXmSOIOKWIeMQZlJ0NEoH2mkIKEgCSt52RwzMWkCo2oJr37qpt1uPd+dT5ToZ0no5Bzipz569OKRatmpUZYjS4jBbAF42r1gIM970XkRqFxHjdPuGa+Aj+tm0OLEJTL2QRB+L+jLG6ZnPXrw8DJdvvr1J+SUpFHDYz/spW7L5oV0N8ebp3d358t5f7j9gR/87Cc+8cn//B9+/bd/97f/8EtfWvcUWH7n7fe+49s/9eN/8cdujzPlSM88EpluqAKw21FCyC3LXovduWvPkSxO1uFxosy0O7K1xFuurheoegCsSVF95IqWXya31yIUClPoMCjiVNy/f12p2GvYLad5osk0/YOBckj5Plo+lKeHbgrMEIMe3SnyCZsScy14dza9GxRcsArwANb+zmqBHmcWWowJfpb2y45Vlnuu+0bphPu4OCLIedEUa8BFZS0aP3IVIE0N5JY6TM4C98tMMVzu0k71NFQVuCe/i6dSUrdZCUHyLB5dF0dKdjP1W91dyA0rIRMp+JpWqnB4+aWHL9y/p168yAgBFQcy291FyMWzO9YGRPwujQSS+qUWeliC6eCCmYOWplIG1K1AaqUfxaR13g/Doxdf+MqHXz9fJs4gkPQXeokhwl0ydtPucp5O/Twe9+NnPvO9n/7cp3/j333hy1//8jvvvPv5H/n8o5ce347z6cQZ+dQrRFz2C5Uqd+swUjX14XDkQcF+mNoQFPOYARkpMnvi1555rmFd/B9NjwSQiIBo3YJ+0y1gZtbu9aewlk7A0Wj7NC9Pz6f+cDz2RzWvYIA4FMQD9YaUiRNKCY6JeLIM2jz5nRx/mOHp+pKWy3BH63bFG7gw9/q64catGriJjbRG9D2Z2FCuyHmKM1Wo6cGqSxvEb6wnZeMYybC2QA0vtV0AJcrT4OzucpEuPofD4UhBS2fknDo+a76kqPIqnSwTWByhNjKXC/F2y2k637u599LDF71bVKKpIvarH6OYlIEvYBDnsy2sGb/qmlmhGg32vQJjqRLovA4ldtJ2Wb94eP/+5TJ9+PQpBXUpuZnzK/nZ00pNhR48eEDDJabl3u3+3u3to8ePP/7et7/++MH9F1+Zzh9+9WtfO9/xkAhmS1rPz6gg7i4pb9AcKBOUpnUpnrs5J2YQVVROdHzcs5IZghIw1mMbPaGnBMKjGmt1HbNIFVKvK4+gseOTpJbdTEMnKIwkRTKmjjbYJULYhRiQRzaHNF8xRq/ko18Thdo5IntKZ3nlLyAZb1vgwrzAKCcl941ZBjXnr8+kJGvFtnLgIrgkRE5yz/gOWyiQ7ei4jUZwDDsnnCFAU6DC2pRxiQzBXO6OBjCFgs/z6XLmpCL+0FlmpIV5epDk1lHC43Hcv/rKSzQW1PVSAQCPPtAFAlmGXc+ulNwZw2BX0WebbcMb4QxwpLLWAuXVMclYlWdfJGsWpDfz8rvbm5vD8bjM84d3T5+czpRopU6g3WFPYeH7N7ckqfbjYRzffP31e7e36/ny9MkHl/PU9/v1wK2j2dwVCqPo0shxI1J+6CxiaXnyCIpgMWEul8vpRLVN1j+QfVrCAj1aGGpiZCwmqIn1awXGgpV2bmY/kncgBLafycylnaFmRp/3anB3eqL+26z0we9IqCBktL2AVJMmh2+GEYqv6omegF0UapWpMoViDsxJLBse8dmPHbkeCxPPdEMuL5P2wNEJUWseZJoYYjXYIGFoUMc1jkeyfbt21DqBVC+qwB44DVjGNYgHZJqnp6cTVe2w+i5phpmneFN+6bTVTfN0HPZvvv4q5SGGqcAWMZSeB+3mAKZcPtNcM85eJHtBNXC8tHHoYEbAm5o+B6cGHBRJOj5DhIiiT/VPsCo5dt3+cHjh3r2vPXn6R1/5YFmWkfKQSCTfHo+7cU99Kfvh9vbQdfPp6Yc0LoqHCHL3PZmlbqFfjhDu98N+2HNHOytg4oelJFa1XBRtyG1xmU7n80wVi5ryp5lrxfRmuRXgURuRXekDNJJTk8501FjJ0kmsXIjc9hNjLg0r5gVAvoInUTaIJqYSV1YBytNMRKUfucobqdThwlau6MoXGtoG/5NAhWSKj86+a2nqT52nGAShE/s9mCiHYZjIF22gFGOMp9Q5Byx3AfegjhyUNTOOu3nh4k3KqqeCV09XMwWJBgJRv6s9R/rINrZwQ8QZNfygxQ/TrqPEhtdeffzC7T3BeG6eJwOOSlzJ9lhkY0mlseQrRLlDGr8DVYQ+CSONaQerSj2IBhotBo9VyF3Mv1X7qPV9f0/aC0mnzscvP3rxwYM/+uCrH3zwwXk6rwvV84+Hm/FwoN73/UgyisascGMODt7osA3Pe+fJQcREDwcd4CRrk/PgMJBb465M0gjIiQbEThcahijOcNBFM/U2Iih4FLBN09qhi7Nk9BC/H6kRH9WBeYErmwC05tOJXZhsIB8PxIb8niyJG/mKRQoi4kNN8xodwmS9rrWV4lfnR8GPEYrlK9wFpZ3stl3ri8FDeI7sMlNXJjuNaJxj1wowOaFAQoAUQ5ajnwlH1GTKi0BDhOYQdSQxVuogOVC3J3kUJzppAJH8XEPfHW5vuFaBhl/2Aw3mk56VbjRJcwzuw0HnT0U8jx8/uH+fi38opClbS+klamaga8UMTkvSNNEY401Bn0VWme0m/QwjhXB0TuHhSDS0zp4rXS6Az1anQ9gJehxIpfSMkcj4wf17T09P13mhEqSJTqZnXyTNYKDOOCQwaXYhJWxYMbwkWuwoKWKvpq+ono6jijILJVgm3JQ2wNzKT1oaAAA8op06ZOnusr4pMkUxLEL07urwhXCp4Ljfn9lk0GodoCdGAOoJQVvZH+HBqcgI7akk3RIBofGdyMsjwiucdI4JWVJOuqc/vbxnaBntYJZfUgfFTHiBlR7CTIr4Y5F+nkzlpMgO5IdiZ/VCLJA8oIIR0t5UgQAGZtg9y0KWqddmzxzg0a7f0ieVCn/oLjyWjAphWAPkrq1nnVUj9+XwE9P0RPlb87q8dEuOK2LZ5oAoHEtQKmZdoANsaat5C/Z+CUcAO95Cf1FZlr9q/gUOwIPcarH2rM/JqC5pGaN1W+JEnJcDdY28L5OQmKHNnJhKA45lKPh0oZiYkobJ2JUyXfaHA03stjSrjFM+rNaLnqUcktpxSnK4ZlyjrhhcGwpqYTfiAHMtkD0X3mNTD4yBY1KZfhk4j91SAtXQQFOI9QIf+g5GaMoFawtOPObIDkgiD9TVVaEQCXV5X6Zro/8AySmHuEq61q8ENJsSOwSDB5aEHdIAIk+Wjh2bvsU5NFS3S/6LiXu180Xk4R98pGCNihJlFMfCSCozG2XUy8qLLVwT5siNVDByrhVlzp2ny4kGvYC/SmrbZZwhh23v395+5I03Rpq3QtVOOlIBDExPDnelOfQVSw3UvDMqA+HJSJr8go4dO003olDSRoaPmb94/Ga0J/vb7XL5w2WxXBBaKHVXp/6vOghUSoJ5wRK9kc4N1riYQgszWYTr3NFwUql/sNpE4aHURuvmOFL/ZMHAgl9VOKMb47Aei19zEANthObv8tU2CqiEirUr3ZIR4JQpn7Jw6NlTRafvssVBGTUCuS1DzogsNnItCx6OGo+vEf1ZgT5KBdxV36rUo5nzaH/mpA/4pFoxhK7LdRM5UNNWz3jI6yWvh3iOueOsiASCLYVppYYN2G12A3AiBxEX29BsaVoYUFteSGIQeTVJSeJ0ZkZHErOWD0pC93Jh+U17mObp5ubm7bc+MtIUv0kSE0ITTuIPF5NBYS+y5wnVaXHU8aeu2YqoukvaCvcb7ovwNAoTNl6eGHKpMdgx02R0Hv3qNOgcQ7LU+JR4SBpHhTUVXExejhFLfotSLlvRXAMvNU9uf9U+F+TiVsYg428wvRu3gA4/t/cLhSb8droDOtELDVE1ZVCdVf2BkiU51OmmoFG8WTEcCmOjocWGSvXV3b2FYzmaRuvSQYlNdlAHR+qRNktrLhW3a4wi+ulWq7OkFGvCEXlhaFsFJF1d8O6LYcuUD5VW6oTnTIWCINTDeBi4iZrmWiAWeBBwWakemIca6fCgaArnGhRHi6T/EFEuNSgmj5Z2iiXsYV+q4OiyLsfD4aPvvHv/5pbnTVsagbJa1XYi8FTCttagQQIK7XQNFqnD64yGC78xHB0KApf6YSml1HKHV2psQd5/0kR4wqefvCcX+6mIstb37NghMBIrlEx3rQm1l9g8mrsTLRYtBFRbbDAtRXKn5W4xfDDgE3zFamjcLxxbVxVV8VcmJJ9Z/JryycdFjX90QopGEhRFgYAF/BZE9eKHCuTFIbeso9Cf0hVGU5CmIa8UcEaqs88zAyheueLB9TFvdWc6mLd0dTIOK0cgin8nZdFpO5GkfIncT1yHJvo2kTELZ89xZh8h3wgth66jRA7OTKB+EVirINivhWL8RfF27qaJ2y/r8Yil0/cdtU1d1xce3H/j8euP7t+nqgMNfYgSAO1Os61b7jABVQ1yGexQaDz5m4YzdqpZN07h3sjTqftrqo/asuCieCVQfqBhi+Twy/0Ow9et58+d/aiXnQRUJDVnoJEL+rl0xC+EJqTzAc/L0snaUTjhSGpUQ8BoULewiB3FIrDk/9AsmIkaFWmPXhWw/BtVs6gFFmQf1Bui3L1FkbupfA6OwUs5bVZ3WmNCgyC/eLPks10kTYNDBW4bWbOwxpgK6MHqgueDehfRcT8Gvc5v7QICG2k5azA3BFK9sSp1WrqjQIqIKI5LSc2cImUgDknR7caJ4z0iZPodzaEOMWleHStkIlObG2JQViMZfTwc6Hw53Z3vdrvdK49e/sibb94/3qN76rgWmcgpkiWdAezA1+PGSkhnSc+2hs++KBe+ChKzp4sYrv7AD2LGB4A9Xj4YFzAmVsMpa8OevLCeql52CcsIYjJEjXDOHg7w2kijwMGwvxMDwiIDYyaRVO/plVlhDQNQ0a1pEujy2N9J1Hu5nM5U0mwuLydNRgbu4kfiQZu+udxFjIyW95qfEz5i4NvuU7um19orIXzztdqPxA2qsG59j3CtufAsrAtMoUqHmpTppETHrbd3kTQI4KVejmkzDbVmHinFdSZSoXnJ0idVZpzBbnwpvJaZGjVxkgYFoIYLtWDd3Rxuun58+NK9t197Yz/SGFGp96OMS2kXKOiV4xt5W/EuUrjWoLIzXDQxLY9Q/ob83ENttlq8WWlf5a0VplqraE31KZ4SInOJrCrPrD754WzCGYnOl2JjRByEcBCuadtzHflCy4K8tLxTi4arnpI1vvhf7Mxmbzj2qGNRrDTKK+ExOBcewAU+LTNdeEAZOVaoel0aA2LzCz9nJ2DXUBDeTmHps5KuSp0jy7OMKfgKtlsprwVKY+AfJaR9V84kTCJ1MFf70PwwPzuNNQVhFyQcqn9YBeib0r1ypiQnTHUTpVsivEKdoemE3CWDzGHTw3z/0t8huJL+j2XvtHt6ofSf9bhMX7ubz5dX3n6jp3inTBKS6n2rXA1ja+tlSInC2FJItAgpGJw7awLQ2ADYVcsybJWgFs4d9cibIEWhjDHMjvrhUPyTeuXyXCgTMKpgA3HYsBTTIIRL0vAnFIq+9WqRmR/VwjN89N7ZIAMSvxJ44pBKt7KJiuTamMk3GUak2WOUsidN2s3pLqCHFDpn1IL4Ou3SjZfQZawGbzN1o4RNHLL/7UmeO1vlVZAW6nkSgRE63EoYQ6J2qgZLh0W0/s9yP4J+4RB0HnNZ+1yk1wGboXgSD1Qg/7TpzL4wUelpvCh1RWH2EVLOmYD6fFWCkvNxoqI56qh4M079/K//7b/53f/4O3/v7/zsyP3Z+SKdDh+MM8Bfk7DFdECT8F/JQcbDTi3+qb4fX1LAFLRmSGTGugm/zOSpvCGICIPeQxVCJVp63HPDJ22U3eChST+UBXAkVHQcICv5VAe4pw0EatlK1PwyLPB4Im4spaaZ/Z5V77CagqCMtjpq7Ut8V/4vPMFHDqvTjosHre5fO076mC6jdNPoPejASYLYL8E1T7eh0/EkEbOpMwN4+FWwgl1b4MIVwFTgs3CIYlGQi2WTZRDusO859eaoVFAjcKHilb4UuoAeDwcqe3EnqqkUqhkthSTwvNtRwj3Tp6TdaL9VdWix14URcZrm3by7//CFJ7sPvvQ/vvgH//W//d5v/fe/8Vf/yltvvXk43rAWzgggzfL5bo3EFVAOAealvBDJYO2vQB/RLChgjdmacn80oHWA3rgm+mfNc1UdloOep4TQLCMeFoVBPyMqPChTPKQMyAQR2KhB7ekpIXGVtaCrHpBFAKDxWd25eCktzdDlgd5OInAadFbISwqs9NxgHcFqyMjraNgoTnLiXYRIFO5yHSjQtFCeqY9fgMRdFilhJNThCj3KMG4D5RPkOgehHB18KWlpbeNt494b3CPUPfmliV9xA8VSSMSFvrqYWBkL9QwdbllH85/3+z1l/ujIsbxX7six0sxrKvMlFwX9ph9xnSvVjY0LTSugnli394fL6ckXv/j7f/i1L53/4PKZz3z3D3z+s++88950YaOI3TruUKk9xlkolDK3lJUW4nQNVVK8CzNFBVilQ1nDSwF4MiBgdbYGsx/rQ5NYECuQVJMgh2Gljaat6q08zys0SdkBhreiqCewz4kh8DqkdCwcVHSxe+0XdRL6BqyhugCIY8xa+6f9Vyw4ReX400VGUO60B7A2ytM8Ps7sOxyPoEDrIbqJ4mKD1XAbcgOjbmL72deglpw2FIeW0VvC95r522XVw+CZgwNxI14Q2rwJM71+wV2LJmcN/qp1aFFLMPCSxHygGGw6NLukBEAk1M6Z5hPt9/vLOC0XdlE51tg3xydPLofjuMxURGoFLPTBQiHfbujHpb/M/e72hePusnz59776a7/xa0++fvnrf/lHHn/qI/cfjd16Q8kaND1Amz+LQQ3EVZ6AiV+UYQLOOCC1Hm3ermEbz0zkgUBFBhxE9w180AO4VrKMek0RVP0Z5LhDm5yBJICj2SIyXMvHjQcG9lkLDKEmtMgVlTP1JuUrbEvFthgaEw0ZhEqkclMXwW+To400Xi7ZdYVFxS6L2miHuPKUU50TECNFTCxTceI4UlMu8mDJQ9FxB+e1rJNMV5fRx9QfK5pRxEDtlsWQkNrf8NwMg3KojuWri1/cCkJbHplkSf5NGW9RpZAwwn0Vy0zr0xKdSDQLWUsyQkOzoC4mrM/LMKUxPlVjr+/H/TBN5BtGRw8loHfd+Af/98tvv/Fy31NTq/1hT1EidkHN03w4HC40v3c+f3j+X7/9e//+V3/5V375V/p+9wu/8PfffPfddeq6idJ+l3EirctNy+g5XhRvJrOlSM2t2atqeBE31hmIdH9OfLBdh+oUe/dHIaUApIrneqYIBqvl0Jh46eW9fMH+QRXN7WuPm0vnkiBFq743FHXSaryCy9lXoagtmJ+C2h0jnNg1Hm8O4yBTqSyx2Uo/XIyL44oNo9CViLYp/Y4vt1F6VIJmA9SU+4mWhdlIVIqxdKSHGc56AkESLc0XpsEGNEE4FjHImgLXCnSJqO0nckz4iiNT7fZ0yvQn2z0UbUSbM15v8UeMyRdh4pASlQ2etK4Q+uz/HwZKX4VaCdUMxn/5r/7F22+/8Z3f9adfefzSvZt7N8cbygiZl+l0efq1D3/9P33h//zv3//6V7/+pS/+v8vl/OH56WEY3vvGjx6Px4WzsZd1SsZmEoGh0SAEzMtUG4LxuVWyah9GgzmLZEY7aXPLvf3ESjch6EHrdARya0Uk02aSKlCAUkLrXJ1LpQk20MwP3xVY/TtKqsxbT5kQ3nLI2Hhp8bRfEPiIr2j6DzjL6TLJBAmHkOhRGq1VnUW7zXqSs0TW2bkgeXTqJ9fp0JxuRaOytOqbE3imiQZvhAKlSdDhuOIKWHW2q0xSzyc6pwEAW1tODL/pymrdowNwVZZl+U9Aq3GfYlZKuiDjssXCVJnRNCrx//kLlmc81pmvAUS/jh4vE1qKZXwoEkx19V08FuN/+Z3f+p3/+bu/+oUv3OwPMzdz0HEeEx2bFML1a3eZzrt57cbxxRcfv/H2u2PfrRQNJY8sZe24I0nVvESZ1RkgmZSniEGRIhRk2Z0aGyYapvUxvsUBxhca3oym8oILEAHD/8p0eYobqfJc0VVCEDGQdI3ie7PhN1UQqNp5YyXFC/3S0O2aW1jqU9kPSXlU57sTFT+xEyo0Y6UKzu2RrqNKu/zDM14Fs7nnKLmtBhqRQ0MkdFiWUSx/xYbhaSBae+KKFScRCZ1WWXRWLGGRSWOLr22+uviWGf8YlogPKnM4+g0kWYsE34xRFRl8aj6Z8sHGiyTvlyRfbDDNSotS84JCzI0xU42x9BsUoC+73binStbldPf0xOO8uDWHDE2mdeyHYaH25hJYoGK4z33uM6+98pCDxybJKCuL/6rym/2ShPohEtMy83vsKrIcIzv+6JQomCEGsmR+q4rqCrguRv3BSDb4n96U26K7binzL8TvzInKHiiJ5RbBWflTENdDX7AXMwVDS7uCpk2dMB4JOyFYcnKXTNLgpGomm/M8sduJwSgtJnlrM7WtOkvNPZGgKMzWV0BCRIwa6p/h8e6cpp2Zqnb3neeVCpbQZaTmKpth5BGhp0g6uAsbwGo3kQuMufbKSWr4So5EdHfm6Ld1GIgHF4Sb2EmtDMVLdV7lYp7fzqSrClFNklfaYYP+ENjJVE70OwwE7ZyKS7MAKC9y8FRRbjSvM0bZKFLbnSTx8XD4+Hd87LCsA9ev8/lA9M9O0oem2hKqVbZfgZkqBRSCWi3gfRX5HyYFKphe1wsPsI9Guyn8C+zC1ThMklezVepg5XEqesmQlHrpTL1tQa7L5sqOyRacDa0r1k96o7BHguIxw9K1AJ7A3k2XpaOAAgOOEuLEY0yeD/PmU7+b8/n89O6OKsgxg1ozK3bianZcpGYt1KsvXHzhQLKkhJSpbX/J4YhKNs/LKMWtmvyZE55qjr+pTSOkK/B1BRizFmzJDeAkrnSy4iMI85onslYX4vY6ztPUFPnU8xH0CRsGhKUDC6kl5qZNTIg3UxE2KzWS1kwtE8hDIVeyyHV+weMHLA+e5Tc7zLv+9jC8/03fdByOC5m/MgFEbK3kfHIFPisatlxol1b6EpAhG/6r0aDFVs59zKwjQ49kzEDo66ICUsji8UFTJYPRB3GiApMvh3y120GhB+XVxn8cBacaWmt9a4cf3SdKZK1v67EL+TU7VdxPmW5Ehvq454FvpERwyIi4mRpWJntpIOuZpC/NQLGgmenEClfXk92CkC7zxvSqtM7QV0X2om9dVUjR0aGgs9h2xQtzsDEAF3ovQquLHxuQDQYc+T8moENtDvMeLa9I6oZHBysASnXSDXxJa0IKweSdlMFgxgfCRUEqHhk6N3ZXcG4WCVqqieOJvbwdnvwjijsLHmvdRleuXT88fPGVl996Y7h3r6PQMpUfIbAtNogufBN6FoBtbSgwohbWqnRiPX76kko6TTEWq8xSyKAYQyABLoOIPKnfnF1iqjlz2wIyrJHtOmeI5RaYxnljNpOkwicDTMIDXQBI3fjEc1ECnTT1FprNyy45ZjRIgzw6LM8jI3+GEiTT8GQKB6SkYiqGMjk6eCFgKP1IGbuxVrt5fMbrVFVSfdHabtHU00xgHkbHzad2AJbYYA6zgNOaUuwtjyrdDN/SvNCI/Oi/TQYLdl0kCPryAjk8eg4ycPtVkXX6E9O7yu8xalPKFRUaSOsiKmZg7KWhPlGM4b/IHDpJz9ldLtNnv/8zj15+QOQvzKNAVA/sh/zJvDG5lRKTLRVGPgq20iDf1k0sf5iIBo2FqkHM1ltSkeAx4K0NbiqYxj5tqqonsQOtJgrQt+BrCjO3GyqkZumh33CjNd+ugKR0G0lW4jzh1lz9TF0s1RsFvSg1d2O2wTSOuVkE4RIFijotSb3/eZXGHn2XZrzICjnzlFplW6DAxFJgfLmpau+pdElN/gbg6AULKW4EoNcWwkZkev5Ghsg94mblTR0VgfpxT+4Tc1+z90+MGz6/TekXmOuAGiWQNKUuEeIj5UwsbrhhPMVYkj2Q5DO3kLm9t3//2z92mLqZuh+yFuY1EbaOQvGJ1XmU1G/cXDWEwHAIPQK04ADynqZHN4wiVbaTdhS8Urs6metB6bgVKq7QJ3Qr/k/StsnRX/Es3LR3mvA3w+Rpse7K5pVfw1KihiU9VUjNe+5JJm5IrS7SERo7SrE4U5mRBBLBOiv3KSDVSJChIJpjeXXulPZ3+CoqfuAkQAOsxoRTv+0mBmDvI3ARY0EuXNxBBaa8YdZ8RI7Fh6nha+uOEJK3CeuKdm3RpgH4t0FQuPvMCCEFHwSFgiZsa41TTyhkTCyeyB8RUKncfx3NLhDFPJQGI2jtCd1144PjC+9/7P19f+x3uwtjKj7CjIhAN9GmPIYLOIcQS+ERvwHNImSJITZ17ZZ1Wd8VPgRTkCBDxpUtQUw9QKnV4sYamiCJKb6bTCZMaCRfFnFCvVvfLhIJ4bRQSYAqXMdil+CmMqZwmypK+/GwHAhoZipwduTasVVOsya541HUhMR+TBRDqMD8hr5Gb0QBiF2UhWmTfB6AphFkGU2g8ztM6ffnNqoZ0hm7igDOknCLhIBY0/QHoy6rpHOVV6+1EYvRPDL0Djh5QBkU1uA7QU8WInUbZ/xaS6hzckEKdpMsZerHHZK/SVZFzdnLGJwhs0/D6rr+fDp/+ru++63XXj0MBw5xUkkwYul2pk0Rjo1HYfZWEs4crJQ5wHigPpsAdFFVqwVvwgEYJByXhv6mwbr+sD+M42AV38FAlYSivDZtRn8xh7jFU7iCEvTDbPIitSUuh7/VHYC2X97bQUBJw4cPuz2FxkXfJV/HKMnJp/Wk8jTMNTgFa40pZcs9KVtk/6r16HaKDGMIROQvKrvkdyJ3TbJfqIV4IegjC9TRt6UHeGqryT8fURJHERx0Z1Rtp4jxbDPStThHldzWU3EBZqy3TeL8N1KuMhH5t4gywu+BjMVtK5M41oMeL38uZVkqwRpY1daXJFvpc9fvzpf53v17n/yeTx7H43mmAdYSxA/2VntMy5XY3gpboNEogvxsE03KtsFt8Qhj9n6p1yVZCCxYN4ID2bY1dKYCOfKyJuXaLq4IN94Gv6QbmpwOYa3Utl+pTqoBHmMhJf+NIwN7CpITxVXcdx1VO4YGRoO2aChcP9DQcGlRGHA3cpZIkhjPEiykln1c8+zdzNyB65azqjDq5aK7CTVLwSGPCtb4G+qLsu5403ORChzPJyJb1kG1nl0s8nC1HBRvG26GoEVm/R9XsxhvkxpYgLnI/vAFrW3yNe04fgdN1k40ioFBYy3xLMvyEg6FLJRDYQwO5VmT33Q0B4WPadrtsi7vffQbP/WpP3k47LuBOuDJadkeuU1tNAUvEQ+fjasLVgQExhMSp/P5UnmwFDTh/0AnfjhUw2IAbElt6bjESobIyIjs6IUKDDRzAIM6UK/ho/muYoMlTpQQ2YgiF79mDA5Lw24S6VgKDI7++afs2FuGYSZvx+F8IRc0dSOEcbTueAHbl8YOUr9cah9rynM4rDF40vXd4bDnNh0u6aQOKsfNY/VtvuQghoZ7ZiQ4a1u54RNr5xrr02aLO51oYCk9+CxADFi3LsLQI6od4UO+S9LMNs+toNVkh/pamGtbkaedrS4TULOlGTS9FRroGzqa90EST87IU7R4U32342ydXfeR1976uZ/5mbc+8sbN4YYmFWmrNlW3NThvUdb0rFRb70eTIIKWh8yVP58I3WBzBgTL4EA2KZocVN0k28RHagKb5wauVN86tOS0C1rsdpbOKCS09bLg3tfJYqklONj6WVlu8LsNkx8jHaaLA89oWWWsxPKAZ8qFJMYL/dqLBQgkufJXcjksuUXJCZOt7XpWY4TxZhUyd9hrqRtZiXZuaCRsN0xtp7h7tttKHS+GMZGZzYAoFd5fBAvoNptqtA+NhlHfIGrtGiNWJNxafGG2ALbjiMYOKDFMkqPSgmgV9OM/RuKZnJZhHIupiKa1MAC6/vVHj3/+5//up777k/dvX5gvC/V/9+ykavP1O6ZflG819sla+/l8Op9pxF6ljibJhGE9m/Kmip5DtHgOu5clwkFt6dLyqyUFWpnUZizVXjCWQcL9fnhiCoTeANrtHaDakVg2fB1d+/wDr4lfPNQSqhuIGSHhgSaccBPcZaHSb5RVyNykjIG1YojDheNU+j1z9IF7jOh4UaVWmfXuaJ3hiK9uw26I/QJ9aT96vUSYJSvn+ujFeQkkSKrM0BqDsA1NFGZdsE3IMRKinp+U1DG9g/tlagU4tp38bZuvFHhi9TaTgtMG8zUxmVQo+zUUueNkhvs3L/zMT//Nz332+x+/9Pg8zRea6LPSzFv0ma+pXAahH5DLbk7/IJKb+UW5ficaX+rD1UIGKwRAeVQXp8aB4OgA0kZH0geHqFeqizzBQLbh813R1HTA+3LFjuFPF/KU8ziZxVuYN5xeLbXLnuOy9PqB+neal4Wa4ccLC2U6G8fxeLzZ75/aYHZeexbE4slXzdlNE7FpkNRJ+ZJML3IyB9PQNHAEWimRvaOy/F0QbjwxvfzgKVd9pini8zIuhz2liu4y0hapIJoeYB+BplEBsPXCkv/QdTc4c0NkYhE3IH8OpzoGpK8G/9h8ia5MX6cw0jKvDA5izN1uJ97/Zd7djDd/6Sd+7HM/+JkHLzwi44k8z+KeLJITwg2ohYJlvLDeaAkDqQg/nU7S0QFybNMs6jDfLHlIokdo4Zg2Vj6bMr+ZegeVMKnlKIaxrP2+P8xXEfuUdj8+X6Y4mfCs52Xg222PV0okRNzO/+AWCx4jyCPzjKy6fdyPx+ORVQZq2G7td2CFMinCi3iRvFyFlz4e3ODucDwItUM4SOVAPtUGD8SneovrthyENFbParycz8u8HA6HYdw3s1zBQYZvqqs997ZN4C3WXTIh44yVltQS5+EIy3dtj1XzCxN1N7qupr/oY/IizlwFyqHM8zBSaseyrrf7mx/70R/9iR//C2+98e7addNC9cSm6UeLghrojcelXTZUCFkL1cGdz+ID4SmkaO1pyzB5J/sZ3f8cwEiGL3+PJ2JqY0WnxaQUtBJYgZ3DvmQckbU+B5TNHokrkGgo+E0Yxj2V7BEt8AaJ0owwfcIUK9LHmxvCfhqYci4eILu3eS+oddrYGEdQYfFkhNAk2hzWsXPZtB4QtoCZWFedqKJiccbdpnla7+ZxP4svA/oLm+MCKCrMkHJFTVmcqNtX4Fp8Q2Uqllmd/zNfTa26wo/wonSArqPHQhjFKYV/npeh63748z/85z//Q++9+S73Aeh2M2XJ84iXMAGaOJg9gc+jHRIbX6ikkapkKJEodh6CAOnLYpA64Nxzn4N0NX9Ip/WJz5l0Pu2KenU59RGgGaaOKw1f+BUZV9oakKtQceGVRSSJijweQIELiLHnanV4YqsoLIfDgbu9UrLHdJF2kbZKzULTyA+ot/GQ2A+ze30k0IYPuBEUN1Xe7CD78lVuJupbGjSVoRLvcQ/68zIzDVPdpOXtW8aFv7YdhjV+ZusHv+dWQULsUt/IyNM+aQgemE8qKaYIaXzPzTu4T9dJLjRVb1I29LJ2I/mBv+397/hbP/fTbzx8dddzCHGlOhVlrwiPSKmwECz2tWlAp2HwMeotp9PpfCbJIO3U0NQAzUzZvKbJm+/KDgwC/VBRwa1gmXatshcWlMRnGC24Ov3XHiCzVXk2RXHKlX7YOj4Igl0h4us0bkK5/BI6JEyld+cDdxc5HJZlYT2Hi7tMDetZRHMSePTrzVvSFUFoR6g3jqjy1gdQwwzacOHYpyZjsvbZRCa5aF7m9bL01NxBOrSwUeW3cE3LwQZmcSszsKyDNZZRnWLrVcjl/EtCiRQhVHUplEio78Ivpbt43G6klKTdwmbhbtgPS9d96/sf+6V/8I/fff2d891ZriSGKz4+0PnhjpYAmLNPildho8dK1t3lcrk73RETYVayfdRh+FrTg0qgKgEx0mlXTkqWNEdXcWgwusf+TnAvOB7TME9BFj8uZCQnuGTrqMQUL6YsHNAVzbrBUl4TMyDq6xtrkG9y9uzheDhfjv3p3HHLAQ8Gcu0kDo6zvFuw7aXEtzkd2yhlSxncqIRFYQO5RllfNeZZQksfRs4Z5qfmXPN+P56IaVtSlu3y3f6r1xQkX9q7yFtyTLS9MYRQCZWmghM6LGpCrTtw9wWqF9nN5KjkcUfDu++8+8/++T9691u/cZ2pn+Wu3w3iZfA4b8OXbDNBrogOEwj5e7yoZT6dT5fLJIfhC89c2LNSLeyrYfwkIY2DajmbtqPjTs5uLVv4C1YaancCjgmWqG/WDlKcYhKWRMFqawCUkL8me0Pa+7bbLsHqvYRpoQup2qJdvPqVaPi4P5y40oGrC7kcQmpNMflC02JA3VIdRLNrMac1GIpb1MpoHDChmeCwolg9RJU2gOO8wyHURVaTp3LoHDnejnTSde3t2cZpYT41RWqbYvUzA3iLSMr9QoZ4e7sNFTrfguYhURyY+jns5nW3fMPbH/2lf/pP3v/Yx/upX3qa1zDsKEzDjVdSYRDezGb6uKaV4zAbx6EMfiFL5nw+cRdbcmmWGTz6q5GgJm14qCOpk1ZtQrFtbYgjbTowEwePJ28DfuQGkqE8yxxz1fALVVN/pvl1QFY1NFqhoZw05k1CAv/azLFCGd1F4TsgENMU4tvbW/L5n3kENEeALZCO33bpl/zv8rJ2HEaiRfEIqBhde6kW6yzjKlfMVMNiV23XdATCYKZ56ubdsMiYbK7VATYfP1ughCADPr5S8eCst+2lLWdtKcOMmlEYh9HSupnCQQZ2j+s098O4dt27r3/kF3/xH37zt318Pq3rfllILVl31JARETn+9QIR1DUa0ZHKKeRw6Xbd+XJ+8vTpdLlgl1kjYxiu480yjK9m5VJ3T3qUNsThfHqeQhYkgbkWLQOjCTDRM5V6p5lroR2LAqDpaxuFR42HtN7AuwcFaAA6zQSLgDLIN3u+txJzBVTXRlXg43i4Oazrcl7PbLkE9YLyCU+HmKY0xGLLuZ/ZqRmk6yS8zbVhTWXIKVQNTbmL7PVoJur6b3HnDpRtzueToZY91SR7k4fiuJrg97kJTsnbXf+vvVKpT34y5DaUqtnGysz9jqUYouGMe1Knzq89eu1nf+5vf8v739ZdlnVPjq2eqk94xqTzWGz4aMyqNUgeqB1tZWAhQnCXy3R3d3c+U8/5eF+3bm5My9Xw2heV765zeeo+q/fUip4NX8oostpC6PkPgAovLUAVV2xWl2ZLaqNJX+tVoG9EU0w7wYr8CjyxrIaAbglu1bNbNpbnYQgHkxoVUieGYTjcHLue6g25CTtgXMutYEyQAWeTKZRTBD8BiOq3agFcvVDeNT7lJyT6xW92pWgNQc2Bemq2xIODvOWNf9npqLZBa+V5m3pbEsHXuhWsiphQVv2arxhf4bhrBLyOPEV4+qmf/Mk/92d+sJv67qBibu3nXjIH/SZttVOPC6ydhulQhjyoxRWVHF0makinRSYyEDsEqsRvxQGVstLlPvBISxLS+b37gWQvpzqDHEtqbLIYN8G2OvVyOQ9Qr6U5ZTaVbupuqqQZ5bMCNWJ9lr4FF2RFVaHaPKF0R+1JYROnSA4fDh2PqqOYdoWnEYzKNGadPWxEU/GQhgqIO9yyA6yrMihf5W7TQywmsk1a0qyTx3VRolLk61myZZBDK5CDCtEzabgyk0AwVBAx++gKPErNXTW78qp17A/D26+9/UN/9nMv3h6erlM3rsNuZFmmyYbQ5MoVICeB51AUbbnAwuk/Llo4XS4XHs7kU0uEz2jWhlGvNXcptmS3EjzjCkcKllCDqOSBaVtixTmF1MzWiU5IZM9V4vZ6b0XgWhnamPL5XBArkSF+c0RoYFNTc086kIlh8fT3PbUZ5WZaM/XE46ko6LRrPNuIh/u0FMutgyPFLiJ8Cken1RGBsBAL9BujaQa6zxrbqmKUou5LUq62RO/7HedR23hnu7ImlbR1t17KVT2v2K3PHI6wYXUWD2q8TCRRavBheOnhyy+++trd5dT3+27arYPOd+bWOwSAGBACKl+xLtAJ6iUgyin13t3dnU4n8f77uF33GKrmHME9UK4rEMkMXs43klaSpm+H1ZUxKpFHyoOWh3vDDdWctXlyBVJcVDWBIhxPSMQ6CgXv1iDW1vlFFVB1nHb368xBvLhaLKq6o7jmxdXX97266NJAuajtQy2ax0rLfYQEzARtypOk95X8p7Xqco+hd4kk2rJf1toUscaL1KWacoX7XvqXqncTVueH5H5yY1mpExDsJR16qTKYqdriqe2/WirQNdFPCxhfenj72e/59NQtNzfHeR2WmXoap/BBgMJ/PkOMQPWevQEAoLbjJ3pJVwBpehZ8SKtSvUtVFer1EjMRBTokYJUhRtrjCr/R0l0rgsYrNCjB1Mv/mQe+dvw7BWcYb00CSmm4KWmgvhxY3xZhXJs3FPcJx4c8ULQaal4pGiW/yQX4hNYcJqN9c0Te6BhWYaaFuqC14sFMmRYT0cx2oP9iG7DljRz+5MEA6u0KCmiMwlJGJFoDzUG2AfRa1eR+lXJFYPsA9ynoLS8PDxOb8jWtn9gzfivXJ+r7DPaEZP7puPbzN3zL2w+7m+H+C8O03D35UEoasKMYLqAlBOKR8iE0p8ZV6+lOnPPMsld8yjAG2RisdSQtWqCge0FjG5IaJT1lxHElhh6I34yDmNbl/9kbK4QUpRl6MCN3Ylf/6K7tLmtl/waMEgCfT50GjRz14eLuiUdduzF9Jk032TXo7FHaDHX90vdr38lwiYW794eXxvPgJY2rgVit56U4puVbVlnGuFnfR/jdrQoBzOROlp0fVq1FjoEP2pQYUjI64stcEq+NRCog5T9tCDz/GpHLzJSLr6GXDG+1pR0ndxhyzVCCYrKSZr2Mrz1685s/8Ukq7F/O80VGh5ExSSFDY6qA6lExB8sAP0aiL6Gi9BaJ3/PlMk3av4FRyIEhpi74C0uzNx0ZN7mWwcc8xzz0bcWrKGdonUo6LVMWJZQsozUlY0MJLlJAyp1u3rKW8bmLcRzQM16YviqMCTHoWV+O3+R7FCfnWjwzBS0EIzulrGIaqTNP0+4yzesc7eHTiCMYl9tS/HJiRtYVNBrV0g5rpTg1w7Q5s8EE1ra6WO1fadAATkDgCsWOyLhfuMtqEUkrRWowFNAAmxpHwruGBK6wet1QSMpfo0ekQWP8U3/i/QfDNJ/PH04Xam8g6hHtjdvW+ffyw4uDEyM/GybSDwC8Nm//GAAAIYhJREFU9gy8ZZ7JcUVZX9pEUj4UTFJfldcK7q6+JKNA8jvIYKZ0IrmjEWOGiB03it7Q9fV0iXgtWVKVRewQ4wSYZKiAs4784ROsRTteAwHB+iDT8UH9V4RLq4Zi6QMjuvSJsWHidDKHQYsXbFKmfE560X5PkL3spomatjgzND2lEBjFCtLicbMbbxsEG5qpT/2T3WCR1c5ihAn00XUrS3k0SslBzelH3Bt57dgXozOOKs6CaWbifd+2IpPlbI09TH80cR1JDxuFt8ADrTBZLlfGp+rdsq7jN7z97mWhIMmuG9bVanEL9m5IUPs5QbnL7yMQoNjgcrnQYB7xBWrrPPNMSCtUEzGqTIfHu3wod8bUGWvUG1mp1yqw09ltil8zuxRSYvhynDN3lDelBYmiPjX7zOu4fZaCcrTwSjjVPlPnbS3ZMLL6XiXbkw4Sn1An2pgmpezW2LTREXkEmdq5xa/oIzUZVjqwPSQUNrs61ZEHVWwxamj0mogASbvTRnHpWAw++CaqPqJQ84BuQiSLSXLGqY44RgFSikiT/XmD2TDDA6n04g1Tv4HqBW9Xf7v9StVI733Ld+77l+Z1GVaf0ukLrfzhla6cnucMtGBh9ux5mafLheNDg4Ecct2i+1i1LedJJgjFD+H5lSMlbozSGknpQnMbjF6qRIe0fustyTX6NkIperK0VbSkE243UcBZUJWQfZY2DACvXC3Vpfkw8AAsZCpqjjlv3Itcbc1vQeUgfOWyDBIMh7xW50NaiKCsKyIFfmDBynNzaeSKsJB0gfAX1tezJNyl4SBet5wRL7K7YiFG8qRYuOuU/OosUAw4VjPpK4GFtUrW2mdS7enaawMM/o4hdWxtHV95+dV7929FmfIooGmUHqZ3SIJ9gOu/olBEMQtN9/DxRZoJ4OFhFbjPSHByWck9jHWsCPeHpazJgEKW2wm1Ueu1q23GtSTDW1qIRVkwR6lmWcWt0+3zISDs/jgi176FK8h3BkOgAax4ptUhdFq5kNwxTezhcaP9jnKZ6LXM1EOOI+0C7SJ9GsY5IQTU8+RNlxoYU5sk9lZxL70cnAmrajHudsn3MDGkAsPvBBFvKqhUtKEb07jcnpsHQX446uPITJMkq+0Fl9VwgXED3HNS2bfwP5bgbGu8d3uPSpIo6KuNdZ1v6sNSp8lSOwj6LJ5uWrEbBDQ9b2L+rZ9zM/aoAG+kaoTs0DuriRyDv6kOjgaLiPgt04aqV9FfVuAQ1CsZgl5x9aw2zxEINOaPtIAJyvUrTy1T5pnKiYvMhKy3bvQLCJ2/XSWsRKm+vpKSgOGlm1icoNPJo5R/O4yD1aZV+9IETlCRk5XvfAwooN5OqP5g8m5IkNV+qKoFV5T6TkDXbUsygjlJR9sqmst54UiTaCtWWGkNP1L1Qb1wU0hgXnU2AeS9+ut4BAVMTKgq6YuDZlipCQctMPoFOydKgDTQw9L0bEwuiH0vv0PehYjamTuMc0BVGUXwtsjSqLZTGRQGP+7CzD4wrjqi/kyBvYYRBQkFSsAhqreZFGct8Y2zVT7T1jBCsJVvP4euBIQWqzb5lDg7YuOW4xPFVvlwsyeMoYbik6MDm0vVg9dbcAN5chbu93vpCqbR4qxopP51SE05Baugyo2na++P7BFtf2fFTtny/WpnySlkf+uMHpA3Iiho5LI3z3NRDAHCCCKFxPf1hdcKoQz2erWHHBWG6g7QHZQw9CMKnu64uZHpITa8Be4efjk3Qbm1qHOy0HslQoeJccwdJKNJICVuRM9Od4FcslUtQE1YrJP2uJE1V32O+8OBxxFUoxK3nLR2QxkPrkl2oTlDFoqmkqK4K/Rl3UB+tY3tWvlG4yq0Y/PCFdUObfla5xq0XlhKLZsiU9YGCdajMYrduM7iHEK6FMkNtWAgLyWBBBloODEypkZWG8ArfbuKNtfFG52Tj6VnVr6vdiOEYnkaelXtzJvpSjGr8h7DeNgcckRnIeC/ynpN8ukUuk/1ClSxKJYreToaY6mUn3hcsS5svIwXoW/DZCrLOE1psnJrQ4MYNNYEpxOuMzT3SFBVvZhkhwN1meTk3qQJhoZcgEYoV/taedqg+6386i32Xr/HXBJC2f4+fu8ZkrmuTkLzdENS6kdZv0g3TT/oRJjjkaoyXSY72g12kxYbJKxUIMOoSDZRY0piguo+MHsz4Q2CovJQ4TE3P+u26DdvtIS/r6EeLY1uQ6Mj/SS1DCAkkfRTFsgcZ6KZJCp/1IGTVpZPHZ/a0qGbW0CW3Xy5OOUQGGO/N3BQ+WlqMx4DkJplOolmo1X4xuQjv9R4BPWgkQaOLtY81BtqoXQE4A+B1vgXvUAfL6V9osXt93sCKCOP3lyhtAUn7UenpUU+4sgzAxF3S79yOXYiSfMKyK6ogYSzg2qfzQb7Tb6b+sK2Tyh9zcLq3LZ9nDmLpnoymmxFrtCmXKBJCQOl/UjyGmtZSo5mImKP72o7pnTok/WxuLiYs9vef1f8gtadM6dW/6u8ef1PHw+4rqdLYlj8r+IN96mLMiygcce0kAQ0vCzXeF8HduJZ7kqiwTYJmLUSk7+gkVYzXPFUIKYqb8rUTX5hD0ecN1nzqULlwlFY/DVK05WGGzSYh+QJTKerbQ1dvjiuZS2iC7jYs+ekxbhG5+looHu0oepwaKnR2+LXdtz2J21cnO/cDEOFxaMsk4tDZA4a2p+O5dl9nG6lipmPxuQkF24vaIvveTBwR9XFFB+I20LmgL5R1ACgVrfLRyEfCUBNf8ySrUsgKWywQl+tfq2gCrk/IbSDSWPiEP0UZzWPbAq7oDQhSiyvFelW9jautM20ZZX9ygTsZa7egdD1VpG3FtfWnjbaFsNQB2przMqVu0otnszFU98ulg22YFi94WxFjTcSoES81Nd7lHpd6L2ogPbtKI4uy0QFc9Myk86sKGlqb9N33aQg838Ad25BvAn0KyRpAK9FVfPLyOQSgTRWY//0rPGyt5J7WXtPyQafafrei4aNeq2HGSVE2XHPbW5MKGnkBcFUzpUKBJsQAsaKkV9+FQlX8UpOa4+otRRULGmMXqtJgzEKluQR743QL92qKW05Xx6bmWxI4+sv+3qUeokRaekjQqsmgYENgA1jC9JsRTXkI2u0sSg1sW1IPPUg1uyI2swP0F2VSaJXMwuYl7nv+5ub4+FwkFH0+ZQ0/ZE9bBqckVHDsYat1NnYUKqfKNYk2n76An7a8MfWyJm9QunSkjzL55j8AVK/UpIEwQQx4UTt1c/Q11HoHy344LvmM1Bdyt6VPMNhGLiUqbMGJn4XUBZR19M8vfJUIEiXwCSkkrKyO8OAQtEGi1yfn6kZoWynbju0AtFwWJlTU2UURS3JOpZaWG+LXzYDzNwlfOQF1ZRI2ap4yO+J7OTZSEyrsMdYsXqmYr6qpzbwLaQ+w0efooOMvVdUJj7rmN8QTtmcBk6VDspbYhlINc9kf9gfj8ehH8L0dSJXAvaUDBncS1LbRBw4NEDBzkpvbvOwPqeQ8KOHRCT/arvd9JW7bLwakaQG9OqXT/E17TDvykvPqkTZ9Ia1VWLXX70Az6dQfw+PI2N926SHZJMajMIF5EiXC0AATVBrVqYN5gFyINgZit9k1cbbiXWF9zhCIC5FIsVIXXV2yBwTUcGsCf6ajx9rxFj2hoQo/oYQFPi/kpbP6E+TGSSqn5KgjFzDtxxZj5ZSX5xgPFjlr1JvVDPFjlJAr76NIYqrNO7T73Y7ivqOpDgAd++TtqylCEzDHLqyTL/S3WA+ykCIRHk16RY6SlZl4Qc2xAr9+EoK/PO8SgYSf2wJYZUYlKbDGQnBmjbWgbJOqTG2YrJObGBkykALOpGUreKhozbjC83JCRcI+B3A4VvpY9c4nAaJ2iu3m+ou6/tUUs3ZLgjO8BH4mWmgSJ2+RQKbsCfxVmtnW3E0BM1UevnWHkv9IV5lNI+TauhJrARENZWnJCflAfXmWDhgu9KT/KDMDZooLc5nfaZid0W9xkYbL4Af3Xd/OByPRx7IJo4UODl5KHfQ2GlUCR+QmDYEe/1z98GlLo8FtNsZz+28mbzJ0H+3kp/dbnCR8lz0XvgxIdvJdudN7VGFFKUE83fqtrDqk0S2BsJa7MHN/dKVXB/WaT2TdB30DCGAchKWSWPO1Trx8M2mgR3+4Xcpr0rgCygio/bUq1A3NAKc+zu6E5HfpZUJh4tuAdIgstpM9bpiC9nh1J9TC4uVktrFKa5rdHbU6gUU21YqMW3DPyMxeCG3UVZemivL3eYy2mJwlPLGhvHm9mYYB2iqptdSk3Lu62QTT/R7mLmZ5UurCsikk3qt4+Ira84wxpttFRpd04+RjJ/9vVpXQDbtTJjbx3DdTWg0ZOAYZNMJV+q1boW/GROYQKIWi08eG8bfYb/vp27iXi/iQtPlhQdDH538GYW9ALljIYI7FNibxkTuCdtgtSGG8R5GxqkLEvUSky68MS8RpoiK24UNOVI+NHwsnmoAM6hBaTm1MQA7C73IAU02sLqWoSyou2Y3F9NNi3QpCX8zLV2o44FkO8vVhcphpxLPUu0q20F2ddd1+/14OBxNl/bbyqwTiVQpfhTf960VcqpQ24vTD2u+0g68lKp432HUPJPyAdU34+ySIAUZnteXI8z+7QjTi++KokdSnqVgcMrQlAwAVbrXur1oCuyDF99a+ylHKDQfHuxKGQeklJkbnD8J9So6/8JhbQukBIZdt802i4hV845ZjYd35YeV9UiQjdCa8ojqLxSJQwxw8VSzVm1zyRM2oTQocrVMu69p3rgWfXXUow7tthA3RpsVDWiAtpjct6zzNF/Ol2WefXqCLkeNQOAAOSVGiN95gfA4fQh1e+kPxyOPmXfxSxculJEriRmG8iEMyphVCzWDL2690IlQHlh9aXmSbVWvdSNbt55Z9bhKJwdvYnGx2jxUaMll0vk2Ju8cFll5rDE9xA8swvUvPUY7qRJcfCSkBVC9ibaw1fvZLSOBUak/YsBgtuOwIDN21kSiJchKcVQTHd6ufiFLATNKk3nVptzQjDzHmr1NNEyOshbYZ721nsS505LCc5AeQpVhHUUXspz2ZeY/Q9NJHQm8h6PwZRp+RyX7nlAFLR9DtUrKVjxQJ+8w0AwK6vumkoX9QRpKu+Um7TNkVKYF8lzPquVZnS9dGgWN4wAG/gwn1NUPYbOlRv6crytecMu0CJwVqMmUIAOB91t2+jI1FFH9akJJkZSoZdP6uz09hdniC1JgS123tPZLM1kNG4xzgWmU2bERc3ivd0H6obHEVCgw+tvwzL97pFt30mIHaefy/JpLOOTEoyfyRvL/hoEHGl/pNuN8L6u7cV5qfqg0FAlc6RDQz98X7w2BXAeHHHb+zsKzZ7noFxW8bPHDihCjPSTkVW4iUdWQ6w+Hw34/TtJpLQLrrDYTElgDrlAUyu1gvrJyuEYaIvSyVwZrcfQrlHYd6a8T33Nq2M/FOpI67LNoUIt0uyHH9vgdy7XiQmFXCiIZzp6hScAbHShLzR9Jr9/1KzeuodFby0Kt8yQbXTr2uDoh2j/sqBzwVppDOzzw1FMxkdc2+9a/PZ0QhSAiiscItRLBGE1Fj/kNgZV07ZW5TYNWOcV6PRhiHC2QNOmqelZsZmsYyb6P9KYQtfxg/UNP1YK+sD4pLqPR0RbdsbaMxqtcDQPGnx8XHM3xSdBFZgjsdsTG5BqV1uJt3nJJxm7r04vB8+v1Kx0tRC+4RsfFq0m1m4t8noscsxrvpw4wYhZhy8l0MTA7vVZQRG0TMMYTQOzMa2xNwZZ401cTzxbDkEi56xeuM9VSbC1LDItG1inIVgZRQTJ3G2Cq3ytVsCxNil82jkDxzdhKtcHN59v32UPUL8vKA6u9G6bfEuGWhUJEpEPzIBU6SEgfgE9LK7eVoofW1yyBX5l2V2qbrmLHBp2dAeV5UNDYsWWO9JL2zCoXsA+JTPgdW1pJigDgL8DdMui7uu4/ayIbijRInS0cgOclGdW6dluFewYD8SVb8SCqE6kCHxqY45ezkz23pUc5ibcIQQFMxKku65gmWNlBSxYcOSAph3rHSbE46iqYrONK95zg6OC3Z/iomrpVhkwMmMO3M8d8/hf54mmm8UDPpdHbKh6tW4WeAyqMChKwkTh9g2SbJjBZyCvtwYGW8FIWYYSnMR7yXamP8dkyChWSgJZ3cVBNgl3Yu2633483NzccupD5PfJEz68uYehu0ILSUsZZuZ70Z+XrglvrwzKR5t2005Ov4VvF/5sXXk2lzRjrOXTA2cqSSXeX4Vyl9ChnY2EdBhMPd1jWANpKh45lStvQvEtuAd5PXb/jOd38ifq1tHwXm/94ho/NidmlqpVn8TfbTRb0z/HFhGClMKizqKqLGlChqmwCDPvDrA0XNokLcCIjDHuJ0HnU+DpmbrlQzAmF0ILFVc2Vms+K7+pynme2fgFMyioaGB1xSv1byoNUmybqlfPu++F4cxwHsX6tHSem2Or93X6yhcH2G6eRCLDiXHVi7rW7XZW19aPxinyVz8x1Ro9CrxnHbz/ASheg+5zWxZqrCetGnWXCdmCMqQxvx+wWx9p4aMzaLgNAEagt5xdpghw16HJlQXwcdoTYhVxz7O2OjZY2a6U4hbngf5tinl/P4KsVHRqEkjpf61hRd5HOx06erU51N3KJk6ZUW6mieyNAAbSzskIVLugp11xgc16z3ce8WfTLdJnOF+oKXwDP2bc1G4L7gnJnZxXuMf1fT8rzOFL3FjKUdHXefz3XESq4ymhaDf2kvpcE+8xzLSBUZT9sf0l+s+Db1gsCa768imC2XpbymspP6keYoqNZCA3VJAMxpHFWY7d9qf41tKCiWMdDEVpey4XFkpPAc104VmzM2PmXw17yqbtCJCG4YhuNCSc493x9fiWnyoiw+LUb5htHpOnfZdTJYm+sT0oHn27hiTcDty4qnp1AYmJWygnTmtqGWdYQRLiK7k3K8+l0t8xT6hFb7QatqSiJMIcXOLscYykRb0/TUqTqSANVELloriyzmutWoi8TZ+qEMgmO6s3bYRZg3L9dvGCpn8Gdw3YMfaUFPDDOQd3IV8iejXijMiYe782t1GbxQep1LASR1a72GI4cR2nWBsiyIMp2TDwqHKyyfJ2xNnOPD26XYk0gAP+BsaC/zMFbn3sjil+k5aVJC8WpNeIU9e0L0Qxo5HhVSAj3VVkmDVc2CYBlRuRAYQSRw/F4MHmMbsgLncKA1RdA/c5HYe2pKPA70UBDsAqwajO3xBPwpRa3GLSCPM6+H7hhrHfMEQPb5iWkk0TNP/cQhjNS9AG7x6UR6lUOHSRDswU80cC4RftM/ZWIotbLQ4N3j0ObkVeaUXFSCnVL4Ij2V8Gm7DHi/DAoGywKwVPLoZwB7E6yKEIzOf5cI9cS9IKGddoLDc5in6gnxkICfbftll8ruFx5fsFzS3aQ9l59NznE3WSPG+kYI8tRw9IEdClUCgOBctkt60x/UQZmSsIxfFdvFs9G0u4+Ffcqdf7M7wTxJOeZklyvJGqXkkh7YQDhqS5naKS3Z/odeXg8pbFI6F8JtdXQNC0NqhWQwE3ogO+lcUYbfDwRXltPgVuhkMyEW9EipoBvIx/ge3LcFA8N95WXkTHHsTptmxSjiR1mHl/dfE0pcINMUhZEiEuLSFa861gMXauU6/CYnF3fT5dp1bnqOkHFNFcDXFdqI6U+FNwhxBHCHW3VYGN+bhVnC71J2SOvCZHXaSsEWTUGvXAA+W6Ui1MdJhfyESczi9hsCLVL5QHUErqnfMSGzpYPtNRaNPArvdrFCwkcArcbOOthgcgaD68EygNas8wKlfnM3NvDgkZm1YMxUeB3oa4kDm0dXdolCbbjUNZU7yi0wk06q42oxOddZ4y5Ed6C1U6zXWiR11eGs4J8o/zT+x4J14wEKUuOQV8I3D7y9O3eom/otzDdNeyxoioJta0SzO6/bIPfbzWOY09TXS7cNM8SEpLeHS9H7rBu86emWtGrEFT5qnTQ+aOQCfqfgDCYB+r4frFkrQrWtHPDSp3CADzPPLvJG4B4krsqPetu7UepWoSlh+sgeF3LqtS8K+t0gXt3bSsch890eCQFRtuvDdyixery6UFqeIdmW/Kw1p3LhTu+I42WRyQ1fdFoL5K4k5cs3bupAJf3jm1uilrnsxufwyIzBqLc1bt4nx5w8Un7Ku1nVcrW+oUahWbnWAPK3KSqueb8jrP6DJQs0Bx3+qEfu/08U6yYvZgbutFavpMiO76C2oQp1vkMFMV76OUFf/LRuiEdwo55jrsDDolvTHRNmsNmIjtysLmdLHXkEEzIQyCF6cJtwVAWkEm7HG0PnZeAZUWlMC7AkZiP+kKHvt+T75m0A3FLSoV+4E48CzqANDGoGb+KUa/PD1rogIcGhaNhg/n6V41S3MpFIQqeq+v5mq1XQb0+dCGkcAUMDbcbERkdIgw8zzew1XoxWQdxUYiaDppQmRNDTeJHf+kqfhW+KtVtBQdoHJ74tXiGQsD82iv7duDl/mw8vaLosPGdKnQWOIAiLEYPtnJl2ivxb2PiStzRXLwWFmdyJl9Gv2PXPSwNR5lseBDpNc/z+XymTNYNrANh5XKu1sNr5WT1xMmRp2rNVOhLdfqRU1UUU+P3C1oqPsKHuq4V6p8ekS25biKLFFwCOIqy6ufFaQIkQQfd8oZBBPVZ+OrdEqXrIFSfFudocjj61df3jjzg8OAG+wKiA6Ku99vQcgE3IwARHBgKEzyzRPF/oMCK0IxOXm9J8NVg619WSWZaoSKxnWHAJ+7hIEjwKzAMwWb+0ThUf0f5fq171lpZwMzRHAL/iEGqGMt0hGUZ+TS1TWi5JbWbctcGHqt9uZynaW4LDOftBrWKQbtC7dq1zXvmUd1EveJ8ppZ0C1NvbsOUt51+86dvQiiULIwe2ARU/FKSH8Vf+Ic4vRGPE6duvBlUu6n0YxIo8wdNqGhdL9ZYkblhaUqeesRYra1FtZW/YlhmIUkpydRoFl3glLjEyhxjUPPjt5xoWZYOuW6kFIy+MG4LoAMVKXHJPZo7w9SQTwVgHKSNQy307KRYlVka6ZxKzaZACX/Ulm/3uuoQXbPEWwPzHn0oErHsed118+gAg8OC51r4xAIcdFLT5XK+TNJ4ILSi9jr9uXZZQg7nxVFWTnPKqPhbLLQwszPD9WyPDZCUH3iCqd8kq7Al6GugpjjPxnaLQIt+MVXSOBOtLoPPC4akLnfXNMNhD7uM2JE3QNI11fxebOAQeOb9BNlSxYHjQaa6WNNJc4uBbG4gxIZYTswh7JMMA8UaarLFc42jIHGVpB5NrKw5KAC7Mtc3F1KsqvwwwcWsCHvLbq+ufslQ9pPzm2GOYwCsZHsQLTbIyG8ypIg+nMdZes7b9Q4tv1WB4tS8hqnX7ymKd+toCsUtGRqlC4bfl3kL1B+El8Rt2OlZ7rRG76bDsbL0rlGZPU2zUTBD13/4P/CYAqMh7yTdFFkMgiGziVysjb6C7DeoMc3zxc19n0NGhbiPpHDnATpjNPzDiTcEDy/W75eBOseqm3T6B/aYc1iaGRObfBJ3H4o2Al9CKlKPuPLMLfrXN1BCP+GGN/HBu24xFtDLghVipN72gYmOdiWcQjacYhKDuR+KB8Yf+LhqVebO6NeRDWIczJgCZvCveh4v04UqR+o76i+BEpXVBkGcUKWNTqRqn0JHFLmWrrAT98nR+1qmUbmrpOiAnoFQ1SCiorMqIQh4E0Z+gbHEIsaJYSXEGDgqJaziUILNom6K7CsAXYMVzUFkfi5dvCGh71mJOXJPYmGsbfM8Wb5WVdd0TEW4w7Xb0AL4jLj508IjvwL+BT47PaZoHoAGGv3SJTyOSMlXLszuMN0aFxavPMxXB1xZyy7wXcX4MDsidD7U9khDB5fbuNwuwonpkFDx0U0mvg3BQm9zkf1oBvzMJqr1CfDJ0bHsxm7pLh0lblnIP+Lr8UVpg8YMb1HTV/3WCauTxmqUEDUvBpGErco0uIMhjeqmWUfW3Jnb4nEkW5FWMAKTBBIzBHyBQKlC0g7U2AuKEz0Ja1ucWUS4VuAcQhT6NRAyQ94cnC0KzSFDPX/FAWVpJ3EEnpFhxJn13MQ0HYkTg9F5XdLQ03Rm3QSIdWhmYnvSpyWYEeny/6lHjI0KEBoxHT9Rj3XaQO5pjZEy2iMKRSjTaCm7tjuq25AycfXsCLBKyeZ0aISTwqebMU7kYKHlpxX63VEBco0ga2JJZAA0ky0egrmRoe6plwaPsbuddpdlOe/6PdUGg0IXyRbytowW4viB3jzkcwrOexMdxEBGBsiVNh+kxbe4OrTvycgRCcyBP9kLKmmmJfij/W1AAuPg0JvBi5YU1dy9hiEV2DVK3kj+j63m0IurYuYSUsEXrsSyP1ca5Z1ErbYTrjg4gFK5vR29xHWkRQMY2Z4uaVBUBmW1XEDtoluGmQ9LQuex8LYoOOKQUvSzs+YS0cnKGUmWuVjP55lxklpv78fBJW0Au5513hWDh3QwNxH+m0p4akcN3DILwLh3/mqNHvkFw9nhqCXZAtQNyJxJBkIobakrRzzfXcxiKVH0h/JN+264Hae7bpzWgTIw+/UyGU9UzkGQIXrSqX7U55lng7EU92MXCWoOFvXlUOfSwDvy//MoN//UFWk2aeQltMv/UjfwwDKhCw05pwAQSnl0JQtjkERSLYWQ9D1/fgBGrWFD7EA3wIPQil2smQ2guZ2aDlsI4cWGPnKBTWptIl3nq5wne7qrq44lAjYhNB4KqVREkX5FDVeReURRqNQqGoGCFtapbGCZ9o2NHauP1/rRBhx1ZcRiud9iFt8pPhY7Kh0GYaSaEmdxo9AfUI2LHukgLVfgoOTz5HIADak4HtiEFK93x2pN68CXwJ71GjiJYFSF4yQYjgp5oEtz2QcjMJ0Hx74Fc9bwSHJzwdqWZTcM3b4bn14uu/U4fGVcT092/XDe3xvXuRuXnglNJpPQ97jDvMwBo34bu2XthXwZ95jtW9yOGu4J0euAIprFw4X5erA8VNYcCUI0WvEm4UvKeqZcbrJ+l4kDBYTig5GhaGw8aqCXGWtiKosCZdXQUd1FsQc+Iu70wN086PEUSjQaMoqSkX07coxI+zVjY+YgWSl4pr2+aBfSGlhHK5M3gcGhCKkdk2QGiclr+Y/zLLhJ4cKHztNHDBethZWcqFRQBkYrpxLEG8S9qSYfOfxksbtu7DvKQWTgcBtTk3HMd3ueB0uLVjTho/U8Sd4BLdwEtSYNiLNMICefDF237/q9FLJW/QJDv9H1OuWrHhZ6nigNAuYgYGM7InPMsBHuaXGy3o5vWXYzu9PYhiYfNa+Rs3CtgEOmOGnoRGZ5AEfQwEy01BTpY1qjDkMKfTH26ZxNN6DVkfQIEXMyxUL7U3gypCw9kMpGXRgjEFw2ZqqlSv1A5zd23bybj7ddt9x9bfr/llyPNMiuBIsAAAAASUVORK5CYII="}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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block is now supported by the right gripper, with the left arm withdrawn. I’m placing it at the pad’s center and will verify the release.","textSignature":"{\"v\":1,\"id\":\"msg_0df8288370165955016ae26a6d4848819d92e39831a6de33f0\",\"phase\":\"commentary\"}"},{"type":"toolCall","id":"call_ca55b142e5ad465f96f510d6e1d8e95b|fc_0df8288370165955016ae26a6eecc0819d88d31c614d38ebae","name":"exec","arguments":{"file":".kinex/tools/cartesian_move.py","args":{"moves":{"right":[[0.12,0.157,1.015,0,0,1,0]]},"seconds":1.5,"settle":0.32},"description":"carry block above blue