diff --git a/.gitattributes b/.gitattributes index 768dd373c5e01dea007cda36b2a523e5c7a0839d..62cccb72b42b78e3fee6e004810411cc3e4005aa 100644 --- a/.gitattributes +++ b/.gitattributes @@ -60,3 +60,5 @@ experiments/robotwin-native/kinex_policy-1/combined.mp4 filter=lfs diff=lfs merg experiments/robotwin-native/standalone-2/combined.mp4 filter=lfs diff=lfs merge=lfs -text experiments/robotwin-native/kinex-2/combined.mp4 filter=lfs diff=lfs merge=lfs -text experiments/robotwin-native/standalone-3/combined.mp4 filter=lfs diff=lfs merge=lfs -text +experiments/robotwin-native/kinex_policy-2/combined.mp4 filter=lfs diff=lfs merge=lfs -text +experiments/robotwin-native/kinex-3/combined.mp4 filter=lfs diff=lfs merge=lfs -text diff --git a/app.js b/app.js index b6d90e11e6743b2b9bddb7b8e5f1089c3bcfc771..08f6ad308a79b65344455c6f38631b279689b90d 100644 --- a/app.js +++ b/app.js @@ -17,6 +17,6 @@ else if(tab==='artifacts')target.innerHTML=`

Recorde 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))}
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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('')}

Predicted actions and interface conversion

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

One task. Three control choices.

Execution

${[['Suite','RLE-Bench task03 · RoboTwin 2.0 · ALOHA AgileX'],['VLA','StarVLA/Qwen3-VL-OFT-Robotwin2 · steps 40,000'],['Agent','Kinex · GPT-6 Astra · high'],['Policy interface','Native run_policy · one inference per call · agent owns the outer loop'],['Control','25 Hz native joint / gripper frames; original task-specific limits'],['Selection','First three task03 tasks, seed 0; frozen before outcome inspection'],['Scope','Frozen-policy behavior only; no encoder or progress monitor']].map(([k,v])=>``).join('')}
${k}${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 ↗`} +function protocol(){$('#main').innerHTML=`
Experiment protocol

One task. Three control choices.

Execution

${[['Suite','RLE-Bench task03 · RoboTwin 2.0 · ALOHA AgileX'],['VLA','StarVLA/Qwen3-VL-OFT-Robotwin2 · steps 40,000'],['Agent','Kinex · GPT-6 Astra · high'],['Policy interface','Native run_policy · one inference per call · agent owns the outer loop'],['Inputs','VLA: three RGB views and language. Kinex: public RGB-D, joint state, calibration and native tools.'],['Control','25 Hz native joint / gripper frames; original task-specific limits'],['Selection','First three task03 tasks, seed 0; frozen before outcome inspection'],['Scope','Frozen-policy behavior only; no encoder or progress monitor']].map(([k,v])=>``).join('')}
${k}${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 ↗`} async function render(){try{const parts=location.hash.slice(1).split('/');if(parts[0]==='episode')await detail(parts[1],parts[2],parts[3]||'overview');else if(parts[0]==='protocol')protocol();else overview(parts[0]==='tasks')}catch(e){$('#main').innerHTML=`

Could not load this record

${esc(e.message)}

Return to overview
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b/experiments/robotwin-native/kinex-3/episode.json @@ -0,0 +1,15 @@ +{ + "episode": 1, + "initialization": { + "expert_plan_success": true, + "requested_seed": 0 + }, + "native_reward": 1.0, + "planned_episodes": 1, + "reason": "success", + "simulation_time_s": 54.160000000000004, + "step_limit": 1700, + "steps": 1354, + "success": true, + "valid": true +} diff --git a/experiments/robotwin-native/kinex-3/evidence.tar.gz b/experiments/robotwin-native/kinex-3/evidence.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..32405a82e76c9013796165fd4d2fb95bcd9e028a --- /dev/null +++ b/experiments/robotwin-native/kinex-3/evidence.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2d3884a8c19a136e796d8a761058c2dcb24b467aef261ab6e3872c8b91f4c487 +size 12619391 diff --git a/experiments/robotwin-native/kinex-3/instruction.json b/experiments/robotwin-native/kinex-3/instruction.json new file mode 100644 index 0000000000000000000000000000000000000000..50237e4c7db89c6e5947368213e456d46e004bb5 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/instruction.json @@ -0,0 +1,14 @@ +{ + "binding_paths": {}, + "bindings": {}, + "context": {}, + "instruction": "use arms to grab the bottles and put them into the dustbin to the left of the table", + "native_instruction": "", + "policy": "original_native", + "prompt": "# Task: robotwin/put-bottles-dustbin-aloha-agilex\n\nuse arms to grab the bottles and put them into the dustbin to the left of the table\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 1700 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/put-bottles-dustbin-aloha-agilex\n\nuse arms to grab the bottles and put them into the dustbin to the left of the table\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 1700 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/put-bottles-dustbin-aloha-agilex", + "template": "use arms to grab the bottles and put them into the dustbin to the left of the table" +} diff --git a/experiments/robotwin-native/kinex-3/media.json b/experiments/robotwin-native/kinex-3/media.json new file mode 100644 index 0000000000000000000000000000000000000000..4356603f8730cf4cf622d55bcab1c0c4166ebd1a --- /dev/null +++ b/experiments/robotwin-native/kinex-3/media.json @@ -0,0 +1,101 @@ +{ + "native_frames": 543, + "published_frames": 543, + "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": "c3ff59536ab0", + "task": "robotwin/put-bottles-dustbin-aloha-agilex", + "state": "complete", + "views": { + "third_person": { + "received": 543, + "dropped": 0, + "segments": [ + { + "file": "third_person.mp4", + "frames": 543, + "width": 960, + "height": 720, + "fps": 10.0, + "first_stamp_ns": 0, + "last_stamp_ns": 54160000000 + } + ], + "error": null + }, + "left_wrist": { + "received": 543, + "dropped": 0, + "segments": [ + { + "file": "left_wrist.mp4", + "frames": 543, + "width": 960, + "height": 720, + "fps": 10.0, + "first_stamp_ns": 0, + "last_stamp_ns": 54160000000 + } + ], + "error": null + }, + "right_wrist": { + "received": 543, + "dropped": 0, + "segments": [ + { + "file": "right_wrist.mp4", + "frames": 543, + "width": 960, + "height": 720, + "fps": 10.0, + "first_stamp_ns": 0, + "last_stamp_ns": 54160000000 + } + ], + "error": null + } + }, + "type": "mp4", + "spectator_views": [ + "third_person", + "left_wrist", + "right_wrist" + ], + "fps": 10.0, + "topics": [], + 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"text": "", + "images": [], + "timestamp": "2026-10-28T21:48:23.264Z" + }, + { + "role": "user", + "name": null, + "text": "# Task: robotwin/put-bottles-dustbin-aloha-agilex\n\nuse arms to grab the bottles and put them into the dustbin to the left of the table\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 1700 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": "2026-10-28T21:48:23.264Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: ls\n{\"path\": \"/workspace\"}", + "images": [], + "timestamp": "2026-10-28T21:48:25.910Z" + }, + { + "role": "toolResult", + "name": "ls", + "text": ".kinex/\nharness.md\ninstruction.md", + "images": [], + "timestamp": "2026-10-28T21:48:25.917Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \"/workspace/harness.md\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:48:29.788Z" + }, + { + "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": "2026-10-28T21:48:29.793Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: find\n{\"pattern\": \"**/*\", \"path\": \"/workspace/.kinex\", \"limit\": 150}", + "images": [], + "timestamp": "2026-10-28T21:48:32.349Z" + }, + { + "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/build/kinex_interfaces/CMakeFiles/\npython/ros/build/kinex_interfaces/ament_cmake_package_templates/\npython/ros/build/kinex_interfaces/ament_cmake_package_templates/templates.cmake\npython/ros/build/kinex_interfaces/colcon_build.rc\npython/ros/build/kinex_interfaces/libkinex_interfaces__rosidl_typesupport_c.so\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/msg/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/msg/rosidl_generator_cpp__visibility_control.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/json__builder.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/json__struct.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/json__traits.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/json__type_support.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/lookup_transform__builder.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/lookup_transform__struct.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/lookup_transform__traits.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/lookup_transform__type_support.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/wait__builder.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/wait__struct.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/wait__traits.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/detail/wait__type_support.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/json.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/lookup_transform.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/kinex_interfaces/srv/wait.hpp\npython/ros/build/kinex_interfaces/rosidl_generator_type_description/\npython/ros/build/kinex_interfaces/rosidl_generator_type_description/kinex_interfaces/\npython/ros/build/kinex_interfaces/rosidl_generator_type_description/kinex_interfaces/srv/\npython/ros/build/kinex_interfaces/rosidl_generator_type_description/kinex_interfaces/srv/LookupTransform.json\npython/ros/build/kinex_interfaces/rosidl_generator_type_description/kinex_interfaces/srv/Wait.json\npython/ros/build/kinex_interfaces/rosidl_generator_type_description/kinex_interfaces/srv/Wait_Response.json\npython/ros/build/kinex_interfaces/rosidl_typesupport_fastrtps_c/\npython/ros/build/kinex_interfaces/rosidl_typesupport_fastrtps_c/kinex_interfaces/\npython/ros/build/kinex_interfaces/rosidl_typesupport_fastrtps_c/kinex_interfaces/msg/\npython/ros/build/kinex_interfaces/rosidl_typesupport_fastrtps_c/kinex_interfaces/msg/rosidl_typesupport_fastrtps_c__visibility_control.h\npython/ros/build/kinex_interfaces/rosidl_typesupport_fastrtps_c/kinex_interfaces/srv/\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/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/local_setup.bash\npython/ros/install/local_setup.sh\npython/ros/install/local_setup.zsh\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": "2026-10-28T21:48:32.378Z" + }, + { + "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/left_gripper/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/head_camera/profile.yaml\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:48:36.946Z" + }, + { + "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": "2026-10-28T21:48:36.951Z" + }, + { + "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": "2026-10-28T21:48:36.951Z" + }, + { + "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": "2026-10-28T21:48:36.951Z" + }, + { + "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": "2026-10-28T21:48:36.951Z" + }, + { + "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": "2026-10-28T21:48:36.951Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \".kinex/devices/right_arm/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/left_camera/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/right_camera/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: ls\n{\"path\": \".kinex/tools\"}\nTool call: ls\n{\"path\": \".kinex/memos\"}", + "images": [], + "timestamp": "2026-10-28T21:48:40.809Z" + }, + { + "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": "2026-10-28T21:48:40.812Z" + }, + { + "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": "2026-10-28T21:48:40.813Z" + }, + { + "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": "2026-10-28T21:48:40.813Z" + }, + { + "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": "2026-10-28T21:48:40.813Z" + }, + { + "role": "toolResult", + "name": "ls", + "text": "(empty directory)", + "images": [], + "timestamp": "2026-10-28T21:48:40.813Z" + }, + { + "role": "toolResult", + "name": "ls", + "text": "(empty directory)", + "images": [], + "timestamp": "2026-10-28T21:48:40.813Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"python\": \"from kinex import runtime, finish\\nfinish({n:runtime.operations(n) for n in ['world','left_arm','left_gripper','head_camera']})\", \"description\": \"inspect operations\"}", + "images": [], + "timestamp": "2026-10-28T21:48:44.257Z" + }, + { + "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": "2026-10-28T21:48:45.225Z" + }, + { + "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\"}, {\"deviceID\": \"left_camera\", \"action\": \"load\"}, {\"deviceID\": \"right_camera\", \"action\": \"load\"}]}", + "images": [], + "timestamp": "2026-10-28T21:48:48.552Z" + }, + { + "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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:48:55.910Z" + }, + { + "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": "2026-10-28T21:48:59.568Z" + }, + { + "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": "2026-10-28T21:48:59.571Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: device\n{\"deviceID\": \"head_camera\", \"action\": \"image\"}", + "images": [], + "timestamp": "2026-10-28T21:49:03.303Z" + }, + { + "role": "toolResult", + "name": "device", + "text": "{\n \"deviceID\": \"head_camera\",\n \"timestamp\": 0,\n \"frame_id\": \"head_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-dlsu4lq1.png\"\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/25fb6783-1.png" + ], + "timestamp": "2026-10-28T21:49:03.607Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking bottle geometry**\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py\", \"offset\": 1, \"limit\": 280}\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": "2026-10-28T21:49:09.219Z" + }, + { + "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": "2026-10-28T21:49:09.222Z" + }, + { + "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": "2026-10-28T21:49:09.222Z" + }, + { + "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": "2026-10-28T21:49:09.222Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"description\": \"measure scene and inspect schemas\", \"python\": \"import asyncio\\nimport numpy as np\\nfrom kinex import communication, helpers, runtime, finish\\nasync def main():\\n async with communication() as io:\\n frame=await helpers.camera_frame(io,'head_camera')\\n d=helpers.image_array(frame['depth'])\\n pts=[]\\n for u,v in [(70,80),(61,42),(78,107),(201,30),(263,48),(275,15),(160,170),(20,95),(10,65)]:\\n z=float(d[v,u])*.001\\n p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\\n w=await helpers.transform_pose(io,p,'head_camera:base','world:base')\\n pts.append({'uv':[u,v],'world':w[:3],'depth':z})\\n states={}\\n for n in ['left_arm','right_arm']:\\n async with io.subscribe(n+'.joint_state') as s: m=await s.next(timeout_s=10)\\n states[n]={'q':list(m.position),'tcp':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],n+':flange','world:base')}\\n finish({'points':pts,'states':states,'schemas':{n:runtime.describe(n) for n in ['left_arm.joint_target','left_gripper.target','world.wait']}})\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:49:17.718Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224159.011935530] [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 19, 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 7, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}", + "images": [], + "timestamp": "2026-10-28T21:49:19.817Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py\", \"offset\": 1, \"limit\": 220}", + "images": [], + "timestamp": "2026-10-28T21:49:23.215Z" + }, + { + "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": "2026-10-28T21:49:23.219Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"description\": \"measure bottle positions\", \"python\": \"import asyncio\\nfrom kinex import communication, helpers, runtime, finish\\nasync def main():\\n async with communication() as io:\\n image,depth=await helpers.camera_frame(io,'head_camera')\\n d=helpers.image_array(depth)\\n pts=[]\\n for u,v in [(70,80),(61,42),(78,107),(201,30),(263,48),(275,15),(160,170),(20,95),(10,65)]:\\n z=float(d[v,u])*.001\\n p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\\n w=await helpers.transform_pose(io,p,'head_camera:base','world:base')\\n pts.append({'uv':[u,v],'world':w[:3],'depth':z})\\n states={}\\n for n in ['left_arm','right_arm']:\\n async with io.subscribe(n+'.joint_state') as s: m=await s.next(timeout_s=10)\\n states[n]={'q':list(m.position),'tcp':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],n+':flange','world:base')}\\n finish({'points':pts,'states':states,'schemas':{n:runtime.describe(n) for n in ['left_arm.joint_target','left_gripper.target','world.wait']}})\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:49:32.523Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224173.802050693] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": [{\"uv\":[70,80],\"world\":[-0.19695410638592586,0.004840793255465603,0.8648806102948801],\"depth\":0.661},{\"uv\":[61,42],\"world\":[-0.19843608488027142,0.01836181260217995,0.9437713751659849],\"depth\":0.606},{\"uv\":[78,107],\"world\":[-0.19720784117987777,0.00647091530623034,0.7836032015382537],\"depth\":0.727},{\"uv\":[201,30],\"world\":[0.05952994588289075,0.18251674300724796,0.8356375733823433],\"depth\":0.791},{\"uv\":[263,48],\"world\":[0.17520652312780038,0.09531415164087742,0.8614856295206998],\"depth\":0.718},{\"uv\":[275,15],\"world\":[0.1815178275905498,0.10234625219325044,0.9355097052456853],\"depth\":0.663},{\"uv\":[160,170],\"world\":[-0.03103803993853389,-0.11372648082994058,0.7397051536755269],\"depth\":0.6900000000000001},{\"uv\":[20,95],\"world\":[-0.4427492319227125,0.24131101888369177,0.5484832795119379],\"depth\":1.056},{\"uv\":[10,65],\"world\":[-0.43384341948444355,0.24559314590548487,0.6666948752399482],\"depth\":0.964}],\n \"states\": {\"left_arm\":{\"q\":[3.7421762044687057e-7,-0.000003416102799747023,0.000009044338185049128,-2.5705126915909204e-8,-0.0000020763914108101744,7.544119284830231e-7],\"tcp\":[-0.30032403719682815,-0.19382880994801913,0.9420051083628493,-5.304990567434886e-7,-0.000009308971204628978,0.7141433685930687,0.6999994635766562]},\"right_arm\":{\"q\":[3.727569151124044e-7,-0.0000034167196645285003,0.000009045151273312513,-2.5587102214785773e-8,-0.0000020741485968756024,7.532827339673531e-7],\"tcp\":[0.3052327434365579,-0.19280673228662837,0.9410048642737745,-6.068416298149924e-7,-0.000009277349876493806,0.7106343746258521,0.7035615008751448]}},\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 }\n}", + "images": [], + "timestamp": "2026-10-28T21:49:34.430Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Planning yellow bottle grasp**\n\n**Reading joint options**\nI’ll locate the bottles and bin, then grasp and transfer the bottles one at a time, checking each pickup and release.\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\", \"offset\": 1, \"limit\": 300}", + "images": [], + "timestamp": "2026-10-28T21:49:51.670Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in /base; link\n defaults to the model's tip_link.\n \"\"\"\n\n def __init__(self, profile, directory=None):\n if profile.get(\"type\") != \"joint\":\n raise ValueError(\"expected a joint device profile\")\n model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n if self.tree is None:\n if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n self.joints = urdf.commanded(self.tree)\n self.names = [joint[\"name\"] for joint in self.joints]\n self.tip = model[\"tip_link\"]\n\n def chain(self, link):\n link = link or self.tip\n if not link:\n raise ValueError(\"name a link; the model declares no tip_link\")\n return Chain(self.tree, link)\n\n def fk(self, positions, link=None):\n \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n q = joint_target(positions, self.joints, check_limits=False)\n return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n def ik(self, target, seed, link=None, **options):\n \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n Joints off the link's path keep their seed values. options override the\n solver tolerances, damping, max_iterations and search budget.\n \"\"\"\n unknown = options.keys() - {*DEFAULTS, \"budget\"}\n if unknown:\n raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n values = dict(zip(self.names, project_seed(seed, self.joints)))\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n home = profile.get(\"home\")\n if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n return 0.0\n return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n joint_target(actions[-1], joints)\n previous = None\n for target in actions:\n joint_target(target, joints, check_limits=False)\n for i, (q, joint) in enumerate(zip(target, joints)):\n limits = joint[\"limits\"]\n if limits is None:\n continue\n margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n low, high = limits[\"min\"], limits[\"max\"]\n if not low - margin <= q <= high + margin:\n raise ValueError(\"target outside joint limits recovery tolerance\")\n if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n raise ValueError(\"joint limits recovery must move inward\")\n previous = target\n\n\ndef command(profile, actions, joints):\n \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n if not isinstance(actions, list) or not actions:\n raise ValueError(\"actions must be a nonempty sequence\")\n for target in actions:\n joint_target(target, joints, check_limits=False)\n if profile[\"limits\"] == \"strict\":\n recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n return actions\n\n\ndef project_seed(value, joints):\n \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n values = vector(value, len(joints), \"IK seed\")\n return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n raise ValueError(\"joint state requires unique names and matching positions\")\n positions = dict(zip(message.name, message.position))\n names = [joint[\"name\"] for joint in joints]\n if any(name not in positions for name in names):\n raise ValueError(\"joint state is missing modeled joints\")\n return joint_target([positions[name] for name in names], joints, check_limits=False)\n", + "images": [], + "timestamp": "2026-10-28T21:49:51.673Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Writing action runner**\nTool call: write\n{\"path\": \".kinex/tools/manipulate.py\", \"content\": \"\\\"\\\"\\\"Measured, stepped Cartesian moves for RoboTwin ARX5. Invoked with Kinex exec args.\\nWaypoints are world TCP xyz and optional quaternion xyzw; duration per waypoint.\\nNo motion inferred from publication. Uses declared FK/IK, camera and wait interfaces.\\n\\\"\\\"\\\"\\nimport asyncio\\nimport numpy as np\\nfrom scipy.spatial.transform import Rotation, Slerp\\nfrom kinex import communication, helpers, args, finish\\n\\nasync def state(io, name):\\n async with io.subscribe(name+'.joint_state') as s:\\n return await s.next(timeout_s=10)\\n\\nasync def main():\\n a=args(); side=a.get('side','left'); arm=side+'_arm'; grip=side+'_gripper'\\n async with communication() as io:\\n model=helpers.joint_model(arm)\\n m=await state(io,arm); q=list(m.position)\\n if 'grip' in a:\\n await io.publish(grip+'.target',{'data':float(a['grip'])})\\n points=[]; elapsed=0.; targets=[]\\n # Default vertical approach, fingers close along world X.\\n default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()\\n for wp in a.get('waypoints',[]):\\n xyz=np.array(wp['xyz']); quat=np.array(wp.get('quat',default)); duration=wp.get('duration',2.)\\n if wp.get('linear',False):\\n current=await helpers.transform_pose(io,model.fk(q),arm+':base','world:base')\\n cr=Rotation.from_quat(current[3:]); start=np.array(current[:3])+cr.apply([.12,0,0])\\n rot=Slerp([0,1],Rotation.from_quat([current[3:],quat]))\\n fractions=np.linspace(0,1,max(2,int(duration/.12))+1)[1:]\\n else: fractions=[1.]\\n start_t=elapsed\\n for f in fractions:\\n p=start+(xyz-start)*f if wp.get('linear',False) else xyz\\n r=rot(f) if wp.get('linear',False) else Rotation.from_quat(quat)\\n flange=np.r_[p-r.apply([.12,0,0]),r.as_quat()].tolist()\\n local=await helpers.transform_pose(io,flange,'world:base',arm+':base')\\n newq=model.ik(local,q)\\n # choose equivalent nearby revolute solution\\n newq=[v+2*np.pi*round((old-v)/(2*np.pi)) for v,old in zip(newq,q)]\\n if max(abs(np.array(newq)-q))>3.2: raise ValueError('discontinuous IK')\\n q=newq; t=start_t+duration*f\\n sec=int(t); ns=round((t-sec)*1e9)\\n points.append({'positions':q,'time_from_start':{'sec':sec,'nanosec':ns}})\\n elapsed+=duration; targets.append(xyz.tolist())\\n if points:\\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\\n wait=elapsed+a.get('settle',.4)\\n if wait>0: result=await io.request('world.wait',duration_s=wait)\\n m=await state(io,arm); g=await state(io,grip)\\n flange=await helpers.transform_pose(io,model.fk(list(m.position)),arm+':base','world:base')\\n tcp=np.array(flange[:3])+Rotation.from_quat(flange[3:]).apply([.12,0,0])\\n files=[]\\n for cam in a.get('cameras',['head_camera']):\\n im,depth=await helpers.camera_frame(io,cam)\\n files.append(helpers.save_image(im))\\n if a.get('save_depth'): np.save('/tmp/'+cam+'_depth.npy',helpers.image_array(depth))\\n finish({'tcp':tcp.tolist(),'quat':flange[3:],'q':list(m.position),'grip':list(g.position),'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None,'time':result if wait>0 else None},file=files)\\n\\nasyncio.run(main())\\n\"}", + "images": [], + "timestamp": "2026-10-28T21:50:14.970Z" + }, + { + "role": "toolResult", + "name": "write", + "text": "Successfully wrote to .kinex/tools/manipulate.py", + "images": [], + "timestamp": "2026-10-28T21:50:14.974Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"approach clear bottle\", \"args\": {\"grip\": 1, \"waypoints\": [{\"xyz\": [-0.198, 0.04, 1.11], \"duration\": 2.5}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:50:20.195Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224222.313883542] [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/manipulate.py\\\", line 60, 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/manipulate.py\\\", line 38, in main\\n newq=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=406, elapsed_ms=100.044, position_error_m=0.10653722123952583, rotation_error_rad=0.10622118351177572\"\n}", + "images": [], + "timestamp": "2026-10-28T21:50:22.923Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Testing top flange feasibility**\nTool call: exec\n{\"description\": \"check reachable grasp poses\", \"python\": \"import asyncio,numpy as np\\nfrom scipy.spatial.transform import Rotation as R\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n out=[]; model=helpers.joint_model('left_arm')\\n async with communication() as io:\\n for z in [1.04,1.,.95,.91]:\\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\\n p=np.r_[np.array([-.198,.04,z])-r.apply([.12,0,0]),r.as_quat()]\\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\\n try:q=model.ik(p,[0,-1,1,.5,0,0],budget=2);out.append([z,q])\\n except ValueError as e:out.append([z,str(e)])\\n finish(out)\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:50:30.833Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224232.937157450] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [1,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.95,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.91,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"]\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:50:33.588Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"description\": \"check reachable grasp poses\", \"python\": \"import asyncio,numpy as np\\nfrom scipy.spatial.transform import Rotation as R\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n out=[]; model=helpers.joint_model('left_arm')\\n async with communication() as io:\\n for z in [1.04,1.,.95,.91]:\\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\\n p=np.r_[np.array([-.198,.04,z])-r.apply([.12,0,0]),r.as_quat()]\\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\\n try:q=model.ik(p,[0,-1,1,.5,0,0],budget={'max_attempts':40,'attempt_time_s':.1,'max_time_s':2});out.append([z,q])\\n except ValueError as e:out.append([z,str(e)])\\n finish(out)\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:50:39.106Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224241.131440963] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,[-0.2496410241379745,-3.8412895605530037,2.487698982425237,1.5250552017336654,-3.1154129244818,2.924150273604751]],\n [1,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1170.321, position_error_m=0.017156539321827088, rotation_error_rad=0.0005849824564675265\"],\n [0.95,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1184.417, position_error_m=0.0004092112180844326, rotation_error_rad=1.4402656672529403e-05\"],\n [0.91,[-0.24688493993230948,-3.825269561715616,2.5398416965020116,-1.6526536288735123,0.005872565741773644,-0.2145231983847298]]\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:50:45.291Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking IK seed candidates**\nTool call: exec\n{\"description\": \"solve approach configuration\", \"python\": \"import asyncio,numpy as np\\nfrom scipy.spatial.transform import Rotation as R\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n out=[]; model=helpers.joint_model('left_arm')\\n async with communication() as io:\\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\\n p=np.r_[np.array([-.198,.04,1.04])-r.apply([.12,0,0]),r.as_quat()]\\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\\n for seed in [[0,1,1,0,0,0],[0,2,1,-1,0,0],[0,1,-1,0,0,0],[0,2,2,1,0,0]]:\\n try:q=model.ik(p,seed,budget={'max_attempts':20,'attempt_time_s':.1,'max_time_s':1});out.append(q)\\n except ValueError as e:out.append(str(e))\\n finish(out)\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:51:01.624Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224263.608290980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=594.872, position_error_m=0.06611405415402853, rotation_error_rad=0.0019896488433462475\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=590.202, position_error_m=0.06586970481906577, rotation_error_rad=0.001980616266574562\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=582.975, position_error_m=0.06614644516120394, rotation_error_rad=0.002008056238735402\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=579.950, position_error_m=0.05696908984691351, rotation_error_rad=0.1997905349017585\"\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:51:06.403Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Positioning bottle for grasp**\nTool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"approach bottle from front\", \"args\": {\"grip\": 1, \"waypoints\": [{\"xyz\": [-0.198, -0.1, 0.87], \"quat\": [0, 0, 0.70710678, 0.70710678], \"duration\": 2.5}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:51:14.411Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224276.413911057] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.34149891422440415,-0.2565050430306144,0.8897903932734834],\n \"quat\": [-0.09308770694337504,0.19088826103653672,0.7954797807205011,0.5675458299374028],\n \"q\": [-0.20009401440620422,-0.2250864952802658,0.1718592345714569,-0.8337344527244568,-0.5068660974502563,-0.01791577972471714],\n \"grip\": [0.9960487864234231],\n \"target_error\": 0.2132543705144551,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.92,\"waited_s\":2.92},\n \"file\": [\"/tmp/kinex-observation-_g33q8jf.png\",\"/tmp/kinex-observation-g6aa97sf.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/eda38a00-1.png", + "experiments/robotwin-native/kinex-3/observations/eda38a00-2.png" + ], + "timestamp": "2026-10-28T21:51:34.228Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Stopping after motion error**\nTool call: device_stop\n{\"deviceID\": \"left_arm\", \"reason\": \"Measured TCP differs from commanded pose; inspect joint mapping and tracking before continuing.\"}", + "images": [], + "timestamp": "2026-10-28T21:51:38.540Z" + }, + { + "role": "toolResult", + "name": "device_stop", + "text": "left_arm: stopped (hold; affects left_arm)", + "images": [], + "timestamp": "2026-10-28T21:51:38.631Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Printing FK transforms**\nTool call: exec\n{\"description\": \"diagnose measured arm tracking\", \"python\": \"import asyncio\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n model=helpers.joint_model('left_arm')\\n async with io.subscribe('left_arm.joint_state') as s:m=await s.next(timeout_s=10)\\n p=await helpers.transform_pose(io,[-.198,-.22,.87,0,0,.70710678,.70710678],'world:base','left_arm:base')\\n q=model.ik(p,list(m.position))\\n finish({'names':model.names,'measured_names':list(m.name),'target_local':p,'target_q':q,'fk_target':model.fk(q),'fk_measured':model.fk(list(m.position)),'tf':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],'left_arm:flange','world:base')})\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:51:46.090Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224307.591027112] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"names\": [\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\n \"measured_names\": [\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\n \"target_local\": [0.19748021568092256,-0.10296987920505801,0.08799976895842088,-2.1073194747276565e-8,-2.1073194744748758e-8,-0.010000025916871026,0.9999499984907548],\n \"target_q\": [-0.5538732839669482,1.995022269064291,0.16137099170018213,1.8421592169091363,-0.5113192953777548,0.016807289704789778],\n \"fk_target\": [0.1974588856049695,-0.10295700740880999,0.08803969389974405,5.656337319998357e-7,-0.00000421306646243146,-0.010000411984881847,0.9999499946207624],\n \"fk_measured\": [0.058972431162814636,0.00270585894917854,0.1515630860129737,0.07115997195073531,0.2000997192265956,0.15152750906073412,0.9653682068284917],\n \"tf\": [-0.34149891422440415,-0.2565050430306144,0.8897903932734834,-0.09308770694337509,0.19088826103653644,0.7954797807205009,0.5675458299374023]\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:51:48.098Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Solving initial q0**\nTool call: exec\n{\"description\": \"inspect inverse kinematics branches\", \"python\": \"import asyncio\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n model=helpers.joint_model('left_arm'); p=await helpers.transform_pose(io,[-.198,-.22,.87,0,0,.70710678,.70710678],'world:base','left_arm:base')\\n out=[]\\n for seed in [[0]*6,[0,1,1,0,0,0],[0,.5,1,.5,0,0]]:\\n try:out.append(model.ik(p,seed,budget={'max_attempts':30,'attempt_time_s':.1,'max_time_s':2}))\\n except Exception as e:out.append(str(e))\\n finish(out)\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:52:01.148Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224322.596944567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [-0.5526204907538345,-1.7623647377941796,-0.6392799169876033,-1.1322713387442476,-0.513594805751331,-0.018117428053433485],\n [-0.553866303587819,1.9944921125413446,0.16136065569054742,1.8416406163084864,-0.5113207015289282,0.016809955678195713],\n [-0.5538701711987519,1.9950389022424122,0.16140448051613965,1.842137792565355,-0.5113167030912799,0.016803814033760394]\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:52:03.306Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Preparing the positive seed**\nTool call: edit\n{\"path\": \".kinex/tools/manipulate.py\", \"edits\": [{\"oldText\": \"newq=model.ik(local,q)\", \"newText\": \"seed=wp.get('seed',q)\\n newq=model.ik(local,seed,budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2})\"}, {\"oldText\": \"'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None\", \"newText\": \"'target_q':q if points else None,'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None\"}]}", + "images": [], + "timestamp": "2026-10-28T21:52:16.178Z" + }, + { + "role": "toolResult", + "name": "edit", + "text": "Successfully replaced 2 block(s) in .kinex/tools/manipulate.py.", + "images": [], + "timestamp": "2026-10-28T21:52:16.185Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"recover to raised posture\", \"args\": {\"waypoints\": [{\"xyz\": [-0.28, -0.15, 1.13], \"quat\": [0, 0, 0.70710678, 0.70710678], \"seed\": [0, 1, 1, 0, 0, 0], \"duration\": 3}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:52:19.874Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224341.829452565] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.2808323314765084,-0.1521085564748633,1.1266195287624303],\n \"quat\": [-0.0017668889194129447,0.002406581741459667,0.7083999131961318,0.7058049655900566],\n \"q\": [-0.16894401609897614,0.6368672847747803,0.7560085654258728,-0.12360157817602158,-0.14953598380088806,0.009546547196805477],\n \"grip\": [0.9981070431795988],\n \"target_q\": [-0.16866906820474564,0.6489774621097011,0.7660709003489753,-0.11556851724076331,-0.1454201730553973,0.009877023164163508],\n \"target_error\": 0.0040701808169305775,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-mkfvkm2u.png\",\"/tmp/kinex-observation-k28drqoq.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/8c8cb210-1.png", + "experiments/robotwin-native/kinex-3/observations/8c8cb210-2.png" + ], + "timestamp": "2026-10-28T21:52:41.812Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"align with clear bottle\", \"args\": {\"waypoints\": [{\"xyz\": [-0.198, -0.095, 0.875], \"quat\": [0, 0, 0.70710678, 0.70710678], \"linear\": true, \"duration\": 2.5}, {\"xyz\": [-0.198, 0.035, 0.875], \"quat\": [0, 0, 0.70710678, 0.70710678], \"linear\": true, \"duration\": 1.5}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:52:46.135Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224368.240218130] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.19568474524799342,0.03129575416783295,0.8768179791471703],\n \"quat\": [0.0009593362831939475,-0.0018856024907780693,0.7054160408881407,0.7087903310816559],\n \"q\": [-0.3494529128074646,2.0894923210144043,0.6539878249168396,1.4443292617797852,-0.3007594048976898,0.014579671435058117],\n \"grip\": [1.0012966367331417],\n \"target_q\": [-0.3411276660362571,2.0986666201389337,0.6667057780316544,1.4364761952238168,-0.29715006055619353,0.014478228366471838],\n \"target_error\": 0.004731478619983621,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.72,\"waited_s\":4.4},\n \"file\": [\"/tmp/kinex-observation-7woacofn.png\",\"/tmp/kinex-observation-dzfqc4pj.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/e8dcb044-1.png", + "experiments/robotwin-native/kinex-3/observations/e8dcb044-2.png" + ], + "timestamp": "2026-10-28T21:53:14.423Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"grasp clear bottle\", \"args\": {\"grip\": 0, \"settle\": 0.8, \"cameras\": [\"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:53:17.676Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224399.517077974] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.19641768011592214,0.03397012328379588,0.8762348087384827],\n \"quat\": [0.0009134239479954816,-0.00042023378935098735,0.7057645168018754,0.7084457889518926],\n \"q\": [-0.3451070785522461,2.094144821166992,0.6640962958335876,1.4365533590316772,-0.29736000299453735,0.01581699773669243],\n \"grip\": [0.6974994838237762],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":11.52,\"waited_s\":0.7999999999999989},\n \"file\": [\"/tmp/kinex-observation-qlc7bhx8.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/59f4e1b6-1.png" + ], + "timestamp": "2026-10-28T21:53:23.526Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"lift clear bottle\", \"args\": {\"waypoints\": [{\"xyz\": [-0.198, 0.035, 1.12], \"quat\": [0, 0, 0.70710678, 0.70710678], \"linear\": true, \"duration\": 2}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:53:26.995Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224409.028210939] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.1978245238769104,0.03624807484249387,1.1136391307316316],\n \"quat\": [-0.0006687220835096329,0.003217325161495589,0.7074333523842097,0.7067724199231455],\n \"q\": [-0.34395521879196167,1.481361746788025,1.1216462850570679,0.35927361249923706,-0.31043118238449097,0.018503844738006592],\n \"grip\": [0.7039102004332977],\n \"target_q\": [-0.3444226970084058,1.4740169827439866,1.135291463367972,0.3440870017483791,-0.3100354832143796,0.016756424773925072],\n \"target_error\": 0.00648453086441232,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.92,\"waited_s\":2.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-h2h_buoz.png\",\"/tmp/kinex-observation-wcac2rse.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/38f9e622-1.png", + "experiments/robotwin-native/kinex-3/observations/38f9e622-2.png" + ], + "timestamp": "2026-10-28T21:53:41.027Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Tilting camera toward bin**\n\n**Moving bottle toward bin**\nTool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"transfer bottle above left bin\", \"args\": {\"waypoints\": [{\"xyz\": [-0.5, 0.015, 1.06], \"quat\": [0, 0, 0.70710678, 0.70710678], \"linear\": true, \"duration\": 3}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:54:02.070Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224444.140137075] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.4945684814634984,0.015371463224506068,1.0604537555111784],\n \"quat\": [0.00025644590441672847,0.0009540466669193152,0.7060608183652334,0.7081505099905476],\n \"q\": [0.5811179280281067,1.626004695892334,1.0479673147201538,0.5658470392227173,0.6207801699638367,0.021735304966568947],\n \"grip\": [0.7077647921713917],\n \"target_q\": [0.5932278082889996,1.6362225123927316,1.0560940418899667,0.5683202118899915,0.63010988492782,0.019804060908021025],\n \"target_error\": 0.005463082701501553,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":17.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-d1x88zez.png\",\"/tmp/kinex-observation-3psdpux4.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/1c00b1c6-1.png", + "experiments/robotwin-native/kinex-3/observations/1c00b1c6-2.png" + ], + "timestamp": "2026-10-28T21:54:18.982Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Lowering bottle over bin**\nTool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"lower bottle over bin opening\", \"args\": {\"waypoints\": [{\"xyz\": [-0.52, 0.03, 0.9], \"quat\": [0, 0, 0.70710678, 0.70710678], \"linear\": true, \"duration\": 1.5}], \"cameras\": [\"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:54:25.878Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224467.850536046] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5171615828512802,0.03131842426267671,0.9044438328481127],\n \"quat\": [0.0019280949082640983,0.0004485915764353847,0.7052955620133471,0.7089106089062986],\n \"q\": [0.6077774167060852,2.09543514251709,0.9070724844932556,1.1807374954223633,0.6567703485488892,0.019012924283742905],\n \"grip\": [0.7038915062492545],\n \"target_q\": [0.6142882451032351,2.1089415504442486,0.9061590178895118,1.1926114491230337,0.6583185828385036,0.017082770022362744],\n \"target_error\": 0.005435301723780054,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":19.240000000000002,\"waited_s\":1.9200000000000017},\n \"file\": [\"/tmp/kinex-observation-f9hecmv3.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/b27b5e13-1.png" + ], + "timestamp": "2026-10-28T21:54:36.362Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"release bottle into bin\", \"args\": {\"grip\": 1, \"settle\": 1, \"cameras\": [\"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:54:39.636Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224481.635084896] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5184455702180112,0.030694684254432475,0.9010313035127829],\n \"quat\": [0.0010387084556719025,-0.00015139120055665463,0.70592582635397,0.708284989147669],\n \"q\": [0.6109477877616882,2.1069490909576416,0.9063937664031982,1.1924670934677124,0.6583145260810852,0.017104366794228554],\n \"grip\": [0.9881577410481193],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.240000000000002,\"waited_s\":1},\n \"file\": [\"/tmp/kinex-observation-2opki9cb.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/ff2d32db-1.png" + ], + "timestamp": "2026-10-28T21:54:46.160Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Positioning camera over bin**\nTool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"inspect bin contents\", \"args\": {\"waypoints\": [{\"xyz\": [-0.5, -0.06, 1.02], \"quat\": [-0.35355339, 0.35355339, 0.61237244, 0.61237244], \"linear\": true, \"duration\": 2}], \"cameras\": [\"left_camera\"], \"save_depth\": true}}", + "images": [], + "timestamp": "2026-10-28T21:54:53.930Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224496.015300089] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5020418026936535,-0.059801965512401384,1.0194523611193695],\n \"quat\": [-0.34889554343130097,0.3472040172825245,0.6175663469696127,0.6134599230978837],\n \"q\": [0.5938779711723328,1.7595466375350952,1.7936086654663086,-1.1486849784851074,0.3013532757759094,0.547741174697876],\n \"grip\": [0.9976837824691426],\n \"target_q\": [0.5867695945404096,1.7695588958198807,1.8155074267669495,-1.1762405787419286,0.2917951876543838,0.5514962828484409],\n \"target_error\": 0.002123224962566933,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":22.64,\"waited_s\":2.3999999999999986},\n \"file\": [\"/tmp/kinex-observation-or0xdy1p.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/a2501cc5-1.png" + ], + "timestamp": "2026-10-28T21:55:07.663Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking coke reach**\n\n**Testing left coke reach**\n\n**Testing bottle reach**\nTool call: exec\n{\"description\": \"plan remaining bottle reach\", \"python\": \"import asyncio,numpy as np\\nfrom scipy.spatial.transform import Rotation as R\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n out=[]; model=helpers.joint_model('left_arm')\\n async with communication() as io:\\n for name,xyz,yaw in [('coke',[.19,.12,.865],48),('yellow',[.06,.21,.87],61),('coke_lift',[.12,.03,1.03],48),('yellow_lift',[.02,.12,1.03],61)]:\\n r=R.from_euler('z',yaw,degrees=True);p=np.r_[np.array(xyz)-r.apply([.12,0,0]),r.as_quat()]\\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\\n try:q=model.ik(p,[0,2,1,1,0,0],budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2});out.append({'name':name,'q':q,'quat':r.as_quat().tolist()})\\n except ValueError as e:out.append({'name':name,'error':str(e)})\\n finish(out)\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:55:41.528Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224543.725672103] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n {\"name\":\"coke\",\"q\":[-0.7682474838893932,2.8205441857806255,2.174066940855675,0.6465166651221519,0.015836629018606843,0.005026753078927928],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow\",\"q\":[-0.5519639959914996,2.793350612445425,2.1392099935800983,0.6542416277664851,0.005085650161614391,0.005436249092634174],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]},\n {\"name\":\"coke_lift\",\"q\":[-0.7837596704018495,2.0847705295916814,1.5159528392357002,0.5691906155097223,-0.006951084853310415,0.013880856415633741],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow_lift\",\"q\":[-0.5705797685571973,2.131406981991661,1.5887848123532708,0.5431653775860207,-0.02002556223756075,0.013504624618480239],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]}\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:55:44.476Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Preparing yellow pickup**\n\n**Measuring yellow bottle**\nTool call: exec\n{\"description\": \"measure remaining bottle surfaces\", \"python\": \"import asyncio\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n im,dep=await helpers.camera_frame(io,'head_camera');d=helpers.image_array(dep);out=[]\\n for u,v in [(197,45),(203,15),(195,5),(270,35),(260,65),(264,48)]:\\n z=float(d[v,u])*.001;p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\\n out.append([u,v,(await helpers.transform_pose(io,p,'head_camera:base','world:base'))[:3]])\\n finish(out)\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:56:01.407Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224563.007108510] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [197,45,[0.05447197977048632,0.18363281695971267,0.7914746286877076]],\n [203,15,[0.06005972182630685,0.18232398834126862,0.875493007530382]],\n [195,5,[0.04204036901406122,0.18984502660198344,0.8948837863355172]],\n [270,35,[0.18028540144849753,0.09326873128529545,0.8962015643731865]],\n [260,65,[0.18068970414215718,0.09767136948762245,0.8120035427331231]],\n [264,48,[0.1769171417661466,0.0945546611955852,0.8621660116854928]]\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:56:03.819Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Approaching the yellow left**\nThe clear bottle is in the bin, confirmed by the wrist camera. I’m moving the remaining two bottles next.\nTool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"approach yellow bottle\", \"args\": {\"waypoints\": [{\"xyz\": [-0.18, -0.08, 1.06], \"quat\": [0, 0, 0.50753836, 0.86162916], \"duration\": 2}, {\"xyz\": [-0.006, 0.093, 0.86], \"quat\": [0, 0, 0.50753836, 0.86162916], \"linear\": true, \"duration\": 2}, {\"xyz\": [0.055, 0.205, 0.86], \"quat\": [0, 0, 0.50753836, 0.86162916], \"linear\": true, \"duration\": 1.5}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:56:10.787Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224572.865750018] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.05147708742033642,0.2045278187007235,0.8629828751979567],\n \"quat\": [0.0012442963942044286,-0.0021552669022041654,0.5092927264227239,0.8605897543917034],\n \"q\": [-0.5445034503936768,2.769740104675293,2.054563522338867,0.720240592956543,0.008363209664821625,0.00577180739492178],\n \"grip\": [0.9996614374897697],\n \"target_q\": [-0.5484997194158352,2.7834866635540485,2.0749658177550847,0.7086049164412447,0.00849461950524203,0.005581543401104956],\n \"target_error\": 0.004640195326699464,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":28.560000000000002,\"waited_s\":5.920000000000002},\n \"file\": [\"/tmp/kinex-observation-jhdi9t_a.png\",\"/tmp/kinex-observation-zkmskjon.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/ed3b0775-1.png", + "experiments/robotwin-native/kinex-3/observations/ed3b0775-2.png" + ], + "timestamp": "2026-10-28T21:56:37.608Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"grasp yellow bottle\", \"args\": {\"grip\": 0, \"settle\": 0.6, \"cameras\": [\"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:56:41.700Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224603.816933881] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.052825427222747336,0.20584596398041766,0.8627488049225395],\n \"quat\": [0.0014806761308315045,-0.0011401104489938575,0.5090236070610991,0.8607505301775115],\n \"q\": [-0.5453270673751831,2.7777156829833984,2.0716705322265625,0.7096014022827148,0.008205556310713291,0.007084341254085302],\n \"grip\": [0.7739523974331943],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":29.16,\"waited_s\":0.5999999999999979},\n \"file\": [\"/tmp/kinex-observation-7uqteqro.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/82847509-1.png" + ], + "timestamp": "2026-10-28T21:56:46.905Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"lift yellow bottle clear\", \"args\": {\"waypoints\": [{\"xyz\": [0.015, 0.12, 1.035], \"quat\": [0, 0, 0.50753836, 0.86162916], \"linear\": true, \"duration\": 2}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:56:50.222Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224612.139791819] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.012475983380957172,0.12415535121015182,1.0325882299616729],\n \"quat\": [0.0013523926468964947,-0.00044605020363605173,0.5105743815543966,0.8598324098181513],\n \"q\": [-0.5547859072685242,2.128363847732544,1.5929771661758423,0.5379608273506165,-0.01129952073097229,0.01545263547450304],\n \"grip\": [0.7797582962296226],\n \"target_q\": [-0.5614852376589563,2.1161047986303942,1.5853120475938565,0.5314918774258239,-0.011183534532282036,0.0136917382097596],\n \"target_error\": 0.005427175903790725,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":31.560000000000002,\"waited_s\":2.400000000000002},\n \"file\": [\"/tmp/kinex-observation-dsx7lnz4.png\",\"/tmp/kinex-observation-wfdkz__a.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/4a031853-1.png", + "experiments/robotwin-native/kinex-3/observations/4a031853-2.png" + ], + "timestamp": "2026-10-28T21:57:02.594Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"carry yellow bottle to bin\", \"args\": {\"waypoints\": [{\"xyz\": [-0.5, 0.015, 1.06], \"quat\": [0, 0, 0.70710678, 0.70710678], \"linear\": true, \"duration\": 3}, {\"xyz\": [-0.52, 0.03, 0.9], \"quat\": [0, 0, 0.70710678, 0.70710678], \"linear\": true, \"duration\": 1.5}], \"cameras\": [\"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:57:09.118Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224631.131968514] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5174761649606938,0.031407337429610896,0.9046801446093026],\n \"quat\": [0.002290593707506658,0.0004161799849168633,0.7054030393235293,0.7088026044589666],\n \"q\": [0.6082080006599426,2.0956389904022217,0.9081711173057556,1.1805373430252075,0.656909167766571,0.01906382292509079],\n \"grip\": [0.7724612653255463],\n \"target_q\": [0.6142413871259487,2.1090307308805722,0.9062710823865341,1.1924842697081466,0.6582675187588196,0.017143442076574332],\n \"target_error\": 0.005500372306525918,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":36.480000000000004,\"waited_s\":4.920000000000002},\n \"file\": [\"/tmp/kinex-observation-92wa3sed.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/e0aba462-1.png" + ], + "timestamp": "2026-10-28T21:57:33.308Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"release yellow bottle\", \"args\": {\"grip\": 1, \"settle\": 0.8, \"cameras\": [\"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:57:36.550Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224658.817845982] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5184055082681959,0.03082700826055433,0.9015009198137535],\n \"quat\": [0.0015373910546378198,-0.0001890281441048716,0.7058661041915254,0.7083435915222107],\n \"q\": [0.6107003688812256,2.1061878204345703,0.9069343209266663,1.192107915878296,0.6582370400428772,0.01719825342297554],\n \"grip\": [0.9859253439036283],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":37.28,\"waited_s\":0.7999999999999972},\n \"file\": [\"/tmp/kinex-observation-7jmu9wgv.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/40cd7359-1.png" + ], + "timestamp": "2026-10-28T21:57:43.262Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Inspecting the bin**\nTool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"verify second deposit\", \"args\": {\"waypoints\": [{\"xyz\": [-0.5, -0.06, 1.02], \"quat\": [-0.35355339, 0.35355339, 0.61237244, 0.61237244], \"linear\": true, \"duration\": 1.5}], \"cameras\": [\"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:57:49.482Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224671.685813675] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5021647469780711,-0.05918474129308639,1.0190715021736796],\n \"quat\": [-0.3482738420558144,0.3459375267960569,0.6181609954097833,0.613929590627374],\n \"q\": [0.5939311981201172,1.7592520713806152,1.7904003858566284,-1.1432219743728638,0.3032253086566925,0.5461763739585876],\n \"grip\": [0.9966154369440946],\n \"target_q\": [0.5866290400105713,1.7696589255235398,1.815409985763567,-1.1763832425627365,0.2921926314641686,0.5510894336527324],\n \"target_error\": 0.0024925658369937304,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":39.2,\"waited_s\":1.9200000000000017},\n \"file\": [\"/tmp/kinex-observation-zoi1ji0f.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/a57d6a5b-1.png" + ], + "timestamp": "2026-10-28T21:58:01.167Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"approach final bottle\", \"args\": {\"waypoints\": [{\"xyz\": [-0.08, -0.1, 1.06], \"quat\": [0, 0, 0.40673664, 0.91354546], \"duration\": 2.5}, {\"xyz\": [0.093, 0.014, 0.865], \"quat\": [0, 0, 0.40673664, 0.91354546], \"linear\": true, \"duration\": 2}, {\"xyz\": [0.185, 0.12, 0.865], \"quat\": [0, 0, 0.40673664, 0.91354546], \"linear\": true, \"duration\": 1.5}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:58:07.771Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224689.703922241] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.18104127421884691,0.12025246257982117,0.8680142777299692],\n \"quat\": [0.0010549760702521604,-0.0023176494770264255,0.40876259422019845,0.9126371990516093],\n \"q\": [-0.7572907209396362,2.787534236907959,2.11370587348938,0.6789280772209167,0.022206220775842667,0.005521690938621759],\n \"grip\": [0.9995792779055509],\n \"target_q\": [-0.7616688198560753,2.80332162175892,2.1382883773455337,0.6650406542444128,0.02232729054880098,0.005286691102754581],\n \"target_error\": 0.004982079625815344,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":45.6,\"waited_s\":6.399999999999999},\n \"file\": [\"/tmp/kinex-observation-i_l_tjm9.png\",\"/tmp/kinex-observation-imo_1j6g.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/9deb50d9-1.png", + "experiments/robotwin-native/kinex-3/observations/9deb50d9-2.png" + ], + "timestamp": "2026-10-28T21:58:36.686Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"grasp final bottle\", \"args\": {\"grip\": 0, \"settle\": 0.6, \"cameras\": [\"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:58:40.778Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224722.893358124] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.182839956300261,0.1214320373928262,0.8679558242217614],\n \"quat\": [0.0014541573105776343,-0.0014481062315274576,0.4084345353416347,0.91278530813939],\n \"q\": [-0.7581510543823242,2.7972278594970703,2.134784698486328,0.6662819385528564,0.022113796323537827,0.006825706455856562],\n \"grip\": [0.7865898013114929],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":46.2,\"waited_s\":0.6000000000000014},\n \"file\": [\"/tmp/kinex-observation-i7quvwry.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/7af7cb21-1.png" + ], + "timestamp": "2026-10-28T21:58:46.117Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"lift final bottle\", \"args\": {\"waypoints\": [{\"xyz\": [0.11, 0.03, 1.04], \"quat\": [0, 0, 0.40673664, 0.91354546], \"linear\": true, \"duration\": 2}], \"cameras\": [\"head_camera\", \"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:58:50.728Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224732.814719563] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.10941368722195692,0.034232347568097815,1.0377322213464852],\n \"quat\": [0.001230415620162098,-0.0004807913597038286,0.40934807110091775,0.9123773405795583],\n \"q\": [-0.7624666690826416,2.06129789352417,1.510847568511963,0.552495002746582,0.00831757765263319,0.015920152887701988],\n \"grip\": [0.7925870445641605],\n \"target_q\": [-0.7676939502135206,2.048071049242703,1.501995544659548,0.5462311917833058,0.008597172733907732,0.014152281034482525],\n \"target_error\": 0.004837287321652237,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":48.6,\"waited_s\":2.3999999999999986},\n \"file\": [\"/tmp/kinex-observation-0b4c68lr.png\",\"/tmp/kinex-observation-a7qeqo65.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/bd40c7f2-1.png", + "experiments/robotwin-native/kinex-3/observations/bd40c7f2-2.png" + ], + "timestamp": "2026-10-28T21:59:05.212Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"carry final bottle to bin\", \"args\": {\"waypoints\": [{\"xyz\": [-0.5, 0.015, 1.06], \"quat\": [0, 0, 0.70710678, 0.70710678], \"linear\": true, \"duration\": 3.5}, {\"xyz\": [-0.52, 0.03, 0.9], \"quat\": [0, 0, 0.70710678, 0.70710678], \"linear\": true, \"duration\": 1.5}], \"cameras\": [\"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:59:10.489Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224752.694936316] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5174527559986333,0.031418531702469416,0.9048850739188827],\n \"quat\": [0.0022403785285909222,0.0003952878515179147,0.7053842503524314,0.7088214752716665],\n \"q\": [0.6081580519676208,2.0949134826660156,0.9082513451576233,1.179675817489624,0.6569015979766846,0.019001225009560585],\n \"grip\": [0.7880872325463729],\n \"target_q\": [0.6143294472492913,2.1091152341605635,0.9063292866790853,1.1926127068902617,0.658363172809637,0.01708281387648724],\n \"target_error\": 0.0056889921239493485,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":54,\"waited_s\":5.399999999999999},\n \"file\": [\"/tmp/kinex-observation-0ip5hs7j.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/2e34d545-1.png" + ], + "timestamp": "2026-10-28T21:59:35.528Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/manipulate.py\", \"description\": \"release final bottle into bin\", \"args\": {\"grip\": 1, \"settle\": 0.8, \"cameras\": [\"left_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:59:39.096Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224780.954123050] [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\":54.160000000000004,\"waited_s\":0.1600000000000037}\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:59:41.887Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Reading head and wrist**\nTool call: devices\n{\"calls\": [{\"deviceID\": \"head_camera\", \"action\": \"image\"}, {\"deviceID\": \"left_camera\", \"action\": \"image\"}, {\"deviceID\": \"left_gripper\", \"action\": \"joint_state\"}]}", + "images": [], + "timestamp": "2026-10-28T21:59:50.835Z" + }, + { + "role": "toolResult", + "name": "devices", + "text": "{\n \"results\": [\n {\"deviceID\":\"head_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":54.16,\"frame_id\":\"head_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-_7ve1dm1.png\"},\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":54.16,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dh4luhs9.png\"},\n {\"deviceID\":\"left_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.8859383490952579],\"velocities\":[0.5435731939294122],\"joint_names\":[\"left_gripper\"],\"timestamp\":54.16}}\n ]\n}", + "images": [ + "experiments/robotwin-native/kinex-3/observations/5306339c-1.png", + "experiments/robotwin-native/kinex-3/observations/5306339c-2.png" + ], + "timestamp": "2026-10-28T21:59:51.121Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Updating defaults and checks**\n\n**Adding an error guard**\nTool call: edit\n{\"path\": \".kinex/tools/manipulate.py\", \"edits\": [{\"oldText\": \" if 'grip' in a:\\n await io.publish(grip+'.target',{'data':float(a['grip'])})\\n points=[]; elapsed=0.; targets=[]\\n # Default vertical approach, fingers close along world X.\\n default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()\", \"newText\": \" points=[]; elapsed=0.; targets=[]\\n # Default horizontal approach along world +Y, fingers close along world X.\\n # High top-down poses can exceed ARX5 reach; provide explicit quaternions.\\n default=Rotation.from_euler('z',np.pi/2).as_quat()\"}, {\"oldText\": \" seed=wp.get('seed',q)\", \"newText\": \" # Avoid the below-table IK branch when starting near home.\\n seed=wp.get('seed',q if q[1]>.2 else [q[0],1.,1.,0.,q[4],q[5]])\"}, {\"oldText\": \" if points:\\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\", \"newText\": \" # Do not change a grip until all requested waypoints solve successfully.\\n if 'grip' in a:\\n await io.publish(grip+'.target',{'data':float(a['grip'])})\\n if points:\\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\"}, {\"oldText\": \" files=[]\\n for cam\", \"newText\": \" error=float(np.linalg.norm(tcp-targets[-1])) if targets else None\\n if error is not None and error>a.get('max_error',.025):\\n await io.request(arm+'.stop')\\n raise RuntimeError(f'Measured TCP tracking error {error:.4f} m; arm held')\\n files=[]\\n for cam\"}]}", + "images": [], + "timestamp": "2026-10-28T22:00:13.590Z" + }, + { + "role": "toolResult", + "name": "edit", + "text": "Successfully replaced 4 block(s) in .kinex/tools/manipulate.py.", + "images": [], + "timestamp": "2026-10-28T22:00:13.595Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: write\n{\"path\": \".kinex/memos/put-bottles-dustbin.md\", \"content\": \"# RoboTwin put-bottles-dustbin / ALOHA AgileX\\n\\nUse public profiles and cameras every episode; positions below are observed examples, not immutable scene facts. No simulator reset or private state. ROS wait advances 0.04 s native frames, reads do not. Load devices first; inspect schemas.\\n\\nReusable `.kinex/tools/manipulate.py` runs with `exec(file=..., args=...)`. Args: side (default left), grip (0 closed,1 open), waypoints [{xyz world TCP, quat xyzw optional, duration seconds, linear bool, seed optional}], settle (default .4), cameras. It solves flange IK with TCP offset +.12 local X, publishes typed JointTrajectory, waits once, checks measured FK and returns images. Defaults horizontal +Y approach. It stops on >.025 m final position error. Caller must choose collision-free waypoints; IK is not collision checking.\\n\\nARX5 home-seeded IK can select a negative shoulder branch that drives through the table. Use positive shoulder seed [0,1,1,0,0,0] and first move to raised safe horizontal pose, e.g. [-.28,-.15,1.13], yaw90. A failed early command had 21 cm measured tracking error; stopped and recovered raised. High top-down grasp approach at y=.04,z=1.11 was unreachable. Horizontal side grasps work well.\\n\\nHead camera paired RGBD: world points from K and calibrated frame transform. Bottle surface depths are front surfaces, not centers; add radius ~.025 m farther from camera in world Y. Table observed z=.74. Left arm can reach all three upright bottles using yaw roughly radial from its base, avoiding need for handoff.\\n\\nObserved successful grasp/transfer sequence:\\n- Clear: center [-.198,.035,.875], horizontal yaw90 q=[0,0,.70710678,.70710678]. Approach y=-.095, straight advance, close .8s. Lift to z1.12 in 2s; verify bottle moves with gripper.\\n- Yellow: [.055,.205,.86], horizontal yaw61 q=[0,0,.50753836,.86162916]. Approach [-.006,.093,.86], straight advance 1.5s. Close .6s. Retract/lift to [.015,.12,1.035] in 2s (pure vertical lift at far reach may fail).\\n- Coke: [.185,.12,.865], horizontal yaw48 q=[0,0,.40673664,.91354546]. Approach [.093,.014,.865]. Close .6s; retract/lift to [.11,.03,1.04] in 2s.\\n- Carry each at z~1.06 to [-.5,.015,1.06], yaw90, with 3–3.5s Cartesian path. Lower to [-.52,.03,.9] in1.5s. Open above bin. Silver-lined bin is left of table, mostly outside head view.\\n- Inspect deposits with left wrist camera by moving empty TCP to [-.5,-.06,1.02], quaternion [-.35355339,.35355339,.61237244,.61237244], 1.5–2s linear motion. First two bottles were clearly visible inside bin.\\n\\nEpisode ended during final opening wait at t=54.16s (~1354/1700 frames), after .16s of requested .8s. No further waits or motion attempted. Final head image showed empty table; wrist image showed coke leaving gripper, opening measured .886. Native verdict is owner-only; terminal/rejected wait alone is not success evidence. First two deposits visually confirmed, last grasp/lift/transfer measured before release.\\n\"}", + "images": [], + "timestamp": "2026-10-28T22:00:44.520Z" + }, + { + "role": "toolResult", + "name": "write", + "text": "Successfully wrote to .kinex/memos/put-bottles-dustbin.md", + "images": [], + "timestamp": "2026-10-28T22:00:44.524Z" + }, + { + "role": "assistant", + "name": null, + "text": "Transferred all three bottles over the left dustbin and released them. The episode ended during the final release; the native verdict remains with the owner.", + "images": [], + "timestamp": "2026-10-28T22:00:51.307Z" + } +] diff --git a/experiments/robotwin-native/kinex-3/session.jsonl b/experiments/robotwin-native/kinex-3/session.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..5f02b82ae18d445ba7bd8b43b02784998f2dbd9b --- /dev/null +++ b/experiments/robotwin-native/kinex-3/session.jsonl @@ -0,0 +1,115 @@ +{"type":"session","version":3,"id":"01a1847c-4a80-714e-9e3a-ea9217d4e23c","timestamp":"2026-10-28T21:48:20.480Z","cwd":"/workspace"} +{"type":"model_change","id":"1eab2f08","parentId":null,"timestamp":"2026-10-28T21:48:23.002Z","provider":"openai-codex","modelId":"gpt-6-astra"} +{"type":"thinking_level_change","id":"6bafb960","parentId":"1eab2f08","timestamp":"2026-10-28T21:48:23.002Z","thinkingLevel":"high"} +{"type":"message","id":"d86b90dc","parentId":"6bafb960","timestamp":"2026-10-28T21:48:23.264Z","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":1793224103262,"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. Output is truncated to 500 entries or 50KB (whichever is hit first).","parameters":{"type":"object","properties":{"path":{"type":"string","description":"Directory to list (default: current directory)"},"limit":{"type":"number","description":"Maximum number of entries to return (default: 500)"}}}},{"name":"web_search","description":"Search the web using the current supported provider (Google Gemini, xAI Grok, OpenAI, Anthropic, DeepSeek, Ollama, or OpenCode Zen/Go). Optionally include URLs to analyze alongside search results.","parameters":{"type":"object","required":["query"],"properties":{"query":{"type":"string","description":"The search query or question to answer"},"urls":{"type":"array","items":{"type":"string"},"description":"Additional URLs to analyze along with search (up to 20)","maxItems":20}}}},{"name":"calib_compute","description":"Run supported offline calibration/introspection using archived evidence. No device\nmotion or profile writes. 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 conditions.\n","parameters":{"$schema":"https://json-schema.org/draft/2020-12/schema","oneOf":[{"type":"object","properties":{"run":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"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])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"timeout_s":{"default":120,"type":"number","exclusiveMinimum":0,"maximum":600},"operation":{"type":"string","const":"urdf-introspect"}},"required":["operation"],"additionalProperties":false},{"type":"object","properties":{"run":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"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])*$"},"value":{}},"required":["kind","pointer"],"additionalProperties":false},{"type":"object","properties":{"kind":{"type":"string","const":"file"},"path":{"type":"string","minLength":1}},"required":["kind","path"],"additionalProperties":false}]}},"timeout_s":{"default":120,"type":"number","exclusiveMinimum":0,"maximum":600},"operation":{"type":"string","const":"fit-mount"},"points":{"type":"string","minLength":1,"description":"Input 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absolute"}},"required":["operation","measurements"],"additionalProperties":false},{"type":"object","properties":{"run":{"type":"string","pattern":"^[a-zA-Z0-9][a-zA-Z0-9_-]*$"},"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}]}},"timeout_s":{"default":120,"type":"number","exclusiveMinimum":0,"maximum":600},"operation":{"type":"string","const":"sensor-bias"},"samples":{"type":"string","minLength":1,"description":"Input 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":"baa72b9a","parentId":"d86b90dc","timestamp":"2026-10-28T21:48:23.264Z","message":{"role":"user","content":[{"type":"text","text":"# Task: robotwin/put-bottles-dustbin-aloha-agilex\n\nuse arms to grab the bottles and put them into the dustbin to the left of the table\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 1700 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":1793224103261}} +{"type":"custom_message","customType":"kinex-context","content":"\ncore.guides:\nDevice guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-e88685ac8f5146fc8f9fe953a6b66d44/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-e88685ac8f5146fc8f9fe953a6b66d44/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":"0333c05e","parentId":"baa72b9a","timestamp":"2026-10-28T21:48:23.265Z"} +{"type":"message","id":"0398c2aa","parentId":"0333c05e","timestamp":"2026-10-28T21:48:25.910Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_bc30fb8bf4f94fd8b419ada61ba9c495|fc_03880fcdb65294d8016ae26da951048192a45a47ab0d529d47","name":"ls","arguments":{"path":"/workspace"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":11304,"output":19,"cacheRead":0,"cacheWrite":0,"reasoning":0,"totalTokens":11323,"cost":{"input":0.11304000000000002,"output":0.00095,"cacheRead":0,"cacheWrite":0,"total":0.11399000000000002}},"stopReason":"toolUse","timestamp":1793224103280,"responseId":"resp_03880fcdb65294d8016ae26da7b410819282b089ff330cb05d","rawStopReason":"completed","durationMs":2630,"thinkingLevel":"high"}} +{"type":"message","id":"9ff17a7d","parentId":"0398c2aa","timestamp":"2026-10-28T21:48:25.917Z","message":{"role":"toolResult","toolCallId":"call_bc30fb8bf4f94fd8b419ada61ba9c495|fc_03880fcdb65294d8016ae26da951048192a45a47ab0d529d47","toolName":"ls","content":[{"type":"text","text":".kinex/\nharness.md\ninstruction.md"}],"isError":false,"durationMs":4,"timestamp":1793224105916}} +{"type":"message","id":"db44cfea","parentId":"9ff17a7d","timestamp":"2026-10-28T21:48:29.788Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_878b9e39f63649cfb186f0f49feaca5d|fc_03880fcdb65294d8016ae26daced9881929db63b0c45ac2b84","name":"read","arguments":{"path":"/workspace/harness.md","offset":null,"limit":null}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":208,"output":29,"cacheRead":11136,"cacheWrite":0,"reasoning":0,"totalTokens":11373,"cost":{"input":0.0020800000000000003,"output":0.0014500000000000001,"cacheRead":0.011136,"cacheWrite":0,"total":0.014666}},"stopReason":"toolUse","timestamp":1793224105941,"responseId":"resp_03880fcdb65294d8016ae26daa966481929d6a5e6c4610f413","rawStopReason":"completed","durationMs":3847,"thinkingLevel":"high"}} +{"type":"message","id":"3f4014f8","parentId":"db44cfea","timestamp":"2026-10-28T21:48:29.793Z","message":{"role":"toolResult","toolCallId":"call_878b9e39f63649cfb186f0f49feaca5d|fc_03880fcdb65294d8016ae26daced9881929db63b0c45ac2b84","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":4,"timestamp":1793224109793}} +{"type":"message","id":"1a9ed4b1","parentId":"3f4014f8","timestamp":"2026-10-28T21:48:32.349Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_ed9eab7888164c59b45bb5130fd938dd|fc_03880fcdb65294d8016ae26dafe5288192b6bd79b67294c68a","name":"find","arguments":{"pattern":"**/*","path":"/workspace/.kinex","limit":150}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":213,"output":30,"cacheRead":11264,"cacheWrite":0,"reasoning":0,"totalTokens":11507,"cost":{"input":0.0021300000000000004,"output":0.0015,"cacheRead":0.011264,"cacheWrite":0,"total":0.014894000000000001}},"stopReason":"toolUse","timestamp":1793224109829,"responseId":"resp_03880fcdb65294d8016ae26dae43bc81929b837b1050474f0f","rawStopReason":"completed","durationMs":2520,"thinkingLevel":"high"}} 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Use limit=300 for more, or refine pattern]"}],"details":{"resultLimitReached":150},"isError":false,"durationMs":27,"timestamp":1793224112378}} +{"type":"message","id":"5c9b060a","parentId":"113fa28a","timestamp":"2026-10-28T21:48:36.946Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_ad66e252680d4538b87eab931768f5bf|fc_03880fcdb65294d8016ae26db31af481929c07f14a6d2dd873","name":"read","arguments":{"path":".kinex/workspace.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_3722a81d61a040d180d587e39167096f|fc_03880fcdb65294d8016ae26db4d4f8819280f43ebd32167642","name":"read","arguments":{"path":".kinex/devices/world/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_39836cc13d7249189b500a4153c3f1d4|fc_03880fcdb65294d8016ae26db4d4f88192ad8b7b6ddc54d965","name":"read","arguments":{"path":".kinex/devices/left_arm/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_42cdcaea1a8243a6b4935539b4721a45|fc_03880fcdb65294d8016ae26db4d4f8819298d9122ade04e809","name":"read","arguments":{"path":".kinex/devices/left_gripper/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_2e8a67dcb44840f79a97d150c580c9c0|fc_03880fcdb65294d8016ae26db4d4f88192a13da7d1e8a0a437","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":2830,"output":164,"cacheRead":11392,"cacheWrite":0,"reasoning":0,"totalTokens":14386,"cost":{"input":0.028300000000000002,"output":0.0082,"cacheRead":0.011392,"cacheWrite":0,"total":0.047892000000000004}},"stopReason":"toolUse","timestamp":1793224112401,"responseId":"resp_03880fcdb65294d8016ae26db0db108192ba227ec66da36390","rawStopReason":"completed","durationMs":4545,"thinkingLevel":"high"}} +{"type":"message","id":"f8b2e594","parentId":"5c9b060a","timestamp":"2026-10-28T21:48:36.951Z","message":{"role":"toolResult","toolCallId":"call_ad66e252680d4538b87eab931768f5bf|fc_03880fcdb65294d8016ae26db31af481929c07f14a6d2dd873","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":4,"timestamp":1793224116951}} +{"type":"message","id":"d322cc9b","parentId":"f8b2e594","timestamp":"2026-10-28T21:48:36.951Z","message":{"role":"toolResult","toolCallId":"call_3722a81d61a040d180d587e39167096f|fc_03880fcdb65294d8016ae26db4d4f8819280f43ebd32167642","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":4,"timestamp":1793224116951}} +{"type":"message","id":"b29485c2","parentId":"d322cc9b","timestamp":"2026-10-28T21:48:36.951Z","message":{"role":"toolResult","toolCallId":"call_39836cc13d7249189b500a4153c3f1d4|fc_03880fcdb65294d8016ae26db4d4f88192ad8b7b6ddc54d965","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":4,"timestamp":1793224116951}} +{"type":"message","id":"4b03d5cf","parentId":"b29485c2","timestamp":"2026-10-28T21:48:36.951Z","message":{"role":"toolResult","toolCallId":"call_42cdcaea1a8243a6b4935539b4721a45|fc_03880fcdb65294d8016ae26db4d4f8819298d9122ade04e809","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":4,"timestamp":1793224116951}} +{"type":"message","id":"a18540e5","parentId":"4b03d5cf","timestamp":"2026-10-28T21:48:36.951Z","message":{"role":"toolResult","toolCallId":"call_2e8a67dcb44840f79a97d150c580c9c0|fc_03880fcdb65294d8016ae26db4d4f88192a13da7d1e8a0a437","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":4,"timestamp":1793224116951}} +{"type":"message","id":"666c9adc","parentId":"a18540e5","timestamp":"2026-10-28T21:48:40.809Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_0d3d767d5ea346c8bf0d3eb23e215a86|fc_03880fcdb65294d8016ae26db6e3c8819283cb40b646910d27","name":"read","arguments":{"path":".kinex/devices/right_arm/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_e0aad2fea7bc4afd8926eacc4eb76b7e|fc_03880fcdb65294d8016ae26db8a05c819281b62a8f666b16f7","name":"read","arguments":{"path":".kinex/devices/right_gripper/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_1ef4fe3713aa486d97cd1ed2e04391d4|fc_03880fcdb65294d8016ae26db8a05c8192a272724573a650f4","name":"read","arguments":{"path":".kinex/devices/left_camera/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_9cd5c75ad17d4d61be75a0be6da3f4c0|fc_03880fcdb65294d8016ae26db8a05c81928fc0c2e351277310","name":"read","arguments":{"path":".kinex/devices/right_camera/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_3c5c46e6b77f447ab13e6db9d68861fe|fc_03880fcdb65294d8016ae26db8a05c8192b8159b0fbc1635e4","name":"ls","arguments":{"path":".kinex/tools"}},{"type":"toolCall","id":"call_6ee48ab143544893a17b1e7101670f45|fc_03880fcdb65294d8016ae26db8a05c8192849eba22b16eb71e","name":"ls","arguments":{"path":".kinex/memos"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":3073,"output":173,"cacheRead":14208,"cacheWrite":0,"reasoning":0,"totalTokens":17454,"cost":{"input":0.030730000000000004,"output":0.00865,"cacheRead":0.014208,"cacheWrite":0,"total":0.053588000000000004}},"stopReason":"toolUse","timestamp":1793224116981,"responseId":"resp_03880fcdb65294d8016ae26db573fc8192ad9a4be6a254f480","rawStopReason":"completed","durationMs":3828,"thinkingLevel":"high"}} +{"type":"message","id":"6ad7aba9","parentId":"666c9adc","timestamp":"2026-10-28T21:48:40.812Z","message":{"role":"toolResult","toolCallId":"call_0d3d767d5ea346c8bf0d3eb23e215a86|fc_03880fcdb65294d8016ae26db6e3c8819283cb40b646910d27","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":3,"timestamp":1793224120812}} +{"type":"message","id":"a199eb65","parentId":"6ad7aba9","timestamp":"2026-10-28T21:48:40.813Z","message":{"role":"toolResult","toolCallId":"call_e0aad2fea7bc4afd8926eacc4eb76b7e|fc_03880fcdb65294d8016ae26db8a05c819281b62a8f666b16f7","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":3,"timestamp":1793224120813}} +{"type":"message","id":"c58fc317","parentId":"a199eb65","timestamp":"2026-10-28T21:48:40.813Z","message":{"role":"toolResult","toolCallId":"call_1ef4fe3713aa486d97cd1ed2e04391d4|fc_03880fcdb65294d8016ae26db8a05c8192a272724573a650f4","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":1793224120813}} +{"type":"message","id":"90029dfc","parentId":"c58fc317","timestamp":"2026-10-28T21:48:40.813Z","message":{"role":"toolResult","toolCallId":"call_9cd5c75ad17d4d61be75a0be6da3f4c0|fc_03880fcdb65294d8016ae26db8a05c81928fc0c2e351277310","toolName":"read","content":[{"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. 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"}],"isError":false,"durationMs":3,"timestamp":1793224120813}} +{"type":"message","id":"5f0b7483","parentId":"90029dfc","timestamp":"2026-10-28T21:48:40.813Z","message":{"role":"toolResult","toolCallId":"call_3c5c46e6b77f447ab13e6db9d68861fe|fc_03880fcdb65294d8016ae26db8a05c8192b8159b0fbc1635e4","toolName":"ls","content":[{"type":"text","text":"(empty directory)"}],"isError":false,"durationMs":1,"timestamp":1793224120813}} +{"type":"message","id":"0809828f","parentId":"5f0b7483","timestamp":"2026-10-28T21:48:40.813Z","message":{"role":"toolResult","toolCallId":"call_6ee48ab143544893a17b1e7101670f45|fc_03880fcdb65294d8016ae26db8a05c8192849eba22b16eb71e","toolName":"ls","content":[{"type":"text","text":"(empty directory)"}],"isError":false,"durationMs":2,"timestamp":1793224120813}} +{"type":"message","id":"15419121","parentId":"0809828f","timestamp":"2026-10-28T21:48:44.257Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_41bfd194a67a43cbaf4a14fa8e8d66f4|fc_03880fcdb65294d8016ae26dbb54d0819286e20b5e7a42245d","name":"exec","arguments":{"python":"from kinex import runtime, finish\nfinish({n:runtime.operations(n) for n in ['world','left_arm','left_gripper','head_camera']})","description":"inspect operations"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":3356,"output":54,"cacheRead":17280,"cacheWrite":0,"reasoning":0,"totalTokens":20690,"cost":{"input":0.03356,"output":0.0027,"cacheRead":0.01728,"cacheWrite":0,"total":0.053540000000000004}},"stopReason":"toolUse","timestamp":1793224120829,"responseId":"resp_03880fcdb65294d8016ae26db938d08192b0ac3ae27d459825","rawStopReason":"completed","durationMs":3427,"thinkingLevel":"high"}} +{"type":"message","id":"61ba377d","parentId":"15419121","timestamp":"2026-10-28T21:48:45.225Z","message":{"role":"toolResult","toolCallId":"call_41bfd194a67a43cbaf4a14fa8e8d66f4|fc_03880fcdb65294d8016ae26dbb54d0819286e20b5e7a42245d","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":967,"timestamp":1793224125225}} +{"type":"message","id":"3c3b5ec8","parentId":"61ba377d","timestamp":"2026-10-28T21:48:48.552Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_82441a8f7dc44b07939b49439c7b3a4e|fc_03880fcdb65294d8016ae26dbf39288192bd0a0b2c00671870","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"},{"deviceID":"left_camera","action":"load"},{"deviceID":"right_camera","action":"load"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1268,"output":98,"cacheRead":20608,"cacheWrite":0,"reasoning":0,"totalTokens":21974,"cost":{"input":0.01268,"output":0.0049,"cacheRead":0.020607999999999998,"cacheWrite":0,"total":0.038188}},"stopReason":"toolUse","timestamp":1793224125253,"responseId":"resp_03880fcdb65294d8016ae26dbdd05081928b289a718243c9a5","rawStopReason":"completed","durationMs":3298,"thinkingLevel":"high"}} +{"type":"message","id":"38fa5f9e","parentId":"3c3b5ec8","timestamp":"2026-10-28T21:48:55.910Z","message":{"role":"toolResult","toolCallId":"call_82441a8f7dc44b07939b49439c7b3a4e|fc_03880fcdb65294d8016ae26dbf39288192bd0a0b2c00671870","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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}"}],"details":{"kinex":{"title":"devices: 8 calls","isError":false}},"isError":false,"durationMs":7356,"timestamp":1793224135910}} +{"type":"message","id":"faafb34b","parentId":"38fa5f9e","timestamp":"2026-10-28T21:48:59.568Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_fa8eb77a463a4d20a8347c16ce760642|fc_03880fcdb65294d8016ae26dcaffcc8192b691e40e04f22d3f","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":282,"output":39,"cacheRead":21888,"cacheWrite":0,"reasoning":0,"totalTokens":22209,"cost":{"input":0.00282,"output":0.0019500000000000001,"cacheRead":0.021887999999999998,"cacheWrite":0,"total":0.026657999999999998}},"stopReason":"toolUse","timestamp":1793224135934,"responseId":"resp_03880fcdb65294d8016ae26dc849ec8192bd13d94a32639161","rawStopReason":"completed","durationMs":3633,"thinkingLevel":"high"}} +{"type":"message","id":"725ab015","parentId":"faafb34b","timestamp":"2026-10-28T21:48:59.571Z","message":{"role":"toolResult","toolCallId":"call_fa8eb77a463a4d20a8347c16ce760642|fc_03880fcdb65294d8016ae26dcaffcc8192b691e40e04f22d3f","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":3,"timestamp":1793224139571}} 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on head_camera"}},"isError":false,"durationMs":230,"timestamp":1793224143607}} +{"type":"message","id":"fd506274","parentId":"25fb6783","timestamp":"2026-10-28T21:49:09.219Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Checking bottle geometry**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_03880fcdb65294d8016ae26dd2076c81928ebac3d953913462\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking bottle 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+{"type":"message","id":"2bbe6185","parentId":"fd506274","timestamp":"2026-10-28T21:49:09.222Z","message":{"role":"toolResult","toolCallId":"call_13b2b63712dd4c5aac748b6bab34764f|fc_03880fcdb65294d8016ae26dd503f481929ab9870397455514","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":3,"timestamp":1793224149222}} +{"type":"message","id":"234c383d","parentId":"2bbe6185","timestamp":"2026-10-28T21:49:09.222Z","message":{"role":"toolResult","toolCallId":"call_467d1babc64044308d07389bb35c364d|fc_03880fcdb65294d8016ae26dd51a10819284768a26ef999f42","toolName":"read","content":[{"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"}],"isError":false,"durationMs":2,"timestamp":1793224149222}} +{"type":"message","id":"6306099c","parentId":"234c383d","timestamp":"2026-10-28T21:49:09.222Z","message":{"role":"toolResult","toolCallId":"call_8d4f7e04f0074f4687ef209aa5ce60e6|fc_03880fcdb65294d8016ae26dd51a108192bd5d2aebbd140ea7","toolName":"read","content":[{"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"}],"isError":false,"durationMs":3,"timestamp":1793224149222}} +{"type":"message","id":"84a74e83","parentId":"6306099c","timestamp":"2026-10-28T21:49:17.718Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_9a7aa1cc485f4fa891b4799b37e30f02|fc_03880fcdb65294d8016ae26dd713ac8192a2f06874f3b85f5d","name":"exec","arguments":{"description":"measure scene and inspect schemas","python":"import asyncio\nimport numpy as np\nfrom kinex import communication, helpers, runtime, finish\nasync def main():\n async with communication() as io:\n frame=await helpers.camera_frame(io,'head_camera')\n d=helpers.image_array(frame['depth'])\n pts=[]\n for u,v in [(70,80),(61,42),(78,107),(201,30),(263,48),(275,15),(160,170),(20,95),(10,65)]:\n z=float(d[v,u])*.001\n p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\n w=await helpers.transform_pose(io,p,'head_camera:base','world:base')\n pts.append({'uv':[u,v],'world':w[:3],'depth':z})\n states={}\n for n in ['left_arm','right_arm']:\n async with io.subscribe(n+'.joint_state') as s: m=await s.next(timeout_s=10)\n states[n]={'q':list(m.position),'tcp':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],n+':flange','world:base')}\n finish({'points':pts,'states':states,'schemas':{n:runtime.describe(n) for n in ['left_arm.joint_target','left_gripper.target','world.wait']}})\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1477,"output":362,"cacheRead":22912,"cacheWrite":0,"reasoning":0,"totalTokens":24751,"cost":{"input":0.014770000000000002,"output":0.0181,"cacheRead":0.022912,"cacheWrite":0,"total":0.055782}},"stopReason":"toolUse","timestamp":1793224149242,"responseId":"resp_03880fcdb65294d8016ae26dd5a77881929e0242c5d6d9ae2f","rawStopReason":"completed","durationMs":8476,"thinkingLevel":"high"}} +{"type":"message","id":"b64d7ea5","parentId":"84a74e83","timestamp":"2026-10-28T21:49:19.817Z","message":{"role":"toolResult","toolCallId":"call_9a7aa1cc485f4fa891b4799b37e30f02|fc_03880fcdb65294d8016ae26dd713ac8192a2f06874f3b85f5d","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224159.011935530] [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 19, 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 7, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}"}],"details":{"kinex":{"title":"exec (Python)","isError":true}},"isError":true,"durationMs":2098,"timestamp":1793224159817}} +{"type":"message","id":"729259aa","parentId":"b64d7ea5","timestamp":"2026-10-28T21:49:23.215Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_fd8143c43ef741b3b4edd36a06cb0763|fc_03880fcdb65294d8016ae26de2c52c8192a16482607ddbdabd","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py","offset":1,"limit":220}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":571,"output":39,"cacheRead":24576,"cacheWrite":0,"reasoning":0,"totalTokens":25186,"cost":{"input":0.005710000000000001,"output":0.0019500000000000001,"cacheRead":0.024576,"cacheWrite":0,"total":0.032236}},"stopReason":"toolUse","timestamp":1793224159837,"responseId":"resp_03880fcdb65294d8016ae26de04a30819291512ef927119257","rawStopReason":"completed","durationMs":3379,"thinkingLevel":"high"}} +{"type":"message","id":"afb46e58","parentId":"729259aa","timestamp":"2026-10-28T21:49:23.219Z","message":{"role":"toolResult","toolCallId":"call_fd8143c43ef741b3b4edd36a06cb0763|fc_03880fcdb65294d8016ae26de2c52c8192a16482607ddbdabd","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":3,"timestamp":1793224163218}} +{"type":"message","id":"d13b3f67","parentId":"afb46e58","timestamp":"2026-10-28T21:49:32.523Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_e5ff0661f776449e803aa94caf102aa0|fc_03880fcdb65294d8016ae26de88d7c819296f7ed199064f5d2","name":"exec","arguments":{"description":"measure bottle positions","python":"import asyncio\nfrom kinex import communication, helpers, runtime, finish\nasync def main():\n async with communication() as io:\n image,depth=await helpers.camera_frame(io,'head_camera')\n d=helpers.image_array(depth)\n pts=[]\n for u,v in [(70,80),(61,42),(78,107),(201,30),(263,48),(275,15),(160,170),(20,95),(10,65)]:\n z=float(d[v,u])*.001\n p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\n w=await helpers.transform_pose(io,p,'head_camera:base','world:base')\n pts.append({'uv':[u,v],'world':w[:3],'depth':z})\n states={}\n for n in ['left_arm','right_arm']:\n async with io.subscribe(n+'.joint_state') as s: m=await s.next(timeout_s=10)\n states[n]={'q':list(m.position),'tcp':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],n+':flange','world:base')}\n finish({'points':pts,'states':states,'schemas':{n:runtime.describe(n) for n in ['left_arm.joint_target','left_gripper.target','world.wait']}})\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":330,"output":354,"cacheRead":25088,"cacheWrite":0,"reasoning":0,"totalTokens":25772,"cost":{"input":0.0033000000000000004,"output":0.0177,"cacheRead":0.025088,"cacheWrite":0,"total":0.046088000000000004}},"stopReason":"toolUse","timestamp":1793224163238,"responseId":"resp_03880fcdb65294d8016ae26de47a5c8192bd51e9f60cd83e47","rawStopReason":"completed","durationMs":9284,"thinkingLevel":"high"}} +{"type":"message","id":"17e9766e","parentId":"d13b3f67","timestamp":"2026-10-28T21:49:34.430Z","message":{"role":"toolResult","toolCallId":"call_e5ff0661f776449e803aa94caf102aa0|fc_03880fcdb65294d8016ae26de88d7c819296f7ed199064f5d2","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224173.802050693] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": [{\"uv\":[70,80],\"world\":[-0.19695410638592586,0.004840793255465603,0.8648806102948801],\"depth\":0.661},{\"uv\":[61,42],\"world\":[-0.19843608488027142,0.01836181260217995,0.9437713751659849],\"depth\":0.606},{\"uv\":[78,107],\"world\":[-0.19720784117987777,0.00647091530623034,0.7836032015382537],\"depth\":0.727},{\"uv\":[201,30],\"world\":[0.05952994588289075,0.18251674300724796,0.8356375733823433],\"depth\":0.791},{\"uv\":[263,48],\"world\":[0.17520652312780038,0.09531415164087742,0.8614856295206998],\"depth\":0.718},{\"uv\":[275,15],\"world\":[0.1815178275905498,0.10234625219325044,0.9355097052456853],\"depth\":0.663},{\"uv\":[160,170],\"world\":[-0.03103803993853389,-0.11372648082994058,0.7397051536755269],\"depth\":0.6900000000000001},{\"uv\":[20,95],\"world\":[-0.4427492319227125,0.24131101888369177,0.5484832795119379],\"depth\":1.056},{\"uv\":[10,65],\"world\":[-0.43384341948444355,0.24559314590548487,0.6666948752399482],\"depth\":0.964}],\n \"states\": {\"left_arm\":{\"q\":[3.7421762044687057e-7,-0.000003416102799747023,0.000009044338185049128,-2.5705126915909204e-8,-0.0000020763914108101744,7.544119284830231e-7],\"tcp\":[-0.30032403719682815,-0.19382880994801913,0.9420051083628493,-5.304990567434886e-7,-0.000009308971204628978,0.7141433685930687,0.6999994635766562]},\"right_arm\":{\"q\":[3.727569151124044e-7,-0.0000034167196645285003,0.000009045151273312513,-2.5587102214785773e-8,-0.0000020741485968756024,7.532827339673531e-7],\"tcp\":[0.3052327434365579,-0.19280673228662837,0.9410048642737745,-6.068416298149924e-7,-0.000009277349876493806,0.7106343746258521,0.7035615008751448]}},\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 }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":1907,"timestamp":1793224174430}} +{"type":"message","id":"e8af5c83","parentId":"17e9766e","timestamp":"2026-10-28T21:49:51.670Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Planning yellow bottle grasp**\n\n**Reading joint options**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_03880fcdb65294d8016ae26df0ba0481928f6c0ec056997090\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Planning yellow bottle grasp**\"},{\"type\":\"summary_text\",\"text\":\"**Reading joint options**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4m3_4wgSvLhrMFhyO2QdbR12oA4-PBvBHH5LnSYf2QHKNDsrLeUQnvGy4MIFsTNlPrm8nmkw5mTISikR1_UMLujFNs-nsMWaXLO78mmazjg7S2vp2orbQxBeBlBdid8_medDyn9pNW6XQ22fvgJEmtuFsu8T4jKGESZb6kPWzuNz1bm9ExF_TTmxs50K3PKeRKhtKrHssBTsi9ziivT3FCWAkANllkl5RYVo_xn-WWoeOpEmGZopi-aTV5QYMQC2Y4YU-0IjSF7aAKLlfTV8XvZCMHb2X_Y2jv_ZVfaIK5oYbs8jI_XdMEgMtTc4trlrAnju0YUXNFsAfStyt5FHeZb7Bk56-SfjHS2l0yafZwbFkgn09rWwR24Oia6jHdawqO6yiHrhayCaQ_m-tVSRLn9uHvsdrlKFDHK91CBxvgWa3thUSsCqRv8lFcFyPkoX8B_m8lUsnDphXRTqF4bq2Nj7GNPqh3H0GXBbN940vN7FNmcDktTusHDT1otNDKr5jCunbiogT7V1KlrFto8sTyjQ7kzv22OGkiS6kgPyH5WSslk4RNEi4Jm-MSwcYAnWxqUFtAuaS9wuB-RL9Xk8_zw483UciXkre37G2FMQodx3yZEZUPkvnPvSqrPNQ5WwPH-U7NuJKlRyt6XYJCrj6UNqRaEcicmWTQr1CiyovbHtLVnXGI5TC976u4iD8NXYa4_Yd82u8IsORYxnHA71glr0kQo1sTNVeewQQns5N4ZCITX86peGMNIO0GDz6nckoPU_P2P9OHkFX7YGUNjesKs_wxyUFN9KJKNmgsHgiYyEcvfj3ZTXEvARqmRxJsJo2WDlG7Ye5BHD15D629187Ns_jEOI_nNJsm2jPN8BXCgepwOGfSuzDFVIw1kWsYe0b-MWTSCnirTeOw2HJedpBCvyFPn8pi5HQuP4r2EaCtIgPzZaWc6lce88uEp_3DWBNpdx7OLZtBOSPQTWhtop9nTPTBOcoELDZ64n6TW8lmnTBJWcMOq-mXtcC7Z-NpjNqWOAAfxyQ4fRKaIOlIsqxttQPNgpi7eYlTMJNQJhI9oYT9TkfRRBOVcpKx2HcNWINFX2r6yqewXy0BsyhYALKghomxibnE2yprn2h8pbrldpRCys3XBXtF4QLQiOlP4bKo21UpG-v9a9_flJotB-wFDiFG_SDxK8kDvb7y9BBbD4nvk70elWiiGPn6VdnvcBfV9LAvkGsqdXlkg3yyhvE4PY2ekSwWlKJm-s1qI2MKF-2dJ22qqA-OApPS6YFOBSoNsN-UQwLAwHWIUIxpsF3cungzVRawvnGWCamQFcvQB5ZWBUIbflJf9cHzI63_uRPRMz0qCIElfohLQbIlqYobtPuswy2xGpYCn8pZFvzspByjLRj04JPz9VqWvBQD-pTJs51jBfUAPDyuQVjSjWS6yBFSgI1p6Q-FBdN_HaEHfdl6CUet8uhcJWzIBIesXgXt1oAlzt7K8j95zBC2Zao2Wi7Bo-iZ45U9vb43qLZ6FYxPj2zrg_iDvRoIjETV4CstQJr1wRMX7Kpof328m7WUMMxalMLq-alZsmcX9msRdzp5_-tDAzjEAnVDR26IfE6pGBy9NbeF49DKDM4W0ZDRQIem4ygAXRxahv87DN-wt9nGa4uiS1t2OPZbCaVmFKbUs-VBA4d-f9gA3Anx5jFIrBNNiAXkM8z9or8RLSITVc1hlJNkkQiGETHRrfi-zbfoU-0QFMoxEnYcRAEUxpfL2U4WOBdq6GOW3VFtwIB6yyY3TTPaTHbbKozogVtqCSuVV1KKrzdb-fkymQLT1PcuOt7wrzfFsp_gszf0aTFvC1KvO-XfYRbw5CE1bSBJD3IynzDY8l9_e9injt8HOtvQT4JX98z8mZlaS-AkYL3OnhfCPfi4F2p9DH20YGZ6o7oxjRVbzx_1RcWqFuryWUk1R6KlzCZKOT5ftIbagsxOcWbPGdGQaaNzuhp-j-LOUFZ3haJGvRcYnpcMis003l-nww9mFLtsKHouP8Y9EGPYkMk2ImNDesuEl7Bt4ccIMEx-TLHf8CWEVJEsIuCa6Yzn0WXSOzcXBQDxhg51QNN_uSlNG0kbvVaeBuDrmUSWzy38g9bYyCl0ff1wf2wInQhv_ypx3fbLdp3ikhLXGJcO7k8JQo4AN171jGVjoAhsN_fyZi4u4QBdKhnaj0vpeGu7CrNk0J7n8XyWafsjILrrxP-tUxRvF1eyihvPS-uTrq9ytCYt1WrKc1MN0Phr_fSXPItogWl8DqYKDaSbKx0XNuUQioGAPmRZdMz4F4Q-6vcaxxtrSFHQ2gNz84qGLFT4d_Nt5261eyKB2fmlMOFDLSm_AvSktC4cbaw4vIwt8ZDg1uXJSq0xo4pkrlnqfmvRAQZ0_gmGLk_c0_zKMxNu3uQACN2VG3pjO20dX_YenZfA-f35zGgYoCJm-fb8g4eJiRvxr0YOSDX7KPgS0foJzBMqXj6p6J3Rm_Vi-APvbYpBN3Alhl8wo4e5FMiEtrooNv5wRP403E8Ht7C_fUlWrntmHwIr4auuMlrbCzxH877cThyl9OeFwrW_n7mF4j4evNjeKP0uxxeX5alADGfXOddc-2KzUwdksqZqiVILUns16vP_Ktmr0K1ft_gvGOsmtlsJB4dsVG69EvuWdn9Pf7D1VDcSmB5oQJ99y7-OY2d7YS5pD-kYCtVc_wUCm4n6ZRloW6kPsrIW34i8oP26SLla1wuIgMOPTL18po0llVqVwTAl7YfLEWV8-E_eyteu6xQxa5TIIy7ZBQS3RNZA8iLStSem-bHWuwSY8QXY9gILneQxm8CDwVhsFXw-I6HzsZ6Z69CLP0LwGpLkcuZ162eNH-uLVqHaxkou7Ic3cO_PgPHL11ukwJg_V26H7ZNJv61CylARr_eSp7C0UK6EsrDbtT_TMKtP5wgQjy2YVoRoBQd3XxpWLxr7FtebkezR0jKM6dK2BtdY5H3kEYXMcB7qwuz2LZDd-IdECoV-fap3qxdY6OE57A5wkCdwRlnPxXUuw5psR2t4UNzceSDVtWa9sQcZPA6a1CIaXd6IFirrPfB7xH_kRuJnNLe8ISnTCGeYSbedlTdYepUjYZnWBcnGBehFPIhduH6zUjF-TscDJ3KBEYxHq7oXG9lPdSST8p2Vz-UyWUq2DZOT8XI_2nRJbSQc7a8zQKN2bI5hGF6oQE7dDI5LOwYOgA5_WmP_2DmlDFkwfOTr9FYtcsMcf7bT5BpGcpCXBrujGi48vf0_B-OApdMAetm5dMuk_Lfmt8FRmwGDADNTkgKnten-1XeBOvUy-5BCvtAKR2tewPo9JOchVhi4ECx4FKjjg1dmzKbiDbVS7_iuapISArXlnlK8rXAKIVftDI6kAc-sWa2-RuVv5aUTYH5YnBtZK-IOk_v6chL8rM09SsP7P0Ayn7A7s3zZVeLO3di5NUnZ1yqrrR3gm-QFDj6ubGwSGpK0mpYws7uJ-T1LGtkAX_6aiGloELikLsCur4-GcJiN6-fzUudWOQPCJ4WnnNDpm7tzDLSDnmhhmuwlep7LkKA-jSgMikw1CiLgy1hIqKyRpMDerOLPAewDHlOOQ2t68EyU76wB6stIyGSl_C5r7aEbmiuazhJBhgEGVfMwxvrht4yflPaF714e_s4DQhUzk1OXG9PKkk4tbKvhTdLoQJM8p9SFTcEdCJOEUv9wh01VzYSg6ztsaeZQBSnLhlGHy83o3EtWg3pUxLCN-gWATRRWbhTTABIudyUB9dWDlTJx0vMF9Y9X7qbgj6RC_oaZnowBdW-eUMTmShuRzwAVUGf1Jn7DLaz825MxGAGfIwmSZS-DVhXMqV6l9tTT0_I_kTqrYgHsoOLgAHKkjt1zMhmobPXAkerKtzhZsjXkD2UmyIvKpVaJDDa9J_DVOaKMQWLYuYNYPD_dIrAPOWlq4ayfnmvNHGkgwIDngbbZR_dR66wWOfyb_7WGlnrRjDSx7rl6Htz_F_hwxy7ltyLGawMcGcMN5FhIp4CE8sflYHrsnHcEqu78hJ0gSZ3fuCE63nXTZbkyOjf6yUcxWnwdrQP8E-I49YjnGtoFuzl728gIt8gScd0BQprLwV4pHHjMJ0XCzvY2Wu22PQXpxfmThq1JFAsRO0Zvvmqe_hzP99WRKK-OgKyKVi90u67ahg9dmAP-3u3DCR0aYJacneB6mutpF1JvKdOc6IKfe8N-HYXtOjdHtfINZsuqUrCrxDFwAQbvcpX_nMxiguhpIIfKAcpCFp70_s5eLYgZi1JjZ1hNzHOSHvdKc0jIthWD4cm-XB6Mhc0hE67d4tlxqVN8Lvow5Rw8vd5VIaZa5F0r7qA4wfn3X3Yr9INGLVPMt-LmH_E90Xevwvonky7XdfSRTb7qIuljU=\"}"},{"type":"text","text":"I’ll locate the bottles and bin, then grasp and transfer the bottles one at a time, checking each pickup and release.","textSignature":"{\"v\":1,\"id\":\"msg_03880fcdb65294d8016ae26dfdfa788192924af801f222ea09\",\"phase\":\"commentary\"}"},{"type":"toolCall","id":"call_c9ef1243fa444c9f85a5355698087337|fc_03880fcdb65294d8016ae26dff555081929e10d71a55311cab","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py","offset":1,"limit":300}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1468,"output":321,"cacheRead":25600,"cacheWrite":0,"reasoning":247,"totalTokens":27389,"cost":{"input":0.01468,"output":0.016050000000000002,"cacheRead":0.025599999999999998,"cacheWrite":0,"total":0.05633}},"stopReason":"toolUse","timestamp":1793224174455,"responseId":"resp_03880fcdb65294d8016ae26deee93c8192af42261d708b1423","rawStopReason":"completed","durationMs":17216,"thinkingLevel":"high"}} +{"type":"message","id":"506bca54","parentId":"e8af5c83","timestamp":"2026-10-28T21:49:51.673Z","message":{"role":"toolResult","toolCallId":"call_c9ef1243fa444c9f85a5355698087337|fc_03880fcdb65294d8016ae26dff555081929e10d71a55311cab","toolName":"read","content":[{"type":"text","text":"\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in /base; link\n defaults to the model's tip_link.\n \"\"\"\n\n def __init__(self, profile, directory=None):\n if profile.get(\"type\") != \"joint\":\n raise ValueError(\"expected a joint device profile\")\n model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n if self.tree is None:\n if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n self.joints = urdf.commanded(self.tree)\n self.names = [joint[\"name\"] for joint in self.joints]\n self.tip = model[\"tip_link\"]\n\n def chain(self, link):\n link = link or self.tip\n if not link:\n raise ValueError(\"name a link; the model declares no tip_link\")\n return Chain(self.tree, link)\n\n def fk(self, positions, link=None):\n \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n q = joint_target(positions, self.joints, check_limits=False)\n return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n def ik(self, target, seed, link=None, **options):\n \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n Joints off the link's path keep their seed values. options override the\n solver tolerances, damping, max_iterations and search budget.\n \"\"\"\n unknown = options.keys() - {*DEFAULTS, \"budget\"}\n if unknown:\n raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n values = dict(zip(self.names, project_seed(seed, self.joints)))\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n home = profile.get(\"home\")\n if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n return 0.0\n return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n joint_target(actions[-1], joints)\n previous = None\n for target in actions:\n joint_target(target, joints, check_limits=False)\n for i, (q, joint) in enumerate(zip(target, joints)):\n limits = joint[\"limits\"]\n if limits is None:\n continue\n margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n low, high = limits[\"min\"], limits[\"max\"]\n if not low - margin <= q <= high + margin:\n raise ValueError(\"target outside joint limits recovery tolerance\")\n if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n raise ValueError(\"joint limits recovery must move inward\")\n previous = target\n\n\ndef command(profile, actions, joints):\n \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n if not isinstance(actions, list) or not actions:\n raise ValueError(\"actions must be a nonempty sequence\")\n for target in actions:\n joint_target(target, joints, check_limits=False)\n if profile[\"limits\"] == \"strict\":\n recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n return actions\n\n\ndef project_seed(value, joints):\n \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n values = vector(value, len(joints), \"IK seed\")\n return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n raise ValueError(\"joint state requires unique names and matching positions\")\n positions = dict(zip(message.name, message.position))\n names = [joint[\"name\"] for joint in joints]\n if any(name not in positions for name in names):\n raise ValueError(\"joint state is missing modeled joints\")\n return joint_target([positions[name] for name in names], joints, check_limits=False)\n"}],"isError":false,"durationMs":2,"timestamp":1793224191673}} +{"type":"message","id":"a487405a","parentId":"506bca54","timestamp":"2026-10-28T21:50:14.970Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Writing action runner**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_03880fcdb65294d8016ae26e0308c48192b6ea33c2ff852295\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Writing action runner**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4m4GQDiBx5wuaBBn50w11s2WGkljmmJldS4sRYRotgOjiORqR8wkGlqRspjrqGGK-_9kGrs0X1h8E16Wc1RjLWnwzKcypDYwLefjbca0pO5jmqc28DJRPkmmZZhQD2FEDfX_KjGZN8jlOrTFJUnMhVFDdGsCt74r2VZi7zteXp3OzkiI0KEv_yEtGu032dbUoXZdnDQq5R3jCH0yFDQgwKReNcSMcOTXErzM08l9RdsQnQbMUXkI3BQ5eIXKM_H9ulge39BdiKPrLsF33Q3I8pXku9jWwbnZzFI50sGtuSVLC_qHGXF_Jpq6JDI8Vh77p05kJi39vcOBz2B2R7YMFaCAMCgyJK76V6y5fl1M1FoCMZly0socU1h4YcLaPmmWhGfLzEJQQ7NP6Uhqtg-zxM5Q8jHQlh_2oRRYciTxHBDg0QSbozKAPm1BfHUuRLOKsfZCp4WcwljE9Q4eZy_IvUv41pg4V_O-wxw5eeWelbkPUJBlSryPGgUfR__D9xRxhigNfHYEV6Vg3BXiikpf5sPZW66hHfBwq8HWatq-bM2wQPjpfGdLR4DBBXKtr7VHhtLhi8DSthsVQeZPdmf6TvTyWKwCyoXAB1k3b9QTQQXNaUgVJcj3tGhfyoojigA2KUE_9sSkicpmyuMzp_6rOwFpa_y_onn2ZF3GHTa3-JSUOk73rTWfr_nQdPNxKSIVNla4QWcHMK1BzBq4osrSO2WsiUHZ8eYPBjRh09x3b5XCAMlBlWaZEJmly8840PlSMdxlIJhwOa3_VW4qsLIValoSqe_JHMLkDvqUW2c5p4PNsK_gWUSKonCPHgjBdjeyZY5amup-8KlKV_WM6p_xUxAw4DZj8FKf0Aq5--PpxYa45SFtzOO4sfdkqwVX5hDnYaTgWSNasy_YQ_BG3N_D66BdcAB_Q2w7jqIqtT4UTWHb7UiRVSyTTrnsOMBsfiy49PNcARlRrSjlGKdyBq-yFMeROB8rkHzPE1GQFDrLXw1jK8ItPoHwP2aN0ndv7-U_R5O3v0CbJihvrMcF1_s1OKcUgpzEfnlD5XaxI4hUoQAVFw0JiwIyy26j_KlIoZKtLoBOdBA8nKoANVNA88gs_j8ccs7qEoUVwiyVr_lBi3BlaOEKrZ2-hd4AL1Phr6xyWYboXvfL_xXQG05yx_BHSR4dt8AiVxs99jyQM1_exmMdizcA9RvtTu6S0Diniq97RlehszTWw960nwJOXGi1wHslyo8lxrhtY6794B4dxfL4YqKEfdrNnO33hjEjf5jRA95Jbwvtmx_7lj8Ov4Z9WSbJKDLadq_pmdUijbQ5wVw3S8JWrqNa6FGhSF-6Db-dTiprymwMx-ZM8vAuynvxS78BGXwnWiZ1jQg7sdN0aBvQrsbXgN4jurHAwENWuTI_AU5GCakn_ag5ejfBGodgDInqZvwj5Ylzfn39z_ZBPOWZn1XmAiBDoDR8pD4EJRJwFO9tJTcOOMKvhZmQSaGnOy36u7lmj-mjC1-k-fjKxnzAhw_jBpzbqYhWwZ_H2lHZw2oPI58vmqCPqu515hJx5hnT0kFkOQx5lIqBVAsj-13v-oQ0tRQfbZVrBS5W0fwgGDqtLigvMXH5jc-mNX9gVEA11OOwUxpJ3Ct5XjTdMow3_Flj2dy79aQk1ZxFsDefUA-9VzPgJyFRPO0JzBdkNJ0DeUM-KI_lGr0_lDuhC1roj8rKXAonOLPL12XLhQJ8q5dvbPy8nw4jyCElJHGkV4CoGEanDEfNmGOKGHKqjozg0gitkHtVRTdlD6QgwNl0haU14qtuJWAVy4M3QG1zfuRo4HT2roqNgUJJMHS6m_d3ngJhtxLftLZQ3wRluNQnB_UM416bRZ50nBYORIFLTV3GtTwNVne0zmHgCArsBlrAsqzSIaK3JAnS9nX-RPljTuWKFqeBA2vE41bCrwTZ-WXbA7-viPtiWYAyM1tqNqNldl3GFyU-HPpDUrqC8Epxrj0VN61D8xh1c73pds0q1-oqCgsmbqiaWOgkEPKDZYp91_UTw5hD97ZHMkJHRbsx6m66qBhM2ZkmXfdK8vgnntARKIouMTKBfAObEEufnXwmOSDRR_roG0oLWA-lcqi1E9ebmvj3VsxhmnWYhMReR78cwkWgJun9qTGtj7enLXrLkvHL7deLC_Sl8OmQHySuqauIRDoOWcfASFHW-rPCXMSRLMqR_gPoUVxGdqnb9nyZaA-aOc7D8kk-OEsFyfX5Vq5_RuTbjrXl1kOCRtoS1woS4Q==\"}"},{"type":"toolCall","id":"call_0dab35d986494f688df08ec008796574|fc_03880fcdb65294d8016ae26e06f8f481929dff7a1ab8dbc133","name":"write","arguments":{"path":".kinex/tools/manipulate.py","content":"\"\"\"Measured, stepped Cartesian moves for RoboTwin ARX5. Invoked with Kinex exec args.\nWaypoints are world TCP xyz and optional quaternion xyzw; duration per waypoint.\nNo motion inferred from publication. Uses declared FK/IK, camera and wait interfaces.\n\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation, Slerp\nfrom kinex import communication, helpers, args, finish\n\nasync def state(io, name):\n async with io.subscribe(name+'.joint_state') as s:\n return await s.next(timeout_s=10)\n\nasync def main():\n a=args(); side=a.get('side','left'); arm=side+'_arm'; grip=side+'_gripper'\n async with communication() as io:\n model=helpers.joint_model(arm)\n m=await state(io,arm); q=list(m.position)\n if 'grip' in a:\n await io.publish(grip+'.target',{'data':float(a['grip'])})\n points=[]; elapsed=0.; targets=[]\n # Default vertical approach, fingers close along world X.\n default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()\n for wp in a.get('waypoints',[]):\n xyz=np.array(wp['xyz']); quat=np.array(wp.get('quat',default)); duration=wp.get('duration',2.)\n if wp.get('linear',False):\n current=await helpers.transform_pose(io,model.fk(q),arm+':base','world:base')\n cr=Rotation.from_quat(current[3:]); start=np.array(current[:3])+cr.apply([.12,0,0])\n rot=Slerp([0,1],Rotation.from_quat([current[3:],quat]))\n fractions=np.linspace(0,1,max(2,int(duration/.12))+1)[1:]\n else: fractions=[1.]