pad"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":671,"output":225,"cacheRead":44416,"cacheWrite":0,"reasoning":111,"totalTokens":45312,"cost":{"input":0.006710000000000001,"output":0.011250000000000001,"cacheRead":0.044416,"cacheWrite":0,"total":0.062376}},"stopReason":"toolUse","timestamp":1793223270228,"responseId":"resp_0df8288370165955016ae26a66c6c8819d92d061224940fe5e","rawStopReason":"completed","durationMs":8938,"thinkingLevel":"high"}} 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{\"q\":[0.05061691999435425,2.6250691413879395,2.372485637664795,0.37307676672935486,-1.4840843677520752,0.12098056077957153],\"grip\":[0.7465521422299473],\"tcp\":[0.11225161767899541,0.15427922863367133,1.016213647769426,0.0003283806839788719,0.00014140216044888562,0.9999977304223577,-0.002100314664298886],\"joint_error\":0.016504502293561263},\n \"file\": [\"/tmp/kinex-observation-2umqg4z9.png\"]\n 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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"devices: 3 calls","isError":false}},"isError":false,"durationMs":127,"timestamp":1793223331413}} +{"type":"message","id":"15842b86","parentId":"245fef43","timestamp":"2026-10-28T21:36:07.936Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Releasing block from pad**\n\n**Ending block-release attempt**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0df8288370165955016ae26aa698e8819d93bd953dbb9035a3\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Releasing block from pad**\"},{\"type\":\"summary_text\",\"text\":\"**Ending block-release attempt**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4mq09xW394Xa4NeXmFZdWZrZRDpy95t1_0fsf-8OZcZTeRG0qI2962K2E6clSAcZwQ5Xnm2hNLGG0sX2ijDPuqApFhnp5l1rIHPH8QDRLNVt_fuNFi_4INO8nVFtnsAFzoXrhuohe9HfMOJp1CbCAja2yFxyvki7-CBEgxlIR6jBdfInFut_MArQEZIQEh-f0JonBOrrTQchVgI50PKAUkEyhwcRxbeY7qSTOKwFhaUAAoG7u9074y5i_T5Zb_SEALQgeR-yn95CCb2Hh5F7D2zer1eKjKC6kf-mKMRZcSmZe7h1SgcYYlNdamf-qcd-vTyAl-9IBXVqzKDU5OkRof_6YnNvCQKQYvfQQuahLMLqaXhb0LZLb1r3U2F8a27emICyRsZMghk454lpBYiJz00k75jxo2YGTKFjTJN65NXIMO1pG9h5dKbXDgAEC4s_sPb3qXPjITzK6CSb7UzNay5rhBxT3VdLiKz9gy8-B0KPH3Z1wPQKQST90MTBHS4EU5YAhjTtGLhx0__PXSB74bP8J2-wRCLhMmeBmuDA1kiSCmK2Zmvy0q6RQSnJsgp7khhk_UY4VJ5HXsEb-eXuy_i2Wau3Ef1pOKfcrdKiJS0Zu-Od20aIyLImhQcdd9_CnIcjMRlkzPvQrYDZNVTDzC0duem6R0_GHjAOpto-u1gnb4pfP4HS4ocZzZmudjlS0wYpnt-GiLJFU6x0-qKAtWCaACa_pmvLsxaf55PTSd_FHgxCXWPxohrcXdV61d2OV1XBcrpqT8Pro2_ZD5RTkMgEk_WF_Vk7A2u_7_yiSMSxd2bjURxmncvZpU_cvorP5WCljKsSykTURhIfLpfEMeA0WTmEvNCL8oBE25DXomBH7jCvpihJeLyG6S2UkxEgp0eMVoXWEfzMWobJgSqh_oy0xW-l9jiSrSKgnWH4438ca9wiZcGhkJHX5kpzoCWOwX1o4Ar_iJfgE-mlpDGwcP4Dla5QN3j5bv6qBA59fvC8C18wTR0yJUbJrSDP5vXR2Fa-bFh2oVZ-GT72P8ja10t-wg_AtDfeAJnitjsWZExn0c63tWC2K96eaogLmQ8HCJDhCfovJcNi_osTd2vr_EuT3dhC-Fk1an48dWz3qn62b8F1JEc8hNVuNMMh9qjvLuKAJt_FiBteCAQHcl6a3i46kcb4dZgv4ABx1JRp1noSmxATZJ30gTqp-KqQ_2_x6sTfAXtlj5zQTzMIGMuIQomRsYwMHBE3_MDEz4n051n78f4VPErbqo00gDODKBPedVBaSZc9wn_PEruyAXkXJKzbzEa-Bb6XMVigrPwkKgwuoqmYFip-Jm31e7Z8_8nV8AYwNuMiSPsHXJi0h3xlacI4QOAN6trIojOn4X544ubL1RdMK5sqF5JS9ueFxgPpSMwmvXIzNac_55nuft2CSSlQniU71MJCaLrTCDw2ES6DyZvLnSg_aWi_24An1RGvHuODXwhg_YDcZPmbRoxyNxwVjSdBsIsjcvF8fbIAjOJlmwJHmk6sVLKsE-FEya8K_2uk6kpDtbcccMiZNSYtlSKfQXTxIKHjRZIZjMMYVNM-9X3yF5gUwPz60oVVTUBh6XNHUSyo3kRpQZI-1HxHjHnixdKYWjQYmf12pvX7FHVVNzE8RIRZsGiDfIsdzcUD7Rx5thx8eC0ETG-v_ASpo9Cet6iJYpWISR406D9qtrrSaxfEhuDQ5oh2hiDAxH0Skvog6zzP36FiFmKIGruKuncrw3teH7ezpckDD0RnYED_8MDGAvPXvVC0PGNGoDzdiZOg4HGjtpLADSWlT4urMmDU4xVsTkBmoeHNAOGaMg7ONK2RcvGcrzf5UjxjHHjCeULqh2E3aBhumwFjOCqXSOCUmGmhieMOxjMm4sKSZcBi3VfS66U7dulMrQJgmNqAurXDYAlyoj9U5Am8gzLzYEW7aMvu5QJ_HDDBdA32Tk5uyF0PrqFj4XNxDWnWsz0a2Ndgxphu4EaTQo8y6USND_avlGfIHfG6Esh0ynEKG3lIouO2E45a28w3bV38ctO8MoQHd1Je5gAU9syomIZWfPR_eQjTgKHwg1YAN0dCu96xyyoKZg9W-0t64KAtltYAsRaLD5XkjPkersGtkFtuE2hFu7dJhaYctfBuNAatYi9Sg6_nUQu37SLjIAYCrADge8QR0qXXHN6S3dyE4uI_s6nWjjQbzvHJug5s6mX0C420Gtg5NBA05pJSdIJ5Wz26vi-6rIN7eYcVMKgQ0vuj3-Kz_9Rq5OH3qOCLnHPl93GvzYtHRwglsmsKnHTV1-2cRTtGCAsBhy2Rb4KAUnUWeXVCtCdEUs3Mgw5XpV76W9zUNdEOmmo52aNn0Har5h8ZEV7w7uxlzK96XJMTOkxNw7VpCkqFgzYDvlchVGPn8bWJQ2myw1nKLSvvIFqO4CHDBS1rGgC25Kv5iUeDOMrJLXsxyMJydcc2MtLSJ2eY3tAqMxcOHUVBSvtcvqX8bAW58dqJL6P5VxVtx1swAGZljvi0g00dqU3effESNS9auV2W382cUMZ2l70QMD4UicETIzdmS_PnrKOa-fyTbLFURQfS6GaJEkslQhmrQqF28Ua7KVDh0lLiGLekyN9jTm4OU6NWsj5uWal2gQ4rSAATLIfLCCR2AIf8RBMm1gPbJtpuorOVPsEiWn8xL7vFGZLM-0iIYAbZSp3tH7e2caNHQG_7Wi1gUqyJT_6kzxjcrIhM-2Rjp5PZUoxV4dxCw38vaXdY7HQQGwm0XkZCOo_ZkvB_z7iCgWIeJ5VrzrG2jrzRudZMsI6Vrmm03g_KVYeni3i6JZgwupjVIqroZkzTBJsJLgR-XLvThsyoP6dE-TvvLDz5ug3Qq977LUzQXmsM-AQSclFSaSNjXzJiE_OLNHGySd82mLIiuwEA-KaZITQVAg8Zt3uuS_q4vxHgPidvigH3OP43TXE4Z9fd8LrjPq_IPTn-LMn_-UUIbiLYrrtGSCy60rHSy6eFm83F62HNE70c-UF1ZxLmAahUzQTS4hSicjQoAS3NNIuFlCWmf9oUeWQnmydkv8HtPrWKctkuJVKwz4H_5PnPUOqH9j0XiftUxWvCYHltBsM9JQSWOTmUbGBAB6SPiLr_IuygPUTrCDcfoHexp7WGCOxXrbR0Vr9tJ67bEoJfeqXWdWOTxSvROA2jUzeG-S-mok0vO0fU6FOSVsO39gmSq_GLP_vkZaggH0gZY0r8h6lzFvZoQar1nOdygHCNapmUDB5w6LLmJrMRPgfrN1y8S_hq86yJXS-r8c-qHUVdQDhjg-fRKufcYMQXXWRwkFKYjU1DTXUDh8p42Ov321UA26PdSlehr1Dd4oqg_WtYUck0j7t9mfheP668UDSsSYPiiSTQhcrbOZ_pCxJLPvP3iWLhtVvePihuSjdQjrb4xkvJUDkGCU57FWU589hEm5hGmb-lA_-yveeAfkM2IOkZUB44aymQgDtqGwGkLJserWy56OY7aatBPZcEVja6vZVF8kq-xxbWuYE0ql-m8TF0GEtwE8440aFyGsve1hrFPP06uXv5ln-donxhwZDhV08Sg4lLcSB7Ch1HDCBgR9yE03Q7Xe_Uc7EZ3wLn_sYa45Dz70uNCJCuQBDhAocQXeq8CQmSNSjFLMnsYvCqdyBmLZw2IUo0z9oS2RPtK4unZ4OSOkNsWAH1fFo=\"}"},{"type":"toolCall","id":"call_9777e5a5cde145f7bb58fc5722c35986|fc_0df8288370165955016ae26ab42140819da786d33d904a69af","name":"write","arguments":{"path":".kinex/memos/handover-block.md","content":"# RoboTwin ALOHA handover block\n\nPublic interfaces: read harness.md and refreshed profiles. Native period .04s; 800 frames =32s. All movement needs sequential world.wait. Load arms, grippers, head camera and right wrist camera. Normalized grip 0 closed,1 open. TCP +.12m flange x. World arm base transforms are calibrated; use TF and URDF IK.\n\nReusable exec scripts:\n- `.kinex/tools/cartesian_move.py`: args moves={left/right:[world TCP poses xyz+xyzw]}, seconds per segment, grips, wait, settle, observe. Precomputes Cartesian interpolated IK then publishes JointTrajectory; returns measured TCP, q error and image. Kinex FK/IK requires list, not NumPy arrays.\n- `.kinex/tools/color_geometry.py`: args cameras; red/blue visible world surface bounds from synchronized RGBD and stamped TF. Bounds include silhouette/depth mismatches and red highlights; inspect images, use interior pixels if precision required. Wrist depth is unreliable inside .1m.\n\nSuccessful transfer geometry observed: vertical red block ~.06x.06x.20m, table z .74, pad top .746. Detect current xy each episode. Head camera clips tall block top. Pad center from min/max xy, not perspective-biased pixel median.\n\nGrasp LOW with left TCP at table+.045 (not +.095) using yaw +90deg quaternion [0,0,.70710678,.70710678], approach +world y, fingers close along x. Approach from y-.15 then insert center. Close .5s, lift .10m. Keep vertical; rolling a central grip caused a slip.\nRight approaches from +world x with yaw pi quaternion [0,0,1,0], closes along y. Target ~.03 below measured block top, ~.12-.13 above left TCP. Too little separation (~.07-.08) caused collision with left wrist/camera and displaced the block; stop before recovery. Lower left grasp fixed it. Right camera gives useful top and side geometry before insertion.\nClose right .6s, open left .44s, withdraw left frontward ~.17m; verify block remains suspended with right (grip ~.75 for .06m block). Then translate above pad center, lower upright preserving grip offset, open right. In this run transfer and pad placement observed; release wait terminated at 24.84s after .04s, before requested .8s. Last measured right opening .761, velocity positive; full-open motion not verified. Native verdict remains owner-only. Never retry world waits after terminal or infer success merely from termination.\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":433,"output":745,"cacheRead":47232,"cacheWrite":0,"reasoning":154,"totalTokens":48410,"cost":{"input":0.0043300000000000005,"output":0.03725,"cacheRead":0.047231999999999996,"cacheWrite":0,"total":0.088812}},"stopReason":"toolUse","timestamp":1793223331452,"responseId":"resp_0df8288370165955016ae26aa4be2c819dbcb52936e0e8ea90","rawStopReason":"completed","durationMs":36484,"thinkingLevel":"high"}} +{"type":"message","id":"322916b3","parentId":"15842b86","timestamp":"2026-10-28T21:36:07.940Z","message":{"role":"toolResult","toolCallId":"call_9777e5a5cde145f7bb58fc5722c35986|fc_0df8288370165955016ae26ab42140819da786d33d904a69af","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/memos/handover-block.md"}],"isError":false,"durationMs":3,"timestamp":1793223367940}} +{"type":"message","id":"3824164a","parentId":"322916b3","timestamp":"2026-10-28T21:36:12.040Z","message":{"role":"assistant","content":[{"type":"text","text":"Transferred the block to the right arm and placed it on the blue pad. Commanded the right gripper open, but the episode ended during release, so full opening wasn’t verified.","textSignature":"{\"v\":1,\"id\":\"msg_0df8288370165955016ae26acab3fc819d8b81285de4d79e70\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":177,"output":41,"cacheRead":48256,"cacheWrite":0,"reasoning":0,"totalTokens":48474,"cost":{"input":0.00177,"output":0.00205,"cacheRead":0.048256,"cacheWrite":0,"total":0.052076}},"stopReason":"stop","timestamp":1793223367986,"responseId":"resp_0df8288370165955016ae26ac89900819d8d1815f5c03e9997","rawStopReason":"completed","durationMs":4053,"thinkingLevel":"high"}} +{"type":"custom","customType":"kinex-active-time","data":{"milliseconds":628273.851421},"id":"e6ec0fc8","parentId":"3824164a","timestamp":"2026-10-28T21:36:12.237Z"} diff --git a/experiments/robotwin-native/kinex-1/terminal.jpg b/experiments/robotwin-native/kinex-1/terminal.jpg new file mode 100644 index 0000000000000000000000000000000000000000..4c15a0539a1ca4f011ddedee56c1df170dece03d Binary files /dev/null and b/experiments/robotwin-native/kinex-1/terminal.jpg differ diff --git a/experiments/robotwin-native/kinex-1/tool-calls.json b/experiments/robotwin-native/kinex-1/tool-calls.json new file mode 100644 index 0000000000000000000000000000000000000000..d20d2aad00698bf1a4f09b90afabfd572c81101b --- /dev/null +++ b/experiments/robotwin-native/kinex-1/tool-calls.json @@ -0,0 +1,1023 @@ +[ + { + "name": "ls", + "input": { + "path": "/workspace" + }, + "output": ".kinex/\nharness.md\ninstruction.md", + "call_id": "call_557120d31d354bdcb113ec14e5b73fe8|fc_0df8288370165955016ae2685a2e50819da406f073fdf4fb3a", + "is_error": false + }, + { + "name": "read", + "input": { + "path": "/workspace/harness.md", + "offset": null, + "limit": null + }, + "output": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n", + "call_id": "call_8340aa3e10fc46f0aa98cbada5362911|fc_0df8288370165955016ae2685c096c819d88323bf779a16f98", + "is_error": false + }, + { + "name": "find", + "input": { + "pattern": "**/*", + "path": "/workspace/.kinex", + "limit": 150 + }, + "output": "devices/\ndevices/head_camera/\ndevices/head_camera/profile.yaml\ndevices/left_arm/\ndevices/left_arm/arm.urdf\ndevices/left_arm/links.py\ndevices/left_arm/profile.yaml\ndevices/left_camera/\ndevices/left_camera/profile.yaml\ndevices/left_gripper/\ndevices/left_gripper/gripper.urdf\ndevices/left_gripper/links.py\ndevices/left_gripper/profile.yaml\ndevices/right_arm/\ndevices/right_arm/arm.urdf\ndevices/right_arm/links.py\ndevices/right_arm/profile.yaml\ndevices/right_camera/\ndevices/right_camera/profile.yaml\ndevices/right_gripper/\ndevices/right_gripper/gripper.urdf\ndevices/right_gripper/links.py\ndevices/right_gripper/profile.yaml\ndevices/world/\ndevices/world/profile.yaml\nkinex.json\nmemos/\npython/\npython/.pixi/\npython/pixi.lock\npython/pixi.toml\npython/ros/\npython/ros/build/\npython/ros/build/.built_by\npython/ros/build/COLCON_IGNORE\npython/ros/build/kinex_interfaces/\npython/ros/install/\npython/ros/install/.colcon_install_layout\npython/ros/install/COLCON_IGNORE\npython/ros/install/_local_setup_util_ps1.py\npython/ros/install/_local_setup_util_sh.py\npython/ros/install/kinex_interfaces/\npython/ros/install/kinex_interfaces/include/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_generator_c__visibility_control.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_generator_cpp__visibility_control.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_typesupport_fastrtps_c__visibility_control.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_typesupport_fastrtps_cpp__visibility_control.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/msg/rosidl_typesupport_introspection_c__visibility_control.h\npython/ros/install/kinex_interfaces/lib/\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_generator_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_generator_py.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_cpp.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_fastrtps_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_fastrtps_cpp.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_introspection_c.so\npython/ros/install/kinex_interfaces/lib/libkinex_interfaces__rosidl_typesupport_introspection_cpp.so\npython/ros/install/kinex_interfaces/lib/python3.12/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/__init__.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/__pycache__/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/__pycache__/__init__.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/_kinex_interfaces_s.ep.rosidl_typesupport_c.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/_kinex_interfaces_s.ep.rosidl_typesupport_fastrtps_c.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/_kinex_interfaces_s.ep.rosidl_typesupport_introspection_c.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/kinex_interfaces_s__rosidl_typesupport_c.so\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/kinex_interfaces_s__rosidl_typesupport_fastrtps_c.so\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/kinex_interfaces_s__rosidl_typesupport_introspection_c.so\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__init__.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/__init__.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/_json.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/_lookup_transform.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/__pycache__/_wait.cpython-312.pyc\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_json.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_json_s.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_lookup_transform.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_lookup_transform_s.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_wait.py\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces/srv/_wait_s.c\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/PKG-INFO\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/SOURCES.txt\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/dependency_links.txt\npython/ros/install/kinex_interfaces/lib/python3.12/site-packages/kinex_interfaces-1.1.0-py3.12.egg-info/top_level.txt\npython/ros/install/kinex_interfaces/share/\npython/ros/install/kinex_interfaces/share/ament_index/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/package_run_dependencies/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/package_run_dependencies/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/packages/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/packages/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/parent_prefix_path/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/parent_prefix_path/kinex_interfaces\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/rosidl_interfaces/\npython/ros/install/kinex_interfaces/share/ament_index/resource_index/rosidl_interfaces/kinex_interfaces\npython/ros/install/kinex_interfaces/share/colcon-core/\npython/ros/install/kinex_interfaces/share/colcon-core/packages/\npython/ros/install/kinex_interfaces/share/colcon-core/packages/kinex_interfaces\npython/ros/install/kinex_interfaces/share/kinex_interfaces/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.bash\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/local_setup.zsh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.bash\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.dsv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.ps1\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.sh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.xml\npython/ros/install/kinex_interfaces/share/kinex_interfaces/package.zsh\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Json.srv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/LookupTransform.srv\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.idl\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.json\npython/ros/install/kinex_interfaces/share/kinex_interfaces/srv/Wait.srv\npython/ros/install/local_setup.bash\npython/ros/install/local_setup.ps1\npython/ros/install/local_setup.sh\npython/ros/install/local_setup.zsh\npython/ros/install/setup.bash\npython/ros/install/setup.ps1\npython/ros/install/setup.sh\npython/ros/install/setup.zsh\npython/ros/log/\npython/ros/log/COLCON_IGNORE\npython/ros/log/build_2026-10-28_21-18-39/\npython/ros/log/build_2026-10-28_21-18-39/events.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/command.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/stderr.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/stdout.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/stdout_stderr.log\npython/ros/log/build_2026-10-28_21-18-39/kinex_interfaces/streams.log\npython/ros/log/build_2026-10-28_21-18-39/logger_all.log\npython/ros/log/latest\npython/ros/log/latest_build\npython/ros-ready.json\nskills/\ntools/\nworkspace.yaml\n\n[150 results limit reached. Use limit=300 for more, or refine pattern]", + "call_id": "call_aae6560e66c84c358b4fd70c7bb8d73d|fc_0df8288370165955016ae2685f34b8819d8019f73c6e473a17", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/workspace.yaml", + "offset": null, + "limit": null + }, + "output": "identity:\n id: robotwin-aloha-agilex\n name: RoboTwin ALOHA AgileX\n kind: arm\n model: ALOHA AgileX (dual ARX5)\n source: user\ndevices:\n source: user\nsafety:\n source: user\n max_joint_velocity: null\n max_cartesian_velocity: null\n keep_out_zones: []\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\n", + "call_id": "call_73a5e4b6bb4444bca2d098facaed338e|fc_0df8288370165955016ae26861d2a8819d88ec34b3281ae1ee", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/world/profile.yaml", + "offset": null, + "limit": null + }, + "output": "name: world\nruntime:\n domain_id: 83\n observation: time\n nodes: []\n ready_timeout_s: 30\noperations:\n time:\n description: Authoritative world clock\n transport: topic\n direction: output\n endpoint: /clock\n type: rosgraph_msgs/msg/Clock\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n wait:\n description: Advance or await duration_s of world time (finite and nonnegative).\n Zero is an observation barrier. Waits are sequential; one requested while another\n is pending is rejected. A terminal episode rejects further progression without\n exposing benchmark results.\n transport: service\n direction: input\n endpoint: /world/wait\n type: kinex_interfaces/srv/Wait\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 120\n stop:\n description: Cancel the pending wait.\n transport: service\n direction: input\n endpoint: /world/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n Kinex supplies frame transforms independently. This device exposes no task instructions\n or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n base:\n parent: null\n translation_m: null\n rotation_xyzw: null\n", + "call_id": "call_6625a9cb6f434c88b5bf2fff5cb3be8d|fc_0df8288370165955016ae26864b82c819db4dabc0cb6072c19", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/left_arm/profile.yaml", + "offset": null, + "limit": null + }, + "output": "name: left_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /left_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /left_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /left_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n as left_arm/, and helpers.joint_model('left_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fl_joint1: 0.0\n fl_joint2: 0.0\n fl_joint3: 0.0\n fl_joint4: 0.0\n fl_joint5: 0.0\n fl_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - left_arm\nframes:\n base:\n translation_m:\n - -0.296999871731\n - -0.419500023127\n - 0.782000243664\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 0.714142563298\n - 0.700000389985\n parent: world:base\n", + "call_id": "call_ada7312797534f87a5c4cbbf16b557a1|fc_0df8288370165955016ae26864b82c819d9cad53b113c442d8", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/right_arm/profile.yaml", + "offset": null, + "limit": null + }, + "output": "name: right_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /right_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /right_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /right_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n as right_arm/, and helpers.joint_model('right_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fr_joint1: 0.0\n fr_joint2: 0.0\n fr_joint3: 0.0\n fr_joint4: 0.0\n fr_joint5: 0.0\n fr_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - right_arm\nframes:\n base:\n translation_m:\n - 0.306300014257\n - -0.418499916792\n - 0.78100001812\n rotation_xyzw:\n - -2.9802e-08\n - 5.9605e-08\n - 0.71063355368\n - 0.703562409721\n parent: world:base\n", + "call_id": "call_4f4713cdf22247a083c3322102fda0b3|fc_0df8288370165955016ae26864b82c819da747d21af5abb784", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/left_gripper/profile.yaml", + "offset": null, + "limit": null + }, + "output": "name: left_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured left_gripper, the native normalized gripper opening (0 closed,\n 1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n it.\n transport: topic\n direction: output\n endpoint: /left_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /left_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /left_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - left_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: left_arm:flange\n tcp:\n translation_m:\n - 0.1199999989\n - 7.305e-09\n - -7.152e-09\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 3.2247e-08\n - 0.999999998609\n parent: left_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: left_gripper\n open: 1.0\n closed: 0.0\n", + "call_id": "call_1424db23db3e45cca0546ad2f1550656|fc_0df8288370165955016ae26864b82c819d8f47a10a70595903", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/right_gripper/profile.yaml", + "offset": null, + "limit": null + }, + "output": "name: right_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured right_gripper, the native normalized gripper opening (0\n closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n from it.\n transport: topic\n direction: output\n endpoint: /right_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /right_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /right_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - right_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: right_arm:flange\n tcp:\n translation_m:\n - 0.119999992883\n - -9.757e-09\n - -7.153e-09\n rotation_xyzw:\n - 1.0e-12\n - 2.9802e-08\n - -6.4393e-08\n - 0.999999973161\n parent: right_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: right_gripper\n open: 1.0\n closed: 0.0\n", + "call_id": "call_91eef032bd0f4752bc461a87f7840538|fc_0df8288370165955016ae26864b82c819d8ff98e4e0e6674d9", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/head_camera/profile.yaml", + "offset": null, + "limit": null + }, + "output": "name: head_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /head_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /head_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /head_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - -0.03200000152\n - -0.449999988079\n - 1.350000023842\n rotation_xyzw:\n - -0.948683301192\n - 0.0\n - 0.0\n - 0.316227769682\n parent: world:base\n", + "call_id": "call_ebfb289d66754647b4e0f7ccfea8adcf|fc_0df8288370165955016ae26864b82c819d9e50eedd577fd641", + "is_error": false + }, + { + "name": "exec", + "input": { + "python": "from kinex import runtime, finish\nfinish({d:runtime.operations(d) for d in ['world','left_arm','left_gripper','head_camera']})", + "description": "inspect operation contracts" + }, + "output": "{\n \"output\": \"\",\n \"result\": {\n \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120},\"stop\":{\"description\":\"Cancel the pending wait.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_arm\": {\"joint_state\":{\"description\":\"Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_arm/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Stop arm motion and hold the measured posture.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_arm/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_gripper\": {\"joint_state\":{\"description\":\"Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_gripper/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Hold the measured opening; fingers may settle briefly after the stop.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"head_camera\": {\"image\":{\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"depth\":{\"description\":\"16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/depth\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"camera_info\":{\"description\":\"Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/camera_info\",\"type\":\"sensor_msgs/msg/CameraInfo\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10}}\n }\n}", + "call_id": "call_1f5da46d3fda4d33bd3e155df8df3148|fc_0df8288370165955016ae268671804819d9189b4948ddb0c5a", + "is_error": false + }, + { + "name": "devices", + "input": { + "calls": [ + { + "deviceID": "world", + "action": "load" + }, + { + "deviceID": "left_arm", + "action": "load" + }, + { + "deviceID": "right_arm", + "action": "load" + }, + { + "deviceID": "left_gripper", + "action": "load" + }, + { + "deviceID": "right_gripper", + "action": "load" + }, + { + "deviceID": "head_camera", + "action": "load" + } + ] + }, + "output": "{\n \"results\": [\n {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}", + "call_id": "call_16eac218dcfb4206bf7a1f0d0fc0b989|fc_0df8288370165955016ae2686b9f5c819da3ad030b7dfae385", + "is_error": false + }, + { + "name": "read", + "input": { + "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt", + "offset": null, + "limit": null + }, + "output": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n Return a stamped source-to-target transform using Kinex frame history.\n await helpers.transform_pose(io, pose, source, target, stamp=None)\n Express a pose given in one frame in another.\n await helpers.relative_pose(io, child, parent, timestamp=None)\n Express a frame-attached child pose relative to a frame-attached parent pose.\n Implementation: frames.py\n\nKinematics\n helpers.joint_model(device)\n Load a joint device's commanded joints, link FK and IK from its URDF.\n Implementation: kinematics.py\n\nMotion\n helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n Plan joint references and duration without device I/O.\n Implementation: motion.py\n\nPolicy\n await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n Infer once and submit the valid action prefix to the commanded devices.\n Implementation: policy.py\n\nImages\n await helpers.camera_frame(io, camera, timeout_s=None)\n The current image and, for an rgbd camera, its depth from the same acquisition.\n helpers.image_array(image)\n Native-order pixels of a sensor_msgs/Image as numpy for computation.\n helpers.save_image(image)\n Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n Implementation: images.py\n\nPerception\n await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n Locate a detected object in a stamped camera optical frame.\n Implementation: perception.py\n", + "call_id": "call_e32dfeef88cd49a98044fc70a66a051a|fc_0df8288370165955016ae26875077c819daf1bff4228d0f538", + "is_error": false + }, + { + "name": "read", + "input": { + "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py", + "offset": 1, + "limit": 250 + }, + "output": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n \"\"\"Load a joint device's model without device I/O.\n\n joints lists the commanded joints in URDF document order with limits and\n rate limits; names holds their names. fk(positions, link=None) returns the\n link pose [x, y, z, qx, qy, qz, qw] in /base. ik(pose, seed,\n link=None, **options) returns commanded positions nearest the seed, or\n raises ValueError when no verified solution is found. link defaults to the\n model's tip_link. Measured links are also on TF as /.