\n start_t=elapsed\n for f in fractions:\n p=start+(xyz-start)*f if wp.get('linear',False) else xyz\n r=rot(f) if wp.get('linear',False) else Rotation.from_quat(quat)\n flange=np.r_[p-r.apply([.12,0,0]),r.as_quat()].tolist()\n local=await helpers.transform_pose(io,flange,'world:base',arm+':base')\n newq=model.ik(local,q)\n # choose equivalent nearby revolute solution\n newq=[v+2*np.pi*round((old-v)/(2*np.pi)) for v,old in zip(newq,q)]\n if max(abs(np.array(newq)-q))>3.2: raise ValueError('discontinuous IK')\n q=newq; t=start_t+duration*f\n sec=int(t); ns=round((t-sec)*1e9)\n points.append({'positions':q,'time_from_start':{'sec':sec,'nanosec':ns}})\n elapsed+=duration; targets.append(xyz.tolist())\n if points:\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\n wait=elapsed+a.get('settle',.4)\n if wait>0: result=await io.request('world.wait',duration_s=wait)\n m=await state(io,arm); g=await state(io,grip)\n flange=await helpers.transform_pose(io,model.fk(list(m.position)),arm+':base','world:base')\n tcp=np.array(flange[:3])+Rotation.from_quat(flange[3:]).apply([.12,0,0])\n files=[]\n for cam in a.get('cameras',['head_camera']):\n im,depth=await helpers.camera_frame(io,cam)\n files.append(helpers.save_image(im))\n if a.get('save_depth'): np.save('/tmp/'+cam+'_depth.npy',helpers.image_array(depth))\n finish({'tcp':tcp.tolist(),'quat':flange[3:],'q':list(m.position),'grip':list(g.position),'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None,'time':result if wait>0 else None},file=files)\n\nasyncio.run(main())\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1304,"output":1068,"cacheRead":27264,"cacheWrite":0,"reasoning":62,"totalTokens":29636,"cost":{"input":0.013040000000000001,"output":0.0534,"cacheRead":0.027264,"cacheWrite":0,"total":0.093704}},"stopReason":"toolUse","timestamp":1793224191708,"responseId":"resp_03880fcdb65294d8016ae26e00209c81928177a56538106353","rawStopReason":"completed","durationMs":23262,"thinkingLevel":"high"}} +{"type":"message","id":"4df9f281","parentId":"a487405a","timestamp":"2026-10-28T21:50:14.974Z","message":{"role":"toolResult","toolCallId":"call_0dab35d986494f688df08ec008796574|fc_03880fcdb65294d8016ae26e06f8f481929dff7a1ab8dbc133","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/tools/manipulate.py"}],"isError":false,"durationMs":2,"timestamp":1793224214974}} +{"type":"message","id":"7210e35c","parentId":"4df9f281","timestamp":"2026-10-28T21:50:20.195Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_45ca89cab14d441c80d9b258f0df7f6a|fc_03880fcdb65294d8016ae26e1adbd48192aa2a6f636e4f6741","name":"exec","arguments":{"file":".kinex/tools/manipulate.py","description":"approach clear bottle","args":{"grip":1,"waypoints":[{"xyz":[-0.198,0.04,1.11],"duration":2.5}],"cameras":["head_camera","left_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":217,"output":74,"cacheRead":29440,"cacheWrite":0,"reasoning":0,"totalTokens":29731,"cost":{"input":0.00217,"output":0.0037,"cacheRead":0.029439999999999997,"cacheWrite":0,"total":0.035309999999999994}},"stopReason":"toolUse","timestamp":1793224215001,"responseId":"resp_03880fcdb65294d8016ae26e1775208192add9a134711286ae","rawStopReason":"completed","durationMs":5194,"thinkingLevel":"high"}} +{"type":"message","id":"df02d221","parentId":"7210e35c","timestamp":"2026-10-28T21:50:22.923Z","message":{"role":"toolResult","toolCallId":"call_45ca89cab14d441c80d9b258f0df7f6a|fc_03880fcdb65294d8016ae26e1adbd48192aa2a6f636e4f6741","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224222.313883542] [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/manipulate.py\\\", line 60, 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/manipulate.py\\\", line 38, in main\\n newq=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=406, elapsed_ms=100.044, position_error_m=0.10653722123952583, rotation_error_rad=0.10622118351177572\"\n}"}],"details":{"kinex":{"title":"exec (Python)","isError":true}},"isError":true,"durationMs":2728,"timestamp":1793224222923}} +{"type":"message","id":"8ad02130","parentId":"df02d221","timestamp":"2026-10-28T21:50:30.833Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Testing top flange feasibility**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_03880fcdb65294d8016ae26e21592c8192bd3909fa7c03be48\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Testing top flange feasibility**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4m4lFi-BEzfKtWtRy0Wv395Sdz33QMjU--o_4cTk1SoKiQhIJbRFBl9_ssbwa6mEGx7JWS0RHl5u8hLtzPs6fFKqeH8iluBb0TFHF1S9nKeRbMcENPEcwIZ7A22G1oy9p5n0e9PaH60vCpedWILbiDH7nXKrre9-fBaTTa9-m3T-885hqW5Yu-emP34IX3oWBKujrgW8664RVj9JjXNObL9E0zI9V3n8l1j0gUBMdC-NFrgfl5bVqNFYh0Ti3ceCFXNZoCbnPXDE2vfTvkKPN1rzFfgi_Qmtmig3qiK1gF1uIRGPK1EC6w_ajWC1pcb3CCQsjosLmwtEuZoW3WTQAFg3gxCgkVHY3rSKeVDBsUQDulDbAjX8URW0EPk33F_vpywjqM0EPR7ygAmmonF_YQWO-k1-cslnXSvcNVSveQIy1NI8DtFaAlLxsCXZFRnAFkZ3FKm9GVeBKyqtllw6jL-ykYmU0ycvxuwpQWc8gZx4acxAEz7G-BOTBmLBZvpNeXhM4sHrB1OqadkBM8VHrJH7ZaUvbue8xDOImh_1G-pceiP6qTc99nzGDG6k3OhAlWxHfueWrbk5qq1D6_JOUPe8uY3_DAVo1TgIkqdoguOCb1W5R-tIO2PXnceBedl6uo6_ODDAsUrcDTaeZKviqyeUUw25nOeKkhGIjy0QeLAkthpy-qQXKfihU2xgkF72Cnux01hPe110nskgGomkoOSGQUSsEBSOulEVOdu0xGdfTSlm5Dz1HBKtXra0SzOSSV26e46ek5QzsfPHE2uqrqDEDH6Y_QK4u4hGYbHqZkK37CZCuMh3Skk29BXGExxZlU22OsOgzQlA0luUguvd5WLyJciAdmQEoZf2-uv8cpwqrm2UFn9zZ4m8dGD182CtP4AV-PMlL-roT9-xX3xpm_z7wn7cAO8ad10cnkF8a5wyEW2iSmmyvaGNcchnxpy2ZAvCp0xZLWZA76fQC4yNz9ifVBy1OSL3QJ_8-2nCXnrZKTVKDyiTg8FuOjteIHGy_AzIxeFCuKMTRG-P7W_FFPOU6ZP24L57vb7D5mY2UK3dHKHXFE62xUq7-4bVGbnWceXcsEWKDK1neBF_ZTOVynv3gkMwrFOYJQLQkea9ereVdxPJwxLvOs_ntpwjx2f24QqsbGbM0OkHMzVMZi9qqybB7L0cHslen9jidUQNClinXJsYiDyrQKGz6PQ58_C5KFKkKpf9myHBkfuwzC-0Qe3_8IMP8Bn-JsGWI4rYDs55w_PBZepjWvHPIZnJM7o_HYLFcRt4U0TZX1sz9vpHxn-8JZDZDhVutHXzpvUK8IlZv9oD4Bq9U4iRyM-_mgbs8MGUZxET1kW5x-zsyWMrc21iGNX2AepFmNJAk0DGsegowmOWIRxcYGze6kLRhVibHW4N4vZGCPgHjscxfTPiTI61zCkuD8Flv_Q0uQjGz7zwiEOm6R1_ifrYcF3wj9DPjMWrxppWi2PPLCGwwtNt2CDHrVfGQLTtSxxb_GTgjRFFlACPt2SsB9o_qpFS7crk5wUVks1cBucHopvsWo6GZaMuyHaaXKqOErFitqtRiQR0t7pjgX55af7lywKfsub40GrG3r5Z_XBbVXV4mItHYm6mEA0tpig6ygTtJmWLVTtyZTuAhXvztb4cpgXGBzYYffQkcVXTFBl4vCXA4oYMWqn-DV-abUAABH8Vgs8-j8hNzu6WQKIqmFb1QXn2bWfkS16ittt_BNjPXqRNc3jhJqgktLWg-OpOg63TW_KfG4UFQ0rfkdc4soq3cxnhptkS8HjafNIFy2CXJ_q2c00rFA1C9Hm6OXxp20bxyFwNCma752tUTLCGC8AD9cX4NGNPsWTZFHw-7itK_Hc8qAcrvoQPvFCDNGZXf1IUBB-3-q8Bo10dnF7wqt1-DCX0K4YsoikJWetsh9wdd7xVd_tyxUu0XA==\"}"},{"type":"toolCall","id":"call_d766eb69ac6d4ec79410f9f2129fe45c|fc_03880fcdb65294d8016ae26e231ffc8192873929d00d4373ca","name":"exec","arguments":{"description":"check reachable grasp poses","python":"import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n for z in [1.04,1.,.95,.91]:\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\n p=np.r_[np.array([-.198,.04,z])-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n try:q=model.ik(p,[0,-1,1,.5,0,0],budget=2);out.append([z,q])\n except ValueError as e:out.append([z,str(e)])\n finish(out)\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":846,"output":280,"cacheRead":29568,"cacheWrite":0,"reasoning":43,"totalTokens":30694,"cost":{"input":0.00846,"output":0.014,"cacheRead":0.029567999999999997,"cacheWrite":0,"total":0.052028}},"stopReason":"toolUse","timestamp":1793224222957,"responseId":"resp_03880fcdb65294d8016ae26e1f668c8192b0ed0a20c30c70ed","rawStopReason":"completed","durationMs":7876,"thinkingLevel":"high"}} +{"type":"message","id":"054b78b0","parentId":"8ad02130","timestamp":"2026-10-28T21:50:33.588Z","message":{"role":"toolResult","toolCallId":"call_d766eb69ac6d4ec79410f9f2129fe45c|fc_03880fcdb65294d8016ae26e231ffc8192873929d00d4373ca","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224232.937157450] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [1,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.95,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.91,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"]\n ]\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":2754,"timestamp":1793224233588}} +{"type":"message","id":"23c00f42","parentId":"054b78b0","timestamp":"2026-10-28T21:50:39.106Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_45460ad2e45c4f75af849f7a18986ecd|fc_03880fcdb65294d8016ae26e2bc4cc8192a79f14f7a6b63d6c","name":"exec","arguments":{"description":"check reachable grasp poses","python":"import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n for z in [1.04,1.,.95,.91]:\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\n p=np.r_[np.array([-.198,.04,z])-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n try:q=model.ik(p,[0,-1,1,.5,0,0],budget={'max_attempts':40,'attempt_time_s':.1,'max_time_s':2});out.append([z,q])\n except ValueError as e:out.append([z,str(e)])\n finish(out)\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":250,"output":252,"cacheRead":30592,"cacheWrite":0,"reasoning":0,"totalTokens":31094,"cost":{"input":0.0025,"output":0.0126,"cacheRead":0.030591999999999998,"cacheWrite":0,"total":0.045691999999999997}},"stopReason":"toolUse","timestamp":1793224233616,"responseId":"resp_03880fcdb65294d8016ae26e2a278c8192a955d374d2fb986b","rawStopReason":"completed","durationMs":5490,"thinkingLevel":"high"}} +{"type":"message","id":"efe179cf","parentId":"23c00f42","timestamp":"2026-10-28T21:50:45.291Z","message":{"role":"toolResult","toolCallId":"call_45460ad2e45c4f75af849f7a18986ecd|fc_03880fcdb65294d8016ae26e2bc4cc8192a79f14f7a6b63d6c","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224241.131440963] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,[-0.2496410241379745,-3.8412895605530037,2.487698982425237,1.5250552017336654,-3.1154129244818,2.924150273604751]],\n [1,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1170.321, position_error_m=0.017156539321827088, rotation_error_rad=0.0005849824564675265\"],\n [0.95,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1184.417, position_error_m=0.0004092112180844326, rotation_error_rad=1.4402656672529403e-05\"],\n [0.91,[-0.24688493993230948,-3.825269561715616,2.5398416965020116,-1.6526536288735123,0.005872565741773644,-0.2145231983847298]]\n ]\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":6185,"timestamp":1793224245291}} +{"type":"message","id":"96d5863b","parentId":"efe179cf","timestamp":"2026-10-28T21:51:01.624Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Checking IK seed candidates**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_03880fcdb65294d8016ae26e39c7ac8192a2c6d356e51a4fa8\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking IK seed 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approach configuration","python":"import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\n p=np.r_[np.array([-.198,.04,1.04])-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n for seed in [[0,1,1,0,0,0],[0,2,1,-1,0,0],[0,1,-1,0,0,0],[0,2,2,1,0,0]]:\n try:q=model.ik(p,seed,budget={'max_attempts':20,'attempt_time_s':.1,'max_time_s':1});out.append(q)\n except ValueError as e:out.append(str(e))\n finish(out)\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":413,"output":393,"cacheRead":30976,"cacheWrite":0,"reasoning":115,"totalTokens":31782,"cost":{"input":0.00413,"output":0.01965,"cacheRead":0.030976,"cacheWrite":0,"total":0.054756}},"stopReason":"toolUse","timestamp":1793224245318,"responseId":"resp_03880fcdb65294d8016ae26e368aa8819294657b621ac100c6","rawStopReason":"completed","durationMs":16306,"thinkingLevel":"high"}} +{"type":"message","id":"8b058fff","parentId":"96d5863b","timestamp":"2026-10-28T21:51:06.403Z","message":{"role":"toolResult","toolCallId":"call_e80a33c48f3e491cba937585b6e9c711|fc_03880fcdb65294d8016ae26e3fa1288192b59901d15046922f","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224263.608290980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=594.872, position_error_m=0.06611405415402853, rotation_error_rad=0.0019896488433462475\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=590.202, position_error_m=0.06586970481906577, rotation_error_rad=0.001980616266574562\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=582.975, position_error_m=0.06614644516120394, rotation_error_rad=0.002008056238735402\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=579.950, position_error_m=0.05696908984691351, rotation_error_rad=0.1997905349017585\"\n ]\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":4778,"timestamp":1793224266403}} +{"type":"message","id":"f0a68f76","parentId":"8b058fff","timestamp":"2026-10-28T21:51:14.411Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Positioning bottle for grasp**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_03880fcdb65294d8016ae26e4d5eb08192959c6f3dc7816fb8\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Positioning bottle for 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bottle from front","args":{"grip":1,"waypoints":[{"xyz":[-0.198,-0.1,0.87],"quat":[0,0,0.70710678,0.70710678],"duration":2.5}],"cameras":["head_camera","left_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":437,"output":189,"cacheRead":31616,"cacheWrite":0,"reasoning":94,"totalTokens":32242,"cost":{"input":0.004370000000000001,"output":0.00945,"cacheRead":0.031616,"cacheWrite":0,"total":0.045436}},"stopReason":"toolUse","timestamp":1793224266423,"responseId":"resp_03880fcdb65294d8016ae26e4afbd08192a134c421c02c72a8","rawStopReason":"completed","durationMs":7988,"thinkingLevel":"high"}} +{"type":"message","id":"eda38a00","parentId":"f0a68f76","timestamp":"2026-10-28T21:51:34.228Z","message":{"role":"toolResult","toolCallId":"call_a1252c09618b4174985aa43b46b91af8|fc_03880fcdb65294d8016ae26e5239188192b89199dd881dd9e3","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224276.413911057] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.34149891422440415,-0.2565050430306144,0.8897903932734834],\n \"quat\": [-0.09308770694337504,0.19088826103653672,0.7954797807205011,0.5675458299374028],\n \"q\": [-0.20009401440620422,-0.2250864952802658,0.1718592345714569,-0.8337344527244568,-0.5068660974502563,-0.01791577972471714],\n \"grip\": [0.9960487864234231],\n \"target_error\": 0.2132543705144551,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.92,\"waited_s\":2.92},\n \"file\": [\"/tmp/kinex-observation-_g33q8jf.png\",\"/tmp/kinex-observation-g6aa97sf.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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finish(out)\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":634,"output":328,"cacheRead":32896,"cacheWrite":0,"reasoning":126,"totalTokens":33858,"cost":{"input":0.00634,"output":0.0164,"cacheRead":0.032896,"cacheWrite":0,"total":0.055636000000000005}},"stopReason":"toolUse","timestamp":1793224308119,"responseId":"resp_03880fcdb65294d8016ae26e7494d08192919c5168694be16f","rawStopReason":"completed","durationMs":13028,"thinkingLevel":"high"}} +{"type":"message","id":"b444ccfd","parentId":"d5761dfc","timestamp":"2026-10-28T21:52:03.306Z","message":{"role":"toolResult","toolCallId":"call_ddb8d3f7aa744130902ea9827c5a15e5|fc_03880fcdb65294d8016ae26e7fb7348192949bf1a9101eaff4","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224322.596944567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [-0.5526204907538345,-1.7623647377941796,-0.6392799169876033,-1.1322713387442476,-0.513594805751331,-0.018117428053433485],\n [-0.553866303587819,1.9944921125413446,0.16136065569054742,1.8416406163084864,-0.5113207015289282,0.016809955678195713],\n [-0.5538701711987519,1.9950389022424122,0.16140448051613965,1.842137792565355,-0.5113167030912799,0.016803814033760394]\n ]\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":2158,"timestamp":1793224323306}} +{"type":"message","id":"3e92b677","parentId":"b444ccfd","timestamp":"2026-10-28T21:52:16.178Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Preparing the positive seed**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_03880fcdb65294d8016ae26e8699388192bc55d4cdfb884d40\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Preparing the positive 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newq=model.ik(local,seed,budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2})"},{"oldText":"'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None","newText":"'target_q':q if points else None,'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":417,"output":283,"cacheRead":33664,"cacheWrite":0,"reasoning":142,"totalTokens":34364,"cost":{"input":0.00417,"output":0.014150000000000001,"cacheRead":0.033664,"cacheWrite":0,"total":0.051984}},"stopReason":"toolUse","timestamp":1793224323344,"responseId":"resp_03880fcdb65294d8016ae26e83d5988192be45fcbe8ab5661f","rawStopReason":"completed","durationMs":12834,"thinkingLevel":"high"}} +{"type":"message","id":"6fc3d286","parentId":"3e92b677","timestamp":"2026-10-28T21:52:16.185Z","message":{"role":"toolResult","toolCallId":"call_5c2bd880402b45b7ae3456ce1ffc616d|fc_03880fcdb65294d8016ae26e8d82f081928e2ed821588e2644","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 2 block(s) in .kinex/tools/manipulate.py."}],"details":{"diff":" ...\n 34 p=start+(xyz-start)*f if wp.get('linear',False) else xyz\n 35 r=rot(f) if wp.get('linear',False) else Rotation.from_quat(quat)\n 36 flange=np.r_[p-r.apply([.12,0,0]),r.as_quat()].tolist()\n 37 local=await helpers.transform_pose(io,flange,'world:base',arm+':base')\n-38 newq=model.ik(local,q)\n+38 seed=wp.get('seed',q)\n+39 newq=model.ik(local,seed,budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2})\n 39 # choose equivalent nearby revolute solution\n 40 newq=[v+2*np.pi*round((old-v)/(2*np.pi)) for v,old in zip(newq,q)]\n 41 if max(abs(np.array(newq)-q))>3.2: raise ValueError('discontinuous IK')\n 42 q=newq; t=start_t+duration*f\n ...\n 54 for cam in a.get('cameras',['head_camera']):\n 55 im,depth=await helpers.camera_frame(io,cam)\n 56 files.append(helpers.save_image(im))\n 57 if a.get('save_depth'): np.save('/tmp/'+cam+'_depth.npy',helpers.image_array(depth))\n-58 finish({'tcp':tcp.tolist(),'quat':flange[3:],'q':list(m.position),'grip':list(g.position),'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None,'time':result if wait>0 else None},file=files)\n+59 finish({'tcp':tcp.tolist(),'quat':flange[3:],'q':list(m.position),'grip':list(g.position),'target_q':q if points else None,'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None,'time':result if wait>0 else None},file=files)\n 59 \n 60 asyncio.run(main())","patch":"--- .kinex/tools/manipulate.py\n+++ .kinex/tools/manipulate.py\n@@ -34,9 +34,10 @@\n p=start+(xyz-start)*f if wp.get('linear',False) else xyz\n r=rot(f) if wp.get('linear',False) else Rotation.from_quat(quat)\n flange=np.r_[p-r.apply([.12,0,0]),r.as_quat()].tolist()\n local=await helpers.transform_pose(io,flange,'world:base',arm+':base')\n- newq=model.ik(local,q)\n+ seed=wp.get('seed',q)\n+ newq=model.ik(local,seed,budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2})\n # choose equivalent nearby revolute solution\n newq=[v+2*np.pi*round((old-v)/(2*np.pi)) for v,old in zip(newq,q)]\n if max(abs(np.array(newq)-q))>3.2: raise ValueError('discontinuous IK')\n q=newq; t=start_t+duration*f\n@@ -54,7 +55,7 @@\n for cam in a.get('cameras',['head_camera']):\n im,depth=await helpers.camera_frame(io,cam)\n files.append(helpers.save_image(im))\n if a.get('save_depth'): np.save('/tmp/'+cam+'_depth.npy',helpers.image_array(depth))\n- finish({'tcp':tcp.tolist(),'quat':flange[3:],'q':list(m.position),'grip':list(g.position),'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None,'time':result if wait>0 else None},file=files)\n+ finish({'tcp':tcp.tolist(),'quat':flange[3:],'q':list(m.position),'grip':list(g.position),'target_q':q if points else None,'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None,'time':result if wait>0 else None},file=files)\n \n asyncio.run(main())\n","firstChangedLine":38},"isError":false,"durationMs":6,"timestamp":1793224336185}} +{"type":"message","id":"001b6a33","parentId":"6fc3d286","timestamp":"2026-10-28T21:52:19.874Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_403b170c14244e30a81ccd05c9490501|fc_03880fcdb65294d8016ae26e92923481929e81e637fddfc208","name":"exec","arguments":{"file":".kinex/tools/manipulate.py","description":"recover to raised posture","args":{"waypoints":[{"xyz":[-0.28,-0.15,1.13],"quat":[0,0,0.70710678,0.70710678],"seed":[0,1,1,0,0,0],"duration":3}],"cameras":["head_camera","left_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":215,"output":99,"cacheRead":34176,"cacheWrite":0,"reasoning":0,"totalTokens":34490,"cost":{"input":0.00215,"output":0.00495,"cacheRead":0.034176,"cacheWrite":0,"total":0.041276}},"stopReason":"toolUse","timestamp":1793224336217,"responseId":"resp_03880fcdb65294d8016ae26e90a7048192ba0bca1e3d4fef63","rawStopReason":"completed","durationMs":3657,"thinkingLevel":"high"}} +{"type":"message","id":"8c8cb210","parentId":"001b6a33","timestamp":"2026-10-28T21:52:41.812Z","message":{"role":"toolResult","toolCallId":"call_403b170c14244e30a81ccd05c9490501|fc_03880fcdb65294d8016ae26e92923481929e81e637fddfc208","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224341.829452565] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.2808323314765084,-0.1521085564748633,1.1266195287624303],\n \"quat\": [-0.0017668889194129447,0.002406581741459667,0.7083999131961318,0.7058049655900566],\n \"q\": [-0.16894401609897614,0.6368672847747803,0.7560085654258728,-0.12360157817602158,-0.14953598380088806,0.009546547196805477],\n \"grip\": [0.9981070431795988],\n \"target_q\": [-0.16866906820474564,0.6489774621097011,0.7660709003489753,-0.11556851724076331,-0.1454201730553973,0.009877023164163508],\n \"target_error\": 0.0040701808169305775,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-mkfvkm2u.png\",\"/tmp/kinex-observation-k28drqoq.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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{\"success\":true,\"message\":\"ok\",\"time_s\":10.72,\"waited_s\":4.4},\n \"file\": [\"/tmp/kinex-observation-7woacofn.png\",\"/tmp/kinex-observation-dzfqc4pj.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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bottle","args":{"waypoints":[{"xyz":[-0.198,0.035,1.12],"quat":[0,0,0.70710678,0.70710678],"linear":true,"duration":2}],"cameras":["head_camera","left_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":544,"output":88,"cacheRead":35584,"cacheWrite":0,"reasoning":0,"totalTokens":36216,"cost":{"input":0.00544,"output":0.0044,"cacheRead":0.035584,"cacheWrite":0,"total":0.045424}},"stopReason":"toolUse","timestamp":1793224403552,"responseId":"resp_03880fcdb65294d8016ae26ed413a08192810cb5ca97ee4857","rawStopReason":"completed","durationMs":3443,"thinkingLevel":"high"}} +{"type":"message","id":"38f9e622","parentId":"27de5ca7","timestamp":"2026-10-28T21:53:41.027Z","message":{"role":"toolResult","toolCallId":"call_5a42816f1c5e48ce8e2db25227cad69b|fc_03880fcdb65294d8016ae26ed5f5b4819290feba7059024ae4","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224409.028210939] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.1978245238769104,0.03624807484249387,1.1136391307316316],\n \"quat\": [-0.0006687220835096329,0.003217325161495589,0.7074333523842097,0.7067724199231455],\n \"q\": [-0.34395521879196167,1.481361746788025,1.1216462850570679,0.35927361249923706,-0.31043118238449097,0.018503844738006592],\n \"grip\": [0.7039102004332977],\n \"target_q\": [-0.3444226970084058,1.4740169827439866,1.135291463367972,0.3440870017483791,-0.3100354832143796,0.016756424773925072],\n \"target_error\": 0.00648453086441232,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.92,\"waited_s\":2.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-h2h_buoz.png\",\"/tmp/kinex-observation-wcac2rse.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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bottle above left bin","args":{"waypoints":[{"xyz":[-0.5,0.015,1.06],"quat":[0,0,0.70710678,0.70710678],"linear":true,"duration":3}],"cameras":["head_camera","left_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":660,"output":445,"cacheRead":36096,"cacheWrite":0,"reasoning":353,"totalTokens":37201,"cost":{"input":0.006600000000000001,"output":0.022250000000000002,"cacheRead":0.036095999999999996,"cacheWrite":0,"total":0.064946}},"stopReason":"toolUse","timestamp":1793224421061,"responseId":"resp_03880fcdb65294d8016ae26ee598288192b307c6a826a6b0a6","rawStopReason":"completed","durationMs":21009,"thinkingLevel":"high"}} +{"type":"message","id":"1c00b1c6","parentId":"d6d61b90","timestamp":"2026-10-28T21:54:18.982Z","message":{"role":"toolResult","toolCallId":"call_a9936dc46ebb49889c6e72409761f1b1|fc_03880fcdb65294d8016ae26ef99454819282a3f7f5fb7a584d","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224444.140137075] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.4945684814634984,0.015371463224506068,1.0604537555111784],\n \"quat\": [0.00025644590441672847,0.0009540466669193152,0.7060608183652334,0.7081505099905476],\n \"q\": [0.5811179280281067,1.626004695892334,1.0479673147201538,0.5658470392227173,0.6207801699638367,0.021735304966568947],\n \"grip\": [0.7077647921713917],\n \"target_q\": [0.5932278082889996,1.6362225123927316,1.0560940418899667,0.5683202118899915,0.63010988492782,0.019804060908021025],\n \"target_error\": 0.005463082701501553,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":17.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-d1x88zez.png\",\"/tmp/kinex-observation-3psdpux4.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5171615828512802,0.03131842426267671,0.9044438328481127],\n \"quat\": [0.0019280949082640983,0.0004485915764353847,0.7052955620133471,0.7089106089062986],\n \"q\": [0.6077774167060852,2.09543514251709,0.9070724844932556,1.1807374954223633,0.6567703485488892,0.019012924283742905],\n \"grip\": [0.7038915062492545],\n \"target_q\": [0.6142882451032351,2.1089415504442486,0.9061590178895118,1.1926114491230337,0.6583185828385036,0.017082770022362744],\n \"target_error\": 0.005435301723780054,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":19.240000000000002,\"waited_s\":1.9200000000000017},\n \"file\": [\"/tmp/kinex-observation-f9hecmv3.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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remaining bottle reach","python":"import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n for name,xyz,yaw in [('coke',[.19,.12,.865],48),('yellow',[.06,.21,.87],61),('coke_lift',[.12,.03,1.03],48),('yellow_lift',[.02,.12,1.03],61)]:\n r=R.from_euler('z',yaw,degrees=True);p=np.r_[np.array(xyz)-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n try:q=model.ik(p,[0,2,1,1,0,0],budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2});out.append({'name':name,'q':q,'quat':r.as_quat().tolist()})\n except ValueError as e:out.append({'name':name,'error':str(e)})\n finish(out)\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":572,"output":795,"cacheRead":38784,"cacheWrite":0,"reasoning":487,"totalTokens":40151,"cost":{"input":0.00572,"output":0.03975,"cacheRead":0.038784,"cacheWrite":0,"total":0.084254}},"stopReason":"toolUse","timestamp":1793224507689,"responseId":"resp_03880fcdb65294d8016ae26f3c3fb081929714db273f0b05e0","rawStopReason":"completed","durationMs":33838,"thinkingLevel":"high"}} +{"type":"message","id":"0c11832c","parentId":"f1dacd91","timestamp":"2026-10-28T21:55:44.476Z","message":{"role":"toolResult","toolCallId":"call_c70a91f8fac24a9990a8ce9d010eab94|fc_03880fcdb65294d8016ae26f58edcc8192a2d5104c71efa528","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224543.725672103] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n {\"name\":\"coke\",\"q\":[-0.7682474838893932,2.8205441857806255,2.174066940855675,0.6465166651221519,0.015836629018606843,0.005026753078927928],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow\",\"q\":[-0.5519639959914996,2.793350612445425,2.1392099935800983,0.6542416277664851,0.005085650161614391,0.005436249092634174],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]},\n {\"name\":\"coke_lift\",\"q\":[-0.7837596704018495,2.0847705295916814,1.5159528392357002,0.5691906155097223,-0.006951084853310415,0.013880856415633741],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow_lift\",\"q\":[-0.5705797685571973,2.131406981991661,1.5887848123532708,0.5431653775860207,-0.02002556223756075,0.013504624618480239],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]}\n ]\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":2947,"timestamp":1793224544475}} 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clear bottle is in the bin, confirmed by the wrist camera. I’m moving the remaining two bottles next.","textSignature":"{\"v\":1,\"id\":\"msg_03880fcdb65294d8016ae26f7877ac8192a7bcc4ff06c6e2b4\",\"phase\":\"commentary\"}"},{"type":"toolCall","id":"call_9505612e357f41e0b601181c0afd3dc9|fc_03880fcdb65294d8016ae26f78a58081929076c7f634d33d64","name":"exec","arguments":{"file":".kinex/tools/manipulate.py","description":"approach yellow 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+{"type":"message","id":"ed3b0775","parentId":"6460a19c","timestamp":"2026-10-28T21:56:37.608Z","message":{"role":"toolResult","toolCallId":"call_9505612e357f41e0b601181c0afd3dc9|fc_03880fcdb65294d8016ae26f78a58081929076c7f634d33d64","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224572.865750018] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.05147708742033642,0.2045278187007235,0.8629828751979567],\n \"quat\": [0.0012442963942044286,-0.0021552669022041654,0.5092927264227239,0.8605897543917034],\n \"q\": [-0.5445034503936768,2.769740104675293,2.054563522338867,0.720240592956543,0.008363209664821625,0.00577180739492178],\n \"grip\": [0.9996614374897697],\n \"target_q\": [-0.5484997194158352,2.7834866635540485,2.0749658177550847,0.7086049164412447,0.00849461950524203,0.005581543401104956],\n \"target_error\": 0.004640195326699464,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":28.560000000000002,\"waited_s\":5.920000000000002},\n \"file\": [\"/tmp/kinex-observation-jhdi9t_a.png\",\"/tmp/kinex-observation-zkmskjon.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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/run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.012475983380957172,0.12415535121015182,1.0325882299616729],\n \"quat\": [0.0013523926468964947,-0.00044605020363605173,0.5105743815543966,0.8598324098181513],\n \"q\": [-0.5547859072685242,2.128363847732544,1.5929771661758423,0.5379608273506165,-0.01129952073097229,0.01545263547450304],\n \"grip\": [0.7797582962296226],\n \"target_q\": [-0.5614852376589563,2.1161047986303942,1.5853120475938565,0.5314918774258239,-0.011183534532282036,0.0136917382097596],\n \"target_error\": 0.005427175903790725,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":31.560000000000002,\"waited_s\":2.400000000000002},\n \"file\": [\"/tmp/kinex-observation-dsx7lnz4.png\",\"/tmp/kinex-observation-wfdkz__a.png\"]\n 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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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+{"type":"message","id":"e0aba462","parentId":"ceac11f4","timestamp":"2026-10-28T21:57:33.308Z","message":{"role":"toolResult","toolCallId":"call_41f972da92074626b70d171b22dc5bcd|fc_03880fcdb65294d8016ae26fb339a48192b3d1c1bd58870222","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224631.131968514] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5174761649606938,0.031407337429610896,0.9046801446093026],\n \"quat\": [0.002290593707506658,0.0004161799849168633,0.7054030393235293,0.7088026044589666],\n \"q\": [0.6082080006599426,2.0956389904022217,0.9081711173057556,1.1805373430252075,0.656909167766571,0.01906382292509079],\n \"grip\": [0.7724612653255463],\n \"target_q\": [0.6142413871259487,2.1090307308805722,0.9062710823865341,1.1924842697081466,0.6582675187588196,0.017143442076574332],\n \"target_error\": 0.005500372306525918,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":36.480000000000004,\"waited_s\":4.920000000000002},\n \"file\": [\"/tmp/kinex-observation-92wa3sed.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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[1793224671.685813675] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5021647469780711,-0.05918474129308639,1.0190715021736796],\n \"quat\": [-0.3482738420558144,0.3459375267960569,0.6181609954097833,0.613929590627374],\n \"q\": [0.5939311981201172,1.7592520713806152,1.7904003858566284,-1.1432219743728638,0.3032253086566925,0.5461763739585876],\n \"grip\": [0.9966154369440946],\n \"target_q\": [0.5866290400105713,1.7696589255235398,1.815409985763567,-1.1763832425627365,0.2921926314641686,0.5510894336527324],\n \"target_error\": 0.0024925658369937304,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":39.2,\"waited_s\":1.9200000000000017},\n \"file\": [\"/tmp/kinex-observation-zoi1ji0f.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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{\"success\":true,\"message\":\"ok\",\"time_s\":45.6,\"waited_s\":6.399999999999999},\n \"file\": [\"/tmp/kinex-observation-i_l_tjm9.png\",\"/tmp/kinex-observation-imo_1j6g.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":5260,"timestamp":1793224726116}} +{"type":"message","id":"559c2f9a","parentId":"7af7cb21","timestamp":"2026-10-28T21:58:50.728Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_56372e9eb2f04846a91b3fef2c900961|fc_03880fcdb65294d8016ae27019bb5c8192abf3141d7470b6cb","name":"exec","arguments":{"file":".kinex/tools/manipulate.py","description":"lift final bottle","args":{"waypoints":[{"xyz":[0.11,0.03,1.04],"quat":[0,0,0.40673664,0.91354546],"linear":true,"duration":2}],"cameras":["head_camera","left_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":541,"output":87,"cacheRead":45184,"cacheWrite":0,"reasoning":0,"totalTokens":45812,"cost":{"input":0.005410000000000001,"output":0.004350000000000001,"cacheRead":0.045183999999999995,"cacheWrite":0,"total":0.05494399999999999}},"stopReason":"toolUse","timestamp":1793224726168,"responseId":"resp_03880fcdb65294d8016ae270177cf481929a920c359e8d7b15","rawStopReason":"completed","durationMs":4559,"thinkingLevel":"high"}} +{"type":"message","id":"bd40c7f2","parentId":"559c2f9a","timestamp":"2026-10-28T21:59:05.212Z","message":{"role":"toolResult","toolCallId":"call_56372e9eb2f04846a91b3fef2c900961|fc_03880fcdb65294d8016ae27019bb5c8192abf3141d7470b6cb","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224732.814719563] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.10941368722195692,0.034232347568097815,1.0377322213464852],\n \"quat\": [0.001230415620162098,-0.0004807913597038286,0.40934807110091775,0.9123773405795583],\n \"q\": [-0.7624666690826416,2.06129789352417,1.510847568511963,0.552495002746582,0.00831757765263319,0.015920152887701988],\n \"grip\": [0.7925870445641605],\n \"target_q\": [-0.7676939502135206,2.048071049242703,1.501995544659548,0.5462311917833058,0.008597172733907732,0.014152281034482525],\n \"target_error\": 0.004837287321652237,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":48.6,\"waited_s\":2.3999999999999986},\n \"file\": [\"/tmp/kinex-observation-0b4c68lr.png\",\"/tmp/kinex-observation-a7qeqo65.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":14326,"timestamp":1793224745212}} +{"type":"message","id":"e8e9f410","parentId":"bd40c7f2","timestamp":"2026-10-28T21:59:10.489Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_c7613f9ced8342abb9df935a18dd950b|fc_03880fcdb65294d8016ae2702ce6248192b723afd4cc20511e","name":"exec","arguments":{"file":".kinex/tools/manipulate.py","description":"carry final bottle to bin","args":{"waypoints":[{"xyz":[-0.5,0.015,1.06],"quat":[0,0,0.70710678,0.70710678],"linear":true,"duration":3.5},{"xyz":[-0.52,0.03,0.9],"quat":[0,0,0.70710678,0.70710678],"linear":true,"duration":1.5}],"cameras":["head_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":655,"output":132,"cacheRead":45696,"cacheWrite":0,"reasoning":0,"totalTokens":46483,"cost":{"input":0.00655,"output":0.0066,"cacheRead":0.045696,"cacheWrite":0,"total":0.058846}},"stopReason":"toolUse","timestamp":1793224745266,"responseId":"resp_03880fcdb65294d8016ae2702a9d5481929699ec2e6cc62842","rawStopReason":"completed","durationMs":5223,"thinkingLevel":"high"}} +{"type":"message","id":"2e34d545","parentId":"e8e9f410","timestamp":"2026-10-28T21:59:35.528Z","message":{"role":"toolResult","toolCallId":"call_c7613f9ced8342abb9df935a18dd950b|fc_03880fcdb65294d8016ae2702ce6248192b723afd4cc20511e","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224752.694936316] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5174527559986333,0.031418531702469416,0.9048850739188827],\n \"quat\": [0.0022403785285909222,0.0003952878515179147,0.7053842503524314,0.7088214752716665],\n \"q\": [0.6081580519676208,2.0949134826660156,0.9082513451576233,1.179675817489624,0.6569015979766846,0.019001225009560585],\n \"grip\": [0.7880872325463729],\n \"target_q\": [0.6143294472492913,2.1091152341605635,0.9063292866790853,1.1926127068902617,0.658363172809637,0.01708281387648724],\n \"target_error\": 0.0056889921239493485,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":54,\"waited_s\":5.399999999999999},\n \"file\": [\"/tmp/kinex-observation-0ip5hs7j.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"devices: 3 calls","isError":false}},"isError":false,"durationMs":138,"timestamp":1793224791121}} +{"type":"message","id":"04875e3d","parentId":"5306339c","timestamp":"2026-10-28T22:00:13.590Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Updating defaults and checks**\n\n**Adding an error guard**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_03880fcdb65294d8016ae2705a3d348192947c34536594c223\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Updating defaults and checks**\"},{\"type\":\"summary_text\",\"text\":\"**Adding an error guard**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4nBmpe_sJ-uLduHia21dpJYQclBwioQI_vQrm6BtWHfDjdeSkxmdjNKeMX2Zl6k3PrXMbYClBvr2eJLPxvlTLsBUXD-YPW39UW5wzFH3Lz4TqGgUOh_RRJ0ckj4X7anJoikoDNABLqv6VyI8b4rQVC-R9vmoa38Zo36ec1i7fagbxhvH7HGZOADq2JluhojO1FxKmnofyv1nD3KLy1SSInxxJjivW6Av76Gc7NWfCC-7b5y9IT1oAxG4Mp1F9cTt4hFMe-E8xvtqGJDe9BLIddw1CXrPGH8Agpoakb1cbvBOAS-AICHEdzz1mxcmKA0oKK-wQzuc2ucI5844z83DQzJ2lsm-wl2p6jSAwBOcbxV91dzyszCJ6IqWwunSBJ7YFQCMUt3m5sRyhaJDAwN7OZgjQtCDZyr-AGhckarQtzsXVIljNKi31eSW27nzRhatjLKU5gsiOPJfTAEcWODpu2mh3zwFv1wFfNjswE6zCLr8tGEMJXiIRsfhFaNOz2D-vp2Drr6Rgco1gwEBV-ZylsEs733sSJxgszU0GZNM5N3oEQ592tw_MivWxtj0sk7jciYUEhsWgR6F7cYpWKYVLHnXp3AOoM4SqZyL65-gV1oAs97cBY3nhylfYw_PDjlQwtatPcNObTBI2oGzQOmrvZRsGgV3z3mCSicd2C8StsdTB_6EWtLa_yJDXE2bxrktFx9_YsAGsdRQ04k56zddeK5ElBRKCz8uNR3nGQb0n8v2rj7eVMmouang4K3xCQ4DNuF9ePgy4fO2MbB14aYzuq7dVOP-FWoSrbC6Q5Wqmq75lvJybemGGrJOO5VfwK6APwAaSHUD0dKK_qqpbM5i031JzoBPc4tqgEKrPhbc6BLSPzKSDboS6t7O15CQzyHxCZ1pzqivhumCt2GTv8Lnz9n2oo-4v4Fq40oO6XpUI4_QaomYf_9tqO_TWMCy9LO2I5lDl2ZDenCIYAqTixXMnzSfYl2v-2fh2IxBtaEUZq4mnhzgmgGJryyqdvqvNrclRrDiFdOjSrBT9_zAooKYBVnq_a83y1RA3CCOJTgVooEyYYnUiFIXhvwSHQO2cZxE_NfW03NfjJKqOPAGMmnqneHFDPAh2LHiLNvPFmo5yYjFXjta-HQW68EGDRVDAtTCRzpC-GJQJhUj0IC4vUpDyN_dH9b27PngiQuHEzP9JIKnhjIMmKB0xQmfXYXoscc0swGmM2y0abxFqeN0XTUBpHOD8pqjQRyiiBDUi0co9F23mOy7sElOMXy4tj6GvLQMuAPdevpK3lRKXj7A_MS51jWZ4AZItePTqRarwzzpXd5N5Zhne5hgsFEva868MeokpekjG4RmDxW-z3eBMmUX6-TzG5irCmvG1QuS0l3pB6d8PMpBW48SVzzOW0rqTGI2u9niAVxmVVtyLbC-eq2fy47G7rZTHc6S3M2HcK86TeUXHsJ-Lb9MwypfOJ3wYZqw34N7q7F0Nak-GYjvpd97YuWbj0PRtdRTDrd7cB9hck1mppcq-jhRU8rRh6h8nX1Z0IO9NTfn5PNFC-Bp6o9-HzxbZP5ZAjs_cQxs0P99ysBc5NXmtgBES5M8acizTcGedIyesEuQFMWL942oSRg7bqQfq4AjioEyYi0SBNG4c7wDSj6dVLg8-9_YgW4g3h7DSt5cBfZ8P2pbe4mgnx950PngKUH6dH_vyYpYRx--3YyoBRA1N-HEjwqsy8tNmZAnJZdyrXhuBLVceJr9ScVTgBGjpEL9pKSmzdEhpo_J6Ty5QNcA8xEhmXOVi1WRBb9NaLYomKN2iJeiXsPZ3fOCsx86uXJDPuVTPPfU4OeYAqw6AZt7t--O-evoxdAGackXmcvY0VQtDPX_S3uvggJS-ykVcISGFwYuf3pttv4hlgU9u8nj0qgfVpB3M1rIkNGAeB6ETVlfVdkjNXqqTQM08eJHLAZSiEwiJGf4_dFF56vD12l9EDkZODBXvS62APwy3B-r6GH9pu_E4zoDKcmIQjiFQvZ-wo_k8lWjrsShgjHUI_nmU5wB8pba_q9dGgTXmNFNj7lacf_kE_zBZPuiHfkqmV_rPGTd3VEnwEKTuwJUgTfWFwpqcMQv7tWnhVnncyXaDjKbNR4juMVV3z_ASge-WLMgR5EligAPoMVhDuzlnQQyQ2-iny0EV7QyR2bRhc4HBaYjsiDv4P9zs5cmqoa7JdJOD4a9XH40cp5U3Mzbh17awH3o7eJr5z74HQjCiXCuQRtLKlzVZtCu1WTiXRlm6EucZeEt0FvqpVo2Wv7ekzagVzFiOc41qNW1aqVEWRM8cXV9ptUbHY5sweDWvtkIzAuzQ0XF_g8sxt5VwDNNB5O6NU7lywoE2q7CqnReYkrxlCGUZbHoFUho5M14-WoLGw-EaXWqTzncxwRHKCtlgY1SPYAWXTir5_4QAW-rEKSzi3l0BEoFNCJKthaF1FwDJ3C_kpjwhVPAtGUf2ieX5Ac4O1qxfsL6jtCIuCLKlu0j9FOKI14sHGN5hI6SR5P59hpJghDRI1WYNK4d4Mm-1qnZy5mQYhgC-xUOsAgNML8cUZ6dN_iRwpZ5zffVXdxC0YaiNtR3rMDee3ld_-HZvI_pDsYBZ6inMr94tyNQ39JKKv6g0ctLwzasJ2i6RZHSD9I-6xjc-rdYh6qsws8Nw0uxDoiaTOGChSTD1gjP7Xnlev5tfzcvtwtc6LgWbjB_0CMM7DELO5MMLGr52UcKtV1wi7uB0UBjA_HPGJ0kQXgbNUFuswUFTxwmAnHW77K1QeKA1_3tgpYdWwjJHd3TcMpkRr-3EowPAEtBSB9Br7bPyWa3s-DldDhcRmXESwaZZNS00Z2x-wmW-RMomuxmJ9QPK5X4T2kSbARxymVGv_LCoNrq2r-Hhtipg0C2kV_R_CUg6wkj_ivOM1jBHCZL5J61ODmrhbW0nDkHUKW5dXoYfS7s_nTAFvFJci4Jocw2CPoFDZb0k02-Jcl2u8E1JS1ZaAUE2FxffY3RDWLYIXSUL-YNfdKxJABcRATd7kghInx7llik3CwxVArAmGR_x7dbj6j3wnpLZ8PBYUKsfoZ4KnYMqo9EkvO-xtPTUvPfcptkcUVVhBj4h3_IYSDGLsPS2WUZ2diECZm84YWYi2ZuHvp62kdTsqYlJmUN9BYZAL2hsUUCLmgjIze0jtBfyUcD_ZLE0R0IToleWKDAmHStbKz3j54zvM6SFpq_3ScnAP_yMeimSLHBwqGYUkkJIddj7N5yA4o3LgIUTEdv0bZnKcVnge3nAHzRSThyD7qWNG5vQOX2wxzl3Ano2uwDxqIgPTmeJh963_CcngWt1Pg1uaS2l3padXiBudtjTBCkLs5gWBkMWHJmsUIAAzg_kjnKKde6rbdiUimHilRpUZmBjnmzAc_-WHKQYk4FyxhE9ChnMRzaPd6XR1h8zHumRKX1DjZKHJxsl6Rcv82iKjKLLZ2R8EhWH5EjtRp5gj8FHH9cIgYLGiSB1gG7E8qYdmfRrqsJ7P-E9XO7A4h29hFjqdtejBDCe1UDqYaankVgWW7abkxJSsQImv902U5HatRw-4ZVr1hccyWk13tAw5eJsrdGZwLmChq7lFwDdI39ajEI3PqziVXXFygQCg6m0vNJWislqtUe4BRNfaya9qjQObqbPD-mfXxQK78mln45piqBOC1nA57PRl-e6BJvaodBr3fAyMWLI3ZbohISt4lm3uFgD_GFToSnsxXUKp-Si0Tbe1zoPHE6O95bJUHPmEITITriFYTdh44r3krQmI4Xtq2kCFz57Tak5w7vHK9JtL-EKJOdmdzYExIfPCk-SMRUcBpaaoLPXdCjq19vJQkAblbO41btXMNwMe3Qc-AGbydVDTj0_MG49E5s0_LZ1zg1l-PmtT08Ain7zfZib23RSQUHv494pwvram7bAC7_7IVH5oaOf2DX92Xoear6nOeq_lqQgrqk8q6Vx4Bhw4ByHKftOHPvzXrdBGi31NB_Qeaq7jiUg5L57GXe6rx1-I9_sObP2XxfRbdXR4J6cc0OvBBk38SBrMA1Dmrj1iVMxyczWwno1LdQW9cnOj_ZLb4c0BN_eS0-WHGExpOpnfWWEBL-iEuvyCzdfnmdx5frW4_OPHi27PAT3TS5xHGMT3lVs4NnvzcIX7_C7GaRo2L2tYC2IT9qEi-sPj-FHkFcsLH6ePrsaePhwUpvp7PXczokdVlFEeCtWpoqZoZAqBJd-3WpE60li9X7LNFUgb6NDrXXx4rBMk4Kl_ROc2wpPR-_2ZZQ8dIqXdXbM90H4sTc46AW8D4NnuM_IAZhQHklqOf8wQaeB3bl5poBnlmj3QV89ie_DxE11Ha0v0flxopn6wdBDVLCksBk6Lt8QqvvBlb7jMFEcJBrPXt4qefg39vuhoe8qE-qd_m2ua0z0aymBzfj-ePSKRx-fgggDMpTT87YVh07qO5Wt7o0_x8IeJslkMStex6aUKatBkmJp7uyuNkEzXTVR9_YaroIoGSLvMfFEPlMU5Cq_rTsh8qcoOdhl1j9deIiBpx1iBMSy9MB\"}"},{"type":"toolCall","id":"call_d572734da4e64fccb8c44ec8b9491c16|fc_03880fcdb65294d8016ae27066f82c819291b7d71c53e842f9","name":"edit","arguments":{"path":".kinex/tools/manipulate.py","edits":[{"oldText":" if 'grip' in a:\n await io.publish(grip+'.target',{'data':float(a['grip'])})\n points=[]; elapsed=0.; targets=[]\n # Default vertical approach, fingers close along world X.\n default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()","newText":" points=[]; elapsed=0.; targets=[]\n # Default horizontal approach along world +Y, fingers close along world X.\n # High top-down poses can exceed ARX5 reach; provide explicit quaternions.\n default=Rotation.from_euler('z',np.pi/2).as_quat()"},{"oldText":" seed=wp.get('seed',q)","newText":" # Avoid the below-table IK branch when starting near home.\n seed=wp.get('seed',q if q[1]>.2 else [q[0],1.,1.,0.,q[4],q[5]])"},{"oldText":" if points:\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})","newText":" # Do not change a grip until all requested waypoints solve successfully.\n if 'grip' in a:\n await io.publish(grip+'.target',{'data':float(a['grip'])})\n if points:\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})"},{"oldText":" files=[]\n for cam","newText":" error=float(np.linalg.norm(tcp-targets[-1])) if targets else None\n if error is not None and error>a.get('max_error',.025):\n await io.request(arm+'.stop')\n raise RuntimeError(f'Measured TCP tracking error {error:.4f} m; arm held')\n files=[]\n for cam"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":475,"output":695,"cacheRead":47104,"cacheWrite":0,"reasoning":230,"totalTokens":48274,"cost":{"input":0.004750000000000001,"output":0.03475,"cacheRead":0.047104,"cacheWrite":0,"total":0.08660400000000001}},"stopReason":"toolUse","timestamp":1793224791157,"responseId":"resp_03880fcdb65294d8016ae27057e12c81928dd22a7373d96bda","rawStopReason":"completed","durationMs":22432,"thinkingLevel":"high"}} +{"type":"message","id":"3ca82f9c","parentId":"04875e3d","timestamp":"2026-10-28T22:00:13.595Z","message":{"role":"toolResult","toolCallId":"call_d572734da4e64fccb8c44ec8b9491c16|fc_03880fcdb65294d8016ae27066f82c819291b7d71c53e842f9","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 4 block(s) in .kinex/tools/manipulate.py."}],"details":{"diff":" ...\n 15 a=args(); side=a.get('side','left'); arm=side+'_arm'; grip=side+'_gripper'\n 16 async with communication() as io:\n 17 model=helpers.joint_model(arm)\n 18 m=await state(io,arm); q=list(m.position)\n-19 if 'grip' in a:\n-20 await io.publish(grip+'.target',{'data':float(a['grip'])})\n 21 points=[]; elapsed=0.; targets=[]\n-22 # Default vertical approach, fingers close along world X.\n-23 default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()\n+20 # Default horizontal approach along world +Y, fingers close along world X.\n+21 # High top-down poses can exceed ARX5 reach; provide explicit quaternions.\n+22 default=Rotation.from_euler('z',np.pi/2).as_quat()\n 24 for wp in a.get('waypoints',[]):\n 25 xyz=np.array(wp['xyz']); quat=np.array(wp.get('quat',default)); duration=wp.get('duration',2.)\n 26 if wp.get('linear',False):\n 27 current=await helpers.transform_pose(io,model.fk(q),arm+':base','world:base')\n ...\n 34 p=start+(xyz-start)*f if wp.get('linear',False) else xyz\n 35 r=rot(f) if wp.get('linear',False) else Rotation.from_quat(quat)\n 36 flange=np.r_[p-r.apply([.12,0,0]),r.as_quat()].tolist()\n 37 local=await helpers.transform_pose(io,flange,'world:base',arm+':base')\n-38 seed=wp.get('seed',q)\n+37 # Avoid the below-table IK branch when starting near home.\n+38 seed=wp.get('seed',q if q[1]>.2 else [q[0],1.,1.,0.,q[4],q[5]])\n 39 newq=model.ik(local,seed,budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2})\n 40 # choose equivalent nearby revolute solution\n 41 newq=[v+2*np.pi*round((old-v)/(2*np.pi)) for v,old in zip(newq,q)]\n 42 if max(abs(np.array(newq)-q))>3.2: raise ValueError('discontinuous IK')\n 43 q=newq; t=start_t+duration*f\n 44 sec=int(t); ns=round((t-sec)*1e9)\n 45 points.append({'positions':q,'time_from_start':{'sec':sec,'nanosec':ns}})\n 46 elapsed+=duration; targets.append(xyz.tolist())\n+47 # Do not change a grip until all requested waypoints solve successfully.\n+48 if 'grip' in a:\n+49 await io.publish(grip+'.target',{'data':float(a['grip'])})\n 47 if points:\n 48 await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\n 49 wait=elapsed+a.get('settle',.4)\n 50 if wait>0: result=await io.request('world.wait',duration_s=wait)\n 51 m=await state(io,arm); g=await state(io,grip)\n 52 flange=await helpers.transform_pose(io,model.fk(list(m.position)),arm+':base','world:base')\n 53 tcp=np.array(flange[:3])+Rotation.from_quat(flange[3:]).apply([.12,0,0])\n+57 error=float(np.linalg.norm(tcp-targets[-1])) if targets else None\n+58 if error is not None and error>a.get('max_error',.025):\n+59 await io.request(arm+'.stop')\n+60 raise RuntimeError(f'Measured TCP tracking error {error:.4f} m; arm held')\n 54 files=[]\n 55 for cam in a.get('cameras',['head_camera']):\n 56 im,depth=await helpers.camera_frame(io,cam)\n 57 files.append(helpers.save_image(im))\n ...","patch":"--- .kinex/tools/manipulate.py\n+++ .kinex/tools/manipulate.py\n@@ -15,13 +15,12 @@\n a=args(); side=a.get('side','left'); arm=side+'_arm'; grip=side+'_gripper'\n async with communication() as io:\n model=helpers.joint_model(arm)\n m=await state(io,arm); q=list(m.position)\n- if 'grip' in a:\n- await io.publish(grip+'.target',{'data':float(a['grip'])})\n points=[]; elapsed=0.; targets=[]\n- # Default vertical approach, fingers close along world X.\n- default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()\n+ # Default horizontal approach along world +Y, fingers close along world X.\n+ # High top-down poses can exceed ARX5 reach; provide explicit quaternions.\n+ default=Rotation.from_euler('z',np.pi/2).as_quat()\n for wp in a.get('waypoints',[]):\n xyz=np.array(wp['xyz']); quat=np.array(wp.get('quat',default)); duration=wp.get('duration',2.)\n if wp.get('linear',False):\n current=await helpers.transform_pose(io,model.fk(q),arm+':base','world:base')\n@@ -34,24 +33,32 @@\n p=start+(xyz-start)*f if wp.get('linear',False) else xyz\n r=rot(f) if wp.get('linear',False) else Rotation.from_quat(quat)\n flange=np.r_[p-r.apply([.12,0,0]),r.as_quat()].tolist()\n local=await helpers.transform_pose(io,flange,'world:base',arm+':base')\n- seed=wp.get('seed',q)\n+ # Avoid the below-table IK branch when starting near home.\n+ seed=wp.get('seed',q if q[1]>.2 else [q[0],1.,1.,0.,q[4],q[5]])\n newq=model.ik(local,seed,budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2})\n # choose equivalent nearby revolute solution\n newq=[v+2*np.pi*round((old-v)/(2*np.pi)) for v,old in zip(newq,q)]\n if max(abs(np.array(newq)-q))>3.2: raise ValueError('discontinuous IK')\n q=newq; t=start_t+duration*f\n sec=int(t); ns=round((t-sec)*1e9)\n points.append({'positions':q,'time_from_start':{'sec':sec,'nanosec':ns}})\n elapsed+=duration; targets.append(xyz.tolist())\n+ # Do not change a grip until all requested waypoints solve successfully.\n+ if 'grip' in a:\n+ await io.publish(grip+'.target',{'data':float(a['grip'])})\n if points:\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\n wait=elapsed+a.get('settle',.4)\n if wait>0: result=await io.request('world.wait',duration_s=wait)\n m=await state(io,arm); g=await state(io,grip)\n flange=await helpers.transform_pose(io,model.fk(list(m.position)),arm+':base','world:base')\n tcp=np.array(flange[:3])+Rotation.from_quat(flange[3:]).apply([.12,0,0])\n+ error=float(np.linalg.norm(tcp-targets[-1])) if targets else None\n+ if error is not None and error>a.get('max_error',.025):\n+ await io.request(arm+'.stop')\n+ raise RuntimeError(f'Measured TCP tracking error {error:.4f} m; arm held')\n files=[]\n for cam in a.get('cameras',['head_camera']):\n im,depth=await helpers.camera_frame(io,cam)\n files.append(helpers.save_image(im))\n","firstChangedLine":19},"isError":false,"durationMs":4,"timestamp":1793224813595}} +{"type":"message","id":"fbcfd4cc","parentId":"3ca82f9c","timestamp":"2026-10-28T22:00:44.520Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_f208824e230143d3868ae42789834c29|fc_03880fcdb65294d8016ae270701e808192978ccd6e1a97c916","name":"write","arguments":{"path":".kinex/memos/put-bottles-dustbin.md","content":"# RoboTwin put-bottles-dustbin / ALOHA AgileX\n\nUse public profiles and cameras every episode; positions below are observed examples, not immutable scene facts. No simulator reset or private state. ROS wait advances 0.04 s native frames, reads do not. Load devices first; inspect schemas.\n\nReusable `.kinex/tools/manipulate.py` runs with `exec(file=..., args=...)`. Args: side (default left), grip (0 closed,1 open), waypoints [{xyz world TCP, quat xyzw optional, duration seconds, linear bool, seed optional}], settle (default .4), cameras. It solves flange IK with TCP offset +.12 local X, publishes typed JointTrajectory, waits once, checks measured FK and returns images. Defaults horizontal +Y approach. It stops on >.025 m final position error. Caller must choose collision-free waypoints; IK is not collision checking.\n\nARX5 home-seeded IK can select a negative shoulder branch that drives through the table. Use positive shoulder seed [0,1,1,0,0,0] and first move to raised safe horizontal pose, e.g. [-.28,-.15,1.13], yaw90. A failed early command had 21 cm measured tracking error; stopped and recovered raised. High top-down grasp approach at y=.04,z=1.11 was unreachable. Horizontal side grasps work well.\n\nHead camera paired RGBD: world points from K and calibrated frame transform. Bottle surface depths are front surfaces, not centers; add radius ~.025 m farther from camera in world Y. Table observed z=.74. Left arm can reach all three upright bottles using yaw roughly radial from its base, avoiding need for handoff.\n\nObserved successful grasp/transfer sequence:\n- Clear: center [-.198,.035,.875], horizontal yaw90 q=[0,0,.70710678,.70710678]. Approach y=-.095, straight advance, close .8s. Lift to z1.12 in 2s; verify bottle moves with gripper.\n- Yellow: [.055,.205,.86], horizontal yaw61 q=[0,0,.50753836,.86162916]. Approach [-.006,.093,.86], straight advance 1.5s. Close .6s. Retract/lift to [.015,.12,1.035] in 2s (pure vertical lift at far reach may fail).\n- Coke: [.185,.12,.865], horizontal yaw48 q=[0,0,.40673664,.91354546]. Approach [.093,.014,.865]. Close .6s; retract/lift to [.11,.03,1.04] in 2s.\n- Carry each at z~1.06 to [-.5,.015,1.06], yaw90, with 3–3.5s Cartesian path. Lower to [-.52,.03,.9] in1.5s. Open above bin. Silver-lined bin is left of table, mostly outside head view.\n- Inspect deposits with left wrist camera by moving empty TCP to [-.5,-.06,1.02], quaternion [-.35355339,.35355339,.61237244,.61237244], 1.5–2s linear motion. First two bottles were clearly visible inside bin.\n\nEpisode ended during final opening wait at t=54.16s (~1354/1700 frames), after .16s of requested .8s. No further waits or motion attempted. Final head image showed empty table; wrist image showed coke leaving gripper, opening measured .886. Native verdict is owner-only; terminal/rejected wait alone is not success evidence. First two deposits visually confirmed, last grasp/lift/transfer measured before release.\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":173,"output":829,"cacheRead":48128,"cacheWrite":0,"reasoning":0,"totalTokens":49130,"cost":{"input":0.0017300000000000002,"output":0.04145,"cacheRead":0.048128,"cacheWrite":0,"total":0.091308}},"stopReason":"toolUse","timestamp":1793224813639,"responseId":"resp_03880fcdb65294d8016ae2706e4cc88192bc858321c6f9a693","rawStopReason":"completed","durationMs":30881,"thinkingLevel":"high"}} +{"type":"message","id":"fe55dd86","parentId":"fbcfd4cc","timestamp":"2026-10-28T22:00:44.524Z","message":{"role":"toolResult","toolCallId":"call_f208824e230143d3868ae42789834c29|fc_03880fcdb65294d8016ae270701e808192978ccd6e1a97c916","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/memos/put-bottles-dustbin.md"}],"isError":false,"durationMs":3,"timestamp":1793224844524}} +{"type":"message","id":"5545c127","parentId":"fe55dd86","timestamp":"2026-10-28T22:00:51.307Z","message":{"role":"assistant","content":[{"type":"text","text":"Transferred all three bottles over the left dustbin and released them. The episode ended during the final release; the native verdict remains with the owner.","textSignature":"{\"v\":1,\"id\":\"msg_03880fcdb65294d8016ae270921be48192a4860fe4b01c6b96\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":133,"output":33,"cacheRead":49024,"cacheWrite":0,"reasoning":0,"totalTokens":49190,"cost":{"input":0.00133,"output":0.00165,"cacheRead":0.049024,"cacheWrite":0,"total":0.052003999999999995}},"stopReason":"stop","timestamp":1793224844574,"responseId":"resp_03880fcdb65294d8016ae2708d41e881929f10fa52bb18c163","rawStopReason":"completed","durationMs":6733,"thinkingLevel":"high"}} +{"type":"custom","customType":"kinex-active-time","data":{"milliseconds":748283.6390740001},"id":"42746552","parentId":"5545c127","timestamp":"2026-10-28T22:00:51.548Z"} diff --git a/experiments/robotwin-native/kinex-3/terminal.jpg b/experiments/robotwin-native/kinex-3/terminal.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d0bf6cc7aecc811f68b79ce58e1c8453cac1addf Binary files /dev/null and b/experiments/robotwin-native/kinex-3/terminal.jpg differ diff --git a/experiments/robotwin-native/kinex-3/tool-calls.json b/experiments/robotwin-native/kinex-3/tool-calls.json new file mode 100644 index 0000000000000000000000000000000000000000..77349966fc859bab386aec80e2720613cb13fa6a --- /dev/null +++ b/experiments/robotwin-native/kinex-3/tool-calls.json @@ -0,0 +1,1163 @@ +[ + { + "name": "ls", + "input": { + "path": "/workspace" + }, + "output": ".kinex/\nharness.md\ninstruction.md", + "call_id": "call_bc30fb8bf4f94fd8b419ada61ba9c495|fc_03880fcdb65294d8016ae26da951048192a45a47ab0d529d47", + "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_878b9e39f63649cfb186f0f49feaca5d|fc_03880fcdb65294d8016ae26daced9881929db63b0c45ac2b84", + "is_error": false + }, + { + "name": "find", + "input": { + "pattern": "**/*", + "path": "/workspace/.kinex", + "limit": 150 + }, + "output": 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results limit reached. Use limit=300 for more, or refine pattern]", + "call_id": "call_ed9eab7888164c59b45bb5130fd938dd|fc_03880fcdb65294d8016ae26dafe5288192b6bd79b67294c68a", + "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_ad66e252680d4538b87eab931768f5bf|fc_03880fcdb65294d8016ae26db31af481929c07f14a6d2dd873", + "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_3722a81d61a040d180d587e39167096f|fc_03880fcdb65294d8016ae26db4d4f8819280f43ebd32167642", + "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_39836cc13d7249189b500a4153c3f1d4|fc_03880fcdb65294d8016ae26db4d4f88192ad8b7b6ddc54d965", + "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_42cdcaea1a8243a6b4935539b4721a45|fc_03880fcdb65294d8016ae26db4d4f8819298d9122ade04e809", + "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_2e8a67dcb44840f79a97d150c580c9c0|fc_03880fcdb65294d8016ae26db4d4f88192a13da7d1e8a0a437", + "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_0d3d767d5ea346c8bf0d3eb23e215a86|fc_03880fcdb65294d8016ae26db6e3c8819283cb40b646910d27", + "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_e0aad2fea7bc4afd8926eacc4eb76b7e|fc_03880fcdb65294d8016ae26db8a05c819281b62a8f666b16f7", + "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_1ef4fe3713aa486d97cd1ed2e04391d4|fc_03880fcdb65294d8016ae26db8a05c8192a272724573a650f4", + "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_9cd5c75ad17d4d61be75a0be6da3f4c0|fc_03880fcdb65294d8016ae26db8a05c81928fc0c2e351277310", + "is_error": false + }, + { + "name": "ls", + "input": { + "path": ".kinex/tools" + }, + "output": "(empty directory)", + "call_id": "call_3c5c46e6b77f447ab13e6db9d68861fe|fc_03880fcdb65294d8016ae26db8a05c8192b8159b0fbc1635e4", + "is_error": false + }, + { + "name": "ls", + "input": { + "path": ".kinex/memos" + }, + "output": "(empty directory)", + "call_id": "call_6ee48ab143544893a17b1e7101670f45|fc_03880fcdb65294d8016ae26db8a05c8192849eba22b16eb71e", + "is_error": false + }, + { + "name": "exec", + "input": { + "python": "from kinex import runtime, finish\nfinish({n:runtime.operations(n) for n in ['world','left_arm','left_gripper','head_camera']})", + "description": "inspect operations" + }, + "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_41bfd194a67a43cbaf4a14fa8e8d66f4|fc_03880fcdb65294d8016ae26dbb54d0819286e20b5e7a42245d", + "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" + }, + { + "deviceID": "left_camera", + "action": "load" + }, + { + "deviceID": "right_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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}", + "call_id": "call_82441a8f7dc44b07939b49439c7b3a4e|fc_03880fcdb65294d8016ae26dbf39288192bd0a0b2c00671870", + "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_fa8eb77a463a4d20a8347c16ce760642|fc_03880fcdb65294d8016ae26dcaffcc8192b691e40e04f22d3f", + "is_error": false + }, + { + "name": "device", + "input": { + "deviceID": "head_camera", + "action": "image" + }, + "output": "{\n \"deviceID\": \"head_camera\",\n \"timestamp\": 0,\n \"frame_id\": \"head_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-dlsu4lq1.png\"\n}", + "call_id": "call_a20753f4cb3c40f38c4c5f9931835f06|fc_03880fcdb65294d8016ae26dcefcd48192a129bbbda0ec19b3", + "is_error": false + }, + { + "name": "read", + "input": { + "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py", + "offset": 1, + "limit": 280 + }, + "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_13b2b63712dd4c5aac748b6bab34764f|fc_03880fcdb65294d8016ae26dd503f481929ab9870397455514", + "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_467d1babc64044308d07389bb35c364d|fc_03880fcdb65294d8016ae26dd51a10819284768a26ef999f42", + "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_8d4f7e04f0074f4687ef209aa5ce60e6|fc_03880fcdb65294d8016ae26dd51a108192bd5d2aebbd140ea7", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "measure scene and inspect schemas", + "python": "import asyncio\nimport numpy as np\nfrom kinex import communication, helpers, runtime, finish\nasync def main():\n async with communication() as io:\n frame=await helpers.camera_frame(io,'head_camera')\n d=helpers.image_array(frame['depth'])\n pts=[]\n for u,v in [(70,80),(61,42),(78,107),(201,30),(263,48),(275,15),(160,170),(20,95),(10,65)]:\n z=float(d[v,u])*.001\n p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\n w=await helpers.transform_pose(io,p,'head_camera:base','world:base')\n pts.append({'uv':[u,v],'world':w[:3],'depth':z})\n states={}\n for n in ['left_arm','right_arm']:\n async with io.subscribe(n+'.joint_state') as s: m=await s.next(timeout_s=10)\n states[n]={'q':list(m.position),'tcp':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],n+':flange','world:base')}\n finish({'points':pts,'states':states,'schemas':{n:runtime.describe(n) for n in ['left_arm.joint_target','left_gripper.target','world.wait']}})\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224159.011935530] [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 19, 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 7, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}", + "call_id": "call_9a7aa1cc485f4fa891b4799b37e30f02|fc_03880fcdb65294d8016ae26dd713ac8192a2f06874f3b85f5d", + "is_error": true + }, + { + "name": "read", + "input": { + "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py", + "offset": 1, + "limit": 220 + }, + "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_fd8143c43ef741b3b4edd36a06cb0763|fc_03880fcdb65294d8016ae26de2c52c8192a16482607ddbdabd", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "measure bottle positions", + "python": "import asyncio\nfrom kinex import communication, helpers, runtime, finish\nasync def main():\n async with communication() as io:\n image,depth=await helpers.camera_frame(io,'head_camera')\n d=helpers.image_array(depth)\n pts=[]\n for u,v in [(70,80),(61,42),(78,107),(201,30),(263,48),(275,15),(160,170),(20,95),(10,65)]:\n z=float(d[v,u])*.001\n p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\n w=await helpers.transform_pose(io,p,'head_camera:base','world:base')\n pts.append({'uv':[u,v],'world':w[:3],'depth':z})\n states={}\n for n in ['left_arm','right_arm']:\n async with io.subscribe(n+'.joint_state') as s: m=await s.next(timeout_s=10)\n states[n]={'q':list(m.position),'tcp':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],n+':flange','world:base')}\n finish({'points':pts,'states':states,'schemas':{n:runtime.describe(n) for n in ['left_arm.joint_target','left_gripper.target','world.wait']}})\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224173.802050693] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": [{\"uv\":[70,80],\"world\":[-0.19695410638592586,0.004840793255465603,0.8648806102948801],\"depth\":0.661},{\"uv\":[61,42],\"world\":[-0.19843608488027142,0.01836181260217995,0.9437713751659849],\"depth\":0.606},{\"uv\":[78,107],\"world\":[-0.19720784117987777,0.00647091530623034,0.7836032015382537],\"depth\":0.727},{\"uv\":[201,30],\"world\":[0.05952994588289075,0.18251674300724796,0.8356375733823433],\"depth\":0.791},{\"uv\":[263,48],\"world\":[0.17520652312780038,0.09531415164087742,0.8614856295206998],\"depth\":0.718},{\"uv\":[275,15],\"world\":[0.1815178275905498,0.10234625219325044,0.9355097052456853],\"depth\":0.663},{\"uv\":[160,170],\"world\":[-0.03103803993853389,-0.11372648082994058,0.7397051536755269],\"depth\":0.6900000000000001},{\"uv\":[20,95],\"world\":[-0.4427492319227125,0.24131101888369177,0.5484832795119379],\"depth\":1.056},{\"uv\":[10,65],\"world\":[-0.43384341948444355,0.24559314590548487,0.6666948752399482],\"depth\":0.964}],\n \"states\": {\"left_arm\":{\"q\":[3.7421762044687057e-7,-0.000003416102799747023,0.000009044338185049128,-2.5705126915909204e-8,-0.0000020763914108101744,7.544119284830231e-7],\"tcp\":[-0.30032403719682815,-0.19382880994801913,0.9420051083628493,-5.304990567434886e-7,-0.000009308971204628978,0.7141433685930687,0.6999994635766562]},\"right_arm\":{\"q\":[3.727569151124044e-7,-0.0000034167196645285003,0.000009045151273312513,-2.5587102214785773e-8,-0.0000020741485968756024,7.532827339673531e-7],\"tcp\":[0.3052327434365579,-0.19280673228662837,0.9410048642737745,-6.068416298149924e-7,-0.000009277349876493806,0.7106343746258521,0.7035615008751448]}},\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 }\n}", + "call_id": "call_e5ff0661f776449e803aa94caf102aa0|fc_03880fcdb65294d8016ae26de88d7c819296f7ed199064f5d2", + "is_error": false + }, + { + "name": "read", + "input": { + "path": "/opt/kinex/packages/core/assets/python/kinex/lib/joints.py", + "offset": 1, + "limit": 300 + }, + "output": "\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in /base; link\n defaults to the model's tip_link.\n \"\"\"\n\n def __init__(self, profile, directory=None):\n if profile.get(\"type\") != \"joint\":\n raise ValueError(\"expected a joint device profile\")\n model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n if self.tree is None:\n if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n self.joints = urdf.commanded(self.tree)\n self.names = [joint[\"name\"] for joint in self.joints]\n self.tip = model[\"tip_link\"]\n\n def chain(self, link):\n link = link or self.tip\n if not link:\n raise ValueError(\"name a link; the model declares no tip_link\")\n return Chain(self.tree, link)\n\n def fk(self, positions, link=None):\n \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n q = joint_target(positions, self.joints, check_limits=False)\n return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n def ik(self, target, seed, link=None, **options):\n \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n Joints off the link's path keep their seed values. options override the\n solver tolerances, damping, max_iterations and search budget.\n \"\"\"\n unknown = options.keys() - {*DEFAULTS, \"budget\"}\n if unknown:\n raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n values = dict(zip(self.names, project_seed(seed, self.joints)))\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n home = profile.get(\"home\")\n if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n return 0.0\n return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n joint_target(actions[-1], joints)\n previous = None\n for target in actions:\n joint_target(target, joints, check_limits=False)\n for i, (q, joint) in enumerate(zip(target, joints)):\n limits = joint[\"limits\"]\n if limits is None:\n continue\n margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n low, high = limits[\"min\"], limits[\"max\"]\n if not low - margin <= q <= high + margin:\n raise ValueError(\"target outside joint limits recovery tolerance\")\n if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n raise ValueError(\"joint limits recovery must move inward\")\n previous = target\n\n\ndef command(profile, actions, joints):\n \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n if not isinstance(actions, list) or not actions:\n raise ValueError(\"actions must be a nonempty sequence\")\n for target in actions:\n joint_target(target, joints, check_limits=False)\n if profile[\"limits\"] == \"strict\":\n recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n return actions\n\n\ndef project_seed(value, joints):\n \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n values = vector(value, len(joints), \"IK seed\")\n return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n raise ValueError(\"joint state requires unique names and matching positions\")\n positions = dict(zip(message.name, message.position))\n names = [joint[\"name\"] for joint in joints]\n if any(name not in positions for name in names):\n raise ValueError(\"joint state is missing modeled joints\")\n return joint_target([positions[name] for name in names], joints, check_limits=False)\n", + "call_id": "call_c9ef1243fa444c9f85a5355698087337|fc_03880fcdb65294d8016ae26dff555081929e10d71a55311cab", + "is_error": false + }, + { + "name": "write", + "input": { + "path": ".kinex/tools/manipulate.py", + "content": "\"\"\"Measured, stepped Cartesian moves for RoboTwin ARX5. Invoked with Kinex exec args.\nWaypoints are world TCP xyz and optional quaternion xyzw; duration per waypoint.\nNo motion inferred from publication. Uses declared FK/IK, camera and wait interfaces.\n\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation, Slerp\nfrom kinex import communication, helpers, args, finish\n\nasync def state(io, name):\n async with io.subscribe(name+'.joint_state') as s:\n return await s.next(timeout_s=10)\n\nasync def main():\n a=args(); side=a.get('side','left'); arm=side+'_arm'; grip=side+'_gripper'\n async with communication() as io:\n model=helpers.joint_model(arm)\n m=await state(io,arm); q=list(m.position)\n if 'grip' in a:\n await io.publish(grip+'.target',{'data':float(a['grip'])})\n points=[]; elapsed=0.; targets=[]\n # Default vertical approach, fingers close along world X.\n default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()\n for wp in a.get('waypoints',[]):\n xyz=np.array(wp['xyz']); quat=np.array(wp.get('quat',default)); duration=wp.get('duration',2.)\n if wp.get('linear',False):\n current=await helpers.transform_pose(io,model.fk(q),arm+':base','world:base')\n cr=Rotation.from_quat(current[3:]); start=np.array(current[:3])+cr.apply([.12,0,0])\n rot=Slerp([0,1],Rotation.from_quat([current[3:],quat]))\n fractions=np.linspace(0,1,max(2,int(duration/.12))+1)[1:]\n else: fractions=[1.]\n start_t=elapsed\n for f in fractions:\n p=start+(xyz-start)*f if wp.get('linear',False) else xyz\n r=rot(f) if wp.get('linear',False) else Rotation.from_quat(quat)\n flange=np.r_[p-r.apply([.12,0,0]),r.as_quat()].tolist()\n local=await helpers.transform_pose(io,flange,'world:base',arm+':base')\n newq=model.ik(local,q)\n # choose equivalent nearby revolute solution\n newq=[v+2*np.pi*round((old-v)/(2*np.pi)) for v,old in zip(newq,q)]\n if max(abs(np.array(newq)-q))>3.2: raise ValueError('discontinuous IK')\n q=newq; t=start_t+duration*f\n sec=int(t); ns=round((t-sec)*1e9)\n points.append({'positions':q,'time_from_start':{'sec':sec,'nanosec':ns}})\n elapsed+=duration; targets.append(xyz.tolist())\n if points:\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\n wait=elapsed+a.get('settle',.4)\n if wait>0: result=await io.request('world.wait',duration_s=wait)\n m=await state(io,arm); g=await state(io,grip)\n flange=await helpers.transform_pose(io,model.fk(list(m.position)),arm+':base','world:base')\n tcp=np.array(flange[:3])+Rotation.from_quat(flange[3:]).apply([.12,0,0])\n files=[]\n for cam in a.get('cameras',['head_camera']):\n im,depth=await helpers.camera_frame(io,cam)\n files.append(helpers.save_image(im))\n if a.get('save_depth'): np.save('/tmp/'+cam+'_depth.npy',helpers.image_array(depth))\n finish({'tcp':tcp.tolist(),'quat':flange[3:],'q':list(m.position),'grip':list(g.position),'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None,'time':result if wait>0 else None},file=files)\n\nasyncio.run(main())\n" + }, + "output": "Successfully wrote to .kinex/tools/manipulate.py", + "call_id": "call_0dab35d986494f688df08ec008796574|fc_03880fcdb65294d8016ae26e06f8f481929dff7a1ab8dbc133", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "approach clear bottle", + "args": { + "grip": 1, + "waypoints": [ + { + "xyz": [ + -0.198, + 0.04, + 1.11 + ], + "duration": 2.5 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224222.313883542] [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/manipulate.py\\\", line 60, 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/manipulate.py\\\", line 38, in main\\n newq=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=406, elapsed_ms=100.044, position_error_m=0.10653722123952583, rotation_error_rad=0.10622118351177572\"\n}", + "call_id": "call_45ca89cab14d441c80d9b258f0df7f6a|fc_03880fcdb65294d8016ae26e1adbd48192aa2a6f636e4f6741", + "is_error": true + }, + { + "name": "exec", + "input": { + "description": "check reachable grasp poses", + "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n for z in [1.04,1.,.95,.91]:\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\n p=np.r_[np.array([-.198,.04,z])-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n try:q=model.ik(p,[0,-1,1,.5,0,0],budget=2);out.append([z,q])\n except ValueError as e:out.append([z,str(e)])\n finish(out)\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224232.937157450] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [1,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.95,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.91,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"]\n ]\n}", + "call_id": "call_d766eb69ac6d4ec79410f9f2129fe45c|fc_03880fcdb65294d8016ae26e231ffc8192873929d00d4373ca", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "check reachable grasp poses", + "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n for z in [1.04,1.,.95,.91]:\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\n p=np.r_[np.array([-.198,.04,z])-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n try:q=model.ik(p,[0,-1,1,.5,0,0],budget={'max_attempts':40,'attempt_time_s':.1,'max_time_s':2});out.append([z,q])\n except ValueError as e:out.append([z,str(e)])\n finish(out)\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224241.131440963] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,[-0.2496410241379745,-3.8412895605530037,2.487698982425237,1.5250552017336654,-3.1154129244818,2.924150273604751]],\n [1,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1170.321, position_error_m=0.017156539321827088, rotation_error_rad=0.0005849824564675265\"],\n [0.95,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1184.417, position_error_m=0.0004092112180844326, rotation_error_rad=1.4402656672529403e-05\"],\n [0.91,[-0.24688493993230948,-3.825269561715616,2.5398416965020116,-1.6526536288735123,0.005872565741773644,-0.2145231983847298]]\n ]\n}", + "call_id": "call_45460ad2e45c4f75af849f7a18986ecd|fc_03880fcdb65294d8016ae26e2bc4cc8192a79f14f7a6b63d6c", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "solve approach configuration", + "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\n p=np.r_[np.array([-.198,.04,1.04])-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n for seed in [[0,1,1,0,0,0],[0,2,1,-1,0,0],[0,1,-1,0,0,0],[0,2,2,1,0,0]]:\n try:q=model.ik(p,seed,budget={'max_attempts':20,'attempt_time_s':.1,'max_time_s':1});out.append(q)\n except ValueError as e:out.append(str(e))\n finish(out)\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224263.608290980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=594.872, position_error_m=0.06611405415402853, rotation_error_rad=0.0019896488433462475\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=590.202, position_error_m=0.06586970481906577, rotation_error_rad=0.001980616266574562\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=582.975, position_error_m=0.06614644516120394, rotation_error_rad=0.002008056238735402\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=579.950, position_error_m=0.05696908984691351, rotation_error_rad=0.1997905349017585\"\n ]\n}", + "call_id": "call_e80a33c48f3e491cba937585b6e9c711|fc_03880fcdb65294d8016ae26e3fa1288192b59901d15046922f", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "approach bottle from front", + "args": { + "grip": 1, + "waypoints": [ + { + "xyz": [ + -0.198, + -0.1, + 0.87 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "duration": 2.5 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224276.413911057] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.34149891422440415,-0.2565050430306144,0.8897903932734834],\n \"quat\": [-0.09308770694337504,0.19088826103653672,0.7954797807205011,0.5675458299374028],\n \"q\": [-0.20009401440620422,-0.2250864952802658,0.1718592345714569,-0.8337344527244568,-0.5068660974502563,-0.01791577972471714],\n \"grip\": [0.9960487864234231],\n \"target_error\": 0.2132543705144551,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.92,\"waited_s\":2.92},\n \"file\": [\"/tmp/kinex-observation-_g33q8jf.png\",\"/tmp/kinex-observation-g6aa97sf.png\"]\n }\n}", + "call_id": "call_a1252c09618b4174985aa43b46b91af8|fc_03880fcdb65294d8016ae26e5239188192b89199dd881dd9e3", + "is_error": false + }, + { + "name": "device_stop", + "input": { + "deviceID": "left_arm", + "reason": "Measured TCP differs from commanded pose; inspect joint mapping and tracking before continuing." + }, + "output": "left_arm: stopped (hold; affects left_arm)", + "call_id": "call_84fa2d515dd348759ebb78e7d4a8474b|fc_03880fcdb65294d8016ae26e6a665081928c3d06c44e287f05", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "diagnose measured arm tracking", + "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n model=helpers.joint_model('left_arm')\n async with io.subscribe('left_arm.joint_state') as s:m=await s.next(timeout_s=10)\n p=await helpers.transform_pose(io,[-.198,-.22,.87,0,0,.70710678,.70710678],'world:base','left_arm:base')\n q=model.ik(p,list(m.position))\n finish({'names':model.names,'measured_names':list(m.name),'target_local':p,'target_q':q,'fk_target':model.fk(q),'fk_measured':model.fk(list(m.position)),'tf':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],'left_arm:flange','world:base')})\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224307.591027112] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"names\": [\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\n \"measured_names\": [\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\n \"target_local\": [0.19748021568092256,-0.10296987920505801,0.08799976895842088,-2.1073194747276565e-8,-2.1073194744748758e-8,-0.010000025916871026,0.9999499984907548],\n \"target_q\": [-0.5538732839669482,1.995022269064291,0.16137099170018213,1.8421592169091363,-0.5113192953777548,0.016807289704789778],\n \"fk_target\": [0.1974588856049695,-0.10295700740880999,0.08803969389974405,5.656337319998357e-7,-0.00000421306646243146,-0.010000411984881847,0.9999499946207624],\n \"fk_measured\": [0.058972431162814636,0.00270585894917854,0.1515630860129737,0.07115997195073531,0.2000997192265956,0.15152750906073412,0.9653682068284917],\n \"tf\": [-0.34149891422440415,-0.2565050430306144,0.8897903932734834,-0.09308770694337509,0.19088826103653644,0.7954797807205009,0.5675458299374023]\n }\n}", + "call_id": "call_654cccc60e4f4bc1a854d57c04897250|fc_03880fcdb65294d8016ae26e6fe8c48192ba1d390ee0a59203", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "inspect inverse kinematics branches", + "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n model=helpers.joint_model('left_arm'); p=await helpers.transform_pose(io,[-.198,-.22,.87,0,0,.70710678,.70710678],'world:base','left_arm:base')\n out=[]\n for seed in [[0]*6,[0,1,1,0,0,0],[0,.5,1,.5,0,0]]:\n try:out.append(model.ik(p,seed,budget={'max_attempts':30,'attempt_time_s':.1,'max_time_s':2}))\n except Exception as e:out.append(str(e))\n finish(out)\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224322.596944567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [-0.5526204907538345,-1.7623647377941796,-0.6392799169876033,-1.1322713387442476,-0.513594805751331,-0.018117428053433485],\n [-0.553866303587819,1.9944921125413446,0.16136065569054742,1.8416406163084864,-0.5113207015289282,0.016809955678195713],\n [-0.5538701711987519,1.9950389022424122,0.16140448051613965,1.842137792565355,-0.5113167030912799,0.016803814033760394]\n ]\n}", + "call_id": "call_ddb8d3f7aa744130902ea9827c5a15e5|fc_03880fcdb65294d8016ae26e7fb7348192949bf1a9101eaff4", + "is_error": false + }, + { + "name": "edit", + "input": { + "path": ".kinex/tools/manipulate.py", + "edits": [ + { + "oldText": "newq=model.ik(local,q)", + "newText": "seed=wp.get('seed',q)\n newq=model.ik(local,seed,budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2})" + }, + { + "oldText": "'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None", + "newText": "'target_q':q if points else None,'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None" + } + ] + }, + "output": "Successfully replaced 2 block(s) in .kinex/tools/manipulate.py.", + "call_id": "call_5c2bd880402b45b7ae3456ce1ffc616d|fc_03880fcdb65294d8016ae26e8d82f081928e2ed821588e2644", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "recover to raised posture", + "args": { + "waypoints": [ + { + "xyz": [ + -0.28, + -0.15, + 1.13 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "seed": [ + 0, + 1, + 1, + 0, + 0, + 0 + ], + "duration": 3 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224341.829452565] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.2808323314765084,-0.1521085564748633,1.1266195287624303],\n \"quat\": [-0.0017668889194129447,0.002406581741459667,0.7083999131961318,0.7058049655900566],\n \"q\": [-0.16894401609897614,0.6368672847747803,0.7560085654258728,-0.12360157817602158,-0.14953598380088806,0.009546547196805477],\n \"grip\": [0.9981070431795988],\n \"target_q\": [-0.16866906820474564,0.6489774621097011,0.7660709003489753,-0.11556851724076331,-0.1454201730553973,0.009877023164163508],\n \"target_error\": 0.0040701808169305775,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-mkfvkm2u.png\",\"/tmp/kinex-observation-k28drqoq.png\"]\n }\n}", + "call_id": "call_403b170c14244e30a81ccd05c9490501|fc_03880fcdb65294d8016ae26e92923481929e81e637fddfc208", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "align with clear bottle", + "args": { + "waypoints": [ + { + "xyz": [ + -0.198, + -0.095, + 0.875 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "linear": true, + "duration": 2.5 + }, + { + "xyz": [ + -0.198, + 0.035, + 0.875 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "linear": true, + "duration": 1.5 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224368.240218130] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.19568474524799342,0.03129575416783295,0.8768179791471703],\n \"quat\": [0.0009593362831939475,-0.0018856024907780693,0.7054160408881407,0.7087903310816559],\n \"q\": [-0.3494529128074646,2.0894923210144043,0.6539878249168396,1.4443292617797852,-0.3007594048976898,0.014579671435058117],\n \"grip\": [1.0012966367331417],\n \"target_q\": [-0.3411276660362571,2.0986666201389337,0.6667057780316544,1.4364761952238168,-0.29715006055619353,0.014478228366471838],\n \"target_error\": 0.004731478619983621,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.72,\"waited_s\":4.4},\n \"file\": [\"/tmp/kinex-observation-7woacofn.png\",\"/tmp/kinex-observation-dzfqc4pj.png\"]\n }\n}", + "call_id": "call_6a4e2d05e8134be6a9ad58f01415614c|fc_03880fcdb65294d8016ae26eac57dc8192b75956d54413768e", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "grasp clear bottle", + "args": { + "grip": 0, + "settle": 0.8, + "cameras": [ + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224399.517077974] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.19641768011592214,0.03397012328379588,0.8762348087384827],\n \"quat\": [0.0009134239479954816,-0.00042023378935098735,0.7057645168018754,0.7084457889518926],\n \"q\": [-0.3451070785522461,2.094144821166992,0.6640962958335876,1.4365533590316772,-0.29736000299453735,0.01581699773669243],\n \"grip\": [0.6974994838237762],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":11.52,\"waited_s\":0.7999999999999989},\n \"file\": [\"/tmp/kinex-observation-qlc7bhx8.png\"]\n }\n}", + "call_id": "call_f5aa026654404847acb39b518709dafd|fc_03880fcdb65294d8016ae26ecce6e08192ba2539ec9ab15bd2", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "lift clear bottle", + "args": { + "waypoints": [ + { + "xyz": [ + -0.198, + 0.035, + 1.12 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "linear": true, + "duration": 2 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224409.028210939] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.1978245238769104,0.03624807484249387,1.1136391307316316],\n \"quat\": [-0.0006687220835096329,0.003217325161495589,0.7074333523842097,0.7067724199231455],\n \"q\": [-0.34395521879196167,1.481361746788025,1.1216462850570679,0.35927361249923706,-0.31043118238449097,0.018503844738006592],\n \"grip\": [0.7039102004332977],\n \"target_q\": [-0.3444226970084058,1.4740169827439866,1.135291463367972,0.3440870017483791,-0.3100354832143796,0.016756424773925072],\n \"target_error\": 0.00648453086441232,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.92,\"waited_s\":2.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-h2h_buoz.png\",\"/tmp/kinex-observation-wcac2rse.png\"]\n }\n}", + "call_id": "call_5a42816f1c5e48ce8e2db25227cad69b|fc_03880fcdb65294d8016ae26ed5f5b4819290feba7059024ae4", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "transfer bottle above left bin", + "args": { + "waypoints": [ + { + "xyz": [ + -0.5, + 0.015, + 1.06 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "linear": true, + "duration": 3 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224444.140137075] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.4945684814634984,0.015371463224506068,1.0604537555111784],\n \"quat\": [0.00025644590441672847,0.0009540466669193152,0.7060608183652334,0.7081505099905476],\n \"q\": [0.5811179280281067,1.626004695892334,1.0479673147201538,0.5658470392227173,0.6207801699638367,0.021735304966568947],\n \"grip\": [0.7077647921713917],\n \"target_q\": [0.5932278082889996,1.6362225123927316,1.0560940418899667,0.5683202118899915,0.63010988492782,0.019804060908021025],\n \"target_error\": 0.005463082701501553,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":17.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-d1x88zez.png\",\"/tmp/kinex-observation-3psdpux4.png\"]\n }\n}", + "call_id": "call_a9936dc46ebb49889c6e72409761f1b1|fc_03880fcdb65294d8016ae26ef99454819282a3f7f5fb7a584d", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "lower bottle over bin opening", + "args": { + "waypoints": [ + { + "xyz": [ + -0.52, + 0.03, + 0.9 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "linear": true, + "duration": 1.5 + } + ], + "cameras": [ + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224467.850536046] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5171615828512802,0.03131842426267671,0.9044438328481127],\n \"quat\": [0.0019280949082640983,0.0004485915764353847,0.7052955620133471,0.7089106089062986],\n \"q\": [0.6077774167060852,2.09543514251709,0.9070724844932556,1.1807374954223633,0.6567703485488892,0.019012924283742905],\n \"grip\": [0.7038915062492545],\n \"target_q\": [0.6142882451032351,2.1089415504442486,0.9061590178895118,1.1926114491230337,0.6583185828385036,0.017082770022362744],\n \"target_error\": 0.005435301723780054,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":19.240000000000002,\"waited_s\":1.9200000000000017},\n \"file\": [\"/tmp/kinex-observation-f9hecmv3.png\"]\n }\n}", + "call_id": "call_da8f102ca6424b14a19a4ae4968069b5|fc_03880fcdb65294d8016ae26f105b648192adccd6a6096ca5e7", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "release bottle into bin", + "args": { + "grip": 1, + "settle": 1, + "cameras": [ + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224481.635084896] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5184455702180112,0.030694684254432475,0.9010313035127829],\n \"quat\": [0.0010387084556719025,-0.00015139120055665463,0.70592582635397,0.708284989147669],\n \"q\": [0.6109477877616882,2.1069490909576416,0.9063937664031982,1.1924670934677124,0.6583145260810852,0.017104366794228554],\n \"grip\": [0.9881577410481193],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.240000000000002,\"waited_s\":1},\n \"file\": [\"/tmp/kinex-observation-2opki9cb.png\"]\n }\n}", + "call_id": "call_44615780c683439db77e1a2334c14c6d|fc_03880fcdb65294d8016ae26f1f03f48192b94f333693da41b3", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "inspect bin contents", + "args": { + "waypoints": [ + { + "xyz": [ + -0.5, + -0.06, + 1.02 + ], + "quat": [ + -0.35355339, + 0.35355339, + 0.61237244, + 0.61237244 + ], + "linear": true, + "duration": 2 + } + ], + "cameras": [ + "left_camera" + ], + "save_depth": true + } + }, + "output": "{\n \"output\": \"[INFO] [1793224496.015300089] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5020418026936535,-0.059801965512401384,1.0194523611193695],\n \"quat\": [-0.34889554343130097,0.3472040172825245,0.6175663469696127,0.6134599230978837],\n \"q\": [0.5938779711723328,1.7595466375350952,1.7936086654663086,-1.1486849784851074,0.3013532757759094,0.547741174697876],\n \"grip\": [0.9976837824691426],\n \"target_q\": [0.5867695945404096,1.7695588958198807,1.8155074267669495,-1.1762405787419286,0.2917951876543838,0.5514962828484409],\n \"target_error\": 0.002123224962566933,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":22.64,\"waited_s\":2.3999999999999986},\n \"file\": [\"/tmp/kinex-observation-or0xdy1p.png\"]\n }\n}", + "call_id": "call_360706a3ace14bd3b3248f941e0797c7|fc_03880fcdb65294d8016ae26f2c3a6c81928313d6fd7b01f6ff", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "plan remaining bottle reach", + "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n for name,xyz,yaw in [('coke',[.19,.12,.865],48),('yellow',[.06,.21,.87],61),('coke_lift',[.12,.03,1.03],48),('yellow_lift',[.02,.12,1.03],61)]:\n r=R.from_euler('z',yaw,degrees=True);p=np.r_[np.array(xyz)-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n try:q=model.ik(p,[0,2,1,1,0,0],budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2});out.append({'name':name,'q':q,'quat':r.as_quat().tolist()})\n except ValueError as e:out.append({'name':name,'error':str(e)})\n finish(out)\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224543.725672103] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n {\"name\":\"coke\",\"q\":[-0.7682474838893932,2.8205441857806255,2.174066940855675,0.6465166651221519,0.015836629018606843,0.005026753078927928],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow\",\"q\":[-0.5519639959914996,2.793350612445425,2.1392099935800983,0.6542416277664851,0.005085650161614391,0.005436249092634174],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]},\n {\"name\":\"coke_lift\",\"q\":[-0.7837596704018495,2.0847705295916814,1.5159528392357002,0.5691906155097223,-0.006951084853310415,0.013880856415633741],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow_lift\",\"q\":[-0.5705797685571973,2.131406981991661,1.5887848123532708,0.5431653775860207,-0.02002556223756075,0.013504624618480239],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]}\n ]\n}", + "call_id": "call_c70a91f8fac24a9990a8ce9d010eab94|fc_03880fcdb65294d8016ae26f58edcc8192a2d5104c71efa528", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "measure remaining bottle surfaces", + "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,dep=await helpers.camera_frame(io,'head_camera');d=helpers.image_array(dep);out=[]\n for u,v in [(197,45),(203,15),(195,5),(270,35),(260,65),(264,48)]:\n z=float(d[v,u])*.001;p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\n out.append([u,v,(await helpers.transform_pose(io,p,'head_camera:base','world:base'))[:3]])\n finish(out)\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224563.007108510] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [197,45,[0.05447197977048632,0.18363281695971267,0.7914746286877076]],\n [203,15,[0.06005972182630685,0.18232398834126862,0.875493007530382]],\n [195,5,[0.04204036901406122,0.18984502660198344,0.8948837863355172]],\n [270,35,[0.18028540144849753,0.09326873128529545,0.8962015643731865]],\n [260,65,[0.18068970414215718,0.09767136948762245,0.8120035427331231]],\n [264,48,[0.1769171417661466,0.0945546611955852,0.8621660116854928]]\n ]\n}", + "call_id": "call_2c4bd3b629dc45048425661fbfddf8d2|fc_03880fcdb65294d8016ae26f6e63cc819295374267ef4f87f4", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "approach yellow bottle", + "args": { + "waypoints": [ + { + "xyz": [ + -0.18, + -0.08, + 1.06 + ], + "quat": [ + 0, + 0, + 0.50753836, + 0.86162916 + ], + "duration": 2 + }, + { + "xyz": [ + -0.006, + 0.093, + 0.86 + ], + "quat": [ + 0, + 0, + 0.50753836, + 0.86162916 + ], + "linear": true, + "duration": 2 + }, + { + "xyz": [ + 0.055, + 0.205, + 0.86 + ], + "quat": [ + 0, + 0, + 0.50753836, + 0.86162916 + ], + "linear": true, + "duration": 1.5 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224572.865750018] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.05147708742033642,0.2045278187007235,0.8629828751979567],\n \"quat\": [0.0012442963942044286,-0.0021552669022041654,0.5092927264227239,0.8605897543917034],\n \"q\": [-0.5445034503936768,2.769740104675293,2.054563522338867,0.720240592956543,0.008363209664821625,0.00577180739492178],\n \"grip\": [0.9996614374897697],\n \"target_q\": [-0.5484997194158352,2.7834866635540485,2.0749658177550847,0.7086049164412447,0.00849461950524203,0.005581543401104956],\n \"target_error\": 0.004640195326699464,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":28.560000000000002,\"waited_s\":5.920000000000002},\n \"file\": [\"/tmp/kinex-observation-jhdi9t_a.png\",\"/tmp/kinex-observation-zkmskjon.png\"]\n }\n}", + "call_id": "call_9505612e357f41e0b601181c0afd3dc9|fc_03880fcdb65294d8016ae26f78a58081929076c7f634d33d64", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "grasp yellow bottle", + "args": { + "grip": 0, + "settle": 0.6, + "cameras": [ + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224603.816933881] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.052825427222747336,0.20584596398041766,0.8627488049225395],\n \"quat\": [0.0014806761308315045,-0.0011401104489938575,0.5090236070610991,0.8607505301775115],\n \"q\": [-0.5453270673751831,2.7777156829833984,2.0716705322265625,0.7096014022827148,0.008205556310713291,0.007084341254085302],\n \"grip\": [0.7739523974331943],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":29.16,\"waited_s\":0.5999999999999979},\n \"file\": [\"/tmp/kinex-observation-7uqteqro.png\"]\n }\n}", + "call_id": "call_668fe7f6cc314dbdaa6658b2da33540c|fc_03880fcdb65294d8016ae26f990f948192bd3357fc047329c9", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "lift yellow bottle clear", + "args": { + "waypoints": [ + { + "xyz": [ + 0.015, + 0.12, + 1.035 + ], + "quat": [ + 0, + 0, + 0.50753836, + 0.86162916 + ], + "linear": true, + "duration": 2 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224612.139791819] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.012475983380957172,0.12415535121015182,1.0325882299616729],\n \"quat\": [0.0013523926468964947,-0.00044605020363605173,0.5105743815543966,0.8598324098181513],\n \"q\": [-0.5547859072685242,2.128363847732544,1.5929771661758423,0.5379608273506165,-0.01129952073097229,0.01545263547450304],\n \"grip\": [0.7797582962296226],\n \"target_q\": [-0.5614852376589563,2.1161047986303942,1.5853120475938565,0.5314918774258239,-0.011183534532282036,0.0136917382097596],\n \"target_error\": 0.005427175903790725,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":31.560000000000002,\"waited_s\":2.400000000000002},\n \"file\": [\"/tmp/kinex-observation-dsx7lnz4.png\",\"/tmp/kinex-observation-wfdkz__a.png\"]\n }\n}", + "call_id": "call_c3bbeb94b3df4b7ba3f237205beefb8e|fc_03880fcdb65294d8016ae26fa112ec8192ad6730590b8c8396", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "carry yellow bottle to bin", + "args": { + "waypoints": [ + { + "xyz": [ + -0.5, + 0.015, + 1.06 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "linear": true, + "duration": 3 + }, + { + "xyz": [ + -0.52, + 0.03, + 0.9 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "linear": true, + "duration": 1.5 + } + ], + "cameras": [ + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224631.131968514] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5174761649606938,0.031407337429610896,0.9046801446093026],\n \"quat\": [0.002290593707506658,0.0004161799849168633,0.7054030393235293,0.7088026044589666],\n \"q\": [0.6082080006599426,2.0956389904022217,0.9081711173057556,1.1805373430252075,0.656909167766571,0.01906382292509079],\n \"grip\": [0.7724612653255463],\n \"target_q\": [0.6142413871259487,2.1090307308805722,0.9062710823865341,1.1924842697081466,0.6582675187588196,0.017143442076574332],\n \"target_error\": 0.005500372306525918,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":36.480000000000004,\"waited_s\":4.920000000000002},\n \"file\": [\"/tmp/kinex-observation-92wa3sed.png\"]\n }\n}", + "call_id": "call_41f972da92074626b70d171b22dc5bcd|fc_03880fcdb65294d8016ae26fb339a48192b3d1c1bd58870222", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "release yellow bottle", + "args": { + "grip": 1, + "settle": 0.8, + "cameras": [ + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224658.817845982] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5184055082681959,0.03082700826055433,0.9015009198137535],\n \"quat\": [0.0015373910546378198,-0.0001890281441048716,0.7058661041915254,0.7083435915222107],\n \"q\": [0.6107003688812256,2.1061878204345703,0.9069343209266663,1.192107915878296,0.6582370400428772,0.01719825342297554],\n \"grip\": [0.9859253439036283],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":37.28,\"waited_s\":0.7999999999999972},\n \"file\": [\"/tmp/kinex-observation-7jmu9wgv.png\"]\n }\n}", + "call_id": "call_16521e66b5ff4499b5b47bdcc3bda4dc|fc_03880fcdb65294d8016ae26fcfe8c0819286df4a01dc40e1f4", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "verify second deposit", + "args": { + "waypoints": [ + { + "xyz": [ + -0.5, + -0.06, + 1.02 + ], + "quat": [ + -0.35355339, + 0.35355339, + 0.61237244, + 0.61237244 + ], + "linear": true, + "duration": 1.5 + } + ], + "cameras": [ + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224671.685813675] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5021647469780711,-0.05918474129308639,1.0190715021736796],\n \"quat\": [-0.3482738420558144,0.3459375267960569,0.6181609954097833,0.613929590627374],\n \"q\": [0.5939311981201172,1.7592520713806152,1.7904003858566284,-1.1432219743728638,0.3032253086566925,0.5461763739585876],\n \"grip\": [0.9966154369440946],\n \"target_q\": [0.5866290400105713,1.7696589255235398,1.815409985763567,-1.1763832425627365,0.2921926314641686,0.5510894336527324],\n \"target_error\": 0.0024925658369937304,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":39.2,\"waited_s\":1.9200000000000017},\n \"file\": [\"/tmp/kinex-observation-zoi1ji0f.png\"]\n }\n}", + "call_id": "call_a4fff9ee2ef7437894d454f3c14eb0b8|fc_03880fcdb65294d8016ae26fdb96ec8192a6b9c4af4059db9d", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "approach final bottle", + "args": { + "waypoints": [ + { + "xyz": [ + -0.08, + -0.1, + 1.06 + ], + "quat": [ + 0, + 0, + 0.40673664, + 0.91354546 + ], + "duration": 2.5 + }, + { + "xyz": [ + 0.093, + 0.014, + 0.865 + ], + "quat": [ + 0, + 0, + 0.40673664, + 0.91354546 + ], + "linear": true, + "duration": 2 + }, + { + "xyz": [ + 0.185, + 0.12, + 0.865 + ], + "quat": [ + 0, + 0, + 0.40673664, + 0.91354546 + ], + "linear": true, + "duration": 1.5 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224689.703922241] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.18104127421884691,0.12025246257982117,0.8680142777299692],\n \"quat\": [0.0010549760702521604,-0.0023176494770264255,0.40876259422019845,0.9126371990516093],\n \"q\": [-0.7572907209396362,2.787534236907959,2.11370587348938,0.6789280772209167,0.022206220775842667,0.005521690938621759],\n \"grip\": [0.9995792779055509],\n \"target_q\": [-0.7616688198560753,2.80332162175892,2.1382883773455337,0.6650406542444128,0.02232729054880098,0.005286691102754581],\n \"target_error\": 0.004982079625815344,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":45.6,\"waited_s\":6.399999999999999},\n \"file\": [\"/tmp/kinex-observation-i_l_tjm9.png\",\"/tmp/kinex-observation-imo_1j6g.png\"]\n }\n}", + "call_id": "call_f61b0c3e4a4e4a60a1c31841e6f3f84d|fc_03880fcdb65294d8016ae26fed5b908192980320f6ed1effe2", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "grasp final bottle", + "args": { + "grip": 0, + "settle": 0.6, + "cameras": [ + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224722.893358124] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.182839956300261,0.1214320373928262,0.8679558242217614],\n \"quat\": [0.0014541573105776343,-0.0014481062315274576,0.4084345353416347,0.91278530813939],\n \"q\": [-0.7581510543823242,2.7972278594970703,2.134784698486328,0.6662819385528564,0.022113796323537827,0.006825706455856562],\n \"grip\": [0.7865898013114929],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":46.2,\"waited_s\":0.6000000000000014},\n \"file\": [\"/tmp/kinex-observation-i7quvwry.png\"]\n }\n}", + "call_id": "call_170cc55bab7548328486ec02bd6811bf|fc_03880fcdb65294d8016ae2701025bc8192ad15bef9b2b383e2", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "lift final bottle", + "args": { + "waypoints": [ + { + "xyz": [ + 0.11, + 0.03, + 1.04 + ], + "quat": [ + 0, + 0, + 0.40673664, + 0.91354546 + ], + "linear": true, + "duration": 2 + } + ], + "cameras": [ + "head_camera", + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224732.814719563] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.10941368722195692,0.034232347568097815,1.0377322213464852],\n \"quat\": [0.001230415620162098,-0.0004807913597038286,0.40934807110091775,0.9123773405795583],\n \"q\": [-0.7624666690826416,2.06129789352417,1.510847568511963,0.552495002746582,0.00831757765263319,0.015920152887701988],\n \"grip\": [0.7925870445641605],\n \"target_q\": [-0.7676939502135206,2.048071049242703,1.501995544659548,0.5462311917833058,0.008597172733907732,0.014152281034482525],\n \"target_error\": 0.004837287321652237,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":48.6,\"waited_s\":2.3999999999999986},\n \"file\": [\"/tmp/kinex-observation-0b4c68lr.png\",\"/tmp/kinex-observation-a7qeqo65.png\"]\n }\n}", + "call_id": "call_56372e9eb2f04846a91b3fef2c900961|fc_03880fcdb65294d8016ae27019bb5c8192abf3141d7470b6cb", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "carry final bottle to bin", + "args": { + "waypoints": [ + { + "xyz": [ + -0.5, + 0.015, + 1.06 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "linear": true, + "duration": 3.5 + }, + { + "xyz": [ + -0.52, + 0.03, + 0.9 + ], + "quat": [ + 0, + 0, + 0.70710678, + 0.70710678 + ], + "linear": true, + "duration": 1.5 + } + ], + "cameras": [ + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224752.694936316] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5174527559986333,0.031418531702469416,0.9048850739188827],\n \"quat\": [0.0022403785285909222,0.0003952878515179147,0.7053842503524314,0.7088214752716665],\n \"q\": [0.6081580519676208,2.0949134826660156,0.9082513451576233,1.179675817489624,0.6569015979766846,0.019001225009560585],\n \"grip\": [0.7880872325463729],\n \"target_q\": [0.6143294472492913,2.1091152341605635,0.9063292866790853,1.1926127068902617,0.658363172809637,0.01708281387648724],\n \"target_error\": 0.0056889921239493485,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":54,\"waited_s\":5.399999999999999},\n \"file\": [\"/tmp/kinex-observation-0ip5hs7j.png\"]\n }\n}", + "call_id": "call_c7613f9ced8342abb9df935a18dd950b|fc_03880fcdb65294d8016ae2702ce6248192b723afd4cc20511e", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/manipulate.py", + "description": "release final bottle into bin", + "args": { + "grip": 1, + "settle": 0.8, + "cameras": [ + "left_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224780.954123050] [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\":54.160000000000004,\"waited_s\":0.1600000000000037}\n }\n}", + "call_id": "call_0a856b28618e426d8883daa1a3fd82d2|fc_03880fcdb65294d8016ae2704a638c81929903e4953d76b364", + "is_error": true + }, + { + "name": "devices", + "input": { + "calls": [ + { + "deviceID": "head_camera", + "action": "image" + }, + { + "deviceID": "left_camera", + "action": "image" + }, + { + "deviceID": "left_gripper", + "action": "joint_state" + } + ] + }, + "output": "{\n \"results\": [\n {\"deviceID\":\"head_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":54.16,\"frame_id\":\"head_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-_7ve1dm1.png\"},\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":54.16,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dh4luhs9.png\"},\n {\"deviceID\":\"left_gripper\",\"action\":\"joint_state\",\"ok\":true,\"data\":{\"positions\":[0.8859383490952579],\"velocities\":[0.5435731939294122],\"joint_names\":[\"left_gripper\"],\"timestamp\":54.16}}\n ]\n}", + "call_id": "call_99772b019ce54637a450fa554edd019f|fc_03880fcdb65294d8016ae2705681248192885c5ae7385d9fc1", + "is_error": false + }, + { + "name": "edit", + "input": { + "path": ".kinex/tools/manipulate.py", + "edits": [ + { + "oldText": " if 'grip' in a:\n await io.publish(grip+'.target',{'data':float(a['grip'])})\n points=[]; elapsed=0.; targets=[]\n # Default vertical approach, fingers close along world X.\n default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()", + "newText": " points=[]; elapsed=0.; targets=[]\n # Default horizontal approach along world +Y, fingers close along world X.\n # High top-down poses can exceed ARX5 reach; provide explicit quaternions.\n default=Rotation.from_euler('z',np.pi/2).as_quat()" + }, + { + "oldText": " seed=wp.get('seed',q)", + "newText": " # Avoid the below-table IK branch when starting near home.\n seed=wp.get('seed',q if q[1]>.2 else [q[0],1.,1.,0.,q[4],q[5]])" + }, + { + "oldText": " if points:\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})", + "newText": " # Do not change a grip until all requested waypoints solve successfully.\n if 'grip' in a:\n await io.publish(grip+'.target',{'data':float(a['grip'])})\n if points:\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})" + }, + { + "oldText": " files=[]\n for cam", + "newText": " error=float(np.linalg.norm(tcp-targets[-1])) if targets else None\n if error is not None and error>a.get('max_error',.025):\n await io.request(arm+'.stop')\n raise RuntimeError(f'Measured TCP tracking error {error:.4f} m; arm held')\n files=[]\n for cam" + } + ] + }, + "output": "Successfully replaced 4 block(s) in .kinex/tools/manipulate.py.", + "call_id": "call_d572734da4e64fccb8c44ec8b9491c16|fc_03880fcdb65294d8016ae27066f82c819291b7d71c53e842f9", + "is_error": false + }, + { + "name": "write", + "input": { + "path": ".kinex/memos/put-bottles-dustbin.md", + "content": "# RoboTwin put-bottles-dustbin / ALOHA AgileX\n\nUse public profiles and cameras every episode; positions below are observed examples, not immutable scene facts. No simulator reset or private state. ROS wait advances 0.04 s native frames, reads do not. Load devices first; inspect schemas.\n\nReusable `.kinex/tools/manipulate.py` runs with `exec(file=..., args=...)`. Args: side (default left), grip (0 closed,1 open), waypoints [{xyz world TCP, quat xyzw optional, duration seconds, linear bool, seed optional}], settle (default .4), cameras. It solves flange IK with TCP offset +.12 local X, publishes typed JointTrajectory, waits once, checks measured FK and returns images. Defaults horizontal +Y approach. It stops on >.025 m final position error. Caller must choose collision-free waypoints; IK is not collision checking.\n\nARX5 home-seeded IK can select a negative shoulder branch that drives through the table. Use positive shoulder seed [0,1,1,0,0,0] and first move to raised safe horizontal pose, e.g. [-.28,-.15,1.13], yaw90. A failed early command had 21 cm measured tracking error; stopped and recovered raised. High top-down grasp approach at y=.04,z=1.11 was unreachable. Horizontal side grasps work well.\n\nHead camera paired RGBD: world points from K and calibrated frame transform. Bottle surface depths are front surfaces, not centers; add radius ~.025 m farther from camera in world Y. Table observed z=.74. Left arm can reach all three upright bottles using yaw roughly radial from its base, avoiding need for handoff.\n\nObserved successful grasp/transfer sequence:\n- Clear: center [-.198,.035,.875], horizontal yaw90 q=[0,0,.70710678,.70710678]. Approach y=-.095, straight advance, close .8s. Lift to z1.12 in 2s; verify bottle moves with gripper.\n- Yellow: [.055,.205,.86], horizontal yaw61 q=[0,0,.50753836,.86162916]. Approach [-.006,.093,.86], straight advance 1.5s. Close .6s. Retract/lift to [.015,.12,1.035] in 2s (pure vertical lift at far reach may fail).\n- Coke: [.185,.12,.865], horizontal yaw48 q=[0,0,.40673664,.91354546]. Approach [.093,.014,.865]. Close .6s; retract/lift to [.11,.03,1.04] in 2s.\n- Carry each at z~1.06 to [-.5,.015,1.06], yaw90, with 3–3.5s Cartesian path. Lower to [-.52,.03,.9] in1.5s. Open above bin. Silver-lined bin is left of table, mostly outside head view.\n- Inspect deposits with left wrist camera by moving empty TCP to [-.5,-.06,1.02], quaternion [-.35355339,.35355339,.61237244,.61237244], 1.5–2s linear motion. First two bottles were clearly visible inside bin.\n\nEpisode ended during final opening wait at t=54.16s (~1354/1700 frames), after .16s of requested .8s. No further waits or motion attempted. Final head image showed empty table; wrist image showed coke leaving gripper, opening measured .886. Native verdict is owner-only; terminal/rejected wait alone is not success evidence. First two deposits visually confirmed, last grasp/lift/transfer measured before release.\n" + }, + "output": "Successfully wrote to .kinex/memos/put-bottles-dustbin.md", + "call_id": "call_f208824e230143d3868ae42789834c29|fc_03880fcdb65294d8016ae270701e808192978ccd6e1a97c916", + "is_error": false + } +] diff --git a/experiments/robotwin-native/kinex-3/trace.json b/experiments/robotwin-native/kinex-3/trace.json new file mode 100644 index 0000000000000000000000000000000000000000..1b84ac3a85104d3e5feea845eb12e983b0939b52 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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": "01a1847c-4a80-714e-9e3a-ea9217d4e23c", "timestamp": "2026-10-28T21:48:20.480Z", "cwd": "/workspace"}, "messages": [{"id": "d86b90dc", "role": "system", "timestamp": "2026-10-28T21:48:23.264Z", "parts": [{"type": "text", "text": ""}]}, {"id": "baa72b9a", "role": "user", "timestamp": "2026-10-28T21:48:23.264Z", "parts": [{"type": "text", "text": "# Task: robotwin/put-bottles-dustbin-aloha-agilex\n\nuse arms to grab the bottles and put them into the dustbin to the left of the table\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 1700 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": "0333c05e", "role": "context", "timestamp": "2026-10-28T21:48:23.265Z", "parts": [{"type": "text", "text": "\ncore.guides:\nDevice guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-e88685ac8f5146fc8f9fe953a6b66d44/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": "0398c2aa", "role": "assistant", "timestamp": "2026-10-28T21:48:25.910Z", "parts": [{"type": "tool", "callID": "call_bc30fb8bf4f94fd8b419ada61ba9c495|fc_03880fcdb65294d8016ae26da951048192a45a47ab0d529d47", "name": "ls", "input": {"path": "/workspace"}, "output": ".kinex/\nharness.md\ninstruction.md", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11304, "outputTokens": 19, "cachedInputTokens": 0, "cacheReportedInputTokens": 11304, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11304, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 19}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "9ff17a7d", "role": "toolResult", "timestamp": "2026-10-28T21:48:25.917Z", "parts": [{"type": "text", "text": ".kinex/\nharness.md\ninstruction.md"}]}, {"id": "db44cfea", "role": "assistant", "timestamp": "2026-10-28T21:48:29.788Z", "parts": [{"type": "tool", "callID": "call_878b9e39f63649cfb186f0f49feaca5d|fc_03880fcdb65294d8016ae26daced9881929db63b0c45ac2b84", "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": 11344, "outputTokens": 29, "cachedInputTokens": 11136, "cacheReportedInputTokens": 11344, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11344, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 29}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "3f4014f8", "role": "toolResult", "timestamp": "2026-10-28T21:48:29.793Z", "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": "1a9ed4b1", "role": "assistant", "timestamp": "2026-10-28T21:48:32.349Z", "parts": [{"type": "tool", "callID": "call_ed9eab7888164c59b45bb5130fd938dd|fc_03880fcdb65294d8016ae26dafe5288192b6bd79b67294c68a", "name": "find", "input": {"pattern": "**/*", "path": "/workspace/.kinex", "limit": 150}, "output": 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results limit reached. Use limit=300 for more, or refine pattern]", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11477, "outputTokens": 30, "cachedInputTokens": 11264, "cacheReportedInputTokens": 11477, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11477, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 30}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "113fa28a", "role": "toolResult", "timestamp": "2026-10-28T21:48:32.378Z", "parts": [{"type": "text", "text": 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results limit reached. Use limit=300 for more, or refine pattern]"}]}, {"id": "5c9b060a", "role": "assistant", "timestamp": "2026-10-28T21:48:36.946Z", "parts": [{"type": "tool", "callID": "call_ad66e252680d4538b87eab931768f5bf|fc_03880fcdb65294d8016ae26db31af481929c07f14a6d2dd873", "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_3722a81d61a040d180d587e39167096f|fc_03880fcdb65294d8016ae26db4d4f8819280f43ebd32167642", "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_39836cc13d7249189b500a4153c3f1d4|fc_03880fcdb65294d8016ae26db4d4f88192ad8b7b6ddc54d965", "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_42cdcaea1a8243a6b4935539b4721a45|fc_03880fcdb65294d8016ae26db4d4f8819298d9122ade04e809", "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_2e8a67dcb44840f79a97d150c580c9c0|fc_03880fcdb65294d8016ae26db4d4f88192a13da7d1e8a0a437", "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": 14222, "outputTokens": 164, "cachedInputTokens": 11392, "cacheReportedInputTokens": 14222, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 14222, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 164}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "f8b2e594", "role": "toolResult", "timestamp": "2026-10-28T21:48:36.951Z", "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": "d322cc9b", "role": "toolResult", "timestamp": "2026-10-28T21:48:36.951Z", "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": "b29485c2", "role": "toolResult", "timestamp": "2026-10-28T21:48:36.951Z", "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": "4b03d5cf", "role": "toolResult", "timestamp": "2026-10-28T21:48:36.951Z", "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": "a18540e5", "role": "toolResult", "timestamp": "2026-10-28T21:48:36.951Z", "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": "666c9adc", "role": "assistant", "timestamp": "2026-10-28T21:48:40.809Z", "parts": [{"type": "tool", "callID": "call_0d3d767d5ea346c8bf0d3eb23e215a86|fc_03880fcdb65294d8016ae26db6e3c8819283cb40b646910d27", "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_e0aad2fea7bc4afd8926eacc4eb76b7e|fc_03880fcdb65294d8016ae26db8a05c819281b62a8f666b16f7", "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_1ef4fe3713aa486d97cd1ed2e04391d4|fc_03880fcdb65294d8016ae26db8a05c8192a272724573a650f4", "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_9cd5c75ad17d4d61be75a0be6da3f4c0|fc_03880fcdb65294d8016ae26db8a05c81928fc0c2e351277310", "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", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_3c5c46e6b77f447ab13e6db9d68861fe|fc_03880fcdb65294d8016ae26db8a05c8192b8159b0fbc1635e4", "name": "ls", "input": {"path": ".kinex/tools"}, "output": "(empty directory)", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_6ee48ab143544893a17b1e7101670f45|fc_03880fcdb65294d8016ae26db8a05c8192849eba22b16eb71e", "name": "ls", "input": {"path": ".kinex/memos"}, "output": "(empty directory)", "isError": false, "metadata": {}}], "usage": {"inputTokens": 17281, "outputTokens": 173, "cachedInputTokens": 14208, "cacheReportedInputTokens": 17281, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 17281, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 173}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "6ad7aba9", "role": "toolResult", "timestamp": "2026-10-28T21:48:40.812Z", "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": "a199eb65", "role": "toolResult", "timestamp": "2026-10-28T21:48:40.813Z", "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": "c58fc317", "role": "toolResult", "timestamp": "2026-10-28T21:48:40.813Z", "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": "90029dfc", "role": "toolResult", "timestamp": "2026-10-28T21:48:40.813Z", "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. 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"}]}, {"id": "5f0b7483", "role": "toolResult", "timestamp": "2026-10-28T21:48:40.813Z", "parts": [{"type": "text", "text": "(empty directory)"}]}, {"id": "0809828f", "role": "toolResult", "timestamp": "2026-10-28T21:48:40.813Z", "parts": [{"type": "text", "text": "(empty directory)"}]}, {"id": "15419121", "role": "assistant", "timestamp": "2026-10-28T21:48:44.257Z", "parts": [{"type": "tool", "callID": "call_41bfd194a67a43cbaf4a14fa8e8d66f4|fc_03880fcdb65294d8016ae26dbb54d0819286e20b5e7a42245d", "name": "exec", "input": {"python": "from kinex import runtime, finish\nfinish({n:runtime.operations(n) for n in ['world','left_arm','left_gripper','head_camera']})", "description": "inspect operations"}, "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": 20636, "outputTokens": 54, "cachedInputTokens": 17280, "cacheReportedInputTokens": 20636, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 20636, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 54}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "61ba377d", "role": "toolResult", "timestamp": "2026-10-28T21:48:45.225Z", "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": "3c3b5ec8", "role": "assistant", "timestamp": "2026-10-28T21:48:48.552Z", "parts": [{"type": "tool", "callID": "call_82441a8f7dc44b07939b49439c7b3a4e|fc_03880fcdb65294d8016ae26dbf39288192bd0a0b2c00671870", "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"}, {"deviceID": "left_camera", "action": "load"}, {"deviceID": "right_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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 21876, "outputTokens": 98, "cachedInputTokens": 20608, "cacheReportedInputTokens": 21876, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 21876, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 98}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "38fa5f9e", "role": "toolResult", "timestamp": "2026-10-28T21:48:55.910Z", "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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}"}]}, {"id": "faafb34b", "role": "assistant", "timestamp": "2026-10-28T21:48:59.568Z", "parts": [{"type": "tool", "callID": "call_fa8eb77a463a4d20a8347c16ce760642|fc_03880fcdb65294d8016ae26dcaffcc8192b691e40e04f22d3f", "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": 22170, "outputTokens": 39, "cachedInputTokens": 21888, "cacheReportedInputTokens": 22170, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 22170, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 39}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "725ab015", "role": "toolResult", "timestamp": "2026-10-28T21:48:59.571Z", "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": "fb968e31", "role": "assistant", "timestamp": "2026-10-28T21:49:03.303Z", "parts": [{"type": "tool", "callID": "call_a20753f4cb3c40f38c4c5f9931835f06|fc_03880fcdb65294d8016ae26dcefcd48192a129bbbda0ec19b3", "name": "device", "input": {"deviceID": "head_camera", "action": "image"}, "output": "{\n \"deviceID\": \"head_camera\",\n \"timestamp\": 0,\n \"frame_id\": \"head_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-dlsu4lq1.png\"\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 22678, "outputTokens": 23, "cachedInputTokens": 22016, "cacheReportedInputTokens": 22678, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 22678, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 23}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "25fb6783", "role": "toolResult", "timestamp": "2026-10-28T21:49:03.607Z", "parts": [{"type": "text", "text": "{\n \"deviceID\": \"head_camera\",\n \"timestamp\": 0,\n \"frame_id\": \"head_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-dlsu4lq1.png\"\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "fd506274", "role": "assistant", "timestamp": "2026-10-28T21:49:09.219Z", "parts": [{"type": "reasoning", "text": "**Checking bottle geometry**"}, {"type": "tool", "callID": "call_13b2b63712dd4c5aac748b6bab34764f|fc_03880fcdb65294d8016ae26dd503f481929ab9870397455514", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py", "offset": 1, "limit": 280}, "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": {}}, {"type": "tool", "callID": "call_467d1babc64044308d07389bb35c364d|fc_03880fcdb65294d8016ae26dd51a10819284768a26ef999f42", "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_8d4f7e04f0074f4687ef209aa5ce60e6|fc_03880fcdb65294d8016ae26dd51a108192bd5d2aebbd140ea7", "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": 22870, "outputTokens": 152, "cachedInputTokens": 22528, "cacheReportedInputTokens": 22870, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 22870, "reasoningOutputTokens": 25, "reasoningReportedOutputTokens": 152}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "2bbe6185", "role": "toolResult", "timestamp": "2026-10-28T21:49:09.222Z", "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": "234c383d", "role": "toolResult", "timestamp": "2026-10-28T21:49:09.222Z", "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": "6306099c", "role": "toolResult", "timestamp": "2026-10-28T21:49:09.222Z", "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": "84a74e83", "role": "assistant", "timestamp": "2026-10-28T21:49:17.718Z", "parts": [{"type": "tool", "callID": "call_9a7aa1cc485f4fa891b4799b37e30f02|fc_03880fcdb65294d8016ae26dd713ac8192a2f06874f3b85f5d", "name": "exec", "input": {"description": "measure scene and inspect schemas", "python": "import asyncio\nimport numpy as np\nfrom kinex import communication, helpers, runtime, finish\nasync def main():\n async with communication() as io:\n frame=await helpers.camera_frame(io,'head_camera')\n d=helpers.image_array(frame['depth'])\n pts=[]\n for u,v in [(70,80),(61,42),(78,107),(201,30),(263,48),(275,15),(160,170),(20,95),(10,65)]:\n z=float(d[v,u])*.001\n p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\n w=await helpers.transform_pose(io,p,'head_camera:base','world:base')\n pts.append({'uv':[u,v],'world':w[:3],'depth':z})\n states={}\n for n in ['left_arm','right_arm']:\n async with io.subscribe(n+'.joint_state') as s: m=await s.next(timeout_s=10)\n states[n]={'q':list(m.position),'tcp':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],n+':flange','world:base')}\n finish({'points':pts,'states':states,'schemas':{n:runtime.describe(n) for n in ['left_arm.joint_target','left_gripper.target','world.wait']}})\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224159.011935530] [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 19, 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 7, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}", "isError": true, "metadata": {}}], "usage": {"inputTokens": 24389, "outputTokens": 362, "cachedInputTokens": 22912, "cacheReportedInputTokens": 24389, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 24389, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 362}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "b64d7ea5", "role": "toolResult", "timestamp": "2026-10-28T21:49:19.817Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224159.011935530] [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 19, 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 7, in main\\nTypeError: tuple indices must be integers or slices, not str\"\n}"}]}, {"id": "729259aa", "role": "assistant", "timestamp": "2026-10-28T21:49:23.215Z", "parts": [{"type": "tool", "callID": "call_fd8143c43ef741b3b4edd36a06cb0763|fc_03880fcdb65294d8016ae26de2c52c8192a16482607ddbdabd", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py", "offset": 1, "limit": 220}, "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": 25147, "outputTokens": 39, "cachedInputTokens": 24576, "cacheReportedInputTokens": 25147, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 25147, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 39}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "afb46e58", "role": "toolResult", "timestamp": "2026-10-28T21:49:23.219Z", "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": "d13b3f67", "role": "assistant", "timestamp": "2026-10-28T21:49:32.523Z", "parts": [{"type": "tool", "callID": "call_e5ff0661f776449e803aa94caf102aa0|fc_03880fcdb65294d8016ae26de88d7c819296f7ed199064f5d2", "name": "exec", "input": {"description": "measure bottle positions", "python": "import asyncio\nfrom kinex import communication, helpers, runtime, finish\nasync def main():\n async with communication() as io:\n image,depth=await helpers.camera_frame(io,'head_camera')\n d=helpers.image_array(depth)\n pts=[]\n for u,v in [(70,80),(61,42),(78,107),(201,30),(263,48),(275,15),(160,170),(20,95),(10,65)]:\n z=float(d[v,u])*.001\n p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\n w=await helpers.transform_pose(io,p,'head_camera:base','world:base')\n pts.append({'uv':[u,v],'world':w[:3],'depth':z})\n states={}\n for n in ['left_arm','right_arm']:\n async with io.subscribe(n+'.joint_state') as s: m=await s.next(timeout_s=10)\n states[n]={'q':list(m.position),'tcp':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],n+':flange','world:base')}\n finish({'points':pts,'states':states,'schemas':{n:runtime.describe(n) for n in ['left_arm.joint_target','left_gripper.target','world.wait']}})\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224173.802050693] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": [{\"uv\":[70,80],\"world\":[-0.19695410638592586,0.004840793255465603,0.8648806102948801],\"depth\":0.661},{\"uv\":[61,42],\"world\":[-0.19843608488027142,0.01836181260217995,0.9437713751659849],\"depth\":0.606},{\"uv\":[78,107],\"world\":[-0.19720784117987777,0.00647091530623034,0.7836032015382537],\"depth\":0.727},{\"uv\":[201,30],\"world\":[0.05952994588289075,0.18251674300724796,0.8356375733823433],\"depth\":0.791},{\"uv\":[263,48],\"world\":[0.17520652312780038,0.09531415164087742,0.8614856295206998],\"depth\":0.718},{\"uv\":[275,15],\"world\":[0.1815178275905498,0.10234625219325044,0.9355097052456853],\"depth\":0.663},{\"uv\":[160,170],\"world\":[-0.03103803993853389,-0.11372648082994058,0.7397051536755269],\"depth\":0.6900000000000001},{\"uv\":[20,95],\"world\":[-0.4427492319227125,0.24131101888369177,0.5484832795119379],\"depth\":1.056},{\"uv\":[10,65],\"world\":[-0.43384341948444355,0.24559314590548487,0.6666948752399482],\"depth\":0.964}],\n \"states\": {\"left_arm\":{\"q\":[3.7421762044687057e-7,-0.000003416102799747023,0.000009044338185049128,-2.5705126915909204e-8,-0.0000020763914108101744,7.544119284830231e-7],\"tcp\":[-0.30032403719682815,-0.19382880994801913,0.9420051083628493,-5.304990567434886e-7,-0.000009308971204628978,0.7141433685930687,0.6999994635766562]},\"right_arm\":{\"q\":[3.727569151124044e-7,-0.0000034167196645285003,0.000009045151273312513,-2.5587102214785773e-8,-0.0000020741485968756024,7.532827339673531e-7],\"tcp\":[0.3052327434365579,-0.19280673228662837,0.9410048642737745,-6.068416298149924e-7,-0.000009277349876493806,0.7106343746258521,0.7035615008751448]}},\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 }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 25418, "outputTokens": 354, "cachedInputTokens": 25088, "cacheReportedInputTokens": 25418, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 25418, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 354}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "17e9766e", "role": "toolResult", "timestamp": "2026-10-28T21:49:34.430Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224173.802050693] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": [{\"uv\":[70,80],\"world\":[-0.19695410638592586,0.004840793255465603,0.8648806102948801],\"depth\":0.661},{\"uv\":[61,42],\"world\":[-0.19843608488027142,0.01836181260217995,0.9437713751659849],\"depth\":0.606},{\"uv\":[78,107],\"world\":[-0.19720784117987777,0.00647091530623034,0.7836032015382537],\"depth\":0.727},{\"uv\":[201,30],\"world\":[0.05952994588289075,0.18251674300724796,0.8356375733823433],\"depth\":0.791},{\"uv\":[263,48],\"world\":[0.17520652312780038,0.09531415164087742,0.8614856295206998],\"depth\":0.718},{\"uv\":[275,15],\"world\":[0.1815178275905498,0.10234625219325044,0.9355097052456853],\"depth\":0.663},{\"uv\":[160,170],\"world\":[-0.03103803993853389,-0.11372648082994058,0.7397051536755269],\"depth\":0.6900000000000001},{\"uv\":[20,95],\"world\":[-0.4427492319227125,0.24131101888369177,0.5484832795119379],\"depth\":1.056},{\"uv\":[10,65],\"world\":[-0.43384341948444355,0.24559314590548487,0.6666948752399482],\"depth\":0.964}],\n \"states\": {\"left_arm\":{\"q\":[3.7421762044687057e-7,-0.000003416102799747023,0.000009044338185049128,-2.5705126915909204e-8,-0.0000020763914108101744,7.544119284830231e-7],\"tcp\":[-0.30032403719682815,-0.19382880994801913,0.9420051083628493,-5.304990567434886e-7,-0.000009308971204628978,0.7141433685930687,0.6999994635766562]},\"right_arm\":{\"q\":[3.727569151124044e-7,-0.0000034167196645285003,0.000009045151273312513,-2.5587102214785773e-8,-0.0000020741485968756024,7.532827339673531e-7],\"tcp\":[0.3052327434365579,-0.19280673228662837,0.9410048642737745,-6.068416298149924e-7,-0.000009277349876493806,0.7106343746258521,0.7035615008751448]}},\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 }\n}"}]}, {"id": "e8af5c83", "role": "assistant", "timestamp": "2026-10-28T21:49:51.670Z", "parts": [{"type": "reasoning", "text": "**Planning yellow bottle grasp**\n\n**Reading joint options**"}, {"type": "text", "text": "I’ll locate the bottles and bin, then grasp and transfer the bottles one at a time, checking each pickup and release."}, {"type": "tool", "callID": "call_c9ef1243fa444c9f85a5355698087337|fc_03880fcdb65294d8016ae26dff555081929e10d71a55311cab", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/lib/joints.py", "offset": 1, "limit": 300}, "output": "\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in /base; link\n defaults to the model's tip_link.\n \"\"\"\n\n def __init__(self, profile, directory=None):\n if profile.get(\"type\") != \"joint\":\n raise ValueError(\"expected a joint device profile\")\n model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n if self.tree is None:\n if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n self.joints = urdf.commanded(self.tree)\n self.names = [joint[\"name\"] for joint in self.joints]\n self.tip = model[\"tip_link\"]\n\n def chain(self, link):\n link = link or self.tip\n if not link:\n raise ValueError(\"name a link; the model declares no tip_link\")\n return Chain(self.tree, link)\n\n def fk(self, positions, link=None):\n \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n q = joint_target(positions, self.joints, check_limits=False)\n return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n def ik(self, target, seed, link=None, **options):\n \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n Joints off the link's path keep their seed values. options override the\n solver tolerances, damping, max_iterations and search budget.\n \"\"\"\n unknown = options.keys() - {*DEFAULTS, \"budget\"}\n if unknown:\n raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n values = dict(zip(self.names, project_seed(seed, self.joints)))\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n home = profile.get(\"home\")\n if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n return 0.0\n return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n joint_target(actions[-1], joints)\n previous = None\n for target in actions:\n joint_target(target, joints, check_limits=False)\n for i, (q, joint) in enumerate(zip(target, joints)):\n limits = joint[\"limits\"]\n if limits is None:\n continue\n margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n low, high = limits[\"min\"], limits[\"max\"]\n if not low - margin <= q <= high + margin:\n raise ValueError(\"target outside joint limits recovery tolerance\")\n if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n raise ValueError(\"joint limits recovery must move inward\")\n previous = target\n\n\ndef command(profile, actions, joints):\n \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n if not isinstance(actions, list) or not actions:\n raise ValueError(\"actions must be a nonempty sequence\")\n for target in actions:\n joint_target(target, joints, check_limits=False)\n if profile[\"limits\"] == \"strict\":\n recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n return actions\n\n\ndef project_seed(value, joints):\n \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n values = vector(value, len(joints), \"IK seed\")\n return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n raise ValueError(\"joint state requires unique names and matching positions\")\n positions = dict(zip(message.name, message.position))\n names = [joint[\"name\"] for joint in joints]\n if any(name not in positions for name in names):\n raise ValueError(\"joint state is missing modeled joints\")\n return joint_target([positions[name] for name in names], joints, check_limits=False)\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 27068, "outputTokens": 321, "cachedInputTokens": 25600, "cacheReportedInputTokens": 27068, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 27068, "reasoningOutputTokens": 247, "reasoningReportedOutputTokens": 321}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "506bca54", "role": "toolResult", "timestamp": "2026-10-28T21:49:51.673Z", "parts": [{"type": "text", "text": "\"\"\"Joint devices: URDF command vector, limit policy and link kinematics in the device base.\"\"\"\n\nimport math\nfrom pathlib import Path\n\nfrom kinex.lib import urdf\nfrom kinex.lib.geometry import flat, pose, vector\nfrom kinex.lib.kinematics import DEFAULTS, Chain\nfrom kinex.lib.urdf import joint_target\n\nHOME_RECOVERY_TOLERANCE = math.radians(1)\n\n\nclass JointModel:\n \"\"\"A joint device's commanded joints, in URDF document order, and its link FK/IK.\n\n Poses are [x, y, z, qx, qy, qz, qw] of a URDF link in /base; link\n defaults to the model's tip_link.\n \"\"\"\n\n def __init__(self, profile, directory=None):\n if profile.get(\"type\") != \"joint\":\n raise ValueError(\"expected a joint device profile\")\n model, self.tree = profile[\"model\"], profile.get(\"resolved_model\")\n if self.tree is None:\n if directory is None or not isinstance(model, dict) or \"???\" in model.values():\n raise ValueError(f\"{profile.get('name', 'joint device')} has no resolved URDF model\")\n self.tree = urdf.read_tree(Path(directory) / model[\"path\"], model[\"base_link\"])\n self.joints = urdf.commanded(self.tree)\n self.names = [joint[\"name\"] for joint in self.joints]\n self.tip = model[\"tip_link\"]\n\n def chain(self, link):\n link = link or self.tip\n if not link:\n raise ValueError(\"name a link; the model declares no tip_link\")\n return Chain(self.tree, link)\n\n def fk(self, positions, link=None):\n \"\"\"Link pose at commanded joint positions; mimic joints follow their sources.\"\"\"\n q = joint_target(positions, self.joints, check_limits=False)\n return flat(self.chain(link).forward(urdf.positions(self.tree, dict(zip(self.names, q))))[0])\n\n def ik(self, target, seed, link=None, **options):\n \"\"\"Commanded positions placing link at target, nearest the seed projected into limits.\n\n Joints off the link's path keep their seed values. options override the\n solver tolerances, damping, max_iterations and search budget.\n \"\"\"\n unknown = options.keys() - {*DEFAULTS, \"budget\"}\n if unknown:\n raise ValueError(f\"unknown IK options: {', '.join(sorted(unknown))}\")\n values = dict(zip(self.names, project_seed(seed, self.joints)))\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\n return [values[name] for name in self.names]\n\n\ndef home_start_tolerance(profile, joints, target):\n \"\"\"Strict devices may recover toward declared home within an angular margin.\"\"\"\n home = profile.get(\"home\")\n if not isinstance(home, dict) or any(j[\"name\"] not in home or abs(q - home[j[\"name\"]]) > 1e-9 for j, q in zip(joints, target)):\n return 0.0\n return HOME_RECOVERY_TOLERANCE\n\n\ndef recovery_targets(actions, joints, tolerance):\n \"\"\"Allow a bounded, inward-only prefix; the final target remains in limits.\"\"\"\n joint_target(actions[-1], joints)\n previous = None\n for target in actions:\n joint_target(target, joints, check_limits=False)\n for i, (q, joint) in enumerate(zip(target, joints)):\n limits = joint[\"limits\"]\n if limits is None:\n continue\n margin = tolerance if joint[\"type\"] == \"revolute\" else 0\n low, high = limits[\"min\"], limits[\"max\"]\n if not low - margin <= q <= high + margin:\n raise ValueError(\"target outside joint limits recovery tolerance\")\n if previous is not None and ((q < low and not previous[i] <= q) or (q > high and not previous[i] >= q)):\n raise ValueError(\"joint limits recovery must move inward\")\n previous = target\n\n\ndef command(profile, actions, joints):\n \"\"\"Validate a sequence of joint targets; strict limits reject targets outside the URDF limits.\"\"\"\n if not isinstance(actions, list) or not actions:\n raise ValueError(\"actions must be a nonempty sequence\")\n for target in actions:\n joint_target(target, joints, check_limits=False)\n if profile[\"limits\"] == \"strict\":\n recovery_targets(actions, joints, home_start_tolerance(profile, joints, actions[-1]))\n return actions\n\n\ndef project_seed(value, joints):\n \"\"\"Bound a solver seed without changing the measured configuration used for FK.\"\"\"\n values = vector(value, len(joints), \"IK seed\")\n return [max(j[\"limits\"][\"min\"], min(j[\"limits\"][\"max\"], v)) if j[\"limits\"] else v\n for j, v in zip(joints, values)]\n\n\ndef measured_positions(message, joints):\n \"\"\"Order a JointState without clamping or silently accepting duplicate names.\"\"\"\n if len(message.name) != len(message.position) or len(set(message.name)) != len(message.name):\n raise ValueError(\"joint state requires unique names and matching positions\")\n positions = dict(zip(message.name, message.position))\n names = [joint[\"name\"] for joint in joints]\n if any(name not in positions for name in names):\n raise ValueError(\"joint state is missing modeled joints\")\n return joint_target([positions[name] for name in names], joints, check_limits=False)\n"}]}, {"id": "a487405a", "role": "assistant", "timestamp": "2026-10-28T21:50:14.970Z", "parts": [{"type": "reasoning", "text": "**Writing action runner**"}, {"type": "tool", "callID": "call_0dab35d986494f688df08ec008796574|fc_03880fcdb65294d8016ae26e06f8f481929dff7a1ab8dbc133", "name": "write", "input": {"path": ".kinex/tools/manipulate.py", "content": "\"\"\"Measured, stepped Cartesian moves for RoboTwin ARX5. Invoked with Kinex exec args.\nWaypoints are world TCP xyz and optional quaternion xyzw; duration per waypoint.\nNo motion inferred from publication. Uses declared FK/IK, camera and wait interfaces.\n\"\"\"\nimport asyncio\nimport numpy as np\nfrom scipy.spatial.transform import Rotation, Slerp\nfrom kinex import communication, helpers, args, finish\n\nasync def state(io, name):\n async with io.subscribe(name+'.joint_state') as s:\n return await s.next(timeout_s=10)\n\nasync def main():\n a=args(); side=a.get('side','left'); arm=side+'_arm'; grip=side+'_gripper'\n async with communication() as io:\n model=helpers.joint_model(arm)\n m=await state(io,arm); q=list(m.position)\n if 'grip' in a:\n await io.publish(grip+'.target',{'data':float(a['grip'])})\n points=[]; elapsed=0.; targets=[]\n # Default vertical approach, fingers close along world X.\n default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()\n for wp in a.get('waypoints',[]):\n xyz=np.array(wp['xyz']); quat=np.array(wp.get('quat',default)); duration=wp.get('duration',2.)\n if wp.get('linear',False):\n current=await helpers.transform_pose(io,model.fk(q),arm+':base','world:base')\n cr=Rotation.from_quat(current[3:]); start=np.array(current[:3])+cr.apply([.12,0,0])\n rot=Slerp([0,1],Rotation.from_quat([current[3:],quat]))\n fractions=np.linspace(0,1,max(2,int(duration/.12))+1)[1:]\n else: fractions=[1.]\n start_t=elapsed\n for f in fractions:\n p=start+(xyz-start)*f if wp.get('linear',False) else xyz\n r=rot(f) if wp.get('linear',False) else Rotation.from_quat(quat)\n flange=np.r_[p-r.apply([.12,0,0]),r.as_quat()].tolist()\n local=await helpers.transform_pose(io,flange,'world:base',arm+':base')\n newq=model.ik(local,q)\n # choose equivalent nearby revolute solution\n newq=[v+2*np.pi*round((old-v)/(2*np.pi)) for v,old in zip(newq,q)]\n if max(abs(np.array(newq)-q))>3.2: raise ValueError('discontinuous IK')\n q=newq; t=start_t+duration*f\n sec=int(t); ns=round((t-sec)*1e9)\n points.append({'positions':q,'time_from_start':{'sec':sec,'nanosec':ns}})\n elapsed+=duration; targets.append(xyz.tolist())\n if points:\n await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points})\n wait=elapsed+a.get('settle',.4)\n if wait>0: result=await io.request('world.wait',duration_s=wait)\n m=await state(io,arm); g=await state(io,grip)\n flange=await helpers.transform_pose(io,model.fk(list(m.position)),arm+':base','world:base')\n tcp=np.array(flange[:3])+Rotation.from_quat(flange[3:]).apply([.12,0,0])\n files=[]\n for cam in a.get('cameras',['head_camera']):\n im,depth=await helpers.camera_frame(io,cam)\n files.append(helpers.save_image(im))\n if a.get('save_depth'): np.save('/tmp/'+cam+'_depth.npy',helpers.image_array(depth))\n finish({'tcp':tcp.tolist(),'quat':flange[3:],'q':list(m.position),'grip':list(g.position),'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None,'time':result if wait>0 else None},file=files)\n\nasyncio.run(main())\n"}, "output": "Successfully wrote to .kinex/tools/manipulate.py", "isError": false, "metadata": {}}], "usage": {"inputTokens": 28568, "outputTokens": 1068, "cachedInputTokens": 27264, "cacheReportedInputTokens": 28568, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 28568, "reasoningOutputTokens": 62, "reasoningReportedOutputTokens": 1068}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "4df9f281", "role": "toolResult", "timestamp": "2026-10-28T21:50:14.974Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/tools/manipulate.py"}]}, {"id": "7210e35c", "role": "assistant", "timestamp": "2026-10-28T21:50:20.195Z", "parts": [{"type": "tool", "callID": "call_45ca89cab14d441c80d9b258f0df7f6a|fc_03880fcdb65294d8016ae26e1adbd48192aa2a6f636e4f6741", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "approach clear bottle", "args": {"grip": 1, "waypoints": [{"xyz": [-0.198, 0.04, 1.11], "duration": 2.5}], "cameras": ["head_camera", "left_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224222.313883542] [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/manipulate.py\\\", line 60, 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/manipulate.py\\\", line 38, in main\\n newq=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=406, elapsed_ms=100.044, position_error_m=0.10653722123952583, rotation_error_rad=0.10622118351177572\"\n}", "isError": true, "metadata": {}}], "usage": {"inputTokens": 29657, "outputTokens": 74, "cachedInputTokens": 29440, "cacheReportedInputTokens": 29657, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 29657, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 74}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "df02d221", "role": "toolResult", "timestamp": "2026-10-28T21:50:22.923Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224222.313883542] [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/manipulate.py\\\", line 60, 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/manipulate.py\\\", line 38, in main\\n newq=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=406, elapsed_ms=100.044, position_error_m=0.10653722123952583, rotation_error_rad=0.10622118351177572\"\n}"}]}, {"id": "8ad02130", "role": "assistant", "timestamp": "2026-10-28T21:50:30.833Z", "parts": [{"type": "reasoning", "text": "**Testing top flange feasibility**"}, {"type": "tool", "callID": "call_d766eb69ac6d4ec79410f9f2129fe45c|fc_03880fcdb65294d8016ae26e231ffc8192873929d00d4373ca", "name": "exec", "input": {"description": "check reachable grasp poses", "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n for z in [1.04,1.,.95,.91]:\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\n p=np.r_[np.array([-.198,.04,z])-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n try:q=model.ik(p,[0,-1,1,.5,0,0],budget=2);out.append([z,q])\n except ValueError as e:out.append([z,str(e)])\n finish(out)\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224232.937157450] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [1,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.95,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.91,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"]\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 30414, "outputTokens": 280, "cachedInputTokens": 29568, "cacheReportedInputTokens": 30414, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 30414, "reasoningOutputTokens": 43, "reasoningReportedOutputTokens": 280}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "054b78b0", "role": "toolResult", "timestamp": "2026-10-28T21:50:33.588Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224232.937157450] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [1,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.95,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"],\n [0.91,\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\"]\n ]\n}"}]}, {"id": "23c00f42", "role": "assistant", "timestamp": "2026-10-28T21:50:39.106Z", "parts": [{"type": "tool", "callID": "call_45460ad2e45c4f75af849f7a18986ecd|fc_03880fcdb65294d8016ae26e2bc4cc8192a79f14f7a6b63d6c", "name": "exec", "input": {"description": "check reachable grasp poses", "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n for z in [1.04,1.,.95,.91]:\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\n p=np.r_[np.array([-.198,.04,z])-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n try:q=model.ik(p,[0,-1,1,.5,0,0],budget={'max_attempts':40,'attempt_time_s':.1,'max_time_s':2});out.append([z,q])\n except ValueError as e:out.append([z,str(e)])\n finish(out)\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224241.131440963] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,[-0.2496410241379745,-3.8412895605530037,2.487698982425237,1.5250552017336654,-3.1154129244818,2.924150273604751]],\n [1,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1170.321, position_error_m=0.017156539321827088, rotation_error_rad=0.0005849824564675265\"],\n [0.95,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1184.417, position_error_m=0.0004092112180844326, rotation_error_rad=1.4402656672529403e-05\"],\n [0.91,[-0.24688493993230948,-3.825269561715616,2.5398416965020116,-1.6526536288735123,0.005872565741773644,-0.2145231983847298]]\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 30842, "outputTokens": 252, "cachedInputTokens": 30592, "cacheReportedInputTokens": 30842, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 30842, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 252}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "efe179cf", "role": "toolResult", "timestamp": "2026-10-28T21:50:45.291Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224241.131440963] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [1.04,[-0.2496410241379745,-3.8412895605530037,2.487698982425237,1.5250552017336654,-3.1154129244818,2.924150273604751]],\n [1,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1170.321, position_error_m=0.017156539321827088, rotation_error_rad=0.0005849824564675265\"],\n [0.95,\"IK search exhausted (attempt budget); seeds_attempted=40, iterations=8000, elapsed_ms=1184.417, position_error_m=0.0004092112180844326, rotation_error_rad=1.4402656672529403e-05\"],\n [0.91,[-0.24688493993230948,-3.825269561715616,2.5398416965020116,-1.6526536288735123,0.005872565741773644,-0.2145231983847298]]\n ]\n}"}]}, {"id": "96d5863b", "role": "assistant", "timestamp": "2026-10-28T21:51:01.624Z", "parts": [{"type": "reasoning", "text": "**Checking IK seed candidates**"}, {"type": "tool", "callID": "call_e80a33c48f3e491cba937585b6e9c711|fc_03880fcdb65294d8016ae26e3fa1288192b59901d15046922f", "name": "exec", "input": {"description": "solve approach configuration", "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n r=R.from_euler('z',np.pi/2)*R.from_euler('y',np.pi/2)\n p=np.r_[np.array([-.198,.04,1.04])-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n for seed in [[0,1,1,0,0,0],[0,2,1,-1,0,0],[0,1,-1,0,0,0],[0,2,2,1,0,0]]:\n try:q=model.ik(p,seed,budget={'max_attempts':20,'attempt_time_s':.1,'max_time_s':1});out.append(q)\n except ValueError as e:out.append(str(e))\n finish(out)\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224263.608290980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=594.872, position_error_m=0.06611405415402853, rotation_error_rad=0.0019896488433462475\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=590.202, position_error_m=0.06586970481906577, rotation_error_rad=0.001980616266574562\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=582.975, position_error_m=0.06614644516120394, rotation_error_rad=0.002008056238735402\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=579.950, position_error_m=0.05696908984691351, rotation_error_rad=0.1997905349017585\"\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 31389, "outputTokens": 393, "cachedInputTokens": 30976, "cacheReportedInputTokens": 31389, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 31389, "reasoningOutputTokens": 115, "reasoningReportedOutputTokens": 393}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "8b058fff", "role": "toolResult", "timestamp": "2026-10-28T21:51:06.403Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224263.608290980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=594.872, position_error_m=0.06611405415402853, rotation_error_rad=0.0019896488433462475\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=590.202, position_error_m=0.06586970481906577, rotation_error_rad=0.001980616266574562\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=582.975, position_error_m=0.06614644516120394, rotation_error_rad=0.002008056238735402\",\n \"IK search exhausted (attempt budget); seeds_attempted=20, iterations=4000, elapsed_ms=579.950, position_error_m=0.05696908984691351, rotation_error_rad=0.1997905349017585\"\n ]\n}"}]}, {"id": "f0a68f76", "role": "assistant", "timestamp": "2026-10-28T21:51:14.411Z", "parts": [{"type": "reasoning", "text": "**Positioning bottle for grasp**"}, {"type": "tool", "callID": "call_a1252c09618b4174985aa43b46b91af8|fc_03880fcdb65294d8016ae26e5239188192b89199dd881dd9e3", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "approach bottle from front", "args": {"grip": 1, "waypoints": [{"xyz": [-0.198, -0.1, 0.87], "quat": [0, 0, 0.70710678, 0.70710678], "duration": 2.5}], "cameras": ["head_camera", "left_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224276.413911057] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.34149891422440415,-0.2565050430306144,0.8897903932734834],\n \"quat\": [-0.09308770694337504,0.19088826103653672,0.7954797807205011,0.5675458299374028],\n \"q\": [-0.20009401440620422,-0.2250864952802658,0.1718592345714569,-0.8337344527244568,-0.5068660974502563,-0.01791577972471714],\n \"grip\": [0.9960487864234231],\n \"target_error\": 0.2132543705144551,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.92,\"waited_s\":2.92},\n \"file\": [\"/tmp/kinex-observation-_g33q8jf.png\",\"/tmp/kinex-observation-g6aa97sf.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 32053, "outputTokens": 189, "cachedInputTokens": 31616, "cacheReportedInputTokens": 32053, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 32053, "reasoningOutputTokens": 94, "reasoningReportedOutputTokens": 189}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "eda38a00", "role": "toolResult", "timestamp": "2026-10-28T21:51:34.228Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224276.413911057] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.34149891422440415,-0.2565050430306144,0.8897903932734834],\n \"quat\": [-0.09308770694337504,0.19088826103653672,0.7954797807205011,0.5675458299374028],\n \"q\": [-0.20009401440620422,-0.2250864952802658,0.1718592345714569,-0.8337344527244568,-0.5068660974502563,-0.01791577972471714],\n \"grip\": [0.9960487864234231],\n \"target_error\": 0.2132543705144551,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":2.92,\"waited_s\":2.92},\n \"file\": [\"/tmp/kinex-observation-_g33q8jf.png\",\"/tmp/kinex-observation-g6aa97sf.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "201e1934", "role": "assistant", "timestamp": "2026-10-28T21:51:38.540Z", "parts": [{"type": "reasoning", "text": "**Stopping after motion error**"}, {"type": "tool", "callID": "call_84fa2d515dd348759ebb78e7d4a8474b|fc_03880fcdb65294d8016ae26e6a665081928c3d06c44e287f05", "name": "device_stop", "input": {"deviceID": "left_arm", "reason": "Measured TCP differs from commanded pose; inspect joint mapping and tracking before continuing."}, "output": "left_arm: stopped (hold; affects left_arm)", "isError": false, "metadata": {}}], "usage": {"inputTokens": 32718, "outputTokens": 51, "cachedInputTokens": 31872, "cacheReportedInputTokens": 32718, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 32718, "reasoningOutputTokens": 11, "reasoningReportedOutputTokens": 51}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "bde0e469", "role": "toolResult", "timestamp": "2026-10-28T21:51:38.631Z", "parts": [{"type": "text", "text": "left_arm: stopped (hold; affects left_arm)"}]}, {"id": "ee431b60", "role": "assistant", "timestamp": "2026-10-28T21:51:46.090Z", "parts": [{"type": "reasoning", "text": "**Printing FK transforms**"}, {"type": "tool", "callID": "call_654cccc60e4f4bc1a854d57c04897250|fc_03880fcdb65294d8016ae26e6fe8c48192ba1d390ee0a59203", "name": "exec", "input": {"description": "diagnose measured arm tracking", "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n model=helpers.joint_model('left_arm')\n async with io.subscribe('left_arm.joint_state') as s:m=await s.next(timeout_s=10)\n p=await helpers.transform_pose(io,[-.198,-.22,.87,0,0,.70710678,.70710678],'world:base','left_arm:base')\n q=model.ik(p,list(m.position))\n finish({'names':model.names,'measured_names':list(m.name),'target_local':p,'target_q':q,'fk_target':model.fk(q),'fk_measured':model.fk(list(m.position)),'tf':await helpers.transform_pose(io,[.12,0,0,0,0,0,1],'left_arm:flange','world:base')})\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224307.591027112] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"names\": [\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\n \"measured_names\": [\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\n \"target_local\": [0.19748021568092256,-0.10296987920505801,0.08799976895842088,-2.1073194747276565e-8,-2.1073194744748758e-8,-0.010000025916871026,0.9999499984907548],\n \"target_q\": [-0.5538732839669482,1.995022269064291,0.16137099170018213,1.8421592169091363,-0.5113192953777548,0.016807289704789778],\n \"fk_target\": [0.1974588856049695,-0.10295700740880999,0.08803969389974405,5.656337319998357e-7,-0.00000421306646243146,-0.010000411984881847,0.9999499946207624],\n \"fk_measured\": [0.058972431162814636,0.00270585894917854,0.1515630860129737,0.07115997195073531,0.2000997192265956,0.15152750906073412,0.9653682068284917],\n \"tf\": [-0.34149891422440415,-0.2565050430306144,0.8897903932734834,-0.09308770694337509,0.19088826103653644,0.7954797807205009,0.5675458299374023]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 32791, "outputTokens": 277, "cachedInputTokens": 32640, "cacheReportedInputTokens": 32791, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 32791, "reasoningOutputTokens": 46, "reasoningReportedOutputTokens": 277}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "f361f966", "role": "toolResult", "timestamp": "2026-10-28T21:51:48.098Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224307.591027112] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"names\": [\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\n \"measured_names\": [\"fl_joint1\",\"fl_joint2\",\"fl_joint3\",\"fl_joint4\",\"fl_joint5\",\"fl_joint6\"],\n \"target_local\": [0.19748021568092256,-0.10296987920505801,0.08799976895842088,-2.1073194747276565e-8,-2.1073194744748758e-8,-0.010000025916871026,0.9999499984907548],\n \"target_q\": [-0.5538732839669482,1.995022269064291,0.16137099170018213,1.8421592169091363,-0.5113192953777548,0.016807289704789778],\n \"fk_target\": [0.1974588856049695,-0.10295700740880999,0.08803969389974405,5.656337319998357e-7,-0.00000421306646243146,-0.010000411984881847,0.9999499946207624],\n \"fk_measured\": [0.058972431162814636,0.00270585894917854,0.1515630860129737,0.07115997195073531,0.2000997192265956,0.15152750906073412,0.9653682068284917],\n \"tf\": [-0.34149891422440415,-0.2565050430306144,0.8897903932734834,-0.09308770694337509,0.19088826103653644,0.7954797807205009,0.5675458299374023]\n }\n}"}]}, {"id": "d5761dfc", "role": "assistant", "timestamp": "2026-10-28T21:52:01.148Z", "parts": [{"type": "reasoning", "text": "**Solving initial q0**"}, {"type": "tool", "callID": "call_ddb8d3f7aa744130902ea9827c5a15e5|fc_03880fcdb65294d8016ae26e7fb7348192949bf1a9101eaff4", "name": "exec", "input": {"description": "inspect inverse kinematics branches", "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n model=helpers.joint_model('left_arm'); p=await helpers.transform_pose(io,[-.198,-.22,.87,0,0,.70710678,.70710678],'world:base','left_arm:base')\n out=[]\n for seed in [[0]*6,[0,1,1,0,0,0],[0,.5,1,.5,0,0]]:\n try:out.append(model.ik(p,seed,budget={'max_attempts':30,'attempt_time_s':.1,'max_time_s':2}))\n except Exception as e:out.append(str(e))\n finish(out)\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224322.596944567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [-0.5526204907538345,-1.7623647377941796,-0.6392799169876033,-1.1322713387442476,-0.513594805751331,-0.018117428053433485],\n [-0.553866303587819,1.9944921125413446,0.16136065569054742,1.8416406163084864,-0.5113207015289282,0.016809955678195713],\n [-0.5538701711987519,1.9950389022424122,0.16140448051613965,1.842137792565355,-0.5113167030912799,0.016803814033760394]\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 33530, "outputTokens": 328, "cachedInputTokens": 32896, "cacheReportedInputTokens": 33530, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 33530, "reasoningOutputTokens": 126, "reasoningReportedOutputTokens": 328}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "b444ccfd", "role": "toolResult", "timestamp": "2026-10-28T21:52:03.306Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224322.596944567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [-0.5526204907538345,-1.7623647377941796,-0.6392799169876033,-1.1322713387442476,-0.513594805751331,-0.018117428053433485],\n [-0.553866303587819,1.9944921125413446,0.16136065569054742,1.8416406163084864,-0.5113207015289282,0.016809955678195713],\n [-0.5538701711987519,1.9950389022424122,0.16140448051613965,1.842137792565355,-0.5113167030912799,0.016803814033760394]\n ]\n}"}]}, {"id": "3e92b677", "role": "assistant", "timestamp": "2026-10-28T21:52:16.178Z", "parts": [{"type": "reasoning", "text": "**Preparing the positive seed**"}, {"type": "tool", "callID": "call_5c2bd880402b45b7ae3456ce1ffc616d|fc_03880fcdb65294d8016ae26e8d82f081928e2ed821588e2644", "name": "edit", "input": {"path": ".kinex/tools/manipulate.py", "edits": [{"oldText": "newq=model.ik(local,q)", "newText": "seed=wp.get('seed',q)\n newq=model.ik(local,seed,budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2})"}, {"oldText": "'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None", "newText": "'target_q':q if points else None,'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None"}]}, "output": "Successfully replaced 2 block(s) in .kinex/tools/manipulate.py.", "isError": false, "metadata": {}}], "usage": {"inputTokens": 34081, "outputTokens": 283, "cachedInputTokens": 33664, "cacheReportedInputTokens": 34081, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 34081, "reasoningOutputTokens": 142, "reasoningReportedOutputTokens": 283}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "6fc3d286", "role": "toolResult", "timestamp": "2026-10-28T21:52:16.185Z", "parts": [{"type": "text", "text": "Successfully replaced 2 block(s) in .kinex/tools/manipulate.py."}]}, {"id": "001b6a33", "role": "assistant", "timestamp": "2026-10-28T21:52:19.874Z", "parts": [{"type": "tool", "callID": "call_403b170c14244e30a81ccd05c9490501|fc_03880fcdb65294d8016ae26e92923481929e81e637fddfc208", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "recover to raised posture", "args": {"waypoints": [{"xyz": [-0.28, -0.15, 1.13], "quat": [0, 0, 0.70710678, 0.70710678], "seed": [0, 1, 1, 0, 0, 0], "duration": 3}], "cameras": ["head_camera", "left_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224341.829452565] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.2808323314765084,-0.1521085564748633,1.1266195287624303],\n \"quat\": [-0.0017668889194129447,0.002406581741459667,0.7083999131961318,0.7058049655900566],\n \"q\": [-0.16894401609897614,0.6368672847747803,0.7560085654258728,-0.12360157817602158,-0.14953598380088806,0.009546547196805477],\n \"grip\": [0.9981070431795988],\n \"target_q\": [-0.16866906820474564,0.6489774621097011,0.7660709003489753,-0.11556851724076331,-0.1454201730553973,0.009877023164163508],\n \"target_error\": 0.0040701808169305775,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-mkfvkm2u.png\",\"/tmp/kinex-observation-k28drqoq.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 34391, "outputTokens": 99, "cachedInputTokens": 34176, "cacheReportedInputTokens": 34391, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 34391, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 99}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "8c8cb210", "role": "toolResult", "timestamp": "2026-10-28T21:52:41.812Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224341.829452565] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.2808323314765084,-0.1521085564748633,1.1266195287624303],\n \"quat\": [-0.0017668889194129447,0.002406581741459667,0.7083999131961318,0.7058049655900566],\n \"q\": [-0.16894401609897614,0.6368672847747803,0.7560085654258728,-0.12360157817602158,-0.14953598380088806,0.009546547196805477],\n \"grip\": [0.9981070431795988],\n \"target_q\": [-0.16866906820474564,0.6489774621097011,0.7660709003489753,-0.11556851724076331,-0.1454201730553973,0.009877023164163508],\n \"target_error\": 0.0040701808169305775,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":6.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-mkfvkm2u.png\",\"/tmp/kinex-observation-k28drqoq.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "0bc8609d", "role": "assistant", "timestamp": "2026-10-28T21:52:46.135Z", "parts": [{"type": "tool", "callID": "call_6a4e2d05e8134be6a9ad58f01415614c|fc_03880fcdb65294d8016ae26eac57dc8192b75956d54413768e", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "align with clear bottle", "args": {"waypoints": [{"xyz": [-0.198, -0.095, 0.875], "quat": [0, 0, 0.70710678, 0.70710678], "linear": true, "duration": 2.5}, {"xyz": [-0.198, 0.035, 0.875], "quat": [0, 0, 0.70710678, 0.70710678], "linear": true, "duration": 1.5}], "cameras": ["head_camera", "left_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224368.240218130] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.19568474524799342,0.03129575416783295,0.8768179791471703],\n \"quat\": [0.0009593362831939475,-0.0018856024907780693,0.7054160408881407,0.7087903310816559],\n \"q\": [-0.3494529128074646,2.0894923210144043,0.6539878249168396,1.4443292617797852,-0.3007594048976898,0.014579671435058117],\n \"grip\": [1.0012966367331417],\n \"target_q\": [-0.3411276660362571,2.0986666201389337,0.6667057780316544,1.4364761952238168,-0.29715006055619353,0.014478228366471838],\n \"target_error\": 0.004731478619983621,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.72,\"waited_s\":4.4},\n \"file\": [\"/tmp/kinex-observation-7woacofn.png\",\"/tmp/kinex-observation-dzfqc4pj.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 35033, "outputTokens": 134, "cachedInputTokens": 34304, "cacheReportedInputTokens": 35033, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 35033, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 134}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "e8dcb044", "role": "toolResult", "timestamp": "2026-10-28T21:53:14.423Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224368.240218130] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.19568474524799342,0.03129575416783295,0.8768179791471703],\n \"quat\": [0.0009593362831939475,-0.0018856024907780693,0.7054160408881407,0.7087903310816559],\n \"q\": [-0.3494529128074646,2.0894923210144043,0.6539878249168396,1.4443292617797852,-0.3007594048976898,0.014579671435058117],\n \"grip\": [1.0012966367331417],\n \"target_q\": [-0.3411276660362571,2.0986666201389337,0.6667057780316544,1.4364761952238168,-0.29715006055619353,0.014478228366471838],\n \"target_error\": 0.004731478619983621,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":10.72,\"waited_s\":4.4},\n \"file\": [\"/tmp/kinex-observation-7woacofn.png\",\"/tmp/kinex-observation-dzfqc4pj.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "392a1001", "role": "assistant", "timestamp": "2026-10-28T21:53:17.676Z", "parts": [{"type": "tool", "callID": "call_f5aa026654404847acb39b518709dafd|fc_03880fcdb65294d8016ae26ecce6e08192ba2539ec9ab15bd2", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "grasp clear bottle", "args": {"grip": 0, "settle": 0.8, "cameras": ["head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224399.517077974] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.19641768011592214,0.03397012328379588,0.8762348087384827],\n \"quat\": [0.0009134239479954816,-0.00042023378935098735,0.7057645168018754,0.7084457889518926],\n \"q\": 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\"tcp\": [-0.19641768011592214,0.03397012328379588,0.8762348087384827],\n \"quat\": [0.0009134239479954816,-0.00042023378935098735,0.7057645168018754,0.7084457889518926],\n \"q\": [-0.3451070785522461,2.094144821166992,0.6640962958335876,1.4365533590316772,-0.29736000299453735,0.01581699773669243],\n \"grip\": [0.6974994838237762],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":11.52,\"waited_s\":0.7999999999999989},\n \"file\": [\"/tmp/kinex-observation-qlc7bhx8.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "27de5ca7", "role": "assistant", "timestamp": "2026-10-28T21:53:26.995Z", "parts": [{"type": "tool", "callID": "call_5a42816f1c5e48ce8e2db25227cad69b|fc_03880fcdb65294d8016ae26ed5f5b4819290feba7059024ae4", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "lift clear bottle", "args": {"waypoints": [{"xyz": [-0.198, 0.035, 1.12], "quat": [0, 0, 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"metadata": {}}], "usage": {"inputTokens": 36128, "outputTokens": 88, "cachedInputTokens": 35584, "cacheReportedInputTokens": 36128, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 36128, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 88}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "38f9e622", "role": "toolResult", "timestamp": "2026-10-28T21:53:41.027Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224409.028210939] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.1978245238769104,0.03624807484249387,1.1136391307316316],\n \"quat\": [-0.0006687220835096329,0.003217325161495589,0.7074333523842097,0.7067724199231455],\n \"q\": [-0.34395521879196167,1.481361746788025,1.1216462850570679,0.35927361249923706,-0.31043118238449097,0.018503844738006592],\n \"grip\": [0.7039102004332977],\n \"target_q\": [-0.3444226970084058,1.4740169827439866,1.135291463367972,0.3440870017483791,-0.3100354832143796,0.016756424773925072],\n \"target_error\": 0.00648453086441232,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":13.92,\"waited_s\":2.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-h2h_buoz.png\",\"/tmp/kinex-observation-wcac2rse.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "d6d61b90", "role": "assistant", "timestamp": "2026-10-28T21:54:02.070Z", "parts": [{"type": "reasoning", "text": "**Tilting camera toward bin**\n\n**Moving bottle toward bin**"}, {"type": "tool", "callID": "call_a9936dc46ebb49889c6e72409761f1b1|fc_03880fcdb65294d8016ae26ef99454819282a3f7f5fb7a584d", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "transfer bottle above left bin", "args": {"waypoints": [{"xyz": [-0.5, 0.015, 1.06], "quat": [0, 0, 0.70710678, 0.70710678], "linear": true, "duration": 3}], "cameras": ["head_camera", "left_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224444.140137075] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.4945684814634984,0.015371463224506068,1.0604537555111784],\n \"quat\": [0.00025644590441672847,0.0009540466669193152,0.7060608183652334,0.7081505099905476],\n \"q\": [0.5811179280281067,1.626004695892334,1.0479673147201538,0.5658470392227173,0.6207801699638367,0.021735304966568947],\n \"grip\": [0.7077647921713917],\n \"target_q\": [0.5932278082889996,1.6362225123927316,1.0560940418899667,0.5683202118899915,0.63010988492782,0.019804060908021025],\n \"target_error\": 0.005463082701501553,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":17.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-d1x88zez.png\",\"/tmp/kinex-observation-3psdpux4.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 36756, "outputTokens": 445, "cachedInputTokens": 36096, "cacheReportedInputTokens": 36756, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 36756, "reasoningOutputTokens": 353, "reasoningReportedOutputTokens": 445}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "1c00b1c6", "role": "toolResult", "timestamp": "2026-10-28T21:54:18.982Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224444.140137075] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.4945684814634984,0.015371463224506068,1.0604537555111784],\n \"quat\": [0.00025644590441672847,0.0009540466669193152,0.7060608183652334,0.7081505099905476],\n \"q\": [0.5811179280281067,1.626004695892334,1.0479673147201538,0.5658470392227173,0.6207801699638367,0.021735304966568947],\n \"grip\": [0.7077647921713917],\n \"target_q\": [0.5932278082889996,1.6362225123927316,1.0560940418899667,0.5683202118899915,0.63010988492782,0.019804060908021025],\n \"target_error\": 0.005463082701501553,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":17.32,\"waited_s\":3.4000000000000004},\n \"file\": [\"/tmp/kinex-observation-d1x88zez.png\",\"/tmp/kinex-observation-3psdpux4.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "8acd820c", "role": "assistant", "timestamp": "2026-10-28T21:54:25.878Z", "parts": [{"type": "reasoning", "text": "**Lowering bottle over bin**"}, {"type": "tool", "callID": "call_da8f102ca6424b14a19a4ae4968069b5|fc_03880fcdb65294d8016ae26f105b648192adccd6a6096ca5e7", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "lower bottle over bin opening", "args": {"waypoints": [{"xyz": [-0.52, 0.03, 0.9], "quat": [0, 0, 0.70710678, 0.70710678], "linear": true, "duration": 1.5}], "cameras": ["head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224467.850536046] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5171615828512802,0.03131842426267671,0.9044438328481127],\n \"quat\": [0.0019280949082640983,0.0004485915764353847,0.7052955620133471,0.7089106089062986],\n \"q\": [0.6077774167060852,2.09543514251709,0.9070724844932556,1.1807374954223633,0.6567703485488892,0.019012924283742905],\n \"grip\": [0.7038915062492545],\n \"target_q\": [0.6142882451032351,2.1089415504442486,0.9061590178895118,1.1926114491230337,0.6583185828385036,0.017082770022362744],\n \"target_error\": 0.005435301723780054,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":19.240000000000002,\"waited_s\":1.9200000000000017},\n \"file\": [\"/tmp/kinex-observation-f9hecmv3.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 37742, "outputTokens": 155, "cachedInputTokens": 36992, "cacheReportedInputTokens": 37742, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 37742, "reasoningOutputTokens": 64, "reasoningReportedOutputTokens": 155}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "b27b5e13", "role": "toolResult", "timestamp": "2026-10-28T21:54:36.362Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224467.850536046] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5171615828512802,0.03131842426267671,0.9044438328481127],\n \"quat\": [0.0019280949082640983,0.0004485915764353847,0.7052955620133471,0.7089106089062986],\n \"q\": [0.6077774167060852,2.09543514251709,0.9070724844932556,1.1807374954223633,0.6567703485488892,0.019012924283742905],\n \"grip\": [0.7038915062492545],\n \"target_q\": [0.6142882451032351,2.1089415504442486,0.9061590178895118,1.1926114491230337,0.6583185828385036,0.017082770022362744],\n \"target_error\": 0.005435301723780054,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":19.240000000000002,\"waited_s\":1.9200000000000017},\n \"file\": [\"/tmp/kinex-observation-f9hecmv3.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "d639fa23", "role": "assistant", "timestamp": "2026-10-28T21:54:39.636Z", "parts": [{"type": "tool", "callID": "call_44615780c683439db77e1a2334c14c6d|fc_03880fcdb65294d8016ae26f1f03f48192b94f333693da41b3", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "release bottle into bin", "args": {"grip": 1, "settle": 1, "cameras": ["left_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224481.635084896] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5184455702180112,0.030694684254432475,0.9010313035127829],\n \"quat\": [0.0010387084556719025,-0.00015139120055665463,0.70592582635397,0.708284989147669],\n \"q\": [0.6109477877616882,2.1069490909576416,0.9063937664031982,1.1924670934677124,0.6583145260810852,0.017104366794228554],\n \"grip\": [0.9881577410481193],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.240000000000002,\"waited_s\":1},\n \"file\": [\"/tmp/kinex-observation-2opki9cb.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 38332, "outputTokens": 52, "cachedInputTokens": 37760, "cacheReportedInputTokens": 38332, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 38332, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 52}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "ff2d32db", "role": "toolResult", "timestamp": "2026-10-28T21:54:46.160Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224481.635084896] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5184455702180112,0.030694684254432475,0.9010313035127829],\n \"quat\": [0.0010387084556719025,-0.00015139120055665463,0.70592582635397,0.708284989147669],\n \"q\": [0.6109477877616882,2.1069490909576416,0.9063937664031982,1.1924670934677124,0.6583145260810852,0.017104366794228554],\n \"grip\": [0.9881577410481193],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":20.240000000000002,\"waited_s\":1},\n \"file\": [\"/tmp/kinex-observation-2opki9cb.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "f6845a14", "role": "assistant", "timestamp": "2026-10-28T21:54:53.930Z", "parts": [{"type": "reasoning", "text": "**Positioning camera over bin**"}, {"type": "tool", "callID": "call_360706a3ace14bd3b3248f941e0797c7|fc_03880fcdb65294d8016ae26f2c3a6c81928313d6fd7b01f6ff", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "inspect bin contents", "args": {"waypoints": [{"xyz": [-0.5, -0.06, 1.02], "quat": [-0.35355339, 0.35355339, 0.61237244, 0.61237244], "linear": true, "duration": 2}], "cameras": ["left_camera"], "save_depth": true}}, "output": "{\n \"output\": \"[INFO] [1793224496.015300089] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5020418026936535,-0.059801965512401384,1.0194523611193695],\n \"quat\": [-0.34889554343130097,0.3472040172825245,0.6175663469696127,0.6134599230978837],\n \"q\": [0.5938779711723328,1.7595466375350952,1.7936086654663086,-1.1486849784851074,0.3013532757759094,0.547741174697876],\n \"grip\": [0.9976837824691426],\n \"target_q\": [0.5867695945404096,1.7695588958198807,1.8155074267669495,-1.1762405787419286,0.2917951876543838,0.5514962828484409],\n \"target_error\": 0.002123224962566933,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":22.64,\"waited_s\":2.3999999999999986},\n \"file\": [\"/tmp/kinex-observation-or0xdy1p.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 38748, "outputTokens": 179, "cachedInputTokens": 38272, "cacheReportedInputTokens": 38748, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 38748, "reasoningOutputTokens": 78, "reasoningReportedOutputTokens": 179}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "a2501cc5", "role": "toolResult", "timestamp": "2026-10-28T21:55:07.663Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224496.015300089] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5020418026936535,-0.059801965512401384,1.0194523611193695],\n \"quat\": [-0.34889554343130097,0.3472040172825245,0.6175663469696127,0.6134599230978837],\n \"q\": [0.5938779711723328,1.7595466375350952,1.7936086654663086,-1.1486849784851074,0.3013532757759094,0.547741174697876],\n \"grip\": [0.9976837824691426],\n \"target_q\": [0.5867695945404096,1.7695588958198807,1.8155074267669495,-1.1762405787419286,0.2917951876543838,0.5514962828484409],\n \"target_error\": 0.002123224962566933,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":22.64,\"waited_s\":2.3999999999999986},\n \"file\": [\"/tmp/kinex-observation-or0xdy1p.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "f1dacd91", "role": "assistant", "timestamp": "2026-10-28T21:55:41.528Z", "parts": [{"type": "reasoning", "text": "**Checking coke reach**\n\n**Testing left coke reach**\n\n**Testing bottle reach**"}, {"type": "tool", "callID": "call_c70a91f8fac24a9990a8ce9d010eab94|fc_03880fcdb65294d8016ae26f58edcc8192a2d5104c71efa528", "name": "exec", "input": {"description": "plan remaining bottle reach", "python": "import asyncio,numpy as np\nfrom scipy.spatial.transform import Rotation as R\nfrom kinex import communication,helpers,finish\nasync def main():\n out=[]; model=helpers.joint_model('left_arm')\n async with communication() as io:\n for name,xyz,yaw in [('coke',[.19,.12,.865],48),('yellow',[.06,.21,.87],61),('coke_lift',[.12,.03,1.03],48),('yellow_lift',[.02,.12,1.03],61)]:\n r=R.from_euler('z',yaw,degrees=True);p=np.r_[np.array(xyz)-r.apply([.12,0,0]),r.as_quat()]\n p=await helpers.transform_pose(io,p.tolist(),'world:base','left_arm:base')\n try:q=model.ik(p,[0,2,1,1,0,0],budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2});out.append({'name':name,'q':q,'quat':r.as_quat().tolist()})\n except ValueError as e:out.append({'name':name,'error':str(e)})\n finish(out)\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224543.725672103] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n {\"name\":\"coke\",\"q\":[-0.7682474838893932,2.8205441857806255,2.174066940855675,0.6465166651221519,0.015836629018606843,0.005026753078927928],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow\",\"q\":[-0.5519639959914996,2.793350612445425,2.1392099935800983,0.6542416277664851,0.005085650161614391,0.005436249092634174],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]},\n {\"name\":\"coke_lift\",\"q\":[-0.7837596704018495,2.0847705295916814,1.5159528392357002,0.5691906155097223,-0.006951084853310415,0.013880856415633741],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow_lift\",\"q\":[-0.5705797685571973,2.131406981991661,1.5887848123532708,0.5431653775860207,-0.02002556223756075,0.013504624618480239],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]}\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 39356, "outputTokens": 795, "cachedInputTokens": 38784, "cacheReportedInputTokens": 39356, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 39356, "reasoningOutputTokens": 487, "reasoningReportedOutputTokens": 795}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "0c11832c", "role": "toolResult", "timestamp": "2026-10-28T21:55:44.476Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224543.725672103] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n {\"name\":\"coke\",\"q\":[-0.7682474838893932,2.8205441857806255,2.174066940855675,0.6465166651221519,0.015836629018606843,0.005026753078927928],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow\",\"q\":[-0.5519639959914996,2.793350612445425,2.1392099935800983,0.6542416277664851,0.005085650161614391,0.005436249092634174],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]},\n {\"name\":\"coke_lift\",\"q\":[-0.7837596704018495,2.0847705295916814,1.5159528392357002,0.5691906155097223,-0.006951084853310415,0.013880856415633741],\"quat\":[0,0,0.4067366430758002,0.9135454576426009]},\n {\"name\":\"yellow_lift\",\"q\":[-0.5705797685571973,2.131406981991661,1.5887848123532708,0.5431653775860207,-0.02002556223756075,0.013504624618480239],\"quat\":[0,0,0.5075383629607041,0.8616291604415258]}\n ]\n}"}]}, {"id": "66040d17", "role": "assistant", "timestamp": "2026-10-28T21:56:01.407Z", "parts": [{"type": "reasoning", "text": "**Preparing yellow pickup**\n\n**Measuring yellow bottle**"}, {"type": "tool", "callID": "call_2c4bd3b629dc45048425661fbfddf8d2|fc_03880fcdb65294d8016ae26f6e63cc819295374267ef4f87f4", "name": "exec", "input": {"description": "measure remaining bottle surfaces", "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,dep=await helpers.camera_frame(io,'head_camera');d=helpers.image_array(dep);out=[]\n for u,v in [(197,45),(203,15),(195,5),(270,35),(260,65),(264,48)]:\n z=float(d[v,u])*.001;p=[(u-159.5)*z/358.6421814,(v-119.5)*z/358.6421814,z,0,0,0,1]\n out.append([u,v,(await helpers.transform_pose(io,p,'head_camera:base','world:base'))[:3]])\n finish(out)\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224563.007108510] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [197,45,[0.05447197977048632,0.18363281695971267,0.7914746286877076]],\n [203,15,[0.06005972182630685,0.18232398834126862,0.875493007530382]],\n [195,5,[0.04204036901406122,0.18984502660198344,0.8948837863355172]],\n [270,35,[0.18028540144849753,0.09326873128529545,0.8962015643731865]],\n [260,65,[0.18068970414215718,0.09767136948762245,0.8120035427331231]],\n [264,48,[0.1769171417661466,0.0945546611955852,0.8621660116854928]]\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 40560, "outputTokens": 412, "cachedInputTokens": 40064, "cacheReportedInputTokens": 40560, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 40560, "reasoningOutputTokens": 214, "reasoningReportedOutputTokens": 412}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "5e3b2fbd", "role": "toolResult", "timestamp": "2026-10-28T21:56:03.819Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224563.007108510] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n [197,45,[0.05447197977048632,0.18363281695971267,0.7914746286877076]],\n [203,15,[0.06005972182630685,0.18232398834126862,0.875493007530382]],\n [195,5,[0.04204036901406122,0.18984502660198344,0.8948837863355172]],\n [270,35,[0.18028540144849753,0.09326873128529545,0.8962015643731865]],\n [260,65,[0.18068970414215718,0.09767136948762245,0.8120035427331231]],\n [264,48,[0.1769171417661466,0.0945546611955852,0.8621660116854928]]\n ]\n}"}]}, {"id": "6460a19c", "role": "assistant", "timestamp": "2026-10-28T21:56:10.787Z", "parts": [{"type": "reasoning", "text": "**Approaching the yellow left**"}, {"type": "text", "text": "The clear bottle is in the bin, confirmed by the wrist camera. I’m moving the remaining two bottles next."}, {"type": "tool", "callID": "call_9505612e357f41e0b601181c0afd3dc9|fc_03880fcdb65294d8016ae26f78a58081929076c7f634d33d64", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "approach yellow bottle", "args": {"waypoints": [{"xyz": [-0.18, -0.08, 1.06], "quat": [0, 0, 0.50753836, 0.86162916], "duration": 2}, {"xyz": [-0.006, 0.093, 0.86], "quat": [0, 0, 0.50753836, 0.86162916], "linear": true, "duration": 2}, {"xyz": [0.055, 0.205, 0.86], "quat": [0, 0, 0.50753836, 0.86162916], "linear": true, "duration": 1.5}], "cameras": ["head_camera", "left_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224572.865750018] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.05147708742033642,0.2045278187007235,0.8629828751979567],\n \"quat\": [0.0012442963942044286,-0.0021552669022041654,0.5092927264227239,0.8605897543917034],\n \"q\": [-0.5445034503936768,2.769740104675293,2.054563522338867,0.720240592956543,0.008363209664821625,0.00577180739492178],\n \"grip\": [0.9996614374897697],\n \"target_q\": [-0.5484997194158352,2.7834866635540485,2.0749658177550847,0.7086049164412447,0.00849461950524203,0.005581543401104956],\n \"target_error\": 0.004640195326699464,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":28.560000000000002,\"waited_s\":5.920000000000002},\n \"file\": [\"/tmp/kinex-observation-jhdi9t_a.png\",\"/tmp/kinex-observation-zkmskjon.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 41227, "outputTokens": 230, "cachedInputTokens": 40832, "cacheReportedInputTokens": 41227, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 41227, "reasoningOutputTokens": 30, "reasoningReportedOutputTokens": 230}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "ed3b0775", "role": "toolResult", "timestamp": "2026-10-28T21:56:37.608Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224572.865750018] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.05147708742033642,0.2045278187007235,0.8629828751979567],\n \"quat\": [0.0012442963942044286,-0.0021552669022041654,0.5092927264227239,0.8605897543917034],\n \"q\": [-0.5445034503936768,2.769740104675293,2.054563522338867,0.720240592956543,0.008363209664821625,0.00577180739492178],\n \"grip\": [0.9996614374897697],\n \"target_q\": [-0.5484997194158352,2.7834866635540485,2.0749658177550847,0.7086049164412447,0.00849461950524203,0.005581543401104956],\n \"target_error\": 0.004640195326699464,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":28.560000000000002,\"waited_s\":5.920000000000002},\n \"file\": [\"/tmp/kinex-observation-jhdi9t_a.png\",\"/tmp/kinex-observation-zkmskjon.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "10bf7ac6", "role": "assistant", "timestamp": "2026-10-28T21:56:41.700Z", "parts": [{"type": "tool", "callID": "call_668fe7f6cc314dbdaa6658b2da33540c|fc_03880fcdb65294d8016ae26f990f948192bd3357fc047329c9", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "grasp yellow bottle", "args": {"grip": 0, "settle": 0.6, "cameras": ["head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224603.816933881] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.052825427222747336,0.20584596398041766,0.8627488049225395],\n \"quat\": [0.0014806761308315045,-0.0011401104489938575,0.5090236070610991,0.8607505301775115],\n \"q\": [-0.5453270673751831,2.7777156829833984,2.0716705322265625,0.7096014022827148,0.008205556310713291,0.007084341254085302],\n \"grip\": [0.7739523974331943],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":29.16,\"waited_s\":0.5999999999999979},\n \"file\": [\"/tmp/kinex-observation-7uqteqro.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 41999, "outputTokens": 52, "cachedInputTokens": 41344, "cacheReportedInputTokens": 41999, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 41999, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 52}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "82847509", "role": "toolResult", "timestamp": "2026-10-28T21:56:46.905Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224603.816933881] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.052825427222747336,0.20584596398041766,0.8627488049225395],\n \"quat\": [0.0014806761308315045,-0.0011401104489938575,0.5090236070610991,0.8607505301775115],\n \"q\": [-0.5453270673751831,2.7777156829833984,2.0716705322265625,0.7096014022827148,0.008205556310713291,0.007084341254085302],\n \"grip\": [0.7739523974331943],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":29.16,\"waited_s\":0.5999999999999979},\n \"file\": [\"/tmp/kinex-observation-7uqteqro.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "b26612cf", "role": "assistant", "timestamp": "2026-10-28T21:56:50.222Z", "parts": [{"type": "tool", "callID": "call_c3bbeb94b3df4b7ba3f237205beefb8e|fc_03880fcdb65294d8016ae26fa112ec8192ad6730590b8c8396", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "lift yellow bottle clear", "args": {"waypoints": [{"xyz": [0.015, 0.12, 1.035], "quat": [0, 0, 0.50753836, 0.86162916], "linear": true, "duration": 2}], "cameras": ["head_camera", "left_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224612.139791819] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.012475983380957172,0.12415535121015182,1.0325882299616729],\n \"quat\": [0.0013523926468964947,-0.00044605020363605173,0.5105743815543966,0.8598324098181513],\n \"q\": [-0.5547859072685242,2.128363847732544,1.5929771661758423,0.5379608273506165,-0.01129952073097229,0.01545263547450304],\n \"grip\": [0.7797582962296226],\n \"target_q\": [-0.5614852376589563,2.1161047986303942,1.5853120475938565,0.5314918774258239,-0.011183534532282036,0.0136917382097596],\n \"target_error\": 0.005427175903790725,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":31.560000000000002,\"waited_s\":2.400000000000002},\n \"file\": [\"/tmp/kinex-observation-dsx7lnz4.png\",\"/tmp/kinex-observation-wfdkz__a.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 42422, "outputTokens": 88, "cachedInputTokens": 41856, "cacheReportedInputTokens": 42422, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 42422, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 88}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "4a031853", "role": "toolResult", "timestamp": "2026-10-28T21:57:02.594Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224612.139791819] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [0.012475983380957172,0.12415535121015182,1.0325882299616729],\n \"quat\": [0.0013523926468964947,-0.00044605020363605173,0.5105743815543966,0.8598324098181513],\n \"q\": [-0.5547859072685242,2.128363847732544,1.5929771661758423,0.5379608273506165,-0.01129952073097229,0.01545263547450304],\n \"grip\": [0.7797582962296226],\n \"target_q\": [-0.5614852376589563,2.1161047986303942,1.5853120475938565,0.5314918774258239,-0.011183534532282036,0.0136917382097596],\n \"target_error\": 0.005427175903790725,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":31.560000000000002,\"waited_s\":2.400000000000002},\n \"file\": [\"/tmp/kinex-observation-dsx7lnz4.png\",\"/tmp/kinex-observation-wfdkz__a.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "ceac11f4", "role": "assistant", "timestamp": "2026-10-28T21:57:09.118Z", "parts": [{"type": "tool", "callID": "call_41f972da92074626b70d171b22dc5bcd|fc_03880fcdb65294d8016ae26fb339a48192b3d1c1bd58870222", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "carry yellow bottle to bin", "args": {"waypoints": [{"xyz": [-0.5, 0.015, 1.06], "quat": [0, 0, 0.70710678, 0.70710678], "linear": true, "duration": 3}, {"xyz": [-0.52, 0.03, 0.9], "quat": [0, 0, 0.70710678, 0.70710678], "linear": true, "duration": 1.5}], "cameras": ["head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224631.131968514] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5174761649606938,0.031407337429610896,0.9046801446093026],\n \"quat\": [0.002290593707506658,0.0004161799849168633,0.7054030393235293,0.7088026044589666],\n \"q\": [0.6082080006599426,2.0956389904022217,0.9081711173057556,1.1805373430252075,0.656909167766571,0.01906382292509079],\n \"grip\": [0.7724612653255463],\n \"target_q\": [0.6142413871259487,2.1090307308805722,0.9062710823865341,1.1924842697081466,0.6582675187588196,0.017143442076574332],\n \"target_error\": 0.005500372306525918,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":36.480000000000004,\"waited_s\":4.920000000000002},\n \"file\": [\"/tmp/kinex-observation-92wa3sed.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 43056, "outputTokens": 130, "cachedInputTokens": 42368, "cacheReportedInputTokens": 43056, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 43056, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 130}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "e0aba462", "role": "toolResult", "timestamp": "2026-10-28T21:57:33.308Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224631.131968514] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5174761649606938,0.031407337429610896,0.9046801446093026],\n \"quat\": [0.002290593707506658,0.0004161799849168633,0.7054030393235293,0.7088026044589666],\n \"q\": [0.6082080006599426,2.0956389904022217,0.9081711173057556,1.1805373430252075,0.656909167766571,0.01906382292509079],\n \"grip\": [0.7724612653255463],\n \"target_q\": [0.6142413871259487,2.1090307308805722,0.9062710823865341,1.1924842697081466,0.6582675187588196,0.017143442076574332],\n \"target_error\": 0.005500372306525918,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":36.480000000000004,\"waited_s\":4.920000000000002},\n \"file\": [\"/tmp/kinex-observation-92wa3sed.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "70ce844a", "role": "assistant", "timestamp": "2026-10-28T21:57:36.550Z", "parts": [{"type": "tool", "callID": "call_16521e66b5ff4499b5b47bdcc3bda4dc|fc_03880fcdb65294d8016ae26fcfe8c0819286df4a01dc40e1f4", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "release yellow bottle", "args": {"grip": 1, "settle": 0.8, "cameras": ["left_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224658.817845982] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"tcp\": [-0.5184055082681959,0.03082700826055433,0.9015009198137535],\n \"quat\": [0.0015373910546378198,-0.0001890281441048716,0.7058661041915254,0.7083435915222107],\n \"q\": 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\"tcp\": [-0.5184055082681959,0.03082700826055433,0.9015009198137535],\n \"quat\": [0.0015373910546378198,-0.0001890281441048716,0.7058661041915254,0.7083435915222107],\n \"q\": [0.6107003688812256,2.1061878204345703,0.9069343209266663,1.192107915878296,0.6582370400428772,0.01719825342297554],\n \"grip\": [0.9859253439036283],\n \"target_q\": null,\n \"target_error\": null,\n \"time\": {\"success\":true,\"message\":\"ok\",\"time_s\":37.28,\"waited_s\":0.7999999999999972},\n \"file\": [\"/tmp/kinex-observation-7jmu9wgv.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "25e5435a", "role": "assistant", "timestamp": "2026-10-28T21:57:49.482Z", "parts": [{"type": "reasoning", "text": "**Inspecting the bin**"}, {"type": "tool", "callID": "call_a4fff9ee2ef7437894d454f3c14eb0b8|fc_03880fcdb65294d8016ae26fdb96ec8192a6b9c4af4059db9d", "name": "exec", "input": {"file": ".kinex/tools/manipulate.py", "description": "verify second deposit", "args": 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"parts": [{"type": "reasoning", "text": "**Updating defaults and checks**\n\n**Adding an error guard**"}, {"type": "tool", "callID": "call_d572734da4e64fccb8c44ec8b9491c16|fc_03880fcdb65294d8016ae27066f82c819291b7d71c53e842f9", "name": "edit", "input": {"path": ".kinex/tools/manipulate.py", "edits": [{"oldText": " if 'grip' in a:\n await io.publish(grip+'.target',{'data':float(a['grip'])})\n points=[]; elapsed=0.; targets=[]\n # Default vertical approach, fingers close along world X.\n default=(Rotation.from_euler('z',np.pi/2)*Rotation.from_euler('y',np.pi/2)).as_quat()", "newText": " points=[]; elapsed=0.; targets=[]\n # Default horizontal approach along world +Y, fingers close along world X.\n # High top-down poses can exceed ARX5 reach; provide explicit quaternions.\n default=Rotation.from_euler('z',np.pi/2).as_quat()"}, {"oldText": " seed=wp.get('seed',q)", "newText": " # Avoid the below-table IK branch when starting near home.\n seed=wp.get('seed',q if q[1]>.2 else 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No simulator reset or private state. ROS wait advances 0.04 s native frames, reads do not. Load devices first; inspect schemas.\n\nReusable `.kinex/tools/manipulate.py` runs with `exec(file=..., args=...)`. Args: side (default left), grip (0 closed,1 open), waypoints [{xyz world TCP, quat xyzw optional, duration seconds, linear bool, seed optional}], settle (default .4), cameras. It solves flange IK with TCP offset +.12 local X, publishes typed JointTrajectory, waits once, checks measured FK and returns images. Defaults horizontal +Y approach. It stops on >.025 m final position error. Caller must choose collision-free waypoints; IK is not collision checking.\n\nARX5 home-seeded IK can select a negative shoulder branch that drives through the table. Use positive shoulder seed [0,1,1,0,0,0] and first move to raised safe horizontal pose, e.g. [-.28,-.15,1.13], yaw90. A failed early command had 21 cm measured tracking error; stopped and recovered raised. High top-down grasp approach at y=.04,z=1.11 was unreachable. Horizontal side grasps work well.\n\nHead camera paired RGBD: world points from K and calibrated frame transform. Bottle surface depths are front surfaces, not centers; add radius ~.025 m farther from camera in world Y. Table observed z=.74. Left arm can reach all three upright bottles using yaw roughly radial from its base, avoiding need for handoff.\n\nObserved successful grasp/transfer sequence:\n- Clear: center [-.198,.035,.875], horizontal yaw90 q=[0,0,.70710678,.70710678]. Approach y=-.095, straight advance, close .8s. Lift to z1.12 in 2s; verify bottle moves with gripper.\n- Yellow: [.055,.205,.86], horizontal yaw61 q=[0,0,.50753836,.86162916]. Approach [-.006,.093,.86], straight advance 1.5s. Close .6s. Retract/lift to [.015,.12,1.035] in 2s (pure vertical lift at far reach may fail).\n- Coke: [.185,.12,.865], horizontal yaw48 q=[0,0,.40673664,.91354546]. Approach [.093,.014,.865]. Close .6s; retract/lift to [.11,.03,1.04] in 2s.\n- Carry each at z~1.06 to [-.5,.015,1.06], yaw90, with 3–3.5s Cartesian path. Lower to [-.52,.03,.9] in1.5s. Open above bin. Silver-lined bin is left of table, mostly outside head view.\n- Inspect deposits with left wrist camera by moving empty TCP to [-.5,-.06,1.02], quaternion [-.35355339,.35355339,.61237244,.61237244], 1.5–2s linear motion. First two bottles were clearly visible inside bin.\n\nEpisode ended during final opening wait at t=54.16s (~1354/1700 frames), after .16s of requested .8s. No further waits or motion attempted. Final head image showed empty table; wrist image showed coke leaving gripper, opening measured .886. Native verdict is owner-only; terminal/rejected wait alone is not success evidence. 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+oid sha256:bb3782a283913dd8ece0ae26fd20814e479c6c2a7c9d76c335f3b8339f3e113a +size 9821 diff --git a/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/head_camera/profile.yaml b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/head_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..125433679dd848806379de9f04adcd7b980ff833 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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: "2026-10-28T21:48:48.626Z" +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-3/workspace/.kinex/devices/left_arm/arm.urdf b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_arm/arm.urdf new file mode 100644 index 0000000000000000000000000000000000000000..0679f698a073bf49a5e79246d115ce8506e14935 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_arm/arm.urdf @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_arm/links.py b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_arm/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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-3/workspace/.kinex/devices/left_arm/profile.yaml b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_arm/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c0807cd0c25600a5cd12d5bbf2d59b10b1339be9 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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: "2026-10-28T21:48:48.634Z" +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-3/workspace/.kinex/devices/left_camera/profile.yaml b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7e0d690e557b179c6c94217017801f9d688b421b --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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: "2026-10-28T21:48:48.630Z" +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-3/workspace/.kinex/devices/left_gripper/gripper.urdf b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_gripper/gripper.urdf new file mode 100644 index 0000000000000000000000000000000000000000..ffde595542f57b4e9ac0bbba73940f540888b67e --- /dev/null +++ b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_gripper/gripper.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_gripper/links.py b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_gripper/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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-3/workspace/.kinex/devices/left_gripper/profile.yaml b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/left_gripper/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1ccf51eafba3dc62d4308ba6e2c302da5b3ce6da --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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: "2026-10-28T21:48:48.638Z" +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-3/workspace/.kinex/devices/right_arm/arm.urdf b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_arm/arm.urdf new file mode 100644 index 0000000000000000000000000000000000000000..98d0eedec4a96aa88680315bd8abb3ec31107ea7 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_arm/arm.urdf @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_arm/links.py b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_arm/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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-3/workspace/.kinex/devices/right_arm/profile.yaml b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_arm/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..06eebf3d702e74216a0830f607b4bab598f08f10 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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: "2026-10-28T21:48:48.637Z" +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-3/workspace/.kinex/devices/right_camera/profile.yaml b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d1d4ce439c7901e3828b5646bd5d991c4465588d --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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: "2026-10-28T21:48:48.629Z" +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-3/workspace/.kinex/devices/right_gripper/gripper.urdf b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_gripper/gripper.urdf new file mode 100644 index 0000000000000000000000000000000000000000..dfd1ec14f3c8085e398e7e1f5e81cb2af81e1f30 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_gripper/gripper.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_gripper/links.py b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_gripper/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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-3/workspace/.kinex/devices/right_gripper/profile.yaml b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/right_gripper/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..14817f91ee520a630d5101a4285b6e653ad718d0 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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: "2026-10-28T21:48:48.635Z" +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-3/workspace/.kinex/devices/world/profile.yaml b/experiments/robotwin-native/kinex-3/workspace/.kinex/devices/world/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..639e92de34829138c386d8bcd6eb94cd09f03179 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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: "2026-10-28T21:48:48.624Z" +frames: + base: + parent: null + translation_m: null + rotation_xyzw: null +bounds: null +surfaces: [] +type: world diff --git a/experiments/robotwin-native/kinex-3/workspace/.kinex/kinex.json b/experiments/robotwin-native/kinex-3/workspace/.kinex/kinex.json new file mode 100644 index 0000000000000000000000000000000000000000..c0400a4af73fe4a6eb2ed538d2e3826327c5ba32 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/workspace/.kinex/kinex.json @@ -0,0 +1,5 @@ +{ + "python": { + "provider": "ros-jazzy" + } +} diff --git a/experiments/robotwin-native/kinex-3/workspace/.kinex/memos/put-bottles-dustbin.md b/experiments/robotwin-native/kinex-3/workspace/.kinex/memos/put-bottles-dustbin.md new file mode 100644 index 0000000000000000000000000000000000000000..d7826ec5dd5f60887140755e2c9be327df9042bf --- /dev/null +++ b/experiments/robotwin-native/kinex-3/workspace/.kinex/memos/put-bottles-dustbin.md @@ -0,0 +1,18 @@ +# RoboTwin put-bottles-dustbin / ALOHA AgileX + +Use public profiles and cameras every episode; positions below are observed examples, not immutable scene facts. No simulator reset or private state. ROS wait advances 0.04 s native frames, reads do not. Load devices first; inspect schemas. + +Reusable `.kinex/tools/manipulate.py` runs with `exec(file=..., args=...)`. Args: side (default left), grip (0 closed,1 open), waypoints [{xyz world TCP, quat xyzw optional, duration seconds, linear bool, seed optional}], settle (default .4), cameras. It solves flange IK with TCP offset +.12 local X, publishes typed JointTrajectory, waits once, checks measured FK and returns images. Defaults horizontal +Y approach. It stops on >.025 m final position error. Caller must choose collision-free waypoints; IK is not collision checking. + +ARX5 home-seeded IK can select a negative shoulder branch that drives through the table. Use positive shoulder seed [0,1,1,0,0,0] and first move to raised safe horizontal pose, e.g. [-.28,-.15,1.13], yaw90. A failed early command had 21 cm measured tracking error; stopped and recovered raised. High top-down grasp approach at y=.04,z=1.11 was unreachable. Horizontal side grasps work well. + +Head camera paired RGBD: world points from K and calibrated frame transform. Bottle surface depths are front surfaces, not centers; add radius ~.025 m farther from camera in world Y. Table observed z=.74. Left arm can reach all three upright bottles using yaw roughly radial from its base, avoiding need for handoff. + +Observed successful grasp/transfer sequence: +- Clear: center [-.198,.035,.875], horizontal yaw90 q=[0,0,.70710678,.70710678]. Approach y=-.095, straight advance, close .8s. Lift to z1.12 in 2s; verify bottle moves with gripper. +- Yellow: [.055,.205,.86], horizontal yaw61 q=[0,0,.50753836,.86162916]. Approach [-.006,.093,.86], straight advance 1.5s. Close .6s. Retract/lift to [.015,.12,1.035] in 2s (pure vertical lift at far reach may fail). +- Coke: [.185,.12,.865], horizontal yaw48 q=[0,0,.40673664,.91354546]. Approach [.093,.014,.865]. Close .6s; retract/lift to [.11,.03,1.04] in 2s. +- Carry each at z~1.06 to [-.5,.015,1.06], yaw90, with 3–3.5s Cartesian path. Lower to [-.52,.03,.9] in1.5s. Open above bin. Silver-lined bin is left of table, mostly outside head view. +- Inspect deposits with left wrist camera by moving empty TCP to [-.5,-.06,1.02], quaternion [-.35355339,.35355339,.61237244,.61237244], 1.5–2s linear motion. First two bottles were clearly visible inside bin. + +Episode ended during final opening wait at t=54.16s (~1354/1700 frames), after .16s of requested .8s. No further waits or motion attempted. Final head image showed empty table; wrist image showed coke leaving gripper, opening measured .886. Native verdict is owner-only; terminal/rejected wait alone is not success evidence. First two deposits visually confirmed, last grasp/lift/transfer measured before release. diff --git a/experiments/robotwin-native/kinex-3/workspace/.kinex/tools/manipulate.py b/experiments/robotwin-native/kinex-3/workspace/.kinex/tools/manipulate.py new file mode 100644 index 0000000000000000000000000000000000000000..3e2891d1cd2a509e5cf919e3359bf8a3018ca2a0 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/workspace/.kinex/tools/manipulate.py @@ -0,0 +1,68 @@ +"""Measured, stepped Cartesian moves for RoboTwin ARX5. Invoked with Kinex exec args. +Waypoints are world TCP xyz and optional quaternion xyzw; duration per waypoint. +No motion inferred from publication. Uses declared FK/IK, camera and wait interfaces. +""" +import asyncio +import numpy as np +from scipy.spatial.transform import Rotation, Slerp +from kinex import communication, helpers, args, finish + +async def state(io, name): + async with io.subscribe(name+'.joint_state') as s: + return await s.next(timeout_s=10) + +async def main(): + a=args(); side=a.get('side','left'); arm=side+'_arm'; grip=side+'_gripper' + async with communication() as io: + model=helpers.joint_model(arm) + m=await state(io,arm); q=list(m.position) + points=[]; elapsed=0.; targets=[] + # Default horizontal approach along world +Y, fingers close along world X. + # High top-down poses can exceed ARX5 reach; provide explicit quaternions. + default=Rotation.from_euler('z',np.pi/2).as_quat() + for wp in a.get('waypoints',[]): + xyz=np.array(wp['xyz']); quat=np.array(wp.get('quat',default)); duration=wp.get('duration',2.) + if wp.get('linear',False): + current=await helpers.transform_pose(io,model.fk(q),arm+':base','world:base') + cr=Rotation.from_quat(current[3:]); start=np.array(current[:3])+cr.apply([.12,0,0]) + rot=Slerp([0,1],Rotation.from_quat([current[3:],quat])) + fractions=np.linspace(0,1,max(2,int(duration/.12))+1)[1:] + else: fractions=[1.] + start_t=elapsed + for f in fractions: + p=start+(xyz-start)*f if wp.get('linear',False) else xyz + r=rot(f) if wp.get('linear',False) else Rotation.from_quat(quat) + flange=np.r_[p-r.apply([.12,0,0]),r.as_quat()].tolist() + local=await helpers.transform_pose(io,flange,'world:base',arm+':base') + # Avoid the below-table IK branch when starting near home. + seed=wp.get('seed',q if q[1]>.2 else [q[0],1.,1.,0.,q[4],q[5]]) + newq=model.ik(local,seed,budget={'max_attempts':40,'attempt_time_s':.08,'max_time_s':2}) + # choose equivalent nearby revolute solution + newq=[v+2*np.pi*round((old-v)/(2*np.pi)) for v,old in zip(newq,q)] + if max(abs(np.array(newq)-q))>3.2: raise ValueError('discontinuous IK') + q=newq; t=start_t+duration*f + sec=int(t); ns=round((t-sec)*1e9) + points.append({'positions':q,'time_from_start':{'sec':sec,'nanosec':ns}}) + elapsed+=duration; targets.append(xyz.tolist()) + # Do not change a grip until all requested waypoints solve successfully. + if 'grip' in a: + await io.publish(grip+'.target',{'data':float(a['grip'])}) + if points: + await io.publish(arm+'.joint_target',{'joint_names':model.names,'points':points}) + wait=elapsed+a.get('settle',.4) + if wait>0: result=await io.request('world.wait',duration_s=wait) + m=await state(io,arm); g=await state(io,grip) + flange=await helpers.transform_pose(io,model.fk(list(m.position)),arm+':base','world:base') + tcp=np.array(flange[:3])+Rotation.from_quat(flange[3:]).apply([.12,0,0]) + error=float(np.linalg.norm(tcp-targets[-1])) if targets else None + if error is not None and error>a.get('max_error',.025): + await io.request(arm+'.stop') + raise RuntimeError(f'Measured TCP tracking error {error:.4f} m; arm held') + files=[] + for cam in a.get('cameras',['head_camera']): + im,depth=await helpers.camera_frame(io,cam) + files.append(helpers.save_image(im)) + if a.get('save_depth'): np.save('/tmp/'+cam+'_depth.npy',helpers.image_array(depth)) + finish({'tcp':tcp.tolist(),'quat':flange[3:],'q':list(m.position),'grip':list(g.position),'target_q':q if points else None,'target_error':float(np.linalg.norm(tcp-targets[-1])) if targets else None,'time':result if wait>0 else None},file=files) + +asyncio.run(main()) diff --git a/experiments/robotwin-native/kinex-3/workspace/.kinex/workspace.yaml b/experiments/robotwin-native/kinex-3/workspace/.kinex/workspace.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f4e663d629af8919ee84dc2cfeddd9204bea9c6 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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-3/workspace/harness.md b/experiments/robotwin-native/kinex-3/workspace/harness.md new file mode 100644 index 0000000000000000000000000000000000000000..f77861ba7febc66956580f9e840c24a340c5e35f --- /dev/null +++ b/experiments/robotwin-native/kinex-3/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-3/workspace/instruction.md b/experiments/robotwin-native/kinex-3/workspace/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..48119746c2b5f6d54e43880fa960e23e3fd23d01 --- /dev/null +++ b/experiments/robotwin-native/kinex-3/workspace/instruction.md @@ -0,0 +1,45 @@ +# Task: robotwin/put-bottles-dustbin-aloha-agilex + +use arms to grab the bottles and put them into the dustbin to the left of the table + +## 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 1700 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. 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"native": { + "evidence_valid": true, + "success": false, + "termination": "stopped", + "steps": 498 + }, + "failure_attribution": { + "classification": "native_unsuccessful", + "physical_cause": null, + "physical_cause_evidence": null + }, + "exception": null +} diff --git a/experiments/robotwin-native/kinex_policy-2/episode.json b/experiments/robotwin-native/kinex_policy-2/episode.json new file mode 100644 index 0000000000000000000000000000000000000000..dc73b55f199b0d61cb0d675c9b5168c444fa6754 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/episode.json @@ -0,0 +1,15 @@ +{ + "episode": 1, + "initialization": { + "expert_plan_success": false, + "requested_seed": 0 + }, + "native_reward": 0.0, + "planned_episodes": 1, + "reason": "stopped", + "simulation_time_s": 19.92, + "step_limit": 600, + "steps": 498, + "success": false, + "valid": true +} diff --git a/experiments/robotwin-native/kinex_policy-2/evidence.tar.gz b/experiments/robotwin-native/kinex_policy-2/evidence.tar.gz new file mode 100644 index 0000000000000000000000000000000000000000..b09f47a737da24ac994f7b95a10dc9d93046a839 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/evidence.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c315023c246871e8fbe1a5655a7aa459ea7d56c90c8799d750e67617e3f5d59b +size 8287072 diff --git a/experiments/robotwin-native/kinex_policy-2/instruction.json b/experiments/robotwin-native/kinex_policy-2/instruction.json new file mode 100644 index 0000000000000000000000000000000000000000..117bff786713490f127c6af684bb93646cd98d4d --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/instruction.json @@ -0,0 +1,14 @@ +{ + "binding_paths": {}, + "bindings": {}, + "context": {}, + "instruction": "use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open", + "native_instruction": "", + "policy": "modified", + "prompt": "# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers 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 600 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/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers 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 600 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/place-dual-shoes-aloha-agilex", + "template": "use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers open" +} diff --git a/experiments/robotwin-native/kinex_policy-2/media.json b/experiments/robotwin-native/kinex_policy-2/media.json new file mode 100644 index 0000000000000000000000000000000000000000..d876760a8e02b12bc16ed3154da2500194574d88 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/media.json @@ -0,0 +1,101 @@ +{ + "native_frames": 200, + "published_frames": 200, + "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": "ebcedf7e2ae7", + "task": "robotwin/place-dual-shoes-aloha-agilex", + "state": "complete", + "views": { + "third_person": { + "received": 201, + "dropped": 0, + "segments": [ + { + "file": "third_person.mp4", + "frames": 200, + "width": 960, + "height": 720, + "fps": 10.0, + "first_stamp_ns": 0, 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"system", + "name": null, + "text": "", + "images": [], + "timestamp": "2026-10-28T21:46:18.709Z" + }, + { + "role": "user", + "name": null, + "text": "# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers 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 600 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": "2026-10-28T21:46:18.710Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: ls\n{\"path\": \"/workspace\"}", + "images": [], + "timestamp": "2026-10-28T21:46:21.491Z" + }, + { + "role": "toolResult", + "name": "ls", + "text": ".kinex/\nharness.md\ninstruction.md", + "images": [], + "timestamp": "2026-10-28T21:46:21.499Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \"/workspace/harness.md\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:46:24.611Z" + }, + { + "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": "2026-10-28T21:46:24.617Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: find\n{\"pattern\": \"**/*\", \"path\": \"/workspace/.kinex\", \"limit\": 160}", + "images": [], + "timestamp": "2026-10-28T21:46:26.699Z" + }, + { + "role": "toolResult", + "name": "find", + "text": "devices/\ndevices/head_camera/\ndevices/head_camera/profile.yaml\ndevices/left_camera/\ndevices/left_camera/profile.yaml\ndevices/policy/\ndevices/policy/configuration.json\ndevices/policy/env.sh\ndevices/policy/node.py\ndevices/policy/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/build/kinex_interfaces/rosidl_generator_cpp/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/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/include/kinex_interfaces/kinex_interfaces/srv/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__builder.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__description.c\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__rosidl_typesupport_fastrtps_cpp.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__rosidl_typesupport_introspection_c.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__rosidl_typesupport_introspection_cpp.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__struct.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__functions.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__rosidl_typesupport_introspection_cpp.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__type_support.c\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__type_support.cpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__type_support.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/wait__description.c\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/wait__struct.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/wait__type_support.c\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/wait__type_support.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/json.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/lookup_transform.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.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/\n\n[160 results limit reached. Use limit=320 for more, or refine pattern]", + "images": [], + "timestamp": "2026-10-28T21:46:26.726Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: bash\n{\"command\": \"ls -la .kinex/devices; ls -la .kinex/tools .kinex/skills .kinex/memos\", \"timeout\": null}", + "images": [], + "timestamp": "2026-10-28T21:46:30.011Z" + }, + { + "role": "toolResult", + "name": "bash", + "text": "total 44\ndrwxr-xr-x 11 agent agent 4096 Oct 28 21:45 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 head_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_arm\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_gripper\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:45 policy\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_arm\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_gripper\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 world\n.kinex/memos:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n\n.kinex/skills:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n\n.kinex/tools:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n", + "images": [], + "timestamp": "2026-10-28T21:46:30.031Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \".kinex/workspace.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/world/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/head_camera/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/policy/profile.yaml\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:46:35.380Z" + }, + { + "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": "2026-10-28T21:46:35.390Z" + }, + { + "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": "2026-10-28T21:46:35.390Z" + }, + { + "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": "2026-10-28T21:46:35.390Z" + }, + { + "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": "2026-10-28T21:46:35.390Z" + }, + { + "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": "2026-10-28T21:46:35.390Z" + }, + { + "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": "2026-10-28T21:46:35.390Z" + }, + { + "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": "2026-10-28T21:46:35.390Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n absolute ALOHA joint actions. Use the native run_policy tool with command order\n left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n created_at: '2026-10-28T21:45:05.294658+00:00'\n updated_at: '2026-10-28T21:45:05.294658+00:00'\ncompute:\n index: 0\n kind: cuda\nartifacts: []\nruntime:\n domain_id: 83\n nodes:\n - entry: node.py\n factory: create\n overlays:\n - env.sh\n ready_timeout_s: 600\noperations:\n infer:\n description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n transport: service\n direction: input\n endpoint: /kinex/policy/infer\n type: kinex_interfaces/srv/Json\n timeout_s: 120\n", + "images": [], + "timestamp": "2026-10-28T21:46:35.390Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \".kinex/devices/left_camera/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \".kinex/devices/right_camera/profile.yaml\", \"offset\": null, \"limit\": null}\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt\", \"offset\": null, \"limit\": null}\nTool call: exec\n{\"python\": \"from kinex import runtime, finish\\nfinish({n:runtime.operations(n) for n in ['world','left_arm','left_gripper','head_camera','policy']})\", \"description\": \"inspect operation schemas\"}", + "images": [], + "timestamp": "2026-10-28T21:46:39.081Z" + }, + { + "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": "2026-10-28T21:46:39.085Z" + }, + { + "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": "2026-10-28T21:46:39.087Z" + }, + { + "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": "2026-10-28T21:46:39.088Z" + }, + { + "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 \"policy\": {\"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}}\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:46:40.015Z" + }, + { + "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\"}, {\"deviceID\": \"left_camera\", \"action\": \"load\"}, {\"deviceID\": \"right_camera\", \"action\": \"load\"}, {\"deviceID\": \"policy\", \"action\": \"load\"}]}", + "images": [], + "timestamp": "2026-10-28T21:46:43.425Z" + }, + { + "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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:48:00.080Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/policy.py\", \"offset\": 1, \"limit\": 260}", + "images": [], + "timestamp": "2026-10-28T21:48:02.891Z" + }, + { + "role": "toolResult", + "name": "read", + "text": "\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n max_actions=None, inference_args=None):\n \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n model serves a Json \"infer\" operation taking obs (and task) and returning\n actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n entries in flat-vector split order; named action maps use device names.\n space \"device\" takes absolute joint values in URDF order, or end-effector\n values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n tip_link, frozen at the observation stamp. max_actions caps the horizon and\n action_hz paces actions on the world clock. Joint trajectories submit\n asynchronously; end-effector targets are paced by world waits before\n returning. Returns actions_submitted, duration_s and ik_failure\n (device, action_index, error) or None.\n\n Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n \"\"\"\n import asyncio\n from kinex.communication import duration\n from kinex.helpers.frames import IDENTITY, transform_pose\n from kinex.lib.geometry import invert_rt, rt_to_pose\n plans = [_commanded(entry) for entry in command]\n if len({entry[\"name\"] for entry in plans}) != len(plans):\n raise ValueError(\"command must name each device once\")\n wait = f\"{world}.wait\"\n if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n options = inference_args or {}\n if set(options) & {\"obs\", \"task\"}:\n raise ValueError(\"inference_args cannot override obs or task\")\n data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n if not actions:\n raise ValueError(\"infer must return nonempty actions\")\n actions = actions[:max_actions or len(actions)]\n split = [_share(action, plans) for action in actions]\n count, failure, sequences = len(split), None, {}\n for entry in plans:\n name, spec = entry[\"name\"], entry[\"spec\"]\n samples = [item[name] for item in split[:count]]\n if spec.get(\"space\", \"device\") != \"device\":\n stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n try:\n state = await stream.next(timeout_s=10)\n finally:\n stream.close()\n joints = entry[\"model\"]\n seed = measured_positions(state, joints.joints)\n base = f\"{name}/base\"\n ik_frame = f\"{name}/{joints.tip}\"\n target_frame = spec.get(\"target_frame\", ik_frame)\n reference = spec.get(\"frame\", base)\n # target_frame is an attached tool frame. Freeze its offset at the\n # observation stamp while resolving the policy's Cartesian chunk;\n # URDF FK of the measured joints places the tip.\n offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n converted = []\n for sample in samples:\n if spec[\"space\"] == \"pose_delta\":\n value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n current = value[\"translation\"] + value[\"rotation\"]\n else:\n current = sample\n ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n try:\n seed = joints.ik(goal, seed)\n except ValueError as error:\n failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n break\n converted.append(seed)\n samples = converted\n count = min(count, len(samples))\n sequences[name] = samples\n if count == 0:\n return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n seconds = count / number(action_hz, \"action_hz\")\n prepared, scalars = [], []\n for entry in plans:\n name, prof = entry[\"name\"], entry[\"profile\"]\n samples = sequences[name][:count]\n if entry[\"model\"]:\n joint_command(prof, samples, entry[\"model\"].joints)\n fields = {\"joint_names\": entry[\"model\"].names,\n \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n for index, value in enumerate(samples)]}\n operation = f\"{name}.joint_target\"\n else:\n target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n targets = [target(prof, value[0]) for value in samples]\n fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n scalars.append((operation, targets))\n prepared.append((operation, fields))\n\n await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n # End-effector targets persist. Timing belongs to this workflow and the world,\n # never a device-side duration or a background worker.\n if scalars:\n for index in range(count):\n if index:\n await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n for operation, values in scalars))\n await io.request(wait, duration_s=seconds / count)\n return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n name = entry[\"device\"]\n prof = profile(name)\n kind = prof.get(\"type\")\n if kind not in END_EFFECTORS | {\"joint\"}:\n raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n model = None if kind in END_EFFECTORS else JointModel(prof)\n # A gripper or suction tool takes one command value whatever it reports back.\n width = _cartesian_width(entry, prof)\n return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n \"\"\"One action, split across the commanded devices in order.\"\"\"\n if isinstance(action, dict):\n missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n if missing:\n raise ValueError(f\"action names no value for {', '.join(missing)}\")\n return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n shares, cursor = {}, 0\n for entry in plan:\n shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n cursor += entry[\"width\"]\n return shares\n\n\ndef _cartesian_width(entry, prof):\n space = entry.get(\"space\", \"device\")\n if space not in {\"device\", \"pose\", \"pose_delta\"}:\n raise ValueError(f\"unknown policy space: {space}\")\n if space == \"device\":\n if \"frame\" in entry or \"target_frame\" in entry:\n raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n return None\n if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n action = vector(action, 6, \"pose_delta\")\n angle = math.hypot(*action[3:])\n scale = math.sin(angle / 2) / angle if angle else 0.5\n rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n value = number(value, \"action\")\n if not -1 <= value <= 1:\n raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n return value\n\n\ndef _grip(profile, value):\n \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n grip = profile[\"grip\"]\n if not isinstance(grip, dict):\n raise ValueError(\"the gripper has no declared grip joint\")\n return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n value = _action(value)\n if profile[\"vacuum\"] == \"binary\":\n return 1.0 if value > 0 else 0.0\n if profile[\"vacuum\"] != \"proportional\":\n raise ValueError(\"the suction device declares no vacuum control\")\n return (value + 1) / 2\n", + "images": [], + "timestamp": "2026-10-28T21:48:02.894Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Saving reusable image script**\nI’ll use the dual-arm policy in short segments, checking the camera view and measured gripper states as it progresses.\nTool call: device\n{\"deviceID\": \"head_camera\", \"action\": \"image\"}", + "images": [], + "timestamp": "2026-10-28T21:48:07.442Z" + }, + { + "role": "toolResult", + "name": "device", + "text": "{\n \"deviceID\": \"head_camera\",\n \"timestamp\": 0,\n \"frame_id\": \"head_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-uo5653ej.png\"\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/34917289-1.png" + ], + "timestamp": "2026-10-28T21:48:07.729Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: write\n{\"path\": \".kinex/tools/dual_shoes_policy.py\", \"content\": \"\\\"\\\"\\\"Run one bounded, freshly observed RoboTwin policy segment; no lifecycle changes.\\\"\\\"\\\"\\nimport asyncio\\nfrom kinex import args, communication, finish, helpers\\n\\nTASK = ('use both arms to pick up the two shoes on the table and put them flat in the shoebox, '\\n 'with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. '\\n 'Leave both grippers open')\\nCAMERAS = ['head_camera', 'left_camera', 'right_camera']\\nDEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper']\\n\\nasync def read_one(io, op):\\n with io.subscribe(op, depth=1) as stream:\\n return await stream.next(timeout_s=10)\\n\\nasync def main():\\n opts = args()\\n async with communication() as io:\\n images = await asyncio.gather(*(read_one(io, c+'.image') for c in CAMERAS))\\n paths = [helpers.save_image(im) for im in images]\\n result = await helpers.run_policy(io, 'policy', {'images': paths},\\n [{'device': d} for d in DEVICES], task=opts.get('task', TASK),\\n world='world', action_hz=25, max_actions=opts.get('max_actions', 32))\\n states = await asyncio.gather(*(read_one(io, d+'.joint_state') for d in DEVICES))\\n image = await read_one(io, 'head_camera.image')\\n path = helpers.save_image(image)\\n result['measured'] = {d: {'position': list(s.position), 'velocity': list(s.velocity)}\\n for d,s in zip(DEVICES,states)}\\n result['time_s'] = image.header.stamp.sec + image.header.stamp.nanosec * 1e-9\\n finish(result, file=[path])\\n\\nasyncio.run(main())\\n\"}", + "images": [], + "timestamp": "2026-10-28T21:48:16.830Z" + }, + { + "role": "toolResult", + "name": "write", + "text": "Successfully wrote to .kinex/tools/dual_shoes_policy.py", + "images": [], + "timestamp": "2026-10-28T21:48:16.834Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"approach shoes with both arms\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:48:19.505Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224100.950625034] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.06924128532409668,0.620575487613678,0.4789740741252899,-0.28832128643989563,-0.006016099359840155,-0.20582163333892822],\"velocity\":[-0.14827853441238403,1.9776692390441895,1.3301373720169067,-0.7481668591499329,-0.00023766420781612396,-0.4732377231121063]},\"left_gripper\":{\"position\":[0.9972223205999895],\"velocity\":[-0.05145943300290541]},\"right_arm\":{\"position\":[0.059656575322151184,0.6675826907157898,0.5648283958435059,-0.336734414100647,-0.0007643997087143362,0.10298626869916916],\"velocity\":[0.16773490607738495,2.116530418395996,1.5773547887802124,-0.8487532138824463,0.0016799001023173332,0.25821131467819214]},\"right_gripper\":{\"position\":[0.9918896691365676],\"velocity\":[0.10728505016727881]}},\n \"time_s\": 0.64,\n \"file\": [\"/tmp/kinex-observation-bzj6q2qb.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/a34c0da2-1.png" + ], + "timestamp": "2026-10-28T21:48:26.651Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"continue shoe approach\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:48:29.308Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224110.743159302] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.1642390340566635,1.7305915355682373,1.2296197414398193,-0.7650421261787415,-0.0084557319059968,-0.500076949596405],\"velocity\":[-0.12782171368598938,1.6054855585098267,1.0615302324295044,-0.5280291438102722,-0.0035553264897316694,-0.3419665992259979]},\"left_gripper\":{\"position\":[1.0039003437215632],\"velocity\":[-0.005440424535084854]},\"right_arm\":{\"position\":[0.16377829015254974,1.8440415859222412,1.4337947368621826,-0.8591420650482178,0.0009951238753274083,0.2373320609331131],\"velocity\":[0.13659954071044922,1.7306162118911743,1.2370691299438477,-0.5968512892723083,0.00857531651854515,0.10719234496355057]},\"right_gripper\":{\"position\":[0.9957916059277274],\"velocity\":[0.056688948957757515]}},\n \"time_s\": 1.28,\n \"file\": [\"/tmp/kinex-observation-v9kc6wpn.