\n\n Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n \"\"\"\n return JointModel(profile(device))\n", + "call_id": "call_594c0aeb37864a37a3508edeae992b74|fc_0df8288370165955016ae268779990819d9072a6bd2ad63c99", + "is_error": false + }, + { + "name": "exec", + "input": { + "python": "import asyncio\nfrom kinex import runtime, communication, helpers, finish\nasync def main():\n async with communication() as io:\n f=await helpers.camera_frame(io,'head_camera')\n out={}\n for d in ['left_arm','right_arm','left_gripper','right_gripper']:\n async with io.subscribe(d+'.joint_state') as s: out[d]=await s.next()\n out['schemas']={x:runtime.describe(x) for x in ['left_arm.joint_target','left_gripper.target','world.wait']}\n finish(out,file=helpers.save_image(f['image']))\nasyncio.run(main())", + "description": "observe scene and schemas" + }, + "output": "{\n \"output\": \"[INFO] [1793222782.251334587] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n exec(compile(_request[\\\"source\\\"], \\\"\\\", \\\"exec\\\"), module.__dict__)\\n File \\\"\\\", line 11, in \\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"\\\", line 10, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}", + "call_id": "call_e5af90cf3ab749f9a00db0884473c5c8|fc_0df8288370165955016ae2687a893c819dab6f8abbc44f9f18", + "is_error": true + }, + { + "name": "read", + "input": { + "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py", + "offset": 1, + "limit": 180 + }, + "output": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n Both come from one acquisition (equal stamps); reading never advances world time.\n\n Example: image, depth = await helpers.camera_frame(io, \"camera\")\n \"\"\"\n return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n \"\"\"\n return array(image)\n\n\ndef save_image(image):\n \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n \"\"\"\n return save(image)\n", + "call_id": "call_764ced789f0d4188899433ec60bc7b1a|fc_0df8288370165955016ae26880fc78819d82a1b2e75454d578", + "is_error": false + }, + { + "name": "exec", + "input": { + "python": "import asyncio\nfrom kinex import runtime, communication, helpers, finish\nasync def main():\n async with communication() as io:\n image,depth=await helpers.camera_frame(io,'head_camera')\n out={}\n for d in ['left_arm','right_arm','left_gripper','right_gripper']:\n async with io.subscribe(d+'.joint_state') as s: out[d]=await s.next()\n out['schemas']={x:runtime.describe(x) for x in ['left_arm.joint_target','left_gripper.target','world.wait']}\n p=helpers.save_image(image)\n finish(out,file=p)\nasyncio.run(main())", + "description": "observe scene and schemas" + }, + "output": "{\n \"output\": \"[INFO] [1793222790.779743893] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"left_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\n \"right_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\n \"left_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"left_gripper\"],\"position\":[0.986124409870668],\"velocity\":[0.018415418030186134],\"effort\":[]},\n \"right_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"right_gripper\"],\"position\":[0.9861242744055662],\"velocity\":[0.0184164128520272],\"effort\":[]},\n \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence\",\"points\":\"sequence\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence\",\"velocities\":\"sequence\",\"accelerations\":\"sequence\",\"effort\":\"sequence\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}},\n \"file\": \"/tmp/kinex-observation-ftia7zbo.png\"\n }\n}", + "call_id": "call_1f72f2a5fef14e6c9770552b06e256ef|fc_0df8288370165955016ae2688345b4819daff7e571c13a4374", + "is_error": false + }, + { + "name": "exec", + "input": { + "python": "import asyncio,numpy as np\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,de=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im); z=helpers.image_array(de)*.001\n masks={'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}\n out={}\n for k,m in masks.items():\n v,u=np.where(m&(z>0)); zz=z[v,u]; p=np.stack([(u-159.5)*zz/358.6421814,(v-119.5)*zz/358.6421814,zz],axis=1)\n tf=await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'})\n out['tf']=tf\n from scipy.spatial.transform import Rotation as R\n # relative_pose returns pose\n out[k]={'pixels':[int(u.min()),int(v.min()),int(u.max()),int(v.max())],'camera_range':[p.min(0).tolist(),p.max(0).tolist()],'median':np.median(p,axis=0).tolist()}\n out['tcp']={d:await helpers.relative_pose(io,{'frame':d+'_gripper:tcp'},{'frame':'world:base'}) for d in ['left','right']}\n finish(out)\nasyncio.run(main())", + "description": "locate block and pad surfaces" + }, + "output": "{\n \"output\": \"[INFO] [1793222803.632560113] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tf\": {\"pose\":[-0.03200000152,-0.449999988079,1.350000023842,0.9486832972651139,0,0,-0.3162277683730379],\"timestamp\":0},\n \"red\": {\"pixels\":[77,0,115,67],\"camera_range\":[[-0.16374677337382487,-0.2402380547197954,0.707],[-0.10597470674429707,-0.125013181174009,0.862]],\"median\":[-0.1355138980314043,-0.1914066542090244,0.776]},\n \"blue\": {\"pixels\":[199,55,244,86],\"camera_range\":[[0.09508362866543728,-0.15700467742024501,0.8140000000000001],[0.19417403643976383,-0.07631422464909199,0.873]],\"median\":[0.1445019651556246,-0.11359511544617211,0.84]},\n \"tcp\": {\"left\":{\"pose\":[-0.30032357671522697,-0.193827798419468,0.9419981020126762,-0.000006960933310190554,-0.00000395167326060435,0.7141423608015681,0.7000004917460364],\"timestamp\":0},\"right\":{\"pose\":[0.30523323124039986,-0.1928057309036048,0.9409978556569187,-0.000007012336903923293,-0.000003885552193758481,0.710633293626997,0.7035625927553112],\"timestamp\":0}}\n }\n}", + "call_id": "call_4a4596413bcd40e7a90d0776e1764dc0|fc_0df8288370165955016ae2688b8a3c819da1f7a4725b5bf751", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/left_camera/profile.yaml", + "offset": null, + "limit": null + }, + "output": "name: left_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /left_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /left_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /left_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n pixel-center depth from a raster twin; calibration and the optical-frame mount on\n the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - 0.07000001149\n - 0.031999994336\n - 0.065000047158\n rotation_xyzw:\n - -0.589367966393\n - 0.589367919412\n - -0.390698641271\n - 0.39069867358\n parent: left_arm:flange\n", + "call_id": "call_a035a832a8bf43bfa0c7ce6196f9a693|fc_0df8288370165955016ae268a2ee70819dadfaf8ab685b02f8", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/left_gripper/gripper.urdf", + "offset": null, + "limit": null + }, + "output": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "call_id": "call_c0a71f1a695641ecbf5210f719cfc840|fc_0df8288370165955016ae268a2fdc0819db5d37915c56eb0a4", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/left_arm/arm.urdf", + "offset": null, + "limit": null + }, + "output": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", + "call_id": "call_4b667940abd94b938fdbc184dc5c8612|fc_0df8288370165955016ae268a2fdc0819d981b1dd9e7ba615d", + "is_error": false + }, + { + "name": "exec", + "input": { + "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,de=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im).astype(float); z=helpers.image_array(de)*.001\n tf=(await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'}))['pose']; out={}\n for k,m in {'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}.items():\n v,u=np.where(m&(z>0)); zz=z[v,u]; p=np.stack([(u-159.5)*zz/358.6421814,(v-119.5)*zz/358.6421814,zz],axis=1); w=R.from_quat(tf[3:]).apply(p)+tf[:3]\n out[k]={'min':w.min(0),'max':w.max(0),'median':np.median(w,axis=0),'bottom':np.median(w[w[:,2].6: raise ValueError('IK branch jump')\n q=nq; t=elapsed+duration*f; ns=round(t*1e9)\n points.append({'positions':q.tolist(),'time_from_start':{'sec':ns//1000000000,'nanosec':ns%1000000000}})\n elapsed+=duration; wp=targetp; wr=targetr\n plans[side]=(model,q,elapsed,points)\n for side,(model,q,t,points) in plans.items():\n await io.publish(side+'_arm.joint_target',{'joint_names':model.names,'points':points})\n for side,value in a.get('grips',{}).items():\n await io.publish(side+'_gripper.target',{'data':float(value)})\n wait=max([p[2] for p in plans.values()]+[float(a.get('wait',0))])\n if wait: result['wait']=await io.request('world.wait',duration_s=wait+float(a.get('settle',.16)))\n for side in ['left','right']:\n s=await state(io,side+'_arm'); g=await state(io,side+'_gripper')\n result[side]={'q':list(s.position),'grip':list(g.position),'tcp':(await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'}))['pose']}\n if side in plans: result[side]['joint_error']=float(np.max(np.abs(np.array(s.position)-plans[side][1])))\n paths=[]\n if a.get('observe',True):\n image,depth=await helpers.camera_frame(io,'head_camera'); paths=[helpers.save_image(image)]\n finish(result,file=paths)\n\nasyncio.run(main())\n" + }, + "output": "Successfully wrote to /workspace/.kinex/tools/cartesian_move.py", + "call_id": "call_83515ff0f7274002a9175087a6022bdc|fc_0df8288370165955016ae268bf32bc819daeef75bb1c85047f", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "left": [ + [ + -0.167, + 0.045, + 0.835, + 0, + 0, + 0.70710678, + 0.70710678 + ] + ] + }, + "seconds": 2, + "grips": { + "left": 1 + } + }, + "description": "approach block with left arm" + }, + "output": "{\n \"output\": \"[INFO] [1793222870.183791116] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 40, in script\\n runpy.run_path(file, run_name=\\\"__main__\\\")\\n File \\\"\\\", line 287, in run_path\\n File \\\"\\\", line 98, in _run_module_code\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 51, in \\n asyncio.run(main())\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 22, in main\\n local=model.fk(q); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0])\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n q = joint_target(positions, self.joints, check_limits=False)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n vector(value, len(joints), \\\"joint target\\\")\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}", + "call_id": "call_0bd080577be548f4b8adb745b8436cb7|fc_0df8288370165955016ae268d2b184819da58990c63104de48", + "is_error": true + }, + { + "name": "edit", + "input": { + "path": ".kinex/tools/cartesian_move.py", + "edits": [ + { + "oldText": "local=model.fk(q);", + "newText": "local=model.fk(q.tolist());" + }, + { + "oldText": "model.ik([*lp,*lr.as_quat()],q)", + "newText": "model.ik([*lp,*lr.as_quat()],q.tolist())" + } + ] + }, + "output": "Successfully replaced 2 block(s) in .kinex/tools/cartesian_move.py.", + "call_id": "call_8a1beec4629348509212b3929faaae45|fc_0df8288370165955016ae268da030c819dbf655887159e5844", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "left": [ + [ + -0.167, + 0.045, + 0.835, + 0, + 0, + 0.70710678, + 0.70710678 + ] + ] + }, + "seconds": 2, + "grips": { + "left": 1 + } + }, + "description": "approach block with left arm" + }, + "output": "{\n \"output\": \"[INFO] [1793222879.778297666] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.16,\"waited_s\":2.16},\n \"left\": {\"q\":[-0.4095152020454407,2.26525616645813,0.7202089428901672,1.5543266534805298,-0.3688046932220459,0.013095114380121231],\"grip\":[1.0019286491654136],\"tcp\":[-0.17529561461992135,0.03572927260289499,0.8417034955027122,0.0007973011467065941,-0.001999688929063644,0.7090469286738864,0.7051580095930252],\"joint_error\":0.05355189650084757},\n \"right\": {\"q\":[-3.0567537834258474e-8,-0.000004077900030097226,0.000004151807388552697,1.029454779200023e-7,6.391106808223412e-7,-8.509506983500614e-7],\"grip\":[0.9861380918459459],\"tcp\":[0.30523325599148965,-0.19280648083225077,0.9410027440767832,-0.0000026235312808848334,-0.000008356054671572675,0.7106332329651421,0.7035626540179656]},\n \"file\": [\"/tmp/kinex-observation-2d4xk9x7.png\"]\n }\n}", + "call_id": "call_ef852ce2a3fa42c1b7603121b673065c|fc_0df8288370165955016ae268dcd920819dad51574c26d56fdd", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "left": [ + [ + -0.1665, + 0.195, + 0.835, + 0, + 0, + 0.70710678, + 0.70710678 + ] + ] + }, + "seconds": 1.2, + "settle": 0.28 + }, + "description": "insert left fingers around block" + }, + "output": "{\n \"output\": \"[INFO] [1793222895.152228323] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":3.64,\"waited_s\":1.48},\n \"left\": {\"q\":[-0.3148799538612366,2.548339366912842,1.4422779083251953,1.1141002178192139,-0.26099324226379395,0.009243687614798546],\"grip\":[1.0029944208535282],\"tcp\":[-0.16302799174504953,0.18793424064579894,0.8378929771294567,0.0013159567918552503,-0.0023886792458947675,0.7054448415417577,0.7087595770155819],\"joint_error\":0.035971618500195035},\n \"right\": {\"q\":[-3.054815778114062e-8,-0.000004089587491762359,0.000004202859599899966,1.0338320066693996e-7,6.390118301169423e-7,-8.509547910762194e-7],\"grip\":[0.9861380918459459],\"tcp\":[0.30523325596087636,-0.19280648459693756,0.9410027702409021,-0.00000260137554040625,-0.00000837856983353962,0.7106332330065999,0.7035626539759058]},\n \"file\": [\"/tmp/kinex-observation-vp29og6x.png\"]\n }\n}", + "call_id": "call_84a5f89207ab44108422f12d06faf6fe|fc_0df8288370165955016ae268ecd780819dbfea61b97bfb55ac", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "grips": { + "left": 0 + }, + "wait": 0.6, + "settle": 0.08 + }, + "description": "grasp block with left gripper" + }, + "output": "{\n \"output\": \"[INFO] [1793222907.657551470] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":4.32,\"waited_s\":0.6800000000000002},\n \"left\": {\"q\":[-0.3116726279258728,2.5611274242401123,1.4739559888839722,1.093721866607666,-0.2568727135658264,0.01041070744395256],\"grip\":[0.7422830950130116],\"tcp\":[-0.16291990834412706,0.19365203773945056,0.8379081966183919,0.0014630621736401283,-0.0012951390698152862,0.7051584468060392,0.7090470696378064]},\n \"right\": {\"q\":[-3.0559299091237335e-8,-0.000004090014499524841,0.0000042047331589856185,1.0359769930801122e-7,6.390172870851529e-7,-8.509651934218709e-7],\"grip\":[0.9861380918459459],\"tcp\":[0.3052332559637977,-0.19280648473495687,0.9410027712441067,-0.0000026004973479138483,-0.000008379470005694451,0.7106332330007554,0.7035626539818014]},\n \"file\": [\"/tmp/kinex-observation-b4k5muhh.png\"]\n }\n}", + "call_id": "call_d4d25ffdec764440a8d920893b4b8379|fc_0df8288370165955016ae268f83d8c819d9b0bda582d0266f5", + "is_error": false + }, + { + "name": "device", + "input": { + "deviceID": "left_camera", + "action": "load" + }, + "output": "{\n \"ready\": true\n}", + "call_id": "call_dd8bb15c54724ff19cebc4feb6f1c798|fc_0df8288370165955016ae2690f4434819d83d69eb89c0c6dee", + "is_error": false + }, + { + "name": "device", + "input": { + "deviceID": "left_camera", + "action": "image" + }, + "output": "{\n \"deviceID\": \"left_camera\",\n \"timestamp\": 4.32,\n \"frame_id\": \"left_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-n86hgu09.png\"\n}", + "call_id": "call_0d4c8455ed964e69b3382ae518dbc5a3|fc_0df8288370165955016ae269140184819dad82661f4b93b61a", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "left": [ + [ + -0.1665, + 0.195, + 0.955, + 0, + 0, + 0.70710678, + 0.70710678 + ], + [ + -0.045, + 0.08, + 0.955, + 0, + 0, + 0.70710678, + 0.70710678 + ] + ] + }, + "seconds": 1.1, + "settle": 0.24 + }, + "description": "lift block into handover position" + }, + "output": "{\n \"output\": \"[INFO] [1793222946.551821575] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.76,\"waited_s\":2.4399999999999995},\n \"left\": {\"q\":[-0.6309064626693726,2.1239354610443115,1.2338547706604004,0.8957475423812866,-0.5893312096595764,0.018119316548109055],\"grip\":[0.7441786920482463],\"tcp\":[-0.0523685806723061,0.08833117353535963,0.9531195122310145,0.00023793746021872822,0.002430648176331042,0.7080719927932259,0.7061360268086014],\"joint_error\":0.024221290566641795},\n \"right\": {\"q\":[-3.056838693282771e-8,-0.000004090662514499854,0.000004208202426525531,1.0363435620774908e-7,6.391171041286725e-7,-8.509500162290351e-7],\"grip\":[0.9861380918459459],\"tcp\":[0.30523325598005857,-0.19280648498184277,0.941002772994267,-0.000002599035845421926,-0.000008380945574451785,0.7106332329624366,0.7035626540204932]},\n \"file\": [\"/tmp/kinex-observation-3etlic6u.png\"]\n }\n}", + "call_id": "call_f03aba68b2054258833eeec424f4ef21|fc_0df8288370165955016ae269202310819d8c6b612a31709f40", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "right": [ + [ + 0.1, + 0.08, + 1.035, + 0, + 0, + 1, + 0 + ] + ] + }, + "seconds": 1.6, + "settle": 0.24 + }, + "description": "position right gripper beside upper block" + }, + "output": "{\n \"output\": \"[INFO] [1793222972.262569144] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":8.6,\"waited_s\":1.8399999999999999},\n \"left\": {\"q\":[-0.6511423587799072,2.11344051361084,1.2211240530014038,0.9018359780311584,-0.6114063262939453,0.01861589215695858],\"grip\":[0.7490885907953436],\"tcp\":[-0.04701344964065174,0.08107771982913375,0.9547894699199675,0.0005889016793547773,0.000834883228444015,0.7086906013221871,0.705518665778875]},\n \"right\": {\"q\":[0.0732794925570488,2.1728670597076416,1.6520055532455444,0.6286025643348694,-1.3997211456298828,0.10128568857908249],\"grip\":[0.9882686869664626],\"tcp\":[0.10952570589101249,0.0737831238675754,1.036011892666211,0.001709156521781137,-0.003622655986114087,0.9995123184453645,0.030969023613326],\"joint_error\":0.10358034141029537},\n \"file\": [\"/tmp/kinex-observation-yer04ywa.png\"]\n }\n}", + "call_id": "call_67fe784facee437ba2d1e675cc7fb71a|fc_0df8288370165955016ae26939f904819dbbd85a6b3783fee9", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/right_camera/profile.yaml", + "offset": null, + "limit": null + }, + "output": "name: right_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /right_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /right_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /right_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with\n pixel-center depth from a raster twin; calibration and the optical-frame mount on\n the right_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - 0.069999999887\n - 0.031999976227\n - 0.064999980711\n rotation_xyzw:\n - -0.58936798274\n - 0.589367967612\n - -0.390698619841\n - 0.390698640561\n parent: right_arm:flange\n", + "call_id": "call_665e4d6241744839bf7d6cc9eb33b0e9|fc_0df8288370165955016ae2694e02cc819d80aef0f662e92eef", + "is_error": false + }, + { + "name": "device", + "input": { + "deviceID": "right_camera", + "action": "load" + }, + "output": "{\n \"ready\": true\n}", + "call_id": "call_ad70f40c88794b67a41d4733eee70a00|fc_0df8288370165955016ae269502f98819d9582ca515d1a7cef", + "is_error": false + }, + { + "name": "device", + "input": { + "deviceID": "right_camera", + "action": "image" + }, + "output": "{\n \"deviceID\": \"right_camera\",\n \"timestamp\": 8.6,\n \"frame_id\": \"right_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-hf7pp7dk.png\"\n}", + "call_id": "call_bc90c007683e4565bc282ad130603e1c|fc_0df8288370165955016ae26954dcd0819d93afb7264c1c8f1b", + "is_error": false + }, + { + "name": "write", + "input": { + "path": ".kinex/tools/color_geometry.py", + "content": "\"\"\"Measure red block / blue pad visible surface bounds from calibrated RGB-D.\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation\nfrom kinex import args,communication,helpers,finish\nasync def main():\n out={}; files=[]\n async with communication() as io:\n for cam in args().get('cameras',['head_camera']):\n im,de=await helpers.camera_frame(io,cam); a=helpers.image_array(im).astype(float); z=helpers.image_array(de)*.001\n async with io.subscribe(cam+'.camera_info') as s: info=await s.next()\n fx,fy,cx,cy=info.k[0],info.k[4],info.k[2],info.k[5]\n tf=(await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'},timestamp=im.header.stamp.sec+im.header.stamp.nanosec*1e-9))['pose']\n out[cam]={}\n for k,m in {'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}.items():\n v,u=np.where(m&(z>0))\n if not len(u): continue\n zz=z[v,u]; p=np.stack([(u-cx)*zz/fx,(v-cy)*zz/fy,zz],axis=1); w=Rotation.from_quat(tf[3:]).apply(p)+tf[:3]\n out[cam][k]={'min':w.min(0),'max':w.max(0),'median':np.median(w,axis=0),'low':np.median(w[w[:,2]w[:,2].max()-.008],axis=0)}\n files.append(helpers.save_image(im))\n finish(out,file=files)\nasyncio.run(main())\n" + }, + "output": "Successfully wrote to .kinex/tools/color_geometry.py", + "call_id": "call_2a4098686c3d47a28c6a9f6357ff86a9|fc_0df8288370165955016ae2695ca4f8819d8273fa4384997686", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/color_geometry.py", + "args": { + "cameras": [ + "right_camera" + ] + }, + "description": "measure suspended block for transfer" + }, + "output": "{\n \"output\": \"[INFO] [1793223017.267591956] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"right_camera\": {\"red\":{\"min\":[-0.07704535908917484,0.05037363663380437,0.9359792452628558],\"max\":[-0.010132965239384423,0.11802476366946671,1.0577161411202283],\"median\":[-0.01790569017331503,0.08437329579285988,1.0223647101538353],\"low\":[-0.01968119294135831,0.11215254015326631,0.9426068395053633],\"high\":[-0.025401475412971358,0.08528556780882164,1.0568718285682772]}},\n \"file\": [\"/tmp/kinex-observation-wrxb6ic4.png\"]\n }\n}", + "call_id": "call_392303a6dc4e4ae19df80bdcb6b5dc8a|fc_0df8288370165955016ae269664b28819d967fff24cbfc385f", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "right": [ + [ + -0.044, + 0.084, + 1.025, + 0, + 0, + 1, + 0 + ] + ] + }, + "seconds": 1.2, + "settle": 0.32 + }, + "description": "insert right fingers for handover" + }, + "output": "{\n \"output\": \"[INFO] [1793223029.805918588] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.120000000000001,\"waited_s\":1.5200000000000014},\n \"left\": {\"q\":[-0.5804733037948608,2.104207992553711,1.1955286264419556,0.9432793855667114,-0.6488001346588135,0.010548126883804798],\"grip\":[0.7495477158914913],\"tcp\":[-0.09106256375115962,0.09291270090328957,0.9573460230231323,-0.00019867867634500307,-0.014277350018817896,0.7457316671533772,0.6660933856508923]},\n \"right\": {\"q\":[0.28108417987823486,2.3755812644958496,2.0089495182037354,0.41051310300827026,-1.1974685192108154,0.03320124372839928],\"grip\":[1.0481817857785658],\"tcp\":[-0.00454658121577145,0.09928463248112668,1.0355422699375194,0.00233159746702242,-0.005923685166572289,0.9996083980399332,0.027249296786112258],\"joint_error\":0.07316711639015039},\n \"file\": [\"/tmp/kinex-observation-skgw42gu.png\"]\n }\n}", + "call_id": "call_05f5697e44044c43b3be8540c2ffa31f|fc_0df8288370165955016ae26973b0f8819da4025981a45f1519", + "is_error": false + }, + { + "name": "device_stop", + "input": { + "reason": "Unexpected handover contact displaced the left arm; hold both arms before adjusting." + }, + "output": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped", + "call_id": "call_e5715b20a77640bab42f190a5e5e42d4|fc_0df8288370165955016ae269811e7c819db816fc229d8738d1", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/color_geometry.py", + "args": { + "cameras": [ + "right_camera", + "left_camera" + ] + }, + "description": "inspect contact geometry while held" + }, + "output": "{\n \"output\": \"[INFO] [1793223063.023708069] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"right_camera\": {\"red\":{\"min\":[-0.1384726838486533,0.06276790815003651,0.962540244023994],\"max\":[-0.05922303365747568,0.13124786536745542,1.060490489585483],\"median\":[-0.06550222925211087,0.09469825304345947,1.0415251530870966],\"low\":[-0.11114994769135768,0.06490913732700883,0.9669796277877257],\"high\":[-0.07376581631004708,0.09461002694122791,1.0596298811854297]}},\n \"left_camera\": {\"red\":{\"min\":[-0.15688127335414823,0.12266262357040175,0.713505453118275],\"max\":[0.037058832820139165,0.40054043859581245,1.0031299882724507],\"median\":[-0.07028974145388796,0.40000044254322253,0.880343311680841],\"low\":[-0.12835342427817242,0.4000191366344823,0.718829186316962],\"high\":[-0.06946718738984804,0.4000030238401364,0.998369265895211]}},\n \"file\": [\"/tmp/kinex-observation-95p9oprm.png\",\"/tmp/kinex-observation-8vj3yjvv.png\"]\n }\n}", + "call_id": "call_bb94d9acc5464dd6b9f05f68aecb9b32|fc_0df8288370165955016ae26994f0e0819d81f48545801ee34c", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "right": [ + [ + 0.15, + 0.1, + 1.05, + 0, + 0, + 1, + 0 + ] + ] + }, + "seconds": 1, + "grips": { + "right": 1 + }, + "settle": 0.24 + }, + "description": "withdraw right arm from contact" + }, + "output": "{\n \"output\": \"[INFO] [1793223090.836931004] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":11.36,\"waited_s\":1.2399999999999984},\n \"left\": {\"q\":[-0.5804134607315063,2.1041758060455322,1.1955393552780151,0.9432682394981384,-0.6488250494003296,0.010563568212091923],\"grip\":[0.7530979514122009],\"tcp\":[-0.09109739092829977,0.09292400028290476,0.9573646424377735,-0.0001907213903724974,-0.014287166396507055,0.7457598088703344,0.6660616697989699]},\n \"right\": {\"q\":[0.008498204872012138,2.4111969470977783,1.9728046655654907,1.1586765050888062,-1.5211362838745117,0.7175896763801575],\"grip\":[1.0059774978594347],\"tcp\":[0.13843104276620039,0.10128114809864314,1.0534453784964337,0.005817304808264196,-0.001298347103139134,0.9999791132900113,-0.0024992485451372226],\"joint_error\":0.1610297677623429},\n \"file\": [\"/tmp/kinex-observation-cvsfv2kv.png\"]\n }\n}", + "call_id": "call_e308aa226da84617af9899822c0d2548|fc_0df8288370165955016ae269aff950819da6e7504915ef17cc", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "left": [ + [ + -0.09, + 0.04, + 0.97, + -0.5, + -0.5, + 0.5, + 0.5 + ] + ] + }, + "seconds": 1.6, + "grips": { + "left": 0 + }, + "settle": 0.32 + }, + "description": "orient block horizontally for clear handover" + }, + "output": "{\n \"output\": \"[INFO] [1793223113.323128756] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.280000000000001,\"waited_s\":1.9200000000000017},\n \"left\": {\"q\":[-0.6108473539352417,1.9071658849716187,0.9704519510269165,0.9435008764266968,-0.5798410177230835,-1.5156503915786743],\"grip\":[0.18181818181818182],\"tcp\":[-0.09249703641535548,0.04354302206857258,0.9685886998113872,-0.4883985901176337,-0.49195225124892156,0.5129229388662707,0.506319917094927],\"joint_error\":0.03695103243016362},\n \"right\": {\"q\":[-0.024585271254181862,2.5281856060028076,2.199103832244873,1.4551665782928467,-1.5355974435806274,0.8628689646720886],\"grip\":[0.9977146685123444],\"tcp\":[0.15515497764595165,0.105221917700341,1.0726259319873463,0.0034350023240606736,-0.13122159004534528,0.991332287535084,0.005421324142934947]},\n \"file\": [\"/tmp/kinex-observation-ytn17zdz.png\"]\n }\n}", + "call_id": "call_d319f968a02046a78f2fab44a70a25bf|fc_0df8288370165955016ae269c72e30819d9131a859ab2c6a6d", + "is_error": false + }, + { + "name": "device_stop", + "input": { + "reason": "Block slipped during reorientation; hold before recovering from table." + }, + "output": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped", + "call_id": "call_00a46d3a1de74717afcc835ecd9791bd|fc_0df8288370165955016ae269d774b8819d964cacc5ae8a6955", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/color_geometry.py", + "args": { + "cameras": [ + "head_camera" + ] + }, + "description": "locate block after slip" + }, + "output": "{\n \"output\": \"[INFO] [1793223137.713473185] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"head_camera\": {\"red\":{\"min\":[-0.14741029664711341,-0.22615111740019297,0.7399306176168698],\"max\":[-0.04856247048801367,0.1220448308461407,1.051112871594798],\"median\":[-0.06579691841584068,0.06822012458681859,0.8542956355803348],\"low\":[-0.06418249593562522,0.06932628355036818,0.7452554129330493],\"high\":[-0.14378272558189698,-0.2207285243094647,1.0450464282700715]},\"blue\":{\"min\":[0.06308362714636938,0.1010429531080218,0.742188561370138],\"max\":[0.1623789745629382,0.1994037568596595,0.7463403147189581],\"median\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"low\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"high\":[0.11265531983118941,0.14487610727830103,0.7460899470953907]}},\n \"file\": [\"/tmp/kinex-observation-2g7v533u.png\"]\n }\n}", + "call_id": "call_eb02c6d16c854fc6ab801dc3c0348daa|fc_0df8288370165955016ae269de4638819dab357140c3c38236", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "left": [ + [ + -0.09, + -0.08, + 0.87, + 0, + 0, + 0.70710678, + 0.70710678 + ], + [ + -0.09, + -0.04, + 0.79, + 0, + 0, + 0.70710678, + 0.70710678 + ] + ] + }, + "seconds": 0.8, + "grips": { + "left": 1 + }, + "settle": 0.24 + }, + "description": "reposition left arm for lower grasp" + }, + "output": "{\n \"output\": \"[INFO] [1793223148.217007165] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":15.120000000000001,\"waited_s\":1.8399999999999999},\n \"left\": {\"q\":[-0.7753829956054688,2.486633777618408,0.6931433081626892,1.7631256580352783,-0.718991756439209,0.010876113548874855],\"grip\":[0.9957061274485155],\"tcp\":[-0.0874784424367745,-0.044104560377094204,0.7831832909467894,-0.0016927903651302151,0.018796832322172552,0.7041006341624548,0.7098493576298149],\"joint_error\":0.03182466435917464},\n \"right\": {\"q\":[-0.03216967731714249,2.560060977935791,2.2720913887023926,1.4922796487808228,-1.5310924053192139,0.8622229099273682],\"grip\":[0.9966695552522487],\"tcp\":[0.15903478459190437,0.10693136194152325,1.0789327091897247,0.005149923323030299,-0.17022306684924737,0.9853442948891231,0.009705994625001104]},\n \"file\": [\"/tmp/kinex-observation-6nj6l9eh.png\"]\n }\n}", + "call_id": "call_8b8659ebbcb24dda892dfd3c4680f63c|fc_0df8288370165955016ae269e7cad0819dbeea659116997c62", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/color_geometry.py", + "description": "refine