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/b3f27e81-1.png" + ], + "timestamp": "2026-10-28T21:48:35.586Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"lower toward shoe grasps\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:48:38.229Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224119.654208670] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.2097044140100479,2.1003403663635254,1.4922027587890625,-0.9227323532104492,-0.012336636893451214,-0.6160438060760498],\"velocity\":[-0.007071767468005419,0.18269474804401398,0.0708129033446312,-0.004062924534082413,-0.0012147556990385056,-0.0011546239256858826]},\"left_gripper\":{\"position\":[1.0023565834218806],\"velocity\":[-0.03414872864430601]},\"right_arm\":{\"position\":[0.18805797398090363,2.2374651432037354,1.7380448579788208,-1.0270179510116577,0.0015673550078645349,0.25923529267311096],\"velocity\":[0.004303688183426857,0.19159452617168427,0.09309601783752441,0.0003027580678462982,-0.0012894750107079744,0.001212606206536293]},\"right_gripper\":{\"position\":[0.9993701875209808],\"velocity\":[0.01965627201240171]}},\n \"time_s\": 1.92,\n \"file\": [\"/tmp/kinex-observation-iacietm2.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/ba86406e-1.png" + ], + "timestamp": "2026-10-28T21:48:45.205Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"align grippers over shoes\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:48:48.695Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224130.042645948] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.2132786065340042,2.2452938556671143,1.4555960893630981,-0.7601785659790039,-0.010994759388267994,-0.6320369839668274],\"velocity\":[-0.006282667629420757,0.41175612807273865,-0.1620422899723053,0.4812483489513397,0.005704400595277548,-0.0010892432183027267]},\"left_gripper\":{\"position\":[1.0031733025204053],\"velocity\":[-0.05907964977351102]},\"right_arm\":{\"position\":[0.18439644575119019,2.3707516193389893,1.7036454677581787,-0.8743376731872559,0.0015077369753271341,0.23946310579776764],\"velocity\":[-0.0019623737316578627,0.3756299912929535,-0.1430189311504364,0.43219083547592163,0.0005497047677636147,0.0012198584154248238]},\"right_gripper\":{\"position\":[0.9994854005900297],\"velocity\":[0.010186451783573086]}},\n \"time_s\": 2.56,\n \"file\": [\"/tmp/kinex-observation-bs7tom9w.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/62980368-1.png" + ], + "timestamp": "2026-10-28T21:48:54.401Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"grasp the two shoes\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:48:57.044Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224138.539332553] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.2147672325372696,2.32867169380188,1.4041498899459839,-0.6313791871070862,-0.00845736637711525,-0.606859564781189],\"velocity\":[0.006417857948690653,0.021585844457149506,-0.0001902952790260315,-0.0025077760219573975,-0.002072217408567667,0.00007816869765520096]},\"left_gripper\":{\"position\":[0.8222877681255341],\"velocity\":[0.03745188428597017]},\"right_arm\":{\"position\":[0.18369433283805847,2.449954032897949,1.658321499824524,-0.7642399072647095,0.0036793008912354708,0.22171318531036377],\"velocity\":[-0.0033897748216986656,0.0317833349108696,0.027323372662067413,0.00080084428191185,0.011743731796741486,0.0016810968518257141]},\"right_gripper\":{\"position\":[0.8141168518499895],\"velocity\":[0.17688775604421442]}},\n \"time_s\": 3.2,\n \"file\": [\"/tmp/kinex-observation-ik8vfitb.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/a0cf0f52-1.png" + ], + "timestamp": "2026-10-28T21:49:03.681Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"secure both shoe grasps\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:49:08.044Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224149.569556886] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.22005799412727356,2.278794527053833,1.4649302959442139,-0.7562634348869324,-0.0094643235206604,-0.6023668050765991],\"velocity\":[-0.0010886157397180796,-0.41600582003593445,0.30070409178733826,-0.6667615175247192,-0.0043848371133208275,0.001474970020353794]},\"left_gripper\":{\"position\":[0.8442490317604758],\"velocity\":[0.05986423400992697]},\"right_arm\":{\"position\":[0.18073023855686188,2.4141697883605957,1.729750633239746,-0.8848705887794495,0.00425342982634902,0.21443425118923187],\"velocity\":[-0.0021621412597596645,-0.35253795981407166,0.3380318582057953,-0.6683180928230286,0.002519281581044197,-0.0016803189646452665]},\"right_gripper\":{\"position\":[0.8242001289671118],\"velocity\":[-0.007260924162851138]}},\n \"time_s\": 3.84,\n \"file\": [\"/tmp/kinex-observation-pf35xytz.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/72947485-1.png" + ], + "timestamp": "2026-10-28T21:49:14.033Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"lift shoes clear of table\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:49:20.005Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224161.483818059] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.2337704449892044,2.106487512588501,1.5987350940704346,-1.0601452589035034,-0.017284579575061798,-0.655439555644989],\"velocity\":[-0.20414608716964722,-0.0711108148097992,-0.003488592803478241,-0.0486275851726532,-0.0066644977778196335,-0.30675390362739563]},\"left_gripper\":{\"position\":[0.8484432345086879],\"velocity\":[-0.03571745262227275]},\"right_arm\":{\"position\":[0.16433337330818176,2.2665646076202393,1.8794596195220947,-1.1764945983886719,0.0008793658344075084,0.19070172309875488],\"velocity\":[-0.11569812893867493,-0.1641731709241867,-0.04941084608435631,-0.07024315744638443,0.014065418392419815,-0.00043169036507606506]},\"right_gripper\":{\"position\":[0.8275782222097571],\"velocity\":[0.0033681344410235233]}},\n \"time_s\": 4.48,\n \"file\": [\"/tmp/kinex-observation-ys4yasbv.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/1e12ad2c-1.png" + ], + "timestamp": "2026-10-28T21:49:26.657Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"rotate shoes toward box orientation\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:49:32.051Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224173.489829010] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.5958328247070312,2.0936496257781982,1.5494917631149292,-1.0726386308670044,-0.014319771900773048,-1.5304045677185059],\"velocity\":[-0.8067498207092285,0.005487743765115738,-0.12532629072666168,-0.005442067980766296,0.033786822110414505,-2.0603280067443848]},\"left_gripper\":{\"position\":[0.8458616760644047],\"velocity\":[-0.19374703141775998]},\"right_arm\":{\"position\":[0.04124896600842476,2.116722822189331,1.6913503408432007,-1.1479504108428955,0.00006936372665222734,0.08349316567182541],\"velocity\":[-0.31583401560783386,-0.21932876110076904,-0.32688185572624207,-0.0031804367899894714,-0.017355522140860558,-0.15398237109184265]},\"right_gripper\":{\"position\":[0.8272719356146726],\"velocity\":[-0.14341287314891815]}},\n \"time_s\": 5.12,\n \"file\": [\"/tmp/kinex-observation-1x49af4e.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/6db711be-1.png" + ], + "timestamp": "2026-10-28T21:49:38.590Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"position left shoe in near half of box\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:49:41.455Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224182.890409604] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n1793224182.892589 [83] dq.builtin: Remote secure participant f8544f26:920a72fd:9ebfedca:1c1 not allowed\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.8931366205215454,2.072211265563965,1.4770663976669312,-0.9812818169593811,0.00011473950144136325,-2.233191967010498],\"velocity\":[-0.3547639846801758,0.018884602934122086,-0.08769574016332626,0.07969250530004501,0.04081038385629654,-0.3869875371456146]},\"left_gripper\":{\"position\":[0.8453784720464187],\"velocity\":[-0.04491062665527517]},\"right_arm\":{\"position\":[-0.08277181535959244,2.0239410400390625,1.5335749387741089,-1.090429425239563,-0.004047530237585306,-0.042060717940330505],\"velocity\":[-0.07115635275840759,-0.04377933591604233,-0.06454810500144958,-0.005736846476793289,-0.0161428265273571,-0.04591057449579239]},\"right_gripper\":{\"position\":[0.8220208341425116],\"velocity\":[-0.06982283666729927]}},\n \"time_s\": 5.76,\n \"file\": [\"/tmp/kinex-observation-xkceikpd.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/d9dc48f7-1.png" + ], + "timestamp": "2026-10-28T21:49:47.540Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"lower first shoe into shoebox\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:49:54.215Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224195.699174388] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9944702982902527,2.084920644760132,1.3925881385803223,-0.8199701905250549,0.009655511006712914,-2.410446882247925],\"velocity\":[0.10853226482868195,0.026367992162704468,-0.03144228085875511,0.00848725438117981,-0.02719578705728054,0.06114771217107773]},\"left_gripper\":{\"position\":[0.8887087458913977],\"velocity\":[0.9880326688289642]},\"right_arm\":{\"position\":[-0.13358038663864136,1.990712285041809,1.4891928434371948,-1.07383131980896,-0.003542549442499876,-0.09856189042329788],\"velocity\":[0.02053144760429859,0.003911168314516544,0.016390755772590637,-0.00030988454818725586,-0.006688009016215801,0.001631051767617464]},\"right_gripper\":{\"position\":[0.8224071806127375],\"velocity\":[0.01656769309192896]}},\n \"time_s\": 6.4,\n \"file\": [\"/tmp/kinex-observation-40hcihy1.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/521d0ea3-1.png" + ], + "timestamp": "2026-10-28T21:50:00.974Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking shoe over rim**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"release first shoe and clear left gripper\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:50:09.056Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224210.329004551] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9979008436203003,2.105285406112671,1.3758498430252075,-0.8019817471504211,0.008222300559282303,-2.3118176460266113],\"velocity\":[0.08126653730869293,0.017701422795653343,0.006158708594739437,0.007415026426315308,-0.02043578214943409,0.0001324489712715149]},\"left_gripper\":{\"position\":[0.9908248457041654],\"velocity\":[0.3809970888224515]},\"right_arm\":{\"position\":[-0.13651666045188904,1.9356255531311035,1.4003468751907349,-1.0447499752044678,-0.007218408398330212,-0.10203051567077637],\"velocity\":[-0.0007513259770348668,-0.006915716454386711,-0.016261789947748184,0.00021159276366233826,0.0009361580014228821,0.00038932450115680695]},\"right_gripper\":{\"position\":[0.823714012449438],\"velocity\":[0.004685608754781159]}},\n \"time_s\": 7.04,\n \"file\": [\"/tmp/kinex-observation-ly14eio7.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/30687d2f-1.png" + ], + "timestamp": "2026-10-28T21:50:15.568Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking lift position**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"withdraw left arm after release\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:50:23.664Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224225.085355440] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9609145522117615,2.103285789489746,1.3415217399597168,-0.9416824579238892,0.12990465760231018,0.26933538913726807],\"velocity\":[-0.012519998475909233,0.00815054215490818,-0.06386817991733551,0.14375868439674377,-0.025883832946419716,1.493956208229065]},\"left_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.4523076117038727]},\"right_arm\":{\"position\":[-0.14984078705310822,1.793803095817566,1.2386841773986816,-1.0167691707611084,-0.005433639511466026,-0.09398231655359268],\"velocity\":[-0.0026481677778065205,-0.0341680571436882,-0.0263235941529274,0.00112156942486763,0.0046378737315535545,0.0006861891597509384]},\"right_gripper\":{\"position\":[0.8233322040601211],\"velocity\":[0.006881222361698747]}},\n \"time_s\": 7.68,\n \"file\": [\"/tmp/kinex-observation-79svv1xn.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/99df565b-1.png" + ], + "timestamp": "2026-10-28T21:50:30.601Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking wrist cameras**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"continue clearing box for second shoe\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:50:39.047Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224240.559793638] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9466960430145264,2.0996274948120117,1.3347556591033936,-0.9375165104866028,0.1452205628156662,0.4871319532394409],\"velocity\":[-0.0024265546817332506,-0.006117640063166618,-0.03794396296143532,0.018360305577516556,0.21688298881053925,-0.12456168234348297]},\"left_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[0.13143813068216498]},\"right_arm\":{\"position\":[-0.16619357466697693,1.7558674812316895,1.1857863664627075,-1.0019826889038086,-0.005586357321590185,-0.12964484095573425],\"velocity\":[-0.0030482755973935127,-0.009106620214879513,-0.005098237190395594,0.0009567426750436425,0.005949969869107008,-0.00099231768399477]},\"right_gripper\":{\"position\":[0.8231995837254958],\"velocity\":[0.002671432924795557]}},\n \"time_s\": 8.32,\n \"file\": [\"/tmp/kinex-observation-k1r8fgfw.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/3fc7db58-1.png" + ], + "timestamp": "2026-10-28T21:50:46.529Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking shoe location**\nTool call: devices\n{\"calls\": [{\"deviceID\": \"left_camera\", \"action\": \"image\"}, {\"deviceID\": \"right_camera\", \"action\": \"image\"}]}", + "images": [], + "timestamp": "2026-10-28T21:50:54.269Z" + }, + { + "role": "toolResult", + "name": "devices", + "text": "{\n \"results\": [\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":8.32,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-g35vn_3s.png\"},\n {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":8.32,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-s815sybc.png\"}\n ]\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/45ab9995-1.png", + "experiments/robotwin-native/kinex_policy-2/observations/45ab9995-2.png" + ], + "timestamp": "2026-10-28T21:50:54.601Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Withdrawing left gripper**\nTool call: device_stop\n{\"reason\": \"First shoe placement is not visually confirmed; pause policy to inspect and recover.\"}", + "images": [], + "timestamp": "2026-10-28T21:51:04.818Z" + }, + { + "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": "2026-10-28T21:51:04.954Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Repositioning the left arm**\nTool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py\", \"offset\": null, \"limit\": null}", + "images": [], + "timestamp": "2026-10-28T21:51:14.887Z" + }, + { + "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": "2026-10-28T21:51:14.890Z" + }, + { + "role": "assistant", + "name": null, + "text": "\nTool call: exec\n{\"description\": \"lift left gripper to inspect first shoe\", \"python\": \"import asyncio\\nfrom kinex import communication,helpers,finish\\nfrom kinex.communication import duration\\nasync def main():\\n async with communication() as io:\\n with io.subscribe('left_arm.joint_state') as s: st=await s.next(timeout_s=10)\\n model=helpers.joint_model('left_arm'); q=list(st.position)\\n p=await helpers.transform_pose(io,model.fk(q),'left_arm/base','world/base')\\n p[2]+=0.16\\n target=await helpers.transform_pose(io,p,'world/base','left_arm/base')\\n q1=model.ik(target,q)\\n await io.publish('left_gripper.target',{'data':1.0})\\n await io.publish('left_arm.joint_target',{'joint_names':model.names,'points':[{'positions':q,'time_from_start':duration(0.04)},{'positions':q1,'time_from_start':duration(1.2)}]})\\n await io.request('world.wait',duration_s=1.2)\\n with io.subscribe('head_camera.image') as s: im=await s.next(timeout_s=10)\\n with io.subscribe('left_arm.joint_state') as s: st=await s.next(timeout_s=10)\\n finish({'position':list(st.position)},file=[helpers.save_image(im)])\\nasyncio.run(main())\", \"timeout_s\": 120}", + "images": [], + "timestamp": "2026-10-28T21:51:26.652Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224288.135853199] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"position\": [-0.949935257434845,1.990056037902832,1.6935598850250244,-1.411597728729248,0.14556893706321716,0.481610506772995],\n \"file\": [\"/tmp/kinex-observation-om1ylyes.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/8a586584-1.png" + ], + "timestamp": "2026-10-28T21:51:36.959Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Reaching for first shoe**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"reassess shoe placement from current views\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:51:45.839Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224307.245751174] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.769152045249939,1.8209655284881592,1.2780230045318604,-0.8482129573822021,0.0019667400047183037,0.5423021912574768],\"velocity\":[1.039013147354126,-1.9829304218292236,-1.5112085342407227,0.9554992914199829,-0.028640955686569214,-0.41967734694480896]},\"left_gripper\":{\"position\":[0.6473405157977885],\"velocity\":[1.9632707942615857]},\"right_arm\":{\"position\":[0.01240176148712635,1.8635932207107544,1.3279516696929932,-1.0523301362991333,-0.002492120722308755,-0.16756686568260193],\"velocity\":[0.9300477504730225,0.4109690189361572,0.5477051734924316,-0.11741548031568527,-0.05728531628847122,-0.034550949931144714]},\"right_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.06072843785990368]}},\n \"time_s\": 10.16,\n \"file\": [\"/tmp/kinex-observation-r9kvl_ew.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/0cab8987-1.png" + ], + "timestamp": "2026-10-28T21:51:52.090Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Inspecting shoes at right**\nTool call: exec\n{\"file\": \".kinex/tools/dual_shoes_policy.py\", \"description\": \"recover from lost grasp\", \"timeout_s\": 180}", + "images": [], + "timestamp": "2026-10-28T21:52:04.535Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224326.069034591] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.7760891914367676,1.900067925453186,1.2369452714920044,-0.7888695597648621,0.012184467166662216,0.5715698003768921],\"velocity\":[0.5874661803245544,-1.2688400745391846,-1.1548991203308105,0.9930360913276672,-0.058601319789886475,-0.37943756580352783]},\"left_gripper\":{\"position\":[0.23286564004692165],\"velocity\":[3.2529833641919224]},\"right_arm\":{\"position\":[-0.04621698707342148,1.8618661165237427,1.3253315687179565,-1.070886492729187,0.0016111346194520593,-0.17026476562023163],\"velocity\":[0.5801291465759277,0.2907960116863251,0.4152977764606476,-0.12846273183822632,-0.05734775960445404,-0.23737221956253052]},\"right_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.08739067918875]}},\n \"time_s\": 10.8,\n \"file\": [\"/tmp/kinex-observation-2l0llko8.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/9ec9a799-1.png" + ], + "timestamp": "2026-10-28T21:52:12.246Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Positioning wrist cameras**\nTool call: device_stop\n{\"reason\": \"Policy is no longer maintaining shoe grasps; hold arms for geometric inspection.\"}", + "images": [], + "timestamp": "2026-10-28T21:52:27.776Z" + }, + { + "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": "2026-10-28T21:52:27.853Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: devices\n{\"calls\": [{\"deviceID\": \"right_camera\", \"action\": \"image\"}, {\"deviceID\": \"left_camera\", \"action\": \"image\"}]}", + "images": [], + "timestamp": "2026-10-28T21:52:31.562Z" + }, + { + "role": "toolResult", + "name": "devices", + "text": "{\n \"results\": [\n {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":10.8,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dvobb360.png\"},\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":10.8,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-v8oio2c_.png\"}\n ]\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/cfb787c6-1.png", + "experiments/robotwin-native/kinex_policy-2/observations/cfb787c6-2.png" + ], + "timestamp": "2026-10-28T21:52:31.893Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking shoe position**\nTool call: exec\n{\"description\": \"inspect gripper poses and visible shoe depth\", \"python\": \"import asyncio\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n poses={s:await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}) for s in ['left','right']}\\n frame=await helpers.camera_frame(io,'right_camera')\\n finish({'poses':poses,'frame_keys':list(frame)})\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:52:38.162Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224359.682511338] [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 8, 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 7, in main\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/__init__.py\\\", line 98, in finish\\n result = plain(result)\\n ^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 12, in plain\\n return {key: plain(item) for key, item in value.items()}\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 14, in plain\\n return [plain(item) for item in value]\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 19, in plain\\n raise TypeError(\\\"images are not data; return them with finish(file=helpers.save_image(image))\\\")\\nTypeError: images are not data; return them with finish(file=helpers.save_image(image))\"\n}", + "images": [], + "timestamp": "2026-10-28T21:52:40.820Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: read\n{\"path\": \"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py\", \"offset\": 1, \"limit\": 150}", + "images": [], + "timestamp": "2026-10-28T21:52:45.319Z" + }, + { + "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": "2026-10-28T21:52:45.323Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"description\": \"measure shoe surface coordinates\", \"python\": \"import asyncio,numpy as np\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n poses={s:await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}) for s in ['left','right']}\\n points={}\\n for cam,pixels in [('head_camera',[(315,160),(175,200),(175,170)]),('right_camera',[(270,100),(270,180),(250,40)])]:\\n im,dep=await helpers.camera_frame(io,cam); a=helpers.image_array(dep)\\n for u,v in pixels:\\n z=float(a[v,u])*.001\\n p=[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1]\\n points[f'{cam}:{u},{v}']=await helpers.transform_pose(io,p,cam+'/base','world/base',im.header.stamp)\\n finish({'poses':poses,'points':points})\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:52:53.367Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224374.798465320] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"poses\": {\"left\":{\"pose\":[-0.04576036873904488,-0.1518396196357057,0.8105108280162003,-0.06397159671261682,0.6655944712893721,0.1116262315077717,0.735140271677748],\"timestamp\":10.8},\"right\":{\"pose\":[0.3158530877393279,-0.0837637020994616,0.8411548580346625,0.5416649392500444,-0.4740655883365559,-0.5247640136212739,-0.45440471118866305],\"timestamp\":10.8}},\n \"points\": {\"head_camera:315,160\":[0.3105280301800081,-0.047369169413844336,0.6644731372869409,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,200\":[0.0007164514080967438,-0.13173156963338484,0.6424513366211899,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,170\":[0.0025748496589661773,-0.06011766118549816,0.6424117683126856,-0.9486832972651138,0,0,0.3162277683730379],\"right_camera:270,100\":[0.3420385028964361,-0.0843225323833221,0.7120579827021101,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:270,180\":[0.3485558820624562,-0.12394106388230323,0.7274574502446138,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:250,40\":[0.3286115784257454,-0.057996926368941285,0.6946765327046098,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335]}\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:52:55.537Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Repositioning camera toward shoe**\n\n**Preparing gripper alignment**\nTool call: read\n{\"path\": \".kinex/devices/right_gripper/gripper.urdf\", \"offset\": 1, \"limit\": 180}", + "images": [], + "timestamp": "2026-10-28T21:53:11.085Z" + }, + { + "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": "2026-10-28T21:53:11.087Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Estimating shoe pose**\nTool call: write\n{\"path\": \".kinex/tools/cartesian_segment.py\", \"content\": \"\\\"\\\"\\\"Bounded world-TCP moves using calibrated transforms and measured seeds.\\\"\\\"\\\"\\nimport asyncio\\nfrom kinex import args, communication, helpers, finish\\nfrom kinex.communication import duration\\n\\nasync def read(io, op):\\n with io.subscribe(op, depth=1) as stream:\\n return await stream.next(timeout_s=10)\\n\\nasync def main():\\n opts=args(); seconds=opts.get('duration_s',1.0)\\n async with communication() as io:\\n commands=[]\\n for side,goal in opts.get('goals',{}).items():\\n arm=side+'_arm'; model=helpers.joint_model(arm)\\n state=await read(io,arm+'.joint_state'); q=list(state.position)\\n if 'joints' in goal: end=goal['joints']\\n else:\\n current=await helpers.transform_pose(io,model.fk(q),arm+'/base','world/base')\\n tcp=await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'})\\n target=goal.get('pose',tcp['pose']).copy()\\n if 'delta' in goal:\\n target[:3]=[a+b for a,b in zip(target[:3],goal['delta'])]\\n # TCP is 0.12 m along flange +x.\\n from scipy.spatial.transform import Rotation\\n off=Rotation.from_quat(target[3:]).apply([.12,0,0])\\n target[:3]=[a-b for a,b in zip(target[:3],off)]\\n local=await helpers.transform_pose(io,target,'world/base',arm+'/base')\\n end=model.ik(local,q)\\n commands.append((arm+'.joint_target',{'joint_names':model.names,'points':[\\n {'positions':q,'time_from_start':duration(.04)},\\n {'positions':end,'time_from_start':duration(seconds)}]}))\\n for side,value in opts.get('grips',{}).items():\\n commands.append((side+'_gripper.target',{'data':value}))\\n await asyncio.gather(*(io.publish(op,payload) for op,payload in commands))\\n await io.request('world.wait',duration_s=seconds)\\n states={d:list((await read(io,d+'.joint_state')).position) for d in ['left_arm','left_gripper','right_arm','right_gripper']}\\n paths=[helpers.save_image(await read(io,c+'.image')) for c in opts.get('cameras',['head_camera'])]\\n finish({'measured':states},file=paths)\\n\\nasyncio.run(main())\\n\"}", + "images": [], + "timestamp": "2026-10-28T21:53:24.544Z" + }, + { + "role": "toolResult", + "name": "write", + "text": "Successfully wrote to .kinex/tools/cartesian_segment.py", + "images": [], + "timestamp": "2026-10-28T21:53:24.547Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"clear arms and inspect displaced shoes\", \"args\": {\"goals\": {\"right\": {\"delta\": [0, 0, 0.2]}, \"left\": {\"joints\": [-0.23377, 2.10649, 1.59874, -1.06015, -0.01728, -0.65544]}}, \"grips\": {\"left\": 1, \"right\": 1}, \"duration_s\": 1, \"cameras\": [\"head_camera\", \"right_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:53:30.996Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224412.462059980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.34138405323028564,2.0631067752838135,1.5276085138320923,-1.006088376045227,-0.01025577262043953,-0.4240250289440155],\"left_gripper\":[0.9917404543269764],\"right_arm\":[-0.0485311821103096,1.9509698152542114,2.119028091430664,-1.7710597515106201,0.0023190497886389494,-0.17672263085842133],\"right_gripper\":[0.984824080358852]},\n \"file\": [\"/tmp/kinex-observation-umnt4jud.png\",\"/tmp/kinex-observation-tqaq0u11.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/2335c3b2-1.png", + "experiments/robotwin-native/kinex_policy-2/observations/2335c3b2-2.png" + ], + "timestamp": "2026-10-28T21:53:39.781Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Scanning foreground for shoe**\nTool call: device\n{\"deviceID\": \"left_camera\", \"action\": \"image\"}", + "images": [], + "timestamp": "2026-10-28T21:53:54.394Z" + }, + { + "role": "toolResult", + "name": "device", + "text": "{\n \"deviceID\": \"left_camera\",\n \"timestamp\": 11.8,\n \"frame_id\": \"left_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-r0rh5wg1.png\"\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/1202e4b6-1.png" + ], + "timestamp": "2026-10-28T21:53:54.552Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Preparing right shoe grasp**\nTool call: exec\n{\"description\": \"measure right shoe grasp and box geometry\", \"python\": \"import asyncio\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\\n for u,v in [(220,25),(220,65),(220,130)]:\\n z=float(a[v,u])*.001\\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base',im.header.stamp)\\n poses={s:(await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}))['pose'] for s in ['left','right']}\\n finish({'points':pts,'tcp':poses})\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:54:05.543Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224446.993372814] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": {\"(220, 25)\":[0.33912656027719457,-0.06270945464371132,0.699152728908267,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 65)\":[0.34366378336074566,-0.0970220567701838,0.7184123702623861,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 130)\":[0.369180866644661,-0.185229663768754,0.6765957202615971,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145]},\n \"tcp\": {\"left\":[-0.1764191407332653,-0.039818355705945996,0.8221104464598411,-0.5187487037504172,0.46380859131529467,0.5349435100330139,0.4791834868324896],\"right\":[0.3142798555779541,-0.07013205333858658,1.0078345324518556,0.5420236400350569,-0.471363514483094,-0.5270262727561408,-0.45416970251444916]}\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:54:07.704Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking right-shoe grip height**\nTool call: exec\n{\"description\": \"derive proven grasp pose\", \"python\": \"import asyncio\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n q=[.1836943,2.449954,1.6583215,-.7642399,.0036793,.2217132]\\n p=helpers.joint_model('right_arm').fk(q)\\n from scipy.spatial.transform import Rotation as R\\n p[:3]=(p[:3]+R.from_quat(p[3:]).apply([.12,0,0])).tolist()\\n p=await helpers.transform_pose(io,p,'right_arm/base','world/base')\\n finish(p)\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:54:16.362Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224457.863503532] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n 0.21555207762772288,\n 0.019347892345493667,\n 0.6978195099953525,\n -0.4912582223427763,\n 0.5016447242922935,\n 0.5008975819995253,\n 0.5060825445689198\n ]\n}", + "images": [], + "timestamp": "2026-10-28T21:54:18.870Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Positioning for right shoe**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"align right grasp and survey near side\", \"args\": {\"goals\": {\"right\": {\"pose\": [0.344, -0.09, 0.82, -0.5, 0.5, 0.5, 0.5]}, \"left\": {\"pose\": [0.12, -0.28, 0.98, 0, 0.70710678, 0, 0.70710678]}}, \"grips\": {\"left\": 1, \"right\": 1}, \"duration_s\": 1.2, \"cameras\": [\"left_camera\", \"right_camera\", \"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:54:32.690Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224474.085063475] [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_segment.py\\\", line 41, 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_segment.py\\\", line 29, in main\\n end=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=410, elapsed_ms=100.172, position_error_m=0.004569528253270413, rotation_error_rad=0.00035777856102559653\"\n}", + "images": [], + "timestamp": "2026-10-28T21:54:35.336Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Reading solver defaults**\nTool call: grep\n{\"pattern\": \"DEFAULTS|max_|timeout|budget\", \"path\": \"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\", \"limit\": 30}", + "images": [], + "timestamp": "2026-10-28T21:54:41.260Z" + }, + { + "role": "toolResult", + "name": "grep", + "text": "kinematics.py:15: DEFAULTS = {\"position_tolerance_m\": 1e-4, \"rotation_tolerance_rad\": 1e-3, \"max_iterations\": 200, \"damping\": .01}\nkinematics.py:50: DEFAULT_BUDGET = {\"max_attempts\": 5, \"attempt_time_s\": 0.02, \"max_time_s\": 0.1}\nkinematics.py:53: def budget_options(options):\nkinematics.py:54: search = options.get(\"budget\", {})\nkinematics.py:56: raise ValueError(\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\")\nkinematics.py:58: attempts = result[\"max_attempts\"]\nkinematics.py:60: raise ValueError(\"IK budget.max_attempts must be a positive integer\")\nkinematics.py:61: for key in (\"attempt_time_s\", \"max_time_s\"):\nkinematics.py:62: if number(result[key], f\"IK budget.{key}\") <= 0:\nkinematics.py:63: raise ValueError(f\"IK budget.{key} must be positive\")\nkinematics.py:127: \"\"\"Select the nearest verified solution within the existing search budget.\"\"\"\nkinematics.py:128: settings = budget_options(options)\nkinematics.py:131: deadline = started + settings[\"max_time_s\"]\nkinematics.py:134: for seed in seeds(joints, targets, original, settings[\"max_attempts\"]):\nkinematics.py:153: reason = \"time budget\" if monotonic() >= deadline else \"attempt budget\"\nkinematics.py:204: for _ in range(options[\"max_iterations\"]):\nkinematics.py:222: positions by name, within the search budget.\"\"\"\nkinematics.py:225: iterations = options[\"max_iterations\"]\nkinematics.py:227: raise ValueError(\"IK max_iterations must be a positive integer\")", + "images": [], + "timestamp": "2026-10-28T21:54:41.285Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: edit\n{\"path\": \".kinex/tools/cartesian_segment.py\", \"edits\": [{\"oldText\": \"end=model.ik(local,q)\", \"newText\": \"end=model.ik(local,q,budget={'max_attempts':12,'attempt_time_s':0.1,'max_time_s':1.2})\"}]}", + "images": [], + "timestamp": "2026-10-28T21:54:45.527Z" + }, + { + "role": "toolResult", + "name": "edit", + "text": "Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py.", + "images": [], + "timestamp": "2026-10-28T21:54:45.533Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"align right grasp and inspect near side\", \"args\": {\"goals\": {\"right\": {\"pose\": [0.344, -0.09, 0.82, -0.5, 0.5, 0.5, 0.5]}, \"left\": {\"pose\": [0.04, -0.25, 0.94, 0, 0.70710678, 0, 0.70710678]}}, \"grips\": {\"left\": 1, \"right\": 1}, \"duration_s\": 1.2, \"cameras\": [\"left_camera\", \"right_camera\", \"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:54:50.313Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224491.855424014] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.007556676864624,1.9853966236114502,1.7999980449676514,-1.3873368501663208,-0.0012377145467326045,0.3198549747467041],\"left_gripper\":[1.0055662935430354],\"right_arm\":[-0.11214446276426315,1.8662967681884766,1.3592849969863892,-1.0733658075332642,0.0006734680500812829,-0.12759973108768463],\"right_gripper\":[0.9959410239349712]},\n \"file\": [\"/tmp/kinex-observation-oewipf9p.png\",\"/tmp/kinex-observation-n29q4c9q.png\",\"/tmp/kinex-observation-bxtc_9a1.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/5469803c-1.png", + "experiments/robotwin-native/kinex_policy-2/observations/5469803c-2.png", + "experiments/robotwin-native/kinex_policy-2/observations/5469803c-3.png" + ], + "timestamp": "2026-10-28T21:54:59.252Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Scanning for left shoe**\n\n**Lowering right arm**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"descend to right shoe and scan front edge\", \"args\": {\"goals\": {\"right\": {\"pose\": [0.344, -0.09, 0.699, -0.5, 0.5, 0.5, 0.5]}, \"left\": {\"pose\": [0.12, -0.4, 0.87, 0, 0.70710678, 0, 0.70710678]}}, \"duration_s\": 0.8, \"cameras\": [\"left_camera\", \"right_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:55:12.031Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224513.394437567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.421788215637207,2.089327096939087,1.8129761219024658,-1.295375943183899,0.0016572437016293406,0.11011556535959244],\"left_gripper\":[1.0041607076471502],\"right_arm\":[-0.11952149868011475,2.052452564239502,1.1140962839126587,-0.6284643411636353,0.0016702237771824002,-0.11548030376434326],\"right_gripper\":[0.997136029330167]},\n \"file\": [\"/tmp/kinex-observation-ocl_jdlh.png\",\"/tmp/kinex-observation-yq84hi1q.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/0633ed76-1.png", + "experiments/robotwin-native/kinex_policy-2/observations/0633ed76-2.png" + ], + "timestamp": "2026-10-28T21:55:18.074Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Grasping right shoe**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"close right gripper on shoe\", \"args\": {\"grips\": {\"right\": 0}, \"duration_s\": 0.28, \"cameras\": [\"right_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:55:25.840Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224527.355764551] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5191649198532104,2.114835500717163,1.817434310913086,-1.2732782363891602,0.0029058295767754316,0.061535708606243134],\"left_gripper\":[0.998820808800784],\"right_arm\":[-0.12290065735578537,2.0888092517852783,1.0617690086364746,-0.5257982015609741,0.002643134444952011,-0.11187005788087845],\"right_gripper\":[0.8180002976547588]},\n \"file\": [\"/tmp/kinex-observation-bbmzia4v.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/85d47354-1.png" + ], + "timestamp": "2026-10-28T21:55:28.887Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Preparing pickup lift**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"lift recovered right shoe\", \"args\": {\"goals\": {\"right\": {\"delta\": [0, 0, 0.18]}}, \"grips\": {\"right\": 0}, \"duration_s\": 0.8, \"cameras\": [\"right_camera\", \"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:55:36.807Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224538.386130877] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5487600564956665,2.125786781311035,1.8208038806915283,-1.263669729232788,0.00247021927498281,0.044266678392887115],\"left_gripper\":[0.9992801709608599],\"right_arm\":[-0.12398703396320343,1.8660025596618652,1.3495227098464966,-1.042709231376648,0.0013285059249028563,-0.11811474710702896],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-0idhe3ah.png\",\"/tmp/kinex-observation-v16xz1o9.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/bf55277f-1.png", + "experiments/robotwin-native/kinex_policy-2/observations/bf55277f-2.png" + ], + "timestamp": "2026-10-28T21:55:43.381Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Lowering gripper toward shoe**\n\n**Lowering over shoe toe**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"reposition grasp to shoe forefoot\", \"args\": {\"goals\": {\"right\": {\"pose\": [0.333, -0.025, 0.678, -0.5, 0.5, 0.5, 0.5]}}, \"grips\": {\"right\": 1}, \"duration_s\": 0.8, \"cameras\": [\"right_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:56:00.050Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224561.581901781] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5512616634368896,2.1280250549316406,1.8211685419082642,-1.2633064985275269,0.0024033302906900644,0.043757226318120956],\"left_gripper\":[0.9994734796610746],\"right_arm\":[-0.10064993053674698,2.227466106414795,1.3507928848266602,-0.6901805996894836,0.0034024433698505163,-0.07860491424798965],\"right_gripper\":[0.9747358587655155]},\n \"file\": [\"/tmp/kinex-observation-0me27shi.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/eca65601-1.png" + ], + "timestamp": "2026-10-28T21:56:06.136Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Waiting to close grasp**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"settle forefoot approach\", \"args\": {\"duration_s\": 0.24, \"cameras\": [\"right_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:56:13.238Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224574.797605845] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5518105030059814,2.1282575130462646,1.8211756944656372,-1.2633064985275269,0.0024045207537710667,0.04374692589044571],\"left_gripper\":[0.9995164898308841],\"right_arm\":[-0.09451431035995483,2.3065285682678223,1.3511804342269897,-0.614876925945282,0.003993599209934473,-0.06940840929746628],\"right_gripper\":[0.9861905845728788]},\n \"file\": [\"/tmp/kinex-observation-dlwemtpp.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/1194c8b6-1.png" + ], + "timestamp": "2026-10-28T21:56:16.979Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Computing TCP camera projection**\nTool call: exec\n{\"description\": \"compare tool center with shoe surface\", \"python\": \"import asyncio\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n rel=await helpers.relative_pose(io,{'frame':'right_gripper:tcp'},{'frame':'right_camera:base'})\\n tcp=await helpers.relative_pose(io,{'frame':'right_gripper:tcp'},{'frame':'world:base'})\\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\\n for u,v in [(220,190),(280,170),(280,220)]:\\n z=float(a[v,u])*.001\\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base')\\n finish({'rel':rel,'tcp':tcp,'points':pts})\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:56:27.896Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224589.387025232] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"rel\": {\"pose\":[0.031999988504280985,0.04039803789401142,0.07136523029414477,0.5893679914824401,-0.5893679004513149,0.3906986006032956,0.3906986716386298],\"timestamp\":0},\n \"tcp\": {\"pose\":[0.33640272493657036,-0.02633167088001903,0.6917798048149151,-0.49874693434276596,0.5009926290810037,0.49905228115506656,0.5012032539440671],\"timestamp\":15.92},\n \"points\": {\"(220, 190)\":[0.323961443569907,-0.027427434548870908,0.6438170953602051,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 170)\":[0.3420592549054097,-0.019472769566128686,0.6447653287773406,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 220)\":[0.3453272239232121,-0.0403433584497698,0.642715433205389,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303]}\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:56:30.087Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking shoe depth mismatch**\n\n**Visualizing right-camera depth**\nTool call: exec\n{\"description\": \"locate elevated shoe pixels from depth\", \"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,dep=await helpers.camera_frame(io,'right_camera'); z=helpers.image_array(dep)*.001\\n tr=(await helpers.relative_pose(io,{'frame':'right_camera:base'},{'frame':'world:base'}))['pose']; yy,xx=np.indices(z.shape)\\n xyz=np.stack([(xx-159.5)*z/358.64218,(yy-119.5)*z/358.64218,z],-1)@R.from_quat(tr[3:]).as_matrix().T+tr[:3]\\n mask=(xyz[:,:,2]>.66)&(xyz[:,:,2]<.78)&(xyz[:,:,0]>.25)\\n pix=np.argwhere(mask); pts=xyz[mask]\\n finish({'pixel_bounds':[pix.min(0),pix.max(0)] if len(pix) else None,'world_bounds':[pts.min(0),pts.max(0)] if len(pts) else None,'count':len(pts)})\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:56:47.844Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224610.029616113] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"pixel_bounds\": [[217,104],[239,163]],\n \"world_bounds\": [[0.2889084230314078,-0.032890889789939426,0.6600515195421714],[0.3053804617258074,-0.02516070082117358,0.6626498111904289]],\n \"count\": 86\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:56:50.492Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Raising the shoe**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"widen right wrist view\", \"args\": {\"goals\": {\"right\": {\"delta\": [0, 0, 0.22]}, \"left\": {\"joints\": [-0.23377, 2.10649, 1.59874, -1.06015, -0.01728, -0.65544]}}, \"grips\": {\"right\": 1, \"left\": 1}, \"duration_s\": 1, \"cameras\": [\"right_camera\", \"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:57:04.555Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224626.173253423] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.4864189326763153,2.1139965057373047,1.6437352895736694,-1.1013754606246948,-0.012391126714646816,-0.5226401090621948],\"left_gripper\":[1.0006968649950894],\"right_arm\":[-0.09558303654193878,2.0816221237182617,1.7370487451553345,-1.2321414947509766,0.002054061973467469,-0.07567489147186279],\"right_gripper\":[0.9936021512204951]},\n \"file\": [\"/tmp/kinex-observation-nsqsy3z2.png\",\"/tmp/kinex-observation-46fp2462.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/7403242e-1.png", + "experiments/robotwin-native/kinex_policy-2/observations/7403242e-2.png" + ], + "timestamp": "2026-10-28T21:57:11.739Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Measuring right shoe toe**\nTool call: exec\n{\"description\": \"measure recovered shoe forefoot from wider view\", \"python\": \"import asyncio\\nfrom kinex import communication,helpers,finish\\nasync def main():\\n async with communication() as io:\\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\\n for u,v in [(200,80),(200,120),(200,180),(230,120)]:\\n z=float(a[v,u])*.001\\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base')\\n finish(pts)\\nasyncio.run(main())\"}", + "images": [], + "timestamp": "2026-10-28T21:57:20.502Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224642.187029003] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"(200, 80)\": [0.3319128077876595,-0.021384687358054988,0.6807880606684285,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(200, 120)\": [0.3320860201227272,-0.047179444335913354,0.6896982618039296,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(200, 180)\": [0.3313421415530356,-0.0816889616404346,0.7110633133544213,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(230, 120)\": [0.35305419668191745,-0.04801774041168197,0.6878528021042477,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695]\n }\n}", + "images": [], + "timestamp": "2026-10-28T21:57:22.896Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Lowering gripper toward shoe**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"descend for firm forefoot grasp\", \"args\": {\"goals\": {\"right\": {\"pose\": [0.334, -0.045, 0.669, -0.5, 0.5, 0.5, 0.5]}}, \"grips\": {\"right\": 1}, \"duration_s\": 1, \"cameras\": [\"right_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:57:33.712Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224655.360224304] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2415974736213684,2.1075475215911865,1.6001873016357422,-1.0607448816299438,-0.01720820739865303,-0.6539632678031921],\"left_gripper\":[1.006791507655924],\"right_arm\":[-0.09445098042488098,2.2661819458007812,1.34000563621521,-0.6393835544586182,0.003709397278726101,-0.07148103415966034],\"right_gripper\":[0.9967559142546221]},\n \"file\": [\"/tmp/kinex-observation-b541py7r.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/a2352307-1.png" + ], + "timestamp": "2026-10-28T21:57:40.894Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"secure forefoot grasp\", \"args\": {\"grips\": {\"right\": 0}, \"duration_s\": 0.4, \"cameras\": [\"right_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:57:44.080Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224665.686067778] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2397918999195099,2.106983184814453,1.599143147468567,-1.0602302551269531,-0.017222831025719643,-0.6552022099494934],\"left_gripper\":[1.0052635290405967],\"right_arm\":[-0.09369406849145889,2.304854154586792,1.2598658800125122,-0.5211624503135681,0.00392327643930912,-0.07036155462265015],\"right_gripper\":[0.2204216422343796]},\n \"file\": [\"/tmp/kinex-observation-duv2kayc.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/6c9d803f-1.png" + ], + "timestamp": "2026-10-28T21:57:48.001Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking shoe grasp**\n\n**Leaving shoe open**\n\n**Inspecting shoe position**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"check whether shoe follows lift\", \"args\": {\"goals\": {\"right\": {\"delta\": [0, 0, 0.16]}}, \"grips\": {\"right\": 0}, \"duration_s\": 0.8, \"cameras\": [\"right_camera\", \"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:58:13.809Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224695.262363581] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749706149101257,2.10660719871521,1.5987578630447388,-1.0601166486740112,-0.017234427854418755,-0.655457079410553],\"left_gripper\":[1.0032016147266736],\"right_arm\":[-0.09411819279193878,2.071403980255127,1.4278910160064697,-0.9290177822113037,0.0019836677238345146,-0.07476944476366043],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-itxcfdys.png\",\"/tmp/kinex-observation-z0_kr8cf.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/1e357bae-1.png", + "experiments/robotwin-native/kinex_policy-2/observations/1e357bae-2.png" + ], + "timestamp": "2026-10-28T21:58:20.839Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Stopping after shoe contact**\nTool call: device_stop\n{\"reason\": \"Shoe was displaced during grasp attempt; stop motion before further inspection.\"}", + "images": [], + "timestamp": "2026-10-28T21:58:28.445Z" + }, + { + "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": "2026-10-28T21:58:28.541Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Checking the right shoe**\nThe shoes slipped during placement and recovery, so I can’t confirm completion. I’m stopping the attempt and leaving both grippers open.\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"leave both grippers open and verify\", \"args\": {\"grips\": {\"left\": 1, \"right\": 1}, \"duration_s\": 0.4, \"cameras\": [\"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:58:43.618Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224725.282645542] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749713599681854,2.10660719871521,1.5987579822540283,-1.06011962890625,-0.017240097746253014,-0.6554499268531799],\"left_gripper\":[1.0024560825391249],\"right_arm\":[-0.09411459416151047,2.0698814392089844,1.4282174110412598,-0.929033637046814,0.001976696541532874,-0.07476774603128433],\"right_gripper\":[0.8706039027734237]},\n \"file\": [\"/tmp/kinex-observation-rljb8q34.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/9c30e840-1.png" + ], + "timestamp": "2026-10-28T21:58:47.503Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Waiting on .32**\nTool call: exec\n{\"file\": \".kinex/tools/cartesian_segment.py\", \"description\": \"verify final open gripper positions\", \"args\": {\"grips\": {\"left\": 1, \"right\": 1}, \"duration_s\": 0.4, \"cameras\": [\"head_camera\"]}}", + "images": [], + "timestamp": "2026-10-28T21:58:55.175Z" + }, + { + "role": "toolResult", + "name": "exec", + "text": "{\n \"output\": \"[INFO] [1793224736.756986163] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749713599681854,2.10660719871521,1.5987579822540283,-1.06011962890625,-0.017239799723029137,-0.6554496884346008],\"left_gripper\":[1.0018847584724426],\"right_arm\":[-0.09411459416151047,2.0705742835998535,1.4280035495758057,-0.929033637046814,0.001982568996027112,-0.07476842403411865],\"right_gripper\":[0.9748014916073192]},\n \"file\": [\"/tmp/kinex-observation-u51xe342.png\"]\n }\n}", + "images": [ + "experiments/robotwin-native/kinex_policy-2/observations/bec6c641-1.png" + ], + "timestamp": "2026-10-28T21:58:59.921Z" + }, + { + "role": "assistant", + "name": null, + "text": "**Adding motion timing logs**\nTool call: edit\n{\"path\": \".kinex/tools/cartesian_segment.py\", \"edits\": [{\"oldText\": \"states={d:list((await read(io,d+'.joint_state')).position) for d in ['left_arm','left_gripper','right_arm','right_gripper']}\", \"newText\": \"states={}\\n for d in ['left_arm','left_gripper','right_arm','right_gripper']:\\n state=await read(io,d+'.joint_state')\\n states[d]={'position':list(state.position),'velocity':list(state.velocity),\\n 'stamp_s':state.header.stamp.sec+state.header.stamp.nanosec*1e-9}\"}]}", + "images": [], + "timestamp": "2026-10-28T21:59:09.908Z" + }, + { + "role": "toolResult", + "name": "edit", + "text": "Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py.", + "images": [], + "timestamp": "2026-10-28T21:59:09.916Z" + }, + { + "role": "assistant", + "name": null, + "text": "Tool call: write\n{\"path\": \".kinex/memos/dual_shoes.md\", \"content\": \"# RoboTwin dual shoes\\n\\n## Interfaces and tools\\n- Read refreshed profiles each episode. Stepped world, native 25 Hz (0.04 s/frame), 600 frames. Only world.wait advances time; reads do not.\\n- `tools/dual_shoes_policy.py` captures head/left/right RGB in required order, calls helpers.run_policy at 25 Hz, returns fresh head image and measured joint feedback. Current policy returns 16 actions per inference even if max_actions=32. Task instruction embedded; override via args.task.\\n- `tools/cartesian_segment.py` makes bounded world-TCP IK/joint segments with optional gripper targets, and returns cameras plus measured positions/velocities/stamps. Inputs: goals.{left,right}.{pose|delta|joints}, grips.{left,right} in native opening units, duration_s, cameras. It does NOT perform collision planning or certify arrival. The TCP offset is 0.12 m along flange +x for these profiles. All goals are planned before publication. Recheck calibration before reuse elsewhere.\\n- Both grippers use 0 closed, 1 open; policy uses -1 open/+1 closed and helper converts. Closed empty feedback may bottom at ~0.18, not zero. A shoe can stall grip near .82, but that alone does not establish holding.\\n\\n## Important limitations\\n- Policy achieved initial bilateral pickup but failed first placement: shoe slid toward near/right box edge, then vanished from head view. Continuing inference after loss produced wrist rotation and empty closing, not recovery. Stop and reassess instead of blindly extending policy.\\n- Manual recovery was not successful. Depth-based targets require renewed perception after EVERY contact; shoes slide readily. Do not reuse previous attempt's object coordinates.\\n- A 0.8–1.2 s trajectory endpoint can still have substantial tracking lag; gripper close while descending can displace the shoe. Inspect velocities and position error before grasping. Verify retention by camera during a small test lift before transport.\\n- TCP +x approaches; fingers close along local y. Do not infer safe finger clearance solely from TCP height or simplified URDF geometry.\\n- device_stop holds measured gripper opening, which may relax an object grasp; when intentionally holding an object, preserve closing target unless stopping for a fault.\\n- Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\\n\"}", + "images": [], + "timestamp": "2026-10-28T21:59:35.070Z" + }, + { + "role": "toolResult", + "name": "write", + "text": "Successfully wrote to .kinex/memos/dual_shoes.md", + "images": [], + "timestamp": "2026-10-28T21:59:35.076Z" + }, + { + "role": "assistant", + "name": null, + "text": "\nTool call: edit\n{\"path\": \".kinex/memos/dual_shoes.md\", \"edits\": [{\"oldText\": \"- Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\\n\", \"newText\": \"- Finalization: command both openings to 1, allow settling, and verify feedback. Report placement uncertainty honestly; the world interface exposes no native verdict.\\n\"}]}", + "images": [], + "timestamp": "2026-10-28T21:59:43.482Z" + }, + { + "role": "toolResult", + "name": "edit", + "text": "Successfully replaced 1 block(s) in .kinex/memos/dual_shoes.md.", + "images": [], + "timestamp": "2026-10-28T21:59:43.487Z" + }, + { + "role": "assistant", + "name": null, + "text": "Task not completed: the shoes slipped and were not placed in the shoebox. Both arms are held, and both grippers are open.", + "images": [], + "timestamp": "2026-10-28T21:59:47.604Z" + } +] diff --git a/experiments/robotwin-native/kinex_policy-2/session.jsonl b/experiments/robotwin-native/kinex_policy-2/session.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..378fe4b7e96f1d3aca2c2f1bfbfa7e828348fb6b --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/session.jsonl @@ -0,0 +1,142 @@ +{"type":"session","version":3,"id":"01a1847a-64ba-730a-b6fb-21be31ad6e87","timestamp":"2026-10-28T21:46:16.123Z","cwd":"/workspace"} +{"type":"model_change","id":"6c58aa2b","parentId":null,"timestamp":"2026-10-28T21:46:18.429Z","provider":"openai-codex","modelId":"gpt-6-astra"} +{"type":"thinking_level_change","id":"6ce80a29","parentId":"6c58aa2b","timestamp":"2026-10-28T21:46:18.429Z","thinkingLevel":"high"} +{"type":"message","id":"20d7331f","parentId":"6ce80a29","timestamp":"2026-10-28T21:46:18.709Z","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":1793223978707,"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. Output is truncated to 500 entries or 50KB (whichever is hit first).","parameters":{"type":"object","properties":{"path":{"type":"string","description":"Directory to list (default: current directory)"},"limit":{"type":"number","description":"Maximum number of entries to return (default: 500)"}}}},{"name":"web_search","description":"Search the web using the current supported provider (Google Gemini, xAI Grok, OpenAI, Anthropic, DeepSeek, Ollama, or OpenCode Zen/Go). Optionally include URLs to analyze alongside search results.","parameters":{"type":"object","required":["query"],"properties":{"query":{"type":"string","description":"The search query or question to answer"},"urls":{"type":"array","items":{"type":"string"},"description":"Additional URLs to analyze along with search (up to 20)","maxItems":20}}}},{"name":"calib_compute","description":"Run supported offline calibration/introspection using archived evidence. No device\nmotion or profile writes. 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":"c215ee7e","parentId":"20d7331f","timestamp":"2026-10-28T21:46:18.710Z","message":{"role":"user","content":[{"type":"text","text":"# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers 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 600 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":1793223978706}} +{"type":"custom_message","customType":"kinex-context","content":"\ncore.guides:\nDevice guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-25e2e270ec6943b1b9c010e7ee1c402d/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-25e2e270ec6943b1b9c010e7ee1c402d/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":"a9879048","parentId":"c215ee7e","timestamp":"2026-10-28T21:46:18.710Z"} +{"type":"message","id":"741ff487","parentId":"a9879048","timestamp":"2026-10-28T21:46:21.491Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_2be0a7f253ee4abf86ce531d7ad43b28|fc_0b5218887c37459e016ae26d2d0c78819f8cc39f5feaf7fdcb","name":"ls","arguments":{"path":"/workspace"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":11328,"output":19,"cacheRead":0,"cacheWrite":0,"reasoning":0,"totalTokens":11347,"cost":{"input":0.11328,"output":0.00095,"cacheRead":0,"cacheWrite":0,"total":0.11423000000000001}},"stopReason":"toolUse","timestamp":1793223978727,"responseId":"resp_0b5218887c37459e016ae26d2b2f00819fb944bdcbf3b2e314","rawStopReason":"completed","durationMs":2763,"thinkingLevel":"high"}} +{"type":"message","id":"c4aeaddd","parentId":"741ff487","timestamp":"2026-10-28T21:46:21.499Z","message":{"role":"toolResult","toolCallId":"call_2be0a7f253ee4abf86ce531d7ad43b28|fc_0b5218887c37459e016ae26d2d0c78819f8cc39f5feaf7fdcb","toolName":"ls","content":[{"type":"text","text":".kinex/\nharness.md\ninstruction.md"}],"isError":false,"durationMs":4,"timestamp":1793223981499}} +{"type":"message","id":"a0f31098","parentId":"c4aeaddd","timestamp":"2026-10-28T21:46:24.611Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_d9fc0acab16d4ca2a46a36ae77694e1a|fc_0b5218887c37459e016ae26d2fc904819f835d2d1c802765b8","name":"read","arguments":{"path":"/workspace/harness.md","offset":null,"limit":null}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":104,"output":29,"cacheRead":11264,"cacheWrite":0,"reasoning":0,"totalTokens":11397,"cost":{"input":0.0010400000000000001,"output":0.0014500000000000001,"cacheRead":0.011264,"cacheWrite":0,"total":0.013753999999999999}},"stopReason":"toolUse","timestamp":1793223981527,"responseId":"resp_0b5218887c37459e016ae26d2e5dec819fa609a3baeb35c702","rawStopReason":"completed","durationMs":3084,"thinkingLevel":"high"}} +{"type":"message","id":"e62f1e22","parentId":"a0f31098","timestamp":"2026-10-28T21:46:24.617Z","message":{"role":"toolResult","toolCallId":"call_d9fc0acab16d4ca2a46a36ae77694e1a|fc_0b5218887c37459e016ae26d2fc904819f835d2d1c802765b8","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. 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+{"type":"message","id":"9674672c","parentId":"7198fef0","timestamp":"2026-10-28T21:46:35.390Z","message":{"role":"toolResult","toolCallId":"call_6705f0d2e2394a30bb581ea44d385871|fc_0b5218887c37459e016ae26d3831c4819fa634b638d90788be","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":8,"timestamp":1793223995390}} +{"type":"message","id":"bbec448d","parentId":"9674672c","timestamp":"2026-10-28T21:46:35.390Z","message":{"role":"toolResult","toolCallId":"call_e58691f7e16147eab395daf95a28a0c9|fc_0b5218887c37459e016ae26d3b4940819fae10e30592a8b3f2","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":7,"timestamp":1793223995390}} +{"type":"message","id":"431bc40d","parentId":"bbec448d","timestamp":"2026-10-28T21:46:35.390Z","message":{"role":"toolResult","toolCallId":"call_60f5baa90b4a45b085b550c6542a738b|fc_0b5218887c37459e016ae26d3b4944819f8262615e431e6507","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":8,"timestamp":1793223995390}} +{"type":"message","id":"89bc21a1","parentId":"431bc40d","timestamp":"2026-10-28T21:46:35.390Z","message":{"role":"toolResult","toolCallId":"call_12bf679f95494399bb47080d1beebdc7|fc_0b5218887c37459e016ae26d3b4944819fae385c7f7afc6f23","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":8,"timestamp":1793223995390}} +{"type":"message","id":"8c5f6959","parentId":"89bc21a1","timestamp":"2026-10-28T21:46:35.390Z","message":{"role":"toolResult","toolCallId":"call_88760df5480447ddb8fcb3567c44240e|fc_0b5218887c37459e016ae26d3b4944819f9deac9b84c192193","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":8,"timestamp":1793223995390}} +{"type":"message","id":"fbbe690e","parentId":"8c5f6959","timestamp":"2026-10-28T21:46:35.390Z","message":{"role":"toolResult","toolCallId":"call_03371d20a1964430b350f091a1a0b420|fc_0b5218887c37459e016ae26d3b4944819fa8b82bd928c268a3","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":1793223995390}} +{"type":"message","id":"95028f0d","parentId":"fbbe690e","timestamp":"2026-10-28T21:46:35.390Z","message":{"role":"toolResult","toolCallId":"call_b9931bd73ee64dbc89d5daad25d34ad8|fc_0b5218887c37459e016ae26d3b4944819fa63c5ea7c8a37119","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":8,"timestamp":1793223995390}} +{"type":"message","id":"ab261e44","parentId":"95028f0d","timestamp":"2026-10-28T21:46:35.390Z","message":{"role":"toolResult","toolCallId":"call_dc800044efb24048a2da7659cbdf024d|fc_0b5218887c37459e016ae26d3b4944819f8b44321c1a8b9031","toolName":"read","content":[{"type":"text","text":"name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n absolute ALOHA joint actions. Use the native run_policy tool with command order\n left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n created_at: '2026-10-28T21:45:05.294658+00:00'\n updated_at: '2026-10-28T21:45:05.294658+00:00'\ncompute:\n index: 0\n kind: cuda\nartifacts: []\nruntime:\n domain_id: 83\n nodes:\n - entry: node.py\n factory: create\n overlays:\n - env.sh\n ready_timeout_s: 600\noperations:\n infer:\n description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n transport: service\n direction: input\n endpoint: /kinex/policy/infer\n type: kinex_interfaces/srv/Json\n timeout_s: 120\n"}],"isError":false,"durationMs":8,"timestamp":1793223995390}} +{"type":"message","id":"4b601a01","parentId":"ab261e44","timestamp":"2026-10-28T21:46:39.081Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_aca37b9173a049859c26b1a1c7afedc7|fc_0b5218887c37459e016ae26d3cf1fc819fbb5c0783e4554891","name":"read","arguments":{"path":".kinex/devices/left_camera/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_2cbb4865d07c4343a74eba4b21d3e0a0|fc_0b5218887c37459e016ae26d3efb30819f8bd7f5954aee7123","name":"read","arguments":{"path":".kinex/devices/right_camera/profile.yaml","offset":null,"limit":null}},{"type":"toolCall","id":"call_c3f15cd1c50341aeafa8ee8bde74be5f|fc_0b5218887c37459e016ae26d3efb30819f90097de3aae79959","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/helpers.txt","offset":null,"limit":null}},{"type":"toolCall","id":"call_efb801f592ff478d9cd43e403a7c5552|fc_0b5218887c37459e016ae26d3efb30819f9f12804ad88a7c4b","name":"exec","arguments":{"python":"from kinex import runtime, finish\nfinish({n:runtime.operations(n) for n in ['world','left_arm','left_gripper','head_camera','policy']})","description":"inspect operation schemas"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":5036,"output":167,"cacheRead":15104,"cacheWrite":0,"reasoning":0,"totalTokens":20307,"cost":{"input":0.05036,"output":0.00835,"cacheRead":0.015104,"cacheWrite":0,"total":0.07381399999999999}},"stopReason":"toolUse","timestamp":1793223995418,"responseId":"resp_0b5218887c37459e016ae26d3be114819f9b18c7dda256ff02","rawStopReason":"completed","durationMs":3663,"thinkingLevel":"high"}} +{"type":"message","id":"0a300e73","parentId":"4b601a01","timestamp":"2026-10-28T21:46:39.085Z","message":{"role":"toolResult","toolCallId":"call_aca37b9173a049859c26b1a1c7afedc7|fc_0b5218887c37459e016ae26d3cf1fc819fbb5c0783e4554891","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":1793223999085}} +{"type":"message","id":"be498bfe","parentId":"0a300e73","timestamp":"2026-10-28T21:46:39.087Z","message":{"role":"toolResult","toolCallId":"call_2cbb4865d07c4343a74eba4b21d3e0a0|fc_0b5218887c37459e016ae26d3efb30819f8bd7f5954aee7123","toolName":"read","content":[{"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. 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"}],"isError":false,"durationMs":2,"timestamp":1793223999087}} +{"type":"message","id":"3e876673","parentId":"be498bfe","timestamp":"2026-10-28T21:46:39.088Z","message":{"role":"toolResult","toolCallId":"call_c3f15cd1c50341aeafa8ee8bde74be5f|fc_0b5218887c37459e016ae26d3efb30819f90097de3aae79959","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":1,"timestamp":1793223999088}} +{"type":"message","id":"8d7b1094","parentId":"3e876673","timestamp":"2026-10-28T21:46:40.015Z","message":{"role":"toolResult","toolCallId":"call_efb801f592ff478d9cd43e403a7c5552|fc_0b5218887c37459e016ae26d3efb30819f9f12804ad88a7c4b","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 \"policy\": {\"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}}\n }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":925,"timestamp":1793224000015}} +{"type":"message","id":"9f951d64","parentId":"8d7b1094","timestamp":"2026-10-28T21:46:43.425Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_990c26cdcea84c32a82583ddc2e7e432|fc_0b5218887c37459e016ae26d4248ec819fa2ee0f4e25c3c532","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"},{"deviceID":"left_camera","action":"load"},{"deviceID":"right_camera","action":"load"},{"deviceID":"policy","action":"load"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":3460,"output":107,"cacheRead":20224,"cacheWrite":0,"reasoning":0,"totalTokens":23791,"cost":{"input":0.034600000000000006,"output":0.005350000000000001,"cacheRead":0.020224,"cacheWrite":0,"total":0.060174000000000005}},"stopReason":"toolUse","timestamp":1793224000042,"responseId":"resp_0b5218887c37459e016ae26d40f1a8819f9ac474828daff537","rawStopReason":"completed","durationMs":3383,"thinkingLevel":"high"}} +{"type":"message","id":"135ab1df","parentId":"9f951d64","timestamp":"2026-10-28T21:48:00.080Z","message":{"role":"toolResult","toolCallId":"call_990c26cdcea84c32a82583ddc2e7e432|fc_0b5218887c37459e016ae26d4248ec819fa2ee0f4e25c3c532","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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}"}],"details":{"kinex":{"title":"devices: 9 calls","isError":false}},"isError":false,"durationMs":76653,"timestamp":1793224080080}} +{"type":"message","id":"36dd6397","parentId":"135ab1df","timestamp":"2026-10-28T21:48:02.891Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_a1a74d8fbb94481d864fe661fa79d7c2|fc_0b5218887c37459e016ae26d9272e8819f8d056301e2d7cbef","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/policy.py","offset":1,"limit":260}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":328,"output":40,"cacheRead":23680,"cacheWrite":0,"reasoning":0,"totalTokens":24048,"cost":{"input":0.0032800000000000004,"output":0.002,"cacheRead":0.02368,"cacheWrite":0,"total":0.02896}},"stopReason":"toolUse","timestamp":1793224080106,"responseId":"resp_0b5218887c37459e016ae26d907e3c819fa8c2c6e035301824","rawStopReason":"completed","durationMs":2785,"thinkingLevel":"high"}} +{"type":"message","id":"4fbbdcd9","parentId":"36dd6397","timestamp":"2026-10-28T21:48:02.894Z","message":{"role":"toolResult","toolCallId":"call_a1a74d8fbb94481d864fe661fa79d7c2|fc_0b5218887c37459e016ae26d9272e8819f8d056301e2d7cbef","toolName":"read","content":[{"type":"text","text":"\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n max_actions=None, inference_args=None):\n \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n model serves a Json \"infer\" operation taking obs (and task) and returning\n actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n entries in flat-vector split order; named action maps use device names.\n space \"device\" takes absolute joint values in URDF order, or end-effector\n values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n tip_link, frozen at the observation stamp. max_actions caps the horizon and\n action_hz paces actions on the world clock. Joint trajectories submit\n asynchronously; end-effector targets are paced by world waits before\n returning. Returns actions_submitted, duration_s and ik_failure\n (device, action_index, error) or None.\n\n Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n \"\"\"\n import asyncio\n from kinex.communication import duration\n from kinex.helpers.frames import IDENTITY, transform_pose\n from kinex.lib.geometry import invert_rt, rt_to_pose\n plans = [_commanded(entry) for entry in command]\n if len({entry[\"name\"] for entry in plans}) != len(plans):\n raise ValueError(\"command must name each device once\")\n wait = f\"{world}.wait\"\n if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n options = inference_args or {}\n if set(options) & {\"obs\", \"task\"}:\n raise ValueError(\"inference_args cannot override obs or task\")\n data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n if not actions:\n raise ValueError(\"infer must return nonempty actions\")\n actions = actions[:max_actions or len(actions)]\n split = [_share(action, plans) for action in actions]\n count, failure, sequences = len(split), None, {}\n for entry in plans:\n name, spec = entry[\"name\"], entry[\"spec\"]\n samples = [item[name] for item in split[:count]]\n if spec.get(\"space\", \"device\") != \"device\":\n stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n try:\n state = await stream.next(timeout_s=10)\n finally:\n stream.close()\n joints = entry[\"model\"]\n seed = measured_positions(state, joints.joints)\n base = f\"{name}/base\"\n ik_frame = f\"{name}/{joints.tip}\"\n target_frame = spec.get(\"target_frame\", ik_frame)\n reference = spec.get(\"frame\", base)\n # target_frame is an attached tool frame. Freeze its offset at the\n # observation stamp while resolving the policy's Cartesian chunk;\n # URDF FK of the measured joints places the tip.\n offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n converted = []\n for sample in samples:\n if spec[\"space\"] == \"pose_delta\":\n value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n current = value[\"translation\"] + value[\"rotation\"]\n else:\n current = sample\n ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n try:\n seed = joints.ik(goal, seed)\n except ValueError as error:\n failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n break\n converted.append(seed)\n samples = converted\n count = min(count, len(samples))\n sequences[name] = samples\n if count == 0:\n return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n seconds = count / number(action_hz, \"action_hz\")\n prepared, scalars = [], []\n for entry in plans:\n name, prof = entry[\"name\"], entry[\"profile\"]\n samples = sequences[name][:count]\n if entry[\"model\"]:\n joint_command(prof, samples, entry[\"model\"].joints)\n fields = {\"joint_names\": entry[\"model\"].names,\n \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n for index, value in enumerate(samples)]}\n operation = f\"{name}.joint_target\"\n else:\n target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n targets = [target(prof, value[0]) for value in samples]\n fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n scalars.append((operation, targets))\n prepared.append((operation, fields))\n\n await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n # End-effector targets persist. Timing belongs to this workflow and the world,\n # never a device-side duration or a background worker.\n if scalars:\n for index in range(count):\n if index:\n await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n for operation, values in scalars))\n await io.request(wait, duration_s=seconds / count)\n return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n name = entry[\"device\"]\n prof = profile(name)\n kind = prof.get(\"type\")\n if kind not in END_EFFECTORS | {\"joint\"}:\n raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n model = None if kind in END_EFFECTORS else JointModel(prof)\n # A gripper or suction tool takes one command value whatever it reports back.\n width = _cartesian_width(entry, prof)\n return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n \"\"\"One action, split across the commanded devices in order.\"\"\"\n if isinstance(action, dict):\n missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n if missing:\n raise ValueError(f\"action names no value for {', '.join(missing)}\")\n return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n shares, cursor = {}, 0\n for entry in plan:\n shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n cursor += entry[\"width\"]\n return shares\n\n\ndef _cartesian_width(entry, prof):\n space = entry.get(\"space\", \"device\")\n if space not in {\"device\", \"pose\", \"pose_delta\"}:\n raise ValueError(f\"unknown policy space: {space}\")\n if space == \"device\":\n if \"frame\" in entry or \"target_frame\" in entry:\n raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n return None\n if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n action = vector(action, 6, \"pose_delta\")\n angle = math.hypot(*action[3:])\n scale = math.sin(angle / 2) / angle if angle else 0.5\n rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n value = number(value, \"action\")\n if not -1 <= value <= 1:\n raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n return value\n\n\ndef _grip(profile, value):\n \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n grip = profile[\"grip\"]\n if not isinstance(grip, dict):\n raise ValueError(\"the gripper has no declared grip joint\")\n return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n value = _action(value)\n if profile[\"vacuum\"] == \"binary\":\n return 1.0 if value > 0 else 0.0\n if profile[\"vacuum\"] != \"proportional\":\n raise ValueError(\"the suction device declares no vacuum control\")\n return (value + 1) / 2\n"}],"isError":false,"durationMs":3,"timestamp":1793224082894}} +{"type":"message","id":"2b4e4ea2","parentId":"4fbbdcd9","timestamp":"2026-10-28T21:48:07.442Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Saving reusable image 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use the dual-arm policy in short segments, checking the camera view and measured gripper states as it progresses.","textSignature":"{\"v\":1,\"id\":\"msg_0b5218887c37459e016ae26d971930819fac5cab7a66ca9874\",\"phase\":\"commentary\"}"},{"type":"toolCall","id":"call_490e6b1f64b34e829277af881f08870a|fc_0b5218887c37459e016ae26d971ec0819fa660db6d9d5e632f","name":"device","arguments":{"deviceID":"head_camera","action":"image"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":2402,"output":75,"cacheRead":23936,"cacheWrite":0,"reasoning":19,"totalTokens":26413,"cost":{"input":0.024020000000000003,"output":0.0037500000000000003,"cacheRead":0.023936,"cacheWrite":0,"total":0.051706}},"stopReason":"toolUse","timestamp":1793224082927,"responseId":"resp_0b5218887c37459e016ae26d936664819fb040500701f39b7a","rawStopReason":"completed","durationMs":4515,"thinkingLevel":"high"}} 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"}],"details":{"kinex":{"title":"image on head_camera"}},"isError":false,"durationMs":211,"timestamp":1793224087729}} +{"type":"message","id":"d27588f2","parentId":"34917289","timestamp":"2026-10-28T21:48:16.830Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_dd58d395e4f74264a0b4ffc91c55c48f|fc_0b5218887c37459e016ae26d9a2314819f9adac34e651ab34c","name":"write","arguments":{"path":".kinex/tools/dual_shoes_policy.py","content":"\"\"\"Run one bounded, freshly observed RoboTwin policy segment; no lifecycle changes.\"\"\"\nimport asyncio\nfrom kinex import args, communication, finish, helpers\n\nTASK = ('use both arms to pick up the two shoes on the table and put them flat in the shoebox, '\n 'with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. '\n 'Leave both grippers open')\nCAMERAS = ['head_camera', 'left_camera', 'right_camera']\nDEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper']\n\nasync def read_one(io, op):\n with io.subscribe(op, depth=1) as stream:\n return await stream.next(timeout_s=10)\n\nasync def main():\n opts = args()\n async with communication() as io:\n images = await asyncio.gather(*(read_one(io, c+'.image') for c in CAMERAS))\n paths = [helpers.save_image(im) for im in images]\n result = await helpers.run_policy(io, 'policy', {'images': paths},\n [{'device': d} for d in DEVICES], task=opts.get('task', TASK),\n world='world', action_hz=25, max_actions=opts.get('max_actions', 32))\n states = await asyncio.gather(*(read_one(io, d+'.joint_state') for d in DEVICES))\n image = await read_one(io, 'head_camera.image')\n path = helpers.save_image(image)\n result['measured'] = {d: {'position': list(s.position), 'velocity': list(s.velocity)}\n for d,s in zip(DEVICES,states)}\n result['time_s'] = image.header.stamp.sec + image.header.stamp.nanosec * 1e-9\n finish(result, file=[path])\n\nasyncio.run(main())\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":340,"output":465,"cacheRead":26240,"cacheWrite":0,"reasoning":0,"totalTokens":27045,"cost":{"input":0.0034000000000000002,"output":0.02325,"cacheRead":0.02624,"cacheWrite":0,"total":0.05289}},"stopReason":"toolUse","timestamp":1793224087755,"responseId":"resp_0b5218887c37459e016ae26d98486c819f963d63d66016230f","rawStopReason":"completed","durationMs":9075,"thinkingLevel":"high"}} +{"type":"message","id":"d726f1ad","parentId":"d27588f2","timestamp":"2026-10-28T21:48:16.834Z","message":{"role":"toolResult","toolCallId":"call_dd58d395e4f74264a0b4ffc91c55c48f|fc_0b5218887c37459e016ae26d9a2314819f9adac34e651ab34c","toolName":"write","content":[{"type":"text","text":"Successfully wrote to 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"}],"details":{"kinex":{"title":"exec 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jG23Re3Dh9IKo+6bnps1NJM327QKhhzDFZbC4eeeExenrkNO5norckJ8yxm5lPLlqmnck5rtqEPciRZLElKmq4XZTQF90uTVMZn5iYDD7LLf0yT/1wqcTxtukXdkIoo51KSrgxSIA2akBol2I+VD9gfch+zjUzfd8NIF81p2dk7CGlPSqyEFJwKUshi1c88gjv8RSbaO4G5S5OQ5DJvUds0Nryk2QO78jx90qcN37m8lNmLp/5wzsYJi+b6A3xmGzRCxT6ua0wVfbtf3PbeiRG4/tl19gJG4nrKM7DbKW3mBOAO0Xy9EhdXWO+lT12PAOTd0mcO2E6L1IBWPx+573zM3a+bMbFsvnJ7jC3iDG4n+pNNmQxivg6lzaqS9zdUR7mgjhhAlxViA17Bslsq4hwOmcIAkNnVpTkyOEi1hqpHagWAgFSiQMCSFTO5Mo8Ykrq2DrNM9NAv4Z1Q3dQ7T/xxGNKsaKwaX0u/TDJAcp5XGr8ZeJ59nAJgVOmfuFVO/XAdKlGv4aRhByloCWMuc14RnIJNba6/W1GqU9eRQmP2EH8aZrhnB2Q5XmOMkSyF87cENOXn31yuHQsa6fq8UgyWloZm5gXHrOCmk+3oE3QT99hso/Sx+Yarx9XNhtR+9n1cnapcot1rHD58CpHspNlsPA/CaHIdWwH40tGU35rQ7PCVot2qC9ivCwLG1mm4BDxAAofSRcWwSt0Xdc2rU3DgvYbXJqZyu//Z1fI16nYkG9ZVpcvX3v4sUcXoqQMaK+iOEkS5jSW7OUG6sQ0Hskld8KEBpMjd+fNnxTGMdFIs1P8M+Ms+AsvIP3MDt4x2KxMIieO0dDHE+Gtl9wZOJeinN88DjrRn4P1mM5TbntMWUVyJG+XPdEvsfOSzDLGmZGG+RvNzsQYMfHRrsQ4vBx9PrlrYGrBAJ281dhqD9sjmInh+1TxjU/NX88PEfJWaSOYZHzbNv3AEKZVajAKpdyGSymqsiwKif8hkMuZLJgeSs67ASPXTJdaatUXlKwFjTrEbkNuSiFFURb3Tk8HrYStFA9eM+/4zuReynrofUgnh6LwjV/2Fcerpe5MrxTqsudXLSoK6e6Z8frMLbD7MZZs7o8kAXn83NSczXf+vKTK3SVjr2HyVXKf5KS5dw9bM/CqiTcjGUaehzDiEQknTEVwtLPSx3ub3f9IBp8mLefbb3yP/H18JVNg55OnhhuPFIUdbHPitggqjz3SHRh3QeKLSQefbt6c0abx0PCpv9H89oszFJhTvlxZrMBDRmBitNKilFUpb98+ff+HP/iZF15ou8EY5TI9iK6E5EUpy6IEDQMGQFRF8bqnXlEv9kjimoUp9X5BxQwcQtwmaTgFl3SPoqi45u2mIUObig/cytjhese3Dyw5tmrHSbfBS5RFvawee/JhQSX/VhXYkW2c8btQuBB4pJ2T0SyNCC+Q8EjiTs4dK6rjfZiy3OzUbEFTf+XICxA/un9qdXy5qQY9nR6nBE0uH71HRqIT+Zu+3VR3iHXG+ZOyINh8zGDEKeffMx3NxPpKqMivZVTCJm4glp7v6xW9qzGeGsq57R4ePW98n/SFc8vFGU+JDTU2vKKLNl5kcxot2ApqBISuBb93e/O2t//2nfM7vVJk6ab5rob1jLeM88b+DQey0p/6zO+VRUmwBKYo6qEbCimkAgALCV9SyoF4QvgN2pizcxxMMFkWggMVwuvQAS7FF0RnM0q3skEozStRP/Tww4JLZgaJHC/vr4/7IViTYYXzW2akN9qa87tlJB8TDuCFsRVvozSUdMXHHGCy4knE9yIVYVJvd+GJI8kwFoJ89F7pfWfGkUqAzDSbvEp6JN+lkiz9JgieULk/ffCI2ObGtpMRzQ0onjUxLFm2PEGejBnTiBZzXpAp41HTGU1hyJlMsmPSuU+1ABeKD1vAlSIgN2Jo2Lvf95E769O27TyoURTmobrM2ckWaAnCtugHqkHmsu0704OAxUAFSag9QOkBgK8IeokPQ39yfoYcDm1U19X1wtrJQADwQSMHB2O3lq8w8rWx4HTDoL7w899w5ZHLA+xspHwZZGIFmp0torJXjyr9okOCnpJIj5yDONaQyYdR2V9CFXYP+pUcxWzH/8ylLbo9nH+eK31+QdOtckHAdCc/mGFY7qOZu42Hmvm4diQ8pxdHwRgruSbvF26YDzwu1Hhg/kN/QeQBCRuI/sGp/pT8bia+mGRIE+UnjCkP9bkPo+Ee8yjtGMZR9swuytX+nGl5TdRvPruJqRRiWS3e+7FnP/nCM9vtRikUCEh4e92DHOl6SClarLh9UInkHmSgVVviV0r1Q2+huqw7WgqhtWmbrZC87wYhUTOshl5IyXkhmMPm8nXwQa+OhXq4leD1ovic1zxd9rIfWlZIPMFndWZ1EONdka2fNaxS62IuwjQ+opCIK5RSz2TWx6ZiplymvDZ+FsK/EaZphsQTyReZj98w89Sfa2epjjYxtca6+xwqQYh2Tl5wdqiT4VzwwSiPLA1kjogwE1HZ+gbl1r1AeuZIr879zSwTo6M1GqXf5OfESYqbKyNud7ofRMDCmmTwh0Ki0QvGv4L6IiDEmOgHtmnO2mZDBXuqU73NuoDghMtKurJev3Ah196Z8SQAbVV9MQzweik6OGNFUXIhLNLioIau7xFjQhI0cqZlURhthmEoitKiWcWgbJpBRtWpyABhvK6Wr3v9a5eLpe47TbnUDqUvZp7MapdzulW6cPc5cu9Mghbg1yx3d81x8ZQqcq6dG6ph3XIySn0oSbTJQ4hl+kBGcbnFbbcIjd7v1czHNktrUSLMqrY7j0zIBs0mGHw7j8wR6N/AYknkEzqjPUyHHiCcoq/aW2uWgEYXmfHHwRmYmsZWSxxRdrSkorqbrtZoMtyZju8GSXvf/ehNeWYGMxilWClffPFm35NWykh8amUpGLihcpCIK4kkKcPivlr4x5C4x7VSxbbZLpeLRVn0XWfgrJbWB7+sl13XG2M6+K0x6IFK+S2HgHvZU7Afo9ubttIBJjBEd/9Fr3v9EzeulVw0DFmVBNYBjT1/tzgLkc16p2Hc7ZHfjaYst1fT9NawlfOrkvUIKzq6JKflTBxOlLCM9ycaVHKOP89yT/trvsIjj/Dk6VbV8RdkQjybjOwGnn+ls3PxEfSE7MOsDCSszu6bZZOQB9wmDomogY9EtNOI3OJl7CGL8JrwqEilLkiSIIzG8WTv5X5aN3G6G5IImn/plJRdOUCmHPi3yWpHwqMoRiv0QpbPvXTn0y/cbFCcJ5iEs6kbeuLrnPHeXgb6tOOAOkyBXfqdsACszYy0jUINqm27elETuh2RloHLauj7K5cunZ+f375zF0jOnA9qsAoyAZ06pGg3ZZDoVhfEGK03DGlYhXzVq54qdNE0LZR3MWHTaSHEaPXMbADQM8CJDyP/MZLAQVcKMxx2UMIlwiW5ajfaKVlQIZBiuCQ613LSSlQBx8DHul8y5sxBlzptwobz0xFJIyPmXIuesdeSS6dHuh7hHkleQsj/y9VlbyFM2JejqBFrHBNslgaQnhqDPsFWDSzaTDdCQmMJ95zwjmTCEpaR3MsBAIWpHrnm0+9SHptKtaRALMBcyaJ6/taLm+2GUHM1V0JKWahiIHh2b/pSoBio3ULie6Ic76am37kyQhpVKK0EK9u2LctSqSFYz6fnZ93QCyGPj4+Gvmu7bts0/dBrRl6uALfpahl8+AhxX+j1ksC1Vov6jV/0+qJACnQoafUYN55jufdLNlbg8e7zJGwTZWc62ROMp3QxI4ePxqLXzxI3eLoOcwwlvf1oM4QdmY01UakTh5Antkjoc6OOH7mBWTstvSCknnv4+tGTRlszc+wHthNfwNdRTV87soT89WZnyD8z47HThLjAlL0PIVWWEwSfhPTHKvJkgHTk2SlukpO7WTCYiKea+d1Soo6bI+rhQbFOHp9cMzN37kLHUhDdZazt1ufnpwz6LAWH4CAGfdGLamUxb6h0iAQkyGWA+wmJHvZTkqOSMNoV5XmQXlvg15IQhCGdm7Zt2rbvQcOHB4dciM1mc3p6hqRo+1jK+HAZl65CGNLZur15IRkTh/vHV69fWZRlN/RRKw7vFV43qS6eETIJ659AHWZTNh+GScgrrohd2pH6maYX+yszUyoYonbQSQGCt88CE84Gkku98cMigPx00OkdxvWi82dNj2iMj7lkpM2cdOceEIKn2W2zHZ/SXEq0GXOIWmrm+8mGngBoek4z5+3Lj2yt3Sdui+XvFlTleO8oPFOCdjvEb4EUhNsLgMBgs82V/O2dPaAOxZTs9dFqtT4/tzQKvAvKwbIpzF3fbTbbYRigrSKaCwi5rm1v3ry53mxge1ImBexT+Ji5NgPSPKwPVWtdFoW1ciFK4XS2CLKs7TpZFMvlcrla3blzBx/aXh9UbwTpTpcADtpIO+kIFzH2pi/4orqqDDeiKBWNNmwj58mKPDPlajEXLl+/QCARJHTkf/JMPH4WTGmTXeijeEEYuRunaxQXNlmb5KO4Pbx4cqxmp1tjl+BI4N52X7jr4vC3322pVhkLtROROENN4/ulM5Cwo5Sf5ZOVcau048KIpbo138lusrcJf2Y+kGRsJrxG5uQYGQe7Xo/v1C+8C84XuSZ8OeM7+UtP63mtqRmeYEpRfvKFF2/euUvAVVbO4id80V3fdR1oBH8DPFKbXmt95/btu/fubZut1hoOahdPRlI0Qsp2QpDkrM2gVEG9T7p+8NBWtgEEU8OglVrU9eVLl7q23TZN2/fInR6GsgQMNAHlIRiNVGomRImuQK9+1RM1K4YBDmyIZmrOEq1f/5Ij+vVfe7E0doBYY8J7k2aiCOPMqZzNz+0bKn2Mpwfhn58086A5KXIRIc7cLuNiD5CWkUvP8Z3G43aFZd6b7Vna7C5Mn5R9HgEKrETMS7l33iAYKjPvnpDKjrvMLEK0ftOZsMcugz4szMy3+UumZ0TjNZBmyney3J9cD8ytEP+P7e0EmhJNr7uus1LZKsuoCKQ4rq0jgs7cIwA09MPZ2en5etP3CPFSyElRyxX3RoIVBXodUY8n677SRsEbRW2c2GCpF3e3CvCm0aWQZVUtFsvNdnN6doquaDS8oij2D/bbplXDwBnrumF/uffKV71KsqJrOo78r1S1yM1ML439sqRE52fUn5isVyC5sJBTTTw/kqi854vBQ7vDt7xjY8xs4Jz6MnMz7pHEHPa/jRX58P9ErR4/OpNNcwQSB+KIzj5y5H0Zqb7Jht19R/unl4azDsjJOMc3Sx8/neT8IjeDma/MraJJVmHkNI63vY/m7e7khzXLBBL+OoWkzOKTEykR0Gms12hAJoUjNWjQJIMpwQK2qU/+QAkQBGFZDlp1fccYq6q6rmrvCSPhCj1aFkVRDKiA0ElfBZQxcHJWDUr1bV9XFaK6ULt1b4ZSilJAo67K4vTktCUoPJjY/XCwv9+2LUzyrv/iL/n8Gw9dRVSrG0JTFPd2ucXvvWtepw6TmJmGSTxymnUUF2xm9XetWnbtWI5lf+a7Y7IzJ09xTRWnj0vdx5luOpGiIy63mxiSf91sORT1bKxxj48U27jV4h6deUAu/sMaJu0Edohl36kq39dREUseM3Yi5YQ5TUExiQQO4dGZlhLhRB9BtYwnzWeeNwtGM5Gxq0wA7Ziy9AN7peCsbbfkkgL0jfUYEWAkNfr0gMu2NoFadILwbFHQYrkY+p7K8knXRWKjKIpCAvIKoDcuGcQwVsni0tGlvu/vnZ4Cy44hmWNR70GsD4NUWkvXZOH4+BgOKjh0wA36vi+qUg2qluLzPvc1+/USiZMhicRtkTxDxlvDQYmlkTtXdZhP238s1XNHOQK7WH4wCtNISgwwZMrQZIuE85MPR4lR4WfcpnOCM89HDFHMaZpi5gGdjGVGZMarg3qTZl2NvEzJDScCM6EXf2N/YrbBk9KCmVmKEDF+qOOTovchGErJ2yT/jik8zNfUqpqRiO5IJXyCOxU3YeKKyVN0wm9+M0Z631VNkn2cpZzQFjaI9aCpqwntiWy9PfmVIr4riFEjkoSiQiE7Ots6umzsljIykVOJYgYp4MdK97p1Sg9Df+no6NbtW81mU5bl+XoNPZ4O8nWJUlSc8cVi2SGE1Ruli7oqqgp69TA88YpHy7Jqh57D+CYCJEhagrDNaCZCZV7A6nyvGP9p4uaKczRRlzKnS3rTuGWiP3qSep9FTxMCHRvs4bOMLbNdAnhanpT7z2dFbT6eJHo01d8iiVh6cf+6Ubus+emTIkMLzDbI8fEOjTlhXpmeuWf0M8aOafFdg0IaF39Gs8rjtQnpxSIKYzlNnBySxskw3IgTD9O8mrvbYAllorm963lczt4mGHh+DEjYMINQ5mBZ3jsx/aAM0/2g+q4HUo33dFMLZVmWrDfwWlGbUWqgjehPOjDYuhQ7KkvV+iQNH7+W0K5LbfSl4+PzzcYYtt1uOed7qz1JnjAY3II4mmD79bJpIPTLquSMHx0cnp2cHh/uGzVwLjUCWUNBANSOZgIpRupMpnrGRWOZlUu59g2b/dSklu84vJr5lzM7KvuRifB06ndIwnkvUm4Q5rshe/54d02eMZPKM7o22TRxWwbBmWIBBQKxl/gc+amuHCYk50XZ47yGHkVpFIlj15J/xtRh63+G5XcnJudlnCN6PFNuxxNFY5I1M9aSJjgRc2s8dqCkbGG6L/z9c4Yc2jrmddt29F3bv/L6jbPXfu65+uCLL97Sg2uH7W9nm3vSJSBdXdeVLS7y1fVJAxTqvlBIbopC8i4GaDx4D7EsbRZ1dXR06dnPfKYfBuIGzu2tuaaETXRXYmyAK5uQQRjjR0eHNx55+B3vee9isbxx40ZdVb1CESFFkmy5ouNRO7IZdqVS7MpVmuOmjqFlPCspuJ9Nk/JsbZZ+xvSYcfPwhJkTLthDk2O8gyLsdPjebZlAMlMnTYRJSwaR3vkiST96x5l6SRdWmc5HLuAmg45KTJaWGdSf8Q2nGXpZwpNXnVzhoBN+Xk+O8ZvkQZnRO8HWzekzfXTWBM+fm1QrplGpzAYJW42qdJkywoh+aD/38Sdf8cijv/5b73zmhRdPT04sHN2g4ceyuc/28ULIuiptv0E093a+qyjrDHChB20zp+ESg/B2j0VpIRda9crwk3t3mTFD3xeLhdX+Qb+Ul2kfBzsZGV1AAFnU1XqzfvSxx5ms/v2vvu1H3/zmZVntlXLTd0YNmknbLdwmc81S75gzR148MtDs3KVacubejLfyfDxJr4lJOiOBPw0Kx2OHQ3Nk0483QBhtSjdRZciMJR9ATXWRhEfZ6OR4f4Vzw8PmIruj372UmDv49PxgY6S5NFOIxeRJ3ug0u9I8YmJYelmyPfOB53fPn2q8IhbyAt03mV6cTFySkDP2IOQ3T4G1gmBIytbCcJ2GHrpke/PF71CILjiJjSpgDh/JxR/68q8437Z37t394Cc+sW4252dnZ2fnm822aTsYxeBA6vwUCDpqQFW99mFjp9XQAwqlAJdTFWWjurQbMqPCBtELdGAZOhtnKstSSER0XQ6WE+QITdl+StY11jWbhx+6Zlt2/8tf/IUrV66UVdX1w2sff/zStUslryG0YXuPpioWEk82j+fbmRUygw24S/VM75hCpY+8aVmINGejo02dCZspNYUiCf+x37vJTs4FcpC5F4pG+63j/+n1M5fEKfATN6Evf4fEIzQqws2gJzLDcgpVNlIyYwKE/3YkncJMRJ/2eGDBIBi/1dRdwUcj9DcNNJuCusZ4RQKgkSvM8T5pT5S4cUbw7iHSHk4LpAz/D6UuUPyIigYpUGvM4Wp1vNx77KGHeAGxRph1et1128323r17mpsPfPRjn37hZ61abHuWuZwfAPMwzU0BqShkXYPAHN8gZZhpcpUNQOvoumFD0WQqEoYizklVtnFn65SDlCfnVj90j9y4sVyuEHg2+sVbt3/v05+uF8tCyE8/95m95eJbv/KrymppSsKeHs9POv/Z/g4PshzcI3gmKnHicUrrBPLtNdW57RCmK5duhcCko9bkycYzXzf8kfsiXDi6uzNzkr9m9o2rrg6bc/x1nr488pmM3ihlHKP3zJ8+ZgRupCNPjSOWlP7dabugreYOqwDnTxqdkAi3GcqdzGYMsyeJQPmST9lacswrJDveJbWiYq/W9JroTfDC2L6zbW8GN47uDWQhJ5rDLQQv9sVir9473FseH166cumhn/vX/+62vk0pHD5cHVCSDSsUFSVxpWw8KQwKHVW6XpZSdeiHYmNTpDOTkwzwOu421pultK6lBJNou+UeAlZFURweHm7X63tnp5qZsqiOquNm23761u0nH3/CjTelUP/WqSzKiC7+ncgMqwUlUcIRHD8J1YByPb8YGXBD+vjxvvd4BWML3PrVE5a8G+d7lwU82Z8j4s9RNyZu4cmt554Tsd4uGsncXdJz0xuk5Z7eGnXfJqvjHj4rsMcLnH4btdocziT9hI9u63Rp378rDmZ0WtJhdpwDknLC8ZzO7Z90CJZnpKzUbaQYfwrKHPIhAVNlPR22eJD4tmbcyIWUy3LTnlf1gqH0QDuKsSwJeY2YPAfTLFCyhMJ99AouABtpg05lXZ+v10rr8/V6tVqRj4o5ovWjg8SmrEuLg7WoqqtXrqA0gjClbcdD6N5CqgHpYW/9zd9+/NFHF1VFpf2TpcstlrELZ7pHwz9Jqc0oshhCHeF3XwY3btI0bn8WzKSsgnl8UYgbpLGt0E4if8NA1JlSP9kSAZhoStDBGzO67QPQY+SN46GlxoIX/rkHLDlvsiKez6X3SyhzRHKjN52fnLCvd5zOLyBgd9ge9T5fb1fyZ4j5RDU4rQKMX/qvotU7ulH43KXx2NZD/jubAhkHHMKOya1SSAzDqpPz7ete+aob1x/6+Mc/plE8RNSHOHDAaOeFhW5H4Lcs+qH3o8CWReYHglQoYjIGyVwCeDg4nfgIsp/pdLAHC8AF71RRHB0dt13PGBI+RWEfYfoBlRZlWXZdc3ZyuqyvYPi4I/UZpddJpWUwcjN18wKvy/jPxP+Tot3kMHDj+yUKpdffJt/vBrKLY/fOR7ugiTSYVRTdlo2j8cDa8X3uo5KOb5uJwXgf6sYxp454XmHdEtEjNPccO9RwR0fBOTPOcujsfAU1KV2NPPK6WzvJOWakXja3JVKml/sDkyyZ3CmRTVr2jacI73mYK9ZP/0zcKHGwIZfJluXb8zwCbP5Meg4iR8iAgv/q4atX7Aay8xUihHSdBnURbwiNyu1iIMVaAtduGFAN3FZlUVWlzZEKxX8xNg4kAJtUovcODk9OT8ui2LYNxDBHtlfXddqws/Pzo4NDbtSnX7x5+dIxPdqi1SbcMSRixZmbX6AZVhq4wOylsYHNOJEkceBEGeV7xk4WOAtijwEJwliSHwnbnYnj+hdNqGz8wqPbx9FHXj6ah6QIafQw29xqpKlMd+PIPT/OUppthBGXzPsG7B5N9m5O5OlzgmDymuYkVDieKZ7TX46YPRnaeCVza2K3RuM3W0KfqVxI2G4isR3f9i8zmiQnmiJentsbYZNY/BxoyaKoDvb3bfKVS2KM5Ifcj6Lvu0IABJ7Bt0zTQHW9VJDQtW2jtV6tVqRmO/05pM3Y5YF2bkuCKQVssVxst9u+kGfn54vFQnKxqOu+66u6KgvZdy0vyre/6z2vfvqVe/UKDm0C+pnOdNReR+sdXnmcKpzvY8+w5nUs+PvC9oiiJj/XKbL2bokzJE8Ynkvf2XXcV5TOCgT/mVUr06/G8isVapkeEe+eMjfvZBnZcPGt0vuP1b0okAK1Jt+lkaKQtzP/+pl8DZ6aiSjLxXCi6rBwzzF3TNXjdC/tcDf7J0AzTK/LT06tJIcUOz1nzDPzN/Bz4cvo7SNdwyT7UvBtKdOjLdHBvqAoj+s7iPiuQ4DXxhSckwFLmU024kwUjO3dDwOZzoDZWtgIsO+2inkigB9AxHq+WEgEpYzWZ2dnZVm2bXfv7r2rl46Xi8VyicvLouiVkr2p6mXbdat6RVfGrA7rWPBsKdTLp6sy5wrJmrz68QUZmOl7+VVTaThe6uTsBL8vUa7ydd7lI0vEnY8XThhBvsYzuUlRf8+Y2jRrMDx0EmrLtcJ4+eiFQujOZJG+yUwlQtXLHDv2RFEekZ67b363jAJ8sHmOxMI0BGceS6YmXekg0fIYkJXTFv7JGzjZnIxMtmQafR5n7i6Nc5AmqLj8G584uHvfIqAT1DUnlWyKBQVZhRl0XdcwQlGDb/HWteuJhgpDTeWEDg3H1wsYI6SkrC2cTiXESIG27CLuZKpD9K0nAOKjtT44PMTnSq/7DTdsf/9AM3bz5s39g30pZdu0eH+J+v/1+dneYlWWlYfMDH5cR3GpIE5j+KHMIeH+dhtmThfnpx9L94zgMjtzfq/4Uz3pzW6nJIAc9laqHEVm7bddVL7TqqFM585tzVRxmCAGxA098zIJD7RTnL4K4ajFnL9UXQxNZEfPGL9dMIKdYmrFh9sq9l6jzTvSMjIekHCWHX45H4AN6hPz90yUAf8gH3aMKsusqZIVX6RE71hRBMrL9RKn6MchBGblN2CSxZC8bxgo5T2797FoG7b3mUGulGEFl6WoFwvASBIMDnmaQGsClURAiaX+wBQ+tm5kyxKs96Vt24FQpymri+ofjFFgAE4gRZ1dAEHPMNZstx/96EcvX7q8XC0W9UIPQ0+g8m3TLuqFKZgm7tdutnfvnj907RUBRDpoaFbwpvabn3rP2yLXzYVf8pPmLJeMM6TnILnGImKMc+yY2rzBmcCr2R+pVHWyNrCScE7Q/8d386Vufpmnxm/YZLkamOMaez9CyNrxL+x2UerFifagd9omDoKUuYRpigak7+VjT3I3Hb3XDGeZxNiSNx2TbcyXS+/jGSDPT/ILGHnBtC47LNVctlpgJIHT+ueGFooxb9Kf5wshiB6S/Wz582jhIn54sIm8DA9JkoKxAdK0Nrys5dnpKeFZwlsEXGfGSNVFHX69WAB/AylUyNdwjM3WIhpUEg8We7Lg6EI6DINtOKr0IKj/t+93SLq8Qn7l+vx8UIoSpPcXdY0UT2NKKZvtFjxisSgkAkxGm7e/+92f+9rPkZVtwSZTbcXOBrV3sW/vNWRneufWoSvupIEmOfyp8pYqcKOKJp+7cpEhG0Mi8TTfh9EN0Cu3iYcv/hu9OFGNixb6ZK8GF0hmF3hpF0jQpR4nU0G6GIHMg2dGPTbTX3LRM3rxxItum1mOJWGYOS9tomM1zRKyb2hN33gkFmCGn2BHR5IlnYvAZfisApU6oulwSUgpA7LBEtqoSb7yKEM8CEPHbCN5WbFG7y8St4HLRw5Y/vSU6Br1a4oAC82Rq9wKq5UsoiVzKyPtt0R9quOmrgomxK/95tt/7bfeMQw9lfhDxnLOmqYpy3J/b2/QyMuE/OwVsKRRfD+ofhgWi2XbbK1eDemqNSFb8q7vqwolhFOlHlXEVYWKJSlOT06pAcRlyQXAMgnTQ/SDLBTg7grZM6CGHF6/dnz56qIGWoBP83QOj+het5qn1RttLpnfnYS9F8ItCcvPtlJI2fKuUcpDSQdOq+QNFZ9dkkitwKyj/hs6ZcUF9z3RIwVkP+dsoLiDUl09VVEThcB5IKJbwO1Wf3qqGPrsYC9Xx2OZWbv8j+gV8oSXv1qIaNgS9VSNthzEgSB7IenXNLyWEz2ppMznYSQdp24OJ+RYvF/a/D3xhDrG6zle3Fxp5rKdagvOGGWE1YPtg0gtpWeg15BVfIP/3DmR6KGE1xyZVGw27xmHzcTKTRErDcGChWS9Hozqq73Df/2zP/sz/+bfvO+DH3RIlQaYlV3XlVVx5fL17bZRSgHFDoAcHFjtZVmWFX7hnLcDYdBB66KKBaPrquz6/nwN3zIiQC6A4gZiy5Is+DvnwMFr22a52EOSllGCOpWiBIINSpeGq6vH169eu1ZKtHqwYHfBCee5ZHQcRJbhtJKRNptbhJYj5ljkdJeQvu7VZvrJZbxyhrQiS01pO7lv7tmNhJSngo426kSrnJB3BjI0s6+jqRb196AspNZEcDNOaCC91cj9vuPwBOFeJmobGegt6SJE2Nkb+sij91umhcnJOeN5yJjc6Hue/bTFtNm3UftPmWnuD45Pd+vmtkdiOdAcUiYU0YQjShKekkrrqT2v47aOtkfuiyArnFjx/gj3jqgNwEOUHgAYu3fpX/70z//Uz/zMRz7ykc22gUlMVf9d24qiuH7tumVJUgjX4Ntud0vDgzZ919vqX/uyGLmB/r2/Wt2+e/f8fA2vcoWR2ilHHogsCOPHjaUnqFuj0ZMQsD1wgsHSLmXRt83x8eXvfvObmWBaoyeqW7sZ8ydsuKmo8S/udUknG92WDvwtcy243Zf2l479Iyfg0tFOybf9iDCdNmX/8UJoxoD1JwdBFjaN+2tk0ydiP3wbXzQ36JLt4nDBo7GREm6qOkZxHNS5qfc9Od/NiecZXg8NZl6SKDF/j3FkKleZneUUCXXGSo3ukdj1heUGdDrkyEKnjMtLw4St+lyWBIPS0onTfEC7RJ1FwaR9vgKCIzXyZIMFh4OTScPPw6VVEZEiCfnmdA6XqZTxD1IBXTNipYZqAbH6K29760c+/JH1dq0BWNkBTKdXdV1du3bNJlaQ3mrQH5gaN+BulIkFp7Tq+0FpSbAewtEplIaiLFd7q7ZpN5st42xR1d6AMUUt+x6JXK6wQYihHyTvKzQmlUYP5+uBGd0V5aUrV3/g+77/cz731ZVYcKpnjpZYzN9wc+iVVXKhuThTEnBwPsJMUAWXQNj0SbpL3Nfu1yzasXPrZnXEgTGEwsNEg4zX5C7v5PNk79jzpt7t4ATJk9PGJbJe4w+v7zMoEkKNrvYYEUnUiuSZyfhnw9FxopIIXCr3xo38JlMZhfJOgyL1JiYFW8ls5UNhM3I5Xd/4S7Luoy8Ta819EO3bYHmZXg/l3mJQXb9ZV/WSKzb0fVkVaIYi+YB+2iUgNphGXjLkm+UBmil7F6LhYKMlM0AJjqhFYoZDDsv6X//8P3v3B95z3m4BZ1ci26plXAl99drVqqoAUmkVA60BqeP6AcNqhf5cFAVwcEIPH+tgAygOQGQLIXshYMe2vZRFVZYWtrIsyqZtbdwYEr/rzs439ZUlurQwNjQwnq9ee+Tbv+nrv/BNX/LY44/TtoIIpmKrCJWfaoE+ESNzwyR7JGm1kC5eVJRSLSmRX/ky7zC7xh+OU5Pwix16gicyvsW4Djw8LvN0TaR7+hpROCafu5kgdKI0opxOiONe0a031mIS+eR+DXTo75fOTciwGg13qqL4Z0cyci8+vXLMWNGcYKKJZIGB8cFz0yVXuYPVmbBee5KFnvETHMz3aHbZptyco3eRMlzocrl/86Xn/9E//Eef+vjHldYHB4eXLl/ZPzyoTPlVX/5Fi9WB5GVZV4tFvVwtpFaqNHvloSgqyHTvGfBzkQas3ZuTC0YyjY4Iopd3Xro9qL7vVNtsrFV4sL+/XCyVIvg7f6MChUWUuGXxNLquAxDlFte4e9N6Wi0aBi5jVV1bH8X67Ezu7wkplTLrzRrQPuQBpXuyW7dv3XjkumBsb7n3xGs+9we++7te8conjy6vBgyRRC+3/oBJWDMhh8gbdwiOTImcK/DKROw0fjpRy8e39adO/NR2a0Q1fbpJM+0sXOaVQM+x5p4cVN54ks+TS6+womEq6JOWEdn7BCYQJ4k+plLYyK+jfZt1fUreOrlv2u1mMjvu14T9xgn1/NnPk9MhsylJWHkYWfzF2HVJEqgSuzvxr6czncbE84/SnitWp3W/8u3Q7x8s2tPbf+Uv/JWPfeyj27YhbxPA5FBJz4p//nP/jBoBg5CMMbXgAxM3Hr3xP/yf//tHnny8MIWFcqa8iTQNMEtVIiFNDmPGLh1fOjo6bNtms75H6Bd8UdeXLl3WanCiQ2HmNDMFMqcJGQeeJCGoS7BqkHFhNU+XIGfhZm0YCdNVL9quVQqN0cpCFmVlu4T7gidcBJF7+er3ff/3vukL33jtFa9As7Wm11oVoiDTwHmc8Uvip3MbKsys871GWZrKGlcmYJUdZ0R73TtE8XOlzbseZ8Vt3Deh+is80NvoqUgeXZRutPzjTH3L1cFZE84znUQ8+oHtuFf83rPdMQXnem9G4cEhnAWx02BY/vy8OndWiZ28jqfplKflnrP8lrnq44yK/IY8czePhXTcMiM2MDJ1oh7uBXJ4U3zQ9G2BjP3y//rX/+d3f+C9Q9NKWcDRAKOXy072srCpHlVRSm1grwpe1suXbr54ulk/ClKjuCy5sMgcTW0xu8I+OCWUDZA88YonvvXbv+MX/9Mv3Ds9MaKoCrmoag2EOVtFTAkZaK7ECiRdKQ31nVKa9/f2P/PC85YZWdJ3tRFCVIvFsNlQXA0RIxK8etNs91d7i6pq2taeTC3VoBpzLt/0pV/2dV//DYvF8vx8vVdXKF32rJeaJ1o/5ER0JVpQYjWl0x82UtwUwZ61CWWBfHMR6PdBjrUyu/WyBybiatcuDSOeSqRk92UJ+nOcZIZs4pykgnRsUMzszOweQSZmVJNP//gO6cbOBzlVnJPEkPExHsZoFvP7h+hOduPcoRV4hslnMn3vsRWQvO5kDuw1CQugqLpHjauWx7/2y7/4q29729np2d5qGeLFbpvRnuZCKODAasVNUZVG6EF3675RRpe0zb0+6ENG6Qt6HQUClgmm1I2nn/rLf/kvlar/4Ec+wIRe1IuqhjcJELQ94OBDZlEB7zTwLU1dVYNWd+7cJoiOJNWfQC6REV2WZAw7IA4pxcHBvl6t2qaBu83ayRQQYgbNhHnbffBDHxSIHJn91YIZCYMe8jZsvQQYNZcP+Y4c7Yt8lyUuoAD15hMEozoFF/54m6QbIP90etghz8mZOaqOr+EUi0ipE5V6x7PyJ7gZS6hmzPF2DSbMTb6Z8wyl3a8QB+AT0fIsueR1sjWbmrcpu4o1uqm7LCrIEe/ELX0AIp6kbzCncU2mYsp45kg3S+yLD7e5oD5WxBhr73Wbk3VdlpR6HB8LNLgBkZQCaiVcSQjtAHm5qKToIYxddJmSqaB4I3xGDu24AqFA0A4HljB2640bj5VFpXRLURwAagDXnTo5WDg6WM2M89X+3nK1NFrdunOnaVubXR3UCipmEIvFYrvZ2oAtvaUuZbm32ivLcrlcWlc2FT+AIQw9Cp26pn/723/z5os3nWMedb86j/rE4kY/4blWFH5EZmId0hi208BtggN0BDAvm0Ew6+sLusou8tm5l1NjKjfQ5rOp850yo2onisWOY8Sucmtgx8XZpp68qNt10TbdzZNm7psxzck12bhGwi35M/CfiZ2RmFD+u8RkzrzEAeLCP2/mZvHz1EUwDkzbf6fL43PzLAWDGpQSC8lLFMbCW2z7GRHGHPVV0IPSw6AJrZEMSSlLuIYt1o1hZcFK9PqjSBRAbwjSxpcWYztT4xOKKjMpTT8wjaDUam8hpKyqqixKtEnpUZmPnCggVLrhFZuz0wY2bIXGLf1gGzQEm962D4UNbvRms8YL+MiYZaPL5VIwtqpKvWLP3XoJ6LE2H5NzUxb3Tu++853vfOzxx0ByUA/cbhyriF7PTfZ8SjT5vnVBV9cVilx8wgxKlNIMAAhyLdowV5raJlptOd3OM4pf+tS5rTVZ5vC1c/ZYV2kC1JLd7UKRu/uI1vH4BTIKz9WJPPVh/q4++/EBRxEIcz7KG9M2szFnotFH3pyjPIrTGZ06v3niAdnJWtkIiy/5/D6vljnlx6o7BIUQuh9Wi4pLdBrizEjq+pkDFlOiBVrqAgvWGNZ0KIbfnp5jT4J8JBOD4NJi54CcbBKyyxq24gfhX0A9GqOGRpry6NIlxtj+cs92NgPqLPonpdPJRNsPp2fnJ6enp2enyqi4CqEAmBsp5WbbhIaI5O6GBFfUyLRAoFcuD/ZXqz07GRKed5y2Wa/f+8EPtRvrvSZbASqENRpChmwUvYlcdTPoRuv0YcePKW8WL9/rgQyKAZyykLLiXXO2Prmntp1mwr3MnIBM5FOWDuwxsWf3wvhT56lM3MmRkLLfXib1joRChsAwOTcZ9uiMnVvXNq+bgzPa+QC6LPwzc1IeBM9WMT+TzJuwoIlIzVi2PzuplBq/RBgdD7eaPtF9MpHPF+e1pnKG8g+4qMtSUiNs+42N2nhBQuCPamjbjhpxwpwclG427Ud+71OfuXu7XzdGdXLBy1Ip3UEomw7aaCV4DT82KwT+k5Jp9BRlXMpqyTi/dHR8uFwNPdqOUvcVNVatDCsGSrqght0Z1q0HjTVFUcmi2Ky3nPK54JuijktkvltylYqZwuijg4P1+ty+vtK6Kthg2L//T//xT/zYH7u+elhaKGm4vVPVyP+SicfoWon7wS0VxcIJHwiVTlBGaiMG3Xd3b99769t+7Tff/usPP3Ljh7//LZf0JV6Tpg3wLzCPECPN3WHzMiUy5dSQzrdQyFNPNQS6JBGQM0Gq2bhVeoeRdI2zE8e6O99p/qbZkwPv+iyun35+0V0mZ1z07qPHzWePT5zi02PWdpkYz0m0O34T663CR4DFQOJDido9WWgAWOCwGFLKF+PaWr1hYPDmknpc8uKf/NRPvPWtv/Cqp1+5PDo+qldf/AVfsDw6XNSL1WJZL5f7e/t1XdeyRPhHGCYUE9AZB7Xlm+15w5955tmyKvrNUBbS+3A8gXpBW2g9SAnvlJ0yn6Du+jmgkyjVEwO3HR/ZvRmTxm2+tKBSfpT60tHa9qfEGm6/9NJLn37+0Vc8BoNfG45g1UitmtPNUkCboD9quOm4LIQRDOoKCqDOzk5+5W1v++13vfMdv/6OF24+f7Y+Pz48/sI3fsGXvf4rFgJZZEwLZXooNz6QGo5davrclpzZhIlLyV+W4mXMSJTpPcd+lokelz0hnaCLhnkBjTwIBV1wTaTlRK1N81i8r2sHfedEldtRO88bjSUfFB+XTc3Rb7rjwm28reg/T5KfY24qxCV5pUpqNlS0repsIlTilnD2MoK07mrDTGP46fr89q2XPvjhDyEw02lRAGsd3QVJ7BVFuVjUx0dHj9648dD1a5evXrt66fhL3vjGD3zk/Z/45LPveMfvPP/cc7fv3kXFAvTq6aSRQFRKSQHLlqSuVctxFuFUwn1lOLtzcoLKfq+RugID+oWsal0U1E5JiKqqBhetMmh6yPn56ekHPvChL/zSL2UFpWj1Pcz4nO/NLE5WzcZ0b0FxdWuQv6lNp89Pfut33vPb737Xf/oP/+nZ555v2m3btn3fo4FLf+tn/vm/eOzGK54qH2arfd13A7xbQ1KENOpiljiHkv03mqx5xp5+7u3fvJx4etxPcOXfh2BxkqfgdAefkTOSGTkdj/TxSQPWi46p1IpfuUeMGYkbZeSVaUJOxqLzO2Zs8IFYTLYeo6umo0pPmLWQUw7tszxsIIn0N0neZHJBe0PSuXgnb28dRCTtUD5v2i2yKuCv6V3RJAWpCLeKs5vP3/zoRz9mXVgFK2xdPVXrdYQYLUr4rW3pEFnP9Bw/+7wg+xijQtwLgV9o9qQkA4UDDYHbTg0Qv3aIVnl2aWHEeCAXtV6UVdMNRVEorZaLherXRiteoG/wP/j//K/f/oe/+fDaZWW4BIaWks5K2rVQ9LmNZKGTaj+U9WKxHFhbDd0LH3vmX/ybf/4Lv/ifn38e8raFxiK1UW3f6UFXy4pz8atv/dWbn3ru+77vzV//NV9/6fCqqAvDC2O0dIud8M488Wsqn+5DjJORj/MVX5au6zd+iFYnO2OaXTWyQeYeM5lg/rKHNHObZFixPejk1BijmWo3F9nzF1PvOON8F4MZ6wq79JXR8P2lbl+Qh9jyQYgqIaGKYrtj1xOxeK4fSzt9ZUuMsvh3Tupk8X9kEdtT4PrFc5qh1cbRGsp3C1ucmAD35Boaivu1Ngq5D0ijhotc8kLKEvhzgKE8X286yqgO6onNxkqrD2jjQw7TKASlhRSXr14+OztHvgoXn3r2mf/4K2/9vu/7AQkWokVRWi95Ti9hWGS1YkcKZQYJ/3tfFuzjn/jY3/lbf/vDH/3oi5958Wx9tm63JdV7cCHQoFhrNaDBomRIL2ma5kMf+sj/8//9t186vffmb/32R649anO5bfJX4K/xuZGi73NMzxnfxUrLjFBmKSZcNxZzYbWzWdnJXEKZ1QVEkQ5w8rBZYp5XkeJHjr+kqaS7ZsfrJXaQ41N875akOnjni04fwu9jEyTFEV6buz+fC3o4CSlkYjBWlHDeuIrfQqAoGF4jg2p3uiVRnSVDCNvQtCR02osh06Ba+iUOZzI4s4pI3gN09bJCg0Jrck9TaTEsW68PXUHCow1bnQJNPdxfrR1cfKkcFjkUpnDyhrvqeM3KslgsFuv1ljHTtM3f/rt//9pDD33tV/9BNjAuUZrIlHW4hS2YR0OIMw26k4v9f/GTP/VLv/q2D37gQ2dn97Zdz43pOqRk9gJlW/BileWA/sia9BLMVNf1xVKuT89+5p/+zCseeugbvu7ystpH4RdYg/XZe7XKFjmNDbCZPK0HUuv8+O3yxyt3SrzsrsGs23Hegw9h9gaJfe4nPOswN0MKqUaeOewSHeGCUfk4J/0+VRESxMEUg3pObcj5T3oezwstx9dm6dPj2d7lZPRFcIQhhW4HbP9gz+U5V1XNqn4Yts22bVp7NoWOqLOCpQDy/lD2EwFbWHC5jHu4NMLp3FH2o811kJRmDHCswgHXpbTrqAbGsRpUWRZOpUaDQQpncb7ZNlSpjz/d070BEOjZll7Z0DCwaZ1vDx0jVoua/E6mquuPf/zjf+2v/vX3fvC3meyV6T3Jj6qJgo3isqNLxtuTzX/4hf/8629/x3PPP7c+3w5D37Ttutk0TTMgbdslNtdlubdaIUnVpYKxQQ+tUndeuvPud72/67aFHNCWDWibITAzK/oe6BgJsAmpOxs1poMlyKIX3PCB1PUHH+5Y2qWJJ+6B2YPyuE7K1OYjQjbj7T4UnMSBUvmfJqqPHjZ/owvKkUwcYgCFiOgQyUPHYxt9nLpg/HsVvF6UjLGzs01dVFCeoeohpjMMA9I2QKIgEPxJsJADkh1V27VN02w2m/P1Zr3ZNE3bo6Z3GHrqToJjQFzI5oI4uBDQPvKg7Od4BIQ8QKmqyqOj+Gn1I0WCJCB1qJXRAl7tmjP86aiXwCjpVJ+bkjh4HPMk7B+tVNPif7ZQaSAAgdViYe/Gpf7YRz/6t//m33/uM88ZVDu1DkFqOnvuL6j1vdH10cFXf+VX6qEHq/H5VwWh/6yWS+BOU0snsMO2tfniRVmUpTRKc8F6rd7+jt+489K9Ht1XqSlFtrkuoKrfj7hLqCG94X1veV8tPtLeZ32rkWoeXGMpKVlsrXiMe+1mtxo7BdNj5LXylJsE6EOWRmB3IaibvkJk9OHfRHGIgwsLeoFWn7OUieRPYs8GkZMezqNXP/n61XIF16zS26bpegvWyEmiNG3bUR9A0J0FtCAUV5KjhIdhDWkK1gaatVlVaJxgKZpYAGBwAJyjUFRZonu3rfYBRFyCLOaUYUjKxXK5Wi4Wdb1YIGIkhWi7lriL7aMW0y1CfllSgE9hMm0GC39HA0O2p5VvnC+Xi8vHR7DIy+Lw+PDtv/H2X33bbxRFUReVnSmn+9usU782AS8XSaGMvfKVTwpba0EppkVRLhfLsiqrqkaWG1gFTIC6XixXy73VEhB5gCbRTYPKkDt3bv/O+9+L+BoXMDCoitY9cBdVJQs8ShGY/rLr8HmbUWm94BL3/mNsvenovGz/bI8dcmj2q9kTxwpHSGXaod+OPp15SOpA36EUpezGP3M0FBMr2B5gdiZyN9uB6QtwboTut4w1y8WScjaQC0n5jMj8R+CDhGXTNNtmu91uh75vt1t09CUAVkDNAcFD8JLcSxLxZBJApZSlkAV2JeqZhEEmJvYtZJ9VuamTNhO8bRvymdncC5H2aRJ93wECdlFXwBMw681mGECNVELoaghdrjHxgFwTwhvbPr+c86ZpYVUjAIX4U1EUVVXv7e09fP260ezu6b179+795E/99IvP32RcqZ78WCS9x2m7bpii5NV2ff6VX/kVr376aWDTtq0a4OhG42IhmmZrETBXy6UsCjCQAS9P7WAcvklRyfW6+ec/89Oq64oFjGAqnXQMMaGvHRsmX845NXLn77k0iR+H7LMgYkaa6/gJadZ3nCHv0Mx0xfCceSs6GI0PwgBmzDP/b85xHSLHBSGi3ceM9zrov/5FZmc9fm6mw3sg/pbcIXaRyN1xVtbBDFZsubx86ZD2F8K4SLwi+DcpC7TXLqv9/QOLFYdE6Ap5l9azAjrvFVqVldXR4cH+wd7+almXZSmh8O4v95DAuFqh9cFyWQOLGW0Q6rouIHCKbd8OerAduUP2oh827fBLh8d7eyvFoQPfvXsPbUiTfO5Ykxe939555egaqdv90EEJUD2X3AC+gA8EzUH6PMeQ6up8sx264eZLN2/fu3v9oRsuG5Lu4APhiZcc5jgSYBZq4Ep857d/x3ve975edcaAjC0dWjK2sQrJhU/0RGYJ5V/BX6WU2qrhuWef+cTHPvz64y+iLG3KgEP9VQYjObvCczvtotMeNECTGLt2CDvw+abXhFyqLME/CeZEn85FtcsJDV/ghsq17fiXV+BCTvsFIGAz95w9baeb3mYzJgpzgFvm41sEzGVLdzuG46FPLxou3cYnHAltBjYMl/b3qS4A9XU9Z/0AZIygnFqgZlctD9noIFS7TslCfNeb3/z4k0/cuXP37t079+7cvXXr3vr0rFcdYjeCq7ZT/YC4i+BVTYW6ZCB3XcMNH3qtSmRAETAsAHSQ6Uh5UzDSz85O19sNXG3kQAvx3ghJYAnYIsfbNyPCtbcTQCUQvTFD39nypqZrCMKLLxe1QQ0//NgH+wdKm5Ntu16v9/aWrAb4JbKpnB/EwjprjjixoCKtgRvB0KBNsHr5xBOPlyhmRBNjW6UMkevSSMiocGqxi2vZtFCluwJairx3evqBD3/o9V/0RlZKl31iG8xc1MFjKlFyH+COqMTUM3uhRyrl+C8zOJtQQ9jrI+nH7z+atKDg4ofE84L7NJV1Ge7P6PrMNZ9eM4InmDs8WQbq3HU2D8Wb9xH+UV1xeTC7ouh0U0p7QHH8YrknOEzO9XqL1n+2MDDgdngIRqr/Qd4wRDclQBZMfuHnveFP/sifYMtCDWeD0l2Dpkf90DVtf7o+GdruQx/8WGvYvTu3b9+6fffk7r279+7dPV2frpGDobTgIGZ6KBohFUw6RGnDilYpafCFj2X55ciXPQrgoFSTSWobjjIhts3W+tEKegbRlSnKEugefXtwsLe/t3d6crbZbj7z0q3Hn2Z4e6Wp7tHZ5aQjwKAQlJQmlnCMt1tTl+rTzz4DxVksgfhHaNV9D3XG4Y+DJTscMgp4wUqAktMP2+0W2aZ8+RP/9J9+y7d848HB5dHaxgaydk0ta54m8ty/AHCyQfI/R8Jwh8h6EDGWx0OCgJqtTRh5oubvdv9jGqZJQlJxVOGXC0koZkV578B9yG30bmNuyic2QwLSOWXDWXlTXBU3nuxGPo6AWJAU5apGNRBJ2qJAvMO25rMZmR6YnkoF6f+2eN4Gev7xP/mpH/rBH1gVR5Iv0C1l32WA+KxE/cYv/SoEVrsGJi8wIJHD1W9UY9Tp+el2c/YzP/XP/uNbf3F9vimheOINoMsrxIFtQkliPvl0h9TTn/i/LPXiP3iA+6EqayngOIP7jTKxSimrum7brl4sLX7PnTt3Dw6Orl+/evvWSx/6wPu+/I1/wBjWD11VljQRBBeEqmgzlKySsmSCDe3P/6t/+573vfcD737PJz71zHa7RjsIwAkgNGdBCWyei4uI0aBb1DPDu2AdDBDl8PLVjMl+AGQXQRP7RL/7G4P307Ue+HiA22S4HbNf76ALL4Vn1dAQNBjjQHwWxy4990KDdy40G4R3DuUzR/gBTmfeA8EnboioUrjQ67yl5Asax+GPZCC28hx/wSd8dHgEbh/0PEqgsGmLRA8kFeO4SHrDjkTd3p2Te58+ufWay8dINdJaN71GnxQUwAomCHGuH3QrmeSyRKYl6g9EuWIrxi5fu8I4++//6huu/61Hf/Kf/cT69FzKoiwKzKB0uNDJU52XIxKsR+b3p/lqJOsL4hydu/uh325bdGoB+taCMkA6ZkzTbFCwIYp+GJrtpl4sq7J6//s/3HZnNV9KJnWH+mitFFJd6oVSbaXNMx955id++h//+jt+697d0023ababtu2IyTiIeacRONXHtkGGoS8ZMjeHtrVKPlBIEAcTe8vlV37lV6yO9pGyUliUr9Rh4RXN3JE22hf/RY7Rfp+A5Iz7ZO86knIUry9cbMhOLh9b1fd9ULhbpjxkmQnp2bMMMi3cDHXBSY7onOh2ldbTjyfCfmIpWP14+hojvLvwQvaeJBC8huGyHLSUfLFYwmkETGbk/DlYKsjdwvYIsQVDVMWDNUHeARlsTIi2b7vNBreWBZIRGaQQpX6QrxrwzVowdBTkQLaiZETC0rJJskOvi2X1Z/7sn92225/6pz/ZrDereonCRUGeomCLe+MgBo/8HxF3Cm429O22fcHBJmxomlqu4ATUW8BZp2GvInNUc62kkEqjIGG5d/grv/qOTz7z7Kuffo2sIZ/N0Fl+fOvmC//oH//DX/v137z1wovrzdmm7YQyvOAKDifNNMc7ERaJ5SvUThwefLgB6xrvqVRVAbvAqkllXfVdhyFVxcn5aVUuh0YJwF07rO48+pBvjp1abrYh2cs5HkDcx5/3AUmfes/uf95Mepk9dvu6wvc+4W4+Ghx04TEB7uYOae1qZgenzCVYNZObBJlj8vOTn9mzbdqTuyptemFDsi4xypK9NySBM+MAIfBXUWLfD7SNSYxJxRWCJTZUY0FhyXi0/hhJ2SdUXwBjuWk66jochk0ZRxIgNY7REEF5XZ7SOCk7mWoDWbftqmX1F/7CX+SK/dN//pP9pjOyEkaDgAOIYwCB9IGWMfVSqhacV8Fy77umb7t1A/wB0iVAb9Diha6rumlb8CMFL3E3DNXQr1bL9fn5b7z9nZf2jvdWVW/Mp37vU7/+W7/x2+98zyc++slbd26fb9ccYJnSdi5l2gxUpeWqLq29YUzXt0MPNsGMkXgJ2CI2TQb4gFU1DDBYpCxLpImyH/3+7xPlUg5bshes5Wx1jljok7SS+y+hNKc76sHu5z289z87tYH9TnVdkXzOUorhftEdLmYrqd6e+JBShXzOzEx+z6Hmkl/DSRfZDTuHnXxgErV5omon2cOuaZZLZ6B2nMb9j/opJCEQokkJS7ASvCKBypaLRVEW3ZZqCQF+IxZykfRkczoHor6uwZDFdnSDQ42tPVxBEnRvuHutWZe9XtCGqUcCmdVIdR4GXhT/7V/6i9evPfKT/+wn7t663QOVkgqZ3DZ2PCDkbtjk4lgMTao+DF2b5Ul4eMNZ05RFia5qNOiu71arlRx6wUl40qRQ9qUwSslSVAX/G3/jf/7bf+tvCs6HQTdtOwxAo7bqC6I89nVsuzNLVuQqQxhdK+SsUN4IFXPBl0bQtLosgFhPMPXSckG0FK8kN3yxXH7q2U+99g2vJyjqAHsTLQfHY90OGLlq4i+pFsb+NziCa2Vk1M6oB/Y3q2+PbLyojqfnedNo6n56mQOMMs4hDl9Q7TP3fv7ItOnPwkrJX9iEJUqigy5hxwJYOVpNMgpdoIU2lk1V9pW91ksLKaqUgGKLkEZVyLqsNluq7aGY5WwEwjXWtPkRJG4s5LruB/hm4dOilmMW183N5OQuCWsiEpCygsE8NH2xKH/oj/7QtUce+g+/9Au//TvvLIItaIWvI9/4IwYWIX4LWVWIQgvG2749X29Oz9fOcLDqOON1XSNDTKl+21A3cX9/ch2TnBbb9WboBmN02w+ohmJUpW9ptkcZB82Oz5TmbGgBj0PuK8hVLgSyzGh45O0GJJExHZQDasWGOSJQEXAmIdbb9fs+8vE//A1KlCBg0kysr3riG56Q7oiMX8ZGewBBPvo+0lZSBDx6YlRls85643SoJKT0X5zXTHX7GS/RyxCn3pSNg7/4/PuOz3gSo5xlRVnK1uDOl9XLPUuxHmncTyvimvDIkg7LAKWMHF3XJVADmyqioCUPDpo65TJytOYFWg5XvdJtJ+rabmkvmIMqkisvjh2FMhvNFOVcMARmWVX+oW/6hj/0Td/w1l/8ZYuO51bck61zq5Fyb4UwFVfAIwQf9tB1m8369O7dF1548e69u03bUOtgKjPUumvbfujrekGV/TQzfk8q9FwayroERIjkHdUhg/qMBqIlwPjIOKWXQYYmee27riMeabFsy739/dXeHpWJINptR4VoO3KwFFzNSLpEfozlrkoN67PNz//8z7948zkg5vpKzVEgIl3/bJmThMjMQfP7potdkcdU852BlRmD4Afd0dG1HVxSIOCp+EJEzvTZD0Ynjmg/ax5xwVPuP4CRqs7TefMw6dht6JHZ9R12l+X0WYfnoPS4BPAkNd61s7el8tbvgsQkqtC3jVPdXSLUaUzPtWEkS0RQfNWwXO6ZErU0pI464zRPBEzmcMY2IZlkkHYJcIq2hXJ+R/3BN30tyLcsS2og6HI0ISPtf5F8YQ/AljSgz/Oz8zsn9168c6fru7qqpUAwCZYuM5T0hQKoNTp6O0d9IAqrEvc9AG4qCSwB1+uBAlsOGwAJKF3X4z/6EwneVGuMsgrnvofjyl4oicCAWCILifaoeBdZL+qyKl2yKPJPizv3bv/au36nRZYonA3kXdAzmjGFlJMNn0jkqHzdZ3fl8/9ZB5CDrhqTJtM7Tm+atjgabYesXmHuYQ8+rB1fhHzKGd42V7D1WR8zl/LwIzGxrZkbEnxGownQxu6H58kJYXr7WCtU1ghWLGCjUQkRmXuBU4SVDrUDqfMIxUrim7/uGx+7/jC5xHwP8tzqHbESZxnF/6y+7WDVceu2b2TDjCqqGv8jyDvAvqZuqwB8ZcW0LTmS1Op4c77RmoF6KZ+xBGY84T5rZWPc/TBIFP4miSpkaVMvYkhzJJiQMeCsFAtT4sO75GQCAyNzWxiBWBQ3vJI1E2bbbBTdLQSSvP/CuVb6DhY4IVrDgjGM9033/vd96Bu+9pv4AtxCD2p3esbLliczCuXc5w94q5GuPvZXTU+esKEZNIDwVVAcsy+nWB+TV9qlsHhV3vVRiiby+CHZ79G1/EB2yRQ9a3QZT/w+9q5Og0ySh/3TMjPJ18gGT5i3SYgBIGuqV5IbyXi9t6S6IQgp39DHGcwhDBYrrUKeUyGfeuKpt/zwD914+BHIXhsZiuOJiPljWJVs/n17TCIQiyZZVpItJVCsKDoK4xZymCjGoT9K4MWCg3CU1yLXuR/WbfPSvbu9VoiyFkVZFnVdVWVVlAVqoDiSthG29dVLKQY0aRSwcwshF7bEkRq9ULTJOd9JQFIdBNz2XAvda72o9m888sS3ftt3/MN/+Pf/6I/8QF0D5dO2tUJOJdgcBktecIuZbT1nVNXoNB7xn37xF2/fu4XUcGrj5AszvMMgbA67wWOgIduR07226/N4j5Ghnfy1S5bmVDD9PL9/+MM9y2pmCYTW/Y8LTxvXB4wFqm+6ntrrc+635IIwaZN6j51DmHyUKkfpXPiVpfbbWYOOfEj+uiCix9VIlHSrBt31gLVQJatWyxVMWUpE8JDsYSuFpyJRAnVz6N1Zrer6z/3Yn3zd61692l9S2tTkXWf0lZ0v7XPDUBdL7R5YsX9wtD47VUqXBZI6EMhihgK9VKYMhWEgCxVSUQ2AnmJclCUlQUtByjdaHGqlCiHLsjpfr+072XB2Cg5F9ZNI2BqQoK3wRK2bDvlScNaBZchSFNSDAu6vvcXy+PjKa55+9Y/80Pd87ud9zo1XPF1W9QvPfuqhhx99/rln2q6nJHKrh7sCDBuwtM1YbakFTZiRhTw5uf2JT3zsiccetQ723O+TAVYEx0Jm+gZrfm6+M8t19L1H2LS7fMIZ4q66v7iew3EcX+vS/zPf5gRAKBLYSEhGSRZx6fLbTwM1cRSjp+34YgdHu3geHoAV8fibTxnMexkHt1+Sj+I0xMktkNNIvhgUzqmTZ5/93CeeeN3rXv3CzU+fb5EpZHMk4ywlG93uQXKisWW5uP7Yo8jXjYmTkxlIHSxOGu+YnYBgQuwJKvT52UkhCmo6al1nNq1RoCSPehkqmKAI9qBQiYRzafVa8msVJJ8JmFJC9natAgAtTNMIDBtK6KAso9wP6JRKLZaLrkGnRtQQIxzE0aSCDI/HH3vFq59++ru+9ztf+5rPee1rXs9qZbacKcEG9j0//MNvffvb77x0syk622PCZVEHb5OXwJZ0rSgGeNHQvft33/N1X/bVbL9WfUsOahpV6NOdKuLOxeEJzu67B6Azd4o/M+Svxe9390OYkKIfVNb/6X5PH0Vlcg3DvsdURY9sP0Vk35GufZ8PHsxo9vs0+p9Hpkeihsxo4l5N4vlXkdmi1YGtdaM7pII3+X3SZ4xWyM2D9cuSMr5crWq5+rZv+fazk/W73vu7qOAnF5edTq+7k8pJDm/EY2zGr+6LWuqOKd4bTemTnjh3TJN3bKcjjl+GJplWApuibZr68KjiNTCXUccPxNq265qm6YGcjkzpogBgjY3iEHodNj+KC7xrpSxQT7/ebAC+4XNCPCePliYVD+M4PztbrJbNtt3f3+NMDHoQgi+LarFaPfzII5//2jf8xf/ux69cuc4WnA2CaW5apblG8tUwyKL4/M977bve8+4Wg7Fo0T6Ml5oTiY5kA+LC8H/1b/7dD3zv977i4AlcZIU2frqSRj9VdktFz8QD4sSms+yumV2i+1nGOVMO+vx9jkx7nSYYTjH8dg0qMi/Hsi5OCBlfP5qq9MpZs3iOEBOtx++h2eJAE7Xx8YvTHnQV7JPnemNtV7ud8Zlk9sk7TfMHv+prnnrdK3/mJ37ugx/5wEc/+uG2AcoLOVrTgjiLjsMGNhjDl4t9INLChqPOSVoj1SGu7RgQLpuOmSUJZzgPTnF2dr7ZQgz2Hby+RYn8YSJdhHCLQqKxkizunZ4ggwOIGLIiZXsgrGZkUFKR4ma77fs+OpbytSCTGjUGhZTLulqfsaqq1+v1+mzoe71crq5du/L1X/u1P/iD3/uKV9w4uHIDDremNWsQGPFSkD2mYGgZK//Un/iT733/+2+/dLM1qDpErkpA6AyI1WEG7BqiysncuvvCb/3O2x9+5Do3mEUwUagbqC6O4SOX7jW7X6Lm5d5seuKFOuKs/nyfI9d2HywWnYAmBaMgmqgPwD8mH4x17um7T2XclHpzPTAd1H0KmHbyPTO5LtKwayFiTcZsX9jzwuSk9rQHvA4bwuYyAtS5b7fdtf2H/pv/5s/3qv+f/sf/+6+941f1QEJAgRa0NegEp3RKmIVHy/3v+67vuHLp2HpZbcg2zYxKX9D/TBZsPKhsGUjlVMVquYfoqVKIIhlsZYN/0fPIJlQMSrXbDeIxhLVRFCUzatv02+2mrkpGTRssBE+sJfZ9HjyYREysBpRIWe4dHp6uz1ar1dWrV7/g897wIz/8lld/ztNXHnpMq40wJesVtF4mOQozUO5B2WfUAsIY1vWsKL/g87/gnb/7rhdeeJHggjgFyXwg3SOVWge/szxpRH3Tv/c9H/66r9ysDvY1NwOA4KFIA0XQyuFYIJHviZRu76PNjkRetgsf+Ph9VA7NDesBaD6kO4crMrSBVC+YEcij5I5kokLJ/0RX5vcfYbhy7nF8ep0jPleRwNESRSJJITw7vVvK5N1vQYPznNiYcG1ZCBcjGfqF2fszP/ZnPvTRD9++c8umXgmqyBu6Ddw4pXzo2sNPP/2aH/q+tzz96icffuhhpgjnkWGfxemOCl826zOzkCqFSVwHnY/2DvaMGbablqENEhxIxpiKnFRD10EToE4LAg4qwPP1fb/erqnEouBSdn3HgXIhiYam6+FwQCh9WpdVZThvUbzUfdkbv/iP/eiPfNmXfMHxw69EXeNg9LahlEnGBe7u9HC35F7TQB0/mrP82B//47/9O7/z0s1fAqi1rQKxvn3XX9u9MVUbwwqwVb9G81/6z7/2Xd/93Y+Wj61K0TStjXhTRbNND6WIN2VWZ3tilgBz4yR//RnZy1+WHh0kwYyDZccYXq7Sbr0UO18wU8JHOWEZTUd9d74E4/eTXB6eOxfJ4tPT6Z/QK9KabLZttWM+weOR3SzRYwNzCJ9ZSFeq/GWloGpd1jx07eDa5cu379xGWTuJD1nKa3tX9o6PX//a1/3oH/2hxx591aqSSjE19LKqOHU1mNdKLj58KmPCVf3FnBWPv/KJzzz7bF30TKDTL6r/nEO4aOmkbWM223VVVudIakEmNxzkJYI3OJE4AVxfiQMgFOtFGB/vYx+02Ww23/Yt3/rjf/bHXvH40wtZqLZFfRAFe6QR1OLJ3jx4QpNOK0KwQauhk2X1+Z/7ul9526/1m60Fkc5QBxzzdL2enPxFR5a27U/Pz2/326s9q2F7a17qQhHSPIcfDS66mqC0H3B6H/CITHac65rqpFNuPOrQkB+7NP2oBs6dlrxYpN7MnRRvFH46KvI3zpw/Ue6NHzd5mZwrzIzoPodXg3dWcptYy2+dScBdpWxo+EvC59Mbj9huWghu1TnbXAxbRIoBGRT8+GC1t6jW59v9471FvTw8vvL5r/q8P/7Hf2R/73B1WGlWaiQ/E9Az+uDaioPcFnt5mpnL+/LEC7la/PX/4f/0N/7OP/iFf/PvKJ8KQS6HlQOoW5Dr+XrTD2rTnAGIC+FWSnn2bxbN9pAoH1iEV51tjdSgNbI9GNvbW3zd13/t9cuPFcAGgQpORcalr9wiqTkTrbD8AVCVuutkUT326KOXjo7X6w3Eu5/nQB6OmziVxXrAQQwPP/zIK288ugBkGLedViyPJfk8INLOjE7fcccxkbAXM9W4FWZ1pLnL5ugg/fICgZwohxceeYRlpxY7f6fx9puMN3nn6XDGydsPeoQ8i3ka5v60GG8BXLkxfY+A0ITDRGMh0yfi6EJ3GFdVQDgbopC87/pLlx4S8hPHV/aeunHjh//oH3nlq55+5KFXUpUAFcVJgLE6ljPiV46fpXvmAeg494HYKGnxute+8ave+MFff+uvnJ2tBbqTleh6uKjOT8/qxaLruj1j7t5rq6oC1K3L7MqSdBPeFRQPL3BDZQSxLsF5N3RPPfXqP/BFX7LaX/ASpUHcwtDCJ5zslajkJBm81hJG7icAsb7jO77z//evf/6FF5+nKLKzusP8REHMqFkxM2VVPPrwjR//sT9xdPlYiAop7la82Vz25LEvI/tx7NfZRcbzu9grh7vM5vyikc0XqTS/anQzKz9nbOpUELsfSTztwl21i1bz+46GZcPoUX488DHSyrOEr3B4fYZPqZBaWKPTiPVZehVxRyFBUGK8UBidQrJYG9MN5i3f+13f/OZvevrxx65ceoTVmvWF6re27aBVTKkoOOEpoXo10+FHk3cRJUcF2lEYK77zzd/z4q2bzLBFXRmjt9tms976JCpU2GqtgMBMCRHRgWYV1ABmkyrNkXStYWkFMMBw+na4eunyH/uxH7587RICyKpHlinys326jE3BCBEd+57uV5p5/EB+uBm0XC5e9zmv/Y23v31rulDf67QCUgZcUI5GXlXFl77xjX/yx37s6aefapqO0rqDmCbd0Y6T6jkBbZ1P5cuoQ8p3z8yxc42mXDrwf/c+8axIN9EkmpG7cxInGWG4Vcrxpnd5gLE/gCBJNPGL3n+XWbCDNfCxRhrdB/FTStQjFCu0rwzuZ6uyBZPGMpj8/f30uxu7BC9WSHl5/+Dq4VGxqpgsWdvrrWZcyaJE/odvGMKg49HF1ggfIfikPOlBjvhmdAEBpxcf+fiHu7YXklVFVS/qbbO1TR+sRkxFgqWtmUy834Fsg4nrUekcRbsQHCqFCKJeckAZqKH/wR/8gS/98q8qgMXhXE3JwuU8KRXIThKnVK0ZUw9dv3p06dLmhRcoJyPoJs79bcgcKJbl3mL1ZV/4xX/lL/535d5qWS0FMk+A40HJ5c70s2VYKHwoSxffzhsNpsGRFK49UwbdBO9EaHz5dk9iM6bUuDvFYiziLZOao+Ewahcqj2rjzkHuorxxmCMfe6al5V9ccPM8nWM8+PwiYzmx200ZKKH71C43qosopzByrUT8jgqEcodEssUpzY8Ds7FiGh2MCGCW0DW86PDlB0FVTt9jqhHNpp7Ppdjb2wR1lbECzQuMKrQErCR85aXvEAiFw2rapFCE2obwGu6VHKJIbEmBj+D7s2KNDjRNrMvXfv4Xfee3f+f14+tuojUUm7kX82MNuTrBHrEOLSultfyRH/rRX/jlX3nh5gv0Ttrij6AmEYAJXFbVsqqvXbn8R777O7/um/7QpauXlJKqV5R3b4EMUCAWK+8SKL/grJ/TsC44Uk1uelxkJ+90rSSyMjLwydnjD6ZBndHpvqdbvGOA19px7Pwy4Sz8/nbuA2kzqZ4x98SRCczHg5lcaiMpaERLKc4jMzh79TReHBMXMxlFwV+gbVAHedCG1TjtFvUnOdgcNxybmrjjffJfLpwiNzWOO6A7oTCAzkDh89AjAsxZvVqhsZKFxnTBBt/RIZk7m1QcYDuSn9SVjQ5U9vftsijf8Hmv/wt/8S89+vgNXtD4bKHlSKKlrzFiXY6g0HWRcTN0fTfcWx4evP51r/vN3/6tbdOCZt3wWCXFYrm6fv3K8f7xD3z3933FV3zx/vGVbquKhUTRleOPEBsOOihOS577nqVhXbzvRukLu+b+/pXEU8/SBSt/0XDu85B8n1jyS1QOz2xmzdv7Dz3cNeFBWZrm7/ewBUQ8ZxPBtpxhHBYlRxAwMaXv+SFmpeFhzwcvWBC/XvO0jHRQg1SoxbPgE4QkAaeKB8AI/pVRbnPQIpNoRCam8pmMPrCEEML4jCnQhYRUSpQ5An4P1u/BccVMWy2XtvqRYOUicnq0eyMETxDAFgabgEWY1NwoPVRF/Sf/2J/+xm/55seeekSygrWa+MZUmOTJxrGRmrNNXOdGoPMgK63dIv70+KOPX7v+0AvPf8ZeuKgXe/srxszBcv/G5as//Mf+6Gtf81S9uoRqjBI5JwXqkIkHZMLWLX9eeBuBExLbYYLAHI3Q+2+6+56RapnenzT66H/bY4di/vIuTpfUZkK8rBs9sEnIEuE/otuRue2ijFYjxGeKICFtUqOjhSi7o7fB1r1knAwvRKl/yKEE4A5Fm8PyjDEYxmF8T5CZazrxdKTqh8ugzKk68BUii0JpXRbloG1zREYtHthmvSlkuVgu2q4V2tn/qGz2hqgvn7K1wvifq/+HuQ7wj6IqtVFNuzk+On7jG970/W9587IWy7piLTBtGEqSggWS7BmXHO6krQvi+l8RykNqGDVSM0jA3jbd93zbN739t972y+cnwoiqlPt7+03fbdrmqSee+PEf/9M3HnscXIRytfCCgtK4qduqLVhyrgU7P6PS+cz1mVpDn0Wa1P31xhF/Dnsy+zpxlr8sXXequ/PJF/fVH5IL8ptE1utndE4S7wj83P/IBj8S6CY3eu2szbNUOs+CVACgGJCptikPgI3pIEgoR7Hk9XHuFsJ5pc9SqQnFGS5eu/+zGbESMojQEUtzZwYpmrPt2fnNXiLMKP4uLP4blQUggxKmqzFd2+hK37mtLh0fsZJakSPvEsO1GRVYDSoGcLveunBJRALgxjCAXWn95CNPf//3f99Xf/XXXruyz1TFejQJdNMdmGW07GIDYifuXVdkC0nmejKioIJKvQQ1gimOll/6pje9673va5tmVZVobtzxP/BFX/Lnf/y/Prh0wHXBCy7RskkwQ91rcJ1jgcHcDoB+VqP28z6fRJHHFx7sGJPjjlMe1D4Mqu4F54y/nlqiKRmnbpMLKHnOuM/HnMqbBE3vAQd5n9PS5/jMO04SMVQoOHOPAKJIn/W0LsC2qRqe2utgt6M8dkvhREKXQGPaUO3juxdIWLjhHVNJaUEWqRbGVxbkYOOpFzZqC8mMBVn62U2MlcAo5SF1spQYvqLcqqpa3Xjs0Sde8crv+O5vettbf+M9739P2/RK9cZIsCVa71A67x6JCsReq15x0/X9lavXPvfpz/nxP/6nHn/VjdX+FSRXVVZ2WSi5HWvq+pNRhYfH6HAC2DuxLMYg1Xi5foQHe6vLR8e3hltVVSujXvXUk3/uT/9Xq/1lWe5RPgbQAri7FVm/Tsy7rA/PWykRyzWnyudxtD8f0Ch+mcc0bBXlW26V+992ViZOP73YLk9/ySXCrmMk0aOiMrpvmhrnz8zk4/QW8fLUehirpaA9ZfuJRbLxTRgclrL1AydKFupWBapjmeLanK03PbUIs92ylFL95lz1qKtFCQ0Xi6o6PDioFguXVO+J0EJME4A7rqJMJZvr4Mc6As8L5r/jMDmTmx655yWyjZklQK9sEp4SwOttpx+6cfXJp576I9/7li944xtf+3mvZ4x9/3d+39/7X/7BO37nnZ/45Ec3Z0h7Qu4woeZy58+z+O3QjQ8PDqt6+fSTT//wD33/6z73DZceOlKNkSXlMEN4j1S3fN09w7IAHfQ/B6ubFZPZFkrkBFTabM6ab/uO7/rgRz72y7/yK8OgD4+O/+s/9+PLS4d1uVcV1MlNk6vZscnMMHJ6Ov0r0f4GEa9cWZ2Z23E0L5vVl6cn+gTRoGz5lDISK+GRbvfNTNw8sx6/5PQtYpBsMqL5D3cS2s5HxLOjaN8litP3mN0gyXd2O5geUHV9Uvqb+GCAt0yIFDZ7MYR23K3QiO/85LwbekK+QSY8qvYRPkVNjhUSnJu2a++cqGN2uFysEMbNrVYHJsPh6yVo41wCT18ghQ0JekE0d0cvm0bNdwc1uKEKI84Xq9Vms/nqr/7y/8Of+/Ov+8IvrA4PTKtYS5Hosvyv/uyf+VOM/dxP//Rv/NZvf/ITn2rO19u+MXro1y3y1CSvl/ViuXjoykNvfss3P/nE408+9rrl8R7rDUOGtcI8UuekXbvGTqv/aXF6LQ5OwJYd6aDc69dcDbo5O3vNa173iU99+uTevbf873708UcfKXUtS+jMg0J2tSMVkq9WK3dRYz95thGGoELNOIl+u+QbzVNTTNVOv90x0eM/Z1cm26ST03ME0wu0mHwsUwsgOGRGUi3oeS/rrtM7jbKm5kex+9YP8B2tPir2jCVnIDkR2hTpR1bl5VIrgrKyGh8lT9FiDW1/enLWo1kPlZkSMNswDNumsXkBDktZWARife/0jDO+XC7VKOHVKvEwnfXo86A2RoUtdRH4Shv/0VQe5It7EdPjhVHm8vWrq71ldf1qc77+zJ0Xn9q01bLGZiklQ5jagYt8z1ve8j1veUt7tzs5v3Pr3ktKb37nV9//xq98fcWXh0cHe8f14eoyq7Xaas4LUK/A5ZxJFzGyUMw0X26QyQ//5ta08J3Lkpmy5zrT2fZnwH+6RWmD+oav/QpmhtXR3uOPPVkh7Yb4IzpmWE+ZmyPbk9gbZ97nT/q4tEDTI1FjZiMNs4TtlaP7w5QHS3PW+rng8hQ2bsq6dzLqBzsys+HCGwWxGizTUQapNxf/S1oYycMpl8q6ZXjwYVDMNfILckqhha31IWtmbdXz07O7d+5uNlval9KYgVPKEuesrqoB/iBKWLK9ewhYmnN+cn7GpairhS+4c1h2nloTFddCq1L+IkV1EoOXJJhNYHaxVmuAzmttD7CLLBD0H/jSL6mkFGVJCd/DwdHhF7/hi//Uj//pJ554wqwVYqu1YEahy6k2DB0/6b4KaZ70L+YGspr1aHsAaDubGk4dXcgrnWiIqfgJ9Bk/gY9qGCyKL6WRhOnKyMj7K8gnAfO1LLUZjOn4tq73OMwcC9BPNneem5HCZIaJk1Iu6poyZp0ulLAXjybrp5o8XZPJjRbO7GJkP3ee4G6Uz89UVkb3phU5iVTdSYAZv+QPqnFPpWm+aqNHZX9OPTT3E+0XKf3Zj2Hot+u1hhfTKywufdZXr0pRFEVZVWgmBlxYwEepfntyd3P7LiAZXf/uEHiwvhXrgPERFtf7krBNpZTXLl0WRWk7FdK+ooY+tHnqugYF2T1JxEv6OCDK0wl3BjqhpVog51Ax72X1BA1rSttu5ilHmHDp+KZpzID+Y8sl8jfe8653/Y//l7/6gd/9AN+T2nRsUGBjQjE+4HcQs8/BEPAi4HZL5HCxcsFQzVyJsuQFhK81SqJtkK1Q2BARGtu3nfK9LwIUc5TCnnrpoE7faE6ohewBUr0iNBNhyTakzoQXj0CEfgR2/WDPU+aYO9MhZSrKbcFB/dt64i2+L/HuqrR8sr28yrTOC7aqH+Pu3R6GGf7xtJKIwB0knMCfP8h44jxNBjEeb3bFfd9u+tAHNa79Eo73tnFNfCj8AxA0CAMF7DWguvVG6WbdnZ6cGQ5Q1BDqcG2SYnQ0zUL3vB6dP4ambVBQPh1OQAv3/la7eyyWPBw5JI2VhscVnQpoU2FsM5pzYidfPC8+UiW6tj8+PESxPjlyCsFa1j3zqWf+2v/tr/3qf/pltEgcNCStFozb2ijidDaJGCoxJ0HMkJthUzTJqUeCN/Q0yAknW7uM2fiuaq7CwANnh+tyEG0XeJag4UIv9hcxI5ZfIM7GTycDmNqw2B2gqeNT23TNtmu3XdsObTt03dC2Cr83QwdITT+YC6TryPGWTf/8qoxOSJXl/C7znp5w2pRfjLbEWEgn0K4zxDUiud1Mavr5PNL77+PwDD7ZC5MvnQpLfUB6EoODatabe3dPlBkKGEtIeiR3bJrKA5A6FK757FqbUEgJDvjjbH3WtU3IKY6KmTeH7RiCNhlkj5WusTDPNqEjOvdyK3mHB6PfcBTIoSRmJLhAVMuWZxTs1ou3/s7f/Xt3b3/m677+aw6uXTbbgZc1zFrH92zNUKiNskmR1k1lB53Y4EHWBuV5dgPTnamPAhYAeGGBXNMbTfzEgWnK6OwfB2WiOZs+zt4P9m9BQLMEmgnuSKueG+Jhom1FKJVW2tma2jD33bCzWy9VSu+/5dP6+R1684S5THfGBZxg5o67DIeE3cz6Y2Yev/MZFzyNFtYHYd0pPM0icI0B4KqC8TowNOMqOO/77cn5tuvRPcuxL99EyC4yHMqEtRyq6BL7i6IYCBWxHumTypYHWF5POjxQqGzkyQ3c3sQaYjM6MG5neQzB/rhrkhdP/Ve7CmOcU6eo63qzaUQhltWCM9AwkOu4UcK8dO/m3/8n//RXfvNdf/KHf/TxJ6+Ytt9bHbDKB3Idk00hwazVmpDKRNGJCzOq7glECX8S7Ak+cNvPJuD4O3iNRJ7EjROD+O65U0ic0R6zVo7tV45ZRLKWzROhphEO6NJuh6SA0zBq8dRTr7cEIuUCw3O8sy+wOUfyNlhEo5eI3iP707yMsoMH9izNUW/ikdkRUNi11cKGsT9GGUgX3Sd/GS82EN9nXjnzebZeMbaNQ6kxd1FqKc9O1j1agkBHxC+MI92IWm3aSh2l2KBNiYpa17XMmsPo7Utl6CXBxHXdtpDAgXOALWDhCFe52tUwQlt07J0vnu3717I7j0QU40DBg3/N+ePuKwlGZMOKvu+IIWBCa1ZzIZQxfEDGdyl4u928+3ff8/84+3s//P3f9aYveo1qCyn3SH9OksUyEgy0daHVlP4W5YjVfkFSJUdbX4Lr9OUjQeWI3sbYl8LRkOMX5KUeadFB9fbZd4FlIBJOQf+QJUKZIonrLN89WrOh78kFWDuMg1QUTid/l1B6UMVyomAlIofN24Op+N2RqpcuB78gWS+76dg0md7tPu+RjMplWUyOGe4zoWEK7HMVOpzA9IWhSen8ln5sA0FCOetA7spUBZaZkj/QmMfuI+ClMVxWIIsPYScYze5WBp1BaIywWRVflhLliODvoDrbbMhuGmZM5Ubn2p95fEQ/c4liZaUCJaIYpAraUdhA4Zhw/FaZmSoqXwcepSyNYX3fKT1YEFlVFBUsBXCGwTQf/ugH/+FPrG/f+8av/fIvu1RWMIZJW00kjxeDKSHPLmpGvVkqcjIwcisRa3MHkt0gGylj1SLIBpkwVSUTvpArI5n0co+y3adS2zLCaOVbKMTwEF1gvcEYQcHpVCdbLf46TTa8kHZz+pyZxtRpOT/N8xJ7p5Z9H8G8Q6Cnz7/vus+OMTDT8fxMJU1a4mPtdauSubW2rl+lQNP2NlpRAgIVqVG2BVRLiZ6YDF5ZShzsjQGwhBTLurYOGIPosY2gMNv6w8pGW9DQKbWAH4vsRytIQTg9tmbfq7pGyTEFn5yyOJ65iEEF9mHLdYWm7tejIohZUZx/SH8VqKgAZrqtgEdEGz1CMQJoDYQPIrpePfPssz/9c/+2a7vv+pZvrNgVVmLjZ/Iv1ZzDk8ZCKVmc3B0zc4QSJ2ud2OmCueKSMWZobFfCWSgwSVU49xAKBvgsdZvWrV0PjImjmWwf+6lNFysJ/Xa3yrr7HWdjMLM3GaWv7dSNdxHP1PDdQclzrH/H8aDKw+4rRpG45PF8t7xPIO1CoxtjRR76iLiCHLttUBiu2emdk8XxwdAPSIkXouZCroqzTVNUcrlcnp4jFlVKtP4lHk4KtM26krwqUZoHjZtSJcmNjL54QpSUFRKUOpzu+9oCi3rOSBqpnV73h9/MZaA4ZuGoIzFWsilIboM7CJQTUikRICY9AhykzQA1GqqARDdtPOPFmy/93L/898ro7/yWb91bHbKqRugokuUc9WbfJosX/h3p/fHkVJTZtutkePiSzHGv5uS18mSMqbWW7XWLN2B5uGura1u7+lKUVBJ6M8d9Ck3LKMr02iHZ5v6Y/2yyvtl0zdxgjlhnmNf9KO0B8jYuusmsdrDrvNmihMzSmGj4Y6Fkc2/8EnPnybNVBdbtaAPCLqAh5aVLqx5gjd2yXkiB0xtyjp6dr7Vh3QB9mxcD2vMRGEtZ1mUlqBDJpuQjHNH1pOtaaSZ4URZtHxKD40bE861W75K5ZiYv27XWmxXqdMd2zezCjBifLnr05kYNEqwF60+zcLEDI4x3YFFTmBQf33zp1s/83L8RovyOb/rGpT7gcs8VBwTALjuKzPUYCDXYfna8s0udWmDuLcffe8dZ1J+Sb6gt6XT9nQUSCwGduKZlTu4YfQ+hBU3SoS3az3bz2HKLSCqZzjezerN0+9lU+T4AR4jztUOsB710lppm7/+ghu70TLtQ973en7bzRDtgQFxhkYyrbrGt3u1L2TUbjJJGoLUg40A3tyATnJPBKpq2XW8AQi4AYMxx+VaxRW2EGJjmlGFtRS65mvDgvu8Fh0tMcLlY1INuQ1vbMDVeYEGGJ/1Ts/nI1iNk5vseBJ5mH8QgcZKpQDcIqXtsRZsJGu7rrkSjQA3EZkBkFubk7snP/OzPc1l9/dd85SVu5PLQosY54Tf7oJm3uGBU02LSEE+2Y3Z9l13GTJaVaFMsY8lzuu7ebkqsNp9M43Hu7eVkjjiEeLx+onja+IHPuiUl37kq43S/rGrhSYQ2zkP4dwcPn+//8iD6bTIzY/0ot0XnZMjMCTtusHtgu/ZlqjTuvi/nqMEZ0HIBHUyIfJMVISewQULg3bt3+dWDvrOuUCVkBbdTA3uVG6aGQZTwWnEh9pero6P9Gn2FEOBBR07DNpu216yoAHy12W4GrVjbdqU4JNA04t02IpPKRS86I08POWLBLPRvFBKNUiyn7HYXszx8S708XQGVQvo2zYN3EdkyPngAlOIayVpc8PK555//qZ/+58v9+ju++g+xgbEqlkjGFRpvjAuGMqocS31J4z1k74vwHUO/FTgqLAalzZwk/6IoieZiKar/nyXUBL/aYhC4DuOUMoM4vhWqWaKi5SvJ9nI6RIwTeA78coVpSkvT1ck/f5Bkw+kJmXJuXyHwhovOpr/4vNDOzvnsCvXz7M/06VOVLRtYEMKUeEMlax6kjpbJkHtZyGHQi1WlUFdkodL4YFzYVmmUPxiF/CqYjcq0fXNyqqndNcBirGLFOb90vMcMb8EDFHlQdUNtssuiUEMXJUHCaT3WWEK+cR6tDzQSrs9dytnTdMlGR6hYRNUBaSFWeIOS6d0oyEZ5Y4gvuUv6HoDMVckWVfmZZ5752Z/+13/wS750v1gQOu2kfDOzZkePf5B9PvbgeX0Z5Uf9MIDejG6aFs1KbdCHDJe6Wuwf7JUFwnbOEZjOj0tA87naXBhlBtQ5u0hAWuSf9CFIu+Wk7xH6MObS48FFY6pxjK59QLt3fP90ytIfE135voHrXW+S3Xb3wMJpIzdHPGECszpiZ7Pv6kYIu44T7LFluLEhJu2DgspoyoIVskTVodWoAMki4ayGZm3hD41Af2nWt33bDGgpL6m8lqqUOOPn2y0a7xpFLjE9aNGdba9d1RQkEYFtuP1p6TWWjaWITInp5sseXUkDldCELZfN/y7OmCxB4fRTL0FtTooNhI4AogwQ6qC2VHW5rMrbt1988d6t1eqyHkQRhzEVp6NQ5HRX7NglQaj5sqy+H9puSzpT36DDkvUigZJDd9Z+e77tN4uyOtjbq+qapsp1fKDbhsV2cQmlepu/kzza7xHPRAJ0S77Zgs6zw5kUWcFudrVrZmaFefbhSDOZszuysSUPGvGIcSXuAxxjS2fHlZ+Fc360g7MwU8J0CAJYCHQbAw0Hjc2qXhzlCzdfvP3045ebgRQ1xIRhzm7W3XbbAqOc8jo4oxR4+HAlk7JalIUofTAZy6tQJIPet9SbfjCMF+Xi2edeunH9urVcM+HpyEcLm4o/87I2Yp99FRr/5drNxaVlcQl9/Y2fIRJjmAJnhnsEO5tTZpgBAEnbLeqKy+HsfE1ZlIRn4XsCjh801rguGFZYMCts3b+UWoEwNYB+BrVt4Bbsuh5AIkTcgLlz2W0A/dWD4aZXw+nh/l69WCDhNVTDZ0kPgW6DGR1JN4VGDjpQsm8zy36n1XYfRSM8fXTmgxix971kqiTvEGqBnT94YWJgJWFaHZnt4GUPegSNJ1ybB/kDCVj/hyQT1KLlkCPaAjJwZs63XdN1i9WiO2ttnsWtu+tFVREutCqkNKhZQ08fG9tAVU9ZHO/v1VXdbDaEWcmLUqph6FBYpCWX6OGJFlqINCmSyzTIVMLFZFtN0DMTBIlUtU4lAt8ZQrnooHrgSPk2Pu52qnXPO2AIqtmzuStU78GKXpm+VXdvnsvPq7imkPSoGVjUov1KZ56eyfhG1YV+Z5DXEAGb8/Pz9fl6oCotUCkZs0qZtmvQs9gh5biWGTZQfLY+W6/Xh0dAVIgbI6VhrzFbz12SmBcT5tMJDm/jdKXoXUtdGPeb83iradbHbDLXqOGpj6a8XHP7ZRz5Thp9FTn+VF1Pv0+9i7OLHtIB7jtlKY8LM4a2AWTBumo3qi6kJgzAO+wP9ishObJy+dbWJJDnU0ucQ9mPvYPPwX4hgLp222w3DcQ1NdIyDapcClHsL2tZsK5vW5TNQmDDoRWM3QkzxcUilWqzUozk9a6ytuyCiZoWnkpb15YXe/0XSQ02RVjyQvBCGiGU0m1Dx6bhSghWcCN7w3/pV3/lzuktVFfaev0srBNKnKdqZFKcFKqXkorCxOeMg9KUi7bpt812UICkbLsO+cqDarZbShdzst/WMVlhDF8+JZmenp40TWODDta7ZRFGvQMB/1G9p3QFSaEEKoxzNO+Rs+az/7Iwj2dFZqrCxEzVhA/GUYxK+Ka3Sxhi9ga7/ptefkFmyY6vzK7vd2gjKYtP/81mcqdHQQhOgVkU2FglkcpfrNeKlQh9QiQTWiOBhhtWCHHl8t6qrgRQngrUNlCMSTAOSKyu7VVv06iR7u6AA4Ch1asOBjKUdoAsP//8i6BQQlVLimUDGIctavQGQYQ7zVHH42zlMA2jbRTEzgzUFivwnuTTMYZRaqAF5kM6WcHl0dFRVS0Wdb1Y1vuXDh46fuh7vvMPLw4u1XW5OqoPYQ0vC5fTv8NdNh1QVI/9/xM+lJiWEDLUpoid3Lu7abdIPYfTQRdFYe2PxbImnuqQ6GgiXFCLRCSTBS+E3DbnBVorIjWTPNa4Fi1UqI+p745ETg6q9w31+zZzMttDCXiZdd67F4xTn1DOeDbmd+L4nF1Us/MOc9dERyX7LI/APhIf/PiEzJKYKVOMN4oG/Gh3ppbXAygVydVCiKpECIRMVl3IgoSnrcJXcGQ6Jc4CtSMT42i/WlYlNyWKyI1RG3Qj4bKQlWzWrRzUcrUsUbXPK460Z7K2uaLeKSiyqZiBBjjIUpqCitGcQmcd4CmrtLpdEhyCtAxuUHtRkuwRZy7VopNGeyM8Jb80RVEvZamlKCtZXzo+3ts7WK2Wq/3l0eVLjx4/+oM/+K3FEog5IICyZoxgBxhjLf0rqdY/NJCYaQMzdySV+q7mw8Gj0GitUULQgvR/eXrv3p27d5QeYLpQuMgSsNWUCek5mSayXkGFDKZOISrGxX5dat0yhlRV2/0CPpCyBFyDnQzyY3NEoEDOPRzbHvpnIkrcL2klW8Y4I4HvFh7Zj/j7yL00NXSdBTiK3IzMRDcPFy9CHim48HASYKRJTbXCidU9z4nGLaof4Okjt5bTQTgwjGVlpR85SmylAUD/RYuKX3SQhSfKQsxyzuu6rOq614PsRY9UZ3Q0Epy13fDhD3/80RsPD0wsFpBkdoOpduigSPNCouU9sp0Y75pG8uK5my/duHpNGSMhwVw8Iom9OKXAZ0nEI7y2dSzl3+SMcgZ8cAzGVrzuc15f7ZfHl48eOXj0u7/jDx1cu3ztyuWqLsvlwpMrZYf2mnWayZ5JwVCvBLQgyDSKN7lK4Pv5XL16GiqukUNjydf6JQgwiNyLyCtnVCnA266z8TqidZcH6xIqKPhj5aCtzVYIWuMXm1O+oe7hhRA1So4K24pYGMpN1xolGTaRg3zYlvsVEsgJbAiR5BmgQVvtaYcaGj2O99xoyWYl8wwl34/2ZuKuE40raOOzd0qZ0IM8cWbc6ZMuPmHHnR7cVbNDojv7hdvaXRCroWp25LHbUAPV6SECQchYkJ+CV+hNX+p11xMqA1BmUenAuChe9cTjCnUF4AUCHSqRaYwqQzVQaRJresYL3vXDzRdu7tcHVx66su1aoNFIacF9iGC9H8uCzaJOZvwaAfHdXhF20IwWO56/cbYp1Ir/5f/1NxYrzupiya/wqleMHEJIk1DIOAqbr7SgXCjLQqgL5gBlfxBkduKfHXGUSTiXam57iEgCEsi6TBnla6IxeYIQuulkV21vpQAZHlaAUOjWxQ6sRA4BcguFhexUYU6a5qHlQQGg0EYpairHGdJSED6wXoCAnEB3SEYV/BDJa7k+hig/iz01XpaqOtIq029yOucPSMM7MrouoplkcS/Q22de7sGSP7NBhpe5eKIeOK3UVpjqoCUFJyD8WJKhZUdZlfhoGBBUot1wcLDgXDTbppRojYIiYGpEoAwTaqjrqqhLJkpAdjC+KOVyUVCngOr5F+91w1AQTCMv5K2bL+49uVcIfnZ2phUrr5RlVRISLdTH6LrAYAi21o/PjjC8rHPAkMCaNY/n5ie8rbMai8uPPYqPho4ZYMBCx6QaeoRPqSwjybKy+oAXVq47SbJA+VPGKpVD4SSgEwt5kTB+75ez/j9J3gO06sVlAC5wyY5JPZB1L8Cmdf4km2dF0PE+QA6tR3BRlmXXqQo9NEzTdYU2lW+0BRWrKJ0vlMB18NSiN4NVol3gDo8lfQjxBvJ+BCSF3d7aXT5nT70jL/3MVM4Jc+8/v0iwj3WG0SP4gz1r15EQ/M40rFnqTf4dobLk2cTJXM3a3tbxGXLhLd91A0ODBTJfUWaLhEn0MGJFZbSSUtw73bZNV9eSvMzMYCsqsbBRUyYGbvhQC7O/WJCDi3eN6Vh3sFiKBbpZ94D60E8//WS5qLquQ8iXF5/69DNPPvm4YOWgFSGm+5J0Ei6E2hyc7U7Lcyp2KJsKvUhHa3oBQ7MYr5SJRQe6ijoJR5cleZ5xAagzYIinpxHpGB1Npe3odwrZWrXZOopy5d6RJSGU2Jy3nvHt5mS73VgjNTShCkBkNtfCeQMI/RZKASnQ5GY0tZTAPdBsYGLTdXVV9ds1LkJHG2F6LrUuK8URbPCzXJWVEAXhKQFZntD/qC4c6V1oDRORBFM0kigFZvdzNifutMzP7DSvsbPRmUtuDbKUntRmGZHixJgZUdrIrNp5JLw7DiY+/0KhMVIlgk92dKOZv3IfQCp2wpakrFfjCsjsCzpbGCRkikICmhTFvYg5Co1+Ktvt0DTd9nzTbijHcOiHbd8OfS2OF3to4mH0IHi7v39cycpw2Q792XrTNI1Ww2Ipj1cHZuBn67VCCkJ71qq6rrXpZVE8/8LtR65fh3OLohnUZggEjA6GcLkWLl8yeGczTXgSS8t4cdrrNJ8svD68OIQQi38sCqxX4VMZEoIWo0bOqUJ0AbfwnJKi7aBgquhK9p1FuvBPodwam5XOt4AZILPW9YAIagjB4lsvIdESoH3LkglTFwWYIxz+ZtPrvmutWYu6UGauHhy2A3inUkrirlAH6noBvclW9nMBJwbnVVE4ErUU66zlXVs2TYTI5jk5IRR+Oq0/BqIiGvjkpj6cFdfZL2SSKUcuwNRGn3Ez+RvcpzlbYA1zKzrWmcK4ZuZj/Cf//enPgbVRubp1iNgjskEDtBjBTGl4R9g5VSEqzo0UVw9XTYe6ew75QLg5AE8ailL2fas3bLmoF2VZ1SsuqnXb3L5z++Te3WbddPA6Kz30q+Xi6uXrx9euXr50fHJ6YpTpeyUlZHzJGUHVEkoWwdtQJR8ImPe9pV5arIh8Go8x9U4mIVXdUtuHprRwlm0mBqZa2ZyeZu+dxVl2Cx+vLrrEKld4nRQEhsiNK5PELut1h6ZNZUkeLxx2y8JmJSMI7VkFgxoM5ZZXZYVcba153zVdS03btOQIu68IRgywm0yerLfV3j51axtKWSHNq+uYKSV8dpLyVehwFOvbc9iRJnVR43ecfffxtvRYphYjKMjwEP12+byJY83CGAQDKmj7xGQ9qLAbIkXxg36UdF33CbOO37uvRhzBk3fcJIHZX0B6EyGZ3sdtkoSlXXSrdLJmTQJb+0sgrYCP1CFNIurRjMuyuHz56MXbLzAhiqoQUvZKr883PeK5gyT3VlVVHeCCByaLtul0p9p2e/X4MtP87ovP3bp7a9sg7c8WvpHlp++enp5vtpfas6tXry1Wq+22hcuMw+RS8OgMlSw0ySGXn+EMsL6HOLAlqnOJK5buxuVAuyc76rP4I0CZJ9ttl9/PbYRZjnvBg5NsOBdzDUC6ie2b/M+/CDEwJNMUKFigrFdCryIXozFFAeAsZviirkqUkQBDiyJADMi/rGybFhp1URqtzrbbopSHe7UW7Mrh/nZoBVtQDT81d6XmGoBf4VZDDlkopECHvZWaFNlvO3ZbNgkuZcU3Obax/jgp4SHRRrZCx7npAnvzhJzE3hzFcLLlnMTO6Mo7yiNRUb6LA090/c5dk3mLLuoZexDFFyjMGTbZ6Ji7JNXnU+/s/M3D/iY0coJ7dkhULCqVzndpX01pLopbt06feuxRWZeLqu66Viz2jRq40Qf79XK12G7Vydm5QjqWUmhdiZDy0LU377x07+QUMBkOQ5FglOFTxZ17Nbx488Wuax97/Mlr16/efOElo4fNelsvVlhQuG5cxZsNQgVtlgZWSfBbj9cx1ocvNGRCllQqFTwB51J3RvlJ9lRKwztZaTh5Lt2PjNigLQe5kj3XbVQt0PyCzG6wNYfAaYNnpYRkLsoazZlIW276XhtGCedmtbcQhm9lsWkbMOtBMa2RHQsXlajl4uz0fH+lRVGpAdCesIdJpQIxO4XZT3sAzg9maFL9PzNdI23F/ksVqo5ubStL29nJn+qYaC6r7ao5ldeK2zDxoWrK6rABa8aptOSBD+p1ACiO8pucQHRLgjOM2feQ4TYWT85AF2JwwnNuOaMEnHJ2/rJjzmPL2b+hlzkoX7ddOzyf567qJNSgAMwUsJNCdG1T2+5IFAhCko5hhRTbTjeD2fRoZWYjksCV6/sXb92+e36GTCFJxpRFRKVnuIWg556frl967oWrjzzMOLt7cn50dKnve60GIwk4PVlA6qnNmOkJmsdgxwLhhibbOUG9oRqnZ878mZlDy75NgXCoO3vUfCz9PQfpySZ37GxNVyH+7nlTvDLUKqS7IJiIFu8CQTz8VqAZhRiwfIMoK4QAKKNciw78TvK+R+743t6i2kdLQpDLwMxCd3B3m7pemKHbbpumbQrJDvf2ukZ/5vzFR649TGExVgASCTQMjzeUchscSuz7iAOQc7qg915wWJcp1Rs7BMNAS/7d05z9nEEnxTj+H2eLJ84tK6QiWyFLPnaUCU0D/H6xGoaHX6FZJqnh9XVilkgtRTaEAz+bB5FM5fzEMT7+PU7n5CbTv1KtO1IvGghSZwz3pfGSwqYSWB8nOSKRbdQNi1VxcLA426IZEjNmf1EUZdF3vIWuZghSWBqlTu/dvaf7ttc9ZC/gni0Qqps/6q1glSao8Nvt3du39vZWy/2jq2XRdYiS0OoClcpJ68CQsVc1zGVU0VUVUiyw2SykcZ4oHX7xaTOjiZqrFUIzbj9diciYEchTxT1xcY3ry9IhZZzD0mvMnZx4bWILFboaaS59V9XoSdPyblEBA2hQZr3ZyFIeigPVtctlvb8HrBM4jltlFG+HdrtpkF5HlE85z1Wz3iBvRgqtz87Om729ehj6otRI/RgGXpaIIQwDuKjVKmeYUbBXCbs0miITCRMsVUfAJDTSuGXmxfWBEydbfblj8A140GGLOpro9AHRwRsodB5VV3oBNWk1C1+gN4FhDvpeA8nISEDb7DSng0wCP6PN4CqExsrbeALj+RP9LezAlKWH7yzUM3SXOIcsaOKul4L7DJGEqqqrsmn7/QrvWRQEXMFMVdfbVnean5yuoXZp3g7D6Xoz9FtqaobooIZoUAgoW7RpC2lM3JcR6qNt33V8585eURtZHByumvOt83XOkKPb0uQJ2w59h6hxVZXoxu2cW5RxmAIt5GrszITbiAy+8qh0IT8rPDzxQcxLmIv56OjM1MPqpVa07WwNUcZDCGZAuHJ8KfjeslZD3/bDtqE0GmNMq5aX60VdVEXRtH0LDorWM0qbXnUaqVQal0rk6CCqTu1jContulpWRunzs7MVE8u9Q6vfQvsCMCcmRVRA/KPBhB4xYXgBUTwo0rZZVDBlvbxKUp1C/3jre/JvHfatT2j3Pm9/26AHB1M2qLQEJBqkjtdu/M3CJ2n9kB8dSQbfJocaEljuELvcW9MNTofs9adKsk9sjDpLEMOjnZOygFD1PqroifMZbD/LcihTHf+503w2Dxtpn/bGg+6Ukp/85LOXXvck9g+U6IFgopF/aYbeAlcYxttenZ1vmOilLCVH+NIavdC5bZJvYu5RHBWF/c22O7lzm5dLXpdCm7ZtpTjySYKjBMlEs2RG97rv+6LramR1lpSsgKQgyuWYdrf1EzKTe+d0EGsDTyZ7nmvOr+H8+ZNvJxLKdS1MCDm9wi6sgHykuFpF7dzW25aSQFQhixpA3bB+1/A5IzKEl9VGd8OyqhZ7dVEAVvN8vbE8uIGZ27fdthJFXcI5ZtSwPb9TFAWvayGktpawAnhKaXSJnHa7fQm4J443KMApE/KNKciB4dhqEGiE5+CqHCNol1WCXYjbTa3rDOVbTNjM7aBDe9oNI3C+zbD9vcvLqm/WOvbb3CXco9jMEzykkNWQqSWczW6zyTR4EWx3jS67gTkEdSxhMTuJNjPB2MU5vXb4UX92twoeB/IfWLXWzO5Y418XFw/d9uFrV55/4Ry3kKJe1BuUAqIftm50s1nbznW2W0BZAjTHAG+jKAqIbIKjof7DluV63hvMkF6rVqsOeHnKSFTFGEoWyKgjKy1zWQz26AdASRdA5imxr4HlQ0BbaflwqkXPxpboROuFTqohohycHGnmUNxEKWbKVC8aewp9WlPcccmrZs+lOmtTSAqtCUbwy/AslYWoqPUK53zbdgzpMZSCAjRPttpb6togZ7WDri2EqMsKvdhp/1En4GrbdVf2V70C8Kbhcmg6pMSLgbK/SLk0RnedqfqqqomerBC2BWJOV9Bj72CSQmdfRdhmOY4rW9hRB4TnYftdl6sALErrANUtmaBEvAXcYH9yNKGtDuvowYlPG3JzTduIEfhnuUttIjH0HHxk9QqrO9hImrV93bu7p00LzxL2nOUazfH6uBGn1nJ+w1QTdUW7pAVbgrZTzEMbt2gB2GQBqrzHxW1n8at02/asqM7OtkaLTUOER3l8nVJNt0GAgyskHijquhJa17nyNTul8PfR6uiu641hXd9xJYuF4lWR4OSEFwm+xahV+o8BX9V1HfRFKIZ2IUJZb7gkcbsk3pCUNdhMrDn3TDr58xr11PgesZ+M4IOsIiQ59FWOLxcfHcNUtriE4DxryQFiZ1tR2YIlex8F/wEvqqJe1mBsXd90neoVH+CEhYNC8F6rbdcWHXJulIEHS2lz3qtLxwfttr9z57ySom0KrVFaaIG5bWcVppQkIzxQWVAC3RaLxm9Kct7XREVsbj1sA3GHAeKSLiIgntuYDmDEUv+oiZfTrv1i2kkbZUOFc1IqC/svgeG1vbrcXiHF0KEg0zNtnM7fOSLsJmQa91hiREQVegI1kVqzs/sp8PQgDOKL2hRAb/3aUltLLcZ31csENzkjDasRqu3bVhVcXj4+WHct5ktDCyMMWogDPai+g00L+kRZki1y8feP0+iQX4k7kYdPoGM4IlCMoegHBm3wK2QW/Gx817NQro0GZBcxYeiJSKxKRN5IPRmRJI3Op1Lu0o3TY5Td6tZ0x1UjHSdEsT0OH3D0popBonSRNtsz3feDPtwr2WAWFSqPbRM5WP3EAqpFudpbKa2R8kbRGmSoKrZaLWywRmkjCnnrpbvPPPvJoli8+lVPXz46vHd6eu8zL1za318VZde3fDjr+7pAPyboNFYNptbA8DS4JtIhIJsZm17F8SN3VcmI9wG0wfVtIIUC6TneurPbwravw/+0tv52AgVH9DkmZuUGs6PqZJdYVNFYBRKtnVAF7i62UC8UG0Kdl3sB8s3EwIzPbrNdqpI8FoJrTa3OCMCQb4+03V6gLIcq5l/CqbqjhL9UYyRQGsdhkAhl6wSpUYJnLMJxIGKGbmzO92lMR80SPvbM8699zeMlVLkSqVnaqLZT/WDQq4AQmFzglhB6kBofps6ujUBadSQA7zvGrNkOpLYwzQ7GORMijQX9MpT8Z9wMPFgjeQFxJuw0WHIogI+xiJ3hN+JGhIk1yYf0FnfGFzM6zbOgd1FtoG1rzNjqIuoGqIRkSKtKOGcqx4JTC3eopDRtC+cwHMpSmm6gpsC2bxwhoWwaKneAi6IuF12H+N6m7RAy1ErIsi6qK5eOP/Gxrl6s+n479IuzkzUWS22OlrVknVad7plhsH8QfvY7Yxh6ql4oE0UxpdX42nF1EEnQAPsBjUtvN+L/YPC2lZLVWikShmI3QwCa4GlYPUHKukV7sKojatyou4fzo5Au7ZRnOwjN2EBJZOSwQSGK7TjPYL8a9LNGegPkGLUIsrijA5BkqBzThkqo97zj9E67domlNKjBbSYoklTrguCTDYn5unPbPtqmjyfOSy+o84Ibq6PZtFncLewDW4Tgt52CkDQ9JgF1RbpTHMVkVuHnNqZNV2iDgSrKjqKyIPXYK66fnZ/C04M0HwkWpXSDXB8EiqnikBgOvWpRyAKmrI+mWQHj0B98pIWhmKEbFPqHEbMt4PpC6+xbd+49/siNqRSMpEI3ns2f0Nr0bOBtKwSveU274EEOam6WUePUJploT/7fCyV2/DK1gV3TMgTNkGOVeEnHjw4lglrKaui6jine880wbDad/ZZmDbpnTz2t6kVVVaWidA5A3hGYNzphFIAGMlocHh5/zud+Tq9MIYqzzaaquClF2/e37jV1YY73VsiJNoz8XuSIJLlrXR3O3R9dGM7Ss6BHgfNYyxZFahLe7E5pUfCCIaZIrecsPjHObLRYFAwZtEPNhhalYMIANE0XrShL09CiVIVBrHKrRA0C5yX8TYXSQmiMidqSE4YoaTOUCNQPaGYrh7KsNaalN2JFtq9iYqiEwLSXWpalMLzvGyP3YGxQ0wKGemtudMdLWQjYLBzDqnQHNa0smaYUl77rjKpUhxFWpXOc8sHSA7x9sYM0SVFeItleD7gJJmKg3vDE12CmWsovGFf0iQ+l2MwSFLMMjDcD8Mp1zwGkoc2CoU590ODhrKCd0CvgA7NSFnA3olAFsWJkbTBFJaUQc3qQDLVo5+u1dUZjsXzZvfMFjEQZbVFb9dQPivZVr5XeUHnDag8+DQG6B84EisztNnEafrajg50QXZ5R6bAgPBzWIkU8K5fjMXE+j2kOf9hIyZS2U7sqPX9Kq2kKjqfvaDVl19siP2zn1I2Va87Jb+RKAZc3J2frq5f2ulZZXx1oEiXcQgPReeBkpkIJ5abph6ZrtemX9ZJrbJqm3Q4QseViuTecnTZtI2QxgA4AimI1IDG0jJetNrJD7AhoabZGXOkBadKsQImo5zhhoIkOSLFeVS5K3Vaa6YIXxbBB/lfbiUIOVWVxjFE0YnM4e8YaxaRm2/7sbFOvasWWVbGgZE6BBHmA+JY9EwuJTTKcn2+UOqqLYcsaIZd1QX22lOkGuUQIGzNe1FXXN5AP3BQLobtSyHbQrG0LM5QHq9KIcmCyggRjZbEQFWDMgZxAba62Dev1cgkMEyjm263pqXfQoGXR941qOZOq50W5VWxp+q7TlRZsuWKyZIRugRbwqBiHOMGHVQlRXFCXMXxiWLMFqRcV8BI6gJYxQP9KNvQMYR5MNOsbVHkWksHtxFQnWatAamVRFpUcurZpl4uKcWmQKV+w1bLqGlW0g4K/SijgTdg8J4KSFN26HbCiwKCwDdCAI2WtEERw4AR25kqSCxMdrqQyF1L00AagGFAtDhqOam6ow7AB1wI1Wyy8DHIkNTAy28uqDj6BgnpJqK7tKKpUwPOSUcSIROPNivsI3oup98Iv8rO8+SCFbb4Y072DUzSc6MZgA9xmuSg2DXQbW8+0XCwo2AsVnLKFFFYNWTrdAJ23F1o+cv348KA22DDttuXPvvT8C9utKAoz9KIQdcUVAg26LipQfm8OanKPGd13fcG4BuARwBxIxYRPA5LbapLBS5FnjluXm6gM+/S7zW/80mkv9qVWZXW+aZdDz/b366q80ypl+JVVve6HqpSnnV6ottic94yrvtnUewvNlhVshr7eZ007GNPw4tj0m7I44qYYNG/P+qHaK5jQjaLYZVvsHexV5+vNrZYXuj2WfKgPa9Nu1bDPdSOqs14tOUdRVlEuGB/KWnI8oubmjC+79nyl2ntiUXdryYtttVdqIAYuF4Wd4EYPA+NXSr4xdaU6SoUTraiXrOvKWmzXnWCnrVnxvi/L5WrRd3ATVZwtBR9EdQ6MZHNJ9ufV0b7pWduvB7MQaHIvwBPFpu1X2/O+RCdAJotOlJUaNl23Yv3NoVr0242oAFW1kLIq9TBUQ1vX+9DnN9tB88ZwM/S6rvliUX3eV7JXvVKIwooIKvSWVw4OwFDkEglYfWdbD1o0FTCuUtRVx4YaTB9lrhGalbZelEy4AAMG9SqC8rCZghIxJFSdQ4GzzasTKvIuuUkqxVS6kb6uBtM0jZTFyjadj5mLqV/M26R0z+CF3mUr76TICwh1/G0iZC3SDRLiXEwv0829888ZHnAh2DZxhp2dbbXhVWH7mlIcHlga5tPP30TQd7WPkI/gZSGuXDuuysIYIOkc7S+PjsRRvdcztVyWH/vkp40Qfdvfu3d3oMrvvcVKGnPWdZTwgRY7jTELgIRxQ1KXG0HiA28FJ5TrRRicjU4QIwxvzNAW67792Cfe975bJwOgCwj5QRaq3NPdOQQ6eqcbDdGHygzdD1R2gZC2xvZC0h5UfqptFhJbFptGqxL1aRUfGshEgYQy1jfXeX9J63u8bIVYs4VGP69BSxtRxEYutmdl21izWpfLJarVVcG1wkuxpjOiFIUQHQMyYQGks4J3Z71G8m6B3N1CcsRLFJNIq8GmJ0NzQPbaOa9ZVfR937dDsSD3C66TwFXH/wFFZfF6Uc4JcSiNhCZSwB/FEEUxKPTrQRSwqAuK4iG5V0im+xYa8wDCgc+3F4YvpKlR4wvbgzaFMUKi86Bgr61XT914vBZPAzypocg1OZSkEHfOtleuX9pbLvpNA4dCcMfDlwz7d29vnwPUgbYpipyossV6ieG3o05IlBhTcL7ZtoyZ5aK2vvyC/DKc8UuXL1MJROq+837O0CxpdHhvY0iTwRv1Q7MFfuOiXsQ8u7GJ6f0OIOApJb48cs4I8D6nhk6utp2Co99RaMslqLvkbCSsYiP1A4ygui7LUiol+g55Woax7Vnz0ubmU0+9ShZVJcTV44NFXaPOWXend89u3rx9tr6rmm25XD38yI391V7BdceY2D8871rG+WB6bkzbdGfnZ49cObaSVBjX6JUeQSl0WEZoTuS+Taw8UhjIwmWIFBmxXy/vtc1HtNhumrKUqHFSSpotCtc4NGvYhJyVokIMgzCbJMq+BcgJqyyqUmiO3PgCRRgcrT/glzGG95BbSzjL0SBz4LeWVyuBfG/nskfHkKpCbQfTvGRGl3uX5V6huekarbmSAIUC/6JuYFzuIUHVKCNkNZiuYGaApnoIpxpcbtjMcBagg6rQekAtUE/5TAVcXiU6coqBVaJyVTbbrRayFExzKQqgIkjESZB+CJoE40CotUNGpNVMCXAFjyPYGdwCngfysckFYRtJkmqay8rCOXNCqVKqwFmGV2WxVQWwM3T/OcNtYnwIHVomC0gsJFchY4cL1AKBASG9gHyKQpRlpdZ6f2//2vWHi0K2XavQtwdo7tQi2yCzr1dd17AWyfWy4MeHS2jOHFh68B2g7IEqH6heAfvEtXxPnc2xTsWn4eUSNQQXSO/s+77ZbARDeSwV8OzOngK/e4D40UWkOobVm2MYPmRBSHbQcm1if7h6lL+fXkgeCHN4uDg5Jcpn7GBRNJ3WfccY67rhkesPyerKsl6ZojpcFHvLPaX47ZsvfOZTH7v94osbiAGLH1Z85ubNUsiD1eJwD83oZF23fSuBG28GzS4d7yvdC15RTSKTPUAwBQHfW74D75RNJ+FCu6rGmE1BChdXgzo9vwd9jHzmAOACFrEkbGE4kyTtXwEvF+yXspKm6+oSQPkCUKd494FztJ2gV++UKQtp4LMHbCry7yEfugUa2YpNu+kL2JmSIxSBFIRCasYA4FQCxGyAromK6J71QusOiEECfxcVCmq50QNiAdq0bhuga5wpJXiVLGBZo727QRbvMKhlXfVDV9U10A5kqdRQVou22UKNHXrAnTOzWi2GvkXpjza89wEY8lmg7sRmo9Aex9RQEpqlXPIJu0jjKNfWh4QRacOZIEUCqzO6aZXkvC7KbdsdvfTJvm/Y3krInmlYV4VgLQbiwu9YRhSfURYa+a6Q46HM6enJ6cmdAU0CelnIg4P9xWIhZYlEPyjGou0WZ+cnmmn4zZjpGnhMUAkEpyz827aFmk/ZyX3CNlw/abUSN7oL+gR1GePsus4CZtE6JmH9FPiVNp+3lR/E4r0PnefYyCMxbuOIhH+Bqi4q6J+NT411BWM6rRYlsPWh2cEvBO1s0EOz6WQpK4CRFYNSZ6cvrh65vm3bmy88/3sf/chmfUbdMRAZErKAv0fpXrPTzXbTtPX5pixrDqcJ+NyqXnR9QwhkaIcMzxj0QmhHcE/YyluNKAucUECypRiCR/Mjfo6Bcc7WKE6BrgtuTBENHssJ+aAHIOVC9AOXXvV2rlADyRnfW6zunNyr6tooUI6mTBKuFQQFzR7iIECB4FQBw6UZgN7tmlGwmgwzu4cKzrt2KFD3YvcVoc/AR206mKsVeT4RaKuqilONI5NSDaooOMpBKEd3aNtSFAWcZRrVYHqwPmZbegkm2lKFAakpZVUwbQry88BRRD18XQMhX78Sa+ICHHeSGGnVSTuhVPhjIQ1wCdUrh/qOUOGhAOogAJhzuqye+d33X/6qrqx7aO6EBkkNLLXkkFKkBYOANfnxCdQZSBBVUdy9e3e9PhFSUr8A9ZnnsDmPj47Ap0pENxiH0dD3A4oitBLwh1RaDTalg1YYLN07vVyAzBNnJiJT+3JiFtuIPwXBKTjc952FaLW9Q5IrQkZNSOTYiU6WFxM/6JGTr41oEqwGQBCoHCwLGbu8lOxd4zpr1TWKkIag+2jy6Te9KqtqsSp5z/oBbv265saoF55/4RMf/xg0EMSbSfcjZBM4iXsFuLqeN8acb9Y1FzXjfLm/3N9DdYiQ5+v18cqIeqmFDff1RVXBleVZCdcK5rfLlXJWlM8gwMBhunVqvXdZnTwHsFL4OWDRupgiytQgGQZlITHxT6+GTgGhi/bjIKTo+k4y0/eupxZBlUKjJXsMXaelLD2gCevgDyPjmrb7oJwr1DoYoPSrriJIUclEPyDEBfVXIcaC5qy2zRcFWuhaizpKxD0gtxTpqDaaAm5CgWiybS3UIrCFLf6+EFRw5Xil/RAaZYjru0ibW1ZfDW1rF0OBhwtJ+JzxNM3MdRJDniM5bClJ29A3XPVqs1efNpuHmjtMHVPTQRuKJsLlAGs0qNhFYR9Kg6WqNZANleTL1ep8U4ktTG9qvYDmo1qp07PTRV23bUcpdHiXpm3abXN8vC9hoNb9IMqS5K+UA1XChBzhkeSafjhLLxHlnU4d4JXtJZgGnAARCSCnL1+NNL3jA+rVF4zJodY7ngnxiyBth30zM37v1o0ZYy5ChW3O+f6yUHpoezLTyrKF46bQA/zGiC3ysqzEvdPzF597vut6DnFN2coE3EDsHHtcDZ1NQ0XvDMEajqJvoxZ72IK0EkaXCA9I0PsA4FsJbAY7JMLbo2pe60ChSYfKSiklEI+iLE7OztftOSGBSNV1AExCnJCuJ1QvmyRCCNcOH6asK0yOUhvaLt0Wfkgn3p2CZcGuLV3glQiBgFxsrp+IHR8S6knVtz0mXUgEbTFtrBH5+cgcgV2KqC/i3WQ3UhED5XGQIwkWL8iaYMd823OLREhpYmTCYkIp2G5dAFYN6bqWUUG8RSzzgigTHbEsLeQ1WDzRpBrDC7KQD0QpI7CibL6nvavmHK1/iOLIldDcxToXttkRJcNCm1Kc8Jmtf1m1nUGv3EFQbI8ZtqgWG1mQFQBnZIleuY5zOAgGre7cvgODE14KMNBFvbTqc2ibQllVY09Vms4SP421gMnHqf1oTQdrt1A+vyvxHjU2JKUlhJtGRyb270ergQRHZB/gmDDZZPwCVsylQQRwC0fpJEGsrI5siL6H9gMYMltytW560mH5pmsojQknLJaLYWDrk9vULckqpDYn1qUYk1XmNrWFxhj6zhi+f1Au4FTTJeBiq9PTk+MDXhQL7HWBDD4Izdi3zaf4+UIVQArb4DbTDN3G+cl5c9apgVDFrWbss9qckeRqkiiTzxrP8HlCZsARBfeNQyRN23LYdAItAJhqfSAEWmCB3TgYBOdCDUoUYFmyqD2uGI3ZZkkocsBaUqZptl1osNnJJod2DGp21iJZqRbi19qMNtGTmmN4eFHqnQFtxV3KBZotU1UtOnNa49O+eXCgeLXZFkOGtBi745ya7HP4MowSvyUo/dGmn7JCMtV3oih5Uby4WL6q2fCy0FIPPcHWUWseuCwK9GFgmnVtuyf5vb4XfbPoJF8sYZwJUdcVecXgnPFNc4xkUJYp0ZR1fU8t9EAvsiyKCvBXIGAnoQhG1alnkQ7tuEdeJq/QJYEMX1IWyMgyOSjRhHNJOjqqAZK8LscWvN87YFtOPUmz0jjApqUZV/NCm/IXBtU2LbWDc76rxH2V6NEh3d69pEtwsn1wljUSrsiuUV0/aK2quua8uHP3pOl6oc3QNITE4RLeIW3IHgMYgqtGs/+gG5uAUjq0zbrrmnbogKWF5RFbJGCTmEYSH/D7U+g9vyKBD2Afwxaj35Ep352f9j2klN27jp+6qkDf4tEKRmzxGliIdV1Vdum1QvNoy2tsMROBkxMxoJ0b+aPJHzgMAAzjUBU66jEDWWp7V9gkMlu/CD2FmpljwyDPTBUlNh9oDFRn046dI92vBbmALBMkiU3+LWjvtqaV9inccIbmCSq569PrrH7LfzAxWajfAQLb9OlYIR22ecw6jXQdBJLdDzZRAo93LXkMkjwKwYZho4fy3qe0bqIggTqB1nyq17fvng3DUFcwfhnSADqk6MDzJ5bLvUGZW7fu3Ll9u9lsDfwCoq4EvIGILFocf5LOTKheNy2AchaLBbDjMY2YLErtdTMYUN9igoPLkc4bHGUGZAgOWzOa1DSN4mGkdDtm6DrZe5GOv/KEjxnqTeg9QB+NPNK7sM5CWboamqZpW6Q3ju7pTX0zdWFHlu0BoGCPEfY+HCVGNTSRwujVaom6sLbZ4hNMOeK5oY0LKJF6NvvyPcuppJCmYOvNdrnX1VXfl5UUoiiL89OTg7oWqBamGMJQUsyfhI3XFvw6aaYsKhzjgyCjsRmGpkfqUqkG5Bv6J7pKPmpIgwCPc+NRyUTbtkVB5r3lLHCcERAB3hTwI2if6MIJbiU6pduurxdwFANZCI1AKH1XQ5UgJCbfMJ4ID7KR1OYC2WdAUXTRdhI6BdRgKrEh7E4qGnbdbZ2Va7FcFfxnpLRbSDlPktCWnTOHWkBaG5Uo2+JaOPpzIJKBcJ36ldJwsqW9WPCljmnZuId2L2ybK8oyXxeMPf+cUqosuUIpCfgWwbMjUwqsU0NJ7sqqX28wt8Mg2lZCJea/9NZf/u13vgs1urLa21vduPHwF3/xG65evbxcFMyIrjcVFaVyCGG0Ej4/Xx8dHFCkmHVdf/fO6Y2HH7apSrnKmlun88A1Iaw0IiXKjycapiYgzqh36aYuxGwsDFfaHS0kB2YeBL9nL5C0O2hYwyfeNI2t4UqPkas6hLX9316TIA2M4jhINaYUKV3Kcj2sN/16UZdCyu35WnXtGjKeiNeVmSHd3vN8ryV4DRvAjGgRw9t227boeSVNUZWLskNLZwQYkDSnkNolKJxElmKcllgLaScGjIPVQnWbtu9FsaRSAWvfkfFsFSXEl+zEWMvSViuRqCQouWGAf6uQgJGH0stFNwwlOWGRcOvqc6GD2FR8ZAEZDe0P+CMw0SV88hDZbdtyDocZ9hwh7CLZ0WK+kRpjA3nG6KZpe2VUv63qisobKIkYqSMYasSl8LjcwHsjpYMAaGymgivTtWwpQpDY2jzf5kYUUExCSZbPTh3baGFmo8KTIrj4WiEO6Do5aAXuVVS3CvGJd713/+uGYzFQuIAr5IkI1PpoLZlot61gbN0iEwOoOF0n2na1KMvF8lVPPPWOd7yzbTtVwtg6/9jmwx/9+FNPPPJVX/01D1252iEjB0OFT6Qoh0HzoRWHh0VBaViSP/HYowVcia7fwNhl9YD0EiCAIxYTRV7JEmYFdVebZCuH+sM0+hpDTsnnIR87ge15gBiyQRffllJbEnSSC91yqbnk3wT/LlbLvutLwxsFowUgG9uuFdikQ7PWbde07aAGZ5FGHB9fe+sbB9tvIMQN7wYg6yzKarFcVZVURpdVfefO3eVD17SU5LKEMwH+HVfKCDp2s2S3FnKwHb/76AfftW42qJoir10wCxzDgNOF4P9J3UJYQgIGhIL1lLPEpQHYA7V9G3rbEcI5u22zGdeNXpPERpypo1grl6zve5vuhxSQAQaGKxU0g5LUbxEhorJtOyCBUb2Lk2+MFdSunkPHQSZp26EXLnzMrsbfrwuKVwmpwIJFkZXoQAaoLA7RKgfK70vifam6ZdeuwMdl+yeliImSF/5IIQ5Tm8puEY+LIICVA8ActT1YlCd3Vt0tzVZ8IMOH4PtR3YJqRMLfUHpZ1Kow97YbsjF01Zb64PDbvvWbP/3pT7/zPe/b21tpo87Oz5pm+7Hfe+bZF3722/7wN1+7dKlEVinIqayq9aY/PNrb298r0eCS16sFGBnC6rbmhWgwSxBOfh0T7c7P7KYlTxyyJyhFz8NvBGsXiByubDklp9QCyclsmhO2s5U7HUjmx/+CQhsHOa3KG1F1qFh1xiFe5PZJc+1oT6LPKKayaTvKzOyb802/XW8btHulxu2hg2FInLNwxy6ob4v7uJTIV0NotO26blHW4KjwAJ0pM1AaPim2eLat2QnQbRkcAb2oUoOsFdusN4SWRjahZ402g8GXoCMrSWlTlVgPNSAVAblehrddQ90w4drtusZQ+NE2Ues6lKpAcMKDCtcGUvC1Xi2rtlfLugQiDCWIDwwoE3DXKwRjOSOjgpa5h1sdGReKBP4w9KTECiPAyIjtIO/L0JkECeUQValTOhIn0NYOaoLFu4B/W4gSucEIxlJrOhsYcsgEeH2C3cb8BDtibnc5rpjozn5y8zMjdpxgJRIeGgBFG3iON01/UpRH2zu6fVRIZIvA2lX94RJ9ueEG5VyrvsKPvkayren71qiODBD+l//3P950qhbs02en/+pnf/5X3/H2k5O753dP/9W/+rff8W3fcuXSkRq0qIvN2WaxPJBSLhbLoiirohTwdUG3tkqfR1aKsifGh5zoTNjUxUEll8KFhDAFmCdSfiK4FX5J2w5EMtopHVPgu/Fczx1qQP0ZTDsnfpMK8Ck4y8wReBeQ3IW4tHc4oBibdFHOywovheYoUDO2qt/ofqP11ugOeYGu/YE1G5zuYdFWCZcXBtJyUR0c7A1Dv9ms+6HTnPdk6r744i1um6pRGhIKytFoxwLrRCcFaY1UMKV7Bh2qVV0rGAo2bKmt1XUskIT1oJFhE6I85Gyk2iMPl41djnaq1P+UvFCw+aEJEwOypesUmCU/FaqgRYf0Qhici6pugCWEBEmb7QSTleSQGRpCN3A0SZFGB9Q1ABTCGq1WIybl1mWhQKjaZs0WVpWsIudFhXZHg7Ctb9wau7ZjLhhDQM1IAA9+q6Af+r9cDwo/JcG0TmY5/0ErKnoDXwAhVMHAQpSDc7Y5lWjMbbPudb/e9luUo6HnN6UEmL4b2tYodd52m4ZihetNf+fWvY9/jN95QZ2fPXb12l/+8T/7f/yLf+mRhx/iUqzPTt73vveenp9xzi9duvTII488/thjjz/22P7eflWVxvB7985cXNwjMCTOztEGzxDoxwTkiSM2PfCFFZSBQg7gqBy7W8yFkUYRquyLB0+8xIAGSifxRtTEoN95XfZ4GwmyshhCUKC3HGVEwHmhuUIWPYVGwXGRcABdksIlCG1EAH+HeI5mDnQr2JyMs+ViaRTbNttts12UlKReFOs7d8QVRanLsEKoKyOkKlXVWxM7OPNsKSMSJHs9sLax5aMOq87HFXxtOAUlrJ5pQRgE72BmQ6e1tU6WHlxlaQTAgxChyiifokA6N2U1s77voB0IrgC01pGirkP/WXIGuOIRS1yQpTE5igLCSNIiNZgMS3j9YC1Qapf1P1kXvoBlrhVc2eRKhPpDSSaWgG282mu/UCWQxO5j8h5FyJd7jxCH7WLbmGLSuSOkJ/md4YWx8r9ZVR81w8LI7alGAySoJ6SttMh/JC6jFMqklOqXjHVNc+X42Ag+NNtywPwPzHRDO9y9p+7e6R9++Cu+/E2f+fR3/cKv/PInPvGp3/vks089+eT1hx++fHz85CufWu0dCMEbOBEXgzG1kjB9bE12So9RW01yDqd6Zx5yCvlmKaWQIg1WWSDamFIhvNBe6N+PHC8iuFEWlZtX7CZbZpXAn7+sBJFobFvMV+uUfOHePRiB3nAsy6KqF+ftFjFVgp6wiDG+XXDwldtsW3IFkdO1ruvV3urSpUPJ9Hve9xF4sRf9oiiYLJbLWrXbsqzQ/wZJw/ChAdrC7sFQcm31Z1oqpXS/3uhtQ01nuRacPMg+mBtcX67olNJnXVTZiSwC1XSzRQSMC+CWIxUUGVHk4aXUKKveIm2INGtqXGFYN0D5LQS4gIOzo4PgxCifxCTGaviWIxfCtpghtAo831Kviyc7yiSWgbfSQssBjSzIAQcIIyST+cVyPmfXYjOI1zSlzyfihbCiPTwQ2ChAnEg0j0OGigIB4BGSTwj8FkJ+bCjedPtOq/sCka0BYXPwVnAoqkAlTa3ruBp4s5XtQq6WZVmenpwerFYAqWwbUxR9p26zF1tjfuQHvv/rv+EPvvTsc7/3mecefvSh1ergoesP7R3sbdu+aVtZlItFffvuiWsmalljntbvxu+7aqaRnlRIhWBjsBqDXux8Bdx6Y+A0ob3n58PWg8xQTeqrGhPUbNLlyDDGn6ofgGAQoUBTY3faVDr0d8ruFrqJOC8uQEzkpcOjs805dU8G6DOy0uta1nWtFpu7J22rqmpFm91ThgVqA2oZ8pClEOWiXKwWh0fHR8eH/XbbdU1VLtumbfquKAQ6YDDx3LOffvLVCy5rUi3hvKXmVYSukbnlceAtC1O0ZzAyGWuRHWkzKW0sNwb9ghZlNymA0Sgwk8BXum1NrivoCJYTBTes9ZRQ+heFZuk8TjmAXTcQ2oTDxbFuG0hXCkqTIepo0me14Eqr2Yf+DYnb2c+frcnyZGhd4VJiJoOzNAW0CGCjXuvytkHcBN4VmG/nEWJ45gcKAJQ+EMBh4NszSAgpWRbsM2//5fIN33KEAk8jy2rwcWBBUESEDww1DQxsswZGT1nuLZdIaO0aqZWoF0IU7Oz0rGk/dHpW7+9fe/TGpRuPL1eVRPxcn5ydd5TlWonq9OTs/HzzxCseLRGrI6C8HXSRxLxTJcJNRUhdSgCCQ4Kpx+CiVChTlhSFCeDkzgttMX8S6b7TBs5R7Mb51Yl/DA7SHt5Q0iXTaor01WLSzeS1Rk/1C4W9JLg52Dtomi3uD06vq6pY1IvNeXG2aff2V5QbxZEZBVczQG2QSk3AUovlarVaLFZ1UVRGmTu37243W21Qatcr3TVtD/IVslh0+kz3vSmr0kdJfAaCnWBnrREFAVAF9RX37nUQmNTcJYmDpekugUZdQiVdbjPhkym0IG3kF/H8mERlugnweHotmKSkxMLmRZgX1p7T2ynv0iq0tvlljJAHeWbPRpgHyrDLMXZJF76209bf+hZM1jC2Y3CotC6XLlmzlE1ngiluhJTgfZldUoE08mklMspBchooMLQcinVl8WzFvnx7IrdnqrwkUT4pgRZLPRFWCygYHXA/bFWZYUOvGvxbFrIsFxWKWxQgQSyUg+rVnTvnZ2eny0V9fPn0bCC/YWmLXAVnW4KLWKCZtCufytowjiRaNImdTJ1s8vFnNujhEGPIk2+9qRiDSy/AJdQCd+76z/LwqhH0SQRlLYEFBNPsKbsemb+++zVkkQyUgo+kSlSl0a1RJA5EyRJFqNitaKBDVwFjhXTOuq6PLh0eHR1V6MnQb9dNe950fbs+Pz/bbNfr86qui2LRNNvlciHrGs1KJeu2m6KqjYBADl02bDcEjzlO0R2UwAquRXH3TKHsyapUNhOKMplD2M4X2Lh3o/RD2yknANT5qaF6ddT3UVOCxOKmmCQcSlYYuvtZ4GTMPILAVMUE1B342omCUYznnEy0It4vQZ45S5MYd+Jhctq1V34p9dLF5ZHRRRyRLHOvD4wINIVFdmIzKooZP3ePiJG/xDR05oIHj6IvLAKkxgyDmBzq/jA0e1Ur5UF3rx/2tXPvyJ5C9MAnEEVHSSYEUavboSuBGkpFjKKXRc1loQPiJA7FBsHWTaNPBskXe3tEw3jvYRjONpt6sahqApRNaoHi7nWTHJX/iImVuZIDm4OF568llAja3Ba1y8bU0dBYKWSQeh6JErAwUxcaqEH3mRJXqtE78YKiQWrolNjbQZvKnpKWJY0fHyofA0iy8+9wVIUrgE9QQSsAdReLvcWiWQGiCQWxUHKKUhayKuqDw/2jo6N6WWllbn76+Zdu3ymKcujVZrPlkgMfS5I32yDDbm8YdF0Zxqp6+Znnbj6+2mfozGTzY/3orbjx9TIFl4NkUit5tkH/B0psBlwXSCKCvDkrgCI3fq/Cc2RTaGdgxBx8KrnhgnYVLEkrf1Ay59w+JGrI3QtHVAEYNxLZIXJma2jpI2mp3Y2HdHLEoIQAqi4Rue2FKimUZjPaLLAfiQKfIknM0W8f5GMlIDTB6ku2RRSjCZFm2mVQk3Pp67QeXCLt7/AfsFSzQ0BYi1LpYnvS7j8E5FNU2nME/FGUgjiZRjyVQBFl0cGFDikN6E6n1Hu9wfXHQNK9LJAQgqpCgra0+7+sSiTocb5YLGHGIc88FvXmCVcpCad8KzGJR5qGnzLbJdIXhFqoYzXIATzEEj76XbsL7yt/RyTmvVahH3QyQI2yQXLQR0T/IOgTzSoIopxgs9fJk/rt6gPGEzViNaL0DE1VbO7Sau9gdXi0XZ+UCNNVgAarFnv7+4u96vT09IUPvXj39GToFPUcFstqWa8qznhpXcJMV1V9frYG07ElnsxUgutm3S+rEmYqymaDdWi3rdtAepCyPDhavTCsKT2YYiukZE5rue0MOL2LvrLBHsKMDpWJsV9hEjsYV5yFNiOYbIpE2yipvRJWOQJaruWRM2Y9GIYv5POjIpc3VfBSdqVNbfZP8rVTFhnEegcJKNjbb7Y4InY/tUI1xCzpnWIcwvOhTNHKvSIxXJKX1XBmKL2G7CIWCzntoHrNnpPysN32hHwpiZPaYsBBK8yw7VEG6SZKlEzBNY0gLgwMNDSzTgf3NGIYBVWGIRvT6l6+MIUwm4orly4xrXsCAnboOaFPXdQ+RoZjtr+TgHEmyVzXM6ug2cCeE8JKIlnJSbY0jPRZ+YfTy+0NbC1y18Os92jbXgvz1yRXT6Ezk/H4fRoxqNzZcJmWfMEXTdN2QG+GTG0EPzq6xHS32FvsrUqmTL3ak5y/8MxzL929fe/uWbWoqrquF7UssLKUroMyj6IsjGYnp/eobo/gmSmbXzWbYnOuD4944aK4LqXaH7ZGjfK35DM3P/Gpmg1IckLWt11Jm7SUN/Byer+t0nDkEQjdNzdLm766LZ817bDxVcoQo4oi1N9YS8XSCZRcC0plp9BauS7HwOZa2t3jsktpim3UNxQbkM7sKiWdTm84ps4GpYMz3D3BtWCJGnJQFDyjH3Pn9Jh0MIgkkBiP3OiFUY0xaGdgEtBtmhOhdKd1vb290Z1QcjCEnkcBDGZYjbwW/zK0hVCrQTlkyK6xbiE3DUQtlHFqY32eDXs1irSR4+Pjqig69PdJdH+H8BDewS2AG2Kei5ysadTA7ee21UpCHs6cIEw9JHdbktpVTrjrmBPUcYi4JVXtA1jIWW5WHXHJJc61kpu2c6zDZdyMW4TH10UprmE9qwEHPTDBkMe8XKIA5dKV07O7Z+gfyfndU9UP6/VWyPL40jHlUlHHLspzcP1FyCVLaYgCaG0GAMOiLDryrLZMV4StYevsvNTLuInhpt/2dafPWIXSPc9xkdlERfyuiY/3DRGabUxBzzpThv97i9mdkHzp0hG9UUG6FkW2KdXDhpSsxot+1mVJWVW2Ss72ZKLqgHTT+BiRv6XL5rDdbgOLobiV7ZwM6CcidbfCLg5pq9tc7M71JQ/Kf+4AzZY8FpHGreHfN/IBm/IhOsr6DpTLfRk9ShiVuCeL5elLJ9TgewCYNOJeZGjouhSlkIAZoyljpHE4cWfVFOe8txVjGBLkRGghlYgQIMyrHn3kkY4guUbeW6CC8buEve4kWIC7HE+CY+DhgQnLDvdFNAw58o6f+JiSy4vaLYR3qdiZToCDsk97av0WTZRg2YTz806Z+S3zQrO4892VDnhIGLOoF6UsbaCyKPiiLqu6on4VXALjRm03SIQt67qsCwsuiENyWZX1oiqqoqqB/VQUclHXB3urHtA7Gh4sKFio3ztdN6xvuVYoYLeUQKlXlHJgy3KonLw3Xc9Qiug3tePzwL60pQZ+U9jCMFvlQ35m21HNGfjW6WgFgUtx9UlKNruZ0j2Av+VsbakGaFKE/0F7znWbtdURkhCR7eahlgpBUCZLYI1bF/HzOjsEFLZvLOq05Y3klaTIkwd/oVw1W1YJEI+gMVJ0xzEjR8SuMaKlGec/sxDNrh2rb9CZU2+gYVNoTLGtMOZu8G56ULAhiueq6lO/+/5iixI8IPpLXi8qjEoAkcwCmnojyAfuKMxu8wWd38H1Z6BuoyQKXa8ZW3wCkIDh0Rs39vf3qFuIbbHsX9wGJ5Id6yPoYcozis2POAOxS2kir50ExiZ1/MKmRycerMwFnPxxsX7th458d599FVhuEkqIDjd/YTRiRiwid1m7GY+BBFRgwmfIhayrqhs69C5Y1JU25bbmQty7t+77viQDRsJ1iLY3cO0QhvS9u7c+/bHfu322Wa3qq1eu3HjFK46PL1FNMKXsN9uKXDKdZvtFjQSIgfQp2F625D3ETSjjiVS7vt9QCWHQkShVyLCC7EXv0bGxXZtzgyoip/vZ7E6/VWzdkaRMSutssjnSlDRF9XFUmlhbuCTsPoXYAs6wBUv4jyJMiMb7tmB4cq8RMKOkC7etbGJYCTURKreNCSNgDgRfwowMyWPIGYZnhTRth5sDxoHS9tqg9woBdYX6/ejT9pqp20u+dtYXjcWeYE5GI3smrU6jvBwmmdlT3UbUmvrHsMASg4Ng0Od12bR3981Ws0PL0nSvdA9oDmomUWyRA5I4j2ishNuCukIvYkhvIFEILxYBlNo1Ysag9bAsDg72sZ0oDzwwkmjzuRmfzXAKLugoixOSc3ZvVKBD0ksAaAAJS3JlWRV6NvCb5s1cfMSQP+zEvkf2VaDdwESC3unHOfJZzZUW5n/Hsm5bXAT5wlf7KyN0dw5XFje8LORqudcfXdoSUiwS9AmPVHBZVuJ8vfnoJz7xkY989KVbd/oGWr4A2C96Oz/+yse+5Eve9NDV64dVLbq2b5rzZjtQpApSuUQQjjs+bSPpTgeGcGaDGYxGJf9gXf7//8re/UmSJSsT80dEZGZVdfXj3tv3zothBDsDzABCBox2BWi17JqklZl+0P+p3yWTZDKZsJVAWtBqwWAY5oFg5s59dXd1VeUjItxd9p2Xe0RmD2zBzFRXZWVGePjxc853vvMdircCpRBp68sWg3lc70DnJnlhzCBh1JOAIGi1gtdK7zfhbEKf55Np3sKnQSnyJpRD7Lf5BHmcUvpSjsXfltNh3PTp9DDlj6e+79ObuEHbEdxwl11MwfVu8jmNJU4RpZIZ9RRMJYFrKZhiELzbuLzLh9PkTm43DTGDTAnBY2IHInLh4Z5go1EUDlzHQzcPgpg5d/M4jMcAzTeELqdhYH8oCBt1ZckoOqkkwBRRDnF+8mFCqYyLVQL6cWWIZ52okjNRUebp9jgeux6mphVpr42ZqChBCSiceh9O9yW9T0FLjsGdDqcdKoz+6ZPrw90dNBG1JkaHKjVyysRzkdvDHNqhB8pPkYwAFIQhleK3281ut6uuSFkUTS+u2UYL1cp/KMtSx9kYhsUhddSoHYHM6pB/scCc77BP3hUe/+OMVy9TWA2cAOfMlAO93NVd8dEgUkc/7/Pq0WNwnSRoWF+oz7gwbIZ53hww/AOtboANu+5q2Dy9ur5782pCYR5tWJtN3B+P//sf/fGPfvjD+8dH8A2hS4gaQwnpMOUf//j/e/nB+7/5S9/Y5LQpUHvF7BBfwnxK0yljmgNQI6jegJCjYpV0iFLLa3g8nfhwoeGTQsFAoXcz+H7I2TOHQAuiMvSQVomxKFEAx+aJPrl4NxQQzSjbjbHMM00AQsiGtAGDIE6jQ1Q/P0LjLz+5voG8zelwcmWkmJf4V32JvQcnmRsV4H0pBGNdOB/7eArDY7fJvU9o9KVC8QyhWXaP0FQDoMuRJ44cipjpEdOgEWjlwe321/322Q76YsbPkVEj9nw56hbMDur57G+AfjMbjG2eamfSQsXZHrJ8LEs4IQ7mwrrnclyTTXKAdAxdun+Vn33NoYHWffHm7ivPn3ILc8Rcjn5OE8mBEvAvkwUR+2gfB2FeCNgguCFnUc5pQsrCSjq/+Itfgz75bARSjbDOo1j+bVs9WGzo1q3V5N8M5aJBKJmMBuS0i/sfZLINSFx5dyzDJc9gDSg3+gAmXr+89vNLX1C1LEZp1qUL/vnzZ6fD4XR6gIq36FiWcZw3w2acZt9jVNA8zn/8x3/2wx/88O7ujjkQNPds2G63w7a7v7//gz/4g3/1T//TZ5vBjePx9UO4f50P90+evciHfZluoIEGLVgPlSyQb7gYwYLmYPRH508PdxDdrLz96H1Mbi7QVWIb1VONT3ObeU4kT4O5WAMWVBQdD0iCmPD1vAQhp5F76Lseg45c2cdxfzjcjScMHrrZQrIDCqlIVkkch2I8hN7cBsWBWsAhE2TOKxsVTJc4p+j8x1/QST8nhO1czKDHnnNChb3rHh7ut10sA2ZQgXgXcIloSG7hFFVrotONMXUqbVFAzqeZDjsXDyghjCIhUFbgLNeH42aTp0rRdDWt5Fo1s75Lf/h8DwSr6zfb7e5qnkeIItALrq93E4RWSM1M9KhFUATquQGccFoAghUoBKbi+ox4zwdpgolQ4aRzG3kZ2j9I2Kyp+coFGndv4YPFghvBTv1ien8LRolZL8tpeJIUGCoX+h/bZdRKUjRvz5Qkip8lO6rrW/FxO4fkf1uY4vKnSUOkfB4/cJGYkwQh+NAPw9X19cN+P8JJhWGz223Tc9R+/OvXr4HHuPCDH/74Jz/96Zu3b7x3m80uxvj++09/+7d/1zn//e/91dMnT//g9/+zly/e23/2SXc6TA/35eH+dDrduhSmfTg95O0OB3/wJLIzOaiws+QS5YNQZI37GcIFlCY3MBz2B3VEGd1oWRswJSUjq9DpLyxj48RaRimEK8rnC0Yp0kJg/AgPfYBLpYQVcTJ3DxIKjnchFTpLXrATORTCcc6sW17ggChGuNxI4cn/8e/lQSMJHPrhEOPVk5sTZhdgOgTBw4iAmYMpT103TpWZVOTdfilUbE48625pZj/KxFK/769IkNlgOHzV+g2WYvuq21/dfYHZaRi4NG3CsD+enmxp8BqkO7rbJ9cQ5uAuTq6H8Unp3GkchwA58b7vRx9Q3qD2D+B5gPpD5/zLF88pgudaEqr9fdcRbWnWQFqZnma65wiPXK+qKlgcYb0vjT0aEixcLSL3QJZ/nrXH/+d8ndt2e1mVHUzl8pko3eQf6kc3Eb64reZIag189ckNACBJf/1w8kBU5UPEuttd8S9Rtolxe301p/lJH6GNPR+nafrrv/nRTz7+OLt0e/3kN7/9nZcv33/15v5Hf/ODt2/vg3f/+r/518+fPDne38+noz8cy+Fwf79/MsT+8diP45tPXl1vn1Dcyr12c0DLopSEiEiVg0thhPAI+oelN4D8H7Af4V3RbVTKf/Uh1aSJCsB23/y+gfJlWJ4CO2SA1NMgqIcMnJIht8xw4HKP0Kt5RBy1+KpgCU0ep09jfgQWcLvBLgFCg+4RZEbUFCUKsBJz5tsn12jvHEKaJkiEqIuuT108a7sJKwJSS9928/WlClsK9Ccx3Bx6F4hfUHeFk+9JeWkK8ePd8Pd/+b33vz0fI4ZLf+mDDx/vP00jKvRMFu83w7OufziNxwnkNTJ7GutCNgw33nWQp+fKgKwQz4JIt9e769tbCoPQx8rHNSH2Adz7BdTzHwAhtX7OqBOKgLWRqnU+4H9JQaW6+GWsWn9yiTPV/kMxftAnoW+iL14UpdYxgXbW/Hznr3q7BgbY/TBBAhaFHfLByw/e3L357IvP+HeYDXVznbswPN6XY5rHU+f91fbq5Qfv//p3fi2MYbO7+sb7L7ddH51/9t6zr3/9F8NpLPuHeDyU8fDwcH91c33du2465uPbdDqV6ZjyFUYATDMyyIjSDV9MLj6NGFnQH04ZAvIM5zGkjHIk9flJu5HBAHwzVrlT/QWpCa/raLKCvJ+phMaVTPonJ6Dkvzg0l77cjkdUUquTBzCFGHozAMTicUosSssJaQhxGIZxPA2QKqIJwkQuwveYvilDrRI0lgF4l5KHvospYKhxhyhSIONWV1VxiwUC0pxbi322yggbHnXLSqTQgTTu5VXyxX9HCiT+LvZvD598xR/v865Mp9m7T988DrebzdAjP8AEKwxae7EZDnN62J+gcCrldApAIDLejYQgMG8A9tnh25ur7XsfPNvudvz4wJWhxx0wArabx0mgoFq2v4TQLiyhBhHtvLzVq9ZYtWw8+CudjbTCm5qVXNjhWkmmlneQWJIMi9S76hv+I1oV3pV+C3DZOuDFtXE2Qly5+cnN7evXbyAN1YMiHLt4fX21/dLX3rx9/Wn+7A//8F/8YR+eXG9/+uO/m0L4/l9///h4uOp23/39737lww9vIfC+nx/u4uk0dN3w7FlJOU6HjGm7I8hHp72/eYoSDmk/+BngpJRqQdNJfUq/9Nnd/zFh8jbfOsWqGdgnWNb6QC30b4KkWnBb3NyaxEI/Z+oxtpmsjLTCwGzBUmDIF80bYO/yqHrEnBgRiEoKpjEh8pY0h4eNkDNBR8hmAzd0c30V0AoP3BlTC/sBSrxzwj7G3LgOo3HQFhMpY8AAdBg9yqHc+VjT30t7Rsmx7d01G7SmfHJkWZgm7BQVBTB3vdS3SPPm6Pb+WZnj2M8doXQvn3/weP85T6zkLhjqyfNXm82wu379cDhNEwvxYuNgkA2gK9TbqCqP4Qib/snTJzfX2/c++DIpEbFCUI2NkTiTdor/udjU2ixs59uvxfrFCytW3RIu60qC/qD1K7Xef1Qm3Kw87T3MhMXpLEMZtYP5He913glcv22CAeO56haX+K16bQ5pUenNOb334sWrL7746c9+tgsYbAeya+h2/fbq5vr5i/dOx1NAUbh7eftRcunXf+XXynRyfbe5ub7y3fHuTXpz14MWS2Ms+ghq4nhy89w7DAIthykKE4grKhAtNSiuZH+K8W32kLrSh0D/HTI1uFiXSbvbBMDSb7XVl5dQGlyWuZOEIlZTMwYX7WoiRJBf5aFONMQQmTDoj0BeAM0wSVs61EhlK2C2ETUwOIcxB2Clct84ciwMxWX9sC4M8EMRdZQ5kR0EiNQShZPenuvDqLwo+dVMuPIktVOAtfAu7IF2fSSZMPiD3qgLgZuondIeDSMhisnp+XH/0Td/c9xeRWj1oa00bvvPXhUfx1vfo77Nq0XHvw/dcxSE0Ary9v4wFbfZbUcV+/Cu9H386EvvgV3bxafPn9KBiDFawAU4VWLqKo32wOC+XAj8lqhCuwzbUkyDzLa4lCRlQtY1hhm9rP6JrRS/c8cUIaG2W9RzbqYLrnXrM5leAQMG+bb9m0ZCefEl97B8dKoiUOOqJhesoXzb1mCCV1R1zZ17+vz5/nB4PDyyQ4OCedcPw6ajERj74zi5k+/9EHed32HMH0qLp/39K//w0APh4cY+IhHMI+QjiSPRlbE8vu3HU+p7lpkuCUxUxLGU0EfS67ruAtWarHWEoujIgrISBzXSE2doo4WdnPu0vXiC5cpe5uZhfcyyVPJX9Cnww1lE7Xkr8HiOA8bqkSAm8eRQT8Z8TBKdFnIVwKrTCZNcMfegI60iujREWDMpJHJFWHR0pd2H8317XAvW9jL90u7HxfY1fEuBy5X/snY7fLcJfmS4zi1SM16gMc27ebrvuy0kb/BKdK2F8OL9l5988tOc5tudh7w76O7EzEJRG+dX54f3djtUBHHwEVo+YGRt3OwgvRbi5nrnQzwcjrxTOVUxai3mX3bxhIUT+qxc3VLFr6K+qyXQtHelPVXNuFGDliGOPAVaFqym0EuXvzYy/aTm85lPBwMW5W47bs4Ho63fSO9xeVMX4LpyhuPo7qcNw2WGF+9/cJqO7nP39u0bTEuBiFCe4TPSaZwTiTORwDcNqkP0N7vTwR8OGyy5KNYwysPNNQLZuDTu3753vDttd3MomJImPEiqrPDjLG4DD0/0VKJs8taHQ5eoqCr3m/Uuj8rmSTRcWRk4uOj0kUdLCAoBAVQHqY27kF/M6PKgyTo8PyJCm2KOXcdCm3QEIH3ld2TYmbwo+poCxHoopKSJh6JNybR+abWmu+PhQPS5EdxTcDPO8NP2jhY7d5kAr36/eI1EeiQXPMeOdDWrt1Bsk+e7uQFy3z3JgEkI46F8tvnog6/87POP78bjy6fba65BA3zqcuiIw8F9qSS15oirwzFGRJGsw7DxcnzE2K2+w3Bg6kukcEuanHl+QwD1huWcVga1urla061Zak2I20XQPsdVwM3HNwTLL5js2glfvIT6Kt4ifA5ZDaXpieO/bMArw2g01vr5gN06elhCIOLtsZ756ur6ePVwPPaHu3vxz9R8xhsdfpN63hFtQdB5Ris+gao0+5AqsORiiCMAJRoRQ53ScTr6GSO0UWvGmE3UGwk0ocafhK5/iEqxHp0+Qi7VNMj52mpXm9SOJ511Iq8RsTi4feJ001FJfpU4LXzSEKPIavKsfYU75dCCnCRiaR9PKNl69r7URScancbdg6gdd+Fh7AOhOLREYH2TKyCpah4KQR3nAKsR2V5QLqzHuG7Fn5euWY+jHO6NXUteRYEvhrk4qU3XfJp46mHou367o7mLdCwHnO/B+eub7ZeHr0xzevXw9uSn675sqMSI5jwkA4DiUOylWUQct6B5A4s4z2PoNlcu48kzUG0HhzRcQ6OLCORc8RYpT0E0JQZrd/Dy5ttoQox/uVNWETAvFdw+//aCwf5DybBk17xXWNtBkjTTbBIVuUXdvXEhmtY2H3nJlPnxa3LY3BE/Z+k1B9IbvHt289TP7vBwPAyn3XabwISdxyOINyiceswXVjkSodVD0peUM5TzT+UDsmUt2kASLR7HeTz62EPlnwo1ZKYCJ6VcegRf1GRgT0rrLZqBNP8nrruuxOLXRn5XqXR+C1KvkRWD/A0FrzxdDdrHyD+hjGUrquA2HgOcKjnpTCVidjA4woHrCPsW6nakBEH60JxW4vxj9GgY4J+05YEpGfzciboZYwrc0tiGvzo0t6pT6NZa4MwGfVrErE5JEucafGiMbimffBYfcc77Td+5zQCyWfaEWkGIlMcpxR4DlZ/6pz/9/JOc9n3fv3zvthvyjrwuojMUZ3iaBEjQLs8PDw8lDFfPXrg5o2pBayLKaHKFouRFiAyxMkkVeNHsaEgc3yYfZ+qXbD1qV9oS1ZO7X4G/tKAaQp9/mdGcm/4i5KGqInVyWJt4A1+Zb6+qbrU4v8Yw3qWxs5igo89bmmPIuZaAYek0FS/mFx++l9x4wHDkDkRl77sdhm9AS4UagMUy6QLQzkOZL3dmSYpByQh8EDH80XaAlG/q5gPuMw98z8Sfoim74DyVLTYADz5p1rDelsaWSjZasmDbphV1OpTcK6IhiHylc8njxjaiWF4aHkgoiyctIfdjPXro3VKYzVfFy0fIsdghfwJ6fdmz0eDjvkd2772bZsStfQ93TaNK5T6oVZgLuoi1KQzHL3ikcEUaa33XJmbYnq0P1fZVlUwwv9tuFTsVneWZtL2JDQ9O2PEEEnQiNIl3JIeZ5BKDd90QvvryIwzCmzFa+s0nn7kwODSmxU0fd5seQtk+nEaIbW+H669+42VEhKGjPfgRq3HUNjlu4YLwB3Exl8HIGWC3lGmtaZUVxmoCzF6wOlV6ZxZN0ckMF03HrmDFmaonqDCDaK5EvboGt6hAamvEF2/oHV8tBlAJPJz1SWBHGTJyPIxpKLELu5vd0+fPD4973wXQLiCfPHMLGte55ECk1CfTCGLGfPTyuYpBItIyIBfnbj+Nk8Qs1ELsCw1+RPlMWNVwxzYCaoUgC+Bny13z4trd1ia/lhtKh1stoWhExYZKNQzEjcF7HshAea82b1Itl3EV1mrX8JA6PBrCV+M8qWhM4Aa7z+Ictc1hKlUeac4rPVsakcHDzRB99BB4USfC0VcDJDeJG+3uxYM1nlnjm1Zndt1SuGHw0gvslBTorH0aINIGQQaNu+HyuL0VoeXgXfQhkLRVngcXStfvMvQtkQBl72eQ54fQxafX3eZqA90WCK6R/Wrf49njlRsETb1DixL2xBIQfmdEa01J1sbQPorFDmotXkrOsh3PrOgC/Wq9G+mL9O/pcpeRgjwyxhx1+YQHpsOQFyny+U1dQNQkKOA9ziwsO7/IPgP0gHPadcOT3W46HmWuvDTBG9/W7kgmJ8FXoykBT50/htw0UkPWgOvoZZIBQnXZHAlKglAUYB/OALRsRU6HuaRQT1VdQQWbNVxsKkrtC9WgZToprTn9N806EbZO33V9JxKcMLYusnIbC3HyVDSW+9PnwoojWD+Ohwy8gGemvYqSJudj9CQ6Igib/ABfMIb9jTgZCRcPoKpxqdqIZezsrenGoHKtmcgb6Wvqnbdi403JwvplTYTZm0vXJTqgun0i/ILeCzdOny5DX6SdpCcaZfFxAzo5I+moFUGfJaLwi27+TU+JLc0K4PCCuzH4Glt8iT4rBEyfx0SIhfDDP+CjLOGuKVeDPF+0SdMt4m6kJhPjfzMKtUhm1p/IFsl8lCocqAevpDgX3OzqRxduzZSBz8+VZooqW3ATZcgoafxmGK6eXF09Pj4+7g90hRLUKvjOfszcHHfidlCE5/H2GvPS2ALY4FPEV3BxJI7OSbSM1w7ZdRA6ByML8ZmisrIF2ULkzRZPTIalSZ4nNqxhZ3t6ch1RiCMiRadYFxsMSziklIe+x1A+QqF5gAPhzPK0aSOyXAu5C+oM5i0NJEwHtYAtSnAUFgUAbNzvj+IYCuJk0uglkW1iIKGwDOvGZVDCUcE39Y105Rz86DmmEHFzKxWpkvWrT12yYA3LCVsKNI6YhxvT7F4S3zweQynD9Hh0ZRdRLoKAtc5X42fHehZM6iLoD9kXxRCEchKcjMYleF1SPrUyQMspWtX5pLUh9J1LmHGIgR6N61JEwm7IAm8Oh7gsLwtRk602zl7uCTnWbLxovb1/8LhoDI2TQe5wqanR+oP41Q1/dTnazDL9tb1eNG4LRvmxWNVCdMq5lubDzc3m9rBLNB0Ck5KoEYf0LKTjlE9k9hAE8uCodSD/ktGScC2NKQudh4MDG6KLJ7AdmILMo8aoKjxPoQcGeyJlB7IQTlYp7EXRoyX3C1qx+oHcV40wZB2JOSCDESxS5BcEaqPFa5Ds4ZWbYcB0CpbFdA7j59mA6Q+6njrWObUWIifXh8ify1Yi0XYCmdG0EMKm70/hxF3+AeOXUEQBfF0yhBpphglTwXAlOvHJsLta/7IYhD9ditNyW3x7PEN4KdJaKS3arAu1AdhwoecuLSWEBeBNt/f5eHu4n3fD5nqHkasgQ/IYHak/kvY9VbY1/ecSGgJzlJSgiAZBSs5a2PEukxf1TvpDDRoIyI9dlzESkoRXZNu2PMlqo5IHSSxETWNtB31FvhqhHqVJyYevQuhV2v2u0YMa0VK3mQg+rA1Wjl21zJY71zJ97cS4hJXVS1JFc1GJoi8WQ9dQS7i9mPpVRqAI/tntk+MIeRAcqFRMQlcfGwtlcdRuC0INle/hgmiSPdrpMoQKYdmbLjztodTDgzwAa0CYPzPkxZIZ/Yzxi3FMJ9pLVJLVs1QG+1XCkGaCFoE1ttw85urF1PFWk4C8CAW9FHXyxqXWQLx0Q9Fvt+2naaZ9C48KjbIZbfDU7Cm8fzWczB1ybCIM1CjKgFVKU4KgKu2zoQemhe9Bh0L9hSP2nDKRQziX5ooAvjiOlecoJA8FpQhR4pO3ZhMmE9aIVJqr439y4sCyPk66OfjEgZc/Fv+z3fDTv/je176780AGoCFLsbWUnCQIp2BbxMpUBYk3B58Z/C+L2PXYFDqFKvUtNylhKigFE8s0z/NZ0dD2AN8vxw3SjVy97CJqbv5ti2Ac9iUKLUfJMsY1LHo5UUVgHaS/VARDrngpkbaN2STD1Xxbf3/xpLDcxzBbQ0JkorxkouJ/qUEM/APkrduh3FztjsfDNE6Y2Ik59DzyikeNkQ1g74N5gRM95hLBJmSJBiJkcAzZQR+ZMN6MnSzRBxWTXUz5kLur23A9vd3zxG4W2Ch5S/K3RG6wc9WstMIWVuTTApJF8DyFjDJVsnRePq7BqnvXTJh8F58VwJPI8VL5Fl0NMYY5zq0+HeV/HONTSFw/Rc7XprCDHBsT5LiFlupMUJ8hSlgiB0iOu92dEhvxOamdgqu8zNyMImikg9AKzbS4jeqiS9bQxW4/7km7n1xxzTfdXTccjl/0ZT+Wa5uoLtRT+oYp1YIOkvnUkSKoDs4Z8lkifmCX33oQvXw5kWwPc9pLcU+ANLl2+ddxwZoi2HeVpVPDL9ks6/zRgAUxCmRg3A/ciM9JO0V9fRPoL1nYNHcHHqmZ+91gU5bJNuYqH1B/cdnpvvPLrySvJS/jN1PMgGd+AF0r7sXt1eHwuD8cib+AqI+BGYjJ4Mq1RCN+DO3aPvalo5gi58Hla8rzCnX2+CxtQIp6kkZan3f98OyzHx2PrycETlLIm4t3fa920FR/29xbnwz3rOm3Fx6czd1sydQS8+pxxhRLajmH4caQWeeZCGE4psCyJERH3IhG5SIOwsZJloQOWCTV8L3ciduhuxi+ukCkBjaffaJB4ZCDL76g/VgoyjQ5o0Z/nErI93z1TN81I9XiAlUieYaLxdCMWdfqkmSTXR/zI5FzfCf0ESKWzDTqfIq53P/M3bzA7F8PWilrk3HmwRhAW4uyY5XORYFUkM/T6Ae6XhYaVEOwVt72YekMVK6KMweOfXrLoGv3QbXdtX01b7r+IR/Xst05B24wT2tGblxz9dD1BrSZisMYxdzMlqqxcqnQRHd4pcX/LyA6TTOW2EBdmwpyq/5bOxWaC5IWGiCnnXy86sLTJ9dv3t6P6GUX+ij5XA+uP4O2VNSYc9rFkIB5BKjNlX6I4dr1A5RfqP3M+7tDClvuIRDP2WPXlN7P/SH/PzE8uFjypEkSBPzt+ozK3KRAukFr4rO0drYENgkq6NRMRTyIyNMirOUZKJAPonw4hp4QN75UGsTKzXIk5+MgjyfesRYeOSiV/cC5NzE3xMoYCeJkjbM7hNLUHUzF43o6NTEfn+tL85XkqvKNRJ+WEn6RjRR5G91/OvuNSrDCgHfcRCEJivw2pXke42fD1env/2b34TfztBv7FF2C+oIo+bXRr245tUrel9phyw3fLK9d50BIMNTIP1c2MNYFLJmOSG/c3VMTXr2N8wJ4hccUMViYbPNNGyAjfzkzlfZ1i3dd2jDhsIRVNqIhFYpS/VL5H/MglvGuIOjmAKmxgpzRZ3IGjLcbINgyxg3Np6Kvn3J6erN58fTJq9d3pxlKDqS3Ci5kQXs3MQFpMyIQpXwxRzQJD8NwFcMO+tkpzyPKu6lAXir6jsEGcjJgV282t9Px8y8+/ftjztNoCRv5PQOQVhiinJpwLYq4Gh5XYxMxe2mMMNycvZsAH6KvBVMCn5PjS3apBCbz8CUEdQIyY0MKCGNYu+4HcUmSIRIyRMvC10HxOOGykoDwMyARDtL4oskGNW2zQo8OSTMEs+6liq0b6XANwcv/smcgspiYdo+JCng9lWlJ7waDydD0+VMf/uSP/sdf+eCXX/S33S/+UvR+Rn2eKHSKTrThZO0TaH4iLlXz5OptOIE1ffP6C9nzSDFoSA2G79FJ16ZFy72w9uMq0SGBnGYY7VcVtuAy0jLRaN6ttfv2z/kDpJCi0wpk+UnIv5k/by2TDEu08FV96yZWrwSvxcq2d0h+iVyT4h+VcKKWLFN8AlQiXffB8xddjK9e3Z32+3k8EfOSap8FwzWoB4cU6SH1FOJ2iK6/jgg7SdJ5dqNzh+n+cHqM8aaEgfYdBdzAR/p8nP/t//w3r3/yA5zyPLwe16DEHbnmevQbcN5CVlyEWTxmZSJoakCMKyVHMhODykKIabmVj4ecSzkaR8w8J+qmUMgA3CTEwHRqseuT05aFZhlxwxlHVkxVYp4QTrm3atZS9QsFKr4kzmMor8HpUDvfbOiXsLUW3Cl7cIaxSF2Sj8Zmh6iipbYGc+pApKcCnTq0JfMJgkQdhYf88e563A7xf/nv/8Wv/sr+618O8col0GnlwGp0moxxfiGT49oyTYnXgn1Veq0Jnb2XBQ8US/RdP8d5pBkSfG+mXLn+lMYJa5Hc7v1doTVxVnQ6IR99y1toSl5rgyYeDRAdzACy4WhiPwiseRurHVo4vQLILkHc59Xf9Z8J/qyCGIZsclVL5C4F1cC17Od56PzT2ye9D3ddt398gA3PyIAhTQYBJ7ILhJ8FfYYxYPCSbGwaEB2G45zeeocqDRrMQNmE0pUrE6aqbFOafno63lN+TY+JuprIKzZBpWSdrYPRLSVgCh+5imst+vJkyD27P4eEk+Q+yAjJFIMNyyXoSEPQ6h6FYdKcHtLfKBI5FVJR0yNg1hVEHBYzGAuU3h8UWiUya3BIJVkdBiwZvRAeGq47o+jynDhtZs2+WvvgX4C2Tf6ea5a8qXgceYwRAxJwDRpYob8OIyPSNN0NYfPR07/9d//nh//sv51dEgluiQ2X3lcS62oAyobS3Wdorm5lPYws1a8QEuWMhIDFSKQOiB7Igp+hGwtf3IQm7UsufiklxhN/p0J4ci2L0HlRY+IfkdeVDmCLieF50dUP8nujECDboeLIzVUtKdpNSluPoGaN1XxVp1cw0wrKtTE6S7TDjIZtDnNwbtj2z/31k6t+PB6O++PpOJKuX1fmeQilRwEm9y53GJPDnWtUI0vx7XF6PY2+H26vb/vtzvfQN4sdhKCgCpByPswHyBkgYLZ59NzjbrP/NExonrbBkYpK6oYRuIpDVzot8doYMXmcTykuoVIDAxVsGHCeUdEBdk4/5BwZ7Q3UjlWhTjVWxXs5SpeLq0UkPUpE/L2UAXgvfikVLG2Kqj3MLfFZQlDtZ1nYhvzGJhtyWiTxd/VI+A49EmhvpiAB81yIvkqaPh5nVsXnWX6e+gGhtH08xn/z2cN/N53G/ehvQ1eQX7S5oeZbl+2p+UXTYVF/yevVAMA2OMhAQpSruzBFIGu6qJZFLMZHCVdwsXAWHjB5UXG9puBGpacOXl9IbAvyYnv95//mESo65R3vR8wTdN4qI6/5S7XeZeZ+9r4NxFqjkoXPxpegrboL7FyryyyRKr2Y6lx3h9OLp9vQd2DuOrdxpY/hyW7LM+kgZTRPPhcMOMMIlcmn2c1TSflwnO720yGN/fXV8+vbfrPzfQfKPzJKN075RI7lKs2HBNFzUpnVkV+SKmuay0/KuIRaPFC6vzxWw7m4cV/G8/rSdRhnyYRHqjJLmKlzdMiHFhAeu4hpQKj9SoEHY+g1tq5xn5mzuVcNaXRzatmK3B4OLLNORM74p4gKdHQbXJWXmUt6OrSTNNqoS1oMLOprajGWYYhUpSTTIgjO+IVkzZmqhgz80uhexNJcXaAq99F3b07jl8cHV17S6jESZeMcDHFYJMOrbdo46TqizX6zaBwSOAMRKE1lJsQgRsw5aFIjPSuVXFbfzb5bNwMtjcJiOrxTd25GC4tZ/to2og1oYdMkzSQ0gvPKtKeXMTnkDRpya0MvufDpjY03xWIi0NOu03q/bYNWg9TOR+pA2YY4HSZ08oYc+oChVg6UQ0SfJIuMl5KmIApFocwlnaZp/3B6c0h+22+31zeb7abfkpZ0cBCpwXgL8C6zG8Lp6bRPgHiYQV/jFaYfN1tEC1AN9Cs1bd/yBIyzRrkCXLlkgkCqAJHTNBOyW2v8gscG/ImtTIN+wTuhICAkCKHL3/MTEB61fLgsHl+b5R+cKqJcxAVfgbvw0ejLoyYnSd0pEJCoU0+rtjpazVNP3loyrtuDsS55Tw77HTQXIHIiGzaXRP3bVNKHOhfvRdEwIHseZxoQ4SM6dTCBoZsOjz0x7YSBdrbHK7TW2FHLndJZrM1LFmixEEI1a5aJduxAqQuC4f1aKL8cGjeUi+ZjxKtba24bvyFCkXdrrbsmtZe/SNcN06wEXFY+lpHMNH9orUse5KKo3I5GaszWduTKeqVuIfGzvbqqCTXvLCaNAQowSn+i0D56lHhdCBiPiUwWvd7opKJpKaG4uUyHMe3H8HCMod8NfdkMw67v0e0AERYdrcySHS64vru5GseIRmFwoSuCrBuxwQBqhilEMrEenUKHRySQEM9ApnorD5FRxE4o38xDJgEq2ij0CXwKCL+FLLxndaZa8mQXXBMliRGoPN7m5hLa6zlPPV5gBcugBBFlFuulk4vfU722IbfSjXYBXjFCaJNetGAA+QYyS5nYy2TkhIZEzFRgEmLsRtDXUfztYgcx5zSTwC1sJnr34Mvw+GnvIKtD1GdpRNBN07g6PSbMrGsut4gn5bLrfmuThmb3EgCInBw9W8qpvGS6bQ5d88FFqanGAmrGCv0sy0iG/Va/aFWR+nYULBM+zgrxMhW+AToUC64nlf1Oa5l6tqzz3eXlVJ/K1gujk3NUCW31zWw9lTwrYwQQOnZgH8ZPP3v7ZBtDOXpqtUHIPE95miDmnCaX3Ku7Yyr5mEoA+dltMQmcaEcdrFel2IhTjRg3b/t4/N735nECQ5HzkCYxp0EfC0hVXVvLxmjoHXqeSeVWYF+Ju6EmJ+9G7l716yQ94uIPJw4aVBNLSTgeJEqx/OB2zajS2Wij0ZPXcY6sBd13ESrKhiEicCX4rwa/bYFXEUxN2tQTq11INlf5+vJc6QJIEUTQZzJWSdX5LJvT3EVoxDIfgC9gmqdCCvXzPG0GZM55Tq98zPefZFA7ewb7Gj5IM+q2uoRKT2jK0Bc2qM1kMizdCHZqaQgowHUZBtgMtaZbycy85iLZrlhXfU6rZ9XsdUpl2qT5zIiMUV0hfFccyQOD+ErTNXVYY+NGbT7duVhAGzEvTp0LIQVvnWrsmu/Zn7Ul71VUpKcEKzOTz+yCf/Hsveynjz9+NT3uwSGaJ5dmdxrLiPj/xZObaQZp8mq7oW4XeiCgdXQ0UZmnEPIILLg7ahmLV2X+cZcfwJponygDS1HlDdUjN5zKRZuLNPiKhWk0y+ebmMsAtXHiqFAj0ThxbMmVIXnw9c0pf44kNEtjyoixTG4583xcg4AFqaL4n30mXRC3NzTpJ8TN05hItAL7EjoK1AxE0D9B+rXpoo2rasDUWLqgwZUhLiT9RQ5iJX3bMFzhpnVIQxdGNN7HnKGAkWkOIVFK3JznCGnrcPSb7u7ep9LHiOag7ARQeud2MyhAAaFlgtxusoWhaNgl+IuyuVGmJn3fceSGEJIoI6TXTKzd/RbTr0AjAwX1v+UFACDW8b+8eBFS22GBmSyQEjZuBj+RarUiraY/1yrmeQzyc7/qA9f7pF25jJ8boLuGz4u033TiePQaRm347qtf+tp0POVxzPPo8pxO4zRhlFly86YfBHygKokMHEE/MM/s1c8gmAJ2hnJxepPHMW1k6AVD0OoXWndjQYiYrwo6sQPWOJmCYT4sluVjwMyQDhfIEvbD/YBKMCMwQohii7qQ6MvDnwoS1pCSK4dZb0/TudoUyeAZa3okUCnxwbObAbPrGDf5SzVd8Wrq0FdZkhI8TILXIEyNrFlLjDMCKnAQfCVJLAad5lzGUxd7yFJTZxJ/ImPUNE/HH318dbX9/p//xVd/Z1+2GzrK6xGhSg3n2G1Nbc++bCefuzr7zqgxshvIhgdVjcbPJnxp78fyfdqF0iyzjVPVPWuMDSLHomS7jBfO6J4oUQgBqxnl3CS9FkTRwb28ODX3S2tjMUA1yVXiJF3p7b2w11m8shasKs5FTfg0XoyGCg7eYSYYPEZX5jSHvhs2w44UoyjhAyUCpQsUHkguSwbzyFVxDwRGkATfDTdzPkKG1ZApMBzmeQLLkvxB7cDQOEtz4OY7vTUjM1KnNTg9jGrzdFr+e4g90Mxa7mhBZYUMlP+QdLLEs7IBsAGTNcCNT6cRpKW2u1PtUwxKTiEJYahoJGcpP1mgaFLKqpgQF63IjbMKgPxXPeObQhr/W/jPtnWafcplYb7IGsUwVkf3hSlY2914HPlzLSRmYFxq0MXdd/349m4z3RX/nAY62KNYbr7Frl/SpuQVFv/rsjUkZisN66/a6JsfhutAEqK2cW5+xCRArmC3d75sLVpwPBbpogU6nXGm2mx8aXdSx+T/cP+vDmhvUt21uS4c43KZ3vVVo+4m0taERPyvSHCYTpBNPdYfKwdCjqOGs4ghmSim0HvBBnKO0CiPYQCerDJ3XMqlcwrhIjAuuGK8D908VT/Rwo7O9s3Gp+lAY665l4AYtzT2AsPHGGEy6KqKEDStakwAtjoiICWeJiimy5meNAzR0O9qdVA1PmX0veAwRplMaO7Sjk+q1M1Skg5W7DBLic4col+po6TjQjhHGrsbR5WjQlbnEVHGtjWWXkAEr8YDL93aMgM2z2dhyZkZWUhA5ETeHJYZo27ghLyj1FoLZ6RyilKnz12Zp/FhKDQpkpWxmg13IThsYvnGO9d2sLrn26pnpXdYdb/eHb0KaD0nCozbM/6rVrS4jEVCWNFnPQ7lIKFuJFn7y/nv6t9MTdKwx8gH1LUh17GoGmn71oWQY32d+v6GX7ZXQXPipX9QYyVm552931lC3agWSh1WaqlyXIP8HMDogA0rGEsGXGImZiANA+Q5t6QfhB2DxNLlESG3S+6E4dBE7uQDgIUyB6gNs46kBc8S69K/REdCH7JQEXTZublPyGUgHnE5B9rOyGbpFJVNsRCp45ha9PlEa1aPa2ZZxi7kqcFgdeNx+w3VkytBqum/RXRgpye3H7O7bR+VVIBFtNqce9s3Ss9NCzp2YnNm1GwW8dgytYgWjzd7U2/weBqkYk3UVp02YfFDwQSUt53zxwdAHkTa4gp2e740sUjdis0OWgxSrJCQxBeGOjHdjEmoNRmuYAw9VWqgxkWCpIXNhDEtF0PSM1ux9dMCIB1bJEXeLKzlXM1DWbyLWggXKQnYl5mzyxNkfUXLFOO8QYEhlYaLY0vGq0v7UOPni1o9EtdcAvi0kiEcdgkpFDgJPOpZw5DW9TOyS9IdfHWIEXMO6KuDCA+jwiOzIRS+oOGepAgte7+6YA6X7Xqtr63ZGfScFbrhdl/ur1q4cDouCP7PPSIB9OXSPmGNWAo0xLCZ80jXRHcFHAsStJKeimoXNfZzkYpkCtmGSFtEvSr/OdeQ2x3RJk30dnLxhLva4xIfy49Hg++60Gr9DVlC/X9D8NSwFW2+uFgwnSYMEnTgv5K6MAmLaLCNd30Vu3L/ah5H4MIYCi8fWuEzO0wE42/+W1xdLaao7zOw4JLZ6Fu09bK6LemW+mEopRxRkqVybPOHGjSd+7x1UukyRqtI3Nl85MrW2ofF8UyA0hIzXxEDTJmmv5sLNlJgwyv6+SG02HTNwTSit4IU1UL4dD0P2u1M4dcqtVsY8+0RJSxFrYxTxlsSd0VTmcHUftueL3YRNP+W4SHSaek7yNYeIPlBgYxm39QtgJbuRZQoe8XaAZTFIQ3lNXbRE1b+hpXOOOig7JukM+jTWCZRpytIgcecurkHPkb4EfAqQrqcmmg5MtefU5FUlK2bLcmlXwVK6MBLHASKVp6lKnyF9I40lBRxgt6oPRlLiulcrECO4V+MnlhLqh7JqtpDPBY5XSNJ7ckJSFklpEJJYYRFl/KY7g5588VPSpr6YTihLbM+1TU72UhwFXprHbXlohY31e8rHFnfaWVMjTdE02EcNpucy/F4xLAL8fONpILG6KtKkz0G/n8w8tX9Vc5js+nNN/IX1pAmTvR9pJCN4E2zP6EjL2v39YCo5LjmneVX9YMtuRZzoD4Y7bzRVsU2pFh8jiFY0o1AQR7nG9zmT6PJaM6Pw8KBxsHFIWM5iPjicu3VAJlOxapqqC2Nh+MJs+7wtvwZ1a1V76EOzWIAjuV1K/OnyDwdCGoxgVFCM8TvxgTglDODOAVZDFWbZaEQJSHSSIIW0pDzhfvVIaMLlUlVe+IGfRaaZZu0P+cIhOMGxoA5nbF1lgTe5AUNxBGJD1P8N0OzRKyyrs7XWp+hbkxZJwmkGeePNMNN8TC+NXwRSxCOJSLXKGmIP/izP348ngh1ZAReN58+mvYp8xprcWYdWzYXJ5fIny5lBnlnDfQXTIXmHBBpS/jhfrNh6Ty5SXmKTefWwg6bf9Fek4SseYXZatOmr2c+qxAxRR6ACut324trsKo2y9d9RsVZXFEjANCEErxncHXUN9cJfNVg1Gbodv6tY3g7TfT/my5HRnuqR+Djs21IqUWYqotE+5IRV0QF8cqNm/tXUKNcvVY3s+pzCHRR6zrAt1jcRT0zhxAL92twDp/D8iwEbuOBKRr0sEOmLiXXQsTGL9Cb5MhEG+R5lU3PlMvXxOAVmUSTSsSGoU8jp9qsnLIsqO6stVBqQtYzV1FF1WaVvjHBNShtNUq4/L89ycUstIqPywCNQJuDb484i3KSGKQ1lvi32yen4sp0TxigeO8Kn6gHrVHvYvc2yWT7VM73s01JasQr6t5bWAE/RxRxuEElQmlMMCB+HZ8gi0+sG7vxfugHVoE1W7DmU8xcJA9T+SV5BeNitXlwEXipb1yjyg2UZhmGrprUt/iAiKTrRzVRarLRuKU5mFZoQzXdJsQ2EEXWBzJrreJCPSTtgGhnSelhWJ+YZHizG3ab0F95dxyFucstdqR0YcPp1OPqxjb+s0RI3Ihrw77ViMgPqwgGteKw6A9dgHpOKpbWkoLOY+OMAxGGGa3cIMA4pl3wjcjH2Sg9GtSK9Jdtj8ZNaiOkfqYv4KES4kVepEkUyOUSpZzeDAdBFzNNKtEAmc1TLqLJiVZMIqFCVJaL1pU56eDrTzTOtx+G43GPSIqq3DraB8+CpN/T1PXumIfxLnooddYHu+huXGDMa7PRtKda+tKudQOja0qqRHzoEhHWXlpZCZyT0V4H07Lr0Zk70dWtBkVbQ1f7M4Fw8ASESrm4qJqw1iyEGgilfV8XFJwSFeUw29EpEAvDas6EhbGZOQtEpDEbKbhCb0E0kNEhdrHEtXrD9vAxEzXrbaJ1/p/lhdaWa3V2KxKLHmkMRnVls/HT//W/HfZvD7nHNDQLf3gva2nEUtOmaUEuTgSLpb1WnwxHyIxv6eoxjZcrvICR6JiwXU7lvVqMZFkazeaEiu8h01Xbd+j9gWwTZ9PKwjLVlAnYzKxC8zMRrcjUHcjIdLOMB/AZJ8UQPlPoiJRP4ZJ5PZx1x3NJTisL8vSlcFXnuhtLcQlbWkWq8IBFrvrxIMXavAl0iHse06vN7hvjfibsS2Ln+lwXHUYXKibmNGxvLdhTjdodjjtrwqTnw3ImkjmIMcojlzsFO4EYr0POZZombdNuz5ZlzK4Lw/g8+L1i54sAtDEvjZDl6K4ZK4lSCq1/ZZ9mRXUNmjJTQ9luLk3DRSBkPfoHTNJCokrDZ9qVtzVtmhmab5qj5fIBskzK1VT1gjh9MysWrhGksKaSulPu0/ijrUsPEI6ieNbGBRD5yRR/6UfKHKahD9QAYMRDG1wuUqwE9rLEtBp13VAVBhCFKi5NcE3FKriI8tNMKjf4TGkh5li3gFXNA6Hwcu7AW3Qz8EtZJY9EPCjHpp4ektKS96G+OXr80khEpbQ6psMOTeUONYJrDpzcTgpmMp+lHmPS0NCUBSl3YGl6pm0Feq4s8kxDoRIrVMoTzWmeJjSPeTcV37/9nAZrEBl6Ne66waOWO1l6kuQOWlRKHotqiFQomFE4Zs1TM4bOciK1XYuQmboquqrUuBJzT+2Q3FdQh5AsfGJrYtQBhqFglxL0xrq0sEnud/HKdSnoQtLdvuPCli78giNNFhMiiEX7vRv7aT5obYBKVqnHhEEVln5YTlIjH5ZNsTdmQjHfSeWs1XODnWVyno7zGJ1/yHmE+BtNUTLUR7JAyQllKrfCiwY8cFbMyyqfyVfY6BSwSXMva23x5j9vtEkCrqoyEFQQQ4enMDFRYnMgl0RjFo/NYbMAyNTWoCwuqbnIWCB6jVCvucSt9R3T1bQznu9pwWOWO6skCbxDM6d8RRikuRCoeDOKyV2+XIDmR5VZ1oNJKYRdQ+NWF40nmiXny1zuR+dffZLmDO4/rgQ1J36dhTjrLFeZDm0yqp7UUqO6BxWea7Bted4Yti5nHF04MhDFS0ilgBkEAHq6OJ1mGY4pLTnt8dIc4kaTp8P7LDhVWMi2eZsqaBzfYld6G6vIuD4OOQvsrQwLXOYToBmgM9uE1FoiYhtOLKxXk39Vw9CPUpmu9qX1TyuEtr55bSUwEL9+OmWENASvpcCaaQAALE9JREFU+BTBizrmgeuawqoRx0jArlvse/belrvy9xJlqAe2dbdCiuG7bZ7IejR2e23JVx5vC4JyHCple33DCiRLbakVepNNbbVtzQ6oPoQ7UfxJL0khxioxoum3ampZR621Fkn2wNSgtkReEz4Cxip7nDFkwm8NL+pAiuA544xLVpG2DrNjiQsylL//03/r9o9unnvqfFCdr2afNrvZv5vPsNhL5qnr3qkRQ937pBLHZVvcLbQvmM2YCS5H7h4w1RDJMEs4mMGoDdb0TPch9YlrU2oLconPMgC6Oq761woQL1ywZpZ6D2fWceY/xZzrYHq4374Hqk7Hs13vpfT3HXGDoi66W+z6xdM2v+IXN5nw8qxVe+V/1zIztf3jH6l3rkxHSuKkY1s8f8E91LnbbROVMGBYBFN1mMwF15ldmvxpQElfVlvie5CiGIel0uXQ1kY4VFOWXD3IuA7UEJ8qeGaiGzyUU8yMJyjJ9dMLpNvRCCe2H/QYtV1lsu8K5El4aAeziQ2054WhsnxaLZLhWmV0PB+HL6TdfjzcnCzHz77/frcd59Np2s++p8nH50WapaG+Y+Tu2T6pgOdio9c00+K/prRDcnJkw3ghK1OJTB+MmHUa1AUtUj/dJ7RKJOLHEMJit9fPXNSTVjfKT7UuRT2q1n61utyzFWjhAUqrYoc5e+IhpBti7R/t/cUU28tqq4bVfO2W+MlYRnPBLa8uslY1+I/5oRu+lJ17hIz4TGEe/5D5YkMgXVbuvCdjXoZo0rEk36laMF+t1VEYeeeHkaxTT9eKFIsr3UrGJkj2vvBky00gi8fiAzJNqkaQTW+QAWCGqZttac2JuTX2aSqOS5p6+ly08i0v0ffRaLSmXiuqdEWazKAVTacLtrTIkyCB8R0JmyGIm89ZKO895jLkdHW661PqUb+xEbAWdzQBiBZNz1Bpu5NVDlcjSz2+bM01JJSsRn5F1ygyGLmg0xs7iNgOHNi05LzFbrRqIzVySkO/lXAbH2qfL9bbnODCTSWyUSROiZuhdZLOclK3vog2iG9CaH0QIBggxaqZyQpUWHvdxeG34GXVq6gFy7Mg+pL9tunvMuqv701TZKCknufxOLo8Zidjw+g12DfQfBGiZNvKZI+fYF6ZG1i5Pvw36s5E+Iq3JoXnJtcb0X4siS4TlDKCSQxPsEoyWxsXjTNEqhnvleRJlf3rAccThWU4KOeU6v0g1yqXzjgqi3bKU+PBQ1bvlEZi0XxqCDrNGV9xWQG35EIWoqo1bbeuVEo+UKzh46yQhFAnmaAeDRweQ/Ipko4nPYFj358e32w/jCXQAxJC/QVP2/iCcz7VxZe3ukDL96ma9vWPjXjJ3fRoSfV5xpgyJkYFjOtaukxLg5veAtnZXdt91xiyoWq6sypPoz4FplhwxAIkU/Qb5PdtoFPtobHIys+ia4P8CM3gkZa6hue2Wrb2nwINa/tp/fGl8L4eIuapWp7u4sXthzWtcNziXoD4x+APDw+QjgCCSuOhKWCOOIiEMd/0RsvncmONujApuEjxQ9Smxa82xtXA4uZlCX3FbqaOI/ZI2NkqfMVVnHYHkGkJPsTtmVoVtOeugYBGBzUHa96plkzZetsHz/rUeqVK2eI8xYZsEpez9VlN4HDhWG1+wB7IyNiFrh7yBs3jYu43F8/l0Mnu01R+8fgm2GYhupueQC3Ooh/YbKULG9CiBDNhQZ6b9LmGpk1VaAk5ETycuHAMhd6ABrIJwzR5DFX7wlXbrJwt3BVSHW3DITGB1MX6Le6nAU5+fji6/mrzSybr0jCEYej1rc4KP/pn7SK3p16LwdTXNgF9w8dYdFL7n/e0NFNqOwCdi9mHyUWfro5vEzyA0Veb0GtxNTUTscyQz0nxhNoqZMgh/5cZLiej3DPcsEUbA6dtTRo+tH25oNQuVBugSMGdr0QzfOkBaD9a/kZAGDsAJaJtIV95qwYqXYtg16fA4zyts3JxiReeel0P/j1BN02I6lBO55thfUWaRMmhCp9Q0bsyTe7h1Vy4f9hw4PbamydezhCheg9LI20S3erxdKVWcbD9QP7EDk4KKkDFxVRnStFq3ipReU1rmx1FEvz6ayUdqvEqXrCInxe3KhCCve2iWFlxoPblra3ZauErxm7YbKwB9dJqrgki7VWtIpezxVwuoybC7ZUaTVlsuDGkJsuTjU8UoM322bbkwz28D/fZmeuSxNI2dy1I8+sIYjEWsHIVJedrqeD8CHi/8anM2TQRCeUGGNokpBNNFNyKxJw5kaGt8WpVq+IPtaTTIgxzrXJ20dqYWOzl1dYDpl3nWsiSfzW/NE66DjJYYxnrWM1YchZza0BfoMhAXaHQKtWwWleGAGlq1QofhyHf7Wf8C8nP4tk3FyCP0G7UsP5VyXS5/Za/W/zLMKILfnzRL6e9GjqMvDVEcQ1NSm3mGbittIkxWx/cMo7PL7CyMFqI+uzrsme2EBEX4F0/9BGjOpozQ+9rPXnJoiz1jsulNHRD/6S1+4us7HfA3GevkrJqB7AtX+36uz/5N93+1RHJLFJEkdilF5aFNdOfL7aG4MDsKduoxl7LLcnan7VYSTbLijKI0xH5Dx2WKaeNrbO8M7VJCNOirRRUwnfN2+WzK3lQ4nylfWpgsjoNBQCrx02FqHVDMauAu0us6lMR04aFbzvQgo6KbBYy4BlJDAEKUlGnO0DCT3hAGGJX+uHTTffxn//77v4wkv5gbUyk82C9L8o7Sc4X9/oyML0Qc7fhX/1JDcSFaWHikKQVyZpzgk2oDdeTkLcty8oq/6ht02ss6TwtqXkyH/18CjR+8x0FtLo8RqKArEQXN5sNNye2QZQBIKyCLT8y0Udj7dj7LlJZfUB2RRVqbSz6Qq+jvotVjlTlUrpMQ5hKmb17Grqjz1MeKk+bdwQX/bjOQw5GZrfUxJKm7JEXqhqTGqs1c9Xt4isNyJaIXk2jEbhnOGUqhnKZtD4mi1KNAcX7wMaN8fxMrkJxa1obPVuMwI6TQwdmjWr9lghGTbdIs7tIAZanBxqptY306lpbZtE8n2U0XTm3S1MorLlgIxG0007wcWQWuNR9v9vfv03zm+KukTZra4hGSHK6LQvBlTZpR4ixzSXxWXKjz2CsNlfmrXzBtFWxiGuLAqBkcNm5SEQBHZfT9fKMYA+w1Ha7rnITeLZR9+LK6lsZ0thUI85eVy/XwBDt9MTc9yEGkoNROrRSI82QLXpbJ7mL9WnyqUuXfcH9rnJhe0WTrMjCMDBDmQ0lLSS3/4PtkE6iEcUrxiNdUFgiryiFlYzudvYSaJynvcV1QMR+LgnIQ2gQ04nYZNhyiNgCkAyTL/Bc8ZaY7kTBKPw/CXR476cJIxhn+iyae4PYUgE+rC4fGWz4TLqVI746OV68GtlK3SJnCOpxLyVPOSD/gHHoDIZx3k0IvUSDIu7Mk3jMl7IH5jqoLO/6US4Q00rUNU9cMxx8Ud4L4jMpvNujhNI9mg55eB1uPOS0f+PSl2Mv7SSL0OwduhiGC8jV0Lgeer2e2jXPW21/9RpNbV/r7he2IS8U1I8jYFAa7AXWFtEOVGKs0Qfgc0UGfNfj2TbuWe7YPN1FjGuRnt5IY2PL1MGeRPUI1PiCkT88jf4dH9a8bXv2Xoh65JSsPVAN9m6HR3XHzXm7dMR1m9hYuuojkXdMrtxOx7cInaGYZRNE+BMTJq1AdpCj2JQgfUldeh1mIurUUdrKPEdBTlQIVHN7t1w7NaZ3KNFJJkYwFevFKoaKo6S4Muc8QWuWRgdGDHAYp4loqYIXQRCP3aZ+uo4UFSa+PlJ8aTQuyIuE9PR9hymruB7Rx6LDiJEz4Ed0fnEcwPuNXsWqtfwlZQsDn42Qb2EkX4kqr/F7NWM9DNkt9A/UUYnSZMVq9UgcEYlMQ9e54z2RXm0MqmTUbUx35hoWPrXx2zVuWJ9CLWay3s9nsXT9DVUEMUGSdMsgWynnsoyra2mBGl62kjqNx2vTjrMLMJ8YQpgxZFcwjiZQ0P8sRQ/s4ttRkcRziIs2qnccHO8MzH/uD1eHiPi1CtFWa5crWp85FcjTciSjSZ2b5wfWgLQQgd6Edje190utkYmTav4kCGPwPrCcAKUe1sSDh5URpRx7ozjIvH+WVOKZiw55bF5vIDIWZlnSoOAwzZhrTmEu70+h6VFMADo08AdWJqn7WM8Ok9KqGxdXzRNPCnBgkUlgaWj+fiJygvHD5BRgUI3eyMQQ5UQQe5AHxYeAcsK0PMlDp7i+y0ZJC+BkJ0I5lOvk1ngHxjQX0ghBTwVDNT6Pwy/v3/QkcAb1Fd2idqZf2G4aLy5CYYnjBb2ri9Sul6zmeW8eX3KD35ijoO2F/qoQh76fxxFRtFK9ZfqrHVA80b5QO6Eci7qyTRR59tGtS6SLkxVvIpAlFr0SLbGlkizRigr2kfYiA+CUetL6zMVyNFd1GY1aXcQCcmzXkd9OKMn6dzWVrvg4tf2kcjqM++NpGkAFYjXHGp7IHxhBkaBP3ZiCWmjxUDYavXfioqqoTOF0c8Q8sLmlaHFlzj5LPpmQMk9SsAQVD4WCW+xvlmf31Kyr4Qg3ABkNs10gQeQXYY8cPWbITLrg27QDX8d0qIolPsJke+RU4qnErFYrtS8VlNYnSpkHYnOokAOjQpICSUeBEngZE7l7y/kkSmU0hiliOXc9BPezK9s+jo9uuH/tQ9dHfxIwxWCbZQG83Tm67SrQUfdbg1cbYmJbrL7VuW9ahIPWXmDr0cXQoTlptN3Niv7yvpz90JOUfmANWyp7cWUrjVFaLxY+jIOohijWvrjRzVZ8dpmuMn2SNYxE6knfoAXCarGjXQhex/M0u7HX9ihZhsd2gta4d3X12lpgIbd19pGmUPC9n9IXx2k/xtA3QoXkH5DUkmsTCaYAJhTbAf0EaY3tWsZ42CYQPctQNlrh6BN1yJUIfoTgTwEORPBdfnasQssNOPRzmDz1QFERiPvLBYqp2jxEvcGfwsdLzCl7mseyWfLGa8L6nOAdYKxnk1VIaRpwEnFFtClHjmdiO2IUnES07GsJ4CrMLdEvULiAhlB+B3kr9HMhOias3bRBpIYUsL408ay2WlOxDexz6pNEkCLljbl7COH7f/rvfuu7p8n3EBU2IzPsfanIsixwmX3a/I1LA8dWcNzZiaDn+uLYXDE0SCgXTQHjiYZOGFhMLDjmotib03xg05pa5r7vyOrrLWLtaDbcO162MImVb9RTjePnikov/rLNlfXna18hJtzCV+sPqQny8krObrDNiqv1SglAymsUu4SwKd4dHzYhX3cASyiAw2Ppfd5hpkeeqQMOASZzIkn3DqbHkDQ2Kyk+4oeWFymhwnJiihtpLBtBGTycQwLFlvVODS2k0sXCMhKR6h6BG8KTh4uegQojrCdHzro2upa2hwxFlLF6bBgihNvBzPBPELQdjJn0bXAtMVIMQXsqR5olRksTS+4o1iCasoxxZJlazCBTaBpJmTZ1EVmMSFu+y6TmCxBcSJxKmAnUtg/nxKUQ2k+x+MjsDvopvMRQev9Jt/2N02OaH6byPvJ0QnsloVsAUnU31B8sPK0sEtnvQgKpfal8XweDr19QY7vl/ualgFuLEceeBTyiY8JQokSJLOxe0f1qRJc+01Jk/seMGUmmLr/6qomFxgnnv2blJGYdrhy42WXNoNvragtehgI2uYz8t8z1uSS925h1DYzaz5Le38XPaSdjOCNqR+7h/ve6+ZvlEF0aSn6L3nKolvYpb7wfgS8QVZXO2xPtpwHbPfS+bF06uHDj50ckrbGPHqLjdCkmh0dyqTCOqaRblw5+6LxP3l27PMJUaePSQTCTWW8ppBmCn1yYHQoMDw7uLLm8gey1m3w3lHQofkdCuofit27Ozh/AZc69d0ednpYKRAtmnk5Mui2plMHlo4Oq8pVLhxB3HvHC1pVH6osevANqR5YFoM7lexdJWBt227u8L2Hn0gR/D7nmUtwEHr2bKDOIJc/eDy5PuPJykp4t6Y0F8zK62AsJlHdy5h0QS4RKvwtQOU8jxqJh8XtMJ4zD0M9+iu6+zN397vaj7Ny0x4HIAbkez6t9Y/Fy48eqpdaieosQnHla+VXris3XLBPr1R8mDMsE1oAhz3gENOiNTlMtsQvBBqEgYV8kyGAdqKuSwoWP4D3O8bNM9G6MqknRm3yy+XP1r6bGLb+oFnwpMl6sDBmYXWxrd/ajmtBfaCupYcG5XavHaXno9kfE2e5BiXenflO67sMJ/aVXnX9CdwuTc73LZQPXKppwpMHLgh38NJDbbSBOMz9hrGoi0IXQUX5c2fmeKCJUdy0bXw4YJJH2SCBTLH7jRlQdSihdd+3KifzihIw3dQVsk03wu9D3AMzxiTNMJXU+IyinMGKLiDtlF69gC9n5dIPgnxDPAJ3lFPoIsxSpPdSZcxhi2YYQp7ChE6Z3ucOAiJCgMsQt5hgI15VyVUofXEKcHIsPV7PzZcwz5QTUkAgGOXUoY9Ew2xWRxIQYhcB2XyJNLZ6B+3NbBWScOE+YQ6TJ9DQziTYFsoHgNjhocA+DzyX2Uxy2PofxtB9Tnr/AZp9njCqMkXC4pum7Pb+bzdDOMF5wbupOa/a24dimb9TOH2gjyloCkJ3bOGrKQUQVkEN9xd+RSLC+NftgjzISF/FM3OddprOwESo80JwRwUsb+FL/pyK9lgs34K8gByKtuA7DlxHxIhNZWV/L5qgUjHck8OdfXCG1ubY60aeWCRtPzVldpCFh7mo3fPR++smL/XQ3lzj5dIBKPuBk6jjF/sP2kkgRfgxKcIxcEYKKTKx0nOEJWF1JOLiSrZ/SNJMKLuKmEMaIv/R9CDl2h7RNfhpS2qfc+zwXPMsZHrQj4CqFMvl86FzKAdMVMeA8Tx5qfOS8c5lIGyh79CmxVBMU6zLMBpOxuUlPhBV4akSZk4tdpLHDJCkeusjC/tPEBSr005CBBVdmSooJVksztb9RlolPg4fBTYLKOzHtM3Yz4KWuxI4U6lDzdmnGusHaEcIXn6PzE+7UJ4jEBmxgTjSoiS3h5AqEI+BTSbCye5zLiHLpA4Yz3rzot7f4bB7QrN0VgtyZJ1qWatr9Z07Boso6kFRJqs2Ll4IU77at1npVmIV6YjCLgw1ah0gRmoVoj746U2Q3wm6bUDf0pfaquPSA2TNneafV6NZVWhtiZD/HRAR1wHYOnV/E4l0aF2yRAP/3skNk/W1zwKyMe50wXMSx7Xece3Y+zNi2T7rNbkyjT/0JNVHSd+FRM/Rq2DPJywl5iVtosTTdjDXqaQCvagaKSiTO155oWsklAqNmSvTINTNyBSeckwtTQb0/+4EGFkPoB8kfwTuUYs7JjQkOmJJOSqVofh90JVnQmd6VsB6yUB6gmqkuQ42KOO4J8wTwDnNK81S2xe+4UzInKm/QtGSWsQUoAskScqosmlPl1+BUEAjkjFtDLScM3mMMKNpDcOklzWPGFVKdJEQfU4FBzoRmu4JEw/mIcVUKBAWPsnymUzMVj3OK4kxMzgipuJOP/T65F7m/f/7V5//1f3m4ehHg7HvWn7N42J6+bbLaKNgmaS2DWadz8djoitIuXHQri7PegYqTtX9uMTbRsmKcpra7lSNfOABWAqccrRkt2xrkOh6uSCVhDyKSdSm1XBiLUTn1nND41rO8uOlucqLRtk1pV1p740ua4YVX+VZjUmkYKjBkt6ooNwOvrPO5wKXNI+t76hJAWg1Jmyu/9k+3P/jzw/2blMeAIRdyJdju8qzhPYCVMEJEXgwWjpoIVXRhW/iWwmr+SDlZEX6z8hwJOElpl+o0DoAQJI572uIzpo9P1NMnij2iwQQhyQ21FyLEQiMty1QjK6aaFgmDS6MTnSGY+UtaUy0VQYp5KMYg5xngYiH5i/GLuBVeO55hSK2F2uND4J3qBdATwymAXdm5MFBTMS4JDoYlNV0mWsIUXRoxvlURepqHSGMi8cgyw4jk0fFFOTZj0MgpaGwG6emQxC9YTX6YTo/TYfjWb7159s1NmMEbYjkUrZ63Y7Xb/yh8s4Ky+PuLOex5SizngL+4dyWObkuvCk0LxgsxCB7aJVw9APo8rC/5GAiFXgJDaxfVRP52DYJry/o28e/CobUuT12sWC+lO3TArKY/L1y6LeqF9WuvsH7EhVcs4+mWonUh6xHcsA2i9UE29Hf873Ger5/3z56lz4IvMyWqWCiNn2mRsZccHAyhuQynzbThgEywRiNcYh36xHUodkEIb6wzQbrOYNfkvmgygw9T2AJYRj4H8CaT7wSURTdBCDLNcPc55JH4ANB5JW1G/JhrWgJuukTWDFgaKaco17GBslJmdCRBFelo5JIzNTXjcMnFJ+ypgEi1OioCARjPhq4tYengiyKdhc9ESM+rjfMoZRw0EHxGEE8juZnQIgOnCAWnPU/5iCPIjGgxKqFvOgp0fEC0MZ8etndf3D3/6J9845dL1yGB8JBfkAObD7hmYy/yXtoRF8JB0wyudq67Zl0pqS7l0p4UGHy5D2s4oE2XrfkQRyBnWjtAlU2742JLt/Fzq48v/zZ1iEqbbU6C5SlwHtzSLicI68JN2Z804LTFNpVgXt+qsjPb1y4u4/LpJL+TiKmpPdeKaEOx5AtgB+vy7DduePalGP4KGSgZgwzztK1Pz5l2MHEVOTmUudZ19YhDyqQI0WviX7KfZGlaKsvQc8LUYpGEJev0se/HPEypwJxTjmmc49C7md6UJCSJzANCCLl8FtNEoNvwdlhLI6khaHuNlFbZteLNwuA6TCctvvS+DFyJlevGyDcm5nPpSYq+4sLhPzuqPfNeYjVamdvG78FMEPTf87THDsO7KF7g/n/lk9mexBfTPEAso+CIlZb5Ykjl1M/jeCzpG7/7L3cvf+G46UJHUX+NMvjLNst52dLy2iZ0NoJRW6NU8lVrs21ObDCQSpBf4FqaLDb/RlBedPvXa5IePlJF6mQGHLeNrrNUC4FtIGOFoWjr2XSSVcJ7yX4rJCTijKaFKPSZfwz2fQZPLf1zRaKahyHxUZ0td1ZVaoJnw6DJN7VSHHUSOoUPeU55U379P9/+4C9Pn/+I2A3yRe6XvQKtNcnvNKtnQo1cqQIQKPtYyVlUZ+WrYm0/MuJIWWLxM2Z40TQgJK1AakHZ2gzRl5DHXKYhTWk6UVAQRYYVS0yaKbiSSNUpuTG5U9kDBKRxoYJ1FFXLmcsWcPUpD3l2YUjbq8m57XxIaaQsGn+wKWkEtMxtkkjUGaki+gGLb+Kwaz0eIDU5QTTQCH6Omy1OdyKtSOxMJXXyxD72OoHRWU5nrO3ky4Qpoj0u1uU8+mdf/trzb33n2EM/RjOlSts4U7CzBsgGV1lJrbfR3lIveuV/Rdh58ds6ZrwmqPVjFhkkF5PCNNd2P36aHDdDWFv2pLabtVhZvZgl7Zw9AXOwls7/Hc0WS7RdAXTQylFjuHAEVGNco2HNGbK25+XP2zO68b31jF3+4Xq4R3NJNbZvZucCoTmO89WT7dOb/afJge0jW8mGasOh6Wah81Hql67MtKEBLVMAKkMPaDw2uSwhFwpHMTkMNKVW5IhhuDHM2Y1xE4bNAE1w101pBGhLCrd41J0PN9swjdkNlOnMdKht8pwAoh2i60cPehI/WAi3051TRitHB7VoNNPJsesEsZp93saYXn7ti5sXH735wh2/iI9383gCNQAnChWwJb+SYIPJyYRcMkEDPoSqRwik60ApQtfQQoXfJRqDzWmB7ncpBCMBiUJPb90yXtaV0uU0byOU3afD6xS/+t1/GT74BY8QXaeBa5Jfta8W28b+2WKji2rT2pVIxNG0263yQQsqV6ZxuTdJDIkmgXZTmAjOl3hA/Se+77iHQ2xoEQjrR9eTpwmWWZCrthivDOycP7b8t0ziAgerIWGf3ca5X2/vccXW5Zar9iTRv1odiQZN6H/0UKw/r2X3hgFb0ThyhCH72YX05P2Xb3+0mcroAsDjyiPisJM7FogORI13+CSiLlHTgsuIJLmJjMohNEMeaxKJrQRKJmV8aJej5JK1RI++i+99+fDkWXjYj+OD73I39PCpMe9Ph3SYU4i3G0+cERDihwRq/GEctyEdc9i4Ewg4eQ7zOPm+50QUB4rqNJNp0RBVE3zN0eetxyxzUtR9TJ//3c03f2f83X/u3366/fM/Gz754Xh4OJ1OOY04lyguoNVDWttkKQgrMlhcRDAlg2QTlmillJASsmPUjUWA3g5HWg9Ww2cnxAJXjU9nzvkQ+3Sa0qkv082XvrT7+m/sTzM4LqgntTZUndXKjLRoWP1z0wjc7IaFxdZgWY+VhXsVZ73ekbVHcG0njH/Q+CSaBrvMvHLxMXaXkuyWh7wAemqrVilpJuJNg181+aoFnougu704gtfQqqqx3NktnMU3S7epC19b/S16MMdrrrV9ZJUksqgim6XLKWWnpxn04gGBVuXLFPrDr/7+7gd/M33xt9qLz/woZTpbyEIleJZzp1ULXU+uGidrcmi2zSEOmEXg/OTclMbOhQ1px0+kKYkexnnuaUDLszDNb36c7682+/vQDW7YHP1t3G7y9unNt37rbvjgOA/TUA797roL3dXmiXPj3Wfjj/9q+uKz3sXZn8p4Oh0O4+lYplOaj2PxG19m0BpjKmkIZQ6hD1QJJl4i0SFTcWPJrvNdCrvir6838a7vype+7V/8k91f/tHVj//99vOP3+7HXQc8+0RQvWbIXEtboAwgeNpIB6nocNcG9R/LCaiieQIfMjqlvEHPNQZZZ0t0qJ8zdXlyyX3td/+Vf/4L6DkshF4ZS2fJdT7f5mdZlu40Ra7VVTSCv9V4pNvRsrbqiI1o0P7N2VeddBVD7PvAJ77q1kvOi9qHZKUrCHdRzTl/c1aUX7EvWpNvqmgKKVW+JG6LaOqr2nMNv9towS7JsOHFtZyjD/Ubg+TbwEiegf7cMuT6Mc0nqPXWCFutkoCdOLvxyUf98xf+9U+prxI0DxNGtd5ViUjp4uGY+E3Rkp9izlMYNsj3ZtaOYB5CiP08TRvWLSd6HVVS0pTm4P0E4oPfpn1wJ5fKw+i6dNfvy+A/Pr7+8bMpvXeaig+fDNdPc7rfPDu++Kj71f9k+s4furA5DFdxcClM/ThuBwwX6o6H/jSH0yFMYPCNd5+Wwxfh1c/cm9dXJUwJEcOcZnyD46Sc+t3Vb/3u9he+NV0/83n26XDYbsfv/N7m6mb4k//JH95OacppHpwbQegC3MyWx+rY/CC0gaKGjFy/Zv+1BFxMbwsTCLkix7JDfgVZcrcWLVdK6TbE8pUv7772zQe0JRUd4yTOlFsFq2E23katrdF/b9LcZcnR8C3ZGJanNZt14d7bQXb8fkvPtDpT8FrMXJkwMF1yVu0UyWhmoFk3hVEItS7Vfj5PFnlH4nFKF6Fm5DV6ba5DjcEelByQwNai9rtUwlPTGnJ+22fGVZPVFaTfDjfi47DBrdaBuG4RBSNqrMTKN9YvYWen/INIpLmcfDcPz17eDDeP4x0ovnAcHAdzBYgo9h7cD7pfHf9Ns7yCc3N0MWWgPiwipdqXHViDZS6opCRVOw4g3oCfDwLdhAucQx9Dt3HZp30IEeSnh3kos8/54MPzfb6O8/Xx7ePd37mf/r9Df/Xe+JhcmIt/3d2mrp+fPu2+/Tv77fO9B73L7zof++Hmadh8J/SbMPpDHBCrjVOZRlx854fO7fp56jeP2ffpiI+ek8/jvtu5978+dcNACiEIGbwbcpnovnRokR7VzUA8w6CrddswiRq22nZvjn3XxFHUH8XPDiQYKJ6nU3/1/D/+Pf/8Q+dntvwLrfayh+2DJHqqXYSLPVOPnAZErrem8kWNd13s6Hp37anxTl+pwWGkTHhGWMGkGCvf+Y41HVi0iEpDuoVXRMImFCaWEOGs6ihXF9B68rPYmOBn0hBU62W7vngHepLpchjeZSbZPlx5//ZhL8QNz5R1l3lt8zfK7mBYVJ+JLTvRp/BvnIpxGH77v9r+7KcPP7lD7YhVdxQpgFehiDm5xA1DvGGoMu/6DCoSUJcM4JrIXKE4oul61/VhThPOXQnDefwAqMQzeNHUjk1Vm4CgKoG8wELhMabQ0+XmCes9d/NxON6l6cl4ejihGrvvcu9iHz7J+x/86a7ELsSn5XgYrkrYhuFm+I++Fb71G6V/MYYuzcENGdI0AT2Uc+lOczek3Md+7nfZTzH56NLV20/T//2/hvsvsvNdt8nYI+CR0XrRnD5ujUAgTQ3B1T9o27t1freNJO1/aUNYbRBwkhk2I6VkxGeMQ3zx5d1Xf21Mc+iAH9QRPm0iy4ToSvTh8LP5AP6fZZQrH9P+lKXOqrm2wV/dmvKj1but9nyDNMn/kPZ1F+Okimg04Aedl5h8j1ZTjqsXZ039OCsKM34tqhHqEHnm9yrctWdjVFN7vcxubwlYdsnNwqkagqzA+ibrm67XoNpijc4rTFWV0avLlaBEPbU000sSIadxC03yu6EWGkLox/QQn9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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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DoHe14fP4RrvvqHNYXZg73dLzj2aI3ebkeiJWA9i3nne5O+r74IUeq1+GOfu+yb+LluBeWh2HxUm6+KKo0TH7jIz/52MOPGdEPmy8Kw4211fFotLG+trmxkWWZEMizwFdEhiQeDYeiqYuyhPMYx6Su8RKt4BAEpCZJrTJK0WbDwZEjR1OWBDyCxU64GmXZ+mLKYWwgXGRb1dXDjzwyzLMwxBdVKlLZC1r7Kd2tLFpSmBI8BkBVt8UELXCuBeK2J0EOqa7qpgmB6kAwvSgrY6dTmhngLaSAwbdtW5NStb60shq0GqacV5bEW1vXv/X0M0VbRwjXhRZO0SkAy8SOAeWr0EMfqP13mWXYGeOLpOMYsf3TeVfoJOvSYzynWu2Ctt+6txcU7hK33blaj947l6OngZwF9W/TX1V/CQsb5e5jd0Tfxu6dwr5u6sQc+urdgH/TSw1s//aMeFUagVPSdcqaJAsee/iBo8ePhUA3gPciHrOAr45Ha2urw8GQUxqVEkKgas55lqUrKyvz+fzGjS3RiCiOhoMcyheCgZtImYYNylaOh8O2kjkb5NkQnEj8E1IEmPjI3029lZRSGuSDO+66PQ45AQoDCvw6QSljlQBxQiA75WCLtkniCKle9ZZVf/q0uIJmYDpb0yAuJQM5GAw4DxGoowyTATi2EY+iOBKtKAokzQzJGgigkSUqLiND7NHnPvvVy5cv4fYtpqfzwpeRlP+klz//N0MsXiTwDY8+7BRvoIB7MSfX7V44lV5G34NYxmee/bncCF4wUZbe46KJ7Luxi/vOlvjGfSkhexf9a/Kwa5v3bfKOFg6xrVwOZgwgCMZiHlZ1cVC3H373u9/+tremSRxzgKeiKKSAb9jUTRpHAHswWJplVVWAOcokSdM0HY/HVV2VVRlTsDZP09lsVpUl2InUMZRXXTEpXnv9whPf+NZTr7+wu70VIjZLqOBldp25FQA5EBAb5PFd5++A/YxvUOQXeWZPZ2G/WyhShMjLioWBaOokYhFrKMBOgSR3E5VrQV6CNciVUEgSxKQVcEfAIIcHz+EnhLMpskuNAEzPMKyRMzbnZtx4GQbbO9dfef5ZIQD47LKYSFlrHjZextLHvBBMuTmRWImwxJ5bMD/tXi9zr32F4GlHG6VxhVB/1UsN0cMMyuXya7neX/7q2dXLXsZX0T8vvXz/kg4Hd38r2gn+41/mKq7WX9gCL/bufG4oza5EcUIog6oWMo4euv/eW249E0VRzKM4jlkMoi2RD62U00dIh1aj+gnIyTlX79d1s7Kygq/FUQNkNyMgogwYbwCADsIounLl8uc+++mvfvWJUCdwgFcKozDUySLwCgWoDQiOlhyurx8/dsuZKMtEreFJYGLC8ysxAdeTUsFAk0RRlicREru8rJtGMB5ywF8ND1uLF5BJ0rEGSmQMNiUk4aiLGP4CXnkSFUU5n8/IeCZnwdG6dmNtQktQvUQr2xefe2ValHS3DLaNfSp9RusbTn4YxZoPh9ig9m/Pb7sJrS3xsxdwIf4P5iIUbVjwUfuvPg93p7vJqvrmyCH3umhQm/e9EyyXWM6ZfZ9DM7l9s+fsLp7Dfb1pvl4miX3E3Jt6ddtL5SghQ2ao/cA7fuTH3vOB9c3NEG5uFAECjPIe8K4O82iLEv5wXUGBxxGnKHSSxAcHk7KqFGxJAzQQuKW4F3JOQF3NRfHV73wdgPe2Za1ka4040YqYA049Go6SmDKyhD3WAGfRvPOd71xZGQSthMWPL6pgkb0RzULIehHokmNNPInj6d70Lecf2Fhdtc5wpyrp2w0VHnVPimCois859qSNycIPGKva9mByUEMiwPntfG+tSekMRgmrI0IWRIx/4annbxxsCYJNaYOj5/XbG+nSrf7bLkBDXad/qPl7MbSyVOmYk/QIyQbVerKlS8fa7HTvuj2yWlymr9DslRdSM92DWFjFG79ucngfIWNvwVVy/v36LHbTC8k3xbyezn2Tr8PWYK1ORc4chQSybQdx9tPvfv/jb397ngGfxELeyjaOIkrOUAVFi1Bz04oapExww7aN4ySK4slkcmNrq27qhpAfBI0y6GyKmYUSXFkWVVlV4fkoSlpxJBB/Lx8/Hg+Orq+OBgOKfSnzFpIDfMNZPkzuuucuVjasFWFEAEvNjTaarLHWKhOF+ifkXoM4SiLOkixcX19DuI5YXKWkFSgyRNVQpKpluho7U2SDgkIO1d0KZNIODiaTyURxphaZlqIdhdrZNxTaFq2omvnkYMJZECrt26Mklzf6J7OmnaprUX8f4gS+gb51r3YYgfTWtPwLS07mJuUWVnQTs8Eeox5eT9MvWeCCTbB4qr4NsdzzMCKif3/O0d6yD0/eMnfblyvrZXzbl1pOtmBBpC29eKcC6KYRzYpk28zaIhlHP/DIw7ecvbVpFNCoDXlo0IpksCrIIlmRsAoFEpyAIkbRaDyqirKqShnIKCIn1zAL4LBUCxkFPEvy8CGeno7zu1h4tOXhsU1U7pDu45wrL4w0XyhbNsrWTpw+G4QRvq1g6Vrp6T+dDauM6qAt6wooa7yiwdrq6sYacsKdvNL4fBkEw9EIH6lom0nnck61tbRczlGlweNoTq5vjci2MoY9NaSzuLYaRANQ6QwsrCvygcnKN0cvakAP2HA4ORlRtVQp3IzbDv/I5dguNG5BBz4Yyx7qywt77ILydj73OMoXWXYRi1GB3q11vy65qSUR6p5J7KLdlvDn4exyyOYF3jc8+9355hIzY4ni7yoR/JzCwmUc1Lk5nFI6TSsFi979tnd84J3vyQYDYJRMala7moZZ4KsSQHA+m8HUpFOIBsGdg+n0YHJQlgUlUxEC5iGLYZLHQCJSDji8s5HvGuT38yRt2u2Yk/ErGxQv2U2DzqzK8m1vfevm+hjqm0JKnQOveJbqmLUJTeIFVgLVf6JWOc+qRhxdPz4YDG2iGarRaNEk4kI0Kuus/OlASlqkxjzTT+F0Op3NZoyFqN4wSsPu3EKqgq5Oxj5w2XDHERKzhW2uFWm40UkjLVF9i0y9jHpuwr0upyycwGXVQ0jZIfclKtw1593Vu+dxducw49i1q3tqyP3Fy/n4SlT5G5680S0KDtkfX4S+yRdbPEkvduiKPZvnXyKebvbcvGfRS4wTjtBERbUOJvBw0AZlWUcB/9kffd8D97+FMjCcTGxOJrYuMVL6iag9mMxmLeploaU1fAdWZ1sUJSJbACGhpL9qqjkAivOiQlAsvFIXj7DofJo+M4xfDdi0rAodJVNWrlpjkGbJ3ffdnccp3HDYxf4mqXvRfEDtEVASiHSugnkxzupaPPTgI8ePHU/SGJ490lcqUIxvFUUxnc50LwmqisaV24DuCEIqCII4ikugvQmhKaVyG7QJbfwQG8FWKBT1E6oaOYuiII6YqGuIPR5qO1g93UVlYmJgjq5zzFl7cJ8MFv/4m7Sway7hODx3CNywd5AN9vRljUt0ruIzIPybCKAltLyE21327umuRZyjIywXgJnuLS1RpH5imN7tgBTBYRJ1mVXUk4dLJeRhFvoisS+szzhTdoUiCduGN8k4etfjb1tZG1MoWMGInYpQZcRKWTd1HEdJEhNaSUWQFVxQ51PquilLgJtgWTcN2mtQGW/4F5PdS5ODW5J8Xza7e3stAGEKPI1Tq1hPK9tBmrzlgbfESWKiZ1Tz7xSsagGl4WYo6gOnA5YtARSRLI54OghPnTg1WlnJsiyOE8WWSoxVVUXROQU6ofpeKHrYyVSXDAcY2WoW5lmmVH7EI4JQIs7ee2i9xxdCyMkozvM854xbvJPvqy3RN4dabL5a84pylhxqhfdiwtH54SY01L2/qGTs50vpzmUwbewskS3OVxwWXLBw3a96O9BJ8GU3tiBelt3aIYGyJff7JvQ0+4/OL3nSzjnfIQF3Z29tdFb3deEhsj5NG8R/413vfuyRt6Liz5iUDnAQKEOkf8tybXW1qZvZfBaS92rK0HWWiHqrWMeTZDEZu+GEi72mme7sZbPi+OqRfDigmgVdDxFIyUH+cjxaP3r2OJpdCcScujV0Nom5WZ1FgD2vShSIVQAVYcngLfc/sLaywnmUIM+N0DIsb9EURQnkim4gBPsZLUUa2MmwzukCMWOjwSBCJhxZtaqpI47aQ4p4mzt0tpbkGO6eCg3FYw88tDYa0P7ZWNliAEyb097j6cvsJQrM+8X5YzWXkzL0j+yfdvkfV9fSM1w8xnMKeks8nFv7d2ku4+gZ3ze3MqujZRWdNwrYvfhhgmIJN5vIRXfwzYyCQyTdYlmEL3IW1rYsK+A9t8NePeCLi22gnjLU4KeVog7aKIkef+ujt95xC0Mxge3bQAFaYiUC9dcsYFEUTaezsiiSJNHFnLqDka4uIoeZLFKKCqGFzTor1oNoO8/uG4xO7mwNB5khYso+sVBAQjSP/cDboyisAczAKqhu2OkX4RieevcYagxE01LJA1rDxTyUQkR5cuLEmdFoyDlLkpgafYADayGQ79bNipAZJimGIJaqYeAc+Kssz9I0WRmPV1dWGAvLGvnopqpJD9vtMzRIch147ECuro8feOC+LEmh29Vu+Php13xeLubNc9eEYEEjS8mr9zWjoZYSrksTi+/7RP0mLuQQ6mIYtfebzS8tnsuJotmH6p3ZvXv1vuJqt9jxEFnkyQOzDCssOuHRfyDLJV3QO9fCvS7KAO9+LHrAfELXMPtihJq9YYfAuj32ofiSUe85FoWxaJuyle944L73/eAPra9vAAdhESSozOGibWfzeZzECtcxyHOAEasKUWQ6TkOaTU0z9coBcpNCvGH4CAs3hfxiOXv9YO+9RX16VnPVSEcXySMTlCTJnfedhw8u0RNDg696BtoyggJ6gwqiAPhM0yTmeZ7de/6e06fOrqysI70UxSxgomlHg2EaJ2h5pbxosqKBPyMGJqBpMJ1Ni3nBAhQV5nm+trJKQBb0H6lKNO4z6A29PwQIhxstA7a5cfz2225N0xyuN8L4KpbvqDTrIy7PRSy9TeP2HEJx/biuv2uHqRbzTZdczGEuWvvQ73tE3D/+EHnhB9R94eFprN6JFuvyHOvFVdkOl/bY2jGf3ZO4zLpsf501SZfp7dd6N+yvp8fplpXtfbuSqjvl8p8OewhwCEXVTKYVk8lHHn/3Yw89nGYJwlT0OVWzi53dXc55mmaAGDLUw+d5rkK3uiqRsExwjLv+DgprRa8HVo+8HAXfjNi3g2BezEdBIyvKHJskDJNBEkWnj5/kMrKNzWwhknXt3N2ldp24AqxoatzZQsGmw2wwHoyHw9HZs2dvPXfrymglSVNq/9MWKlYOnLclO+hthb6C2mcsS2EdiLaliNc0z7PhICcRgxR5i8ZaXVSFto/gJ1KM88FPfPD9gzQDMpUhDayi+Gbju2yuVtzdwzVEZjMLPvSJ3nJdWPfBHurWOXkua18bdbZogtrjHdfUUVid3li0HF0CdQjZkwP+x77+czmo45VOFS2wSB+p4bPw8t3oREFv04zu613AqknZv7yHeFy2F33h45528Vj/CeoPXUq38R/7DFzxCxoTxDGimQZ1MIgevP++287dpmoOFBuVCPTWoxFwUypkRV2Tqa+c4VAVBVLZXBUHU/eqok1ROm0v1+U2Sz7dTL4VD9C3qhK1bHSrYgr/RyEfj8YBMQQq9CwBeiZaJ5FUlkZ1wYMOpSVwFq6sjGpZF8XBjW1UIx49ulm3ZVnMZYAIWxwnVVPhtpXXbhorImKNsiQEn5saVYRxnNR1vX9wkGd5nuY7e7uoq6LKSRlIsDsi3NRvFugQ+Y5HHz1/+7mYJ5JRubJ50F3rxl6uRKVAvE8sdenQtk5i9b/seZX+h06nOEfq2390dfhS3vc83EVx74iMjq40o5qWdovHL1ZBdqquO1HXYNOugD7uFqsLcoyy6CtT/dU+E6pef96Zu6t65+hAKL527TbYB9ebjrP+vbknoH9d48k9trctuqmlxdOaboFmbUtsB5UY0QKVauqkEFUTxj/88NuubF27euXKlStXJQgZsds0SdIkVaEt6lxH2EdJiA7OEb9R56F6AVUkrBZBvbXC6F9F8W5YzaUUWTqtBZCKilGp9xtDN9kQSWnGqG1cE0QRmlz6qFEvi2pUFQ9DFBvobDXhMWI+yPOzZ87UVbN/MJ3NJ5TOha3foO1GwBlvqZ0wgZhRDqF63ilDmkUR2lOLBqkgwle2Uh7b3BwOB5euXOFhYlv72qeUJunaaPXtj751Y22dR2h9gjaVurFyV1Jqu/l5z9lhVOcp9als6WcOdS3NdCw5+vAwrCUkp2V073wO6di1m3tbdlbvzD2JsmDR6tKbjoS7j7vkrrnTzkYxrN3x+eL1+4g4p9ms/V1Fzu05vXBjoKEAZlu87fFvs7fR6oEvwjucFrkdDMVEjL2mfc5P9v7sfhhWp3cJqNHUbR7mv/DuDz7/wss3rm9VIZpBtYEc5ANEp1HorvdJYQeboAH+KeJNQwwFugWlKoMtNEwXvShqgWKBOqjUkAHdk1TFgQPJatmEdfnq66+sjkYsjuoIPSidiiJTBuTmAE2VDxXf6p40gHbArY02No5IFpZCjlaG+9vTyXTGKtWYTnDOGqHBWIzDbKYOfohpwapvUWmYJCl6ZR1M1lZWVsbjyWw2yLI8y8uqkmhhK3UqnFrbMine/YOP333nOSXkgG/RIwi8NlhWr/RIWdPrkgaTPV70qbKng10t0tf0bvvKnj4yR1jCdZbXR4KqXeqGOXSk6B20QNuWmN1b8ZS2WbnujmmPVJrJdMLXV+wpP2+dS0yKvlbWy/a3WzfSdNiiJ14C2yPXNpVUcmFJowVjMSyaMn2W7Y72BbB78QVrYrF2VFW1onVl3dRl2g7y9N477/jeM0+99tpriO4KMRiPFeLQdBrXXxYNemUlcYKeqqoGHopHJVspTEZZlog6nkcSWGsacOGm84jlW9kW8+Lpp545e+JMmiVB26RxGlKhY8947NwAicu3okkjiobrFFYLfQ5wiUyz7Pipo60UjzzUbu1dL4sZHNlWhDRAg/LAFHmmtppkF2M5lL8WrBFFWVhiHY9GbdMM80ygR4nyIuD9RgETYbi+eeztjz8+ysaUCqYO+UbkcDT8UYlo/y4WDST14EwVoss8hxiKi7y65GWUk7nGQv9mb1kOsfh9bx06clClPsUp1tXX8/oYeGvoL5D+st3cvZNT6+Ceeevycf89z2rwrVd9GZfv9R+nDYQjPY2O7LGg7nPfbUbHjubLfY7uHqUWUZ167yrMzcKch6JMgkWqceWrSyTEw1kcN21VBflH3vO+1y9fuXr1+sHBjFOpEsxmF0ykwzIg/qZpkC2iOgFKQqGrljosJNUeUbaWvu9U0lowExBLVIz4iY9/4u7bbz2yebypVofDYT4YJGlits7rwafa4wgWRmmW5gMex3VT03AidNVpQ1EWc9HWeRQP82E2QMkvBFRI3YMISKoiTEK2ScjQmoPQZ1ESz+czVc9QFMXKeGU0HFZ1zZFf4uvr61EQTKo6y9JiPkdrWRGkXH7kPe+97eSJEF20VFZMW3lGn+nbtDaEpmprGTkPyedm7+WphJvx8FK+W/x9QXP1tZc9u3873jV8PeY9oo4Nl9zMws354yx87brkBrVe7g7vDW3o+5iL93XISnzm9FwTSSyiG//a+1wA5fifuI+rY33ZvwRV1dDCuwkOrlomxnZElpGOznaron8WtE07OZhnMvnlv/Ezz7/48pd2vhRy0oUmGOMFlXS7D0IrEhA6QTETfkANMVXCo91HjUJ5ZJzdkn41EUulgmkCTLK3u/P5Lz7xznf+UJqmcLopEaPC3EjY6IbpptO1SlJRo4EojsMqKNWpQp5heEQU1bxtRJZEbRiEAXAnqmYwQNGRzni1AF8ABD7IEkTDqmY6maqK55CFo+GQGkhDRKH0AjjJeDXPZCvzNL2xvS1aedux2x58+MEownQVSXDKLl8HsCcsbtMX3iMNs5N6CJhu0H8IYd2U/u15e+pn2at3FY9T7DtdkMaqKSt8lhVG0XccxWEHDHia2/vGEm3sCACHSHpmx4KedOxZfeiihWCF/+LW9IAU/vwX/4qBv7qlW9zd9hIZai1HXyQZY4geH03/6ehn2bm9fXWuhvJVNfEtCMRUiLWVwQ8+/MizLzw/nU5Rn2eG3eluFroHuV4o1Qsw8CdxUBJHMoqoGAFChX5Sgsb2XdIEq3UyhqRQhcPXv/Xto8ePbK6vp2kmR0gVKZiEddaJ96gRJgsxo5BxialnqHrER6ilgqCA2YDi/kBIUcyLsydPXbtxXQ10a4RI4rgFiIVy0aKtyiqJEtGK2XxeVqh7rqpqdWUVBYZqU9CvAGMQR8NBOZtJHh3Z2NifTOqm/hvve++ZE8dQTU1diFpl9XU7C9nmNvv2hbGjlJ1cZO/RyzfDhX7L/06Cu0ra8+7cM5sFuMTmnrnLnWgWdr7o3ZdrphrG63GusxZ1RRulN/xlBIaL5+0ttO80Oku2Xnl3Ame3lka5vN96jZ3ZoYLVHb2jlKcdgHGTl705EwWyGQdVJUQIYTXfRK/HpAHNO/gRjpmxWLxcHLxRCk/JRorgve98+19+8XMXLl8GCkl3a+2/lFdAH1FYWcGTqT0txXeCppU0kIEgz11UR0kC1SGW2JicbHmwt//ZT31+c/PI/XffnwPSyKIoNtmxbvcVnjkOo8n0QFZlGKU8QY4LKpBxmnJYtujoIau6DoM2y4DHqmAqKymGlBe8aGAwmyRJAibnsymNdwLojDGWpDGae5hiYFVaGUXxnIXAeGEkmjx79ta3vvWhPMoAo9ZGUDd1Qm2OitM5vWu66iYSgzTK0Hs6N6cxx+A0GtxRR50SXAwNd8Fab5iIy7kuu/eubzjLzvFwP3Uup6nz8JU7N+pfzlel/Su7J+wGri16wh619zXZISGnvsfcvd0Fnxe+ZeaZyUU+X/iKdXzpu1a4ay1IOCRlrmIMSl01JbhC0PAfTENUSR6qCiTFQ81xGFcDwnS4V0GSI0JI8RDjvuL8a9/8XlXVcRRTxQBKBpwleTtv16c0pQIWixbzBIWUER2uB8uqRRtB6WD9Ee8RUcyuXL/6p3/270+cOB4nSdPWq6trcRyrB0MBItpUGjVY14A3ByEfUBuvpm3gaCOYJuM4aeNkVldNUbaNuL6zPZtNKWmNYvtAyphHtcAIJSFElqSBDPLhYHLtRhTy/fn+2upqlmaEf4Zd3DQNQvASeDSBeYjNNKjSJPrZD/7Y8Y3NgKHwiDiYtkGNB7b2QkexWnY4yUltu/jEZDfS1XDemyrZ3+NbV9UYI2c56Tk/e0ah57f5h5hnbzlrIWDtU+1iiMyG25exkbV9F9/tzJVu/WYImgkOeyHtXjbZuVhvP5brys4gshdjne3qQ0Bct8gVWaaI3cpyG0DoR//1yFFYvYHMkrgJ4+dfeenilcsH+5hgBtlvzDcq8EXGVJGlxKgTNa4SJe7qxIgYU18K9CsXwYsvvXAwnSmEBurrqH63Z9f1n7iTjCakCDxf9IaFGUz35eSGCL2lJimrTgBU4Md5WM6byxevDvOVNmhjHg2HAwSZCD6CDCzEQtvIQNTN6njUcj4YDOIwPNjbnUwKuAJxG4jm9deu/vnHP/byK69euXJ1f2+/aRuY5bQsACcp7ibR0AOzFNpGTOezsqqyLBsOBmmaokqR8sPoNtK0HPmnaF7Mqee7KKrq3C13PPTwA6T0lVKyjbs8yJ2fSvLmURhyM5Ove+TusrPdZpvI046MK/s9Sld0sZgK0ge6eUgn0tvpo44AFaUuPnojQIyy0s+0k8jmhIuxKbsY93rOHvUu4fVBculMWXIL4YWlQsI36B3+cgJlRjpYU3xZNqA7yL2FPhN0K3eHPnf2j0UFgIdrKZho0zz7/lNPP/vSi9P5rO1ypmr4MhZEqF+OWjdM3zZQNfInjdyRTdsIKcDAUl69ekktRsEc3CZw/q707s/x8OiHSM3zI++VksNqrANNiI14hCZ4LWaIZ1k6GI1vO3v28cffvr42SlIEdtGKmkdZlvEkYoiMAeqBWdosKMuKh+3qYDXExHGYG/Nyv9qfffaJL7/w0kvPPv3Czu6Noiqo4h9xZNXaDsBm6uajyqiSmI1Hw52d7a2dnZCFVVUlaYpYF2WYqC+0aluLO+IRH+aDlZVxUc5/7sc+dHxjPUtQsagadBlnRU/kNA/aesXGUnGaLyur1GML+6zdeKfJ3rha0m5xR7uu0Ddmus5rOcxI9OYGoN1H6TnO7tS7Xs7J08ju41dhGOfIhZuzUsQ7VzceuVulzx59BIaWZ+5KOoO2d7kuf2auqRCqZsygIwOdqemG/6T3WBwB4etjJawdD6eL7PW32cmvqYzmMM5fv7b1/Ksvb+/t6kG9iutIKagfVP28ms9Ls35U3yj8bbSCVB2cQx6+9vLLFSbaK0xv1/O79+oJTj1/2i6RHkoUA1mFVA1youSnU4GeqBtEjEaj0Wi8cv7OO97xznccO3bs+Oaxnb3duirDIGoaWQlZCcFaETWsQdv4NotZlIfT/SIUtaxC0TQ3JtcmN64/+c1vfuPZ55/69pOT+WQ6n9dlifRPFGP2KBUkKVeCckiYRR4nCUPMjU0mk+tb2+ggQk0pVX4YmWxAMloZ6jQanAGSO/O6Onrk2AP33B1BDLZChroJoCInJxfUs3/NrAhjpdjt08c5mnpRXeiHrUGFGqCxqBYMQMacxepbj+P68eQeAtNZjQ5D9znWdaKX0YVdt6PXDTn7X7B30M8XuVNOvdU5Vm5PH3f3ag0fY9taFIO/p+YBuL8Ys6DbIeYLtZ660ovVD8R1p11bwDVo1Jo7y4lCN7KYTm5sbxdVjfZMpO3CkKEfnQBeiuLMoEzkeMwFVcdU1cUYqAlAHOAsl1V16crV6QzJTqcOY8kDswzrqBcSGHp/8XWMckC5QpoIaHgpJMzROMlGK6O777jz/R94z5HNzbXheELtp+YFEMtpmiHC0zRtUMsAWg5t1pu6Lcp9Kb76+a8dXzuyujEe5asvvfLqv/mz//3J73z/4OCgQkK5IfxHEMcRY8CO0IqoUWQQ1A2mlilmTtJkOpvVZbW1vYNpSXEMV5YWXzcNR0JM56xQPYxm9pzS3Dzj8U996IPD0ZBxjrnEtkbYt1Z1O0ojGjvzxTfjLFH0JeRyK9CYSr7Rq69o9Y1Le15YvGedGgLSduQSUKbnzrnvdcOBfAWzhJ/76mq5f3zI787GdSuzjNznE8tOqi+bszVOBrN7CksMER2wsT8z9X2FQ1tYev+ZLr4MmMyZJ9zzGSiQK2Q7o/EgyFmG8Dep7ZSs6wr9boAhBKHlWba1dX1rZwczdxF0pVI83bCRsMshFHVVVrP5jGr7Sbt0oxHchTpcbZhXhe1MoFxvQzSvZtBUVZPFaTbMzpw6844fenw8Hp44fuzk0VMt+FTMMb80QmwtCMbxYI7SIj4+sn7p2tb2ZDIqi4YFX/z8F55+8qlnnnl2e3enaOocxb6JlGJWzKZlyVQbgDCsEJ2DL42HSPPXWvTWQGoJyW4pUVzE5WQ6m0wnQjRhwJMkUfHpiEeDweDa1WvKgFGQZqSFUYUI2yGN+A8+/NCpkydQfoj0ktOAwsx48hqydOagQ2vdtvXHBNuEuyEvE6s09KLlvJHmFsTkEqur8xwXXI8Rd4zjjqAWJZDzkA0vdMzULdJe3jEj9E0ahWTv2dj3zqm86dqeV+yvwd1MbZ9ooJY+tdlNhzR1KK8H97TXt2d0zYpOCHYUzJwn01udYyf33IZlm9ndgcFBaB+V6LLCEFAGuBHZvXAXm6aoytls3tQNNCEqe8Vrr72+tb1VVbVyblGGYwgVhUTULAfTqqVcW1nR7eP8qmrnqRkL36yYuNcCVnS79ujIykYbsztOnH7HD/3AaGP9+MbxJI+DOsiyAQvCpqoDdIRNh1GSIkTEUhZNRHni6NFrW9d+95/846t1UV7brqqirqvJwbSpoR7DiM9mcx6WQRg0FWldTr68jiCEcBjIdqhRMKyYUPAGpQuoco44Jh3DeUAFFP4jpxf5oaYpqwrJbFNaZdNAUZpkaXbfg/cd3TgSZynAG6iq7jrYOYOP/eCS5T0HK+DtZY94/BCJ68npZ+9QnG9Iu2WYi3rdkOVCnMfa3Z6J0KNYc2f+sn3j3yFSo6aXLUExtrNL7pkWjemOx6yTb2WTSdn51+kW73Jh7yIdFMS5JTPsxT4S5okE7+w+NtxUGejN9CWpt+PGiIG+oziuaNB7vWv8IjDmElBhsDFCrU1dVUJs7+/t7e6FjMUJwIWdv69TtYjYMBZUVYUaeNVKVvOv5mF3KQtZ7m6X9ebStka/8xu/ztbyoxubScjbKkiTaIIRppGM0JV9JR0lMYAQalAogapZDihVuHVt6/LlKzt7e1SchDtBD9sEti51ouTUyIYqG80ztR1+XHvF7DKiBSoULluO9A89G+ppRz4GCrMw3iXNMnKDUbQchhB7QIA0bVTV7/7Bx8/fdkeeZowHoYzA/jaZYVtgOY9cRyB8+l7gDPvzkk5LbBkd+l/sGEyHpkkcL3kyHZXSPx5Mu8Mvd3nWv86ru01f4y4u2HvP6EwHjqYHTTtRYEcd2l+XOHOeG+F+aOxlV3faM3UC1nSV6c4pl/G/f11rC3WrsNaVOr+/mfZ5OpYYlASDtwvwExxbmimgZ/qo5s6NEHv7e5ODA4HeFVGaxNTnmaaWqGJA1YZVBiV1YlQ1vQRwRrsYX8cuoE0X7s1uPrTdmTNn2kGSNKFgMhslWZqOV1cHSZwgjaTA/qp0icwJakYb8iheiXkcMx5RzSFCStrGJ5ND5VBV+2pzTY3S8LGkurc75YABjNQGL6lOMxwR3Ku+ohrrxDHf2d0dDoYMNdCCeli2LA6Pbh4//9CDaTrUbT61nHKs3h6l9SSeufpSzujxthNR9o/pWa/+dy2UduEYG0k5TGl2/9pU/X/Ey00VmjDUTUVJ1+DTPdaOsbSawFuxYXN1T8aqNuy3eL0lhoD3sRMzWJC2zOnE0D1L5SP2TmkSgt0iOybWutkYavqMysLHcOsA8RdVRG97n6tRuGqwjyr70WN+pACtYqSJLdTTEsrsGwYJFUU9yFFFyNQgXA8Htyw0vnRnJIvaLAK2Mk/WhsMREqnUrIO+pUCYdCcoNTDfQx5na3vr/G233nrq5JVrl5WviNoINKlDz0c1uYn0JwLndId2koNVg0aIqP4gDu0q+0klxyAxOBdUkxGwAMUZjNdVVYY8TWL4HlHUiDpN4x99/B23HD0SRUxibCLtl0OxVtW6RqHmBivKfdyzz29OcMU5j8sES5nfUoyDC/IsyM5QdNv5LHtmTjB66TM9TPh4RzjLfWMdvjSo4q1oic1ht1Y5n3qGvTf00tOHS9wW/ax07Li/JO85ys4z6QFOO9ljiM1msKxn4xpQrii2VgT5aHr1mGJE7crV6ajPDGxoFUBLgQ4MIwzTrAW1htXYOEUzbQCcv+EAiRRskWcpbFnyHB0i0CEqHW52jGnXqLakGa2PVsZpnqNeFkMN1ThwM0Swu3Nrz6O5JESGYCF7y/nz3/j2t+d1qVrkpSlKK+q6UkyL1DZtn6FRa/LosSnk1iP23i/D0D08KNumWs/SpAjVQyjksAPKspDtMIDeBthqbW3zwYfvz9Icv0muHhwZIzp+1eVqulWovywnUsuBfg7HNa4sgXRGhHZxuhuzmr8jOO9fjxY7cLChLOcfhx5dkj1U9fqXdg87JFD8hlb4zbT8G3K/xxs9PW2+rOduKUm5gDQzI/V6C9IObN9wlvoH59Y6w8EZsqz9GH2seYqdcvTPTC0cZRSGWYwga9fZlQ6CR0m0G7Iwy/ITx45funQJxrYEuEhRvhraBaQHDbzGuLGmbqVULWVGoxGhlxbrNrr4oCVJxdLdQVRyGK1lg1Ga6kYeCrGsxg7qgJPuheewcBByKVoWJvm5W86NBivT7atYF+VjlU2hKm8xGZSgkQodbaeEKno3vec73at2RvcBolpH3ZoHjM5prjm1m2dBlmXT6RSzVUkoRZz99Pvff+vxk4ieAbWNURJmjoz2gvwwpYvBcrfIiiyPYsw/Rjp2tNifiW6TOouK1Nc7TpcJgy9wKc3mLHvenT8XwANBuXD0RQY77Dz+MZ1I6kzIw15eYKoTX4tWv++6LslRuXxmcI83SWF7Tot0vQ6jpbQO0JLDS9vZshLvq76n5eC5VRCrYaHM4jgMgho9mnS/ZEFgfjVmAMAnGvDJ0fAYfA5NQx1wNPyPzh9FScDqsELzJ8aCsq7F/v4wz13iDFtlri8KYZ1Q7O6WVGyUJmlAAScvX6dux5CJmeqiD6BuGeF0Nv3Q+37kE5/9q8k3D9oWs3wJ5NnQMDXi4qqlkgNPkZixKmqLPcNZZZWcRl66Shk4cuSZRV1Xyu/Ks6woirqukyQpanHu2OmH3voWHmc8xOgK6FFT5mBUonPXJpLRcYKNVloycnIQVGlqoZHaQbFc3Mk1Z+N65NYPnhnudNjDFwTePw6iwHlw5vnor/mQZN/AtF8ztGnf8dfqXrXzCVyL37uCk5JzBJ7Vke5e6ue8qDkdx7Ur/PFX10E9u1vqu8JM73NnfPQDl/Yq5jPdvkQ/BgMydZN3VpBSHoPneR5Qi6hIjcINYSrXNLVMtXpWc4USjNMNMW6AeqLbZohBEFRVnaYpvF6OKvwgYEmSNE0NbAVK9zQpqSkEmoddSjVPurtdioZieikgVHpzlFG3QA/6WVrTB6sVrVw/Mv7JD37w2RdenMxmCAhTdK5BGQNaTFKxnlazVqAnCVqE6IiAqpC0mCVtUbOIRxpsqsdMUEs+BkQkfsEk1TgbDFrMB26jkP/yT//M6VNng1qGaDjp27YabecVtKr6b78RS0eCnb/bdWXoGNQSQWeEWR3YNcszfKs9MJcpugZEhyoZCpV4fGTWYUSexXX1vEQbLnaUoXHZ1Ea7t+LJG3+V5mQOWsohJGOpu7rcYhH89qQd3emwut1yS2KOGeH6I32HXaNqNKv5llLQ1+r6XzuT07fFbY7X3rkHMjUjixQ0kAVhEsUro9WEJ7v1ngippq8RxbyEu6nnBqIYQaEi1ZBNBGsoaUzIXxB2Uzc7e3t5liVJSpWz8FcxdUxUs3mdZXkCK13nRFXm3NBSZ5SYx6AuibeifJBR3ZyKsWlNqJrgWAFLk77JICXixZriIGzaZDQcr6ytbx6ZlRcQW4dIqmuaQY7ZRxpuoVWdoiohGsfLRW6XYJFAeJOmJHQH5w2GDwaU7sVzx4kNl4CB0yStm6kQddvee8/9733Pu9N0wGP0dldwNnVJNZ7FCFMnaOLyo3mYPcewC6NYntSMZxSFiQV3VpqZxmShz04IoYvBunWtFjNv1qUNB0eB6HN4yso17p1+fAaponD2LhdZ/18vuKfzO+SgeWCe3WRv00ZDzMk67jb1O1brq0PNaDrLObpYwHxf24Faruo3ba7WFgR5oSzvpFIL6e7LBOa3CQ5Lgd1jMy/jz3YSQeMtzPqp/kVIRJ/iO0+f/lu/8FOf/9pXX718defGzmQ24xHLsqSpqVa4BTaJ8/bkyZMAae3ulk1lEXnqASVxUmEEQ5MhVBSHbY0qWFWWw3lJnc2zFMkfIz90ussqT4Vf058SCiIMZLRx8kgQp1iskZUUGDcVwoZkFZ5YqVLgs4OwboJKiI/80i9+7Znv//Ef/zEMetV4Dv2ZaZ81/1I8Stvg2Ga6VShW2BJxDKu7gtVNw1TAt+TIMkwY51FVVapk3wgD/BdFPE/ToihXs/Hf+ehH73ngfjYcBFVVlA0LVYdZBh9FpcedR9iBizy15fqjrvWht4/KhjsCUxLN7LLR5x1mymH9Tl54CWeHsxb9PSturUBxguT+YbbMxbjAmjfdkKBVVotZbPd3axJ0yR5SK/5VncCmoyK7pfiyybKfurgOqtrNpf3sZg105o6p0zYmDn3YMbn6yTS4CHSwo+us6K3VPKgOXq3ftSKii1yQqaYZQVlABGAmyGDCw3PhyXMPPBxU5d7+ZDqbVRVG54oKVjSoBOij+dFjx//gf/uj//n3/3UxL2o0Rce4YFAsjVngIUK86G1BBbBFVbRCELWHGOaNfDLQxNQlCsODaP5YZ/tYtae2S7bg2iiM004u4l0URgW4noHeq3a1JCw1LUqOwik0niqDIDh+9HiAGqQw4Bzr0FVkiqbQkM+9ql4ESQJKNgGMEcdJI+DThjxM4pTAWBHNhqIukgoR2VEa2hEMBvn+ZPJD7/yht//ID7HhMJiVLZdxEnM1f5Vi6R3zGEa27NqFVnSiWaMrlEmsIocqfKZtWW31q2+rmJwxH8kX6exnJ7ppGdRB7RradTxpR6f2Qy72wTkZxe7UtnDCspJZgOfpW3+9y8s46tmieoxdri+Bd2lXbAzGGB76sF763NxJt2wP4quJwV1/B5SidimdgAClacvX2oC+86xbiwbGFLLGjBFhjnGvsEsor+ssMHUxuxj1F0GMuodpPtBLoqdeVWEYra5trK5uBLANqYqXBbJqmkCKus3Wxvfefe/a+sbO3i7pPaAkoJsw0SuWMoh5DBc6kFmWAsSlYj0BS+JkLoq6qjFLLAxiqDbMcFBlEp4oJNWIH3BxxmRVofWGII/U7+NmHT0vxK/qpEIuApHk0Ze/+Jl/9I9+93vf/x4KDBTnkN1Pfry6gp2khiGjVLSBZhq6zEr3Q9X8o4zqJIpCHiGfRi3sXGmqPOUsjWkAaXjP+bsfevCBjbW1H3//B46fPJFmOY+AzSKhY2xjT/s5Pp71lPqhGN+J03Rt3SZ3fyw92S+5eUrfhe0u5Km07mfH3+l7x37O1KoT+54fPfMq1Bf4yi0f9vdAndQKG+NdGKnh2vTuJ8aNNOe2Z1Ulq1bQeffp10drKeUd5J5+0XrpPyLpOAELxy7fVOfxLqaCvRdSSWSaK8PAXAJvqi+JVoQRlwGLsyxo5Ed/9Ve/9OUvlUivMswMkWDXiMdVXcXEJg0q2ENMLWoEjd+GzqAm6hqtSaWywFfHcRRS6Y4K7mqng1rqoAdIgI4c0GhdJM73BM2eWNNQ8xts+jAKguRbX/nuhYsXVZt1lE0hbxZyxI3RqUPjN8zGEv4RaSQau2gcHrL5gKAmc4V68DEgztC8jmxTpwxDGVuq0ezZMyePnzg2n02ulcUf/cmf/N3f+tsZz7U941zVaB8/YuP87zK3saUcHlf55M6P6icn7Sk8qbDkB4dbFgnJJfxDDjEhKstuNnDbY2CPMhcNZ5dUe/Kjd5jVU136QhOJSvh07og92GWVJdBTs7s+nsV5OE5GxeVwf4qDn6xnij66qLKmV72sJZLUXtVUWSil5n7U2S9mN2ADmyBRX4CaR9HW84M4iE8fO2pgasBgZoMsQusc2MiybdMkCdE1FXWHUcyJmKnPMUANuu8iAtVC1KIRVR2i2A4qraobnBZwkYj0JBXkk0FocLcW9qUlqePsdOaHupFWISzn09lk76BFDxHYIJAbFFvTXrcub1bGvI5kIbIsaUiEYg8hYJAHrKnQHAcFg5g5uERyGxqCIOCcD8ejVojZfJYlWdyUu5OD8ca6rNGk0jVKF/HubsJMiQhFjaaorbMye4rGNVMPAUN5WqXjO/XpErbpPL8etS8DS/oxZO+ALmxtwq4uEzvmgb9uV/9Ye7Yz0XuefBdNsu6me3rvh94lTPKni3KZExiB6ASlltx4/wLuUdI15Z2b7YwO7y7oX28yjAGnutrbBvpdY9udoeioNGpkZ0tDQ6RaauSHMbY+TZJBnqOjhRAc/SHbCoEehfEQ6NnKUDFgLVDb6Y3K3zGyUMEgS1QEUoIpCEqJIaEKshGhYHnhBvWOWouxn1cCh1Qt+kXefe8dPGTVrMhHg0GW01zvBsEsqmd2g6wmiAv7gHx6av0sW04YF1jEBJa0srMz5boNNU40VD3b2doeZMOqiqpEVG27dWP7zJkzmGLhRqo6+Jlrb9r70Ncz4qa7PWto9gRXJ+RcAutEfD8w1TlbSxSOp9zM8tzzOtTYW73Pvd0PHgkugyJqKvOsX3sVZ6d8UlWH6iJB15ew1rr+wEehGY25cKNuLMve2aIZ27OTnfccE1F6N+gBdBZBmv2jertjGN81s5xoomOnm72ld8h7liJoOa+CQLz0+msIzeIlRuMxZ2FRlwYOyWBvUwS3hS2JiG/biiiKlOvnPioV7jLEGMYxlR7AO0TgW9SNaqnjUpSZ4uTdstNDwXkbJfRBcHTt9J333Hfh8utFOZUsqMpS4xYxvgVfUpgORTYK+R0wpLDTJDmYTAjyoa6sq47MNver0NSukveO90LGi6I82D84cfK4Qnhdv3G9FXXIcwxxcoMQZs/9R9UFxpwOVT3VaE33zlrVxNcduoibNPDNPnH4hsAyI1Zrud7pPB7yU8T6d8/hWfKw+sU+vcva5Zi7cw5ZTunOErugdR+FugjVsj93JsnSI9zb9b7mbJA2xwIHeOOt2HyuVtldVyvcQ7obuAeos9ouLmbbOx7Wp1EeqRA1kw3P1/+7/+afPPvi8/OiCkOWpFmeZbaqx2RFAFgE79FITTKegf3o0hpG79vxoqoqIWThbEYAxCDIs3QwGJR1GTn6wdnMPkh34YWuBFHAgx9437v/37ceKcvqU5/8q49/8lMvvvRyg4Y7qtLKDOKl/LYqckBhA6XHZWIGOxBL6uSt53uQtjO5AOJgHfSiCge0AZlND6pqbTwap2kym0wQQYtUdyItI82+69Fvrq/VpY4sKSy9YeNw9jilpzl15tFTD85umb+WkmnvOMNQnobzcOz++rqoknsXjjJWn/V52JE2PokvWbfDBz3L4zC05SJRdR+4pkh34a7WwF+Avyhn623cW2+DIvxlosKTUPZiPVei2ykbZO+vXMtSNdmv8ziQf6F+jYPRl778l1/44ucuXboM4zaKkiRuBDK9Zm4uRX70aluU5KiKHTOcQYMtSOMRQoRUmmpbQ10EKHEDzYyxSnTD0dLtX77hDhKT/FoY421bnDp6ehhk9/36+b3d2cULF6eQEDTdkEwCpa0ULysUB+WAQ1Oyr5NLVvLonxzcp90++y8AWhydOkSLRjyzoghYsD+dtqT1XXvCe/ALz8ONUnhIne4gp3TPtbDd/TC2ozmka39jrPC+2XtIyNOAN5exRL97ZP83x2noUrL9U/jJaNfedkCE3Vo7peb59kbIunusD+8z7PJfvEGqS+6xW1DfPFkqEvCyHpBnjDhgGvW2+cgRFQt+vi+FXTymweqrEcE2vaIA+xRL5ldevfbsi68Esh2PhkrHNvB4YWKrxKYBm3ZLQ8dHjLBH0tQUCMCjtPMcELJiLedoVpMkSRSD1pI0iYFoyqKF5+wgB5a8rGaA097U9WS2O9vb//rzr5WzeTWfra6slEWhmvlJtPzRE1s0BkgVDDM0lKSEk09RhhQMD3fmqLP1XWRLtO3e7v7xE8fRh6cS3/veM3/z5zEA1RsnaP3vLia55NULKy/wii+rD9kb1x7scYaJ4iwoaNfJM5reaNPFTfe83UOk0uL63ft0lVRfk/d/800W7xRO0teVYu5h7heXG9AOW6j4Jr3XtxJ6lm5P9DH1l3Zyei1il7y6nXESg/7HXtosdHA4Kg5HfTV0Vp76bCB2wxHGhi78/vee3t3dC4OwRh6oRQ8ZJFdQmdRbl0pBqfyQqohAQzwV2VXZHKO3G4AroPnwDtAd6FqHhpBUO6caux92v45E0zVESpkGMqyLqti5sv3kU9+7eOlS20StbDaPHl3f2Njd26vLCvEO4JclQxNcrXPQ2Bq70TaN9asPCSc41+69pbQ1dfpjccyrqhrmWds2USSroMoaZNwBoOk/Rx9gZYimF3v0r9UPgnT9zwy6yY+y9Kr5nJlEDgjSf7ksfZhw8a7u8kbPt+uJh8WTOl/rHesrWMfucESbG1awDbg6E6T733DV8lCAux4lko1sdZm+f1X7q0n8WBkfOANbLOjdQW/qvdPf6SUBF4xsc2qdb1dMhtFEehx1e/3qlRqwYQoj0fhrZQDXTR3HycUrFwF2UI4exwSvmjpmGTHlPkftYGMiGAfzq+JZAjFr9m7btiwLzNB28mcUvW4Hg8Hqymo5n1EQ69AeHs4jU6qPMrVhzKa70y9+6YnXL12aTqZQ9y3auUdhePLYkWtXr+5RwgrlwHqhpjZRP7auo0nPOXOFthbyRuN2P9CZ0EeeMVE3uzvbaRKNh0NE85sgCqMqILi1PQuRlW+29R+nc31LRgYB7Lmii03Cnat4PpwTuLew1uVE2b99X+rc3JlZuKXlD88e9ibUrzWNLcS6p++dorZD6cbNI7zhS+2fL0cdo9XfFuPud5kv5t+iMbncgXL6B8+s8G0EA/U15qKafICjQup6w0N+sLt75cZVFPypds5wRqlMoJUMwMcwisJjR04cOXI8DG9MJgfD4SCOIjUMiKLHzr2pfTUt31UgV0/qohBR28J6rZsGOScd4tUgRuj8iCGZHIZRAhP6JhaH8zTU3wJ9nNuq+OKXv/rq6xemsyl1yUKvgqpCr708HaysrU6n0wByR5+2hdHhNd9zYHD9TibdE7MSukvEGXlP5jcNSmUXXru4trq2uXbk3e/6ERgYUcgAiPZJ95ByO3cirXXlenRhqpvM47dG25KQi+8P2LL8Q61ur5rVu3uTTvM4RIer3hxXLLwO9YoO/ULHERZHYURod8IeD/fN28Nu/aYfLXFFfOHaPTJmP3dWYWSHY7B6jq5ZOBgGZioV7lARvDpAj13CuE1y+3gcXr548fq1G9RMItIIiPk8Hwz0GM1KVFV98eKlX/mbv/CDP/LOf/z/+2+eeuZZUsuoCtZFNc7qvHbgxkq3Jj11jqda35BzpGa9249gX/NWEv0zHcRytYy7EYaqNS23qFuKExaODw4mk/mE+nnBkGjQpq+q6iYdDNZWV3e3t+cotqKqhpAwEpBaXfmKeUoWV+QShvvgNJtbIKiF+kScl01FfgamO/zUz/zkAw89nGdDKVrlP7iS2+qoHoWYrfEi8L1ejGrjVNRywYXtiEP3P1n06Q9rgNM7h/tEF73RZT+/4cvSt+tzLj3sMC3aFyuLPujyK3qqd+mm9dPlPRegEw1djP2wC+qXs0ddPYbyNU2Jo67j17nslkrwaZRCDB+2ERUNJNHcK2jOPJM8CqOrly7s7uzVbROranNSzdkgk7KdTgrO+byYcR6ur6+GnN1127l77733+ZdeFqLlDBVyynv2S0w8t2cRLKUiRxQzUpBFC6HU+sxmmNwo9OG0YUwZ1efxhRdfuL67XZYoP2qqmgJSbVlV89kcPSIlGwwGKKoCwBvBt5CwXrYWwL2eMcWWubuOcaSpwihDNUuqbtr5vGjq6gd+4G1vfesjaTzAl1T5iGoUZo2iQ+7NEpw1B1whpl335d6Y/qp2unopjf7uHbatro4yJO00m1vm2XSU75pGS07sZGgWLfM3u7reCjq511k3CwbcErv6ZrZ9L6hlrmCfiHdsLzXBfFls0sTG7aX0q6psMkqQcLvU4g14/iDN8yBoJlu713f3KFdTVyWgfIloaoaitoCFu3s7xXxaTosgjU8fPS6KhkXUhpyK7MajUYui9LBq0Cx2Nq82N8ePPvjw//Hxv6Dp1rZvkifHtTGngDEL5mEnyjQEwUbQtDZTe6GUP7DQ3c73noYmYY0AxQ9hHAT8zjN3fCn98ra4Dp2LKkJEwmfz+Ww+C4JgkOdpmhRAfuFKbYlsEuDgauKTJa1eS+/e0zUBdaOhuzYeFLjDLLW6qtI0q+tqa2uXM55lqHDQh5jo8yKV6NP7ZGDRlyY4Zb9kygQPiVN3RQA9OnUszt6TuZkl6xkiHd95StiJZ2qpsZSP3wS8+o2VuZdcs57tzfCKfw07/TC5suA8Owf6Pkqgha6eZK29Vxrap/IcWo4bNwp1aWi+yDDYqw2iNg1Xv/Llr/zu7/7elSvXYDAa/Y2CV6pnl2jp3rIALaw2jxz9rd/81UEyzvJY9XmK4ohLlOExVCag7l+04trWtYtXLqMnnIr5Kk96OZN6by6lJMeocTiYEFqwsSMKfy17/m4ox3lGLcJHQdLecfudV25cOZjNUDPUoIy/mM/rukYlI2oNI9WfXQkJFW2HK6zRED266G7ILtsYQU6pPB1D7aJZFMf7k4NWyhgdSOIvfvnrly9duev+u4NCkAehG+UZ5L0F33rmmWPBOPrMuk+6erBVdGICibZoXvdcVItauBdzVX3hfv7qECr3PE3P0XTGrvjP2GEp3yNdNFmX7PLCO+5H1uhy3Eb33EareHbEkjP3b9YBMzn7r5Xn8o1ZPENHEkzHGagTG6UtUesDlWJSrubGKQ8uoS6TgE9n093r17evfedjn/zEiy++1FAMWZXmIuYcoAxOlcLGnAsJc3Lr6pWvfvUbZ06d4hHNA0TVDo8pM8sjDElhtfjO97/z9HPPX3j9kponbIz33t0fYlYtN0pchaoHFRCOCQWuiGMdcry/lVqKwWGQbcMC/tgDDz3zwlM729tU96QaWZJOF42UAeexahoG9GabKHMa00fdIJE9r4cMNw+0U5JK82o0FqoFhajKoiiKJM2oJjGYTPZeufDaXXffaUjK1FfpeW5OjFjHGhHoV6VOeg3qeOkzHzEq7H/OGXLNjRtLU2t0qqTdnfIN70Na7NsPnXt3rchlj7jPdQv+uHfm/vu2qcJSmb2wMO8qy+ABNw9CL1ufedydb75gKSwXNr5x5JxNqopxCsTAm0OORWMPHDpTLmUYtqxNcllN57//z//lpz7/pYP9nTZgRVVwHqJZhqDWNFZo6SeNAZdt2wRB+Ad//G8oQUnNiUi1AlJEEWJVoCuDdjKfibrB0ECUx2MgSxdMdzlg4em4t+5YWR1X2yaQ6sVp0pBKI72R7WPRBaBWTDpJsujOO+5+9bXXiqKENa96wIYhetSLdpBne1Fcl5iBgn50BPhEXJoEks7O2wnri3g841E61j6AoGHERRDsTQ5Q5JGmcRLHPKpYXVazl156oX7Xj0Rh2AqMFzanNXq8K0EwIVUq3KYIh2SIRmJWk75ThNUJTs0lDBQB9yZB57CAxUyULeXrXLrTuqjHfO6+ujELT6I6OZnuraXPd8EbvbkB/Ia6zC2ZdFnadWF7+vzNm8f9432jq8+x3jcd9vWEvHnXBiYcN7xFQASWoGrk4HKuwjIrPqyCJmVyNdv4H37/X/yHz37u1QsXMUuPOBCJXZXs1Pa4Wos2QhV4wYYotWWuCcooAcNt0A5kss0pS6I6pWtzzMiVRXdniTHd7yTUdYGBcU8u+GwyVWDLQ56PnTusr6KkGAdaJE7e8eAjp06c1hOPbDMqlAeiH/YgH1BrL0BM0CMESl91pDPa1Cvhtk0vTdMuFx2otpPazRdFsb8/CdFlNg8k7iSKklY0zzz70nw+Vx6P09HNPYXTZkHNdiXXvWkaPX8CfhG+3dBAmCgMIxkGIto/2P3ud759fXuLxzn1qe8wyZ3B7WyTJThPKnbYJf/B3fTVqam/5mvpefta3gXGes5D95WbXNs+rTdezNKEsGHiJc6udwVX5HVM0K1ZKikM81lHrJyzm5aKIHaMBwrDjZWNT33mi1/95teuXL3SCgRZ66ouywqjjwiVQQB+Hf9S5UT4X40fpH8JkIyqdoCT1eRrNcCWKEy1ZKVCfcgUaGDdNkiFje0YpH6bvf42dCUApi2VKvcJQzVnNwwwgalqaq2Ilmyi59PQTEEFrSb3GUnnNLzvnvsG45FammJSUqywJIbDEWYRVnVZVZjjSwMWVBFC1yurW54tFFSSRQfJteVO7cWEwMjCra0tvSIVyiJZGIXJE1/5+uXrl4XEpBXb36P7H0mswJ4b7fdgFGA4cpIPeJ4HaTybbFfzqTJpwzgMIinrKszSC69e+c///j/4h//X/8v27o1AhC0eOtlZxkQ/hAZdPl4kaE9iueTpm8Hds15ynv4bb/D+IUdbfMzNJMUhS7jpiRd+d5+0PeJNnrN39c7+YdZG0BET54+mehrshdgV50FZN5OdrWvXbsAd4zxJE9XnGcpGtGVdlVVJU38xJFOxa0UhnqquqwZ/amQ/GvwBI6OHO3xINSJJUgk+jUoiQYDelBqkAZfcICH6/Gtkjv1jhZ53I+T6gn0xY1C0YjpBBIqAHI6i0GbdYftKHXkInCKiKHnnw4++fuHV73zn22VVtYjdWaOj5RH8+9lsjgsTuEQ34qeO9fpU3ZCKzqu06A77YGqkyZvpvGiaWmLaKmJjSkgG1EAoicOD/e0Xn3/2/O3nmICb3+2Lsl04BrMitxfFgQiiQYyZzbMC8yWEvPTqhX/zsX//2c9+5kMf+uBHf+HnY5aUgcgi3sQomFpfG9V1/dS3v/2Vb3z1Qz/6fmQOcQV07+12xfVy3Wkp6g3TD9YDshxOuY6FqGdJLq9y/+vy6uLLNeH/+pr+zXzDsZb7ERrfJLHRgH4Ny6EBhC5EwpReoAJ0Y3z21omIFGppClaxNJEsSLJUpXCEaKqqghGposYKStG2GBFqwpaq24ebm1MaxoRaCfqoU0ZUiI/Z1KDpsiqzFFV3JCM8M/BmN2RvXb9P8Cxt4OJFzV/rokAjy8j0FPFDKfbXxYIVFUeGJc7ZKLj/3nsuX7lw6eJlVcKHo8mMCFiYpFnVXIcboKoJof3Rtx62isqQuY0QtHeq4yy2Sli02N/pdI6UFw1/CMNQzQFHkKBuFJK8KqdPPvX8j3/gx4NBFmCUqww0gFQWdRnFvK5FEEa5TIK4DZiQ8+KTf/Wpr3/nW9944huvXrywPz0Qorl67fK7Hnv7uZPnopW8bOusZdsXrnzje99bWV+7dvnK7/3T33v3Yz+U5YnlUSc6tQT072rjZTakR5m9gG3vSfSeQOcc+hGt/6jXzYTA4ul7AbfDPvrrX7IzZXrmYNcbRr9jPRdzuCFuvKF7WTpS0PQhQq2QZHkcH93coMFB2P4ZtAJ1kUGmhL5KjdxoKpKiXAuc0m01fd1vcs6qdabJ6qrulqSF0X8jSWyz4UNlt9d910uA6xuE/aqK+wNZVRXBEKmv6yGn7G2yFZSOewkUdvTA/Q9e29rd3vnLEgUMtN0hEzUCQceObU73d3f3D0oyQCgonWDeCgW9VMWS8vop2ounQMkmUdcYHqObU1MLrhicj/ktCL6RMd5iPjrxqJRzoDjZX33yc7/5K7+8efZMXdVxzIMwZmHTztsozaIkjcJ5VVfl/u7nv/Cpz33j61/+wldvbG1N51O0BVXdFIJg68bWZ77y9ZX3Han2d7/yhb/87BNf/e73v7+9u0MdP8XzLzz/+S986QMf+XDQoIpyIbq8rGGO8/I7gRgw5pvgPR1I9+r4HWvbjSzf9HUIg/W/eRg19N5cesHFagR3ppz5wYkl91h2wSHxaX6h7V2gj9c2KQWGNY+5pVS6plWCgvLo4GBPChAPjeHGOaIokrCIVcUPvkK9JZA10dpKG4bai9VRMQU5IIVFOAs7NhY/Gme5nRcFRYwpsKqNAxPz0nFhtSNuF017t7oHJaWN9HzPkLHhcDAv5tQnE4ZFAwdR6NadHk05IqGrLdEdV7VDzlpWVvXHPvXxL33hi9PJFOZu3XIWjcfrd9x+9sb11z/+iU/u7u1XFCGcz+c0yxuToKIYCWNlDOuqaGr7rvLGqBlGUoq6/ukJ4BSJoBGn2qlG010eRryuatm0axtHf+93/z+PPv6Och+4tihOsdP1fFrOD/Ymn/r0Z772zW98/Wvfms+nk2JaFiVjaPknZVuWVRCwNE+G6SBO042Vlel0NplOi6psA1jkqJ0KMQH57Nlb/8U/+x/uOndOlCKIGPXttTGVzqZSsUyX9jognW/r2IKJfvXdgrR2aoz7UaalpSg3YWqPa5ZCrd4Ef76Z1yLATu+Mze47QBD7rwMhW1xLF+F04tKSLN6qLMqmRSeqzkm25yFF0UqZhOHqIPno3/l73/zOd+bzOY3GReqoUUNSImgH1QpKd49xgr/UuBwn132S1ex4Or8yGP15QdDA8IYlsL0xivtTm9NU5fF2gR5M2Pj3KjUVEuqBc1QLU7wdrxkwF5Vqg6eKGZZuv/PIDJ05YpRApHSXaRR+6H3vz5P80muvNEKM87Ufefc78mx18+Tmpz7x8e9+/9mdve8zGQ4HeZKks9msbuqiLEL05eLOdkDIqGAzfkJbWcQHeYxRLMiJq6ARxclUOhud9WRbFzV+YG3VzF555ZUHH3gbOpXIdjafDMajKy9e+L//V/+vb3zjG9PpDPEGFTqUqKhE23iE+PkgTUN07Y24DBtRX7x8UYg2jhLqM19TATeZBzy68Nqrn/n8Z++68w5Eo5E5RszdFMJ1/T08vvP2s3s+HSzcI+NFeu+T8BJ7+5CQxWE8fJgp57mey657KPe6mrMLADiHe1AyfyFmNZ1+0O/07rITCG7AXBrjU9XiMaCpOtiM1UgqXx/IthJiXrVH11c5V4V7+BvTfsKE8ZAmhKEyR5kOnMeOcEbDyDCh/CFGroStVOPLAiFRe0e1D9R/hqrwFZKLY9K39geFaFA/B+iRHcWox9uaiFV3vzyKoMJ0pynCftYqqIYIm9qLBp1dwcBO1NOHBi6RgjaIRsNQ1CFtIAdh8qH3vBeVhmkYBEkQiABVUMF7f+xD3/7O9156+ZXJZIqGrwxjylBsoDvF0tZTVEwLSeNriKZGSRNYEx10yUUx4QEyqqnWGIaPEnk8jOuqfuHl1yvRDLNYNhAI1Vxs702e+MpXd3Z2kgSTHlohyrriPBxkCawA1b+eWvJOJ5UqelRNA9u2wIA5htBHIMWsaKI0Kuflv/znv//Db3vs3K138SwKeWB2QT0Nh3t9EL7p5K6FomdELXLZm/Fp/RY5Lr/1+PAmJ+sBxLwUdJfFvulqvGSyYd1FZPsht+jy8lL72Glx7ebs+mtgNAozilBY082U9w+imVvUiSlKTh47c/bMbVKK4yePnbnltoOd7S9+/kv7031ViYuOyDY24wQn7dApdGMU7WgwuP++e7M4pcG/YVWVEB8MKMswCssaDKcyzFK0ISKvQS2gEsqq2t/d3dndaRp4ymp4EI06wL2oKRRAhMHNpH7RbYvuNojCKuwQDAQ0uGSCcd3Uzj4NZ6/7Vp23YTq6S0KPOBrmLIzyUgRBFURhU9Vt0yb54I5zt22sr1Z1ibXJNueAUpH/q9plwRi2FiY0O3GkaNskSUgV8yiKoToDICgE9cgNpKyqWusuPUA4qIrqs5/+7N/+rV8brRxpRRnFGN94Y7Jb17Wuk27lIB+MhiMaXgGhUFZlU6OHpmoapqLzymORMOaRxN7bP1BbwusoZtHVS1dfePbC+Tvvqao6ToDQ0k6dDpTYPthL1KINA/acxTc0TV2V3oHifczHTZTtYa+b6e0uOHfIQQtcuJy7Dv2yf6DV3wsHs8OjXdJ9S42oRiWt27DQWayuSWfTIPg7v/0bP/crP5umsZg1w43NT/z5n33j69/cnx3EUVyTr6eCX6qloU1wqr2hS5Du5uG5W06fPHUGY8nCaD6fh1KnSVdXRzJg82Iu0RcaKKYIoCMo5TiKg5AX8+kTX/nqq5eu7Gxtbe9uqXhP1wHLOtlAbUfZIFtb35ge7MFVRNi8bRqMuJ/OykY2BgvthPnMAzRStcPmuEaieQHSpOJuAl50GKmcSZTKClGB9tTxU6dOn9za2caYlEYpT9gnbYvW0PBAaItUECIEBBybgBytajer5q+im26VoJslEndlXctWIChnsFo8ZDyNrt+4eumVy0c3jic8wXQKJo4eOSplkKVpwNh4PIp4hGBFXc/nBeX5ujGO5CQBZSaaJqiYaJrxaFjVaFVNDfIhWXnI4kHwvdef+kH2dl4HTYuwOBYQMkxkuinQ0NviBeDNG72cmNWbCXwtkG/Xr3fxI3vqBbtZ+9y9eYF//ZdnJr/BanXT+MWAlrV0OuvZvNQbHlv3omRmrBsHjCmIk+x4ksowKKP55ig/dfIEmawhnCvSLhZPZPMqpPZQk8NQXIff52Uxmc7DNkzCrGkqHqUVYA/zoJWzYkZYXbi+FHAVk705tEUDiKUM+Op49bFHHr3rrklbi698/YlXX38depVqJ9pWplkWx0me5Suro6MnTv36b/1csS3/29/93Ws3rqNuF13TobA51JIuZvBcKk/5emkkvRt6czsq1Fl0/YdCyTBFeNQ27WM/8kOPff1zTz/9TFNVIUP7TJXZhCiCY9DBWNSDVsGDKIpDwoEAoTqfNY1I01R1MaGCTdOwT89uItrgweRg8vkvfvmWM7cOR4NEBFduXHvyO0+urIzrqgI4pmlms6JpKoosqBotM+yobWmoE55fWZd5muV5rnT7YJBTH0w+HORlWfM2fPdjjw1EWgXISzfKEeBRmobwR7oQkxV2HUF1HH2I1j2cwN2H4uek/CEli5ke74Ol79t3HJ+0+4ZjkWlu8M7egzUe9rLdPfRu9EIFbmDPN8K7D3SD12UFwpL0qyIkZxqYrl0z3Zu0h4hnrFIeQR3EomWYqrm6urq9syUbZfQ2CsNMbqypLbcZLZq0wsKwruprWzvn7wBRJnG6e7Bf1nVZFIFsyrKeFcVwZZjGWVWVtQrNkrag3FJw7caNKAzjNB7mo/vuvefY0aNhxKIkybPBKBvfdsdtp0+cXjuyvrlxZHPz6HB18If/6n+umkr50qhGoMbr1J9L2IL+Zc/Aq+LpSW8LIjJFN9DuduQUHUIZtSAIbj1129p49cLBZYW2pPkqGCNsK6R0UI54OAxY2TQzilcraRrHUZaiOQG1OGic6kK1DuU8AxHDguCf/6t/9h/+4uOnzp6a7k8uXb60tb1TNXDHFdJdDVtC/I1mwdC0C4yEEygZQ+AhiqMY7f4Q666qSog2SSFKMPeRuiGcOHoyG6e7+3tZFBFEjMKSUrSxRMOvxfZAuplPP8PSd/iW9VTtHa/6BfiPann8yovbWvW78GCXvnpeb6f3vHd60OalgO5DX4dIrqXfWnZs1yuHuWdQ2J5unqz6fsfwug+7DGRDaRdMNhDxwaR633t+7GN//h8uXbkkpvOQ8wRoYaQn3JmJFqKkKlgozBU+9czzD99375H11em82j84yAY52uikfOvqzqzYrqXIUnjCCk+HUnmdY4YUKZu6Cpp5WfEWYK+cpwkHur9qipdeeu7yxQvD0eCee+85s/n2p5/+7l98+q92dnbV/dF8Ij3VhAr6+zp2mev2Bnvq5/JsWQ81sA6D4Kd/6Zc+/hd/cfHqVdVJiFrmgisIv6ocR9X/T8lRavDRNCwMYTAb5KUNsqmwl62P1JdHqaeMGCvns6eeevr5F58XKIHC9sG+VVchKBrlmSlFRzF5ldSIsR5Y98YcwDz1LEnzUYJopZRRyI8dO/HIW97y4x/+8ObmcR4mFHtUTQu1AOnqV/q75ffZdhzYN7Gzndesb9P3fhcDxb1yCIU2WBqM6tbw10km+ReymWhvtT1A1fJQ2LK3HEdjedi6m4jQmQfMHu/AIWyxsDLLSc4qs1GrZQqZUuu4oChuv/XWr33nW2VZs7pSrSGVhrMFWHo+IKVMVERasLBAx7mqaeRsNi3KAhjBJC2r9sjGZsNaTMqGVV1HPEqjeDjI0yxVWBClMIDqrURRVavjcVOVgEAIRF9LNOg5mBTTva9N03Tl2We///prF1TNH3FGo0iXaFcHsbowthd7XC5GeyDsnu1to1zkbQhRl1WcJo+//Qe+9q1vT+YzNCtRl0bTTcrROKcKEZ1qUWOJWzVDfjxTzsSKfIeTRibKMOJosRujYxAGSVljDfkF1bULqth6AYyFEYUWzJplTNHMCoXNqAVjIbv1jnM/8ZEP33PHbeduP3/06PHdyT6SDvBW9LRUSmtRP+rObvY2yFlj9/mCu3zYq+/0LrWHFi/Xr9g5/Mjul0Xud1fnMVevlYH9qlHPHtc5MSXft/U0qZEHy1fqJDQ14MnQKHMLQR2w1JKFOde35huiVuPo1C2nsjQtZkgNU3M59UVnQLzunAH6dUbttXuzyaScVU05Lwsh2vFauLa+msXpkUhW89n+waSdykGaHdncTNIYOOYkHQzzfDhsqxbD0OqybGACXrh4sawa0TZlWQRwnlMZsrqunnz6e1vXrzfEuUh3Y2Sniv/SBDJo4L7Ts1z4HbaxPtP6dhQMBimqMk75nXfcdvT4seLCawBFwEfGEjpOMoJTy0UV1jLoBgfjEixxLM0jUuKgKqnTZ0QgOIKzKbO5gjamrn/0NSrH0jUq5EbDvEH1dqK4F6jPuiw+8uM/9Zu/+sunTp1qUPsgZ9MJjSbniNzRoEktBigL2duXDiyp/17mZfY3c/Fd28ugf8SbCSndXIUue9czkJ24UVcEbU+8hHAWzr3YMMY+4r/+ko0nYa8utVus3DgF99fXOyyWoB8IUrXExqhlmIsySwd5nm/t7DhwG0PfDsqGSA8hF6pR5Wmazcv6ytXLZd28duHKyniYj0cotUcDKQL/S5Yl6fFjG6PhMB+MB4MM2cw4Kpqq4k3TiIDJiLM0Se+441zbsus3bly7dnU2nYq4TVnWxmEUw5AVbUtQKNimqhhKlfGo4dQ97+UQH+XmprUxWzwjEo1eoeNkJX74gx96/G2PqS5DtnuNNY2Vc0+aWU1MNp/7NqkpQVSBdm9JGoxucDN1BQR1De9CVZYgv495DjRnOUnTGGUoxLq0K9pyRvpK6m5ebZvGyc7eTjZYzbNRg+k1dJEAiWhqHKjDAIaBFwoGLaThUGJdsssLv2qYoIuO7XZj4RTum/bnm9jqC4z35uqelN3h1Zr2yUFf+qaNk7yAsfuut4SuyHRxGdLuM9A2hJ3QoH9tG/UwWZSg0eW9yjTjIa9m1bve/tjpzU0akKJaqyt3WkdXNYrOBuCAXMCVHrz//PGNtbZubmzvxHFYlnNcPiCGQwcApJpOnDy6vrlx4tTpU2eOj9fA3kVRIMmqihWJ8qum4izIovDWU6fvPH/3kWPHUPqOpHET8TBJkwaZkxqxNSA00ZRaEayawW6rmw4To1ZNLttlg9jUT9PdfZJVaFNCmvnee+7Y2DxCn+qu8yqS7FUV9q5kzqqZ2ms36C1PydOQhXmCSEBZlmVR1lUVUL4OlkuMcWoJoalRgwJRBn9DNGidCbA4FTkR5BSogJpk3Xe/++Trly5Mq4IJGadJHCOJpU1mU+el6it0495lFLnEK17KfB2NqB+1YukYeWH7l5ZQ9AoQD/N+3RObn3330zLpksffv5tld6JnrC/XCH6n1UO2wIMHm6X0ZXpgPiWMPHWzUi0ybEOHBarRJg2ZwiGAujIZDs7demtAWEDndvuOjq1iB9oXE61RmlMhS1KXVTkYDDDSQAKwgNobzk+fOrW5ceTo0ROj0UoDJw6hVIq6gPJ1qlkRJAVlpKg2RoM7z92WJKmUbRbHgzxvGxUZ0tXIqkJZd3aUMKFV5YUxTZdLxcU9Ng6IyoK6Uzw9u5EEEsp041/61V/74hNfu3rtutvmq9OjTqF8/6n7D8B1cKwOVM+KMB7hjHrNY+YwIBngXqrRBFRdSoZEXAsAeJykmDGDmDMNXqO9EWGYpCnsqkBy9A+YPfvsM/ecvyvNUrJaNHpMPU3VtVAzsDUye/gmvdAexPcQn9TWtyxVxs5vS5/PTXjsjQ1Td5LC4hM47DyHnNox5DweXupcd596lYBmETdZvezEC/4Kw4CjUxSqkkxi0pVTXVxAj56gWQDUnjIfZetHVkXTRgFr+lUY9H+n0+lrwMG381mxPy+AfmBhGsWA36JMogaQvijyPIuzZLQy5jwsmwKQIUC1AmQxGRQvWZy6SrgN0IxKcp4G4XiYv+W+e3b3D6hlXXh0Y32QpvvtHpp90VE6YUbdZKgaqbPyu6bTN33irpQ0HprrEusKC/qNoCtNUUZZ+s4ffPuXv/6VvZ0DJaKMt9Uz0A2mwHTh8x65pS/zNvX5pfpcMqim01kjBKoUUBcGJU8JeqA+SGhppB2S4JRWIyHYAGueJioibXOJYcSrqn7u5VeFYGEUiaay4Rbbptq0ElIyzLUoF0ld7awfY3H2c1GXLpzBdf0XnsPSB/WGsawlC+n5vm/i+EOu7B51mDHfx28uMfccmegcqpzfwPyvR4xReR3gdKSozCicrrWw8tiUzFeWNFpOBnJ/Vg8Ha4MsO9jbVcoIuQv9WMwmKDrVGhhR0rqpD6bTLImEwIBfNa+rbprV1ZXRyihK4o219eFgKGVTATFAMSGQJb5LQSmsUkEjceZGsBCjQ7PBYHMwGq6uiRpqthEyzwZo2IUAku0ZosGeenrTQuBqWb+UfojL7KQxSXQLbar9hYzSBljIoiiogXK648TtmxtHKe6Lv3SfkZ6F4wTF7EfOP/ZTh7bol5AFGJ3KGKAqbcAZapdkIMuyytI8TWGTJAkaCKpyStVCRXmwQcDIWaaRa9genJHy78ETX/7SlcsXZd3SQHQE4BT74iZVbzHVZESz8M36zPTDz753b8qsl5D48qCiY7H4Qm7hSSq9/maiXgvhjl4vjpvws99mZNlpD5FK/YstaH5Ho/dakeGlfSstT1WYg/wooO71eB9XgpFl37EmhUJYzPjPfORnfvAd7xqvr0RRwqNY4Q7s5Ezvoor5KYJdlsBXRCGKZFUJXUquVo6Y1WiQpzX6pU+BUiCogorIIJFJmKp5MZ/N5hVGqCAbXFR1UZZNVVNqJOQRS+N4vLaWZVkjatX6gxge1oMy5g0SywqaHry9+8eli17TfrW/1JLXg9irYZ/IXpW8jmpx3z23nzxy5MqlC20Lox+gUuTibB+FzqvWPLpoYzoJL32wDkyqJoFtGkVNgtC6EG2eDTeOHXv0kYc++iu/9Od/9rE//pM/GQ0H+weTYl7GSRJTewAjzJRDRFKIoUSFwUxiPI13t7a++PWvnzt7W54nRV1UjeBx19rLGtDOInsxV2+nevbhMoO5r73f0FlecqgnCG5mxKoP3Dcd7bt8DUuEhettH5ZEW1zpMpizGywyjrRzMr+pSeBZ/EqnGuiU5C1va1YHdSBQru+tyaJiNO9LWU6ngyj+rb/9qzxiT3/vqd3J7mQ6mRxMDPgJUWtlq5IOhBBXTEtKOBC1GI0HMdW4JWkWxTGjkvUSLaXqAHgpjPUTaLmDUJSKSNU1gauoWha8Q7dTVuV0Ok2SrCybVsKmiJNoMBrCzEbU2rq5tHAyoe1WW5diMQ5puEbtTkvKFu9AX1ECWnBAI2XAEbaiY2iCEVQiMizDJAlC/pkvfe7ylcvUnor0HmFTie+IiZ1AX2ePLqc71eaEMNhEgIipo8tPFLTtaDgerqw89sD9v/arv3bP3feNjm0GQbB7becvP/t5JoGVi+NYlWOSV6t3w4wp1nP3VAteeO8s/Hf/9o8fvPe+B+69GwFKaFpNZkr9AkHvTZHohonfVGMdRt9vTlEucvqC43rYl/rXcNo/Lj2Hopm+6LGLPdT079+IJ9h6kShf6CnD12i+hbUs3iLTOrKLIlJQEXAqi9XtLV/rYXwj4bwVsgzEyROnPvrLvygOduu2/P3/7d9/6StPzGZza6yqL0MJwlriMedrayutlLNZMcQsgzSOk+FomA+yYT4krC/iKqqCqSrnErYhLGfSwWBi1YuLokZUaUSltTAGRTOdTg7mtQxZnueDQTpeWQU8QQ9LMO4b2Q8LA751OrcLJZv3bfwP883wN6EQwwiFlDLhQRjJoGGBqKe7cRYHMg2iKIhS+jIP2uYv//Tjf/Dv/uzi5StofWAkhVqHzs92/OtVyDvutXZ3dcDb8DnNNBd5Njp67Oi9d5z76V/42fvvvuf42dsCzHnhQVWgA9IwD3nYCpFmWVXDUFGntJVEtnJbL4N2F25AzLauX//uk08+cv+DQYi8FNQ7VXXCzAIGXqM4HOrzyVJRnT2zXF6Mt0DkLkn3ndnesVrULY9uLakXXlqfu2i/q813ScHcxDIxoc1Mx1hehgBzYRvddexQdLt7CypTKcDl15dLlgOy03piudFgRiURt0fUmy5oVvLRjcn+Pffc9QOPPf70Sy9Vly8j/GnCa+Q+w7INWXDi2EbEWStkJeosHg/yIeoP8ixN0yhJinkRcBjMqk1lDZhHg2ZO8NQwzMT0gujQDoRt1usTEmWtLTCLUdsOHn/sB779zW++OpuhCsr0fqQwrdbA1nhxH+tCF2pGaGdqthegOofmmsZREFRNXe5e3f7EZz75naefY0X79/9Pv7062EjXRm0gdq5uf+wvPvb9p77/1JPPXrp6mXq5thGPoABRmmz7Z9rYld1lPyJke1nQQWrcvFJ9TVmfOHb0wz/2gR99zw8f29hYOXVmnAzK/QljLEmzuq1jxAXQ2KAUVcShgcsSsxTN49e1EQvPuG3Q9K5mkv3Jn33swx/58dXRuK0Fa2WEDDs1BEBHrl4RtT2FM2Z3kVjNrz423yXhvi96k/izfXaLo9lv0kVtYcHu1yx19y/j/G1lUu9Tj9eXeAX6EKOGbWOxJSs1AHAXntnfAukiyS3UUhflOXUR3dikTsJoakJWl5zL9dGo2R+WVfMLH37PE994Yvf6tblqhWHAJwQniMNAjEdDWYE7OWOra6tZnqd5lmcDagWBojZ02wE4Etq2gtOLxi9CdZ/3HrpaGS5RVzVSoYNcFWREVAYbMnby+PHTp8+8+tprasUKhgkNLDEbyd6NAwPvHpBCqoHdYA9HPIgTMpJZOZ+kcXpwZftPP/mxZ5977trrV/Ymk2s7O1kU/YcvfPGus7d99Vtfffb5F15/9cLVq9en84mo2jAByLGpm9l0pvpsKGFiBJATAPHaelvTCD8ofQdouErIBnJ1be3hhx45feLESpaN4mj3wuXxLbeyGIHuuq3qpombmlMwEj16UFpE/Xps2WgHtFY63pAEbQjFq4Odna2Lr726ft8DMkIhcYQIA43hWIJ+1ic1K/aAQSai5GLWlkR2tPn4htmA3tWW+aRv9I3FX3wmXeK4L/2O+43+Lx0aSwtfu05XvFnnxWlY51zNwSUvXF96d2zOYDpL62hy16rV7q21uGFVgbSiOpIsTdsgKtri7rvOff/7T8+vXVdEqq5BCZTmyJF1tLlpalGL1bX1tY2NMI6zPBsNh0ma1E2b5wkm7wnVkLaqRS2BBKKqna6EVQsFQlPhzZiHarqEgu6g9QW4umCDwSmMdKFJnBRyQeyNvmWglB33Op1ldaBdhgjOt0GYBxGTddWK6vVXL37uC5977vkXL712aVLMKhThlUGLe9vanfyT3/3dKEAvgnk1F3VDnjLNGoVfriAoqF0iWKLiFoVG1krY01AdORh8DRWP6W1v5Wg4fNsjD58+cbIo67396cpwsJIPDg52BqubMI0EzUOpp0Uxi3iossGqu6XOY+mbVk/a3LgBwpJ7TVE/Wc/ruWyqiAXIJnlyRW+UXu8bOKFEqZYSPbNTvbUQLFxo0uZuTkfWrunSmaM9E/1NvN78kcu+pFXqEpNk4S0vVmU1ovnM0ZwECHJv3Y14BuYfH41i0D/OUKsu6GWKGrsaEeiDkFoWxwFPBpNJGQz5L374g9/+1vevXbtGGCFETFCyxrioSxbyhlpK5HGyuTKmRovRaDgaDPIkTZsWtUZFMa8qdL0kICDugmZ36nCponcUNWo3mEBgROYQC8A8I6bDuSr7aTc2N2DwosRPdReJgMpMKGDrxxOM4jWbWoo2Ek0bo8fb1svbf/aJP33+hReaqrp+Y3cyn8qmDtqwFlhvLdqCutyHHDWLbU2TvakAk0ooBA2OQ8QLyKcwVsa8fb62OlHhWu1zddaEtAAi3TykiXFiY23jsUffevb0KS7Z3v7Bk88+kybxrWdGu/v7DU/SbEA4KyElf+yehzfGa3vbO1gU6dWmF/lwtsA8a4hFlIaGwcbG0c3NjSiNsf0VrCPdpdczkZfzrjHorPPXGXCHNudfeHXRVu9RWQvAifgcZvbe5LXoELu/LWi8nnvvnKV7govnWXSGD1tbr67BufdeUpu513TsYhMRMD1wrALQRzu9ctRqSUpzUGoAGmbtrIyS4+uDu86cfeIb39DrB6ovCmR9/MhGAsyWjOL46JHNbDRE+6wkSfMsThJq7caquprN9uq6DABkUpFPnfBwM8uUt9H2lm7KgWERFSoaqeQHhh59dzjM4ySqS0SFozhO4zgfDdI8WwhidUIQNnjQijxJgrn45F98/rNf/PzLr7w+nc+KaibhnSMhXdXVvKjqhkrva/TYBTClDVuaPUFlC8hcNS3S3spwJX9CcEl96qg8uMsN6aicNag6VLSOIlAHrTCI1wb5W9/2wMbqxmA44DzC1rdia2fvm08/I8Pw9ImT1y++fsu58wgAVmUTJeOEnzx6/JXXX21rATFgughb19cLvyg7jgRzxPnZ4yd+/aO/cvrU8STm8woATFUFobVD12zD+lXL7NNDLe2FINHS16LdqN7w7fOOr7SM9+vzXAh5p+CXPX1ztu6CPkyq8zo9cMWy2zrkNj0T2L9/n0cX0onmCQXagLEcrLBXFJXquGIhCOCnuxx7FslD0dQbRzemW005r7d5cfL0iYggQJKqzeM4FqXIx4OEJ0nUbg6yY0ePRINhNsyzfJCmqQJyqvZcQC6T7jX+s6Jnuhxlj8zMNb1CgWQVbgftojD6MFGSB4Z2wPJkkERpK+RgkGbZIE2SlZXVPBur+cC0CTqDpoQT4RkIeMmH2WQ6+au//MxTLzzNI95WDRrDlWVZNVQPidC4scFhn1PJHrx3anEdUAeQlnBkKpmm9w4oFAaGp+FJ+I8x9IulgAFVS+rIN0WdYWtD5cVpOkoHb33kwVMnT6wfXdm+vofaA7TvhHSoG3Hl2o1vyu+/cuHS+ur4+MmTw+FqiakZTZBm6+triEmSK9yFnHW0yaEb1VaQnnwYhY8+/Ojf+e1fv/vee7N4JGu08FMYDjWRzsx79xmr764tvFybUSsD3+90Kd5Njy7kUG4a1lJ/mTKxZY7uTXTzEhO4W1D3j9ev2jqzC7LhDe0L94BFo9D9jJrbKNqW3Y3oUR34C/UMaCSuOs4qXN3NLASbicATD0JCWUQvv/bqnbedzId5FCN/g/J/yQMZbGysJUmc8GRllB4/cSwejAfDQT4cRZhOivlaqv0DDTJF4xeFUzaGgLlZB+mpoX/olNdSe0dkm1rJsgyYX7gjPKza9sH77t7YXNs/2F8ZroxW1h9968O/89u/kccrhIXWlVWhSt62osLUUUCFGZbczP7dn/ybJ599Coq3ldPprKxKAi2RjiWAGJiTVkKKsG1ErXqIUaEPZWu1sWx74JKRLEWLQYyxqr1CDV8CgLgaNKOO5lGMCWZxPMzyE6ePrK2vH10/tj4e5tlIlm0SJQVqn6cU2WuRRk/ka5evXLh2LU/TC1evv/89742SHOPKRDAajYHO0ppetaDvAsjWqIW4QKE0y9L07jvu+gd/7z89ceupmGfwAuhgg52kEcRWePeJzwnQOLxpPNOFoQz931yWc3qoLqNyM+JUB4hc+rQywNHfNsxz2GtR7x5ygMO67tIOE103FWmd7nWcpp6M0SSv8roo+W5NMyYND1YYOkzeQ+gxUO0ZVHWRdydd2sqYJ2p0mKoEzrJo7ezJy1uX0cuF8yiMG1mEPIJ+lPBL8zhbX99YXxuub27yKB+PR9lwCCcYaEqIAmSF46xtGVDPdG07KshZrQZUK3IQDXp0MNZk1DqqEVUUA5OPqBUL4iRuSnHutnOibn7+Iz/x0KMP3Xb3fcFsEtSziDAQjvfLZYjkLRK6smmns9n/8od/8Jd/+VfbO9stTTmpKtV+tZvcrf/TnXLAwdo2VpuFZuym6IGKb8l2gAComno8HK6vb5w+fvJtjz28ubmxuroasrCYF/sHE8naOIrzIaYQygYaezrfh3ku2myQI8JcqwZXQDJLWT//wktHjx3dWNtgLMizcT4Y1U3z8oUXTp+4LU1zzvl4ZQVPCNBssu6tQWPavuoRihQhyEfpmZO3/s5v//bZc7fyNI04vAkFhKWRiBAEXfrDj7x1SUPXrO7I1wy5cmiqD51S3Z+MynjjyFiXPPG0mR/vtj3AevTcO5FvZB92aS/j6AYSDDJmobrNYUcn9OYe4ZxO/0hYHbtdqvxTFespGDFepvBPgfEg9EOtACw+yN7cAoDExtZ04aMCydaiDnn01HMvbF/fy0aDuppFYVw1TczY2sbG6dPHTxw7MZ3OwjjN0nQ0HPIoYRFQnAp8T72ZYABT53DdhccRYXq5QrCKgB3U7A2lCpy1SRw3NM9Aw7Tp8YeMzZvm7/zGr955573Hjm4AQNm0QZIEg5FqqYMQW9O0URojXSTq7e3Jv/m3f/T0M89cev3K1etX9w72ae45/FjdnpPMDtX+hroE0GhWx8HpHgMdCWQyhglCUzZCrK6sDMcrt992y4994IfPnjo3HAz2izmXYVkWaZyUWb06WsnzPMmz6WSKOattMC9mWTCcTmf7k4OdyYRKgaDw4zhlYTibz9BMA7GEOImjPAX6OU6zq9f3B4PJkdVYBnIwHFA/M/S1VRa0zluR2a/4FoHxOMzS7MTmsV/71Y+eP3/nIB0J3lL/aMyrgn5GEBvIOMccIovOUKzBYjlqvQuZWN51J2F2HOz+aioojS/rDObp0b0uaneYbzmLulbA4gfL33RKANyPOu7uw0U9W9E9u+MquAAGVzm7MsM4u9rC1PyryuqUkgjsKUkFmYAoKRPYhF7RoqeIrcvsOPYqCKxKBXl4/vyd04Mb9z96+vPf/sqL04O2ZVErfvgdj2Xj8ZHV9STOBAEt8jyPeQJgj34hghVINi+Qv9VRBz17RC+ji4tDGYAa7QMkdCBivaTjaFobLRYWxSA/fvz4+voRIXmIULRKDwHIQRNChKjbOkrjb3z9q3/2px//1re+u7e3NynnkuYPNeiIp4zlMFAuvUDvD2rQQ32tqAZSYfptIz/Vj1M1sBK4gFzf2Dx2/OQtp0++54d/4P7zd5w+e2s8zptpc+Xq1qQsgTJBO5sGnXeEqIRgGG4Y1C0Apdu725PZrKmqphFxlGTgV4xOR4anDfJscOLEsSzLRFO3kQLfKBs/eOGlF7N77zkSHR0BUAqJDgYmq0lFF3RveRYqBHyUJSvpyk/85N+4/+57B1GGLqKiaeGF4wkY9av1gjLgnP88BjNE5hzTuWBdDoO+o9mwqzl1NJGjrpzqaZs57dnaPrV6azI2Y3c682+3dt152jCduY6iuu78ip/9IHDP+LXYG8Nrui26K8g8RdwhaE3hnlVXRs2qeSdaLwd6b71ycXoXCRvAHFzb0qsqckWWGyBQsATRIjZ88sRtL77y8pHh2gvA+oljJ06cu/2WrevoBcu4GI9GcZoNhnkYhY1t9kLYACEk5aNISVDWQxl7NjerA+HkN6Ysmc0LatGk9kqhDxgNPFSnpeEJrUjSAfDXpgcHGs5VJUaUQ/awMA55UAWf+NgnP/+5L21tX2do/Y5OzhiF2tRwpwk9CRMaNVOYJWOJSw/3tuUbAGGr7cLAXmjbtbX77z7/8x/9hfPn7rvtzOmnnn/y+Pom0C8sBoyrrieTOUSasq2lGGbZtRs743EuRDMv6+2dncl0SigOmcRhlkdpNiBjKYDrX6MBXcjHCDagQx0cacTWZtNBOkwifnXr8pEjG4Nhhi5GkCVIx6m91vE7BnhWHCcogQj4+97/vsff9uj6ykaUKsZFwFxDT4G+UvFnE5mwFly/eYzT/MV9WQhdP2JlDieLq1OW2rYzLK1xdGoOvIko2BMbcJMlV6v6l845dN7pEseWNyy6ZgGS5ZzO/0f/7PxGhedQmJYPu+1x/+2YXlOTHRKtauisHa2mM+vRd0EXqLYyShG3ao/leuo39fzNvtC8MNnKJI3O33XL3nx3mGWibtaHw9/8xZ9Z31y9vr41mcyyZMCztA0xYRgtTjsPlxRoi1KHAHoOxYNEtkbza0nY3bIaeCREG4YS9VPQydDeCi6h/HPKMAUIDDdtGKESlmKt0FAqCg3UL6rh2+DG9Z39g4MKfXEFYyEMa4SAFCYcLeHRP1n1yNQjTqhRnyrnIySnbFs1QVy0bRglo1H2I+/6of/kZ3/5h971nooKfSIeHj1yrBJtGMhYyjxLgDsP0baqEWJeFkh5pcn+bH9/sgfrvyyns1nQyoRH6+vrccQHw8Hq+jrcfZhLLQO+Ktjbn9ZNOy0OGAtruMYonpwkBxvjtRdfeGlzvJZw6E/kv9RsQQJJ6cnJQHZgmwZ5duftd7/vR39kc+NIiL6xoWp/S5Fw1a+DBl6oae5UzKlqx92gtmZnRQxO6yCH+J3fuooaR5epwXFUn2kYq1PLUhD36rnVbm5HfXVJyNllUfUiaFFXt60H+BmNq6J55rNOT1p71L6h+cOzkruXbv2pKq8tE5rFdELQV+IdLA9BFs33avXKLu0uwJwKFGdTVazLns/tTe9ntJz7c8omyUGV8yAYj1bOnrl1Y/Xo3/2tv/nwffe080aUQZaO8ywXnFVNy3lEHQEQ7VV334KweZqkxIoNop2AaSqPWNn1jjVDIJU8SwglIQmqAb2EPjawENXoEhwZ8bBB83KyLBBp59DRXVdKlWrmwakTx1T/uxqOYoiJ5GooixodSKYyZtsr2sN4QT2iTyDNS0VRJP8oGgRnvqnZ669droNib38yXt8gYIhcGa3ubG9T8RBhU2AiwBOo63pnd3cyObhw8eKrr18YZHmapLgzKRPOV1dXjpw4sr62Bmha08znE4raw5WIo/jM2ZPr66vFvLy+tXP9xhZVbzUAK8dxmiRbW3uBDJM4Kqq566QpnzZJMOUsidPbb7vj1371V06cPpGyFIkDYnQ9MEOJdjTjUyITMQEtI0lwqrpia/Xp1o3UUcBBuTn2m7btbAdE5wPrTjvxKM1jVNqmgjudNdgVNPb8yp7A0Da0P4lDKXb9vl2Tq1IXbE8TXNLUrl3ijk1N04hOTTrusKu79VKtP2v5T+2i6tdOTq9KEFCPBmrGZPteBd5L62SIDnpiC0e4PGx6Wbjv2QZYuKt7bj+fot1G8MCD92Z80ERNPhpsHtusimoG7oWnnSQxhpWSlQHzu6kjRGHQYpwkPjiQZAQ4DsFfGnFvy4qQaYk49cYBl9bw1qIUqRnqvkamH8W3YDza3onqeVNLHVQWoQds3czjKL7z/rvYn0Rxm8yLA5o8Ao5EdbHaPtTJQy9JHmLAIaki1cFEtW9WNh5hJM0ce9Fevnzli9968q6737YabgCkItqyqnZnk81kHDNWY34nbr4hWEiA8HJ16eK1qpiNsjxD8VDZtmK8Mjhx+vj62jrMcngMNOmCpAiidVLI6aSVzdp47eTJ42tra1WBLU0GacSiKG0vXL8wiNDYU8WgdYABbccwNiWKkTu89ewtf/vX/9bt587imcH/wH9qxyi6AKmuxkbCIEFDADWHh4FWONwG3TRN90lRo5lp+JPvx3r6xjCHA+TtW6XWizMAE2mqwnRRq80F6uy5/oYKph0CTjUmvuF2J+So1+P2J+y4zBiCBqhozm/cCAfwSyKmGx3h2cqLC3LaMmnrn6xgHd0hOARkF838Mktjdmd74Qe9Jh1Y9qNW+jsm8ucFtGx1KVRpI2reVE1wemMzOpg346gsyzxNoogP1lfF3lTKGrqSOnE1xVzbTUhcyeF4sLK2UpYl8lBU7U/CDZgI1c1OA4dNEauSczHnsgTNDYbDtkUwXQlHQBMiTn6InqgIkqO7oJY6GO3DYgm1uj4ejbL82sGBNYQoYqMVESCRNcYFIlEaRSwIJRaG6JbujwzOVUIS6Vwq3WB7Bwd/+clP3X76tl/6xY9iRFnU8hAEVxRlG/H9KVAhRVXNimI+LwDyzLL77l6bltPpZK5CZXmWnjl7dmN9va2bskYhkR66SJdS1CaknMzmVdnk2SAbZFGU11UrGQ9CnmaDrKhjyY6ub16/fIkJXYWFXByyXLi9o5vHfvZnfvbWW06nYRoIsuk1WMM291e6RT0B8ihMObFWNRQc7IAZiqkQxdATOpxAs44l+YaoH9wy79vUsVacJrqqcHeUeVS4U82w2jKzvEVOoyFV8poth5tJBtbvpVaoiqK6Kb5duMm3pI3itXPpnD+6eZDmXsXoS+uiulHBjhCzFVsqxUllPUY80MhexcOhEVd90WRXZ9xix8inlRqfmaSvvrZbSkO+Cd4sq/rOO863dXN9b/voaDWJUVvPOFzfFRmCsudlPqDOHxoKAgZDeVOcR/lKnO0xdCvHZFI9/cgYFbgwWVJ1VasKWQqRQvk1jSClzGVlJKJoUYZgw3ZKopGPEOkiQTo/Y/Ld7//wLf/j/3Lp6sUW2okxGaacS6r+q+uaY/QuiqQIj2xq+lTzEcPAOhZnm7zSUN9XX3n1X//BH9x777n773wkXskHg+HqeGU225/sT2sZgH9L/NdKwTgfDleyNEvrPGhvzGY7LAjOnD19ZPNIKxqU45rHgvHG1IxKB4NpzkPRlPOq4rNwkGCEaA1N2UjZpACpsrvO3/X8Sy9PZ1vUVBBMHAYoNsrS9G+8/8fuufOOUT7kAPHQ1EN9CyYCohSWYgaT1NDmFugLUQcdwVNUQ6zS0jzUZbkfP2Drh4H8iJA90J3ERnJZMFS44Ll3Q0DUXwrWYHhJ2du0SU5exbZ202hWZ+428bzD6tS9T0WY3Ji7znEpztMh5m7ugeYLJ0ql3nJY1qLd/Wif9SBUwQ7dni3oM841GpUtreR0lbGuf9HK2BK/7WvmFL9rDtahQhAYqJmJoE5Gg8tXr6zLNoviQQRwcsuCQZ5XZR3GKXX8UDttAiJo6yI2Vtdee/H5G5hIVssWc7qzLEFzd7i4lC4m32o6m4c8zLOUKhsYVRxhfhpHuxjEwdTGYrYwZb7NKE8Fs0RBPwmbtpWVCHjJkuzxxx/9yje/pswlmoKGQJfKtAzidK+qUbMRxXARu8IavTmq8bQqFVTGXivaiAfjbHD98qVPffrLd91yfyySphaDweDK5cssYHGaDHMArXb291fXNwZ5XotmVs5l28ZJlmX55ubambNnaOREQ+gVYfv3UEcv/EjhcdRSUqs6OZ+LG9Pra2trYZwC44ZtixkPH3j4gWs3tp/48pcO5jM1uzGMeBRlt5y96x0/+AMb6xtxGpMJoZsGmCdrU7kuQlATv6IPAqXr/IYNzates7rPllv2r9VAn/YMD7qVO0b7OQafdRkpAEEPz2gsW39srVY9V880HbI2ou2Q6oSAuqi6wWzqLuaKkTpv2rCbVcpmwqQbh+vCU969dGOtF8sGbS2aDqURYI8cpQACEhB09Su1OGcgd7gtPWfDyDwblTdTA0w23fgp2nkgHu6wUipMphU9gQtvOXV6urWN9hpJjCFdES+LIs+iMkvErCjrJkSJcFgLjKtuGLiAR2KY5+PxCjEwihNEK6s6EGivqCp5JTQLg1qmIJaEP6TEDSBTjeRwLfXQNk69gqC0qQ+uEpoY7as6chABQhcFiFfdfe9dMY+roAkjoLd5GK4MV46fPHX23C1bW9efeOKrhFiClKGJa3q8kJI/aFCCXCoKfZUFjyIsxuGphvxTn/70w4899COPvSsKE9FMs3wwnxc1MtPxxtGj1/d3xyvjNMmKckZBbFGV1er66snTp2MeFboEX7cU1Myl4n7YC6R2tX5ESg2A8rIsuGjn83k2GAzSQRiw8XDjxz/y4aIovvzEF6QMYvRAS04dOfIrv/QzR08ej6MMkGwT3ezynp2nZHWXMjK6QKdVSqQoSGN1I50sHNdnTIehHVXQJV7dCLOvX1xwr1K1zL+6lRNd/tnEqWxaVhuZJhjk+LSmE4GK4fm6sXNZvVRYxxU2FuSwrHubStItA2IZA8CqUJNZpQk4Oq1F56grKZoSKdYkxTBnl38dqeD4yea0RoZ0wvCQdgx0qhCSEVmhdmW8/vqrL50/f850YkngxFDLFszapmZsZVUFrKGxeHXI6yRNT505df3GNTiaZL8oZ8dCnpQJx4EdZmUp0iRo0DMGF6YxoqKu0GVaWXYuFMRUOECBkZ5U8ackBrgrCM4cP7dx5Gh97VISRbfedsdbzt//O7/xy9nmsVOnT/23//i/fvappyfTWYUtVOly3ZZRBZ2VVUA5K5VlUb34hJAiivnrF1//73/vfzx79OTRjWPDATqtl1XVVDWLonEchwF87DZu4ygRQhTTeZ7HR0ebqytjnMPJt2oBqfSezjVoTKnuEyswF72pmhFGdQfz6SQI5IAPoyQa89G52859//vf259MAhaknP34h37s7jvODaJchlT3hcFHJqBj1FJnexFBqDRsi4AKhaHVcSZ+SUMCYCLpLBspQoY+052/1bVG9BSI6ya76tZGobuPKTXhwZG6NXcxaXJhnCiy0r/GhLT2sbqY0YzdZbt210Y9+vTfjdOxn/SZYTmb3vwQew4bGDWXsHIHPp1oWKNiLjYR7EWxur3SRe6d+DEZZL9cVTsFrgRE3oGJ9txtZ2fzXSGaKM7ANjwkgIQQdSXboChLPG6KcAatrJuaYcQRi+No8+gm6J+q+YFURuWwHsRDmGdqN0tZL4pOUVyGpnDWKKSHmqC8Q0Djy6mI3UhWXRhM90dZLCw9FlV17yMPPP6DD3/tCXn6+Jn/5//tv7zrjgfZSPez21jbWF1d2907UEXuaKhJNr22OZXrpTLQJptA6C3kh+blLImTC6+9+o0nvvGBH/sQyB+NWlndNtN5sZIBqHmwPwkCludpK9qyqAfDLMsGkZqfgNZCpjLL4ntUOIf8BOOiksxAnBhDwCuEojLRyqKcYzZDnrSB/MD733v12uVPf/ozaZ6dOX3urY8/uraxASFHJpOXQXH9Ut9ZpSO7o7upWRSHUC0vTYZcBbKX4ghsGNiYFE44WHNpl/TwVIthIQ8r2HGTra92rErVZMZEbhxfVIeyjN3cUbNXWO/YGvqOLUc4UqxjPvuPtppN5ceyfXCXbcyETpx5W2+9E4EUJ/xPSm36sGezmm4eax9n42ylfXXhat0pVR0Feg5ZEqdMNEjAkntcBrWELVCpct8gZFmSTMsCjeJCWUlCFobRkc3jTdPu7eygpQW0aEj9bkFqhJSk0jgKHzVCxioNHPF5iU4yw3wIuYD0Erx9wCWoc63tp05tZe0zRygq5tDv03c+9vjf+qWPnj556/qJY0HVoFldWwdJ8jf/k1/79Be+9PrFiyohnFAEnWr0VKW+mvag8xx0V1TThZ7VoiybaJjO5sW/+/jHTtx56+MPPRCHPE+ToiiydLCzuzOdzgcjXsznw+FgkA+kkPkwz1KAwqkRGNweMv1tG1ctkhETtqBYzb2NslFqUSdh3Eo0/kR37Rg931mePPiWB7/17e8MRoOf+8mPnDpxNEZHPpVO1eFHepxuzEM/YR1Msqzjc4/Z5Sjieg6U6eTSc/Y6kvGCqG4/jmXBLeseGtilDvQ6mtqS4aKLbSNDrgVpPfvuKEcSWL+k4wmtEOm6fjNCR+epX/W3u0dF98WWIj/96q2eFlQ345jiupt3oJo8AtxqhgQ553f2A/8vcLhdtWsVdH3MdGkxOkK0MoiT0erabL5LUzNpRlaInmmzaZkNM8xboMn1avwlOtFRtFmG4Xi8kqbpzmi0uwseRtvZNAs5h4VYV7PpZDKZYg6LhMebp9oFKosiTtPhcJilaU6dKRGyJbg+hVX03mEMEFYKh5ZgHRSy5XL4kx/8JbbS0hjUIIhDlBWa273/7vNf/NIXZrPO+1MgLV0iZRxEixbWiGgKBlRlmSbxaxde+6P/9Q/vOnNqbbwmZFOPhju7e/vT2byY56NBCbtfDgcDKdsowdjeWogSGC2qSyTBpXIhFCbWVZcdMpZg3RopRbzNY7RAIWunaeuGx/F8Nn/g4Xt/cvLhl166cP7Bt/AAs1dQotAn+l4G1Tx0IoYuKu0cbfxP1dOIBLMTLHKoqQsn25KGZTUGPVqzOtoN9/hsb/MnOlm8qGWWXcYmn608WvjBHKa9ZRf9pFvE+MVL6pRu6YD6ouevHqaMTSjO/tr3UVWKjyFH19Q1h1IjK9J8yYVl2JHCJh6xbJ8XjC4TRMeXERxlLWchGmLIRgYxgRlx0nyQNoRYDoOwESKNYzILkEDFMeSSZXF84tjJzdVNFsoszZMkQ/ycZt0fHOw998JzV69cK6sm5KyhIslKyGE+vOXWW9bW1rIUUdgshYxo0SiagrfGPGk1pAoroZQbul0okq2Dhiod8Klq3gywVcDjEyfOjMcr89lMhc0JlGmQqja97qsn9L/CJDGG4BxncZK8+MKzX/ny1z7wo++NMXwt5pzPigKIawLczIsCULIQQKiiLBh1vlbcYsJCesIEcZLKERqIGkEsFPgWgTQh2wYoMeTJpIDXT+CYmMtHH3rkRx//0dMbx+B9cyUTvAyEdRrV8Atr0FmYUJfs6HgJ7xFSBHhSU/rcc85cEnbI3VCP8ZT71OWymJn74dcCuGc1utXYWl7TWV2t4Pat6ZxBywEuX+qSKGNnLNyK/45f73yoXPKrLF3X3QuFWffa7qMqKA8pEiHQ7KXiLLWzpq1NScfrhLylEMdNt37xshUqyUi3j4yOqDePHHvt4uvHqRBNR9SUtG5aqmXnBOxBJRMVxaNvFMJMDG5uxHmaorsV4rukvSNUzsRrq+ubm8dev/D65UsXZ7M5Yy3aS6fp0Y2jp06dTNNBwGRNrWyQ2KNUKeEeuhtQPGomzcMAR1E/W0lUgkK/yJXF6C8eH107csvZ09vbNzC+U7sKnVvdBS0tIs0QNrzShgLuididTP7lH/7R0VtPPnL3PWIHXXtQStHK+XQ2Xlktq3qYw92HH88RUqZwmIm+mA0mNtagdl3jCFmiCxtt7ANT4ZqGMUDGMGwBtZoR4zLJxcaRYZYmPFRBK9MsXisK41KrOizNwDp3airCetpZ76vu2meGxvlE5WkgNyflahyfigyVG2VrQxiG7JWr6pqglvQt3TvnN767p8R1TtfhTdNE0FWWZiSi01LHuROTpvV4sLdF/VCTv0NuRGyxn7W75TS2jsMDpu6thBpCQwwPU+qs20v5Onfu2ebO7Zub1d57LZqw5WmWQw3YKmKbEKE2IEkUzYlvGWPzom5Zi6hoyNGpR7Zc6RBy3KuqCuZFnMZ5kuX54N7z995z13mozaaAMTovVkajdDBgLUOtLrWsjuNIKDuZxwFaG1PKRzLUTGj0jSZflNkGGOKg++oq7qUbwd2958M/+q1nvvn88y/U1R7N0NYYdRPlt6SsxbZuo6NyVdSRvqx4EifPPvfsP/2n//1//p/9xjBblwEbDofTai6mwdrGJhFbi8GtohIB+toENB1D43y0mtJE2X1grFpdHeX4NiwI0NMHGWnMRqIJrkExr5BG7IqmtcbtPEuLx9Wt/W2tle7/0NGVI7HUk1UDIJUP0rFfZ0g61Hq43byMxhdDYdrPdtKdNgnUqfJFrnFxktZF9wm4/xU/OuXByFR7fWOxWLnjgSed+9X2dVc4bQsPPfPH3Zouu6SOpBnskTob6SRv3GrHrhpd4hTz3fTVMwpsG6sWLJQnSUpKyCSFqOaOikpgMIchS5OkqlAyoDpDkFesy/ZCheLFJHmdSqmqsODz/clBQlCFQZ5znoxGK9CiMpgczHRKPAwqxHTC+cFE3uqZXUxKG8RypH/ncClZbhNPsGUDzt9y//233nbrwZPfo00T2tVx/EdPgxhlqDxBKiQuZSuGg+y5Z577xH/4wi/83E/JRqRZHjKuJlmopB8IApKsigbQwFqreR6qq+PVL3DwdeMrsqZRkgHHAl+fzWdFOc8GWZyg516aDy9e21obr8VprqdVdW4P/WBD6hoNo9FKtnbQi/VYcjNwZXyR4m3WAvRtc7NmXZiznIJs62pLUXYqn6ucIWvNgGUTnteQqKU+pgtZdC5mNY59drZbn6Z/mzHWBr+WRwumiBe8cuPW3iIMsFrHnlRRl0uOCuRlbt75jLQ9B0SRGDgkSLKe0OzEGTGwGwKYB5FXMOLJT/tgbO2ku05bytuGcZgkCUYEYoitzp5Syxmki5E6otFqSRyVVRtT3SlGMVB9bF3VIhTKyO+ImDERCkazhVnADiYTjvIeOZ1OW4l51EkMWBFjcrJ/MM7HKytjNQfXDHZiMlQmNBmiZucdTwtho+7GGVm5QRB84Md+/IXnnn/1lZcP9mn6E4Wb3WdnPSVlTvSGloCHWxnHCG596Quff+yh++68++7ZdLCysrK3uxtyqotAW01gO9MkDmQj6hl1ctdBBbXdwH9Sd9eOF8g1UmNL6cJmbhp5S01dzmfTfJCncQpAzXBQz4u96cHRdGBBdrp3ApnQCm6KmJm10DWxutzrPusOY6h6viCB3+cdm0jpkEBLbMou5rLAeX5VhKJ9A9kmqKO2KFzCXDyrZjG1czq+oLWoSiA4hYzet9VARpUYa22QyPCMNYZciWQB0i5na97Qi7QVFdZodx1yP0FuvB2ElkIq1lEVKVyNPHFKGc2sYDKglJjRfO1msu29aXBBFwSwqXL6RbRNGPBXX79w/o5bsQUEB6CQMBXyMVZUFZcyovZ2jUDrf4KgRLCAlRPo3I86P+2iNocJXwZOr8oqThAYijFzOGacocFMHB07eQygEWoFRtVYWKs2QshOtizmPjIyFNWjxJJ1SODO2+66/dy5733/aa7NRW3CqWeIUFGHRDL8S5tKwMdAAGEqkzQ5mBx848lnH33k0clgfuzYicuXryAaTxg26t0KtNy8mpdlBevfl86o8CDMhLMnqnRX43gRdac0lwyC2XQu2qYsUDCRp4M4Ral+lCaXrlxfH6/xODapPz2jUCWxdQcIJ9js5jI8g0yRf5fEMAV1njPmbCw9MYPGdXKjyyMqS16OFHACUI5a0aFic8wC91pGU/BdJZy0Q63vwyhG9zYJyotSDx3110YKFXaosER3YgOCcQqkjb9lbQ+9LOOI22iEV3BkWNbpLUDo6FZNcqa6Vo7gEAhb+3SGVRX7qHiNs/t+DMLf1IUAPoLMQducOnlsZ/uKlJjiQDWzak3Ew4wlnNfos0O3qfQPdWFCKQNcNW3mWZpwn4oOCVOyRiGANDcqOsGFoiSMAdLUhoYC4IXUldKNrntBIue+1I7zUNY1S5IP/MSPv3zhlVcvXtzZ2TMDHVEWT83mO21sDc+uZwLab+m8Dwr4o/jzn/nce9728F0PvuXa9TNPP/M0ZclV6I8HCSyKusLdkCHcMY7KxXXbbMG9FDOH3696r1POOOZRtBIfTCYlvTD4N0ggI2I+20VnPBo94TlttJV6SHQvi2FUpxsXNXdqYLX69tUYR6cSyXCBOZFR5U4Ju8FYeBftlKX6pDNoPGvP/qQsfVvV40AeLfxF05JWgYqnaeONNrQxW8eFBZYIPYA1Apcwa+Zm4UshKUAyWoPwzCh5R9g4frE5t9t4z26Jvm21YNvsxXi1ZMq1ODG1NASCKGrCOE5Qi2J7MFMGiEU4AjFXNItzt6O7rFLRHebE2Vtrm9WVCDFEKSFUIN4b5+n+QdSiCyy2DpZ8ICkhTAwmGsxr6ToKaSJ1OolbksaVlNxRkxU1IEz7RKidgL6KaKKYQVZiVDWCWMaptrJbNytfbByI8ROIF4j5jOeDj/78L1y/cf2JL39tPp2yIGxQrjQ/mOxKNAJQmMoOPKSMUG3yUe9K2bYUYWsDIf/8M5//zTvuePiB89/89jfhsVCVCTArESLviCTXEwKdqSFklgwMOlSJKtOyQoGfVGsya1cmcXjq5JHt3RnAmw3akZF32g4w9bdMk5zqAQwvaoSxHT7ai4hqeuyes6U5/K+r3CQifAgg4ql7BqH7dSOHOwSz1Zu6iqa7qOtDWvY2KcHOgXNf1jEyT1aZFqY5hiFWW0fcIYd979XEgVTthnIvdFMb03Kuc1Q1gJaazynFbArYPUSI40N0qzY7aZBQGrBi2kU5kWMk/PFChKKVTQNQUSuDRDGAnWUUAGwcYqZsBLpTnZZtYKJbii8NPVNJWycUx0riNKWAjEBRfAxkRensEBJOraAoD/V9oVC5ic5rFLgD1zSS3LA4UXtnYxhiAEwDpkWkMy9q8LZ6VhSFVsatuYw1wUxbFYdOEJymSoa6GY5X/uHf+8+qv11iih8Tu7PZ3s7eFz7z2edfe317e29358bOznZdoXtFiMiv7lYBADpavcsafXDQw3rSzp746tff/b73vvuH33bHbee29/dVvbGavstCFidZEOdBWTWixlCGCI6PDTuQWe/shuoviRwyDA9CrWEb0zgZrgyals+LqirLpkGBoarOfP3i1cG5IU8ipS86/WnrafxhHE622+EP+5HBXymbqGlExFGNjXXaCXeOnWOjNZZ6XO3g2JgOjXuK1wEoGpvRQsW6eLTiMQW4MT0dlbBz2nTrPddd9FVsRBUg0vdVuKMlwLxRj4SiobPauJjmNiUowMAUSrGdBozQtarQA247noaBmDoBxk7owPHnMFDxlurnjOISwicSqdl4FgO0QIOQOQ3E7IFirdDsdZ11YnbakMUIgWQwFG2ByDeVDfKQ+kmgIFdTjYLiVXXR1FXLgjhOwijWTOuJV9sWlQSW6QFkazbUx+TuwsE+emQzgQb2pHiAKLR6SDbW0J1eSQVrIKqtJj6HmGiDKEo3NlK1IazdYGFwVj7y8MOCixtb88n29hc++9nvPvP01WvX9/e29/Z2qrKBRIrCmHNgOsOqriq06RL1wXTnC5/79Lvedvfd5+/4+ne+R2OHcRMoNMGOBHmeNcX86o2dOIrXV4eqZ4Dqe0TYMt2dQFm2VFYBZHnIYF8lWZxlaZKkWZ40LS+u7VD1cR1FMZcYajM5mJZ1mapKZpN9sbGYhXhHt30dp2kwgOPMKtYm56tuUeSoupQYGewC9J2IrnFbXavXVqLYzO6Sl1qmk712OmQQd+loOrkFOgOm8cad00l9RVEgThYDoRMREvNwMhLoGm1wUgclam1A8UWHF1TnGwX5U13olEJwNdwiFxnf0824GWlnm2I5zgtTVSVGXCCOQ8CAznuj/Qhb1oDhqGlqiESKq227fLkNhvYjinoZKlnFUHIjRSxjeo4rK9nBfIbhJKZIEoqDh3GKXrNFMWtlkNLGWiSOjfhamaTixcqKVplXK+sIJCkHaQpsonpsjgBEXw1bwmNcZi9QaC0p/2s04FuDSylLDC0YBCmn/+Xxjez4ePOOX78tiIL9venu9vZf/sUnnn3p5YuXr+5sX9/b28NpMPiBRdQob3owme6XL750/eiR0cbaWIhWpcziAHE4zsO0zup8OBqNsjShtLDyi6DTWtFq/kX3MDjhEQuTkCdZnOZZkiVRzBtMgBC7V25MJwWTIU1yKmWeqS7caRrtHOwfj1OK+2vftevr7BJaR4COo2YpzxbE2qprLQNlJQT60epMh+rnRJWdht7tNz3atdTaEyBObLnLdGtYkSoKJr9T2VstYpuYK608euvq91JfikZZKCKoZwIMmU5AqnrVvZx6N2RoWKpdGxsUN1LGGAFuKbPx4M3GdjkOZTVbHnZHkLpbTofqKpJODBLBk5zQoTTNTAaqgqpb6sHkLXLBtPFpvs+/cKd5xNZXx/s7B5DNUMJBxAFqqMpaBzrtIkN0Kc5kRu3UG4byeJiN3bM0eHL7FFBsRGajA9LBcVHITx4/niY0qMFdO91dpJIBXTzBbpdjXjjUa80d1QbA2JCosSdbS/E2E0GMNnFBwFZW1laGa7/xm78ZROL61mS+u/vv/vTPvv6db7766ivFRFaNYDwQFfvOk9/em//EGls7ffzUta1t9dSFbJIw5lFa1lWS5YMsRw2y7gKpabttBZAZCniM6kqeZdl4PBiOBnGSAkddNrNZOZlMtq5vRTFfXT9SFE1VllKg+adKx2/t7G2urYU81SYfjUe0aszumQrQduaQ6ybaPdIHqkiD/gAdf5BMBjolhJRQ+ok6iig2sS6BTvHZ5+wlLxfyuoZ9lRqix6FLwBU6HToXlWnQu3QipVS9Z2rOT8oWSFzWtixNwyhWfT5UispMcdbZBDzzWtYopiPgN53Y6BQEt4K2AUbAyfU60A9XUXRZQEuXDjmaKLoDGrcJLuZ37eo6pJhjzFnNgbafticUnDOY4dD6qWlNb9I/qPKrRSv396tRlpjCdLk6zGBMlnAMXe5HXRoVxqsu7KqHToBuL8ThGkytl0ld2iERzH6rLA4cmDiKh4OBvmE3WE8/6emEDo7K2z39UDpcqEOq/bbHGkFgFqFSUFCLZDIkQRAc3cyC9aP/xd//L4q6+P1/+ftPfP2rL7300mxvv2SiLIor13buvP3u228/OZvPWxlUDWa4CCmTiKdJJhoxGo+L+UQDW8howjwa9BGCe5Ong9FokGZJkiHafnAwn063DmaTuoJ0PNifFUWxtjYWrUD3HnTbbFsOIg9DXlYVOpgFja7tYUBwajVid4OeD+FrOoSwExF2Y35asDohVWItQo2FcGqQraaGClRVQ9a19kK9HGonG7qG7E4WwnMnTSREd+tSbfegdwU6djiMYilYGS7WpTaeLY2DRXsT6mFItERTXelhAiRDLVBhOhnXV5dOaOtDGQAq0KXbdC3LjfXiZObdHiTU4PYNhtTZ1S57bMyPLqhmXZqOpFUncLis3SirvlvqJ/18PQbV0VCEVTLWyDaiUUGABoP2sqpqdGmAjTggKEVpEar2bVAOgJAWlI8K8eqKLrSzUlLLpkJV7BYjEWV74thmEkekm82itfUBygEDe2kSx7owN6pJtg/77kI8hgC66m8jSxTKWj8VASxlLcI2TILot37rN37rd377T//4j7755DPPPPf0jatX/vBf/8Hbf+Dtq6ON0erV/e1piKAitdUMwyxPhWhmcRKVYU0VC1BxMowjTFXOhoM8zyLMQOOT6fTll18tqyqJ8JWyKpI4yQbZeAxVzgJezgsTIBUBQ4fBGnqbCdlE1MBd+TmEfjY8apxJ3V/K2JbuvrkFC9ZEdX7T+6HnzaEZAEOnB2BNgJSPeKx69Nrd9HWDpWf3Kh3YyTq7qo06+LcBdJQYzKKznCdsYm2WZ0wBLjWNUydER0Dd3IxKyhV96Z6xiladPXDNca0NDYbAzyP7sTrHtDCxQ1uwYmnapHD9HJPhW1KcFBZHfzMtx5Rt6OBCqCcUjTJBVGtxsKp3Rr8PiZekT6j5eqWmzCLNEXIerIyyeVHMBcaY2LS3zRtRTpS1Uk3qjOCe2NyB4UaMyNVPA3B9VbvfBCKP+GCQg8mNZaC7WWruYpHOuzs+hpNc1iHTZW6g91DMI3QTH4q4yEPWIhDkReA2zPmLSuBHf/Lnf+4nf55du3jlf/qX/+Kp5164+NpzR+55axamRVJBfKuprIgqR2mSD/JSlDPZIO4tWkSYV1dWTpw4kQ7T+XR6/frWlevXdnZ2cHbMNsZOpVkC2dm0MQ/jlXGDqed8Ni+pUSB4MIo49RyTu3uTo2sx6kR0OE/vpoYbqcw8jZ9U4X0X6mQ0cbcTjqGrBIHTL0bLCJMVBNGp4RCq75g1evzKd9O8z1CWxvepGKzGZpP9TwMBIJ+0g+MZBuZxmQiPabSjtTfyujSWU4VjSFVQFwhXXmjmsMjzri7YYrp0pyXHOvP0ba922RjeZlvp9nr+nktrPf4NTAN6DSl0EPM6NqFAIAQlRHMpagLgg2G7J25jA3oBvTgBppZFmOCBDG8rMCqM6DNIk7goS+rIZtjJwsLp7pFbCTA3OMRIIwRZVa5ReewoZqLEk6IDtRoeRpgPlqaEUhFwT83SbEV/1I1VUvhZ48f0n3qXU9Fgww6N6ykI133rCgzUvpKFAy8wQVcD6ulTVEGWHTt16h/+l//ng529cNCmYnDXnXd+75nvVfvonodpo1JGIQYqD/LBbJpJ2YQxnNcIupVP59MLVy7t7e7u7O5WZRWGYUoDzjAoJUkQlxJSBDgDniJgIegXPchzil2r7j8Ar1V1TVrd6AF7+xZsRFys65C7ah7DDnb0iS5e03PpHBHnMbXZIAKYhegAzBoSvXAJwh6nue3YNVnZziMqtGxzgcTA2pLssHVL4qodmsD1501Q13iBFuvhrdvIJcteul+cpXWCPdmqij6s06gKx5Rw+dM56I1LmqSmY41/se1nDRV2WQFldKq2x23Xld5ZhjlhL5dkkrzqJWTT1OHLFy/cdesJxoCVBRIxkMMsnhWRELWzwyYNax6GQnpYNJnxWQjBr+Ge5FIh8twmHO3dT504Ecexaunn4QJMpgSQCdOt0G7XEo2r1qGQa1T/YbLRi1Uk3eY6EGsNBFUDaygTpWLXNNMBHnIYjTc3YfWLlst4ZfVIUV4p6pJgt1ANSZo0Qg6GK3MyP+OIN1W9tbU9Ly4XZTGdzHjIx6MRj5FzU+NeEQYD+AaGkwoXcRmnw8HJEyuj0SCJ0KlAtqIoCpgrGKje0EQqJXB0axGbz1SKSAQNZUhi9wnbPvuGRnUotYu1LOOhzvikp0ctCOD5xBg1oxS1S4V6tyGnYR6jo4MdrGlWrDA5lvStErIunwWmdCEg4ybpGHZIGDjgT5UV7QJn9Hdt7MPnMWu4EQLV0GjfHXCVnmKsDjfl7M1SoePaEP4WMkcoaliSMTJMZlDfJuoNGkDx7On88mtX5HbSTZs+VHckbj93aja9Pt0/GA6zNqJ2wrKNOKMyhgbg+UXxY+k+hEvcBdsVbCGOFRTVqgFqMRfGcTgYD4H41h1I8EhsqELRJ1rGuprEtVmM/DY7qoDytDtq8riLO3ewQe52u1GgzlPW3jG2hHLcqjc7RphWVV2xgJ09cZIzdunKJUVD6DkS8TSL8yyfHEzTvN3d3S3mM7TamgNpmWf4DwEGU9ioOjbQ0COUkfAYOdjh6sp4vBIbMC8NOYIzjHbTFLxJqFG6YR2je6lDn7JIW8kEE6YSxSkWciW5krfmIR766gSmTuSpvgUMgEDeZTW1XYjwMjqd1cDGWBawnqNVy64e6xGok6TuFqvMBctwNFBDN+NTQwYss5nCHO++OserM0bN3G1zJQeu3bPTLAW5/rim7EVN0klDY1sz35Fx03uaObRHBBmpJuUoDxM9YsnidKMBzsuznTVUQh3UohsxZt9ydKVkmGNIodCAjfO0qsVsTmPDFL07Fq0tnTSz16jgP2g52jN3u6HG6MFgDNiR9ZXheIB2iIRUsbN1LDpHd+RwBeQhQAEjq7VTTHBX82yMfnGMZ+d0+jP7fFzrwbYHNpEThuCUpFnH9ZHNY0VRbu9to2UsOncGeZaJUZOw43vFzmT/YGd3P4ljBdnCVN+QmI8wRWGExpxwdSoan4T6X3F8c/PU8VNxFNVNrXpxtxJwTtnKjdXVMAx2dg/y4xkPY624CUuFmYmk6wz5AIrUcjUz0m6Oft6d56bdONeQNW93HpZ+wAqxqXphyrqRjeBJKtH+F0VsiHZJJGzqqhRVJVQ8xsw3tuGfHpydLUWcmIS171pbj8qsmsgNOWsfb2EBiAbV2DFZx5fKjFdA1G5hHof4/OzoOnMeQ9CemW0tG9cVkXr7NfAUbSvc/tXGXCdMuoTuU1Az9JfSWZ+by9hu3wyHScmS4TDJRlW1H9QVxgWQM9UKETG5OkhaURclFcA44UvjXumYgw6pwJYE1gg9KFSOXqVHyV+PQj4eDBBzUe8oJS4Ict4hnQmJ5W2Ww4CO2DMfuxPfexDCHusucV86B8e3mTSGRpVjqEQvQnuBXB2vHEz3UULNAzBSGOV5EgZsLV7f39jEBDOJTgXUYl6lgsMoBWxjf7L37DPPX7l2fWdnfzKZ1o04dfLkf/o7vzXMBujC1aDaAfPUqmZ/b9+2XiSprIk4JNS4oD71OolqqnSgttvGsBEd77hNWsRZo9WxPl2cm90Ng/wH9wI42rb1fFajjQFuSnf4bJqqqjA+TkdkTKDNaTGlNLAmGSeb2dt9y0z98JhKtJj+XRq/rOKOgMcYsIkLMDVtLroxnrbUwERhTep4uV/mp2QdMWMXaAbEmS96JqH02d/R9tqi6GSCac1A4QXIe0zeJHPUMyX8BTobqEWZKsJteR2w+OXXr9x/7pRq2kC4UyHLKgmCtTzel3JeY/Cd06XX2DB0NgXJQH0izdMy/SEocQcQISZq5WkyXF3vPDNjBtunq9Bdarzooa9uz7oaq67q2qVMr8vEwhmcBEmnxs13LSxIoWpVhBb+6frG2sFkf2tvV8VqqqpER404Ek07Gq+tbWweHOwzHjHOgbSJIsbZ9t7e177yzUuXX9/Z3i+Kcj6bRVF64sTRX/6Zn3jLPedjnuwf7CHpjv1tD3b3hGhXVschYLWI9WN8ox7/a9GIhmQNTSA73cCKDnRBuevTmWfvpYC9HbZmrxdnViY0Q813I5rJdEIgUuJh1ZpA5XJJOCsPpqvksEUKat+6kKqhPgtV64YPGKuA9KRp3aX74GooCN0BcDKUpTaYeTJc3JMbRnYIhTLAeuiSrmCwDnJHFDpuYOW/BbAqWnc9Mt3Uyvm6e8XAlNea9fg4OW1O010puwmBLHSu6lsArn1v7UkXVaON6Lq899wt21dfFm2bck7dfKRsKlkXGKMZJeM8CQEVbjCNHjPuvbHquvCWuvBkaYLaOOqvriJYKgWcp9GZM8cSFMO70l4HKzolGIbIfN6ch7v7W/oy7KflU0/HeI38e/ZW94Z+y4ykpNPBboyjaGV1vHNwUNZo8EwuIky7MOGj8cqpU2cvvv6KepOm1rHLFy5+89vffeW1169cv5ZyhKGPHjl6xx23f/RXf+mRhx5gQbK/i+nHRQkoB4+i0cpKRA2cFaZMtVaSLeGwFXjfYBIsHko/QgA9wcNOFyFjSS+YeJ0ytvEj2xbAcj215lUaByBVmmEa1AAzBW3LYdAm1o5UZ9Zzzpz9JqimHumsC97NXDqNoPK9RTj2IC9gBBWKhAo5YWEQ2p3aTKnJAFqoqf4WOs/T72RpG9d0sVKbcDOP2bjsms2UUPFtXse4tggqPVfOqHgnpRVYYlaLcmjQBACM00DzlKjRg3LzzZ6YIH8n9ToOdu0UfcK2bQoZZ3xj80hZTQGwgM9XtWUBkDQFbOIwHKZ8EPNJwcqmqRAyU8uD0aZn4sAQwDJk3VgjmYc8QQ17dPTE+mi0quZv9kw3izhQ74JY3WyCfb59lvX8hS4MYYSW9r87h7gnpV2adsOi3ifOCHc6XV1XeZyu5IPrdYUqk7CVJKrCKMjyfG11vZjNprOD8crabeduX1tff+i+h9752Dtu3Nj+zBc/f+utZ1fWRredPrtx/FQ+HArJrl+7MpvPRN1EUZQPUE5hB6NRbSXVbgrBgFInCdKiG66KX+iKS9iUuD6IG51oMVa0t0M2XuCpAYcuqDrMakkdiFLBI9yeakIG2YFiRFV6wNFaRcFANCFbD9o+SahJah1scMnQMjKk8IIaX6iKk11EBzJs8MR4pIG4QRAiAkP+mKhqlsQNA4WRxtDZZhosqcaLG+vOcSbJEdIFLrrExPHJ9XQT5UzqmWvalbfRAZcIO/SYiSq7IbHWlCvYjJ2GbfjJE73NujeVoPC6Lt8gV9VcwyPRTqh0z1HXmmF7yro6c/q2Z579bliXA6jfBm3kdGS3bYMmQnY4WhvlQsrdaVE1SPVR4wtT4coAZED6EPMNsOQ4CtM0OnJkLU7ilXGeoH20Hajl+CPKyDKRS+0D+y//rcWQvvp6Nw7KdjGzfcLdFhOObO3EgGsL6V1T3qaalqLdMASu0pWV8QFGDhbkVCAcBUEWBnk6OHbsZMuOr66tro7WpRBBHA6OrN998uSdb7lXBC3KicGXbHtnp7na1HWtZpIatI8W4toHBffQJCeTNjJ4BmdHbMxEVebpPfC9O1vcafIzzrPvnBId/zNaUTU/C9rG+P8BepRSc4OQcRbFus9wl1p1g6Sq4SAiosCKm3FHsCvg9WnrE0Ey0p+qWE19TWLQq7Je21AIhlpu5cHTvClwru7pRzADjFMmbQ26oWy1hpfqenRVqUgF76YgyWBEKAejjbLO51D9jA1RaGBDZxdb1qdMhZo8AhGh/G7mTTFWTZQ04XUejMeHeiSnan+BHaZ2C4YWOzGkydlXbFoGqaRJECSDeGXt6NaF59D3hmw4NQMFTWRoGCD6MaK0MD6CIfVyXlSTeaEnEND0yySNmxo7TpHncPPYep4PkiSKoniUj5MYpc3e0kw2W2kI9Y6aSLCUdX1vYElIxMJU9bGO02G/6wYCXNe3/4Jlouu/7X6pcl2Rp0meJojiUENA2/smYMFoPErSiIfxvJiX5TwMWZIkddOwFsMcmhIAZxmg0TRjQRonsFqswFBqUGXQUdXUsEDe2N7OT2amT4rVoo7nplQL2IFGP8ICtzlbw7Fu+3AnVaLqa3VERYUyiFuAssPfgomG3BpUAiktoSw9JMtVPbgBm2uT0rAC2INMYqX9KBhLCrZpAD+iiByhLMmIVOXZAKvFjArQGwAvkcXCSpBIpwB4EDSw3sH/qMNQfjaM8yRgmMauGBiZzSixkSs4dKTAKROlKvBMVyJlD5rpmGRN2vEsNsCtuY+0qwopa/ApIbIDDX0irKs0YF/VwMLiyanLqkOwzvhT3K1ypnHTNdo4B5zGvbtf8MJjmng7bYUfGyG5DG47c4bVs/nO1aEQNKMXGQ/tzhBQWphx2cCkcp7lme5vqjiEolAKWwJMNRqk4+c0TQb5CEUECmpuTDXLiIaHsFekgbsKDedTK4KWsK/lUI1/69KPPTe4b8x42+R6ZAS916NQDXBF66c8G4yGed2Ig8kkQH9N9AGEuxZiyHjIOE1tkA16XtWT6czU5SiVpU1Oas6h+xbQwlVRmu4ai363VRVEUcPbFgMiY+2gmmnAVqOCaCC2Sd9oLW3rPMk0JoSstk51ZJF0PtmeVHOuT4Me1S3mksMwp/MTWAxl49RwEVmPsIXnT73DQxWGUTFfNQkepWYoLqASdsyXZG2LSHUchnBbATGQoWhaThSnlI1uZqUfP4Zeku3XBuhhLHkbYrR0GwghkWGzcxgJl089m0IM2gkaIWsp0O0pigDsZ2T9w38X1FeJ4PocypjiYYI6mqHHNzxt1SBGzSiX8IpUazizw2jtTdM2VSQCNhk2LgQWSRdVKFtWkiijd21vZDXQ3IbaTf219Vc68qVCZjwhasBvZmx3ETIfyup0vVb/YoSKZKdvueNCU1UH1+EJyygUBI0iLgArI6WLLo3gbr1mY6JphxYFDgQvUX350LAoi4FrMrJHX7THO8Z6RFtZO1zHf3n++7K37WaYLIqPyfLST913+r40vQdwERI22q4yfaEUc4i2ieN0kDdlMS9ravEna45aPHJHaRtU/AlYqgaTcgyqQQVdRMTJ4oNBqSOyOh+uutYZnDO9hGwqRs318U1SdZ2RoZM0NsiltLeqhlZWsJSi5klMy4SQFtQbRAK8J6BXWdBEITiOREMc1E3bJLKt4ozqcDE6S7QyTFiKDmmYsVjJICNTjCORRuJDNG3IGjKNKQ8huajnbZAGmOdWo/lZFCA8GswaFlXzGuDboE7jgQwET1gchk3NRDNjqWRtyYMIJZmK0pkMmpjGdwVVNW9ZKAWLI3Q7iFOM2RTNBJsvCAdM91xHPKjziNdRCssfHWSgsUMWop8ooyajbVPJIA8bNETDFIywDsMYo+eitG2LAKNKMDeXfJI2kCUkaCUkqwKW8ibG9IOglDxJODKKgUwwWIyVjMctWl5x0VZtECeRqjSgziEG3erRqzapFFAvZCFy7k1N4T9bZGbI3KNmt25Rz1lmDH0CIhaeOHv+peeqpthORZtHeOwKjKCuqcrlUThkHYSuQQIEdUQjh9HvgXPGeZKk+WCFNKujHLssjgNYpbNFyo+/6avL8fc53ILq7MDKJZLAf6uP16QoEbWecSthLKCVhlSIPI7ZYDCdTBSz7e7vowthxIWQcazswJZc3DCJY81vSu1RVIgajEEFkANMbTE0SMzyrZqTDTnQ1CKJQEa6nbt+nNagVppbBzNo/o3K3DApRBzFdThoRROVaiB1OKvpojSMWYpmhoHmKQZUolOHHKRx0YJfOM8ShonmQpTjYg68Zt3WddvQAPSWRYFoWJKuJOnWvGHlvILFL4eibhG9igMZAvAe8Bp8GNVNNExy4GHKomE5TRgX6SCPyrpgaSo51TIiScWjaCNNqpZxHlRlPZtjMGuYp/EwDaNsLAMxmSZBIFjW1qxpSDMzuVcFoyis4nQcsaJqB0EUNDwHNDVImShY2NbNvBR1FOVJHPMkkGmQRkEcTHb3KlR3srJFU5aAQ0KELKLAGBoeoJikqUPcVdzyKEW1API9MWDHWRbHrG0mgKJxEUWjKAnLVtZhUE7SrCpqkWerZJNQe1AVujPi15C+9rzDELM/VY9lxCPJmvWptKsc6RouWKY04e5GtnGS3HLHfc8/+9TW3o2NqM1CkchIl4E5A8VtUTVhtzSRIxepMkcKVyBbarMVmfiaZRZbo9Vxq0onUsPrfrLSN6Q7TtT6R8cOrHGuIQA2MeCr3y521eNe86kOX6kpGbapgWEsNbRaBsNsuLKysr23J5smTTMYeqQAq7JUVh4VGGkAr6vq0fFM1JyFGDgFSdglHqzFrqx3oCma5mA23Uhzm5TWO+3IIl2oQE3dNGpfPd1QDkZ8eumZ+CufDYo6ihFUbAXnRRHFSR2l4XSXsXgPgaZqPY5m+UrEqmpWD2U9Z0kqqxnp8ozLJvn/N/amS7Jl13nYHs45mVlV994e0JjYAAkKIDhIFqWgRVuyQmGF7XCEHY7wb0f4qfwMfgVbvxxhWyGDlkiJksVBGAiAmBrdfYcaMvNM2/Gtaa99MgtUoXFv3aqTZ9hnr/lb39o/3p/7FU1r+1Te9vubnO+PT3E9fjp84W4+p1T2eRjOTxgKtN/Pu5vzOOfxhILieCKXan63lPf2+Xj30U0M5fy0xLQbjw/7l7swLbFb52VHkX9ZpzHkw/lh3b+4zbvbdZr7fBz6dU27tZzGx0Mqn8X9zfE+L3MZ9rmEodt/YXn4ZLj7YLz/UTykME3om0ndfkfzv4bu6fF2HieMJBnL0Hf98BRQILlZ1pcpnWaox0Nc5ww1EmK6mU6If4ZDf3iRc7d03XBG6hYcrWk4nsf3u/O7/YsQ8n48T7vDK/jxfbl/ezqdH07rMceP5nfdf/Y/hG9/CyxVeSfVZ4dD8gkp8lzi0PfzGUMvaeg2ssFacLJEtKZ2JDK3jcsJCEnRLXE53A6/+7f/kz/77l/+/PNf7p7OX3wBOF+MK4q0lHolimTWLPUeqLk/zus6pDwv08PDuMzpC9/8aorwi6WZ7CKn3NhQUOo4Y1j3qSuVb+2oiLEvz29I2Z4fB+Ch8CqnML9wx3yaWnMiwszMTkS5PRyO5/PT6bQ/IMPuK1U62rTi7CsciOkO4PMtFDkzVVPVSvw5xqrO6/r6/unDD7ix39Ue26KQogaFA4KKB2mZ1uMpz7H8m7/8tz94eHo4nwJcXfhJSwJ3YVqmfn8Lun2MpzjNFO9GgMeQYEtlfjxjfNxh1weElet5iSPqSGjL3YG2vNuH8eH83TUvMeFx35tPd2u5L/kp9lPKI1L0Xb/fZVggWNmb08P6eMoZhvHY7W/m8xrjIc3ngHF+oHcq64xhHwCBhe6mK6dUpnPo4nwEJqwbcphucp/6u3mdyzqOU+ix25eyTA/5kHb9GHZDWiesAcLgNJ4LxtVnBOvgOMICTMzAlFPpe1CThxTnc6bMXBcjvG7EObtyPo/TCplHFnfFrHtsYkTluxTW8xTWaQhLOtxN83Q/AtzcD8M4rXdp+vXj07d+5+992H2bk+COK76pShe3g8lvRTOpSpSfltp2MeuW5tqVTLBAfxBkGLsAvXHpt3/7t5bxm9/9yz//8ac/yyEOu+G996gNMHfDjumheTA8dbkjnZHWebk/n1PO56nkuP/Wt77R7XqUIQBuuC5DHtouA74t01Nz+5tCbtVFF/IsH6/VeocM36Ss2mXUT6I1BJuI+m75WlTW4fwSt6Sj66AsL29egEpjniqpk4xM4KhEDHcFUGmSiVwUAi2iTAQCWQMywz6jzZrHl+FiSOQu55R2QB9Z6r55cmq4k1rEvC4I84jLFkZ3mpe3p+P35lLQok1doNSqj+E2+fDy7ub+6Wke57I7dCk9PT4NXbcf+rt+/9nDu1PZ98uUj6C66Pqc+/35PMZu2OVU4JkCIXtecaZ1Xk9z7Lu7LiEXjq2O8xPiA2Z1yR0olJbuptzuOMTDjPRy7vcDwhIaDoYR8JjhmofhMC8jZqymO6xGiLm/6Trw4Q5IQ6czWrVyCEMBbjB0MgkQFQF0SBegeVOM87QO3W3eaX6IXjWSWaizdHOZC0DlPNwWx5DTWNYJY5/L+ZgDYHDp0NNGpyVlQCelBOOwi/0Otb5QltSXHSmFNZxK7MNwCsffePczVLM7sMlhqZTx3wye/ycxscSu705jXIGroz5w7r7SYpcUqZvYWMcQa+cBkUuRQwba0ZiG/M3f/nY5/caPfvLjcZzePJ7WeYIXSaSYJjhUi6Y06rr23W6/333jG1/fg8NtYCrKCi/Y4En0pxLk00A3cbeb/otGeE2IK+e39Yuo3aZtrVB+RXM4V1qu680vWz8QCBsaTLAKtUtOsm8cJZS43t4cns7HUwggi2FkHPeuSYFFm3JUBaDsQRcmI4y0LLGiCu8UzWriAqemhtHxgzUfbjKFzaBQddAS+wZhEBWOkTwD8WeXQ+7Oy/J0ereWOfY9hW8wQjiQQBmlzG8fn0pZjgCo5K7bkVcfjvP4DgPZp/3Nq2WEPiO6nULuKDb9GuDxMkHJeZq7DKMEMwsYaVqJMyhFsPlBkHK/lBVUJPt96HCZx9O42+2R3O1zvxvOT2f4K5h9SQowraf5RGMNSFYCcCPzWnKHhAwg/8QHnqiBErW+EpiijRIGOGuXexqwnA53+xjj0/mcKbS1LUi55Bn5ZqmdwdOZEQtSTCjgWRnniPwXAfbJNarlYHzRMHuIX6R684qvnNLNzc30eP/y3c9LKLthKONI6+G3cDUwLJGcKu263HcdCJUWwIRgJWUj2KaViNBNM1crVEelCI8ut2IjlNvvfu03vgE3mkp5VHMk7UAVFGF0oY+AUgNpP+xv6k4iWlya4eIZdETedOwVc6jwNgYjBye3K4WX5moqEqMOVHRPVaXXSXiNIoQDTh93qxGk+krpY5v0Z/6v/GkEIgLcK7f7w9PNvoQwJcxJ8ZNEGWUkhSEGV1F5SEa8lDXmTmhnKmYfg9UNesEcNzS9aYL1Il9byfxrS4K1qSkJs0BGQVW/zI+nU8z75elphosEL5HxiYw0wKTiGJBpW5bxdOz7PC0LhsfmtE6xzOd1WfuMgeNdF5d5BhkVFWNRBiqAJcuoGKTEmEyQei2J9gC6ZC2ZYwma6oTa0zyjd3qeevyDyuNl6Ya+kMAjf0OkBjQVTjalsILgFSwpYuwt5w6XFShTAlWi6EdEijTYS3KB0JWpMC8RdVaR8qWiG3Yjj6deEKcQ9a/25RCSQvqOeYEphS9Ca6lD0ugME6igzRjLLnbzvD4cdj/5t39x+O/m4S7iuZgBprYn+ixPzdJ0Oe93Ax6GHg8TQKk2XbsM1Qorq4LGgVqaqt09iHRQnQZPRZ9v+56CiDKEgW0L969WJAaXxPGWYCSUq5TcjXkBx4cUuK93D+kwWMJCb0b/NIVbe1ZF/l2NbyXwZzVV3e2LpLRFuuroInvEc5/kx2I5jZDa7peVECJ+uBwhxvMCxskzODsvISJ6LwpXVjiFSLgwRUmWjNH5/JSAskJtCiEb9p1/UFPf2ruJStMaaBY5nOhlHh+Ojy++sLz7MSzVClJr0P/oIyeCKJGGQfDUAcAYxnlhkgj0+oaAhgrwJxGJPu1hzEnG42RkdYCyYHoixNAYQktGDJKcIhqXdRYb1xxlBgVjMARNCb8D3iZpaVjCzf6wMARwLGmspI9I3IAIjhaYiXEpBsQrH6d53/eQ2JRn5h6U7aH4Bdka6r5JVhinkX7b6nWRTeOLK+My2A6oyoDxJeI0pZTSeVoeuu7h6TGOr+f1I/KEWS3U5JWmhN0Ud4ZDDcOyrMfjcV44j0UAW22vM4ZQ+Zeehz+u6e1qupjWjBp9ocsYjmAWy3CfNZmrIB/OUTPiLUaUXlBRZF21CTq91CGJRa1QvjftmpBeGFAPu6r4s/pxl4tud7/53EoIg5jT0sF1uF0zVYRfKnBEMRx2PX8wgNgZQxmBRPVzjdSTNq+eQn/UimEwjPqAUl96X6ZHeZL5kspsiCGyc9Ye51dBKMuFjHlZ+jS8PZ4ent4gA9n18zJx7dkyJ/QnlazoKefCggSJUoWLEBVz3SH8KHrR6xP+ynFeCAVRU6WWSUdyCB/BA8pKUg0NxxDlDle0ia1KPUj9SY7whKXXjkwsrr7iT94U1AtFdGasTEkeWQ0xlp69HMxSRy818Gm4unieOLKqiHIJGBcPUTMxDPY3PE9VvOjvXs6CY2EyroR7RqNlB/TJfHo3jwsSAuJOOm9RYUYO5EH+ekq7PeiET6fTPE00CaU2PFS8d52AvAmqK+yIDrMJP/Jj2bsWfKnU29PymAl+eniARGAtD98In8/x6tMIe2Wbr3KS0PznP+vO2nx6C0dvvprUrzS7MsGYhMNa/20IJqqHQE7yGkN+9fKmx/ykMAzD7e3tMOxJvGWgryQKL+6N4yXVHMoEp8kyUyi3+248HZfTOSHK1HE+ji2tUSzcvB4AqFqQquiOp/mzx6e5hNRBoszXMAdDGJjpaYGDQgIPaErkgVmnQGwS+cNkYqQ+p/gWhl3K5tDUO5GOIvchaAiCRDMejRjSCA9DKTmhcUa+DsPjCbDILjnHIClTDTOWZT7LW5HJmJZnUBdd5y961xS3TWwIta1PM7/G2aEv3L1vXVZhJmqQqH7t/S/IygneLqwxfdb34fgAVkKx+H69bQ/qXCJlr+M5mPv9br/bIWCdFmIsmoFMmCdgTFUhMtu2uwXdoxtSHrq16u/J7bunlB5RCfmqYGoMyD3gaoGuiFANVLkfuKHtbfJYTdTenmeT2qs/Zu/NJaDb4q9+hrJXSLdWEj9bbrfnpWEc+5ijCNiS3a67vdmfzmcEb2D0Q8oVIx4wfwD9AGQOZxaJVgXLpZEQY4Su3RxpvD6UHOfxND58vtx88AFy0SmvzBomt1NttdaS5fXGZdn36zofT8QzB5pQOqkzOSRjFq2Q1xgDxrqfzicElOzFCzEmXhKF0OTW06EMjyQmL2q4BWRSAXoksuw1CwG9JvU1tw/wBMeWBhTFzBc4vQt3O5HcE1VMAnANRChc7sSqyrgJaoWvdGfsYnCtntVoSnAf6sV937LsX2XeVKNl619jMc300shDnY4nrqqITlQthvsa57dLWH7502U85n6gwEFOIem0LRCisiRTL1fe7dFygPmgiHTozBi8N0fM+yR+bJMcdTt1AJOFzHKIkyFlX9CLqkPh95CtjmZWKOUJ1he+9drQVU9sni+y0G7wcH04+5n5oM/I8TUVYf+oHrbmo7UJk9q7JAMsNKDi+DndKTLMFR4CGtMrnR6nD25v5ml+8+6B4f6uHTrT8MP1eDztBhDuAJrDsHoezMWNeqKja1qBIrfy/svbDonf8fQ093d3od93GSfk+2mIriRI4F5xAHUXXGaaxifqS7RZBvVl8f/waohQeCa/NKR4j/GO2C7DMFgsRCR8idPcZOOrTSODj9C6k+EslF3nQUS8uES5BOAxST651tJEzoaEugzBQMI5pJHG05AzP5dl4l4m2AmaY5Z6FGYkL0w3JgTmHFPSFCVt9SOXJaJEpH6Wwn548I9Xfmqb1J7YpHBXECFMFW8KJdlQR4QyWr1Mto7LGu9T3/30R2md+g4oNParqOAnD+9FaKPZY4oDtXacTud5Gs2DZa4coM25f1iFwbmy2HcrjeMiph5XY7FjNknc6lxccnFpSptmBqEWgSL5RtRtCBx+QnTSxn9y7UtTb1ewHS4g4B4cl3PenK2aYfEdSHgdt0OFbW8JJUyFUzW4ZMghIr0PXr04g1oDLUp0KtrlSKciUb/f7TBhAEigLq1ga4VZnueh6/mV1FXWBaYaJ26AKj/LfDyV7mWXiMeXp2IxhFJBLEwzi1Iq/gkhv3kxnI6PnB9DLpGSirboDMWfpnk3gFGes2cJbVLdiJ5bomWhUbPMW0CcAZRdlxw71a7I50cLAxPSSb8O4GY0+VL6iME4oVgYIOnROUEc19Q3wjkeXgICkaMQOoZxBXAMhZ4OaHDEy1A0M2pJ1EchjGpK18EgFiOgE8GS/LC8Rm3aZQF2EYHtOxXsTalWzqnhRj3eGMgSLbtUIXP/pi9/+Sf/5lv/zfxqmDPaCoSplS/eOqOug6emkMJAoORlGsZpnMHSjkOIooSKkbrFLcCzG4IBWBIG7VGTvtg/7ttUZVGtsHtWCZ8tVeNEmqMjYur2zqxjU6MsH/cD1yygU0uXAm0ZBx8HuklSz6gAk04vxDI82x9kdSBng6v2cs1zaypIeO67w354Op1IFaDv18ZA84hgebB1PZ6e7m5vxEHV9DH3YbI2W9fS5/je3S2jq7h2nKczhLPry9KlNAUkkyveRf0TOuuCZp6b/c2ff/fPjucj+gGRDwYMi5NVBqsnwGuaSGDmBfcAhjweg0OGlANAlKNp22IyDcZn9adxpFbehH4jGZcQMMYSNU+yhLn6q1TNJmZ88mlD7E6nqes6zlUTnYjtIyDUULmZJtRfiYGTBkojgJ9hBGCq7ZUz3SwlS9nPwP3htsnl5s5Ksc3eGxaia2dZa46GXYbWsfFxjc98MgMgt5WV0JMvoJM90a2xvrufH18v772IALd0ERiwqltcGGU5ZovQxb3DNICu35H2F0ZSoUabpQG1KhphDFJ5Q/0nKXuJEPcQn3ElU66PZK6pSacTHn41FGSyf9mkicVToQdYkYV2QrSxnFvTf0Uybc7UFaH1P3V+PJciBL+hMtx0yrpPtf+yV1swXDnku8PuPO7Wx3Ue5QnVm0bo01GpGxPD97LtaHejrMqt8UbSBum9PRBHN2B/Syh9yp99/vYL7/0aErwYwdRNlOuWcErXXwnuoRHPp+U4oX+AmzxP53HXI6VkhRPCxKKZDq0XBE3saHw89ahCyIl1BIMDwPtFcgW6pLLuUmQWrmWZciA0FPJPaP+A90gRZ4xUyqIRmpA97Dmwt81IKZGzA0gBiMhJe1EPCdLTgQCmmcdjckQQY3g6PtKciDTLOHpmUSNnmY3wsmaAvsJC83HhO3L3CCXO7OWL9+58NfPozPy4pELz5mXOgAbTfm/DdUxhn8KRWsjRykPuy+cp/PrpdVy/XoB8h0ercyZdPGgYfG9z1E+H4s6kNQlIRzm5pXR5OsdxHIUc3AM1m3tmd1/qZwt8NuBBuQFbt4zwaXgRqcIpi2JJQ3l611Yl32pib2EL3KxRjVudLGrl08mQTwHV75632076KsTZBYeWrrPMUD2h+KyEOWAvep7HGIdd98H7L0uINGp4hrgSDoEJKsjLJCYHtk5kXs/juO/RD2eqoovxvdt9Jg4KRoLFWMYYn0r6Yg+q2pRB1YwJpURRYbJrRUDk0vBEM7JBy0RT1/JaxkLDwWqmFbsZ3BoJsrpg2yVgp5CRolapVc6DTQLUh3AyRphfzfuhAxqxEeE40SnJukmqkRktcmGcJiqYEYuMsOEsqcA4oD5Ejb00kkKmc+chQ6MxmwKJDRWriRiAXgw0C+FRhBCLXV826AuWi++CU5JG+MWKGqOAyBFw6RoDNlWYYp1YqJySlhqpn7MdgTAmn1BVogm60BOlS90tisL3lONbwzypS/bMl2TS6t5k2nTwGiCrrf9DCN1LUuA8ylM60+nP6BNl/Aio685LB+gcc4nbI1z3csUvooiV09EgZNioC8420EKCE8uti1zYpdJ8qkqrW07ovJgKuZFHYm1TX5yswQI3RcH6WpsctJ5YvARl22FkGtEdrPNu13/w/ot5nj6bIWFmfqn7MqERt0a5jA1Yc0bjG5COMdwedgOSD+BuBT6PS6CpTNPy4r0PMXIM4AsQAqwdup2UXa2qOepKAogCSMFpWs5oQEBkRgbOPT/lCQgzXIhvBQGn8fvQnFR7eC2b0cGAi2EHWKmcgtisOUtptKe4Nx9Px3HEMNFu6ADTh9mkDvOETzENDsQYSpAICSivf6aRNITk4+Qs1T95BiGXzcmAMJ9dJi3AXTbWsUfYDuLvqTZS0n48nbdW+J3688g/33QvPrXuAkXoVgNMlAEcZWC4pAyqnuf7Lp/u7yeii+N2AGHcrCXmbbq2/akUnKkcj/Zn5mhGar7v+rCDQwTkvLYWVypWCpKcTfeOcikrRTRd1/cmw66WTwrTihom1xyOKHNTuBAkdv1UJdQfXyoE+cMqfiZ5jVtSY/NG/tpcNh1IPQpibJ341uV2ABg/x9luhgY9wu3EOKNQhqH78L0XL1/c7XZM/ibTfXiCO7cBsyoG5wlJToeK0fre7e6mTz1mLYzoXJcbKOMU3j0tdy8/wIxboWgBaYKkIlkaObLVGS6hrEPf7U8P8/nM6AUycTJTydaB+CdMeyNTxvRRjGeyHCvzr3HoxCANqtwiWmZLjgZ4WB6Ipdk+wusxIh9ZPak10jHk8sjkICqVy4gyEkhuzIRbTTdFhp7qAxwFCok8567QYs0VUSv/cGdOdbQqIJ21lS/uu3cuUqANma4JRQr08kxuV2mllCv9TJup+SR6458t4fbTv44FHd1ECOdkf+MLqrJoXUgNaBmrSi4MO0ToV0VfZE8dSLVE1+SdfKZY97ulfzCsAGVO0f4bPeLtYVV15NZ7TMe25sMq1M0YcHdyPazVE18/ofNzLk+ov6ZXwylSFxf5HMblyV1JUz63rKfj8ZNP3ixnBHm7w+5LH7731Y8+fHV3d9jvhgHcYBmt4rGjCa2YLEpUikg2xvL+7e6Lr24hvaH0sQyKaStrGcfl/uH45a989eb2tssY0EyVEYS11O4j2kGxu+RmkVPbp246j8s0MVl7Zpmk+xYhZn+Ukj1wp6RMzflEllU+ISJenv7OK0BaW4NRRR6TccaEJMzRVlgo5WR574tLxFGbJXJFvxrBpYzwlSQzYzBEzCWawC1wLofJrgQtU/W6+IoGeGh8KIpCfV1BXnjtOfH0KPKXaD3bHpo5UlNNfd2CJIvcmEuLhkf56Xf++cO0nqjTyTZL9YK2Mmy5U9nXpmX4Z4wwt8lY7AgrMbCCsk2Gn7N/mpFblmmeRiqC6I056W/lRkVYocXt+bVLGbzQeolreqR5zApJcEG4P1prenqypi9J8hIKn/Sqj5wUbEFBCuiKc/JHvWgt4VX7n8PL/W3u+pRQKen7brfbffSF99aCEYTj6fz4eDxjFiHcVTDfMTAaY71RnQdSn8fxkTpB0FjW83l8+3AqeX/36j0JC4WWm8iLQIaD5qAa0ctuQiUw7btyekuzENkzorFXukSSjlXuaFSqKHstHiMZK6uXUWETTEBcE3YZVParZNeY0ebLSW6fXHGwawIGykTE7M0K57uVEXx+iGNaBidyGEs5RslN8XGcAlSxF/vCDrfg6JjykpememPttpSao960BNYuGm144zei77YS0/cFEiRq5pnS8MNUfu/pze7pzXr3lXkeSUKsfsseXmNwHYuqq0dXKRBtQ1aY+htpEywgTbQ72giLNtz5pVU1hyZKkLoVNHczB4iNnjRXvjpsytrGiCMpqmxzVGhmaLiwr2WhfdLq4ijr37cZO46q27sUpLzh8gkerUkr62vxEsoFV/86K6oJyqesY1l//vPjV7/6IVYDQ++5ew+Du3OOw4AuPEOMEK8qseyguIAhtlxrI5pDSNbD/eOb+1Psb771t7653x9AN8UXVX5RtsKFJkQrpIidN+JTnZbuszfUbqDFWbTqCpseLwhVeW0phbQRTWEAMMIh9B4mZskitK6xoYiDOa8MSBJLg9sSZk+SNNYIJkD6vZUuJetKx0vyBRLYocmQ0SOMRbEzEERRfTq6Q+qqBeZeyUSFjsrkrjV4isuvZ5RY2faVOCFW9/GhoX4xEQbDs4NRHIEyr4zDMD5N3bufxY++CB0EEFXaYpfqznUI+CrLrkeQXry4ZqxYUBjqcp7BYdzIQguT8I/tvuddQOZryZRZoG7i2gHUmmT9DBPWXrXv6AfmcorPnYlr0grp5rTN3xvxt/frWHr5AC6pWVhY//Aw8YvrqJZ0wQyc3bmsu77fp24ZZ7SQofeI8qbUBsApFlg64kNECpDfNehQQQkciC+foMDYwk9nkDzkw6tv/9a3b+8OHeo0mnkXv4qiaICNO5ppQCYO9gf8kqARXOfd67fUQKteJTmomoZxkQfbYvVggPqkMYoyD12iWU7sGuh1q/BZ/EQz0HwASmhT8Fz3pxQtuE3CoWnkL+WvFY+NMBVpRb1J5po3e48NsEgNklbihbNTT/wHbU5Fn7YSLbmclWZDW/OgKA+5bFv5IB2AjCMPw87bJldmWzk9vNtDcxLXgSk9fsqLOU1ax9q4jXZKd7u0o8HEBl5CdP82hU9xEevEG2eK2sK3fFZYaBnbojkwScuzN1DdnKsGj14O5nHy6Xya2h7EuxMbh/lS2ug2VWFtVAa7iZgKBiOh5Yj6y83V1Z3xathtTNpelNTFG3o6HnMX+x5CUNYM15Q8ExIJyz5yBzugg2vql4iUMRhMw3p/Gh/O4bTEj7/2my/fe3V3uOEr2aBtc+KlqNB10AK0rjzsO8Yy5PDyxfCT5S2jkMhSUfJDkHEqfozdJdJQBfaSGSxzBxJLC3asRtUutW4JSYVQEzLHrgtY8rhtSF6FJ1Dn1K2NGRHItGeQITc4E06DgUeiyekgGe7GlMx2aT4v9V4yclgZPzX4FGtWaf6dN9CkaqoIXNHgTq7IJnTLOs0TaJMLs8XL9qKUQHiX0nx+BL8Co+KJL6DW/typtaZinKqN12ghmxdpdIfBi84oo5DXocnY2hbuZs5WT0LcJAkxJYvAX2RYOHtvWo6eyxANGje1AsXXIhTgdaimhgWXK8p/X/6u3gIZIVpgn4dC0lXGyVtQx0/q6TT4H/U2N3elUC16G9TvBht7Ps3zGW25fZfmCO6KGBdyTmjLyThxQf2BEHHXzaV/eJzuz+npvPv4468M+93d7Q3hLDh9Wf29mkDnEe/UcEBpW6mvhLC+6Lu/+sn3/7/YTzgDEtrafVM1ry0rTaCjFkKmqqZfchFV6AdsketEXuM8FyPFniQHR4BeMa6ccd3Uh8D4Eu11rirV42fc0sp8vAUMB0LS3CAcq+2WKWhW9EK5S5IVV0fcbaWyulKsXK+2xqjv5cfq0vKt/TLNKyxsoIHzHBDwzoklvU19fvw0rhPqZ3Gu3HTOk78wRfaWWGs1o1VqPpcDfjLCM+NqQY4jvc7mhxt3h/kMm++EWcMBiQkGw6+Wgbq4Q+a+FfZ/Xp5LP5oGfF8RYV3Bxt248rsrVp0HCEinqisGUXc4yhmqdau30Xyac7Jtjdm9v3bhudQeQ9yn4TRPD/ePL2+RUUbrUsq4okzrgY8HWSvLOiG/dTyNn755jHn39d/4Wp+HPVingFvSOSFN77UsnlgA+ON8+/AYmSokhelUHs/h7RrOyH8KHTwNEC/MlqlKHo83jVNPWGhMBoCdZ2dUxwTaw5oDZRkOW3qoAUFH5BBAAyeYTXGV6Yo0xULLEYxCluqihdJOlBCfW0TDFlSIA81YkrYkIA0qyxE4LMk6U/mO5UnV5AYBuN1OhuEw31O2pDhdQnuk+lxJXmOciINeDi5cg5OgvivdY+7v3vw8zEve9+xCeQOvKrBqkXbydoMrtH/UbUr/JmCexg+UB62oJL0tlz+y7uBm79IHRIg5u2LvXmZwsDKmyQ5X419+Lzzg+7q0PPdlLsd1b5+8KeIbI+MINj58SVGtLf+6z1r27jIYbkArtQJef0mjynO+2b/66S8/7XI6HHBv6LYhow9g4Qws0fl0fHx3BBippN/4xjdTN9zsezJ63K3kF1iuxT8RcyduEs1ChO9MdfaFfPbQhbWcJoQHFFdKxmld113XzysCbgLfc9wN14EvI4yYEtPCePK1hEVIOxnV2FTFJ2i2FXQDM5MOEGaCAMnCxkbvHuRVuqukWOV2k0kxWrV4ALZxNiHLIuBBoXZBFwruUCi0mTWURqNJPg0EQjJ9ScXPoLveDeQxESq3pkkcjMF7gCweOLQDdHszMSRy5TYtZcnd54fhu//qT3/rn85zR3xmm73rt02zp3X+sd6qhszOUdRQCLeB8RUol2tgzYCLNgS9+Go0GT8VxcMK3zdLWmd9aItiw0xt5wMJuL+YDSf71QLt9Jd7QW6NWKHWZjEOuQhXSjHbBkrqcJO1y6W92nZVfDqI3x9+MgzpK1/84jzNP/rpD+/v78cR75rCXoLtLOsupW/85seHu5e7fs89DLY5zM30gZphSBxHPxrHOoA6GK25LmAdnNHAUI7jdGZ4DAOewLgJmKS013IjDtlOyrRp6khq8TKeXVLKTMyClDeS68TtI716yDbTWGd0DRGpTplWGvayBsxWYqGXchMXeLhrmKZar6CA9FAZ9IGsKygnoQXoYTm1wt145CU2uQCyspyoZju9EgwbxW2dLq/21YjNxaiaurameur343q4WFhz1RUZRvubp7+kcLfO9wUdyzVXHDR2xcCJ9aHvHp/erstTKDcC2rpwlq9sb+VI8tjsVn55T5ikIBJmULC+PcbHXBUdjcIuNAprehpKJwQgdvFrZ2oRHeSxAQt9gS/7FZ/Tn5iKaC2m/uWAcHo/3PHHbrwk2cyOu5L11kQ38rtJOtq5JW2CGmwoXYq//mtfgyECEhgof5K1vB92xKhEPHYyzEVS4oqXMdoI0cBSp9RVpdFTmar53AMLxbmsGAuOsdxv3xF5FStMcjKJ93gXCxjq1tSHFSjuUgYMMkNanAkKAAJT8j5ge6nW2odyt5RX6zKuYbdOfYzH2O/XU1jKU8jvxdNU8rmkvsxTWd+NBUD71P2iv1mJUb1jkDLPHqRy85ozyGbAWSm+K5H6k/syz7fzMZV07vbTMMSuT5BJohfSoTM2mG1H1VDKWkFR9KkblulmPt0SYmzapac8UIcQy6LQhpkXyikvCmVB2jmnNCUgj82ccn6StjwNgCVSIw7IurB+9Hh65IkGjmIuMJARtCNz6EsP5vwnHkzlMxDP9eYIqaRrGHAZp+2R/EWNS+RaEnOQ7GWi7bsQE5fuuRqOcklMcJFUdSBKUi5x0kgWd/1tXZUZOZxMXv3a5CSufeu6jcSI0tA98qzUHshgWQklFfOumBszwVtBbRKq7n5qezWdRvM7OBdMAaAdQ9+jO0yIjqiiW9bUYRSPUq5RB4IgnOtkCTu3RKWUUZCmbQeo5OilY0TGCE4o5bviLQjNkfMe4w6BrgfzI6T2jPwLJYHk6aWfnnYB1ged5SG8m9ZPYMVj1xMxBToppIuaZr/A4mXyY0+n6XieMJiNCmg0VYj6gan1t0TKb0MUZ5ryRtw9ayE+zpRSt+Z8zANFtxkjnJYlYw4Zl8cp7mV0IXsLTOVJ1GFMlUV2s9t1qYtpH0FhTpOHLKDTxLi6a1Z8IpY8MiMsoCwb/BnBvbBkwG3DTJcCtNVCwTbBIIrwhWj5agllKuup787vXg/vf4x9IHkwsFsI1azPzVZ0P2871qMWnku7Q4P/EMHBG4N6RvsJfolvgXNA6tunk81l9xw16upWf55y5SSw6KuSFKFVqHx0vLkPlJH4f03q7MrX9UKZ++3m2AZAR28mAYCCHhupqVqkyBACAnhcO1ujXbTQ4nOG8mQSeqnSI27wrgeJMaFepJuTXQBu1oRgh4g5BywXTCqqmWed7skRL01EtOw+Q8ZwccR8y5yWeHp6ohiBIU348DBgxGseqJeFEub8TqTbiG+E82CEP+GV0ecrGGwHERV6c4w/Kj2g0bYbMk6I4UjTfHoa0YREUO9YUN+n9AZbMapggyFApuPaoB5y9bUtiakunZNGUTD+iaECBCpkRpERVLXAvcGHASsoDiw9gk5INEiWFxmVq46uc3Ik8Kd+YrwlM3TqdnP1AAeyd6RYQuiRYz+U0bZWrINPhAYPXHz3IXVvflY+/i3AerSNT4qsbu+7zbxJyG5SWZLBdC6fUCaz1WQVQ/sb7cBClfSMVOgJtBpX9zQFdBjJLrA9aXpmF92JVXsfBcPNrhv25pH0Sa4d2UiWL7xraxH/GATWoF/KxqSjIQC3XChBWuv8txlMwazTtz5fWwuqlTVUJ0xr9riW+3RMsoxm5REKSFNztlZ5qoVQnmymvT8LUZRnELHZtKJ9fCGiG+q+FTLXbhi4bxZTEyR+lMQNLwx175J0CWpKqZIJgcWBnZBNqdAJsEi3HPrpidKu73vuEgFbOSGlmSeIF4ALElT1lwqJkOGRt0ADibVAp7AGDi4AHeQagvbrA0tY1nGa+6Efp3E/DAT6X5dpTn1HvVYCDxWrQK/EmEy4GsLvk7J6deCevB4XlhlEmuo3+OlTv0vLHIjkKDjWJ7HeMIv9J2XaffK9cf3P+/QCL4XowyqbbI2cW+fSDIMvKV3aKw3gNaEpxCiQJcJ+baCgJkzOrHuDpDtLWo+EZUmdsyaorjqI3yFtBebE8gf5/dHkK0U6f4UZdg+mdy5QQdpqqQP5J0aDMMW3ZWyMBuvaKds7M+ls10cdXpEDDou4cUQiHDX37K7jeEJF2l5ROlDRhpb21LSe76Ox9ZTFSMva9WEYn+I685Qm9sZJDiGl8NHNGVUXgImvlCCRDDAtn4i0KkwJtl0btwQfXPJlB4zvlqQeXDx0/zSViVlHyV8F5TtpQG6YVgSiYNS5oK7mQ4D6EXMGYozn04nYRaju1eV+6SyhAWJCl5wjMbOpHSoEPr2v6A6XGXOJGbfhRP/LhhUJGFOX8kq8sS2vAnkwXYlL1/84xL/4f77z6//of16GkjuYenmftaJrH1Szo36XVGHrwFxvOLe70ZAhBIaNIfZzRuuXyI5uTs1fO2+92bvOSHHSjhuxXXZXzJLiSvVgHNO5PJ2rkrmvzepak68dZ7VCtkzu3kQsraUPJQ3pYWTKYkb4eOl1uQZ7lXUprjjqencKQNPD4B+jH8koHul3romCF0WQPJIHMUQAF0AdIl+XjvenPp1uhhLmvF9Ok+gKbvimo/Y9hhmoEyaFY1lCq/rqLmBbb8JZdy8dLS47/5y7nvkwiiR52KxsVAJtd0BnMrJKr4Uxopi0cB7h9xLxPVipmUQIpWk6OKd4PI2YRhbjrsd4mNL3N4fhEXybqHIR5Vjf5bj0HTcw4NwYUocYggZWSxmi3VX80DXTsN1nG+hQfbf6L3YqtR0oVKye6G/QG6R03+8+v3/zO+k85Tu0atn1qyJxkakYKs0PGajZa4hWzvR+JWqT3E4MHY0sRcWShi20R1uhrM0k806yWSnqt9bh88qdaqii9sMoIxmoqwL5qqRI8d96jO3xW8/ZqVN/1xbhSgVSPEe1hsrLLKN4PUJ6s341n2dXUYwiy552vTEuHMEvksWd6c4K9ZZ0o9lPf+O1l8xITaqrbM6HvQllb81puQnz73zy2b+cpjfEg6bqpdwAnWgvkWFVbrysuXRNjFUdK/2lpR+3nPmEtcJT8/AnIkaEgHVdtxsAuECX1bJONIohoSULQn53F3PMMyZrQuSRlArh1cu7lOLj4xGj4uELg9BzWaa+71/c7ed5vks3/Fa60xlc6iTK4zTjoRLxKBOfLjndwrTqXl6zeaWu7DaLJTN0EWSfNKkZnwoxtzDK4TALyxqm6dVaXoRdAkj+hE6LrveE0O1mNcfWLatnwm1fTWOM1S1Xk41m090wzMt8PtGsXAmX2kGHVRu1+1kp56XXk+IPSkXXXkJZCFkmKXwxEqviXOxR3LY253sbMFz5UrOprpn0gVM1US+vJW9NSEo4pntezuLAbLWCaEts3q16UFStpNXh7dvBAHl9266j01O+O1vuyfxqichqSNDcj/0dh6f+cOp6QjEYKoAQ233HRKzi8Up0bbdtO4c2gUSi8qIdy4zuIs3bM88Ub3C7R+68oXQKvjrMgoLDvMxrh4xaD4Qwgls810T05ZRaTwNNAz0eqf+OaEUS6pzpsBuWNU8zjYAFEiYPfTdNmFkB54am02LqN3VTAQ+GSEEYZ1VhSeHwGr9TAzK83EXqENnj04rkkEse/QeiEeeQuK2nD8blS3//D0K+W6eHFQgnbsFv9kybXamBr6oc0ZjyIp/Z7OqCEaUBpcZzTrthR7Twk3rPusOae/anqHZBmozFJaUOOpIOm1XYymaRWqC1lFZJliO8wvIKs0aBdRmc/jTYvKsGW8pKnVKllqjZrGqNFN0m9WHLfLgknOti5HQxk0QgjZEyMyc098yGVFPujRMhN6ZxUFPQqm67ERiaoNvxAyKt6eV+v18nmhokcQarE7bjlfxOwgkNbfkbH4fpa69kRyoO+sYhdQQD0pwL5+uIjJPwebg9kNzPM9Fflh4M2eHp6ZRypFpAYJrFsiI+74huBH1i4oHjv2maqTMYg3qEV7KgUPZ4PLGBEBcK97z0YI0yV0FyjGx/WHq540Yjweo/XLEEJtTi2YqV4/0YY9jneIpxFh6wUDvICd6FVPxpep1ub+7vhwMRaqCzWimDdYfZrtZ3L+g3g9n7mEk1ikep+60jG4vYS8DI2/c0/YdpVfQ5tw/qd6CqlxqFa1Bp/qnjtdOtQY8NAfZf0iG9uVJLJmR5L4sbmvdgx1vJwAa1bpx/Hrop+61xT2rF2toJnL/B7gnfJhPaCC6aSG8ytWzqGIH6NJvxZ8034iqL51wXVzWV2DvRNdUboWWG9pjOY9ftemoZ5REHrlija2KS7CkcdLPQyCJx2SwAFigTySaP4eSQSD8uWVpOiZGrw31FgJ1P83ldQfQzTvMZpLHj8XTOOR/2GO84zzOq1NR3KZGIpu77QRTGCC6ujOoRXWma59PjeD6f+x6VLF4bCkgR8oKkMuJPl5iTUlWVG2XAUVk3LnfT341b7cnGef8CN0r+BbH4FKErsHcxr33IN8tx9/IDzPejJF+zNes/tIGvXlb5yRq3p9ko0jmxRSqYxRYvsuvyBIZZd/E6Z7H90otbaoQnhtgIIBu3q0kei99kSQRKqd0Q1do219iG8Js7UVfOd2+KfmSMGdhS1f4q2MqaSWupyQah22s3gIVFD878VwtMTiOqtDwT/oKKcBNebVbPeIt0EE/7burNWvzuT025uHU6YyKZFIqJ44aQW+xSW5AruWRChlBmS/aJ0DDK7hGRttE6nN0lJ40kVedQyCsjzBn7IQRHMjOnNqrL+XQe+z71uUOSdKEiLdUoerTdxQkNySpLNNKRRpYSSgzJLYxQZDY21MYoSaVNPtisgNQTxKUQMVhHCdkNIpb3oRoZnzd1boa8ZvvhxuXWDCDq0hEzkWNFHtqmAZVRl+KrWxqtuqSu485mh8ZQ/a/9muZ5mqHUnasjd+Vnajk2m98Rtkp+sctd3wk7SpWhJpnnRU3CpdqPBK9Z4k9NZskcFy9v9GfXBp9O+/hwuJVe/2Kqe+1/qNXGZjZRrQqLZ8C0DxeKSVlnqt+9/b39kOMbWN0O/2Pwo66KE3Vd27rcrYV1trdWlqq7pxZYPQUNaugpuA2HZ+Ui84v4B7UYAopg+CPli72t5foWjXoRJh29IXpsS4zbZAChU1e8kZR21Vtim9tj6DbCB+Sr2LTSYNSZqHl2w5AS9v28xpnYMEHjhQHCsFHGy0c0zhGON4W4Bc6zUD2zHA3wxW3aIL4wuQbBHz0TuGIyMxfxLFJx5wkKIqbEYhARjsbaqfhIGt/sTPPyubFMbyEqaz7/N+z7w1NJBSHGEoFd1fl9LTeCNEBtCW1cNKoG10Mi9Jpew+uOd0IeY99RrW0Sgk035tdYZXwWs7bz291os1OlJ2wmjqmx0yQWBwAXTow+8OZ+t/+owl9jg9rgLJXe5jhiFaa55aaBXdVVlWJzmUby9BkADWR0MhpgmxXRB9Pctks6uftX6RZj6xOm1SZrawDXUTXLIAIfUW5JPUA/wEcKoYkCuZj6qN6RVMXZabb6Do0jBEyY8BaiEzz2wJgsmU+vnoq2y7KGDvlWmVoEUnKdhMv6YZ7nw25A/RR5EYSFnOhiL6/rkKlGRxdZUj65zuZFZtvqkR3Go2GCEuMWXSALIDTR/hF2jZxcFq46q5u+56mGtlkIrGDMZyrFG/hvNSP8DgX0H80iUZpUcGx5ADBVABWknmyH19cuzWfa7FBpOax91IYAqzqVjHWLrNh+GQolpdT3nZDhMbRDrVxVWvqRJrblNZHinCMKbGoT4s4CH1Xd3q1h/xu/vBxcfZwqRD6Rq7WldjalexDvKbQKw7vStExwlzKjnTUEaGLXi7tt/QT+icTY7qOuXCE5NrkXUQPylslcx4L65xoyb2ia7dwsgs+L1ISpvkv/tNKLLOLKMOG6Y4QZl4ynlLC0U5SeHmIGq4M4gnyRUCZMV5KhW0XnCAnVqw7XZe51kJVMIMTi62TwgSGSZ64/G9WGNPCafdkP04ypngzhZ63Ao1/oxkiO1fWkm2ekL+8BgcULdInxFgyiFzZvVemuzUDnEfrXWPC0yB5iDdfltov9OueI4cu2Bc1/lXhUs1PVo9UYXb8Xr6xO+67KxZz+7ZZymBNELmvfzVPAkMMKwdHDfYJV7upComwSLo2Gd3iyKsMA17l7uiDhcJcxHJZ0bEuL+9af3hR/5O42F2d0geUjbXXc2etzWJfj5taoLQbwPUbwVUt7mYizLIKh1xWb4Zw3p2+aB7NMi3pYUrfh2QUl5olpcSgJibl42t3JYZvVVOwajEwLERIlKR/NbKmYu4e3VKGHYasioOTWiqpPD3U9zTCt4F6nbzBlAGlkXEXUCOeWNPRiDxLkb0y+RwW9BDEWfAJ0JPXVyocQUaO7cZ6lj2FZgA4BzI4aPfRJREmRb1GDLkb0EDM/sztzN4i9ZUnFIdyvbtc2A0MKhpOUgU4OaVceBhS83nVpWSdqi+ZePUwwr+VJHj7mVacYSGEv9PQ3YpWNq6r5qmepEZ+TG6wJ4JUgwKNoUYetGcbFJ9HMRVDjwMELTSAy0m3Bx5kNB/hMHTQBqlyYUWey9d+adPd37OBO/hE1W6Z4LLFItWfQ0jjVItZHq7rDarZ1FRnMjI1DHRzPrW29FfeL5sZdpO0srw+EZWoQH1mBHMzjCi8AdzOXZSQuKYlaZSPLkFEp8ijW05En0bOTzZG4kS+vAzHVubTYmGGYK1xW3p+8FnB0w9oDBx2aTgwCN8pAAM0QaJVPqlvWFS3TNOGDp2UmyugA7CRqv5aFI4YR7gJeFlRoOP4VxnN7745dRLGZBPmmBgzSFerfKJ5TVyykFYx5plXZzTaLwTA7HnNR5GGl52QNZXw6DSHm89OKbBtxffJgHfPMaRSbmg3b3lqzksi7EW7+gdyq0L7VYFSEt+4sia4FyiqcGRbMqRvAbt+FyDgxI+XFU8Qk6HDlN5Ud7D0FY3mHzp3P1rfd+m1irZVeCej10YT1rIoQN2/yobKETfVVVlQdC3l7tS2DtiDNHIOHqFgGe3BvTpvbdSZXhbCRZTloa75bzeq/uHl1mdd8s5uWMqrvJZUU5dbiTzFXBr8IklXes9yeoDQW8vwKROO9S21zei9odQBC39IrjGMhFTP0/UKyzUcPvZR14DyDC4aoJHWV9AVIpcpi5nle+p4H+Yh20OkWxNFDKz8AclSmaR46dKT2fSdsYbbjWR8JHV7d8ZzRo5IYexNCleeySRE8BjQkSl8cG1qBo5YQdvthOp+Zro5zZsxZEkoa+sNpPd+++/m0nhGx19IVZRWYa+HSaDpn0Xlcbqc0n9oE6hVg5uDW8rAhIebA0+BuyZe2fb8NtrzhkdOgQxGlVs1waqnYkkTwlpjL9Lmi+jNNSF4grnmsals2Cko9e24evDyzGGlLRNtG9soKyWeOt7ou0ch7V59yd25ek4jNxtv34YThO/3DmxGW/xzxC5lftG+G3Rr6UM7rmMfpCFiS2h5iKtZkC2sw81Oq8Gjcq2BVGw2mzak8gtDvISgvenr048osFRqqEFG4QiEnJ/ihBIuCe5sTj1bkOIbBeQoOlkeUnkMOdKlzkBgqcVgmA84OBEwzKu0oJk3zeiYyXElh8x61sWysGio5jOl6UWzqUhHfCGkZmbhja0G+d3UZOBso8WsCLdXCBWoaZ8+FxBjXfv9JDN//4//343/4P92UpRvE9dC3p4Go7N/KNF7fuLdyzYaRzNZWmBsJbjYyad6Q0YsJIkshDW7Cyyrv1fuyihGtP484E5voEB0ssTbeXkesuMqaGPxtXy8/6FWRvfKlP3ZdjeQW6HAe58C2n7JnbQSTNhr1KZD9pYZedchZVgUCpcpaJLC9hAmR92eu3rqcU1sY2rOs0IVjTA8lHG66w8PPH8F/IwFmKuUGFBzEXcj+sOpQ82Slh199FFHM6rvUchH/Rl9Utc5M1iN3K7+1LkpGO3JmqaexWiDk0xYt/puNGKeeuA1fyz/iBPIq8SAY9sP7Lvc9aLhTiru+wwgbeh2SO2Wt5JJOGvjVNa1tkcb4xDGGbkbbAKIM1ZfRg4kakgLv6DBTzLM1nk+fp93jPN+Ob1caVUduez2z08scqBkHS9XOPqziHypBVt0+de/WmnVjpmpShwY6UI7PsUlZCOnO6govFdfCoC6oaZfNExeXemC9Lq6r/Ku+6jNubJo/gz2eM2Ka2xDz67q8/aebt6172RRoSrGn5QBiQH1Fs5TXJLE5n6ofL8NXxffymRubTQPyQl/WYT3d9F34wZ9/9vj6PBHXLA8lKmHhWUzcNUxhjA3cMgdDC+EaWNUchjaRNY5cTfppU4844Sx7jB4RK1GZ0sRis47m2NgC1DrSxUwk23O6jOVxgdAiVBY75axB2NrD56VAhs/CN886y7WZqcFVTEs1dM6/8Ddj3L729qRZWthRiJCB8Tv0mR5tzXFdxun89Goow8MvwI+BYYI6lMM0u8JytPQge9Ua2HXDyotpwjHdCO5fl7+vT8xPjXvGZuWUpbPw1jZfVYnfcPiTwKA8z0QSSrrRSRF6hJEu5RXr6jVSo5qqd2HHXnHuLfODvNpM6Pgm6q3fKKRPPbq6qeW2SHI7mF8mJWru6booGtc/b/zKm9Z8Qqu++isZOubPuV0HiAN0/DqFt+d/tyzHQA19FFqjV5athLQxkjA4sE9NaNXLCBCyifddtUJx4gaermaSZ/DStEE17y4ZjjIPWwAxaFpCZpmipebmYXk0zrELnlweWafFo5NB25UZO8neK44kPjxyvAkNyOhOuaQaVaVMlWcyKa2ZGF0M3QZmgKWyAiWOMD2yx8HeAej1U3o1DH3Mb15+qf/0p68OwwHxMwuwoGVVOl3Xct24totE+dgxrqu8brRWopufiFwo/kygc6Ad11cnoYReebNznTXiiMZqrpb7420t5J6tot9+Oaf0P/LrUgOY9UNm3CbZuWNNL1W156co80+pyYa8J9K+7rdeEDeXv9Q8qvQqcNKKQ3ZpvmB18S8fCZ36IU4rJln+8vt/8ePjgqkP6jOwmVIrROAhHUTE788ysPIB7nFo4gW2loobkTqcsbF442QhNP5gWTUrywnijhL2zDJcS1b02Ow/m+UT6gHi06RyMSVU6SS7oecMHFP8MdCKpkCSgvA4bxNPpjRxasPu3uK35rXp+NXKkq0CzyvALXbDgNohn433wlrCVMq4zFNM/2YK3/k//49f/NW/n37xV/uyS2Cep1fNPAQmmd5tq7gA9Rf1Mdw3VaRNmpqtUS79anpSIVTDdHZxhNzLa4Lpix2rM+sq+k3FhKyCObBXQkU7USsdjaq63Ntue7h4QnYyZiOB1f7SSLeuezXDagXoT4p7O/afuEWx0uI1t61Reo0pKrdQFZCNaDcvsn2u6mjJ0HqWrtSn/c3h4f/+377/oz/9OeYAmE9K47Y41yxZI6eMJY/CBUc1QzxpSUNB8X6rrZQorHHX2KGIZvGquTZyZ80fkxDKuWV13XLAL5V3x1gwGXqqDi+jR9D5zHMgkNYWulw6B7nQSCzx07AxZ6Os9qfR/2p9amLABIXuU/xcE26uoul2wEVZjydVVByfU3E5ztP81yF+J6d/8b/+L7d/9r8f5zd0e1XYrKLi98NG6btO0ovN2uRK/Ttxu8Q5p+LcolivFErOs/Lmx+3Y5jJEQT1LWt7fLDjAbBxro1McivviMhcXcFmH58w0G/4VJGhMH3fNVPpbc3qJEz9c7SQjQmAAURDCeGZeRBtGbPsS3NK7yPiaUu7PBwAAQOJJREFU4+0+Z+qjff2wWlOIYb+Pn7/+6bt3jwWmWMbhcd6U7J0hh9n4yTJxbdnKxeLNuhyPfU+Or2SOlWLP3odxgJmWY1stYao62ybOGlerDFWJZqVUw2GaIi6kP2w0OOmNJDOBqGWeMKmy04iJj5lY+MQOCdZUzqYuti26AHsMY26rKy6DcMXJs0AV0inYDq9r6XpuoJCfqmeK+Tk4yTg/7br7OP/77/zR28fpPAIXrh50a5auemtmAsRWbzy8JnFlu6s6/3oKs1xMzckKR5ba7VbRIwatcOfm3Q0KUWJBtlSWvUVFpYgpMA9Af+AC+woJbjx0/1ba751fIAhP6lDV9sEmIDHXSTezbTIz5piWgqqvJi2s3+q6JnA7wpa/VS/tO/FnMHdaWTgsNnavE0SlZV7H4zSN07vxPM5ExaH1Q+osEPJTWxNzFmRmJMOgGRqpi25EfPYxRYMIaVdNy/K9VVVE8kjgRU1vsfzTQjLWqsmAVoHlgIbtMK0ovsGccZUoShxJCpwjAiLxSdMMCDcAlfR7/yAS6Wk3HKPlLJOu79nevt/MEioz9SyTdTFREMkAPjXNRAminjnleXsOxXFjS5mX/Fd5H/OciSCDqYPrJVT4XK5V7ES9JYNPby1flTKXhNJFtdV120nyHqh7UuVtU/OsvqGF375+iXUYp2lGQm6b0q+mrN3SdmX9eSOwTTx/RXr0LVAyR2M4Sl+hKq0fuLDw7adJzSjtEDQ9RxEhE4KXZoTajMOqVK9LskMz1/in1XX1OU0jOOXCNoOOUd+gTMjtI0vTxzSiF0q7AlX78MQREz9JavI+JgtWn9emXtOh3KZlGR6hldRNbs4Jm3YDElrOWZFtUq8SShSEYZI2U8xmTfyad1A1KvnJ0yQoQIvq9enEBqTE4EqclqtNlmnm6ZDCOMoNjx7cInlm8i9qStGjlajiQDue7wzDAcWvAQ9BJggHdwYu4LJGbWI3DFRcXMdxOg392/Ny9/BJJkCilUq9i9xukE0UKaBx9d3M5NS9wRA9TiAb7uDa/uO+F84m4ok0F1X9j22aybn89C7mcRw5/KxSwsCUBqDY3sDGprpz/4qvdlGkdQGvkMdPt6q2Pt/2vDw3SkEaMhNB4UEyza9FbjxbD1Kvx+kge30bCo4rD+jdc3v7uLul5GXaLQ/7h9fMBGWuEZ+1AiqtZkP2UIsA6jqpNdSJ2yK7Yr3Faolul2SsdKuwKRPqSXbPGlSTVxPkiFZes2pmzBtXRH99QxE8deoycX6bepjgXVMjoQBE2SxyndmjyjgY1vPxYfo0/FhCuq2mrpLx4JecixP5VRPKRWa0j3SclSO8Nkh/lnkpI1CfkPJ5mQGwWdL8dE+1AHZP2myzbQCyc75ErK/eQhRvM7cW2VmHZ3agmT4uths056rtq+mo6p+XtYxw9SajsIHjQ8mLOkPiygkvY9rLnFF7+JVPcPoKzMHohqkhf6PsfAZLMu21p5l8JBCjmD2vHtG1VW19o2ddbD7cGUJPC+bdJ3Hi7c5XqkWtYVz6sIvz+PqJAWFW3WTjRs8gnffsRkolya5nvAe2b9krJjdVIwVXVKvOjUisJpzc+pHDqXkTkWl3W9VJFRCxPLXxX7hkMXwBzlqwg20wb0Z3KHUObDu5tTrvQhNTQjygdlJXlq5NsmxcQBz31nSXVLbQYsV1bFdaY/o+bAiuRpApho1KoPjWa4Ggo3yeu+X4Dt2FApZxqqLZBMLpX+FQbjfVfrqaK9qewW1HV5eVX/FH6bHgByEXbdUk2zUXglMvxtwO0zyP45kmm0qdby2lSxha0Sw6Lbzf0+3XVcjlNoO8vQt0lpP5vfiEc37tc6J9FDhH5Zbcgc2J0ys635of0Ccz5YzX7ltvpk76tJ/KXy4dVNWfhbAW3/FJ4DXm8uJuH777nfV0f1xoNpjwPrHrZbvYO43VRHNTqjT6qBXkR9exDV4ruawwOavcMqqwazLgRnDvasfNjB86trKJ1Wd3RSXR5BxMInLBAEYapyGzyJi/lrB0jOhaCcm4qNIBY7ABSEUvuCYNCgANXMrPU5sssrn9RIpgjjo1HfLcYr7RFOM4SVczdyOvpUznowHjMfwhhHdrNz28DpiDmtHJyIFt7fVV2i0fSF5s7Gs/F+YqPUhjZ+2FaFxXV1jjDpyMYVlLwEBy3vmbPevZBdzrK2WapmkcubZIZOOS4agKwJke3avWwqvEMu2W2vjcvv3arAXeB+M31IL7gNds3sUaMWiL1DzcJfFNdTEujndeUaMA+ZfiVcqRAttoHly0XTXsUnhts3j8qhOxF03zGp6e/nIXTwG4eWZkrK/UvFxJHTcd+lqCkMcX6AUTALg55rhXMoGaDuDKLeV7NS0kJ5Upb/WJqtg270gaJLSbU5bVfHZ1YU0SXLWOmXq0Nd9rNMoKE1aBlItxAOhLIF3AyaRCPCQkpTXUrwU40o+W9JLFk9kSfDIdjFzkEQiYxfMvWTvg410qBZPW0tvPw1oQGV/ptrNUQLMH7O+qiJ9NgF7uwa0ucEEr2yJYIw65XODmPlFfhP8F+XwLgGYTslnySyJRbC1+jYev3aHz1Ooj/2qDzWk0aqqQUXf6XF6M618a97KvCqEgT6mnnHlVI1albNZqs3IuqnHl+6vrfnmCRvL9m2aCkzWGaR5DWd+smB7Km80G29KYb2eBt8siP1I0AmdzdEPRToYVUr4rrXPSg9B+1USuTOmBGaKxTKqTqxzqPeNJbFy00dmIE97qHVYU4gQodIyYsUgUdR26XrDo4j7QjxUhZ/nm7ebgn1hKzART+vnF06UpfVrl1tZiCbnp8Zcynpm5hOpxqeuZwAhGnDr7y9Kl+xeHH/zpv3u4f5omGXrS5Ep0e2iGsombKuK2CnWz/VnQLl3f5/6hQ32IuET4700MtMejDSabk5A8TBoJ0wB39Dldv7C95Oe/nD20tKa/cBVEzOYF53cFUF5c9Mq1hA6I9gSlItu2wV95bxdi6iPki++e6biS27qSHuQHI8R/mOczhofze0FWhTFiNxgaRnVSlbQmbmoQolXbK70ozk+VWGZyis0i8/wmXQklqKiRq1AYCWSrAgfsQRRy3PIy2d5R22/pJRbmjFZe8T4s5ypMAyJdAjRia0nEVepQ1qIgPqWoz1raZbdZWIbpi5S97Fqh6tBrAKYaQrc/wAflIhPjWmH8RQ2wy/3Y9+H00E+fg16QAyii4fQ4Z98+obUP87/s7TiAlS6SD3DitbzXZstU5YUAXrLrpmBFF/hX3Yo/Z0YWcIyeae40bpwa+uWuLrz95vXWm2r+7YygZjGlW5q7GFnmCD7JcwlrHVXPfRE0NNcTUlWdvrk9rD164wJ5e2v3u63L19Bbn8IfYHvbA3dET66UdL0/nomWsTAFNN0wpmZ3Hc0Xl49bAUaxFfxwOBoDEDAZ1Fk7OpYMGY28F0tYY1ngHPFnAYvejOkhPBLN+e71vVjtsN45Jdg0Gd74kPIjklnp3aWTryjbcAOjde7jh5CZyJuJ/uKwUsVelJdmLKy+bWkdM9HiZWgDoebwKs+bzjbVT8gAnCJ1DblvaYykXkieR0oN/ef7dU14lqRzTKyRybJadbvoi6q7w2bCSVJRbvlX7ETWoJ6NR3NRVrfGYDoRivqaNDtwudfN+ytwovMZ5J+549FB1Yr7Yy/msNQtXyuJ9fSihbkYyDALBqAwBMsBwa4/9tY0syKl+Icnhljk2qxU8+2V0KO1tNvfeJqfzfVbiFP1YnQ/MSFOCcfjSCgTKWlybxDP/HOd0j7VVve3rrf6moJY5MKvMkxV1L9gsAgmZW2DM83v5nwv5wm0jUGNjGm+a878VmmrwVa5MvsBh4Aj1BmjikOGs4oxDnoJMrz0H5hlRcZ1c9VlqN4Cn1s9ZC56yWrbtuZw15ZRoSZqHFf2SxG5UEMw5aSVkosTKJ/vdsvjm9xBgMklUg9KL1XXodH1V+znxiRcFJObE11u91o/IcYsBAJWThK95ovC5lQ3doQi4fl8RiRMA4VV5zfZnJZx+8od1qsIFpU6UgCHSUT1ot4yV/BWyn1b0kvNTxN6uty7N6KUsOkgDOaoWLbfHPW6/fST/o630ZfzmJ05uFjupmJ6EcOyjxXXJczjWGgq4TIteFACe5M4qUt4GSbJbeiQhnoDLvUnYiGDBtWJk/kstAOAZyDpJciXCDkFq3zL1Tv16+GQh+rYuqC5lqx4iVkx0Y1y+C1kVOS4CvedxOAcgTPPAjr7eP9JRcdtGnlaV1hXtgMpmDHM3OrFkpSVMVTV+08x7HrwgXBTFLZeD5wPa1/plyjlOK/d8XWaZ3D11P1a25y8aHoQf5PD4xtX29/mbxuzVx/MkcB5A8+ZIKSih555BZW0uO5GJws1z1pjroLBGhBgPNQmZja0ig+9tjvP4LzSJ8W9mdSgC+wRB7t656gAa8rS07rVXJt7Spd6JfZTTM3rB3aungFqtFZ3K4zVyP5HhM3NStTTOM2mbUO0b8HiuJ/O8zEQVJX+o2nyjON3tFeC2ZZXI7qNnUXtAjKzLLlu3VbUt6EOXx1yJ0XajE2L9KpiKsS2SKWxfWt+71ZnutWkisjSliD5HKXQpZAjU8V5bw0QIYBkuVjAjcfSvUhSXStaolPUxrNfToMRTV5U6bvYUCd3878Yycc31jGTrbXZEksp6Y8+5z5TZ8W6psfYD/dv1mXtEghobOtd30NN6lxF2oprZm8b0fwbXEu9X3M4eBxk7PsBM4CE1K9Kr+uEc6ZJlTgvBZd1wEboAquLerKPbu071X9apOd2UCEVpdlZSFeJPaDdOmHqDlvg9sEkbN9qNJZB6QsHxJUm3FPo4boVGgahZ95G8wxyDUWuKCO6fEbfU/t6hIuDza2tgMaxOac+pdO7scSFIEssb9KxZzCLa/encAh7tWJjbH/XvET1Zq1DyEJF/CdED2IbxZc2q1gfRws22tlfl0UfqzYlUjVIjCjBz0XXiHuv0bqFu1boEdVESQEMbTBkiIGzauCNbzdqhuMFRl8qDs1pGH02C/cSWEl7QW/SZGxIKb+iEnaA/qS0y9/7oz86Ho+hRAYSbOTXRb/VNXPSyrfQbt8mT7XR9hVZ/ZxoE4gQxfOuHxKFG7WsUh+ucYrlJiqwEeH9Mk21uOa2vClnl4E1zewUuXIj0CwiolCCBznT9HT+REqYgad2qbonZkYat9n7upJ1JJ9cmm/aO1SZ1Ge/FnL4n19Zy03475R+9bYt3pEbZOsaY0Yf/xrLKT29KWgvR3tML8VqSSY7q0vaR/MGzJBscLKa03MQCw4Z3J0LkZmnuBDCGinn8g6GVhE8k8KGK5zTmB8tbN02pmoHBOsD+kvo2qneI9rJZNFI21kbazO0cvQ4SVGXtWp95c2035iPSMkenQUguS5JpAm6i2hA5hWhCp0FvaVklIAYnsZzWdB+lFOeu/77wwGtJtMTCKk3GKl2P11++XEsLbjAycjVvbUNpLdGn3vUgPQmeL/O0HIMAheRn/sXfrdAsiYkWrguoPUAuW8Fp9SMhpUpVafK+2A/Hrn+dZ0gvfJklAkEppx6Ry+Gk7XxgSoZ1cwklZyskwlZzeMz/GqzbnVjt2/HX7cpBGw/LcN7rkWksiYqJznHZe37QzjcQhXmHl5CAFKU7DBjopgI2uXYlUiNL6cwCrW+zOHYZuRrJbbmueSWaBBSWpnJneTWQBE0ohFlb/JzRUMp4tKJnLuO8l4q3tP0uIq/RKWUiCYCV6aqNWdTtg8PPWEEFWezZkwxqvuRhjzhpmw8ipaZBSClpojmxWrAwnGHkBtUjyQSwm8qCxhzyHQv0zQtpXT90A/DQpXxe/yxpvEhhaSd6Lo3mnSvDCj0wufem/t33TzODdQCQoNjtBdvPzLPiu4XyhGsvdBHdm7nN7ut24ab8rTzjPnxBGl1s5Pk0ewUMpRHAxrRlVr9M+8ccSBBYfC/TP7bTF9qLDbJlGe+1MDwpmF7srXOG21WjVXrAV+9Wlssc5A1ve7mMH8x9XNTzGuad/sYvvPPzm8/O8bDuICunF+z47JSieKuLIVYWTzD9lHEnZsZNm+hcZvrs7FaBaMooR7t9miOptUqlI99qTGcZbaot9ySS2Kzdd5DbVbkShJzSuhoZ+BqaBAME34Rs3y1wzp5hkQq57zMs1EHKC5WWqKkOY43RmWjFNZ/H/Lr+2wxs8WOKTkD4DSC6TZiPEVaBzw3P8S89Dk/vV4C9Tf7LVBxYD7sVVXbiKv4PxrKbMIcJ89GUOimC6ldYgkXMCe/pr7rRmq9aDxeR/rqw2in/ylVuSzgJeE0gtc9tWOGb0N9Z91VfIxIFr+WxUgD9LlhkMdpXTDB3Tsatg/NULbxg90EN4DmqxmHq30flz+84hptDqnVlq1BbrWtrJ7m7NMUSndew3z6Dzfd6THMwOXW83EEinmbZkIleDY3ms8j+XedJ+rUpiUlarWpWUiRQ9C8KoInhY4GcPPHUKGlcjE3r7EywIhDMp4kfkK+pZ6pXlozZtoJIfOchJmJs9x0DwDT277QrWvsbEwH6VaaoNSMfpEHw+gW3io8wpLDbJUNInV2b3cpGDLOJDJyzog03lzm80zTkmPoQYmFau+yjDwyfZzW110Xnz4bS+lFTWkSgc4hKJBGT9st63WuGY6LXVOtcTW+6ve4yWQaCxEfcJdz33UA5RLLqUhia4g2e9njYYgfNAQBpwpxUOPzaFStu8ZvMRMaataXgaiqC4hTXlDwdaWufm1/LNlIZlzQkFLVW/1Aa0kvztI63c3pLy5qOHRnkDWF4s4nuTXBqACNG1+jFt9VKygSIIAkfvzNvC4VZYVe6AXFZnOEoR9p2ofI8ySiDGJy5wmF7P0SB3OHDnvh5WHwErmdsiWAhSwEG9YppFxgp9QuM623KycbRXc3IF8kb7qNfLTBKoqnLqj7qP/zuWiSf2aB5oheKuiuz0VBE9WHwe95c8lIcXG7AzFyUXOF7FwMNAlSXeO3N4T0cA6HX/7kPJW4S9ShoRIggml2ausoN5bZbIct1DbOvRZKi1dWQe+yaCwoZe1i6rtuoqIj4+4i/rTD/Q41M0K3QRuW5+lyStQgqC5V5aoKdX+a8dTyLU1CFRy5/Yorok6A+edVeGpiYHOv9C8QTnUd8XmrNF3KqN7D3+CcN3LoX409jb949Q4a35q2j4u905r6sIRxIWpb63igDyviWJWAZRA4981jGVzGmG/SeLB0o3jHRxefid0xi1CeSJvwpCdPeXJEAoSeirNKPMyRqDKVikbSQMqLXlmmtN4qiS4FM7LDTlInGWNGI+sSuWFsItkVniFrTfkbyT4xXxt3DqthgnpChdnGw/CaEIiS1lMobyNn6QgoT7gIClV0GhgvVI+YIoU+/PBP/mQ8nTHJSVviZI2lpcTh5NvN0qb5Lgnhmn22UdGeHULLHs4P0z4hzObL1JpRE7JONzRbsP4teXhWf9byVpMDcidaTr9SGdZCt6yYkiuyQUb3rwrwNgPV/umz+LyRxXYQAMtfzpasWd+LgOXq17Xf16LqBfBjkzDwKwkyENp0K4ZwTWPb8S2WjaaoyLLQPtMnEHFmRnVeW/Hq3EM+o4xqBUTLvMzbao8oEAstpFpNX8TeyazUEfiDRF/NCoBPRDVuYfoQT1qGpJAFFFiy+FkSwNsKuFgGC2ZwZ1FidCjUvVlZ7rJgedbj68w3vUuh+9Ndbjn1RHMJaw2M9BwORIv/nGNaY/+9fj8vUxofVpqnK6Dopl3Kb6zGdVV1xI6w24/P2pRLa7Lde+4MUoqDrWogN5Yyef5CrHx1rc1htgDXpUMbid3cKg8Q5677emfqP+skWA81aR0StVL1LqUCzKgAUR81KtiCYGqKy1pKrq/i5sbFWD7jd298SU+OJI4dRv/N4f5xXQM3sqv9hM+wp8BE9WQFL9g+E+4rPeeWWV+TxNrYcKGcGW1OrXk1G8EduU4+WbUxvLHWPSqYQ8yCKe1nCtd1ybghVJaOHQ2/P+TOEhtiLuc6a2CxPcBkMIamdGrUz9VsNrBNjbrSXFdNH5CazpjlzcwhaylEucZ0AdQOgQBheYq5rCEdP0cUwEEQqyhzVJzLWr1IC7boMH4409byp9e3z+je2gCgjr8yh1vRJQJ80vWMbtES+xXP0RCWglCTBr3WiTWD63fwxg5KaZvF141sthvGmEfi31C052Vn1vYGN24CI2w0DNO189rmqu5zJ7LYsj3YDXq+WPGNJ82RaVUd7HAoTcg6LfPxNIZw5iHsKjh45q5nlHodNeDKb3wP5Nz6RAPfdN08nPtQiJKzb+4ViCyrxyiGvdZ/TEGrsKkh9GtZUbf62gVpKHlyDlzVU6H2IAz6qT0SEhUqOlsfknyzi1fFSG+uVCN/pu1D1iwp9Nh8Y61vpAVu5zMl0vKwFZKZQFF+NwxgrswY1BZS7MFcHZf7zynlB1C06cRt/NTsJNtRrLhrVpF+sd1Y189Td3D1Ic3oWNoFAeOA8Tf6pq8CvBpJZZ8fLZQ+JSA34nVfM5RczyNRm2TTLmejkUKkZq9nC6/+fMwso+E9Zu4iALbmWgn5Gzfae8WujvTsYor+cChqFWb/PvS53T9sqEv90UoDPGKXD8d7POS65B3yKM4+M1wL3pzXP3ZryiPHcHzJQlPtNLFDw29I4hLpYhFklWSVVf9xMpN/5RCsrAEkt6QbRqSV3TZ7h/wTa2asa8oHmIl0vTXOdGuKTP0hWyweEWqtdvLY0FwhAYvJnNJkjdHXyqbOXIHiRhnSe+fIt85QF06MnELfg47D8gUs/0tZh9ixyV1L+XEMH073xLxPbr/Vgi7CwsZ35oeRlZeS0HZrabX/2s7c7sc2DqRNIrWp0HXAAhUqxNpJ7cDNGZVXRUjtbH2bY5xj09yE7Q8GvVX5l95rbWRV8prnRNcZxQqfYP3DvREquk1Msr0F+rZ1YnQLPetDN7Lk9JZzztX2N9G/dCRj1fI45ziN09M5DZIm4DvhFKtj0tl8kYTj1zIg18cTbj2qmaH1dPdoD6y4cePucO4JJ2XVQWverjnhAuRSP0gysuoa2ftQt07jISUl0fNrhlrdMecE2kvwm1uMGUkkpfGUK1OuyzGB4+BtYg1DjdaOi8LcY/Y1LwAUxRDAXLEuqLGs5bgs+fH1tJyVFKDiGSBEBoDUQLcVQtsXDpuoFqw11hcbzXmeF3ZQ+pbVG05gxmVV7sOmNswziWDZ6xh0eqF3N3fvlr9CYeztt/szIkWB+LDdkvYYeqLGbNre428EWe0dZn8ntZ6/cTWUJUrXy1w8vroyu9reaPqf3Hm8oTZSaIFqhbKmuaTQH94bpvnpgYWN2DBkP9NBPHfKwjyRmcZOOu5z08jUwKTZC9WtEg0KY47LxOJIvm6JK2gqiJpaJro/8wr1wrhPbjGxArasnAylN719ZTp0s/D2N9McKIKUEc1qTpXxi5SXrj1Xeim9rFwrNPpYpNyUm3mC3jUKLAOiORgNaFgqtPsQVGkOKXfD/l069m8+hzzn1K0YA6uCtanWibPUbDruRfZbcbsz7Z8XMWd7Ur3zzYnwhXw6urhymWfORl5/iaYQaLk8mNxarzdfF6rIaeV2SJmsAnXvg+97u3V8s1Z16OtVxIuLEQOjSQCaYNxX3zR41Gyr3d9mBa9YYbn8c79uH7lFU5Krk1LepZub/bs//qP+8TXqrgAYyv5TwKWtp99s/LfYMJ7HqblrtXu1FNeGaZxHEbHRj9DxWOx5nUCqKt2xnCrfrLu1jJgDymaIzY4noK3GnlEZnFIC7MMtq/ollmbj18dcWVLKseSk/FzorvlwFm9ZMs3naOi8kCdZazAbS1FrdPTF6BH1gSSgY/s6LUtY1nxz8/Oh++Gf/OvpcZqnpq/GXJoWjXWZAq0+hXzsesT73M+rQ9P8SF0cdtwwJT1zVKs+qdsYorBMfKgNmoE0uqUuTPC2dOPiAgFz6Y9sVyATKPNTrj+dw8Ipf4x/KuhODB8UZdJgzw2ZXe90AzfbfFflurm+syrSFbdxBdx9SlNg/XmHxOY05/7lGkucnsqOtb96zvgQIBOGD0BGgCCPFBjztqTiqUmuQtmNoNFFnor90iVgAw1AMng5xB+lbBAtXeoiqFiVwhEBpFFeWSZbY2v+IfvPNiix9car2pHeegFLKkFW5YKnE7AAV3k2bSNhgOpgdrMR0KIXkgZYyyWtkaFOGXZ7bhsfFVxXiA9oRxvfsKg+UB5NeZqeuuHt/cPH5/vzekdj0cxy1X3gihJmH5oN32Rdmk3td3A1HW77NRtfLU3dlhIsoPeuG+cJ/YYE1DBQAE+E8nuTM3rds75Aa7LtG14bMxzVt1LrYQAsxdE00rO1bt5tZTEIiOYd0yof6DNpTaLluhFthLtZ2/YZq7oyMa6b1mlit/BliSQb0xq6/N1DF86QS+Ka4UlIkK5lXigBQ7HuSlOFAJqjnWpTdGxibYyF4gV6YUxeJ/FhoP5q8ScJAMYeT+ogsYxtZALYGWkHaujriAASI8JwFcZnmxgTR6zSScqDE68D349qY5MQpcLkBtQIjLsRaXH+TbUL8xQDI63SC0dM64huN5H+Eb6zEjpi2FTZ5FKQOGPUFGFbUQ6pwZHsXA43hHubeWfpQ0hW8TbPYZrGeVpvcpxPb+P0xdhjg1lN3lx8efcu89NsHwPEauNF3VYOJ3llK1Yf0raVmTAPesbdYuJyzjOpIa/B6kN7ZFRMnTld293/3D811yAeoz6ZyRcHwGY/7Jb/pi/VMyC+FgFudNe2pPDc/bU/tISSrZu+qPI3Sbv+tp6B1y3FOJV1Rq/r+e28hnmRPne9YRoMjzZoyq+KNAAVjISD5FJlKyrfXc0TtuBhiH+d7CyhMWkK/JBqOWVhLHoAtfwY6dVHDPjjHgndGPLqxDxasFTlr1mfS8gRBmuEJYETbqVuSG48EhIwjt6XZRlHVGS5iGWU1ArorsZCPH/i+u86AleQugP0h1v2RUTEGitWW1oXjCA6Aiwg6y/sJ0w/yo2DtOVTAJMjGuO6bnp6O8A1MsBKK3cbU+P2QCOushm25lolzPXyebGV1obGOFZwPfNrEpd9Rx0guulYLswZk4idRqSikoG1q7HRs2DOehdVhO0X1ceDWRAC9+sLsz11XQU9sQCw6rRa5+3VW2hP7hT8lYKSrpPRkkkQ0R7QuN6+oOLczbrqhOAL4/w4BlgcBaGRbSHdj91pForSRSESWpy2vFaGOJtVu2ytpbO+KkYOq0dHzV+oNGGkmCK9NO0bYprXNWBesYCT2JGWVijJ8XpHRmTJGoIksKafEy6Kc5bqQLJA0ZXBXEM8K+ITkHRJAEzNT9ZiVYMsF3DxVAGysqA61plK0C8TpRMUcyUiyVvBuHV5XlNg65liD263hOZBTs9aMpAXheKLbsifz93XH992oWB0kszNqKa82amScfOpprodXO9P+5FalhRMnHdbL0/lfqHOHoGXupxHnIAnummyTfDUWu1UTxgCbB7+xbZ31t1CDquXNuqK/k/sdRM8eCkzXhFcJ2jtFWUb8MgMj+Z00t3cVr3PizDVAldvVpr30R6uP+SI3PSrHqG4AtNTstHfvD2ezud5L5MiaKoAoWqJ0lm6x9mi5Zi550bjQ3Jr9cWR0OulWBg8Qk5ETemTyTuFGOlkRrFYPO2Fw2vqCqs0mrrEtQjBCs0OuFhPrQ4JDo7dezmLln2Y6E4FmLg4mTqJQM5kFV313vw0pYnVe5MREMBX8FOCW4uuvuiIQ6mrifahIQvkhxfK/pTEMguPpFLGUESQxTWYD2l4nOPw5tOBFOaI8hJz2ah29tbL21TxIWpWvnaYbUtNPn/9nEvtdmS78ILwwSh2AMNnRJMbKfMzTEXHdBfu8ZUv1xNnW8h/ziQE5hcZLPIYrWRzcTKntpzscXNZ7nqeOsdc3psy6Van6SUk2WVuy6YwvL20nsz3R3lnuzrVaqWqSkB3bUm5D314enpzHp/GHJBJkAk1+GNZMtggItwgSl5hoqIUXTkwprCUCpBShFQUQquwtYlYhjxKTERNSWSguC7DP+QZ3ClQ9oODUmvz1YVQQeRnIh0gL9QwfaQXYOZlyoQ420QzKl6DGFtfqWRbuEbMXCBfQ/pKZcIDQ58UL81KhhMEAuukzkZx8mMuKa7cyojGKcls82wHGZ/KU8ALik8UrJQlhyUmcLdL379Tv6AeLOcndOz99E/++P0//B/D7kBokItazeZfzZtoNrq5qx6J7j7nXOdLMd+Gb9zApWU28qLJ1/BziCRBYme03kyywDrKZwMNvrT0LZy58WYlCbFoT79SKVa39ppF9iZCQhpirBfL2TAmSCTlZtC6JdFvryUTLPC1O3DfiGJrAmGXbJMsqvbtSgEthDKEGMbT0A27ESEVd7bHBJV/l9PUpVnSV3A2YZzpjAzt5t01EzESrE2E18stxOoGSFzEiGpXTCInhXYxutXJaq0hZgwrpooC/oTGoJQX7qvIGcTDh4QDL43fig7RxohIE4TwK24GYGMpqAsqWpA26a1zgVspzMqSiO8HPPKK8J+sIvpMKy0Wx56kt8SgMU+7dRdS9KGeAjsVnHQvpBTIs1N+ZUk9AdUckD1LkGIGpWlPLYXlC540nNP847771nIaz/exvI9V8zmiaw6jV/xeOq+6q9XAbDffpqy8+Zg6z7IwNNyYaD0n5FB0wB2/IN3pHP+yq8fE7v4mWlNnkAN3yY39NUgOdSAhpbJJ4Mnu29rjxovl79C/z1PeN8DJRlNcyvDVxXRifeXgDZCk5qi2+YtqqMUri9gWqNmFafyDm+4PyuORnhjM1yXMIe3W1MdpKXmNZQrLDlD72MWylDiG1BNoeFfWJ9hleNU7QQuGXSrn2O3o5fShTGs58eybWIYYe85Ux7AvSwzlPuYDGkjTKebbuD6WdIhxSvkuhxMnl8N6G8pDyWsofVnO9F4Szhn3sZwCMJt9Wc+x6xKeaQjrQ4m7EkAehTQY2p2PJQ7oPcQy9gFPl0PpIx4HPFRkNhZWPLpgMyXA+7CeML5rXtYyYc4Y0i2sAogSQIQMo1kobMgRZHS5LGdpFgwAuOlIFbKjYKwrIc4Fuo8GyQXy12GIcyxdWM/k1PexTLFbQ9jnMOzy/RpvwjTGOO/2X5jHiHGLoeuh8qTM7V/8pbB58iHDpG53ZZumaWPDa9uq/qz1ejlHmToQa6KdTyzzlZhTugglBlbv+iqwr/7ELK+m/X2dmPznaaIYSQDAlherz9zE/hpsscElCiWaYVsfT6BV0hhvJRh/R67N4Xr+fKOiLlVt47r+CnXJJqVLcaBm/hVFnmWflpJuXsQ4EXx0Aj9T7Gbsb7jRXYZzLYYBJRzK3yDZ12HELnzhjvmlyxLXMlJFB9Y7xV2Ityl3Me7I6k4FQdvTSl29OXQr/xmOpezDeiC7Nq5rt5RhzV2Y8Si5e1WWIRLHDMXcsazHGewauxUhO4StTAgfQ+nC8gpUAeFuKVOKS8hDjjclpHUmNCKs7RTSgG2TYX5JqBKUS+rRkIi00ETtxitVd3cl9CmxzSd3Ax9Cix8lATGPgtQTlTjLAsIqqIwBATARviFzwF59zOi+zhM5BwHqBktUqE2TBpuS5xzKjDe47iDvyAP1mF/VH0rZr9Onc+nOj+uwg1sUQy9Oe427WAQ0upTdJ9/WEXK2jzbphe0xFRL2bGa4Qcc7cmpKZVGP88zduCo4bbpNYDScwZd8up6kshmbSBt0y+IeVwWW2Ib85xonO5lxYnGZhFMcT4pJRxB60bIRP7qi1+VO9RlnMNTn/JUK0PiK/FvYfM6/zTr/Gs2Ch/3rlx885veOaT0vw5Lm0xxK3oFcjmsiK3pkUBRfS46wmULISrkZmQ26LAkJZaKAI0PDTtSCNC7kFpg/mP15WilrmuIc0pDihEEDS4pLt87nNffw1uGJTgkDchndkAu8yhTnjtPVNN6GnkpwiyuENhwjoD8rmfcO6a91CNP93Hdl7qYyhXhgG8s5ygIcCw1N0tQO5E3S/Pzysg4nRGIqQD1BbmvPBVhf4xISsfusbMWpAD7TAZgZxDAsBZTFmIgtkAho4EqnjjqK1hBmcbEzI6pJKCXaRztbgtezLmOY13FZD10333yQhxendYyJZuURP3N95+5tSwnMpS63xYitb12d8HanX0NFbvzw1qBwTwt50eOMjeIAHa5vgtPynRN/EZHNtBFzpEVLhbYOrEHbivQV2mKtDOId5xoQb+7YiTmPi1HJalOClw7BM974diW2/nNNQ2zuyH4t17/0kDSLsFCpK5T5xYuPHob+dOpC6sYILDJcOUSSwovCfg4yMkgtUc0WVSZukifWVfjVa9L5GMq+KPyBBBROIOktZcFeLHAyyZdETF3CCEbKsBSEfhTrJtqQsN4JZFUrcb+nUfhSGB1Hc29ArYPxuZCwkgiCCUkEr1dMhGLPJRUy4t0M9g/YwhjWjvk3SIMxuzSdgboCCXdFkBJpdl1wb1J0ZZDZjNkIyEthshk+SiG9wu1SwfmflozHBIJFotmZ5mxRNz9OSCHgGlEqWws9pORC1R8WiBd1OCGd0u+Pw2G/lv6jX/vyP/lvjy/eC3ma4MhwMKYcAbZpXHWYhK+CsDYlTZ+waqW6QW0979F5G2x/c+pPqNZRF+QTOvsu8HUSaeEBqN002+1fA+0mqNS0lx3PAwibw1QWGtO/aatzFyVqlCazb13WLhq//tXA0qq8+fKSE12FDXpApX3ePi4q2JWvWJeulMgZ11J+5w93f/kvpze/pA6X1CPHwrUTZKWorYDfLZso5uOHHMJtxm8yElghY8NS/gbDh+hjDiwOWyO8kFxZXWeuVDGl3Vi6AT+eDJy7xDiFFbElwztDx7nZDCmUsJH9T84i8YgjqAha40RYGlo2iAw30FLtmZs58E4yOa44UpGYWKcN3yM9thgaKQZVxgVYWngcRGfJc8QpzzXH1MOLR3aNA2uMC8cVlcNWx0UiCA74G74w5dyohoXFYvZ2PAP1aacUdtN5PU3LH/73b17+5j5BF0hDYbm+9bebq/EZHfnH85JZjcQloktb+S+sh3ZSSjY35dxHggk1nLE+2iaHyMyl1VFUXNmUqhBfWE19OFQzGLhggKR6Ep/dFW9aixbyFCxBKH91Qs7lb5CO8fza1xZ4ozJqRKFBTatsLjyWK4vTEFPZs/IuptTscZr3h9sPPvjsZ98jlxj+KnetxJjhisjgd1hioWji9YCcU6VGZnZHZGHkeO3Vc+tmTeRMbhFjx9CLDAe4LGUGWCsPIczwCMmrRMeNzAAlcjuMogQ0QMI+2tU0DRHePgefpNRxU4wIpdmF8KvhvoK+UpqMmN4yrYhstf6ld8v4KGlxbXqHGdgoSE11/wg/JTqOVgMJLU7D8+RbBA20BkJDWXuUtL4WNcHLXJsS4/Cy635bwZ29H+ebL7x/+No35x2mF69AEEt+zKVS1Se77thu9seVPFWTC31+u16hgVGfs85vJIA3IsoRGRR5sPaavFc0ibW5SxU1Nbf2kUuPlH4DAA3PT7l8yOYDNcioD4MUREe3q3qmCpQ0L7YCuPnHrzLNPga/LLP+R5zCUqEUXIgmQZIprCUv+fZLYf8iQ02m0nUEy0ImhTYZVyqIOJ89TroOVch1UhRVRiD8IjZ2P/gwpXwFUwLzCyecSq0hdAlwoonMZAgz0tdlF8tTWtf9ej7nwy5DN5/LDr9mvBLEb8UpiRGeUFDkRROdqfj89HQTlZb4ftI6zqXrw5KWaaIgF3ewLsSNlzKWAfMRuGRJxWJLbVbQt/T6aES4IO4ljDcdTs4Fl7oFr6Eq3UV8pO4Y3aU6jb9jeVWeO3llPOKAn6uEdX47p6/84T9dP/x46Wk8qp2kAejJZy/9ZHakdfrnFoDpQmifL/G/9zFqPVLlqsbfPmKOmG8JN3qZQRld1bmLcCE4rgWuSTsZalxfgRbyL+NGqjnDAvt4V+9s21RYK5L1bnhsh84QZY9M3zZfvjrC2jplwyLk/mrG+cLZblZYrmeOs5tO/9wXo939aIkVHto8pyH83X/y4kd/fvz0xzx7BPK7diRUwonFQAXIIfYsFokqrxlBK5ksYr/DuSeJCVEYYJNIg0MIncFoRxKCvKZpXs+g5F9S6g/d8NS9OM6nh7R7idzvBCaieYoBLvGAvTNTinueI/oGJoI+EEyCgmkxfSwq2vNKASm3HExwF5Y5EDu/jq7tMV60W/Z3p3XZl9Myj8hVk1wgow5nBDAitorMU8JvkwvMmBMuQEdjOTfcnAZP9LegwGsAp8kkVfTFvWRDo7JuYm6woYTdEocvf9R/7dsPaeocjZHfHtffe3OQxcAa36p+aUS0DX7dnRuRkTv3Mx64qIoUEQZ3XRrPIjRtdMfiCAyRZpidp19jX21yNemt6L16i56WcvsITi2YxrHnN0IRwCc1zNnQqsl7bhZW6FQuFFv7k237w9b0VuXiFYTlvTRMcdpNWt7ZpuRpnV+8t3/16umTH2DSNmCgZREwJTYr51BoZF5EspbyOUhP02RsxmBVQDK1Oa1pN9FQlFTKEKZuWaa0Y4fYUCqphyWeKICdukPa78v6Yj/H6fzQLUvuhpR2JSxTWPoyZ+qnGLtM9RyGkowMhkg0XZbceVx/hRBmtsaUZEbuCxUbrEmfArrMkThCBNx1wzB95euPL77w8t1n6+ufje9eR0x5yXNZ0rr0PLtRaAYlCmSaAstnOEiQM3/yl73G6jdKNCGCKtnT4lIWYqI0QsPyojdq+iykX//7/3h+72PwV1KE4Ou6ZiKaqK/dLrYNGyXiNmQ12tdk8kpxx0fd9XJ12/JicS46AeIuaXnfA8kQHepG4p84wLYXCFnw2haigl2vzp0lxFxkwBN+PVtlJy+tkmgrUwuZX5aQpg9+U2yWS9q1G6+p6oqWR70SQLjTtqvLr9Awz62zrUMTKKRSdl/YHISzp5cffPSU+7mgr5/iTHYLkaFBjUXSsBgTDyRGiiDzWGd6J5TQRvUSvjQ6FDDBFhogptTH0of1XQk35/tzf7tLERNKNPYb8+H2C1+ebu/mt8d1OoVu7XZ9vNkvy/HhPO7WPpR0l5anbneISyzLq/V8DgAA32JW0M1+Pp3mkFcUsbpI0XuIc8xT7OfQrYVTJ7DbiEvJO6JABvfWkXB24XF+98uXf/e/PH75y+kXPzj86b+aP/lBPJ7WOSxxyYWqzC49ZI4q/Y2gWhqC6vgtSVG1AFfbmq0llvxG8WhbU4dMHM+1pCGkm69+Zf/rv3eaSrevVqo146ZPmgYj8/G2dkJdaosQrBPF75rL+pEZ8NawxIu9yNEaFA0EOHeR+3Olex5OI5NxRyoEOmD21ptQZ9rxA5gc2vFoTBUC94tTOJFy2q0+MkFcV5pEyQFw7bR2PA/Oqb6SE3dxk7QZVNe9zQBu3f+6jK2bb2bfP48gU+kKNCcodmkpeRh/7x/ffu8/vPvshzN7xZJcgIXNKH/MJXVrHGIoJ3iPeUaGBsWUngqwHdHxp+Ucx4mc2rGs6ykf1jCRluhDAkH+kFHghQ2kbNOrMJVPfvDY3R6WEWfrdvlwW25ujzdf2n3ry8urr4V49+5FeAi3cTrlLt1O03y8T5/89fnTT7qQ5vWUzqdxPE7jOU3nNI+AZaELEpMb5mU6pHLKeU85s9ztUbNZu1zmFJDmAFRjOOR8N/TxXZfjx79/9/7f6v/sX+Tv/dvls1+gT4BzyPQ/KZFp/w4rU4opROA49nbm1NqS2QSLlLYdLMYhEOQs1ryhMRG5c/O7tPvK7/8X5b1fk1noubbFb1kSKd1SOSNsq7rWFoN+mLfZNBc0wXT1RNWFtIcUyKQ70CRZzyKXw+p3fT9NoxozAxRT9jPEjmDn2hcjbGPKECmGayu+GwHlKWnawV/ZTp7x8OVOFQoCVQLEkkzr2Za8N3iQKl3uEjUhqVSs5hZLFlveQ3MqUUJNBbC9tMv02/bz+hUloKmMtx/Fl6/ymx4QP8l700pgC+eS42mNO2z/jPaB8XjqDoduWNf1vMzzuuzKPMX9Xd4tO+SXA+EBO+R/80Sj9jABNqxlPK556NHly3CkOOYSpiMqRl23zqflYezP9y/LfP/5D3P5zofTcbc//KT/Ql7WY8jjqw/iN/7O/K1/VH735l3IcyzDPqYw5Y5GQJ6mMoG2Iq4L2CLf/jIcf9l9+rP4+vUB2Go81IROfYoP5nLuDrvf/0+7j789Hl6GdSrpdL67CX/nH8ZuGP71/5WfzkTcWmGAm2TItpOtjY6ajIf+U1+0dZWIaER2ldV75K0naeml3OTu9qtfvvvatyYMVSohdYRzqzTj6tDW2YLNBrikTb2EZFxEce3utS4Rp5PctnaBsfaAuR2OZ8mpH4ZxPGNim+EQdZoEdSOh9Tuhk0Tbyxwky6zvZs1r+ILsyzTNE0gAPVGBN8b+k40S5WawEJDa9whKCxq8r159dmlmdm/AbwDrenOhRmtL29tqZLKep2F1axk+9aaAsC/jmqfuvS/e/vKv59MjogimtmN/m5BGC/XWdxDfvHalY54Kwipg3UMZpjHNcyFoxwLznmnuepe7tKdnXUM8xwyZJ3QhZYwBxehCmdeYwdJVpmXEiLvd7jbE9PSwTvc/WspN+eQQl0PM54efPv7sz24JKrPG+FBy7A5jf7t88EH/7b9zyi9Pw26fhvUmxtynVx/ubv/evt/nx3VKHSakLSjyI/ncpT6XXZinfn8syyGMt7gfjDY97V7OX/xbp/SdWwTSYkWUswD/Zq/PbS2u4WiNU+sTdY6lKx5KVZn3tPjhhu2vfTMUwGu+Jy9v+92r3/0H8eXH4PyQronqG7tIrlUhPg52P26c6M0+UvGvqVU/zqvZ8/Kgz8Crt1s0xjB03dQNp/Vkrch8PSRGhFKnSRbIqldiyw1io711ALCI7uAa8MSp3PbJLVsoWKWOtmWNcmrZ2b1CftpWrBWQts0PmGJ3Etf4y/wzlwm3x9YjLB9nMJ0KZwNyiXKsHToLd+kf/Nf505+ln32fZI2KmdyVx0PA1zClcN8NOwpeeA5aoYbeHmnbFHtY1HUJEzCLXQlZulh5OGgYEuV0Y6QqFd2qFpAJ+YU4tkyoF03dPM/doe/7Mb04oI1hHIHJKod17I4P03g69S/K7rbEvnv3Wbes0+tu/dGffPB0/zoNH5T5s+6wC915eNn95m+Vb/290/D+jIaLruxEv0aEx8iHDWXucjfuDiFOeYJhGx5+Mf/FP1+Pny3LGXBLEsGKSCF5NPZ4ohkw/1EbIuW9MR+Ae1c62li469T3LXKqhiONyaRnuDzx5UdffvVrv3te5gGRYtZKAhfDlW+8bllv/VqD7HEIrQmqG0x+qzJbyyVbqZEjNuhq2WF1q5vgICHSd9OSkWayxeBmqhxBoEQwOapruOqR2+6NK+mT0xg/QxMteKCzl95rUXrrg5IORacdEVIQuraui8N6bVqhmgRelTNbgPoZ01byKwbhOZpD74z7t2KxiweZskWVRyA8IAosAGCN5bF7sdvdINdDVWCieMJ/kK4UdgAtLPvz45SHXVxznNe8j0AMY+km3tUlIqdFwFcUimVOWQeWCWoopqQPIShl61J2mECZS1iGdU55R/2B6zqd175f0eSOzDbOif28Hks6d4f9zRdK3sfpPMfHmM6Hh3k53D3N5245H8OS12MK5UX4xfT6Pxz/9T/7MPafd4ePwvjZB1/d5UMsOb940X/569OHXwm3d3nIh0/fHH/6Z+EXn/ZPp+HpIbz9RQnHHqFAxvBxkUdOf6ulMKl12rziVaxvvgE7UOekHeywTdEPYVOdHddyF7rw/pfe+4P/Krz/UUrwU0Ae5TJNNirEWg6McEpqAhubqzLe+NXN9qx7qSJMWmBmIxIWFWyqNpYQEw+YVotAigwHkosS50mOXVeWOKPCIFqJJgVI2pnNlpMJLSjJnygeLRMqhG7khHhN9qENZ706LxaE0hAzLlvo84ri2bgf9BEfMTh+M5/vbg2tt7bahCEumhTx1eKbwpML+ZNV/eURbRjMmdBosB9K//6Hh09ulvPjPK/Mta2uTtyDjSN1aTqua47U2rOOEdyRhIxkxD6B8aVAStP6wDihrC96Vzo9V4sk1GUwLCmN85j7u4RZzEsiuj3tJBb8U4JnnMt4xhyDOM/rQ5gfY9qXu9uupDF0qe/ndcFN4dLITt/MT0uYXxxL7MP6S/aS5vjJeP7+H4159yqFJaXbeZrXKU7ncwERcIrTLeC7VG7y1Gsc9ail5elJvERc4BYdxpLKy6xUMuoui79t56THi8Tdx8wFIgkxhlNYpu7w8m//wfilb1K1HKk4e8uud96yIDaWvO62JjI1Ve6K1m5HqyLinrn6idbW6HgsrwQqh1ZbJ5aTcPGHJioTzTWh2WVEMx6cCu+HCd2t5xWNb8Y6alX16qALoy+RqBD+hukna/5Z/BLnlNTSQfuk5mbQpgQGgglXNaPIzGX+2fw29p6NT5jp9lCEQGOnRUwbWyvluyuq1jzz6hU5fcEPC6eSOifXGTS4f/cfdz/87gdxuS9rGUeCtSyJOvh4aMgU1mE9opMGoMd1SXt+esLco3GA2vFWhMJ0+pUcZkatAzNMiDUGZsGm0v5mBo4OQP9Q5qcOVrbvcxnLTBDgFDroBKorrz1g0ktfxvXpzR5V6pRjyePDw7rkeUroulP7BiRun+MQljkO02Mod8vbV6W8yeB1DCHczGfQBqzlDFB0nHf9+7FLeTgjyYV2S7pJJqk2inwSYAvi6OGpAE2AC7VHAIfx5ASsmzy/sL05UhvGlxdxzZEXUAnBag8pd1/+6P2vf+vEjhLND3I72QFklRbbG8FqNsTfbzqNvKftdoy1y9WJAK6sqU3lqoXkbuSW6gY0OXaYFAlmadQqMD4sg2Qc0hr2XZjTDkYBC1cA2lI76se2S68kN60Q6mpZEMUxCZKSryjVYn18g1o0FFN1JSjXQEUZxIoyjNodVv1oxlSKGGpB9OKLr6fMSRZMWDzPjTBGnio6wFIozFHDllGGEfEUZcooC9bF7p4ol5jH67R8lt8/3e27U7i72y33Y1mOx9AjfqSnQLsQABhrJn0K07qO2K8YjYm+nYENDxr9Ue/FFFt62gWVG371cLGJBzItKS/kM+1Qs5rDArDkUo5zOMd4GEnyB7Stc9Z4jaCqCKAH6Hbp9HCO4VC6CYiS4zQ/rGHoUpjnY4jAae3RVZVzzBO946m7e5XT/fRuKtMh7VFlQoNBGtFbT8RBOM90BNPtMJYypIB0HbW1AFwN1x/+M+25KoDAt4CIBKuCF48IAkWRTM0eNLsGq4/WKzQkUsODtjEAyI2oBP4JeKCZpFMFJ4Vyjrvh934/Hr647ztgzhgLRu2MMitDixJEAmKun2Uxm+QW75IKw+BQrNH2+p1oJ9qe2lerkQPPYq+aQA+X0tdG3M1wcROYBP+U84SvltZ4HJfU9fN9V47HfLgZu0Ncx9Ah44JGM7Sgk2NiU7O4CT0sM1o7lcEbTBJk4GFDhU2cxV5Q8wRR5eZR7xDzQBDo15zj0KeBiUHULRAHV0F41cALxSLHQ7Zwwr9MMqdMUaxR6D3Q3hEORBLBTJQ9LmPMpyGtZuN/mF1SQMxlIagDJwlIoKmXvYRlXkrOU3j8Yr/79PbF+qYvpzmG3TnHjvQl+nTRrY4ZXPuM46d57UNcyGc+p/LhvLwebsK8oMxBea+Q1g/K+knXvT8un+YBOEpsvO6mT/MEQ73E8mIprwG5iscSvhrmX+77L51PP9l/8aaEm+n8JuZjWV5Mj/fD/lDifdi/gBqYbteHeD696+/eX0/Hdb2J4djd5bh7uRwfurwb381reMo3fR5oADLUVsr9ucwvlvA6HYZp7dcyxvNhevjs0H8j5E8O731QlscY7tYw7A+nmPd9XqclIh83BXDdwhQzfhuangYRE+cEra0gRKkBnwq3TMG5YB8kMvJLj3UX8joueK4pLjH3IffcpEWMlnizKe1DefM0v7jt9x+8mro17dGSyd1MhEfh/iRmi2MKQK5nMKEY7hd3q/aShUbHIyt3Fe8CzZwSshoHo+GbzuPtr4xRoH+Tn6kj2BTPJ0a7TiypfiWT9YPHG4S+K+hPAfdBNJCWXG5vdn08v13/f0njA5kFz806AAAAAElFTkSuQmCC"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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"}],"details":{"kinex":{"title":"exec 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\"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9979008436203003,2.105285406112671,1.3758498430252075,-0.8019817471504211,0.008222300559282303,-2.3118176460266113],\"velocity\":[0.08126653730869293,0.017701422795653343,0.006158708594739437,0.007415026426315308,-0.02043578214943409,0.0001324489712715149]},\"left_gripper\":{\"position\":[0.9908248457041654],\"velocity\":[0.3809970888224515]},\"right_arm\":{\"position\":[-0.13651666045188904,1.9356255531311035,1.4003468751907349,-1.0447499752044678,-0.007218408398330212,-0.10203051567077637],\"velocity\":[-0.0007513259770348668,-0.006915716454386711,-0.016261789947748184,0.00021159276366233826,0.0009361580014228821,0.00038932450115680695]},\"right_gripper\":{\"position\":[0.823714012449438],\"velocity\":[0.004685608754781159]}},\n \"time_s\": 7.04,\n \"file\": [\"/tmp/kinex-observation-ly14eio7.png\"]\n 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"}],"details":{"kinex":{"title":"exec 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\"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9609145522117615,2.103285789489746,1.3415217399597168,-0.9416824579238892,0.12990465760231018,0.26933538913726807],\"velocity\":[-0.012519998475909233,0.00815054215490818,-0.06386817991733551,0.14375868439674377,-0.025883832946419716,1.493956208229065]},\"left_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.4523076117038727]},\"right_arm\":{\"position\":[-0.14984078705310822,1.793803095817566,1.2386841773986816,-1.0167691707611084,-0.005433639511466026,-0.09398231655359268],\"velocity\":[-0.0026481677778065205,-0.0341680571436882,-0.0263235941529274,0.00112156942486763,0.0046378737315535545,0.0006861891597509384]},\"right_gripper\":{\"position\":[0.8233322040601211],\"velocity\":[0.006881222361698747]}},\n \"time_s\": 7.68,\n \"file\": [\"/tmp/kinex-observation-79svv1xn.png\"]\n 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"}],"details":{"kinex":{"title":"exec 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"},{"type":"image","mimeType":"image/png","data":"iVBORw0KGgoAAAANSUhEUgAAAUAAAADwCAIAAAD+Tyo8AADic0lEQVR4nKX9eZds2XUfBt4pInJ49aqKAAmTWrZXd38or2V/QPuPbltmU9QAiiBIQKQokrItyZZld3tYEgkCqHpzZgx36LXn3z7n3HyP7QAqX2bEjXvPtPf+7bn/P/7N//bqq+u6bd25X6dh6YZhW9ZNX33fDX3X90PXdX3fdfReR/90/Ebf9V3X05/04cq/butKd+Nr+qHv+37oh4H/G6axH0a627Z1fAlduS30fbpiHDq+00a3pa9329r1OhJ7qH62bf26bMPQDQM/hm/J/9HX+CK9lP7k63kkNCW+E02r79Ztm2+36+W2zDM9hYfW07i2tetWHqa85JOxW7e+X1dfBFqGgeYub2xD128DTdsmQLeyq2l4PS+oLKl9QEMdhmEcp2EYD4fxcDiOIw1122h16H70/G5bt3WbaXvW53Hbxnmh+23Lf/ju3Tgd137a5nldb/zMVbdjXi7X2+18Gcfxm9/49uGrx3Ecu23t6YJtoNXXxePls+XftqHrBhoqj5pGyPure0bjlsXV5aRz0nfDsOrO68x6/lUOxUwr1/Ujrd66zHSjrl/4o24cln74+P7jh++/X+brw+PX4+FuWZf5fO7WtaPj003jOE0TrRIt1NiPQy/7QmtJi0lD67p1pVvy4tOZHHk6G22lbIANmmfK05Ovxg859l0/+J9ynHlX+XpZJn7RHOhCpgUayKC7ziSkR4fpw+/G/9j5kwPnY+poi2WJhXLkQfKdfusmHvc6DN06zrelPy3T67v1PCzHy9RNy7byhvJp1HnT1m38jpIrj0Uoln/lufJR452ir8pTedNXOvg8paFbh24ZaG17uptOjZaayW/t9dvGK/g5eh548PQWTVxXRrZt7FaZoa8WD0gYC9+Fxy5bR/fhG8iFsi70aDp6XU8HdKG9XreeSFeX23ZBNox2Qs+szbKnSfCd6RM66balwihoCWXXaDn4WPMYZHjCHHgF5OCvCy0Vs01aXzoRi9yb1nnsjyMxvTMzimGhNRy3bVjp2K3dsA392A9EFP3ardsyr7dlmdftRrzusPbjNgx8wpXJ2rnS/SZ66fqVn2ZMUE+t8CiaPf9NZ5X/si1mChpkoZRAiLXqsRj6iSdIlEBkPMmeTV3fH4Zl3X799s13v/7+cj6/fvVqvi3Xy4dx6KdpGIcDs7ZhJLIdmEaYkJhrd7JMdvTt7IxyCuVgyX7Js+3Yypd4bHoy7MjHOVLSU8FgqyB30P+p5BJeoAvJ51ho0k+k7zTxOeUTTrH+bZGCw9pto6wsnye9SLZIDvu6rGM3D/fDcThMa3e7fZi3vjt2U897oWPUX+jMhkDUPbRfeXOZgxvVKcfWjaahyS80XrnUpiWTEFnpDLCnY2e3keeIIBU5IBSsM++3kXmHnRe7Lb0lQzH5SU+R0fA4RbLJjJyyiesJ1Zqw1OkYqcmCyv+FCehmy4E1cawSVRln8B6+oTCgPDzbzo5459Jt3UIjJZJltKAHRB5u/H9b14UASN8dponl3jL0JFdVYChyEELlHRmHcRqHfujoHWbHPAdZUlsJWUY5rP1gzN+OH4s5FrixqsjbVDTpobcNM+bq8EnFGOOtvl+37XKbv3/39t37d9NhfP3VD/t1vZ4vh3GYDodpnMaRoNtApOsvPew906FQMw+ftpV5oW6F8nF+jciMbeODspyZ2WT0TDIIlIki+FS6t6MoDJxWUs6P3SNOmB5Z38UgX2WIPBr+tpOTUI8uIf1poqvruqXbhmW7Tbdp6Ifj/aG/DCDJ/DAafwImk14h53Sv7UyaEJYlIAHcM+PVr8RmC5cPLCMEGdfI8WP5IJvku6APFiwemCbIGTbMSd74pLBaPWokuUg40tlWqWyUqOfOBJEAIZGpAOt9cRSyxwnh7fB76rHnseu4jQiImpw+CQgx8dJp9NMqmy4KyXomVKkAqevXlaC6iKQQIspkaGMZKfWkb9jBsXPofJdGpCfGHqUraexbz4LdonjJoQsRreeQv2g0YEeY9KXpOC3b9v7dh++++/7tu3d3d6f70916XdZ1ng7j4Xgk6hX6FbLlCfvhd+rtjKKDdlx8xeCMBvVPVw3wMMvh0hNmUyEG7PhOAaDOxDi7zT7ItquuMcHj0idEtK+SwSyTzsgeffWCNc5r11/GaZ2XfhqYccEIbJedOH3eSmAoSF2OqZ7sHzoGs/uKKEwSs8UXYk398IR8hVnhJfZ02wzkPP5E3USXl6arG725wqyi2oW+ciSZjGAoHI3tUTF41SZ9APoYR0+mahh9mMKoDFAPVExAJfq6zt2yEO5mcUh8xD40jVrvzgqCnj1Cn+kMGb2y3u60jCLC19OAJ6w6HlldcgSasUuiWyHRszQf16179/bDL3/1q/P58urx1eEwzdfrtq0jUa29SNOlkSgN+0Yw/faK8WB77fwZxLFh6JKYlissWM6yrD6fbBAPBkRJxVUKFkHhDEKOMtBKkrXwi52fIEMTbvlld3AuGozez7kQoMpl2tl1WlhP8k3X3XbBkmCHiTYfdOj0uJN8cOKEKinQUvi8y0mqxHNdFZlXxafKBwLlxtooDcK3bLcUBdpmBUx1mAQStBgjDCXG5FLSbECxIb5rIt/KG7qGE2YMUbxXOfhuddCVYQNC328zE+Y2koWRTI6KK2PbBLkwtjHEObDsDiioIxE1yJV4QJ4udUOe4gGvRVhiodWW+SL3AxPmh0+ffvXdd7f59tXrV9uyrctMon2cgnzFYGUKL/GrIcCB2406H4+Bm3KRQdPlRQ1sr9+DGxgrzcdUPiMoA6iueIyrWuXjnSHGLzwKYDJ5IUNcidxW+AC31S0li8209SMPTgWFbxJuctzNMaV8omcshEg+qaAFsJgQ+x6sTfoXIBcsSks+26muX8JdkwgIAJhMjaXao8ZioOEgY19Pm06AKn/XtAcHYPp8XRnnrj7+GLFfp+JdFXQEFLphZNVauuV2EUEpguE60/HvtlGsM3q96ANqiqMHkCapMjgZMmKlDFrG/qv9T2kDdTpUKGHRwjKvv6IAEXfGMPTj+On5+Ve//n5e1sfHx6Hrb8uVjZlj3/dsZCbK5f/YEG6qb5AACNwaCIV6GdDVLhC569YcOA2ZhOy7sWsmGMEKkC/2NdA/5Q8EI0qbhUpS/4uvrLobcSmr6bt+EohfCYWScPITlYadNn3IcqhA1uiais7ofgo3ZeqZcTLyd7OUQyaSR5TfN3M9rm5Bw1u1xApbzbaNt0MGotQbu1Qc4WLZ238Vu2pywS7jfQGdKyusxjnXbSFjptoHutvtZlg4j9GsWGKgYwOuYXaDYA7MdUGBThXGucgKCFNPuiBneyM0DUVvg1Lv+Ve//u75/Hx/d+rXbVnm8DX2bnAW8lWZ64q64YkSqPb+Z3wCgNZsFI6IeMJiFVJwnCCgXgQ2Z0BTBdBwFu4eNVUbuoZYEv5fQGjQE+0Q2qPLg+VPtj1ja77ON38nD1L9HsGREJnjkW+A6o2cJxVL8GWsmYPOKd6pWVN+jGInNWcVT1DwZ7poifuyxK6PImAyMBsXt/C9B0zR4qc7rwwY5AbJkAH4QUiYIQ0DmvV2vQqVkq9YTwLuPevNXceG3MR6XJrqA8OsYi73Cs/VGo2eFV7WYsZZPtKNlm07f/r0/dt35/Pz/fE4bN0yL2IXEwdRAGfXehsaVyKjPn+2+3x7E0+IOb2DlQJ/Nz8iwVNyzLKz0JYCeJ4rxrpjJhxV9QcTTuN0Ge2FbJW3g8TgCCThIvaudWKfsWv2uFdAHjDujFPqTUtb7DYg+yz2EsZmYRfFWgezaZACDCQ9DW8Af4MkheAOf0dli7v8Ws9JFo4GfSbh6ltWjxyFaiGZWQaHAQFnY6Nbh45wpvja+I1tvi3s8Cx4kTgATHj2/TRO7CVMcjQsIjHssLGF2EouFttj5Vt25sHiAkTGIRZsetm67vnp+fs3b8+Xy2EcSfKae8+oV8UvB/2Ey8jJOAyiSNa9OwjzpoIHzN8jWZJYESvU4AEA7GXYhdaQ3OzDOhiQ9ANgkhk2304irlTc0gRCpQCCmhLnK2jCHR5+jf5JoR0afWJHW1eqEjQoeo1bV9LOeTcACICx4daCeQNXASXM3k9jyBAHjkp4ffC+QcRJzQHTHjM7MzGG3MFhgXTdEaom6faG3SD3Sg9yt7calcHdFvul0Iws0RI7Q2d7WckciX7FkLymr7Ph124JCF73S93TJZdHuNnYFfD4AbsKdcxNxH3fz+t2vlzevv9wvl6ZlRBpqIjlC1kIm8kKGJc7TDMiATttV7/Q5mvrDE7hinhqWa3CVNQHda+PwOSZtlcLbpEzRNEzHAakqy776LbyCBswYQVPBeNr8ISYubnBcB9E85vAUASbn4AGfMdo3ERrA7HiATDG6MELSg+NW+9jzmwybG5YNQBfGKeM4hP1w7r6GyteCMmGNG4wEXi7BD44veSqy1+NzQ7G65cDEbONOiGseWFbk9ze3LfuN2ExskqUH3lE2FwX3ki+kZ8nF0PgpgwrB6xeXlCgd3Mzmmw1z9Ttdnv7/uPT83kcBgYOzHQM73MkqZKvfgfiDsx8lSQX4Oe+Er+4V7EBaZaw7LhdQFoaPSGEDKDJgmUoXo8kMpGxHCBzBeqx4hGR6JZtjTCPwN8yVZuR/uH+Oj0Tobnp/rO0Yc9Rv1H0FcYltdbAP3SNAflgnEtw+Mb6KCv0sAnTw8rFRZTeouc8ONczMmXnrwCrKB7o24ZBL3YqMokFpBahU1y2Y26Iics33fUBT3ayNQ8wLk/itDJfkb3jMJLWCFiMw3FDW0SoZQdmpRB0pg/HT3KBB7wF085r48ApNgGC04oXTDnEMEVmbt3z5fJM1DsOPWFnivRUvVmipwLqi91Zw75alme/cZ8HARAk3nqZ3fsVflkJwmG3nOdJeF3PoUnCTnkSFOulLNYlNIoH1YHoEwJNnDHAfECcW2KkdxiicjspEhnj992w9QSh0VVT7AxwtkR62d1gNNrge62bVkv48idNuVztjAuNHfrP76aTZo5FdA8gVqllqaPIzB5qDsLv4lkO/0r6igYcqooljDL4CPi/eDctqFptMKSimZ02EZDRh1uJ9EFuepIrYX7mCE33wjNewIus9up7MRJmi8u6Xm7zp6fnZVmmcVzo6MrwLG9gXSmumtNmODnFvUYReOkhNDXN9XhOIdCp/ZKN8/33H3aG3XTtEQMtpmSQmPdikDwT/B+/iEo1tQdImZNIVnp3XZZlWddlof/jY8Qm33P8qLrSxJRgdkvZtGEYt75nHRhPoaxDCwcilEKxhI7m/ZMMYeIit5O66/jPH4Vv7nH8NPA0iMAA9Twyz1HGnmbWcNjXdykeV885qBv4BaxOxusFajQk7I7rakJ2cEOC2qN0ffUo8fVEIYyfY12zabDfE0mtVxkeF5Bd//bYFkbs66en5+v1OlBIEzm0fH0l+EDSE9iCpd4jdBaBphuCKLZPXvWQkVdnhpn+jb9KuaXbUkuCYFqR3qMMSX7I5lL20pBIN+Tzth4OQryUpDUvM/1OhO4RrBJ7pmEsEB3rMfaycl1PgRwlE93bu7RF9ccvKM8aTKfjgDOmxjJk7sU3X9SLPzPqxjj4p0eqyckLfdA3MoyzW9NIpwcJGQuOtLDrwvQUIexiC91800g048tUAcWSg1mhOdtSM28Q8NmIArW5m9VgumdV5oXa2ftk4YxRwlPNeeJqnFw5DP1yXT59epJYTvZ16f7L2Q77s9CvSBsNUwFJ6SyhOoX9Dptu8HMXs+mCFt9vfBNNNw2lP/iLBeBGtIhxUnX1WH6lvFgIsxSWHKYs1jRyKwKwhXnTvcdh6MZhoq8RBDBPb8LHzck0Zp2jJ16GzGobslP9IqtHMbn/tsmz8iqd80tjqX4LVRxkXnDo2Dn1K9ccJkKvCidGjDz8rfWIPCI/4u35PoyAGQlvc0d50KqyaOpa8QDn9WpvIWTK6T+cp2SHAeWwutHRbFBipAK7oKSyJ5dWpq3rLpfr9XqbjiOdVcstcwvtOI2HiV6UCi0CR5IWMqk67e5YnbdK1Ea+nMY4V7EfPrMcaZ9NIK6Y1bYbdPUUGEbxiRwQEMoRskPZxetKaZ8D2zXWkVQLMWujLDffBPBYzdJUu99kaShxZF5+Fcxul9TLkA+lCZN26crPKMkZKb385N3B4Bcjgw5u5bFY8lbhKS5vZFDUBVklAfPZz8ciC3Y0ZZl/EbLS+EYkvhgJ9+tM3J1+2bae0pdFZNUv99FQHQJO62EKSC7dNMMSiCKK8K9kjQA1jTgT+tu6UeL+9XrTNNWII5RqAgQyD5SlT6OLvCPTd/VYGtkGtm8c0R4BAkTtx5dxYObJ3MPSsUAoYYsLC+6bREoEktpNQIBrPCJpDUTGZNEY2CWF2VTmNdaBahCCSwVh6SyB9fnF4WwNGnkS4N4smjCuUlfSJrshxTrbs0wVvM3O04WxlWRUDTAva8KvO1dpHH8AwtIygxqoRw3kj3bgyQsvI9QQZQ4uRXgYgtbBySptMwVOCsZUbCDJI4DlZLXtCDCJjyNvA5eqaC3TZ4femPLupRLyvi3LervNYrtSvCAx5+xZnYbhQJkLhJ/FToO2q8LIjFjGYnHLDW0A430rdC2s4GZOx4WERUlieaKtZfOTHjc3RUhKbZhzV9aJGBllgzomcwKG7PRqpMJcwogVBx0HUxFosK1M5m3GiMk9GnkKH3leiSVngJ7YAM0psKaaVX0K9RjHBoSlDKgakY3CzxpY4+2FmTqtpenitZFMBN+G6DL0qSshxifqV1JmYaCTdcZtIVcMZ29TSJaALr53GJbkYjgAUsooqwbOz+tX1o6TslIC23gzFkih+LKu13lGnMLRDjKqlTL2JfVI0hbABZzWPIRaMM99KNY33ts7nUUsZlzpwsfhiguhTLIFDqmzs0KLS5jLSUOehBmmMbTA2yLSkxTRU8JFdsTnjjnQoSM5BC4Av90pDmS6e7VwSqJSywoPcvoi/OIAx8+/fD1RBEBOqHRRbJYMH0LfYJX0/0BVLwZgg74Uh79E2pitBipVYAGHXyJn7WMA9wGjzCCCM+PoHzsupmA5Q1TG4txFdk3smaGPxXCbilM7TCNJNNcJSh4fYYnrstxusxCfFUpj2xXXHiDitXob4vuMOzfQbca/O7byDQVmNsxUSYRw0zYX9n3LjvDyYgfG/mWXRYW/AZYxqNNGl46NW9pYb8LMLs3L1XI+pELDXYvl8LOE73nJDokIax5yLcJRMUMDBYmT7b0aM/cwP50HLgAO2D95mRjTkC2CBq0w+f9pMs37ed63DQ/5QaxmmvueBFQ2FzzJJiV5DFzMxFJK8wh0vECqVCSKrV6WWliPPg57xJTaPMshGt3HL3nszsbI5UtRYvS88KZQGNkmCYPk55TkBSwwp7ONslIglUHXaL16/zeBw3KZGjOKSWWBnI3WUkXIQ1LTeH3FYA+ctxUHGX+PgxHrKay9siHq1hAzZB8UWa6nDAwKwNx+Zn4TZAvOolpjMMtwbv/nXmHDwDSN1lgMOmKsNX7mMCJNJcIV5M6qkHwBb8mPTq8qZFPYgp5pCzEvLjYIsXNPeVPONFl/yFyp9awkVEIRGHrwSlgggdCqqDTo14Cch1m1g5sz0epip53OGwRRSJGyuY1UnpQzjizjNwn2IiFUn+PGsyRB5ZWdXP7Cvx32WU4KoPPG5fi2lenUP+i1tayB6a/kJy9esDz5/eDvLoOwyoCGiHBmFFn0eVl59Rok2Hxge+YITaHsSEyp2M+dp8QKQEm/iFGsAXAaUbGOxYfxfv14D8NKlsPyZk2oUd5JeLRRlm6h2oH3vhgeB6gOY5+pC1Ep1aLF2P9PERHuZAyh4Ozf7AXFwZfx4KLowpq3skRdn41GBJ7h4EPMVDxMqX9pg+XsgJGwM9WbFPotI1hsKesHNym0TxLZ/NAvDBcDzptXtJ6blyEplAa2WnCl2PZC9KYP7S7wPVyUMjSNDftUbZfrfgYbrQ6qamGuUqiJszqFL7wKqNXMZmjfQRk8EFZjfOnm3Uuf4woXiCpCZfidhObqm8v8M2EUF1ioMuDmLCRhisXfEdMMoa967ji0uNuoOC0rvUJ09clWp4R9VazQ9dPqNSo9aDVHC4Fc4wmrRcqqOvmKHA4Lc+DS0Baz4aarik34yjqJtuh7w20EA4DKY7Cv2XnddZMm4FmHXwHCdUKsxFoNHJrAeR+0mewt0QVGrWnYlZThpk8mr9xW7hs+DGxEXyKK4kt21D108iVSr3hcoIkXce1nRETbK5ZPIqTPvyh0YSszJ4iLoy4DPMUDdGpytbqbcnLcNWQ3dVjCpgWpxexJhQbQUzyz8Bbh1lqBiSuzSfBTFvGNtSw+duIuqWgrilkm2xMNSAvAi2CXqCPDz5r6a1GTuuyo8YUNpwTC8aO39Y6plHHbsOTqOSgQWTF5JXMMTqzclpF26mujqfu7RwdOh9+ksnDBL4UVwkcv3yHnsSwZETAOuFXDA+fxBc7i9rBFWEjwtwJVjKLcW6rPiXZdkb/rq3hm7IspYAVEqKbKawVOmHq1/YBHllRJY/AFP42x/PqzzhbQhXRlXVIL4SnZJOJfduMLcoX6VRQuaBhu/YApk0n29ox8jcVoOP+yUJzVKPn6qvyW+wFLtP8GfK/XRbYiceViOVTyr4KJOK8Q/up+L8W1DaaXNx1C/ctX8wN+QBn8Y+lZbcSI+hCxcikTSQSsdjBkTh6Juvv6jFArYy4sBNcIhb3WKMRkuZreGb1kP4Ljs698Amtk4yNzC4eR4ku39EiKFx9sK57DEG0+aXctnhhSFTBohpdIi6pJqnlPpkjXvW0GkOAsKfsaYKxVo4tVab0SfCg1nowwa1TlnjIR/qyta1KO42apmWMJSyEf5JbpqMZ44meAlM6cuY2cZZhDejtNIJl6rbAs6DtYKCstGsZdVVdlEYQx88nFa4/HWnVW1BW5TIo5V0ZMOSGcTojARfFXMO+ds1mw5GrU/GvFt414jbY9hhNLNbdV5JBLu1pcZpLFEdyPWfCofK3ytk+RmbHYIatspvm+/sVKkRNyDEktWNjjZsvnWjFE6M9DHGSZOZoyCICFNkhxTUCzEvawnB5SV4L6gImADEr6YCjv0w+ehCiRf3DmK9f8pGxBdfpG1asglQhCwROQwyDd+hQk3MsAUBgkkx3IYMCXiRlUSeg4BZSVTVa1wwihdqPtWGbW5fX8HI3YSkLYT4gcU91nbVk0iYE/CSdPUYeiR7IoCV4hEAvZUhx+KcAcwiYUW1v9Wgd4UfLvrVbzrwyEyywNH7/UY9W9aJxYd1HV9y2EcOZaYl/SVKEcD2+MuWLbmVtb5B3f1NIbnCC1MZwzR5w32OS4rLISU7luAApwEM3DWq4s1vtrARs2Wkm6Dbd4EcGrmq/17fLYSbsR3tLeyVhbT06S0FDE3d6JkbwI3DBHfvcqkC6xcomXwyLmCB5nJxCO1rq3ayAC7IBKIoRFVF9fG3rK1GyZoq4LxdYtgvJjllic3z7pUc7q9Yulsp/uLwKkEpefsX81Bpa/rJ8lSRzi7zO8Iz85c4XGl5zgoKGH8eCGJS8cPRE65a6OIs/L9oWBqUY4UeUmqfketgyXvmr9LoxQZTYHHEc/z0GVQTBfoleRPVTdlVKWwjxJ0kWSUn/ZgMVIMDob+RP8iWG4qZZdybrHGdiwZYshhqKGPg1JiG/ZZSCQAd2ko4+r7RSa+G8m6PBXi8s9f9+eq8vocsCfpgTuHFqqIPjGhXktr1eLcoCplWocBmtV368cweWB0DCjxin/nGz+/AuPsfnUUk+D3VG5Ylp/kL8GXZISYg05WUwpkYtKJN1c/5ZfCSE+UNUrUiJ8/yKDzepLoQcxtM2XlgrmUxFRa3NAIkUiu9aO4dwj/r7IYG1rVEtMf6IFveTT6M1LU6S3vswFT7+HlEIbfbSOKUsw5eOeoFFm0nU0qTwm/1vABb+hjg9lqN2/PFwe5wsd3TC8QA8u1YWWmzcAfZ4V8jV9E8FCQcdlvoZcIu/V5dLhiV9g3v6/8Cq8FWoeQI5vIwnramxOzAbgVuxUPkx5Woh9GneBMAhnsxbeDNLE+8Z551ZpZDzC4mEKecwGzkfwfcxyq9YJ/m1immBPvkB4YBg8U9UYylbnQhxDP1mVCQz38gdUhz9pucXogjC2cIHWaLINmvH9lphpvQI2Fse64MtAsfVtC99RSOW4oHA7gDgQ3qjsTWzQ3ao1sSJYKw0jD8I2Mza1Etb4lWxsCzKvSujVUgm80q2BNB+HQ9phAm3lxqtTGkWLSJZT7rvT6myU4VKaZ+vxLYUfttOAmiTuw/oqzalqxDvoyqzWFa/K1Xn8BBx2KJ9WS/6914uWzARr43Cy13exH/yL6G/SoNLYYhaVeTD4ZyI2NyeEu6WXyWI6hJulQDVAjBB9Jfdp3CnJ0VOlvdb4Gx+TngLfAIovpGdxs/RezSfor4EKuxMLB8OVsnE+tLjNL7GoytcVMbexQyoeBFxB+bEWtIRzWMTbFsfphVG1SQmNtiA6pAM91oYrCpwg+6oZU0u4OxAJ3auqMyEoV9ZXTmGElexQjyYQOt/xZvA7opS5ktiMzI5YZ7jtLtdeKGUuY1G8SOBypRgiXqmDpSkuUsQ9wctsj2rcbS/KqdfTYfWF5FDFGfYLfX4IdPV3ND9lPG7cKP1tp1Xj5mPdmkNVc2O874miMKwIosxMzAeYAopB1PIFkwREyz2YM9fIL22tT7Le/kSRQJqARrUMDTuCpRVqDgRI9wuYhrBkX8JVwOal69FanxhVuDRyiGLjiTl2LZ9K/z1/z3u669GpP9QW9HYAfThgoFLlWKpYulaYSa0sTWvzyWc1OHO9TMD+X4I1QS6R5UdUtW5Uq03CtjmmcuSpefyGq7q1byCOri5yIaAwdWlL3/KV8Zw1Vr91oFmd9MErDZeRIPkgF0A34dz4SuYA7aRHJF/nARn/Nhciv60L3U+EoxMUMEeiRwTuh7PjpsNPXAK4DpEQxsybI6ZYv+ZUqlf1JYejFT073aT+iFC24gWfbmMYJXGWFPAl6jz4p2L7pMcXCgtYSXW9itBRIF0qMsY51W2sjEZJvUg2KtpbNvyABa6IX62SSt5uE01Sb3FZF7FjaeF2cyIl0xJqTDomCfO2Oqooi+RJTQjUqZsF8o6tlquuJWngaB2qklubB8C8AwUd1fi5XD6H8ulSKdNlEjkaquweFaB2Y+q+IlzYvXV9ZkupaFjJFmAxM5IG12ealmW94OF5Meml8RHggPxpS5XAr2mNIT0hPlnkrvVuwS463fq3wTCBvsFsHirAuBFQFV9ufecTJ3RgiGljXPK/60fqG0dJtnLiFeGA3AnBEzna5artFZjZ40Oui8KIQBeit0jwan4TkxCX1aO6zx57pdDMVQi8e+L5kGAnyx8nuC8hckBRatfCb1r7E6VpJl0LB2scF8U6vrWBEbF5UcQ+l0pRnACj4JfFgq9ycVlxMGKXgN56joV2bcK1QlisHVIp/Yi6ztjeOr5VnuOcVFgitWot8CyB1CiuevkVOm+FF/KSQyQYSKm2L9wu0gtrIIgILNXPNVpTyY+yReoy4ghAtIbkkm8WCA0Csgyg2xmDzh4iAvyBJq6NCLNxuFYC8iOjwiZuNEWYkOVZQzqpC4lQDURA42JDd7H4xGsgA9sSaoom01seDf9lLAScZlbFllO0RBXn4UTnIokz1d2ApL4a78LCNjGj7Um4aBpW6+KLPql91GY5zwpzVPunBt/83ToDX8eRkS0MJSG2HDcIcim4Io7CDdHYB7qZbPdZ4jQ/C6KUnTUo2bx1bLIOyd67uXSoIc+oQisa4/QNz/K14nd5YzUuSeKVoCVueXddbhIt1FyDB67WC11KVw8CsxFwLNYSj4/YzxBdNOp5FQtbnZegdaqGtbDeK1hWw7CYgvHuiNCSAPIj1V5c75nQmxPfDinSWuknJjsamda4sp9stVKvxHdKajz0NalOn9YSlrbn5fSdV+oIJPMPTnWqMlpGjhV0s+veS70nOBLLqg34hlUoKwaW3G9pYxVaaptTQw+Z9wAb1f+rQeYLBu4V4ArBUaaKFLyu/KM0Asfa2pYnllvfBKOqSvhVge60zwlDp242BYBm8bvjnGWOaxWL2IYlHfHKVkzFesh+AEmmWn9tNlnRU732dmF0lrJ22G4Yl+4/hGa5ersQsMMTKGjsh6B8WB6MzdA85Fvk1aHwNolrCwwFmbWqnuQ3yluM9q2jCb24zp6WmQeLuZ5dBRs5Esw0TrWE1/mAeXG3FiUXbXcaL4BkEv4rBGwyEO9v+wrjAP6si16OT4aVNhcYgN+OghDCIZl2CtOya/PJl7t3jasU8nlPLS+BPLRmD4CIzytpuP2qw6/LMRtTDqOaQDv/tr4rHJatuxJcZakJ0vEoiU2Yv9Qns6bAnFEAW2m1s3w4MKH26sD4G+hQ9T0xPKt9kAlF9M1xnLg5g7fLjCRTAD3uC29vtW8mGl63JHm5xrJkPxm7NZERdbnmmSp2UVlPMrbNBPJ5gUeqz6WimKvuad09Za2ee+2PVZoqHBIJEZR0EhV6ShU6cG65ANknG6dF84HT7WEzE+dLGvIOp0AVOwmmoCJjl83bmI8eYEBjH3e87zgK0+b0OqDjTH6VW86HieuAQ00r2YTOjiOKWKMKwwJ3BNrD6OBqYjEuNbauC/fKM4L2tqKyxKAIgQPR3vShlWT7UlSZDDHuW5wpaZArJiPxAq/rOPZcRicVQosyyUbDqEOnhXaHCEaYZddJZ/9s60qlqJk+LRXEg8dVUaIqf9M4cE84ihIjWqeWjyxYqYlUtxEbminT62Ldm0gucwsJb99kVayNicZ5K05TnKXYcbeAJTaIwa5lhFbz0HNd6CR8/d9UQdU5WbSOzRavGixWJnW4LgKv8bumH8NzW0e4FZNYrFpDpNgCJWVFy+niWW7drPGsFtRvxqQUODtuUA5XhsHOUozDKh7Uc0FoGjgHGVdpdLZ0ySaMYW3QOFi6PaQFr5l8nlJL1OdnW7inPoMDoYd+Ok6TtAzNNyojRbNVWx8Woc8FOu/A3mQofp7n6/UqWZaSCi0VFOVrAualEpf0MWUmPkmFx2EdtoE7727bvMy3KzWFkVH0lAI5chsJBtZiUje0LeWtpbETi3CDp9Z1Gj1RhcYHUSW6W/xPgGzQj0ujIlmhDfYmorCnp0vt5MVeN86nK5FNoAqfeI3CRD5eF9m3tLLS71qEa/ppvRKYBiN/LplskRzlNHfuGfqLLjSqNw38CYskv1mpIz5S4qs0Tc+ow/JBGX+6o16M5GrFzMsRy8ryoigomAOXW/uFNhFD+vFRtabmn4uTJ+Ec3dZN43A4HLRHpmFasZjISgfpmk0DSkFqSUtTXbUuo5Us2OSORJksCOcbNXO53W5qzyeiIqhsDzB9Qh+s50mwPNfZG0XtHYZxGqbTUUwMHFrGc+P47lhA7ShhOrOIZyqjr8QdlKylnHPlWtv10BAdWufVrZU2HThLYHS0NdCkUhzeFwRpuMdycGTNa+xbkGhSYKXyycFGCvbyMkG1Hputim74NGdwDlKqOja2bpyYS5anWPG+VHMRZJUMp6eKjQPV7W5NUhCn2EDN4eukhUOxTjEgGS1/2ILMooJHaHA7ds+C66MCVrBNGYvYcxkfUPnTrtsO09SPYy7z0godgxVi5zb9YVmJ8Q+Hd0WB683cZIKTSQIT/d6438M2rCQ8lci0D7orsvYcDURVsC3YeBy7aRy9J7co89oTdFtJwps+bQkxZOrfCJxzzS8pfM2t26QBha6yBSEmVRp3Kp+Iip7BGczb761VwBVZSrFUbcfC5u2uBbk7Dec72HnVFE9NGS1GZtBPcW7B9Ysh4cRKDaCaQaWnIUDk9n6AY/Bc7jzbLoGHZ1rE8e06uJPAlzghQmN+Nzccu2Yka8+ijPoManGzRj6zVrfdeikEH+1Hq0UNyrMl8HPk0ZHFPvuDolmqQQHVVSUAWpIahr5j8etsNI0UHVbofrUi+QqZTYKx/Yk7ccoDer8Pa9yS/EQGKvJC83oJuFypm4EXGvJuhULzEq8NuFB5pKy4rDZDmHW+zes2rus2DtwRlEZCrgBNulLb2HY4kP1rGDjGkQoJjcfD4e50Oh1P4gnXTsjj6OmsMZGdU5dOn7VLYwJOOufL31fLfz4qBcfAIN82orXyxpjSADIeZmG7Wphy8ZEKwz4z7MYgjPINh8lmo+/xc0v5IhQA4V4tQgPrKOdi7UqPS2HTMQLBtECu0qqRlVb3QxZEvSsgmz26OhwADcQM9o7mAuzaqe28s8wUvZeL0C/zPcugaKG4s2LBeuMVmpYLYCW2kJmdl7yUwGsOIKErxA5tM2aTN28qRbDxemlnMevUq6nLVCwO0RiKwH6YhL+aANTxcN7kfFMavs23ZT4/P9NVA0WkMVMh5/PhOJ2Op9Pd3eFwGJiqj4fD6XhkEU3FrSx4Ipt/kgkjHx1qbuZ9SHMb0gr/mrCLul/VtiKBJ7iebhj1eyLapjolmehKKq4VhQLpvUh8yeaf8vQ/T7P1CdyV1aUVT+nphWGxwjZm+a57YlIp2f+kzWzCngJwhD3HgqhLrHKLobZTA7e0T7tL4aNVxtGzF4f+mOd5W5bjw2kcJ+3621wn8wd4qyekYQ4EEeet0Bn3K1f/WaflLpVwiUjU5TPSSIZh9MQ6+oxGRs2wN9ZRxcAmkpQDT7Z+1YLbKdtI2KtHKorhWRjm0E0TBSOv63qbJ828OomaTt6p8/n8fHmepmEbu/P1+uHDp++ub3q2Zh+PBxLUw3h3unt8fPzq1av7u7tJFooezrzZ+LbyZ8ijtCgUIeCdQ4fHoiVJygvLgM4iY0OyJDwE3ktONLYUOI76RkpR0X6B5H7h5cEYcjvII43wkAJ170mefZOa3KflBi6gRgQ8RXvreBm9Wa8kpRJh/UPHB8h6k0AzGlgGieriDlkONhKPyE8r1y5ZJRtTxIHqSeEHzSSTbmPfUxMGPpWm/ul33R/Pvlt0BNkthPCksFY4dvngurRfmfI0b0F9OoZ5JeTQjV1rR7CAyF/jwXpqy0vDZOFJV/Njesxy8G2M8BpP9rF8Op4GYeaR7tlv3bIsopPf3d3fne/ImH293g/jw/3D9XL98Onjhw8f12U5ng7T4XB/f3+7Xtf51n/zzel0z7ZsS9WTVcUIeDPQSJlwwgR2EKtayBHjYgXu9o4pnMHyBCTNy5NMNGQLLM7lAckmpdIsVMSs2N6j6vbSC9V42yWz/JRYJbRLA22IMNEWkr+XlhFzzR0DCSnG/enUYXh9rISxYSlkAYXpSA3mjgspTR9D8WHwO+nNL7yQa9UOh8pBEM07aJHmeb5db+QBnrh3WVoPr6McG6nqcxinFBeKg8dt6NaeXh1LqxUMkOerlUps9Q4OjBdHPBYhhGEd1C7FgRzrMIwg93VIcbS4q7p0CyyWKVQU7aBLU5Y3pYD96W693a7zfJvneWRDxzgM7z9+vF5v67o93j8KF77N83hYRi44ac3eTH4pEzGlkiX0Si06eigrK0QMfepK5ThrtlXVrZZASgcbojgTQ2nET8a5TrC/JIOmgv2ykJbvgQzyumpOwYFZbQFcMlvUcflQVSv2edyLL5UWKdJQcUpMypdNQszo79XNqdkZF99Hi4Nsml3v98cQgWqs/vyWtK51aFuFZWZjLTUBpiOsCed+y9SvC5/t7hBJgVDNtgpXQ5jd+dQ4BCOVZPClhQHqE9gtRHYtxcJsMR4k1MRztvUH/M9xhg9XdRbnOa7ryEDJgjVNh9PdSfDA8/PT0/Mz5ZANw+X8rHmh9G2u3e2WOhZ1wRItyVehvFWS7ZfV0gldFS3C8dJnvlVoqQLyLb7nywbnzzx37sjb7fVaImKX5C+okQU6/qKX22ttMXdHkN4OVBNsTl8F+4hNx1fANP5P4bP4MhMx6uxljcCK657qVNoxLxd7R01vMpXK3T/Zjac3tbJLxfTa6+pI0ocraHamcKiZCHg85DR6lcJQaLn0lgt+XlYyJrs0TODQmqdt7D9gXytJPG//BdFmZj824Z0WyYQ6N47qx4mhvusXRr12W9xOXxzMFkze2BSgIuX2eSzHu/vpePfq1evXX334m1/84ny+HKbD6Xg6jGTnK3qcg+hSxOMS2OcxWUMHyBUtMsoa26ZdKoO7pT1NWnG93/EICAqx+B1c4Qzpdz8qHr0v8vbiuiQc3IIqbYrls3kDikcXRFN+UDicWoONaAnfPLwmMLavnSg/xvc5VWAAnopRcho0rTIKhFGME0V89Xm1i0hvKVjK4qCIZ3D32o20yo36mLkAxwPtlv9iuUQyUnzyQtTbPEJunV6VfbEagXGDOHBkebYySuYS/jGQSUmyHc1ckKRuuc9bFc3sPDMHyvjHjujIziYG726bpuNv/daPSERPHKbGW68auGVDeb6B1nEADcU0P4bQppUYOGsG4eed9WjjF18NzTbIIeNN4GvNmxaxm7vZVn6Xxqj5kyJWL2jEsxyzGqxr5W6nnWWJIgvtSWVoA6zaFQuJ+C+Ylg/GaAUxkTptRGoXGTmw2u6KcWYCEAFIXqbRWjObQCLDkNJZ8dBUfvEDG0H0LSGVHiSCV8zJUg8Pm0gHkTDVqv7cCxfIEMigR9CvRa+4ZmmZKjIKrWGk9sMizBuWyyRfDnuKZhqtnQf2JlyDNmvoNuqHM4zDgZobTeN0OEqIdUTXBSdCXGsNnLV/g0RiZRe/KQC6IwAQLNrF+MIeHbVpK2ws/C8zDPLIJeuUXLYnbPNdS2iVgj/aYngvV8si3D0bLhtzzdTSwhuxtI5cA8ChKy/momsAtjaNBPRAIb/R3sOs8N22koU2410YoZ1Zi4oIVQZPhiPqF15N1AEDwsUSEbps6yJJeVoilz8KA24+45psQP3PpA6eJsu5DdzH6hlAGP+w6c0RemCGl7XywQnbkjFBZblbYA4Aik4niM8KvFpyuIgR16/ZA7Zh7MkDTP+nsE05A6YJKPV5LLU9AAiTUQjXhXYZvyNjbMxQpRiAVHOvHck0YJBfoab/+GrW/j53rjLCM4rwL+6Sq0US+47sAGCVurDqMNZW7EyZoIF/1WgEDDuK3cB2Umhy9iD2VbqD1E5PsNUki1TkEUWJSSTYe+1aT2hhZ93iklaAGbM/tjmJdXedpklcIr5QnEovPN7yKESD4RclAHHjeV1haLzqT84FFXqIOoB/stKaRm+KW7RzF3LRatuYI1tI1qTL6NkpGL3G7kCJOql/ldfZ3GDCuEn+c+A0y2YZRDZOWNnKiO9xtsYldfZcmXIqio9wNfeJC1TJ1hfNK6Jha+bYazCP1j2NQIskmlj10CObVFzLYQbRgTU9dc1ZZTAGW9tE2i3JnkCyQj5XRNNL78suJL1rCS4D9Yf8tAFKQSyKWwBBb+vpJUuHYSTNK26oFF0+JW6THl3+jm8l0ENUSM9dtuU2n6KFmdlMDPuicUpDLln8ujsTroEVjRxEGM8WTomiXEUQUrwVWC2YZRggEs1U7KykZbMFKVtxD46p+KFYKTHbsF3b8RJoTL2Y3O1OVne1ZViueZgd90YqZFBgPWloVo4/uF7iPmmVEOGWxIsr563VXtRp/QlpCC2CT6JBHpYutQCCSNnX7uYh+l148tydj5XHvfHrnth39g8cGw+BQmJuWaD+yszRTFP3Xmd6BsJfoUZBD5eJUyh5NClCLk5nLYE/A5d3Xs7+uPwVPbPvu5FS8GhiapsZLPAOU4Epv8eiGcHFHe7YinPH2dyQQiFoFFFwGV0UFBsHo+iaw5eLhcwuUALCU723EOEmS+qUR/362do5KpnKTCCBd9fYGxVUkgbfodkj9zBQhmZHWJRiQfzmYTZ1EVbHXvr1n0/6yc8pNGQMmNpxdKinwxmH3c4ho2+hcUioJhCezbCKwOw/N3aDiihDqy8BlEpjbFoA2cxrVT11XzmzN12miJwZOgcdMQnb/ohKWMTFxKYW48uEXeonABdcj1237Tbf+m2dhomDGSVCO1K9ewjLI8AttWcXTvoL7BBBH2lqIJR6g4nRHTj503RcmgXk/so8A1jZBsQALB30XJWSsRUwxgycB6BaGQihMllPpwfJgpu5gOix5nY3qUoZoKzatM+jqOpVyOXGd8KHL5Er1sA29wH6ohcA6c+PCf8stk9rWjToEmLLyuWvL67f1X3YG6KbTKSsWhoT8j0/L3zEFVxxu7BVClokQWO/gzeRs4IGDoXTvrI4CFDrsompwE54IPOhMHxAnhJiGPNt4CgO4iEWbOitYSjtflkk6Jd9TuxY8aHIxW4O3F+7zv+LmUfelY257R1Q/lfcPcSSgP4kJw12N0z1NedzqWcBZm7aCO0m+ACiZjiv3hNR74kHV/UjzgfG7wQ4L0bzxZT12cpcxn+d05p5XOtEtpUt/MvFqFizHZe3HuXCr9ETyFYkouirfchYGXHZLlqubwAJgWlGHjOgngx15Bmoq5URvaEzGqksCvKhr0dNTdC4tDpVkDYlK/fORMotZ1P8AsO2QUBbUw41Jkh8uw0USEhP5MhthQzyc77N58vlcDxMI9WTMKuzB7V64AM/zDfX3Sc2jN7yvZIC5IEodqnSDI5ZeCHIVGAVVtTJVWL3fenZVBMOdNIFzplfYX/y6DOZpnaNtJrdHuYZJ5ufLkMIwe5wMQ6FFLWj5kgFPkrHcI908XA1QLV/DNdBCI5STlhT42uFtXvnsUb3IkPjCYYWVRjpud2ZA+oOTdEDMB1u0h7brgMLEyfjmf6HuiLhC+mWWfZbtI4oy/x5PkoaziXAWrzFy7KOB9CHXlgJCxUpWTjAzLyffjGn03VUkOp2pVRXcmwGn3YK/PT0zEFa49Itbo+STYwSm3ZIPIYQNwDxQ58qxHmMho3W68pY0G7DX5GmanEeZjjamgFk8RwgFq1IGS0rHEVEKQWTGJyJEDFqAP4gwgLvbaBBb2Rij3OgyBsbHjozEsNy1cZJn3gwiPgwX1hoMag0WW+fVAQuoGrplgGOiWueJVUp6/ZFs/G0uLtbAHdEbD7TpaqwA6eLxzbGI2ddfmvMKY9Ahig/ufhjGp7RiwcwStIKGYdb0a++u8k19AK/RnoNcKGHaV2Wre+vt9t8u94djspCvcQp5QHRyXp6ej4dD5IAZNEa8pFreUY8UC1IeYvLQZXMWy5Ur9YiBHIWROHLGszApH4gMl33MjgElWGBHOIesnXXw5cN52aFjkMN8A9+VbME4v7ENgAVRy6F1RaZ2JGjQCQzgqZzJ3gvYtHqvCUeF29l/u+JRvG3bU91dn0l83MKYLxHIcUrZmlFdR1Ry3E3AnHo1LjJThVfX/l2eldu62N3Ch+SrW3O2/VOMPQnpaMbvGCVlgsv5VgS4/Ms8da+uy3rQb8VOmuajfwMzNRINoppOMFIbYsUI7XNRMDzePeQnFlWYmNZVwo50vgEAgcCGKQmhh0Ijb2omYZ8UeHoVgwu3GxhDIRyWOVc3KFkhyq8W1bBPe1g2m6RI/bAodxQf5CRuVCxNg9wbxGE0GbNB27gLYHNVyGedWZ5UlYWSN/FKjBZXexIXbHP3Q1j3/bbQP8J+NCJELLrzBCddfDGTNIjfKHwOiPk4iqYR1ClHBb/laBeFB3TxQzs0qbggobL7a2IADEBKBcqKTWK0hxG2ftvwdq6Pbb/udM89vT2aC3RS7sbV1f3iCjuSFmurv/tEiOtZNpfeNcGKl6gdaG6Mz3VrpAgvxTHRG0Kt+1wPKwzGZ7XnpueuWhzs7LJS2dk1fpnmdLnD3wCTiQY2ZwxhjsezOGgR54hKpfO5zIAUhnLv+PNWFB+IKXmISKuooQPTjzj+nlcO4vZcFpfDOJTVsGVvbQeJh+8YXA3kiVDJ6HpodGFW9hpknkLz7w0YrdqXKZXJmwT6TUYxKtbIrlp0G8QW+WSMRYMZNdSk41R7byw+EprxMUXEXqaJkDvcOVwQ0Y+nDoSSwbc96sL0Ji8m2LcxKKSqJdCitRiexfiZ4/nzlU14oApWvAzHXTKa6UYrEGa1jjklQO1bpT7Pt9mjqSlhMiaBZrcSnAvm2RrZbyDoqsmiAuNyn/Ph0GqDJhJIT4T29uwDZ3UKXPftIlMEoZ2kF1HTRLYrO4+ULbRsyOA6uaNh+Nxmg5hAYFad77szn0s11n1/n4YlnUVPzD0KHqR2aXFUzlbfE/WL9Fw4+igTFePnzkyIBc75tI+VI70zVRva9Sgu3C1xBHwLbF6C1ZPAmknY+h6LlmeFmND+7gPMvXMkYQYy9gPIkfWBr8ay+cxMnCQ/J2t1xzyiL6KcEFytFL4fEDESvyKomaKZSwrzD8p/cFUzcvqAPxyOVMVO/EA+3bLXSn1YpgOpADPy3IY+g4vQ2Kz1YHDlnh2a0P6zMCdrn0Ha2aMUR+RDG8dCrMSCW4cvadHTuW8KniMG7F06CsjFZakxLe5rM7BWiXWvMXuBrjC/mQcSyV1fCO0QH4+sMjiCo6YH4IvA6gNQFtcltvIwNcr1ceJvoRPWMEtfyONvQHx49iICwdsxSUxbj2VSWhoho2rXTaVimY1EjkEFPJfLUv8BaYn72XMpp+h6xeOGGaYN3DVivw8g9Hbut4WqYEiaeJxa7QKOijy+qqObKuBV9OgP6UsXEdaribxu4lVbMkkpQc6vpSk33f0c10oHRCPP7B3hZyGq30zfDlqQOe0KP92L71qPgbwV84CCHGLVyivjiReF2sRoRk7pp6QhXQacXZJ9RwmZCnmX6G2wnPhmN0uHIaOE/pLrIaSI5WZbJJrWrm2f7QtFP1Mts5/K2i5elg12vg8KbT2j29zoGOZUBQiiYemGvnuAMQhwP7AESqnasONd/xwedPNYmFaQCiEGRlNhoGUWgGpOGpzgSi+0IyA7Xa9BpQrdqOuiJLwackI05eB3Uke/7rM6zIf7+4pctK2ELUMrj5HhWaHruPy6+tIiw/o1nJVJWQF2Q2aYBL7aL9AtsqPhGYKcQ1X6JFIkEBWLx07KAGZQvXS162afbzBwf/xSPcEqyT3xXZvURqHzURTJDic0gxOO+uQVysfl/K6CkTm1QSHoNfB0BIiYEWX31Ez2pFknxum3Ae/nG7kh1wWBk5x+wF+T49lEwqEuxVf2ZcCoSVJ4/oEInCoIST0/Ei4ouXIk7zTcLZiACwHPL/pNt8oPtniRZP5GH0cCabkxdg+Aznk+1IbeWTLlPOW6CumMcYbNSjhbMNlkd5iHMBlOXIYAGW9Be02sNAbIrDMrCuek3T7LI7keld5GptQrwoYsMpjB8fEeqpwCBqqUjFCix3T6fhK2a/xdgwz7VSOH0iDj+XXb+J04HcvmIah9RWNAy1ZHD5AWYRDUYENtqpJhnCJZ97Uj33hDRXDvhNlUI32tIeBZ23CI5vcwmAlWFoL2vjdQjh2GGPmvF43JOntJgp0cCqSTQ4oad4uVypSlbjh3nG1mce7lhZTowj4yxvBLMss6jZSmp9pqdSuRqCRqlJ5WeZiibDEHBTASge4R9L1P6q6WCrGkMUV4WT604HYSwjcRKzZTWyVcGxAhf5xehrmvQl1we7EdyNC0MtNAKsjX5KfkKCb5o4q2RV/o5oHm9USRQXZGfXa3NAF0nplTmJyPER6Wvj8zX3U0FeN4oFIQ/bF8yHBRIMlFCEV1iGoQao/m2eBoqBHdevhNIut0DHxA8fppFF55EWgnH48AX629T/ptdT3y21euaVPhpQyO3BM1YuYh5JGCSusJ4zLL+tSsA4cu2ZHT4780PfS6O9wPBLY9vJ0XqYOni0vb2oGMrkrtiqTXcSF+CGLMxzCDNgyns168uW5CekV0NKmGa4wWLgyUiW323EYVKhjxcs4o96MC1mH+HyhW3b1vvOaxGjStAP6xDcRydjJVAPfrtabFm/7DOCvRYeZfwK36/AcDGMHP6A8lcC6cOnM2BfdEZhwhW8i8IBQ84L7p2pvu3TuH5PJmvpxaLDkKqXK/cgbL7NhaooiWbnX9Trf0mnqC+dTfK9xbBGHZFBtuFYMD/JlSlHQzAUvzBoJgvJg4kbjSJ3EVm7hMJNZWqpxBC27Lu8UG6My9t8XmNJHZoc9hu0BFEZzYavPeK8+QXnmeByCQ8D/qtWK1VY/kKu++iSEdC+cAdshIxiqyJG96KX9BA0cjvVghhh5bKTl+NP5Sebg+rbXlN0JXWrMJK1KRO5mNASDTMTmd4iPfTdL943pTBFp3FpIKtpGsUdcrFkNL/GkeKPxTVtQsJe20IMPS8KqOG+J3lCrgWS1KGyMUgT2FIpUJCNnN6zL7SadMnGY8cQm66y2oDocxje0VZ5Nat22MTYCdRvtoy0dWKgu1DgetgO1O+L2CKSo09ui2YkJ3VwxddhOF90LdDRqw4tzG+ExhfKTOAHkNRghxkMqqF0zi3gg5HYYQVmwoN3MaFj+FBEYPvYMtP0LNtWCRDcK5DDpDlLClylrPcI3Xf6CzM+TSmRaWDx8DUyMuBw3N2Wcq5eAcfUKtgcOVUQkNp1sGrWXNI+X2uDhfVcTX+N4CwFJB7x+4ZidRBEprrsaqZKc9n4HkZxIxBYHPtaQHGpIbZvpQNPWVevj2ig4rWGaPn18ev36awqvMOkcO/HCK324A+3w7EmEkHSOoFtTgz+soGu7rgZxCqvkmozbOEodaNGTycUteyG2aCl4mczSviBBwyDjLaPI5IIjgZbNJ7TfCBTIfU71tgbSkk1J18biQEAWWEcWMhlCMgMeVY+8LwjT9ybo3izhJnX4XeoUo274F1W1iqY+1wgt1ia0jvIbEK4IF2z7hpOMOmJ01UML4G5vQiBZ3NLOs0NhOATtGfnduJeCi4vPLUV4AUzzlLx7uylEmew8WPeKY+iCYvIq1LoBddA5TO/fv1+XGXiZgnKrOhIPQb5XLOH+2EJvVT+WW+0DE/v59xPuJ4+EtvUMdQTtbcf0PekDCjWQOl02ZGBuLROIacafcH4kjVHvV2yTHD00minSsClVzpGwtYV9ChR6t/KCgApqtjJnoTjYG2G+L96x2DepiQUjb0JXiLNK51oTtsCI25RWVpTcmBreNexRoSeA0MmhG+EKLANJ/NeKReyShAzK4JnXNUsLof+CX1R1Pc9IsyC4FwIVq7HoLFihtTI6u2uPU1OkJuVXRYjLn8irPDtj5SJpK4f59P10uX66Xi8cI6W2c7TI7W58tcgFJrY6DFZXgP/yBhO8XBCBCJLf3pMuZANSLFeHopBfqrop/rC1oxiudSBo7o7/zVUwz+fBGKTsQRSWyfHNnqtSBArHpIEdWniPaQuZBTpdax6h/CNAzyC1jzGZmxzRyxqVMayOGkxPRLwb7JnygWWqoZal/Su2tBGKVFTdqy638YD+0DgnL6QMxMCqnmN+vzbxWJhR6ENVddgAJBFM0e9YMDytJQJIhewl5KjOo83nP7r1uK5IFWfs+FjbKrBwFQqKh/rIGIaeemESJo70O4wv40PB0m4gTXiZl/Pl/Pj4iq5gIB2CFg9PrgAfZadSGJJTr83EDLCCY6yZi+HHKh3aDqLuB0eyjNwrUHuTifwS0UQqA9euH8ay0l0nsxZMq6ucG68L3VlD3ahojteENI/NSruHCmqhq9phgeT7OJRgMzInB/wt6nozaiYCJpE1Z6uSXDBJ0X5/aElfMn089C0ahv3G79nvVVwyYMlYaVgPXL0y4KDSwz5XVQeoozHNQDbxaN8O+dNj0ZwO5X1RX6fDdLsN21xmC+wK5HAdkwbtClOtm4lSpSfQSop6LqGIO62hzt29/HsZXLB8Y9vX5Xxxx3WI0UbsXKC7tE/tKYVqxjFirK0u68Yt6XUeKqMwed22O0AqXeVBaXqApfs2kfIy9mQXcxiKCGBTLOf9gyRj1/cyrCvw2OIIvoCfGh8py4Zeu/aLoiRviKbxckFEiviwECZQcVg+QZXz7UqELs9n+4FpX2XmvhusSgNxLF66K3KjzFHakBduqHsjBZvkPeSr1SsbqHZilMuVL7icnht5+NiPhNqgGCFeiUWXJWya0MtEXyFvbBn2XAqsPHD5V7PDqiGmDHB5tBvE5Nz0/UguXQHw7Ikx1VFrsZiqSRYg/W0cDuP44f3H9bfXcWILccPwaQXgyrE2lzOUHacqqUEdcXZefdKDPPKdgXV4t0I1PmmbX4EXKsyDUtBglPYfmZI7BoKiXAh38AKDsQnKIiMGxJ69IIvHRpCfbwVR9DahPytvsiYcljoWSrXB9Zz+5WkKRteqsxCElkOjuoGbaeMb9SsPP7J8KtO0/MHDkS/ULp860iK9HesCMspRTuIce6N86cVoj36REsarRju56x/GY8ssDIdacXNz54gPhDgK36k8nvSOtIHFNUNkX9kdQmeiJuCSQM9Wc1AhTBIgqfCs+n44HY6fnp9vy8ylqkJmlEsU/vBwWkBag48r7aP7k5Zl0aPZDEwF+QC8MUyOVN4JNtqKdTqDT0ukL7VK+DGpWI1n5LmSIl/zImQgKCRSOTVvtMekOmKpbldi+ZaTSL3EQ+h6JXsyq+n02G7Hra3gFf1Tqqk4RZvGTCTMbiTIpGy5/nz8VYXZklpfZNnyaBtKGT8Piv0eIAYF0YQhD7sYM54PYIL4m2E6ERWKWsgpqXZOi6qJCGlDRiYCuP07Sz+JKwfqDbqDMWi+jo9N+tyWemFxKDOVePD8cLjruvduX9EufTUpGtZWsDeO6/NyeT6fDodK44HnAqqBzakUl/SnQwYqrDEOw5JMGxDSr8SGCE7PtU9SH60jac0pKXSdBLK4KAvLnDOLSLWNcVphjKAKLUoNhXKU6IM6fdjCa0LNAghrW+VMjwjWBib9hCSzzfheUJ0vQoVPYPFAoPdky5S2aK1XcXyadBU72O/jVPPxghiJzfAf6lMIDah8Oa1WH+05ajM94POdDxhf5Pye44HKznDMsHk0hARiKR3NUhTRIC0hM5ZqjTotjmxYtBJtjBjAijIO8FIyWiCEyf2/JGNhcX812sC0xazmXPSUPDC8fff+1eNjP4kjKryKMYe0vEUsirE8HC/YshklmSN6XSnbzbA1YipIMArpGatAtyMkxGY6caEmZobBDJvRf4RZJoCosDxy/lK1HUvpTbCbd9xI9wXfYDaDKd2ZfAIWKHZpViHEKsf6uZ6jiBvFNSh0m9J6qOQ0DBTCRo0V5dm19AwOY8SD921OLUtjG0BxiuEKfFT61XZ2P0IXHhF1TYFL7i99PQ4xpEsaOgX0UdP2WVyIAhY4XTMiaPq+PxyP4yQSW54spY2TATQ2MrvfZMG58VeeWNtA6GqVljQja6z4XVgDkAJTWro9pXLbcWKnDDeGn77//s1v/9ZvHqaTK6g6Hqn1DwjOiAZGB7q+CTBXGNjaJM4t/iJLGqGfvGlxnJzkLBjTtpD9ZGRiN1KVD9BbpwrwBh5TPHz+psveaPWL+25EDAhgK0m8oQjZCOqtSt/lu/KxMMyLMWt0Aw13cTC2A4nSbwb1Fu7fSIWLZD2LPoGxculUZPN6fqslhKpx1fFpMMISUu6K/nIotvN5f+DR9W0MhYcjm/o9UrfYcesO1OJyobpNWGlKaZmtz0Tt9kxrTmkhqvWQ1dcXz9UkpCopxsUnrFHg3UCKUH9uHPuNu9ibyAaZKtdwbCJFRY3T+Xa5XK7H+zspXakpLWlBIy63AcPiV6+gQldQRqR4p0dt08RiR2bO9UbCXAz3MhKOaGeNjOcIbyp35wwh75v39NzU1eROYAU4OnyHbXqJl8rzq4NCI5LZBY/7rbEBkXET/y7ei3kE5W0Oq9XGxFPgJhQhWrZO2H9AY4H5lKfJZHTq7LkUbzPrwLocpZCwhQARgF5GB1Bx9ipHLSgJgbc8MtR5XFO6xulLN0hCOjPd9DXYb1MyCuBg8sXa9pl3UP7TIhkRjJMlu/hm9DdZuCjnG9OuSNmZrxFw+EKztNbfdXfQTCVYgE07IjSnYewWjnOGHXTHp2JIjjqeDofb86c379599fWrJFqRGfsz/UNzyGgoRIplcGlEDZAif4FbJHHlmCBBObX1BovCAsFbuiYrhaJY7CAUSrZvObPJbk6YSo4Zge0Dw4mXe3YMDtFThmlUdAfYhdNral0AdS3n2xi1PWbhRsGKCLzBmccDAk0kERWeQY17t0COskVJmbOgmC+vu2OsDJubr4I+S3bq61+ZXluvTIgq8trXFQ5fsEGl77aUkDB8QKAVUggsp4uP5EAuxwon3xpJZuVGL3MSLpQsG4Br7nqiRq6oHHqT+VswuoNVYMbQ4+HX37/50Y9+eDzdK+q2wVXrnZbV98f4YR6azYwR4UDEzC4gHAeo/GbZhgAnTavSKehCmNSwSBGPbU5qc2+hWLiBWXXP8TtFWDJSL3D0mKeTko/XSlS6ngykiwkqcNBMgCkrVIVLLmECNpI2zp4HrcfX099W9UdNBj+Cm8ZpLYgE5+9QDaBbDNSOU9PgJHcw+og/Ypy+eztGhH2iTdeIS9H/MGsTiPyMeJGDFbd34IUwUKbgcYIG6vxWNcvwx3lQQzwSRTzY6mCgjiRItHGkJL8XBXkKJqgIwnaFkfThcDhfnj9+/PQbp3tgKSVN7kJn+R1HrseScR0Z04ZhOm7bUhzhuDSb12MZhEYjZtpSdGJ7bGXCwLilyH5cMXfemJSm/wiTu5snnGMGa3GuSX6UHj4NioyxmhI10JZ4ZeDiP2cKbDkH2WzROWasQC8agDMNVFG7NRvG1m4VCZxfun95E4HxFvZkJPMiLA+YVrXraBWEDPj83cq18sUvXzYInKvFHYy1oKc4qoUag8QYZwv0q+ohafo653Ecc/MLXMakaxQ3DJJMViR3xDkGS19krYumeJjGbj1+/+bd16+/GQ8TF/W3sCHgPaU9oZC4wVfMLE9JSD05gYfh9PAwPz+5EHKlw/8uJKHY8wbOJNyWJaapkWdspQP9zaCzsRll/xCvqQQuu2Lqq7FXsbdpJe5s9cnbh0Vmod0L5hhIKV0ZBcVua4AGcmxDS2kxXV8IMCX/sSlBBiYRXBZpXvNmTSGY2JEJx8ucosXOJchSzLR+z20MVj1d3jWCdZgROicCrHTrL6LZnesyYC6wdzGXfFqhtwxo/Slgx2bI7bOHpZvLU28AxQlS8gdSMf4kA5T0ci8nl8daCMt2khMZ+TBOUtJQiSPzUNtN+XwYxmmalnl5++HD5XJ5PB4ocS8ehrMN5vHiDgDW6vtlXSk9gyPD7B5GXnGQIilVE1TNjiCWcuo2Ss1GucKOkIyvQkr2kcfqQRL2JKDEEpIEZbp8VZzMsWKkXa+eyylSRpMx9HLIb3WGKneWWu+QOyTCU8yBQpY0HbdRGsqM0MmOdAwpUaZYTwzv3Kgui0fP8bYtlh4QCk4oTxSiS1pe8+b5RlMLYA2IeQ9DGto0bRa2ckkEJ7X37yBqGyOM0LtYDnfK6cW7czU1DHApQPZsmTV+RyhWoUZbROui6eVqF/YwBv1uHnR82SwmBtXpJ3UGUxFANOP7YPK3mI5XPmThP4y3/v37D68eH61LB9ZVAFDSWuVgM3JmLEFgo76ENKjL5cJhkMCuPVQ5ZuDcIhgAmcrZoN2v60BtgynJPxzVnJgkuYUuvvjFucPkyqeKj0JXQmIIRrwgF9+V2BYVY+DKruJG4nt6sylVoqGWti6nGdzChaUuWcvNJRpTG7NNTLmn7jotFAeOmCZF9j/wZFPpe0XJcgO3G4K24fszmfsPzk0S0+ko2ZvJkhEGHrR0gUJoYEkqqCrOd4APd89Cp/uCV8RSJsALXAn609WwtvwV31KDRgUlm45aJgNhioIoU1hxqYzIuTNbDJxnsAsiJIrf+DGsy56kpzGfeDktblYHrck2V8pQbX0/jeM2Ds+X7dffv/3BN98e74+OQ1WYFAsS8XsJrbuA9sxEPoXr+Xy+nM+Pp2NUhXJ+H1viq5bOG1j6JUpc+25ylr8MjwiTytQb1Q8s+/RsielbyZdIIoPkjbu5SOWPlbZHq3FzVVD3ZqV42HANqFg26WxtKJyU1WLHFbq3cdvo3ip6XSWluHVpZOX687aSjrAsVMZAWBghD8tvcwhZyLbQJtbN+gM3j1qSH/ZPxJD7v5oGUtAHyF5OnBPy5QxY00yqJ4XQTk3A7Dk5btUlYLpLqejCdjT4Qsv6mj9MF9TEK5oAjid+KQene6GnCgaFyuLeC9BNR62HenL/DhJJAtG0EIwVBkbIamCRdXc83i6X79+++c3DD/uJD59iiLRMglphKSxVOy2zYk3Bvc/n821e1sM6cm8kJOM8Qg/qQvhmUVPyECoOT62NLVuY6G/pFgpY9xJ3A8k+jvcmE1WIRalTKwmbpiJLCwvFSQxZ6XvijrNqAdrrr159Q1ceNWJFNukHafAjpSqLNFctSYyL2zbPM3dCGaka9trTMlHi5EL9NHi6wvuYTEe2T3tyuqoXK1vHdFeFpRp1KQGbuh/m38+dJT+Vdga9Z3fxGY9hJDOFFK+wwNjwe8LVdv4DfhYWMwSmLxJfBPklbbO4C3gA5R2wP8fXoGmfTTWWwcXIflshvCN9SWJBYEggNIGXpUxr1Qbpz2VdBoJZA5fVob0SCYLL5Q6SqM8kcm0YyJXEYu277747Hg5f/+AbogJJKzWVrJxJgP1K+XDIwWWt5tscfZ7SKUKuUCN94H0ensotSKQojyivC7WFIvBLMln6gm8qx6wKCGdW+n7KSPTE68d6wpUdcGsLqQfCjdey2IAT5icmnNWByyVobx20q+u6LBwnL2kYgiA4BWXbqAsHQ4SFqmEHAbNCKwo88yJdCf6cq/iPw8CFDlT5JOLndZ9qCmiewqTQ5X0JP3+6VsSNxPBNuf2AOkMAPLVDJu0wwYbrvYOiMuYEsIciAiPigOlXM23lLwJdZRIN66qk41IcMKD1eiVdpxEjBz6gZkNo8Q/5C08eD1N35l5hfTf2/ZVLVsinUSRN9QDbHDoAdFCGcTwcjut8/dtf/vK6Ld98/fp0ogyHCEvUCTv2KabkW6z/kATmwnTUuXuyKhHmxQsU7V8r+ojBSrhK0jMy8IhUEsQ2ezrBw9pxqJzJRN51ae4iyR2hRYK+apJbxrEsVNhTgbrV93TAChV1atOHFxdVEmZtOpwpGg4KZYFE2I/UiGLYqAonUbUfK+6ipjawbbERCF6SqCGuvkaNacRjZ7PghH7IfAulrHxpiGzofMaT096GBHDqJeFbZALazoMV44udRDn8K6Bqgt0v+Yeypmfqez0I0ynti34cgP7FhCvJJbsrV4yfi8w5OinORjViU0yNBnTjqPnQQACSpEh3mqYLMxFRlLMigQojRycyGVMkdnfo+v77N2+fn5+//fbrh/t71gbZIoR+t5hc1W9RjL1bNwvxUlTJRI2Ylm2ihH5BFtaiKatYRQhQ+jieHXE0UIlA1dwOVXQmPzfmCWkoEcmX1KxtrlsTsFpYG7NHI3gHTicedrPPmoJLw+EOoWqLEIsA+cAkRNwcV8syazKDmgzoC1D3SrmQlFtX3Ywc/tLNUB+NQFmam4FpNpaukSFvaNcGjwfWnZe2CVqQmBTfwhy8c877vb+yrN4BCPmj0K85mzzf3D8E0sxzj1+8QZ3yXGA4+hsk2gUDy3dCcOFeh2rQYH/LM0KobcUaJBhA4hTpj+M0dhfO4at1nzBE6IkXSwlhsm1ct3Xsh+vt9utff39/f3d/ujtSvXU2VVCuhWQsm8oaaVGytqr3EnKmis6km07TeL6e53W7OxK4k/Osbi6faGjTxUxBM1EpAH+qXdieTdafzY6arRNPTNVd1o2l1Lx+xY1Q9sgIlI1wJmV1KTIhnzkVzhyu7RX3Vor8JDVcwsIVEjDxiuJNVUU6sq2HYYxQN8lXVoGJqE0js3Y/npKmA/Qiear5UVE7TFvNoumlOs2BsvLxsxXQCE9V9l7UU/EJjbpY+/UTgh+4mpe+pv+mcfct82LBuXz17D4Z3ZfyzcsXFwa0auGCe4lqkwMncFjV28gORFGnrRvJrDnfFuq4S6cm+6Wq1WUzJ1OhtUwax36lIuyMvYfz8/VyuR2mkSmYXMbjgVrXsvmCoxSi1os6auZlvlFN521Z1tvtJsnAIu9Yo1PPdO4zpxRcQh2YpreR0/VW1KT2IYKq28B672qIRhW0dSVkQcyETUSsfgp1RKy1/xsQEPxCAhWcmxQBQLiXwiTCAyT9yjlCzj3+oagLXlDNNfaa1p1YJcvRiKbEmAmThv6pKBoy/JW6E5aOg9h7P/zxo5Z/pTdJNUOxmLSMYRHoVRzVgnr9wOciE576lciiGlP7BQE15WWh7gRVt7C1Vl0yNUtKx/HpYg9A69GJA4QPycjBXJ0VzQY/czYb+SzjYVrIL+GROlqfyVUY+5ozW7eO0s5QzBNlQUtnQFYEOIHjumxrz/rYOg8L2Wm5ji1lPmqJOV4i8sSwKYn/laaiNCzq+DsMy+1Gcp5HWuK72KKG+gBUjV5jOgMDZQmrWYewHafwdDyk0+kUayZGKd4ONjMvwzoElGVUHQhU/bo5oVEoBKSTglalGbPLY/Mi42qEADzJ2LAxCH+lPzjbhIQGZpFCoB6GFX5kjZVWD4LJf1a8B6nIgQqhFQaz9UTAnPRFPNg6XuOtKn2hGzE6jcBGily4VFXhEZlBu8absX5T+uEg63ftN1CHq1dDVUA7pMhRzjy3NZCUHRhtmniEYQV60dVNjCONXI3Ihispj7QbhonarJCJkkJ7pLydaE7OdGK9cCk9GNg2XEYgtWzGgcwi7NMghwfDxHXbqDvpyq5UDnYQ44yGPZpqNkoC0jgMNwvwYtcOwLQ8L0spwsijtO6wGGpC5EAIOsXT4VAaJdQaROl87B1emU0R6ffUP5nCn2i7BrIheejzwKVr3cBliUG2SQlwZVXZy1qXoe3hewczgAzQ7q1EMlB7qIlNvOykCTK12n4ap2aUpIUsCeFQtPnGNbFa7g9T1OCoarhLpQ4UYst9YFodNGHHWG2w7JmBtaQzu0Q1oODYX/6yvBJo2eeHu3zhuOQ314A9lb0U25aLZm7tzM9cfNSP0vnKdUUTpcoSjgJcy/dQH97Fii5J9Te+pUYOFZlOCgSVDTAXZ/qUiCE9HmS+ojfFKkvWXRmkhjRq/VcewLrQENlepSDWzaBUpnM6XK43rIHqJqzSlOm5NdXGwanwbiBc444HbIs1FB4plZHM4Tbyt63itmEiJlTPXZuITNn1xF8ZCG7wi/QLg0eujbrAcB1UH8Qu+E3KIKq0UsmZknRMm3ZuoL0AiGRZ8LLsTdTrIzCaQVTNLIxY4yjGrUlLlri1MotXW2ejB6U4hJNWLNEVU69fAkAg7Y3t32doEanf6xf5R4AGCoXTf/US51E4N4JCmtC7luYQdZbMN/6ruN2tyBEC34SWEuxH6oWVUMAL37ITnA6orvow3a2XJ3qLyGk7SkGvVGUjoI4vmi6bFNtRUyN1S5PKIwYGw3kjNKtfoh+c0WM6mIRYuH1TFL3j8UAyBVVJp4ed9LJy03Wf5Fzl9lmwT72LKD+gZuGhU67auCUoSyM/1XRdBYKQDLXSRXx1nAFA0Ooo9DJ6yuc1+hnkJ4hQ+0gXViOaJDZCok6depklJXEu2x/z1S0hn5L4gRsYMgBvEbBaLbclgYCv1Th8uVeYL+e3S9pt8zGJOjO5Jt8oXit4oT4djr3bH/ns8/sYB5hnJD+MXUWLgCT/LNzD+VssTeVgTFODkKrmKhFklfgNArqv7+/eS01cSJv0RFodr0RiSryxiDTy8In32DhemNvhaZZMERUAzRYQK2FpewM3fOORjIHl9C4IeUv/gHJJp2GDd7oLDsiyVpNekptt50RtU7byUglUI1C9qGTUE8Da2kEtvlkKjkDwyInHwBXM6U/4C7fcYoohJQCo13il1rm21QoQHYydfptAjdEnpTkEs0Mid++GV67GaqTOwcLbmagDN6l8JY5RiEMYpHKm5jWfRdm6vTU72vs6VrcqP48my2XgZf7NEThEEuBjW3o4uE0TvFbjDaWf0j+MY0dV+Lj6eQRiqQ0L5LBRLuUzajSxmj50E+GgCLG6wc6T+BJ+CEHvhjnKE1g2ijow7FyYM9v2ynwi4KimwdtY+A1Nvg0JDIFW3CdN049i1ZUypbScpJ1J8MdKZjKQzMp3DRgX+a4BijVaUxcODFFIw+AmCRNRsHL4NQwoDbShPFifxumEWhqpPjSeAwkQOsLty4ttFW0K4EYLsOvfS39iEBfQQHHx34kaAahmEd28VxFe8MKjYkIoPHmCKUBVBwE5WwkqB2vMpuf9oYX3xW5utljTXgKwa7CsDwP/CiMAyUk7yfCAKF4FI/AIMzdyAs1asxI5+qxFd+M0rWIG2ZWWkDMJgspH6RLQrUEePlGvz2a4Foy+aqeNxAO39sPSGLH7ApKHyoqAhHUq/DdIlq5jmASOjz2dsLXLcJ1bdYwneSCISj36nhROsf0NL7b8IoXdY/+qxc4s0JfNZa/pXaG4Qw1P3BnfJtBMy2NVPmSPel28JKO8X48CIkdphsdYwaYZI9NpaTzQIFw8BwC8ShlAvkbjllYM+dEhcyFVCNcqwVfL0rfIaFuV8ix76DzAFPddwJqEMVeKR6B0hzFUfyK38pXy9U3wgVKUD2QcpaJhql3HhhQpj1nRqlzXyFkdjBQVdTY7VxyzIV6teaaYJ84akKp9ATqzyPAYSzM15QJVAIcTPpbswEDZMRknzXJH0YEhXwqGb/U5pCKvrK/2r5Mb2l5ZxzTLWB4olNKrDSgvQ8y87a9ulAXDRqyVZarGy0nD3rl5KTliHfKAahmLgt3eA83Blfbi5lm+7bzKiAOnFPmq4UUnbee8xiVCKwLGsT/B1oM1KorTU1bOgO3Je8ThqqhglEvl0MsAGFfFS8kkWAcAkBrwQxCCijjiT/tHBfiybgYt4wCAV9HCiWNfSpwGsed5i3UaBnwsMthDjykqWzsqcYSzM6Niqa24RnyuqmiBcDPpOpQ1IRX8F64rVt9PidEWmMCweZq8fKGUrhKql1/c4jxpP7md44LLX1ATVLBvUFJj4WMqKIoQ6yE0L187h7tBclUHwjRzd07EBUVpNyzOXw7BQzRjHvaHKlS4TmVDVWeKK+mHL0L29JEBQI4asbfFfcHRtBrzpJYBMyPv3dZbfVndkaicbtBAJ6QYocEQqr+DtDmrhuMoxmNPUSF6s9hhw5bZy2aA012LgmTbq+M8ZTPlRbKL6P+LZOoZsoyJu06oAhATN8Lfo1ETYWFKxSYBzbUM6uZUd7Nnhgomr8MuWoso8EGFVAlVKr4kZbEknbDapsrAW582b3a0dw7TzhjfjYn6iNO2xDXO5l981SIM/qtHxftY6UI+E/0TbG41J1OjQPSiMkNnyQTzFgZHs3imnTnsoniJ+vKwX4nmIG7A9x6VX3NVCuAuJSo166ZO2XEUXAdugh36hXnBd81Pyv1DKPkvEF1kRuIRaBaByQCbnxRk6D3O0w6vrO1KBKXl4rkDSVVLazeEFjkzlgf3wKDFqE1VjtBIzJ1JKf4yKQkA1QFueNZmoQMGgotL04J7BK6viZTUSTHIeyST7VcQsBfLq9HucEZgxo37G+oKNJr+0QfsDWhHyqcLkB0YakEA++UvPNOlHVmTnENVFFxXTM1yTMTnCgRTzKY1r2QYFvvj0A+H0zBNnNaqIQjbThEv0JHwltrSbmfNYNl8QKm8ebV+Zmrq+2FeZj3KrpU5VRbmg+i5JXfOGm5igGFO1nPeE3KWhAqSv+uiGR6203ap73rYrZSsNJpFu9sZes54v4rnh+ivAA846LKgamRNgfIJ+xG76xLcEXfE0dpgYmE2KqnjWd+IaNrGZsSEwQS9saFVE6wsM+bycdzsGDpAJ7o9C3aSgUhB5rFPjVdIyR1nLo6yvgJBgfY6q59AMFY4txpD/HIQvxFVIkV+jLyLB2eesyOnRYqsC6X1Hg/X29US47zdQmhZgWOSyIxwy/SAlwqj+VLqzWpwJPJhXimnn3IbMH0NbI5C2OmMpruYcTTWx8qGOaGrNOq3jcobSEC2FLVgM7hpmP5kK5YVk9GID+E2UpZIyh0o/X425ESHgkYE0c7MJNQuigzTTZ+HtR2vAi9wvJUUgynBwuJB+LvN1+MEjDSjcqb11FDG5rA9sGdt5IYdyRXXcevTNOHjlx1M5Uxw2tUr8Yl0ToBysFVheoDGNHXkmAWOn68yBbAqOv2ZcaevW/6TNB+iTibc4XlZuqGb+pFClh3YgJOrwDLamjLJDEPp0k4MRo8WaVzD2Ng0H2q6No3D83n24jQZUvjBjDCBYuYaPgn3lqwDHKvcaBHFl01XYkFWKm3VngumYIhAqsxZPKPXbHPnGownIiqMGpLcbZ3rJGrM0Js/jLZM6r+DL4QuU1vLTPZRVFajwX0l9fIIoYJpM1cQMJekroABHyLsQ4gXoBr2mrlsNYqg3lL1bL2+gFz256of583KRXD5X0qhpcB0/j+3HdUqU0l0MkZRWJT3BMAzign4qMBaLHy2bZgOFFLLQXmvHk4JbvnhjUWOeMli2vwIHtnucpWfFPDAxk7RylxF3TqOA1dWZhJaosmV/H9Y3xS74Lk/Qsyrki43VZN6QCKJwRlcUC9YkkXjHSAmmSviSrGF4lg3WX4tXUosqPlNaChz04ktYcRy5KU1VxZSry2KLzVDaJH2+j33jLb0sJdmE/+4bb5B2WVeaNgZE9YsJ1k+x52Qn8HFfgMelzVDaWQy1pa4krxqo17GQhNVmVO3iZZJUiHgGaNKUvqFXHzLrMH1sMpqQ3IMHe+O05Eqv3HqHiUmJf0l5CXojRaBoQEGCWkXG58XNxF9NoAAe+ZhzDNlOi7LMlEHqQZQ1LOGFqRwvkFvMjgJnvAXcRqb1otz0hFq1nx+lMDiDXYnkFmqPJfA3vJav+lUNo4yRjgWm1YcFESToEykvTfHQKUexgojetXoGeZt00v12HK4YrGT6RFy1DQih/MwufVXRJkrV4EFgF/Ss3eaSVdDS7dqA268JLop2x1gaioZGhLzS18UFGURPfYA2zI+Qlwxapb0HW3THjYMD/7BwcTxyAfKlpT/GIe7vh+3bvb4X6EJNQfbz4IW5V7YyUm7dVbAHVirDTcp7LiDAYmXZaFgLH4wdA70b0VASBygBBDlJCVXi5fRgTo1m92A/iNi5hR6qUgJ9i5Ne0KfkEcjK+1GxTZ7GgwGT6mZe/JaqXUHqR+4G7g10naWa+x/27PzmgT3008J5bkVurx3sGwUdIVVu/yDBSiXJiAHZZpIDt1pAfedV2NyodzFMGFqn7tdocvZAEEmwPN87TPLgNUU5QXZGrg7LfVtG8a1v3XLfNNO8MWQbI9hIctZx0Hii0W6UMTiOG7Xjiu66DnzNk0u9BPm0H3AJoNtxOWBBvKlSjCVCyNiQSq2HY4jUZj4hAP2+OqWh7/1CmkvRGtyVaVqx8VwLbdIOxtIidYi3lDQKKbFQwKfW62YhRULYV2XcauQjiCmoRSaeSJ55TDlPg6Y+QiDk5fgRQhIogHWjiqTFnWhdxYyGzzS+sKRVwDNRMybl/C9i3MLS6zakOSFeuGlN62t1c3rPve+H5O4Be5F7TYqIF9wxMg1UJQYtEznmGoczwtXtjA4oNImgh1TXnEerB8t96Bwrca+G6gCM7WjZf9r141epw0rJ7gVsoCmNhf+txLVgSJeXEqbhhbSP0yUJONap9zIj6WFAGPAOxCy2YdV5eHBSjF2y7pPNomVabYfem7JoiQeBkOFzZKmZ80RmICL0CytNgdr7ePgSz0SWs8K738lh6por/ayOb3CA51BNlAvlP1wVUys7rT3mYniv0FXLaWWP3X3SDRllPJAK1VFqESaRw+E6uWl6xsQrikZoEVtG/g3KBeOb/klR6/ZmZPwvgoRIPGSwJoBLcBfQsPSnuFh1iw4LcqociJKuaYCSR76dOBjrNFFkkFrqXFVBkCyTsMa4kI3mWGlZQEIyp8YYUpFvEi0SD2fdHpAifF1DJa0dCK1LFEnA0OqHZcd4Mirdb6SMuysRs06ET6FoVRhxzJ7JPAoCBmzspD4xS/Wq8BWkO0usErFFwpcnZFmKGTyPw2GXiYUjxoJCJHvPhj8FYi8GJiycQoPWha3FRhLac8czegN8muUx4z1eQF7qRKF4KB1pR2grF/ApBU1yNB2aco+NSNRom8ItkXuU9inspcHNhBnCtNhzwoXzb977D5+v82LZOl7n/VK59WneoCUl6RBOYKiFoMogD87F8i7r7UYPd6pmybN6kd9Do9Bot60FAplxGoldR9VMNgE5QTPy0J5C9zpQCtjZNZrYRGh/DrB2qjFNQrBJkjDTrsRxNDae68bU2K1OEIhr/JdkvCMcE8dPto+sZymWCvXdZWidsavLeu8loi+dS34aVc7mJS1ppJo0jAi4JPev5wCtIFJ92/K0pfxNaxWkqHtK40RlMnlcEFCzI43mnjENcukWttYknei0qaA5gwOJHaSV03bHHIk5WD8xe24qveWzDUGH1P3oUfeaPW1FDVmDhkUEApinG+s8zIcqaKl618u0Yooifa2WPqVPM8nZQ4mcf5SKfl5mTn6eVZosnXDRNqKsQYwN+lNuI4QIAGPd5YhKn15WRzfsZd4t/4lxrJKtKiBN5/CloFfiQAD1DH32qNzFC8QDS9a1K7y2++sa0OWuWiupsKrzKtDrWPCnQUR/zWAMqEQ908LUksV+yBOuN40OZD3yDOeuh94U363jIqwt+JYx9di2BLrYfVoRE8TxJuqUMD67rgjbBmML3JzxGFauqWgXAS9ZTn77oU55Geg0PXAAkhK9UWXLe67fl5noQltesIIhMnPOpkaqQTLab+0jLY9X08PFZGXOtQrR1ByTxT2CWkGAigHruHFAhsqLlIUMMC59gWpHPTcaRiTXRDG9dgtb0rqo2lKEq9C0NJUkIDDk6L7zIks5ZW4gv7JLgYFhJic1KJlS4ybFMXHGe/pTVKHXT/+kiiNfEXCfzkv6LO3wbNcefL1JhYU29hdYPr1l4ynkI+Uumcmw3B5fdhK7O+QIDBlmBwVcZaGul49w0kLOV+cvBaOyk+sh1W+8upGvLDWLtdg8EJNNgoqRhEC2X8zilW5o9EFmrHg1qqN7HZum7LmY8ZW1IwWUZIRdBW0i3Ui7bfmbMsU1Holct20vOY7t/RyXKrZ2nelyRM3jon8Db2jaZ2kM3E+sEvRWM6yElSpurSUX/cluhrDqz13PfXZYJuGSpuddfB6YXHP5jo13lcGCkobpN++9NAmShfHjrFb1yp3X2jK88B5wF42KENrOBnIEs5CME5wNXkNFrLdXrlAP1lah66fM4AWfpgYm/mFK0li+jmWL9hbOEcJgajpP24CpPKeM5StApU/CzRMEFbIZ9Tun1kzHy0NvNLDPnPir7l5HYNqPiD6jQxAxN9+pFXBRfNaQjslr/ZDirtarUxWLbP1rqEGgqmxtEFUsN0Msfo/6Y2U0g9cVmZOX3qlkpKmloAKVVCyyNqzQWvYRrGy2Fg1BEy+0VYIbJmdoj8nkV8UFH/HL8Z3I1D45eP8wu2tXU92M6R9lYPRDJ3ZxxByeqeH1/35A3hmioSSNMb69HzhTBLABzaptzTbUt9TTMXwcG92stIpVlCvkkVxxk3UhOPXoialgtVtJgOWcA9qUaGFlTWsKjSMYFVAc7kGrIIOWHjnI3Aci3W3E5ry+HAxTG9xWtqTAPDIPVUuHPJFBvPWTdIjrXnzyjNiEtyDZWIAoaq6tcf5CTVunzlvQuJdkCaTHQehSHC2IPhieJ7900Q2eSVeorDw9Kbv1F+p6bcKuDB3B1wh3ZPjexrz4/keKIE+A/bL7dDj3w/HYz9Oy+1KBq0anxcgBEvPJs3BXBDFNJPZSxVaewNAm96bUPz1dpu4XYg2LVW1JOsXIJfL0GMPevaar4KVubL8slIbl3mepc3KgV8CjI07OHyO0gXOjKUjuqnjQDPyvJK3fG4zkObgRvKfpfnwZyaz0nKXz9kBn7osQTSO8Kbd6lJ7M4jyYvlaM7+Wrmy2/C0dGQ2JhLUxT76oRJsVgMzX+YSaAyzG9TJJ+CgDw/kuWMBYqoP90hPciQQgwtxymYIiqP2zoyvGiWX/7ZysFI01Hm/Xi1jGLDY2czdUkGrco3+Drbp6Nih4jRVwGr/NN4oQoirsOtlcw6tAy8U94E6QlCAVApZlIcK9UUsmMtxN4+EwHQ5Hbt1kzuZA/56fHu4hSUCyPqz+CWyiLXJwsxAwIAUBS4XoymzRyv/JUzzWOb6H3j53BcWY84IYVHGkrXCLi9q14m+Kpd2hq3AyeJykHy+Ww3qcNW6V2nBRzl1YdWClK8BXYfI9fIueswS1P0O+TXkLTrQvQt8lToH9rjFRyW2/nHxt/xtkRG+vx9Pp6dM7PQTxjchVUML11g36vh/USgiWxerK8ad3jdmty3a7UYc0uYn5Qxx4CVw1RJK6BvtdgLqtstVKUne5Xq/kOVqW42E6Hg6c/jVpKkIIvQRiAxRyHXRtO1jvQlQYdIJuSMYEOoLXq8kkyEvexlZE+i4SrFyT98hhf66XUZxExEPci9lqJMgDNXKk4eksEmhS/oq/nyKKnUSFhpmlTp0U7od2JRKbVpx0gNdfREkxKwye+FIqEYoNgbvDwMpn5Z8Nr4hHS3jZP35PFgCenW/qkCY8s9V00rYPh7tHiZqhrvNFREF2bhUWrOrJ5XuQ6wIEnW4gFQqY3CgNixs1ZVkvHB0qsMLUTX1BmCuWGSHdZVnP5/P5cl7m+XQ83p2O0tNgGEczI5fTEMFi9UbC1RtZ1TY5CYGXY6NrrR9C4GRrZURS6gLliDf91XvCoAob6wJlmfQbgZHSidhKl7ICOh6CJDPEYmJDDnCA254BQaYlK2mvdex5GstC9V+IhrkTY1qiit/HzV8mwxoJ7pxHXInGW1hBAgU6NFjB7zY5C8glK1ICE1HL9rZJK0CteercE6i3oJPSxWifmzFiHaaJ5NLUfXjiDDura9Fw9sIzUqaMP9PGnwZTTtVEi9+Pyrgu5P9f1mmiJFu1FmONGLsSf49P3PKhbpRtngkzPz8/f3r62Hfdq4f7XlpOR0+DLh82BJ+x4OCiwM21VTc2pfgx3a1UCOGNUEkzE9epVM4LldSKy2MRSoATWgSAct8xa4dILTB66szgR+sF1RqfkM6+kF92cCO9pQgsHdPMUW/aGAZO9e6zQPY0aeazjqIvmUnNPgKS5Q+Kce29NJTBv8SnjZsJdTMnFVoJlgrG7Aw3/tEmO5HVwPs8DONho2ynVmROegdqU+BC7K+gGDqrD9Vg5kQw9MNtvp3Pz7/x9dfjyA0PbGeLIjVyK612bM8IPmaJ1Nfb7XI9Pz9/7Nb1m6+/6bQVU3YGbSHtYcbxtw4hvIJwVTj7sI5bhkaZn9XhghnsxJYWkkx1A6UYeLdCVCGOszMZPMVxx2jDpKucxq5ZB7ZC6Szb5PS5yh6QJcOs0xpQcizZIrj8J3WWBcMpHhLBAlbVAG7QfG1fcPhxaPVFxbtN+v7CV5AEuv8NQ3IRJ1htX8IqQ6sYQsO+LMiQv3d39yiJdxpk6aZi+SfBtNzsJoYRHazydhu1OAnI+XAJqNdtZMEahtPpIB1IQ4EAF6y6capJinHTKk/21+v16fz09s2bsR9+49tvp8PBek7r8/tqdjbglh0+TZNbrmhw4ra/yb6AwaRACNqupUAbrkniZGHkl6gOy4V4KU9rHwE5V+rON24maVeih9q7pJDCjrgTwdkAcO/SuV2AeoMSprw2bF86JYmY4RHdVm2NqpUBsxKZyC36HdRqZvF3+THg2BiFg88Yv94cp2Qfuw1RrvksPAGrpmt5upnSvHPbFgq7iPcxma5158Yb3p5T++IeH15fzu+X5Uqhqw619IjFgaqOthBu8Nog2ALSpW9lYMUDuN0I8kqmHmVaGJLP8SsN76Q+Uc4yo5Pn8/nNuzfX8/nrr17f3Z3kwGBZnMiO6NOyZXIshC5c4Eqk4QtoJGayswh8bA05+Y5QEobQr57qojnUbr2n8BQMuFQ1pMhGirdIB3bqTTuauG4g68Z0Ki0rlkFcoDGr7GraOgKT/daN3LF14MaLDi3rF1RoKOXULg0jXorrUjC9h6g1gXy+u0kxPzXF44El2LGwX+yagV21bJXn6CkHILBtbod4Qc0GWKWnkdaPatwlZGTXOhQrqfhF1SN92HC4Q7VUqTvCjYqp4i314oVsvDBFG//Hcy/MlIMHu2VZztfL99+/Wef5B998O07SQJPLe1TUy6+dViv2KogoRK6ya4Puake24PRMxNke54ktecvS6pTKcyO2El3DzLo4vSxZRIwTg1WEv8rFC9iIpXFk6k16YTfLSG4cZsPbKLPM/ihZKuPxYl2gnxR/x2PfODNbQtJlVwGLloTUrI1ajTpN24MsguKKaxMHat45x461QZf+K5uDtE6lJAMAaRKYp8viXQo9LFVR8t8S0+V0Ok7uN5J1fq83hdrEJgc/7yqr1elaMBNnXtZ1mqbb+UwpQRKxwywFAhjDHBgsSoamqc3dvCxPz89v3r3tt+3br78ZppGOBBub6ReNB0nUuxXHoswXcM8CtEP1GZjs9Rr3IbQqIRIH2U2vO6ez/EIOnAHUoiZEqyiiozLDpm6qf8cYkBVdUwJ2Bt1/zi5UTild5MS905cEVrT81PiQatC9hqdzCymr/1byM3/QZw8gyN2Wb6y6tghibgRf5WoCrY1Glya+ywCaIxy0kZ7ltiUA9tIkDPblmSl36Ltt5OoczMr1ZPhwqpkUmCqOYdggdUUAU5WPtvd12WaG0D03rpdzLqoR4Od8J3EnWguFy/X69Pw0bNvXX38zTQcKUukpy0qOq3l8i+3o0iKHpVPRsWg8tCKoB8aAtOWF0jCG0sEBM7+sUJZRMHJEt5KhkcCWs4A9nqVlUlbeBNKGWAaPftZbSdsYldur1YWGDYXvpj0vghIKx22cJfWjgoKc5bDPzgI9It5Drew0P8m+luBLCd76O78cL8cZzLdByZLeVk60g6lNDpUUUSofGTPyS4CzP0aJzGm4ZZis8QLGvsoB0LO6bdN47Pt+0dribVcFsBY9OHGNHtwmOy/eyG9u2zgMhH4vl4cjF8pkfY4cHZz6YyzEp4dMSNX16+369u2bwziR7D1wUR5mcBJAYDGSjZOwlYAlz9U1yEy7hntsm6UVFFqZgz26x5e/b/IxPOpoG870pxN0SYohosF9AEvrpwn4O/WTluK2N14QgtD1cpjtsDifqSBfOmRwINQwal+St5qQ1eersKHWsQgocFKJZnX3n+tituOpDkND8h34zrdlKdB3CzVUXynJLa+TAxRm4GJq5ANphXCqRdkbTtwrA7G+G8bT/XQ8zNQVQbqKVcMEOFZirIBOFmHj1jv5mTFJLI9k/66Eojm4YpiXRdpeq9HYg/IUF6jr0WQf3et6m9+8fXd3uru/u1cG4Z4Ubf5ZL0m3hyEgKCSjZuebUPYD5mbHEIwQRt6Rw6TufcH9IEHl84KejYBLonWajuNp4wV7FQo++ZSTcxUN0GvyQng1InV/82cjKWAFZfkg8gHZNJwYEIjRbkob5tkuC3OmagBRFCmOTdi6cQsMFsHUXfwa8MEUCLONYNw7PMFuIH/reMNWXy5KTK+CufAhRXFgSGwcpoKeasCadiN7cfmdgZqt3J5JAGK14CDDrMjqsciTgCPvrglQCVrZ4WIlVnnnhTg4a0AIXPk/GjA1t0sTA99/fH88TPenO/fzkpPEHVGZFX5Whdlw4M6EVCxJvbywV9k50N1lS5phJy1nGXkSZZhEouHiyIeIdd+Rr7C9YxJP1hBYQbkpaWu0NklPMVEqEGqctLPB/laDw7u9FRlHXNcS9tjHw5u9ylHWT1gMc5ontPqtGEoCoX63RNXlOCxWFe5QqmrVzSED0vgGXlsJZlg6XnQ2HrIh2i8Aj0MpDes9CSnJX079mJhmxol6NUzbdpMgGfy+MO4I66tqOJUviPfYYSjx4kby2zLP/Z2KXI9hLO9pn8rRX5b148ePQ9ff3z/IDsvmm5vUlYzKeeAvBGJOpYBYPXhJj1AcsUJ3kX9z8pg0bICCds74wY+cGCUMJCGA6qIQF5SrwVpHQpopikgFd6infGspalcydV+WLJDLV8STgDxqBirVJxECW/w7LlMd2poYloa6qitmovRAnPrl587qNRXLYVIFRhdSJoVnW7KFjiebSbzha7EwyW4f6cTooPWV36m30gI+gkDjie5kpTuQI+kgexrO5zK4Vn+Uc9e7e4G21pLa+hT8ct2oDjgVqLrN0zj2tFtklPaQjSwtfRmJfT+dn5d1uTvdo+YmIEcqUnjlDCdgGXiv19eajc4uaNmeqXb5cBVRqhycNSVIDa6QdeA6Klb1Jq2gXlnpFJUQNT0Z+IKdD46J0MbkhJo81SecxTYwm5NafDlWvJ8C1aQVaL6yngbezZLJ7v2J4q6t0phSpEdE+K4GY0n/7EIIpVtjHJHZ42MNXlICwiwDaMI5iYelYyHGBMvr28GbulkQIJUDgGDysTT6o0gYCbjnQtyjFGm+6zZwv29xuhdPSZRbPBrdL/v6UimC4SfVOKUOY9vhMElvE0lZaWbqG8Lqn5+fz+fzw919gAXj2lKiXf2iVk4yvu27sfkaJx9NZk7mK8o2J/8HBH62CfddRyVOunEcKOhIw1Ia9pBSAsAS2w3zThqAs1IFFGVFaWXkTPDcSC+6gmHRZDsReb2sLIGhjFIBfWtKLLFxUrIdouRvBW21HRIpwsr7fUSGoTcI0RupOpyOWTKR28AS8tsRKXBBi40i2i5u8JJZAIVkeK+4F0Yk08Foyz/xvUb8RJLXajbVLVweH+7fvxuX+YZXl3iuBHqBDd0/HELWP8vyA88/24rXZZ6nnry2HZNxIyTHd4QHcrldPz19Oh3Jcp4Oh4UTutR16k1LXL6Q8uBdBFjx9Kh9hAppVmk5WFHJa5KaH1ay0nhKS4MyowoKD4hITkFVnMFldUaw7I8iR1uOFEQp1YW2dZk3yQfGXZSnNdVVPLVRlMm/y1KyXDuvbKtX7cbJ4FMaf2nGWksK7t4tLapzmEBd4Iz8jIw2T6ofDo0bVet5xbX8JOIGc9I2y0edEPqP0sDqL+ZDKeImnsn6p0gtUoMVQuv5KYNPG7fKIDD0b7CDuRELoSS+uDxVPx3Yr7Fx31OYj3smRAOVU/d8Po/jNAyjqssh0aUXmfFhKDK47St4W0G8mObghjshVaUd3QZHzpl+tUsaD4BSUMaOctm9PgGonagy5qNVy2NXyk2qapE+LtTniY/aPFGyL5l+pfym5aIS6ziOx+44UVE7oYko/JJODvoJU0JHFRBcVpKRN5O2BBf4/EMmiFclDpF51PIKqe4furIYIwIO7EvGpCko3XljsUrIFhYggO/Z4uc+oNL0Xb5pi6v9vtw/2az4/xnzkt3RvZbWxUx4OE4ijauy9TUCSDO+LN5FYRyZax4OKDlGhyNHdNZQ01E9S+x5no/chMWy/pzEwH7mfps06vZqdLm0NYZLW7sk1Cidw2W2ZNSlrcOVYJic2BCjCMUbeqSqOjqF2Fx/Gz7ybAZqsLgsVGOEqRgD10jcc3ci7oxHxa/XdR3H6XA8Ho+H4+FwPN4f7+4n17EiBrNASEEXukAhrxHoF2XKbSp7r/qAo0Kd7ccWQ5feRPqPt4tfWk+EP9XfmJTnfXiLfFS/7X8VSamAb0uDlppn9OwYxmad1UJPv4R2M93F8WPVlwMPRCxXK1Mgyhh3zZCVlOJ82yNQhzRWdJjGK2tlFI8tQtUFAFhT/Sma84+cNwtJS8LP++thCADpOrMsOBuoXGjhyfVcoqClfFh1Z1QqUs04+pStTGSqE+EMioCHZ9kEI9XIJ2iVxNz5q9h8nm/XG1f4ohxbYb7ELqTd9DQS76Anz9TXcuv60+n0zdffPDw+3t2dxulwOJw4od9KKLv/2kmq3P06K9qvg6AIoOGXDmBDMLvuiK0vwbrk6lmV6JHtaS/op7VT25l/cYhfAJ6t25frVktV7ekaxzUWzDIqqd3ezsDbU8lA2607cdaLKQQlgrE/1sWG1q58hksRAkYY6zQOy3w7nY6H00lcfvSEFNTj36FvUPnowEL2sYlyX8na2mlzrKbll6uPSEjMmAw6tE1DdVqH691VIy1HKbGLa24JkiVFDl1KPojopCBaLahnxuWsrpTptJw7fb5erzPVqadBkcdqnLhjO1UuoHpfhyPr/2z3GcfXX7/+0W/+9jdff3OYxnndrtcbA5j4zwe2B1SalKFmpWKH21tf6bzw9NDFMFgKqzj5PSLXxzYfQyP4J1pz5YsZzvqxAY6Dvhd1EeSbvDCz3Xec+QBcpdB8hDP2QGsc3HwljUaHDwEgVtg3GgKnFGifGULkxrjTiIIzVvN0ecZtiin66vn5/IOvv9aym1ax1YckQMohgHtFuEWoLIlFNrjiC2IfB1yAv82VLXhHScX/QpSv7himWg2EtvsoI6WXWNGljKWfb8u0gYIgIOVzmq8XdVC3lI9DUPGVX7fb3PXd8Xi4v7s73R2ncZomkr5Un2GaDhMV3CSKnihE5+tvv/32mx+cDnfbcltv19M4TGIv2MVRvqmIgiKeEjY2nKMvnnaUk7EXEOPiFBN4TfNdJWEYyuY1Rrs3i4zYS0aCTCwPzRu7l3dL72n5peZYiugFBcrsM4DAefUl85H1PjR+IyD0yIsu5H2Acbmn+YZzTVuFc9X6VDN0PtG41pJZA0NTJPPlSkVeT0fLM+N4rFJSxoglO50ljxqx7dC7bU9DtxAp5zF2kRBvnZmi03KK4YDvo68JfkPq9XpWHoLlznYSyqNUdAPg5pic/6+cyHmoQ2eT0FRs5Ho7Xy7Lsgx9fzwdXz2+enx4PJ4OLH2ZiKeRSm5Oh5HoeRzsz8vz8+X5WVjAOHhvpCY7TlEWMGnjX9lYjUygPPL1W0BRBfvMVzF7AanTZg/C2wrGLPRY3joTaT1tUBhaAC5DI+c3LyLskOiq2lHhqCVqRMZXTGmpyxZklSNZ7eBKCnjxAChzH8VXTXyVgtbnFAcxNbnQL4C9J0F27lc0L7eJXodaibJ2yNk5ygZeSYyz6Nko7w5wQbe20TVog7Moj9PeaxHBGJsGdysBSURp+NT0fymcS/092xBKc4JZ7k9O6x2xWiGb53m+XC/zPB/G6XQ83d/fPTw8PNzfH0kDOZB9igxV/DodqfJm35OZiwJllst8cSfTtlA2UtF7vXb/VvucdqfxaU3Hnyfo/ScZj9WgJ4DB1TFvyN8m6yiV9fg+mpDq+zsBZH3BYyqwxFIElbnWpsZzaoGyUUM99wAkptLmf9UCQbs4EFjUCkP4lgaGJLxclqvce0o96+yfCXgC7zGyOB5PrkKnKUHChrpP+DzfblSCR1IgwqIOZON3qELpIugfGV/+n74JkwtzVfIfgV264L1ivlbH+NIPwywp64lLhRML1PwW+JAnzfRapml6uHu4v7s/3p3u7u7uHh9evXp1f3c3kXlqYhRN0eQbtYSnZoyCWQzm0jHaem6tsreNxfulczjON9blC0/QS/m3/ReAawQn5D+N2ENUTNvDL6o84pP3CuLA1sYYizHt4oQYTzAvNG77lUarDKGVxrq6MUDLUpCU8UiOcShnfM7fLMTQ7mKlP/PVSWom6GHHSITSYRrfnS9kcRkHsam71mjfcvMxh4RzlBXFEs038o1QB0uI2UKLU7X0RneIXOBLaCEPKoX1DxeP28wQVFi8Fi+mgmHhiXQClVWRVirJ6vkptuKlJIFN3tZtud6uXdc93NPr7u7+eDrePzy8/ur1/f2dGMxmLmFvRgE5iqA08UJxqogUcDAghJHUENUYPEtnqYtcMJi2CEmWTl9yfxfODJAlBhfp802tMLdS3tRinYKVOMCFEdXlgew7dVitStGk4Lfhw85xg5kpnbFtVp0o5rktl6mlpBtXUp0QTSjuVd6kBbYGAJTrLhtdRGfUa2L8QkzZ7YBM+1UP2Lquz09Pv/M7f0++4T4/31/3xFoungi39TbP27aOVO9/rI+Ej7sYQGFo2wzAIqEkwrU/wyjtJie7RUgLqDtnJMRnz2fLgR1SVtWIS8pZJY0yMxKZB5VDo6bGt7kfhuPheEeGq9PpxMWuh2GZ55vsopNu35MKPFBREp+tcipSPQYJpWxJqqa48SJCPrjMGZE4Gwwob0M8Sv82lm8MJ+SOqQ9NC22Tmmz5HMvuv2C0ibGU2iMSRBtCVJk35RWcJufOnqHyasMbrRewOqdePld6GKWNKxpNvbh5SzdK9XbSeL3cm88WWHlRyXkbp+np6SO5gil80khEuRIeY+HNspNaTohaHHFAPkctSqBioSVV7gNckE03xvancebCIh3V4DyfL6S2RZfHYnpstLzU98GjGe2AqNbtPaCQB2X/snuP5nnuuu7uRLD5RMR7PB2O4zDebrcr5XFLiB43jaXOE9Nxoh4Uso0a1snmAy5DRR2oAooUQm/HgpO7KDgsalFzpQfUMiqfII8U4fNpHlIx3q2caFrbNQv6rJGrLKcjzV1qrkNPnGSBIF6Kqd55VUzEXCQt1mkIBGCN/6g7QMrBEMOmnA8KRe57qlur9zOBhTDOP5E34OjHpSFCAksDLInRb8v68cOnV69eTRMJimKjy+W0AUlzBXYIU/wHqUgS2h9cELO+fGSBU7cSIOcn4Uz8YnD0ODGbVKNQ0IhxDCJ2mqdUg65bNmqKKGGPGillvgovaw04RjdJUo4ooGqZKdv4ME4aUHU6TodD123z7cbh0Oyp4m6LR7JhnQ5HImB+0EqV7rmxG9MwPZlC3kxhDINt81iZAtq0NlcIucaRSS+snpCDNcp7QqJIJfJ9tTwMBX1F6Vb2hEhJbe47JlgGgwoaLiefD3a9dlhOIsjIefgOQ8CntcE5R9JKsD0XWpGDMmzHcWJEt1J2msGQeHzw6T2DpaDcAssa8DXuLlvSUyGry7qt9/evwuqdlqiI2VGDj/hUp8O0cfuFYZq4arucL1nuyHIJy3KyCneItDyktJDFYawC6kTA7YpzinKEFzIOziEgKTovy3rcJjIRh9M6no7RRsENiAK3bbs7ncgjRDZ7+jkweKYy6RK2STmhFDJp4pl8SqwWkfWLSiZxWKUIa9KBUwh0mC12UGKunQqHIhXIqHSqmg7SmQQrY9p8v7evvYWm1cwGREyC+CBP7PkhfCJ2Nh5VzH/PB7ZrLvZ3TVybyEpV7NgQYb1GsyHaR1BV5AlPD8mDSETzuZNz4fVXX719++Z2W9y9ksaI250JOtF1ugDMIHAohXcsjIcfHh59hb0EjjtzkwRV7Y5mdzgcLvNyvd4GcqhQzkBR9hO5b465DgsyTit9JQC9XY/UC9A6TF9mCwTizZEgnDJCTWRus8SqjhMZ4PQIgQ1Nm6io/ieqLa0WNZJkD5FGbHAPGklWYMqlCEkSzvyfUjkRuYr6YWRzrjYL3AYL8sZTgw0ZqteudrbtLntxkWsjcWzwUQjV/ACqX5OLE9Yji3Jn1bBbl/sd/f5aoiRuVOSwFrfdLa8GRzVsLzBn8QOPpP2qjqUcJIzKady6EWz3AqLTO9Kua3eLEBTLujBA5ZwkDxLOU8fB5n/sB/bDiMEbK3SKloysdT0eSGIE/VuiLspspWZXRhlFH6bp7u5OZAvQWl51sCe5bYlr7Ww+Vjx2WbDydfyFkMM+HL1HIXjdhBRcwoWztinmTAxCs8vMDm2zIQKbQC4go52X9cb4+XQ63t/fTQclS3EQUU1goV6Su6cjSWkR00zpYscypinZhdwbSdOj8iHH0geVvb4dTay4IXPvEm6DChMqdzBc+dOEElhZImbdN8oTiPBoZ3kLE9EhFMbeYg5QbBw/34syidsWb4UEtjkCXXulc4maUjAfRBSCP+wBhdHe2DnRKoJT9evQ9zhWKD5LzChFJdYQt8EB7d+Q8z6OYeyfPn366vGVmRh3FIw4SUFIBKHHiRqUj9fE6LFyvgowsCEjUNiibq7dXc45yGRjGf43inIPvghqDzLPINrupTFkHAvlWjLKeVtzGz29SfxjpeZv3TSNd/dkviLNtqMIDUkkHEcKlpQgDlJ9KZBSyJdSG4StkAbNYHtdiFdQvHQhNLMdK28wQNPCvmUrmlCnU0TShYBGijpcTo3FW7bkHqTjMBq01Pahc7UAPhIpKN8qKK8FOF/QFAEpNJha42LeZsrq55fduhhspiCfuVYuVmnBgItUJv0iKu6yPWzLamzd7joBgGrMtvU+LyR5LG+3h7t7kiEtHwGqrDJ8d5KQj2Qig+5tXg4HqsKzaSB14GFrB2TUAdx5s7fSKor6GT6ighzxE+MlOYEocHN+16djNC9bQGYnkDUFMcWoCCyx+J0O44GbGyu6ZlJkMKL0y6rvUSQvUS97K9hnPt9uJPZZAFOhuHVZOKEfaU8lG7+3Z3DC9SuAZm24goWttj/MVsU34SzrkdTiOBZ1V2JYMDfZidEoYEzVzAPNurKr5w2j5mcoM7/8ti1pppVk+THUSpdCH1HG4b9afMRPVMgHdiS4R9dtVCBxKOwgVd+rcARgmRCQRbhjhcziI9HHhuH9h/f3d3fjpJHPsVyJyuJR+j82mXcbGYHmeaFaF1wSgJs3WsE5oRKrRFGk9Pu6bO0DmggyLFggm9OHlcpr6FwcvJmATRcgmlwWr3JW0IRTtaJoLoJzPE6Pj4/UqI05OOUAszGbxC+BZ/UrHY+qIQujZ92ZZO98uy0zm7JXCqJeV4qFTkbJCCvtPvfypJIKpeIR2b2PEcyecEu7YVvysu4ZNBzlH8pLjE8GhBdR4D1QinVQoLszzpfiTjEOzbC4QSrJ6c+9bErgkWrmWhyEICmu9ZbFjh97mQudGMkTMNWlHGrxZHQ1ySMtmtpUrJIURTzeLtevHu/LwjKmlIVupR8kbMo+GWnyw1GeHNU1mn3blF6r1Wi2fJ1PiOU+GH3QDgpQp16AA2HFNhQcVm1XjX2c7r7QIQlTYZfQLDOQ0oZ+BGGRNIxhXhZGHIfDRIXvA/Ns29iTKiGylwzUbOMiwxUbuEXpXuaZMpduN3qmWC4poq0jCVwaT8SdhCpoYi39Z62w/pfnZbZo9YuoN7I1fYmx0wIAaPXFO4mDVwHPqHNgN/EKAbvRV8LEvZO8d4FGXV6fFTpBA1zi0y1BR1iLV12VcocNE0RweqxqLIJXP3X2GbuG9j05rMaMVU77iYmx6Vn1p1a/R73k9AmzkE1CAqkUO9BqYXcATdRnpsYtMS9dCRMu2ziKN1uztRhYKvFo+p5NRpttCnD0rAIjFoPgKr2Z1NReaDpF2Pxc/BamaeMP1oI0SjC4Li2xZdIi15aratxky7bShQQ0DhzhzJ9p1snAsVZiu2LsLJZnKsHl35zZUM+Zw/MyL9tK7WakjJEGcgRBvCBudC0z5baRb2LmxcdfKHLjXsIARAHkYO4o8ZotYGK4j0In8lEgJXDo+1mXy4L0pSYCZXRZj/s29lThBIr8fh5vWgIrWEZakcTVals+u72dd6FeO1LmL8Lyqk4eXjjHJkjglssZuyBE0kQnNzKBZJQA6V7smC4LN+C+jmN/PB7FgRtFU4zDmunZMZSZjIyeudeMF46jcA52h9JOB8lKUTsLenLq7cyi4XdTl6jyF6Pf0LlDUy3AtR0gZPFZUTYZEO/zcMgCZenIdZSNI0dyEbHCP04mMnsywlOa4DQdT0cJqTwejpo8SBHRanMm2Xu5Xi4Xxs+z+PwtMmzl2wEUaRzV9I61fNXDiJfsnt+aGvdu/+LDacyyIwWtuFbjhzV9yXAYELA7D+y+Sol6634YlnHRdJBssivvbtLwi9VkRWKKhc0IrQnFRkMJ7cvFfArDwRqYotTnRRngYHtNO8Szm+RgMaVUYCCDZtW3y61ZbvPj6R6nZpwNsD8wUk9rV66jlabIGyxYdlko30dq35uOpRLLqDfsvr0vJ+w2iF+j9cDGweOcVrOXKE6TY2e0bJkkt2MkBMYBGNEtJlQ33cplWW7XeSQCno4HSdCnvH1yEknK4N3p/u7+dDpNlITk1Es2qtvtej6fn5+fL5ezlN4RbCJrTcUy3XSRYgRtu1o6XjPFKIlE3OkSefoBaUUo7zCSOFUaLUi9hGPrHP0YPRluNzIBZcghUj50+AC6iK0EIyV8Uaabu4jRu9YYXguOgCvNg2VB/XZAAHYQSwVXJs/GEjk7nnUiNK2A1UxmVg9AWwVrcK+tRtmEIOacpHDeLn8vmzp4UJfb7fx8/uHrb7wRYV4SxEYgyexciVzlmES1QXB2P70pGTlaShYyDLXHd7iDOpX6QWI6PHX0iKw2ZVlMBKFMgC5Rmspd4II8RiDnx8mVGlOluDSsRhXEalFw5DhxGiKVzDlQdj4pvZL4S4YrpV5qd2/J0RRc+Xx+fnp6ul6uXPhOxG/wQI7E0umEzSY0w7wnre1J7/ieV1A5yBt+ImnvPqa4kVj+JIYJ0E6sbARWxVFLFo+CS/DUDSt7CCEfDSkFSIDG1xVMKa075YmghIuBofhU4tYL7dHAYrT6b1hP9cs6Kf5WbdjjKjDkObTSHECQUELAY+YKzmt4Mkxods6Bf21bd76cT6dTP1IEJNQS6Vo3MyJx1V3NM17tQr3iHLCglRnzfG3alblgc/ZQCGAt1cp1YfkxIXT1aCDzDyHc0ovdVxwFr/RMWbyMHiGpiGLYQOx8F65VIrHf4zSIl0icvaz1kgQ+nY7TeODS03wI1/V2uz0/Pz8/Pd+uannmoJ3Fizkwk1i1rGzz5AGayi5TSGYorY+micEUaklspoTyBuXVpfwXlNWT4b5bCdyWNsdWahUYF+Hhid7sqCsr452W5nOULkMmlmkaOy5TpKUQIrEvDy9wXT5hQSUK0pVeUxBRgE01w0q5b1DSoOAA8EB4WQ+h7u54dzgclus1Q2ZnD7psFZqocVcq8G5hS+QAOz+ff/D661Sl23l4/Cj+NV21o4qppPMOw+l4ul3JxiphCz0b9oScPBwaTRh+/PqgvyQ85TIxiS0LuV7Z7a6Od4fGgYFK4kUjt32EWA5kcfAaI3FtnmfbdLtRhX0i3r4nQSuBGuwpIpfRHRHz6e5EpmnGHfyl9Xa9EvE+PV2vV9J7lXpF60r4iN1I+xk2KjRQcc1gCehA8WuSKuX5yAbK6gg2zUXxGa+5oCOyoEvhfuQzWc7nqmhwVz/TEAiN5R2tvKszJbWsDOM2dlM0rC7AZtSqKmcZlKE+E/tGmG7dNusvlR5+RqUwXIPgvGmXiSH32jSs2/xBxsd4VR1aAwBBx0oDvc23butOFD7JNvvKAIJ0bOI32cn1MqLh8XQ6DhSiRKZXdfvagjqHC+tX3LgPHoT2J+3CyV5aLl1EPfzIqCv1mUpYANK1EOOI71ydBuDH9bGokSpU1HSljJkIwQAOj6NPDtNBSJarTRJyviPVl2KeVaBSusNyvV6en57O52eO2bAX44kYgESQdB2VldV27nto1qjB5QMevdbVexpyM5M+YjleaLiQOIja3+IwYZeIAigUQYTVec6UJzfIcl8kGpkiKPaYwownomGwW4GAsc1zz5QPVKMnpSIKQ9yImXDzjpOv55r4wFSwAkMoeKXZMehF9YuHceXqOpjWW699m75L9BwQ0ea3PT0/HUciPDI7jQaww5lTSl+/qVuL5BPSnzmIn9C4VAtTH4n773xJgXqxiNuW2IKFf2i7sJmDn0j31MAWhOGFYlv8blgarSi+oyJ+OWXXHO8OAXUlpHK9ZAh2FOrcj9Mg+QuUhMS2q4f7h+F4CJfnulwu5/Pz0/lMJquF1F4JnGT65XMB+08T4uZmvr4IKptB73yOUhnL5qko23Hvy3d7VSC+ZRKvMTmIGl04e5SeePcvtEcLDMWwsYpm5yp5ZgLJpn5i90+eGhSmFP9Nurkak0xHZBYQink8QQPf7Z1gMWjYTHNAc4kufOZdVrG4krdpb+0O8UEiDH+TSzpty/JwuhOFwDXjoj5QfCvZeGH84gnhkshcIkYvMmuuKlLSBCW83hH23IUfKdbLZZ/mSvH1VmkgtIgg0mSqCloGa7bhYwACOpKR+5iHBme3FgIm+/PtRlar493DPdmZuboGR00eKaF/OJ1smitT7+X8/Hw5i8fIRW8kD2oWkmwUt2KeBslSzie1QXC2+3X6qou+F+xRFUMOs131nbBeNO5iQq9dns02s0H95bWtguGtkYTLVe9EcLrr+wO3/rGJwzf7l5aFzA7gCCElkA6A3ZoPjFmZg44LQ0PMVM2dbpAAyxyLHXVMgQOoWjLkCJU6Hkc9jBJbT6HL19v8m19zO188EHpEcN4BgB0wGTWaVOVfaWXQ2piLmiVBHuPZQiQ6kAmnMndGodwsCWaK+Rh5urabqFceE38GXAgqlm6TA2UgcP0T8P5JWQB2384UerHMdw9UKowdvPwfu46Ogpz9QC/zlUTvpwsjZ6lBqQQs1OvG9BgCB4EIDos4gFAqM6rNtTvLY1DoSzsUWLos8doitmrvJYuNoha/I0IqP+OzqQg4N/iriqmBy5d5oUyuacxfa8Hp9ECRuuzRZZ2HapSm0Ti3Dylg34NbuXjC+BuUnHR/1tRjEshPYJ5ie3fPyku8U3/hBBqqCEF5CNi2LE3eZL3e30VeuKLjR4h6MzDJc3C2MZAQ6hveIehOILQCXyFcoQFZpcLpWCBnNDC7eMaVQ4Oi+GzD9k5QgXMbGKRcr9d1Xe7uKCf/7u50f38vBWO5mA4ZnjtK+ZQvLvP1+nymTqvaZ8XoN5Spkq1zlUdS6JhBYb5pWq6GDGjUfnqJTjI9Gp1n4kDt/wXq3bl9PCcPfa99cTXyUiFo1YMu3qLg1L7fKJ1LKEmdln5x/qp/jc+Ic0KJAw7hq3VDPZbeaT6NADvggIvTvKm8lhyjJ1XJMysDcgB/6heuL5/RZf306ROXcRrIJJBqFoT0RUOZUWUpqp3ogqiDL5uPrWGETKy3y2hYqVZ9wECtXmk9G8PQ6IxmaAXLjtPLT2mNSQHmqkBA7tK2l8zIyzI/3FO52Lu7+8fHB7I5Hwg283uU68t94Dgg8nY9X+h/MxPvPDN+FqtVMKIqa4M85lrY3Zcm8JAuaOHbtfMT/6Zz67+roCwPQexwvlz3+XOab/mN5suquLVoKedt4IxckJWfeqyy8RjxEkjPuLXvqO0zAMbm8CT1Oo6NWroj1UaramjSOSpZZXqQzyQLYLNZuCVem2SJZh9Xyg8082bXW2EmMynqj6d+1+tMLbm/et1pjYiylqc1Kqko1u6KXCTEHlwGGDkNytcPNnhLX9XUW4uiZPuRMGiKzeTuoErMwEA8Si9gNY48Sb9Qa9RsThY8p13qZSi2q9v1end/Muqll5SflP+Ox2M3SR7Rut1u5zMpv5QrON/mK/20yNIAZM7FjUQ1Kkg7M2ToW9th6xcQZ4vqvvClnM6PV0tcxwMbblYEkSgBa3tXviEaivN17s2unh3flK9LU2YrXRsetmI9XF3V+sIOFfVx3uBZcozi+MdR9vMb6iUMzf5B45dEyfvYLWjD7a8+tIYlGjChnp4wqW/bx0+fNsbPa4hH2UEcs6kc6GasOREqvUA6xnIw/CDZmorNpBcW1hAvuhsU2OBPzFFsAm7PU4EcshcIOEN3CJ1x10HfDwcqqaHs2/P8pYDK4TBRr6PT3ePjI5VrP91p/SvOM+qpmCSL35m44eVylkwjKfEh9maHEw73/WDIoki8CwVycHxZk3A876fwbqYbIfuOf7OEKK4o7pM+8yeCSUbvWH8dtjg0IuvhWjxjX3IXNnMQuKFTBLAwaqAzsXSUIyYp1/GwdCvVy50q4bzQR1YhRWUC2Fr4+cYSwPCZ8HRiW4CeePijtc3Oltq07LEICLAtaNz/FKWaqOJ8vnSU13rkL1Hso94x3R58tIlulQYCjYaBGh1CsgLo9UY2liewpV8NyATLia5pfGemsdhXQwCx9jAIANxmJLMT01vnMSoT7BCXRCp18R24Yuzd/f3D66++ogRgBtsa1UdFsEUbmW+X8/Vyvl0o1ehG4SwUs4HUm6S+puhYSw4lYCvs3hCbGdd8Frx+tnJ64sfpdtZOFN6pbgks3RXDUiAns2pxrqEgn+uSWCKu5aMuACLcT7bbM+DMFV0xuTxRyUGLAFo2V6pZutBxSod0Xhmj32SkQiDM1dKZ6BRFxqI0xC9IUjQPxbDUg3qdb8vtSi28DxM7JaUVsePdkguWbAPZUwhdX9LYrepeaSWY+wXItm+zD85hgUd3qndLTVrcNtmLOONagKcphK6L3TSAYaCOZNIRRsubiYuB+YcWpTucHh8eHh8eT8cT0TnFY1l9drrzfDtfPj19Oj+fb7fr9UKtkhw2WzIGGq3MJKDbrjxoWmkHCi0GDkotmNt2GnyVnxQwt/Wk4qCWIlMhgufgYivAZKJBqtkNKfFBbQXpVmjA3mygTPlB0pMdCmJQ9KDTxMIcSXAjEvMHSOY9hVrwHpV29XrchjbMS60VqJDQlVjljq8eH89PH+bZmIUvkp/STL0+r1rIyWvdqKceheaz/YbDEzZOa3VajfoBOCAMtUAbUWERzvIPCsyWYNmq6nUAGjzaC/RqZa+sq3NnXwpxEYME55yFzSAvodumwQAH68fl+EbqIzOQG0Iupbut/TLPFKfBtSXv7+9evXr18PhAdb/4solSkfigLJSJ+enTx6dPT7fbdWHtVwrlhClO7SO2YDouz9VnTrRJczPONDekFCuWJdhLxAcWhyQpDUsVSOjLhDlEzwMx6pFIIzEcjcPAQfBnDbyfrF2ulX4JzoC08WVZh35Ri1EsWVogKLYn6i79MQ5cEtS5vquduIz4zNbKQG1A8wdpUUgqszSOw7KI8NUeAunWOW6yIJOIXNAqH9uybNwRc368O1F3EYspwDzEkhOnsn7FJYCi1SagsZMxt0pURgvWNNbOjX9JbmoNScYhTMKSnsQ6DyUtOtEHwjZ9sVwr03RlRKLMSkM2WXkqCcRq8JF9vcfD8ZGblT08PgrOItRNJZ4pz/x2uz5/+vT8RNTLaUbLJqwlAL/3ZbJzo8Nx8zDrAguFUjbURTgmDVxZXdn+3RYgfWYQdrftWawVEKAyufbVYYRrXWC38QQLcKEy7skzbCjabYpC68q8rsO6UhpNq5uFpWvypAwYkfuBT0lyWWVWn/gR6hsFugSuoYyMMYVEDwIGjgE18xjKXVQgqc9eGT/fbtd1nu+++kruiYYO17VLrRdmFxYrxOn2bYk5hGcqcIeZB4aszNkdsAf3pas2LGFz7pPp1mEdtjEzFO1CHjXV0oIXw+CqdCRX1Xlg2U+UtTaOx+Px8auHV6+/+vqbrx8fHs7ny7LQJ1zs+dgP/XqjWvbs6eUkQYq0Vy06/NK2W8lkbhtKQ11oqFSMjDP8QX6lI4i2CDCbtOBebekqlqF5VHZEvMdaJeaSzvTe+1VbUBC+DrBVe27C1uZ46is96WFb13mZp47K/KqvAuWpbYHE1nENUctS18BuXLckiDUJGZyPOCArV1UasaSCFGtoLNPkDgVh+fHNxUELuOsrunKC28J9fciOihZ3XH3v/OQrXgFRcI6YAAQYrfQrjje7u5K0Zz+Hc6+veJFBa7MJc2USSTJmoyEbeanXkNXqMQ5fVElNN4NNJ+jEJZ0JQHGos3gQqOZGP1F8+P3Dw+Pj4+uvXn/zzdfDMJyv17GjKKzj8TgeJq1FOXOBWPb7mr9XAJk3yQlrWrYdSPUwMdX2HMjhK90I0EgAItFC65WKzKAiukvXDZnlXLRxdfseVigrTJUCNMoU5aB28eWmu8TJKAoxq+huvdy3RcTZLWTb17xhx2R6CyvdQBopd6YMgdXO5CnX2kQ1boKVsbHRSFatxdNLzm0abXiAs+Ey2H14WrciYlkKIy5sUCWbKp8h97Km1cWdAc6JASrgvnHvUZzRaGuQ7u3XROHCDc4JmkH0thJ7rF7xvqdAdl41nkELIQYg0EqHnu0ry8YRePQSUwP7BlnTZr/ONFKd+of7+8eHx2+++Wa6e7w9f6SqsdNIFXPuTt04bVQH4fl8ebYSk+j0df8XSGNfRqivaFnJ5AeuTk9Rj3SfXNPZ8Dmm619QKB3LuuWjvOWX6aNGkJ95TjqtQQkVU8otXvafGWJHDw2bo0UuWwijezmlES5pYxHBrE93vOhBqpXtNnmsM4E0GoMqoVp8GyRnQ+meckFA7up7lmOver4UZFrmGyftGvxMg8r+nCTWXdVVdGuVNZKS50Z/5UwKD6CuX/JkB4MRJCQiVDCwxFUMPVWc4vbD+iJiZoOweP6MQIIj6hCsFBEANTWaUAAWpeVrKLyITK6OQh9MIyUJPj48/uA3fuP169fd7fL89DwN493D3eHugb5xu3788OHj+w/X52eGY+w00kSjQg2OISV3G49BL6dsJD8bUZqldVTyK4s1OyBIJ9mWBatd3yxLwiCMBnj+zKuk4qjbnwXR7vRUW7Z7mf+kdCyH8ybIh5ZZXEoSnAXOQykOsVCVGEu5IQDGkRtJqrYZZkojtKHXrl3T43gn5FBTGRftBhgL6k6hlms4cm4cQFDtWIowoJriVK2KAhioi4qJwhgRhjWWTBUxrkZaeGyT5NgAdkJFWi2vewy6F0EYbI9/JV45DNOJ7MHqIVD5yeEtGJgCy6hebUnCNIXQCFytFVzykEU6yXOPGqHGKFzemQpufP3166+/+Xpelzfff9933euvvz4cjvPTp+cnytF/enq6nJ9I9oqo1do/Wv4noIkdKrBAy/ta7E7GnepCwyluL1YcM3QaADrP4E+vaRILIvr0nVJQfmZQBUIuPotqoGVsFqT+7cw9Sej8D6xIZFbIf7Sy7LrQ46yVXRYSy9rJjMOkRiJg0eKctweCDIlTPNQJBVRQ+wgaNOiR43BobrZSbHM6H2YwRIRr/5DqS3GB15m8HWTBOn71mkormn0iA1zVF1CcQzEciEaEwKYIgYIpFlJcj1nta+sLwGKpues2HdgezH39OCNPotCjpq+sr0cm0n/sWGIR7eZsKHFtFMxUrvBdO4YuK7fyJSPV4+PDt99+Owzjm++/f356ev36q3We37x///HDx8vlImbnjY3O4pX21rC6fN4OFpxxqSN7LCAxk6nB4gtrE8AHtA4WLkj8etJKEiYvSAAYXP1qUXShzX72ZQylJfed5xfsqPo6rB3aUPzWpk4rr6BmX0LB3jswjdttU+w61EzmfYSRrFvWUKn4gmyFSSojK8ognxa2mWVlwTEz6gA4bSWgdV0v18vz5UJVXSjC/rrcLsfTgV0xGC9Zl2nJfybxC6l6UGEKLGbxtbLgZyVo+KVQQE20PGxy5ByPI3GwgZ12VrofhVu8NMBAczNVt3X/kt3fmKMYn8kARS25qSe39Pu9O50eXz188+3X94/358t5W9bHx4fnp+fvf/X9snJf39tVyiTZOHW0WY8H9JL1+0wLqmNQMo1wopfoQXY5Si7olIpG33pGEJQAvMRDV+xVW4q2Dna9lZ+B1vsfl/guxSC0YEPbyAdnR9dQFTApTSaFrPW6wO7siRypbigQFljOsHReaCj+I85UE2+rQqhJZhAP67l6cVLwwChSc/V95kjd220mi9jWLbfrSH4LrUHhcNf3ql7YAMzhpNWKBWCxwfSN5FKQxtmktueFT1K/g7dZjlGIMmf6qbMaPU96WVpX14RAFVReKFmINi4tfC1GPc48IA9Q3/fH4+Hu/u7h8fEHP/zht99+S8nAy9wP3bs3b9+9fTeOpANTQRcKc57DURQvXYCi5oq2XYI4F49+sd8GKTOdDQ54doqIhFI6F+IUFjRHC8hSRyiR4/D/f18vfRWHuYOw04Ew4GnwTx2n6hZMDzXDevqo0hukQipE2OEIaNW5xIuWMlJqjdUD0hAgDAXT03AygLCfahWn0P318fHxw5vvlpmFsBnG9+x96D0Q0pLUtp6De8lPsqyn6TAOFMlLafJmr1frkcuMrIOkm5qV28L1FaKgpyQBYoWrEnmXAZ9i2A63QLzfbHFWN7sHQGhjAP21sQTKHZ3aaRGtOo7Bcq4Z21OtIl6T4+HQc53n0/H01Vdf/Ue//aP/+D/9j79+/ZqaJ2zbd+/fv/3uTdf3v/Fb3x6Ox7dv3n66fVjmm6SCx+gdEJe0Y6Yrz5ZC7M9Yn7JKIPEUTCkej5J32FQy832U0gAXo/olp0xk8bFX8eaLcHLxguCkUAfLERY8uPWYOnCkCD7JrqhgSo3u9jgnK+OQPs5eHR1SGFbhAaXGo5/FG5YmofXcxnGTX7K9vR5c6KEmE+Z5FqTAvu6l77bT8ShhTUUhEPue/2M5hWCWkXMoxhovmqE6BhCxhQVSbfqO2FxHWUT+OD834FnegPgjB8vxeqywnoxSY9KEDzUigBLC/62U30udXBeSiOM4cmn1TvoXSbHY4+H0wx/+8D/9T/6T17/xW5RkP3XLOn/6+Knvhx/9zm//zt/77cv5+RPpwNdluUlD8LryISBn2BBbOy6+LYNltzZX2iQ/MJycwszTPtX5eTuU1xbnDSUzM+t4s3n2v0Rg1/pN66vVQJCd7NTodG6pslk1LqevrSZsPyBImxJInJobFwSqv4FmZPDODajymxtxZRh2Hxq/qNcsiMiIBWVsQ5ELQIk76kTIB4fzIaQ55jr1/d3hSOVglbuYz8fwFcSbwGkE85XQKnnDKfSFIKWagJiohWwkmZGO50JOICr7yB2Euahkq9Lopj8sRhpqdNkVprvozoFXQQ0LEcOut8tIhNMMeo5+5XiM0zdff/vq1VdMvbSed3f3v/mj33r9gx/YA2+//tV3823+6pvXr7953R1OC0VOUncFDR3RcIFQncoTGZY9aSKtyYOKSihtgu3lPenAFuoDhztLB1BLCpvyZyUkcs26dczOC+/ZJOYXvArlVVVdHP83MS1A+y89tcQK1hQtXaJoFTRemxehHiq5zkFBEamQYYkUFwVQiGjBxpAkrlXO8eBGM1HT6ZN4BfGNWKB/iX+khZf8X8rxUeYs20jHoSf5TfQ1cwww599w8wc/8SoDvM5VCsUKMSLClqQZv1iHpGik2+1G36DeX3Q2uWYw+2rI/kQNbjgYY+s5Ax4CxxLKMS3ZhVcApga3do4H+DHiQi0fWhzgXJaZAPOBOieQX+r+8eH/9v/4v3/9+tvn50+X66Xru4f7h6++eiX3OX96/zf/4a/fvntzmI7jcXr/4d3Hjx/efP/mV7/422WepwOVkeTucMxKCyALp1H7VajhnC4RZuFWDlGsKBKrgabSQc27XV7XePtLLwDsXjxMP298vXTqJp3IL9fd2Hl2Mzys0DEbIjqi0Sp9NPqZOpBMJyT8O0JtJBXdERMSlj+3UALdRuWZoaWnZvTmn0xphcGIpMUQ9AhFLiPWVP2WOaRVJLF0X5flQNovtcgjdYsyWknDtOzPpMLFA0z5kmBIc5RKiUjCovO8cJ9qtuHeLt3aUWzXwEiZRBOhhp7eGj3oaF5v3D1YOraZQ26LDfWqOegZC4+bKbY1ZDR2G5fTDw6SZF5D3UNHAsmEl6nfRdddLs/vPrzru+Fyo9QOUiv6/nq5vvnuV58+fvr1L3/59OmJYqLvp/Pz+e33bz68f3/+9LSs84EVZm8AF5AJhaZoTUrYEW2iJKnXat8BqsiRA8JBzJYmCLj9CzRRvL5E4O7caI+q9UPjMS88wUBee1RN9P7isNxfGoSWRirLZqpF+74ChNhAjdI1RmRzGnqqlhZaNdc71AsG6vsigb70DgcJulSGrhFMDpqGFGwFq1UpaxeS2EjeapQAicFtIuQm55ljqw/HjktJGGPVYJXA4mEiM6BrOhzrvZt4U25UN4ZS2JfrZRr640RxxdLxiwwz3ByJhbC4Wxm3cCtNCvofVv6YtchOd0/M2nKuAccrmgz6BFRjWiJqKfRDLGsUecb9tLuNbdqceXKb522lNgvz7fbv/u2/e3x8dThMp+Ppm29eEwFfL9fr5f3799fLhe3SJ+ZP57dv3j59/NQPVACAmJ8jYQ3EdgtmhRW96UM+JXq0rRVeK5ADv6O/o2B76Yyns1qd7hcJBVUWlG019o4eBA18lEa5L4EzttW7Zmdpvme+fcXG4Jal+mH3hoZXpumZRTT1fLRm4yI8mB9rwxxupbUKASlBSzMwpR4TsUFQ+lRLulfVmQQs0STHLRABU68tSm0dxv4wcbcfVlbvTse1296+fXc5U9DR4UjprBpClcJfA7AmWaDVC6TGOkVS3663y5VqT8zXa7csD9PhdGS5fpikSjrrGES6qeA0c59RU39YwaDpqCXIYxC974WX/gfrmp0X9WMBNbvT24NupCQsy14K56L8ovF6my9XStklm3M/cArRvHTbreuePn1kd2BH5azWWc7Qum6kHMy3Tx/p04lLZ5nUVbYK5KygtyJk5UEqkpEGTAGTwu755Jb/GomnO2eTtb0ZONJ5s+1CSQflPUHzTvUWfBfjErAPVwRX8rFqrLtcJBcFKa/V8eAhrZ/rAYb5IbzGsmkd+Wa0M73HDyZPEK8KH0EmUN25Yen67bb0nIY69pTB2K1j3683Mq7I9hNxw8prQo5yCpmNMRGGCIJEp2E4TMOR5CCZx8VCzhcPrx4e5m3p+uHTpydJQkodeK3tdkG0YYXWel+Mm6nE+UykO9+2+XboNortP55I6o4UccJwgfE5Hg+ZF/VqEI1xFLOOnoJeutwRVLF24Oqd0ph0W90IAWvZw7URhkS28tViGpeWdhI1TX6jYThQDkR/Oh0e7k53lFFIy7Uuy/n5SRK2ZOXYLHcRbWGmTMJe7NjjNjDAUAqWHAs76A4GolqDy2ZV2VBNkgIgDKFbR3/3jJdXF1atl21LtYO2fHATCiR1y9iDE7g77vYf9wUzU97XWo2KJWD8c8EkTIfUqGA08fOWMQqkYqx6kBxym0U3RIakBBKdddtAuSyXy6c333/oltvDw8OBoxuO07h0d72af0qcID4VDgnju+QUCjKGD+NhJNK9Ox7GsSeERzk63k+McN+pP90/PJMYGigLJ6KTkorvz5Sxi5A0OMpnl13KZLZab/O4dVQsmVrRH3tVqsm4J6fZk/HFlicV/1SZYOFMMNuCIXpRNyg1imlwocBP6YDDE+CXWqRU3HIJMlJutfyy2tTo67QAHAIiMdOUNkj+AmKE0zSeeqqA1XUddRKUu7LM1xRR1aZp3JoqKNWtaAoDQx5mFDp249hqPVArtB5XsVBjeECQcXlCpSKHW0aQvrDWQ3lcTSn0P17sj7b3qgRV/qPNFpCG4wylcKYd9RNEXfX6nCa8YytLVzTuq6ngHPxMZhnJKNQID2C7dK6IYLzIsn2L1N2uPw3X7vl3f/8f/4u/+PPnd2/JJrquD9/+8L/4z/+zH/3O37s/nSYwB6DeTzVf2O7CY7EWm1pPpr87TXcUvUs1YFjUaBMTHXhH/Rj562wUniafTGEEdNOYMFizPHt/BE18JaX3cu7WldvhHkfqRk9tgSTtUcZG4SIQ1K/ldqU1mA6b/0dVchy4a/WCBZohULEKjpEi6zHr2Kr8S1yE19/mnvH9trG5TDotcL1+7bpABnBLVBWpqVorswBFUA5+dTUgtllmJmyUqzi7Fqx6kTqFbDVR1oJ2jLgOshv5U0wnLAgmSk/V0rI4wP2XEUM8oKT3+uz/nfBAjoqKdtNJSw51qOlbemm8e9B7Z6b2CNNISVBMHJbMrDqn1nuAS9hPlSLoSM/L2J+Gdf3VLz/96//uf/z1r7/run4hOfP83S+efv327W/9Rz/quiN1M5PoK6Rkmfw4UDj0topoVatV1z/cnR4eTiP5pKnFHuQKQ60/PmXS8lPM2VbQZu16Kwfls3C9nUt/eXNAVn4pani+nPt1pkLJ9/fDYeqGae25ZohBXGowoomGlnKominlabJVr99Gwg5mt90kadKa2m7UMoKr+Y0HK9fMoazixXWzkXRUGMdhWyfr2Sl0GKYjITruQQdNmE1nJRk73KhFlhTHTpm6iuhpiGxQV8HOj+S4D+HMwQ8CAeKpK/5yUlZ0rYeGAzlAzAJnrUPuyry2IgKzTQ0ZZvoRd7btTcES/zA3Y33b4hF+1FzOONJOiZH+iBQCVfp6WqaAfRrfp310h1IbdbKEDGTj4CNXDIxHrzDP7TECwudh6g7LX//1L/7+//Mf/OIXfzNfF4m4pmJX3PZHkl19BoYszeSxbYeezyhLRJYndCYf7o731FFaTlO4h+0G5IlVLaDb5LK1I4vOwlGQHJqF6xD+GY9nkvgHCYEgY87l0q3L3fFwf38/HY5UU9pLwVjMaUQGC/y234nk2H/kFqDMgjeqb8U1pTquVtV3/fFIqUiC3t22jAtDSoTUizbeoVYMWwcp4OGkIXo9O1+lPNhyvd0oXWSUwszpaMsNtQwI/+CsZAY6vJg8GyZuN1OlFbWoT09qzGeRxb6qyhKJVYiSWjimKKHi+MrEgUa2v6soxXF/9luY3l5lsBjphNlKPaB2meYqAFYt5h15F/ibfgj0nlTx5jBZRqwb1e8/nk7kV2S3BKohYTy0PDF6sSa4LetwON7W67tfXX78j//wf/xf/s3z9UxBfMIA+o4A4+1yR2Roqmfk8Wmk1NatXz28etP3bEqhGlkDFTQeDgcOBTOm4UuZ+jqbdXYceioiy6duVmAgiwi7BoLCZQT3KCAiul0vy/VynKYjWa2mlXxCTA+Sc8GKrohQ1f6sHIavL8d1iLrrRlnrpa3gmry1J+pXQqsgKoGGtPTdNFBNm/C8O7uwalZuFpLkUOnbDaZrFSkG4jiW+3qldNzpMHSc2h3whJE5N02WuGwCBNwRWPh4GCZyPHISJUllQ3W17IdSpxMmdOe1EuGA297tEn1xqEH+lsfdiQLsPTUvqmgDt7bxQJ+100Z1h1J9LZ+jhF+PtzTeRNeCcggi18ZhvLu/H8fp6flpYSho/9O9UPYXxc6Vu8zTYZnnT++f/ukf/OGf/+VfXM5niYbdOnKEdlQYresWKSOOWo8/ncPg2S5KR4ZKWRGNnA7DkUMSqKCihVPFxgpycXoUCTyNp+NxHonwxNoSbK80rNgd+Igu63a53VbiM+dD3z88Pk7TgfzLWg1GRJx8mQxIYIKWdl3h+hLcyX9pLTE9G72eZUmm7/uDLKJ1DCTl3a3iJFQjgEDcTeTwZvINACefcekysGOLwUmbT6ndejmfucL7QcxQ2MuGRkd6iVjCpB8wWa2tX4YroVXmnskZCCRM1hvnNXLlVDhGsjkKXKNozQIBl/atJlK4Co3gMQcfEoYJxWDKr+QP9/y2EUxQDVB/ReWtWL3qGeUj0t8NVmZso6eso+PxbhonCjq6zo7qQQb7iEMGrLe5O5xuy217mv/Fn//5z//8Tz9dLyPHfjBf53Z4Gi0VufoFjBONdFkpT3U6HubLtSPqHe/v6BSxgynoQ4ebtV89qyvVcyIbnEA+LmssEiRPGAwSbLYRmHi+Xq/PT9O6fvXq4XA4LOwgNXyoMl+gpmiFDglYs1SdU4YZXtIiTrXXCjjTOK5aa8bDvqWEUQStBmvnX8mAFZpc3pIUjM5/KvOgFxnBuKLG8/Y0U5AWhVgGa05mKQlSEfVXK6XYksMvpV9aNsOBeylZ7R9qfGyePZhHgssKioojXnp4jaAMtTQehpDrBewZFiCsPrtDr/GwPeW8ILgvhPTpqYWPLo9WAxhgcBa1QXWAD4eJ9KXrlaqm6jIE3Izdsjx9CgCggnHLern+sz/9+Y9/8gdPn54lPEoIg9m/Stjb7UZBjtwRIAU5O7fmIU3DRDEJXXf/cCBGYIHLGpKnooIPDNRSd0fu0PfHw4FkjdSprUBNUJGcFrLn0pE9Hqnyzm2eH+/ujnf32zCsFBzBtln2rmqPXflHyqyHRAqLbNBuQJYtp6f2xgfEZyvRI1zBzvwxCvHC1RJZeqFsmyDxAvG65wqJ5TaaQqnZ+LP4+9gdzfYxNE45+Wp50JBmfrpCG4t5WUyMxKMA68Fzx0FyPcVCa943anbp7Ivj4AUtuXi3cOSENKziKOq7VJC3Bve4Av5BgamB+ez4lOyqvYYIwS8q51l5UQInETNBPZy5/zpVY+XgjYaoNucdHWmJtZooy7Nf1z/7s3/2Rz/740/nK5URXmYPx5LDJdVMKVKXHZggjxJj18NJ+HK4OxF23sjyxXmtQi09hQiZ1izH3RQ9VRSJvI8UxMtjlITbInwXV9z40zhyd6+70/lpPBwJBzhgjyMuxGX/Wl0OjfBO7NapF7h1j0JEzdJ+ZDxLy2NozJoRbuYI60hHyT+y7TWvkhmgSM7bMywcMhAQzE0DRKWjit2xSCSMTCLXuRFjBIAvJBhZGkkHjkRh0G3gZMrpDLmf7By7RxvXw1cY75joogL4+nabTeDM495l9j3qtcV98Kr9RzgDhCC2Sv6a0JBP9Yix03S6o444IxVtYJqp7m/ypdt68mewPL4tx0O/Xoaf/PTnP/mjn75//56+tq5sDtEiqFokj7GzBOtqrBVxAMCxgavJ4ns8UPCQhgrK0DXXpSeV2upM5EkqYyJ0Kp5ULjClVrTQ1Kp1Na55OBxef/XV5emJEolk/OJYcVeo/V9YhelQqsZZ0R6lXvCK5rZn/FJuYg1GQrbSh5RSLJcJ+4OLNNoNMZfX/9BIchsv1/iQaBMmTSvVIx4pMU6Z00gL2ZIf3gCEW6K0eHyVGAnQ12jYzqDNEFvkqaSWEABDJLmnby1r0gNDl9SPsH6pGUQrCgdNA3a/rOfmvPYLcoPBq50/B7W6ENgISkKdANmlIAoOpV5ckLAvpkM+8hmQweJAmd/c65k2enWrT9xNeZhWkKAH3sb+YRj/w69//Rf/w3///bt30zBsM6XaiRdW65Qb3iOnDqthEmoZxvLE1VWlFB3tqikQ6iPSIXGkPgcksoZdrrA+i6hcK124ggMcV3c7qEBGMHH+HQc5q0M1Bfx6jLIwSX+ksU35EYchRbnHgcea0sWueUSE4gsHFgr43agUj3ZbqpOf8A62AWjwI2siFCEjuyO0Kno8+3tlU0mPCMUZGxe5+zAWEWSdURAGzZivWg+O+a4pmSEfbCeXGpHWQjZJ975BqLW1SG6WaCcYAfADP4jtV8CVghMUBF+jAueCenUteRIjiYKRlS0woo6Nm04T9bya2N4oMTkaTeSKoq+l7YB4A8lePR2G/vzLX7753d/9f/2f//v/QhF5HJ3rxSxd9nLNPI5DkNYhvE5C2zJEf4yCUfaOSiKuzh5cLJKZypVuKWiTpBWcaZnX6XSijFil3pbUtU31FSI3L8V4cvSwKrzSIlmzk0C189hBk8ti8bXC646m8Rfc5c34gDByVaolfBRtYIxG3DzgotWVbJwWxnT4SfOvpMMrNjiJ+zBiNhIuam/EwXKht/tSGtbMIwmRsU/UXUEhBrtA2N2g4PTE5wXSqmgeVYrWuJU9uxxqivs9TJ7ODB5YeKDGFpu9s6EIS5iZKwbNZzmSCL7vvkGXMXo8JG72QDkBFH/Ybz2l0Wrr35AKgQNVOeNo266/bNt8e377/bvf/71/+K/+5/+JSJRJXDMgNEE1agj3lCij5Y7ZO2LhROobtVNLAQl06I7DtFILDyllRaFSFrbNrhHO0KP4aPHEwgLIGEQBFirLICTZMlHXQCZtIdisKlItDkpvALMyREhaBiS9u0rkfyGA3c4WsioEku4SF3B3EBbhEiI5WdngCRubcyUcSFfH5miA2KgRfOZcjZeTupGbiHzzcKH4jVPbFDbGK/xXjRU3HCESWG3zNua2+ljovoXztCm564FlxlkoXBnb+puOM8oZhtsTUHzgsCxufSyA+iD9oPVCxianzxUrKwvF2W/caoMLJFH0MABB0bJkwcEsI38MHeXujeTN7W/L9enj0x/++Cf/6t/869vtNozjMi8seVkLdlHOyi+ltrPdSomZ4pAM4YegN4TBbWmHfhOjrJloIiWcrhEHLBVxDIO4V8RRG9t4kLMatOIhNdXqcVC1uo0PUVedQ4/Xbb7dKKpfaJVJY6OmNFy+jsiK+aFScuLO0VBFRtKZ8dVUC79o7PttzB3J2GxryjB0ygtPvp19qy4bsq4kUlDJK6jurB1r1SsFm9Awtb88znVIAW4lpKQHr9JsJIfb4bcxJpdt3f1nTUS1RE2fgZQWKBmKEyYUVk9JMc2VNAXWnyi1cgWH40aPQ9CzKe7FzYM5RHSftBcUxecwHqaDhNiwpqq+fI3F10D8fFOuDEyUv9zmy7bO2/V8vv78Z3/6z//qL87Pn6jt6LIp9VpUsBOwImEJ9uDqhxz56AnBOh1neuu2HU+H5fIsYECllcZAWU6y5CECZcA2sP2GUg6n2YI8wdpR77CeJAuspkA0lZDqw90oGZhyj8ksRoJKand1VE6AVpGDQy2uoxQBcQqMglYNo1K6MaWBZwYN5Px48H+KbN17BxNIneaSN8tFdYypEineidjUGfBSpTj8tsjQTxrAWvGVs067NqpSlmS1c/taDsPgfbZpOO27gHgrTdS+4GH3V4OwvhfbitozoKgSEahTGSAzLEswQ639bd54tMoIN9YjZE13yLNAdhoy0iiCVDZuPa9UKoZqqPek07t2c9f1Y7detp/95Kc//eOff/j44e7uuNzIai2LwvYvLm7DQX9aAI4ImMSVlC+13gsey4Y8mHHj2F+kKZ+fJcUSaoNVb4sOny1ZBirVfsMZ9qqXSucn1DpaokOJnY2367yAvUYq9YwU96AVYGkRpdwUZwvqvaDVo/lvAU+LHNtEYyclguSxFFIOASnXWiPI2HjH1I6G3bAO+TvQGym+hsceJ+2AXZMvWAdXuWtpjKLscKwsMIlSZiYaLoyFSYdlZzpxx0JCKLPAGPk80BZiL1TR/EGaZcUCarEco3BXPa++cSCAeDp4iCFMo4vqNxm8B4ROs04RAmHHl881Arez6FYmYA1WTxFWSiGaMxhZYu6ME1Vq7ucDxyb/+Cd/+LN/9mefnj8ejkdKp9GcPrVLueC1Aqz0k5LgKLuYVV9vz87nx7N+Xbf3ZmKW36PnLAISFUIrVJTERo5WFkRKYcbTNM63OL2ewuqeRdWBUX5rTDE6Zul9caeSuiH/l472bKw2Pkr/Skkdl3gaYKWQQW3IvUSNuPXTApszBWN1Grt7yIhgClAUxn/Dw5uPjVE7RFuo04c3SJ/JwJl/Mi8m3ccqqoRL1mKXQkLbIxxZxOmMgVFZEjZiJRD68muXUKF26q7QzU83AfvStar1FxkXZqG2jA0onpwAsQ0GvpgeUYR6l/OEUgLeC6XvKTCdUkzFKyimXmPUzu4E9OpcXTTqMjHJsFzrlv4f/pN//E9/8kfvPr6nSMOFbFeSZkroV4qwcmMfpWBrRkKwUzu22CFa20xTRsZwgBZsIfMvCwRrLqZR+4aWKRqRjdTq/rBFJjcUNwcO3qCz8wANhJWKjrgb/XyAI8NJUYxfaA0P9Bs3y45wJdHk+K5WrIvyhE2+oWjafPc0mos6d6v/JY8l9M6d2PV85nboIfVZc/pz4WsbTeUFpDiHKiQKpGUiFITu/C7QIYwro0d7D2SpHil6gLqRKmRehQDvUHhxyQuf1ouV5R+Ir/LrVUAltLmyYkiGHMHEn21k7fDKMOihkzEYvUX2kque+2oyYg5bpPf4thtKwzKvXKHzkNBepnmi3qUbh+mwbX/9i+//6l/+qw+fPo7jJG1WND1H086lFo2UczT65Uh6qbfu2qbhdx+/ChVfZ72BoXqP5UpmD0pUZyogKiZDLb9J82TVVMRjZtJyTEpaUYoSwT8dDtuNIrFdVFABATIaTKR+TES9khRplcPUXihP5wRMzuCzWruo5fcWgcW+MP22S+DSxJRKNbXYORw9l3mRXO5SV7UiHa9GQnHMspk62dpONEz1dMTUYKTPuclyftQ/aCnHKo7g8AcgwJeBPf429UaSS3MnY/yeh2eF29nEUnlj5B070hWYSF6/liag6LiI/wrCqzlI1scAUBfbhXsYHnOlBAuEkyROKuatmdiei4odGeC0YEi6iwqze3FBRfr6bZgu6/lXf/ur3/sHv/+LX/7NdBwXah4sJmumXpe9qvVKbK/axLR3mgcESokJxHe+kgL7F6mZbomqyrHi2EpxWRa/9BC3tUpeo3wkRTtcF1Vr7T6Ldno+HKbr9Swd5WWTpTgJKyIEZKTMPUhK3S2DuvoIr4zjhmJ5ofvHm7p7nQvb/2KcKQ6Ev2c/3M7Hl3k/Fqd71VbMK+Z5AjnMUZVeXntSfQVH23G2p0ldMZiF96uyRIY8aj/M4rfnjwlCl2pt82ul16rauSz1AtzUdwqZ2rpJUy6bLofpV/m+yrCVSUJUT5LtpTIsX1L/Y/jgOXtk4uPFFZs04d5QmJzpWJV6TOYAsagFJeN5m7t+OlyvT8/vLn/8kz/5n//Xf3ddrgz6pH4S66buMWLSTS3cRQKzN0ZesTxKMWEA9CBZsiFFT090JUKGrDxU9OphIG3NUIlM7UQlKQeuyIcd7dzgF0cIATnX5ZmkwZsaodUoptUnPZtBWK+dQPpNo9ciglidfmpbNAm8uXVRIZHrvBqwiOfWgWs4IVC9BHcyyGTXo5Aj2Cn2tg5BniHmKF5lWRdWiM1AahqVxdFIA7eIaIYnFi5v/IstkGu/dFPY0h3If5E+/MJFOx8hM2x+NUEYE3PC6oBmalOy/w2CJ97LCgUgQItW5ZJQGgDn0eqQmyoVTO2PiMoAvU5/Roi8WV8p95WMxgpzh3k89Ms6X5af/7Of/8W//MvL5TL0XCjLmgN5rBVFO5jVOehXk1SlxIZatFyRMh4TlcOZNKjGmrS0hIEiYORUdeYOS09K5MKphuSpBZjI2eiDxXLVLzhavm4URD3ebjNFXHNlN9tHKUxH5if+qsgS4UBOwmxZljhvOfYOgDGuY7MnabIXfU0wROy5FcYuQFx58ox6U1Qm6GYek6yHAsWEHnqooM/XijWLt4mOVILjdhxDdEfF8ej7bgC9sjypJF8nsz3ltikts4+tyOfoe//zsEW1bGEN2Wr76oZyhSxxUhwepO0P/p+wLTlvtcMNo+LIL5FMH/fzpbEhm8YlyaW3TO9VlhMfkfGIzP3rMPXHj+en9br+8z//0z/5s59/fD5PfU9lVlmQ5jpwVos1ka85oVU+alq5cmuoPu26GeMHqv9i8QNsDBWXCyISPuGcoiSxXgT5NhHDNAmynJLmLz4eJwTfaN+ixo73E7cyd4c2e4C5sg9FGpv12yRjotOMW4Eiy/PSO0ATNxg5vQzGv9CO0SSxJc9rj5mgX76fLRF0sw9lE3JZwC6rNXU5jcLAEukfaluBfsqiUCMNN9bTwp7l6RZhqO2TJ5B1wA4ScjXuIJEk5e41l6S9XLGQO5ZAHa5Zlr13CPTYBNXH5orQCWwWmoGpjQooo06pljwWXhywHg5iqmT+NtwEOKdwH6kNhSs/zOp3pcJJJBamw3ldbuv853/+z//JT/7w/fsP3TjcuOKMHTO6nss4Wdd3Vnqdfj1P3aJJpKY5RSvwgTA3l2FL7oK5zfMT8QjDtXHcASG6KV0yDdm/RNQbSku/Ca+DXdLOW1qEAusc2T3Fwj1NB0180hJztBFqn+V4GAfJlg9oB1vvYuKvaAhlzGczgKsyKeri2GAQFhf3wN+ylTtfpEHJNriUTyRWNsVAuKbCHFgKM46WNKaQQGYi015MLkCBQaYD6beMSEALpUwi3X2zRkl6xtt8TA0TGFzx+Rc/BkR8DZ71s8qXHpf4r3YALSjeag5FehcHlqu4BajZHBjaDmJuakNMXwJO51xcvcWK+9nDSil4t22Z12s/TP/9v/rv/uhnP/v49DQdJm/uTmTDYRYMt9noLOHCDpwT4Rp6IulNyq2J/0qB4EgUid+Ik2ih0iZ3Y17SuE8FuxRbhFMhKXKuMKc4PGEctv1+R9PsJBleWygsbI13G58UiHWnMsQ5eaXDbKRx3TTJrM60YZ9jTBn8qHjc9sQyf+Z3jXj3gAZWusTbgiPVa2UDtUdLVxraVyJj9q5bJU5Fy/YYg+WZx8Ctbb6KrjUecNNeaRDs5uAUGGJ9zgMu5l9fpOBS3plxqOqjHZ8X93TTkW+3Rg547QNBxGoe0bLeIWylUAw8qNIVzFimnLaaNjBzLcImG+rEJfmnolJu3UIJPvPpbngYHv7kp3/2hz/+yZvvvlu5iLHWLdf8BM1SMK2XfuoHkdtqDM0Clqn7NqVCeHtB232rCjX0HdXAQNYjFcjzOXaHOqNozpWg48YCc6Qvc3EvUj3gYOnXWVKr+PajhSu6ddvxeFjnWWugs31do1BsRd1ri1IgxEKoo5l6Nz3GavN3PgIxaZFfGMknDRXYbHrBH+yTgJ+hBVvdO+B/IOy8zbB5b6R8UWjCpAvbc+H70PAoHUkfuO0qve9RBtobKdlq8bfMrBoa8EuMDF76zUSbLlrjXUD7/oFLuggih+wzK79kpX5NvlpOiN5J8+6CyMIC0sL8qYVsxv5JgJvBws+JigEmO3K93K6X69rf96dffvfxn//VX/7Nr35FcfbEkKlhNEdHOgEn47N2/tAy8EZmVohRRna5XKlWWz+s3RJ6vh9CHqR0HdD2DJotEMGrmdfzmeQwES/HPGzq45FatlKMwh1JylTIq2XKS8BDvWnXrcfD4Upx29xzvOO+4QPF8B4szxJ9yQXYUoyM1JuFap/OoE4rLgvazXGmFQtTLcORd/I0gfoY7lbXjG2qxs3N/m2MnmNy+l5KzYtFuoTL+gRH5QWbdXJ0HsEXrCtVcOG4OTJv4uVyT+RYUbDDD1CsvMu1RiHnWK9YZd81FBtpB6Epc+SCeHV7sxVrhWyt8lK4752t4ggiNyFdmNBYMHK/Eltl6eL7ClhldrA+0JAOpAcfT8d1u/3yu1/8/f/6d/+3/++/pRTZ6WDNC4JgJTQKXUYueiEWPvirKA1Sx9wEghE5zrDvltvNtyfyyakMvNtSgDyEdkkKbwuJCpVu7OiiUTmWsQEpgHOB4CY094UqTXBx7HmmqvTzvEhtvAhWAx0Oc8fQdNzQT/llQhyPOp585WoJJmLMpFJvHBWPEOJ/7DSGjPcoH7Q3g3ISzjV76Up0RMOUyi0Xsh3ejpqLqgz2c/Ckj0je5azISeoNii5WLNDnfEpglgt3WPM6oZJ8huLaHAITMTZKrVoeO3CxULLGSNlAvJdVbEtR6ENrrpg62780zdDFgHdjYGW6d/ge+VZCjzdq8jEv6/Xjh09/9ON/+q//7b99Pp8lKIp9eGJqFqwM4tf+CtJVCYSrq4+WAIxIT5B3LU5Nouip7I7PyNNRK1mEoJT7kTHGFXkhnYLE++P57baJ8W1QxZS4uQA61+UYKSKJi+NdRM1eFkk2EoNjItuIdNLTgWQVgNoEXvGCALgktbNNCz7xvo5uTEhCr2DuhYxMxyiKeobjImSKtAvvukUkj3rZY6yAPipRmucDuig56sjPvpKqE0177OEqcpOCm046EmI2XVcIAKOVlCm6mQLuoNQqdUpMvlpFKDMvq8VKq7wlXlWkm6ctjmrueR2g+nvgelcgAKqlJtvpJCgMoRrD0nxhXY/H+9tt+fTx/Kd/8vN/8Vd/9eb9+4mcwDfKqic3rxIwKsDZauXlUR3e2EnkYdHHFOM5SXaC4DYjJG1ixPdbBkpj52hptSgriIvsUgCl9IMkLUMD2Q2alsNjpRr7otiGQxB5tpOFglLc4IEaiB62jlpDTOMoRCAtORFDQj0nGFeQLCBeZ9H5FV9TMywe58CTgI7N+ed8Eu6clAFE90DUKdTScHZEWwqflZNDJndqHiP8sMh1BlqLQ4wqfAPU8rOJgBfKB2fYpImlOshIrkq3R9btlVmAJ8Sg/IctrtxDdXZvLa//SXAd12CRdLYA++GXB7222r4dOznsBfjsWmABmZMTssdXGgdJswpfEh3aZaFY+m7rl61frs/LNv/FX/zVn/7lX7758J7jEKWUJHcL8gBJaQ1gv0FLIHXUgNHN5Q6LUuFwhHdd/AcZckg96UbSYF5CIvxgakaFbbOvph9uNw6JC5OwGo9KtBZ9QrpfSF9BIMqdmFGQJY8K99H5HgYJbxY7H13BBm9jL6hQmcURGESWCfwK6AtgJOgdmXBihOkzVKqBTcBpNruUVt41QjNY49cFIeO2qRVs4XW8MdFbie3kWg9w5/tjtumwZMkaqYShKCRyErA9glATK2ImAPjh2EzSDWRyvg2cAq0hZYBvOxpu0E8ulCT2Ym5oqdpthF803Dz5HWMhMbI96gWHMX7VNqX1hJ1budJr10jpceOy68o9Kams1NaP1JTgD/7Jj//4j3/26++/G6dBEt2o52QouWoqcgrWeOjkMgIxLw8X/zQ/mEM9aTjSw5YEKwdmqb7RDZen97fbjXt3myKjnYEtNlEDa8VzqrutbSKUxBhPEzegv4klALxFQ4xjfs97NCcZV0DlivTUVcJjIxRqaJk92YBkUA26hAVAYd9lnl39DmSZ6CneSlfU93FJDP3MLWXc1EJnZG67iiw2Ldsic2bgQzTcb9RXiV5HbRrn9SdDXqThgMFV3xN4RtlIFEBoEmYUU4rUUhQ8KF5EV2xCfXdaVVwV0kmFrReFYIqVSn2m1XoiHrRUDAJq0A+ar/OfAXyL7Wl6kGNB4H67gjsmKlfaqSjL3Mupl626LrdpmIbh7qf/9Mc/+/mffvj4gSrCrettuXKjTQLOgZK1qW2DeIN6HbNrQmxUiuGIlJHr0Q2cJwjxZ3yq1pmwlcwx1kPgjzl105ob8AwF3MzS3UqRWUb0Fr4LAtB7iPmZgdXhcEk3Q1KE/8bObuJnVJajwG5pX/L2BDJN/lc3d4U3r9hWk5YwqFIgBwCHd408Y9/5P69kb9JMnEoR2KhH32Es2On7jZtkEflNVHDbg1jIGlVaZFIgEYZbSHQbd0YX4w7bxQifR5AqcXTmPNoKlRO61P2FnSztUXZ3K7lv/1rPKrsexSGGK1d7Ff/qIqTUK9T5EyX6xjRwcvGeQZHy0lgoX32/Z2gDck5lWcaJOrh3h/XV8dV/+7u/++Mf/6N3795Oh+Ntvs3UmX6W8Eh37VrKEcRJAt24hQU3zkQXmwA67gYk1dbZFRwR/nJotvU6nynAmaWnrrZ22JPkPRYNrraBgAkM7S8twsPDs9gr4zI2eneIKbOhB2OapjT1st4G3KSTXSsS/OiZeWiwy5In7eIm1zZEazpLQY1g307XZilcEL+Le/P7yq0g4kKTItyRC7ZOb/Hk9nYtmcB2ew4UojQvj+RlPcKgBVq0Nf0pDi0PZfXmZsADmQs4JuY4AUpCFioPu3/+kulmftjDGuRJzaXWhfIWmWMoI5bDUVLRF72M7Hf0XV8JD19t3sPOtA0yBi7UK07RgWlkXcfT/VfD9jf//m9/9rM/fvf2zTBNN+prfVlmLmRlGfkINq1BnqgsiYhL6RgxN5x1THx21KwUrTqqXEUolMX7zEYoxE8qSxgMB5VErCXxbG58pvuq/mDySLEnJDzovBJeqjqN204YACaNinOwTfFm82283fqOcoYZoxc+XYP1eccSp97wgvLf5nmIO/kJCKOYm7FKzO0kqn+6kqujcdnrYZXF+1oITchG8jSUpQmZGXNx/dc9Sy7yw81iaU/U4FuhiGXFy8fGll2AO1Fmg1KIvzI+0fO/QuT6UnvIVwoaaSx+BPMh0LZbwGX2DuKOjA3A2hCgu9aLPQg79sZsRsq23H/EQpO6tHMJU2qrsPXjcv3bX3z3X/2X/9W///f/pxSjQM+utW7XA6wmaKNe8Qx79nAM3QPQoYOh+FfHiYKKORbKDp5MUYrisApk8lvFgDJdDXE0ZdqKFam5w6M31VtND5EgXnFc5XOtSM2AP9qDYkO1KaowO/FULQqiOa+ByqSnqgrhBLSfoEVZc+/eAG6YutCHBPEcqYVOKSvgJ+LwpL4Ybwzu7WOxbH5F0xHraEc+kIIqrmQloZpF1N2K1GDuoO7Pkrj28Ft70AysuYXmdeRD0qKETpBu0Qjrs5igIjYJadgAJVKEuiHtvULtTLRov2SnTfHaE6HJLVBfDywmo2qAVHab2D9g5ObGA8Ckn2h9xoFq46/r3FPbodvH7y6/97u/9xf/8q8+PT9z6xNKA7LUooCZAaBNJAsQjZY+6ZDEQ32mkgJB3cOMAyou5oGxFX+4PL2dr7OWhSvbTmjQuBqVIo4AnuZtAiUtbtvGcZxJqW5uEuB+fI6BRmfD4zCQAccMO1rpR0oVcGk6MbD5GdV72+BVh9IDsxnMTIsTftAyz9Y/S/sc37DFN+o3p29kAgAjsWF6c5h6BBojZCxGUBbPj7KTVjob87KQ7wU69NjNMXdIk5bAVC6JIVyRg0W6FUBC7QJqwcs8yqxEZRJIh8ElCqprbHqAgh2ytZuESLRz8MJd4/HpQAaAx7AfPfotv6McDdMrskuBewSS44MVkZWCJtdu+bT+g9/7vT/6k5++f/d+o3h9ayCd0gHN8myhkpz6E+AZxINHAOCkdBJyLg/H4zzfyNdqkWkmpwk/Ux0Mr3jnFMwnw9Q5QsYUXs8uLm5Y6ktgUEC75IoWTIYPtSy7wFc/heYqpIBEn0NokPSsiRudkZmcs5TxpZE5OsEkFhJ4s3c2W7KEpBN5Ogx0zw5+gqI5fz8dLyz75W9C2xMjL4vuSVZouSLUCzZAabA5x4eTJU+aPqO4T45xE4qBSKQK50oldcCj5zON4HR8KVfS4HdfWJSuDaIKSdy4XX2L6suWrVBdGwYej2vD/XXhlOUq3w0HGa3YE5ZXG4m64fWBLleETLp1naZT16/Lp/lnP/vpj3/6B2/fvRvGYVluAjbNRlV4WcJs5WCoQnNxZDRTzjmrqjaeqcdSF0LgydBNjGWWinCAnECREa2Kg2aEOElWQ6c+o1quqEjh1LNlToPBS680Z5jMWcUAKnSx1+yPmNaxowgUEbygPauvVb+hNYZ8q5NpKxuUN/2GJzjU58iuSGfLOJXnRcCdzD6VTmc6IF4CB9S44v7xcCBhaUa5dtRGlkICpmWgNhSBmU3SmFHPE4ntY5IanEo6UWrEaFltxjel5nehIjrGTotT59SGQPblTuC4gLQx11IlVS6cvyiTgziXdAfUbDMurkjfB5i2NPMiCm+Rk+PauIUWKBiepvEwLOfbH//xH/393/9/v//wfpwO4ihi6nXzrPt5Pbk3MKdH66t0LQaZwaFj0WHoT8eJ42BZd5bSyHZGqSyxlq30rdOqLrE76qbkPxlPW2M+4IKC2Le1m5fDOC3jcKXisnJjpnfXBrxPJ0gwiBnU70g1/HEbDtPhdrsac4yS9BhRATmCYHPxuun0CvRiZOcM3W/jCa9+EmCN/QwkZhNnxp4ewstPPHoITGEHTm+HOCSP8T5lx0zJYhwR91z0agSJ4icjBL6XPV22aVt6aj7rlVlcQPvI3XoegZ5B0VFbw4wKQA36w1cHoGoQiTKV0nedT5CTnyk/Dezs9gOQNbZ2GWPFw4Lg7fhUxjA18dnOac4stx4b+7Fbusv0pz/7+T/4J//w3bv3wzRSL29u/G7lqxw7o7nK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shoe placement is not visually confirmed; pause policy to inspect and recover."