block position for recovery" + }, + "output": "{\n \"output\": \"[INFO] [1793223161.661026146] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"head_camera\": {\"red\":{\"min\":[-0.11061178078762514,-0.2457689362177062,0.7399306176168698],\"max\":[-0.03470604127310729,0.1427277026477306,0.9475643068469688],\"median\":[-0.06918190786485898,0.07990095152521826,0.8826486653824216],\"low\":[-0.06299468250458584,0.07044721886777061,0.7465854293892895],\"high\":[-0.06552924214969097,0.10698729422722852,0.9434633257962752]},\"blue\":{\"min\":[0.06308362714636938,0.1010429531080218,0.742188561370138],\"max\":[0.1623789745629382,0.1994037568596595,0.7463403147189581],\"median\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"low\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"high\":[0.11265531983118941,0.14487610727830103,0.7460899470953907]}},\n \"file\": [\"/tmp/kinex-observation-iop8tv52.png\"]\n }\n}", + "call_id": "call_8581f5bf27f44880b0d46ef9b939d9c9|fc_0df8288370165955016ae269f703cc819d947d80dd777ecefe", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "left": [ + [ + -0.068, + 0.1, + 0.785, + 0, + 0, + 0.70710678, + 0.70710678 + ] + ] + }, + "seconds": 0.9, + "settle": 0.24 + }, + "description": "grasp approach near block bottom" + }, + "output": "{\n \"output\": \"[INFO] [1793223170.440709280] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":16.28,\"waited_s\":1.1600000000000001},\n \"left\": {\"q\":[-0.600791871547699,2.6094110012054443,1.232616662979126,1.3856762647628784,-0.5472012162208557,0.00989964697510004],\"grip\":[1.0027754415165295],\"tcp\":[-0.066016572510952,0.09042241913998889,0.7872048785646522,0.0005284683532509702,-0.0016981723383381042,0.7057319257426141,0.7084767363290981],\"joint_error\":0.03893985785541654},\n \"right\": {\"q\":[-0.03247563913464546,2.560077667236328,2.272132158279419,1.4922877550125122,-1.5310924053192139,0.86222243309021],\"grip\":[0.9964158291166479],\"tcp\":[0.15919550134342006,0.10697694676322184,1.078935974341295,0.005124039207660705,-0.1702399443739578,0.9853400206235566,0.009856435426625896]},\n \"file\": [\"/tmp/kinex-observation-56ho_eio.png\"]\n }\n}", + "call_id": "call_58753371f6d1448cba7c50573184d598|fc_0df8288370165955016ae269feb958819db51ac98dd38910c3", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "grips": { + "left": 0 + }, + "wait": 0.48, + "settle": 0.08 + }, + "description": "secure lower left grasp" + }, + "output": "{\n \"output\": \"[INFO] [1793223186.546428863] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":16.84,\"waited_s\":0.5599999999999987},\n \"left\": 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{\"q\":[0.07760625332593918,2.268001079559326,1.7254252433776855,0.6678985953330994,-1.4511550664901733,0.11310850828886032],\"grip\":[0.9975609156218442],\"tcp\":[0.10518671059120635,0.08316107064569606,1.0163136369861834,0.0003802638280060978,-0.0063742821117829885,0.9999764069935545,0.0025316759748120754],\"joint_error\":0.01376317773439939},\n \"file\": [\"/tmp/kinex-observation-7wv0mr12.png\"]\n }\n}", + "call_id": "call_934c885fe79a4fab8ed1cbf467f05ed2|fc_0df8288370165955016ae26a1a9720819da1374a265adcbfca", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/color_geometry.py", + "args": { + "cameras": [ + "right_camera" + ] + }, + "description": "align upper handover grasp" + }, + "output": "{\n \"output\": \"[INFO] [1793223211.784921453] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"right_camera\": {\"red\":{\"min\":[-0.12343917734098808,0.0605901090306426,0.8646018955545028],\"max\":[-0.03777323677106045,0.12671369821273332,1.036432952542873],\"median\":[-0.04040799159067776,0.09224365837616032,0.9880418815912079],\"low\":[-0.09459596192475068,0.06060752452967939,0.8692415165210006],\"high\":[-0.04925679433343136,0.09392724195870489,1.0344916769744117]}},\n \"file\": [\"/tmp/kinex-observation-evmbcelr.png\"]\n }\n}", + "call_id": "call_22d70f83a8264fd394d871fac7ab9364|fc_0df8288370165955016ae26a28dc0c819d857ea530bb2ec73a", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "right": [ + [ + -0.07, + 0.093, + 1.005, + 0, + 0, + 1, + 0 + ] + ] + }, + "seconds": 1, + "settle": 0.32 + }, + "description": "reach upper block with right gripper" + }, + "output": "{\n \"output\": \"[INFO] [1793223223.295463317] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":19.48,\"waited_s\":1.3200000000000003},\n \"left\": {\"q\":[-0.6064146161079407,2.2680065631866455,1.1165276765823364,1.1589206457138062,-0.564342737197876,0.01636873371899128],\"grip\":[0.7491666525602341],\"tcp\":[-0.06978309887078618,0.08097908591451436,0.8845432426730855,0.0005590646864575862,0.0009305853341717258,0.7085652278336617,0.7056444851075103]},\n \"right\": {\"q\":[0.374615341424942,2.502243757247925,2.131927251815796,0.397475004196167,-1.1541471481323242,0.024385878816246986],\"grip\":[1.0017903392965144],\"tcp\":[-0.06014010459146446,0.09413422683807998,1.0075094922363719,0.0009083332956943216,-0.002139926032686006,0.9999960431704957,0.0015840740357489504],\"joint_error\":0.02744833812895031},\n \"file\": [\"/tmp/kinex-observation-65ioy38v.png\"]\n }\n}", + "call_id": "call_3efcf60ab16e49f7b8f958d1df555eea|fc_0df8288370165955016ae26a33a008819db8cf019a718b5711", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "grips": { + "right": 0 + }, + "wait": 0.48, + "settle": 0.12 + }, + "description": "close right gripper on upper block" + }, + "output": "{\n \"output\": \"[INFO] [1793223236.977331379] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.080000000000002,\"waited_s\":0.6000000000000014},\n \"left\": {\"q\":[-0.6069324612617493,2.2672977447509766,1.1154533624649048,1.1594316959381104,-0.5664319396018982,0.017290595918893814],\"grip\":[0.7638818540356376],\"tcp\":[-0.06995436989865308,0.08067682635973633,0.8846298546530436,0.0008844987711302622,0.001196786566270506,0.709116506111797,0.7050897574944884]},\n \"right\": {\"q\":[0.383068323135376,2.515486478805542,2.1559560298919678,0.3830629885196686,-1.1423680782318115,0.02483583614230156],\"grip\":[0.7542767009951852],\"tcp\":[-0.0654515172886107,0.09498601759678144,1.0070095627793196,0.0004312189457866284,-0.00020835919012824746,0.9999947648052887,0.003200156040570001]},\n \"file\": [\"/tmp/kinex-observation-06vm8zsn.png\"]\n }\n}", + "call_id": "call_48627d0884674034aa8408dbe7ad1023|fc_0df8288370165955016ae26a42433c819d9b68b93b6b31ec42", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "grips": { + "left": 1 + }, + "wait": 0.36, + "settle": 0.08 + }, + "description": "release left grip for transfer" + }, + "output": "{\n \"output\": \"[INFO] [1793223245.217918798] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.52,\"waited_s\":0.4399999999999977},\n \"left\": {\"q\":[-0.6075312495231628,2.266892671585083,1.1154754161834717,1.15908682346344,-0.5642680525779724,0.015033591538667679],\"grip\":[0.9626180990175768],\"tcp\":[-0.06932564109333014,0.08054488784278918,0.8847849114380533,0.00010642963722792084,0.000344471082472518,0.708141468352586,0.7060704857249375]},\n \"right\": {\"q\":[0.384282648563385,2.518695592880249,2.158612012863159,0.38260990381240845,-1.1413120031356812,0.02465474046766758],\"grip\":[0.7497303905812177],\"tcp\":[-0.06626623117850927,0.09513037725465906,1.006063905169769,0.00026264212673088436,0.00017109248919661276,0.9999951122919751,0.003110809964219864]},\n \"file\": [\"/tmp/kinex-observation-fc7wmhrj.png\"]\n }\n}", + "call_id": "call_97ad15d705734b009f8daafaa9d70368|fc_0df8288370165955016ae26a4a827c819db0f21d2c31d4d03e", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "left": [ + [ + -0.12, + -0.09, + 0.89, + 0, + 0, + 0.70710678, + 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{\"q\":[0.38584333658218384,2.520796298980713,2.1592612266540527,0.38251370191574097,-1.1410601139068604,0.024593651294708252],\"grip\":[0.74832460148768],\"tcp\":[-0.06717741407694655,0.09484375843603249,1.0050543348354926,-0.00003133563748486725,0.0008554720467265697,0.9999966224054394,0.0024540504005274295]},\n \"file\": [\"/tmp/kinex-observation-s9tov1ew.png\"]\n }\n}", + "call_id": "call_01e8151c27654816bbc769bfdb904ac2|fc_0df8288370165955016ae26a5457e8819dbdc0a8413c14c9e3", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/color_geometry.py", + "description": "measure block offset in right grasp" + }, + "output": "{\n \"output\": \"[INFO] [1793223269.388614648] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"head_camera\": {\"red\":{\"min\":[-0.15821772589290822,-0.2987673906932839,0.8398380605979726],\"max\":[0.14645363087424962,0.06300409512308258,1.0602012979118725],\"median\":[-0.0769250015221273,0.06051332279327992,0.921461141233301],\"low\":[-0.07438347114092453,0.05827808110437249,0.8424585763634667],\"high\":[0.14441538763929712,-0.10189180637681439,1.055024763226523]},\"blue\":{\"min\":[0.06308362714636938,0.1010429531080218,0.742188561370138],\"max\":[0.16214894027171578,0.1994037568596595,0.7463403147189581],\"median\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"low\":[0.11265531983118941,0.14487610727830103,0.7460899470953907],\"high\":[0.11265531983118941,0.14487610727830103,0.7460899470953907]}},\n \"file\": [\"/tmp/kinex-observation-u10oxgvz.png\"]\n }\n}", + "call_id": "call_adac36b50ed2476dac845a7d3e1c6141|fc_0df8288370165955016ae26a610660819db7445f121efb0438", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "right": [ + [ + 0.12, + 0.157, + 1.015, + 0, + 0, + 1, + 0 + ] + ] + }, + "seconds": 1.5, + "settle": 0.32 + }, + "description": "carry block above blue pad" + }, + "output": "{\n \"output\": \"[INFO] [1793223281.404363054] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":23.48,\"waited_s\":1.8399999999999999},\n \"left\": {\"q\":[-0.8029563426971436,1.903110384941101,0.38487792015075684,1.5342161655426025,-0.7633680105209351,0.021171852946281433],\"grip\":[0.9984204416925258],\"tcp\":[-0.12033357727388883,-0.08948404422128958,0.8902496235827575,0.000004593705238664185,-0.0005345615343604223,0.7076974864572219,0.7065153797936703]},\n \"right\": {\"q\":[0.05061691999435425,2.6250691413879395,2.372485637664795,0.37307676672935486,-1.4840843677520752,0.12098056077957153],\"grip\":[0.7465521422299473],\"tcp\":[0.11225161767899541,0.15427922863367133,1.016213647769426,0.0003283806839788719,0.00014140216044888562,0.9999977304223577,-0.002100314664298886],\"joint_error\":0.016504502293561263},\n \"file\": [\"/tmp/kinex-observation-2umqg4z9.png\"]\n }\n}", + "call_id": "call_ca55b142e5ad465f96f510d6e1d8e95b|fc_0df8288370165955016ae26a6eecc0819d88d31c614d38ebae", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "moves": { + "right": [ + [ + 0.12, + 0.157, + 0.916, + 0, + 0, + 1, + 0 + ] + ] + }, + "seconds": 1, + "settle": 0.32 + }, + "description": "lower block to pad" + }, + "output": "{\n \"output\": \"[INFO] [1793223298.768252578] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":24.8,\"waited_s\":1.3200000000000003},\n \"left\": {\"q\":[-0.8042228817939758,1.902944564819336,0.3838373124599457,1.5342135429382324,-0.76336669921875,0.021171730011701584],\"grip\":[0.9993253485722976],\"tcp\":[-0.12008906515642134,-0.08988933922742132,0.8900465762383285,0.0000010772669426179937,-0.00009598160339580161,0.7072440127824126,0.7069695164360356]},\n \"right\": {\"q\":[0.04501097649335861,2.72459077835083,2.1980509757995605,0.6329107880592346,-1.4919719696044922,0.10310453176498413],\"grip\":[0.7487442723729394],\"tcp\":[0.11575673991909216,0.1556721693320849,0.9233802663062167,0.0001254167071825891,-0.0017099633021399826,0.9999923417559874,-0.003518057067655621],\"joint_error\":0.011100574249183759},\n \"file\": [\"/tmp/kinex-observation-cc3ukbqz.png\"]\n }\n}", + "call_id": "call_efcee2095f0c4fc1a8b625d24f4007f7|fc_0df8288370165955016ae26a808488819db85a3e2520c51774", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/color_geometry.py", + "description": "check block base against pad surface" + }, + "output": "{\n \"output\": \"[INFO] [1793223311.719651992] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"head_camera\": {\"red\":{\"min\":[-0.16217849814359225,-0.30906706361326325,0.7456756236374391],\"max\":[0.16107405276545833,0.1959731213087743,0.9920060863424123],\"median\":[0.0997301809589289,0.12246768758288817,0.85327489402623],\"low\":[0.1336706950183812,0.14817067406106688,0.7460941018292808],\"high\":[-0.15934001440462964,-0.3056585705895022,0.9859815823860145]},\"blue\":{\"min\":[0.06308362714636938,0.1010429531080218,0.742188561370138],\"max\":[0.1623789745629382,0.1994037568596595,0.7463403147189581],\"median\":[0.1071707446695593,0.11953112527781917,0.7459859885183578],\"low\":[0.1071707446695593,0.11953112527781917,0.7459859885183578],\"high\":[0.1071707446695593,0.11953112527781917,0.7459859885183578]}},\n \"file\": [\"/tmp/kinex-observation-syl6cojd.png\"]\n }\n}", + "call_id": "call_362ee813eb8043ed89a7878277728ebb|fc_0df8288370165955016ae26a8ce66c819d8150edfe352a3e17", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_move.py", + "args": { + "grips": { + "right": 1 + }, + "wait": 0.6, + "settle": 0.2 + }, + "description": "release block with right gripper open" + }, + "output": "{\n \"output\": \"[INFO] [1793223323.319521528] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"episode ended during wait\",\n \"code\": \"operation_rejected\",\n \"details\": {\n \"operation\": \"world.wait\",\n \"type\": \"kinex_interfaces/srv/Wait\",\n \"response\": {\"success\":false,\"message\":\"episode ended during wait\",\"time_s\":24.84,\"waited_s\":0.03999999999999915}\n }\n}", + "call_id": "call_d417da052c89404488e8e334db4f4b99|fc_0df8288370165955016ae26a9937f4819d9a4e435b14d8c0ba", + "is_error": true + }, + { + "name": "devices", + "input": { + "calls": [ + { + "deviceID": "right_gripper", + "action": "joint_state" + }, + { + "deviceID": "head_camera", + "action": "image" + }, + { + "deviceID": "right_arm", + "action": "joint_state" + } + ] + }, + "output": "{\n \"results\": [\n {\"deviceID\":\"right_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.761039051142606],\"velocities\":[0.8033322339708154],\"joint_names\":[\"right_gripper\"],\"timestamp\":24.84}},\n {\"deviceID\":\"head_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":24.84,\"frame_id\":\"head_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-j_sgq_yb.png\"},\n {\"deviceID\":\"right_arm\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.04493730142712593,2.7255666255950928,2.1971681118011475,0.6346826553344727,-1.4920541048049927,0.10271850973367691],\"velocities\":[-0.0010401532053947449,0.030199257656931877,-0.004802028648555279,0.03646739572286606,-0.00745355011895299,0.00008076801896095276],\"joint_names\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"timestamp\":24.84}}\n ]\n}", + "call_id": "call_a35d585c0d0f4e70bd3ea84c2d1293c4|fc_0df8288370165955016ae26aa31414819da4c32efcd331f803", + "is_error": false + }, + { + "name": "write", + "input": { + "path": ".kinex/memos/handover-block.md", + "content": "# RoboTwin ALOHA handover block\n\nPublic interfaces: read harness.md and refreshed profiles. Native period .04s; 800 frames =32s. All movement needs sequential world.wait. Load arms, grippers, head camera and right wrist camera. Normalized grip 0 closed,1 open. TCP +.12m flange x. World arm base transforms are calibrated; use TF and URDF IK.\n\nReusable exec scripts:\n- `.kinex/tools/cartesian_move.py`: args moves={left/right:[world TCP poses xyz+xyzw]}, seconds per segment, grips, wait, settle, observe. Precomputes Cartesian interpolated IK then publishes JointTrajectory; returns measured TCP, q error and image. Kinex FK/IK requires list, not NumPy arrays.\n- `.kinex/tools/color_geometry.py`: args cameras; red/blue visible world surface bounds from synchronized RGBD and stamped TF. Bounds include silhouette/depth mismatches and red highlights; inspect images, use interior pixels if precision required. Wrist depth is unreliable inside .1m.\n\nSuccessful transfer geometry observed: vertical red block ~.06x.06x.20m, table z .74, pad top .746. Detect current xy each episode. Head camera clips tall block top. Pad center from min/max xy, not perspective-biased pixel median.\n\nGrasp LOW with left TCP at table+.045 (not +.095) using yaw +90deg quaternion [0,0,.70710678,.70710678], approach +world y, fingers close along x. Approach from y-.15 then insert center. Close .5s, lift .10m. Keep vertical; rolling a central grip caused a slip.\nRight approaches from +world x with yaw pi quaternion [0,0,1,0], closes along y. Target ~.03 below measured block top, ~.12-.13 above left TCP. Too little separation (~.07-.08) caused collision with left wrist/camera and displaced the block; stop before recovery. Lower left grasp fixed it. Right camera gives useful top and side geometry before insertion.\nClose right .6s, open left .44s, withdraw left frontward ~.17m; verify block remains suspended with right (grip ~.75 for .06m block). Then translate above pad center, lower upright preserving grip offset, open right. In this run transfer and pad placement observed; release wait terminated at 24.84s after .04s, before requested .8s. Last measured right opening .761, velocity positive; full-open motion not verified. Native verdict remains owner-only. Never retry world waits after terminal or infer success merely from termination.\n" + }, + "output": "Successfully wrote to .kinex/memos/handover-block.md", + "call_id": "call_9777e5a5cde145f7bb58fc5722c35986|fc_0df8288370165955016ae26ab42140819da786d33d904a69af", + "is_error": false + } +] diff --git a/experiments/robotwin-native/kinex-1/trace.json b/experiments/robotwin-native/kinex-1/trace.json new file mode 100644 index 0000000000000000000000000000000000000000..d9119e7eb211f3c336bdf6f52ed1e28f50e3a49c --- /dev/null +++ b/experiments/robotwin-native/kinex-1/trace.json @@ -0,0 +1 @@ +{"schema": "rlebench/kinex-trace/1", "source": "Pi native session", "usage_source": "pi-normalized", "cache_coverage": "normalized-values", "reasoning_coverage": "normalized-values", "info": {"type": "session", "version": 3, "id": "01a18467-8ce0-753f-a7eb-20be90a95c04", "timestamp": "2026-10-28T21:25:41.216Z", "cwd": "/workspace"}, "messages": [{"id": "14c17edb", "role": "system", "timestamp": "2026-10-28T21:25:43.964Z", "parts": [{"type": "text", "text": ""}]}, {"id": "dbbee242", "role": "user", "timestamp": "2026-10-28T21:25:43.964Z", "parts": [{"type": "text", "text": "# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes\nin `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place."}]}, {"id": "a2d6bda0", "role": "context", "timestamp": "2026-10-28T21:25:43.965Z", "parts": [{"type": "text", "text": "\ncore.guides:\nDevice guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-3106dcd2e6594338a2b1e037988e4bc4/config/device, /opt/kinex/packages/core/assets/builtin/device.\n\ncore.python:\n{\"provider\":\"ros-jazzy\",\"project\":\"/workspace/.kinex/python\",\"venv\":\"/workspace/.kinex/python/.pixi/envs/default\",\"executable\":\"/workspace/.kinex/python/.pixi/envs/default/bin/python3\",\"present\":true,\"library\":\"/opt/kinex/packages/core/assets/python\",\"workspaceLibrary\":\"/workspace/.kinex/lib\"}\n\ncore.recording:\n(none)\n\ncore.skills:\n\nLoad a skill with the skill tool when its description matches the task.\n - calibrate-camera-extrinsics: Measure a camera mount from robot poses and board observations when a task needs camera-to-robot coordinates.\n - calibrate-camera-intrinsics: Measure color-camera projection and distortion when the task needs calibrated image geometry; RGB-D depth facts remain separate.\n - calibrate-dual-arm: Measure the relative transform of two arms using their calibrated TCPs when a task needs cross-arm coordinates.\n - calibrate-fixed-arm-mount: Survey an arm base relative to its rigid parent, including a mobile base or moving torso attachment frame.\n - calibrate-gripper-grip: Establish a gripper's grip joint and its open and closed positions when a task needs grip commands or policy grip values.\n - calibrate-joint-range: Resolve joint limits from models or referenced measurements only when required; world geometry is separate.\n - calibrate-sensor-bias: Measure IMU gyro/accelerometer or force/torque bias from static, unloaded readings when a task needs bias-corrected measurements.\n - calibrate-tcp-offset: Measure the tool-tip translation in a gripper or suction tool's base by pivot touches when a task needs a TCP; orientation remains a separate fact.\n - calibrate-world-geometry: Record independently measured world bounds or surfaces only for tasks that need those spatial facts.\n - setup-device: Add a device or repair its ROS nodes — choose an instance or type guide, provision its runtime, fill a profile and verify the interface. [resolves: access:world,access:locomotion,access:joint,access:gripper,access:suction,access:rgb,access:rgbd,access:imu,access:force_torque,access:model,profile:model,access:other]\n - verify-base-interface: Establish missing base control and feedback conventions for a task, without requiring a full mobility calibration.\n - verify-base-motion: Measure selected base motion, expiry or stopping behavior when the current task needs evidence about those operations.\n - verify-base-posture: Verify selected height or attitude references and moving attachment frames for a base that exposes posture control.\n\n\nproblems:\n(none)\n"}], "context": true}, {"id": "989a0c21", "role": "assistant", "timestamp": "2026-10-28T21:25:46.605Z", "parts": [{"type": "tool", "callID": "call_557120d31d354bdcb113ec14e5b73fe8|fc_0df8288370165955016ae2685a2e50819da406f073fdf4fb3a", "name": "ls", "input": {"path": "/workspace"}, "output": ".kinex/\nharness.md\ninstruction.md", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11322, "outputTokens": 19, "cachedInputTokens": 0, "cacheReportedInputTokens": 11322, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11322, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 19}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "3057c142", "role": "toolResult", "timestamp": "2026-10-28T21:25:46.614Z", "parts": [{"type": "text", "text": ".kinex/\nharness.md\ninstruction.md"}]}, {"id": "ef51bca7", "role": "assistant", "timestamp": "2026-10-28T21:25:48.800Z", "parts": [{"type": "tool", "callID": "call_8340aa3e10fc46f0aa98cbada5362911|fc_0df8288370165955016ae2685c096c819d88323bf779a16f98", "name": "read", "input": {"path": "/workspace/harness.md", "offset": null, "limit": null}, "output": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11362, "outputTokens": 29, "cachedInputTokens": 11136, "cacheReportedInputTokens": 11362, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11362, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 29}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "e5134a1e", "role": "toolResult", "timestamp": "2026-10-28T21:25:48.807Z", "parts": [{"type": "text", "text": "Use Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n"}]}, {"id": "762966bb", "role": "assistant", "timestamp": "2026-10-28T21:25:51.841Z", "parts": [{"type": "tool", "callID": "call_aae6560e66c84c358b4fd70c7bb8d73d|fc_0df8288370165955016ae2685f34b8819d8019f73c6e473a17", "name": "find", "input": {"pattern": "**/*", "path": "/workspace/.kinex", "limit": 150}, "output": 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results limit reached. 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results limit reached. Use limit=300 for more, or refine pattern]"}]}, {"id": "bd24ee21", "role": "assistant", "timestamp": "2026-10-28T21:25:56.807Z", "parts": [{"type": "tool", "callID": "call_73a5e4b6bb4444bca2d098facaed338e|fc_0df8288370165955016ae26861d2a8819d88ec34b3281ae1ee", "name": "read", "input": {"path": ".kinex/workspace.yaml", "offset": null, "limit": null}, "output": "identity:\n id: robotwin-aloha-agilex\n name: RoboTwin ALOHA AgileX\n kind: arm\n model: ALOHA AgileX (dual ARX5)\n source: user\ndevices:\n source: user\nsafety:\n source: user\n max_joint_velocity: null\n max_cartesian_velocity: null\n keep_out_zones: []\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_6625a9cb6f434c88b5bf2fff5cb3be8d|fc_0df8288370165955016ae26864b82c819db4dabc0cb6072c19", "name": "read", "input": {"path": ".kinex/devices/world/profile.yaml", "offset": null, "limit": null}, "output": "name: world\nruntime:\n domain_id: 83\n observation: time\n nodes: []\n ready_timeout_s: 30\noperations:\n time:\n description: Authoritative world clock\n transport: topic\n direction: output\n endpoint: /clock\n type: rosgraph_msgs/msg/Clock\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n wait:\n description: Advance or await duration_s of world time (finite and nonnegative).\n Zero is an observation barrier. Waits are sequential; one requested while another\n is pending is rejected. A terminal episode rejects further progression without\n exposing benchmark results.\n transport: service\n direction: input\n endpoint: /world/wait\n type: kinex_interfaces/srv/Wait\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 120\n stop:\n description: Cancel the pending wait.\n transport: service\n direction: input\n endpoint: /world/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n Kinex supplies frame transforms independently. This device exposes no task instructions\n or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n base:\n parent: null\n translation_m: null\n rotation_xyzw: null\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_ada7312797534f87a5c4cbbf16b557a1|fc_0df8288370165955016ae26864b82c819d9cad53b113c442d8", "name": "read", "input": {"path": ".kinex/devices/left_arm/profile.yaml", "offset": null, "limit": null}, "output": "name: left_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /left_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /left_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /left_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n as left_arm/, and helpers.joint_model('left_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fl_joint1: 0.0\n fl_joint2: 0.0\n fl_joint3: 0.0\n fl_joint4: 0.0\n fl_joint5: 0.0\n fl_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - left_arm\nframes:\n base:\n translation_m:\n - -0.296999871731\n - -0.419500023127\n - 0.782000243664\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 0.714142563298\n - 0.700000389985\n parent: world:base\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_4f4713cdf22247a083c3322102fda0b3|fc_0df8288370165955016ae26864b82c819da747d21af5abb784", "name": "read", "input": {"path": ".kinex/devices/right_arm/profile.yaml", "offset": null, "limit": null}, "output": "name: right_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /right_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /right_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /right_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n as right_arm/, and helpers.joint_model('right_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fr_joint1: 0.0\n fr_joint2: 0.0\n fr_joint3: 0.0\n fr_joint4: 0.0\n fr_joint5: 0.0\n fr_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - right_arm\nframes:\n base:\n translation_m:\n - 0.306300014257\n - -0.418499916792\n - 0.78100001812\n rotation_xyzw:\n - -2.9802e-08\n - 5.9605e-08\n - 0.71063355368\n - 0.703562409721\n parent: world:base\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_1424db23db3e45cca0546ad2f1550656|fc_0df8288370165955016ae26864b82c819d8f47a10a70595903", "name": "read", "input": {"path": ".kinex/devices/left_gripper/profile.yaml", "offset": null, "limit": null}, "output": "name: left_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured left_gripper, the native normalized gripper opening (0 closed,\n 1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n it.\n transport: topic\n direction: output\n endpoint: /left_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /left_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /left_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - left_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: left_arm:flange\n tcp:\n translation_m:\n - 0.1199999989\n - 7.305e-09\n - -7.152e-09\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 3.2247e-08\n - 0.999999998609\n parent: left_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: left_gripper\n open: 1.0\n closed: 0.0\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_91eef032bd0f4752bc461a87f7840538|fc_0df8288370165955016ae26864b82c819d8ff98e4e0e6674d9", "name": "read", "input": {"path": ".kinex/devices/right_gripper/profile.yaml", "offset": null, "limit": null}, "output": "name: right_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured right_gripper, the native normalized gripper opening (0\n closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n from it.\n transport: topic\n direction: output\n endpoint: /right_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /right_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /right_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - right_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: right_arm:flange\n tcp:\n translation_m:\n - 0.119999992883\n - -9.757e-09\n - -7.153e-09\n rotation_xyzw:\n - 1.0e-12\n - 2.9802e-08\n - -6.4393e-08\n - 0.999999973161\n parent: right_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: right_gripper\n open: 1.0\n closed: 0.0\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_ebfb289d66754647b4e0f7ccfea8adcf|fc_0df8288370165955016ae26864b82c819d9e50eedd577fd641", "name": "read", "input": {"path": ".kinex/devices/head_camera/profile.yaml", "offset": null, "limit": null}, "output": "name: head_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /head_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /head_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /head_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - -0.03200000152\n - -0.449999988079\n - 1.350000023842\n rotation_xyzw:\n - -0.948683301192\n - 0.0\n - 0.0\n - 0.316227769682\n parent: world:base\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 14101, "outputTokens": 223, "cachedInputTokens": 11392, "cacheReportedInputTokens": 14101, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 14101, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 223}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "d21d533c", "role": "toolResult", "timestamp": "2026-10-28T21:25:56.823Z", "parts": [{"type": "text", "text": "identity:\n id: robotwin-aloha-agilex\n name: RoboTwin ALOHA AgileX\n kind: arm\n model: ALOHA AgileX (dual ARX5)\n source: user\ndevices:\n source: user\nsafety:\n source: user\n max_joint_velocity: null\n max_cartesian_velocity: null\n keep_out_zones: []\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\n"}]}, {"id": "ef397517", "role": "toolResult", "timestamp": "2026-10-28T21:25:56.823Z", "parts": [{"type": "text", "text": "name: world\nruntime:\n domain_id: 83\n observation: time\n nodes: []\n ready_timeout_s: 30\noperations:\n time:\n description: Authoritative world clock\n transport: topic\n direction: output\n endpoint: /clock\n type: rosgraph_msgs/msg/Clock\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n wait:\n description: Advance or await duration_s of world time (finite and nonnegative).\n Zero is an observation barrier. Waits are sequential; one requested while another\n is pending is rejected. A terminal episode rejects further progression without\n exposing benchmark results.\n transport: service\n direction: input\n endpoint: /world/wait\n type: kinex_interfaces/srv/Wait\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 120\n stop:\n description: Cancel the pending wait.\n transport: service\n direction: input\n endpoint: /world/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: world\ndescription: Scene reference and authoritative world clock of the RoboTwin 2.0 table.\n Kinex supplies frame transforms independently. This device exposes no task instructions\n or evaluator results.\nsource: default\ncalibrated_at: null\nmethod: unknown\nbounds: null\nsurfaces: []\nframes:\n base:\n parent: null\n translation_m: null\n rotation_xyzw: null\n"}]}, {"id": "f75054b3", "role": "toolResult", "timestamp": "2026-10-28T21:25:56.823Z", "parts": [{"type": "text", "text": "name: left_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4,\n fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /left_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /left_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /left_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF\n as left_arm/, and helpers.joint_model('left_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fl_joint1: 0.0\n fl_joint2: 0.0\n fl_joint3: 0.0\n fl_joint4: 0.0\n fl_joint5: 0.0\n fl_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - left_arm\nframes:\n base:\n translation_m:\n - -0.296999871731\n - -0.419500023127\n - 0.782000243664\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 0.714142563298\n - 0.700000389985\n parent: world:base\n"}]}, {"id": "d75eac4d", "role": "toolResult", "timestamp": "2026-10-28T21:25:56.823Z", "parts": [{"type": "text", "text": "name: right_arm\nruntime:\n domain_id: 83\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4,\n fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition\n stamp; do not infer motion completion from a sent command.\n transport: topic\n direction: output\n endpoint: /right_arm/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n joint_target:\n description: Joint trajectory over all 6 arm joints in radians, timed from receipt\n by strictly increasing time_from_start and finishing within 120 seconds; velocities,\n accelerations and effort must be empty. A zero header stamp selects now; a nonzero\n stamp must not start in the past. Points are absolute native joint positions,\n linearly interpolated at the native control period. Replacement clears pending\n references; invalid trajectories are ignored, so verify measured joint_state.\n transport: topic\n direction: input\n endpoint: /right_arm/joint_target\n type: trajectory_msgs/msg/JointTrajectory\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Stop arm motion and hold the measured posture.\n transport: service\n direction: input\n endpoint: /right_arm/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: joint\ndescription: ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the\n sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF\n as right_arm/, and helpers.joint_model('right_arm') computes FK/IK from it.\n Commands are upstream's absolute joint positions (the arm part of its 14-D qpos\n action), applied as native drive targets once per 0.04 s frame. The native URDF\n limits are +/-10 rad placeholders.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nmodel:\n format: urdf\n path: arm.urdf\n base_link: base_link\n tip_link: flange\nlimits: native\nhome:\n fr_joint1: 0.0\n fr_joint2: 0.0\n fr_joint3: 0.0\n fr_joint4: 0.0\n fr_joint5: 0.0\n fr_joint6: 0.0\nstop:\n behavior: hold\n affected_devices:\n - right_arm\nframes:\n base:\n translation_m:\n - 0.306300014257\n - -0.418499916792\n - 0.78100001812\n rotation_xyzw:\n - -2.9802e-08\n - 5.9605e-08\n - 0.71063355368\n - 0.703562409721\n parent: world:base\n"}]}, {"id": "f106c879", "role": "toolResult", "timestamp": "2026-10-28T21:25:56.823Z", "parts": [{"type": "text", "text": "name: left_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured left_gripper, the native normalized gripper opening (0 closed,\n 1 open), stamped at acquisition; Kinex publishes the URDF finger links from\n it.\n transport: topic\n direction: output\n endpoint: /left_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /left_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /left_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX left parallel two-finger gripper on the left_arm flange.\n The grip joint left_gripper is upstream''s normalized gripper value (0 closed, 1\n open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - left_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: left_arm:flange\n tcp:\n translation_m:\n - 0.1199999989\n - 7.305e-09\n - -7.152e-09\n rotation_xyzw:\n - 0.0\n - 2.9802e-08\n - 3.2247e-08\n - 0.999999998609\n parent: left_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: left_gripper\n open: 1.0\n closed: 0.0\n"}]}, {"id": "705ec8d2", "role": "toolResult", "timestamp": "2026-10-28T21:25:56.823Z", "parts": [{"type": "text", "text": "name: right_gripper\nruntime:\n domain_id: 83\n observation: joint_state\n nodes:\n - entry: links.py\n factory: create\n ready_timeout_s: 30\noperations:\n joint_state:\n description: Measured right_gripper, the native normalized gripper opening (0\n closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links\n from it.\n transport: topic\n direction: output\n endpoint: /right_gripper/joint_state\n type: sensor_msgs/msg/JointState\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\n target:\n description: Native normalized opening (data) from 0 (closed) to 1 (open), the\n gripper entry of upstream's 14-D qpos action. The native setter runs on every\n 250 Hz substep and moves the drive target at most 0.1 per call toward it. A\n target persists until replaced or stopped; out-of-range targets are ignored,\n so verify joint_state.\n transport: topic\n direction: input\n endpoint: /right_gripper/target\n type: std_msgs/msg/Float64\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n stop:\n description: Hold the measured opening; fingers may settle briefly after the stop.\n transport: service\n direction: input\n endpoint: /right_gripper/stop\n type: std_srvs/srv/Trigger\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: gripper\ndescription: 'ALOHA AgileX right parallel two-finger gripper on the right_arm flange.\n The grip joint right_gripper is upstream''s normalized gripper value (0 closed,\n 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes\n past the finger stop. The TCP is upstream''s tcp pose, gripper_bias ahead of the\n wrist joint with +x approaching. A closed or stalled grip alone does not prove a\n successful grasp.'\nsource: introspected\ncalibrated_at: null\nmethod: introspection\nstop:\n behavior: hold\n affected_devices:\n - right_gripper\nframes:\n base:\n translation_m:\n - 0.0\n - 0.0\n - 0.0\n rotation_xyzw:\n - 0.0\n - 0.0\n - 0.0\n - 1.0\n parent: right_arm:flange\n tcp:\n translation_m:\n - 0.119999992883\n - -9.757e-09\n - -7.153e-09\n rotation_xyzw:\n - 1.0e-12\n - 2.9802e-08\n - -6.4393e-08\n - 0.999999973161\n parent: right_gripper:base\nmodel:\n format: urdf\n path: gripper.urdf\n base_link: base_link\ngrip:\n joint: right_gripper\n open: 1.0\n closed: 0.0\n"}]}, {"id": "17b8d0de", "role": "toolResult", "timestamp": "2026-10-28T21:25:56.823Z", "parts": [{"type": "text", "text": "name: head_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /head_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /head_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /head_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: Fixed RGB-D head camera (D435 model) above the table. Native ray-traced\n RGB with pixel-center depth from a raster twin; calibration and the optical-frame\n mount are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - -0.03200000152\n - -0.449999988079\n - 1.350000023842\n rotation_xyzw:\n - -0.948683301192\n - 0.0\n - 0.0\n - 0.316227769682\n parent: world:base\n"}]}, {"id": "8e36d79e", "role": "assistant", "timestamp": "2026-10-28T21:26:00.106Z", "parts": [{"type": "tool", "callID": "call_1f5da46d3fda4d33bd3e155df8df3148|fc_0df8288370165955016ae268671804819d9189b4948ddb0c5a", "name": "exec", "input": {"python": "from kinex import runtime, finish\nfinish({d:runtime.operations(d) for d in ['world','left_arm','left_gripper','head_camera']})", "description": "inspect operation contracts"}, "output": "{\n \"output\": \"\",\n \"result\": {\n \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120},\"stop\":{\"description\":\"Cancel the pending wait.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_arm\": {\"joint_state\":{\"description\":\"Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_arm/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Stop arm motion and hold the measured posture.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_arm/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_gripper\": {\"joint_state\":{\"description\":\"Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_gripper/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Hold the measured opening; fingers may settle briefly after the stop.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"head_camera\": {\"image\":{\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"depth\":{\"description\":\"16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/depth\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"camera_info\":{\"description\":\"Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/camera_info\",\"type\":\"sensor_msgs/msg/CameraInfo\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10}}\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 18793, "outputTokens": 55, "cachedInputTokens": 14208, "cacheReportedInputTokens": 18793, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 18793, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 55}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "582be810", "role": "toolResult", "timestamp": "2026-10-28T21:26:01.104Z", "parts": [{"type": "text", "text": "{\n \"output\": \"\",\n \"result\": {\n \"world\": {\"time\":{\"description\":\"Authoritative world clock\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/clock\",\"type\":\"rosgraph_msgs/msg/Clock\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120},\"stop\":{\"description\":\"Cancel the pending wait.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_arm\": {\"joint_state\":{\"description\":\"Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_arm/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Stop arm motion and hold the measured posture.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_arm/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"left_gripper\": {\"joint_state\":{\"description\":\"Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/left_gripper/joint_state\",\"type\":\"sensor_msgs/msg/JointState\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10},\"target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"stop\":{\"description\":\"Hold the measured opening; fingers may settle briefly after the stop.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/stop\",\"type\":\"std_srvs/srv/Trigger\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10}},\n \"head_camera\": {\"image\":{\"description\":\"RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/image\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"depth\":{\"description\":\"16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/depth\",\"type\":\"sensor_msgs/msg/Image\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10},\"camera_info\":{\"description\":\"Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms.\",\"transport\":\"topic\",\"direction\":\"output\",\"endpoint\":\"/head_camera/camera_info\",\"type\":\"sensor_msgs/msg/CameraInfo\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"transient_local\"},\"timeout_s\":10}}\n }\n}"}]}, {"id": "01cb21cf", "role": "assistant", "timestamp": "2026-10-28T21:26:04.491Z", "parts": [{"type": "tool", "callID": "call_16eac218dcfb4206bf7a1f0d0fc0b989|fc_0df8288370165955016ae2686b9f5c819da3ad030b7dfae385", "name": "devices", "input": {"calls": [{"deviceID": "world", "action": "load"}, {"deviceID": "left_arm", "action": "load"}, {"deviceID": "right_arm", "action": "load"}, {"deviceID": "left_gripper", "action": "load"}, {"deviceID": "right_gripper", "action": "load"}, {"deviceID": "head_camera", "action": "load"}]}, "output": "{\n \"results\": [\n {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 20034, "outputTokens": 78, "cachedInputTokens": 18688, "cacheReportedInputTokens": 20034, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 20034, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 78}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "aabf9fd8", "role": "toolResult", "timestamp": "2026-10-28T21:26:10.567Z", "parts": [{"type": "text", "text": "{\n \"results\": [\n {\"deviceID\":\"world\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_arm\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"left_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_gripper\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"head_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}"}]}, {"id": "2ec51e42", "role": "assistant", "timestamp": "2026-10-28T21:26:13.473Z", "parts": [{"type": "tool", "callID": "call_e32dfeef88cd49a98044fc70a66a051a|fc_0df8288370165955016ae26875077c819daf1bff4228d0f538", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt", "offset": null, "limit": null}, "output": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n Return a stamped source-to-target transform using Kinex frame history.\n await helpers.transform_pose(io, pose, source, target, stamp=None)\n Express a pose given in one frame in another.\n await helpers.relative_pose(io, child, parent, timestamp=None)\n Express a frame-attached child pose relative to a frame-attached parent pose.\n Implementation: frames.py\n\nKinematics\n helpers.joint_model(device)\n Load a joint device's commanded joints, link FK and IK from its URDF.\n Implementation: kinematics.py\n\nMotion\n helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n Plan joint references and duration without device I/O.\n Implementation: motion.py\n\nPolicy\n await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n Infer once and submit the valid action prefix to the commanded devices.\n Implementation: policy.py\n\nImages\n await helpers.camera_frame(io, camera, timeout_s=None)\n The current image and, for an rgbd camera, its depth from the same acquisition.\n helpers.image_array(image)\n Native-order pixels of a sensor_msgs/Image as numpy for computation.\n helpers.save_image(image)\n Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n Implementation: images.py\n\nPerception\n await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n Locate a detected object in a stamped camera optical frame.\n Implementation: perception.py\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 20264, "outputTokens": 39, "cachedInputTokens": 19968, "cacheReportedInputTokens": 20264, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 20264, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 39}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "28197cb3", "role": "toolResult", "timestamp": "2026-10-28T21:26:13.479Z", "parts": [{"type": "text", "text": "Kinex Python helpers\n\nImport with: from kinex import helpers\nImplementation paths below are relative to this index; see their docstrings for\ndetails. The builtin Python tools are interfaces to these same functions; call the\nhelpers directly inside exec to share its communication and cleanup.\nWorkspace-specific helpers belong in .kinex/lib, which exec and tools can import.\n\nFrames\n await helpers.lookup_transform(io, target, source, stamp=None, timeout_s=5)\n Return a stamped source-to-target transform using Kinex frame history.\n await helpers.transform_pose(io, pose, source, target, stamp=None)\n Express a pose given in one frame in another.\n await helpers.relative_pose(io, child, parent, timestamp=None)\n Express a frame-attached child pose relative to a frame-attached parent pose.\n Implementation: frames.py\n\nKinematics\n helpers.joint_model(device)\n Load a joint device's commanded joints, link FK and IK from its URDF.\n Implementation: kinematics.py\n\nMotion\n helpers.trajectory(joints, start, target, period_s, fraction=1.0, max_velocity=None, max_acceleration=None, *, check_limits=True, start_tolerance=0)\n Plan joint references and duration without device I/O.\n Implementation: motion.py\n\nPolicy\n await helpers.run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10, max_actions=None, inference_args=None)\n Infer once and submit the valid action prefix to the commanded devices.\n Implementation: policy.py\n\nImages\n await helpers.camera_frame(io, camera, timeout_s=None)\n The current image and, for an rgbd camera, its depth from the same acquisition.\n helpers.image_array(image)\n Native-order pixels of a sensor_msgs/Image as numpy for computation.\n helpers.save_image(image)\n Write a sensor_msgs/Image as a PNG and return its path for finish(file=...).\n Implementation: images.py\n\nPerception\n await helpers.locate_object_3d(io, camera, model, prompt, depth=None, min_score=0.25, instance=0)\n Locate a detected object in a stamped camera optical frame.\n Implementation: perception.py\n"}]}, {"id": "f1eb1ed0", "role": "assistant", "timestamp": "2026-10-28T21:26:16.257Z", "parts": [{"type": "tool", "callID": "call_594c0aeb37864a37a3508edeae992b74|fc_0df8288370165955016ae268779990819d9072a6bd2ad63c99", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py", "offset": 1, "limit": 250}, "output": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n \"\"\"Load a joint device's model without device I/O.\n\n joints lists the commanded joints in URDF document order with limits and\n rate limits; names holds their names. fk(positions, link=None) returns the\n link pose [x, y, z, qx, qy, qz, qw] in /base. ik(pose, seed,\n link=None, **options) returns commanded positions nearest the seed, or\n raises ValueError when no verified solution is found. link defaults to the\n model's tip_link. Measured links are also on TF as /.\n\n Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n \"\"\"\n return JointModel(profile(device))\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 20772, "outputTokens": 42, "cachedInputTokens": 20224, "cacheReportedInputTokens": 20772, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 20772, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 42}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "6fda1c44", "role": "toolResult", "timestamp": "2026-10-28T21:26:16.262Z", "parts": [{"type": "text", "text": "\"\"\"Joint device kinematics from the device's resolved URDF.\"\"\"\n\nfrom kinex import profile\nfrom kinex.lib.joints import JointModel\n\n\ndef joint_model(device):\n \"\"\"Load a joint device's model without device I/O.\n\n joints lists the commanded joints in URDF document order with limits and\n rate limits; names holds their names. fk(positions, link=None) returns the\n link pose [x, y, z, qx, qy, qz, qw] in /base. ik(pose, seed,\n link=None, **options) returns commanded positions nearest the seed, or\n raises ValueError when no verified solution is found. link defaults to the\n model's tip_link. Measured links are also on TF as /.\n\n Example: arm = helpers.joint_model(\"arm\"); q = arm.ik(arm.fk(seed), seed)\n \"\"\"\n return JointModel(profile(device))\n"}]}, {"id": "199e64df", "role": "assistant", "timestamp": "2026-10-28T21:26:20.658Z", "parts": [{"type": "tool", "callID": "call_e5af90cf3ab749f9a00db0884473c5c8|fc_0df8288370165955016ae2687a893c819dab6f8abbc44f9f18", "name": "exec", "input": {"python": "import asyncio\nfrom kinex import runtime, communication, helpers, finish\nasync def main():\n async with communication() as io:\n f=await helpers.camera_frame(io,'head_camera')\n out={}\n for d in ['left_arm','right_arm','left_gripper','right_gripper']:\n async with io.subscribe(d+'.joint_state') as s: out[d]=await s.next()\n out['schemas']={x:runtime.describe(x) for x in ['left_arm.joint_target','left_gripper.target','world.wait']}\n finish(out,file=helpers.save_image(f['image']))\nasyncio.run(main())", "description": "observe scene and schemas"}, "output": "{\n \"output\": \"[INFO] [1793222782.251334587] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n exec(compile(_request[\\\"source\\\"], \\\"\\\", \\\"exec\\\"), module.__dict__)\\n File \\\"\\\", line 11, in \\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"\\\", line 10, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}", "isError": true, "metadata": {}}], "usage": {"inputTokens": 21024, "outputTokens": 164, "cachedInputTokens": 20608, "cacheReportedInputTokens": 21024, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 21024, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 164}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "696bc35e", "role": "toolResult", "timestamp": "2026-10-28T21:26:23.111Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793222782.251334587] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 44, in script\\n exec(compile(_request[\\\"source\\\"], \\\"\\\", \\\"exec\\\"), module.__dict__)\\n File \\\"\\\", line 11, in \\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"\\\", line 10, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}"}]}, {"id": "888980b4", "role": "assistant", "timestamp": "2026-10-28T21:26:25.414Z", "parts": [{"type": "tool", "callID": "call_764ced789f0d4188899433ec60bc7b1a|fc_0df8288370165955016ae26880fc78819d82a1b2e75454d578", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py", "offset": 1, "limit": 180}, "output": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n Both come from one acquisition (equal stamps); reading never advances world time.\n\n Example: image, depth = await helpers.camera_frame(io, \"camera\")\n \"\"\"\n return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n \"\"\"\n return array(image)\n\n\ndef save_image(image):\n \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n \"\"\"\n return save(image)\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 21584, "outputTokens": 39, "cachedInputTokens": 20992, "cacheReportedInputTokens": 21584, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 21584, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 39}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "85aa9ef6", "role": "toolResult", "timestamp": "2026-10-28T21:26:25.418Z", "parts": [{"type": "text", "text": "\"\"\"Camera images: current frames, pixel arrays for computation and files for finish().\"\"\"\n\nfrom kinex.lib.camera import array, frame, save\n\n\nasync def camera_frame(io, camera, timeout_s=None):\n \"\"\"The camera's current image and, for an rgbd camera, its depth (else None).\n\n Both come from one acquisition (equal stamps); reading never advances world time.\n\n Example: image, depth = await helpers.camera_frame(io, \"camera\")\n \"\"\"\n return await frame(io, camera, timeout_s=timeout_s)\n\n\ndef image_array(image):\n \"\"\"Native-order pixels of a sensor_msgs/Image as numpy: HxWx3 RGB or HxW mono8/16UC1.\n\n Example: pixels = helpers.image_array((await helpers.camera_frame(io, \"camera\"))[0])\n \"\"\"\n return array(image)\n\n\ndef save_image(image):\n \"\"\"Write a sensor_msgs/Image as a PNG, keeping 16-bit depth, and return its path.\n\n Example: finish({\"stamp\": stamp}, file=helpers.save_image(image))\n \"\"\"\n return save(image)\n"}]}, {"id": "e1574d32", "role": "assistant", "timestamp": "2026-10-28T21:26:29.317Z", "parts": [{"type": "tool", "callID": "call_1f72f2a5fef14e6c9770552b06e256ef|fc_0df8288370165955016ae2688345b4819daff7e571c13a4374", "name": "exec", "input": {"python": "import asyncio\nfrom kinex import runtime, communication, helpers, finish\nasync def main():\n async with communication() as io:\n image,depth=await helpers.camera_frame(io,'head_camera')\n out={}\n for d in ['left_arm','right_arm','left_gripper','right_gripper']:\n async with io.subscribe(d+'.joint_state') as s: out[d]=await s.next()\n out['schemas']={x:runtime.describe(x) for x in ['left_arm.joint_target','left_gripper.target','world.wait']}\n p=helpers.save_image(image)\n finish(out,file=p)\nasyncio.run(main())", "description": "observe scene and schemas"}, "output": "{\n \"output\": \"[INFO] [1793222790.779743893] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"left_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\n \"right_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\n \"left_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"left_gripper\"],\"position\":[0.986124409870668],\"velocity\":[0.018415418030186134],\"effort\":[]},\n \"right_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"right_gripper\"],\"position\":[0.9861242744055662],\"velocity\":[0.0184164128520272],\"effort\":[]},\n \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence\",\"points\":\"sequence\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence\",\"velocities\":\"sequence\",\"accelerations\":\"sequence\",\"effort\":\"sequence\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}},\n \"file\": \"/tmp/kinex-observation-ftia7zbo.png\"\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 21855, "outputTokens": 167, "cachedInputTokens": 21504, "cacheReportedInputTokens": 21855, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 21855, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 167}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "01d7a694", "role": "toolResult", "timestamp": "2026-10-28T21:26:31.346Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793222790.779743893] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"left_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\"position\":[-3.016615224282759e-8,-5.666987590302597e-7,-0.000004835828804061748,7.701128623693876e-8,4.63164752773082e-7,-8.158995683515968e-7],\"velocity\":[0.00000265039443547721,-0.000010050378477899358,0.000024748725991230458,-0.000004255349267623387,-0.00001595680987520609,0.00007239235128508881],\"effort\":[]},\n \"right_arm\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"fr_joint1\",\"fr_joint2\",\"fr_joint3\",\"fr_joint4\",\"fr_joint5\",\"fr_joint6\"],\"position\":[-3.258066882949606e-8,-5.65274206110189e-7,-0.000004838918357563671,7.693390813301448e-8,4.6469963876916154e-7,-8.163781899384048e-7],\"velocity\":[0.0000026605034690874163,-0.00001006854472507257,0.000024771599782980047,-0.000004258563421899453,-0.00001595234061824158,0.00007239227852551267],\"effort\":[]},\n \"left_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"left_gripper\"],\"position\":[0.986124409870668],\"velocity\":[0.018415418030186134],\"effort\":[]},\n \"right_gripper\": {\"header\":{\"stamp\":{\"sec\":0,\"nanosec\":0},\"frame_id\":\"\"},\"name\":[\"right_gripper\"],\"position\":[0.9861242744055662],\"velocity\":[0.0184164128520272],\"effort\":[]},\n \"schemas\": {\"left_arm.joint_target\":{\"description\":\"Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_arm/joint_target\",\"type\":\"trajectory_msgs/msg/JointTrajectory\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"header\":\"std_msgs/Header\",\"joint_names\":\"sequence\",\"points\":\"sequence\"},\"definitions\":{\"std_msgs/msg/Header\":{\"stamp\":\"builtin_interfaces/Time\",\"frame_id\":\"string\"},\"builtin_interfaces/msg/Time\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"},\"trajectory_msgs/msg/JointTrajectoryPoint\":{\"positions\":\"sequence\",\"velocities\":\"sequence\",\"accelerations\":\"sequence\",\"effort\":\"sequence\",\"time_from_start\":\"builtin_interfaces/Duration\"},\"builtin_interfaces/msg/Duration\":{\"sec\":\"int32\",\"nanosec\":\"uint32\"}}},\"left_gripper.target\":{\"description\":\"Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state.