}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":401,"output":102,"cacheRead":34304,"cacheWrite":0,"reasoning":68,"totalTokens":34807,"cost":{"input":0.0040100000000000005,"output":0.0051,"cacheRead":0.034304,"cacheWrite":0,"total":0.043414}},"stopReason":"toolUse","timestamp":1793224254632,"responseId":"resp_0b5218887c37459e016ae26e3f1dcc819fb971b92508f48298","rawStopReason":"completed","durationMs":10185,"thinkingLevel":"high"}} +{"type":"message","id":"d82e271b","parentId":"c395dcb2","timestamp":"2026-10-28T21:51:04.954Z","message":{"role":"toolResult","toolCallId":"call_bf129035ee174f919191145a4b6393c6|fc_0b5218887c37459e016ae26e48a838819f88aaafacfcb21b14","toolName":"device_stop","content":[{"type":"text","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"}],"details":{"kinex":{"title":"stop (First shoe placement is not visually confirmed; pause policy to inspect and recover.)","isError":false}},"isError":false,"durationMs":135,"timestamp":1793224264954}} +{"type":"message","id":"82c8bad8","parentId":"d82e271b","timestamp":"2026-10-28T21:51:14.887Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Repositioning the left arm**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae26e4d04a4819f8343b54edb07918b\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Repositioning the left 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+{"type":"message","id":"cbfe32fa","parentId":"82c8bad8","timestamp":"2026-10-28T21:51:14.890Z","message":{"role":"toolResult","toolCallId":"call_542c0fcce2b948eebf6a714e6f1fb3da|fc_0b5218887c37459e016ae26e513fb8819fb2800ee5de3c1296","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":3,"timestamp":1793224274890}} 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left gripper to inspect first shoe","python":"import asyncio\nfrom kinex import communication,helpers,finish\nfrom kinex.communication import duration\nasync def main():\n async with communication() as io:\n with io.subscribe('left_arm.joint_state') as s: st=await s.next(timeout_s=10)\n model=helpers.joint_model('left_arm'); q=list(st.position)\n p=await helpers.transform_pose(io,model.fk(q),'left_arm/base','world/base')\n p[2]+=0.16\n target=await helpers.transform_pose(io,p,'world/base','left_arm/base')\n q1=model.ik(target,q)\n await io.publish('left_gripper.target',{'data':1.0})\n await io.publish('left_arm.joint_target',{'joint_names':model.names,'points':[{'positions':q,'time_from_start':duration(0.04)},{'positions':q1,'time_from_start':duration(1.2)}]})\n await io.request('world.wait',duration_s=1.2)\n with io.subscribe('head_camera.image') as s: im=await s.next(timeout_s=10)\n with io.subscribe('left_arm.joint_state') as s: st=await s.next(timeout_s=10)\n finish({'position':list(st.position)},file=[helpers.save_image(im)])\nasyncio.run(main())","timeout_s":120}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":391,"output":353,"cacheRead":34816,"cacheWrite":0,"reasoning":21,"totalTokens":35560,"cost":{"input":0.00391,"output":0.017650000000000002,"cacheRead":0.034816,"cacheWrite":0,"total":0.056376}},"stopReason":"toolUse","timestamp":1793224274926,"responseId":"resp_0b5218887c37459e016ae26e537bf4819fad0636d7e1575062","rawStopReason":"completed","durationMs":11726,"thinkingLevel":"high"}} +{"type":"message","id":"8a586584","parentId":"64fdbee4","timestamp":"2026-10-28T21:51:36.959Z","message":{"role":"toolResult","toolCallId":"call_f4dcde1faa784a6b96a77eea113080fb|fc_0b5218887c37459e016ae26e5af5ac819f8a81e257365d403e","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224288.135853199] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"position\": [-0.949935257434845,1.990056037902832,1.6935598850250244,-1.411597728729248,0.14556893706321716,0.481610506772995],\n \"file\": [\"/tmp/kinex-observation-om1ylyes.png\"]\n 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e3BQTPq7Qtohm8wBO4IICb8tbYM12bQNd72k4Ybe2fVxgYNBp88lvDwCHAXvdlvxrUszTLW/txiPKrwzn6XQzeasoZtGiABzCzZsP7QuZrNrf9Ktlm0czE1KJuLarQjlLrLMHO00kK7IbugCY7uXNojC22Jta5z47rpGFHlBng2fvgjsTLtGzoVjxIMG++L4H1wafLBbvGSVevHMNlvdaC+rClR58JN4Cvkn3Vs1d9ny6Vmtj+czLTmnoIrAGSdMwrXGbriANHxmC357cEXpwk3TOWB9zWrNp/tCRiuYTzhOcXkF07jAMTRmKdmMAQKSBOfRNqEWjdVcqXkXIOpiHO4qcherXyP/spKX8hA6o2rdPdJ/ossTQLuy04W592EtJB8w8iME2cRbaGy+ohqc5WwKIA2FLZ27yBcMkV7tINXcGMpFOhlmwIF1kMCLg7OzR3qW/Sr4mLtEaQdlcA7e4k+7DEA5un7q2UZAcZIYSGIy1VJjz69eQit+466grv6CIOQ7cQf0Pg5/K0/+nIlMNNq8J3FsbFUZfrLFpDMi1iGeSFx+XE43Ykb0sc6Wyz4fx9xL6t2rReyrj5Usc/HRcAdT/2kp8YvkmWQIEZoRPGFYZhOM0lFGQcVHtvZjs6lc2kdwneJmE+7PXDEN1xA7Wg293fptWe/odft4d3H1py2CJUXb5jy5W0KGVrheF5MPuGl1i6M3IzsYVElpgADXEPGFuv+bbzrDd8sS9a0s4yizlDJbYuuweGlbD1ukMOgpP4PvTMgWt//FvZnoe1BHQHaD3tl9S+Wp6RXuLnPyRSYUhuGC3ehq+QJRs8MSmULSPP0ixAF5SndMJDBA0aBDvcZsKMdARzcG9zidA9HKLHk+RcbB3bZIgxvYTU/UMrWGJkYwtvEe3wjNhUuUMqfFN7ln3EMbghkDBDyYrbPGiL++hZ6zOX9b8ErBzVYLw7jJdkN/p/WvKAuyGwLYU8LTAELcFpRjstke870JAt9jKVnhPohlrYi7FUx8zcV4HA68egGk3P/sIXdJhAdxW8RITFOPeVY4js0WVsNbCbgBELCy1qTggY9rAJBqC0NA1B93r22pH4Vj2kcS0At+SWPiWYi8O8oMURGfjW5LwE4Psi42mX+pUgr1Cyt7q/7cQ9OeOI6VmL1VJbawvf2Hdd59veBMhuKFyHvXRJK18gzS4webeeZyJ/iJoO4HqIUfCQR/BwV5h5XS4HBt0Z2dQ7oxsDk0Ibqqzt1bOyjqZyg1eH6jjYNjSDaZcGxsL3cLxvG48YJPrqcOhG8vjCGFNwPz8i+2ybgtkFBPmIzi7n0ujc3NlgLbBpGW3tyIE4GsjA7eTNFh7gBh4VDtJ6oTvQjmzGL17+rbMRfC3scS/7BsQ04IWtRfe8N5hzL9Z1cbaPt3envoSU+VN1ZlEuAnQU4wCzfaVRupf5n0NV1qNQr3WyO2PZXZOfUyucrb3bIa+w6wgAIdz9ZeDTImnBmTCOGzqS3MNtmdSUZuuq8AGAsMUlgdrDnV62tlgbuz1H7tlFsqWHwqEHjU7eToBu7eCk8Gp/QGtxA/RtL1u7XHZ8bg3GICVDQAq5SkfEd3+39sR/HvAMv35GGxhF6qc8bMJEifjXXRriP1wCcku3tvtuvz9mK1zRUfIbOcmjw3eDnfEsKKxJ5SlyIKe7AljmE58KyOZjv5E96NiHKY7g96/Qvcnw6tb3IQXpjfYy7+nbhtAE6hfBnXMMhe1k+s/PbppZX9Kjx9pENQuXbuher6GjpJ4nMiM4A08v0bTWzChLz9f9K+jANXvKU49WAKTfXZatEbqCA07SJvwMGLk24z4OIaQ1qw7KW2BwNKOHp3fi9rws2OKIHDqCYrGdl/fBnl7+gV1peDYuDsR+HJDt1rJdCLc3BvipdcDE+FE7WOSRkau9mhcksD6XcAaMQXlAM76UDhPv0JQeaNTLPg7VVn5Ll3D0WyVMAkIoxntUCxkWlFROjIUw8A21tj+cSNdFGTjeQyxwCec9B+vjHrqL7MVhBysMKx0GMtuZJWtcIwjjdnt2tf1agpB+va47U/emfjtUuH1sA7yt1DuMufQCdcqDowxXzybgkGrJ6lo7bSbAGUP36Pl6GM3qXZtfWd/ZmbEDzODTYdiCwGgjNwNzchuWdEA8vdpoJ+yESS7M+jcS5LMNsi43B+WG87dWxVbMiSeT2Oxs22Ssq2vYdVu8sSAXkogQa9j70YjF7FP9W79cM2IabcdpT8UnW8FWnVfw1x6lpIYPMZoTrDDUtvh8+sbuVFrpe4+ldR3HYPveJT/ZDZ5dsfrarQEC/0k/OQtHbkHXstUsM8w7xDJ2TC8N9ENet3JTqMT1RZR1p9kxcPC628FUu/jAJ6CDbzsqaw+6eEHCHiureOPj24wcFdpFW4ZSz9fdrT3nFNLk3iBK+4XHQ7smdjdnSP5OrSUEcti/j/KH4SBtMs03sG2p966MlgLPFxS85br4y4+ny3/cifStoR+lOrf2m/yI1jlO1IGe9os8ci5TDQ1naXV/YLPg2oH9havnXXWDw0vnjW01yz3dUrwDST3YAwPSviWBY2zBjS07ZXdtgRDBFrK00JnvDEJZe8zr2EKslpTPwjutUG6tHH73Q+Lu2TRn3uablsGBbZBH5fBowjjIPiA0Ij+PZQle2AYgzn7xf1YHNhCKXXaYuYQorLOIsWNqaw9t6hF+txTWe+4JdEhOEyz9tYjUipPzpUyfRQyiufqYq4u7dg6h8I5lONx2sHkajTPzqc2tegtcmjHr82P5t8JvKFf3uyy7pLWfePldsz96TK58Zy3cQ/AA/Osb2nTDGXpIaP/VYlJBkKT7zvWNcVwzDAkRbsesKu2P020Hy15g/3oewyVsLoWEQNa/jdwv3xKtegXbECLCG5xNzr+dPg9igxxoaK0T6FNHf3Kna5iuYUJp7FsNRwqPaQnfW7LwLq70U4D2BrTHDMjT0szKMBS8LZkEf4WowgWDkCC28c3DYehr80BAuxxkW9vVdbxgwahd0uZJaGvLAjLLGVVXMmEiabjJ7SmQJu4TcumtHrlDesXJOwPsPvXJT5IvxLJ6d6AOlJmqad8nnfHPr7HtXjOGc+hL2J2BqdTeMfPYSS8hf/5eb8gJSWI4nse/vhcxTbgF1qHe4A0tbgegpI7DZ8ZzzPa1a/zaU2kbHJdJ9kfyqGApLVphTinYP2Oa6yUU7EMbn+zteEeYqpz80jV/h5gcaiF88a3RfQtJNzHqENm/Rf68lszRD9cdgVHpQKo0Ult7RAPO7ia+5sCE0Zmfl3ecmabFDxwIOhhrf+g2pn1cISgF8pPbydYr7YOyRXWcj8sydDdBeaRo4CM4WttuDyYwCHOKzp5mv3WgPqADlkIxVWBJgaIeQzUHGGwCg5FYJE2bbe54oDxAWp3fnJm3PAcehZZAyh3/bUrX8yEHZkpS9vPq4/PtIazp1o3ThRZnavSlB/x5sznbGTlECeEy3HJ2Rga2eC0Fjz8Be2Fo3oKR1tG12XoLGNvoHgC7YxAWYDy4uZLOIVq2bbn4iz3jFtJwOh4aClpejdYGmX+dpZ0tpmUXa2eAOV+n9HzIvtApvf1iSQez4DxUb3MPe+hB0klQ2yk8IgcGtKE9fCa4v+2EY55tvyJul/MB8O4TKEkJgRwmt4GdXmjR6iW5HA4Cyt+2L5ndsUJ4SE7ZnvhlhqYIhsBOYPIHw0Gzl5Px/TXrdLuJLfGc7sWe5NWWbbSnzf/ANrYc11kWoJMbrd7GUJ9u4Hvn+I29NdSjOgtx5mu+U2w5yy63k1AAnL3fIHZ4AA7agxAMX+eig8bstMKoDQdDjke3JmkSyTql73qWg2hm3uYpoPZzcAICM4MwNGCrcu/CQCXbSJ5MXD27xl3OnaNpUVV78j2iegtN3GNOCfDWNUYPAqMZ08ctulNhd+iNzmCfoV0QSugX4zbNKR3WwmRlmoBUWl7nSEPrtDp63LIUpSDsuPMt/mDQ1Jl6iEp8RYzKWZLScvB5w4FttG62GXfBoa9dcMhCNAeEcFqMKnLloEUyPQswHbSDQ2Ly8xFobO5h7LQ9h4CFtvSEcD5hokTwiuDkzStbq+lbfMgS3PdGlWKHxDeMLYy+IlbqI4mM24gcyx3xl8b15Ncm7nkDvKEGRthkG9fCK9cR13QyDI8iWLhbqY0B5vzE3c5JkOdZFtVsLRhqaJ843tj/xm4QsT+jjvbPLPMe+gwvIooQMjo/XBs/vUGi82L3rMchd/ydzF82M//Gtt2h9RLPD9uLY+8OeH6gTfahkp2qV0JcbLidWB/dshzTQZKZScAo+S82godPNdgOL720xV23xrCYMF+DfY8TXkM05l1x2btaeNrH5pgg6T4OJRNrCHQA5g5C++3l3Jn/6cDfDuAXQrfYttwO9Bxgmy5i7RPo+Wlt9X20kf3VonCdTismMx/PsqU1ODpq/wffK+sH7j0zvpLWp6Hsa7lyV6dtcZP2V60Heq2BvZdXq3vmRfvZqwHYU6I+55ZOu+1kr+6iU6BgsRXJfmu6BUH2NBfNwy04Au9aaf6dfeCkiINsMHeuv4QiXpsudgdo6V6BF7FFDLlcy3aBsVgHho4M8jTQ3sae5pTYEbCOqjogYX0XOYSd4KL72AZbm5tAgFvmENobYWZm3Vw8vMhK9d2tZioMCyLlYX+YGexaUQcWUFPk0qqEInHdy/n4DNT6ybHssRX4lS/rs+mF/kCaCcFtmRM3BPBO7x9GZ9g34dI9U+NU2m1h31u7jAgNX6FoQyIGtYcNaTVrc407GvJRJqG1vvVPuC0KBADLyB3/7psvox7mZe3Waq1f+jeCs95W17oO4vB6mGwXTIqAv7OvqZP/qJ03yJvmmc2z26qpHwM7xSPgp2dx/Yfext7g3cHnIe8JWBK3+oRTtHKMuM7lHY2dmTpqmBAvc1I8u8HKdR1s8SQt5BluS1vUhSvODga8Nz4IIHUY6HVGrx8eteIjD4b1UnT6m2vO6Rdsv+jDJjsQdlNmyoMZw2wJYbZHK9jZcFMdhWS17vhptuDMG4C84YJVdu9MMjRTtDfABczRf36Q7hNedLVY334XD3wwoOB+7fb1YwJIMMM+Mt+eC3uLgy1noIpgHVHwfv9KjgA9KLlsLcHnDDrpH24gcZwwFB0s/BtCZYcNHmvf35lS/70sIRMRmI6Gh51xUy/zs7RtTcx/Gry1Oy1W37S1Ge4d+H1gNGMg3V1B79taHK4f3y0aOBWDmZY0RKIZwodtlhzAWf8vo50GAe2r0RgCCEvKiTFXA3OAh5RlmUwm7jTJDnasrR0ZTux0I3aKwW4EpKoLkPbrftDofWEbz/hLvQEnIObs5AIHH+/c5nG4x9zh17Nsngw+RfvoWQQlex8fucvTAjHCb/YyRhkQ5gChAxC2/1mx029+uFy2pw5SAssUJwNhdFjiOaXtchnUtuuYna0D2YEGt7M70ce/OHDX2dl2CbAZg2sVbje5nmU8NO0TsPvU+oR3xPG2d/rLsU7DWAnZdINYDH+7GkWtUDVe6cGTXzprGMHgvgL7NBhGEa+dZu4O0fUadEDjeIiZGi/x4tHRnm0HMsMohmUiWksXt3NwZ2M8a8blvZRTus8Ni+GyFYNOn45G726BdzgUBw1v+AypnENJN5bu418tYuHn0/duX4zKzRKZiXWSt7fOzdJPTfamvHWYsn0fM9AEMmzv9O3CWf8Yt0NUUsdxIAYhDNyYWmcND5zR9ZW9DDUc5rViaG9vYjHBjJSxEVg/EguZaCtw+25BiNHlPu0Xd8DEI7i7iFUiN9WAc7WKpBZRpISIJLoJVSNkFCEp0FLGJEPj80oDksYRXJYQ4CVkI5RslBRJ3TQUPIwTxEhidwrsyNxaOEL151/7k2qRPdoelCitwNQKBbUwEKYzh4SQ3eKf5v+yMwlxqnVMjK73wqZfi2coIVfzIUPByHbBfXy5TxV04OGVfk5i3BusWM5maTIbGWSZSXh0CI8ncCy0hUo/gJHsw8Wxobz53T8WclV3mIlzYjk1iRd98rgRCESeUXrkdcjlyZxnU/QPY7HMUeNav/t3OURkZMFsppE/LdlgJIoOAeMWnUjCAz/ssTAljZLHtUpFlA6H2WRtPBmVdRHHMSCxkHVj+KkUMskSJXTdNHEUJUmSjIZmVKXrxUKpRmvd1Eo1jZSyVnU1L9RsVhXzoq7ARam1jFwHJXeGDAhCP1XIJZdcYe67P1k7nhOAiKF5D9HyIQMMMbm5hr23fE9u2wPzZXDa7YMMx7d/cqGCj+K1O3kEe5Wdybf+dDBlX4PAw20YHanSMo2OFGPdvAEldF9652CwtDYj7ovtXWpRZHMMJBlk45HUyIGR1zhZyFIGp2oFWy2NouiQr/s2TjycZsAUAHrSgq0jfH56oTZheJc7DBsOg2kYHVmayf9OB3QEiLRXcxASNkBIHSs5HA4nN99WNk00zIfRaD4v8wSUi1SIODLRpko1Uuk8y2Gg2DZGV1pUNbLgqG4qUlGbRlWVrhoRDYajyWpezQ7391VTk3COAXBCRNaTZ/NGrF3NHzqvh+YAIdS9vPW95WMnqoNyik3DExHZonjwgH1lR4ZxBNCYA2yQrYUyts1O0nQMPRDZ2ujbdv+ZdzOMsllw1qHinzPsMihRS1cbyelDtl9yqZcEdsaBCOO09go2lg/JDQ7OzBceD2Ma/jNfd9ZJBJ5+U5iQpztMSvLuapLopFQKRGhrV3VnyMire7GP5HLe7W717VYsgkO6wORjuA43bgQ762ihRz8PwYGzC/DPLbttQjG8OkBnJ1cC6kJrxjiKkzQeDAYrK1XTyESWVVmUdaTF7uxQaF2URSTjOIoHgzzL0iRKiqoCTJYyjmLcRhCelQLGq5CyAVmIoxTBTNW1zNKVtVOTjRPTrWvFbKrqWmHAkCc9jniR1TqExZC8uW8Zpw2cu8ztwI1ZWsEZg8RvFXNEa2OMi42QgRTNKghWsXHUnGStVhKaE2XdFJls6oWyFlqwFfNj9rIcfWCK5TjKRDaKlvQsHf3h1pbA5MEUNAIm0l9s8L972hhYujZTHjTvV9fR7q2k04ZDz5O8zNlS8+gVntcZ2GCIyBVyEOXs5shERglYbFSDJXYcvUONz6gFjOubczUvMnbcdmtKayINirF3nOYsrpdTRz+sn7nfFjLnegQwbh1KazNBoZwJW5Jl7cCguZJ5SaeDQbJ+7MTpUyKWosYhExgsjkpVV2mSCq1XBuOyKsuqWsyL2XwRRVGW5zJGbRlHMXYrKzhFMB0rYyWxELqq67is8nww2Twh8kFxsCPKoq5rQvYeyu3W7xDMJbL6bHp/nAaLCS+9yQI2IZLA5VWk6qpRjY6lSONYo2LfKCW1imRcqUbGIhIoQ1jpxtpZLL1EBm7NNA4ZkKczTdoztDDAx1jxGFt3cGKiOQjQvEpoHKg28pr5CdwrHDUTHlJakqltomRMHfACzwk7Sn0In94BEbjrw4ioUONp000DEgEodiKfgigUpuf6feQiboRMCQMoYQilVZymyXCcC6mkgrxCQ6CIRHfldC53GdKH6GOMJl6psJqqRW/63KqvnLZ7juGWGQo3HqCs99PpRJZpkFwPgwEXdO9icqGBfhPrJ+MkzkaDyclTjUA3YqNFLHWtANtUk8WpHGZuSgMtdFEuFvOyAYBrlAKFA1/TaNU0tVZEHHCL4zgm5k5vjCKlxaIsK9VkaZqOBo3YSOqyKRZ1U0fcEmQ3BakA0ziYuMjcUsG2ETcxC7SwRsccyaioyjgW+WgwLxdCS1WqGkQFnckoG6RZnOaDbG+6gLMkJIY34xJgKuYUu+H5YdN1pGLGIOg8+6ig4FdgA3RMzlMrz7KAwhryy/gAXhz6Aq8Can7CExnaAU/UCAi8wM5k2dawbg+5cs1siuEnJh6eeRaZRu1iACwAsrfYbn1e5QiCP/zlhkCM5WXQSBKAndJa1TrSTSTV4Z5KMgAmi5AGFNz6LQ0GlmORjsQRL76FZpdQ5nfky5FzN3UDA8g/rWgNwdr2gEOBMkjsIyYT4X+csJpUKuJJhr2Zg8elwCNxpEUUZ6mqGsN56M2NblRN01VKJ0lizE6R0GUxW5RJkkZJPD043D84KBbzsioiiQatJIvjOIpkkiQRidaEjfg8XXEcpWkSiSSLoQ5ZkkdwEBps1GD/cuqdgzRsnMEUgwhmToQShA0Sl+D2QG6kV9cIFgMhSqFElEZiunvwe3/6/r/8y4evXdk+nE611oNBduzU5j233PMj7/mrp45vJuPNoqyaGmgNzBLWHoNBXgjVAMA484FDXPKuOZeoRSXiAVgxkcxohGZkP2lZinyjeYeybaXTkf6QibR4mSbq4UDO62FmfJZ+0oIU95yXxq1VzUOelQwdw7T5870XkS4jKHKvJzM5BsKVmSDT8vwijCXDrR7RFNC6acDEKnVdom8jAl7kAon53IAyciMZr+kRYqvljC1Vla2boXRbnHA+Hb8YLisFepQngIAk6LVltMZsm9XZrNuWZCqZxFWlkiQz45m9JrICNIpwA7ao0RIszrEATKONE01dHRweXtvZ393fq+sqz4dZnudZliSgKqdJbAgKQisK1IaSI62RIKyiIpcmMs+HQlcEtSiPt2HAL9Mdc2CnMLQfCSswzRiIEa4FiEJTNWWW5CpV853yd3771//wIx+ezubVvKiqEmUspWMZNfFwlDznltve+773veylL9zcPHVwUKQxzDKSsZLKWg19JmZLEHSb53CTfeLu87otO24rYxmkaduaXfKkXSuXO80twuKs+8zLJaEPpUUU3BXEAbZsXW1tuP1k6++2V7dlem95iTtBIsvIAd8zO1QEaNkAG5K6qkFSamw1LI/rdlbOUxaka8DnwK8DSdVKB07uNW/s7ATdZoRCbziBaYVw7G8moc1TMPihygrFPRD1SLOlr6xkbeaPzAAYhl0L8F3BpUameQH8g1QMP2qwbCV5lhn3qtCiKUWti7q+tLU9PZwBo04TtHClMVwRyZ9OgHdTIXEIxVqpFcxYSl1VxWCQg9eKKf0OKO2emOhJwyFc6K9ZsIl2xV0E0blWutF1XddKi5XJaH/38N//h1/+8F98eHZ4GKVxVVWqaegVtQLdGCZU63iQ3nPH7X/jx3/0pfe/fPO2m0VRoEQiJVrsQhbBNJwjAZCRHXMs7uC8eulg0i7e8UMrk7Wtu66YkAyiBgOnNsuVcIDkWAoHR8eFgjmyN7W5lF1Yi5oxEdqZErj9xsFwC/OX0Ygg2im80Tv/tYhTROAkAgRuZ5lYHON7ZF1zIU4yRPWiBlfVOtQGB6kWheVXVur0r7F2Mk4RjQxv2LS12pEYaVUgz6YMwlhxwQgSnMVbLYE0PqNsKKIFsB9gWK4rpYQajkdpmhqqMV80wN/UweH8YDHTQqdxGoFBO4mRmgB1wIGRj0URCNcOfxnkwOB1VdcrowEJ3rTVXDUOztsoprgDOEVbpNG6xMiA0TRVVaRJHOXp7tb0V/7jL//5xx6YTg+jLK6qEhAbyZMGPAdyCXYRpQGVa5WNBvfccc/P/sw/fPlLXq5K1UiRSAVkHmztYcgj83caVsllVGN/sY94u1AAsJ0o+BCcOUU7mhni5bRWf4Vw18/i/D3dSMDWSYQDcbGe47PT/wIXuZljOJFe+ieXTt1/Q/pVI6NU6qaBmAXgwCoIsAlWaBwc5qumMeMRmTPMz5xSS43h8jYnV+SPBEsp89Q7OuESP53Zwc2GFGanFVj8bykQXmbx0nR7px1XC+IzcVz0MYNZV6QpCMeNgqJoSZ6b6VS1rqqqrkFrBOOXAqMUao2gqqLRBn+Fi0K1rEiAU6N5IQLWVVWVVRRH41HuQIbxBWdxJy8KfNLAbEBwIaoggE3CiWhgp2BeFlEWj0dXL175pV/63x/8zCeA98ZprZoafGCkJoDerVRjVAO0AistgG2X+vTNJ37yr//o29/2XZs3nRaH+5WOgboYX6mR7Dm0tdMN7RcthOGn7LbamqJbCHwUynbBX/ptbd8TgHN/fXAr7rLeZn7Pj2CSvVNrv9iz9vYbv8mLKZwgISVSl+hCsS43dGYCRll9wIXfuamQGcxPy4pEbhFMH7DlK5weblPuDLrzlIAA1Z3QwUf0a7Cj+o9aUntAC7gZwNhJLAIb3mXyA13euLGBkV8ti5MkBSm6AXUbdF2gWQ34frVqskFelPVsPgdPstCEsNLyXvifxWmvbVhzP26PKquqWJQwbhTlKdABlPftrrg5QUkcGUtZSK2rKpIyAflFyRjC6USkgWcWZZTIeDzcvrz7v/zb/+WzDz00mx0mSVJWZVU1oN/bU0bd2Rss3eaA1FFV+XD03Hvu+3/9s39x10uetzg8TEUi0kgLBWt36ggqIb62xfVhLxTbrJEi+LKN9K3DxJ1wH2nOPAM8DoDRY5F79/KXuBtaMkXn6u/MsnTt3XfwhbV/aX8dvthxABElUqsGILU2QQhOOrUGclbmkyflcPW0vUJPj0PsdTzQ4RCTcT16BUaF9rBk27Cz4QFbbgj+pwOvUIMz/xoE9pZJx0sMcKEZGF6SRFGapTKStVJJmqVJCi6lBriaAD4GGF1qXVQluKkicCehRuwwmHxLfl8DjUHpqizrqhZKR5HMskSIhixuRElkJFWlFk0zHOdNUx3u7a2N15okE0pBg3YtdayVqBOZ61LKSXLl8qV/97/9uwcf+vThwYEWEcySwjnx3IkHO1KJ3NyGPNEnQiyKWtT6znvu+Gd//x+/6c1vUFWpZKyBfpmwcTqeltTaiU1ogaJseUS5jc7H27XdZF0YMENKYy1wyMEUTh8Pzr5v6cKt2fW9giNeGAQZitnhZJ1Pxn61VA3owdslqB5MkERo+C2WwE6JmdiYhDBUnXHTkJS502CvCqLEDYg4/yBHYI9pTCToYqCR2IKdZWfvpBz/M2TMbFY+6CSYKHMlwh/OccPqlxntGK1taJGK0ZaMHiPdQAg0mH8hEiauVDWbzwF9QSNOUpS/bYJDl1cEZwiGJxDKKwrbTJMUdGeB9nAlqqaUSa1l8n/8yq8/8ciXf+yH3vXC++8fjIcRCPAV6DFgNY7KuDk4nP77f/v/+8zn/nL/cCqEKKuarFbGtUa2drYfiMF+T8jMVjfNoix1I2+7+Za/89f/xg983zsE8N44SmOQ3LyTEY3lzAHd8a14pO0BxXYAXRDv0a+ydtmi7ErVXYH1BlklG4iZWHrv4C/vTsEB95JVhPPt+bQrD/QgMAhkuq5hG4Hi03cezFkgUx8BCRDaJVgw44bTfJ2o5vUQxgj9bh2x3EAJ5iI01/TdyDw2JnjcM1kzTMt9wakIS+IwX4IiLGSeZ3Eal3XdYLHwWok0SSKpIi3TJK2bZrZYCOTCeQLWaQPkbqTuVnJiXTdNVYNQXZVpgtxbA73IclVMp7/yq//5zz/xifMXL62trLzn3T/0qte+9p7bbpcKokLlJK5L9Wcf/MAHP/ynT37964v5QoGMoIjhWms8+cj8fjlrgiOLjtBooaq6acr65IlTP/kT73vPD/1AGg8UYm2DsZkoVrhoVrP1Af6y2Ik2GrTVIhbhwe0o/ZwrNNFKz2ks5+O+o2ch57avHsNaPwIvHeB6CNy93/wM3hqyOmtIhjKjcQJWaCsnGvim8n6hA9eZ7d2YVlphJMh5orkW4jmww6kWAndM1H7lDNSCBXnDbOtOj5eMvHs64dblJbduALsHJBMBwJgVWLeSOMqyNEoSLUXTqEVZRVGUQJgiirMySpOkaGoN+nCKEnQQF9s+mGBXzZtE0xRlCZ56SIloxmujch790i/90kcf+MiZs+fLumjKKhsNn3f3ve989ztf87rX3LZxixilH/qjP/293/u9rzz5hG6aKJJFWUoNMgVpuxCwbZmtPWkfVBQGJ2GgeQTSWVFXqtAnTpz48ff+yLt/+PvzfCRlBrbppk5Q0iCi72hnF4EDJAiIODs4HqbFT3mJ/unfIBlN7AYisxNtb/wNYXVLwAwGcEOHoMmW0/Ox/57Lj/1Ci/86yOxihm4NkUNGIQa7FPLbkPgtuVqszVPSjh3KtYZtFW+0VUBYMXsfyhOCQVcmDtq+BHeywGkmY5EhzuXh+Lg5Rvl9QBHfRRt9gwtiaisYfhVkMkk5zrMCWJWK06Sq6qYqirJolIbgrGEqInDS2Hx35i9z6hrNyaY40BeYHQFuaAVhNKXQ+Qf+8Pf/4pN/fuH8hQo4vyqbutg/+MLnHz5z6cIbH3r9e37g3Yti9z//9n/+xpPPKNEkSdI0TSQjwl7KYYB/Q4OxDbFrQw85szSKG1mSNFJvbW/9x1/7Na3qH33POwd5IkXUxLGdsA1R5XoME8pcabYuQPENcPceCXjhs6Kr2JkPGMz3IeAN8uSgZ1ogJ7lLX39a5uMjQJpDhKewnIFZoseCDg0sQ9Icmi1sgGLPlGyDOPYCO7/OVvspWL+m/ctwM9O/2YWat593WOQCcbo1ENvP2bxpH+5H5QqZ5ZyiK/xUAjLhTO5sh32AUDg1d6EoCmYdEHTjg8WssnUyIEBCqXnTLJpqkA+yLENNWcUSDFvmzUSEwGOMo6EnD25qwDcM4RYKbEZxFKkomxfibW99+2Nf+srO9u58a7ssSl0rGcVK6q1rW5/59EOykVcunj938UqjaxnLBmK6EXFtxqUXlml1LGwvrHvjwnSwLQbsWwySeCKube/86m/+dhpn7373Dw8BbKI4y8iDGGybhRl3RlYqdkjtKXr3KHvgOvwrlCZFh1j3fPBNirP+ZcYCwpo0HPnIcuoQSic9E1w2hitb5Ak/UEUMEITYdojIN6nKzHbogmH6Wj/2OtQ6k3URdpzydwxJ7cc6/N0IxX6VrQcDMYxvhQ+25Xm2ZicMV21X/XYUgmlwfHSnUToJHLIjtGrmi5KMRXVdU9B5XdVZnAyGIwixjqM8zyFfL9RMYIyGor7qBszFDcRUIPaiiwuQu26aVMosjn/ngx944OMff/ChB+uiBLMjGpPTJFtZXYmlTFLwPzWqAdmblF7I7kCvL2jCzvLMcNieBLfO+723nnQSseuyPrF5/O/93b/1nh/8axozJXWUgOzGqnEGkiGPc+6FHiaJ4otCe4Q5QtE+H2OpCThK67Ka3Y2rvyHWcGHX7MGz8xkdHaXmlU/PdnsZfCsuj7738Q0QyIEIbApB2foyN4DAfMnBaZgQNgYIwTNG4ubmplAx7ZWZvVoRRqf40gqMVTpiGcQOyCPt1OyHH4fTDB4WwlgYfUN+oqquF4uFkBFEOAmRYJ0dpXVZVRjgkY6GwzSHTGETXo5o1cD2oxsA/iK/lfFvgRcaTGcQLwKFF6WIUqWy9Pd+/Tf+8IMfeObpc1VdZ3EWYQB2HEWjQRpJAVZnRCoSu9Dhi+Jzm3LaRXvMZaYsezD0ETrBoBpJtWhufc5zfuGf/NSb3/Idsql0MgArOVAgf8CmxIsZJcRIw0ScxB2oEx3uuoybtbFTt25fJjl/UxevlGulp77xQ1n+CARuw3H7jp57/aBe1KZUH0qjQ0gySlHIgZla04tU9qtQ9OEeM6bUOogJLSZ81aFlOxzS/ewN4+wXSnrIpiUdXf5veDKn3pYLG7UwUEzcO41Ik0SRUs18voBkX4zEwkBRPZstNtbXdAPlAeI4TtIUYF7rGgrwgJCLjniSRZ0MYVoyULEAFNdVVVexjPPBaH117TOf/PgH/uxPPvTRPzuczmIRJWmmBaZPCeK2kOpIwV8eC8N/2e55I1bXWKhZpgTMsxaLonz+C573i//jv3jVK14iSq3TRKAP0kEKS+HsnIg/A2aGaH3RPtC2atP+UvtDefaM98avZx+10bn4AS99WC9VFFqKMVyQMFYDjVYNOAh4EgnnYK3kEj4dH37eJ7w7DyyXjEPprXUiJrjDvL2Dn911MzV7mUhlmZG9n4nAwVSdwB8yAYtL7tEOvcF4SfhfksRSy6JYkKmp0XqYDYaDgYiFKupaNBCKDOwKIRjRF7CCJWm4CpbOcIyMGdQcCf6iqBHxcJAOx2PR5L/z27/+xx/+469/7QlVN0mcgu8K3Tpg+0Jl2oR6cwMJ9xq5I+pDX472JksZ467qul6U1Ru+5Q3/5hd/7vjGKTHIJKY9Q5UIfI4FxpgVBcd/45cPuu9RD9myBJ85p/T/110t4HOGMvuRFQOvz/pvRDow27WEvnlDsUYEhtMGHmwLRHIObCRiKy8ytGPFkUwWAf29XM33FTosnob5KM601YI4vnI2lv/duibY6hmjdajG9Dy7Gi5RGg8a80JyxcQHQLBXwKfIZyGREEI2Egq/gjR/xN+qKof5KBlmTd3s7e2RZRjknhjTAJ1PhwEhbSbSVNVANhTFazZCA4XNwS2VUnpElo6U0ld3pr/xq//uQx/9s3JWJmls84axsoAQ8/msrOpAgfAuI/7D2yi8pMYkJSOGCAhZ0UKXi3qQ5P/o7/69H33XX02zXKdpE8WRqonkOtXD4HMHx468Ohpbx7jYJ1ppJr350Or/2xhvD6dyeu31xvumJsYfMs0IwW6ikwg7Mzgrgg2XD2fZu/PWjO3Sb2nzwntdtoCNfTao68T1QCmzx+JjnQNdzf/gYqA/Ovunv9Fp3BxyucWGs1HmXvOmVfcTYsQBgS2btHUVqLZYBMmYGrRfHTVYnRYcSxWk6zUH853qcH6Qpdl4OJJSFosC7Mx1BQHWWQ6cDXN9SAEGtEXEBZUYERgMUVSCEJBH5pB3oqSWi2YvEtGxjfW//w//8frm8Q988P37u/sxuWwgYDmCwiNZjmmFkNZrZYjA6dvDea3X23+A50W6tMKwsyRLDufz3/i9/3r7C+94y+veKGsVRZBDLMBh1hGBw4GX/+2AmwVbhlKkj9Jog6T06fcOKv7vkaRdjJ6fPxMh24JA/+PdjTHfde4O94iQA3Nnm7qOpawTlQQSrkcYHpzhhK+Wytk21NJd4RFaYhxYL3hoMoMfhpWtdwXwZveB3eVZZktYNwjMX2VkZSYIO4rt7zfDGMpjsYtKQ9t6fnYVdA/EZZm31hB6DPFtSjfTvf39gwNI95fxLD88PDwsygpKc0TReDJxPmqXYwBYizGVDRil4QOaJzLVqMCaPjRvibmLtapX8tUfffePLormj//49w/291Nse9uIhjJRkhSKJVEYGdqlwaNlDqWLvZ4smrf6vGNbu1EpFUfRcDx4+uknf+nf/od7b7vrOTfdhKVOYqGhsloIlAEsduCx9e5elhFoWZxyiwATmAXJhfstUbv+uy7DhezcAu9I36SDZ7sWLs7hnby3RBckvyMkngGbUDXE3yXAiF08HR+3n/NyKdiLOjYepo1CqMc5H2Qbb7n81i6A3VlBQGOYmsDkWSbhm/kzCPWv9/Ffrgw4x2crFQSbjtl0xgBt32Y9LFBxB5IzsW4HmJQLKINXYvGPOE0zpTXYn9N0Op1e3d7K8kGSQsEapXRZleHSYbvACwx5iuAKIg82suimgiTkRhaAQlIIKASSp5VS+/X+2mT9x3/0vRcvPfPAX3y0qADBmqhpGhDqkzimACyo5yNEVVVFWcIIPIVyycViLQ2oGXIG5XRUlMqvfPkrH/2LT7/vR95JqY0i9tSvO9jST7wnmltgWqnp7DSCEWTvzHlTi2/y6piSGd+18T1ey+x5uv9q052+JXia0LFj1ZA6U9aLRut6Lj77sT+GBt+QYYtuBiqH4cIerUbC2ViAFR41rJ7DqsiZO000gVc9naDawnaDKC4rxbmZrHXWfcY2s6VehTw7kM89BQhuYrDjvvJAawIunM/IigeEyjZ+pFEqrpsGIjGUquq6hKsQGnAM4hmxUMbh/sH0cB7FyWgwgCBEjOUqRUUJRzieCadZFIj/wHOTSAIiVkW1KGBQFL2hpEaSplDLejKeDEcyWVS6PLm5+rzn3vOZBz91OD3QcQwxOjFIznEC2RRU+BbaRGidxDGUqG4ZnNn2GSeWC700/iBvFaCPkzSZzg7/46//2ite/vwXPe+FQqQCyg9hPa42zC4D1b6rT2y+/q2d67+X8S6frlzyyw2QjG7M8I2tzsppSqo4FblOP/PgX371qa/sXdlLQOWiKoaQZoJnhpBEQiJDJ2+WpNv9vP30u0ba0Glk8hjNaHaXTOYZ5QYb8sHcA8ZvHwjWlDJFEpKv1BFGCtqVt/VZ940ZuMuiHb1yiM0eDWKs7RoajSkhmgRg4p9CRJjznwA6wZKObRwbjYZS6zkkD1cgA6GADCdABTqapqjq+aJI0ihL0qoo57PD6cH08PBwsYBitEW5KIuigfwTsTJZ2Ti2fvLU6Y3NDSXV/mwwHq1tHjuxt7MLpjKpYQqVSDRQASHEvCgODw8jGQ2GA1CnDXqGEa7mV+O/skZNq0x5t4ApvTka5hcvnvnQJz5x1533DPNIQrdpIrltF1FLrDkKfMNY2BvDDXlD6HPj1/VQ9+hJyGfL39uBl3K5AqGFHD759a984QsPTw93mlokSlWNkk0FQhFlihqQ9bjLTE+BuunjIz26WoJtGRjbWGsyapW9ciZu5wglYxiPtaFA7bZA7IUDehnjuC5c0Uk7HrV92HVQL8CJ80zapwWg+ktiPnp2zXhUx8dMl34xGmwNmQwkcTZ1A/mAEgq7YoagXCyKqqlMxQD6gVsuoaKGroC7JuPxcLq7v7u3uzicz8oF8F5UrLUQWT6UmSiKcnpwUKmqqlWcJBtyvZpVP/j27/nC57/w1a89IesGUx+BIDYNWJikFFVVgziABQygeiakPGJEd0fiJRu412684dFaIo30Eck4qovq93/vg9/5pjc/7577yqJKsNiBK0Hp4bkbDeRqW3Vhuw/ej7z0db5f9tB1xu2JFfkmZxDIw1xdD3/p/BHeD1kzYqbOnTt3bftalCSNUgkYXxpRgcZl0MTCug2uxC1GIHSH4HmymXxLqm/JspwhM/IcEn1jXjQ3+TQjc8gOPT22eW7LPLvuCyuUu4SVAP9JzTKl6mzykvM28bH8HwocoZR04OzRZIQCYMbUwhoyAakOa5zGkImLCiMk1ttnwLAUpxRoZTkdBrJiUOb6ykoap2VdHC4O9w/2q0JFgyyL4noxa4o6FiJKcxnLVKZRJaqq2Z/ub23v5Ek2ytN6lJ86eVxBtIWCCu5Uwx7FaSx8BVJ0lueuvgB6jUEB4FgELkWYPzsWCw1EZTw8wVaoKImuXrv4yF9+5t47b9cirbWCrKwohggtXjR2qYOxpet6Lu/u7jGP9A3zLBH5BtB3ye+9Y/Xc6bs1HHH79SbGYvGJLc6KwwsXztdK6XkhYplAwjeWJkbVh2uYPKnIhyJxSw57abt6ZzAQU4OcoB0oRp6HWsQl9dQ/4wa3NU7ZGPzbgHlScoMhTOZ723vGYLUhJMYQjIzHsGF8Cr7CWtmoWGjdUCMaYlMJlKCkarFgEgbRuSzRage3xlG8mB8eHk7TJFOqLstKK2jTkue56bdkylMRBwa2BYU+03h6MLu2fXVneyuL5LWta8+cP7O7s100ahgPbr315vVjm+NxrgQU9a4hnKs5mB7M1tfqeq2qo/FwlCQJUhAoNtYA0YGcQiFllqXrG+uAtE6Tx3wjal/spBfqEWM7XtgjMhKXq/ZIVEtLSKmIVaMeeuRr3/k95cp4HAksmscTULilNiCtoXWj12a1lB+30U+6JJkbvVoyfugaQnZyo0p7z2z5z3aI4fUGsEvg7k36RUkRR1U1P5zux3G0KBcQOoSGDXsoljX6fWbM1dRTwrEdZfFhc8HWeTu+Dx31JiPj0THar9sx+umabHhCEK6KfpomK1xbNnht268YHLQGTeoPyo7YqNzUhgJg2OwSxQ8jKlPNRoxqhpIHVAcuS/Iojov5TGZppBUV1gQFlbRTU7QGQjwGw2FdVVCSqo4uX91SSgzzQZZC2wdi7Q0alqhSFeywFPOta1d3d4pZ8fUnvvbFRx/e2zuczqZlWcg4yWX65NNPHT92/KUvfvHx0yeSPK4Op2j0hnTGoqoOFrPBaOhKg5pIakhYAZszFPZKkqqqmgY6UoAfCirhRk2BmQ5W+KAOT2Y3WExVR8oz6V5xFGutPvXg55555uyLXnJMAInDLiY+qIwdn3fhukq5N4xzQYRudzriWajCbZ2T/WhXo+m8rHdq9maDbV2ZofeTZVjtcYlZ42H0uimrtdUV6PZTllI1umgSBFcI4o2gq62tkeLmH7oamNvWnUN4xkZftuEVjl8TIobmIUInp0FbHdmGdVjRzSIom5XroWjEXSP5O/MwKWCGmRp/CWII+nExFNmosxhFilIs9g2NpIizLMXAjQYQTCwWZSOjsoJkQajhrKO6Kneu7Qih19c2ZZKISGMJDeP4MS5TwHk5Hk5Gw1EkRJplo9HKuQvnpJAp1K2DlEM0SsGDRheORVkt9rf3v/ylL33lia+cP3dpZ3cb2qNB76pIRE0ty3kxm+7tzeezl73yJadPnB4k8QJqN5t9aBQk2aMFy6oZ6PLBOG1wJpVYU5bAx5S6ZdmVxhXGNtnk8XLZyrETX2lDx2l0cLh37sy5F7/8pboC2hdBnT36skV8GWxaIetorGu7co5EJn2DKNwapM3rlr5+yWgGUpekXh3x7iML47WMeOili5t069LW9v4eSE9RXNZVAjuKUh86F0EwIq3O+KssLzQKkJesPTo5Kdjl5rlmOl5+5hYqNz/Hzp1Q57VYRzd8yHW4Y8aWwnoaEPtFjmrmjT018TZo50VMGPr3Ql/CKE0kNPvSEpTEKMoibHpCRTXg33Jezg5mBwd7i4P5E1//0qwsp/t7T339qbNnL0zGaz/1D//OsfXNJEk0dGugulNeVTfEHDOIILmvLtdXVxazzfliHseJwlIeRVnN54WCerRSSV2Vxd7O9AsPffHhL37+0tXL1aI0DduwaRlVqNUxCCjnL5zd+vC122+743n33TtemagGMhEh8AtSKeas3xqcAxXuRTkL6JRtQwipyVjYK02SdHd/j8ZoBc+xpnz9SOSOqaqLMxcvgFEhzXRVuMAPxN8Aj5lZ1A1yo/C+7NIMDI8Y58gXHfXgkfNz0vIN25753/IGpkE8EOS8GOSobIjlCUF8A9KMiGsMDqxAumACsGWCfMcDR4tvQ2iNOo7JMnbNe72yepduUCfDOT3Z7gyzfxvB1qzeKLTY0YGsdCQDN6ZQM6w6ybMkglxZDBUS81khdARp76pJIxFDr0HZNMXs4PDSte3PPvzQ7v7B/u72+QsXL128crB3UEIzwQpaWKCc3DTN6mTtJ37svS+47550uDov5tj414juRFRs/Q/EP7RPQY9CJaAQD8ZOQ6wkZe6XpRCqjhQ4eRfVZz/94Gc++7mdnR3IooduvrhhVJLWJRJjG7CD6cFXn/jq9vbW8150H1RsL+cylnVT702n2GuShFVf2r6BmntmM03RTIgOg2Cund29qqxsRoWxleBxo1JuW24GzngnQGOvKUj6j9SFy5eaahHnowREAdP8spNtzWF5OWDzm25ULJbXRcKlk+iN+vgmNOD2wPqbt4zRyjmTIz0rFrUqwFyC30NrLou6Vg4zs7cKoEuUAZnT7oN/sYt38NYp2z/a/NPCUQ8Ets6KE9RtqfMglIfCgPE3BXXWjHcSOCoxdQlth6I0ljIWCfyMZRQ3qZ43UQ4B9iBiyhgCiA8PD8uyWsxm873Zo088cm1ve/fKtYuXruxs7ezvT1GvqJTA4gaQiAAxxBIStuB/TQkMqtbNYDB890+8+1Wvf2WUDqATGDYGhpwiI7MaqYhUasIKCc0P47ouNQY2RoDPGCQBmUm1kHWsRVM0n3zgk5/93Gd3dnawVD4ERZMB3TWeQVMXtMQhiahqiitXLm1/eufC2Ys3b54o50WSj3Z3dpHvSZdAZAPhEKdtpXmI7kD7M8SMVCXTexEMnKuIpGjXi6MDcCQmk0H78HB2eHiwmo8wbo0Ot2VzCJS7EJ26jgkPNR7WjuJyevlXPdN2zKH/OSdNfzM4zOzGXEVoaSLXmZ3BDPsAGv1x+dPDfVBSogjqrmiNIrStY8eTvoPgK+RvEVlzOVUwPz1L9nMzZmv7iP2QnMsot1NJYmtxoUhdYrAErWhUo3RYsCFBZCC1PABeBHIvVqSoG5XKqGhUHMuqXohGL+bQf2xnd+dwf3rm/DcuXtlaHO6fP39pZ2tnMV+AQ7UqG1BvtajB/w0iKDYN9Wod/qBa7KT0AUJFaphk3/eDP/i2t7wlSSaHxSKKIwh3hoRq69LFXWiwxisBBzniZBTNIMCqHqQJumSrYj6HWs2JLooikskDH/vYpx789M7uXgqhyyjrUotP1xPDHiWZybGjL4rHVXPp/Pkzly7cede9cSQvXDyPYZJgyyB8BCaPklEEpnL0IGHvRSGhmCbEbTY1WfhsF+8Ae23dMlPqyG0QHS+xeKgvD+KNLIpSFNBdGQJFrfuxA5qOBXA7tN30VmQpB3U+1A3bvmSLj3ef6rV+9SMYC6PkqlL7LucFZaY7blda/gJ2AyN1bjSQa+rbTpwYpGmhFYibjUoayAQ29dytQur/5wbFMhJ0tKYZbMcIbARmZ1siw7VbrssIMKSNIroQ2my3FERpbLMGEQ8JeFtAGEMuqtMoSsB3oqXOkkQrsZjP54v5dHpYHCz+8tHPXd66drCzc+nile3t3cViQXqpkhCnIhTEIIKTxrQqggo41P6TgpHAzhxsm9fYARBrUTWV0Op1b3rT29/6lkm6FiN3A6aMzJR7j+lHCu5WynsAYlSX5byYI63S4HKHR6JKq2I2L8v605/65Gc++7m9/f0oiSH1yNiZTC14oiG0k6ZNOVbaUA0E1kVCTA/2P/mxTz/3nvu+9Oij3zjztFINualAdAfuqlmTF2C8sOd4PHt7ezUY5xhehVyWmZkcOLVK3ABBTdI0z5MTpzYxGAUkGHBCWbukG58lJ/ZiCrMwWxO1/u9mt9qYcJwY6abdvlo43INhPOj5CFnAaJzhDS03+A0U3OTM0HA5LL1UVPVgMAQIR/cF9Kql7uvWYkwMz+UaGELs/bKtJTlagze5aGoXaGTFZjSOUuIcuk+otzYAMnSIRycNyppF3SQqWkB42CwVKklGUoqynE0PDqZX9x565KFre/t7W9uXL1/Z391bFCDaqhp6HUORKIhWApEWeBSiKq0HjUAQEox9VYHB4RFAamuozrOjs3wILEyo3t579/P/6g+88+Zbb8X2wtD0zOYdGsSlXaOsJWxTaHosQfhyWUFodJoKLUo0O1dNMyvmSsqHH/7iZz776a2tnSRPqawOr7BnkqTMyBYlcO9o8tBnMo53tq+ePX9hsbe3v7cPbBm3uyzRmISnj42LgUg10LEMDnR/etCoutWbyutHvayFS4fWMAHW9iQejVde+bKXTPIhVOfEUiRY6Z4/auuOHyk+mmgDllgaMM9vSinV3rFzIxn3R17cN+wQtTWgdQH0fPjs34f/GJMo6irxaLI+Hk22d3Ypk9ckMwjoFUxWKyoibAshOzz2uqoXA2zvTK+0kyORajyBHwYYfZJmSRRBzF4ag8QOTCaKhnlWzEqhazCiNeCsBB20KWez+WJ78egTD1+4dmX36tali1f3dvfKYtEAH6ujGJpXF/M5OF3RahpBS0DIByI7MzrCsBy6RVSiu8QOXZilW4llER15zbEOIDPRLTeffvd7fvj0zTcJqFwTQwQjVsLheVY8vATtQxE5mSvscgAZ9nGkKugwBvU6inmi1Bcf/fKnPvXpra0tiWFxsCIFTYlpc6MINFhQvV2kluuKTVIx6shxIuuq/vpT3xhKWVYl6SeAlyaNRKRpmmUZZEEA4QfmuLe3X9c1tDW9QRjqwL7r25bE0SBNv+ft73jFq18xGIzruhbA5pEMmXudh74F9oGG5y7jTWy5Poh/OFNqWzCgyymcLQvZDaFsexrd8Z0nxTFGY8E9evwl0SWt2lpe3qY/rQ7cGlsrkcdra5MzZ6AnYRqlCe44QoqN+UUUtBhsPfrEp51K63kO5LGDjxHqq0IfPxOnV8EBQCBQnud1A3Ra6aZooANQVap5MTs/nT7y+CNRIp986qmLZ89fvbI1m80Q5STYaMHbg8ntYEvCV2JP6jiKalUvFgWVacMgKDyhKAbqYNV120yUbQAzvNGfLuKr6xCg4iTA3FAAPr6++cM/9EMveMHzMii8kWqwdEHlOJuX4/KLjRoP1TmA3ZloEAVNz2SSJdD4E0vGzotCKFEp9ejjXzp77nwKJjhAQqB3zs+DJkO0e0NlHKhHa9URNNtFUAELMRoK2VXl5z71yTSCYiCAXXFMRQHiKB4MBnmeg5eproej4WJR7Oxsk1/KkqkWW/Is2Cq+HJgwt0qp2Ab+xEn6oue/4Ad/6K8N0hG45CDSyxiuA8Ye7HSvFG008PCwXKtdB+AMnaUzcrujtjo8YzLiBq5nwx2NMBpoj4zQdcZbznxZal57KkaRZvHkSKsJBTeOnx5Nnpnu75Z1k0CIggI/AKazIVt2hZpobNPs2ghyUkqAT3BdmqbSYMNMoFlB3TSxakDRhKoxUDq4PJhf2b70yc98Yl6Ve7vb586eu3r56sHeFMP1AAOU1IDQULnCpkBhA0DKb8ejw3dANCDcAEUey2qxWGRpBq/BGEQlGzh833+Im+EZ2Ie43JOnFnpLqG/fZDj5Kz/wfa941StW19YG+TCSUVUWUMfTZvgza4GxOKUJhDojMoMAT3qrNnEUuqiqQpaprD73mQefevIJWFscg7UciU7QQhh3HJEZyZSdJOiyWF9aaQ2dE+N4Xi7ms8O5cfoIaiMaJ8kghwwKihITUsxms+l0CvXPsGImohoZqlzRbAaKIQzaKYHkgeQFTInj4ei59z7v//nz//L4TSeaCvtaQlC48aWRO9qW4zFuP2vcsb6p9jn0MB1uD9a9zE/3YAeTra5/cXvxEdKu4/6BfWipwuznetTVjQjuPuSWiWVa3v6Ot772lS/+rQ984Mmvnk+ESLSqsQVWhaZEABnCLuw5LSLQniJgyQp6GOZxivmHIh7EqYgwlKjc398rpgdfePThnenBdH9v+9rW1rXt/b1pWdZFMUfDFOinVMeHOoShk5PkbyMvOrnLeBAhBsNX3SFQhvrnTZOmKZaAiKDQBToiAz++Idkd2YUZ9rrb3SKgkJnQNGmUfOdb3/6ql710ZbQC6TtRVBYVSAU2bctFgdnXAfdB9uvrWgEHRq4FdXJQnI4aXRXyq098Y7q7HyP2gtPIGr7NRKCzCeY/oZDhuigaK1wcp1CRB/4/ny8WZQFJi+A0QjcxJOuC2AzZQtDkBcp3JFE8PYArz3IcGSvVMva7FNq9A9/kqkHPB9UkUXz3nXf9y1/4ueO3nhALdPJBzR9XGjLYV7MoFzDAz6PNrugQW9a01tHZ+ALdc4xHnPOyy9m5nPixHO36QvBtMLZ/P/d++fl1RJJe7GWT8inpVqoBJBVybf3U/+N9P3n54kEya+ZxUyZRlkDLDDD9QlMAmQ+SqCoqKFsMclFSN2WtqtlhcWV7+tkvPHgwm0339y5fuLy9vTM7mJYVWJJKAJZaYVcBY6aKIEPUOCYjaD5NaW4wJeBhxtXp85+oyoGL9SJHBe0KCkcK6iKnVQ3RS+QYMTHsrhyx1U96SFhLw+3ZVvMoFqWBOd373Oe/8a3fvnH8eJKnWTbAaKWaJyh5YZ3Ed+RmYB6ziUY15DCoOM2auo4ijQ22m0RXn/rUJy5cPKuBXUk0LNltMUmL4CvTqI8YtxBVeYfYY1JSYNvKstzZ2Z0eHMRJPBwOdAPUDf1D2JUcJGrwrsGZJ/HVK1dnhzPK1qA9xyJ7gem5zfuYFcU0eEL9CN1c8f0vesm//Bf/7O7bb1NFE4H73Wq6R7OcbhGm3ptu4JLdgdnnvcbgo4a6MZXa29Od74HLCJYAGXHQ9dRxdQGPkBmusz6r8mGvSrBIN/rUWpzExUjmSb2YZ3GstJgXi7Kq6/nh9s7el7/6+KKur1y5fObpszvbu7PDw6ZWVVk2WIoWo/AhX86VLqXEV0OiIyzFRJqSaUdpNG3PrRxdsUo18mG+PkxCoE9MhC0wQilTgE7rtOKb5+yXnIj2xZfbrxz2WoMyuV91o+++8573/th7NzePZ3meD3KqJGbCTfh+GssBT5CipgiqBvwt4wRmS1pJrZo8ympVP/G1r+/v7cUx+MOQallTMAbMmHKwqOyaGArkxK6Fk6EOdTM9PKD7m0alCdixScCOYyTHKejA0+l0hoUBKMKE+rNBFQKs7BGI7CaMzsVveLsr9mpD1QmMkfH9L7j/F3/+Z+645zlVif3RYwjvk2A7tGkh/TveVobbwOy39sbZ57PAyWd7WcbCgMSSZthJrDfoUuj5Cw2zhFw1+g9pro0+X64qtzzGjLK2olFB5VVyMkqefOzhp849dXn3yoXz565e3ZodTGfzgkL9AEulbsqSbDN0ng00oaaoLJTCrHhnjWDGcm2M0w4CULTzhIqHZBm+a8ov2140Tr2xRBoMOTYqSYJZiFlYTFUOL5pRtL7TtcJTYKSV0WxcAvCRWNd1c/LETe9+z3tve86tWZIM0kxqQXXqXHyaFwZZmz8KpLa16ZqqqsFUmKVVUaN3V1fNIpbq45/61IULF4jyKVUbXdBQbqtWGDiwJQRMfKZtzIsPlGVB7Bjs1Wjyy/LMGKbQS5emyXx2eHBwMMjztdXV3d0dSFmpofuZUnUUxwAElDlosNUEbbAAHGMMx5nAVNI0vffOe//Hf/xTd919u1QDCMRFgYOCNr0I65qGM6j2BD2A2vC38FqGg6FoLrrYYA/9m0Biji4MpAzqgrOP/meiAp1rxvBct1AFhiKivaD1JBB+AyWFO9VbA3rTFxzW0Qoozha6Vv3iv/oFKJNWIsa6GoiIfKa3AKEWxuUj7MCkADrIpmT625mq3vQ02YxNUK2j6jZzye+Li/mhNVPkT2vmdutoaFtyh4xWnH26dofMWdf2yHk23bJYuRx9LFzeHN88+a4f+uEXPu+5k/E4GeZJmmNkZRi7zYpcWrc3pgHHIBpAAXfM9E2wY3BZl3GkoxppSpxcuLB1eHggsd4VkwKM4ceRaCaGBAZ1EtooaSNNsFeLCXKOKWGQkA1jsJtrOztpkkYyHo8ndd0s5nMo4RXHEE4CnjelJSjt1m7AtDS3eyaiE9oCp0l69+13/fN/8bN3P/+58zJNEwWhoRKr2boK4VzTY/UVPCgG7IekN/Nx1xorbuTSfRr8MhZ8fe7eJQU2TJHco/gfxgh2DMg+c8AQZXi+EQotGtAMFH0ErvcPe9autzu3FsKzvVJaJYtiUZa1ADM0xdnBZRxKzmJofUhWzMSwLBzMRx3YMjV0aM6W4QzAll/5YsyMC7L8B4y7CN1iKH+glQtjmIiuu+B+vCvwQLgqHG164EwU9k/HgVFslKAWro3Xvuf7vv8FL37+YDTOx0MJYcw11K/qIYyGKjuxit6KVcZgQlEMHmO6CRldDQ3PinprewucPIjijN5a9toPSq7vsjMdg0ibmDYOxnugFOAtKcBlVYJlutFRFkHeWZKsra5DalRRHM4OkyRJYnAiAgNPM1sNxSYQ2gBK8EtBgR5Z6yZNs7vvvPsXfu6fvewVL61mKsZOTBB2xcKM2uaEFiS6SivySL7X/vabMEsJFl7NnnLg7IJGl18tocBHGZp6f/qIiA5nX6fQJlA8BFQyi4UAFwUYopZNuzuFZXUK4NME0tnAoUiAQJGFVkQk7mqjlxmTZdNmtCOMlbPp2tbS4/K7/S30mzHdOLbTUl+NWOnqJNItjohbnKTMNdgvIkOGRhDLthIVhTEwCYvs3hDLGUlZNlUs5Fvf/NZveeUrNzY24zwFFzd2RzBGLfOUiVdx/7q4MyNTQJgjxZ5BqiFIqkIvirKYzxbTgy9/6Ymr165AqR3b8c6ipREvbZYuhZfyXhl+40iOJg3ZmBWwq7DWYpAPqrqczee7u7sYaJE0dQ3krwaJejQaaaUWZQFUBuvuuAgJvy/O6o0viaWsoEqPfPE9z/vnP/vT97/k+WKhY6kx0JX2k0Rsh58B5DPlyM++DarWGxFC8TevwupeSTQwDzsBLMDwVtKV+cnkaC94+sASL7iwQghBAi2ZOJWA4ofG/tq/OkYIuG7RE+dp2Cu2hwTu6zfR/Qhy8flbAhRrb4zbPtYN1ilUrNuSCZVHoZzBKAlzzDRtJmRCjg1hCFo9OBqCgUYygbwl2UAciQFQSwHcTYFNgrpZk2P3rrvv+7a3ffux4yfSHMRR2HTqLWbm4pIeLc6aRAZ3qDhVTDag2jSwTMxtqstqfrjYmy72Dw6LorBczpgzW9Bj7II8IJ7vvteDnUzq/EwwkZ2d3arCNGOlIFjW6h51Xed51jSDsirruoZmMBhiaXfbBGrZnYKIuQTiRqH9+Ete9OJf/Nl/+uJXvlhXCchjwJtdPVKaJBUS8BzWRbfwDT/KuGSf6+J3731HXH5vaA9JJ2Sshd3U1ab97zr8SQ0KrBeiP/CL1uwBniEQpdHVEqwP6Mn3YL50DZ14I/c1eiWiREOWETgB7FBGAfaPsPircJoevd0r3UvJjoq+XXA7EBv1crKL07c2KMOJ3Wm7XhkmqJ1KvRA1olivCLwr4AsREvJ/AFeiJNqQ+riMpyLd0RU4fOzY1sFjLCjWkAhaDAA66oO3nLrpR9/7rpMnj+eDPM+SWmkoBAmp8K7hGEtZcPo/5VEhGptwTezDArUpIUIspqzDRbk4nM9n81mSRJCd6+t2WtqATTNo9yhQkco4G0Ll+DLqtwnEWhmaYTURiNbSWs1nM6FFVYMmPMjzuoYyl0qDbI23RePRuCxKSK6ETAaRRZmjJabULkaAg2aLbEMrffftd//Ln/3pu15wjxCZkuRIQ50DOzAFaaItxuWIZgjgHenVexK9FMBA7MgKNOIImdvAdBdSb+zq+jKMo8RNlGer9zzOiIS1VdZNE4FP0cQQhq9aoht3TdQ2KACCkym7hZxWPGS1vQ2tQbtfubgbxkEIiSg3zp6Jy1XELuNGBqHQDmpD4JVnkh9MHBjTh7WOxqpeU80VEVeoD0axPFXWr86G96Xp5yPxxShdYJ6gcyM75LPJDKj1GnojTh4/+aPve98LXvSiOIaiOlgVoDK+ebv1hmO7erCGByMK40dNJGJI1QTTLFQUSMELC5beWGItAAikhEBlQ0eIoHDoIF5qPL2ep1s6h7hN4kQEJeuMBdEgPKUrDAcZ5jhCFGZd1xSJFUUSo0ngKKBL03hcwZegHlSylikWkjZFriyPwQJBcZrcedtzfvEX/uWrXvEyIYaYWlWTywjmEaP1AINzrAehBX9LkKULQjxbOGQOlmcz20V7aH0kTnIPxLLvr4vSjk76NFnH2M0cONdzJZe5XZq+wmbuiUrALN2jaPfNxCnsXNhHWzJ4a419yECyRfb+dAqmpZoKhUGaNnovjM/fZIZjsm9bPkefL6GDVUNA7qUISoysRgiJoY+WVKIsKuPFwseVEHldfU+UvDRf+aguHmjKQoiNqvz2ZPRiDTUZD0GqAMZi1W4M4bJoB7KtAHMxZV81Klofr77rXe9+4YteNEgGAlKGoIoyRVsj7XFsl3Fhq/dS0y8yQQNFsJ6wqqqHWUanU0OaRlVCaq6GyLYEswis+T2I0peCAspN6Q1Lxl0sB7mNjE/dQgZ9AcZkROPRaBhF0cHBYdPAayEcvW5M4CuUKIDa7htS7u7t4ecQggcdWLRKQCKzVBdCy6NbT9/2Cz//c695+YtFHQmorlmBUI1uZIoQY9DPIc9Fl/bYkLrYclQ80lKgZp8tDyKzz3t9t2ew62KvhSLuX2OCOX+Zt6/YVLLglUYXw8wVCDfuIUydVXbNbZYkQDaSPS63Tq5UdlYYhMIFlgr7E5MGbUFpJzEjp0Xfhi0H78KZgJlECnRf0Bzj4SAfDUcbm8duufXW255zWxpFf/Hnf/H0M9+gtAHMcML6fLqqVdTMF+9cHV07EOfqg28bTb61iWcbKx9Y7D8dyYWCgEcTiEwV27D+JpnBKKk9ErKCgEL11rd+5/Pvuw8K0OWxiCGN1rQC5STeyg3WeWSx15actSeELlOkDjZjUZdVUVYlFJ0D9p6lkOjoI4Q5q6FyNxD4QV3IXK6fzXCgWVHtSxNeir4+eIpQX8rBYKKVmKcLtPyZ+VZVlaZQhxI7DMbj0WixKOYKanRBigj6vSCKm/IctIqT9MTGyZ/7qX/0mte8LlGplgLopNQRUiUKrr9eJGCHt7UUYAtLHbtRB1zbIy+3AAVXwF7oE9G5jmbfnHM5cmrpwRJjm5X+/c8QayhSgCqNhxNZQgfbcyTDK8wlsa4L6/KjqbKN9s9FbWT2lV9N4JBLZzKuf7oVhV60IVOPTvK4UtEJBD4NVY7VO3/4h++974W333b65ptOr21M5CCXMyjUduns+aef/oYxRtCPRi3i+KNFmQ6iSRK/epi9sly9U2b1Svbnkfy6EAe1gCpWJsvK7haUdDPiOs0e02/1/S9+xZvf/B3rqyuYRBRBrl8DZWusncggrq274cTn9oU7BFZfDHcB1QDNSAbNsQgW4PDhosmyLElSbBxqHO/GaYPBKoSElScKZhaGD1OzM7Q5W4nbsF/kwITcenVj/WB+qESzWJSLYi6FyNI8xzIDaCMHn+TKygR+l9CoIUmSLE2hKDzkToGr//SJ0z/7j/7BG1736iwdyUTqoow1arxgkoc8p459xcF5S9tkxp5A7O2zzLTxJry5h9Xbi1u5/YcEmeZXvMF6IZcQCz9I14Zmak+ZRDFPnXrIj9N4grm434zQhinwbeTsl+e7RMxYp6EcipMHWAGqPlrm3x9Oy0b+UZ0rVDFtSAV5EVCITtNBOhhoVU/39kx+nLOcCjnIhq947avvfM5dw2FeFMXOdHf3/Hz/2v7DX3jwS1/7CqbWQuhGU1cC2hpDLZurSm3N68O0vDeWcZLMh8lvRPOnZ2JeKRFDaKFrjEi4Zx2Ahvc1QtRC3XXn3e/90fesH19LkyxKEywNi28is7zR4q0Ebfy9Fo1DXKaRY8zpK+sKcgmMWRj0UZSbdNmI207dsrG2tr21BfhukpUsUbElbygUs2V4sDBhqYqNT6XMXNf8RGtdFlUuoYJfU6vDg4MojlZWJqPhGGro2NPUSsdJPBgOm6bJ0jTL8ySK5vMFeIzT9OT6yZ/9xz/9lm/7tkhjSotsRC6bAjU6stthtdouPNjeq8arZ538Fgi9RH2k0Nw2rRypwkr7r+306j/1Mk4wrBt76SSWIzfGVOFu+1GcRszk6XDGQcgQPoKBIGAvcZN3c76hy8blAbvwVhIjobcoCZ+SlSHth6bWCxVQo1AVqPCmRqNVcEg2BRpFgS+878d+7Lu/9/v/8qEH//W//lclhGei2RY3oq7ql730/sFokCfR7/3uf3n6zNOL6d6la7u7W3tNUx7MDsHJE8drErJ+i1iWTV0W1SKWj1cHz2sGN1dyvLrylfWVncP9YnGAOXyumJvXW3H2VFoVvdpK3XTqph/+az98cnMzIdc6hsuYGBUTLkGo4roFuaJ6Xge2XfxAICd1GaRfoQfZENp81oCK2NEbOkOCUp8lp0/dfPHK5YP9faog6XquYFAk5AV47YODk3MVWeghfkwiNBFiyCKMk/l8fjibR1rMF3MZifF4PBqOUOcHLZ3gBeqhJcnKZKUsywQTkgWksICH6djKsX/4j376TW96yyhPH338se3drdmiqermBc+/e21tZX2ykiQZRts14HXq9kyxVo3OCrxiHOJZL4B2hd9eFVEGti63Nf7FXhH1n4W23O6gSzEYvyF7IaVeB/IFt1h3Lv4dZYljmTfaeadLuRCn7tNc2fK2ZhShnWrrtsPRAh/GZz1f4Nl3Da9tl2vXZFhBZ/hBOvinP/PTr33Na/7PX/6Pf/TBD5Z1nWXJc55713CQbR7bTJO0LCF1kWqBYLqTPHnbTYMkrUT9sY8+cPnKpSRJB6Px6sbK3s4ulXgblYvvG4zvzoYfrIvPo+h5smzuy8eri6YA/U/Mtq5Vla6aqoGCHzHm5RBTYuhLuBLHTd2sr2z8le/53rvuuj2JojRPpYxVBYX2vH3AZlx4dLXtjHxUmhXR6WBpp8BnQwIt4TNFzqLlrK7Kg0jf+9znXtvdOn/+/HS6BzXwDF8C7M1y6NtQYC/CUD30JNyGdpmSKNYojS2fsVEitmgqqFXisY1jsfX3UnFZGwFnABjUdSy+MJ6MiqY+kZ34+Z/6qb/y5m//+EOff/zxL+xNp42CfuO6EV987PMbK+svfclL7nnu3adPnhJ1DQ5CjNn0yGYh0Iestk0mPhd+OfYu+6IPL/RRGHcDvP56lxEimCLsolxaE7aE+GhOSuvHwjUQfACW/PZA7WH7xjPQCYEcEday4NyXNMDwdqpo1VR1IbUejSdr68dOnjw1Xlk52J9ePH9uOt2bz+eAOUq/7g2vf9FLX3TzTafAqtmA6Dgcrk9W15Ms/vJXvgpV5syMMHVOqCiSt996y+b65Pw3zrz65a99/ovvufnkyVvuuL2R4n/6+f/poc//JfqpIWD7YtTsQY6cOFmpb52sPrdSdSz38uyWpnqDlPvVbAdYApn5vQ5v6wO5PsU6T7K3f+d3vupVr15bW4tSqPqjoOAMVKp0psUw0JmbrHgdXGpNZqJWwFaE3pk0B9cryknYvxVFazBdCTGfLVSjX/iil956y3Me+PhHZ9XMFG+FKGnAtb29vcVsTrKx4bhmJVYZhkys2iIwSbUIDXhKTYNFPxaLg4OD0WBEMQO22yCmF7Os4yiOMwjNhWpHStWx0D/39/729/zAD/zqr/za1d2d/fmUqvlA3iaUBIn2p9MPf/xjX3zk0Xe8+TvuuucOU6oF9A0sa4rzc+UBju6x3Q/gXazo1Qi5UCwtsfgmMLVX4WyN0/aOWW28T0Y4Iu8tGBs1IGysU6cxqVptGZwN252VdVkpjWVoMGSTEVFrMKFHqVYHegCF1seObb7kvvvf+yPvPHXL6eHa8Uo3eZ49+KEH3v9HH3j4i18AY7PWJ285Fet4srb61NPfWGAK3mtf99rJMK+Lw/PnnwHR36lF+EuaxLfdftu8qG66+cSP/w8/MYzFYtFEKtm6dPHSxctwOkruNfHvJHUi60Lo9bJ+43j1xU00Wo0/Nkm/sXPw+sPBy5X6jsHwq0V9KcXqT8b8StjrY6ngm1q97ltf/9rXvHp9bS3JIR4JU/QhKZf2i9DX/Ota5RIOGyJgQ8MRddAwgtGtWMMdlSRswIvlwaj3UZKkeZo1eT09nFVNmcRydWPl+PHjl6pLNaQfwMNJks5ns8PDQ6iqBe3ufU4pJ/hQnxBqibrEYDStQ6WUrCigEen04GA2OxxBB/AJCCd+ty1qUe8JKCoPtRaiCIIlV0aTv/Mj737rd73jg3/4wWcuXcBoE8R2KBCaQOUEENOjTMrp9OC//tEHfuC73n73vfeh6dJ1jA1KpnEI/O9lg62LvUGaX1vpwEdczJrDpZH26D0fmMqFgV5tTT2WrvRa1P18mS2LkiI8EemRwbuD8fAYQMqEgnCNTC+jdJCCIbKGIjXcYAgilGjuufPuv/8P/t59d74UuhqW868/+cT+4vDOm25563e+6dOf/3TyWFxBbrq6+dRNw+H4sx//1CXog1gN8uH65nrSqMEke+ZpqHtqUghBEYU6pGuT1ROnT8dKnL1wvjp3Tqrkice+eGV7+9z5M7u71yAvsFGRjhZKpI1OG/3K4erzRTweJp+qykcOq92m+Xi5f2o0SZN4OD9sCt0AZJKKEhhaMCRVHlvZeO3rv+XUiZNkG8cUIeuKtdnHXnPuGKycP9hWdYe4JfTPC2hJoKETEiIV1Q1qnHocxSAhRwvIBwKluGnuf8H9s+nhzu4uHZ5qmvli4XKJ+EHyWusokNcoopsSSEKLNM+gA3BVLmazg+k+FAAaYCNvanFCySk204Q6RlEEOLD9KJpMVv7u//C33/Hmtxbz4tEnvnI4O5xMJqTjUGMITByF6VD2kVb6/X/0oR97z/rJzVPOBGom3GtD7cHgjmjof+XBfxzBTDpi31BS3OAVMLTgRy8etyRR2jpmDmda+PKAkLZN3YqFGG6LFY68LY7/auxOXkNufat1YlJYkCkf2zx2733PvXLx4vmzZyssxUaBGToCp+jdd93z8//4n9x2760HRfVbv/6fHnv8S9cubatIPf+FLy739r789ScwQ7KJI3FyczOS8sq1rb39/VhAPNLJjeNr48lH/uyjly5cJEsQlIyhCG+tXvWtrx2ksj48/Df/6v+zvXtQFdAVJRmkUquqrAnT0wwr4KpmKPU9qo60+vj84KEsOzgE+n85k783P4gPmmdSCcVaTStCSi2mPTNttFVdv+pVLz9183FkP00Spaw1pW9pbX29LlTD8GKL2xRDae0GyOQl1CCCqHJgV+gqaGoIBUMWrCHAK4WE++FwkMRxXZRF3ahhtr6xtgO9qhqQM7BVL/Bv5oVgYeTm+CkJ0QVAQyRKDFm+JTat29nbHU8mg+EgNXECJu7T+J1h241AHUmoEDLKR2srGz/5N/76d771LZP143/5Z39SFlCEPi0rKLcFqYI+lBOj5iFrX8ZZOZ9/6StfW33V2iDLXTa2qyvDsKDFl1pOphaWtLDG38D4iY3vF8uRliulHtyDgU1eXGeqfB5GHHWBEizayoXl8uQMUwrfj2KDbdzeWMOG6aeAtixQT2zWd3C5hhgmxyicrIAyzBBKiWqcioT6ju9409vf+l0PPPDnv//7v1/uH+CzkYgj3dR33nHXP/2Zf3Lq2KlHHv7ar/zqr50988zedE+XOsnihz/3+fEQQn8E1hweDiebJ48rqZ45cxaKP2RJmkabpzZVHO9u7x8sDsktSiXrIhGlWX7rc247fnzywT9/IM+Hp05OTp0+ddsttw9GyQMPfOzSpUuqqalXBz6gmqq6IgdfWB89Na23DgtaZxWpr0OfPGgGTOVcrb/IyMMOeIaj4elbb05lXFSLHCrephAdYhi0LbRpcdSm6/v9dPGUJhLZC9pQMxAq/mQ51E+HkFdlTwhEGjDawejRqZOntNKLYlYUZVEUG2ubl0dXDw8OiE2CpsxeaI/Qm00oDZFqSoLxLknKokjTdH86nUM/4tl4OBqNRjAM6wDovPE0Z9D1NZgb8yw/vnLsPe/9kde97rVDmYtIPP7EEweHhzUJDmSIYwYbCjODQHTV5IPhJx/87L133nXTTacMevc6Y7vsd5nQeh0Z2N50NOrS1Y7f70GPUE9vO2QNTtrjNdQdqTGwTcpKD0c0Rh0/XGDY60R0GpEKbNGuttESld9AgsNhSn+NoedBAr22mkoLcdPNz7n7ec9fW9188Qte8YUvPv7Io1+AquuRiHUyGE3e9RPvPnVq9erO7m/+5/989syZw8PDJE5UDskV73r397/trd/5sU98/N//0n8oF/PXf9sbhsN8nGZnzpxtaiUaMRisjFbWFnW5u72jtCqrOo6j8WjS6KoqSqn1Xbfcfu3a/K983/e+/lu/PVsZHDu+GRXDnd2zH/nzDwN7QZ8s7gD4fnZl9MFyqqYVtEilmC/4MtbYbIVquboUlJCeQ0q6FM14NGgqVURVmlNrUdMsKjgwdwbMEu2PmqwYZArGCUDZqTQdDkdpivZebBRMSfaUliAaKHEynkw21jfqqkqzJMkKHaff/qY3b+1jSV2wSQJm0iJYmxsDilg9C4QlmijlzkMt7UbtzfZns0MRRcMh+K6oZiVnb5g8jFHO6F6WkcjSNB3k65P1H3n3u3/ge78jSVeTKDvzta8++fTTs/kiy7MY61wZ6R3JlrWX2l2Po6Yuz168cPzEsQxr1hOstjNteTh+78WiBZfdZVhXv4Cql+PzUd0dell1OLDtt4d+QrJJomJkqHdL0qAfXsRwDNq8KTB6uaVQtUPslIOnZp4ISSGcnenwA7IapshFUBhLJZVUqqjH48lb3/a2U8dv3tvffe2rX/LoV1/y5ce+WGHFhlrX3/rmV73g+XdsrK//1m/+2qULFw+m+8A60YOxsnbslhc8N84Go8EYqsRG8qbbbq0Oy82TG+fOn4MKrEJ8y7e+YZRmi4Pplx//2rFjx4/fd+reu+95x3d912/++q984uOfyPN48/jKMM7kIJtsrsYivnr12s61vQc+/IG9/V3rLLbau4I3V2mkq8qZBEgVtNYU5v6xZ2jpL5hg67re2tmPtIS0rqqqs9qccivvcvm5s0xACWVLIHpD5tlgPFkxCU8koaIuS0XVB/lAaVVV8zxL4yRqGqgTOBzILM2lqE+fuun82fPQYgnKFUGIOERCOc3HHzmV4DERlEmapmkC/b3r6mB2CPZrRFHsY2gDUOyMTfoExb1rlSRxGqfr+crffM+PvOHNb5xMNgBEssHW41sSawyQcb1pIOLe9GG3Yc8YaIcdzxuIzXzgkw/edeftmxvrppWiz5TvRkEH28o4VeeuHlyyGOWLrrRY6rNRgzv3LrNlYWkkqC9lQvl8/iebaTdj1/PffhcQewfFEUCVFIQkm+pkyaHZURDeGqqNDr6MBPriNnOxqBVUjVSqfvVrXnvPvfecWNtYFLNvXDx71513rq8fu3zlcpQkMok2jq9vDDa/8pVnvvz4l69e2wK7JSaVNVX9xpe8ZHO8Gin1+GOPV2UZS3HzidOTtdFHP/aRp888XdZVnueT1VEcRWsbK//8Z35GrsrhaLI2XqtFfPfd9372Mw8pXT155sz2zt5D/+XTV7Z3D3anOzv70+m0rIpiXtrQU9Miwng8SRuLqQ4ksyw5yZdlJToNEkAQqro2D3zsY/fe8Zxjx0/HiRxkeZymbr9MfwoLieZcbLkeZ7Ug+w903YZ4xng4HI7GE0ASPOMay4nhc2C3zbO8GtRlWaUpEIvpdApe2mJRN1UNsVLV7bfe+vhjjy8gMgLLdKIRi7oqMGkRBOBExlAxx5TUTaghYFlVhL1pmmJygq0TQhmKmNYJf+LggN1xomO9vrb+o+9613e/443paFPGSSGq/HDnsSe+UhZlmkHINGQUR0mKgQJENZSIVFVpoTMofA1hqjKO54fT7a2tjZU1yg8xG8WcphwBPcQ/G7t0AP4OLwIjt1hqOuNZDEt5eM/zpmxgA0lboLa4BGBvUgoSl8OEW58/3isYh4FZwNljoxaxgA2jotfgkZC1iFN8qILulEVzeWd/7+LuopgmalGdPnHrt77pjXfeflM6Sq5e3Tuo5Fve/KaPfPjDWx/fAgjQem20GsdZeTA7e+bsbDGPU5BFdR2l2Wjz9OmRzoeD5Oy5ZypVr06Ga6ujKMqvXNo7nM2ghXQs11YnItGrq6t6EO3PDy5fObO7fbC1s/fQZz8Lve3L8n/9n//XUQ5Zb3MIFq4hnBGaMGC2GpFc8jKaulNmfyF/qV/dsAkTfLcQDcERkkTXLl957PEnXvO6Y1opwAcwGpmcAQNa9mSs/mgVJKcKYglOJcRgPMqHgzTNJARpmOhNY2Y3nAuMzwmWqsqzDGM7oK/noijLqogTUS2qw/3DpkIhivIcAesxMLPBwtDWMOQM0XECxa8Wi2J3b5eYKvSJTRKI0YUxXMcmYNgo6aOpHKqqQXloXdfHJus/8q6/+va3vG187KZYyK0LO5/67Me//vWvfOwzDymlT5w4vjKaQCX6CuxYIEzbkFdofDOfbayvJxmEVQuIPInPX7pw6+lbBsOBKXzW4TpLpWK/407cD+VM/sMNe11eK1u42KeWH3lpJtya/WRmFBdJFqZaeRXYY2//61vrcvlskKHHrFWQuQkx/Sg7qnJ25omnfve/vf+Zs5e2trZ39/b2Z0UmomRjbeU7vvtNd99+64mNE3/6oQ9XdXPTrTftHe5/6xve9MXHvrS1u5VjmM7uwf5ksLa2sX54eKhQF1tdW33DG77tVS97xcnbNj/0sT+7dPUCxOJlE5FEc1WCSgZIDp7Q/fnh5QuXLn/j7CcfevDq1rWdrd2Dnf35YtHUdanqJEmKclEs5iY73ScOkxjYGKHF+mFx40wncpaC2g3oCwwv9GskYwVcU33ywQcnGyuvefnLB/mwips0JdbBgias4dBSXGvFNZVuYPjBaDgYDIA1QcFooNMmQIpRf+LiSRZnKlNN0RRlmsZQtTlP8kw088UXP//IE1/+0nS6PxgNEuiQ1JDXB4z/YAszkWE2gQuSAZMkq6v6cHbY1CYWD4rIQgITdEp1tXg8OGro1SQiAeWttFwbrn7PO77nrW//bqUHv/P+33rgk5/8ymNf3d7bPVwc1iUEQp87N3nB81+wvrE2kBHwYWwmTILBcDiEppHYHJxU4zQffuHhx+67457Tw1ttqVLawBBwHUn0zNDSSruzASXuswj73807dOvG8H79zcnXtGV0cK4Bsn8zt6c7czhOxqWvh6sO9Vpuubb1Hm2sgYqB3jYkX0IjulTHiVrsTN//wQ/+8Yf/5MKZCweH+0VRVWWNVRdUWark9d/+hvtf/fITx09/5nOPfOijHzl16tTG5omLF7e/77ve8aEP/clDX5jWqj5/5eqZc2ff/Mo3vu/HfuLJr32t1NXmcQip0yI5dnz1ox/9yAMf+fTutX1VV7ffcff21WvTvZ0vf/ERcCwnsqjq3/2N/zLMcmzpVSshoL4MaOK42FhCWTSUCVkYK/XVJeUNEQ71BIJLSnsyapYRafnFfB7MDkQVYqBPX5xEqdzb3f6zP/qTPM9e9dJXjeBuCFXAJmlmCPeY9RY4QYhSk1Q6yKm9C6T6QuvfytYENOZEyvSHSO8ScBvCJLO4jnRTlVev7VZ1Mcyyi0+fPf/kV67ubNWqbOpkMBqKStQUUmJpjvFU2apgWgtIA5zPIDkxTSm7kIrIyig+LA/TFKqmuRIAaHvGvkpaJzEEkxw/fmpjZePf/M//30cffXx3f29WHFLVkbIskVHr3f29M2fPQQ+d41mTQOE7LKcIe5KmCZg9UWahfjE6TutqceHK1eMnTuRxDjVm0RnGj4XLsI5Vda+O0XE55h2FjXrJ188unIQsJmCPNPZhW7DGvaOlCOg+EuMn1C4q4NR5UvtMggx0PkDsbdCdmGRC6D94/2/9ym/8xtmLF6UALw9Ze6DVSaXAdHr/6152y8bGwd7+Rz78R1974qtnz5x5/n337U9P70yvvPC+u772xBMXL1994KMPrI7Gx9L11772NW96yxtFPKgW5dbB5SsXz/zh+z/8sb/41IXzl2Wih6PR4489vL17eTEvpnvT+axAV0dSlEA0oCUEdl+CtF86QJcrj8X+keBRnizAIhQfgax6VdU1JdYyiGhviK+dE/oYDOjjT/BbISpSzbntna0/+P0/OLaxfscdzx2qLB+AXRZaYx6V3m117TjKBwMh5eHBbDafQ/GNOMqTVMcxdTNtikoKDS1gpG6gLocoF/MEa9tcuLr99a8+PtveOre9f+nS5flshn2hZFEuBnkeJzE0HcclmaJEuE1SQPezGsvTzYsZ1NkziQTwdQz+IQGFgTFAHlMaIb7K5RtC7hG2oYFMr72r/+FX/s/9g4OyqaEKNiZ6xiagEnY2TdKLFy9sbm5MVsZZlmHpTFfZEGLhIQwQc7aEllUNHYb/4pMP3nHH7ZtZBt0VedZUiBNBPfCOwhI6ezs73/rKM3ptObFD0Q4dWOqh6b+IalOWNXaHtK8n3cYN3amC3HohS/rp1DbgX9kaTXiiaKqUTaRzIaI//J33f+CP/+T82fMNlnOu6xJA1BSZElLXyembn3PLXff+9n/61Ucff3SxAKPRU9945pab79jbOXHzzXevrq5fu3ptd/vKR/7sT+bzvbvvvjsbDpNBdnBt+viXH3/qqa9vXbm6t38IrQFBOo0arc9fuFQVUAsewn+NyoCtCjF029XyMbY8k2HJNVDfrhaKOZYFtJyFaCFMP3TA0Wa71r7EkZiCokFLhI8SMLsoykwcDAZKxJGSz3zta8dWNwYnT4O9AoTRBHvPsNZzVt+hzAWhdZpC6edyUWosVpek2WhlAm0bikpInUVQPQfsElrM5nOoVSD19vnLDz/+yMWrly88c27r2tU5hGFBr0IsywDhH0kM3Z6qqh6NRhJyD6lPmPX9Yy00srJDWVqoy2Nau1GVJgyKhEEhEWK+iMcjynDw1izb10ErsbW7U5fQHhUYqQTEBiecEvl4DJXocZlKqa3t7dXV1dFwnNSJSEwMqYdC6igDIATWl+nuzrnzF9ZWVrME2z17hHExcBhgz4/Mw3O/0O2gnH3tLBoswdhdPC/oyCtM+el/gAQ9yA2DKHFlmB8P2vBoqvte7xQpS/qsluClxkDSsNlASJEhxDmKvv7ol/70Q39y9uL5OI3qGhQ1SAIHCQhELioxk6yPV//o9z7w8Qc+tbe7r4UqisVnHnzwzttuO3bs+Ctfcf/HP/Gcc2fPVE29vbvzR3/8J5DsaoyjEPpTNxVUk05AaqrBl4xJfGiCtYzUIaSrzsbSNbx92LlyfI60RokRI3EpqN+2Iraii/EeWQNzYNg3daThF4zyh00ZZKMkTQaj8akTm69742s2Nk6t5aM0kzW0iqkTnSInVNi0E3HC+Sdt5RCpVZTIwXA43T8QUZ0PskkyjrMEmjAURSlrXWtRllDmQsQ7O3uPfvmx8xfOffVrT108d/4AGDW0+YbkZWxqDm1KFURkYcNHKNQBHLZuIiABcE5uI0yFyCiqmrqATEyzCZhLiOGQcYJ9WQEbMYAEjt9tSQRVc5qyamQM0eykJw/SgYFIKqaFriZKeyCZ+eKlS8eOHRuPx2meQR02lJps+rZxFlG9YMhMTMXZ8+fuvuOOfHXi7IgByoSI0uZGPXos8x5bk6/Hdx/xKrnt137sEN8hqZsPc/EzJTsomsfCP1AviaImNrVd2AOWutieFLZaREgGWnIz+4WhsPf4WboQY2j9latX9w+nYENJ03lZYEs6CA0iYaquSqlEsrW19ecf/sgzTz8DRaLBmyz2dre++uRX43j45AD8wOkgVwWK5iKCkhI2MQD4RpZSYD2pfIabouXZkhRLJa1uaamTqZ7nmK1xE/nNQS8c9ObCdAu3oZa9UjqE1/PMV9RdAZNyoIRqnufDLM/GKysn1o+94U3fcnzz5Oax9Y3R+tqJ1eFkbRQNkiy5cuXSmfPnqqoUTSKgx08SJwnmMLsSf1S+A1oqR3FcFKVumlhKsDjFen4wjbSYLcA8W82bT372019/8mtXL1w+f+HSfDErFeQuQzenJAOfKgRsYTtls2QgNBhOVy+KJs8HRbnADjIukQHwpsa2ytTcEDCZmp/hvEBPylIYrSTaDMWtaijjDuU1oNEZOISgLCa8HexjUmZggqJGZa6AmTGiYKUELGjXxFEETqqDw/F4AtWwMHwTozyZcZDMjKoZDMePPPb4i+577trqxBYyYQZ8Z1UKFRwP0MsS/bvmK/6ntHbFpWXrQhzuful/JWgMPicugio/RMeZIFuD+w40bIXj7gv4Yh0o+a9aW2HNZlTICRL1xate9or3nz7x9JlnAKEhIAjkSDD9lpURrLRIPvKhj37j6TNQRAalLAUJ5tHHP/7AZz75mSiTdQW+BIxBNqYUbDxv+jaY+GIEFF893Wi1vAVOV38JDYXulG1GpdYauoTgxjkF2LaIMuYrUwadWA12/YuieDQaZ2k+HI7WJiuvet3LN48d3zh2bH1z/eT6ifXNyfrayUGiB0kWp0mjpap0lmbrz3v+6srq155+aj4/1FBVEo4N/Z/QOghc5yCyRHk6WN1c1XV15co1GaVJFF/d2W8ide3atenO9PEvP/L0M2eefvKZrWvXZuWsLqo4gZaG0LjElA2yKUxULNYVB0HxlrwISVyXqDlDpQWMgmqaZjYHidtxIiz1h3qQQMtbEi8w/wFSTKFQs06wH9JwOKJswRpQN04GgwCvyAZqw/dcjVzyXGMsCvw5Xyxm83lRLIaDnLY3grwMDo1EctHA2NRPnz93660353luBEdHdwPg9spvD2z0Jff69t0hAEnPlo/QbvmIvWFZvfNwn1GdI1DfDLRbn4hLUDUOgm44R+sVnscbUydfII3idAatKimahz7zqb29aVNDJgISdapkaApJAhAkIvniZz5ZlPM4wQAg7IiEhl8xrxe6tCK5Me34gHUt8SRJtDGl993GGpGnvT9kam8TYf+dKZqIF6XjYGRCbQ2xhhZSUBEV8Gm0Hg6HaZROViYra2snj514/bd9y9rKxsb62igfT9bzUTpZWRmvrY8wFwJ0CyxUhTwcmn8BwxFKnb7p9GA8fvrpb+wfTGtgmQ1Uaoyi8WC4vrayMlkdj4YJdOSFCOPTGxvnrlxNIvlf/+gDX378sbPPnDs8OGhUU9Zg7QApXeQVevTAoIV0kXoFksWJxCWqpGH2HMMnpRAlXDpJoXpVlEZNU8/nC9B4vTkLu2NZhUJG0MubiiKY7moWApqKBC2ZQgcU4rNOA+OleQzt5KcFQoFSUkeXL18eTybz+Xw0GML7UuoIaYqjOYotZVTVZRRFn/vLLzz/nntuOn0TMwS6bDtmhu6Ct1cb7S8tHO4DJtFvHArHtE91Uhc6Y1Fd3R7zCsahQrlQXxSOgrSoqgZ20jEzceTRDGVeZHHX8eM+U7uLSBLIQ0Save473/YHH/1wVVbAgMEYDB3MMCgM/gablqqSvZ19dAYgH8W9gJI0JNxR5okn0gHnZxl64e63jYCe+nY0An+jTzFnSbgQCZRCAWSypRKbFVKurK0MhqPRcGVtvHr/S5+/trq+cXxt8+SJYyvrq+vjldHG+nA8HGZpBoZiK5phlXKn1tqwP9TSIRNofXXlBfc979rOTlkWo1E2zAaDLIeEHs8xTAzsZDx53r1rIs3vvfX2D3zgg3s7OwjxsLHASzXKMnEEkrZQg8EQw6GtrGeUaUOhdKMYG4ENVkpB3wZ0npUFFMc0co+J0IKhQBeSIgaHDqQi4Z/kLYKRIcdpOFhdXVkUUMUWNRqq9ey23EdpWqxygR/GaE8JJ02l9nf3Djc2BlkO0dFxghXfWxTYmh5j4Pxnzp07fux4lmW+eFwvLvbIw60eMu2vW894lNdLcPjIVzL3j9PP3ZY4PDO8wrrynPUGEJQ8TKZtp28w6oYM39aJK21Nl6gv+UsjWSENrkWcvORFL/rsZz+zt3+AjaEl5KGBd1LlWTYcjXKRJ9CN22ji9kRNHSHwHuDt9qXcbse2sCubhTvkIrMN2tDg7B4fiEylyxGjIAIEG7SAMjoajfJ8NBoPR8PxsdWNl778/sl4srZxfHW4vrKZroyObaxPVlaG49U8kblqIDNexImA3vSYbwm0CKKVDLA6gYFnvymdZdnNp05p1cgIUJr8Tjb4FtknWGtRXGq0iKr3/a2fvHj18n/9rd/cmhXYhBOQDYzOmGyfQpFHYIOYjMH0d1/Kz1T8c9Unqbw+JD+AnlPSzaio2pLQmE4UxRBQATUuyV5lLXam8525S9d1mSRQSNaXmHYnwpDFsl+jiBhiAeFiQFimB9BbOEvTsZwMoCYhmgaM9GXOjwSauqllFP/FJz9z7+13bx4/ZhtUcj2Q3tfOC2yxyo5I3ca4NvDLZUgRfG1SMkJ2w9oYMELaed77RbwBGX6AzS9JsFhSu7Rfiz9152aSIawqSGUGFXaJxZiIuCmreCDf9YPvvHLp8p98+CPbu9u6VlUFXQE2jq2/+pWv+MkfeWcij0NJHdPExA7sSXSoqFpLG9ux7ub6lhP8S4b5JMch1NpKtCY5QEhMiEc+NllZTZNsMMjHk8nxteOves1LRoOVwUo+WVlbn2ysbmSrk831tdEQe29HURpHDda4BiAEQwuMD/hvtho8GCxA3JAPBtNuSUYUtT5P6vfjBA9kaEjUsIq1EP/s5//FTTdt/uZ/+e0zZy6UNagfOAz47BIo+tVgwLCDEFMPzTWLo9wHkr2sYmyad7iSV5jSZPq8Qt5EEo+GI6qhQyoY+XtxTthrA5e2t7+HFXygxI9JVfO5fiQw0oKsGOWCtySGy2P7lCiO5vP5bDaDLKsSan2AiIE2CUP7OLsSUFP3cLp36fJFyG0A2c13Jugx2yxlmaG+3MXzXkVTd5la5xPGZIMaV3RmvpnXURzc/U52B7AZxiB/MX+ZY0lMyqGPOkIs6aBExCmSo4F4SnC+QJ/aeRGNRq/7lje/+PWv/MBv/f6nHvxUpfT66spf//H3fe93v/PU2jAqZ1QXmtpSXX9PTD6Km0K/zs6oFdEh11wIN4m0WZAfTaI8tmUQYmW8kmX5cDjcWNt4+atfPhysrqyMTp08uTbayCYqk+PV8Xjz5OpoZZhByQtqD4VZ/mWtdWriHRA9jJrGJSKv5wXdYwJ5gZCmVf46WJltRBApUdWirkSave9v/O07b77jV3/3tx555Av74F6C6GcIh4OWSyYBjywFyEFBg7VJGFzYMkkYNFssrU6rAbGYeDiNkKVpVZWLxQLHpJo63qRP2YtJko3GEaRb2iKwLrTMoB1Fs3VkW6MsIkg0qiah/ODgcDJeydK0LEuqOm8i0wy+E0k21pUki89eOn/7nc8ZD4fOCW+lGAZYSxnmEcDHoIujlO595qjxSRAyRUrYnWGhnGX2KEeT4A8grXEsq5oCePxxmj1us0brg+axfeZj9PZDT3CUg0ydYVE2r37Vy4ty73mbz/nfRyuXdrdvPX3r93z/O45NJk3UROsn5Z133oF8pgVPbq3ez8ZoB1P1uUgSarg+As1GazhfPam44+EkzwfD8XAwGh1bW3/1a182HI6Ho3wcT1ZOrqytbmxMNo6tr0BN1OFARqmAXjJKNCBjQwkLEox9xrr1WTkpwcegByfQ/Y2drO1Twc46fJhxHGiBhJ6nOC4Oyn/7r//f/+2jf3rhwqVKQWKDK/2KwjV2UUNp1hyiTaGyyaVUvqOJoMQ1JB1QiVlTW8f0EIVdTLMMcoAhEsvI1e5cKKXB9FWSYrq/X9dNnucQ/OZ5uAcah/mu/hd9Dg7nqloUZQxZh5Ajce+996yurG1ubIAuMxgasmHG8COCiypNhdI/8te+79abbzV2FJQJOlsa9BJnjLAP9/izHd1RuDvaynnrk658TgMYMoD3EvX35CEEHwvJdsV0hFVdFfOFrYLK+UHPAhynsGQkEPLRRkklRMHwiWKaFFUpJ5mYJ1BJOYMU4HQSiSbRRSEHI0gnNPvJysPwxXewt2cbrWmIWJQtOW7CYgXVVYUISilXVlaH+XiyOt7cOPb6N7x+dXJsOIAwxlE2XjmWZ+nK2mS0tjpePb6SpwMZZQKiMKBGuWgaUdbWmArRHWw2tl6BP3uTE9yept/D3m9U37J4hE1oiIiwtaPSoinyweCnf/afr26s//4f//4zZ88uIDsJzYVo/48TU2bDcz1TRtt3mgGnLkabkemfDO4OXKB5BdpToGEi8l4Dc4Y4+qIdELaB1G00GE0PDqC0APRSicjoxfsFWId7h5FRWR8ymEOMjp7PZnk2mC3meZ6DkgbFILheaFUuOOhY1dXT5y+cPn4KBSXyaYeqYMspaj5nts6eUzj6GLVZDy/M4NLEAw2u/c7lkbMGIYL0itAPRc9i1aFIQ5pfuCq77s70wzAO96ntigCF7iBu0PZ6HQ5ArhxGeT5EJbNsoD8zhhs3mtK1vWDQlgF6X22rEfDMG7JlQJQXhihgawEoMTHMR4N8OFlZWVlbObG++a3f/tosHg1G6Sgfj9fHaZRNBsOV1QlE7Y2GoNCmtppEDQ4e9MU0poOxiaBu2TMc9rYn2TmSLtyw1fod5bdZh2P3opVjnxdI+5KFzLK/+Q/+/vr6xu/84e8+9eQ3dvb3UxFDZRq7W00DQd0mGcN8DmHM8HsM7cXIlohNjuCAjIpsC8f7oF8fM2pcuTI0dqFjFpEJRd6iKIwx1RaA99KTg+3g4Eka996ig+nBZLJaLBblsMSSI+gDIxC3QUlUu6+qQW5/6PNffMk9z80HQ1RlbJ96E6rEWKLRpZkfKShxw87EfMsO3Wcddx8IfDiB8tz62wuwvUhugJxHNNgnXClJ9MvHsSGsHWZLNweFsrwg2lEIECogMko2UOoOvFd4EwAYNgGAqFiMmpbkXFQo3vhF+PGchN5HtuxvBEG+3RFEEiglxpMxIO1kZXNj4/VvfN3q6vpkAoJwCtw/jsRwbTxYOzZeObYKSXVJliZ5lKZQUgQ6VyLeoo0P3kRdge3r+B663eFmAg8igSuSbFfBc+xbZ49p2yl6H2jtNxZqjjEwci7E4J0//K7ByvDDf/HhBz/z2f1tCNKCSvNYVI7S9KlDk2lh5oq/Q/aJ6VQGxjyo6m5Eaz8V78q1KgxrW+h6s0DfmUaWUBwamtBmeV6WJdIOGYMrE4OLfL2wjkhp1WDyt2OojNjd21lfW8vTtKzLXA0SrItnZHUWVkgIGEXx4eHBxa2d9Y1jRih1i+AY2GWhrU3myumyg9DscYZ4vb8uv1q2nH5hmz50vJ7fTgoPtpvrXK7MGpdYHBKHOhl9h61FQByLFPItU0TUfE0VTi2u1wnVrDlyY6wXxYvH5KzFfDKowxCvrEwG+Wg0HGTDfHW88i2vf9V4sjYajyb5ysrx8TAf5TIfDtPVtclkdWWQDlE6RBmUSkUCFWioDzgpc0x4Z+sN9teBBf+9T/oyvyxhvtY+7N7DNuF6lylyh7oTlAeRQlVCiO/9zu96+5u/69//H//bb//B7167upOKpAL/nenP6IrEQdHZyoSmWh+cs0ixSD0zHVYZ2ntnTL4RfYaKE9SyM6XB0CEBVT6gRlelawioRLJhM/StaZGlTjtDC/oITEc4gIfZfDYej+saWhynoBrHJEe0/MIQfK4aVTcfeuCTt95y08poAO0fbdBZCy8729yxqvUfRB8AiOvasPhLO2P2u5n9olhKS0eHxBPkdZ8s42Yje8nCBVaYXBteQsx8AYG7uhY1OB1EDG2d7UAQCIE1SUnXlZFIHP10hNKaWq1opL02C9yxbqIE4hxX19cmExB9N1aOvea1r1pfPTYYZBHU6wFBOM5HeZKsTSbHTq8Ps0GeD2PwjCbgjIW8GvTx2ExVv2stgcgVBzKUhNET73Z2S+/wZ3fAfZF84W1LzvUo9mupIHY7RUMgNnHTtWhUNlr7e//kH64eX/kPv/zLW1f2NKQNU1Qm7ILWuigKU+rB5SwYKShwHxLlZhERfIrG6ODYMIZ5AMZSARDaTfgFIYVqSwDjjDWWgvbBJc4043UPSuEyvcuBqV69trW+tlFC4nNVp3WUAC93Wjg3jwDPSJKdratXrlxbufVWGdu0AZ4L397wEIH6MD04qfZ54BVQkr6rV540w8obedQ/wnQPyle36f2c37idsQYaVwE+HLzNi5ErKEtxjWMee/SSVGZLzSoMLXQtGFxEM3ojcMNRta3qSjdJDI2wJpOVyXgyHA+OrRx79eteNhhM0iyPdDZYTfIkhwCgyXhtbX3j2HqW5uOVoZCpUIUGtmRT4cHdj9Ea5K0l5c5VKutangKF1+7LUQi5XNENd77Dk29E1uKs3tkk3fGQXS3RulCHe/H62o/9+N+KdPTLv/KfLl/drkCNgUaeUsrFYgFOe6fC+uJzTCUyYqFNnjRKbzhdp1aAJoYyF5ZxiqOoKMuVyUpd1/kgX8wX0A8ZTXQYiAcbaEV4O6orQRzgF6SWUyWjpqyKxaxp1sqqSjFZwjjs3GaYyG5jZGya4uyF83fdcjOQbKQBIaVuyTyh8hKgcysM60i9RlzPHdyi0ZxJHn0teS1rbudv9QjpqaI/RTYNEnVsJA1Ttindl6I1sY6ZDVAwWeLEgEE6SjBYwRulyBJWoY92OBwN8+Foc7SyOjlx7MS3vuFb1tc283yQZgl4O4YijoepFoNhvn58fTIer65M8sEgiTMYCEIdQJkTChrr2C3AuRrrCNeeLD7Y7ots8V3TfBgb73XjjkzVOrYWxi6NjT0ak1kglwvM5E9B5NUgLitRHIp8/N6f+Jtnzpx5/x98AJopgHEYAp5B4kbTuu/n2jdpK1R7oSyQm8lyhX8TMmqtqgaST6qihFpWWUoMFRlzBK4sitfDhAps801uJw8+foWunJ8tIaSF3J8eHDveUBnqQY5RWeTjNaKD+R/F/afp8JOf/dz999y5edMt4EoI9EoG7m0RulUczoLNdY9GB78xoXUZaw2t08HG92QWEV4ydZ8eNDX/0X8R0B1uXwuW3hXyjYoczNWiAu47JqoHFA4ZK5KNJlGNjgYxKC5QoSNaX1ufjFdW1lZXJysvf8WLJ+O1wWiUZ0kuh/lqOhiPhtl4NILzW91YG+XDUZZnmLkDAwOtJR6LIdS4KFBoTcXm7u44zOThjctoM0Nju6XBXizTcnsPqYvnrVyy3iECAsHUcrevUHkdBYQEqrzDucro/hfe//lHHnv6G88czudUUgPs7NYTzCLu+BrsZnmDlbNdybbfyKRLQ635plHlojycH45HYyg9ix0JIeksiesG6wTYyzRbryGWCEvk+Jc4YRHdyRQmB56w7d3dEwfTJInLssAKR+TW7rHr0rSKxfxrz5zZPHZCQvkvAnGm8jgtyYqZDHnDzWgf0xFClvZEyEkWnoIze4enC7343ceUWwKSb7vjsNUP6jeFHylfPdOcPNoHqjVCm9NwWaSz2SMsapycOn5aJGI0HIzH45MbJ17zuletr2+sH1sXtUwGUjXgcoU6bHG8trFx/MTmymScZTmk3WQxmMQwxxheRu4JiMP1BRSNuIt16dy8cCfshvod6ehF7lw5422poO5vj1qW2bY5G7ciHC0ytYOG2gTCvz38hY7FVlUAHWV+GA3z7/3e733kS49dvnw5SqISilHWCnHJNgr3JazR4Rsq99ZG5GVokybskBfbe+O/ZQVF3suyGA6GgzzHVHtsvWa1ZNaFzEixIHKDLVE7kzhfDaxCMYiJop3dvTQfQDZ53SQxRk7TUzSiPXl0MukkyT724Oeee/ddx06cxAIGPIW+dTItzkV7cEREhz8a7b5hmedkPvAqvj9TpmcapGshFv9lqehOQ1KvSk+DWpTFvaJngNblETOQhPiTTn9ytA5t38k7vuM78pXhZG11mA/H6SRbTfLBaDSZjPPBytrKCDIIBqPhKEsgVhHa2huxDMuPE4kArwhDLCcqOyNtV3YNFtRFpiVij1+cI11O2Ob46WPdOhvVZePsK2/kCF3BHQmtj0KzJdsXRUqJohGxvuvW29fX1qYHh4Msn83nUD4OOCNGzgFSkT2SRqGyBNwRwlmwQUXENvsP3gsVoYWkGj1QyAqpAyRjAjaBTEvpXEFzbaNJmXKBiSktyAkoODPqBpNCoPR4dG3r2sraahzHJEijqOVMsMKnZ+O5R0m0mM8uXbq0sbYhs4z6Nx51cYHZYUX7JB2HC4JBkbm2Ijadim4UzfZITII1f/Mj5Tym65K0ZRYhC80SeDcqG4bxKKNmsE/ZCzpsIdAy0MeOcbUUZCGh9gbU6xIied7z7xufWMtkKrXO0nzl2Mba2tqxtbXRIE/zLNJaJgmYZ0gESlGzJbeU8QSR3uzmYGVlSn7ukYJaSkDIvlq3tPeBj8HobYsz9ze9WDKLLisNcHUJG+cco3WLMSRgV0DsRCai9N3f/1cfeewrn/zsJ5MkHQ0GaZJWdVVCVxSoxGUSsVvaQUjxHRG2bJeaE8IH0NcYY91m83lT19QKtCgK8P2C6gskdjDIm9kcQu7Je8HaCeIuGiEAqmOhj8kZCoFEKAkWbFO0Qy/m8zwdLBbzNM9QB3btBGA6SEEg6wvhG/SoMxcv3HrzbaubmyKNYTcgLIcL6qFhqW2eDDksPeWppHCJtwbbgqGCgw4E2pa6ZAAP/HxtC4t7iiKNjc0K8RYDlUwpBO4w6VxMwWhPzlEOJnK7tfsZU+oLyrgSgvWgxDeAQCJk8pz7nnPLzbePBnmO3X3iNMGkT/K/Y+dME7NoS8k5TOMkI4iFamULtDfDltNtyyvhybHtbq2a/71Mb10mIfdw5o547xd1pNjTQ1PY7+S6A1GzirSOxslLX/j8R554dHdrJ4GqypGQUPy1rqJK1qYQfBDvYxUB/jeKzobp2pVAxBVUrW1ANi8rSGGB9kcqwhLTg2xQNoVSWJTBqjTE4NFT4XLzcSiIJgHbFpjKNdAFgmc0VWNlRpzOfDYbj8aLxQINloarQjEmoauqOjg8GA5HWZaDb61p8jR9+LEvP++Ou1ZXViEyB2Rya7N2p9/fZTts3tviEDb8U9MQtpnGDV09NJk0C0/PLES7QnOYHm2cONTRHVDZET5zRKY2Fr3E67gsZqwFr9zb73wZrJtA4JwBaZ3odgydMejsouSlL7l/mE6AlKqYEnFJZfKrxbCAkEwwndMTHkvbPCMNXVt+8jQ1hr1+oVwXcr910ZQLT8ul4qOvQBnuEt3et7c0hSMoBYEANEMp6jKT2V95+9s+9+VHP/6xBzD+mfw+4FLC8Cxy0bKsPgO0fpk2/sOYrMh+Qt3Di2LRYDMLyhOKoQB1ilUvZQWdUOD40nRARSr97pPjB7kOITdyE2zCosBkZcCHejmDcA7ieJIke/vT8XicJEma5fCP5WZY11YlSQovxTpE0MhOQquIZy5dfs5tt0VNYqLWzYm3FCsm/3o1pCOUBYxO9qjI3F5lT6KfGHt1ibV/prbs1PPObpfrbecKzhikoA46ZjQfHEer4yEuVpgX/VBksMJEOJh7vNGKCApWySSwoUgndMcmw+EIg6Ep2Bi7e1Aek9lijlFWWqa/uxYFq6oFn7W2OmC3Dr35J+6NTLruvoqfQT8Hdhskl+JtgL3hQK0HufG8a9pwN7dkPASDGDv/DifDF9177+Nf/OKVq1spZAgBvhL+YtElKLZs1FFXAJFBOZZuR/sQjSoEuIvqBv4BsRkkp6KqkwQ8Q2Bdho4+NVTPgqxGYIZGUnZL6WtiglNw5VAgyBer+MCbsd4oxAYJWc0XhYgO0iylXscmzwyhGUsJg7eZRoAI4Sj+1Oc+/6J77twEXcxl/HTrXLW2kcExpHOzBgltGXAJLndq6ISX5SLeagE8FnLCsExOf2KCMULQXCINFQep4K6LXrMy5tHiQGCG7piIPCHyWZ/YfQVrp/NOwuhIzCBgi2yIEZqjbMytVwkcoDtQN4ugm+3iutqg+3+ADMGP9iY5kcl9GqgXS1SNNjVhdMcqMOGLWve0NGDvVbH3tB4P30P02P8f74cWkErUStdifjirlXz3O77npS94SV03i6JsoGA9CL3E+gB0KD3fW6ggz4FsSoaqI+gqocsKUAg6m80OoXAHhh8r9BVByhHmEiNPNt5/oyOGsbhBYJSta2pcvqjjQVUQaPcEWXIJ1jcHYR3LQFEZvbIsi2JB5TJBbEejpitCQICDlUDkdLr31PmL8EJoNYCetgbbVjTu//ghtU+HTWsgKr7BTC+4TeE9mKcD/3eMLvIU1JT9crJ0WAzEAUkP17GxMCim4iKhnYCAyvVYX8GREXubKXBNJnfrjDcbyWjvUeQj4BTe6BoEzroF2GljG2/UzVxCioTqldSS0/rgPBg7PXC5sOjhPoz87DVEOTFp2VKCOzucln/FLQ0BL10+pF8R3zwuxrResWycZaO72CwsK9bUUB+bDD8CqttoqUcrg/vve+Hnv/TYuQsXVF1rqasCgtSgq4vN76ESHSY5hFpumDrMcFVKFWUJCDybU4dRmCwKVFhrGn9JoEdhDQGVmMNolFjDt712GWg37S2w+YHUHAPmsLmxsb2zTcuHfuWYcQFxldAbMcGwMrt6RxFRi66qQgr50U88+LJ770kmJ+Cbqva0z8S/Q8Q0J3yUT0GYSTXAnOTgxWzJ9E+TA4Ol51ryUX/JeC5d4waAMATPGmrv2gfY1HrOgbmCjsVQyCYdvrYXeNqSgtcl+2xd7HMUghqloBGz1YbA+hB6epx1wVGbliTZkW97Tr67TX3I036Q3e7pwrLd+CZU3mVP8fFbm8BqVC8zaBldF8+cyus2utJKVyX0SYDiHOgDiIUqZoVK3/qWN37uK1+4euUy6rzQaqgsyxmW1KJ0QKx/h3KYbYlnAimQS2Pld6jcnmW5xMBJTGyiZBgJTdOiaDFfQBkd6JTiOn2CdurKYjGC3/7X64/uVpCHISliviggPRAnVteQ56SgtyPmNkDhLduOocXi7BS2d7efunTpuWsbInPO2Roif6hqItXzEzH0fRYy1o2pYZtGYhDLUoiiqKIk0VUJcb4gizew05GIZKYVtN6sq4CSBkFyTibnwdAtjcmkiFF3ZR9zRUVPWESNab9sAhptJTmlyhJaYB3RbMWD2HL8ZpaAlsZsCr+iHAQtNZzYm1hC6GpZOY7Gj7f75lAR7KZxthHSfc519M7NXEo39oI+XeSbuTpIyDUuzsmt8QB/v14KsRHRoVl6U5WRlEUJzb6o5OusajIsLhWnWblIDpNydDx93Sue98WHH97dPyQHHFwYg1GA7QdKlpGRmbgvSsLYmsi0QQBNOEli6DdJBZ+xaB5AGZKKg4MD1JTQvkG9VJAO1JjW7xJUgsPthku4eC8D2ZLafadpCtVFFFSuLopiNBw1DTSXoaY5MmZp04Yp0BFCo4F5MXv0yXN3bd6WrK+oQ2hCkw9SAT3ToLw14EvdgAGt1imsPo90grmlkSjqqoBQfJHmMkpziBeFoNEIBIxIwG2NEIVIYRpxNqRQbOsGYCDkpb+Wyuk2wdgkfM1dVGdsJSSr2JggOPO9GdGYp62pi7+3Hw5D84qDt/A0guhpA3Sgw0QY4UcfJVRONrir+7v/mxkS+PH7/lSMZR1xtbXlzv2tvT4qTLLVdaPvRTeE9haDSagLNGJLEZ0MahRHq6Ijf6ygCqQsLz3SfOovMiUyXTaDgWzERCYiqsHQUCVCNW87OPiTTDyM/Abk3iRB50DcxMhcjRnLWUXJOGLcxFgXmjJERZalaHYGzIHuH1jOGpNDKYneQRn4h6gteNvWEyYw+Fhr8jqhzEbJaFjeD2t0CUHhPRS45ZroQvlbgnbjZTY7Rj2xsG1A8sjDD33btUdW8zSTVTRaV0UZJVJkAzGfY44UObsyUc1LaP4iUhHt6XQIm5NKWaZZLOpSlE2V5HmWVEUji0XS6GkjD4pZkujNN/yAuOsOMR6huOlMsMxR1gJLrhsiDMNLkwTXA59jd2tsq2zC1DDQ0BNBP6qEY4QOOxCeY0GjndLQD4vk1fMSsG3XZpkis4cRZYECelihCT6PBAZp0BjyaHl9ubLq59bn2u3FPe4966Jueyb9b2bfMuxqz826P1tT5S/jar+JP3EjyH7DtcUvtCtABRSopq10pYe1Kr7y1KNPbE/ni2KhItHUKs5lkkVNKcpCa5WPJjqptAakyrIBFprX2MBQAABQmT9blIhSCbzFBKU2wxRIgkIBupjPgQ5TrXeTsYIp5hRxgMZqaqfS3SEbWs1LY2D5Dlg7lEIih1Iko3Eih5OVJB8MBgOTx4SFrCkqCeoZQycqrLFK7hjoPa+h1RtkCENe028/sav2r+Sy1CoaJFC7c16BbNDoKMkgZxWyy4HdiRJS4BoRJWkilEh1vRAySqScJDqeHNPz2f6iiiKoSgEyvFIvTrLRffeP7rgNcNfBIWwKhWcwM00byJiMifups6ysKqGhGQ10UzVmdqtjuF+CME6KRZXYMdCyo8Aa7agFkQoDbuYPw8OCeh6tFgj0ZhPmBhXRgDY1osQ8BG6LEkeEwhwtwd8Ap+uNcbn+dQP3BGpzR0JeatZydbAMr3OqEqt5YW+23VEM6ppQHKhDgD0WoFb0UKbTOt6aFw8fgG4L3XahQQv0CKa80UbpvJjpSE4G6dZ0Vugigx6/WIQZMrxilqUYQImplUOlm00TASpxC6nFtVKoF5FtwwRKm3BOGVW6NtZpyDC1/klXjMd33vCpx9gCFkAoT6PVQXLXLeuTUS5EfHFfKyjiS7V6QGhvIHtfVTVEgTQFlvKGaCugRGCgrlUO7mIotF0Wh+ejGXY0jVEA1HUJnBbbQUpVoR0baA5mi8OkMijfI6IG/Fcp1BBIEylifVjVRa3itKqBxJR1lKXq9MrgBdNzQLsUiBwsTMhFX/mD7ACBFYixKVyKgj30LodNI22ToSE3NnHN0VZcYGYTe5RGFvH8hHMu+pQyuUJwRi5sglRMkUhyRtSNSjHDb3E4RR241eTeq6lhlvKRuNtWWbs5WdfF1W4Uhze2XBeHu6SBWQFahjErCZPTE521pmObUXyoHFEwLGmgNvsT5UZkwNBaAXtYxFpGtVLzqjiEkvpxU0Q1hERFNbRog4I2Oga0qIVYnwzWDsXuwaKsaqxBgy+EBtwgrlFlHFMQA5xLZm62p5yRKcAoXZvZ+3A5Kq2L+0ahVCTBIlACe0TJEKPw4gRbnXlzpcmRENAPLcuiSRrdcduxURrlaYxADSWuGyF3SzC1gSUpgYpN2F6ini+asiywH2ccQY4qTB8Ub9y0ArurrQ6VUIWMoNwTtF+EHGEgK9g1DRbtgpuARCGA1yU2hcJ3VHWhiiqLk+F4qFWKHU20lFE6SFU1352Xo2vfgDjNOBEKGlObZfkTZ3YNB2+M4/lfINQJaBNV8F6CvRa0HBpac5ePK2YVZEPd1ErhQeoXY91+Kng+LicDJTPopCnVfP/g6tbu4rDAZOtQvV2Kp8tjWsIt6H78TZueWiqEC1bpGbA9r/YkWeVK/El9NMHPSQ5YUw8ZQQ9SDKzrgrJ2TPIYYCy28kKPnMtAgPqy8BGgY1M1GYi+lBcL6p+GXAIwEGNYBUp1ejxKxqO8OSygoQGYXKmclS9eaYzGiI3AV5BkWI8uQAuGe7CCHRScwVuwog+2ruvFoohHYGHNspTiK2gPMagRdzUGZW+QxmmarAzie59zYphCKQYsbGdbiQIz18NBcqAgVAN8znG8KKoY6osIjOKM0zxNwLBsrOJYvQD6JUYYe5hGB3UJJXcltI6GwvdEZYB5W86BQoWpbA+4TdQJG9dTOSilm2IxhxoTmM4lhYzKKhlklxL9jUe+dupt1XhFXQeMe3Q0hr24/dht9Xocy4zlFWgqLIhxmT70wBe38fcHENr/HefO9naIpEyiJM5UXZ6/vAViBqag9IUut2Or+Tqvu6rwuhH2ae50gNhainPAs6ARlvTDqat/KZ+ny49GbobIAO2SMYaGztuUBUFIgbeAWdkoyFgbHbGXPLOoJ/vyZdhqpIGaE1ihspgVFQo/CmqgmPxCeKxRwKPRBNUIcXwlPVwMy0bNF8CdGgx5dLEBCENg5ELct1G5pmeEKcSPZVzZFhlB2KwaG03KGmOky7IYQGejjLIgaP8APkF/jhOp0iReH+V33HIsS6I80nkmEmh2B4I9OHhhJ0FIEUIPpMqirMKip1AHN4nHo3E+SKEeiIyaCkvpUq0kmAvGgUkgBCk2tgFHiw04IKcLQbuxn9kcSRT8YUlQ/1IAv4ejwY4ukN+OdW2B8mp4Ee6wnMbp1nx2S7Ej1Emw8RCRatlwrwO8PDub6VBtExS3RbHxNWxn3MSNhvQUL0QGUHidKwxT9BXkyQYRxyLR5fxgvr27W1UlxkZjDllYNpXFXT07XOVax40/eiRRaEkfQUR77zhMbObfWGkZrERQGgOcaTQOs9Qb2ce5m0wYO+EOj1xnpguaDkYQgBUr0eJgsZhDpXcQDmsTWk7JJKAwQgo1NLVXSRStjeKDIldKQM8SsD+b4g6+eIqsXFQBER+npppEJPTGEuobvddMzdilMFYaZl43dVQDTmLrO+zVHUWTYboxzu84vZom0ZBkcA1KqQIRF5HLqNIWIFC2TSHYAbRWqDKfxnEaZ2lOWnmUQLVuTFSz0iHKM5GI0khqqM5Fe0oxTh5fvL0C5WXI9QB/FVrgwLVUusaBGBwDx0kN4iTYjWAPILZEN3GxDVEiUKybYi+OsABbG20ATv58bWhziyX0che7BtOhN4REfqMfsgPw3k5qPclG2keCiNaMPI7qYnbtyvbB/DAGqz3cBKpRyPSsH3zptSzUc1lc8lKh94YuVzHLr65nKFYsIdw3jElC4xGU84P0dgqRswP6YsvmJdRGyX5NNd0A7FVoqsClWskP6nCg4zbSejo7PFDgpQRmW/mYW2SrpqsYht02kyxazZOqEVEZL0ShIaqSGR1MwoFRd/kWO5QirdUandnCfdMjUKPzQU5TJdUOopcjCJF40Z0nNkZpBBI9lgPGRsioi1KNGNvsxQEXnnsGzWcjjWp9FCfFvMiSDAI5AOtMmpSrBAS5DBraxg+bvUgvMLaSyFAbXFxnJrKBQYYz/EutFV3fKG+nMA8rMBwIGTdSXEnzajpNQLeOqe1jB4fYBpGUy21dbR7AjKD+RxBiwc7EGx47kc3hjY5IWNsk34IeGRuzCClmXjTV3nRW1pVq6ohs7ZhRSOlHve98VlcAQDeMw9dRrNsSSytI3aewdER9KNoKcUJoKza15v1QLZuBZzLGXGTQ2KKUPUrXUsS904TsmAQAtMhCq1zs6GqEcGZ3pGiKCCsMCt0MMzlUKWp+eoH6KiTecj7gVk1DcFOiJeoOY5zPmF6JU4KoHVd8I0sB08DVCTHGdZ5EAO7IyqmoAD1LzmRTN5gInE15jUQ9ivW8mCs5QfiJhdbT/b3BaDAYIBf3AoIJOYyiKJeVnu8pVTKzkenlgwtwNfsJrcy2U5UX4K4gMwdVdZ13JJJQ5bNSjajqRVPJ/Uu6LmQysvWcDGf0p9yyQnVYFwv7WJaiyMy6XM20dVLs4oloM6JgmEIvnBsl1hMJu+FKNWDDr/VwBOHZs3kJLa9wAyDTWxo/sPUL89GOuvrm4RCtN22Kr/zZXZ0Yj9Yw3MTl5gZRflVVlpQVhpYo7CZIuMi4ioEWK2nbE6GmNDiul0tDq4PHXhxBocAWVao8xNAHneW5PjigkCbjaCcCjI4cMM7oZjWPi0YvSgXVLYATR5BchAVgqWBnOyTfx8lYvkszsvBhrAhoOIEazmWNMR540gmkoUG7rLKAjFKspGOiPiBmw2+3gT7aJZyo5caUCqkGsToAz05E7YuFFuWiimQ8HA5BGabwbJxYAz7cMi23dTNH2mSjQ7GzsaWG9ixcFX+KWTSLwei0bq0hrGQgVRMJ9KHreD+NBuefhrK+g0zMSfvogGHgUupcXLpt6bwtwLPWUM+lyYYYR7IBOxYuse3L9R3PHGt3vg9HE1yjYPKeS1XN4+2ru/fccaKom6IosP8m9pdD2MUMr2d9HflMy2AQqPNB1FjPaG0Ev3Gc96+Bbj4VJNtR+1zW39efjdU1vQ4ULMlVSQ73nwtSBuPpLswoUlonK3lVHGJmDkT/4XmAhdpSDaxZgaQkwuYZsWhWMzGdNTX0XJfQnyKOITMfohtACWQTZF42LnRZFy73XmA5aOhgSMhBoUSj4bAoiqqkcuHRJIfPKerCtHm1uGNyhe07uBdOAVdUK7muS1WJpihmURwPskGSZFCsY7GABkw4DJaxh9KbaTPV9T4VJbcCk4HgLEsrdObCthil27B9tIIZMd7VZDTigFkviOcRSDGDslIqG0zj6suf+8ub3y7WwBBtsP/ZA5I/ePZsCGLBHz4WiBxxEYTdQREiIx9aGGJ6pwnUYS+waIy5bBB4U1ek/KXgPZDH1gZS1td2drHKN4Sna01CtGmt8mx549GSAP+gO2zXnBB+yZRAu6g2IVvyZnhQNViqpqrIatWaAmMzLlub4WRQQd0CLQJ3KCuFM8Y5a6myPH/myS/vz2bUX5ji7yDSgwvBFKerQSbE7hQ6jdRKLha1KmoI1IN8BCwTi2mp2ALYN0WynNC/3FZ1tTIz5fJqrcqqJM5G1p8kknVVCgnloJWWw1Tde8dNwzRGE5dfrJY6llAihCQGy7TQBmfDQqBruSxHcTIT0CqHzMZkMFJQ1LZEt3icxXqoi1iXsYCuLjYTykw1xWTaBq18gG1QG8ylGxB0m4q2rXM3RgKrciqhZ0VNPdTVZBJNL+b1loiO4+ZAHb32ybchfQnkG8tLWBXEA08P8Ln2KabUkY3eaL/JyfWcSjj1H7LNigJKpjXggNBK1unl7au33bReQ1CBy7MwT8bK9UZ6Vny4k33mJxn4oo+u/7jM4sU4XPhp3+WtOeCZqGuoBVVXqBNQQyJma3R6l0VIm2HCLQ38rU60Fh2Bic8VG4gCWsZF2RTQuRd1TiiwnjQshcCdHfpkoFoRcEcpNyex0vXVqYIYYChpFUlA5BhS8hGHbQdSI+C6RsLG4+TtVXBjrSqw2VHEhogS3YwGyTATSpWzOhZCDfLkeXee2hwl3rFs84GpNhcIyphEZXbMRmJTiSw0eDWDeA7u32gFMoao0At1yBEQEJZGaqhnSXlQ1wWm+IE5AJ41jXyA3zZYe9zGcMIKqcCW5f4uhtQHJuIPMk6hvaysVSQXZTXI0lzqsqp34vTWxZ6YF9g4Esv3eNWF5di14edZ8SoP5JY/eJ7k+1RZo08bcC0/snKhOUDsj1UXRTkvCqoEbPiRFPNinqTHsPqGaRxLKZZw1tC79VlrpkfjOtNFjxj2+i/lXPdo7MVL6aauimIBxRltq2Nbu83Frdm22lQLw6fccM8ZU6WDH0wYM+flmusSW2ikbnJRgsEBTkfqpoqjGEwNVi51BdmthkgmsTiPxen1OIrqK3vQhkxjVW34Auu1Y7MF0+rR8CBTWcNPFH22cAv1SgPvbgSq4/ogvu+OW9aGmYrElavbe3N9cnNlmMRZDsYnfN4UBjFlvAzPMVydnZXRq+kHdrVqcnGYNKqWK1rnGtpOgCMX4jtElatp3MyglAHU4xJNBV/T5DFYVIGEiIUybftRxlZwUY5cdY7aBCRhEQEBdnWJBbvKEoonYO0+dAJTbRls+M5BLvBK9shmnJZ7RhJYtFuA3bLBkBoM2+vCI9t32FGxor6RqlSDoiN2U7FpTWC1anQJOSsuBxlddOBHgYYJ8KTvTvjNeH16jEnhJ0ex374PAiOBY5yhqNEdE5TeqiyhNJRNZEOYcA+G/lusP2rYp80C4SM7u4KVy00MI38zYxVG9oYqydDVHmQdCUH8SpnsAiP1+WgF8imZesp4NnksT69nUSK3p8XhAuUnAjfIKI5TqAoKyrSpm8Wwiphz1YDCH8k4S6M0yVZH+T23bYyTaGDcPRCnfeuJjVMQemjChCi6hLAEY7lpVCQ0CPx8K5A9kjXa8BmsQdLE4iDVC91kAgo1QXJjpKRuCqlLaAgL5naodwseLNvEhSpXgjOPEi/QQWCETgPQ+CHW3WDyZguDFQZLSXCIWUu5Eqpsmni+GxyNh+flZW56oNQySl7zuQ2vpj4rF89pcwDhkIl6lc2qaF4DssYsSomBwkiQ9WGavruIe92UaQrx3XA0VLTEpIlCCQcdQ0I/s4BwcF962V1tRZu03cDdVN6+4oOWmy3H9fCRrgIDEYx1WSwwlYfLuWhK5YPguWJMj9N2edtt+yTzxhprSQBJbG10KHTEaDpM5jMxX0AkhDHuulJ+iMjOQOyGQYEIgiuhGJ2+eT09vpJd2a92pov5QpVYz4a8ExHUZ8ZITgzcxsQIUCBB3AINs4GGLol8/l3HJ4N8mCbDFHQoahRhoqNsXC4ycuctABqA9IFKOoMwaw4kZMSWwlndGDVzVMlqsDUraCCeSEBjDXZYY4FllgVjeqLKlySIUCUtMjKbl1g1PxCDLMARUTUiFBU/kLZDOrDb9Gytvu3ieZhbioWQjWLde7V0JcbqW0DKM9g7EYq8zLY7VFPU1VFZF1rRSVcg4thApDd4H6y7zqAwVcOMoNxnraF8oK9vBdngmHTBsyWf1dWr+rd4aIvmta0/7Q/D1fU+1L6gTBLY1oF6ma7ORjU03ShY3IX/vwnCcHKaLUJntclQcDeoiojq/MBG5KP4JfSzJ5AQVx6UAmwqpC7aFWCCAWWDo9yDVh+/ORSWSIaPQaxvXU/vPjW+aSPfWElHqU5EqapC1VWEgVyJ1JChAxwZ6tFiu23gfUTeR1kyTEUKCQJgB0ZOTj4hIFvoRYK4ClCzrWSD6YxoSaL0CUZnka2Zqj4uEI0kEWxxRZwAA8dhAHS92nRI8kBZM73ZcSoX5roTky/MNiL2KdDGFcwCFAKsMXW7GooasXHXUablMFbfeOC/FfMGvHoKi3K3cdhWpW3Dr30ZV9x4ZwJXd4UDeSAwWjgxIa2kohkkCObPPqERqWBdS0mDPaU4IsribxpK4KTQurKq4giKuTsR2vGYGxCe9Y2EYTk7UZiQyC8G4e0v2p+EwrOJ/gC9H3gvZG96vrv0PYHc64zQXgfjJNIlabq8vACSDE01NkoEwQaatdYzUdexgPbpMYRTxjUEPvs0iU7MlN07hhxS6FEm8438pNJVnS0qtTOtyrqJJFSEA5gUYl4Uh4vqsID+wjAC8lisvk66vgQ3FJFqikoht4xFDAq8wv6U6NkiDw9amJ3wZk0/cGG3Sg/aJPZBcoOJ4zT0DYeBDCmT4+qxt3UQlqqZVxhfPVejnJPAIr9HEvLbuIAajWuMG1XE0dksu/9gO59eEcNbYKcc82yFMNtVhPC1rNGcqahgMbRVIDmsYYOnGzS4sEjPvUjsnf4rL/cbyQd0pgaKvShoF9zUqdG98FAaUCKaCIratWDpiGsZercQ6/+iq6cCWEAoGipEVEIewBLZ3KC9E/+cVGPGN7day2aoE/A3sgzh9j0YnyTrKBKVHu7MQAUGXwyQ4TRNKrBpkTHcojATxBgt8U5lOtEI49TjBArADzcSrJ0OsAFIEsmizp4+vzsrERFNgJaslN6ZFsMsBlMx+VVRzUXfD4jT1B5FSQ0tIeoGgxQhU5ekAmjVoRqQgQ3+mMqShgWRtwwnSkwAi0+YvAizKhPC7eMknUzsYN6IR6j6uJ0kE19rd8MQO3+UVIESKwpo2jbk/lCJcxrLaSOb6bX42GkcgrmRjq4y61kkF5Qt7bAc2ipUvWIjoy+YxqHQuc05o2fYgcZgEN4KLl4GwXw28K5jw0koc2YaXMFpapSioARpKAnw/WqtzxDJI0wBvRu05P62cmKrzwZFD/rGplNrVAlhCZiT4TUS/i4v9uDeOMXPWO3ZIhluujyA61w2chqYmEjQmJLKem3nWtlUjVLY/BhqsmQZNdP2sYB8dJpNC71px6jsBu0GFq00c0NYbfJIDDKT7WcChWFJ8VPntxYFehEplwgVYEwDRjk6SirdHJbVE+f2HjuzP68gr494C2q+wIpd7KfrvSfABmZqQFrXuildTQhGsi+KeVB5y2dj2O4GlrsaYRRjjEAXDhwtVO/dSrIMbazgagUl0O2N/Cxd02yUlFVCITX71+BTrBzGBOaORaYHJj017SjJvuOnZ8IthLCh6/CxLU7IJXYjcDCsdo/aev1+dkZ3VuLqzi4UACVLPjn6hU5SoLYg5TGOxKfbUueZD+2bTktoXV0PecD6wl6EYVNSXTdFWZSLol0EkN1j/Uj2L4dz7oRCxm00rnbQiL/XiVgcvcG4pKBkRCyb+NjGpWo2AxQguRZgFYhxFIFHnsAvnG5IlewWMHust5SBKRfzaHEJsZS3nly/crANJSpRNCa6VGB+4ggaR2KfBQoS01CsZlGV13YOr03r6axBd67a3VvkiVxUzaWt2bH1EWQLIxLbOg42tZjEcuo0jVm4FpecQI0li1EWsaWsQ+etWyLSCuafcodmswosdHto49wKJqZSrJJphAS4LLwrXTXRhXx413TnGFhpUzyHrgmyA8O9Kh5XsQK5yYde+cGDKA3405Q5IbNA+024eyYfy0Vdt+eDtkl0zGNFaDCagFs/ElC6BEyOO3v7J06coIocCHJ9qko/tLWwu32jjV9Zpvr2jta602unbFKWgleAvXOAaeNU5dKWz9pn6GrmZWdujSvsexvfYwt42pVYLkG/09kZURJZEphQal3ncbrYu/B0nNQgPWEMCXBmWbrQQTsfd2CM+7j8GoOvGPMAhijSmslbhOU1wB0MVQOg/k6yMhxOofORzwwWUbyoAGqgTgAk5TQHlT5z4fL2YQO2AhwG+nqnkW7EM1t6WqlSjMt6KPartRHUw8WUaCPlWkuTU9ooPgZ1frenFFANijRuB+pkLP2B2K8hfM43Zk+cK4oGe51aYQMUnVxqtj+HwqoubFqTu5BCIxIhUqWy/Yuimols3QZKXI/xXCfTYMkj/GEPruZUXW42U3h5k27zDyk/WOghgH4iTEi9IlVDXTHs8YGVBrB/OgQeYL0yo0g4MLqh2Xv+0KFkTr3s4S0excxe8fySQKy0Ugq339mq33VZLooCCqoYkTK4j28l479MrmO0w5sYTegAt4Pa6ERzI7n1gkBGA5cooYsyWlzb31ZRBUFMWNhfK6kaYGimyqM9bROJ4Gw4bjAiRnh2hCFQpRzLUErIYof/NbXCNkhYb67OoyqOBJTdIjEdxLDs3LX5vAIkq7SYl9XXnr52fquczqoCtGHowALuB/TCViIu1ACQPo6vTqtF2cQm6ZEm4HJ2ne4BYnndKOg2AQTFSn0Q8QWhuWi1Jo5hxUmzMBRuWNusJaqY81EzLukBjTI9ZRUltYT2McIegnkBEJdoP03j3atKpyBCU9MCJ2OGYBgOziGWpHkzSTe4oUT9hXXaqUtEel3zZ5cpau8OoAl0WQi/s/cxgZjqH9U1tIxQQk+GOd6HQXwxuNyZEUs+2ygOwxO9uHtDNiynDXQZrqNNTufh31L11qYoShMDGJBBik3ya2cs3JD+gKS0lCLHZpjkHigOVrKzHNnZaAWEbwjw1xyU5V5V1ia8GAPOaUgsqwG2Ci8j2CAwM692dBaphyZQC2IkomK+ALKbwGMU4iBjfXpzeG3W7FORUVL4dFOK7Gtn9xqh5pW4eTODyldY6wUrcGCBRTLbRlKBiVNkkYAKVVE6O6wmx8ZKVdTJwXkYScUmLd5UF8IcX2qyCS0gIP4qVU2ZgF+3UWhYoeVYeZpi05wu4/71db4sABhnoHOu+4OgYGwIWIkpRSUiUmcFqVjrJkmuJskzn3/8vu/WYliBIdp0ru7Izy1A9Vy6lQDWslwu4XD0iIMcxF2bZuJZVVes9BtA9bdtkIKh0VonWS5kBE0CoGoiZK1FYGUE+2GWptvXdjGhvyXCBtGFnct97uJQeneEiyTtBEMX4BJqB70kmcmzuoHGIhV0yTPhVG1zk6Vh9FT/CjxzdjaJ9hwsDrtqGIbXsubp8AsaAI1lUDYaBFao7U6RTaYRBuirqMq4QUzJWPyL5EDy71GADd2AyA9hzCROxTJK41xHKk5yoUtwLkDvhTzN4o18MV00YNcx0V2grG6VqiggmaGclyeODTB/wZTXgKIblKSFNe4ODmaT0VjEsRLVzrzaxKp0UOOSJEDDRI1QYCLDoH4XrBnZLGY3Kx1lSaKgbSqyZWR9ViuwwgQVfvEJ3BRnYsoEBMhjU4T9uYASgcXM5Vg1VZToBFOqhWna6ICqKevZKJsudoTeF2pkih+ROYnzA309qPbtxUO49FIBu98jDvuYBCqgy9iuAQPRunK8o1DozLZNaekXQmUsh3jh8rXV1bFOMwHIi/X8cPCmdn7gNvIELtPWN6H21nc5nXSph6dDF5cIVP7XRheg+5bGcOUScdhYNvbZGaoYybMqGRNeTJWH8J0kqzFWTphBTIHpc7QHpksrmIqapp4XWEWRgiVB4EQz0giTZcBGjEnDVKzJK4lCx9CDAO7A6jNo2cYgwRNNMS/BqQpt1dMmb2ZVvb+n8xWtKqmqOqrK+fliiu3pHK7BZJIkmi9A0l40clokSlQABdCgDCK4yhIqDoAzBqrNqUW5gASKON3an966sVgZpSoCTIQqV7i1tqI73ExtH7BUMioKAurZZLrJy/2B0JlIomFcpBnEdVmDBfk8KKwBo8goLE0lNRC5KpIFFKZkMa1YhAfcaGY1GqI/NdRBkkKvN9VVqA8NhS8FVco28SeIKE1dQbxDU+3vpKPjMmqwHTEMEDhm+2VMA5cWai3Sd28OkoRZnTafmA0XCQjkIOAAFpAEK6UZuZj5SqisbF0Xk9Fob38XLGIAT2IyGS+2d9BNL5M0o46Pcrmgu0Qq5hVne7eiXwd2n9hCOcu4bvABnnRVQjYzGduW1vwJ1NfgfU7TxdAXq66H1cdMjgAhtpVKXE6nl1NMACS6ezHiF28rsBY5+SdrFGshQDJJs8kQAyfQD4stmAlCWC0B0x3D6MCAHbKq9dWqVCCVijRFFQkivuv5DGQQGScXd2Y7O/NzBzgNUlcptLTRkD0YR1BVXeqD2YzaI0AxZ+j3CTI+GUggGDOFPuBQzRxKaiZ7ZbSyNiCPMWRVUFcvrOyhFSCcEDKW0EjaoY1SeoYtLSOp8yibpAOU8cjOh+aWCLsWek3WkC5oJWZL2ZqgSnsiVF+CDIERFLUTSiSxgtfkEGUN5e1IwI/Q6o0pX4bwNkrvxcnh1pX103fDKUgIxnImM+tF8IYkc7A20t07rIxay0zlWFUvYNYuMAfkrjYfNmVmHfiF+cBensOzx55YZErnFVzAcHls49hiPoXawGnaiDrP4zSKIC1Y6xRazizFvOViqCNVIfKY77piRptoBaa49iDdUSHJF3IVsCC9ryrDpZvAcWECjhzvdQZRE8Lvo/q6JNbYM+wIdpq8oR9J1YDkgCpJBIn74NqsoNF2BcopyE22Vyz2zALjMWblurO0LzduEFAbUUhE1AAZqtZVBdWXYcG1hJgbITOZZEmaf+PcdizllXlz6UAdLqrhKCGzpTl0CtiI4xqrMetGxRA0i2XfkAljOJcC7quhagcsFPgZ8MVnruzffGplmFIyoSnAAdtDYOIq71rARn2YxI0Y9IUMS74T4FkVHpTT8Cg8bODOmphDDWjvVGMbCEvEDUuqolKpRsO4hsLvhm1pslOhoV6IWqkh9CsQ+upTzeL+OE9rEBmCHDTH7xyD6po6XAxoSwF2vl07gs1dIDLOxiT3IdYABXXKiY0usM+Ji3ZsMCQAWpr+oWZpSkdpnskoXZQ14C9tKjF2dE+GCNyfdNFFsz7ECyxDbnnLlNulY7T1XuhwCcwXohEYZbQb3pYSCDXJaORpiTNFOvHaImoYS2A+tfzZsHqvS/gcCCwGDWXbZRRDhdpaJHNsxWlC662Sjam5ZLMykwlrOzkijuVXXdwTEgyIeSaTBgrqWDY90lG0Paun+wfjIfQN0GCgVhjqSLMzKf15moHdC4Xeuq6hDpaIsizOsW8IJDDgNGDKWqdNhdGgqmx0VTYrWQwxKFDU0jAvqJMTCivYkwyUYTBKN9gkkZKvUHxoGWW9zmSz8c3eOg7FtC5fVorOAl3WwKPwzoM0j6EzAab7Cm9Uc3KWUumWTNSVs9CqajiQqkaUMDGf5I8nKcofO3M2WBt0APWOGqEhwdJJn0wGjgDjPeU4iU0eygimQLOzWTTWpurDugBCjInRGbHQLNJoVelyNq+++uTZFz339sEoS9J4c3Pl0rUpxBFDVpafZohnfX4i/0kbLXmjiPDh62MvixQPPkbRra6hwoaJa3XY60353J5g1S7TPMF0XCChxJRmt9qFi1O3BxgE0gdM2/8frMPYclpC8lAUQ7sPsArG8KcQh3tUfRbcLOx1lMbAoq2o9QP7y/xh+uLRU0a7xvU4wl+Val5AHE4pxMXtgxLbfEKPCH9+CA44fYjFRnGAOheZ4CcpsyxLswz0WArkUhoqR0PJdaA2ewcVADvOyPmm7IFgBLUzvFCWDJAPmUKjNui5Ylv6GvGT99y2fNst2vZzsYTSC6XewumdOPRxnaQRxbZRrxlNx03mQh0LXSh9aZA8/dAX1GGpyyq2QQkuy9mwBZY64QDA97zyuS4eNRjMha4p7l3kehv0M0/TNLWVApEP265TXHg0y6b0Fyb/mbJlqrnz1lsGsQQ+hk1MtYJWTFmeDsD1HUiS8obZozXjdEVlhlFLRzviNWwroK0elRexmTCh1a/vaQp6MPmsnsQ6/ZZW6qM9eJxdhxs7kcqOY1QliJFMYoDXBFzpshK1kOLwAMJfAb3BooWRGDrx5myKiGfcyTgLDBk3eXom6ReThOwXWkfzSp2/drgAm1YkJGSIlkWVpOg9RK0Bunu66HEMzoqiqICabyArY4Fo3eSmAkYSxzb7xbA7io9vlPjq2Ws3nRiuAGECNISnapBCkRCAXR1jPNF6R70R0edEKU2Gn1IqpWUs7hx8vgZGbNFMqBa2EXO6ThzH/mzbObKBgcJtDcbCJmgqISCbMYqmUbq/2BpFi1qvg7/YuJksHrvmvwHSkWJkPI72LqMJWLbLjI8cVo01g2IFTIklswxAs0FZ1aquKCqVsvhNMpZZNLRmRHcvkFSfwIXmPFSIoCKhEvF8USV5VkFai1iZDC/u7MWKOjN47rlcXG5/GSozhkoxN5/dlDaGMbxieOI0e/MhEHvEXhBObXa5m0g4qnf+2EBRp8ASTHk1mEfBdYQLVvvKz42pK/bIkXlA+itoOBjTJ6TKdH3PhWufr9U2cgTo66UbKfQgkmD5N0ZKFutsTslCuvUtOWsHRXkVjbhwbTYrdVGpGgtak7cReyqgFou5REo3MTRn4pup8zw/nB+qWqXQ+RJafoMpK8ZiERidZ+pjQsUP8MxR75d5Xc8W9fpkCD0bTAS3QXCaOHFk4mAYY4BFv0KHnhUGLMz7kzd/u3M2GB2EE7ZZgjMHO+HVr1Ob6o/ENMFJXFUDMG6vqFqWVZ1kYJ+zp2mnxWKzeLaBkY59tA2dindVMdhzongIvMEBkxwEXaSg2aLn866Mkd0bQm0QoU1cocUnYNc1BAOpqonOXLz2gvGogZw0gINMiqyc28g3jgmeDTIFkzGi5ReTfTwO3FiAlyMIJMqoBmqiQs9rK1IYWcpH4LKX+HKwJD+j6mTEBC469K2Bg4dfLGGV0WwcZ8YiEJj7C41j6R3YlCFVUTZvwPsDlk8jriJRT1OwUP3/G3vzXkuS604sInK7971XW1dXb+yFq0hphhQgjQUN4bENwYA9H8Af0Z/Bfw0wmAFsaIyxIYkURVEUSXPr7urq6qq33ZuZEWn8zhYn897i6DVZ9eouuUTG2X/ndwh5YjPH7Jy1X1kykNzoF+8O02+/uLkby5zjXBImXZPQ8SdncAVj1tJMWT1AO3KBpyaoaE5xQ7rAmTyOonEgbDMHWjRLUGb4YOqaklOSPMdXN+PH74FvWFI+5vXy7HGxVRDhpjQcYmoIUDOGhs0wJIftMPzJpsbSArroHCpuHpQRWfAbhFcRMoHgEmOo28U4hPnJNH/4J396H3tUYUBmgYi5Bl/8RDkarwZFlIn0hlswTLw/1dV3l6RfVHzKup7htn3smmZOKdOMdShNyieY/yo+PTkYFILYV+FnUB4T2ayPPnrv5cvnSKkDKoNl2O8ub5vYqkO/MbJ/2J0+Zat7owI9+a4GPStOD6dS6WeC9ILV2QGj1i3Hpr00aqTRCOhxZ5QCf2i7F1ZIaUtW1V4l3n5GWkYMbtSTQDuHQzcIT521i6iShgPkB117RUzkZl/In+QpZJZUtbBqixHgpzlO+Tcv7j99cXM/kt1mIgDGyrIPSUkptr1EpkED0yhqIAi1noDONvQ9LOSytC04bvljqGcRggpCDsGG4mfNR5TO7T/++sU3P3qy73rMJqMtxd/NKI/p3qObA2W+3usqP2eqtiJgxD8VP4PLgYo50vflmVV3R9PRrDeQxiPoGLJwCl+LwI2HEcB4fOH2cOjm+FV3+ej65slVW9qGKXe07undPNtHAtfhqR0ahvF94hQQE3Zp7S4N0G0mz+q9Juz6KMhBBn4OwyqpLi3z19S9w1hkXgaaMIznouvGpGgJE8RLTP0//+r33/3mh93QNU1452r32YsbbmYwL3HjELxZFs++c74+7l5fE0Cuhap+GPRAE6gVXVu3+846ZLW/aWYZmV/nwumWcE/KjnDqU8jrUuixXhjJshDlatNAfHl91fJj2BBYjrtd2+8iaqpkLpmmQiJunhdkLr2rO6r+52+BXyPG4xzmjLKJdvlw5yApE6lkI43MDmSKcaLpRx1sjggLxSB48A0hIogpeuraDmazaeYwHw73IcRhGCgYrqMPUgqHvFzfHJurcJwIjC27Sgc3CfJT9zEF7TUtx9tIs6nyL1teUYTMOKNvipRqNdakoGaPJAtPNCNhJPA2A8GClGFFU+c5d23cTVO361G7m6YIUAoGxbkmJ3lufgewv8xhBc10FYoMSgRQfglqrhqTNf2qq46ZC61uAU11pHoSqA6EKXUjQcx8wkZXdoY6XEw/DAJA4l158vDhOOVuNyyxzPD8Okfsbv0S5yNYM33nZfdEpE9suDwL5Uo5cxhkOScILxGjVi9jeybFQGzfcTlSK9x6L6v6LC70OJFhKybY2CT6ZpNSB7JXGu+n+RhdfuoV6mK6ey0VSbK33EbPe1+TI7K9jcTHoiAR4Da883j4/RfXE8aLCRskaEY1focLRXTR7NGaOM3Yu3ylzBZHSNqubafGwlQWw9hiSsN+f0HeBKihgeioJBLw2OaZai9oeoGpJ64PWQjd+7giSY/Tn+L+rdH+tWjAVkItqr7pXT+vdeti1QPYFHvCYaGIF7ihAn590/D4VVS3crv0l5dNE+fjWFLb9T0RzKqY6p5aCzD+j/Quz4OjnB/np3KcltR0TFVnhlegIw6UK13Zq+3OP3Ci2iZNqBcA28M1ou3GB2wGvlCKc40a6VpTmhOco8ePrp5/+uLm5vm3v/0R6IiX+a1HVy4LvY4rV82rbzKtb/xxi1Ohql4z+Y/pOajbBv5hxuatB1ubU1e087G60vkRF3B1kyrjiRntem3W4+doUlx+WjcXERtzHw9zSQmziVjC1IScD6FrsdEPhbK1LFaMKdJGmbCk2kto98EXLLErnfCibS92/fXhKNqabZ9OR5TO+QK2dNjSeWaVwqP8pCBMQkLzUUPXdtSMhrHAMcZpBjwEr3fEyR5j2zZCWCuj17AezBDIQQA3AFfHkS7UWgi0Mip9C2YyDeqglj0ErnOfxFsrSJ6lC81Em9pVwDQ8TOHwCqiWA+qNWIDuMbZNfLJMPUdoVKGnSZFVL1RHy12DvERrSP+RKQAkDZjVpRxKnru+79qOagnCVSJUufQH4dtoBKrcofc6eWwhUfqTmaGQvG5vwsrTXmWaLy0Tw3PmDi+Cnsc2XVxeHO7B1BBCg6A6FbbApx1I6sRvQZFvML91tc9BqWpqbuUr11/osUB2MXmNJwNZ1dc9yWpf1WBprzXxyshPLS7W5V1Lr/OknSoRDMj64eJQGAjWdy2VfDVtIV8hXwCAmUSD7QFyXBo1V+LO8lRwMsEKq18WjiWlhcx6kkSEAYWUJnlLsbofUuTomGCBscYDDMKi3Syd9UgUlaYFyoCEEONbCNfGgB7GgputFIc8l9Ix4SMGjtN0AKoe87NwXcsSTZq3bFciDQZMPasaUDQadVI562Rdrx4AsRJde0bV36ZoQnseI7iEMCxXGAVKiBOcYNoaBZsqtwrgq03O4kbbPmAJYgpunmWpNp8gp2UseQLcDI6shCTrvaVzBHiG8dr9pDGOAL2oDlLnX+6RGlJ51pV5dTyGxlF58L68evzwOI8//ek/f/+735oLBl8hOj+R3k3QeSpyJx/fJOn8z5tzYe4IJc95hOcM+nJVieo4qa/ijbecs8btnEgVFF4dCuZblM3Xcfdi5Ww5g2KzVHXyDM1WpNd7E6YOiHkKUdiIgVoM/hOvkXANwhGtLdp8JqbLqDZBpp8RQWTO+x1mDIFQWYEGlt5jkApXcXD8mRg3UKVFpx1qC7pA1FOBC8AsWaoYzROKSZqLZhQHcYZQZI/+pAWojH/1R+8/frSn5gcSXXWTHc8m721qydaZDAxLppm9XAupWR5bN7BzmdJYq3oxkQ4pq1qCn6M7dWpQdedhSymBzZ9cD+GYLXnfLN1yRDozdaDpmCgbyI6ZGiFOvvHMWHaGeAwiUnUSi+ouIZMZlzDNU1vA7coRimkX2S8cxfIEPSs7a9qAYZXE+Oe+tK6piOO0ictpm6HMntBRhmluBNM8wPOKZQaUcvkXydxZL9q/+MYMVrXN+jD8WTibCbBzzrQZDfbkHXjnEVfrpivAJPjGwcnOkLlcZovXEUK9FguJTHHI9UFvIl/SYmARtL5AQfQU+gueepzHME8kf6zNmXoDXyHgH32VYb1axDiTJ+Dp9G366NmDz14S5yQJHsdIdH8ivxZNAgaGU1GuiQwGo3IVTIVev2EYxmkmQaO9C6YBSBmwZD0pJ5pvQINemnfefvC1ty4vdnC8JcdLq8ONguSscpaljqchBxGjRgncJZ6BFmM814TwYJsGqJq6Un86+2vfsTw2c2KlmFMzj0exkLS6NIZKxj5+mZqPp5E5TQimNiKLYI6vSQYlGYk5TNLOcptNywgo3mvc8km86hBxHi3hpquwV8/BUG4WYkNzm1ZkGZBbOCjy6Oyu5LG7u/UiUutc1C1MsdZ7Tx//7v7m73780z/+9tdzEmJ316nxJjk8m3b6b3b/n1aAT/1wSlxxl4xZNivOOunySYeKjbEyEiOYNL6s/vqqn8ulsNyKMSPcKuoGCLFJ1GeHH4dBV0i2meu0YJIJtP8cAa6wJysXytVkLQwI7Ek2uqlq7RKGjg3LrmuudruvbohwU1IalBwVAyxcTwyHmueZBZgzf4YbEv8uhr4fum4kfULTdeAh4+a6HoF9omHfTROePbn65N0HD696UiPSHaUwCaKG4MlIdNlQSsLJLKBGUnaS51FONs1WqyEl/9KP/DJYwzputO1WM0WshwkWHjEddTkeQS0Uy27ohdEAbZtpOo7tvLTzLU3QBaCACBKAijPPX6MSrtaLk0PYUc1RkuLkR0mcvDh8JmwCYXhQmpIVdncCRZ4zxjNWT022G9is0KuQSAzPCBD7WRDzmGbhLjKhFNJO7oouM6XWY3t7e9tc7DhxuapPvsFT/gPNg6Zq1h+vFu4NPyIRZYLw8mpJ+Oj2tfN+7WwSeLGjZl7JucP7ue62oi6ucF6boVDITQNxVEdWhdMP1ZOtxU09ZD6MzdVFQPcPNRLSAFGKh6i+ggyEbB1mkTTDJNM7NEWkbGK4kkeX/acvIwq+pCyYqd9BeCXJyLuOD0iudYlE2C8bnu6ma9v90F9eXLRNc7g/3B2PqQNndd+1+6F9dNF868NnD/bDbgCrRtc2E01LoIlkuEAaMI0wUjJ4slqkBoTEjrr4Geehz71CvZ3SlcHIsmwC8Kpd3F7fM9eve+bSSkUWd9j3x2OL2QuB6UTwva5Ba0fp9rch7m5fhmVslgETTkhQyf+o/iryFpQe0kiJbwNHAJZGzsoDuxUkF+KUc4S69LMk1oEc+ZBy0/qsJAwmrCT1fWj6YGVudVQsoURreO0GjBBXRGhieufZ098t5ee//t0nH3/Unm/3O1GFJ6+vJPXNP2+OjFXzTDyWy3cX2NKc/qOSz2hycu0eVziwwTlcDKyJVfPc/Ccks8APgMq9YKMinISZFF6nGk7zg2igWXPI4O6lrLI4PdQ3a9LKSRfZuU7j6CRru8Em9uRF//r5VwcAFBQk6hst+ELEBYFxZrgVQFqY+Vybfigvldu26dp0czi2Mbz99FGXlq89e/CtD59e7rpSQITbI0pAizOFvQ7jiXZjdN/KHheAG6edqVtKYgXJXdUaEbPhMshEHyKHeeoWii/J8aaUAqyF4KTbnINxQAvJC+m7bl4m2kYygblpQJo9LuHlRffp3/39+382dW3uBhCiWOePpMPpGeogcr1u5NiYRlKFVzDKMMty8/RfxQ5ZyGrhoDZY2hPl20kEJudKlNgQ4/i3tCl9SeBvfDzLeSqWH3qEtOTXPngvhPnTzz9fl5HOCuDK31wZQ/nA2R+LE87iKGvqTqW3ZnTWZ66GV2AC9SIc6m19JVUj6tms7GrwZqkq6rdtj1HhDV4zeZvaSGJKQzhvagYxhSnPh2P7/hCmr74kSla53KUMmHwRobHpuAKlXLNDmBKWi0Ywib14MaSLXf/l66PobCmg6C/qTFhDE2sCHlNGrm4tvlJBeEht9/Aqvfdw+P53Pxya/nK33B1GJr/qe9wscEINcU4piZYWkOCPcBaLq8XW6keUGBQvWjJb9WA1U9SW4bI++jEX9wremj0wgzooWsYMNNeK+Dm1bXd/OIRl2e16kHuR3kV/7BLG/YPp9uZyuT8sD3n6JpfLjZmQJFg1h20rEjCxxqkhCIyk42lqN5MLrDexRajahyKlTAVbV++QER00uRBzFmo4xfhXbZJT1I0c0rJYqtZSTJlmTgNrXJY9TWawxh3dlH8gNfUHA97tT3VtPdJdpZSEijEv1Ye3FAY/WZU3545pIFOT0jUXoi/Uvl1//Rb/snqs9LEWIAs4kUIMTBvikFfnBqg7tTosQejbrg8/+Ztw/eIAFlmOYEB1h8hS7Ku0EGr0KlfDl8kztnUvCWlrTGE/9ET4K4PqKc1SQ4qq3UHyltuu3cVhxsB7Mi8kV9IWAjK09Jc/+OZbV7ELC0aw5sPdIQpbVCoYKkjueWoTkfUSK13FN8ucJ454UUlzGpJy3e6hkymWHgPbUjWt7DOHTFu9+NFvoiBNMjTPwVVwLrPwYHTpVY4wk0QoLxn1XKbr1zd93y33r+YLkPpRVBuJ3o4Ht2liXFsV2b/FD7fgQ26JFJ6HS4qTYFiSupW4u00rVBKCEQ33CVKD7gjiS+MU6FJZ/8vyiR43A6OrJs9cIit+GbmslNLb7777+1//yizwiZ00eMzaDm7spH9ydU+f/9lab/FLXZhUL6O2lfFTZF9H1oJVnUOjaxagQjdqEK3WymP0BCBh3xRPlH5jzAYKd1zjFbaLNSpQ7D/FJX0TwiF8/sU/tM0NVRyIIgsfaoYdzxcFIp7BBOTYi92xsqrZLHXK2PRd7duuTUd0dKxL2W5FpbsOWPl8+WC/9AOPCsaoO30upcBLGMe7UHYMGUansEqMOcucJmVfWJPYFutVUeSZZgpxq2k5PqAdzkafaLygiTrWzO7LXO9n4jpYyZTQw6HWxOYSwY+gkeUFbY6Ci2pbGLWZNgRqdcsC+qGlvJjGb/zmF8Pbnwxzvs1T1w1hychFa8+RMWQIThKYtQ7ZBOk+lo5m2zD2h88XOWefsT26jar/I4tndlUYPytFNuobkgK3Q0osocZ3LTW8wJhrN4dvvv9tKmm9SeJcy4V/dQWWq8K5lcHVu28+i3R+25nq4SVBoGlVx/ntS7tyfFUssoYq0Xz3kvpUiJruJ/PCqUufoHnINHQAV7nMi8AY1AabQy7bK3ThmMNvf/Oz+1jySGUE+SgGfOtCWkhZPQq5YWNf0rYXhl6m8PE7jx5fDnZDXs/qNtPDENVcnst+1773zluPHl4xQztHqikiNn71+g5jXloAEdiK8vZgu2Mdkyy3iPp8ekdnfyAWFFUkwxaFfFGda32e5plKfMw2vHqO2gwtSEh9U5IKgkC2ErLEmswHBPQVQnrAWgn/gzwz1YVRwgO/Udv/P3H39//vfzn89h/HT39+zAPcTRpSJQVfcd1J+tAqECG9bRc4iWWJOt3ZsnPYP5YRvmvbsd3vdbdrP6vYKk7mk3eu2HF6RSpipPmszGVhtPMSdfsANBrnNKIOrImZVdgomm8LFvbG0qeI3hDrngiyPxISmUsqqH9o1X6VFCBWL90QKrMKd3aoZ3Goob95dg7laC3898uhYrz2YeUTUMVtS9S78FzN6mpII3VgA5sDEtSmME/hr//Djz/75a84FBD7y6zFlpDg6Hf1iLVTXVSuFFr186WEXde89/Th81d3xxG1I3EjavRARJaodaXLy2G/79++2n3vG88ePdj9+BdfvHj1CrxcJACUpo4///WL73z0+GLYTWmeCuS2A4iSKyZY17ZrMAiATDBAAi5dQN2qES2UzCDAYBlmxLDbUzelWk3nOYOHUnnC9NUaNdC5bHmIRYQ/oi4Gllo6vcVlS9T4EciqY46yXQMq7+VVv/+v4/3L/+N//1//9YfXT5+l7vGSMQnKT461OgArJcXHmpwwMbuM1pKeUr+RXU+oz8KIAfbEtGYySIDZPjOjgTpi4qRwCM5nEH3vMk8WT4ttaMBs1kqpYZXLsds4NadOen01xqEuwr/0R9Q4RgchOaEuX11gE+QaTrscrP1FD58Ai3U9a73IEs+VuEcei8ZhjPBHkbRtCTLZguyKcI+8ExXmb1SpNermudLh9cvf3d0eJ0oQsuQTf5U0G/AEYLmU6o9X38ysDI8uo6QqnlOzfP29R799/tXvv7jR26hEiakJF0P34Ori3acP//ijJ/uh6ztsjt2+fXjR7ob+7vZAbAK4v5TS3XF6/vz62dulb9ulBFDzxPnB0GW8i2IEskRNIsA2R7xCxKU2lgtF8jB0rSsZjhRGBfSgJlZ4KqrZ0LC28uNpcwfFgySQ8OWpNm87G/4FF1QpZgSbPLqdG3AkU9+BuqbMqgwV+lnf78fXf/9ff5R+MDfNDE4LhsmzL1BdA6htg7Fud70KhbdTzj7XNLTmYnSCh8qg7ufqTiIVx5fChXFiFjdIKQ9eEGWmepBWgRfb8LY88ZUodQima92Mav1rDsKppTOCeq64tH2t5iTq37oDJAauOs3f9ioEUfzzNmq3Govzxv3imwukTHdVgrkMkclnRBgEojZAMwz9t3FdFa4lA4GA42kwlWF+PQL4SHtYlQTtD2lMooXjEoVweDiwp+VUfHYKwIC59E187+nV9f3x5m4CXpP64vZDf3E5vPP46lsfPHm47x9cDsQhi2PiWsryvY+f/cOvXtzdHfiSCcCD/377xV1p4t1Ynj9//cXNGJf4P//FJ5d7sOcQVoWGYoovq14MZ5h45yuUW0Ngs2IrBJXEO9wLIJJJrzMPjnklmsuzOiuYymVQcYyZsuI+eEHuR8wvzxNtAD4XfoSckYrjfiBepdRd/nIc/zLeXt7ejsMDNIIygbeuN8cX1OrA3d1rb2L17OtuFn9V2TtWW6PKvaPq0GSsrhyJKIcfzIEF/cpoc0kH0ODletYKSeXEPS0BNYqFNoaW03c10K5ZaLX+PoiufsQ6uWVOoZdr/77kls672QoNPC012GvyAbkLCwbU/krr7SrpUP2IWmQWf6Pm9KScR0lIrCjWh22v7v3qJMleZQeJzthMS2kKSGAPAvNTWn1LIHitZWpcK6AuvWAQUf08Pc2mbb77yTv9sPviq7vj4Zja9mJoPn734ePL4WrXF/AdRnT4F3DDIwlD4IrUNu88efD7F7Dbtg/zEn7y2y9+/tkr6g2eOdV6fXP/YNcvy0LU0Nv9Z9fHQ4kkaSqX57zI085Rkg00IfJ4Pavdu2NqeowzXpgcQZaW5xlpSZjgvwSGIjVEhp+qOjhOQdcUwNvc/9d2LQg3sf7o4sCUjKurw2eff3D84j6/ncBsIbZXDsnWUMfNrK2QNzkbW1Rtoq+v6D7VNMB6F8usKSnlkoPBZAlU+mqWBHZCwuAabKNqc3fy6gDThJ0lxFY5OW3I9ToMqGH05udcUmobJaw/eWLR+XIJPqwARLdsNQCrVFUieN5V8OAGf4J1lsoO7dSTybpEfUL4X1mJ/PVooMHgWD5/s8QGVdpDuL/JRP9reoOvEA61y4VYKdjf47q4XVeGPfAlhCGFb7139Y13Lhty8uEj0E4GjpeQdwuGpFAdtGsJMwYA9OOrvu+acZyFEIC5rDDpG02vKPvSdKMvX0/vPsF0hZYqwHxec44MsGmAKfVaxLmlkpHGUi6osgS7YRelUu364EWN1mhCCNjFE2VsGYYw4U0i08P10whyVM4I8ZSIDVd0uXSxqU3Pee5yvGuGcQLyjI0f6jea/DA+UHliPkVosEv1kTebZvUQq50mRSeZii3MuCbjpPwsJXWU7qlZKcOBwFcIb8nDeVb7UERG8SdNwog3IclTEodN4xqn3ZSdVfriFNVnH1AO1a2cb8ztqfXlc1bqipX3ezL1ZPXF6lYTOFjSXdV/ttEK5vVa3CpOrSy2kB7QcBECPbuKNMsWgkJKTkrHk+UbQl6aEsJlDOX6tsRZEKx8Bi10SP5bqGNqQ62FD5yJtGz39gd7vE1x6ACligE0uzOGklFfAZ2FH0tDDiQ7mUvJ3//mO2893FOUTt1SjKImsC6JIsXnqf3lZzdHLJ5wuDjQlAbr7NPR25p3EYyzq0zKB8yU8T8ksV49rBqA6Zy0ql0VVIbf+TprAoy+JsPGVUdzFjcRtIMLXiOKT8wMQqFKXNol30HWD3BRUZeXJ0AGPKIoJQ1W261byy/eOljwVlMg9v/q8gnVvBXWTDPUDIBHtLEekTopjwL2NUXPy1uFRrZoGtuGZpoTmZpBXlbNEO5SNEtsNsNkiYMCvSRXKxXBXVsXtxo0rJ2zEHUFbPasE0E9kjqhFVnFLXZyWm2iPOMdmJbkq1dYJLfRIHWFiQICw1fv1yrJ7B3zpJ+ZpTnmJTQYgBB+8qMwHjDhgGv+tNGJhg7woGqSxfeyW6lGWFrJVm6L5Mtk/Sjdqh3SstBMi2VKGfVEWgwi22v2++7Z4wefvriWSjvQkGjlIyAGPgx6oFS6odXoW8bhcZhqCAfNndWyl9R40QHKppgmD+kD0CerGWYSFyZtFjZ0NYCgngCBNmf2IUotj7BQRJMQCYmLxCwsnIYSz2WmtINJlkKrueDIrQjpRejy4WYOkaiDZM8AU8y8nr4gKvbWllTRgavdfJrxlZuXt9gK8oBo7sXcJAuq3qvJAwp6GvSiYOSETDnk9TLdYZkF8WjkRUo/6vUKL48liux338fBuQ3bRybFvOel7nSmpLR+xfpBeAiPgvPPFLQqiMHWUHPLQkPBJRYft52gs7ReYP6Qb2VHURHSKyfQeu1K3qrrLlubav1M9Z6XX3bphlSnVbgsXS4Z19pyYxmNaq8Ms2menMPlqC2Qh60mjmIPWBfS1xS+wwemlKZwhD99sGubiEHf5OiCP4dmSS9LGdrU73YPrrrvfvjOJWRYXFZLk7NFnd2EO7aBGApoaTl2SnWFOfxSSMzquVO8ytlWfi44RtMgSGZHGh4EIDQ8IFUITWTZdaMxJZduClB9timhS4HOjdWnvA6R+8TCw9yWdJe67uZ6mad2P0jTGTEBUbaAYRsWcgpUXR6TX/8qvRrGVaY8vX1lxWZ6eAJjrbKSbxIFNcqAac3g1aSAn4zEeeyE9QAQNUkrGHJ2Y/VeTkP4Ggo7aXN5mlXwabb4DabQt6eTAJpakxaUdTDtb56/q/xqfAgloeIPMDDx7JnlTiXKofQqzbtm5IYlkpz5k9ShAebEJNPYMv6zhLY8nzKYHLR0SoEWdqRsu7o8Io2q7BSQ7ZFN+kSdHpNpwqsVlBhLijHo8HUNqEiLl/ydT97+0S9+/yqMyLLCnEFP7Yb06KL/o4/efny16/u25LntWibpoeegl6ib1/jrFNS68gTrhnSZaFY/rOjBxljNpHNFBXwsQQOp8SLdiBrj0SWhAKaZa0XCMktlKF2ZRiA/OQ7iRi/oM+pjxijlezQ29L/42x+99a2/SnvoLxIO1I3Uea7GsAIedGXd9rdPCRjA8GGniCbal6SvXNHn3F7kTSDujWb4efKrZKP0Crwry1BubDA2MTw5YEOxay6Cvzp+fM67rl6DdIDqP6yCJ8Uus4DuJFq8pcRJvR8zT7YZV2toLj23cRv7p224Cvbfml9tXRNIrYR2Tdf1iCMsIWzp8Jp+ta3Lvm6dgoNt1bVhHO+OOYMthtcXK1NK3jWg7NUUgz4BS0h6v0sWRc5iPpoYBAkoXUjBQAqyiTydDmlNeosxVdCKsblo2/fffhLCa3gYcen3u3eeXH73g0sCaSxD22TwbBLPu7qe9carkJotNJ9Cn3+N45wqqbEUPk19otwKYq6p7UoimiiF+eNJfTBCWNPTOkq5BuOShINKnUPory7j65tw1JGunIGTcpfogWO/a26eXx2fh/iklIz0L3rMKAjS8Q5ui4kLaHvbLbg8Oy/Lfotq5xD9gX4k71S6n835BBIj1N2y5IRacfJWcUyc9REpQPSLZoYV3NICNvnXOqCtVkkVrkSy2llP9QVTGBU2JzMHXDTN4y2pk6H2ODmBlQmejvmgfrHuOQnwkMlQcdZdVkkBqsuhxh9HxXQZGV3jAgcvSBppqDarz0CSyfMc+jZ89fp+mlGTk3WmbBBG/rWeVWRdWVY+BuuskIYlXTe9as2BCf5Ld7Y24RHmgVkyavBKPiI13C/vv/Wg6bqhSV9//8HVvr/cdTlP92OOJUy0Xr3OWDENo71GboOKR6+5Dw+GcWTuNQWhPouW4dhC+kRIRXCQ5eRdwiFjVRArBSes3IR9kO7+MGNEUDOGSR0Axs+I0yAzXkqYkfu7IWcTNQMW9Rq6nrg19bSnLqy/KIMpivchapXWQkiTvN8md84iYjueTR89AkNXUm6cvquJMO/9ctFUEnppwewYkRKn/M2FXvnuazVSJYv3vdy72mN7UKu8lny5aF7U0Bcu1DVLrdfiZJhJc6TFtHYdUZTFzdSGkRCF4JCPmmwnmHKDGV/EzKgKVL0MP9ZQupcqj7OtSkTGYWnmEG4xgVd2vWoKbkA3l89r+vX8E8Ek+sUl1H5dMFs/r0rFvnA6Ew2ASFKpIkaGltJE4bsfv/Ud3CtUHG+d48SRJzsXOBcIPbR0JHlmB3cz8JchZLyTpjVPzbxYGK+K15MH8V8AYDDHNa+5nJlVmKbBmQzQEv5KLs+usmLe8QcBOeQpS7sut1vQ9TJn6NSmbrwtOCmSRFB1jGfWx27m1dkp9bPUcfW7WKVFvy7yyD0N6lwiCnZB9HrRNjGeoQUtRU0TZm0D2GK6IJYoOOOCAdKiNE1duIfnAWYrn9R9zqXUbUGqh0B3ZkJMRxT6OR0nYayL9SE74ZE9z4axosklxKqSxeRzgFUzeYo7irfslNEklsm+63punjbBEPtW/+QD14/oXpTeVezGPIYyTjKCVhqkAGBYJeNlWyu4Vy1Ntadrb8nQaduIrO6FKjyMHqHCCDXzUGqDeWpj6HqeP0bYBtqUQlnFarRgmJDl5UTZWE7Hn0sy0oZAoR2eBV8hk02d3paNJCvFYYutoYR8PMjREOtUDeK2Cv48EfVxd5f+kFGzZBve2A39NB4LWPBpgSmMFo2Aln1c8nUI4fi6yTl1zcGPp1VtarGl2DAHOlo9Ii+HKySg6atqxCQVTQ2bGxu4teu60SV8wBpgIzsv0uRSmkqZ45SLc4TEklW0DIY4KfGNZ9z8eCW7ZUq33aDKw3b0WgNpBbIeyukB8TMkexW2xSBm/aMUCNzyPAv2v3YPSAI+CQOjoiaVf62aGxdnOpfZLYem+NFNm3ogkkdQLZuLzjqL6BcsXykHtOPZDjHtoyQNjn1RmOB1s7Gtlq9T2x3XM6XWwuUrnV/pfBptE7CtqGZFL0qmVuur6mpYAUlsrM05M/pEkVo9qmukqwlYZFdDQt2aT0+uPsmhOPssQOJ2O4+RNRhfJAsxa5dpmlHx5toTfqTHgQgbdXaLZviwEHO4nmP7+npJQ49pDWNqOkJfVXMoZ1250xttWRfQ/8MrOc1JawQAENpCLRLrkTrrgzufUwINbk0upRUaeAsdzVraNkM/NwgMTF2akdiI3+bf+vEztaLN3a4CidqnvUZPUfDKnbf19tRsuuDVFJUHDJhKZ44mzt1hTKzUF+z+WTvB9nao+XaUeSYR81ZD/Dc2Oq45ql4Y7yt6CeYBsnNzc1cYBiRTiEXMVpm06n/rnpfYnzslqstSi0grJbp68OLZC2jBeCJ4Q6NQJH1Tsgr8Iw62DBwwLUqUtLrk9sPnp45E95IJvrxA0b4WYVnEVBGLLNeKujYHEtEHKDSEKpBQVoK7kkifD8aISk5VGoK4QTGbzgsG3Bj7rgNVPbMarHIIRL7J7RFD+s3f/e2HPzyGeLnr2tB2lH50omvh7NkdXfezLYf28NtKeWWv5ofQKI4Cyj9ElzRWsIE+QeoaaQpMUubxWKLPNT6xjjQMJq1JLJfyObl4PwdGL8Jf8ql2qYWhWt23+EjqQPXRmkA74mT9VPW1DU1QH5TVJLlZnoiOqAd/vfSCHADTBowvJJjDKlVs3nmu3zwtAchFc8sJcJfwTG++knFmmssRZeVUmdZkvAgLUk4mAGqQsRKktcvs8yYM2zFRV9VLAEz33NisW4LZjR1nx1sSQlLLsK/Yk7HfbPnN+dexNLz4dr1Mx2H5IN5nZvX1EPxQqb1esNIwqqJP6byazoAXzfdOPdLY36Ceb5olY7x4h6niKAFiWhI/ZRwSQ6E4szel9mft/uvHl7lcp3DVYkZRomS3dyzVbd6kjQXWYlMYvIjLA7NHbg+qEqmCgh8YZxmF55R0DdD8VzknkBLMApCB7Zxmnq9hSc3KjCYnW1BBoRTAepaKOJH2ZNY0BOomvMEI121aVULVdVrpWS8hFyFrhVgHTQlSu8Z7VO6jfiveDcLSX6NW88PUMlCgRc4zmn1bUKYLH0XFfq9V1CoB6u/Jy3OJYb8LKYf5MGLqo7GE+w+abVxVR0zp+kYlfxpXt7BVUmpVu1+mlZMzSjCqYgeMjhWr5OuS6pRh3CJRtN8V6KiCyXkKtbGm3er1O7CO6lBRbDwOgq9Yq4hCWq+LC5eLY10GQhJ+Q1hUDMOVyDIjM0VaRrZNXmj2zhQxpQrYySWXvmtzWYAsq7fPoS1KUyXnGxSdSjpct48/YBXlkjjWu+/yUuJ7nS3yrkR4Yyfc7hFrVVDcLqCqXY2lPjGIoufkH8TJRU0pYAJSb8Zp0BqLYsC31nnq8d2koHMb2YWvLmgUS72+GbdS9W/1o/X/6iTX30x2fWbLbs/ORoGZUDpUU6oNfXRjaDMGDh4uFkW/HXSwXVu9XjMGYus1BnZq1wVoAXuuhIsU/vN/vL5+eZvTXNmnRVv7kLk+OnP8+bdawRLDKFzR+oXtFrFvV58BJ7PJZKIOqKfKFoo8GZ7sUhWKdKM636jS57iIWT8vIlFLaFoIq8NTJaGObi5hriZlYHDFCnql/3EuEjJsfpDG5VyX1AduOo1SzXSPE5jE6V005SzozyhLS/AMYWOnE5EvBtbNqU3T4aYj2GzFn246ibYzxzZ5Q3sk6zDCv1fVVMU80FB1aYSqB9n8g4poslu4nxzofNwOcIp6RGpywA8TqPMmk+5mdzQXLntZO//jzdZWevVp1Fuvm7V+VytbCj9UrEkdUMJgYKoQEPsLySIojMCD4sjEeb3sifBNEXsYk7N3w9BgGqN/Lk5Wti7rxiYrStae3JLBpBOnn/U5x5YmCKl/ox9fATpXiyi1rVqYcIVi/YQYpdVLZmb1ip1S5giKYxLtpKguvPlcmkzgja7azuMf9aA2VrtuD8t728eMppKwxUKzo/Ci6k3YkSUjTfpxGmfiUuC3rH1C+A+066vyVnDFm6cTSYdjRFg05zxOoOqr2r6SC0CLPG/7cPu6oA2LsyTqkLoHtN6nakQ1E2h+q2tTkD0hYZ0ummAxmCmAAOpEVuGYZs+G2vRwGYLCugXdNZhrZQ9R8vcuCMHFtecu3seEGz/5zbK8NcgbH2P1foURSqir9W/+iNgHodSRniud8WbNMAqvF290hcFis8M2jYhy+q7v+16YS0/vxQAVtgrV56+3U9UPUyZQw8xrTEPdGYOmFHdlFmUVQnvYte5d48mN2+ISKmIpxL4pBr2aLBGFVZO8X/nqb3sjIFVD3ZQS5aljoNBOFw3r4kjfjBZ3PLBSERQrGByD1RN5y1ofkm9SUINEYGQcOoU5RLshvYeUUqdip2Y9keMAdkxalwsdkDMajOQhKCVFE5rALyD3bcLStV99ucBxQismMXa7UIehcSsjLBeqbhRnmc95o2rADfyiimilP2UUu9iAjVrWq5AZaGnJYOmLYEaFdaom2NmChQjVAQhnH0xN4unV/UHpXVcLN184wYC4nG/9DN2btuNoDkGmk5m1EWnQvVhdtpW/wOV8UT6Kpmj7JnbD0Lc9bVmu0FYU6GpxWHuIDtncrxafGcRGuVdcTl6OpUETAe1PWwo47RRpq3tqbpo4z1IPlly0SXD9w31gGzDxzbuJvatPWRpRylDmylsppuan7OZly1Gq1yE2WNtyLyG6cGUEE084kSYhOQa5dvQEaBBfglvLoTVV7rl+y1clUMcFiA6koNFYLd0UpkqQgQJnnTpXDFylIlkL5jkaqLuwtMehp0lOFTMkpQIJi5d2Sve//7//+uM//fdz2qeGm7SrR8g3IElzFWw5zHZ2kB+kuBJoeYSu3dJ7VJr2OXmYa/mgtqSWUeloPIEEp5ncaj4ocTgpyqXkdqZJLh5npKJnPmb1Lat230SxJ1J+zkVY/2jtnGMVjzI1U2Em1ppCK7Wj2SD10tdd5UhdgG8xoYe2g/ntxOCs3WTXMKJVR3UQVlfpnhKfEt5cirGLYTzel9igcUdiHnqfyMbIeTI0jy6NZkjYCJ6ox2r4pcy6yoitzLRwVngHWnM4zjmxkJsYLWQFhKxKl9LW1nJg6u7Ksgohlhklzh7Kac0Vkvvi5uRA0xLZqtQ0DAGMwG7HXUz04NGxAK9YU7402bgFPxcMtGwGRl7TOEWUvdgwB+rcHCc091IkRSgVHoMuUW0K6Rjbf2qHrx/vxvk6hcsBxlC0U1281e60bqP6zvnu9E35SeVCJoluZRhIldUHvUBwfZJwCk1uJnjdEfPX2xasiUI4gzVZmpJBEIam/3suI9kOduK5FkMNB06v/00XdPpJHwtZMyFDI7npszqyqsvc/Z1kBC03K0GJdPXwM8Yxm6bruqHv0CrYAETH66SFle1Dq/mb1ZsiwVr95fCJVOJSsLLXNyPKzjKWTbskQqdlXj5ptb61zLCJeTetK9X9OrfmUqCVEov5JuJ56JxofWhVsJUH2jXT8MksnK02kEEXzHTPwyJk9pF6idwXpmfmaIVWBXLLdCSMr4IQY/Sk5FfxGXwRpSN2GPFslFtDnG1Itd6C8r9T+z51TSmfVExNajELSgIBawg0O0oXWu5CvCi5G2+gw+A+G+RC/ZUKnLROI5fZPY1fDWDr5+jph0mvrvONFkHX5L1/oPUv6Mq2KTPh+WICc9805Tza4yMW67bBzEseFW6qX5W/Heu/KZbnfs5sOONdcLuTYAaMhVbzbvQDyjBcbY7PF9bL1JS1HJ66yfI4lTl3bbff73e7Xd93tEdWgce532ht15QgflFcPo8RBtw4n8J0nEKYaJuab87sghLVSLyuYqQOheA3VhkDW4U/qPU1q2J4VOWQ4o1oiymmVGHjq1KErTma69SA+piFiYs5wy9E7maiVS0xO7StVh2epGLEyoC4NQj/LCUk6ymxLws8mVvLtGHLg+FEpcIA0SRUG3wVQ0BhYRVwindCyW0ckVq14m3bHW5voHvh3q/SH6stJX+7oojFQast47BR/liVfc22dHUq7ai1aCYns/9R9ounRNARbWKEZVjEISIW/oZ47XT/OBKIGtzXkXlndtLJy+ta9+lnVIktQOFmmhqn7qBF8nU7V/uwCqzjamkZXMl3O0/jMk1t1/W7HZLUQoKqs2O25Td/Vbac9aLNj65oBynXQfcc52nXpHh7Pc5lKqEln1BH+1DPms7sXmsJ2XcuinLtL6tPiR/tCxd19wjFrbRVoDlZEwFsuCwEro+Xl5cCLYK+KZspHVATL1Kco7lujFKUoWU2XETjrdWzlgI+1aU0AV6t6II+PgK6KndSTYXrsILa0sRHstKxS2NLdptKzbJGBQaAUBy+CrZKB7AqfR2WdHwF3qEE7gvrhRQSAN1bKztphUXXbLfZODXL5V+1hCZ/xHa3uXObrbf6DdoztOhFzfNcaJppM6VFRm1wOxWNbWqaOWR2P2r16BS2ud44K7/ujDg4t8S/KmlX2bzcyE/oB7pVZcuse22zVpvDWVsgyy9XI8HNOM991w4XF421GcnQinMaqHYdrW9nq4BOBZl6f6Y5NIcw3s2K7NOqDDl79J/wsjqtaPk5VEQKETfZ5VTHxxOt1aDcoImixIRbFIB5LANNADJ0SFXYnDfS4UNmzrzDLoKrHp+N1ZT6uj7yquHWCCRZf4FO8SxWplqRBAe9oxNqxDnllWTH2LSjbiCqI0oTv31QCUml9dJUYAzExSNLpCBFJxkRh0L75M3LZZnapqMSstBdnjFCoj29I+2RcKtNKefZ+kri8VReBrcTNvSFcmv6+EWHguWYXs55QjUK06ukEUALrki1BPDaVUSh30b8lHyYpu+7/Mh2w2/DAbf1a1DGsZNMo3ZW1j69KXlsErGuHVhwWnjeM4KGtkm73QCKCRqTUqGZqw5tt1K6bda0+7o7fYdutc0UQeYYUhcfxVAOdyRFciSuNbhiMIuKMyJiP+3pyWZ22tE/W33XRVPVjyFAVSmMxWTWf2eFqi2o6y8xG4eP+ISDYRj/cnWwqRMxAAAAJT9JREFUtRZSCQC3D9ZSh1q1rZSHBMbhIlcl6/EQce5UksOzyTaIrUiQvFnLDaK4tUi+aIrCIMe8ydVhYk0CI9y8Tm1+8WLMYQcvfKLOrRO5sy2mD0XDEt0+b/iCLbdFuAYFjsFA2vVjnGkVFkmVLPN02M9gOwwjzFJO0GqKOinUyCWW0kWUkUwwt31P9SZW+7sKgHcE3bfO3Jh7k9YUDDTwn6We663BifU+czwJ/riAXBBM59zE2HV905L0Ele/P/X6IVT9vNHAax4sd820W0UgQwp92Ydu+i9/3d3f3nNLHCOkVTqkYdzDiXXzqWtM7rH6ogprMkBj9bn4glUL1KhQDsVclATfYby/jE2wgpsDGLLu1XxvDVh4No84rYrS3CyBa1pyyyVoLtYCosakm5JmKAhhA+kaV3Go6BEzm1oKUwUfAjwUfdH5KdswJ9L1Y0IjUaQrQzjPNKS7C8vcdr9f0ud//5Nv/fB+2u9It2v1ee0Krzadrb7VaarCsutZG+XaUYsAAjBeJiEQYIyEwcTjxhbVdQKT60MJeiTnmPodo5JinHOep2mE+ApSiQoQ6dhitIrdwtqlF7dN82wWMGxv4awnfW5hzEBBlcwg06FsjlN2sp3XBFAnukO8CE7CUameRtqixXfouSGGMb7sufFRV9hW7zjLxAovM97+OlyCOLPU4zmXPOy6ZQnT/aGgrkiaxLkRAkWQY3ETs8C6VXKAIXShvmbi7BDORdPitoaEvMuB9aVJA4IcTgto2hRAUu25cEOoyWEs9MoKugKd5rEkKcYTACxji6WmENci8JICTqo927VrrG5x4WOBz0Ud+QKZqCsqjSxc3eJ1oIZQnIsb/1bpJCdlUU2taWeNMsSLwgwH7JDlvt/dvXyZp5u76SE4lBQOX+lgXMpUnofRJ4nTtLFEft9Ub8H8N27XiEjjQxg1Y0UbV+Y8uV1N1fVYqJUb4G2KjKQU2t7FdCQyIebpMM8uST9wtaTOIFRx8f5rNV9vwqXUB7e6XaOsLMC8zTMyJIgA2UuszqFu5m26XTMzoWYsOU9CSU4MNcI0XWESq65vVTwOxb56Bu4fyicie1tGpW30Mj3mFPM0N2355T7mpglMtuyaNakQLRezkHQxdaZMD2OO9cAz3YQCUjxJCiHdarD94lZJ7e8xJghGwSwB43FhgqlHj6rUsuA8QMKWw4OklF9A/lIXTt1of8v0J0kUHpnOKGOnl7BPTVkw3KR6GfSHDH9GVoZQruwwRUDT5bykPmiqFXUUgrxZkdHcvJAXwmdIVG9IeFVokQVbfTHx2DcGhcAkZZrKFEu+e53GZ6kduCruaXX85vAHEcdHHCDdnrqlN+asArclH0HSTEUKc6m04dLtbtncULJcwTZuGYze6rphNxwOhzkfrXWfjWFOhDdwT8pibCez8uo21H8zuFMvTRN3TjvhyuDWY39bY7tz4FWdOU/AHWjFvWpuJFJyNFKQG8q2Qrq9EfWvNPz1SYWqlBwYcbMj4ODMMLc55MOLcaKhAbZKnDGCpaKp0/h3llDTvFzWDFu3kBmqeUQ7TRhY0dEQUIH9Q6IlQIML+DSWUkaSPIC9O2Kz8xA+Yg0MPrTi66RcdM2RaXLIqJVtjWoWitTQAhabGY4dDa8XI9xAG+NiYGVR/M0cJFGKmKvLUiygxB5184mDw6VmC335V3sApPfkavzFe19+YR0gDTCsI1YlA3LzMDljTKnc37TkWcvFSbhfmbD8VvF2xEGb1O4rX7CrIjsGB9vXOkC4Ps9qJaWm7jaXoRSYuIaAKzENu93uYpznqWDx5fuMvW+riNT9qzk8t8/PZdpWv670l0tFrHQ5dUjmmXa2RmNap5LrUmN95uiOFE/yWHzD6BBsWyJ2qk6NpX58wVGfljk9JrHiL7kQ092pBspSb0UtETCZcHt8eZhLYO4rIcfnwuNcCggZxRIiTJBFj9T/RWQvGdAc4amBY55oDiKNudRhT+KQApOE3cbwRGLAopYqYh4hE9c2Hc+YRfAv2pDAZ/r4qh1mVjg1ofpoJCUmg6qpF5AEBiOLNPBk2taSUQJnEkkJvwlPwmYwZ/ICC8b2iovOpM1CV8r9NHWnMLOPJq84P6OE9fRvstus4FadG9b6EsUy1941l+bUbPKCjO1tCMeb15cUthFyznJ6Zoq9+rbUv+0i3zYsOUur8Xm9swnUpDZWE1xOILzB2QTl2uQTYujbdr/fH+8PY56l4ytzgQXTCT15jHaLGK+Ec0re4DBvAn+NJ85BupZS5mnC8HjTDtUh31TI1rZvk8DRPBYlMBo0hxJXqH5vk4iq6+ndMM1uuiSwM8ineQ0XCMDgAGd0uL8uYPEhpl72geh/NB4DYQ5tKutgoxVAhJ60dQldu2vzroAKhG0uY0P/5FYMSgtxDZdo1mFIGyLWMfJk42+QacbctiMpWsm5CXK8RtluEIB/pOK/qsqjDABoT7PuLzaZ5MzmvMwTfF4FlshSGiRVEhI6gkbwIZYpqI9aGHegFGD36eJcwZadlMJpK/Eq+U6F10YSYarPecjDWMLy5XP4nPKUDYBgqUw7gQ8QN26ceWyeHdrZg02/oXNU3b7eeIp+2VeQadGwwHsP+8uLeRrR20SEWxgsCgEW8FwVqfU1+xlDW4fZSKg3l71GJUjsEBEm5XmC+dUNBFkzGnvxNlxYsrlrd6tsoaiID4arjtxLO/VKlax+0fhqs6r+4floyCelJdtFH4XT3oYwHl8epmmOoUV3EjYaSRSUI5FAqg/nYglOICm1F7E4a+8bp4QYDAGooUTVXHwhDSGtlQh2iXqDhhuTBDTNEhNI5pYI6lRaRx6CxCScRIHA21oGGFOO3iqF2kOiEARnnE2RS4mmlDjPvk6tJjDEktCqL7k5pE/NRYTvAf+bdauVq41ji09NtT/OR1PtJC4IizCELfPABqro2vUsdAss3zLK1MFXSZeyQ4E1n+7x6hc/+dGHf/Hvp/4yNtIVYAImuktF+MT6iDBvAmy/USy0r1852cgVsbnyCr2pkk+Zj807ru3C5cU+T9P19TWaoQEfKmGg2UiulmLa0l3FSUOGA134NdPE5ukXNYM4z5NEhvpdtlrmwvumCheyOq9Y8vAayJP5ZUZszc95z2VjS04DWn+q+qtP0dU/zROnNGEclzDmy6Z52EMNwqugC0tL7mM6SqM5nZOcPxlLy8egtI0AdaWGxNVcBkBAUggyxOR44C5jB59JNBvqgRdnFPFoaBIuoQWaozSIi4Rtk1t/8KfkPzHwm/FpPPKen4BSRIkZpFyCJM2V51C2SALdNcHMSK/QqEPikxOY+dJjogJnYmj9odS0e5e3gtFF044npnUq+eDi4B9kQUGKuQeOXd17StlR8onUSWKrQHhsYylVRYlrIj8IlzeTYv3y8mLJ18d8O4e3KdigCnFN8dVci8t/uA203RpVcE9+XX1b0jb2T8kAsAt9cir/wuoImE15cXmRS7m7vskBKbn+fgSUUqcbbC2fU8PrlPKmd4lfNRXiz8rPjN5F9ZeiXy3g+yWpDW5njK/ZR/YKcST4/1zsblpiBfLXbzmuTVhx6sr4cmTVwnIXRjKoxL/2rYhekRzC/d0Pd/Hf5LvbJWS0S3DBpAwJ0N8uhDsmcyXFlEPg0R7gkQzLRM+kgw9dDkvcJyTlbwO87YbKhhE2Jw8hw/ELpV+WnrCvr0q4XMrEqbAYWoxZiIeYdk3pkdDr5ohRvwdEetj1XQCzeb/k45LmmIaSJ5LiwFQYBeM6edZ7XpZdWO5A+kadfiRduE6chT6zLD1KI3EXC8ocRHYzBnCDLTEOdCOYeLKUmTTPGOKU55TRtQ9CZrThllGlXZsSbRRAbHF/aH+VwYtWPJBRQVQUpf2UOJlXiBiXiyu1whl6TKCju6driymMS5lybEJ8GMIywT+iFqnqPJrIndnY9sbGLd0IzKn8bBI5xvm6Mi2aHfHevL4lJLWWdIffh2B4yfkeVw/lhE21MrhrjfAGi3U6/XRz3z6DKmaVikdIPtelVtFXN3KD9jk5vsBAxIQzR6xwqMgmqInHzTHU29u4zf5mLcQ1fPAqMveeBdReD4bEtpTYtd2cezTBsWuAFDH3vb8Ff3eZuOpLAwHBz0zuHo8iWmLphWkcs0IexwB4QYLIAusFMWhmZAwy0tqYtxSe0LTRJYRdWpqc71LbL7lZpgk8hnHKZVhKF8qu4CKXUNpQxtB2cRlpjE0M5WXYh4IyXktWlj36NoZdLANMXTugzQqJqkxwByLIoV/gJMODzwk2jfJSSwcbCSejS6FZ4NsT+TpC9v2yXKFOvGTAkcoRBnG+4POG1FHaAvUnen45YNQLx/+RypsItkm4GGVSkDpAT3yLocgsyZHcY3anmfCdVpea4DmipnQb4oljLIcy50iTKICST4W0qgxi3G7hjbv2Jv43w084Mi0tO52XDh/reh95vcf8C9oPjSJFATI6DcOw5HJXyhE50VWmtuZw1uc/B7Y6L9w+fpBj8/BlZK84p7gquSmPhpNhi5BXgX09tobQ6E1jEMHpotbLlJhic3WSStBHUINg7zXXm6wpGYrbmqYL/S48upq6YaTp9oeyHKjMQjRcMCwysYTgng3+kNSd4J7kHhHqHmno9BLmUI7tghgDwIwI5moyhABLEv4Bmy1j26F34X6OKeSbqWkXGG4CsxCvAGwvHAHkJ6nPAzs2LCOJXF7CuMzcwiU+J9V1Jko8v4KPPd+GPFG/OPmwkEANZlNIA8S1RfNARwOKYE8ZEsjyhlS658tEmhTufiiHpWsjWGI6OPQJ0Dn69kyMziEmwjcyNkTAghJNY0FKCnMJIyU/GFedoGSYAE4AoDxKhfvfKatH7SxksPP9dBxSaR49a4bLMUw9XAoex1Rt59ZLW4GY1gkg/bzDZJ/dg+tXNxkew/j9oR/nj/NNEWS2H/qlzP2MMtKahX5TNjpvZuMf7O/xGBD5A+krGueqkiJxr3kxjHHwV+zzffKC8WpSSRGsX2yA3WAuH/b6pOrG3lYQrkFb6mlW92X547rYzJ4aprB7cpn64/RyjG2k7BE3+RC2gjHAhKMjQKwivDETlO+A0X7Elgp5b+AOkSgjOpvh5uJYBDotkDliiEMGq8egFEwEKhBMmKhCSTSaWgzTx3A7mtoKkZ3TRBNJmo6C6F6wjTyip1CnKY8XY0EoS+haiJIiHbgJEB51myIGPpUA0g2IJW5WEGbi3HKcq2RrhBRr0BUXl54WYYJfXdKSpwIYL6p/0uAwt/AXlhEefpOpqs/gS3YcWpo0jChBOoShruCZc6hPq8thO0XgYNZbJgTKS4r3ZXnQD8dHb7/1b/+X8dFbDSbbE+JFtvFq41qK2sfFblOdCJd+SqLDTd7XGIn4Lrd+eoVRnWzRM1tWhscQSWM7XCxDT0AOJ+QuBbQ9wkY+1tHjSotYGYN/UNZHU7LQ829/tDNBqhoO4+AuQ/NgyjlM410NEec9/3NXuj3hFhHtVZibkrHWNqww5ZmXJfbx+381/PKn+eblWPIuxJYhYjrUg7lSSY6TMLexCHL2DTCaAGeOwlQ+N5PD90QISzNKUSNpmk7rsQL3h3mJSKOyvaNvYre3lGijJBDDMhoh4Z2npiE6DkLnESiMyJh1s5YFxA4oZ8PWSaGJkmwy2lOqrFSLXZqmFZg5YtZEXVXSVcwOMC8vZZJh3UXz8txPJPMwxjHRWEVkiqmbX9dqpDqZHQQpAd0ElLIiP1nFLjlSE6FEY+4OSC98kYZ3U5530FO78Mmf3L/97YsG6lamfbuStLdFVX5OHOczBncrLeZXn0u8rMWlen5rofUqQ7s3JY+JUVdkHPquvcCDc6gNcfpOItEazp6XCJF8n5mudWDCFc4Yxi5NULz+lR5ZiLEYxS3urk+AeQeH/0nElKsRrycZZrP+1Ss/cbSdsbbrrzLr6o6aYRDgJ9/Fcc67x93Vw9QMAeDopaENaukUxknqQDWkRBluRVlhOlRJJHLyiCyWQKaX+VbJBs+UwWa4NfMgcLKMOWYg6ES/1VByIMdGXGOBHROGJAWa8g2xwdu4WOHUgJXmtnvabsT5ZgKI8lSMccaaZAz2BHtVKe0eAx1JUVAwjkZGuc3aBspYX56oZkUqzrgnJOcJHErUcsgdSP8Bwbp52YX7V7mwNdMJipz1uOTIz5YRNoRhsXQI86aUVMYu9s3loydf/9487Nq2ZfZSG6mp3U3nhM1UvKVcdXqJhacn8L+6ZU/kRmWtJrQqIND9c9WN53cryIOAWWpIDQLGCz2oVSRHluij2POKYSMtmy/VtG0B+BnuM2dt+LMrX5mFnOdnnKoItbyK4GcrhlhenteZ63FrtdZHq7V0d+UlWdZijSoxjDRhfLkHaF6G0j16b9/8LC9HMrjgaCNaZAsxcaRGGZq4/k6DfaQqW+/fwRqXAKgmjZgwgguVSaSSmqXkBiO9M+MpmryE3UUT+lKul+nQpB5QEeoBbpB9mGIep9RdoUB8QZEyGvoz2TZGe3KBltxdcnNpHdjDJMBYTkuGz93ELpQxXk6ENutDAJsu0erwHZLjyj63UX/qECja+RBd5NKlEEkO8LoKoJKhVSv/CAxSR/6zfifV3Y7XhPqOD0fKcmnCfeyf/uAv41sfp4G4+Th7d7Jf1oWQ89ZK8i+VcMf6hH1ou9qP5+DZ1UrXV8xqKSbXCbg7DveNEiVqwhxz+HQyjUS91dOLFjTP+dvxt+/+5kVn8AY1DyozxjlB0sZNdcX8ObRtmK+eOeulqWXt+FqwcXoL3oZvzPxaQ7I6XKks7yIJOnFJscxzuMg/+Kv25/+QXvyaIMhYWSYvb7hcKfGy9t9gY4PPRYaRhZRjM5O7zKodRVH8B84ywqnQuGpKz0SkoKVZv4QmlzmW45KnLqTbpi9z6nb7fXOV+ztKUR1pbGIi4GMuqVyMh0M3XDGVxUzOgVSnMQKQepWqx1T9mTLOBeBb8szhPRyb1Oa7cLzr+qvl4uFhWfZlnPNEBhwRfYtUHHUdUWecD9/McCKLXjdsfeAsruwarNDl9XnVokZFtwUeP+14a6ylJcZuibswlLffHj74+tQsA4w+gebUup86rjr70Fc6t7CCmr9R6T3TReF3kO8v5l/OG231l71zXzetLA+xzyI3ghjMt0ufvwLL+AhAzgno+mPe0pnXPQP7jErgmfvy3+VSviB1zmXw6OpZwLk/6/yRzp1Cr/gNIfv2kz4Glx8u+ZPu1Z2UlnYqh6vH+0ePXn7+K2ApalZR3DKNBKXNDS5xCjP5lyD7pUoJTaRGlpr8SfjHNLcZrjBlDSDx4EIWnhoGPMUltblp5wltNfvd5VyWcRy/6IfL0haqcsH6Ije9dKFplzi091Ae8/0chqVJZYbQdjEcBdEEmWP8hvTWU+Iq5qlhEAjkMadM9e5h1wyXxxAv3v/k86u3Pnj55X78ar59NR8OU1zQJBqRw4YbbJbQTAcJmhsNJs+i0mUaxrG2UEqngSy/hoMm/ou+bCGPGGH40iXn6avYv/2dP2sff7C0LcHUWEysb7LmmU9dzG1k63esNV3yFbyJwewMt1mVoHXhV99UE7z6ugamzGo3J2DtoQ3r0r0hCqh0OyZGBmP2n1X151/EqE9ynlcHdIUcPYVQxMD15MjW+gfqNWibKvFOOFhTverqa/A2UFaU+o56JnbTIpMOIG2dKa4zol52RQ8ig5NjNz967/0vf/XTKR8j0suu1Uy4OJB3sSFzvJhZtEEJeUpk2pCUIjpVdqYLMluAVaUlN2VcShhju2AuZgs2n9gcQpsePVsurl7f3MVlbpplIIPbx+6uNHdz6MHsR5EG83Tlu2Ep4+F2X/JN1+/K/RRKV5YeTvShD+k+9FqD4YJ2zLKGSwuTjVubqYWjywheH6Vj+t0/7f/d/zb++b/Lrz/rf/w33ae/iHc3xxHGmMNzztZzUM1lWw1OvAyrtaPF5p4LRwDihg5vhcgRyXDE7ir2Mu9rKX1sHn7wwcOPvzeFJrWIfnzN1Em9O4l/6vVk3jU2y2u27XSu0GbU2Ooctqs3kajMtJA97M8tAS77HTxJN1GmAIx+iox4oylb3YSwo597010K32XOZc7zacW6RnyrO1IqoWoJhXjSRUbyy0ZvufBhu1yVI0EVwtlK+8Y78rXgdZcpQ9GJNWHJXdPfffeHV//0D68//6UyrXMWRZxm1dLm4hOhOWwv5sohyVTyBJgvUMQdpbJDyFOZwnzoYqJqB1dlljFPoSw9kpDLVRjDq//v7lXz+HAfh6vj5eO5CUPbx92D/Ycff375/leh3TfN2PZ9WtKSHsYl39/EL349v/yin8PheDsdjzfzOE7HJd+Hmawr9f40S8zLdBXy2IXH+XhsdsgANSj6oiAF923ZpTSm/U3c7dr0Oqbw7HsP//tv7n/8f13+8sftl5/ejwjRUWbV9eI/KWsuhNC8BchUqmPHKE59RBYN1XX3JVdtBQqWn2R2F/kYBcNLaMIcmqH/wb8NTz8CIy3wIwpNV9v5JmfPYYPW3kIVZoNvGrjb3bCTXzf5ecNP+C8oJJ94gzTFBkWYKeY5ppZQ+ChfuDGzK4EQUpG67muzp9qhyoc9MG393Vyaa++rECwdCaPvu75fp6isrF45I3wCQ0/NV1NbThi+vemL9HgP5/2KsyccTkrlVEvNdE6jLWzGJTx6b//08avP+fvaimN5hQq3Jd1JayWTSYiVEzfSECYKgH/mzqHqSVuOZe5hCakBMQI8MYEKMLQNtbkvoSnjfVvG8aYtue1SG8LuVWlvfncZh9scny7Tp6Ef5umme1AevJW/+a/m7/ywf/j20u3vDvc5HqYypjAC3TOFVA7tNFPdZTm8/ny5+Xz48vnx1evLmMo0tSBjmjHyA42/6abf7f/8v3v0tW+Pl09bTNDM4+Vl+dP/od1f7f/mP3XXU8hjzDN13OKm7AFVPPmKa0A85kQoy7pLtn4tJ9S3iONFnq7sQ3HWaHl3SzN88H7//jfHUJoEFFkNfb3snrrPytrntfZ2n1lAa3Qdq0bJk5913moFgKybuLZKmx5xvXjyf0BU2qbLaUYekQ2aSYm6f4Yq9Nladvad0Npyn7laGgs3WXu4HaFe7bLOGAi/gu1wa4VwjpKeeYV/9sLrV3z1evWOT/EnbiHV+Aq5hzwl7ugR54p1PBFvZeAU0+7xu5ft/j4fZv6+DD3ms7GUwkOW7UtVYrkrydfgoaj3jdwPoNTxcpqP3H1I9R0CJ6cwoZqzYH4JKmkNajL5QIniITTNXZnCiwM83RJuw9SgzlAehxfjq980n//kiorHwzwusc2l+3L/tHnr0dW3/uSme3INPvGuv9yFYT88eTftBgyJH+PtEvM8LRmpLKIzDbshPujnKe0OOaT5ft+i2NXM8127K88+Ke1+aIcZWeuJ8wDcNKDRKbUOCyeurDmvJz8TLUSpMq0PpKpQT3cXPYbeuDipYXsJ+abb9X/05+HhBwyelo3jHCvfKOn2+Eb8fJDsXbdtVHyecWrVY1Ffq6apHtjUmUPzi7nyXGDw3Nq2yaVblqWdpKAg02tWAUHtBq7iVC//vAta/waHHjNvrB1994tKr5A54vAMhFjhMc31RblzvQzVQdG1qN2em0dihsDQX7Kqp85LtaUGLRG7zn9yFwD+bOYpNVfhe//jxc/+8fjFP1OLLh/EKOLc+HJRjnKz3P7GPL8cH06L8HQjxdW0oUuFmrfk5gkFnQMozsBBgxJnE1OH55jntGTYF1wUqjNLEw855ZRH8AFMKUzh8LqMhzm0qU338WJ3f/34+sv7l0v5yX/cp90lqYKp3ef28qa5GL7xrd0f/8XUP84gH27DJTUzwCvNJXVzbvs5N7Gbu8sQR+RT5nF397L8+P9s716OeWpKOaQ2FbQ0MoWAOrum32wrObS5ogeUjs3hKSSsFfnhRS5CXuAfnfT8H0vpm/Tova9dfvgnd7mg9Kv6U3WyMAuuOnW0rruOTteR93r/rjdZFdN1yPzmDJdF01u/1iMzJFK3FDz6wAGtT0sD9J60X8HBWHsRm99X0zh8P4MuCW9cWZyl5BnoeelV8EuxNowr2lg6IKf5q0zrY6EMOg0MAKLQI7bWSsWzGK6erbXn8iVttK25z3axruPfoaWVdBLVRNjVabq9eDw8fTI/R6VnRmwsqyKJK5oyIv2pqxCJVYLAAWVyAUhxujmlaS77FuzAQixKF4DaNyF/j6AYHXPoLmjCV9vEQo1DJcUZo4KoBS92ZZknPAj0KCbAmrswPPoKTUXNLfIfS5P2oQewfw5Lnqb9/eEQXz9epuu/+8XN3/6Hi4imgi52y7sf7vvL0A7tw0fpnQ/mJ++Ei6vUtQ9ef3n/6U/C85e7eRmWfHjxeZ5eNksgpjVJSumcMbp+4gPi4cCsv1YPZysitW3bGUF220wLBDQpaecJfyAuy2Vo5sfvD3/2P00Pn6SUc+a2xJV8cMmOnoFxgFiscw4+aRHbBqV4stMcVeI5Z7qqke27q8NqxmT9GREZaoVPLZoZMjB9BCx3YbvLlGnOQDalu61VSKlZY0EG5Fwmrh5J039twa6H1efkz6d0CsIpY64yy7BM9Ja5mpLmqvfrV8IJuCSVJZ/OgAqzxFWo/J+18Ccthy4a0nNQgIdyT+ri/tl7V796cHu8Yb4cbpqv0T7pMUwFqkTMZpIlVadV0AUtDVCyCX4x800QqNAUZUypj3EuU5MPIQ3oESxzDFOeD7G5iG1bUocgBIxnLeg/4XFDZNquvz687GLfN/0xlX65HYGKH+YS7kpIabxvxxTjMUwxXV6V+yUf8jymMOff3IQU7rsdal0p3TX7d5Z8H+PD6X4Yhi9Djy+Hebm/XnpgjJGJjgS4JBvJzO2cjSN8CsAXLHXqBlEKpqZRpHi0aXnj4pwKqhHi4B1ONPAiHcO8pOHBv/43x2ffHhB35AUQbtvPuiOr+ak9ez6Jc+73GoyeGjs5gBZbLanjMP/6+VOvz20qt/dON7aF4tQRmUMbdlMoKFVEmiMueLeVUNRYw6WwaughSoGgQ4zCmTOiXwxPEQmsC14tWU0byUm8c6IMuiLDgvOJan6Z/oojbb1tv9Yi9XbsFdep+TPOuT5NVmzT6uLGiF2melxIZY4QnK5v777zlw9+9bP82a8AgCT+berWJDgGdSdQWw8uDEhgRjiQauB4GBkpChGh+sB9RXHj0o1hQAcCdQLHBc36kmbFWLp2SRDONk4xgKNhCcs8H6clXIDxdpnzBNAWmh94lM04zyE3j55eXt3evxrCIZVpGdPt7XVTQtl/bZ+oOSQcp3HcdVObxyVfxxDHiDbhBqCfy30Tc87D4boJh31pxh5FpwYWDoiRtilpblLIgLlwXoounM2b5CHwO1qAlACEAGbmreCBEq6Ewj50L8gj5m0k7rNuosh5YLbq6NkiXFYfU/feswcffuMWV1dSAGGLuUQO8cHVAR35tCo8n4hWxW6ud75uDrFS/KcETzXN6jf8uTDbhXW2dVfRgZtnheQnYnyggFLXLlPsC7WuhBQyusqof4YNxar/ye/otf1UMRdABpiRlhnT6HzNlfFrVnfX3IWzgVrvwjtCbsYQCuJgFBAHTBK1ntDcWceTugLOqE+vr9h8NfqDzKBUJ7dpknU9nJhtZMymJUZ1QCsllyCB7WF+MbzXXe3vPktdH/ojj32apWuA+ZsimKso0QofG82oGggiOYQhN2hIIp478GwFmL/piGgnlhbNxwTSBCZ5UoLttEx9flWW0qb2hspShVodj9Ntj36jHMN8WIYw38emD6j0Tvtlfp5Lgw7hq5v4ZXt4ucR4l7ow38+pCcucyl0eb3McpmUaYyGi7TwuzSGHfTiOYyFA2HzdtxfwusOBbhCcAQQXG1MXm3hT0KHRhBk1ajwpbjokMBftFJ7ep04m0pbcD5WZ0RtYNH7I2AHcvUWUAGImGAC8KEse8fISqoGaHe7D0P/x9+Pw7AIahZQA5dKc4XaSYwlkrWe5OMcRpCk9j0+GWs+4mwxkQqd70Pz+6qU65HV1RbUK6VFXImvVvBBpYQL1wWGe0cxw0+a7u2XYz92uLfMCVYyLZYpfGbiqQiy+pOYSOPlkG5sDHnCKhjKXOQP3igRqJTjnZSByFKacYCYo4ioVcKohaogmHXoaER/ZJuhQIB7oy0w2QThBAJNtIIjyVIpWVcVDXciELOZxDdSUw3kkrkvoF6iTVINV4W0hXmNq8mP6Nm7DW6YILgxIzJAO6f7pZf5sFx/HfD11KXdo8QOug2Y2gW+HOzkhhX2xZgLq3E+BehGzzBKj4bVIT8RwjPlrS75d4isw47V7fLtbcm7n+Yg0ddmDXmN33+6HfAyxh/GPTV/iodk3y7ybb+MSd92QShvGV02fjiAHmIbmou+uxnHclYfHBZis98v45W65ur9+nfbt3MTmwYPyspTwoly2MVyG493uIqGNGOF4buaUD/v5Qbd/RHwgU9Pt+xAe7fcvb75o2z6k5uk4fhlTKp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"}],"details":{"kinex":{"title":"exec 