\",\"transport\":\"topic\",\"direction\":\"input\",\"endpoint\":\"/left_gripper/target\",\"type\":\"std_msgs/msg/Float64\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":10,\"message\":{\"data\":\"double\"},\"definitions\":{}},\"world.wait\":{\"description\":\"Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results.\",\"transport\":\"service\",\"direction\":\"input\",\"endpoint\":\"/world/wait\",\"type\":\"kinex_interfaces/srv/Wait\",\"qos\":{\"depth\":1,\"reliability\":\"reliable\",\"durability\":\"volatile\"},\"timeout_s\":120,\"request\":{\"duration_s\":\"double\"},\"response\":{\"success\":\"boolean\",\"message\":\"string\",\"time_s\":\"double\",\"waited_s\":\"double\"},\"definitions\":{}}},\n \"file\": \"/tmp/kinex-observation-ftia7zbo.png\"\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "9082ebc5", "role": "assistant", "timestamp": "2026-10-28T21:26:42.398Z", "parts": [{"type": "reasoning", "text": "**Segmenting the red prism**"}, {"type": "tool", "callID": "call_4a4596413bcd40e7a90d0776e1764dc0|fc_0df8288370165955016ae2688b8a3c819da1f7a4725b5bf751", "name": "exec", "input": {"python": "import asyncio,numpy as np\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,de=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im); z=helpers.image_array(de)*.001\n masks={'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}\n out={}\n for k,m in masks.items():\n v,u=np.where(m&(z>0)); zz=z[v,u]; p=np.stack([(u-159.5)*zz/358.6421814,(v-119.5)*zz/358.6421814,zz],axis=1)\n tf=await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'})\n out['tf']=tf\n from scipy.spatial.transform import Rotation as R\n # relative_pose returns pose\n out[k]={'pixels':[int(u.min()),int(v.min()),int(u.max()),int(v.max())],'camera_range':[p.min(0).tolist(),p.max(0).tolist()],'median':np.median(p,axis=0).tolist()}\n out['tcp']={d:await helpers.relative_pose(io,{'frame':d+'_gripper:tcp'},{'frame':'world:base'}) for d in ['left','right']}\n finish(out)\nasyncio.run(main())", "description": "locate block and pad surfaces"}, "output": "{\n \"output\": \"[INFO] [1793222803.632560113] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tf\": {\"pose\":[-0.03200000152,-0.449999988079,1.350000023842,0.9486832972651139,0,0,-0.3162277683730379],\"timestamp\":0},\n \"red\": {\"pixels\":[77,0,115,67],\"camera_range\":[[-0.16374677337382487,-0.2402380547197954,0.707],[-0.10597470674429707,-0.125013181174009,0.862]],\"median\":[-0.1355138980314043,-0.1914066542090244,0.776]},\n \"blue\": {\"pixels\":[199,55,244,86],\"camera_range\":[[0.09508362866543728,-0.15700467742024501,0.8140000000000001],[0.19417403643976383,-0.07631422464909199,0.873]],\"median\":[0.1445019651556246,-0.11359511544617211,0.84]},\n \"tcp\": {\"left\":{\"pose\":[-0.30032357671522697,-0.193827798419468,0.9419981020126762,-0.000006960933310190554,-0.00000395167326060435,0.7141423608015681,0.7000004917460364],\"timestamp\":0},\"right\":{\"pose\":[0.30523323124039986,-0.1928057309036048,0.9409978556569187,-0.000007012336903923293,-0.000003885552193758481,0.710633293626997,0.7035625927553112],\"timestamp\":0}}\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 23252, "outputTokens": 406, "cachedInputTokens": 21888, "cacheReportedInputTokens": 23252, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 23252, "reasoningOutputTokens": 26, "reasoningReportedOutputTokens": 406}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "9a8e8452", "role": "toolResult", "timestamp": "2026-10-28T21:26:44.722Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793222803.632560113] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tf\": {\"pose\":[-0.03200000152,-0.449999988079,1.350000023842,0.9486832972651139,0,0,-0.3162277683730379],\"timestamp\":0},\n \"red\": {\"pixels\":[77,0,115,67],\"camera_range\":[[-0.16374677337382487,-0.2402380547197954,0.707],[-0.10597470674429707,-0.125013181174009,0.862]],\"median\":[-0.1355138980314043,-0.1914066542090244,0.776]},\n \"blue\": {\"pixels\":[199,55,244,86],\"camera_range\":[[0.09508362866543728,-0.15700467742024501,0.8140000000000001],[0.19417403643976383,-0.07631422464909199,0.873]],\"median\":[0.1445019651556246,-0.11359511544617211,0.84]},\n \"tcp\": {\"left\":{\"pose\":[-0.30032357671522697,-0.193827798419468,0.9419981020126762,-0.000006960933310190554,-0.00000395167326060435,0.7141423608015681,0.7000004917460364],\"timestamp\":0},\"right\":{\"pose\":[0.30523323124039986,-0.1928057309036048,0.9409978556569187,-0.000007012336903923293,-0.000003885552193758481,0.710633293626997,0.7035625927553112],\"timestamp\":0}}\n }\n}"}]}, {"id": "0a37bbcd", "role": "assistant", "timestamp": "2026-10-28T21:26:59.090Z", "parts": [{"type": "reasoning", "text": "**Checking possible tower view**"}, {"type": "tool", "callID": "call_a035a832a8bf43bfa0c7ce6196f9a693|fc_0df8288370165955016ae268a2ee70819dadfaf8ab685b02f8", "name": "read", "input": {"path": ".kinex/devices/left_camera/profile.yaml", "offset": null, "limit": null}, "output": "name: left_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /left_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /left_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /left_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n pixel-center depth from a raster twin; calibration and the optical-frame mount on\n the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - 0.07000001149\n - 0.031999994336\n - 0.065000047158\n rotation_xyzw:\n - -0.589367966393\n - 0.589367919412\n - -0.390698641271\n - 0.39069867358\n parent: left_arm:flange\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_c0a71f1a695641ecbf5210f719cfc840|fc_0df8288370165955016ae268a2fdc0819db5d37915c56eb0a4", "name": "read", "input": {"path": ".kinex/devices/left_gripper/gripper.urdf", "offset": null, "limit": null}, "output": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_4b667940abd94b938fdbc184dc5c8612|fc_0df8288370165955016ae268a2fdc0819d981b1dd9e7ba615d", "name": "read", "input": {"path": ".kinex/devices/left_arm/arm.urdf", "offset": null, "limit": null}, "output": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 24103, "outputTokens": 140, "cachedInputTokens": 23552, "cacheReportedInputTokens": 24103, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 24103, "reasoningOutputTokens": 21, "reasoningReportedOutputTokens": 140}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "02612523", "role": "toolResult", "timestamp": "2026-10-28T21:26:59.094Z", "parts": [{"type": "text", "text": "name: left_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /left_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /left_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. Use the declared distortion model and raw/rectified\n image space when projecting pixels; intrinsics are not TF transforms.\n transport: topic\n direction: output\n endpoint: /left_camera/camera_info\n type: sensor_msgs/msg/CameraInfo\n qos:\n depth: 1\n reliability: reliable\n durability: transient_local\n timeout_s: 10\nloaded: false\nmeta:\n created_at: '2026-10-24T00:00:00Z'\n updated_at: '2026-10-24T00:00:00Z'\ntype: rgbd\ndescription: RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with\n pixel-center depth from a raster twin; calibration and the optical-frame mount on\n the left_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - 0.07000001149\n - 0.031999994336\n - 0.065000047158\n rotation_xyzw:\n - -0.589367966393\n - 0.589367919412\n - -0.390698641271\n - 0.39069867358\n parent: left_arm:flange\n"}]}, {"id": "68017fa6", "role": "toolResult", "timestamp": "2026-10-28T21:26:59.094Z", "parts": [{"type": "text", "text": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n"}]}, {"id": "9ee747d4", "role": "toolResult", "timestamp": "2026-10-28T21:26:59.094Z", "parts": [{"type": "text", "text": "\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n"}]}, {"id": "76aebf4d", "role": "assistant", "timestamp": "2026-10-28T21:27:07.599Z", "parts": [{"type": "reasoning", "text": "**Finding red surface coordinates**"}, {"type": "tool", "callID": "call_73994271cf284e0dbd689a5da523de7f|fc_0df8288370165955016ae268a8e788819d94b273983e98814a", "name": "exec", "input": {"python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,de=await helpers.camera_frame(io,'head_camera'); a=helpers.image_array(im).astype(float); z=helpers.image_array(de)*.001\n tf=(await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'}))['pose']; out={}\n for k,m in {'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}.items():\n v,u=np.where(m&(z>0)); zz=z[v,u]; p=np.stack([(u-159.5)*zz/358.6421814,(v-119.5)*zz/358.6421814,zz],axis=1); w=R.from_quat(tf[3:]).apply(p)+tf[:3]\n out[k]={'min':w.min(0),'max':w.max(0),'median':np.median(w,axis=0),'bottom':np.median(w[w[:,2].6: raise ValueError('IK branch jump')\n q=nq; t=elapsed+duration*f; ns=round(t*1e9)\n points.append({'positions':q.tolist(),'time_from_start':{'sec':ns//1000000000,'nanosec':ns%1000000000}})\n elapsed+=duration; wp=targetp; wr=targetr\n plans[side]=(model,q,elapsed,points)\n for side,(model,q,t,points) in plans.items():\n await io.publish(side+'_arm.joint_target',{'joint_names':model.names,'points':points})\n for side,value in a.get('grips',{}).items():\n await io.publish(side+'_gripper.target',{'data':float(value)})\n wait=max([p[2] for p in plans.values()]+[float(a.get('wait',0))])\n if wait: result['wait']=await io.request('world.wait',duration_s=wait+float(a.get('settle',.16)))\n for side in ['left','right']:\n s=await state(io,side+'_arm'); g=await state(io,side+'_gripper')\n result[side]={'q':list(s.position),'grip':list(g.position),'tcp':(await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'}))['pose']}\n if side in plans: result[side]['joint_error']=float(np.max(np.abs(np.array(s.position)-plans[side][1])))\n paths=[]\n if a.get('observe',True):\n image,depth=await helpers.camera_frame(io,'head_camera'); paths=[helpers.save_image(image)]\n finish(result,file=paths)\n\nasyncio.run(main())\n"}, "output": "Successfully wrote to /workspace/.kinex/tools/cartesian_move.py", "isError": false, "metadata": {}}], "usage": {"inputTokens": 26857, "outputTokens": 1241, "cachedInputTokens": 26368, "cacheReportedInputTokens": 26857, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 26857, "reasoningOutputTokens": 258, "reasoningReportedOutputTokens": 1241}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "ed34bdb2", "role": "toolResult", "timestamp": "2026-10-28T21:27:44.864Z", "parts": [{"type": "text", "text": "Successfully wrote to /workspace/.kinex/tools/cartesian_move.py"}]}, {"id": "bc1338e6", "role": "assistant", "timestamp": "2026-10-28T21:27:47.996Z", "parts": [{"type": "tool", "callID": "call_0bd080577be548f4b8adb745b8436cb7|fc_0df8288370165955016ae268d2b184819da58990c63104de48", "name": "exec", "input": {"file": ".kinex/tools/cartesian_move.py", "args": {"moves": {"left": [[-0.167, 0.045, 0.835, 0, 0, 0.70710678, 0.70710678]]}, "seconds": 2, "grips": {"left": 1}}, "description": "approach block with left arm"}, "output": "{\n \"output\": \"[INFO] [1793222870.183791116] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 40, in script\\n runpy.run_path(file, run_name=\\\"__main__\\\")\\n File \\\"\\\", line 287, in run_path\\n File \\\"\\\", line 98, in _run_module_code\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 51, in \\n asyncio.run(main())\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 22, in main\\n local=model.fk(q); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0])\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n q = joint_target(positions, self.joints, check_limits=False)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n vector(value, len(joints), \\\"joint target\\\")\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}", "isError": true, "metadata": {}}], "usage": {"inputTokens": 28121, "outputTokens": 81, "cachedInputTokens": 27904, "cacheReportedInputTokens": 28121, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 28121, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 81}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "c76dfcac", "role": "toolResult", "timestamp": "2026-10-28T21:27:50.898Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793222870.183791116] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"error\": \"Traceback (most recent call last):\\n File \\\"\\\", line 198, in _run_module_as_main\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 51, in \\n script()\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/host/run.py\\\", line 40, in script\\n runpy.run_path(file, run_name=\\\"__main__\\\")\\n File \\\"\\\", line 287, in run_path\\n File \\\"\\\", line 98, in _run_module_code\\n File \\\"\\\", line 88, in _run_code\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 51, in \\n asyncio.run(main())\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 195, in run\\n return runner.run(main)\\n ^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/runners.py\\\", line 118, in run\\n return self._loop.run_until_complete(task)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/python/.pixi/envs/default/lib/python3.12/asyncio/base_events.py\\\", line 691, in run_until_complete\\n return future.result()\\n ^^^^^^^^^^^^^^^\\n File \\\"/workspace/.kinex/tools/cartesian_move.py\\\", line 22, in main\\n local=model.fk(q); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0])\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 41, in fk\\n q = joint_target(positions, self.joints, check_limits=False)\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/urdf.py\\\", line 114, in joint_target\\n vector(value, len(joints), \\\"joint target\\\")\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/geometry.py\\\", line 19, in vector\\n raise ValueError(f\\\"{name} needs {size} numbers\\\")\\nValueError: joint target needs 6 numbers\"\n}"}]}, {"id": "2a226851", "role": "assistant", "timestamp": "2026-10-28T21:27:55.238Z", "parts": [{"type": "tool", "callID": "call_8a1beec4629348509212b3929faaae45|fc_0df8288370165955016ae268da030c819dbf655887159e5844", "name": "edit", "input": {"path": ".kinex/tools/cartesian_move.py", "edits": [{"oldText": "local=model.fk(q);", "newText": "local=model.fk(q.tolist());"}, {"oldText": "model.ik([*lp,*lr.as_quat()],q)", "newText": "model.ik([*lp,*lr.as_quat()],q.tolist())"}]}, "output": "Successfully replaced 2 block(s) in .kinex/tools/cartesian_move.py.", "isError": false, "metadata": {}}], "usage": {"inputTokens": 28847, "outputTokens": 87, "cachedInputTokens": 28032, "cacheReportedInputTokens": 28847, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 28847, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 87}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "5eef5859", "role": "toolResult", "timestamp": "2026-10-28T21:27:55.247Z", "parts": [{"type": "text", "text": "Successfully replaced 2 block(s) in .kinex/tools/cartesian_move.py."}]}, {"id": "e3336eda", "role": "assistant", "timestamp": "2026-10-28T21:27:57.609Z", "parts": [{"type": "tool", "callID": "call_ef852ce2a3fa42c1b7603121b673065c|fc_0df8288370165955016ae268dcd920819dad51574c26d56fdd", "name": "exec", "input": {"file": ".kinex/tools/cartesian_move.py", "args": {"moves": {"left": [[-0.167, 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measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /right_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /right_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. 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Native ray-traced RGB with\n pixel-center depth from a raster twin; calibration and the optical-frame mount on\n the right_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - 0.069999999887\n - 0.031999976227\n - 0.064999980711\n rotation_xyzw:\n - -0.58936798274\n - 0.589367967612\n - -0.390698619841\n - 0.390698640561\n parent: right_arm:flange\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 32638, "outputTokens": 56, "cachedInputTokens": 32000, "cacheReportedInputTokens": 32638, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 32638, "reasoningOutputTokens": 21, "reasoningReportedOutputTokens": 56}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "3a1bd39a", "role": "toolResult", "timestamp": "2026-10-28T21:29:50.142Z", "parts": [{"type": "text", "text": "name: right_camera\nruntime:\n domain_id: 83\n nodes: []\n ready_timeout_s: 30\noperations:\n image:\n description: RGB image measurements with encoding, dimensions, row stride and\n acquisition timestamp in the Image header. The frame_id identifies the calibrated\n optical frame; use its stamp for tf2 lookup. A new subscriber receives the current\n frame at once, also while the world is paused, then one per camera sample; reading\n never advances world time.\n transport: topic\n direction: output\n endpoint: /right_camera/image\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n depth:\n description: '16UC1 depth registered to image in millimetres along the optical\n axis; 0 marks missing or out-of-range samples. Rendered with its image: same\n stamp and frame_id, so pair the two by stamp.'\n transport: topic\n direction: output\n endpoint: /right_camera/depth\n type: sensor_msgs/msg/Image\n qos:\n depth: 1\n reliability: reliable\n durability: volatile\n timeout_s: 10\n camera_info:\n description: Projection and distortion calibration for the image stream, including\n K, D, image size and optical frame. 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Native ray-traced RGB with\n pixel-center depth from a raster twin; calibration and the optical-frame mount on\n the right_arm flange are in the profile.\nsource: introspected\ncalibrated_at: null\nmethod: introspection\ncolor:\n width: 320\n height: 240\n fps: null\n projection:\n model: pinhole\n fx: 358.642181396484\n fy: 358.642181396484\n cx: 159.5\n cy: 119.5\n skew: 0\n distortion:\n model: none\n fov:\n horizontal_deg: 48.085891406656\n vertical_deg: 37.000001358777\ndepth:\n width: 320\n height: 240\n fps: null\n quantity: z\n scale_m_per_unit: 0.001\n invalid_value: 0\n valid_range_m:\n min: 0.1\n max: 65.535\n reliable_range_m:\n min: 0.1\n max: 65.535\n registration: color\nframes:\n base:\n translation_m:\n - 0.069999999887\n - 0.031999976227\n - 0.064999980711\n rotation_xyzw:\n - -0.58936798274\n - 0.589367967612\n - -0.390698619841\n - 0.390698640561\n parent: right_arm:flange\n"}]}, {"id": "a55cf17a", "role": "assistant", "timestamp": "2026-10-28T21:29:52.504Z", 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"bca1e978", "role": "assistant", "timestamp": "2026-10-28T21:30:11.820Z", "parts": [{"type": "reasoning", "text": "**Saving colored-mask helper**"}, {"type": "tool", "callID": "call_2a4098686c3d47a28c6a9f6357ff86a9|fc_0df8288370165955016ae2695ca4f8819d8273fa4384997686", "name": "write", "input": {"path": ".kinex/tools/color_geometry.py", "content": "\"\"\"Measure red block / blue pad visible surface bounds from calibrated RGB-D.\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation\nfrom kinex import args,communication,helpers,finish\nasync def main():\n out={}; files=[]\n async with communication() as io:\n for cam in args().get('cameras',['head_camera']):\n im,de=await helpers.camera_frame(io,cam); a=helpers.image_array(im).astype(float); z=helpers.image_array(de)*.001\n async with io.subscribe(cam+'.camera_info') as s: info=await s.next()\n fx,fy,cx,cy=info.k[0],info.k[4],info.k[2],info.k[5]\n tf=(await 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/run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"right_camera\": {\"red\":{\"min\":[-0.07704535908917484,0.05037363663380437,0.9359792452628558],\"max\":[-0.010132965239384423,0.11802476366946671,1.0577161411202283],\"median\":[-0.01790569017331503,0.08437329579285988,1.0223647101538353],\"low\":[-0.01968119294135831,0.11215254015326631,0.9426068395053633],\"high\":[-0.025401475412971358,0.08528556780882164,1.0568718285682772]}},\n \"file\": [\"/tmp/kinex-observation-wrxb6ic4.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "df80a3ab", "role": "assistant", "timestamp": "2026-10-28T21:30:27.842Z", "parts": [{"type": "reasoning", "text": "**Aligning the right finger**"}, {"type": "tool", "callID": "call_05f5697e44044c43b3be8540c2ffa31f|fc_0df8288370165955016ae26973b0f8819da4025981a45f1519", "name": "exec", "input": {"file": ".kinex/tools/cartesian_move.py", "args": {"moves": {"right": [[-0.044, 0.084, 1.025, 0, 0, 1, 0]]}, "seconds": 1.2, "settle": 0.32}, "description": "insert right fingers for handover"}, "output": "{\n \"output\": \"[INFO] [1793223029.805918588] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.120000000000001,\"waited_s\":1.5200000000000014},\n \"left\": {\"q\":[-0.5804733037948608,2.104207992553711,1.1955286264419556,0.9432793855667114,-0.6488001346588135,0.010548126883804798],\"grip\":[0.7495477158914913],\"tcp\":[-0.09106256375115962,0.09291270090328957,0.9573460230231323,-0.00019867867634500307,-0.014277350018817896,0.7457316671533772,0.6660933856508923]},\n \"right\": 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/run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"wait\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.120000000000001,\"waited_s\":1.5200000000000014},\n \"left\": {\"q\":[-0.5804733037948608,2.104207992553711,1.1955286264419556,0.9432793855667114,-0.6488001346588135,0.010548126883804798],\"grip\":[0.7495477158914913],\"tcp\":[-0.09106256375115962,0.09291270090328957,0.9573460230231323,-0.00019867867634500307,-0.014277350018817896,0.7457316671533772,0.6660933856508923]},\n \"right\": {\"q\":[0.28108417987823486,2.3755812644958496,2.0089495182037354,0.41051310300827026,-1.1974685192108154,0.03320124372839928],\"grip\":[1.0481817857785658],\"tcp\":[-0.00454658121577145,0.09928463248112668,1.0355422699375194,0.00233159746702242,-0.005923685166572289,0.9996083980399332,0.027249296786112258],\"joint_error\":0.07316711639015039},\n \"file\": [\"/tmp/kinex-observation-skgw42gu.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "57b52148", "role": "assistant", "timestamp": "2026-10-28T21:30:41.248Z", "parts": [{"type": "reasoning", "text": "**Stopping after contact**"}, {"type": "tool", "callID": "call_e5715b20a77640bab42f190a5e5e42d4|fc_0df8288370165955016ae269811e7c819db816fc229d8738d1", "name": "device_stop", "input": {"reason": "Unexpected handover contact displaced the left arm; hold both arms before adjusting."}, "output": "left_arm: stopped (hold; affects left_arm)\nleft_gripper: stopped (hold; affects left_gripper)\nright_arm: stopped (hold; affects right_arm)\nright_gripper: stopped (hold; affects right_gripper)\nworld: stopped", "isError": false, "metadata": {}}], "usage": {"inputTokens": 35365, "outputTokens": 47, "cachedInputTokens": 34688, "cacheReportedInputTokens": 35365, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 35365, "reasoningOutputTokens": 13, "reasoningReportedOutputTokens": 47}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": 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a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/head_camera/profile.yaml b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/head_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5deed9becd1123c6cc84fbaea8366db7ac08a17e --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/head_camera/profile.yaml @@ -0,0 +1,92 @@ +name: head_camera +description: "Fixed RGB-D head camera (D435 model) above the table. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount are in the profile." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: [] + ready_timeout_s: 30 +operations: + image: + description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time." + transport: topic + direction: output + endpoint: "/head_camera/image" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + depth: + description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp." + transport: topic + direction: output + endpoint: "/head_camera/depth" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + camera_info: + description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms." + transport: topic + direction: output + endpoint: "/head_camera/camera_info" + type: sensor_msgs/msg/CameraInfo + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:26:04.559Z" +frames: + base: + parent: "world:base" + translation_m: + - -0.03200000152 + - -0.449999988079 + - 1.350000023842 + rotation_xyzw: + - -0.948683301192 + - 0 + - 0 + - 0.316227769682 +color: + width: 320 + height: 240 + fps: null + projection: + model: pinhole + fx: 358.642181396484 + fy: 358.642181396484 + cx: 159.5 + cy: 119.5 + skew: 0 + distortion: + model: none + fov: + horizontal_deg: 48.085891406655776 + vertical_deg: 37.000001358777325 +type: rgbd +depth: + width: 320 + height: 240 + fps: null + quantity: z + scale_m_per_unit: 0.001 + invalid_value: 0 + valid_range_m: + min: 0.1 + max: 65.535 + reliable_range_m: + min: 0.1 + max: 65.535 + registration: color diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_arm/arm.urdf b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_arm/arm.urdf new file mode 100644 index 0000000000000000000000000000000000000000..0679f698a073bf49a5e79246d115ce8506e14935 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_arm/arm.urdf @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_arm/links.py b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_arm/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_arm/links.py @@ -0,0 +1,8 @@ +"""Publish the device's URDF links on TF from measured joint states.""" +from pathlib import Path + +from kinex.node import LinkPublisher + + +def create(profile, context): + return LinkPublisher(profile, context, Path(__file__).parent) diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_arm/profile.yaml b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_arm/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..175eff17167bb6c30828010e2b0aa7b14f25726e --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_arm/profile.yaml @@ -0,0 +1,81 @@ +name: left_arm +description: "ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF as left_arm/, and helpers.joint_model('left_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: + - entry: links.py + factory: create + ready_timeout_s: 30 +operations: + joint_state: + description: "Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command." + transport: topic + direction: output + endpoint: "/left_arm/joint_state" + type: sensor_msgs/msg/JointState + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + joint_target: + description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state." + transport: topic + direction: input + endpoint: "/left_arm/joint_target" + type: trajectory_msgs/msg/JointTrajectory + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + stop: + description: Stop arm motion and hold the measured posture. + transport: service + direction: input + endpoint: "/left_arm/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:26:04.564Z" +frames: + base: + parent: "world:base" + translation_m: + - -0.296999871731 + - -0.419500023127 + - 0.782000243664 + rotation_xyzw: + - 0 + - 2.9802e-8 + - 0.714142563298 + - 0.700000389985 +model: + format: urdf + path: arm.urdf + base_link: base_link + tip_link: flange +home: + fl_joint1: 0 + fl_joint2: 0 + fl_joint3: 0 + fl_joint4: 0 + fl_joint5: 0 + fl_joint6: 0 +limits: native +stop: + behavior: hold + affected_devices: + - left_arm +type: joint diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_camera/profile.yaml b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..e78bcc5344ad41af5c1ff79d775595f510023062 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_camera/profile.yaml @@ -0,0 +1,92 @@ +name: left_camera +description: "RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount on the left_arm flange are in the profile." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: [] + ready_timeout_s: 30 +operations: + image: + description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time." + transport: topic + direction: output + endpoint: "/left_camera/image" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + depth: + description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp." + transport: topic + direction: output + endpoint: "/left_camera/depth" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + camera_info: + description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms." + transport: topic + direction: output + endpoint: "/left_camera/camera_info" + type: sensor_msgs/msg/CameraInfo + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:28:47.472Z" +frames: + base: + parent: "left_arm:flange" + translation_m: + - 0.07000001149 + - 0.031999994336 + - 0.065000047158 + rotation_xyzw: + - -0.589367966393 + - 0.589367919412 + - -0.390698641271 + - 0.39069867358 +color: + width: 320 + height: 240 + fps: null + projection: + model: pinhole + fx: 358.642181396484 + fy: 358.642181396484 + cx: 159.5 + cy: 119.5 + skew: 0 + distortion: + model: none + fov: + horizontal_deg: 48.085891406655776 + vertical_deg: 37.000001358777325 +type: rgbd +depth: + width: 320 + height: 240 + fps: null + quantity: z + scale_m_per_unit: 0.001 + invalid_value: 0 + valid_range_m: + min: 0.1 + max: 65.535 + reliable_range_m: + min: 0.1 + max: 65.535 + registration: color diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_gripper/gripper.urdf b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_gripper/gripper.urdf new file mode 100644 index 0000000000000000000000000000000000000000..ffde595542f57b4e9ac0bbba73940f540888b67e --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_gripper/gripper.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_gripper/links.py b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_gripper/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_gripper/links.py @@ -0,0 +1,8 @@ +"""Publish the device's URDF links on TF from measured joint states.""" +from pathlib import Path + +from kinex.node import LinkPublisher + + +def create(profile, context): + return LinkPublisher(profile, context, Path(__file__).parent) diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_gripper/profile.yaml b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_gripper/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..bccf1ad6a039c690e16f083760893d7870ee0834 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/left_gripper/profile.yaml @@ -0,0 +1,88 @@ +name: left_gripper +description: "ALOHA AgileX left parallel two-finger gripper on the left_arm flange. The grip joint left_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + observation: joint_state + nodes: + - entry: links.py + factory: create + ready_timeout_s: 30 +operations: + joint_state: + description: "Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it." + transport: topic + direction: output + endpoint: "/left_gripper/joint_state" + type: sensor_msgs/msg/JointState + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + target: + description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state." + transport: topic + direction: input + endpoint: "/left_gripper/target" + type: std_msgs/msg/Float64 + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + stop: + description: "Hold the measured opening; fingers may settle briefly after the stop." + transport: service + direction: input + endpoint: "/left_gripper/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:26:04.568Z" +frames: + base: + parent: "left_arm:flange" + translation_m: + - 0 + - 0 + - 0 + rotation_xyzw: + - 0 + - 0 + - 0 + - 1 + tcp: + parent: "left_gripper:base" + translation_m: + - 0.1199999989 + - 7.305e-9 + - -7.152e-9 + rotation_xyzw: + - 0 + - 2.9802e-8 + - 3.2247e-8 + - 0.999999998609 +model: + format: urdf + path: gripper.urdf + base_link: base_link +grip: + joint: left_gripper + open: 1 + closed: 0 +stop: + behavior: hold + affected_devices: + - left_gripper +type: gripper diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_arm/arm.urdf b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_arm/arm.urdf new file mode 100644 index 0000000000000000000000000000000000000000..98d0eedec4a96aa88680315bd8abb3ec31107ea7 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_arm/arm.urdf @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_arm/links.py b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_arm/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_arm/links.py @@ -0,0 +1,8 @@ +"""Publish the device's URDF links on TF from measured joint states.""" +from pathlib import Path + +from kinex.node import LinkPublisher + + +def create(profile, context): + return LinkPublisher(profile, context, Path(__file__).parent) diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_arm/profile.yaml b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_arm/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b8183ca72747032dab22afaa61298fdda9b44740 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_arm/profile.yaml @@ -0,0 +1,81 @@ +name: right_arm +description: "ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF as right_arm/, and helpers.joint_model('right_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: + - entry: links.py + factory: create + ready_timeout_s: 30 +operations: + joint_state: + description: "Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4, fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command." + transport: topic + direction: output + endpoint: "/right_arm/joint_state" + type: sensor_msgs/msg/JointState + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + joint_target: + description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state." + transport: topic + direction: input + endpoint: "/right_arm/joint_target" + type: trajectory_msgs/msg/JointTrajectory + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + stop: + description: Stop arm motion and hold the measured posture. + transport: service + direction: input + endpoint: "/right_arm/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:26:04.562Z" +frames: + base: + parent: "world:base" + translation_m: + - 0.306300014257 + - -0.418499916792 + - 0.78100001812 + rotation_xyzw: + - -2.9802e-8 + - 5.9605e-8 + - 0.71063355368 + - 0.703562409721 +model: + format: urdf + path: arm.urdf + base_link: base_link + tip_link: flange +home: + fr_joint1: 0 + fr_joint2: 0 + fr_joint3: 0 + fr_joint4: 0 + fr_joint5: 0 + fr_joint6: 0 +limits: native +stop: + behavior: hold + affected_devices: + - right_arm +type: joint diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_camera/profile.yaml b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..608c2545cb62a8ccdd3b5e970b3467f6e464d995 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_camera/profile.yaml @@ -0,0 +1,92 @@ +name: right_camera +description: "RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount on the right_arm flange are in the profile." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: [] + ready_timeout_s: 30 +operations: + image: + description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time." + transport: topic + direction: output + endpoint: "/right_camera/image" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + depth: + description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp." + transport: topic + direction: output + endpoint: "/right_camera/depth" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + camera_info: + description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms." + transport: topic + direction: output + endpoint: "/right_camera/camera_info" + type: sensor_msgs/msg/CameraInfo + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:29:52.539Z" +frames: + base: + parent: "right_arm:flange" + translation_m: + - 0.069999999887 + - 0.031999976227 + - 0.064999980711 + rotation_xyzw: + - -0.58936798274 + - 0.589367967612 + - -0.390698619841 + - 0.390698640561 +color: + width: 320 + height: 240 + fps: null + projection: + model: pinhole + fx: 358.642181396484 + fy: 358.642181396484 + cx: 159.5 + cy: 119.5 + skew: 0 + distortion: + model: none + fov: + horizontal_deg: 48.085891406655776 + vertical_deg: 37.000001358777325 +type: rgbd +depth: + width: 320 + height: 240 + fps: null + quantity: z + scale_m_per_unit: 0.001 + invalid_value: 0 + valid_range_m: + min: 0.1 + max: 65.535 + reliable_range_m: + min: 0.1 + max: 65.535 + registration: color diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_gripper/gripper.urdf b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_gripper/gripper.urdf new file mode 100644 index 0000000000000000000000000000000000000000..dfd1ec14f3c8085e398e7e1f5e81cb2af81e1f30 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_gripper/gripper.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_gripper/links.py b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_gripper/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_gripper/links.py @@ -0,0 +1,8 @@ +"""Publish the device's URDF links on TF from measured joint states.""" +from pathlib import Path + +from kinex.node import LinkPublisher + + +def create(profile, context): + return LinkPublisher(profile, context, Path(__file__).parent) diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_gripper/profile.yaml b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_gripper/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..15540c12f7788da54dcd6d00c9f579b5fcaf31cb --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/right_gripper/profile.yaml @@ -0,0 +1,88 @@ +name: right_gripper +description: "ALOHA AgileX right parallel two-finger gripper on the right_arm flange. The grip joint right_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + observation: joint_state + nodes: + - entry: links.py + factory: create + ready_timeout_s: 30 +operations: + joint_state: + description: "Measured right_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it." + transport: topic + direction: output + endpoint: "/right_gripper/joint_state" + type: sensor_msgs/msg/JointState + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + target: + description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state." + transport: topic + direction: input + endpoint: "/right_gripper/target" + type: std_msgs/msg/Float64 + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + stop: + description: "Hold the measured opening; fingers may settle briefly after the stop." + transport: service + direction: input + endpoint: "/right_gripper/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:26:04.565Z" +frames: + base: + parent: "right_arm:flange" + translation_m: + - 0 + - 0 + - 0 + rotation_xyzw: + - 0 + - 0 + - 0 + - 1 + tcp: + parent: "right_gripper:base" + translation_m: + - 0.119999992883 + - -9.757e-9 + - -7.153e-9 + rotation_xyzw: + - 1e-12 + - 2.9802e-8 + - -6.4393e-8 + - 0.999999973161 +model: + format: urdf + path: gripper.urdf + base_link: base_link +grip: + joint: right_gripper + open: 1 + closed: 0 +stop: + behavior: hold + affected_devices: + - right_gripper +type: gripper diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/world/profile.yaml b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/world/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1eb9ce9708db723f84c7a058a25513826cb6c59e --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/devices/world/profile.yaml @@ -0,0 +1,58 @@ +name: world +description: Scene reference and authoritative world clock of the RoboTwin 2.0 table. Kinex supplies frame transforms independently. This device exposes no task instructions or evaluator results. +source: default +calibrated_at: null +method: unknown +runtime: + overlays: [] + domain_id: 83 + namespace: '' + observation: time + nodes: [] + ready_timeout_s: 30 +operations: + time: + description: Authoritative world clock + transport: topic + direction: output + endpoint: "/clock" + type: rosgraph_msgs/msg/Clock + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + wait: + description: "Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results." + transport: service + direction: input + endpoint: "/world/wait" + type: kinex_interfaces/srv/Wait + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 120 + stop: + description: Cancel the pending wait. + transport: service + direction: input + endpoint: "/world/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:26:04.557Z" +frames: + base: + parent: null + translation_m: null + rotation_xyzw: null +bounds: null +surfaces: [] +type: world diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/kinex.json b/experiments/robotwin-native/kinex-1/workspace/.kinex/kinex.json new file mode 100644 index 0000000000000000000000000000000000000000..c0400a4af73fe4a6eb2ed538d2e3826327c5ba32 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/kinex.json @@ -0,0 +1,5 @@ +{ + "python": { + "provider": "ros-jazzy" + } +} diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/memos/handover-block.md b/experiments/robotwin-native/kinex-1/workspace/.kinex/memos/handover-block.md new file mode 100644 index 0000000000000000000000000000000000000000..3a8af2fe3d5e841641740b7fe9664ca95ff54421 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/memos/handover-block.md @@ -0,0 +1,13 @@ +# RoboTwin ALOHA handover block + +Public interfaces: read harness.md and refreshed profiles. Native period .04s; 800 frames =32s. All movement needs sequential world.wait. Load arms, grippers, head camera and right wrist camera. Normalized grip 0 closed,1 open. TCP +.12m flange x. World arm base transforms are calibrated; use TF and URDF IK. + +Reusable exec scripts: +- `.kinex/tools/cartesian_move.py`: args moves={left/right:[world TCP poses xyz+xyzw]}, seconds per segment, grips, wait, settle, observe. Precomputes Cartesian interpolated IK then publishes JointTrajectory; returns measured TCP, q error and image. Kinex FK/IK requires list, not NumPy arrays. +- `.kinex/tools/color_geometry.py`: args cameras; red/blue visible world surface bounds from synchronized RGBD and stamped TF. Bounds include silhouette/depth mismatches and red highlights; inspect images, use interior pixels if precision required. Wrist depth is unreliable inside .1m. + +Successful transfer geometry observed: vertical red block ~.06x.06x.20m, table z .74, pad top .746. Detect current xy each episode. Head camera clips tall block top. Pad center from min/max xy, not perspective-biased pixel median. + +Grasp LOW with left TCP at table+.045 (not +.095) using yaw +90deg quaternion [0,0,.70710678,.70710678], approach +world y, fingers close along x. Approach from y-.15 then insert center. Close .5s, lift .10m. Keep vertical; rolling a central grip caused a slip. +Right approaches from +world x with yaw pi quaternion [0,0,1,0], closes along y. Target ~.03 below measured block top, ~.12-.13 above left TCP. Too little separation (~.07-.08) caused collision with left wrist/camera and displaced the block; stop before recovery. Lower left grasp fixed it. Right camera gives useful top and side geometry before insertion. +Close right .6s, open left .44s, withdraw left frontward ~.17m; verify block remains suspended with right (grip ~.75 for .06m block). Then translate above pad center, lower upright preserving grip offset, open right. In this run transfer and pad placement observed; release wait terminated at 24.84s after .04s, before requested .8s. Last measured right opening .761, velocity positive; full-open motion not verified. Native verdict remains owner-only. Never retry world waits after terminal or infer success merely from termination. diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/tools/cartesian_move.py b/experiments/robotwin-native/kinex-1/workspace/.kinex/tools/cartesian_move.py new file mode 100644 index 0000000000000000000000000000000000000000..83bc037a555015643ef3b26b7e3e4fad65b56cb2 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/tools/cartesian_move.py @@ -0,0 +1,51 @@ +"""Stepped RoboTwin Cartesian waypoint execution; world poses are TCP xyz+xyzw. +Call with exec args: moves={left/right:[pose,...]}, seconds per segment, grips, +wait (grip-only duration), observe. Uses calibrated TF and URDF IK, verifies feedback. +""" +import asyncio +import numpy as np +from scipy.spatial.transform import Rotation, Slerp +from kinex import args, communication, helpers, finish + +async def state(io, device): + async with io.subscribe(device+'.joint_state') as stream: + return await stream.next(timeout_s=10) + +async def main(): + a=args(); result={}; duration=float(a.get('seconds',1.2)); plans={} + async with communication() as io: + for side, poses in a.get('moves',{}).items(): + arm=side+'_arm'; model=helpers.joint_model(arm) + initial=await state(io,arm); q=np.array(initial.position) + tf=(await helpers.relative_pose(io,{'frame':arm+':base'},{'frame':'world:base'}))['pose'] + baseR=Rotation.from_quat(tf[3:]); basep=np.array(tf[:3]) + local=model.fk(q.tolist()); wr=baseR*Rotation.from_quat(local[3:]); wp=baseR.apply(local[:3])+basep+wr.apply([.12,0,0]) + points=[]; elapsed=0 + for pose in poses: + targetp=np.array(pose[:3]); targetr=Rotation.from_quat(pose[3:]); sl=Slerp([0,1],Rotation.from_quat([wr.as_quat(),targetr.as_quat()])) + n=max(8,int(duration/.08)) + for i in range(1,n+1): + f=i/n; r=sl([f])[0]; p=wp*(1-f)+targetp*f + lp=baseR.inv().apply(p-r.apply([.12,0,0])-basep); lr=baseR.inv()*r + nq=np.array(model.ik([*lp,*lr.as_quat()],q.tolist())) + if np.max(np.abs(nq-q))>.6: raise ValueError('IK branch jump') + q=nq; t=elapsed+duration*f; ns=round(t*1e9) + points.append({'positions':q.tolist(),'time_from_start':{'sec':ns//1000000000,'nanosec':ns%1000000000}}) + elapsed+=duration; wp=targetp; wr=targetr + plans[side]=(model,q,elapsed,points) + for side,(model,q,t,points) in plans.items(): + await io.publish(side+'_arm.joint_target',{'joint_names':model.names,'points':points}) + for side,value in a.get('grips',{}).items(): + await io.publish(side+'_gripper.target',{'data':float(value)}) + wait=max([p[2] for p in plans.values()]+[float(a.get('wait',0))]) + if wait: result['wait']=await io.request('world.wait',duration_s=wait+float(a.get('settle',.16))) + for side in ['left','right']: + s=await state(io,side+'_arm'); g=await state(io,side+'_gripper') + result[side]={'q':list(s.position),'grip':list(g.position),'tcp':(await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'}))['pose']} + if side in plans: result[side]['joint_error']=float(np.max(np.abs(np.array(s.position)-plans[side][1]))) + paths=[] + if a.get('observe',True): + image,depth=await helpers.camera_frame(io,'head_camera'); paths=[helpers.save_image(image)] + finish(result,file=paths) + +asyncio.run(main()) diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/tools/color_geometry.py b/experiments/robotwin-native/kinex-1/workspace/.kinex/tools/color_geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..d1e10b15a1bc324536524fae43efdf27c253c71c --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/tools/color_geometry.py @@ -0,0 +1,22 @@ +"""Measure red block / blue pad visible surface bounds from calibrated RGB-D.""" +import asyncio +import numpy as np +from scipy.spatial.transform import Rotation +from kinex import args,communication,helpers,finish +async def main(): + out={}; files=[] + async with communication() as io: + for cam in args().get('cameras',['head_camera']): + im,de=await helpers.camera_frame(io,cam); a=helpers.image_array(im).astype(float); z=helpers.image_array(de)*.001 + async with io.subscribe(cam+'.camera_info') as s: info=await s.next() + fx,fy,cx,cy=info.k[0],info.k[4],info.k[2],info.k[5] + tf=(await helpers.relative_pose(io,{'frame':im.header.frame_id},{'frame':'world:base'},timestamp=im.header.stamp.sec+im.header.stamp.nanosec*1e-9))['pose'] + out[cam]={} + for k,m in {'red':(a[:,:,0]>110)&(a[:,:,0]>a[:,:,1]*1.8)&(a[:,:,1]<100),'blue':(a[:,:,2]>100)&(a[:,:,2]>a[:,:,0]*1.8)&(a[:,:,0]<100)}.items(): + v,u=np.where(m&(z>0)) + if not len(u): continue + zz=z[v,u]; p=np.stack([(u-cx)*zz/fx,(v-cy)*zz/fy,zz],axis=1); w=Rotation.from_quat(tf[3:]).apply(p)+tf[:3] + out[cam][k]={'min':w.min(0),'max':w.max(0),'median':np.median(w,axis=0),'low':np.median(w[w[:,2]w[:,2].max()-.008],axis=0)} + files.append(helpers.save_image(im)) + finish(out,file=files) +asyncio.run(main()) diff --git a/experiments/robotwin-native/kinex-1/workspace/.kinex/workspace.yaml b/experiments/robotwin-native/kinex-1/workspace/.kinex/workspace.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f4e663d629af8919ee84dc2cfeddd9204bea9c6 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/.kinex/workspace.yaml @@ -0,0 +1,16 @@ +identity: + id: robotwin-aloha-agilex + name: RoboTwin ALOHA AgileX + kind: arm + model: ALOHA AgileX (dual ARX5) + source: user +devices: + source: user +safety: + source: user + max_joint_velocity: null + max_cartesian_velocity: null + keep_out_zones: [] +meta: + created_at: '2026-10-24T00:00:00Z' + updated_at: '2026-10-24T00:00:00Z' diff --git a/experiments/robotwin-native/kinex-1/workspace/harness.md b/experiments/robotwin-native/kinex-1/workspace/harness.md new file mode 100644 index 0000000000000000000000000000000000000000..f77861ba7febc66956580f9e840c24a340c5e35f --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/harness.md @@ -0,0 +1,6 @@ +Use Kinex's ROS device profiles in `.kinex/devices/` and the robot +specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode. +Use `kinex.communication` for typed topic publication, subscriptions and service +requests. Inspect operation schemas before acting. Load the provided devices; +they borrow the running simulator. Stop holds motion; it does not reset the world. +Improve existing tools, skills, and memos in place. diff --git a/experiments/robotwin-native/kinex-1/workspace/instruction.md b/experiments/robotwin-native/kinex-1/workspace/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..655004f8664de4cbee54ebc3865f6f44e18aa581 --- /dev/null +++ b/experiments/robotwin-native/kinex-1/workspace/instruction.md @@ -0,0 +1,45 @@ +# Task: robotwin/handover-block-aloha-agilex + +use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open + +## Work to do + +Solve the task efficiently using the running rosbridge environment. Follow the +task instruction above, including its completion requirements. Read `harness.md` +and the device profiles for public interfaces, units and calibrated frames. +Reuse your saved tools, skills, and memos. + +## Limits + +You have 800 native action frames. The execution mode is `stepped`: physics is +frozen while idle. Use `world.wait` with `duration_s` to advance the native clock. +Joint targets are ROS JointTrajectory messages; gripper targets use the units +declared in their profiles. Reads and command publication do not advance physics. +Waits are sequential; concurrent waits are rejected. A command or elapsed time +does not prove arrival; inspect measured feedback. Device stop holds the device +without ending the episode. The safety timeout is 28800 seconds. +Do not launch or reset a simulation. + +## Completion + +Return when finished. The owner finalizes the episode and reads its native +verdict. An interrupted or rejected wait does not establish task success. The +native step limit stops physics even if your requested wait is longer. + +## Saved resources + +Maintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes +in `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated +content. Create a new file only when no existing resource fits; do not create +per-attempt reflections or numbered copies. These files persist across fresh +conversations. Update them before finishing your response. + +## Robot and device access + +Use Kinex's ROS device profiles in `.kinex/devices/` and the robot +specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode. +Use `kinex.communication` for typed topic publication, subscriptions and service +requests. Inspect operation schemas before acting. Load the provided devices; +they borrow the running simulator. Stop holds motion; it does not reset the world. +Improve existing tools, skills, and memos in place. + diff --git a/experiments/robotwin-native/standalone-1/artifacts.json b/experiments/robotwin-native/standalone-1/artifacts.json new file mode 100644 index 0000000000000000000000000000000000000000..0972deb777299fbf34e639203c9ce2a73c90d115 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/artifacts.json @@ -0,0 +1,109 @@ +{ + "evidence": { + "complete": true, + "path": "evidence.tar.gz" + }, + "recording": { + "complete": true, + "enabled": true, + "encoder_queue_drops": 0, + "lossless": false, + "manifest": { + "bag_messages": 0, + "error": null, + "fps": 10.0, + "run_id": "ab542c17c6c7", + "schema": "rosbridge/recording/1", + "spectator_views": [ + "third_person", + "left_wrist", + "right_wrist" + ], + "state": "complete", + "task": "robotwin/handover-block-aloha-agilex", + "topics": [], + "type": "mp4", + "views": { + "left_wrist": { + "dropped": 0, + "error": null, + 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0000000000000000000000000000000000000000..e0c7bc7f00b4843eeb3993dfcb6fe5e92630f6d7 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/evidence.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e17807ae82344851f4a98db25f9d8f03bebc570e5f17af9fa3e31f747219ef6b +size 6785397 diff --git a/experiments/robotwin-native/standalone-1/instruction.json b/experiments/robotwin-native/standalone-1/instruction.json new file mode 100644 index 0000000000000000000000000000000000000000..4e077823a28a46891c7e3cec8542328dbaa964e1 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/instruction.json @@ -0,0 +1,14 @@ +{ + "binding_paths": {}, + "bindings": {}, + "context": {}, + "instruction": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open", + "native_instruction": "", + "policy": "modified", + "prompt": "# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n", + "prompt_template": "\n# Task: robotwin/handover-block-aloha-agilex\n\nuse the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open\n\n## Work to do\n\nSolve the task efficiently using the running rosbridge environment. Follow the\ntask instruction above, including its completion requirements. Read `harness.md`\nand the device profiles for public interfaces, units and calibrated frames.\nReuse your saved tools, skills, and memos.\n\n## Limits\n\nYou have 800 native action frames. The execution mode is `stepped`: physics is\nfrozen while idle. Use `world.wait` with `duration_s` to advance the native clock.\nJoint targets are ROS JointTrajectory messages; gripper targets use the units\ndeclared in their profiles. Reads and command publication do not advance physics.\nWaits are sequential; concurrent waits are rejected. A command or elapsed time\ndoes not prove arrival; inspect measured feedback. Device stop holds the device\nwithout ending the episode. The safety timeout is 28800 seconds.\nDo not launch or reset a simulation.\n\n## Completion\n\nReturn when finished. The owner finalizes the episode and reads its native\nverdict. An interrupted or rejected wait does not establish task success. The\nnative step limit stops physics even if your requested wait is longer.\n\n## Saved resources\n\nMaintain reusable code in `tools/`, skills in `skills/`, and concise working notes\nin `memos/`. Improve existing files in place as you learn, replacing outdated\ncontent. Create a new file only when no existing resource fits; do not create\nper-attempt reflections or numbered copies. These files persist across fresh\nconversations. Update them before finishing your response.\n\n## Robot and device access\n\nUse Kinex's ROS device profiles in `.kinex/devices/` and the robot\nspecification in `.kinex/workspace.yaml`. The adapter refreshes them each episode.\nUse `kinex.communication` for typed topic publication, subscriptions and service\nrequests. Inspect operation schemas before acting. Load the provided devices;\nthey borrow the running simulator. Stop holds motion; it does not reset the world.\nImprove existing tools, skills, and memos in place.\n\n", + "schema": "rlebench/episode-instruction/3", + "source": "catalog", + "task_id": "robotwin/handover-block-aloha-agilex", + "template": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open" +} diff --git a/experiments/robotwin-native/standalone-1/media.json b/experiments/robotwin-native/standalone-1/media.json new file mode 100644 index 0000000000000000000000000000000000000000..a791d6834e4e5e57e6376f6aba8bed4b0363b5d1 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/media.json @@ -0,0 +1,101 @@ +{ + "native_frames": 321, + "published_frames": 321, + "pts_verified": true, + "source_start_pts": 0.2, + "published_start_pts": 0.0, + "pts_offset": -0.2, + "terminal_frame_retained": true, + "views": [ + "third_person", + "left_wrist", + "right_wrist" + ], + "fps": "10/1", + "native_recording": { + "schema": "rosbridge/recording/1", + "run_id": "ab542c17c6c7", + "task": "robotwin/handover-block-aloha-agilex", + "state": "complete", + "views": { + "third_person": { + "received": 322, + "dropped": 0, + "segments": [ + { + "file": "third_person.mp4", + "frames": 321, + "width": 960, + "height": 720, + "fps": 10.0, + "first_stamp_ns": 0, + "last_stamp_ns": 32000000000 + } + ], + "error": null + }, + "left_wrist": { + "received": 322, + "dropped": 0, + "segments": [ + { + "file": "left_wrist.mp4", + "frames": 321, + "width": 960, + "height": 720, + "fps": 10.0, + "first_stamp_ns": 0, + "last_stamp_ns": 32000000000 + } + ], + "error": null + }, + "right_wrist": { + "received": 322, + "dropped": 0, + "segments": [ + { + "file": "right_wrist.mp4", + "frames": 321, + "width": 960, + "height": 720, + "fps": 10.0, + "first_stamp_ns": 0, + "last_stamp_ns": 32000000000 + } + ], + "error": null + } + }, + "type": "mp4", + "spectator_views": [ + "third_person", + "left_wrist", + "right_wrist" + ], + "fps": 10.0, + "topics": [], + "error": null, + "bag_messages": 0 + }, + "all_frame_visual_checks": { + "third_person": { + "frames_compared": 321, + "mean_ssim": 0.9983650965732087, + "minimum_ssim": 0.997618, + "label_area_excluded": true + }, + "left_wrist": { + "frames_compared": 321, + "mean_ssim": 0.9982074797507788, + "minimum_ssim": 0.997366, + "label_area_excluded": true + }, + "right_wrist": { + "frames_compared": 321, + "mean_ssim": 0.9987225451713396, + "minimum_ssim": 0.997234, + "label_area_excluded": true + } + } +} diff --git a/experiments/robotwin-native/standalone-1/policy-calls.json b/experiments/robotwin-native/standalone-1/policy-calls.json new file mode 100644 index 0000000000000000000000000000000000000000..7977a28bda6bf78857d3a4f44968fc4ee241e834 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/policy-calls.json @@ -0,0 +1,27252 @@ +[ + { + "call_id": "c9bcdd27a5664f28920863f46a20dd8f", + "task": "use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. 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b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/head_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..33abe5d1bbdde14403d86023b374a92716991064 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/head_camera/profile.yaml @@ -0,0 +1,92 @@ +name: head_camera +description: "Fixed RGB-D head camera (D435 model) above the table. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount are in the profile." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: [] + ready_timeout_s: 30 +operations: + image: + description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time." + transport: topic + direction: output + endpoint: "/head_camera/image" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + depth: + description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp." + transport: topic + direction: output + endpoint: "/head_camera/depth" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + camera_info: + description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms." + transport: topic + direction: output + endpoint: "/head_camera/camera_info" + type: sensor_msgs/msg/CameraInfo + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:32:47.588Z" +frames: + base: + parent: "world:base" + translation_m: + - -0.03200000152 + - -0.449999988079 + - 1.350000023842 + rotation_xyzw: + - -0.948683301192 + - 0 + - 0 + - 0.316227769682 +color: + width: 320 + height: 240 + fps: null + projection: + model: pinhole + fx: 358.642181396484 + fy: 358.642181396484 + cx: 159.5 + cy: 119.5 + skew: 0 + distortion: + model: none + fov: + horizontal_deg: 48.085891406655776 + vertical_deg: 37.000001358777325 +type: rgbd +depth: + width: 320 + height: 240 + fps: null + quantity: z + scale_m_per_unit: 0.001 + invalid_value: 0 + valid_range_m: + min: 0.1 + max: 65.535 + reliable_range_m: + min: 0.1 + max: 65.535 + registration: color diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_arm/arm.urdf b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_arm/arm.urdf new file mode 100644 index 0000000000000000000000000000000000000000..0679f698a073bf49a5e79246d115ce8506e14935 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_arm/arm.urdf @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_arm/links.py b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_arm/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_arm/links.py @@ -0,0 +1,8 @@ +"""Publish the device's URDF links on TF from measured joint states.""" +from pathlib import Path + +from kinex.node import LinkPublisher + + +def create(profile, context): + return LinkPublisher(profile, context, Path(__file__).parent) diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_arm/profile.yaml b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_arm/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2c0f77218e12ba4e22b85cab998e516b049a288f --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_arm/profile.yaml @@ -0,0 +1,81 @@ +name: left_arm +description: "ARX5 six-joint left arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fl_link6) as the tip link; measured links are on TF as left_arm/, and helpers.joint_model('left_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: + - entry: links.py + factory: create + ready_timeout_s: 30 +operations: + joint_state: + description: "Measured positions of fl_joint1, fl_joint2, fl_joint3, fl_joint4, fl_joint5, fl_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command." + transport: topic + direction: output + endpoint: "/left_arm/joint_state" + type: sensor_msgs/msg/JointState + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + joint_target: + description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state." + transport: topic + direction: input + endpoint: "/left_arm/joint_target" + type: trajectory_msgs/msg/JointTrajectory + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + stop: + description: Stop arm motion and hold the measured posture. + transport: service + direction: input + endpoint: "/left_arm/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:32:49.906Z" +frames: + base: + parent: "world:base" + translation_m: + - -0.296999871731 + - -0.419500023127 + - 0.782000243664 + rotation_xyzw: + - 0 + - 2.9802e-8 + - 0.714142563298 + - 0.700000389985 +model: + format: urdf + path: arm.urdf + base_link: base_link + tip_link: flange +home: + fl_joint1: 0 + fl_joint2: 0 + fl_joint3: 0 + fl_joint4: 0 + fl_joint5: 0 + fl_joint6: 0 +limits: native +stop: + behavior: hold + affected_devices: + - left_arm +type: joint diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_camera/profile.yaml b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a056551a4afc784e1e52502996ac2a4c216b5c3b --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_camera/profile.yaml @@ -0,0 +1,92 @@ +name: left_camera +description: "RGB-D camera (D435 model) on the left wrist. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount on the left_arm flange are in the profile." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: [] + ready_timeout_s: 30 +operations: + image: + description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time." + transport: topic + direction: output + endpoint: "/left_camera/image" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + depth: + description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp." + transport: topic + direction: output + endpoint: "/left_camera/depth" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + camera_info: + description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms." + transport: topic + direction: output + endpoint: "/left_camera/camera_info" + type: sensor_msgs/msg/CameraInfo + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:32:51.632Z" +frames: + base: + parent: "left_arm:flange" + translation_m: + - 0.07000001149 + - 0.031999994336 + - 0.065000047158 + rotation_xyzw: + - -0.589367966393 + - 0.589367919412 + - -0.390698641271 + - 0.39069867358 +color: + width: 320 + height: 240 + fps: null + projection: + model: pinhole + fx: 358.642181396484 + fy: 358.642181396484 + cx: 159.5 + cy: 119.5 + skew: 0 + distortion: + model: none + fov: + horizontal_deg: 48.085891406655776 + vertical_deg: 37.000001358777325 +type: rgbd +depth: + width: 320 + height: 240 + fps: null + quantity: z + scale_m_per_unit: 0.001 + invalid_value: 0 + valid_range_m: + min: 0.1 + max: 65.535 + reliable_range_m: + min: 0.1 + max: 65.535 + registration: color diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_gripper/gripper.urdf b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_gripper/gripper.urdf new file mode 100644 index 0000000000000000000000000000000000000000..ffde595542f57b4e9ac0bbba73940f540888b67e --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_gripper/gripper.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_gripper/links.py b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_gripper/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_gripper/links.py @@ -0,0 +1,8 @@ +"""Publish the device's URDF links on TF from measured joint states.""" +from pathlib import Path + +from kinex.node import LinkPublisher + + +def create(profile, context): + return LinkPublisher(profile, context, Path(__file__).parent) diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_gripper/profile.yaml b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_gripper/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..034db8f28160a95dce305c7f86213ed064195a25 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/left_gripper/profile.yaml @@ -0,0 +1,88 @@ +name: left_gripper +description: "ALOHA AgileX left parallel two-finger gripper on the left_arm flange. The grip joint left_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + observation: joint_state + nodes: + - entry: links.py + factory: create + ready_timeout_s: 30 +operations: + joint_state: + description: "Measured left_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it." + transport: topic + direction: output + endpoint: "/left_gripper/joint_state" + type: sensor_msgs/msg/JointState + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + target: + description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state." + transport: topic + direction: input + endpoint: "/left_gripper/target" + type: std_msgs/msg/Float64 + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + stop: + description: "Hold the measured opening; fingers may settle briefly after the stop." + transport: service + direction: input + endpoint: "/left_gripper/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:32:53.278Z" +frames: + base: + parent: "left_arm:flange" + translation_m: + - 0 + - 0 + - 0 + rotation_xyzw: + - 0 + - 0 + - 0 + - 1 + tcp: + parent: "left_gripper:base" + translation_m: + - 0.1199999989 + - 7.305e-9 + - -7.152e-9 + rotation_xyzw: + - 0 + - 2.9802e-8 + - 3.2247e-8 + - 0.999999998609 +model: + format: urdf + path: gripper.urdf + base_link: base_link +grip: + joint: left_gripper + open: 1 + closed: 0 +stop: + behavior: hold + affected_devices: + - left_gripper +type: gripper diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/configuration.json b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/configuration.json new file mode 100644 index 0000000000000000000000000000000000000000..df0544ba289dd0c274ffb539eb43b14f90c2b877 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/configuration.json @@ -0,0 +1,15 @@ +{ + "policy": { + "attention": "sdpa", + "base_vlm": "/models/Qwen3-VL-4B-Instruct", + "cameras": 3, + "checkpoint": "/models/oft/checkpoints/steps_40000_pytorch_model.pt", + "embodiment": "robotwin", + "image_size": [ + 224, + 224 + ], + "unnorm_key": "new_embodiment" + }, + "trace_directory": "/workspace/artifacts/policy" +} \ No newline at end of file diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/env.sh b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/env.sh new file mode 100644 index 0000000000000000000000000000000000000000..edf16febfd76b8a4854d90c45b51f7d3ac192a43 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/env.sh @@ -0,0 +1,4 @@ +export PYTHONPATH="/opt/policy/lib/python3.12/site-packages:/opt/starvla${PYTHONPATH:+:$PYTHONPATH}" +export HF_HUB_OFFLINE=1 +export TRANSFORMERS_OFFLINE=1 +export NO_ALBUMENTATIONS_UPDATE=1 diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/node.py b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/node.py new file mode 100644 index 0000000000000000000000000000000000000000..8c51a97b60baa14f2c3e5a50e87e3d3bc461ea5e --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/node.py @@ -0,0 +1,5 @@ +from pathlib import Path +from programvla.adapters.model_node import create as factory + +def create(profile, context): + return factory(profile, context, Path(__file__).parent) diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/profile.yaml b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..585ffdd2e5b53ffe91bfaf806319e74fbf5884e8 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/policy/profile.yaml @@ -0,0 +1,38 @@ +name: policy +description: "Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images in head, left wrist, right wrist order and a task instruction. Returns a chunk of absolute ALOHA joint actions. Use the native run_policy tool with command order left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes one inference per request; it does not control the robot or decide when to re-observe." +source: default +calibrated_at: null +method: unknown +runtime: + overlays: + - env.sh + domain_id: 83 + namespace: '' + nodes: + - entry: node.py + factory: create + ready_timeout_s: 600 +operations: + infer: + description: "Input: obs={images:[head RGB path,left wrist RGB path,right wrist RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians), left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases; +1 closes. Also returns call_id and inference_s. All paths refer to this workspace." + transport: service + direction: input + endpoint: "/kinex/policy/infer" + type: kinex_interfaces/srv/Json + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 120 +loaded: true +meta: + created_at: "2026-10-28T21:31:46.267793+00:00" + updated_at: "2026-10-28T21:32:54.874Z" +family: starvla +variant: null +implementation: null +artifacts: [] +compute: + kind: cuda + index: 0 +type: model diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_arm/arm.urdf b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_arm/arm.urdf new file mode 100644 index 0000000000000000000000000000000000000000..98d0eedec4a96aa88680315bd8abb3ec31107ea7 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_arm/arm.urdf @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_arm/links.py b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_arm/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_arm/links.py @@ -0,0 +1,8 @@ +"""Publish the device's URDF links on TF from measured joint states.""" +from pathlib import Path + +from kinex.node import LinkPublisher + + +def create(profile, context): + return LinkPublisher(profile, context, Path(__file__).parent) diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_arm/profile.yaml b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_arm/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d2e9d6aacf87db984ca1db30643cbd6f5a9b4c91 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_arm/profile.yaml @@ -0,0 +1,81 @@ +name: right_arm +description: "ARX5 six-joint right arm of the RoboTwin ALOHA AgileX. Geometry is the sibling arm.urdf with flange (fr_link6) as the tip link; measured links are on TF as right_arm/, and helpers.joint_model('right_arm') computes FK/IK from it. Commands are upstream's absolute joint positions (the arm part of its 14-D qpos action), applied as native drive targets once per 0.04 s frame. The native URDF limits are +/-10 rad placeholders." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: + - entry: links.py + factory: create + ready_timeout_s: 30 +operations: + joint_state: + description: "Measured positions of fr_joint1, fr_joint2, fr_joint3, fr_joint4, fr_joint5, fr_joint6 in radians with velocities and the world-clock acquisition stamp; do not infer motion completion from a sent command." + transport: topic + direction: output + endpoint: "/right_arm/joint_state" + type: sensor_msgs/msg/JointState + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + joint_target: + description: "Joint trajectory over all 6 arm joints in radians, timed from receipt by strictly increasing time_from_start and finishing within 120 seconds; velocities, accelerations and effort must be empty. A zero header stamp selects now; a nonzero stamp must not start in the past. Points are absolute native joint positions, linearly interpolated at the native control period. Replacement clears pending references; invalid trajectories are ignored, so verify measured joint_state." + transport: topic + direction: input + endpoint: "/right_arm/joint_target" + type: trajectory_msgs/msg/JointTrajectory + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + stop: + description: Stop arm motion and hold the measured posture. + transport: service + direction: input + endpoint: "/right_arm/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:34:09.138Z" +frames: + base: + parent: "world:base" + translation_m: + - 0.306300014257 + - -0.418499916792 + - 0.78100001812 + rotation_xyzw: + - -2.9802e-8 + - 5.9605e-8 + - 0.71063355368 + - 0.703562409721 +model: + format: urdf + path: arm.urdf + base_link: base_link + tip_link: flange +home: + fr_joint1: 0 + fr_joint2: 0 + fr_joint3: 0 + fr_joint4: 0 + fr_joint5: 0 + fr_joint6: 0 +limits: native +stop: + behavior: hold + affected_devices: + - right_arm +type: joint diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_camera/profile.yaml b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f4139511d5f7462082616ea16bf5c6927ddb047e --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_camera/profile.yaml @@ -0,0 +1,92 @@ +name: right_camera +description: "RGB-D camera (D435 model) on the right wrist. Native ray-traced RGB with pixel-center depth from a raster twin; calibration and the optical-frame mount on the right_arm flange are in the profile." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + nodes: [] + ready_timeout_s: 30 +operations: + image: + description: "RGB image measurements with encoding, dimensions, row stride and acquisition timestamp in the Image header. The frame_id identifies the calibrated optical frame; use its stamp for tf2 lookup. A new subscriber receives the current frame at once, also while the world is paused, then one per camera sample; reading never advances world time." + transport: topic + direction: output + endpoint: "/right_camera/image" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + depth: + description: "16UC1 depth registered to image in millimetres along the optical axis; 0 marks missing or out-of-range samples. Rendered with its image: same stamp and frame_id, so pair the two by stamp." + transport: topic + direction: output + endpoint: "/right_camera/depth" + type: sensor_msgs/msg/Image + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + camera_info: + description: "Projection and distortion calibration for the image stream, including K, D, image size and optical frame. Use the declared distortion model and raw/rectified image space when projecting pixels; intrinsics are not TF transforms." + transport: topic + direction: output + endpoint: "/right_camera/camera_info" + type: sensor_msgs/msg/CameraInfo + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:34:10.895Z" +frames: + base: + parent: "right_arm:flange" + translation_m: + - 0.069999999887 + - 0.031999976227 + - 0.064999980711 + rotation_xyzw: + - -0.58936798274 + - 0.589367967612 + - -0.390698619841 + - 0.390698640561 +color: + width: 320 + height: 240 + fps: null + projection: + model: pinhole + fx: 358.642181396484 + fy: 358.642181396484 + cx: 159.5 + cy: 119.5 + skew: 0 + distortion: + model: none + fov: + horizontal_deg: 48.085891406655776 + vertical_deg: 37.000001358777325 +type: rgbd +depth: + width: 320 + height: 240 + fps: null + quantity: z + scale_m_per_unit: 0.001 + invalid_value: 0 + valid_range_m: + min: 0.1 + max: 65.535 + reliable_range_m: + min: 0.1 + max: 65.535 + registration: color diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_gripper/gripper.urdf b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_gripper/gripper.urdf new file mode 100644 index 0000000000000000000000000000000000000000..dfd1ec14f3c8085e398e7e1f5e81cb2af81e1f30 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_gripper/gripper.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_gripper/links.py b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_gripper/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_gripper/links.py @@ -0,0 +1,8 @@ +"""Publish the device's URDF links on TF from measured joint states.""" +from pathlib import Path + +from kinex.node import LinkPublisher + + +def create(profile, context): + return LinkPublisher(profile, context, Path(__file__).parent) diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_gripper/profile.yaml b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_gripper/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f58894239c7f48a045d5ab41de766d4639051436 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/right_gripper/profile.yaml @@ -0,0 +1,88 @@ +name: right_gripper +description: "ALOHA AgileX right parallel two-finger gripper on the right_arm flange. The grip joint right_gripper is upstream's normalized gripper value (0 closed, 1 open): finger targets are -0.01 + 0.055 m per unit, so a closed command squeezes past the finger stop. The TCP is upstream's tcp pose, gripper_bias ahead of the wrist joint with +x approaching. A closed or stalled grip alone does not prove a successful grasp." +source: introspected +calibrated_at: null +method: introspection +runtime: + overlays: [] + domain_id: 83 + namespace: '' + observation: joint_state + nodes: + - entry: links.py + factory: create + ready_timeout_s: 30 +operations: + joint_state: + description: "Measured right_gripper, the native normalized gripper opening (0 closed, 1 open), stamped at acquisition; Kinex publishes the URDF finger links from it." + transport: topic + direction: output + endpoint: "/right_gripper/joint_state" + type: sensor_msgs/msg/JointState + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + target: + description: "Native normalized opening (data) from 0 (closed) to 1 (open), the gripper entry of upstream's 14-D qpos action. The native setter runs on every 250 Hz substep and moves the drive target at most 0.1 per call toward it. A target persists until replaced or stopped; out-of-range targets are ignored, so verify joint_state." + transport: topic + direction: input + endpoint: "/right_gripper/target" + type: std_msgs/msg/Float64 + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 + stop: + description: "Hold the measured opening; fingers may settle briefly after the stop." + transport: service + direction: input + endpoint: "/right_gripper/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:34:12.658Z" +frames: + base: + parent: "right_arm:flange" + translation_m: + - 0 + - 0 + - 0 + rotation_xyzw: + - 0 + - 0 + - 0 + - 1 + tcp: + parent: "right_gripper:base" + translation_m: + - 0.119999992883 + - -9.757e-9 + - -7.153e-9 + rotation_xyzw: + - 1e-12 + - 2.9802e-8 + - -6.4393e-8 + - 0.999999973161 +model: + format: urdf + path: gripper.urdf + base_link: base_link +grip: + joint: right_gripper + open: 1 + closed: 0 +stop: + behavior: hold + affected_devices: + - right_gripper +type: gripper diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/world/profile.yaml b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/world/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..76a0f172abc36a4fbc8cbe98ba49f7646437c33a --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/devices/world/profile.yaml @@ -0,0 +1,58 @@ +name: world +description: Scene reference and authoritative world clock of the RoboTwin 2.0 table. Kinex supplies frame transforms independently. This device exposes no task instructions or evaluator results. +source: default +calibrated_at: null +method: unknown +runtime: + overlays: [] + domain_id: 83 + namespace: '' + observation: time + nodes: [] + ready_timeout_s: 30 +operations: + time: + description: Authoritative world clock + transport: topic + direction: output + endpoint: "/clock" + type: rosgraph_msgs/msg/Clock + qos: + depth: 1 + reliability: reliable + durability: transient_local + timeout_s: 10 + wait: + description: "Advance or await duration_s of world time (finite and nonnegative). Zero is an observation barrier. Waits are sequential; one requested while another is pending is rejected. A terminal episode rejects further progression without exposing benchmark results." + transport: service + direction: input + endpoint: "/world/wait" + type: kinex_interfaces/srv/Wait + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 120 + stop: + description: Cancel the pending wait. + transport: service + direction: input + endpoint: "/world/stop" + type: std_srvs/srv/Trigger + qos: + depth: 1 + reliability: reliable + durability: volatile + timeout_s: 10 +loaded: true +meta: + created_at: "2026-10-24T00:00:00Z" + updated_at: "2026-10-28T21:34:14.471Z" +frames: + base: + parent: null + translation_m: null + rotation_xyzw: null +bounds: null +surfaces: [] +type: world diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/kinex.json b/experiments/robotwin-native/standalone-1/workspace/.kinex/kinex.json new file mode 100644 index 0000000000000000000000000000000000000000..c0400a4af73fe4a6eb2ed538d2e3826327c5ba32 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/kinex.json @@ -0,0 +1,5 @@ +{ + "python": { + "provider": "ros-jazzy" + } +} diff --git a/experiments/robotwin-native/standalone-1/workspace/.kinex/workspace.yaml b/experiments/robotwin-native/standalone-1/workspace/.kinex/workspace.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f4e663d629af8919ee84dc2cfeddd9204bea9c6 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/.kinex/workspace.yaml @@ -0,0 +1,16 @@ +identity: + id: robotwin-aloha-agilex + name: RoboTwin ALOHA AgileX + kind: arm + model: ALOHA AgileX (dual ARX5) + source: user +devices: + source: user +safety: + source: user + max_joint_velocity: null + max_cartesian_velocity: null + keep_out_zones: [] +meta: + created_at: '2026-10-24T00:00:00Z' + updated_at: '2026-10-24T00:00:00Z' diff --git a/experiments/robotwin-native/standalone-1/workspace/harness.md b/experiments/robotwin-native/standalone-1/workspace/harness.md new file mode 100644 index 0000000000000000000000000000000000000000..f77861ba7febc66956580f9e840c24a340c5e35f --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/harness.md @@ -0,0 +1,6 @@ +Use Kinex's ROS device profiles in `.kinex/devices/` and the robot +specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode. +Use `kinex.communication` for typed topic publication, subscriptions and service +requests. Inspect operation schemas before acting. Load the provided devices; +they borrow the running simulator. Stop holds motion; it does not reset the world. +Improve existing tools, skills, and memos in place. diff --git a/experiments/robotwin-native/standalone-1/workspace/instruction.md b/experiments/robotwin-native/standalone-1/workspace/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..655004f8664de4cbee54ebc3865f6f44e18aa581 --- /dev/null +++ b/experiments/robotwin-native/standalone-1/workspace/instruction.md @@ -0,0 +1,45 @@ +# Task: robotwin/handover-block-aloha-agilex + +use the left arm to grasp the red block on the table, hand it over to the right arm, and set it down in the center of the blue pad. Release it with the right gripper open + +## Work to do + +Solve the task efficiently using the running rosbridge environment. Follow the +task instruction above, including its completion requirements. Read `harness.md` +and the device profiles for public interfaces, units and calibrated frames. +Reuse your saved tools, skills, and memos. + +## Limits + +You have 800 native action frames. The execution mode is `stepped`: physics is +frozen while idle. Use `world.wait` with `duration_s` to advance the native clock. +Joint targets are ROS JointTrajectory messages; gripper targets use the units +declared in their profiles. Reads and command publication do not advance physics. +Waits are sequential; concurrent waits are rejected. A command or elapsed time +does not prove arrival; inspect measured feedback. Device stop holds the device +without ending the episode. The safety timeout is 28800 seconds. +Do not launch or reset a simulation. + +## Completion + +Return when finished. The owner finalizes the episode and reads its native +verdict. An interrupted or rejected wait does not establish task success. The +native step limit stops physics even if your requested wait is longer. + +## Saved resources + +Maintain reusable code in `.kinex/tools/`, skills in `.kinex/skills/`, and concise working notes +in `.kinex/memos/`. Improve existing files in place as you learn, replacing outdated +content. Create a new file only when no existing resource fits; do not create +per-attempt reflections or numbered copies. These files persist across fresh +conversations. Update them before finishing your response. + +## Robot and device access + +Use Kinex's ROS device profiles in `.kinex/devices/` and the robot +specification in `.kinex/workspace.yaml`. The adapter refreshes them each episode. +Use `kinex.communication` for typed topic publication, subscriptions and service +requests. Inspect operation schemas before acting. Load the provided devices; +they borrow the running simulator. Stop holds motion; it does not reset the world. +Improve existing tools, skills, and memos in place. + diff --git a/manifest.json b/manifest.json index 8187712f7c2d9909feb4347238d3625f3f13ae9c..01d2fa2cfb0100e42768cc1333919f536b900497 100644 --- a/manifest.json +++ b/manifest.json @@ -1,8 +1,8 @@ { "files": { "app.js": { - "bytes": 8878, - "sha256": "8429ad1235aa238a37d76a15ade2e1fbaa4eca1d573c99d6dc371403835480c9" + "bytes": 9041, + "sha256": "b5465f4d1e138ddb04fb445ca32a882a55f38530d131729d3c34f00c3f50ea93" }, "README.md": { "bytes": 321, @@ -17,12 +17,1080 @@ "sha256": "c787bda4dcd8b664d4844e72a39ac6d2855bbbdbbe4b58cb317488b30aee86e3" }, "data.json": { - "bytes": 1957, - "sha256": "3faf31ab4a47409fc9502556743b386690e24fbe177feab3eb81c64371490ec8" + "bytes": 2879, + "sha256": "fb281dcc25ab9104b875e11c4f69e8f9cdb3050e130d4230421d3b4efec59946" }, "style.css": { "bytes": 5015, "sha256": "c9185472e4768289aa8bb101f9031f87988c3d03a7007a8467af90fd750a6117" + }, + 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