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0XAszJwBJFy1+Y8JPYONc7qCHhH2Yfh+iP2gV232g7g9dbLIKtoAxfDBpS8nG4nV1AywDsl6HmAoN2sJA9ZSIo6EGfQgxyWshvu0J1rOtKtlNXpq7KYZrneakoOCS2kal39EJIfF7qQyUuaMeXcl1H0oGLiaxt+YhULcEhuCk6dzo5RN3imXFBcRzlAWJe5PO6Ho6Hc9/301iPVVMDpLiQVT/0fVUVbduRXk0Dx6z5exwww5PLqmzalomT4CNwv+dLOWfrb7/5XXfoXrx6UTXNMPZk9+Jb5qBRamJjXnBVG4nEXngJ/YVZ8c6RteLdV/ZpOGIayU/PidN8Y/p2rqHdV9hMd/5SGeI2+znnkSqrXn1MHiYzMKGdfGgOj7ACNql4rk5b3QzB3S/+lmjPFKtwxywRCXsv552KlunaMpj2mbxNmMtpIkBt5HZmqJtbwK3wFcIMjk5MBXQLqmJJVXcNu3wY35EB2lddeoRnbS1tSjZAxmZWVRUU6EycVwEbBSE5j6SdurOgDh3Hjo2e/RHQ31X/U7eeP6usu97d3S7reu77S9+P4wRJmuXnp6emquu6hv5sqy9xBI1V0VQOx26cBg04M3wm/+bbb6uifPHyrqobQNKEnGLvK4/DbHlyiKvWT/BBc+2lNKV6MetV7Kv0xOfET/SXkrFIHHUPRvbqs/S2o32LDFfn166jyY/esRKxHwmT5aaQPk9Njfh0x3pzomIlLjYjj+TXlGC891MNOTq4rEH7B8ej3B98tMDepxCoLBbTG65Dz2Q/iMXq/f8p5070AucZUTuEvSZLsWTzAKgTY92VKUm89/HxLIHlOPjP1gQ8wMtSAnYEC9j8MZkz0cnLBaiG7Zyp2uZgMBPX6RTJ+E3VdF4xvr1KefKEV8eum6bpfD7P89Q27TzO43BpmgZ6hlyvktvtIod5icybcRzmGYZ0UeTn8+PDw/2rly8RLponhT6JnEkX3DiQt+jDfusFG9MHHmxhBwRy9nZ1tATxswKh60F1n2wivoF8YgGbHpr0+EYeGD/wWA650Saixx+cHajF+rztaFx8w/q2j0oHnt4NMV74RTzsdp+Md28Xjdf9vCfD3cCUCui0A5HsZxV/xzGD6DHeBWKGAMAaZdc1yzQvxfrZmztEiguQFx87ppZlYbSmQ8qro5SOGxBRTVOXFaVeeMa2CtfKYUUjKoV7k3DyQxf5E9PSdqWivVOJbHYEtOUFqSbrunZtV1c1/Gp1vazr4+OHsiwBEVG1ixfAK1us4ecU4725Oc3TXJbIUijL6sef3r757E3bduQVI2Nux7XiYz/Cwhh46rfjynTUa8y4EpL8jrno1430nqPMK6rr9cuvfCbOL/eWinlVe5yfPHJV79CiJxEZumIPo8HsaScfnchHX8YJeayc8WP+3ID+2DzRoSqu8Lu9AQbC5KivO2VMghINdhHfDetPn2HIWblKvUwFcnGqp6fLADTEUnc1ULsC7yaIEmtEFCgyHcnULL7jkgFNUdY1nVjWFw35H4bCCCfFrgX1QHRoFSQSu4nNL69Q+pOtLNRcQAgxMu21TVtV5fF0Wtd1nPrD8cQB6rDIqifxlAi/WYAfzROyrUokHqzzMkxTUZRd25JPS8EUgZ0GitFNESKGWo7kDJuCmjrRxjibQFLJQL9zvqsoGt+Kwmub45PcPT0Lz9Dt9lm6+tvvqDrpmW2iWntD3B9K/ShVPAPxxuSy0S3+gJftl8apDcmROuMTcSmGz0dur/lICfsKAkasBc9b1TnqkGNbObzzpJ0oA44pJDpFNJAagbOxaKyXdenLMFDoR99SI9ccHfM8F0VeN7WMKYogZS6qxMmCnNupKgX9E8CVFNf1GxuYgDFM080jTqULwjZrngNocXvb1t356aFCBh55vfRaNVTD3hKGGjY7H8ymacZxzLLsw4cPh+OhIsuZvFYxnMgtrWZ3gAh5hoBU25JbICPel6B2KE+DTRIpuduoz47iG5h4TEdXXnuGHyvy4X6eyHxs1slb86lFMiqWWY5XewecfXlnNEGLi6LXG8Xaz2Wf1Dw4jCDEFKRPGJIz9HaWJ1W1TPVwZtHecu8MyAKHm1Mg5pxqVrvbEw2Al3ye53GepnlcsnkZkRiQV+w1BwCfUYwf7h9nwiSFG2uciEXWOI5NUxdFqUEXrwXkMjx6XMjI0n/S4yvgO/1uHPgNYWcXKDb0QOB1yNaamqb68mc/7/vzPE2ARlE0W1cr3DD8xPkhtDDzvDRdy58s66JZFcw608WNFjQWG5btuN3HNVE4vAdyJiAIgdauHqWEdW0vChS0jftuDen0t809w9HekcZmnG1m6ue7S6qbV4z7SRSKmGJCpPIaDSdTYrzOvtSVaQahuFHnr3CIZOixAenVE1MCkjFEt5KltLk6ZpdunIFfl5tjVwCRNSNVHok9arjSxU9PF3FBBPozIBmn/02HFpm0sWKb8xrriKIRJ3mebu/dt4zUnCSIEiTCs3g/KXDN4b0s++M//UV/vvz40w8EoCIQSrJnzq/CKw+qUdxVlmU3NzczGEEjzyEKf85jYhJJsRaxzp5sqj/x7FRXK5AgHxRqy3fYnLBq/1wH3Er8fimDjM/NnqTeYVHhbT1ayUqGJ2xVyUhCuoFZsCJshfMhe0JwGkUAado9gzfAC0YvtsP7HHbh+1teZ2QGuFjR3mtDlsmaOj05Il27qwLtTM45h7cXXHR9ZED7W7N5C0ZAli/KR9RV3bR1U/GRIzwrW2MUMwuiz2wzRSkD77BmFfmHjMKdo3r1HC34yaOxeY+MRl38uCM+HFbYW84qn8FD53Vujt3xdPP9D7+f17muakSn+ZqgIIZTyNq9xp8pq4lmXVf1PM9d153PF1bBwgAcjjrf05KoHggn84WM83gP3AzVhaN2BoE544lH3w3nc48y3WnxX76usG6eEXZZ/g0SL9oLx0nd2vhhXBH20RH5A1+BMK77lYIPxWiCgaycbZJc+vzDrqsVqXwNbOz6PencmA18/c7+3814SVSpKwoojrx4uiBk2lZVTohoplcc5RCVNDFs8T54ecZpKlHRSTALbOWGSHCmUMmQpBCFv/zkNcPChJUOWNQJb+szt1ePBD2LU/zyLGua5rPPv/j9N989PT117SHAPm1ITpKzoSAilkFhDEIlW6mqYFHUdW3Qt+iEx+TgY4TLuk6AT3sTbHerVAToZIUm5gm1Tzghy+3czh3Mkt+iCrZuIH9Etsd/F2EZtCmbureOfKB/z3+qqxVNI9aOt3zto3r29YmGW+7o02R8ivSjijjmcYy/HCYYB8JT9eWKDyzoQAlXDaRzje1EjC9ilaKz2IhYeaAyTvM4jGWWf/b6JWBUOHbMv5CZNM1jLNfDI0hKg2qqqkZCIq2Kf0Sudp2gnVm6y0dGya4qgH7Tq8zR3MKHdheNAHNRsjWvivqzL76a1+yvf/OrY3esAEm28i5yRpV6zcEmo+HCAYRdQRbEOI6n0/FyudycbpEyVVU2e1leZXDxnglz4UpgqjFd0UrNuaMQCAkfIyORPYfBa2/FOXYcRokc9qrehj623OSKjyzokw7fFxRCnz3ocsjs5xAE98CIIBIj/T4afzRkm70b3zUVNn0nYgsihfkGMofUiNiAw2IN2C2wBXYcj9zhBX5WjD0QERzyQrdU7n7Vh5gHVGwEOu0L1LyqyCcWXIeuKmpO9WJNo8jzYSRYv50Yxz+ooB9q+yACLIj+oNPmog+G8IrZyBuPH594PZlqDQY8XaK2pTtkZYSwQ3evX43T/E/+0T9q27asymUh3IVbFE+3kRgRTHSBpGLiN/DN59k0TU3d5Fnetp0pzH6TxWpW7ipPob8rKpgWmLwXUFb0wtY3aCa0QwCpU26Xx4MzijV2laS32JxBRSVHbhH9J5R+8PUL5GYurO1ikQRu1a1j/ujEsp9iqjbYKOxcKCIpkuo6Aj+9P0zfjjWP4BEyd+POwjGyJ+CTwwH1fEVGk3gCvAXh8vkD8tcuZr+Ro4tELrqR7qlUQb7lQoIFu52BT6bMfgYJy8bmD48XlIVz1pFJGgp3TNm6tk1NN6PCO0a3/FJhV+S4OUE+JDU6ZJLrNvp91WWwOK8xc79ikgXLm7KsS3s41F37T//JP6mK4vZ0i8gzdIpQDMCZdaL4WT6FFAIgJZqKd6xlUcwLwOHj2LdtQ7PTMJLVSNM94moMJjW47lJIegqCbH3+RCozWhAXCIEv+Uz5wzUtzD9NWa5+dXutpu0HKRjh+CI/tkapWekSCRCym1L1MixrkMXh/qZ1R1JNFmVHAtrmZZ/0cliz8Awx/TjbOpqr/9fsNzuFERORY2RXRhqiaSCK7t+ZCygOmT9it4X3t0qDV38STibneUHpYJBUtsKAXNcVWKUS1bDYcQX0MjELzd0IHmAqTwMMZV2VdV1xAU65txFG5hae6vOInA5nI+KGqeSQGgA2nyhQwT/giMNWxQen4+nmdPPw7v7p/v7QHbWCBT9Q7i8jU/r3/iOCQ8uZ41J5FRWXbdpmGPqqrt+9/9C0jeLE3Z6qOh42WWTLgmwkoXZRMNwcQzkiOWwMJ+HPsM2MwLRQsil/yrpVbm6UFLf/+mg7UvaRz9TR9UkEdFQnIhZknLe/R1EqjeXY+PNoLFv1HyWXtFyHaV5i8oiD0esDXuZHTDEK3WgFHMn94iR+W8tdfhAzOq1Hpowz/cB/zWraXr8b54mbiDNN3nEKx0Jj4yUelnDGcV4q6JkMHFjKuuIMGxaJy5rPwGBRgTwLyoZRs6sWGUhGHpF7Ig9aKmrLUrllSluC7S31xbZqlnw3FGKOFinMjWUCypUu69wdDi9fvu6n/pe//Odt01U1V84VxhhoTMBtfF8dJhfdAnPhOrWytvgKUN5L09bD0FNJ8AXJDIBD83g0UYG+Ap+fsmVeexSuVQhMtC1Ba0z4MMQ2ZTKtqP9eUsF2x5uTpLZwDmO5viYZcVSrQ4sZymVBAIWrtEKbHB2jaOUY/C/qsc4op06Vysx7SQWMmDRZyZd6vWHswqRQ8Npnb9m4bXaqXKcLFM6BrXISUONjF0Ul3TxJ+Eahp4RiZEShDF18hVewIhNDy4glaQ26fP4Ic6zS8RhvnASlYavly0ZqCSN+wjLNTVOhYiunxJIjFrhoVHusWDsKh01UDw1TUSSqOXRSpFn8z6JGrKoD62xQzaMGs6BUYyJfLQXky5Q5UKzfCHAQhk+bGoqax6ibUZRdc7y5vfnx3Y/fffddURS3d3eqlXN1aGUGUtHMInDCyKjovcuCFNgJXlSBBOXgz5fL6Xjz49u3r169QngJR1jwPCEGRP4/3hDe3ErljslMZ8NrTUMVlByWDOoLNU0g+QpWGjZSTpcPynszNFHFIg1aDJVYPwuCUvbXTouVrfJPJ71nGsd5GtESgXs2cB1QrdQdUj7FMMHBUKaiDkSKrkXOMb/fITH2KhnrGmxtA/e9INY4fgOm7+S9Frty16hT1J1cz+rCo91PaQq2qT7RYKmOv9QkdWaHJNY5Z6QDwfnNsB/0vuRUh5vq9vbm4ekslfelRI4cJyoN55eCfc10vMiaQJSVSuIzbEtgkkmFtZUrOCPIWtVYQaQEExzFnZig5GpF/bAHsf7ExiY9EJV06nHof/XLH6YRAa26bWtyo7vK7IEHmifeZsTykAev1ZxCRUbSqWXZ12W+OR7v7+/fvHrFkSKqum68IMhVUVdQ1E7VXk9JdpURX5B2dMjIlcYdJWx1nNroBFeUyr+PhPJVEiPZFGlnkaIcfgo8ZqtzkGsENhh5UKJal6Y8cUIpQ+AZfK/cPkCSw83jepPJsLbT+shLls35F13UUW+xS5LJY7S/Q7pKUuLaIy5SG9DOmKkLIZNBCTrlQR6Js9HcIxGNO8JPs7599z7Ly5oUXG4lgt1BjJcNYHF+mvVrmcRI90eGbcmVrDgCbNPOdBb8Lu9cAy0dpjXqfhAIhAS56j4adqZRGk4Agy2osj95yKl8wLxkRTFf+nkaCcVNNXCXom1bo1hDmwhXiQBc4QfiAxJwdUSlg8lRbuh4OIzDpWsO4zy8//D+5uamrMqVq8+znRUl7YrGDhvYiQO/Dd6PLHXg7B3KdmMGKZU7ldFEW+iVNf+Db4oSfx7W9jnacHVT3YVOEnA0i+JunDxFHziq0/4j4hBdgmTmHjXI86JTtDGi/UNVZERKmr4XVefcnYNqNTp18ZeoYuGU6/1X0I2CChN9aBLFFe+w8SWagzSAIcWVHEZ81sV0UQtm5yGBAYXVNScm7cKl74dlKbLidDxxrqSomWt27pHXztsZxDkJw4V82LwjlHan1KsNiujlSvzjhZFSVyV4s+a5RCXJCdQIu0qmyOXpCXOOjVeWzWGVBeV4LhhxD585WkCgE0oJoBVlILRoG+QxIwHFoayEj1egX1aiHXVri5DwwpFDufelHy51WWVZ/vj4WDdtU7cMaTOaYS6LxlxrPs+rEHA4bK5ugRVfNuiZ7ivtJFXKRbV92TzR+KNxhanF70TUG14xK7kiwlJkWcL8MVgumqlqX2hjoDF50XvCIbbADEAHsNZCRt/+IGwejoYNzSMnf/vVzf1CHgUnt7P+HPikBfBFY2Ll31mNic6sfCSiZvFDaM3KWIWIVltdADtrvsMibFWdHLaCHBaIKIpimma4XNel6dBRBUKRAQVrdrn0zowNI+EtWFCDUmquQ5k0Wzn8kDnl0hL42GCiFnm0FcgN5mId49gP46W/PD2dx2kMkQb+MmBNqN5cSWGNEpnLzFvnOc/zqqqaGjkV6iwPQ0g3VYGd3C1NtQbHAY3NmTGc503bZvlw6QfqjlaMQ99fznkuMl8qpbHBxwcbZWWdZzmKltrY9Bh7f7XEO1Dqmo8x52GYGmxHy1Oq6Q7uvSCU1UgPR31LPtERci6Y7VE0WnblUYKv0C6LDoQ/C8FuvPYKaaNGw37wW2INR5Ou2RrG0ijRD8msE69Y29eFMKOF3BtyVLZtQ8L2eNXoqMqL/OwVjf1bptNz+8JeVmjKoIoXpxapRcI2qa459WGM9RZlWwoqBdLQYBlWw85/JQ/YrLCzapRQMeV1mKeHp8effvppGCbQYVk2VXU8dFLWXTef4jx4i1HcTHtVWVUV1wLhdAUptxwAVjZx5SHaDZEMtLLkb3N2pKVMGUUo/xALApCzae57FMQiJlTO8/j+cs5WGIM8K64anxfw7QO65QtcMUhTI0rBURHtk2ZqcrwVRYoW7sST0ofb2ISSA5klir2n0qAmR4ZF5H6xkkdO9dZcTOlm5vi20/DlpMQ83QUYDaPrlnpHuw/8wy9SmNz2FfwE5hXjZ6mmpDcwS2SzoP45wWEY2eWeAUWKjbnGkn0VOciDkLOo6IFteOqZl1XDsqtAt9PSHVo0NELwYkFZDboN9SjQWAL7HRQKxq+yRhlKXRc7SaKL5Bs+arGoeV6neb2M4/c//Pju/dtxmNqmvqM66YyECmTHrk12hchOcrkpTuBFhpCGL6Tzmtq9fv306uBMoUqxbdN1HcgSndxyaPTTSOYBShQQ/Fl1WJalkLcN1JYZlSsxBlLgxbNOgfF5mYYe1YkKeKETW8uKg0fSfmePWCkbxwmFRFBWLzgJ3ElJvu6cuqpRGMhoexTkqKWn2IlspUqq7aW2vot6B/+78jr+qotnO81AVV8lP6erbyxcPwTZ1ITXpFfH/CqNxpoB79lddGHS4cgMYJuSTcFGm7IQY8oJb+DfZD9YW4kRM3tk6zhQ4KK2W/xTAdpbDl27LGvT1FKK28Sj3Ug3Q1uRyUcoWNO1QAhzLRqHT/T2Xi4CVBzAbMtM8/T+w/33P/44DdNtd7j5/IbLzbGrmi1VyxByQhTvKBUyp8NlVKAnOBJs6DILpX2Yskb3ZD13XXs8dkVVw9eyLOM6UztX9IymAnOLlczkTkx5Tm2koL4XVDELDbltzQBMA2GDntFCOVuqqAqrHSbVc9LTaL0UpVnJiuLZ65o3DZpNsFTTL3vh6WWq6caRxNDaLuydctqgiBLzexmjTQ5naB6KenW84egS5R69Iyrt6Dph5f13iU8+euaOhN3XuV1se/9lviAtuJ1+tr16bwym5VytghYofTti/skMLfv+5nT4YgnKN9RWN7VJB0Ya6byMExISAjaGu4ORrs7fivIzKN+d5XjdoBmn2P+OeuW0ZMah1UlEBDfM8/c//Phw/9DW1avbm0N3QEt6Ii1pEKr2gsyC/O1UqhmnJ0x8RUURvrECNHW5zA5mJzMp3uh+ODg/JPGCpmnatnGRpKwqsRTcrHnmJs6qRElTDglG0D0QMGc7FcejQtNwwDfWrKwpjESXOpClKmbO45HAZeWU4Kkob4sy82Pbre2hw7uiTCd8wcNvSeENilkiRq7Ycm40G1lvS6oCCNwPXgcVy7vhK72rQeH0XGrLAJ1GoOBEAHpuElsJkQF9jW79MOx2EWtzL2fFbj5IVZgr308GmH7KPaS5g5ZbgCh4nIpin8Aan29ifWj2wz7FBWYwgxao1aaTtIl2LFyA6xcvFcIBUrjdgBAhLJYHNiH+ybK4TPNvfvO7cRhujoeyLJquQzIT2aEkFxkRECQE5CAkGupDVuheIoNmm1NuzVXDRV5LoXgbdEiHIaqU5jAMylmzEUXwYNPWTVNVZb4W0wpHqSh+2gqZeRqj+goAxrFi4gqQaA/0adJB4BoEwLRBU3HtoKZHxwmjjSvGCWe7iBKyp2l6WNalOxwJjU1RV4MryArYr9wkyROJP8WRomynRe39OB3QfzN1zDNP9LZYrBa4hzi8lOSze1x7evSvUEj6WZC7biV3GKGedit6ECjFqaW++MueufEpI3vmS3ycXDE9h+f0A99qL35xZAm5rxK1HclRN4dDvyymsCsKybX8NtVKdTuV7RYFnFh+rdQ0Cpx+1aw9/nxelt/87ttpmm5vbuaRQkGIQkkfwpBgELKD4K3OqN81lxZABWuq4UUZQ9oHkBoji0iINXdGYDC5MgtmOc+WPKAacGORKw83zvEzOTuYdNn2Nr+NoYBpFedhnAFio3DokqPzW1UUdVM3VZOX1ZIvuFmUuBNpvskWieEb7aQGbpdleXx4QPfH7kD8I+SXORdhCJGw4u/7gHOjd7PJrfaz7Z7C/72BuAEl6YHEdszokunB2q60hNeUI71Y1Qf46PleoiYEe+kTKXmjZe+FpRx38XEr3YiI+Tgaeeb5fwgJBwKVAZJf0ivem/Dy1nwOwS6JHglpUrVzdiMCr5dna8loXrLliiyngugElTONRuiZVntZKKJDKGhtcWTjcocj57+ZIO+fzk/n893NCUbwON4cjm1VFeTKVuPXlNNgAdB/BTLryhJaKZ2zZZ6HcZymqe/P6ISUwaPMqGaY5VSQhimQALsg27qtkThV5Jfz+enxaRqn7tDxIpIlj+UdxwEkzehuOpuUoAVKuAzDMIzDcIGABQsAFrht667p1BvPAS3MhFVHNLnw8ZjkxIV//YwtXU/sBA0wrevl6TyPU9PCJJayCeJBombFhEq2QyP/icJh/l6jKhFLvE6sywopquZi9Gtii3uCTH0/DYN2GPLy3B9X+U3VMpmPkI4EkvkA+3ahybej07yj96c/mn9JDr0PoibyPqGTIIx3wsnb7uLPKO46l5g0UeGB0EvOGR4uT9dMjAtTmp2rIWgKcNMMw2VFrIj4IennSJagIrLjKDAsTgw29Ai/5gmNPLnzQMj0leerwZSL5BaE/LJ+uH88nU5FUb378P7uBsozO6Y4jT5wKrGJSNLYeTZGTzKkKMqmQcuiQ3cAOHfmRsFIZkZ9HzOVqK1Re+jarsmz7OH+w9uf3g3DUNfloTu2aA6KC8nTC4QG9TSsuradl6W/XMZxGAdqnIqinag43dYIQ3MN+4pYWIXW3lLUYxHZLgG0KkUN+WBtdDpjRVLSZjkjLPhF1gx2/DSNVd1XVCiImZ+4ObBtetjY0A9QNpgdGhOgiivhJCifIMVb3X5mcgjlUeklSN1pmtD6zURHoi5HB1ntR/Gpe4gg995Sn4yliz77SmLSTmRvCEvct65zsUng3Yeoqu2fFc8vvnY/hhWHSs3bpXWwcDwtk4H/3zKTXQM7ZPRrkQYq/ZQteZXnb968/P0PP5DWSAnC81xRolhPDf5MJ9alFpcEcnTqDsoj7walMJnuKmSWCw/nfYPqO01d273/8W1VZl3TyhS8PaZNsMIy+VCgJLQYW7DYLfT5Oi9mJNEu0zSqLV6UTTnN4/gwvv1p6C89dfdGH+C6RoLR+fx0RMnYioxjVK7M86xfL5fLmSiU04TzAsGy+sBagoSwJLzCg+VCpVKnk4r7UI4mGnwz59lXvAJ39UcraHKSMuoALUJv0zhO40gtEKu8LKXLhdo2ph26xEkrexdifJrUpzgp7cMhAA3BHQbrjCuE+8CACAI3SseUtmp38NhwsTXtw2jCN3SQVV5nodJgMLuFSFKRjJjtbWEaMng9u17m7fmZ0z1SgnNQn31Ws/lM9UkOvFLcQsu7EoONskeSMUQqvu2Bu5DjJev6008/HY5HhSuD18I9k+cjqjK5b3ObakXaoZUZJA9lLzi2ZrCOTEQxVZUhzR9x46IYz+fh6f7Fq5fILZQWB2LK2f6FIOPWN+HeMWjkIuBOngIsbVKuR8jMZaFug0uJipuExCiRBj0NI7u3SaRNWY9DNPbT+XIpC/iSiUPVbdvCzifAJr4JCl2oYgehJLkxMpxeeHYBVQQkDBQoceDKcIbRv+6lCp+qq7JyFkO3GKDj1cq4SVkas2liBUpOeMxF9W2LhEpuCpEFVcGWTAplJnz+zT0l/RQdNfGII09QcvaiOTopGVRU0icnxkWrKSAfsts+Bl5Fvuz0EZ7BhxhmdOJ9XQP2EHipEaVh8Fu7fqxIFj+DA/VQErZ8qZsZ6S5SS8dGl8j8OECcoKPN8jCNAmeOsJPTEzQykh6gx2nNS3QYiRNIjbnquKmQXegYKU4JPYurQn0Fz0xfK8vq7Y9v83xt29byByxu5KLwCvUJSxe588Mv9BSSgwXVAsIo6iyryvJCNQfyqmrrGqUEQJMrIz+5KuCaoVwO1zhBLgT88NXxcIQoBtn38zydz3PTNGQSI8eJMn7xKZCeDAfR0cC7Tah9KvNJ8YJ1rai1hT8DKloSozc9Ag5JrLQfDlXMLTlNzjhsQKIpTN59Rz138juVF1cYRhiPakLBHJKnhTEz1sgdYiPl+Ai6OZqw4cjHPE0D6RjkRGR9IKlVsKu2+PoM7gFqRiQXu/LFoYaXs2X86MRM28r0iJ7NNtsZWpAsEXKNxS/l6G5ntYOrDkHCvU4AWhyFnc2oGoXW2AJFhCsLAgSZ5NagJ06HUJFBAEybsIt+qL6xUo4hLy7z2hF9vZ+ezi9uD1QRnlVQN2RyVlHkljOiwmANB5KwJLPkhG+rvz7P87ZruwPcS9R0HH5qQ5JbNhu150ZCK9+P8VSKOtHGvOwDkqwK3UCqfwmWIOKRamIiP0pHRmOm/sDRVuspi9w/Xp7s+D90L51RFZ2liLGy407FnUYtTK20LL6IZFV2C/RQ3zDsEUte99hQSMddbCNJRh48eTZQMtXMSC0bQryk2bnp/GXcKqI3L2+KRW+QJEoC57FzKF6M4InY3n7PPkgv8mAtOlLonQCIn9ZzV9+dKpJ6KFQCu/0x7SJ9mecYBTcWgJCpqXReNc04cJacHwmfPQlOUEQHWQiy4PFMDUiyYsA4+Lze07ycz5d5HpvmJdmcSqbkPuNDseRrvrBVx75cmYQknwURLTxVQ5FyQq0kBLnHAQ5hmSzjZoWggH5Bt7HYtdzYlBtFcgY9SGS9TlAUAVfei+1VFKIDAyqXLHv301vCQuva2arz/fQ82lHxlBQfDyfC3CGRH73lp5XFHNpKH2zkpi4up9maoRrpmSErX9+MyFANPCekE4VQT41mC8gJNS06g0RCg3nULiuqbRh8c2bXLd8yL93mYgf7j8WPk3p+3IwUdRzV9tlrQ1ZO4ZmXLLsY3zOQUiPiEyF2v6tchA3w+7C5WDEKtq98rAlQxV1Lqeol4Xv1kvQ5YeqO6xrwmc7PYrVZKAKZr+uln54u57quqxK2IRv3+JjsRZEXnGuYoyrIAOMOOUYhQmyoRR2HmYgpTNgbLGZSS3aE1q9zyVI8fq4Ow74A1bFkv4Jy6reJiHeawEbmZWqqeoW1XC7r+vD4BGAWR/0sVqp+QH9Kk0BwsszRj7Fs2mH/zm7e0HqkkRtsUw+OpCRHt3LcP1JX+d/AS2OZGUS70X9IUXTqE/M9rgwhSBhF5Gwmth2GH08sPqIvqPeOByJAHA8pdc+KTIlYym/0cy8iFZaRDk/hIwSwRQBDfeGSyxsrLFtwvAwvVhzCjAhFmU/LVBK0h2KYy4KOfAACU0FUIXGlFL0Bt2YSD6kqPha7JMPXOMhK5QspEAsaO5/Pt11L8CQ52BTBpCfyWaDb9Zce0rss+8v4ob+fl/lEeQ5IACZl3KQxxCmVCsPzrIy67ZfvTBRhxWRiiKzAcvYR6BB4ias45A7dYQuDp47LPI7D+el8PB7WLL+cn25vb/O8qOH+cq4bk2GG9E+crs+yddNR4wOSoIh1o1wEN76HsjLdO/Ow+lRZvnkwb90EdjTPhH6CO1xnqczCBJ9JBhenpb9Yifco0/Q54Rzaum2Wha5zisneWqYmxN7DHCQoJAluCHnnJqx+0uwWHA5Snk0xDdPdDDriP14GizB3FA3IZF4WyzB3bYvcQSoyp/hE526JMKrcggTXMBzIPKJabksq8EhgPheHBdr2ZnBbLDM/TngL41LwtWWFp5gKdDRd+/79h3lZjqdTAYRF/Xh/Lorq7fsPnDbY1HXXtpL5kKM+2riuwHCgZIdz24h9YI4y2Y+0sjMXEXBGdhD0yrY5/4nf0L5yZBpk2f3j0w8//tTU9dPT+dIP0zSen55+97vvXr1+OU0DxYGTkGmkGKvoY4yDieZYPidHbCMQY6+pP7kxTzca37NWg4m7x0Y83W4VMmNEMgXRjPSGMiVHdIraS/A+/o0rstWBXuzpqWoi72qQRhuA0pGLMkFS5TJeAWcihKC5kXSAr+6+VL+l9LRQ0Uy9T1udf+9XHakzmL32ieWahuncDyAMLotCcC9OAVBzOpz0oJLQR0yZZBHjfEPSLoB1SdW8XCxX5LoX3JoBZVqpBDyjlrh8uSwNYCoIkpWP7z70w1AU5TCMVQXnMKilKo/ViUl2ydYPj/eXM/L+lwwJQ4eb05dfft41RyIq05jcaVRJY84dlrlcsIkB2AG8JDSdIbtS587loqnjEfWLIrfW09P5P/vnv/rs89ftoesOxyzLpmFEhnJRN8d2mUbvxAq8LpIlgbpCQg//ZW4uvlz2LzKX9YctRCFVxb2HMRTRUGeXcWrn/I4fwYfZGaEWl+IejPYsHWZKgaHQY2xcBzVvo6brmJ3o8isYGfLhCwG7ytfQIVySSjQRO/GEEzF2/E8HjgYhnSnFJ7jf/khBVKorLxOczxbuDuO6RvreSeH2Lqyu+bko5W3te6S/whFN2EB2PKMNn2mPwvLMdBA1XBTZkLhATwoRJLeGVB8nWwugALWwFT0IOQN5nsPIp5Iaa5aP0zBOU4XAD/y6MP4hlxFFWxEEIvIviro9NN3p7mVet/Wv/sWvfvj9j6/uXuR5xaDJIA0kxBROvOEP+W2U4yFwdCjE7f4zY0WqtJPQpbdI4y+KfpzyImuaGtHmZUJlknVBSjP8gvDIoSJHOO4BchQOXUzGqRHsLaRoD/1XpHCld67sOrLtbjsG7TUVPr2LM7I3FweV27GM2HmQzNnLe0G8yDWeZ6UzsjP97B3Nm2+1bAVBvCfs3NDdA+WccDJ0dPcQN09FuO7JEsq4B0shEqvRkyOe+NxLLTo2V/P15nR8Op/HfuBu9IpT1PgNL5zIU/6aIQUpK8xQPLL/wdGUhXx+MMCqRpF0oC8JUwnxTf0e+nGEscBKPE27aRvtMyZq6uHQIYaMgBB7n8jJRBK9yPI/+qOfX/pLWTdamVgz+72PVIdvlq6Fiog3s7LMxU9l1jwYTeUXNIpgxTnQO49P5/Ol78dhqutqnmdS50Hn+OKylBU5CmQhdYdcB7SgErhzEMui/Q1NjCdDX20uiKwmf334Oa6BGFlZ27NjZ2xH/AjriPshbFVyRyvBFRB/tL1yb+bRL4r7kx6lQdkVkeJCNmGuOzzOs0AJ9AuQKtoXW/WdQXETRO1cQeDnoGXsquzptCO+FRqQqikYRo8OD8APkjOfn2M4HLK6lSqdbWJcHKUwrH5ZiMS4dgernA56FXkxTKBVNBMHcAJkcBlGrkfKSFtdgcW7nZGgC98V+cYpAUO9ifiKAZgAtNKUqoAeESikeZ+1Lo/EdNnwjDJ5QlZUzpWiZRP5b0Jcw9nHHLNpmhd3d+8+fAA7KiuONqIgLOqswjhAeJNrz0rpQl/JjZ/KzM+F05Nt9sZsKvyiAnQbn4UnpHB6fGkLc8RaHHcn0JzIOBMyKUA0uL8DSEKhGUoUoVy0BgpEzXPFbiIpKPUU+Y475z2WnnL0LCtSz683hVUN5Rn7WoRKl/IO4vys0cG4e6Z+iDxZ5ZWlDHJ2jGnvgZ06ziFnwJlEjpsKS40iPaJDMxUQAZTTPOM8Q5hZVqRU8HOmSZx7RrmgvkCTTUD8Gb6yX4Z/h2GcZ0Sb0dipWvqnnuxuqt4siA6k03BmIOuFPn8m2BbGMKS4D7z0qGWH8QcDgtuEhmNlaY16N6bnQOQ6LxVI4A/STtGWU7zQgmZFzTpUqMyoHoCo3BTdZAwxeBKlGUb2WyC+lEz39N7EZSN3sEZ10cuJh61eGSSGflNxdvEd5A9RgonU6P6eLbi3NOS2ETOOdQQjJhWr8nnkLnXPiWaTJj24J1oSqu20LKtPWxYzJJ7czot4OFGFyRb9xCsprjaQU6O4+hoTUeB58se7DCJ/fToaZXc2/wCE0GNcUQo6V9jgDIdo9akQJN/KUvwN9CBkoM+wx/jFWmXTipEKPBEBj/M0Pzw+9f0AF50mpxO2Xdovm6PJd0PiPh5c0U6jO1JdvkFSkRn+oHwDePjMd9tIy+3WWv0s3FHbNokaMtZbk0mkM5aoAKgBUM3z2jYtBC+9YN6rkUGNFULGDyeyA1opf0KLC8d0Y4TQNTyhbIDS/eYYxn2kgr0WAXE3x8I3Lo30Q5Hopva78+huF46yw1/avGLNPdbqzdtiSrwN/hqFJa4C5y2IWmTqoppJyMbSM7QbyJ6ULikfokc+eao3X2y9wrqT70fHZ0zq6rysF5ApOfog54R2ShQmhXOP9kdCj+Qn1ip+VCwmEKbjm5IvVBQoHuPHFK9KrhpplmWP50tR5lVeoh7PvHRdO84EgCASkh/UyxDvTHC+sLhklCdXpRb9nLxuIlSYF0iQOfAUJnWYr+TW5+1EDj51L2K+oCMJynyU9MqpSOR25yNX1fXN7U0JqAlhQNjN5WZPcWDK07t6Er20uWL7hlQHPUmuXZJm8gRHY3zzsPGG+nRbqCc+PFKclUpEUcn5ZMD6ANFy+L5xoCbh5/Jk0VFNczRtT0LbcdquNwgiKzT6wVf1UrVZTXzXo1nTMzfT0MHxB2J0FXlOmZNa40Jsns3XDDkZqnrEnVyuFJtNpmADtikpmivwCDURJIqO3B0c4IoSFVFiccyA76fGo2sJP5OMjGt58O7iLarbaKZdQO6YVZpF0XSmUBx/pE+gZ2fRNH3f121HLcUpGZmFmGV9m0Kkz3e/UpIA3Z6pV1EkPEQqrM6wSfarG/xMDXuk/1dVAVe8FIcRPJdDf2gtV1cNRbaXIJPEXY+Hg1Q6h3MOTVFQBJYPDacsmcyhgXu7QqSoGnn6gUVpbUnl5Cg/9XaUAzL6A+yOgD1I3SGULBq3R/bgwvicuYoiyZkPEtoK/6sxKOqwev/CVxjmRhySK6kZQM0PQK3UrVVw9ZXUlNQltzzagLf1bvyEiCOIHOnPKIfAOXmh6ZtfAuNUDg9hh4ZqlOrjIi3LHu3e3GnSFijXv6SAkqDT2cOWo3yaSiv2flOzEidcmU6ZOQIt2DbN23fvX94cl3VB+Jcq38QLktsgZtiF2dQPfIp4jlVRDkV56XsQM0lRGQz5tPICZbB4LnIalgykISkjEkWmqyhTGkVaEORhyAUnTlPrPJdMStl/1MlnLssSvZSwSYKslJp15Gsui5Kz9skdhcp2gY3pUUY1nRl2MhVZQJoe9kvWj2u3wYkVxJFZP5udUumyi/NJNDSlicAb+fTYW6lvSQ4YP93Zh4lfMsoiUpCr5iQL33FatZuXRYc9MNUr2CEbhp6OQiY4NWRHbDVZj/rbCly3NG64AT3nDWkdIet1zGNjm2WnvL38xu3q5aaM9FTl1W4fhL8sgGkweBGCQyMZe5MIg/CNSU3QbyjeLWlYIazWsuYVCtwZFGjNspESeqJ1CcYQ24bFcEZTInQn9f17+GO39DmRHHIPYe5qXg+Jtbap12FE/eN5QsJtzs1NmU6hf1D1KkokktNEx4m/Ps9lySoqx5kz+MXJ96f9Mbj+YUirYJ5PmRhznk9SWtoq4uI+VM5uXUeUBAAMG26qokBGITMYTvMlRAM3T0ExbYqgs7pOYb+FHNVTVmhFDlv2HbysaKxxBoMS7VYhjjbQREbQWSMfhlMvmXY4e0QDE05XUq1PqkfEnhOT1PIVMhdsb6OBczTfSEUPP8fquJOAlFnRcyJhAY8YT2YcONhG/js1z1sBSqUBvCH0EeKhOyqFLaW5iDaGcojkq9JhTNc3cmbxS4EdMWvUPojUZc+ULQTumHjEBMPvZg2JTIAKSggOiavQJ+M4ydQj0nS+fJzm+fHpfHd74jAA46Pt1qvZUloUZtJyIoJXlyLPcBjXjZRARy3kceLO9Nq2BX+oBJ/0oGVfUVEU8zplSOiNeIupSFJPYs2meeIBsUpH/ncKXtF96LkSVFK3HAgK0BH6BpWQGeu6ahvUZkYh94pSL6n/G2O/kLAIXyAqZVDqJ4f/pip4DjaEG86dV+qSeE9ciynW/ty+hk25+pLesQRcUQavu6vfpbJY1OyGFhiOdldB0XwD4t2j/5MzZzakBbulTRu5F/hr6AlLNcq4rRZVJ2LwjRyW0Llio0F6SzhZCc843NIkPi4/2c1GKD/gejXszg7Gk7dxTKYa/dKDLOOGMFg4CqlfPtYcZNha8WZn1zbp1VHdaGKO5BoSfysjoikvONn8aAlxDrK1qdt/8bvf/cXf+PO8KGckhJW8R+HArjIZEqsI/rLSZAknXLZpJrnH1i/JLuOdJFrpTtwjgfswWDVJ/nUc0UUJFfboHToW4PJQaPnMSt2fGeoyQlUAgdvE0G4a9fJsf+DbEnKnlULNWyri9/TwNE1jURZN21ZlzaBoNEkfhmmZCV6Gv1GyqqhIh5gQGlYNNHVjxIpu+FdjmS486+O/Qk3B3E1v43KR7F1hUyj3ByWHMjy985FnokXgCCMj4W9/SPmMet0v5GbE99KPmc7FJ8puR9oqKa1vIXkfGjYvSoohDzfe10UVw2BL4JrlmFKxNfHcTQQmbs13pNuGp9/4laQzydcYYuEgHEHk6xUpx1MYXcppEn0qlsnsq6MRUzELu95l6uq+OOXbbKqqaZbH9f3D48uXL7nKERIBzdeyMj+XJy3rOoxTU5Wc/KMeUXESTtOYKxVRWyZwbfxP/wGrSDsITOk8jxPCUSRNcCCptyjs4AzQEs7AVHOAxsHRXKJs6mOoCDkpRUsHx1IezFvJVVCYQbItM89TP/SX87nv+6cZ7deyNUP5u3kepnFdAdvOi3wcxjEf1yyv60psYB9ki1hhsNZUINrmaHa9lvhVP4ReYZUv4/MUnhLpYhJkoLY1JP9Eugby4B/NiA21C0zs2X1joetUURol1ziILW6qW0hKFiEOeCtcXc1gmioWISxPxPeiKYdIry/P6oRrOPIM8lU8nkNRh7Nqe2JIW/JVcm4A326HYdo2eApZgwEccQx19IWKEmEIggH0K0732kYSw4IgggJxVBbU04fnKVqzTsk8nF5KsIFZlOvN7d1f/fZ3f7Nt7g4HQheSG9aGu0pj4XVdRioIlBWoL2WhPpRlraqG5ouSBdBAUZaVIj1rVVXoPTovl+Hi5CMJbWirA3PxcZq4FBbVhyzbtlkQ4MF/VLyazVyj24C10nMW8JZsoQuQay2oGpYsL4evjstpOPVofzQO/UA/gNEuXduh5uwFDrmqKLrDIS+qbJk4zJbso9eJ4w32PrJ0zwRUY9iaWGb7MxJvFn+B4gSM12GIWYgeqb0nLPW5MGnIpQwGS8TT/QE0AENAGgWILBcA1Hbw/qU2ozPro3mGymn+S15TiHVWlhGWprHVFMJWKAvjzlmMvKMqVkq6zlIxpqbIpcBVcmCLJIfBYUs9/ZumZIPc05/TmcSfUBg0m9dxHue2QQL9NM5UA9nqXIhm7gZobb5x2yLL27bthsP3P7xtvqzqppaKALRnOTuEGSw1jQ+PjxL6IUNZwlJUB5UKaIDgyrFclgsKuxYVd0KB9QlTAl8g7Q84alS3pNq3PPOqqo6nm8OxIxW6JL65cNNg3jHS04wBRytmWCspC6vUxkfY6lpo+1i8TwNdUe+u6+Z5ZuQzFZQe52n8cH9PlaWLtkV16WqvaUp0bky38+ZsbJeahW8dYbxUCjEku0Gk1XEGMzZDXXYU3yQJbPa5p9nnwr4a9gloxHBBCtkSUow+ks+FizpQksglJyOCII17vvAEI9VjD+6i3h7pP+4NkR1V2L/UPieHueY++CWIH7lzr4leKmj9XhtkM7Frzaa1GXnPdapW29qQqZN9/dWX54d33//40+3dqV5rSuhH1kFgWQqatFJbBMMUhefYHZ7Oj7/+9W9fv35x6FoumMzWaUaHZ5qWyzgNPXL0cXPuxqL0IieOtrQsq+PxCMUbJjZKT/fQR1GLcwVsm+5Jhwf5j/w/7DrQlPMnowis0G0pvQsFshKSsVw/2wQvSWMLCZy8YuYxIH8VqkFSETzVNFHBp2maLMtubu8eHx+zbD1f+nGaKwJdRe5aI1b/SP0gnNTEYPYvz+tjJVOwrPKrrJZsNKVxkaYiOR+BtDYSV08NH13xEcYw6V2K2bzk6e7sWTg9QAz3p+h0x/DgoPvyjsTBD78+WmxR+/FFUjKZov8u4iXiV2OXjKqSzg1wRbeVGDs1RiCna/SEgFSxGQaGe20d9HnOZpZNNg8/ncCxX9/dv/+zP/sjqVWaF+g2JuA/9jE4bJtWPiN5CGzGoTss8/z+/vzwNJRFTlokPlqyFVXIR6oWiRLoZOVG3emkCyHDs6mcraGxEd2lsuwAmaBZBJa1quvgiBHVl24EHwk1JRLLvgBABTo7+2tSp4HTpVUeqBlnSmo4LwEoRFlKrAiQC1oDQCaQ8vx4Oo19XxyhEHA64V65RHcs+Ws7O7f5UjqLgDnZJAgFXk7PF7nL4KJg+MqZoileEUvBmxZ+3TwnHWrQd70i46xaAb4661M+2OUEXjqZCqA1JDZL54SeZAYZ5Cto/FekNrtJ2EjnVHxLIPN1v+SvCGwj8zD3VUDxmqURGJJbIOZBQQgHvrs3/0S7AoupymamGBYlJwlHY8+thPs1kTv0+xDmT6EdoKiyBnU2qKpWtg5oykJ1UTirSjVqckhR+dkEEcqoRs5AoMtmeKpgNRv1FKjGjho1JEqgJ7Mw0ePADYS1rgZ3N6PqlhKNUdhw5EN1QkWVMrJ1JZlTfUiaUqxiWYhWHBFO3eU9y/Os6ZoRuRuoyCGnZavyhQygwNY26uKnvNKaTFovXB9LRfYgV6gLlZ01kkvB1gwFBGNTUY/ZFTPtqjCmVZceTfq2pVJKIRI5ViGRLbo2vr/zJAcvgPr3EiXK6EtxAAoe2lznzVL8zKTLCHi/AnIF14vyK2HLp2eRa/RZyM1rEdv4QJhp8A7aBzt5oDLfAMPDoYf7tC7XaUVrEoEFWskrnnt005AhT7YhF5cVl3KwkFQtK7JiEahd09aoUydeaCuTb0dYwKP9MJD1QbV0yUHVNnghTU81B8OY8L8cgmL6jehTPDihL8zedju/I+cD0vUMh1RUpqAgpAobY9RdM78Q/VEBj9J9ZcZQyo2IsJXdeVNPdBRECS9vHqfhPTMqFe7KIRSQL7QBQslo7bItkcg3g6p49elysf6z6dBlIS+3NptZCkhY7xDUMq/YGgpizyq/qjIo9SqMw5IJgnVuoAqvqHM2gHTdECStV999ZNods9jUh39GQmYxgDJdiESJT8sJe7IWiGrQOEx9gWCkEA7OLMEPAKKQ2FwIY3AprKABWIIrj4mb/QkywyE/VteosKB2StxlKMuBMTRlVmiMor7oipJlI7qIoRBnVVaoZVejhRLJYdcAL3i7BbuuagJHjNURxQOw4+FxOyGd0DMEEdOoP4CAVoDZ0zWETlcgE7eDYAxO7NJh3oiWLW4P/TH1+6SiJELa6+5tYxc7JzZQi0vjpH/RiA2NZBrA1nybppg+oydsZI+sTLCi4n9C6McfPiclA+X72GRg307A28C2MdFdx7jOeuPN1viOyxHVuFsQQDYz/gFOeoUNpCmIttLCGRzcISjSFGxkCPSyg4QNvUxjAypet3henlMEA4A0YIRNYStOKCWRZes0gMQEq0QnUA285CmuPnOoDuusW7GxMxH69EwcfHpWoIewxcS5lrUfhyzLhmHoe2rYW8HeparszBkDpfm94vc4nYjJT0uwiwSGCFVvuqY/InNDoGBI2jUliN6WdGmBfqNWJ+kp3GxBlUBS1AmJLTNW44VUAlkpVMRlwvXOF6PJRMaafIvejv/ZJ2dZ8ABo0szAtSqLBq2giHq9CRnf7gqXMPoL3zWSSxRfN/igNieswJNgIIA0VUdKG/tLozJCkfjdhVR5S5ctLdS1iir06OHQbCs9N9plz6fDRwCxIDjVrHQufIZwaNLpdj1DrYWNEpT67BIhHG+KYGdUdk3nYUL68byuhMZf1qXKECy0ztVulYLvWHV6XSkFc+tkM9eGGp45fwrpJpr8Q2+w1TCg/Rj6j8FVVdfsqyL0hUMOSZaCnSLB+3B/bZ5e8NKFDkYKAqP4Ftv4VA+Ae0GJHs9hLVs3hU+Dhs2qIkB0OCFO1Qg0LAdCftbdMm+mQgySsoy7GxWudz+LauOkvuBj+A+XUcvV67eL6vX3dxucwrgSatQk5kjHt/uHSlLRE2WpzBcQUg6MIiLNw9Yn7n7nRrotaBA2xNiNDhVnSLFJpgOoC1PwYgIOk4qpQbf1U/NDdPMN4Ue0XyUV2pT3CAAdTTH5ScDV9GNk2uxJZvmbHTb5unzx5k2W5X/1q99NI8E5ON7D6GxaTj/4ODtGTpHQjCl94fOcR0TeK6E6YdNho0Stn8bh6eFDXZXH47Ht0BVF0gm4T33osCK1LwxtS6VFGGvBYb9wkmn/5LSHm0jqLnC53F+chbYE8EmQsctWbCKKYOVlKc4gcpkZ2FIKINmmE9Say94RlNK1aHM0sO933viCfeQySJ9Qlz7i2oadp2ICFNqC7FU/XuoP91qA0wuUOcWaeVowwnlV47HuigwDPKR0sDnb6fji+5iDYJ/hWRkoMa2pqkZRZGU5Aw3rSsM67c9Ciiwo3A21aEXc0Dd6ZuAJeM0jINB01j3Vx8wpkrxpndv0c/6WBX5C3FCUS/JboRQFiUtgEkuq55SVedc2T5dLxhk1rHWrCm/JkZZhq10c7OkSPtYoHDTRoshrahduDNJxnPXy9NSfn25Op6qBQ1tA5ZaT4GpbcbU7QeeLa4oAlCtKW5IcJomsBWut3EPwVCm9aW6ilEBRF53m5bNHeimk1yYtIkHWhG45y1GL+/AxNVuL4sNqHZmFd/WEbsjUa9tx2UCDTNnFomwpWosSQWpy2ztUnV4e8RIZh9esE+oQrSvkwATbPJxhR/IbGo7K5WpKd7B9t5ToPbIisS287UYf8x9bMVPNMGgwZsQv0euL4Y2M66AudaaGKwFzFyB5RqjgYQpyhGe1EvY2QeANRxQ3VP1Dpi0p/VqCa2tbRy+a9U4ehyyl3FLoVwFGyORDd9x5mesiBwYYsAqpd8FLHqB82n3HP9LOaNDbc1td8e5O0r3eeX3Vq33/8DCOQ9e1JeEfxZzOkZjHriYAALkDGxvnogPh7uS0p1woSmaAupsVM0CinKZGDGDmpi2UagP9HJIXQSkGTnK2EyVT8b5ac+xyzWdwH+xnKXIYsSHCcnD2r7Ijuge10Q54JaBhuBNvaBuW0McV09P/Fht54djZpxQ9FzqnDmt51YB+pcbuxnOmFo/ujzmBkiHK6RWbXzfWO8CCP8PfO3WrcZifHOJmRe8JNU/nfvamcQfuf0VT0e8HyKeYW3DEV8D3SSXy2EPMK0QBFZke4EwEaXKaedBzrVtMsAS0Z6qFpuMRalOoMOvI3R6wsnw3c+QHAILNUig8RCpgPkI/RIOQYWyaCnOk9mKcVeUU8WDo6Y7vy3wl31W8BPTv5XzpDoArGQ6ZR/H49DQvc1M3rBeze4lnyE5eqo4AmCT7CKj6HRcbY6Am+Oy4zFUBVQcotnkdh/HF3c2hO64AZgI7xcKZMoFzsgqzhpzbfCiWeSHRDsWTcsgWMpsQzAch5Pn3v//+cHMqq6pp63zNfvrhx8vlaZmhMRFmsmlasJ51gYqxzBPqcuQLFap0Rp3LsZB/TFlxhy/ZfBe007MQToApGBoaoarZTdvWsA22Z90nvMc796xYkGEF34wTMOlhNC/v3ivfOOSTz/w31YEkoQSbjDMh1fEiaOhI8Mc3V3VNip4HajK4sBEe6VdcJm1TECh6rinheB8IaEJQusVRfsQxVrfSMqNNCM5rSswutlE66e0qVahw9TgN07RCXFBiD0sNggrqysU3l2F4vX1zDlbPEHFjVJ4ibUZyncRMJUhTSQmJUhRWvi5qs7kk1gxkSfXoi6qpi6JCQVyiwPcPD5e+79qma9ovf/bVmzdfHLq6qPIFKEywUUGnimiVTGMxfDWbTXCY+KTKarSEyDKqQk+vN1//LKzC0Ndl/XmGXjHv3r17ejrfP9z/+ONTSWGIeZpvTzdLMSHxJ1I2dTkS/23gyd4W2uKH/T+efI1ZE5atatqmbVCwPvqiE2mCB5AbuVumYWU3LP2iEVME9rCCOjt8QA+z5Tn52TqQXNAnNl/36RWhvaFdHzWK2WMcWwXEoUKCdFexIuHVQOUelRpvSIjHoHQLEBxkXyUlWb0qulH5k7dTHI0FUNbEQ0yOlGC3rF988ebh3Y/DNI5oQkwXqbbr7rfHMNIRRar1asV76FcBmcqvWJnHpydkQ1FBAXhtg2tQjBPJGyU/Vo3q7aITsOcoz/Nhni6Xy1dffPHq1cvT7c2XX3yZtx3icPO4ZMM4rZzPycaHYLQYeq0pbULDzC1I686mORsmmL9Nk5XQGqJX01RNPQ3Z3e3p/uE+K9GLuCiLmlTzp4eHd+/ff/Pj9/VSSDqhtf0MGlgiGk0fiTbdiFbP/kb59IeyKsumrauq0QYzLt8oNnpDd7Uwhmg4QUeOv26eA470eJnrrFavYwdKt131gUg9whG4OKyCM1Pd31fk+45tGdscXhoq+i66JIAluYOfda9l68QUWYXg0Qc8K8YwaP66RU834702+D3u58AyJkmtBXqoYiEdjkgV/d033/+tP/2aIb4otRjc5Qpi0LXS7BdlUo50U7mySuVu9EmZlrzW07Og6gVrwlxiXlqMccl4Cbm78s6SPyouNKwwXXB+fPrszes3n785HI43N6e8rtC0ZRyAxUQpeUBjYJrSYzVLPasAeNVscq5ZG1BV6Fy7zLRW04BfQcNeIi4o44VYMpStob9AiaAkjSzLT3cvbm5fvvnqq/NlBBaa62kFBuqXz50+MTY31uRmx1PVkE8uEim7tqpgFQS/qGneygr8FqWqunjhvIoeAc2Sg+bHEZGO0ygCXFjHw/Pf5BAyKcS3CLxF5nFFdjgB7IuVX385p64fr0pfvhPhfh2Yw3WHD1QfFotaFsAZ4xtF6YWRX88Ys0+Q8D9sCD5imGIqW91QCYRMRV5dxqlAQi/My3meS4Z3wCPNIVCduMEjaBTBCvbPVJtvtWNAB7npuICzVUxCwRpyH0ixDAFUI1Qb98yKjjcpLAv1cyKw0cvXL7quO3QdDGlK6yNuSJVx2WtBUTE26BnVT5GguCJ94HJm8EEvEhyPZOABeIbqACjJUWXLghTkshrnEVyPuGG+5sM4lmVzrJEMxSGI5ETK3z7YlRSncVXnTE6Ew+pipBSuguxta4KbKiI8aq66KeZgP8QWj2AVJEJsTlZhoUq2VwvA8J1oUf0bfsoS4ks4gNvpyHaM7mLQZhuD/aLykaRVSg3yBNUAgl4R/yVCUzuzxIW1+cmuwqXkN5g9AQg0HWWpArhZle1cNdPAr438JOBHM3G2TIHeVdSQJamsCGNpXCRkqzgFKzBnv417DDi3AvZE9lw82eAYavGs9w8PVFZeokKchKR1UYLxElKT5WBJTJhaBx8PBxzftqnRW7i/DP1lHqcF/dAQKFupMlE4VhF2KqDrmePaQVc/G2kPKCJLf2DjTMBJoPEpfNiH7kCSPKQzcXHNrFjLlovuOS9JzOyvn9Z4QxMaFytN+t2v5FgDXBylTLzNGe6T3j3YbeGucSHXXelsjFTe2tbc25vP7iXR35GiK9JKST0Q3J56yWsRP8fxApOVsnCavGZJLNdgqg6EEbBIKpE84YtBROVb+JzF2kAYX2SV+ElES6VltRWgRv+KJhY0BiMEqWgHIbOua4ka0RkqLcLtqhxe2YDEfmMr2GdMuWEGGs6pONo8L+dzf3s6aUDMVhie4X7o6xoZEZK+RcLYTA8qMWteDIpiaWxrzbL3H+5//vVXbdVUFWxXkr1EtwHIrrEwzStCU0YycaBVSymhcIRDwNFe0gmJQoRiVHAaVrECxYVKIANaFFOLNgJBoeodvSrOYLHicToNnf+m+7I/hHw4duGyqiQXSETu2qau+bZBadk5nJujmiiCO/FfYWLh91gUXIdpq45n3wwktDMKL6PVq+V4QaTwuzsEfraBfXmnvR4d/8yrMWh7RGolxJ/qT3wKKYAE7L7fQ7d04R+bWzBFYicg5ysERxXvpHmz3Jr555SIrObjvFRFSaOQloro+guzHBBFxZelKowfoDhqFHSzmqzLi2mcTt0Jti5X/SBaybPs7vb23ft33OKIURqgPgwHtDKOY9O2tqKc3mldVHBhkbdtU1U1t0clDBllTDgadtxXXqykU93akHUVSpBo4Qu5GIW4FpQN490py4zAYVwzoKZiIuv6xIvAxa1VVcsJgRzQF1GPhb0DvQPKj2WonGGaVFE3TQfVo+aFSc6Yu00wC/aeu6VaRz2BNgJvuKo9OzCtc0oHQoqCoLu2qi7RZkzqFomCNNGq7E3Hnh8N2XnL0he/zxAgRy7OZJan21milBZUeKIyrpb871UMxfgkT3Hjd58GCawWazCDdQ1dhQErHZuv69dffP7+7Y/nvj92RLF5dji0D48XJ9tjlI2LZAirEH+pUG8upvICTCF6i1ITMMVC8TZXFYJV87zUVc3eLO8XeHo6lzUVhSSblDsfWJHtt2/fvby7PXSd1p1k6sWfIHlphBad4oI7KG0MfJwrMUWrplyHn4bFIwg0SIOlMDNCBNiaIPNIf2041CyOd04Lz1fAGIMlEHf0Mz5/TS5d62DHELCyaQ4HwE0F3u19KztncufeAVzuJYVjIKnuF/53OqhT11X08T966Ez75hTqhHlsOVhcxDOagpGRW7vNK2WCztUecgiuOoOtE3xIflf/gIe0Ocw3nLHoTs9ViEWwG+WqauMSiRKxrNRq7iRHwsIzhFpUB1LArKEtpKUYUoXy+ld//e3d8Rd1jRPf1fWH9ZwmrgZ8YoDlhHCcQ/SsnNKACNF8e+oAhdaLddRFVWV3t3e1lo/jOUq3BKLlBWoqAZ4J1MyIxgIifWzq8s2b123TstxjJAznY6pybz5FLk1mVW8lxKzVj4S9h4KntO7S9k29SBzeB5JkHMt5ApUWZdc0t9RjGQErrqYCXAzQI1UFCWxnMQmdRNVj3VbGB9EEiEdTrXlV1x3A4rXpGKwz7h7KXTkVC2LHR9yPhijQd5KBXpXEcomA6fnricc30FgkFPalsh4t25Znn31Fq9+7bv8uSUuucJFDWIXwCBdYhK9FicC3wkqJNXADWcLYf5cOKCxLCEBaLE5vQc9kqChACiPqYNCBZvFInUS9QRnsXqltGj0t+n3NsrXMy8f+3OBW5pbXAAONpWkEgyWnkGNJ0FClPklJfTpJu4aTHCmQy/z2/fuf/+zru7s76gyMouooBTijAh6r2fw4ckXPwziOExqpcMtIhlQHPkdDYufZQmWiGdFB/wGQBUez5iQzpxhnJDxWdd3WDZqk95f7h/M0Tmxc88oC0SgH2UoymcfF8UT2GIZ6dW6H5YyY8sqbWORw2FHRI82xSgV1clBjHhxYv9GTOe+s2ZVp/h4zmJpM8V9pXR9nkEcyVXM6wz02hzfRA0yvk8MTUXykmuqZtgHZssZTf5b/pBq0qmeOGiXlcV1nOlrUB5Cy9nYN/cScCBfFl26BFk7bF79lJP8kp5U+ZPG4zmjhSbda1pX6DVAVKHEpq+rEvQ1iXPZ25Dkp1VVV33//+9evX1pCCl8uaBLz/cpXQm1TriAnjXQy/M1g2rKoxvF8Oh4+e/OqqYCyGEegQJktcEMp1DWh/4dhZE0YLUi58AA0aDr7YSnFUJXEDC7lPYXgBU+2LIoWvl6Qj+pZQMxWTVOgAACGO64oSIAea/Pc90slSocTvgYWkPQ/t40RCkfPYST+iEDZ7OY2LyKNnHV47Uw+J3Iigrxa8DG20vzA4z3fmHiJ6DZcSmI/xMOLvhwP+zmZ5d3lyfAif+t+tNh4hJbxkGvVfPQQFBJrKJc8ok1u3BonmUTwJifPMe7iVyPgKKM30xIHqglQ8Ib7XlCPbHRUnOZG7c6mLFGznGcsRE4pOFvkS2ybrKpCl8jmUveosXFpbyLOF/XZh0w/5qHH4w0qT3IlY9US52l6vH/4o5//rMjyceiLktxR0lZ7HgcUox3GAXVtqgqgYE4tYqA/eYkR3aWwFasUvAK6b3ixrCbPPNbEWvlRgeulH3pOGzgc28PhdL6M799/6PthoL4qwGyX5VKhYAIlA4npEwuhVKA5hnz9wLKLHkkY3MTR3GPhcxPy0a3MzLE7quIauzZj3VhSwf2ZFPaTIiADr1Hbcd8soMwRBaDHWfvu+KZ8zE5yIPxdIaey2qboMl93dFMx8iK9gKlUsqkZMxTwAZ4niIt0oPwBMtCioegzIz0i+nlHVUquuMagwmNEXWVRnOULumfnZb4CMUXI/Dyfbw7t26ezLybvhXwCGjHStUkWa3Y5PzZNzTkhynFCrQwtYMBpH2uBII98lyxkbDdbpoCXLGt/uZyOpz/62c+PLfxGT9NTSSkPgmZDZXikHxwPBxufVFAz41d09CisHxYoHJegN/EXuUMaAFwwcrNpGs+XHlrJnPX95cef3jLXPhxPXdGVdIYrZwUGkhVt2dUCl7Ph3EpGSEn6n7QmoYCeSnWhWI+pMUXcU63XloJMT4+IQgQT2vNZEJuMCNPCwxOcKDGCDnE4D430i29cxVfMNfiz6q4izZi8bEq7r2fAvxbfFeYsb0qLykCyOhVfwYAePI1j3184v3RvAJ6JeR+8CNS4jVMg+sjujbvKbm4exAMZfsXr16/e/vR9P80H1pPhOkZ2jkgn7rVrFpO5H3bUEblzVVbv7u+laLNz9PKL3cbsNJY0fcoWUYcHwIrSPofyBIf+gn688NoP94/D/AFfvrs58gHhhqB1UzN1ikxmB5+kK0ietYMqmYHlSqxYP2VfwFytdFapBfIJloHa8+M4nI5YM2DJp+l+uK/brmkqFLWjkNyWJLSzzFVF0rm4jAa4u9SuAui6Fpm67g5erGH747AnK2P/yM7eOsamTtHkkIXDKawkBWhtX96dJVPflVNOz4gc1rEXb9c6jq6KUAkeOuST/mLFJdwOZ7C/UIoMF2j1ynDa+CYxNQOIxmtIiQURCcsY8mGr79ggN7Zf5nFeq1/++tu/+xe/qAr0HK0IUKldBsNXaWctGOVWVxAjOOfUjxPzzDvBErsaO4ySgq47jqOAWBF3hgXewDOMniko3Z5l8zBM80SJTIgn53nWDyCnvh+aqsrWHO0/KU/CEJFa0ChKjOAF1qz+uLJKEDtiL0hXPaQZBs+DBaPA8rjYA2Vo1HX19PgBJeapEhAjoIf+UsHadiWnPYVZoN4pfzsn3OPP1TLxG61+alle2wZvjIoQMZUi4vyb0JFPlhL2JWzMAQu2HvT4hO59sL25v0i1WWfryRneivvILrBqnDH1JmbK3nC8xqIjQgcu1a/3PFL0HPhFYTMNVjh6M+UtZWxeV/iT/757pmoDEkaVVQ5iiBAlyKQrECWdi6UpqmlcZ6CzQjUGWl4reesZSHBzCwFlFGxdl2EaT8VBVCfV7kC9y9KP0+PDY55lB3QiQSRphhMY9t04Qz0pq7ppkJtOoR740rjc3/nxiSzV6sXdXdu2Wk8nYBkC/k1Eq9SODJU9nBAjJcg0lsD0qrIEfpLKZQtCTAhevsjvFFV5czo9nntCdEhbyaJCS2EEacUnvTFmhCeqizqiBpExQWQFFLgAOzlcLt+Nwq1yGlMCSc1WHwWOdGP+anxuQwDUxubYmqPJ6ADK5d55mtCjl3/bl7FUH5dJPpPz5GRkRJWqI+xoLJHVb29ql/eAIfCjY+qlvIVxNNN3Z972wc6wd16Bh3uBLCQbUsDwiyErbTvNPYR6T+jaWS6X+XIZuoMUoyqpuh00aXZMBbywiXmNgQidCzokW3P0RpmmqkTA0kdml2UdLpfHD+9BnpRCgzjQstRtS9J5LijhXuK1kp9JGjU3aKiOZCEja53wVAb/MgxG2HICPpgEVhymFh7UgDhZx4o55V+RW1IiU4CV/QAj4CvVaCJvWX3sjpY9MKGyCkAn1JlBe14bV1da0GBa6lwMuoCydicIsTgEFpU+FwHcFd/GN1PYPzoGLHEkoJgCOTzxyROeo+sYLvFh/x2RZf6HvYPrh5ZGpj3NB7ke/LPMmfM9Go7unBqQCSrRLYgob4qQ93Y4n3pyPYNJU45bFGuKmZMpPB958Tycs+UKjbtdDHoit+sNCO9imobhMpZ5uc4gWmahN139OHK+gdkI6bpESy+I4vzd+3d1DQd0XZdaPkBiykPfP95/aFH9vJymibP56roirRthXPhyCVaJ7i3QTFsCN1F/PU5UpqqPVKYHX1wpOYnGxohUdeNoZSNGJ2sAHlXfEPdmtkW+Vts1yu/RugIyNzyYXG0U5WWdlfiaOpfzw+HQDz2wosRZOHdDa2LpORcYjfPwBIiOO0X+9MVebHwMqJkLdG6UUWssuT2i8TvJB0II2wvkjAl3dPfwlOez5hy1moMoTfPzyoGTc6kRu7UY7ZzJkXYK9IZLRCpN/L1wtXIxSUKydCPH1mwh2AOCjgOc6Zbw3mhSe5DTbZBNM/zCSH3c+TkDILlzka8TnvnlZ29+evfTMlFaATEctAjrZyoY5fTokHXmg5sMmcIP04TKAMM41G2To/c3KnKsKzAY6zL356eKQjUEtKJOglKPrqCc3YIaIxVNUwLnXKKpALVxl2QvbWMCkmCNhroKc0SKq0NHKIKQTEr5fbBzpFaAoJ159MgblqaHVJ1T5DOX9mFjumQ/HhcOHYahrlBriZoklhP0BrAVBoQWOQoChj3lIJhDE4VtNhdo2FKm9w24ijWHZYV/P0jN7enfPTUeJecPwoaSozuJEvCMvZYyBDOpVPkVz47UQEyrPHuu4eOfGj5N1HldzeAVMAvWjeeK7DOfbsxhwqeeKM1u0icz9c6A+wl80a9Y7LYShctldAYlx+6Xji66n/9BywBbxrYr1WHywPqRr8Xj4+X22CLHHuFhyhOAcSw3oaCPRkZlgS15iioDZFl/uVCO5NCRVmzwDLig+mGaRmTAw44FEUlagZSbJELh8nSaqmco2gjcoCtC1bM47Uf/03bwWk0VZb8o2M0tRaX+M6AhTlAzGMtqbkbnGN+VtqgmP6u67odh4SUilzsKfIr7DC1lQNneTIwyZOzcOV4fi9+4BJUouFq/KWDr3fYHAg1Wqos3GQY9kthhYYPcu6r/xaHaPf+zv23k+lL0gybEyfDCvcJcQ7NUx2K9mmm14+wkhjXdGXRoShk2II2GyROdCHacQd8hEsH/lOAeBLjcds9QsSIaSZPTwKuv+wG8O8//bikOVnMQ8ENOmi2KcVq+/+n915+/ELQweXdZdwy06muVmsNiQTpvnq2n0+Hm9lBk2e+//z6nWn82tbqsPowDpS4Qucxzxc0QiVAhWiTeAwSjczmFsK2ahaw3BPtnZi+X8lEeHDc0IgyHdJwgDV1DwXNOXmbLiZUfOACl/nn14VpGN9+ZWE1TNwzK5C9wD1SrikQtHqkcNS9WiOeYrR4LT3eOHRmEM8eKAEXJNDDkvFYRbflvxYdCd2tTcSPYmZHmkN4pMvN0O8yp7lxGqaFp5OESMCLjTiu/mQCWL4au8frgjQ9uX+p6Ubt5P9YYQn64xDgZqpCWAiFnaQgUe/mcXdvDIFiFFewxvOiVeB92J6V0F7NUtnYETgzblQpHV2UxyX7rN7yTniQCyHGeu6Z6/ebueDpAtg1Tf748ns9uO8CO0DF0QhLyMAzH46ltOy6iLvfXHmXUK9TTrrlCVI9HvgOcOsJZjMUpioY6vEr7IvGHUclkVHxjPSLPCirDyiMkZxlp4NqfUXguK9ZiJNPeUbhBXouKOLOi2UyHDcx9YgNkORQi2rZFcCpWwDU5chFrQCp5WVXxjZa9YQBbQ022cMeFYXS5m/Bn2bqpIRl0UavLG6K08h97zIUJatWIIAu8ie0XRAvTe3keNdXV1pM67tjeNiUhgHSNV9hmhCoblsivikgUHYAXhIIW+yV+wr46rhzxieinDbsND46uj3mDveeVF14nCJBlyeq67Ec0CS2Khn2vtzeHD4/9rGUgJLWVQ6OswU5zmeefffn6cGhhsfICl5S1WlbS6AlfwydN0/TzZc1RvRi571IbUvLeVTe0FkV8CuiZtNamAnJRSyzpulYUsjE/sCQmeK0VCrMkAwMhIrWqQFtUe1L0f6ERsRekrBnZxqIb692YLUBfMHQs2jrzHai5IXojUTGwyPfPrkMZaIg0OmXNNMh087iyMtziIv5ltVSX3g/LRi//uVPckvO+IyCSQEysGIh7MKRs2pSi5m62GXHiHL/Db0SPFWan4ttOvVUfSg+x0mOQ0NHsnMSUO4hCpfciGzLk6wQkphEmd6aUKtcMupTIzPW1j/QQHVB40+k+W1sksR2U7YUArvJx1c3KYiI84jot+JsODKi0FJ0v1g84nrIu09xUxRdfvjkeWl5Tvn9ZA/0/jT2pr1KTjirgI/ZD9SRAJyTrxLGsu8ML4kKuAbUbti/nepO84HR6SFIS4Jn6IcmHesZMIZf0DNonKRbNhfyoEXEOZDoRIRfE06I7fnE55ox3KN/DFXoUtyg7PKi1SgA5Wv1hYUhbYEXgTrZ3Jl/ohW4wkMDcOcLOhYlji/R4DrA9WrFMthV24t6Vok9uE5nH2pdZMQXZTjE97xry6SxxfdtUVVCZSEXWHb+Q630NA8cxjCGq8yCwEGttLppAHHiQcTK0VzL6474nDEacqLdD2LbEVxL1qfDL7acaTzPdl+igJZtkzMO2TC/msAy0wSJ//erlT8tPhMsvUCma8h2YKkGwij7gfZuG6dDVn3/2qgPcintW4DklCYe706Hvz7OmBHD9IOQPijcmRx2SjVOUtcWwKSJ4Aysmp5VOjd6nstJcXcxcEKYqEoyPek1QdFcDmpQ1wd1ccy4xzW1luLAGkoGnZKkjbAU1SVNtExqz5l1K29EcXugQuXLzC8mkomQ6CjP/mfXHZIEsBTuRQE01n00exWqwtjKPcBRXX2Z5xwfGm6VOTVdMhpKGjEjbqqvT2SzS4O2jg6fnV+2dxIh18xfpKLjkFK+kkt0XWzEDS38NepdJfI4tWlwrBKICrwO6iPLJkdEWZIau9DoDWCiQ4li/UF4aJhXQb5v1dst7dVeCqeV9DUFvCqpLUPMLbeTH5iFFHFk4FiRztHwHHX6CNs/jeDp1r17edQ3wg1wZT56N13xoi7oux9mTHzb9dHPbXy4lF9DRDaEfRA/Trq6kxHJ9dR0yD4Cp1KhO2oPo7KVmJi+kAuipjo5WKlF7jGNMpBmJM5lB4DoFHpQApE0LsEAVJ/VVVT3N0/sP98fDgStLc1VNGBP40eLSe/TsT6Hn98Fdy7XDCMKC4oDETsNF7lDbQbSb7mi+dj6Ywnc9t07r9AxCe3lpFXyrgBnOr6gj+h/1sI3uGNW5TVxJLBtMm9Uzlyiooi/rWoUCf5EumgA1FfjGY/Aah5vrIj2yTb/yqvuyjChWPNAQJSLhjKDNyxFnMhK3AtdCvQG74m/o/FXxgwKlY1caZLDzmVV9mHPuOOuRa77n+TpNx2P34u4IHAXagkd1jXPOTECYtF6nQavi4CHzvJxQS7J7uL8fRs5WpNR0MwGlyVSYs/m9ze9hJ0AcTJT5wO9xbiDJLaqEAwc6ZHOVIw+PI8yF9HAhHzEjWnPu3kRdQelTEtdzpAIT0k46J9MwuYLHOAy/+vVfw74fJyTbt022FpdLT82a4MfXMxWHzLkFBvF2Pi8U37WyIdStFjozuiRXJWAu6LUKGLZSIue0aOeOPcUtOl/OAnTU72pcbDXACMdoTkXr5xgoUhEBjiyCiq+WA++LN0pj7qPCl4sjsbM3DVw7jVafa/qpfayhML/iFspWRFHgY1IbB9nkXFINUcfA5fGapvFyPs/TxCw4FaZpcC6MJFZcnNZgs7f9eE4u7xC2t2gZrahRCqrolKOsMQ6p1IsgxsPqG+q5Lseuvbs7oZirL0DsAtY5tUiTHj36PPHFIIjaDG2LCjQG3jIvlLZfMNbCXn0ONXk0ohhJAmubq7JieiZqoOZj6v1ln7Zr7QtSZAeTad18pVS3oqQnoRHL1xYHc+SjHcfpu2+/Ox26m9ubDsBscgzkZde0lIBRFos0Dde+31LtnfzVeojJeF9xKVYHDgLtfooOEpg5pWs7+HpEBVHXXkPXmLs7plz3PX8Hr3qHd/RnORpSDV/dCZEcjUSt3sSBNja6dXSh0Qp32XPdQKP7RqlCMRBtm17BZOtQBELa5n823sd9t7guqs8i4tMxzVN/OSMRnJw3PinG4is8W0+SBrCIR+tUfRdFFmWPtXwvmGMjO75/sMBlF0goUCnk/Je//e721anOC+jVYpTIZRBi2XL74khwZY0umZsiPHmFACHIlTIBNXLodm3bIfxMkfGVkYmkX8ScW/VY6eIpv5CJzjXiIeOWeX7//v2LFy9IuApumUtqcZpThpoeKyZD6A2agvlPOSoJC4ABpAzA1tiRuA54WCjro32yuBhIVVY/+/or5AVj+6GPW/Z+hW6LJVMkloKtWYNJBKgzm+TKa0jpl842yt+ljJqdYt36OIZrZ29fq0tJxh0ME4/x6Q9ijJ2A0lc5UoWDRI3Ups2zPSV6+IPZoky9EIK6+DwCDswY7jrQqUK5nSfMiTPjPS5Gvhme99zR3qOgI2GMtEbEynm/w9Cj8gY31QjaBPeVpoaZMrEo7/ra2vvPYt0hsCivwPsvxtEQLjynBEp6G7olaHEZ0pxhYopPGNSL7ntffP6yrVtxCgTb02lcOUVpqaQOXyNhHT0M8PGUZdfU7949No30H+JBsD+aldUpX4d+PJ/Pr169lAYodHSkcyYBPjKgu6a727uyLEFERIVEqXryzcERWh8uJNBlpMBRkzrNVMOANEuj4AAggNNaT4Kr5DVUm4rhdVJgBJo8egijRHSRoZkbCFiC22IAq8aldhS9iKvz9mv/JgnVCptPdUg5lREq4ApAIMZ3OQFumNgY1OB1W7dj0v7cVUJw0nUTNI5lslnJcrnKQmfsM81Ifl5CgntzCrqLi5pvx+Dsfm8dWJha0YQkqLmD7bqg2TQwurCKp2EYEJIh+9LFjfmsck/qAqBBcgGBV4dczqh6c0iRy/bMFfcdXQPVQTb2Q7RdEgNTNysn0JUlETXIlV1CFULEOPXrOr+6u2ngtUKnTxO+TjHieMaKBywr7MCiFJqi90gd5MKTa1GW07LUPCiFzpHDScyfvh+eHp/M6RhEjNW5oTkfuoO4zSlOxWfBRaHoV4Jnsq7O/dJm4LWFobM7istoBsyB/OzaClGsAXm/JQDecHiQq1S+YWKTvgsVeoXFSnoFq16sioDAtW6jbVKIcyRqk1TS3fV4uJ6fO1Jv78WgU+e98TZpFNcK70v3jqA3OtLVddkBfmzN8M0FSrvqNDSvlf+OhdH5l+DFcxayw1faOXGAt2AfbBeR+SRgw1UFV3M2rUsBuDmOf4GChkWBFDPkIJF6wM41BRtA0JRLRlUQpdS4UMMm0u0INdTptF5Zwu9sr4OVqHON7hDTsfBjPohQ4GZudDIXaNaQN1VxQc2n9ebQnk4dJQaZeLPtDE7WnP9al6YCvaL1RIIx0xCPYJ4Ce2L/l7D9oR9QdrlGo79gQxn1klI3DCOS/sg9ZiyElT3d0+B3AY/VLWZZLsgRbm4EssfqEQI8n1YwBZEMZDSj7T01fH+CSjVBYEqrUnF9ccsHwlsgGymIHnUAOAswModEFKqpQgEBU4w3wtPv2yaT7+OoDgffyXZCePEIzQe1JdHI5gt23Mdfll0aSFgpOJmeAfdt4G4VAuUGHLyaXvFNlBo2QTOdOZdOEXBIgf7tuI6cOE21rtVUDUOfDdk6owqHodxVQZsA6kMVNELZPjvz8Ez73dehdm+6cJRnuarDJDc23WBZ4AstyBDguG6W12WxrOO6Ti9evKD8O3OWRoclrHImCnpdFMe2fT+fVYXmOB+fZ5g8XduB6Qk9kk2LchfAe8zL/PT0dHtzY7UppUmhArZAUtSTdEbWMeilgK8nm6ZJeQuFZykwu+ZrP/ZZhlLspAvCP1dVoEauX4vvZgs0pmEs8rzvoTq1TdN1LTqbVlBOKAOyRBFMjrGRR3oaJ/bVlRWVroYPCktZOTC6IDOjWHKkSsomeNOQycPbzEGbipsZXT8sO2oa3zbWqlM12DFhkbtBc/Z7rn8lzdPS7EEnKU3uqi/JcE/Bx5EwBPdegPLqkaZ7JIqDK4Dq0aaBH+p3VJ3zcWQsLCoUTzNXb+QX9MayHHPo0ur3VBqme0DcocH3ykXcmIxj9hF8jH7xjTSDhr3xD/iJ+A+5ko4eHLo9alLBkofDZ1mpZyZcRtwy+uXtkStE+mimeaBjz0JGtVvBC05N/dj3PeU52LOplCRjnRj4kuUabWElAOV25+nzz15b5IL7rThJAUsYQZaiHIa+PXQsHpGNSN3SuWg+oyG11HOtAKC1P18opCTuYWT/Vejw21R1hXqX493tLbmdEcMB6rMALLQ7Hdq2HYbh8nQeh76E33t59/69HAMuRMJlpgsgsXiZnc5nm6AJT1EAO0JweHFheaKp7hu8zpGt623OlIz826mV6t8z8atZ1Jsb7TlZ9rlKeE9dwL4HiRpyO7Il0SYsPzGRQ852DyvmIkhO6TTKNt+XnltcuSzzMA7I7VbIK9tD/CU+TAbpNtvKtoubfKsSrXE1q3khR9chQBxlxw4qr319RKnxqDA5FMtc5KhEkQFNVCzLhBTZZek6tPelMLB4YlP9XBcu1+5o6J9CrX8dlxEWTMlLUlCeSlKSM39dRxTKQkz95nSkrgv4OuXrS5BU9XfxC4Fqq9OhOzRN3WgFdlJ7xZGICgr044wi730GHxVKOpdEqN3hQDekLqQlymvkZb3O683tCQJ2Xuqmbru2RnV3gLdh6SzrVI3zPCFIpPZQRdUzuffTTB1WSAKH/QoiS8zanUKe+nckO2Xl+A0LtUbfUnfHnlbk0gDsC3aK43/jQbrjHX2cPsG/Eygyvbl4jCNx4WSj2cDu2zxxH4jeDkc79+lyxh4fMytjccNiO/GMS5nCceh7gLHItRxylBQlLXJXgy5Cu2L3gH9zOni5jfltFbCIA3ol2ez2PV05WnK9ED5j+SZcr0U+TmxQIg40ETrlhB6eNQc51BlpTwuGk72b64jFd8lrTWY2EoDJdJiGfp5GzhfvL+clK5CcBLujgKILvRSlpSifX3ODKaIrRosgW9g9DXmeAWtIz+fmLDQF+JzUy3JcW2rYxNmRIREAXi40W6jKsi7z/HRzgLyuG9LMsSjsuGI6KoscbStWtDumuAfXEhBjHgFccjfDP04wrgg5qEnZrhzOhgx0Q3lwceKfiy3GB9kdBNlcgeGk14axmBUdW9Dun4iXiLfS68Nemju2YsqDsg8bWkhlCMV5nF83ZC+EYeyc4TgcHEhZQ49+ZZI7uImlc16WBX3oCS6oS6/eXc1qIbHmuYQeIf6BrpWSDkWp6bqW+RtTr5tkslHO8k1eoXkh2x1Er2ZtEOCZTz5qpwEdWZb5QCZc21Vo6EGZ8VEbSN96K3YTZ6qoc1m6sLwr0nfnaeKmChDF01xWVXvsmrZBLUoQLlWaZGs1ygpWPdvUFHoMV4esQH2QkBDwRDvhKGjolLmgB+AzgFlAIKTvcsQIReU5XiXONiQ/oE5WUVRlkUGEIztSPVgWCeLFJSil1FMNKqgU2QtU6JXESKzxyZYBunx7Ozc7MEiFIJlm5q+Rtd/Yv/55W4XcH9f4XhvqjVXxSMLvoQajR/tkayeD7YiHyinxjWJ639zbvpUYMVtVn+BWqBLsVjY2Owk8x6rjxk8ozyEQMpC9ehPO9LCLvKvBcdr0HS+Xd4WxWVzhCqG2DDAyyW7P0ZpAMzEziMMFJjpnBFisIxYZVoIhY3phxtTWVXHm6rmsJyMfaByHc9+P43Q4HPK2hZOAFGDqY4L/LJHbYG3OOSL83hVmkCgthadAfQaZ09pyCqyPfBOGyjARw0FZvDgsaA28WfwDK8VGBKFHyWHGySCcFKmrsSzU4JvxbWGJZfS7xzEExjxHdZ8zhzHFcW9r2c5SxG2qI18xprxIj0wuE+MmsYLN6OI6kbpgn4azEfRlq6UbW3zRYY005WB0qbRISHBPyQj0pSc0NNwONzJfmJ6SYRyhTWnOOd6lZAqqeME8XjIZZFTOyeTmQBcgb9gUaT+ZGNu9lbF7UtcrWC6slHIy6YdSFE1b41xSx2pxRBPW3+FMLGfeqFcPG0v/NaS5c5ZSOL2sUhbZ0+Uy9OOLuxeU38rZjku2LqU8ynQ5g/2Y+8x9Su8BZB7MFc7mY9SVuFBVFIsiw6OJAyZ0mQLGBK7v8IJyEIlCVZ2H13pCZxzhLMIE0asFOy+xr/Q07+2YLaLNKo54BBOOsax793E2jEdP7kjpsN+iyTse7iXEM84TZ0ebk9y96Xxr7urYaGBUuxNwDi4eu3o2os6RuPdRe5XZ695KdF5vo696ixttBseBc0qNxXj2wNXIDEsQr6JWT+fnKRAqm7OcWnwRJjmVoHvbEv2ic3UsQ+4vWmMAYKpnn+3baZwObcPON0mczfKG+gCaciChHP41qk2sMNxVdinEvJXEcgiu+eHh4dC2dV3NKOCKunYUUyWUhyEjAqHGh9JCDGyUKgo6zJhklMhwrnXLMHZhFFQjQ/UbkjPkyGLyFq1ZnmlOWKngQzkMUoh+QUIVwks1FYGm+1N/8iKbSYVGkhc1r4u2R6hGSTccaVk3KUWbKLmOOFO1Jxzl4KZJU3L86Yj8Z66H7hVIRuQIjqlESDCNQcUWn52L6OMwSM8+TX2D6PCMwj80RYTrnJ99RSqcOb5E/HKxWCrvYnfzvn8KHrmK0bYYSuHxJvDj2LMitZE3K+lv4Y/B5rXVsZR4hZNpV2vi7Vwjsj9fWC1lOFKRF3WLrsHMfoKTnldA1z1e5tzcr0I5em2eZZfzeez713d3TC8ANruqM4KEYgpU/I8dTMsdl2Ve13EaA8JeMpZDpH2eqd461pN9/MIQBNXLaEp+sb8MOEuGRAUAsJAbPxg4jRKQSSZsttUVrc06NhxdQVMJSx/KxqiNEamawk9sIcOZV1PB6MYdCzmRagM4ybOTUihPtzfM7RPq1aQqQ0ga4A9c5uDmlFnxzWsvPeLhTMaEbnSu9U3iV4Ag6inz930uOO7aEscSTQxg5D9x4zh3tESBZrAAByS9PZLoscaSzGyGvVgshFZIB+YVBKcWbQwL2+1omtmViFNRFahZJSvFvUfhVV2brs4XNAdcgJguuM46n+BgPup4cs3RFDSFMRoSU2Q6UkcVKUMZFVmSXbKE03isYpM7OB9pvEFoKlNiJBk6QMJRjApBjAeUArTMUvlOkKXTMlFAi2wHKPGMAKupVQrzboAhOW1ICZ4bmrJiYulcVjmnYu+BqBNbIbEpb+ZNYqO0SNLolLXhTWjasDkWKmidhp6Go20BIy+30obWwXWMOaWP+Cg7p+Jzr5jiDBbrymMlswgTiJGSyYMS9OLeU7cMQehlXvREWvVOJ4FZeZNKHbFKYBhIy3x2doTgiQHj1RSIfV+U/hpxFaNLg3yFL2vGafjMLy18QCOZo1QmkvN61rqq5ml9+x49xbqm6pq6blpq2WCWi/OpZ3pDoqlxHK3xAXD+UGLnAmhqg52JkptR/j5bIpqvEO4fhK/a1FzSvW2ApzYRjALU6Mo0oCXaOHADh2meqZYtdf7VsSo0WjzM08TXoNvDPM/naXp8fGSh2jRt1x66rqsQCxe1rywqLjIttjUBAVCDt8RtyWWfqMHpy7cdSY6FSSlrEuK0ZifuTDZHZ9Uu8rq44w4RBXi9z30SZb0HGk+lnB/5jo59dQGi6bhN2Tpq04F/dDGTZzwjlfUUYeOiQTjuFLCeGlJPYEwGFkm7RMj9EsU7nkjcaDudeUrfgVZFY5YufkJ7nGVOrckonCRUlRXrMgzzr775qR8mjObxfGyq25vT8dhVFQRR5MsNZzLnXuGMeWL9gjVRKOZlmZcI0KJAtIhHyeY32LzgNbz1JGICNQYoWRe/1HVtPtw1y54u5/sP90RO4JpNg7twfVyqqq/BFDRDRqMS0pAFbTZTOCpbQcxo592g99I4jvf3D+fzJcuz0+mWRDPKtk+IhQHmHYxtyySCCk3qC9f/uWbgRO+LfRuhXq5cu6OlKrXa40IQOQ5bRfdKtGUVSgYB8qgFvdRHwq4Y2slTdijPqefqbpBr9xHLMSPaKgIp2v7agNLyzPQ2B3hj/7sdZ9aJJRlH8tUdu+SS5Ckwzg1Z0zR8EGVnjXZnmwKk3TdMUgqDEGcVLMYFpShQfQ7oBImofPPju/vzuWtbKj1TT+v67uHp/fn8+sXtsWuoq4jzMa7yFzKKpmmlXAaDl/FlFaiOXBXcbV7npQg2aQcY5ux+UkcDNFjee26tMvTjpT8Pw1gjPmtB3JC6V1IqAjVVx6soas5b4ivZGgcYS5JKWBco27brukNZItdyXtfHh8f7x4ebuxdmFnH0XpuJ4zgU6A8cOnU+T406z116SFXOeC2iT7ZkEuTv1XPyzCu4ZxLJ6w3xve/tDScZnLf9hfwi3ZT5vAuafEygGv9+liTUqtCTzw7OUHY0iE3Nt5BfCamnt/HWuTYR9pOW02z8VNxMJsO2AvYah4sbEzubHc0GGB8mmj1DoSWICMdPVTFcClIrr8q3Hx6RQ8e2nkZzpnn+/qf3X765a6tqWpG8wa7U1Q3u8QnA0lCSQrYFFdogh6ntkMN5KKGLz0S0t2Bf8aLw1eQyHoZhbtusWB8eHpiZAzKlO2ApAMBHSXlaaN1s5QnfoSiubI1klmMswziPxVwUIwNK2rYBHCvL6+OpO3Tffvft49OFs8045jwiJgzlJcvngvufqN200yFPd8WhcWmyjGGLpO1mcz1y2E5Owp2FKvae6VKl7VnxqXeUqhGKmBNEWb17LOJZhhGhJvmB13HVfuc//oop3k8jSdPUQdOJRlW0nbWwH0jDNstQz2nMv8IKOcC3/zAspr+9X1hfj1GXIJgza3IfcotLl6ZgPy/LPE4Sv+biMk1Tw4eLbASPecNf6AxUVD+8vUdGDhXrELxKxquCMGk/UqslkqtM/OQuQoGewKvN2FNRrJmnctISM8HCyuu6PDw8DeN46fsCzRWp6qqI6JBcKkU0aVZUG46bpEFuo3caIM4UrCNNXsoX5EVd1QQFX8dpGefp6XKZUf55HuexPXRfffUVhxaovDyWUdqRE4dCOoO6QoydJ6+dE5nYk7uOF/+xLnZ411ppm8a7Pq+1i6PKa48iMHyvgtgC0wuNhs0O89GNj883USqSkn97a7ZBbfhZrlenmHKAYEoGI18KKQa1Rcs7aYXwzVzckEOlQV9LxdbF5V99/BXNRuHXpiTba3H/Kd2jZhpyAPNXL26WDG181xWm4IeHswRTLfWafeTU1miclg8PZwn3UvGKXFVh7syQgDFm6szANqoWowrMTV25NnYTYOzWDlohr3FVAu1cl7U0dxARraVnfZ4pl0Yn17e5r9jqJkkpeC49lFQfTEEbVGh2eToTDc/TPE51XZ9OJxK5qC7CvmjJtSCGbzUZo53xp2izn07hVicAv8/QeyGOVBN1u+4EyJ74jfwrm1MdvWGhfEu5T06ZBCD2JLAnJj+uRITvvzYDSia9J4gD0iZeDiPPoMz6KYQvC3vaM3R4+pxlFOC88TDMVxMb89tV0WUN5ViSPXUMd2NwmOSk0wq6lfphhBAjp2q2Ft//8OM8LzWKvJOePS8rQCqos0N/4J1m5ds4dF7Vb5/OD5dB/EZcZG7FEB8ezlJmnWmLAClFVc7aTIztUjJl3RbErMZngNgs8P88o15j09bQxtkc5cxbKxztbAQVZiw2JXNIq1xzrTKGPnJ9aa4KIskbNDTCPGfDiNr384zcz1cvX7ZNgxxGBt4gc0pQYMh/8OVQ+ICEKbpo4s6G8XUuYSScRg8g8+CW+FS6vyOilS9uTqlTVsNX1YsVimZsg5kyughcEiGyNsOLKD4pB6uB8meUhmcUaZthuBs9I8TalKSM85iUk1X1QXtTTFEv2oJIG9o1bwBjEx3/c5EluST457wYSoDiEYtX4zaiBRbAVNRcWldDwUREqMhhyi7zjENJl1IGe1GhRagveSJ1svBdiqNUVfvb737806+/qLgbsLzgLio5qCJKLcq/IUmgKrOh57ckz1fFTrQC1uPTzqxJ2BWttPO5ePniTroGUw4y1+ZmdzcVSEO+oSQ/u67qSCQSgJg4yPmBBRnGiF1zHSzCXaFht7rRl3kZqMlzQ97tY3d8ujwSyC5b82XpyUMG+6CgMJLuRCCQCCwVoaDCFgbEh4P2XD+rwhIUyRGOcHygPWnZOgT3iAFOHRbDlHM7PJoUsicTXUjZMys3yOuUyfE/Pfb7yvDHX/551qDON66QN2RSSRxd/QL+huLIoFe0YtECGIgm4FmuIEjSD8Od9Kt+ykL7Rk88DGv6I6MS2AM3G5qn8TKMqHoFxXLOp/zHH98uGbVXwaFGxCUMiVt3opxQWZfV4+XyojoJ3mqFi5jBmFIRliq3F2U5LgjGAulNowbxQYiJ1Stdg5hQWce2r2vNmZWK7V0ulzzLXpxOjM2ALkHF9fksTzMC6CVFqpiH8gKz9asFaCV6hXkAZGIFPwSiMk3LMI7Iaq6binrMwLLg540DkopLVKLtL5dhHKns1kydR9FlpuJV0mp+Oydy53Q+mwK6T8xGqM9ppdvPnBT0DGHHUCVcGxcLg11v4AUL3O0whY+RXnL+OeLo+0X5qe3S5WYqMSFFIDWWAJs4E9eDpjK+qAsBtUred8xMCTgYcG7kxiKMQcYDV9N6X/UJ+qbXxGyiEnbW6hfUf0A/04rmCoSUdcMU6KBzO5QSjTzysm76cWQbzBCK8njCZ1AZ5qWsmu9+fH/sWni2MvbKIi9axqYMuSiLeZABqVxGAgOprMU0TxlIQP6IGKTa6GAeBJaG1wlIqTpb1svlTFEhkdII887gLwS9yguEcpYM9gtVu1Zr1no7wCQYQfBE+1gNLsXMNX2qujoc6m5Z+n44n88f+gHdTcoSedHZOo1g7GWRXc6Xd2/fYlhNzbiuDPC5rAJyK2Qshn2NwpB70RWXt7997WmywWuy/xVnSybnywE3Y8GdniXhncryo7zi2NDzKuCVGXgbgodgtqUd0euzkTjI5ikbVEx4WeUM832rzOWAJXavIh6vUWk9IerydcLR5S3Im3tqfzrluN/3ZoWkaLvZFPwPO88YTBg6rVrjFztV9GLximyCeS1KCE7qhA00ft+PTdUQTpRpXj09gA2HB5ZFMUxjVaOYzLouQw9axP0FzyVrU8HzC3EGJxmAlgg/S+FIkrNkkYLfU2kEFKmktl5U7ZwTHKmNwak7rPN46S9d1mR50V+G8+WyrMux6wjRTQWrponrzlENaqz9RL1vqAIed0LjDH9OduR4EFXzHsGO37x+dTgc6xrVBZoGYeDz+fHp/ITG5RSgOlBTqAlegrEYhxoB6BreQFKhwXWSjfokkaIVKxOVbqevt5p2jjStWMDmtc8RdtS7nQt5aRYUW1IcXAQ9ctqBx38l0EMZoSp+oY46H45QnS5YyOHr8bnfUUSMyBJ7XxuamuJsA1IFFIy5GotRO33ItyRJwDiXboqfvD00QXB5Yg807pZ365QIGkgIxVLdqRktxTk9MHgXWYuWPmN0xKXMCGi4KqmVQQ7lcxoGvi3nyZN3VYcnKF8xX9um/fBwbho0GYV/jMJKkp7FI0exkeLQdUW21ugtCjWTE5AmqkCFajWwIYH3Qjg3LyDWOEvJZTYggWldyqruh/n+4cc3r16sWX65DHVdEaMpydeN1M6qqlAlC7EflPsnFVr66ckC0UpoUj4MeHZdlWXVXy6///57dDapmu7QcfGdu7sX0zQNff90ecrmpanL0+1p/M1wGYa2beXWAHVVBOTYmESxcziSY/59Lyv5Lp567Sia/hsJtSDmr7w8RDq90rRQ/ymrkGx36V5KFXA+DSG5yCatHdOVnuOJh8ilU04S096DyrS1jjwmpNIlE3OsxKYQSDW60qgbv1HVF6o8LGSpkemIeC1tyN9SvDXu8T7jQtZzux/xgByfMtQHJyET9bq1In2epBz1/qHzxqKNNYWiXElqMfJiWZeR7EbKg2dbCEmO4ujADbUvEZl7/QUu2rwoUXoKmQ/8XInKMGqiLsv65nQ4dBDy7Exbs2pehnlZi6VGFWoGVBZaaFKjUJp9lpEbqW6qaVkeHh5Ph44SDOiLWdYP4+VymaHVVzXUWnyzaSGWrTaeBqpoueUd6qVqz6iK080NJzlVEKoZYkcjgmqqydfTCn/BzfHwr/zxn/yjf/qfZnlORXhq+LWpKiWWcxNMijZs7xX5POT8W8JHas869h2p6luhvnlO5C12OvWVF9PvgpoPVLpPlVB74sY0tcE4MMtGfVBuI+mf7F8QWODeBLRMTfy4OK/fmFvaBttPMEhMiShSyCGoE8Fulu4kenk0t8CDose7f5N12bWEvUWzelVQQzUK0ZU/1P6oHOGeoSNL517Cr8jMwDFdsrWh8sjLvFRARpNzNqPic+KMTNQnoJtXKJJTVRZrvtakcwMIzQSp8TLhnahDiTgyB3NIm84KRmGTV4EkpyQ0GfbLIjqweOfl519+cbn/MA5jRRXtGBy2rhmwGhXQmlzcg5AaVO7TWSEsK82TFzIRg/YKy5lbHDHvQd8ybr41z23dFDmqZw7j8ObzNy++eXn/4Z5dPUjZQFG7xDza8SVFTYHdNaEDS7zziQ9lcxbid3dp8Qrn2HdQ61fcPc3zqfEBhxW6koOsCmdktkVj0sxuzUraaBjRlBzuWARVWiwu+enKQnikFEfwuWGP3ymrRGnDdnJ359bizre0UPOcfYJKxOTLDiJRDdmLq6eVhQ79xpQI0tIeIxKzKZsm6xEDBk4qz7nxfCkldMgMwq+Ry1MquNDRLauqH6aWkofzAuY31aYztE+IdTMEHOUrOPuPzNESReSkHoZ0CQ3F4rXrCCv9CLniEQtBLI5Ny8ceASqOJxG0E+QHwYx/SL2F203a3rCGQ5aX0K1ogVI8L4TmKU6sHgRaUfZmNRXV9FqKujrd3j08PqEGbVnBiChzStQQI5f2Pk3b3oM+u2OhwA2zsvaN26Ci23c3Pq7wOOYZG0vXPcK9twmrSNvrBSmlaP7IvCrQLZNSqLdgx5KHYWmVKY9xIitJKTI1dDtr1mI3TobNWl55T5bY2c0MxPN8hlPyyP0r40qHZuW7VXMOOo2JfooQaxZsvIV6mfCZ4G12iI2gwIu2YNYvP5KTaY080fmeXEeUowPymKaphF+K0+3Z4WOAKT1Y7CUglnP/+HQ61nWR11QDnssSqPQMXJaJEtAHaNusKbAqU87rchkGJCfDkHQoF2tqKUggfPv1m1fzNHL3DzvMlNgMTzUVe5bmCYbSykgpCNE9zSi2c8deaMPdRA4Ilv6S/IC38fQ8/+qrLx8fPtRNU9YEzERnhmCamjml6mIQDTvOGEdKqckcy7D1o3pv/Kmo4wpl2BbWcuNxUiNxLVNslHNNjCSdERdoakdrZ8vZdDf3l07Yid9kAom7wCfnfvIrDiW5haGqDiDhCTWS+FrpThKqvUR3ciqF6iExO7IFMVxHYG9Ba1HKs0CI+RvEnRpxMbk3exQl5RbhFljvGeGEM9DPDOgG0gyA9R+GsUGGXQBVsBPYnOIyOtaNcbypDS+Vhi0JIG16ls+n0vpTopSiGDS1ZaKILHzOpHgrwTvi9+WyuNvw+/fvyqpcqdSArj3hK6mSM7VTEdljKRDsRqEUIk7Hx7c4o0E6eUlgUjPbiTvwm6RoCQEC+1JVy7wcu8PxdFNW/CzkMIGAPeUnZBS/rp1FgxNtT8enKMjpUY2E7LOK3XMfsICgNCypJQggqdog/kmBhg3hEIVABMFE9ZYoe1VCnU5QbvQUo/BoKbaM7toaBSMquSl3dOVeA+ZPIpNMJKFIWPVo+y9ronC6gMHONpUhqtQtq0lNDpDpak7ADYBVHNRiiHILFdUU+RDna/HNt98Vefmzr97AaUNRlsOha5qGpWMgVnkopfsrggf+SfVgzvNSdu3arGWeUwKu1VYNoGj2RBCYc70Mw9P5cjieoEID3BVVLzIadsFCUgXIYC+Kqu/HFi0O0YGFK8UjYEuvEkFpXUM7uSrGmUd4kLmO0dzmuvwMQHMxWK2ew7o8TP26btApggtbckJ/JDDUvnW2XZAiwfS7KlTsSOTXT+pWBvgC1LteoUTldtLEW77eG25VWHAMKZqvfbFU44hcqTI9791mYWAxHb1bNJXwS2jlF8/8uZWKX6aAeq3cXUUkwZYWuoEZ6SiKQsgnrcVlD1P92rkE+J+gUeNUk0QScaE4DdHSOdIrB8/8q66FZVToT+GbMIClFBS6EOBEU0X3cpnQ14u6wa+XoW+qk+Qz81fFoeVUaB0RN0kZxmlakAoPPXxG0jyV2rCjL9KUMFKoRpRl+fF4JASItuaVdkJxaTlnkylWilukIe7DqgSlPOKr1BtbyvIQ7pssi3DiBRuXnA3BPovvQcvziRVj0kP2xNjZvK4jGjWUkPmMCiBrPpwYU7BiYfFsOvzuKdzew7bFzqK7zj3ZH2xt93H1Eaaup46koOfKgkAnFiSV1lAx8yAYDSlRpe017X3rph6p0Mo4HX/yw//oIm7NEU+94b7cVQCCVwH3dgIEsxR92fVdC58pm9IFFDSPLkfQuwUdqZBmkocqp+1Oe/MzMA2ZlTBUKa8e6ss8wEFRVuiksmblnPcIx0hszO8JkTC1XZA7ORU1z889SuxWWda1zZlqvsb8SnktwSq4xBTfRLqhCJsXEebaX2v6lwtezCMSIeBaI6exmQ4ULxK6h5Jm3maLHmm+D0XIWSGQVWH9MPA7KdwVND8u2MmZGxyEm2ZSm0G8NQyBLKskB03azCW0saPyOdFuF6mHI5hBKuBMhic7nAAgopenI6uUnRzncJ+waQnpxz/xLClfy9LrRFOIpGqwfCI1eUvDV1TfKOr1L6P8u0+idklCh0wGaGYAaSn0J4BFG5IziJgYzDNn+tXOcwVHIV2hTd9Qp7A8YZfT2SCjxeGgrtxbnp4zyrfIigYNfqAYt0397uGMNtYBnqwP0qwI06ic9p5NI5W8KIqbU/f+8ZGzCCyqonAb6o2kndCZFZDfXL3UqnQrL9OiEHrcxWYBfEsKYijNc4lYmzu+xpUlOeUxaALW6ISGIYmAjL1hG4/wm6x1mNIrT6FmypKZiJiWJZzRGIu80ghAtAfeKNzf75SmYlSFXSWbsH9w/E7ZGxsssDeI/QKn0tK0D59LkyjlzuXufDR+NLYjxkL9k80yid6Or7ny+xaJcv21pQ59ON3F4wRczoCnXpmhibLA1XdGyAJYAS/0VVWNRYXjveMDrypmnDvsPJqm+LGY4nFSqFXIscrzy7g06CVEHUTqehzuS7KQE/uDb4AAjNg/ppSqTyjHHZBsR6V51JdmthMWhlufA8rMTb2piIWFRjkgbDyTvdByErnrvbZJou5lqALEtEuWqvicefVZT2FByh0L52mum1pQWaRvMzRc0vG5kGUJvZx4T4iChQNNMK95ImVrWVlzFj85DZLKyuo5D6auEVTM0sOPcZXIK0cwkNDm0o9VunHb6P2h22cF9TfqJWG6QBBEptMqVwnErQazVzBSrXM7tkgKb4NL6bcS4bw1MOJEAxlXULvEs8RhMaoJgXZ+wZKPRxhCT+vWzcCxwkjoaIdjfpDqNmFoYel2t8BRr/zrDBNpeC2gN3QAXZc5LwU+NAJ8hGYx3PRFBYzjIPx+1NIYbxdFOU9ooUuozPIyjQ1UZXR7YhKhKoBrP45ZXozTBBWaas46JdlmZ8fecRAOBYs3DlkTVnPbAt6c7WTtXKlcJD6lbmTIZDpfnvgsUfMz+L6HYSC3V/X49JRnWV3XN7e3pB0A1MVcRqpY6nJy7+JpGp/Oj5o8LIyD0wlF8/cKgdupoO+GMEEqm3bVqvTIXnl5wf/sETe+tCVllcwOmBAGlmjaKVfZqx29nY7zjQfIU7iHwyDvzVBZi9qFiY5DHyY45cA83Yf8bVjB0kBPZJEFKdRA8AF1R47ucSyFlCyjdUmLs+hhF5RGELPpIgWPnym6RMBstGcTQZ1xvgHil5gr9ygnrdYKzNmu4AfqABxxWGt6MI4L6srlWdM29+cz1ceTzDR27VEXZTRwofCXpBwJZ/SKcqoQBgYisSwtYcMvcB/accrLFYnKXYjHYTweD3WHroIvbo8EImFFaZknQMYPHfX+LsvjsYObnVKapnEexrlDVhY1OXQlx5nlZHl2uYzf/O67r778Ulo00AARUaIC28F3yROID+hu+fLrr/R46gnxEdxY6UrZwq66Ga3zLlPYCEE7TOKW9DTixYmNUDI0tgTmIgRyk/T4RuVu3QiSCLVeqWzC+H+4iyezZDa8UaFoi6OoDf3Iehmi2xqeqakRap0bZ4q6MSUSKUKFOd9ziKIla08hNziwcGLJw4MTzAWsgZ2iHn+SQShh3+BQC5sHkQTHrwxYZWBWFMO8dISOPBy6+ae3qElCpMV8i9CIy/nSEz65ZJwmB1jVZyR2g/cSK9mIV1JLpXDSsXTuJnUa9jBhmBkIaQ0OkdxvNxE3lWQ0CpFj7LAZtDoTAl3aYVhx73wlgT0QPx8HNH4YhwvyorqaivKChpGN5AH4okuHQxVOvKHCItHgtnhfg0w+ivTocEKiL16xG+Pfr4ZVryh6XrjyL27c0a12NPU9qzR9T1XR9MsJE0vv6CZrv6+J+UAnVvu+yy4IKogDtuy+UaPAE5acRan4JttL2+2ykVVHD7xjE0PW7RKWG9R2p6obCjAwJ+q+x9VYGVqJHHVae1R4IvmKaGpNLZEi/proZYzZZ0slNJu5DMPdCdDhtgGUEaU5FtSRhSKKFvaYN2K2y1o0WqSKvWIh1uuDwQE2gkJcYDG8vLgR/ziO45rldVW1bVMjjkMRMsnYFw7KaYPUvIhT1HntpI4dT0fC+M4C5CCV1BJYliIr1fOBCpg12hkj9/f+8eHu5gRDnvB4RMAhh8VpyMEM1iMVpqwLHOMtrstoNU63CcQu32RLJBsr0R8pT35BziUOsA1f2Bujqt+6dxFrMU3rGWM4vO9MCXe37dfYLRzny6YPiRzhxjPtIjpceJFjhk+MVITzRlBAXbghbFbCuRnMjRtocZc3J4Y3E1S8hNR/GNozFaxAf10IP0Y0anSAwqfkVnIuLL8FdKFUBTDJqAHahQpxrOtS10VXNxy/lvZd7DOgmphl3QzDgPI0W53fQXEDoMKqxmnnzmEcpnGkIuxoEMyUyzUGyFjlfjYCKCYFW7znPB1Mm4t4GZ8QhkpqBWkKvIM8Z+jlZCZYSg6KOVB+0tu3b79885pSgbFsAK/ZhkaGoimcyW7HYkQe6AzE9KAEa2xzAlwlputEER8w14032QjbgkQ/CFdcD93wNwxXHbfWjddnx3j9l3gZ993QbPgpNkODMOZdYniT1BlmRyrlrEc3ND+ym9/OKzaMo9UwavL0qnU4tsvstGpWFPn+UkwT3IZz7GskA8yUJAiYVNtUqDpB7T/DLqq6LPYl/dEZc8hrUT0TxHY8Hi7DWQLjIF2yEQjC0dZAQtt4I2+vrIAWZOStdkVveETMF5Z5WlfOFUIKnyD8yJke/BzoZQRitbbLlB7n8mu4PENwoDHbwWfUXMJ5WoJIn3lGdV1f3r6b5qUqFzQkRDKDLLajMXcMrjuAxe6xDdyew0854buuL8WvOqM8ZgnXXruDufbc3fci6zaYvEk21vVhuChXemHiY/6Inh7PwMu60AIZ5UhRPdwhs+3HTX0PL18Sqzv87GQp4wJ26NLxsQhEKbfhaCbHtYiAgRyWA4oz99tvvsvm+fPPXnGJrLwo12xBzcUSHUANlbRdHklEcK6HlYa4LFlZQ+jeHLp+7F15EnWkSWEfAoro0TSwmTTbdKlIRtlmiS/rcjgcijw7nxGvLoDhRndFYSOKGLVCQMxlkHAo6c0Ch5EzRgr8QjOS+js6X9R5Jh+zpHKsEMXZinTCNVuHsZ/nZeyRC42yRPC3UxhJOTSrYMz+gsPnityKfDrJgsenz2vg8ZmJhPaegbixc6OQ0kZk7Qwzuucz1B9k7s5FiXKRWq3RBBQRExf6syCtNzmca0+ntw/9cmQSacJwhHAijnTf1YvU3aPPcouzX7PTrw8X95BBR0maJj6uL7W5x4xfcRkKJqFpgls4n2bUmhKmRse64JgQjmn8RGVZNCaqaBVcMDkRz7yswFTn2eF4qC+XcZjoM4sSAsixrmvbNnZk2U2tcfXYJ2K4KXPr0mWoz1PVjw9Ptze33GyBau9B7WFdeyL/lOgas1R34ptxSAl50WuGSkDESRD+pX5o67IIuhMEjCogVMtScSD0QibDupTz0qEYGNg2KyUovCm7Ij62hAzlfKW2a6Jtu2Mfu5kT8kypd2f7UykUvRGpnN5scc+4+kp1y+gNM+qvnM1dEzB9ic4Q+e+vva47rPcbVbDK7SfA+f2Um0QJAXoUBYQR6TBJemDka9p7qXhS2RDN0Nixd0mbuWRsK2AJRUcQ0mmaskctDpSYhJ5JHmGcPiTWcb0+sRFSpk4nFomh+qyc1oobdiIkWxV1XZ+fejjHSAhSlBW+gpEqpKunzkDjOmTFY8jsww/WuQFU2rXdu7dv+2HoOoR/ynIBuFtLCyqKGy+BfbEdm2X3Dw9UihAgjMswVGXFzZZOx0Oe5X3fH09HfqBWMiCbSBKo+YNiGcEzTsfj6dDxYGFQoOiP4q2fP8LPMF1Tv4OCIucm9ZPYDwre9SuWJuHukXh8mNa9mz3zSmyA6OfoFuntXOsCUc6ee8Cmg4RnPa7Lws6D9l/xWGJN3EKp0QyjpTSKfu62/n0CPu2Mj/G+fraRrLRFDMJeqqsykpGCJXM2LxnAF1zvAhyCI7crqDMlI9cFgMNMVvJSj9cKTZVLXlAfwmqRWq4iSoui/PKzN2jwJ+UyXRFl9XnwnrqzFHQRL5IPh0OWrf35jHuR+c39XMUlJ5KQWszNyzRO6CkIjApmT34vqj8HKxrF2euq4qjW4XAgVzlnp8hZc4cfEx+G4eHx8fHhfp0nlAPsB87qRHMpPY/SUsbUaadU+i2PBN6ezen8ufFXgyMotar3TdcravkmqBo/66Oa9M7VMUVtnuh0dif9nweubF/rp4zSzJf4m8rhFCTLSElt/wURZzzC/tFC4vw41R5jt8Lu0GO2uqlGeMXnuOem57QLaSFKtAHc1YyCGKJCc6M+JPgAnqV9ipw2G1kFfGPKS1HQZ8GQUnoEVf1QKIjmJFDjz/rzz968v79nQJhZwaKdixgmn3Eopab+N6d1HA9HxMMYlUmV8UBFVENjEQMYZdy9I586wkxNXVvZja5peA058URsBK5SaCXtOTTD7vcF8I9pmjLgKOHMl2HAozbnOXVmCCsViipsi7k/53KJLthru6m6+HOKboqz2D5l76vJQK/RhTLyzaRimzPwvd17fsx8TCABz128OxfPIXZUXP+5dEqkAjNUb8TlHligNGTW2CpcZ29bSc07ErhKirN6hoZxBVIuCK1B6EDGI62XcW5RTaoEiFlA0iQMFy5UJffQ4m8+NZs6AOMcs7wRk2OcRgZgKULcJs3FAKBgV2XVNi0CP6Iagv0ty4wuLzmVxoRLm3qIAvIFTBhF2P2OrV3bfP7554fjkUsZcBUuhLC4nLRhxrVoCFcv4K4urCZpnE9dDHDqUU1ceRL85vpEA5oUCFmVQJKNwM/eHQ4HahPDkA9AKXf4qezPHhZ6l0jM77X7wd4rXhz/VvqgLWBjQ+U2pn1Ep93iefN0RyW4cqFmigRRE55ut0rGfS03MpqBN1WZylNEo+6Hk5NUzVAMZJuAOpuSdQkkmqo9+nVlEVF1sEQPC3g384YkfJSUWk7ilyWjM18Bv1EM01AUABhZCAVKu8CYOSnWgoVUynmm+pXUIpSzERjsnNGdx3405YKrwlpePqnp+TJPRZEf2g79l1jzJb8BYNQFSs6CPrK8R0Iipc4zbFOXUvVSbPqLV6/F00ufEf4ZHkTXQchBWulvAmCzXJVKfQwTIftZso/FOHfuM42iU7qy1vcjVExd19M4AUzCJTzZC+330pOAxy3FrytvXxFuCbrBaFN1Qn9t6rpK6PvKQx3p7Lw+QjxbRT2c8437LhpRdJTlFmIM+af7QewnA/ihfoSRpH4leLIq8ovEcZbEUtEAUuR7SmSuI8NkUOZg2wjf4NuNRqnGeRD/xVr96ttv8rz42RefoV4lMxmLkbIADfYfTvy8LO8+3HdNdTx2OSJGgkbm6pyFJj3Al0OeYYkBE0iNKYEDyICAFfk8QS+epfQlI7QByCBNoejaznWADrlWwkiJpKgnsCj5VKdAShsYEIulomUXiGYcanxQBgoc14zPYDeZ2EFCM0QJVDZA3hSvGLkRSPvgfYMRjKJcFDwvKGnL74n8sCfsnjE/o131mxdRRkROEQL74xqznpRU9xX5df27QSjGYM4w2p2nfKRUY4LSctdd+YqDKj5vS1ylXRmvQTM4dFHX1TwO1Axof/YR7kKxiXa1gRUVDRg/3UuUhEpD2CgSAvKptFITksoz1mCXusiQlieV5KBrMuaQ4BAaySQU5tPl6dBWt4emO3aHpv39uw/nYe64fg1RSV7k44C4aFkBR8lttfV0SEdHEvC43+V8OR0PDCEmmuMgrFgfrBC4gnY6aWPaumdcvk5Q1dpDVLaXi//4mjiiCXE2N0BXHOPTOkNWjBp/E9nqY6O6XJDfRYYGaOAjdZMXQE6vnE4Yb7lAetVmig5com4lzc2soLnKJkefsdln5zyA+h01xWdhR/bbMUzRY3xs+MuJpuhm5377aI5GmOAOksu+vU+siWyzGVu6rX3VFWbg47S5ZfhCmBjxe4BmURO8QHs+hP03G2WSNSZExdjHjDZZsGcYeLydEXvwvjPZJE4LrqtiGunYl2zwkt23riWqN0uh1ZWagDL86P7+/vWrF0ue1U1ze3Nq2+Yvf/NtvyyHpqFyoyVIgkLBMKIXeGu5zKVlNXOlLDT7G0eYq1I+g+gm9CsMG6b77eNMcnql2h236Cbfm7jQBCFjeX0srSWOpqgWFInVM6uLbRSsLENSCL0WxJ4AbtVI3TnQ940ApEibJAdmYhkG3mMCJjqC2/3dfnlzCrZBGnpuoq/tGtf7d4mA75FvxvX53kyMja7opiGrJl4Hx/vsidt7fsrL6bMStUhsCWlW4CpUGZTQJe9Z9p4E7ATGsM7oDIYQClqE7CYybYa8yR+OKZRN0OjpVt0u0V207mq08FKiGskA3O3WoiM40dPS5BkS4tR5ntdlRhFteOOsMFWWD+PQD+e7U3dq6kMLMH/T1D//6jPgQaZJJMLKKU5ozkDJsYpL08IwpEWjCMs4TocDwq18qgUC5e7idYy9k0DONpjuBkIX3zV/wVQcw1/yB1pSR4ra00RLVKVDIWnK0wIvA/8ivZpageupE4Qy3Th0bw+F96g6GlOC6YC6V1umu4fgSa6M6DBOdJNlSqL/sQqchn73xWNyHK/cb/NpIJzUlSXHKwY5Gdje/5EpbRuJh9WIfrLwesLQgonizRX+RliuZPIh0U0jsrAJOYJKZS3Q3ccuDgJ/Z530cNg7VyZkzGMzwxhn4QtAWFB0WZFjZOUaSe8YevQfRBFl9k4rTBrV6YZBsvFUhF6Gvizym7q6ub0rympc1zlfb06Hf+VnX+brPIyDcAqCOnHougC2iTCX2pmhKErgEJGuRPEbHYwVkhWOg0gOoS4s4KMMXzeRia9CJWgq0CcpnTCjkZ5PrmaKmUXlpaFsoNFTnk/rCiwGEaUU9+bWM6zfkSjlVmjsvNZGNYoX02q01HmJgurrMg4Df1/+BPHkRGPEmtLNDcQfHznhSnriN4coPSNbEbz3yMAyIsV+QwbR4/w9dokv3MEd6oTGnOC5rnMn7OjKb/72V8S5bzu6Gbmho7SozETdq6u6zpEyS99V6gru3PjhFiIJ51THs7Pkfl6qAmwGGLw/TD9lVZcw1aAXMBKZ8l3xRQgxW4KyAJS5LN988Zq1XzOe2UvMTUsATwQiEct/Oh5/9rMvcrQRmnnA48hgUgST0BaQx6pA5XGciyKv0DNBS0DbFqjJT7qM4iFEZHJSvbx4CowlYWkm5eG5JDRK+nBrUbKKkWMP9/SCphOwfdFFtKmBo6T5E+ZKSBeGO/1NRScJJII8DxAztXgj85dGzGAvqCpQNDB94LoohQVx4SgO4c+ZFuKIXD37Utp9KzhoeV+Nmq4SQKhBHAFR4rt+zDZerwYmZd31GH7E9t29YTygT3rbj8PiL4lITq1lqfS9Vebk8/A4zg4n8ws10ssym0YrjhM0IfWLbIe6Y20nj6CvWl2P5A7exW3fW9alypDiy0YmgSVRPWe6DEVVLvPUdh0cw9rMBHSJQvVsD6tcXFG149C1QHggqLqu1M6ePLzonfv1l5/9/vu33Nr36XJ5nb/IsqxpAYtG/j/hMApU0umhfUM5TxWhwLPIe6deesmWlor5LJtJ4C1ZiU4B61KiDynJY5ryHJihPIBLSRu4kINfHIuaphkYTCosSnW0uBRf4CeYkNbtnTPkSHBjYSJdynmm/uIU/YZhXwnkI45c2i19Fs516t24eNSSd79+nGB2X9LweufbrBnkn6TLhu9cDUzvnuTgL9q+vc9Grr6uaf5eTRBiCBm5wcui8IDwPVQxVccx50uh+SgKX5B9uPtUP6tQSDS5cKu4OFauA916RMRZxSeSrFpWC7lQ47RQL8+6GS99jba3XByL8iBXoJnLps6exuCCLAD0b04HqI4QTgV6xnIsi8pdtE31xRev3n54mpbsfOkJfYE6u/O05CgSgGyBZV2+++Gn17cn6jAMRdbBW0yZYQGGPyQPQTMYD2VBjPMy9ChAgMQvKmSlfQOhrZdosxRMYtU8qeyIaPfEwshJsSxrPyCdiI0MKe5D1xBeTVqpLAsaL8FFRYxE25Oj/oEkUcxYr6osny7j04dHADnMDFd9O5xZy2CM/S75x/BIYcvjQ7N/nHY4YywDtvEffX8XuPkMbcmh/SjdJexo9z5ykDesnT73SXsqVN3dngGcWKr7swOkHpxo6skoWDyPesoX04ROBaI+OERs+ji3uInATQZohbycsRTgnVoOT/gOIXqpfRmsX/HgINcoy+qyyObpz37x9VpquIL1Vbi7pMA6x1qoXAH6bjdNXVQlQ6Dh6uHr2cQt0FTl8zf1jz/ePwHrPJdFAUIX8MNSFtXD5Qw/9u0R6Tt4IJfNFvPYZsMeZWprwv+B41wuTw8PHwZkN603N8e6bosFaXxKvaDIijvZauISP1oUbPYVCxYNhIc0LBQG4ng1yFuyf9lop0ZRwgtQL1vr13JD4RI1NzNqCI7uMPOwrsv/5//5//47/4W/p2GkhEw99aU/XxFUUV7Q8y1adP3iD1IVfufT8EvM9Zx+9wxScTuabYqSgbb2v5985xm9mQfJUm6HxPc08x2jQGqbRYaE+afML6ldAoqirqqRvDuS2arlsENJyvQhwmv3vA0un0wy0pxKEC4SO5JT+LR3MdWpE8tYVPq6A5ixqMoRNWWtmbFETXPqdcTki9NNNjOjiKd5JrxFyc4bGRFNrqnLFy8O8FePw+HYjdP50LYT4JZ5leV/+Ze/HC6UIawtxoM8MiHMJcbY/BZgxfr0+Pib333zT//JP/4bf/bnRd2iHy9yjospm9GSQcVaPlONLRLtZIUS5ps+JqAJdzCE9c5jLgoIWKJzKqZBcBQur8l9J62oD0Lb49R16DtRo3s4xsk9TUkUVz/+9O4/92/8xVr2FVfriug0SnUL+nOa1H79BG7VxQ0iJNSFTz7YnK4Q2fW3duJ+R70Np25755SGLdcuYNmv8B0z6o0exZkknyZfUANoV2HZ1zv2nrupNisqNufDeNO0gC+6qvpymia1e6M+HTsPNHBV8nJorkT+xkMLFXp5VWaqm0OJBShCJ47lJSuL+q9++atpmOqqkX4pikhkyCOu14MIPRNNcVHCBgRSox0pSnbYY0PSwfry5d3peNdfHjkgfHM89JQSjFKPw/Ty5R0jjKVLjqyJ3Afsgh1lBWruUQuJYh7np/O4ZMXf+Tt/gS4wE+TeVE3oxq3OYaRDKmfkJQQVaT9F5meUMxghlclbxj/gH9QAMNwl6Q0MMCuoYSJy96eJmHLNTWaqqqyraprHeSjGcazyYnk6Q89htdKTiTtLQaI9K25cHcH4GG6PwFbCrddlrt1wyz12nG7RF7aRmGuPkbj9DoLZX2S2/j5GZP+bz3itd++Sji1SpA0NKa4XKzPJp5FR+3Vdg+Vv3FPejeiQ0btOOs+f4q9fV1AoCiJDUkyvYB0qUuyhJ4tKSdczdBK2q2b+iUgWYiuKvOlqFEanO3nVXfWLjNud1FVxgQICTx7kYF3N81QV5V/8a3+zH3uO+gBKKY58jYGR5kwhWZLAhK+EmjrNY9+3VX13043zfOl7pOOSI1qMFZba7CDneHJILnbaiUp6DphxBqK7hjCeNG143dCzhSsNCO6aK11e+v5pOddoLA5rV/pDZvPjhw9dd0SSorlkBUi7uzVY0CsHMZJ40Vd2r3n2dY16tGR9Kir21MHNZx8DLQZgooMk7GrSz5sF+6LeueK3WjQ7P69Z7EpwsQLO8WhK4wneJj6S8JEWZdM04zjC1yGMOfJvREPZ58rRKluGzRWFxshaYMDsNUXuK4ZN+ij1K0ALAsr0IRA0PzycduNB7O6ssryfx7vb46ltK4KCSJKA031ZDOYStslvX9xS8fgeRAFfECqh33Rdd6hJoYWOSh17uQspCV8K/XAqyEKYSz7+yzKRrb2cz33fXwAaQ/SHXdTyVa7VJ2AQHlXQ3aRknyZrhGXkArHkoOY6TdzoRfDTLvgsWJB1RdsnquouzTeGcURD82Ueh8s4jm/e3HFNrOAzUXVyc6L2NGR/xlPvkPN+2df3CCklkT091F3wqbzAjn985708yfQkx6dqe8MkZ9dFhnZo+JmQVSgptL/i7h5706HQggGB9CZ5Rl3rqmGAV1ad+GmZ7+1Pe4uh5GKVLkVHSeneUumsVhs8WMHCpczZcRr7QS0xQw8KbYKEDElMeIy5v3DtR3malLHkDqUyp8ycPPk6Z8BjUpNVyvIDheWHqhnRUGEm1kGzYaJiTzJRb4nycNQsSRAdmELXdnUF4T/NRzRSqKmFN2u3TPgOjxWRgDxGbReDhZFGsizrMAyo5oHC0Q5XK6EvsqJloRm8QTkgdBMuT1uVJRWQHpGvjHzkqVJULD2IN8dX9nempNu0K0S7PcDb7+ycTuMD16T8rir6yaTsaW8vOyGixeR87zq55drgxrzGmIKdv0cpznS+SuaOdiPMsu/F4PRdyhgoCjp5+M+nHerFMR43Frf7apS3gsLstlOCI4f8qiBg9hhxrQnuOkZKqHDyAEphl09ozEvFjvN8GkbxWOFNQkNwj2AOzPDKLtYZG4d7WbK3Hx5vji2S+0k6chIPCV0BHSn2GKo89TcBzoRBHJbbWJRF2zXzvDYNpF8/9roOSFoioU3tvFX4ehNR3ZYyYS7BzwmEgF5QmQ4syzzVdcOskSJGDNRZVootuR4bzKs4d5+LoBXZUh7aw5tXbx4e7091J72Rkk2TvLNI5PotjH/eaFZiDtj2m4bqzmEeXal9QKMDck04h/djXeATXq7W3HUTNpZ2rp5U7ID+JDSIm48sV/TckD/3rIfBe844biQgSjGY/Byo6zScHdyUz+WL21D2X3sj2VPv3dGIBy0il2Is3MGECIICIEiXn+EnQu4tK6Pue5TE4G4/A+5cES1L5TcdicoMbrCzrqgWSyGcrFqLvO3aNc/rAogLoQTUzIZaTXkMmsOgoVxOqgX2iRVklp2kHJO7GIZoXjScGEHZfBxBgv9Y+Kho/ZqgElkF+NK8FssEn/K8zDWc6WAigGFQMIkadnPr85mc7FwUXsoNMaFwwllZcitvcdo3h7bJphWYctPSHA2J/uvoY8/B4zzB7gPHpRXcZZTuv7eN6MSv8PX4hD1/0p8nBa+Cbt/8lNeOqRrsS2tosc0Sfl5JvmoB68+OuPjEsPvH9zfRAXLskVraETA4vbkzh2Np67fo+vwDU44tayEAoDVQN5FaByG1hlVrcaFe+pGAGULC3DGChKlEU626+bosTQvgcYbix2bKww00zytAITmivqT9cuZQMU9zfzlXp6OpKIyLZurnOgAU72UyltCRdEgQ+4rhyaBsdjgRDAUkK6EoRXEo9tYUOyvpKjJ+mabz0+UyjP1l6Or8y8/ejNTntCwaBJOzsumadc1+//33T+cf27btuvZ4PNZ1A8wY4tAYrIS+BBAi9ewBm+V0CHp4pbUopWKZk0JeTw5Bu7gsaWxjxragKkl+l40/bVy+SWfYSE83aH4qbezo7Jw0d5tPIemrb+jDrmi4fhCqLe+TatBEd27khPwOf9FDYtNhfVRVU7cnXDOqgBtpKstxAiaAid+tQ2QEO50kdmC4n3YcE2mESpxYqMCK/vHc1oDNZ/Lf5EV/oZ4qVs9CTwgBTsTlxbebUIRjen33sipr7iY0jfPlMhFFQORKj84C55mqrEIoTuMESxGUqvWlpCY0FXVnWFjINODuxOx4A/KLrxfa5kL56sADAat41fxdXWw7pgy0mKZzP0zTeHtsT13zxesXTdWQpC/uHx8WSuGYhnEp1+PxCP/zPHz/0/r+w8MPP/64rqhT+dlnr483p7Zuy7XkHivcl4mylmCuc8bC0I+Q69NcSemAdbf/bmTd8mx2d9RZT8kxFfNjN3M3USajj5xET2h/9/pPErY7PqbkftEbgWLii3eLEEXXpCZFVOvqE4cdvVzYR1VO1JpRArAEVHUlIOe7rsY6H0aJW7pZ7SsgkXbkvTKx/R0MINcKifdc/C+UNmA3l8ANSTLSTlP2xnBKIndgIeiTJc/GZTnd3VVFMQzDh4fz2w+PlMrO59hA7SsPlpv9wUeEerW84EGjHSfUoBOTVbIQKG2Im425zHXpx2B5huKHIlCkkoXIMeq5zUFF6vy9jsNU5vmhaz77+gvKjKQQFZQHQCUrStj48OE9xoH8qtubm2O+Zq/ubn/+1Vc/vnv/17/93e9/+un7H3787vc/dF372ZvXL1++aLsOyBdI/LLIpxV6QFYW+dAPwzDOyzItK8oiWEmeZIviXXa7FYz2jxxJ04FV9btutXpDekcf3974qjqq3wy1l93hTP1XkWkZSagg+RxTStjTVl/x67Qh/WsdhD/t5bVcyWLntL1AUHK6EBAuiqZtz33vY8LOZ7XrkdiatMZ2t6MWkR25ITnnDm1T6KvcjkAWGcKxrmoqKRP1MRG6YR2VjgH8wF2bVej29e037x4vPTmOrH9YrC7kHOUlFDE1DA95uivqyHPJO+PFoZKGy24VncLZCKJ4U9lYrv9qDn8CohTLOtGHcCHf3Bxf3p66ti0raAScU8i9jFiSl/naVXUPb3ZZN03XtQ3Z9tTcsDh27eevXvz297//y7/6F7///U9v3757++5dWRY3x9PN6fjm889evHhJqA9S+OsSjZqmsYWPLa8oEcsTqHgoYnkZqWjPUe6+gN6eju0t4gJNTgYELcUujXEdW6YQ7JMY1Bmdvq2Cf2VCqTqSPm1nvs9U+tijleeenlwtBU8laqOyl40XtdO45luNVzMOg5uz1SHnOSVT9/lXKuIiWySMxS+193UTEJ/IyXAdHGWd17ouX92c9GJnFRAs0o4fWQBZdeguw/z08KGfUIaWsRAyV6eMZJQ+kRUFJeXONbVNprRFdp8B1CUZgjJCEbRSZtrXJ9SP+ViI35ucgmKwoJsJQ0rQYLRr2sOxPp66uirrugLakvgC/y3KR9CRlrJYT92BugxyJyWExCX3I8sObfvnf/T1TVv+f5fpt9/+cH66lHn5bhjP56e37z98/eWXf/RHP6+qdZqndamGYZjGoSjQpYnaxvFSBN/S1aBt2LW4x+jutUHxSqgkNoH21Fd/nvasySuhnV2/2FWZf+W1HYfrHHW1P9jOK2J/8V2fiQ3Huux2cFzuzJDQAs/XvniC66OC/UVZtl1zOaN7WLRi4tXYHZCtmWWT7QEtg34iw+bbI8m1guQRty2BSdg4JIdT9ur2pP2A4aiRcSO5RvvB8PjR/nu99MNwHvxBMCvBWZ4ZW97zun7/4w8///JLUC9RLTmzBfwkKdaiGrPjSix20/w0ud/2ICwCd+ZGxY+qhsOpqboGUeK6qdBjNCddmZKWYbJSjJ6L6fCOcZk7ZDs2lcwilzYTXHmS4wXrmr9++fq/+l/6+/04/PT+w7/49Te/+/6HYZiOTVM11bfffdd1DcDhx2N/uVwu54Y7SjEilPqjXzmI6XHagUI4v7KWudq6YHdegalHDCOI15iG/eXPDvTZp+2/EYuUT/p6+tRrH9GXTcFYP1ni7hr//Bwuj8pV2NKFJnHDemPTtE3T9peLaKfKM69lZ5skvZZ9EV2epEdIpJLadosZaj5gLu2e11VBOAuRcqRmrtMw513D6iyr4Quq5szv3t0fUEbDynaFYxAGkwssZF2LF7d3lMQnjmX1QkvBaHuxWetZlG6Qn61whgktUbJ+GOZ5buumq4vPXpwOx65tGgopQUKT3wxESHOm1CKdteN0aljb7bkvrNoxPK48L7r20B2Or1999ud/8otf/vVv/uE//meXEUHgdVkf7h/KqjweuqEf+kuf3d1k2QqWoGZw2IaPvZ43fp/Fdrj9/7TbR2usPT/+sMHJfkdcKIr1XBnKxy1WZ1VunhkZIfueuCsqTPytBPlFG0+WLREw83PxsqyRFOaAYdtOhKyMJ/RsoH0zdxuC5h9EQ5TwBl2jvRFc6S/FRs/cIzPgH+iTNXv39sMXp0PozUXi+nLub7sDB4RCh6JoL4NTAGQ6j+jYhw4GYlcohUgrdK6XwV+h+JskUyiAMeARNakQv0P0rsv9w9PdsXt1d/riizdt3bCRD0wkNWaWvocJMMD1eAge3KBBGMZSNQEhc6Zu2PN1XX/x+Wf/6p9e+nHqDt0PP7wty+LYUmMkgsecz2eEG/S7/NSPE5Uso1Of7Irrlq9+M3GUXXtds68/WXt19wn6gqjAbp3joiHRiJMfN0whLMMn0HlaFDLWP5OHmkERLI142YDppQw1KcDkinnpvaVvNLJbEDCsqgXdC/QMifm5M85NmN1ubWvIYYzYPtHDyJ4bgvijTrt6qMg2ptpRVrCODyvyEIryPI7904UBFkhmIqu0piI1aPNHqjmbMFQhiCZniu9Kv6NvcN7UtQAihdPwqWMnNTdwsTrpQkxUVCNA24TdCHNnWFv59NS/vD3+8c+/OB2OdVkQfhHDp6R9bWOmy8+SXipNWmRWC5qECvBe3aQnShMHrWwFHWSejl334u7u4el8c3t7furff3jf95dlPpVlBW8YQKBLhbXeNDTaOip2Nlu6w8Xerv0zqgHi7V1iM9nOaHJBeMZ1n9LOudsbO8/0me9viFm4zxVrP/9Y7mJiDjg63n1aGHzEWW3sBMSjTpZ0Xng1Qgyfsuv5BJPzs2jq+lKWqLTzCWbCmi57KqOdhA9qdlBtqSoIw7AsPM3qQFWWE+XEieFpQIBsnaflt9/80HadFX9CpZiS2i/ofVVbtlMuz811bJCKIycea50NGZRgNjh7iEBXcDQtqEwDiQ23bl1b0rKJGjkkyAe6fPbqTV2jicrEib1TaGik5Qzoayrhrb62RMikUpD1flKlRBmQuM7iIkowucvy5nR8uvR5vnI2Ejm9Fli/FaN0EEYKKxGbAf5whpft/UaMuEBZvLFXsvc3Fq3+s3O2VB25oq8+99oQXgCc7F4T6wzpl+N4WrQ2CQ5md7KbG/qOwGEZTGKbmAkfMbqYjg83lQ631BPAmXaEgFiqumq7bhiGjNLK5TJJcIj2cEOuVzULdXa4yXF/AQKQFGifSUJKt40fyA31+PoSQgYfz1nWdi25jQX5xJnrENfWLznZG4Xsr7I2qOVIeYSbGegQqYYcQF15tvbT+P3vfno6Xx7u709d9/nnnx1vT02FygHS6lsdOTmVvz117e3xUGTVMq9WLEEKODNzUZC1Kcoa61JlWV2IMVpHJmREywa6ZoxQAK5Y725P50u/zAv1dkLO4zSONZrDNesCVl55DXx3t+SR0dHcyUcRtKWUZ0pKZuzcPKGfZ9RQG12s3F4VxLvi1978yIO2P3/cqggG9bPX7r6uEbbRr+aXqPg1DDQralZ8WPiO5zmkoxZr3rbNpamn8xzx0XDbSOa7K7w8TlV7VQ35AilQBwAJsvO4w6ek0HOJdY7IKMyCINNSUHbOqxI14KAGazawFNIK+YPB/ZGmfubUDpVqAKjM1wZEottCmQeMAp1Hh3H+5vufurb5+vXLbPqCIz4ERY6NVHoCAsp5dntzQLGeifIyFvQiK9FVW7dHYJSBWN1uRlIjbhmuujOHmKReiOmGKtvQM7U4HQ8f7h8Jr1King7ao3N5aSrtoa74xFHiKuLun79UXCo7dJt+RWfe/eTjJdM3FvDmehtwUgo2vBWPffOzvCMKjT/qcTHSKzfeF7/PvxRuyH988fb0ToGupJ4DK2wG0ghsXnQ1hvtTel3Ztgeq+RQhF54ZUrqV7tyF60KLRGUWCFyhYJ0IQ2kZClOSBkSoRekKTEjpZT6fL6jqzOPmby5zjwYLJbua3ELE66u1unNtlaBzl5FzjWXGU0Jzztemrf/qr/7q//x//D/8x/+P/+sv//lfjv2lzIq6rAskLIVGv7JAcIbnP/74/tC0OerCDxC/hOjgLAouNRtcUDpWSfDnri7S7Fu3g9Vp9rpRZwbxjuvhospc0iSJ8quwBqdD11BvYfgFiuIyDNM6I+MS1ezR3Cw4DE1BdexIWUKQgTt7mZwJr8p4H9I1LTo1q3dfpvwlGrf/vtvfWGn+Q15xO3tfL2ZrWGxrtn70cS7ktiV1epRnpXEUVsB73BEPZd+4Y4mr/qASOXyd2HuNmsS1gjr88w2J6WblGEi0eTup1HIYqGwO0hgqFOWaLHmITyyBLHEQteSGYnIpH0dSejgKRd+qa8CZIB5Vh+aLtdx5uP0qgObi4ekCxVuowRqthhrYyMhb6g/v3n14fPt0fhge+1f/lZeH9gD4Fg+Ko01qDHBotSyQUk91uRBPgmUeUo5Y7xGUl4yLUx/XrCyotoGIdNc9ReVwMIkpfUK30lDrsuKUj0U1RdCGEsVDBsQUkITYz32TlQByRHsWcTpL0rCPTLiHg+AOdSQxIq65OcJep/Ufpa6gYHN/omB7HoLiTu9zLzYHgg87ZklBGkubE0fNH6Xej4noUH+OnmGZOCJ0WJOLt8H9ZuzECS+uptrUFQEGuDB5NIhnxhNvYuBWQasWksG/NYVtSdwFXwOposilRQF6IRGiPU3p5+QEUieQ1A7gvoo5og+lYcWEhBYjK3mkywL106loK7RKQVAQoFI7ifG3npALVd6/eyzz6k8+/6NxHtGSyeJJktZoQ9TlB64ECcPUHjxsf1ITnzVgbjiKmRQlCvLp8rhAsMCtjb0B/kIaFQM2VU6ph4IedzodUTx7QknKp/N5mqaubZ8eH7mCZoBVpdsZ9xGS0xBUqtRIunYCrl3mb2+Wze4J2r3ttQfuqr72zqcpuP4ezz3wD5Ptn1wuKzJnjDk6eIT0wJMIpCndRlZik4pBQNKlqpB4Gk1DVRmP24i6bWymG8rJ8vfV9uXSsE1dc609U+HlKhIyVV2fH560i6bWtKNO2/Zs/p9jPvhc56umhq9hIDnRVJZ1geSnrgbWhIl9tpJaj4QklEOfl/nQ3eRl/v78gGJXMCxthFqCT5V5RjheqK5ltEJil4u1ZYKWqZSAkqzSaw0eu3EwdMTFFbVhkYi1krtGo4osb5r62B1uDsdD27ZNm62r1GxYkAESDjYndl0/cmx1R0RtDVUT28S/c+1ee6+PktfHaHCjpm9+1f5Gn3SLSJ3de4LG9q6U0LtGCVc16Ktfkrtr4TjRzQKdxLfWb8j/RY4wKbwe2qHN+S6CR9BRR1gmzRb3owhmlGWZ4/5lJW3pwyllZwoVqSvL06G17lJcqkrTg6TipD3Ocofo3fByv5hmDbgFYldokqRmKVoJT4xj5LSjeZyaqry7edkd26yohhHtEQj/yLn90nDQ6Jc5wfHQHrpOC4ZYQ6P4lNuSc4npPAOeRMjbHFtWcyT0Aw8+Cy0ByGrLCCRpwI+BOSK7ozkeuwZgShSXR4kfKuXBC3f9AG4OUOTodHI4cr4kE9zey3XdcxJd9K59dUDfuOYAcx75T2sieM2ftpPWbiqzH5KII5NKH13BWGm9imN8/tsMA1wouUdRezJoUeP3rCF2ehFuIRmjFwj+UYntrf/GdO+JC82Z2O+ix1ahy2zvorv8MM7TOBk1VGUJF7EqFLSYKkQMiulcyl6B1qmucIVRvXUOjFFvIcz06TLM42SIymVZxzn723/rb/7rf/sv2uZwe3PXdR2DYbjOhjb9zc1OQTHZkjokSJ1naZi0PSDe31IUZdd1lIChcI6whlKaa7u7pAKI3JWSgCqE+fKqBrK9bijcRSHiuqrQQY4bSahEEM66b6DaCBzdXjmywbPzL61kXtFZ0/tdk1Z+eFeGuonp+u/7nxKYSxAxzkS+Dg55Pki291D+bYOWUsYvdWq0EkPE/diHtTsQ7RKgwA9zlDyv1AStwMSu91CxF0n1Z868kzpr6h2gRvJl9tVXb/L5cu7Hum24jDl36CLiziWdGO7sWQBTGopSFUCTD5ikaSSF0DWWg7v1IkGHasH9//7yl7/42Zd3p0NTFCuqd5ToPJwV//V/87/2r/7iz8Zs+ezNawVoSbtfA00x1dycugrNJOaaKtqJAa/oa5OoXhfmbyLbWJVg9Vr5LGvFjMTHjPxttKiUaciCt8gKQ5d1XXs6HB8fnrI1aw9t1dbTMDKtRwfI6YyMchWWLC5Ft5XrH0C90fF+9rg4LS1+K7GTTWNP3VKihG3j+jvjjKX/zkCCfhldkYJWRK3YZ1cemrKderqQgmeUs2tKB+thQsHqidrUG9xwJNMXuN4d1aMN63kdWh5Pzf1vPjVFHOInalDMoVv/ftAG5mWCGTotbdNAImIoBcK/OkPkBeRZUZXvfroHSkE9cqbAq36ROLEyCtfgcIbu2CitV757ePrpw/tDg/ozWVFX+pgqb37xp3+WFVlNLbIDllvIH+2RqAYGSLfrmqKBC5i6F0m1Dr3c/xEhqtutvTAMN0oFhkRzcaF9W9TQ1ZpuvaCVKoXfHCuvqvL25vT+w/35cs6L4ng8nNHe5RNdOXsoKK85XH+LV3WnlId/dKLA6UJEIvTaK1G5TZvYZrvufPFZ6pVxb0ls71ZB7DsCyvcDMddVHPs09h/K5qrOGkIZ0SZdvyldJ8FSvp+3d5/TqYR9JevkuDxFI4FkKqT8I0sXtWV5WRBkapq1nLk4G7UdodAOKnFIRQ7uplsSFIQxKt4noOl/0fNzIuECjxbdm3r2zceyuu2OK7BTY1nX5YJWgCA+Kk5dk8GMTkuc8MCsURZV/pRleXm63HRIXWCwh8lbNpoC7TIVchtBDvJx2BsucK5elk3LSLEx6kiYrrKwZsGS0JIRbjxIDu6ulJXZzbG7PR17FIUeyrpGTazQYXy7e3Zq4sNkidif8toeVVPcmG/torT+oLyF5NprLrTNDFOlYP+jHQLZH4EGVfcvMOYcmZRXeAH/y3vnHU5UIyK4nZ/Ji5Z/rVo4vcV2HON5tUDKRp9yHqjgr/QGgstG5OmipUvThGrGfA9DyvOZxw+Q/dD+ZxBIXiAfoCoL+pXLXGSI64S6IsH1q/4rvzQijrkvIfUxUQzKsj6N/enu2HV1aB3DI6GikBVVxpS23uqFZ0Wcd5JUX/SpKcqirkB1JqItmusdyKGuiI6BbgjmQM2NkfI8XgYqdE0NKBS/FVvIkgTKJre8r2kKJJcRE0YqI8DbWXfoztQCKpSftrMYzqXJlKSXkQoUT2lBr/rIK2TIX3X8xFJh84zIGtsva/vRQWzVzWSMnoYjvX4D5PTX7poW+0/5lHFamN7EkFmgHzWrnVFEGp0UQ1X7RnKWdsYa/bvH1pksJIeXSq1WzxhR3IiMZGA7Dk8Zkgq4QTCsTzu7JQiH6t2Ydc8LEGZtLixG7GbSiwSAkFkIgKpiTNP0N3/x9dP5jBAzcxCKo1KFOpbtkkrEj+HiWJrOiCyucRo6VH0nezZ4/WVSafhHLGSClBLiTakdHdumaWRX+TgOdV11dUNZ056cAvZEwuDRWmrGMF3y4u7m/vEhy5Ggcjq9pHTC5w9ScJ9Fxqbb+Lju/55g23hj4o+3r0+UwNdJ9xmX0rUHXfELb28TUbMX0d7T49AXjpDS+z87z8jpq7YTd6OUmk7Xx2hj4/L/kh5EHWj1CyIIgj5qLFnf2DNP4qngDfaNJeEBYxQ8dMZCZ3mOehbs3UGDggqhabXxuX+JC/EJVMOcHSpofCv4HHJ0moquoKAo9HBx8c9ZBTwmkpAlCWSauGQ1ASchbwWzwbWhpcYz3xV8oCLhKa3YgkFgE4/iMRJkcvYm+FpeXqb+wjW9iLrxc17Ar0aauHEpzbjCL8zd3FJrDzRiHG3XvHr58twPw5p98dVXXNj9iuoXVSyIjUE1EdOd3rOgdj5J4uI7WvRHq0p93Af27Nl+XvB/yrP2P9y126P8tvT7HxWk/t58+D6Jv9lpFMqnQ1CWxTzBoEqwk/RTbF/uzirc3AwgIWA94uaaj46V6J0rAIpznpeAH1PdVlGvpcs2V1ZmJsWatI0smBLWKmSVD5cJ/UaA4yiyciEtuSzneYb0JFQFXzUj+MZlbmxCYvoq+kMME7TnzYuGsm6RuAe0tsdnRboZ1ZojolMbWu2bdYANPlEBAAzwcDhUVdX3AxIYpVWELo4zaihl0NrPSIqZcFbqTvryxd380/uumus69/2Br52BVNLs7G/Qr7eG51bNNNMofCH1Nl15WKqh79Lonhjdo5ztsD7h5S9+7rtXP3iWXq+49gMDIKtNWv9euzo8XiSVnABSU6kMjOGgPvZKPNxOtTAcKUrYokhbUD2cHPMUjLwZHMnSIIvZsjZtVZ+pXKrUg0NFnrzIGI2pZ8PuKNokvStyeMmyaZ4u56dsXQH6JxwYY1fQqUThiVommrI7YGlKRyK6hTUNhApboFH4cmiphCTjJzXLaYNa05GtaIaK2gnarJCamMHJTvgMctqV+eNTT3RI3FOV7OAn0JtyWfwV3ripadB7LZQEJ45ZV+Wh6/pxRJEEZotaEiVsW6o+XcUdeK0qlst78jA6Yc8q0HsKWxDdel+fT5sON0iDKF9j88jUf2V56PFs9oe5pel4wuxfjXWcPVYX2UNmaYrkZM8nSYbQOdFfvTswkQUaz5EeBiw1WF/do9A4XcSxv+ih/KIKblQIOtTQcY3Avc4pwZRlmpeyQutgLkdD9wXBQFKySbyuZVVQLY7YV6bTZXbk3TbzMo9PT2O2tt1hZntWhSAnGCu+GvOm3sPUaEULP1NVHINwgYBOh+7zz16tw+CyQixX0S1BqGkgbVOh2pAhL3E7CTeVCIzl1FswR7/fkGthsU49rcIDmQThq1KmKF3dpMbH6XS4vzxVBbKReHX1uIW91zGmB2xPGdy4daIL9mov87W7h4+hgp+QdBBswKDvG3BtM8bd359Tg/8gyRxut/Fn0z8J0V5xHW0sCccS2I0hvleLProWwXu7pSUGTd7S8UKIg7/MrhvxHIez4sYUElq2WBWpoaOdkPbWQ5MSgmypF4qK0iFHsUp09F4WpMVxtddlrepqRWs2VyAx2eyAVs44OW9BygFeXBPPFFmeu3BDqgeCTykZxHZkWQGUpvZ/eVUW3QHFYruuO6PC5cL9X/wGi8PcyU0eKeOfOYxsOJt1lXhVnmc3p+PIRTO5oi0Jfvjs9B7hGaJnyTRpuCgJQuVAUOC9LMumavNsrpA2meTa2AnYUdGud8S87vHZ+kL2r3TWwI64jqy1SMS5LRYoTb7/RT2Qe1p1MlvhNVc1ef/t9LpnARLxRDaDSD90ooc7X3EpcW8D6gVE3gm8h5m2yUVXrkkdI3J4bCBJMb6IjJ2azH9x8zFpd6QPdTzMGjiSDkIFcVdAhYdjUaNmToFCE0U9LsOErAJQMqrJHrsmPN1jEPwWqSqy0uegihlZsus6I2hDfRM4FZcFL0doJA5n2irZ28iCUgP0zatXd3fHQ9tN4zivM6gUf2lamEEbxY2oqUUqxKnmM5ncilGGzUCBKJjgeTZOaO5bLQX1NkLtkZJArj4pUIvO07oy36LUDHHIUx/GNV9e3N6+f/8DWhdrZC/4wbbxwYgFPUeC8WmWM+G/t+OdTb+8K8M2V7rbp0p74iuPxGk4q+G6/UdsXdz7cj3R9HeNg4giNFIaqNAnEMoNvBRkjZeK6ajAjJbC1I4tv1VmR6oqgwYlmCSCKc7cl+zF7WLIKgaLkL62AJQbuMGeUaGTwghAO+iw0OEUUsRW8m1EmhFcQYIp/DUnl7wqrdxnJdnLPbMLIBCpMCVui3oALFQ5WTkrWOgtWQY6lwI6tJ7Qexe4wLoWbKWrmH2sVVWiNhBRJoE3UIhdM4U4iQ8xZSQEkdEdaSwGrKLKI1wfb1zm777/fV2VP719e3t3e3t7i1YOqvy4gp/wD5inTcCihH3n4B08bNlalUVLlaapNpoumWWgbszOT1Aon9U7g0FjB3ZHi35GLqf/uvobHysLsD3w/EM64B0NNrIjPvLyWSoJVfsH6FOdJ2/3+UlogAlYqNl1jnQ0dz0Gr8eE2gJx71yRkZCKgacGHuu8CDssycR3jWIRviJcWAg7zQXjN0FH1MYafX7WWm8Cly/QTpQVTHRAja5d0oKuRfhJaXhG1VdaFvTsnC5PZ5jTAFpBsiInYVnqps2LvKbwMqI3nDaQ5QvyHpZpnZ+eHt+9ezv285efv/r5119mhDCZKTlxQXGvlbxTnJSJ+XB7QiBDs5KJGpErDu0SQyGcKOpFU3waJcyatjoeul//9pvf/uavv/v976mkd/a3//a/9ud/9ievb26lpgGvkWb/UykGmrUVbSFeMs8ze7bWde2OJyrs7vZ+ozttflVfxbWjEh2aVDY69GeIisvVToVwN7DvRjLZXLHu3tFYN3IqPVofscP3f7kKnUp1/N17G0wpEtaBpn15Ipdy4ObETiyNEUbrGT3GD1x5Ja8gBULLWfpOu9VKcU7bCcl85LQg/spFsCpxfKXONRaZmjwh3bklbXAcF7RJkrR7ZDLhG9Sti1qIko3OInfdjEmBpPOyUHkK0HBWVMgfQukqam6Ig06KcZ5PoOGmrCjVMc/LDGgvsr2Brfjm22/fvX93PN4Uef7VF1+2bQv4B631XK6H7vjdTz+V06UkICQ5pouyyia6U1vm6zKVVTWep2Va6rZdYCDMa8khbiRBUxO6tW2ax/P5H/+zv/zlX/3qw4f34zDd3d394//0n1Rlefu3/kYBOZy6idXAFh2aFx39oVAeZB4GlLarm4IyN6gwWHBA7mnIH2/LtXOyr1l3O0bjlTtce1as+G6kpBUmiT9QwSX+kGs27M6gd1XDhG7MXE3nZW8nxsjmU01/9bUmQ04A58Q58G3IGPDKctDLE5Ofri+Lomka9IkfAF3i65zfKoxFN33DB7VsKyOWpLQyfy3mJsyAnEeAlW6uZFVO8wIKpj6BU7bO2QJnz7rc3t6gl1JQQtNVpi5FaDi4AOQ0IemPeqZlWfbwdFZIBslzGh56oTQNdy2WPHvSbLM1/+Uv/8X5/HS6OZV5+dmruxcndAzs+76kIpXLPH84v/8f/I/+x+/ev+UgdVHVQIrQSLpD29WHVy9f/oN/99/50z//M5SJJndTP47FNBVDz6BOgmGtl/Pjr3/z2x+/+/b1q7u2qe7ffzh29dPT03fffvc3fvEn3YHTJEJpDX+iCXmpKAzybJdZMefLMI1LllObYMvlCgpLuE1q/cZmrVtafmvHInXX7nxvq25uJNrH5f2ODLLivP4SUR92DPXdG11/Lxon33cjhXfcUT54Gy9NpICKDRDazpHlQ/ChXfN6k9ccbYGUrpRPIDObpmHdzlL1/ODlCHwCJhbYXuqERMWqXHWo7Vp5YxZ4fyTu1gTZqPNspAhskVHp47xApQlomQoUU5Xe0iip1QheWgsWjGLo+/unB1ibPCxEvPOGXlS/kR1ZLMqgk37zu28e7h+atun74cXt4YvPXmbr8vj4OK9LsSxlVbw6nf6jf/iP3/707ul8rqtimdd5PUOwL0tZl8UHsJnffvPt/+T3P/3b//a/9ff+/t/7+enVw/1TndfoHbhOeVZcLmcI7DK//3CPjm0V9WHJ89PNqamq403boEI1ZsuOgOAjU3iJS6p0zmphuPBLk/5jRLsRNDtOkeToPyeWt9uZBJud0LjyhedJ9/qHrhja/gCCWq5i7+NP2JjO+9p3NEN5ecsgnjJrz4F/BhPFrFASWuSB5m70emmy/GE2QdFw5pGGHKl2ZLcsS3/pUSc1qrgRrd5WPbfPcbMir2CMcb1lS0vFSXMIhx1vOVemXMuWUBPjy5e3l7cPWV5UdQWXbd2cn57maQzLReagZmFxFAb68zzP4zw9Pp0Ph0NRFg/9GZXlUbKC+o6RyOWMfFBdMXOP0qJA3bm//tWvLpf+6599/eaz12xMX8b59+/vkUyPHrxV1ZT5XC7jMs9TmRddUc5lMc4TWEdRIrE+K+DiKvK37378X/9v/nf/l//bf/Tf/+/9d3/xZ7+Y1qxqGmoYjoku89LUVdN1L1++Pp2O79++W5YMeNK6LYv89uYmryqp2MMngbgLUyg3N5cAvpcMUmOgKAvKRiJAuJPeuxS6lb3eaE7VuGdee9xBHSo7X96X2e5j5yw2n1Y8EP9lf5aCy5of7lhY+viPGhD+KxtRfF3zNmvW++h5XPFdPDcO95ERJ1bvlWe5aFFZVYfjEfGTy4VEH1d7vP7ixAHNZmchBguvqgn4pI6uIAAiALNfiuD4QFnZHJZmVZTAVuZ1VU0kWOuqGnoxKjh+wim687oOVNhtHPH3PE/TOOZZfr5c2LSom+5wONQwVdG5no1lwkqPYAJ5US/r4/nx17/+6zxfv/75zw6HI5J9UZM6R2kQKnBL4hvl6S795be/+y0RbIHuoASuGvHMEe0RWnT3XCaU0svm7PKbb/+H/7P/+b//3/pv/P2/+3c/f3V3HqESd3U35CjENY3ToTvc3tzenm5PNzd1DaJdCOyJ3CjffE3ygYE+EXhVkMWWeiFYCXA9XV9NQo5tS68/G3mEfzd1GDcBoI8Zl/6X/cuVbeyLAhmvO/96uy2oRJVIE/xmuAURRHGHrToaPCmpSHZcKAx3h3r9P+q29QlqfNZZxMrR9SvJeHtzCEX3NghLMrAtOdPUWJGs6vp0OhVFcT4/LRPqlTPdsYrpnmx3dExbWgsCwuHtnY1ZYizR4Cbc1ggCA2Xr6nYaH6sKovFpvCxLN4xjXdcD4bAMzMPJBsM0Xobx6XKZZ4RRyJVeSW8KWruu65q2qetaOhvQ3lKVSJii4zy/e38/XM5rnr357NXhcOgOh2Vep3WuuMeUFecoSt6IYVzePz5WlHjAOGRAFmF2Fss0LWhugs4sVVEBq1Fmb3/88T/8X/yv/uTP/+N/8O/8e3/8x19nVXO66QryrKNdRVMdTq8olLAM/YB4QFkCOEKFwWy+Yf9CXjsnPLNfT5Q0ysTAD0jovyor3Cui3qCORTbzdUre3uoPekWKdiRDrxL2ZgwpoaVKsKeCvRMZjSchmE+dkWjLfKzJDnNUg2aWBCay5h18nKwMHUKN3N1nK8YjLTWOySaPp6cTdgmCpTsc4Hc5X+YJ/luXRu8UL6/tW9E3jttKpk4cxFAmFRYn+OdE25by1lwkA6+qnMt+6EeUg8tH6pdLNZhx3byu/TBchgu6BBXV8XgSol6RsAASwg2ozTZVk2Jtm3x+kJnotFQjA4q6zLwoED5t87ycR86MRjSI2qlVFbUdhH8bPAJdI3766T31G6ctIEoiHRxUA9xIXl7GfspQkauuIU7Pc/GX//Q/+w/e/y//rX/zv/z3/42/uLn943Wd67KepApAkaG6Hp5botsLqJeKebmt1ORkq8MJBYRCX4wsp4Uj3Bj1fkM+sN9gb7TZcYiA0v6kbK506NDdEMe/zMsby1ubfCcu5OezefMPeu71L+6C1HYp2ZGAgl+Dlm/KAtS3ub9cxrFnJypCBtCttL4STzbklW9nfGWJ9LOgckvEAYsKNa/tqqK8XC7jMBK438VuIgSWDkG3VfhL7EqIbTDTMWQUPHct9QZKmxaEeSDB8mzsexzzZR1HICINSTgM4yNWZqwqdNWGzktdHoqsIG9xjqwhcllxZWZ+MoRiUTYZLGF4D7KMSlisuUq8fhylscO8VGXZZA0XS8hz4KWRxLAsP71/F9gXSUIU4iNinlHLtm4JwgkFYUDcuWqqvKq//fb7/+3//v/0n/zf/5P/9j/47/ytv/XnRZWNS5GNU1aCkYi7D95EMC7VbRhd46VCKLPD8oX3n53/yzJS+emVIKgbfcvYr5lgn0QFqQj+Qwziq6+rUUkvQqOPUlM80S2ff1pE99fN39TDrL6onZKWsVHibEULlQpIA0ER7tVO91qyuchyFCglWRyz11TIipDYMfG3K6MaBjPcoiiati1hdg59349oZKi5+na1Uq2rSk4VtrTu6U5tEJH3Wu8v2SLWSbNlLcp5RS/c9ZxN41witEtV1CtpmDBN8+PlMowTyllA4YR6LEVVc9Z6OQQDLy437OOELZ4d8cBqLeYqR3+XeexZC6Yq8lBLqxVCuwCuY2ZI2Jqv0zwfmnIt1vcP9waHkklpXQRaioV6mi4TufSxif1SIuu4Wi/Lr37z7f/0P/gP/4t/91//9/+b/+5XX/5Rf7mgBl0DR9oyT4gF1C3nUtpJBvSaFS6pOGqHRivjkUtgyZayyOd5RUkTjgCEXfeSYS8b5SNOpZ2j+8nfy5+/LpQB+/grIsJn+yVsJNhmOAYtdy6lRN7GNP+pr/9/aVf6I9l11d9eW3dP9+zjsSe2k2BjnBAQKGwCEoQMIQiC2KIIgoSCEH8HfEJR+ILgIwLBF0QIYpWQkFhDBDjJYBTbWezY49k809PT3VXVb0dnvefe96ptQyXu6a56dd+9992z/845ZjRtmSNhEqeGohsGekl7wldjR6GXl7mJE6FmLZ6D218hEMcMlMyyquqqwuabrqir0q/dHogA+1lsdiuEg1tCtqo+n9y0WLTtEnpnYqSnyNNlWZMKQlOGkEzTzWZTbX4E6nKWA9wqYdAS3UOLV5LHAAg0gfTZJmrXR8dRG88m8zhp66quTqosS6eTAgAkgKbq+zSq+jZp+zyDQlNxFBV99vn//OJyucYaV8BNKEqL2Xt8v67rwACGDr1MQ1DXFpzkgCXJ0vz4+PgLn3/+1VdvfOLjv/DsM982mcRNByWswUs+LSh2C65kcVNhKBnJ2G2ke+yUiQYcHlult313UlUuH9ixZm+frfnrxUI2n0t1pw4JZ1OU2L3x9gh0tBaMTNiuKHjLfH/wwSaDd/DLyF+nkK4+CEM2ocMHlFoAD3G1Dc8Kd9lA+BmZy6Itj8n7TX02jLoyKBdHAEPAeIAozvKqroBuOkrOQ53R1+6wSR/VorM5g2ax5p7iApTmoibvvY+hhCSkyIJziCoNgPZL56eLohpwVKAjk8KA9jJ4vYR9xLxJlIQEshEotoubtOjvPnx4/fn//sY3v3nztRsf/K4PPPHke77x2te+8O9fTKLkiSevbW3tvf9bv2U62wIoCqqyeZJAFtJkmmVpvLcL/vAoKg0RcFTPcSJMKIJ7xhHkPDDCBUJcHdjuSZrURXzn1q3P/N7vP37t2m/8yicffddjFQLIGygYgoMgOMeN37XU25n/Fk5OUA94O41agIEmt968u1ytqD+wBx/2UK184CwtnOLcsVd5aSljX3lbpOqnQDjjagPBjI+pznLPqKN/PUIaJQdn1W+6nydKR7xnRrXw9WmVopqooFYjyVExf+xS1BO58eUeZWBKsHdWe9o4hxXFEtGsTSGKmaVN3tRVhT3BgLVQQXndN4RJQO6uQiXtgs3sXDkBOaNuO7BQZhz3GfQPw0PbsvsOr0zitgYVOi8yYjLorMo5bx5pNfYqygGmBPziaVuk0+Nq+ed/+hfXr79wAl0I+1cvnp/Nt26/cetw/8HxcnXzjVtRFP/lX30OK1llWZ4/du3K49feNZ1BM9EkzR577CpEpdM4brD2ADV5QHYjZX1Rn+1aqLyTgt8LCoPQYqm0HXQ9RINoUqRl+fJXXv7N3/7Mr37qk9/5/g9AWlJbQ7QoThssVYd6OexGGwHWCjA2sAtsFgP0GvxeEJ9O4nS5Prlx4/X1uoq7CBUAVAGksJby9rdm6P7xFV+HMHfLmke+7L/juzmMXutUtFNS9MS2Gy/0ZsB8egf/U6MlDmGem3xk4ysaWuqnxp74GSH0lnHOBq0/uHTMgUXLC7SjACHj3jdjqJWknKRrOzIp4ZigsIOsurahrmgEJSEoCFWbcSsa3XrDa8h4JJOVMFKkQ3VxXGSzvC+LosDmiZhXQ1I1iQHbDBYvZAVB0Aq7t2AbFBwhkRp94gyHNguABcnms/nf/8U/v/jCi6vD43wKGveLL710+cqltmuTSZqcJHVTtl0Ef/bQdvTao49dvXKpyKE1edQDYOO1118rl+DY06MlGFKrxsDvXdeCNRvFq6YSADZ6ybXkT9vBhibZm7fvfPrTn3n/M8/+yic/sbO797CC0rAIOeugdhc2OgJnOewteOaoYUUcx1VVf/ObrzfYdqqq6jhNJrNpliZtTLYHuzfd0RrXzcZUT/8q+ZTA81b2vYOXZfTGgnuLr9BWjQIAQyU2UGD9JY1+Xb0C7/hlKs9JuFd1Ha0/CohCQPzQVRzZJPCcB7eiNz0TnOLJgcW7acs4xuxsftkAY5li6hyUlQAYE3h+EigeTrl/CH5iAoYqbewh52lrU2UDSMDMOKQ1xEAJ5JduTyhIUJjrVQUcIYM+2uSLAkKN4qZpCAVJUVOsEkeNjNDxlOhdIcKCNQIIp9wenxy/8OUvHR4fTWZTRD33dVnevn3r4qUrFy4sI1CuD9sOIFZxGm+f3Xr0XRd3t3cB0wIWKXCCopgcHxwBzYj5bywbjfOwc7pr2/ls2rbNUXmIXMptP+RaxH1c90nSx0Xe1M3z//Xl19+4+ZM/9ZFLlx45u7PoIuAXx6vV/pv31stlkqYQ2yMgAKA8wOAHxEwBv8Is0gS6qkBlImx5AcnE2KMNiwdoXFyLXOvxdtXKzOnccF6EF4xUCnADeifLCJB3SO/Ba4SGxfQ8TT/WPzcZCKegwN/J7Jxq4XY5ZlUL04x8IlOq018F2zA6qCXtkZfrqKLLFZVXK9jzjTS1DQQyNNFSc5kqGnMfIdc6hMMsSKbUxYD7V4uXWDFcsmz0nuLjSieLnUW5Xq/WXVtD4m7bZrgFLakk1HcT1XvXygTRFxFyCMKoUSSJZOD1L11/8PAgnxQxWKRtW3VJHz188PDpp771wx/6wVu3b//LP/7zwcFhA1CwfnV0AnAr5FsQaQf7tcd8e5o2Q9Cdl98IYkiuB/0W1JPtre31alVVNfUGxi4OFNZqqXhmXIF3vU7Smzdv/cEf/cnemd3nfvRHHrn6SBf1D/aPDu7f77o2yZMm6lMIVyVFMYEQNWj0GDIGBx6gSqCJat+XZfnMM99BTZu4FRqXW5GKPt652HxspWKgY+weOMRlcrpzZNk01wKmNjLObje3faevwCsbTNMqF2qaOQfegBWdcp//8wyVHOQW4C4KtRmvQZ98xzaRd5B3uyItsWhXHOoQpvWgnY/3qfSgdzwLGxRoFzAuOm+aBRK4jxDICK+itAKON+l8ufyU9DfowW+TTKbTre0dTKONjg6PgW4xU18FOI0jPQQlcSJmaoZ5SY118ANX5fvf9wy6voDEurar2ubuvfvr8qSCih+zi5cu5RPoxgap0U13784+dIUA+QsG8XQyzaHzUIOV6SBDEH2/Pt5UWzlgR6iqrqMoOnfuPIFtuPEr+xeQvruugjTApq2bPunL1Xr/1t2//qu/eenlr62XJyer5QmAzHqAl6G1T0EzMo4J7IEbD13N0izPJ8l3ffC7i7wBGBm2ggBNXI5On0RJC+xRbHV6UrJh4YvUpk1H1TpRVBnRcL42sfOUdtgro2taHfgt8hqNf9aEcIeTG0FpbdC9NR3mlJWNziO8goVtYICQ64FUU+PdtLcR74SLp/pCGZ3TOq7REwbA1sEEdQzvDRXNvsU0gJHoGRWRhFTcoyRUEa0aujrl3ERJwEFYpE/iIs2zYlI07L/B0BkqxsQ42O4Vcc5wzsjp7AhRAp6zfHh4eHQI3XO7rqlreh/6FS5XDx48nE1mXdedO3/u9Rs30gx8x2kaHx0fYb+GPEo6UNSj+NbNu8vjY6iqg3U8ERFpupzaXGU8Z5BZ0dSzYnp2b29//wEA5ihZSk89GgctQt0SpKw6iss3m7/5279+5NLlb3nPe9HCT7I4ztMU0h9TcK2B9CXAB/KoNEvzDCLiOZQpyNuoRK1awOiOgtG3H+aJbjqZ6uANLt3wtnl8ZDrpU1DlG5E2RrgYX1foOBu5p9NPxynYFKvksTaRotOpg2QlEx4+bR7WIazT91YAdi6A3RvMSheLdAAvo9PP8cfABSa9PFy1dxNFttL6NFdauDpvnWYpJshsLqZ3qdoUEJ9k3tKNfSMg1HRg0mDezhdnkrQqq5OTcjadUAEZTs0xVKsoLud7jtmbBYSRpfV6lWX5g8OD2Xxy5dLF23fvQmk6rOQaR9Gtm2889ujlndn2wYP5fDIlVEYSx1evXsHimmmDNB9FWVnWddtwkJbQE4LQdBQsDRNpT6qyjLposVi0Xf/w4YEWHpBtQuu6R28VR7rjNm7X65M7t+7cu3fvqaeefhTyK6aT6QSc7SBpESoNJIyZVehIgIJhffLEtSeatoqjnqGU4hWUI+92WbpEbrBe/58Wq4xgThyXXPN2SNHCcvrfDjzqrXRwa/ONeNo2+7beEmAmQjDe+J5XVA6CNJ565hwCQ9PD+OWFtQhYmiubMT25L4nBNm5ZmNI1nk8jnL9YSAMsjRjH0p1BPVtGSrkzNfR/8D9xnC8W2XTW9/vgdqZ8YI6rWQer5l+KCi2l+poa3IBd3x4dH5UlwDXe+/S7z144d//ewfL4qO6aPM32dncX863tM7v79/bn83lSZOcvXDm7s5hOs9VyWa3r5fJo79y5STbDhWCcjDBwlMroGrsKxkW3ENKdorIukyReLBbr9ao8KXlj0EPQA7KEOqBRnTzMq4ToQ3PYtEWaffn6f3/161//8Ic/NNtaEEYOqx9h2JssCMSMJ2l+5cojbF1DSxtxQ1DxLY4HeoULnR45dgA2EHb48pL+nLmshox6MN351cR2be0sVVDlKHknwJvFiNU+VCFFXyU37uDY28JTIyOEqUievAtWKl8Z0Vx6AO6REyvMThrcUzORzL3dtqq2IoBrwy+8UgKjmzIQq+GV7O5WNLd/uWPy6LRC4D27fsIXPWflQQyrhAgWFK2JkxR93lysKwInK1m85tQIqjpSGsbkqOP1Mo26O3fvXP/i/8y2Jtmk2Fksdh/fitK+rbq6LJuT1Vdf/Eoxmb369VeStNubb/dN9cbr+03TbO/snNnb3d3eKYq8SPMeAlgqRPE/S77mffqIC013/bosp300nc6qEsr92HNKiVjyAwNMiPPq+hr6JFdNU9Z/97d/9wPf/71PPvnu7QszoNAERTHAXSHHOMsnFy5eTAU0miZxZpyPTgwSWsy3tkb6g3qPZPwtQ1EMhTR4YY2/SnhfQzoCu1OcDROc+j/I8BsUbAlu7015o/LNXQpGqOtUE9rcVvFFI4OPfN0WW0QCbrhFgMvaGV2ScxiIZ0JvbnZY1zQS8TV/01XBG8OZDnSQ8QUJWboUpW5UDXcM2w5K9lqSJHu7Z/fv3IowVQD6M/SkIjocSDi7mPO2IFrTd3HTTxbzro0Ol+vb9+9ACKrpFrPZt3/Pt892Zq987dU39x9848bdqO+2Z1sXLl1+4tpVBDO3fdSmeQEArrqBCnsYwaIlmb5qtrO6nmLPIiZWVNbVpCims8lqtcK61yIpuKA3nXo+uxAB5qYyUdlG3Sr6h3/8p+e/dP0nPvqRJx5/HNAdyJviOM3i5PKFy5M8g2gZoECgQDSp0GLSCbKVtBLNP7StzYInMi6XrUE9+MB/QQxr1KI2ShulRuIzljwa80P6YgwI1A+NbJxBGMw61XT3RzKa6SbqHRvM0hN4cMBh6NuWo8lOI/55pxmJP+yUGQ3dVJpnEGpY4wqMF4IWi93fEclu4GQavcaavAMbgg1b6DAIqUVFEidHq+XW1hY6M0PcqfzE6lRIwY40sNfhI5fPP/ej39c00FdhtYLk4hjq0LXPPv3ebJq3TZul0SSfgNcK6nXEgHpsIeuwLWvEcUJLwaqGloKG1wZaYEjA7HRBba6NICNkPp9XVVWVFS1Aqr26M6GbjjY6JDD2NbYUToqD/Yef+9PPPveRH3/qqae2tmYx1ru+dPGRSQabQ1tKyB9IZrDbok4K5y0Qr8nwiAoGyOhU0gNt8Aoki+YIcpPLoQR0J01USsfnqcoX2x+xV3YkwGsNWg2P27pWjrkhxDQe7VesQvR0t7j7IpcsC0aSbimhCmv33I/p6T0HgBQrdb2PR2v56Z5y0oGVxhu5l+xHME/aEJqqYoI3HwO3KtUBqM8oGHl5HqdJA3h9kc+OdmwRJhb5bFYKTUAYN+7ObO20dR2n0c4W7EkOpiKli4BqEIPyic0DsfBN0zVtE1VVgznRjBxDZGuL3EGPhrQA9Y17R9PC4Pouavo2BUV6CiGopjHk5firC74zC+gaIOakhcTJoq7Wf/bZz148d/5jH/vpa+9+YjbJIfGxqaFCboZp4dBOrcvQ2efVaiFDC2NtwJxcwxZzpIKDOGornm4z+xqfT71W43IuNM/oE68G8iNRcMhIoRacNuXONxeDyQ5WYcxZTbkfurlGjUdrCQeMQWlJhRgVJXddxmz3HedLVCnlmIuXYGIu1Tbv8uFQ+RhwCvOJzXUaYNo0HDJYrLcN4iVW3hZwT+XBKvZF86Yxkiiez6aHWFOSiLWDXkqcciNFhZhzxNLxiLMasMpHmhR9m+RY7BZ7NmGVu5z6J0JBVqhdi2Fa0F1bSOxZrU6gWycGcuMEoMh37tzlyDxrCaZDsbAnmoRnD4vKAX5JVB6yLJvP56vVMUpLo/GEdOxsDphhCx2ksiw9qZvl0foP//CPn/vxH3vuh39oJ5us4zbmhuNAnXXbY0kdcFyRVq7lRLmSRwwVPtmhHzz7cakj7pNBuHOIMVa2FoSYDbUICbKc4O0RQlPgHrW+iyLIWKHiM+DdY2oesZxOfUmKroM+8t30CLvTF353jMaFzIyqI6Mr4mJg8hrnrXvHsZXhEpSMVaqpOUR7R9qgoy1X7jEYQiSbYX9B1TG3Kj2v9B61ShrPFxlE9Ny8RO2Gc5im0+kkTTG5F9dZViWUzmPKoW4L1DGQ7sjBKkBTxq1UgZ7s7u3ev3+PgJd9H0HKPSCyOgjXQQlIcj10ZUXFP4Bs6Cwi3CtbQbk/gIVbgePCmsboNSo1zUNNYdTLIyiL2bazk9XS9+hZNyOr2KpfUzpS1AAZtH1/797dz/3lZ7/ywvVf/7VPPfmed8+mxUnVxHVcdW2cxZDnwbxER5P95pwP7nYxytSd6WbCpUbIjlJLSM+K2TTP1sgcc+YlyumyoK2GKo0ssL4w9oICV7tDlYcnzzu29K7cjz2d9h7+/pjV+PHWgbk2WLsr1cg9fbAZlwhcS8ty4jciynlm1gDiL4TTIHqUT/WfMQ8m+6v1Yfjga6seKVnzlzHNAIAXelRdAS/+qWdI7kemK/KLBBJz2wxs0yRP8/VqPS2Kh8cn1FgEHdsSwTHRo0h6voGoBaQhYDby6Qxczn1JdeeBKqC0HRSjgkYJQnZkRYsqzgwbozCo3oqI0LQM8aY5CnZ0LKukIn80LDRbi+LJZAJ5XQ3lRWhCqEe+bndwRERRdYQ176PoeLl+4X9e+q3f/vSHPvi9P/+xn7rw6MWDZVUl7WIyReWEqloKINlpYSSD2RFntWx7Iv3HGsoS1cRobnoyBtLYb2/Pv9nD5f1qJA0NEBqNkIKNpUe0n4GScRDa4IK7emtjDDN1Bmq+cfX6N9W1bGRdOgw1KYBWI7aLiGE08hjcKCEuSvfD2q6D/Rq71kxUjEc3xOAZu6D0hscgkQRECmHTQfeRbqvRyjyeqyOxoRJHe2fP3Xr9telkerQ83l5ARFSsX+lKxnYHT5bdYWiQgLmb52jnJidQP7KKohPSylJIwYXafaJzC5RTfgNZjam5lDJlj6NzXjmIqvc/dxAddfJc267JADY1aVoouWkEyDBqIdsvDdlbDByDKK27k6556ZWv3711/8WvvfwzP/vRZ5797p0Yqlu6onbic/OOA3dVdTq/51R0zPitrV4Xkxz5XNNi7bP3ZbSvlnk69+BefCSw3AEWTwI6RoSqK8/pnFUiMZx4UhnEwk/5jydrfentKx4G9jgqk42HXZxYm17m6yFbMFJN+l17t7TEGDIfJ7GHXj5Wmken5YlP+oVKZQD1Ih7Zi1Ybs9eRnD+aquvoYerirFhsbx8uV8drDs+30HYQVUXxdGCjMuovJml+SOBd2xbzxXw6jWPo+t10Lcl2UA4w+dEEgBSuRrVxcAaocbpegdD1CCvCq6Urtq6GiNUADo8oh/Vj6LvWtBMoH1/WXSUZQ/JA+aygEctdvVTlgaHwzLYJtNRIiz5erY/+4/kvvfzKK8++7wO/9Bu//PTFy1koeMzA8B9mC5N9zZ4ugfTaV+ARDU9EQGhuoWRvcv7zhrzDcaEd3N4neT7RfDzgQUN1by4XasLx3reN7WCMuNE7BwJTtZbNMxR6jTz3O5ctVt9MQF6bF6vjuHHf8uXr+I7jMn7Kk5FysE5bjSNT+glIoQTaDplNM9hpGd5SL6tZrDnCxaDdJsl0vhUnb2ZZ1kB0N66btigYkEfdZQRTHRs8JSuRiBxOJpOJJjwQM6rrJs9zjO5KcIUngdo7Vqfvux4SFiG2hHuELIP3S9GAIm4cMZvWCDYIo26vBqxZSFE+rCunB3r2MEdzXK6V3X4IMoFJjXn/UGPo/r37n/+3f33tlZd+8eM/R/Wv5RGzGHTnmeLkHTatkVuJshMGRZw/bcPp0Sir0BmfQykVw54W6y9yB8ZC6TxpM3q6rDgkS4bKPEQAewFp7LQiW/XeWZ4BdY8uS5mtGorDK/gY263Qw0fIFOSxjBc0HgVTyUDvFtzBeqQHkxrOxDccbLDMl+GMKQqaq6jF7GnG8hslG3ACgyPqYB/od/tdqy5BCg32G2zzySzN8zzNj46Pthbz5Xq1mM/IN64BZjHrIqJGURmgXzZBSfI8bRokTu0ZgaEjrNoDwSHLfyh+2yUQXoriCArrcQFtajWsvioVbfK3r6aEAUEON0DVuwKrEXTg67aHk9Vew5I1q9ntPBmxYLxj88Yo7pu6vnHz7u98+ncz8vwQD/bFKAOSCZjOJc7EjUTEGyqHIsrdQwlP0aYjpqBwoWFrFTvKtdvi3uGuMcPDHHjKmCEhZFStF1THRLf2aHng+Rp9vb2PAktTOFdHHixeCxvHrMJyoEQJRxm+F2ofUeFHKH10e5R4nCLhCDVIhQi/Y9QlOf/MId17VlRbiR2O6KkEmEK7d25vfmcOvUKbdro9Xa6g9Dw0BEfPNCnRJAdjf5+h1Sj4FMCMRRwFtFBgiRtFAOHIsxryk0xaiRr4qF1iNUxoWwjxJbVpNVTi6PdtqT1K3ZhN2BbFBPEhfpaQk+Y8ZtcDqjT0cEmoFBXrPsrivq1r8EPj8qiBsZWOInfATQgPB7tOGHXTdY419s3GRQ2f3IhaGmpXLJaH3w0sbp24a1DvYSOFXGw0E/vWkK8xjqFTgUHZqK3l7mUUgGH4aNRjZZQgj8g8ExQT6bCHzqCXitzRKhRWRLprfN14w3TsmJso2c3bglQG6zFGLmsNqslq5oGfyGwmrAel94cSCYKps0mabW9tHz44AGqEek8Ak4IOZUkC/FbyHCIZkuKdGEQF2AR0XUuzrfni+Oi40Qgo0j1lDkNuvX84ZIagBbz22g0KAktwC91nBjBpMqmV8NSEMhEUT0uG8tFYvzoBiEgouWWfpBouTFWgHoHyhLeGZi1xA0UIsyihU20VIseCGVzICZcj93Rq8WmnInzbU47Z4rcNnsa/Gx7O4XEUeIfFJJjfRsQW30+rnROR0cOW1uwsMTVQZ6W6R1ZmVmMcSmlNvalchnKTA8DdKPDabd4gAxA47cJRKe3e2fCwvTl4DIq7n2LlCkgIxnIZPjjbf/Z+UqLjQTEoinESzeYLyJ4Dz1O/tdg6qaBDL3YlBjUSEm4UmpwoJcNJhKYK6EzbWiwy6AnMLl3Stdu2yfIUOiX5p4B4DhbpySCjsEVFV3p2unSuIILh1qb/2t03X0B4VhdBp+J2DQBpyzuCRxUSN1MiW8oM2QCZ21U1NDcboRgbrqMa/D4ON6TYUExteI3KUiY5zk9QvKEFBYb3Da0orcoZKvbmrqE95zTHcHJIk+D1oliyRB+ZrVkV25xII6E5LhVMRYETmn9BFSeslPWs79HdGxoheveAS582ytjvbhTHwN3ps/qB18NWjjh1OoFe2+C55XJZImXdxoVBT493ETNFwdft7Z29O5tn0LkPmh6tT9ZgRyJeAwUlFJGFFzXm4y4RIKPrqiryHABZWV4UUB6A6UHmmYIZTGLcbrscpBYry7kq0wGrCY0r98QH1pt7opwsAyoXlKuD5hNYRNLzLASKk8FmGqe5HTVG4BJyNZo6+V/Z7GaMG71FpQTRpR0YP6Eu7euBG6WA1RPdjFkJ1oc+ZBnOQjaYheGoZmuDWbHOaSfmfnfeO47EwzQAtyY1kXD1yo7561Z50ufJTx7/NnquRAhxbGreY1e/8TUIvYyKNH7/lHH8Md228aa7ttp8ScBE1SfnFGB+Ya/tGroFSRWpyWRqreLA/PIeqgHikeoJTYPzbLGzk6dZXVd5WgC+sIWas5i5A2lKys1A9gAACshJREFUUAah63Istwpfpyp8ab5cruazRRxHRZEvZrMKgjfwRVGLoPxYnmXQD4XKX8pSXXktsaaMTFQprK4TfZrjaqnuo/uJQxI4qgfglxVI4tOUGJMrZiD94Ozd5Q2CIArOUx+Hi07IwKheEGMyUzRZ5Jp4Iv8ffxmbwIBMzQr1Y7FD7U9LA3JPv+bT6B1lqhy1MXqszkLo1upytHxpWYep1bj5UsZLrDyfsjxDxI4sfIe2ly0SkiLmyIx3mjP7rmffid6xDQ+2JSgvOM4Q6BIfDzdcmkphXaBMuKVun9D6s16vT9bLJZSzQVA6TdUZu1bldI+Ul5+AldhVTbezu7dYLMqy7KJud/dMXZdikOqWtdz0HA8qltHMIB8QC/HlRbG3dxadUtJ9kJJ4ANSFnQHZpen2F8o+IyfSaJHJ+9UT6iCUDghsxYh2grU4WdT2CQMGPVkpwOseuWrNDjLihhGbTht3QLUeWDxkkmewB+hsx+fiCpR6SiZWxosT6NDqTmdwfO1r6ICWcYZhGZq7Ef8bB5YcV5WZLqpF33HfHXPkqNzsN0D7fHXJThPBdRKJJCQIPxlPdzKq5qCItanxCS9wuxCyxCkWOoZsltHMHbf2lArv1nxnISt9SGF4wYwfan5OlXTX2N2TJ0Xk7k9FfEXkBIPWYXE8nUEHFJ/4tTKADutUd87MT2C/z54/f+HSlQOoTQP5DEiq4H5yoMqOwvoO14OFKzCIBC7rZLG1mM3mZX3oTjZ2P4OWKBhP4n7pulnIl7DzH5fCJPXXMhybPDgmfIVBBQElCRijbjIpy7JqKtwX3gkL3qB9sMaFxx8YUsyYhoRTUelDV/BHvLEEMec8D2yWR/U8xTUfzh5ZhnZssiydY9Rja5ZEIqrlZ/ixz+39pbhdtRzQqQN+FUp9R9VvSUhzl5EZEU6b+BGmZ0kzLdKDgHunuC38phPN3iStoqtvYCkVMc+83ThNldaXVqy0AkTuozVoPF67aSSzceb/A/7A4VYpYebbTFqeECWabDNWg8TghZNhKm9EpfLxsNgwEaPKfdREzd6Fc7PZHNAXfTfJi6atsearSqVOmm/KJKJoOp1g7DPro36SFxfOX8gApQFVPURpAsGF4A0Jt7qMe7iiLEur/TnW5S14g6/V7svQMQmuLOA3UF5DFqFk5pQTwxiNz9tFnk2VkD4BnxuVMxAt0+tNik4WdNhAcI3aPYxiGNU2NDCu4DwNvqPP1NYq4Wc6RrPe2oQi3An0zqDujOPy/nC+8bw5e9UjDn1wWjNZvSfk6dPAiBaq0p0wDeYwWEL+Fc27tux2wHqFkfGEWDsQKnX/BubEqC4zKnNPeykHGlyobNxgjKWSO+6LDUmdOq5ttA6MFErDttGVK1cvXbkCbXC7bjqdlGWFYgeJgLTQTjQiCUBk2C4wL3IsE53MZ/PFfKFSRyUL1o5Hec1kjJ4w9PZUdWNnPCzdO274Dl4GVOB+ITUCgGJpAnUMjflkwIEsh/n2TjL6o+G3MigcD5vScIdSlThcYNAW/9WJ0XptUFMgB7RBhvpGtGnr1nFaFc2SmuPS+0YjVYPcdyZzBMYDFXqEyQQnGhojvkaeQYiedCu1F6tqacnMhdHYYkE9L/Qm+SlnXChYv2vNaY/q+CF6IBbzGW6lKrX2c7sro+Ffs1ZxzrllepgAbxnmTTEy3P9oqmRcUmaS1nWRp2MOyKDzNMcgKKMg6tq+iqvds+fu37nTdx1RJqTetylZkRGYlVgPsKcuTVgKs+uPDo+nRVFNiqosk6jfXixOyrKFXAJRO6F0M9wFJD2WZEVK6nDcHjMMAX6skk6sWGejjirQnuoYHi+2L7g6WRxlWd5gI1V7mhgMbbYruIXl7DTnrK3aPmkjqBogKC4qtUkYNAkgGcuc8oalwROOxsU/7dHRoJB/ItVO0t+9zaBcLEFY0RG1gsnh/QzWNNBxxvZUp4ahPVt+mXFQBvplzTKRHzohJX6x4Iwtgc3jyHTBPtmO83gAL/JGgEMDjiBLZoMy0QcqbM0QkxoAOrAuXS90q3AUaTLihOvYz2UnHT17eQ7CsYVvGyQHe0QpLmmEKR8aBEhyuhd5Diy3HQp1XiQm4bRdEzfxI1evPrh3d//Ne30HAeGj1fFWlmFQFWo6okOn7aGPaI8978E8LsuTLJ9hbgXI2MVifrxarcuyqWu4DCvEU7F1haBg3Q1Ywc3btztovAvloI1LBBfAT0RQJCPczXhkbVwxWB9ud5HnkGOIYQhxA8EeqlpvPTnsZOan7wQgaBxNUkK2Qgvxrx7C19Sijvu7MBkTWkucgB6cSGKvVLfXAPspfxyzK6i+tVuHoUEWjOxkh6+wDWZsM80bCRVLUSPkmPGMbfFZqsfC50cHkwsdcYo7wTEbT01QDIc68K2iqsBwpmZMfXJBXx2NcjZ6rxOS+yl82LEorPTkKNQRtbA1X6cSt4d+JRDrLl17+MJHoNEv97gMjVs+6OqVqzomaZmU2YsVTjGlFuC7GvN1C2Gu6TaRSN4194GD07V9mkXnLl0+eHDQtk3d9wdHqzyfFJNJAqH6iFw5mJyEBTlQNy7yfLVeI56SLZ3ZbArJSXUFZXLaBsukQ3yQ6AdD1iBx0yzf2T7Td51zu3FNLAcxMrgee56dVerBzjwRxA+GdgkyAdMU3PQq2QFNBiExdTGoUeE4sGc6Yk/WqOrSvk/bvsrSHvIn+yhJO3ADCk9gwUVSm/KbZCSaG3VjcUaQeyRetgjzZd/aJNMbiQzUGdxOnqAql6RV0xEjse7rNHyU8NxoJJkPlTXM2d/HszWGpSRweKA1Xjm/HHSGEBweTs6pwQoV9N2IzlMG8B5wdELiS5z0SeaikQF/wuRUj1mYJgwuI40EHRO1V7JFZbKousrJaYNUU3I5NxaAzoFGtguciJYnrGtiYa6VQxGM0cFzJNsOiERMVJcT7+mLonTovlKvsrjrmr5Kn3zX43fu3npwf7+CIlXR8dFhHMdnts+A4gzhFHAYc5gTytfl09li//YbZ7bPdEkbJU0cJ3lRpNARrM3Be0XV77gUEwC/6LxAn8Ts4OgAdHPMDlDvqmoQTofmdqhWJILLjmxteHTmQRLF0vqhcRTq5hTBpcASB8/EmpfglrXMDAsAeBEWmse2L1nXVG2aNpMZFC2YpdisCWpZUuhKUJScFQkQb6RmOVHAAzBrq6NKtAxIxAORJHGG5TCpAaLQGOYFicEVQ2IQ+iUwjwR7JUcxtIuwGUl0khjdoUvrvGMNbASLlKkEhX+w9JE6IUQdAL8FJ17iheqghBGpGyw78LSrMp8vk5VlMC2YQkL9tsT5J8+NnjF2LcNWzgnwAvSkdnAusjhG+aEaLTlSsNkK5yPRcWEfgbiIKMLBQpLbguMu4UOAo4+TUA7KXIigocwtXKdeRYBzeyEBY6h3RTkW9Rbk9fLs6L7MwVlBg4YlYAZjVSriydzwGPfPtcN0Dg8n0tmehW1J47o+6tv0/PmL1cHDN6sqjqLlAXRdaPpF2qcdiqEk6sHVjP36krjP8hy6GeZRGyV9DZwYA9RlWa77roduBzFcC5e2UCULGjJhe/Goa1YPj3NMKaybBmEdsJMETVY9hXgat0tGg93BpfBPYsnKPzWnvO+7um+pXgD0T8uK6AQzhJkJqslJ73g6usgF7NIKTCCCuVfV/wJGqVUc7PEoZAAAAABJRU5ErkJggg=="},{"type":"image","mimeType":"image/png","data":"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2 calls","isError":false}},"isError":false,"durationMs":191,"timestamp":1793224351893}} +{"type":"message","id":"676e1b73","parentId":"cfb787c6","timestamp":"2026-10-28T21:52:38.162Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Checking shoe position**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae26ea3bad4819f985cbfad61b8b942\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking shoe position**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4m6lDEEgh61iUucS6gT-pFaVTw9tfmIjngddf9kcPbw87H7fTQlDTpgksQ1txQ7Op2rj0bGvi7_iBA32tAD1VBwfqQiYbdhUNmQvAMgr1cZ_hhKo5bE9oCzH0jjGtPFT8qd0ny24qOnRI_cqlcKYxbBzYcHmiFeMtoLKb_ug88O8WqhKAwPArJ-N7mbxzhdkH2Kls-n2m1aS-8DQ22RM0fWFFInwjhYNFeBGySw4POkGXW5sxl7LWJbo5L0rbKQOPfwH2hkv8kVXToQJivGlxVtDrEVw7fPsq5zD2TpL4pkVyHbelKyEcKWQWPME-oV32SRTKFOoOCHPyS80CDVGqZZNw0ByphjSOdn4vLf5feYgVVTOugEKVU1CHs7VA1HbyOANKlOJO1G7DqBJdUoN0ZKr5leQ2i82tPSIMO5ote7433TFSsTa99b71Y7EC1Z6LL1aIw131cddzGp4XB6mywg0m7R4PnqorjvdrKoQDJ3yugXP_D8CqS499Q4fLF1uB0rivaVF1Og3B_HOqPLOn-Q7TFS9O5XdNOKx6YiLoywR6dU6dBCtmJv3SSwJxrYp-JCn5hmVUHQQKC2jlBIB_TeBIjOqzocV9yZu9wJMjHEdUCDmriLa2uzR5cCR2psGEU-vpPU6xuQSrent02HVNql7Xc3eAtwklUgtSx3SMrnRMdl0DMUwRaa4-uZ4S_kIutncFb-AT4YdIIqR9Dyk2JDO1hhFFADBNlu4ktWh8XkIY5fzUAu7FuZOe3Pk0xdk1V7qwnhphPB5MxcvvroTZGL5965OxVZiIxc0S49OQvcIZro80v71Ujz2cf8dZ55-3GsgUqMEHwelgUnsEUuzxRtzo3WsxRM8uw8tqMlLd_cyPxQytlAHZIQDy-eNBdaS1iJOLViDWCOMxFmPCFzPO4eEGMV9Cw997YUY5UwkLk69rTiGjh9nUEf0xSRgNR5TPRifFfUbfSb-GRjyNQ_kWpVLs-QUdA_X4Q-gIQwufcvPQXvJX5AV7ZVZIhmOFRUCmKm6BytHPjGWRj_EQqw5vVQEiL7uEd03YYYXL1BukdEz0LH5PwPdOv9QPzDSXF0X1mlaBDC4Fxm8NuCM_PvY6h_E395l-Hu6CHFkr5XJ5r9s3A-KIAN8JHWHpnWIQIhp0beNs_2zdFBFnK59olbaL80ARas_F4jQOFtgKkjpscRjqtbfkNG27qtx6pCRKkfc6Ywmj_vKni305VMRnPV-RA8WWTp9HN5co-z96irwGG57kEkpTtALnBWlNxW9i3NeZyi6sh4tE2UGunBvE_KS_75ZYpg6CkcOQOYKSYcPYim-4KchDDwWSsooaxpmEK2tNtYJwWNZC8tuQyFVA3d0LdN0kKuPvN6Hcv-6-rA4jJaxzebAV2ytRfRKPym3BQUcBFKHw9NX_j3ApdFwCguMshCEPUfkxBrJqFn-Nk5o_wUGpxDkQN2wqt0qkOCjN927CvJI4G85WHQ1EFwLRVNNOBGrUVxhCjAHrA1akgiOg726NxKwM7NfZ5rHSBZwyLWOb9XdyDaUQfahXL2hNTGvQ1YsLfytg5jTw-Ew_hKY8EfRirtKQd-vBCKVKeb_gmvtFFH-2rgAQAE4B69NbID_N6W_oiDvyzaVMXZIU8wpf9D76b_fxHuxZQZwGKkEMzBIWiTtyahdi_ditX_ffBnFOskQqk0ybA-KZqPvHznblpL3xnEsZSicI1W9qlhQMQFlgPHX3hJ_915TD3LVOl-V89s0x0CV0iitvDO85zG5fPWJA1pRRzMJYxt6EGjBEgXSkdgn\"}"},{"type":"toolCall","id":"call_f659d0456d8948aaa2dd289b53c4e0b2|fc_0b5218887c37459e016ae26ea464a4819f92a7b7aa6895bff8","name":"exec","arguments":{"description":"inspect gripper poses and visible shoe depth","python":"import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n poses={s:await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}) for s in ['left','right']}\n frame=await helpers.camera_frame(io,'right_camera')\n finish({'poses':poses,'frame_keys':list(frame)})\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":486,"output":152,"cacheRead":37120,"cacheWrite":0,"reasoning":22,"totalTokens":37758,"cost":{"input":0.004860000000000001,"output":0.0076,"cacheRead":0.03712,"cacheWrite":0,"total":0.04958}},"stopReason":"toolUse","timestamp":1793224351930,"responseId":"resp_0b5218887c37459e016ae26ea07758819f913260f114cb2f59","rawStopReason":"completed","durationMs":6232,"thinkingLevel":"high"}} +{"type":"message","id":"779def20","parentId":"676e1b73","timestamp":"2026-10-28T21:52:40.820Z","message":{"role":"toolResult","toolCallId":"call_f659d0456d8948aaa2dd289b53c4e0b2|fc_0b5218887c37459e016ae26ea464a4819f92a7b7aa6895bff8","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224359.682511338] [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 8, 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 7, in main\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/__init__.py\\\", line 98, in finish\\n result = plain(result)\\n ^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 12, in plain\\n return {key: plain(item) for key, item in value.items()}\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 14, in plain\\n return [plain(item) for item in value]\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 19, in plain\\n raise TypeError(\\\"images are not data; return them with finish(file=helpers.save_image(image))\\\")\\nTypeError: images are not data; return them with finish(file=helpers.save_image(image))\"\n}"}],"details":{"kinex":{"title":"exec (Python)","isError":true}},"isError":true,"durationMs":2658,"timestamp":1793224360820}} +{"type":"message","id":"18061e3f","parentId":"779def20","timestamp":"2026-10-28T21:52:45.319Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_bf701aaf00e144f99c654465d20fa7d1|fc_0b5218887c37459e016ae26eace220819fa5e3bf1ee2b54732","name":"read","arguments":{"path":"/opt/kinex/packages/core/assets/python/kinex/helpers/images.py","offset":1,"limit":150}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":712,"output":39,"cacheRead":37632,"cacheWrite":0,"reasoning":0,"totalTokens":38383,"cost":{"input":0.0071200000000000005,"output":0.0019500000000000001,"cacheRead":0.037632,"cacheWrite":0,"total":0.046702}},"stopReason":"toolUse","timestamp":1793224360859,"responseId":"resp_0b5218887c37459e016ae26ea98554819f8a6f36cb42036d60","rawStopReason":"completed","durationMs":4460,"thinkingLevel":"high"}} +{"type":"message","id":"af46c091","parentId":"18061e3f","timestamp":"2026-10-28T21:52:45.323Z","message":{"role":"toolResult","toolCallId":"call_bf701aaf00e144f99c654465d20fa7d1|fc_0b5218887c37459e016ae26eace220819fa5e3bf1ee2b54732","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":3,"timestamp":1793224365323}} +{"type":"message","id":"3d43df48","parentId":"af46c091","timestamp":"2026-10-28T21:52:53.367Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_29f165f826c5442a94f4599d8b7313e2|fc_0b5218887c37459e016ae26eb18030819f91651be63562500d","name":"exec","arguments":{"description":"measure shoe surface coordinates","python":"import asyncio,numpy as np\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n poses={s:await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}) for s in ['left','right']}\n points={}\n for cam,pixels in [('head_camera',[(315,160),(175,200),(175,170)]),('right_camera',[(270,100),(270,180),(250,40)])]:\n im,dep=await helpers.camera_frame(io,cam); a=helpers.image_array(dep)\n for u,v in pixels:\n z=float(a[v,u])*.001\n p=[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1]\n points[f'{cam}:{u},{v}']=await helpers.transform_pose(io,p,cam+'/base','world/base',im.header.stamp)\n finish({'poses':poses,'points':points})\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":343,"output":269,"cacheRead":38272,"cacheWrite":0,"reasoning":0,"totalTokens":38884,"cost":{"input":0.0034300000000000003,"output":0.01345,"cacheRead":0.038272,"cacheWrite":0,"total":0.055152}},"stopReason":"toolUse","timestamp":1793224365355,"responseId":"resp_0b5218887c37459e016ae26eadf8cc819f959516883e12832c","rawStopReason":"completed","durationMs":8011,"thinkingLevel":"high"}} +{"type":"message","id":"212cfb7d","parentId":"3d43df48","timestamp":"2026-10-28T21:52:55.537Z","message":{"role":"toolResult","toolCallId":"call_29f165f826c5442a94f4599d8b7313e2|fc_0b5218887c37459e016ae26eb18030819f91651be63562500d","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224374.798465320] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"poses\": {\"left\":{\"pose\":[-0.04576036873904488,-0.1518396196357057,0.8105108280162003,-0.06397159671261682,0.6655944712893721,0.1116262315077717,0.735140271677748],\"timestamp\":10.8},\"right\":{\"pose\":[0.3158530877393279,-0.0837637020994616,0.8411548580346625,0.5416649392500444,-0.4740655883365559,-0.5247640136212739,-0.45440471118866305],\"timestamp\":10.8}},\n \"points\": {\"head_camera:315,160\":[0.3105280301800081,-0.047369169413844336,0.6644731372869409,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,200\":[0.0007164514080967438,-0.13173156963338484,0.6424513366211899,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,170\":[0.0025748496589661773,-0.06011766118549816,0.6424117683126856,-0.9486832972651138,0,0,0.3162277683730379],\"right_camera:270,100\":[0.3420385028964361,-0.0843225323833221,0.7120579827021101,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:270,180\":[0.3485558820624562,-0.12394106388230323,0.7274574502446138,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:250,40\":[0.3286115784257454,-0.057996926368941285,0.6946765327046098,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335]}\n }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":2169,"timestamp":1793224375537}} +{"type":"message","id":"23c278ae","parentId":"212cfb7d","timestamp":"2026-10-28T21:53:11.085Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Repositioning camera toward shoe**\n\n**Preparing gripper alignment**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae26ebb325c819f93be8640125cd612\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Repositioning camera toward shoe**\"},{\"type\":\"summary_text\",\"text\":\"**Preparing gripper alignment**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4m7GYPZR4v3IYhof-RwlTLodWcW58RDWElEaRX2djJj9XGxw-GP7GeSRTZ7qbyYtPxLbWrOu4JUqanQ7Lf_hMufODJdWZ9u6BL3w50Crn57Br_MQq9gOYh7D9Xevc3sBthTckoyVgpWhBgXzqWFld7iQaAt5uthCBocb0aHQM9Qw53V4APxCCfhCZUQWoGlhMJxqZpTNjZXp16LXDig3_9JR27QFj56YfSuHv9rftxdoaFKuy9zPLCbyRVFuTx-zeKPTWzx0k2teDGv1QDiJkW_X-RagxyFuvQDzP4NG4WE6UbIbmPA3krfq-GYRy6CBfyvAvxYTYwCkR1k3uEEW8jviAlUzHOh-029q6vkDgBHtBC1k74tanw5RKV5qVWAqB3Ye4OXJPFHiHNdZ2eKC0Fp47fm1EZJNr5S0N2WFZOD-vor1c35K-lBHFbQrt6LNYIxSb9o3Vt6lEyXLNqEtbI5P7ionA_kjiBJDfVAllMIcfJeLeROho4KmDdHdBgwxFT6xBh4xnJBXE4Enn8LjJPlqYEpboGLcY-ebj6x5vUUmhiynzKdCh6fdDiWnrqBn4uNeqVl9yfkwMtZR34Lk8g-7yzpLAYAMMT9a6Rs8INyun-II7xee-m9zpdHqy9v15lQ64djiAKJsF3FZnRe2UhM5Lwm_n4cQ99PPwEL6Poy0CKVBg4PRawxWRSeNjAlBFryZvRCPfoMuQ1lcwsvVWw53n_QljTXAKtW8hcFQTw1BFc3anytHEMygOsGG0iSfn6QB1IjMRUOn9gGaQsFrpwAbM7o0VJhfS7iaPhU1ky_Y-8eb5bDZ9upPruXRyWHnRAyKFf1qvlBnDEoak_Svtm752XEmlkwF0G9wSVwjJTyKaCsA2KDUTaoXE6FvqU_MLuImSJt5ZzPu-GdKnPYIm35jg61QQyUZMJ7LuaNFJXu5MLiG04RgjNxTi-Gldz6qOGlAtvqLq88bOigw-hqvK61S5JlMZhSQI3U_PJjz6Bpyf1qIOh5IODiU0CA4dFkeN5wXTpPIASMPnZ-KxmGu-x9i6VRH4QuBbVSYWwOtTnUe_rn-rrsUEgVbqX0sKa-Pnm6mnJwSMQRof7ASQVEL0jEI2MxGSBrB94rOw-ku27HXD3QRMvdtBJ1lkGZz2vRJUUduUuwp-f8RhOVaD-odfyU3zuTJz5viYn6VUo0VScrEpTf9G5d_2r-SoMUin3hJzH-elRKRDGhgEQs_PfskUKrVCcxsTYSNkisAXIrwex1R9QUOLVXjb7HLKjswoLOQaUH4qUjS-tV71vcFbJsCr-YNehyXOOLZVHdpacCRedo_rsdlyBEcJlFF65eVIknjs8b8jbGQ-ANjfGYLndt0xLjnh91wt0niJzOSwgNuw3xd8nXvbGY0IRUE8STrxCeH5UdQnN_FbAIO24ZDzP2DIQAc9K2UhaT0FAqBF8uecsdRcfa3qMwRnCf_u7PIq-tmS4100JGtRQhLh32eml3ud4i_IgH2TMZUeBz91kc5N6kygFTmAkYedUS_GmQ2mdbm-1Zti9RQ4cQR7BBVmq0xoGCKjMrazN1eGJt0N74dnCeOVG9YfjFmJ7iPZSlR1h97A-wG8VoE80Ysvpz4jHnwKvI8jZ4RnTnCVDQUP-guYIpAF1TQGDRWjWutEJ2Xl3VAOuf1Lz3uQCVuAJ5jkz1BgADw355yY8WFotftAPtLL5F-Rz2zXacfJkwP56mSXHxcGX6C9qP0Igwwj65EVXIn5Rv2MhWUJ_R5VKB30fympSA_ObCL7MZxLDgwfcuwRYC8goa7RBUbbJ7cNx6NtKVcAM2JFWvJVh9keJZx0X4tKXjjxPOR6YdetgPOp177yIjNpWkzQfjf_ryvEpUvfu777NuAgYTEZUUd2VDW9DJwqGFx1jYPS3I4n6eaWqbHhBledSOIOGWQEId56jwgAyJv0m1SS2YyOqGiaTwEo7cp8eMly-URj_F37LzH44OdxHAhaCfBy5QGTuDLM56sWoS9WvL1B6VeCs_Rf5_Ohzzm1m3FcxHnn9i0YHIKFYNiB8r2N-LkuG6sybXiqCAX6uBzkMiRdoHbuYMpeMxn7YD39AD9ujx-8ND6XiNYn9SV9VxWK3e_d7SJ3OERUEychiHa4f7iK278BjIkxQ_iwVByPm4-rwsmO-10GX2XbO42JVBlfPaNaQ38yZ8osC8zUPXiKeVM7dSmJcDfF2KQTekBzYX70VdnHXzXP8h6fmMmusRuy7TyEmDt_h9V7x2bsBgYk5S0pbujGfHMqZe83ClIZBntq0d9qrutau9VbnNxHok9Ois2U9I-Lbk2jcPaibwfzdZ0gu_7WYXwe_tX50dpi6pe-IH_OCxOTD5CRltG0Cx-X6zCc1GbxZB9N0ARbW0rX6d-tbjceoEiR3WHTnp4IAJsThToBc9yEclEUR3p3ZRhFD-xYE_aQcRRS9vQVl1zPPNoTgl4j7AXCONC8ChcnkhMJ5mH3PVVE4i_yySOhsU3BAG69i18MlWtyuPkkDK3iYHDUCpLREks-vqOgEiNi-PPdyzQbyGS0gVzT2kk14_qoRSzkdv5zgErSyVfiSFTlqvsDdqbThMUKrE_xJ80tPh12wcM5LLr9q_9w-4LQ1GgXUj3AecOVPcnPUo-uJxUvMpIqgu2mgL5PANkA5QDjFxifFWs-jWax2V1XTGa-B7fPiXOSBYOguaIyuIoQqtPeVcLR2m4FMbmDYpBpMrgW35VTo50IQGnRuD93aA1FVeEB-m_pAYxftHwzG_peuYDbQIxJ2NLqa9n4fKUYMXdeNXr8bGJxq4R3axmmUERPdA4gLeX_DWdbcg5sT309fmUxXZPtxhKdKb2PURxE1yuwuMOaIJJLe2GDAeeJfX9d4pvv3xVqHMGzcJ-iQo4PcOwiffYBazkllBoVrqelJS76WPAhf_KF8Nyz3gApS7SLrysaIZ-OVbjFzZUSApnDHUe3TzLqddINvjJkwpFZ9ZmPgJz0hoilgfIN5KZ9npF_z3EFPLVNFEVfo2-lI3rBJk2D3JMBLXCOQzlVjyEVKexq8vXQUkVcs3SmIrvR1xmAMzxFYM8mujh157PyMajM73UDL5WtG2kY4bzjWP1YX2VNXxoCKhosvqECNpYvD-FJEnCCzo7ZHbh02SnjP4PAVB4BPYfkWCl1Un5n7XmGxHWt_0VUYwLNwt9NQ6Sl-sx4gVj87V3DG0jrspRVqDxjObEboovkRlgAv61meNmGJjQ9z5a2ZLXC2NkH00TH5qEY9YadOLgOZ28_12jt-ZdSoLc16aLhOQvSn4rldyQz6zOnELmLf5b6-Z-AqX7N3stvbarFtXzW5qzsBCOSvT6xO2Hx64sokB1AQUyBh7wS4c5KgVf8cfs-j-d-M9fOkIroS9XhHvkIB49iqbiiT7nFcaY1bGnPmFkpii_MtU63PDe3K4_jlsI9Izsc1vYSYiXB1iXsK8v0Dehm57becT9TiLAM1S6TAtS7b82BwRm_0WjkMUIESDnyo7OvqANDFXjxTxLVqN8d36MEUmxOOZUW0A7RYpCA568W5ps40O45nX8tha7QLqC64H1WtYdwdS4Ap7-b37zl-GPBCPe33VkXyAxh4ZbuN-z1DpWYbfdzM256RmFpQcxqRPNY6fndA5GPtp2VrGs_gHJb05WwNTW9u3IJrnwR8fMuwPBKXHUAo-9b6iW6P_oRMzqLpExRbGLxGxKjU4JhtAeaoV3SA1w70AK8EZgmVcF61Q6VRh3B7mgNyH203Sc1CBAtydgVKYPLdCtDnOEZo-MhVVpmmjnsRhuvISUXn8O8W2l3kmrGbVycDSDJ21DghOb2re9h5GTVt-JIdNN\"}"},{"type":"toolCall","id":"call_69cb68b39b0b434fb5973109b24a4381|fc_0b5218887c37459e016ae26ec31f00819fa910bf9279b5ea66","name":"read","arguments":{"path":".kinex/devices/right_gripper/gripper.urdf","offset":1,"limit":180}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":694,"output":238,"cacheRead":38784,"cacheWrite":0,"reasoning":199,"totalTokens":39716,"cost":{"input":0.006940000000000001,"output":0.0119,"cacheRead":0.038784,"cacheWrite":0,"total":0.057624}},"stopReason":"toolUse","timestamp":1793224375577,"responseId":"resp_0b5218887c37459e016ae26eb8176c819fa2fd16fccdde9a5a","rawStopReason":"completed","durationMs":15508,"thinkingLevel":"high"}} +{"type":"message","id":"48632ce5","parentId":"23c278ae","timestamp":"2026-10-28T21:53:11.087Z","message":{"role":"toolResult","toolCallId":"call_69cb68b39b0b434fb5973109b24a4381|fc_0b5218887c37459e016ae26ec31f00819fa910bf9279b5ea66","toolName":"read","content":[{"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"}],"isError":false,"durationMs":1,"timestamp":1793224391087}} +{"type":"message","id":"e6e6cdab","parentId":"48632ce5","timestamp":"2026-10-28T21:53:24.544Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Estimating shoe pose**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae26eca22e0819f9f9b0c2dc1336c92\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Estimating shoe pose**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4m7NnOdA6qF5uXqLmc1t9dJTjxHhZdzD6cO_FO5VJQSXHQMBgovRHMAPKjUDI34AN28j8Ck7h2WBTeR58PqQRYC5xrxtswGLyryvo0FWazdhZTlgEaK4aQoXevpIzVKdeGV1aW8X1ribGDijcWcYKNoJsrQ7nljcS5UZYeGFTCSW1TAguF1CisH6rEAN3ynRIYlAubeu-d6zZxH3oLM4sgsGAf3FihQXIf06yfiq2_wLC3MEPDyJjqBBTvNOWRgmQ7r_h1GHR0fEZCJFMQXurzV6ZWt_FtdxFfiNyNAGCDTxdHnA3AxZ6EZA73D1LYnXNzDvpmJJs08QgLsSgXdxupVx2HkcQkAcZHHNh3OdhX2I_TfJ19h5kUsWgb96U8OGpHtf6L0hY0Jbu-CDFVB85C3_-B9HS3tLhiWN89HIjt99wPgg_O8lQ54gk5hu2cUhaSlU1EB-u8KuNY5yUKjkob3AAsOJidurSz1FxvbDEAhVaw8uibum8HlYmo-afsAYsgOSMHXtPoKIialRN5AHIhVW1l5P4OIMX0hFkxZKfdr1hLQ_MmUK3QJwVIFwEUqr4Al2xzDviWOhP65py5hrY8_1Bz2yp1gL2xSVhAk17VLZ18bt0VXfoeFTtdkKm7K9tGhLSTTc01LG7X63uU87llWfQccoO1SfOcGwLlPEhkJRqrd-2mJnOeILzPZJmWkoBK3D2tieXltXm5L3uDG1xrUJ_YxHiE17TemNbfCG3TiqWm1Oq4I-lncMH16FtY6S3opmeY6Lyhc26miUjMlkkzF47QvVsuKzyhy21pDl6uaj_b5Co2Q1f7fxyFRr9sqLI8FWl8vzbgdF5DpFgkVB0W2C5iL4LEpafx0ghJxJ06tzoztdTzc1D8Pm_j4EKOLJr7WYUWjdIcbQqX_liH6xsMdhIBwqx8a7Fd2TRMl20l5v-6uMQl84SXwxp3EX1AIbIpuVP4OoVfoh8yoOb4ltVy1kBBtKBVFyQT5xR-FW2pWbM1bgF0nK4f8YfrA3JJynrP-z_j_o96Ugf9CnWkBOIO_wcxsb85-KD-G0dgcGAEytCZ_ZWJJYGjL_rv5aWTu2NfLFRc8BmLW_ZW5XcsYOFucgYnmh9Zga5bAEWFVUx8algJQMoioDgrl2ZM2urP5rfd2EZYhE6XMpQo0tS9l5FgcuNIoMR9716eTq78OKKaJ_AURwU0sEglJyo7oh9XROqZqOKPKRAqQJkTE5JPmcY7cfT86uqk3c-FrPkFkvIse3SWolKTQlVF5VhL4Zs_KvncC1NJ3bBjWR6r4YuAg9IvKMCj7haxjKbg62wUutvHV-t-I6-rbqCBPiVZKTbx_uxZ7yEZON2tu7XRatS8zW0-TWhIoK3VTUgW77Uef_vV40n-HyStRWaJINifnllJrYJJAnLHp-ALuHoWiMWrfx3VLz9irR_9HJtydif8C2vwuBGDfUN0sW7AC3bIo2jNWnbcF9EQxQO-S_YQ1WsvJt56TfkZSJnrKMOqk-4IGKb70K3W0H8zfhTItUeEvP7sq8hb843QKJWtzVUjyT4icqYxliz-T83tTPXV_ZuYWoMSKo5j6Q7Ij3Ks7uZFcEnX1iKc4uFQg2eNXjV_04fYlcAkfnLl9LmKV0nR3wAnUFkw0W17cT0AQd_CFyixkUXJNPqxwLpD_VvwizeDabdkD_It9I-V0Sk-UIbzif4pcNT5wIPTJdooV78OcxM0ddPW05OlwJqQI6LN9UVUkop-ZFRae1du8KrSfviktGHUoKBPbtlQGvX8P1AxcrAw2XC2K1rmUlyZyejqakHnxjIOe1fvublMlUxLqVHUDq-hPa3oncIXLHn4zlUxp4COoTPRrHD4UAPC31ghXOUndkAXfJoM2suBCa0VTX6XWqMCdEz_xKpEz7eYLFrp9CGKwit4qpRsxQCff4Z2GGXtfcLyV0B1T6-QgNUtx8fKPZPVUgXo81D-FK-R4OHe2uUyvbXrU-FK13sNglpdXB4qXVq7oP7W1vefShZaqbtOXNPkDEi4y_vC-t8M8sZQYghHC1fgW8hQCsIZMr0cJPQ-WOjgpQX9L91ryhvkqHkXENEBgY_5DOjuc=\"}"},{"type":"toolCall","id":"call_f214bab6552e4fac894e29af38414f0d|fc_0b5218887c37459e016ae26ecdef10819f94cee84862a9979c","name":"write","arguments":{"path":".kinex/tools/cartesian_segment.py","content":"\"\"\"Bounded world-TCP moves using calibrated transforms and measured seeds.\"\"\"\nimport asyncio\nfrom kinex import args, communication, helpers, finish\nfrom kinex.communication import duration\n\nasync def read(io, op):\n with io.subscribe(op, depth=1) as stream:\n return await stream.next(timeout_s=10)\n\nasync def main():\n opts=args(); seconds=opts.get('duration_s',1.0)\n async with communication() as io:\n commands=[]\n for side,goal in opts.get('goals',{}).items():\n arm=side+'_arm'; model=helpers.joint_model(arm)\n state=await read(io,arm+'.joint_state'); q=list(state.position)\n if 'joints' in goal: end=goal['joints']\n else:\n current=await helpers.transform_pose(io,model.fk(q),arm+'/base','world/base')\n tcp=await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'})\n target=goal.get('pose',tcp['pose']).copy()\n if 'delta' in goal:\n target[:3]=[a+b for a,b in zip(target[:3],goal['delta'])]\n # TCP is 0.12 m along flange +x.\n from scipy.spatial.transform import Rotation\n off=Rotation.from_quat(target[3:]).apply([.12,0,0])\n target[:3]=[a-b for a,b in zip(target[:3],off)]\n local=await helpers.transform_pose(io,target,'world/base',arm+'/base')\n end=model.ik(local,q)\n commands.append((arm+'.joint_target',{'joint_names':model.names,'points':[\n {'positions':q,'time_from_start':duration(.04)},\n {'positions':end,'time_from_start':duration(seconds)}]}))\n for side,value in opts.get('grips',{}).items():\n commands.append((side+'_gripper.target',{'data':value}))\n await asyncio.gather(*(io.publish(op,payload) for op,payload in commands))\n await io.request('world.wait',duration_s=seconds)\n states={d:list((await read(io,d+'.joint_state')).position) for d in ['left_arm','left_gripper','right_arm','right_gripper']}\n paths=[helpers.save_image(await read(io,c+'.image')) for c in opts.get('cameras',['head_camera'])]\n finish({'measured':states},file=paths)\n\nasyncio.run(main())\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":502,"output":672,"cacheRead":39680,"cacheWrite":0,"reasoning":53,"totalTokens":40854,"cost":{"input":0.00502,"output":0.033600000000000005,"cacheRead":0.03968,"cacheWrite":0,"total":0.07830000000000001}},"stopReason":"toolUse","timestamp":1793224391120,"responseId":"resp_0b5218887c37459e016ae26ec7a7a4819f8ca79368ebee2256","rawStopReason":"completed","durationMs":13424,"thinkingLevel":"high"}} +{"type":"message","id":"d7ca3c2f","parentId":"e6e6cdab","timestamp":"2026-10-28T21:53:24.547Z","message":{"role":"toolResult","toolCallId":"call_f214bab6552e4fac894e29af38414f0d|fc_0b5218887c37459e016ae26ecdef10819f94cee84862a9979c","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/tools/cartesian_segment.py"}],"isError":false,"durationMs":2,"timestamp":1793224404547}} +{"type":"message","id":"bacd7459","parentId":"d7ca3c2f","timestamp":"2026-10-28T21:53:30.996Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_1dda0b3177e74e1891be115c85009f3c|fc_0b5218887c37459e016ae26ed8cfb0819f9b33fcffb6845c21","name":"exec","arguments":{"file":".kinex/tools/cartesian_segment.py","description":"clear arms and inspect displaced shoes","args":{"goals":{"right":{"delta":[0,0,0.2]},"left":{"joints":[-0.23377,2.10649,1.59874,-1.06015,-0.01728,-0.65544]}},"grips":{"left":1,"right":1},"duration_s":1,"cameras":["head_camera","right_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":171,"output":117,"cacheRead":40704,"cacheWrite":0,"reasoning":0,"totalTokens":40992,"cost":{"input":0.0017100000000000001,"output":0.00585,"cacheRead":0.040704,"cacheWrite":0,"total":0.048264}},"stopReason":"toolUse","timestamp":1793224404581,"responseId":"resp_0b5218887c37459e016ae26ed57a9c819fa0528699a333507b","rawStopReason":"completed","durationMs":6415,"thinkingLevel":"high"}} +{"type":"message","id":"2335c3b2","parentId":"bacd7459","timestamp":"2026-10-28T21:53:39.781Z","message":{"role":"toolResult","toolCallId":"call_1dda0b3177e74e1891be115c85009f3c|fc_0b5218887c37459e016ae26ed8cfb0819f9b33fcffb6845c21","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224412.462059980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.34138405323028564,2.0631067752838135,1.5276085138320923,-1.006088376045227,-0.01025577262043953,-0.4240250289440155],\"left_gripper\":[0.9917404543269764],\"right_arm\":[-0.0485311821103096,1.9509698152542114,2.119028091430664,-1.7710597515106201,0.0023190497886389494,-0.17672263085842133],\"right_gripper\":[0.984824080358852]},\n \"file\": [\"/tmp/kinex-observation-umnt4jud.png\",\"/tmp/kinex-observation-tqaq0u11.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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aWTxJthoOluU5M+WA0fw0E47hAFih5e92o61rAjx33WVyG1NY4DFOCBe8L7fZKRSwo3r0DPr5AaqcGAb4MTMUBbWSDR+jowWFCND+uV6JeZLIjVJkxxLSH6VbZy3eZ0oCoSweGBM1lCWCFHJY6gYC9aVLqgVTlQWy+h5+9OtNksj8iC84JoByx+nHELeGVME+lb2AAP1SEQMP3oZvw5/Dzv3cNxGh67zBIHZAuq+0G2OpStPdYYBTEIhotAoDYjTzIsYni2k2mxS5lXeNXF/rMO0RMggcCvfKAdzQJkmVk//MdFJ0n0l+VRjN7+xHFRGnFawZ6rz35AHAqxbwksXaUBKRVe6SJVixxqHy0zM+LQsJ0q9nSm6Yd6XbV3ndd3GKwV1SRC45h5j+pWpyp2ZNVfmQT0AKUJyUsNBCIt5tiAGw2kWUQMnF84wRhWovLzf1K5KiJOvRWa9KcygN8W+/PAybz2UxnQMFjhcr/5it6OI7M1mHXvsKFBMWBxzzZ1yX311KR4ebXsp1BYgVKUZ/k7NLBZDG/QohCNRSxuczQsOPJ6XhSw9K6fkQmz5K68ONSsr+CrG0vCDLrGtoNRAuU+Lo0C2dbusz/P1end3dzqdJLJ/Ynv1tizTleRqisqk+Ws4mrfUhJoAq86ogNLl+OKIBVFE+LWg6yRX5NfalkEOjMWzyGdyj3cGkkkFkoiwioPI7UHa/asVlT2dWqlA+3qX9+7N5OxLRdWgJk2z9cXc/k1Z1+dex5OXg6k7oci+XShCn5KEuZqBqb72rCRtWrZ1O9N9GsfTMCwYC9eYc+2zmjeQmxZ+7g8Sy6LYEAxjxkCUJN2/6jp6LrseQADsuGCLhBHtWr53sqsELEgHzjkoHPP6fL2up9P9/Z1w42Fapn2b53k/na7X63S9XCi4mvxogsZp1qYZBMRn0vE6ma59QpH3VcLpjbY5NYrFCwm9aiDS+EIESkA7Zs+TlQtK1d/DWOVEbcfbKjuA0hDwCfdyd2GxdNJgfPg1S/MKGIVUpOxJz5OEW9u2Xi7ny5lijYULcNQbF/eQlDnWPuWzu4xSi2Tl3bb9wkas4O8O/YmeOS1KjLwzYYYg8sW49FT4GtpFiW4cRsc1HkC4wgsRyOC/HLcX2WWJzSEuQSMOLT7xsPHsw3C9nLfrdbk7LSeqMkMrTqs9k9GWUp4IjRmHqW4Mpo+1AsYBHPUhTLeiL1hmmgYTv7EoyZ0md8h1tjj0pL7yVnZklnQzIfKrpth90gwLwcMUKOFpM+ra14zY7byzJ+ulq64mMgGYsuTaZGrcQRYGist5vZw1tWfkkhY8M8ZcLqYjBYa4OJRkgZk6wlg+zxTfv24TR2JpgaXelA9QInn200zJfWTlTPovJwnPySlaVZNwplS96chXChCu9Jk8pGms6cUGDuqCy08STBPdkTfp6Wl4PpNmfGKfE4udMxut53m+UuLEuo70R7Vii0J3z1OgNBhX2nXNE2ov4ykw/pdgsyEkdkfLNYXPz4TQKk++WoZC+bo/AAXOI70aNKMYbU/CsqtjUnuFBB0/v0T27JPmQR/RCf8JlFCJKti3VapAobRoRnrL66O/VyrVRhWPxL3DSE7xvlQzbhvGbV6WxMByoklnKPWnNC1KlmIX8MFidASdKqKltHVPkq6S1dHm2b7e0NhjsF2KVVhSdAxelnCvi8RxPm+XM4VxnZbTcjcv8zhNJ3Y9cWTIeiUUvujCSNybFnuqa9N1mlTg7IWy6dCBp4ahGF2ZPQu5ql4MNRMXFbQiNya+dbKefEs80dK5TchejeDbZdt6rytx3dCeXrj2w7c0ZLRQAHzomG87IYpHXvBXB1eSalGEhtd1kEh7HKgUIHOCpaUBWA3jgmmEvsQcV93ecaeowfGZOLDnyIkE0Jd6AiISRgQksy7OYEqkRS2YKSaj2gV2AAAFBkgIg3VrlrUShuTDbMlCjw0fbFSfXHdEAKRn+74Tv71ervN5Pt2d7k7LcjctC+mS6zqTW21ZycBFuJwz7EF2SNF5xfOYRteQpMJ5oZxPtTgk2TNpMFITk+UzKaRz0Id/MZkukd2enO2fkXb0mfFrZNoanzKW3wuFKsJuvHnQVyaCtemU1dd52fkHNoOFuUinWomvxoJaiRMQNCDqw+psULEydhAYPx3XYbyMA8VCHxGjtCg2oxvP7sPONliqt+fr/Ko9sWzOFmwr0svNtIypjEymhonKoaCSXUr1cTRGZJ+Dvp0Ziq4o/SEL3tP1cp6X5xMbq8VKwaoM6ZZsqJaMRaLBGuAOnKoOHUYOVQHay8uSeckrzCI2zuE70m1CTSda4bUsSwfLUpJAb9xVeIA96PzwIqR0RaPelSiN3rJylnY7LObllfQppFI3DyOpbLrxFsJaod9MkTAO5ykIUMclinYi7qAJzyMr9n2bhm1ed07URq7UYcAgL2lz7c8KOuxCofpJPGaRrFKDvmiVjYSC2ROkUORzjbEfAJ2t7rk306laOhrShX8DPPfYAV1SNPvhJQUVJeTy+vx8OZG1el5ORDm5JunEUSErpzpRcDXXdbZZddfFKHQf6xKaeRxkW9aj96ZV9UbvbFMBEMLhYEUbh247rCIhIk/Sii9pEyAXvF5FSTyazd6MtNOQ99FExNsvOUqsIwV1qFASqVKjOCtCTxKCQ8k0347Jn5a7IqbZqIds6XXSmNUC3sZxm2ZNJwy5QwOSmgW6dTnnouglLcoMFLoEGdxs6qD5Fy0LxdkL/x5ojP3t83Vy4ofvBFu2TF1oykgsCEeX8/lyuS53d6f7u2le5PWJTUQcmnm9XiSIK/Fe7UE6RPPr4dLACI8f6LwslB2JI2+epB/pMjTS5s3E+tJBEn0EJPTLwSaE3CbzfkHm8+soD7+uSi7JhAUrYZ6Ax0cmEbiVO9BvZXqawEF/rViEr2wUro70uVRaD/hxiOhul9ZYaNDMXzCxtyBiIhuZoLeV68JHWY/OFYTkIMojvwUm6i7rt08HnD0oXE9KKtNyttHNGfKJ2nqhTBtbFPybPmzb0+PD+fn59Oae/Makw3gBfjJaU1V7TnDSng1k1YffgEZvtZD0dFddTWdZDE1CpWMvrG0HezFd+ebl8ZOtxTiPHCS6VihrtjRteVx97G3uNJIlEr+QDoANZzm5BzcZCGuIrm2mbOTGIqqH+YRmhF1p4EBAkguMFnEVPv99nMgPnCTcPJxXXmKxlOpxhzS6tdfdYg/lzeZPyLnRWo+WA/NspgQ38ib0hpVK0IEFAgcBr0VJUil/+fzwsFJqxInPoJGoa7JKqgoMARJOQRx9jte0rBF8LlUrcNBMa3KVdKyf7eS+TB//eeHC1IjjgaONWrle21IPh8s49j6wtgpudlcm0SPpILfG2sw0bfrBAOTHkdxHUnPZXlUupt9BjGxqViFmCkMX8FjMSiMSA1SL7I63OzV7lmOwKMKrKAhJjqmAdnT1kbsh0iA7JlknT6Elp8ezOH5CK51YVHVv0FAjK1N2ujjOcosYCXH66VPK0Oskklx/43JhsEeMS5udLzIEjR6A7NhbDbx0QR27ZrSdMYJ8I6OpBDq9m12Qe2UPldyMNyfe++lAtj+aaZOxZJNSZy9vDFdbs0qPalhJNlBpQtHaY/iSAGrEnNF4HbYlUYME+lms7s0EOuYAMUlCkmNuTH6vEBmM7Jsqw0E5+xbIWziI0+gEvKVm0vTwY8MFez3acpY21VLITj3Jd0Lbc9taSjCOdm/qHMBvkGwH62ozS5zvy01OtnITVcmHKNh4a0CIRjdB35+oxDaJlAEtBRz30qmvwBETau87FvUm0aRetIPPPfca0WUi+VnFZ/B6mlTcNJwqMSRYCMWNeAFlI/nPieoeL3zqzdIeKQeYjnmC29XEeXs3W2rZX/+ypkejLVvV88O/QipFias1L1b46YGBlilwest/YcHRgJjn3zOyeRxhe2V8Lz9U2d81LB8F/18OX/AfijG5021mhAc/twjTPHXzCoPDQVBOe9UHYx+NtJnj5JDuV4LV76zLvbuPiQ+4vgW/t1NILD2xd/ozDfsbOhfvyPz+spUgnuTz1yTut23kpgyVHs30vaphHaNEpda9RuNADOy8oVf9VwHrej/roKFiUOBJ4vJu+1fEsVO0lHzmYlu3lseP4svE6XgiTUxG4VIhWQj7rfxIF6olD90Yx94a1etwnPghljebmgAobgQwdIiZHbMmiyhPaYsZ8o4alARdG9Xh/LBb1bnkLjn/r1c5B0SPEK1AX5uq2mJmIHQwom1Zr3dfPuAI5QO+iBGUCP0ZypOZ88bMX4P0jfmu+0ocRPkSI2h/s6NFwg+fmh5vbnOgcTxXDlfSl4whQzFRmE/QIJQrCvaGhTmBlt+x1+NzJNpL1CRHybOBLY2tHW1DCxHH86hgDdulSjnSvYw/nIN+LM2MWTCQlbxJzkpPR/gjtAHpUwu3BxAUozbUVQFiXymCEltu8PPWZYcf+0mditEa+IprVjAaJbBDgkE+ak27iUmra7rZWATPxtp5gL0tVe0WKmiGhb92SFXlYvbZA0LsELKeSfe4U/j3mFAdgE762LbRkQzhtsnFhZ07CEIyuAGFPjPSgXdawP0oYFClhXTQbprWreiOBLdJjEitNYJYxgZMCxhQhAIy3n6sr+QJ2Q+y1+l4hJb8ZECp+9M1b0qjUqZG8a0EGACWySnJLl2k3g2DYXqcjZRKRdlruQbGbYYlSQx8SGgr4MFEUrRFDoXow3O+h45MIIStob1txSDSOVjtoIy7aahRSG+6N3v3Oh66/mbjkCq6tqNCtpyfSkpT6QKZtUXnSTKqDhZ2px04DtEK7ntM0a0Y2ljlMqi8q33eax63GzvdvSLS8OYFkKl30jT7i42zDVEPfrFIDSpsyiHQCZYDLPPJ196RrQviitm8KYeVzmfGt/LhLFWLCHoQZc4sYpLy+P1049esF6wDWqKa3w9AHXWhnmDa9nI0Luz7YKcPeP03ASL9i6IwEN3yaCUz/nyw1soXumMhDhvEvK537DObr9iBlMO8/WNSAsoFolTsvkosRa7BtgOa5NkXWIR4uWIEncd37E+EplQXpZ2Bg/wL/bfqklsWblgU5UH5mV00607n51l+eE5kx9C07oAd1yQGlnjmvl9GOZkBO32FYIozksQjak10YAib7KJeU2iwAzBVQMuC+S22VA3FXaJZHosys6+5oK10usBLL1UFAFvrSQ+58XguYX5GsUOD6u2tFAZA6Y+1hN3B00B+6k4F+zk6n7arswzHhV9u+LHS73kNDwK2Dq4DuTcN3Kd7QMRr8keJ9NE8eZi9lq0D87irtX0BMLVP70zbsFFVSrwZ/fYpHLToIXUkoVCAiRqxeAyHEuIrKEPzreVH/Xc6QR74RAZO+PJagQH4WGq6jK8211AhP+u5H9zZQMnhhpROWu3lJRes8D8uPzibBSsFC7f93NJT2m92lGMCaX+izpSR9GCfXyCVu2/Li9F9rVzZ/ggaRvcn/9gTSAIDXe6iOjUSeHcgD4SjBCJx0+TkCC6TQfSUEDoiGNRlFVG+SRk6JtrkoWbxAKZRq+l0VqRO/GCTxMNy/PNLQ7xJ88v1EicuUlrnZtx4xYhRMj3uNx1rcTiofnedQggqvymdVdpGaVKuA5vM+sr5eKM3futoBO3dV5GpTtt7Gd4hFXi5PZT7EXBebrCdnksjXCidqrJgY8ro3H+om5HpAxgMR0hOst/3aduoqB1Kcs4vuhwllXewXSO5XotN4EuHosAhr3r5Ssp8YjS3eL5f/YzaLB19g0EJ2ysEt9nIA5aw3/p+QNvTgzJiZ71JTQVREj/sDQ2Sj8x9BXu9CofEOAisSVw1xHGhInGAvV3O1nBcFQL6G2e93NiSERa/Jz2+yI/9aMwyzldeZoBCKRBszAIf28YWIk2sTVEJ1q3ctDhdAylNWUCSn855nDayY1WCcTyLlEebMEZt0Hrnm67CK69j1VfGFpm+lWcg3cpX4u2ZvIFvoZ+Wo/+GJa9tPYZtrC1O7nQATg/cmr/2xab+oiL6v9FmjSVpkEjxw44gJAtWeBjThOtJNNjHTZntkKyipe62nNIlZNlXNDjJ4SVCBS4Bdbevri8h8ErbvHUdx+Vq9B1lfZsCLKY1YELBed2FU/ARQdrpp9Su3Cc3YkGcIUztAALizs6V2/mkoDbS8Pa0v4HBL/Eep7LYR4eooJYboF2GWFW6OJKkzKOP/vxHgaY/IeNaBzTEkKiM2rImbDmSlKQ4mosB2zThZkxJJYEyd/0jJfjy+SnaaHYl2cc+pUe2J+dBlrs3rigu2gj86bJh+0D3ziMJPVBWqDdTT/BE5WftU2lu+gVFV7Pk8k8UYWxJ346kwUdTJTMnm+0YIFlDazHsIx3cpUYs4SHZjhli6dE2SPbKtl1FhLZjk4HjHwMtXFmIT/cPvrQNtO12xfd6uwcBL1+6koDd/RVyLahPbNJLSciVO6W2QLzVBERDRx3g9I+Q92K+f61S2tTBMnZxE6I9Jbos2uFqHKoTOEq43fuIeDPedgTcjtSOIz8DZ17nYW7Ug/B+wzZK9cKdDr00/G0mU9pBNC0dioyNNFaOyPXTCeE2VFt+AW24VT6wb4sSOtbli1aP/nr0ugVAxv1DilwnfABTHnoQtw+PYuyNCXfpFnkqEZRHlYqrq8u469E8jnCpLM9hDobSaPf3a/yG1MGCTrOJPDQ9LNcP/EiPvU8jVYpzKJV5E5r0YmICqIjBSxpJqS786L/UFWii1eIV54baZB+Hgzo2M+mdIWD3bSs5y8daLepybq7fLWNvh0WM4lCmonZuPu4i7QsKAMeISYVqxXuAG2zQ5cjeeY4vS85wZUEfGMXRkX6t8+MFlG/RoDf1vlh8263cFenyJvazBHojSdJ1sp30xBrowpFrGlgBhiDpnikIxFVZ7t4adx+PEyo7g8dYPADnzklit8ADL8As50tF2zi4eoIZMqQDKa9QzdIaaZdytlbGicIxXd/AowfkLyxLngOPi+us6fmR/mOm8Tf4sAtZ6gHOvh4kkWGvsWXJG+CgwZq81Q/sLazfMhZS65km6R1jvnMDkfxjK6tu9JqKkCLHsacSJA5XB0h661cQBUqP1ld6qRQxjVpgWn/Li5EGJgsiyg5JzzOfLAFA1x9/5XE5P+VAaoaVStJfHlG+c5tj4JDiyshTd6ZLUNM6HwbNi0PtiKf1YgugW/rI7A3SueyLgmCkotUdM7gNvLAMEAyW3P0InACi1qZ4rEWQiVwq6YDzLba6bae/TLXZGyItUIaXRe/uFRZQkwgOnihkLVC8xfTXwt3RyF0Ny8zu9iXvuPCZOgjprNBJ60/O9SD8pSr/MIgyzj5KvaxgNWN1AE2iqMqV2qjDqBJnRH7oO0vTg8sTgY3N6LqbDFPsPepd15cF8yCzU5SOKNln71MNHdEuc1Bnd0/ay2ljJvJImVgHphRFbS1mhRpeX87nW3SmJvuQKKCygkB+qT9YfEb1hhoWmCWtuHNbti49dDf9QDVtTxOMlnCLKi05goRclrMoz98AC5rrlibg0ph5E8MWHqWO6dBjqhmuDx4qDd8sfcCGFiJ+Wa+ewNr/VpQwqPYTT4zSyy0j2eEgD2SbIY++mEki3gUR3Oo8Q7vrSodzOGJ7I60X7QhVGo0Gvu2bnIr2MhABhdYhuthER4lyKVnPWefmbnHXo7CqKooljlFn3h/yUVRrQsImqzQJRS6B9MQQp4jmzXsF2+2Qvswp+g/1L4/dOabh5pRW+Vb1jOJEYlCapmlZKIYSmuplEt4aZ828SzMsepQCey9h0GRL76MT/QUFEFDG24ED945OCIZeQeEQdxNTkT8v7rWHIMSwvYYO/Scmp0b6EIQykSvrfY4oDHHqJjCFSo4F2DZxI92ytx/9IDFYcrA1LPGL142IhaS1yIJ3CkeZOdTFzsLNeiQtnjgutgJf8yjiY6dy2EE3TgcycUjgl9r5RgwuFzO9fUX9HlajXMwbB6oxP8sp0zaCZBWPmCAoiN0uYcypFXdtF1HsOIYSKGFvyQs+STnLEiXu9Kv+43J6UVsPJYim+kj829nmFOFi3v+8vyZIMkUi5sZq6qEEXwlxZjXZSYHFwUZudqTzGQpU3bgA8LwfKyUrq3a7ke7ql7Z9EtWRVcOPnFB9k9DtvDPtd7wB+ldrceu3HcS36Md5zBVhUaXu8IhOlYme3ero9QSJiBPjMi+znoTk4ywn/sa3DjNN7MIZiBS9LwPBZTjYriL2AOW0G4E7t4SC0Z1xtSn47pah/lhgObD8HBa7PrwC49gEjSdpVqmikrKWP3dHLv8Sa78Ow2LmJwsg8RU6SjHFxvigiA5bi0desXP10ZpJl04KyWcwoFPjNWqa6zzpFSdM+804i7S8HcX6YDZwr6Gv441lEm6fBJDS6XijK6ClXucKkJGOX5jmEx2kKHHOaY7AKav/shlNGXN7tTlGLle1YFOF7u561Y72PFVFNQDm/qVonoQL6NnGXR4TO7ArA+3l+0wO4PVqBkNOFtCXIEVhuLGedraZ2WTK/EfKZBhIhJYQaywlUOd5tKRmfs4V/Q+ef+E6QMP9Nkn+Rl2EyFQ5pN8MsfcGq22lxNIcilntQ7WVfjhRB7mbng7BPVazd9FtDsCS06FbGbF5GhhyikDrd57a6q6jc7LUxG2qmA9Urs2N+C8MqKsfGRqF+Sshai9y7htCGqcgrcO6id4CUJfPW8jLa2RbR1oosx1UGFPbdubARqDFkVwho8wjjiDTIGg9XiBT+QMKGcM5wj3N12h/O4Lxm5DakZfzMGywJTgLXimhNsCkDfdz2cRe9yGFtYBRsDdRlmB6vbEnmZui0u0g1y7JrabDeZoWiqDkg6YtlCDeOpzL668KAy3R7Jzd0BdzBGQSm+rs6x6rWU0ZjRDRWdQIiUx2DrfNvyRuFXWacmztFC0t8oxh3C0j0ZwvEGgdQlJknQ1x3MdNgjk6HKnDFKpPcHcfklXMa7WRfhPNb2b6lVEdrG/MutOsj/r1tTWQtHqomsFWTjFNWmjiP0mu7MwtFbSJ+cOEbw0uHta6kQfr551hPHObICs2LGUHy+k0jRTGU6/xxnA66nj30R4xRC4upN75SSP71dVORuSiH+63wOylKxHBzlQ8NKKZYkN2EHsp017jI8SLVxroBhJIQ202gUeSlCo8bMUiN1LON6zcpjMrpYVczF104DqpztBeXMOMTrnvTjRUFZbS5pebLdy5loPNtYz0RdhoWciNAX9TWaxyHASTbhZ4iD/26kEo8jhNp7tFc3+L6REMZPVfWNhjLeLwfrCpjoCa3lJZ0aC2vtLfBL5KKa6MZFlYlXu9Z3pvH/tDJX5Dlz4IgiEwlq7UAWSTQLH5GcZCjVzed38sjXekQA57KHkIaqR+R6oy71apAtAaSDtOqo62441ks1UsWHw4oA+VgHe+dJ7P8kpvzo0Q/c0U/RpXf6A93AgpgK8eYl4iGJFR5KSwXrwZx1/NB/0eOcoCwpwMOnwdzdq/3TzTMJppkhZ6T+eIe7owvrizZfYTfHDU7Q3MyZ/fiE/9DFujrf4j58dXkGpOvjL5o2HzDbIoiKaKvlxjh8iw1qJFQ1bbSrMcJCcA+9VyA4d4lC+UNyoY5u5vbfzLzXdluHwdz/Kw6xfG1FA6dIIO/4VXeAX5/2F7r2aLNjYZpQ+6P42nhQrodFwN+bu3Wwglq2EJg3rj1ZePnkAXQLajhdcYBZxEMm8ncO5l5v6xyUxKAopj01GkXJ8MNeUVNNCCjAt6DoP+AEvmjLQZAV5NHWtJPpdCVpKuSIVU9m4yaG2qww+klKzDB2PvN8WSaLfHj+NzgcgsOKZVAHKZ/6stRGUonMHhkP7/dYUWeGuVVLixxU6KdTxhTovSvm6v9DSN07KcRlWAna+quo76WG8QBaa6QoOIlKDBvo6Ux62qxxWZs05PL8fNNmYgP6EX7LaRKh08qIJwBsqLQgS0RkVfCTugUFyc3ZrHhNzYe80g0VPbGG1HMWLldInuAB2JIOyKvcCgoL9auXuF2FmwrmNU62BxC1pHa56V9RAOmwba+znbr0cdjuYUjyY7Tynb3G8RHBFpYg1dqp8wHk8uyeC3BYvdw0jddtqddjuYnImqxRscXa3RARtredTBtWPnkqB10G+xTEb74bbBH8Fq1x1EY+kA6kcOGrdCd2TB/VbeQlJ3U/uJj+3LTq5guHEcS1V0bzq0mvIcr3kV3LOWBYIkIB0uR9ujcIxuOHxVl+D03td04AIV8IgOUzsQAr85Y4Y43+blFEAAwmlKjNV0axi17TjgzBGDgUaohg5n8OuxWM3WxFevwIaPFPNEInIpH/ylZdJJggoQiJS38cVwqXQdyzKoSSjLbcSN5ImqE2ljFMJrqzc04oKcwFeqcoFZ+z0swCZt5EXYz1MAnxVFaE7D4shR3UAdWHN1my4+zpsNmq3UlclYZxXbVothCAClGCrym0Y7Xgc1zUP9qhovtfFNtPMOi2xzftp9dHOC7k0cfxP7pNqivuB4H0fLJq+iBu4y/1VRDlb4pST93mTaX0xRf1VQfCxFp6Vm2doQBcTxF3spF4RI5nwMt5FxHebUU23G8dxPf5ZtkiocVzpIxaoE4hhtX0terBuoPD7PKHUUNcsKzjaOK52NpAVNFDMPJtzOb1tpiEhCmzOpe9PudNDZMcCrHikCgHu92N408AraEhQxxXf2mOgh1bzR8sEDY8d7L09lWOjUAPfmIPM7HK47n2B2mhc+BDhF5pXqUqqXh1skzxO5f5chH6/CIVHo+zlM4UPWlIN+6YKlOgooxAuaCub68pDar00vwnk2Pkmlqi7epaFmq+V6nKaTpUDdCJPS3+d9u9/2xexZVcHWvjtUh7retv262nHetM6SltinvQW8+/Qw9ZZhI/uvy9WjGa+9boZiVoLXeb5oCh00wh9wis2IHaK6AYjFt9Z/Wz96n1bDzvVnBgjiwCQzBVNuSRlOMMlVENjfOb6+jzoNh+3tVldc7Dx0cKDcWN5L77oUUuBbq4L12stCnW5Oh+Aa+pvwI2e5UxWObVvVzlRG1fiWrCm9H/gMYbgo8Mu5cxTptQ/XeVnmkdwJeox8rFJQu3a1xmFcaYxkgrbeQthrnbghsJZmIDjO/yK1d4DOxNtum+/q1tWPCrzxjjRujhJA0RfZ6EFj5dHsomgbVW5Z2N8NJOtf6IyJsN+RjlChDGCOnSzqYiWvB4sUklH/l75IfOO13u0AGeS1r1iB3T8h8U0hDq+7Gl5rgaqZo8CzVseOhdNwvyd1pUeABLdrDefSu00hQJ904G1diQN3zpzu94IWaMvUKAtjQG8y2DGu9Aj4bWTsZO0cpSJ1ZhSB62hq6faYUndgAfqiVo0qePHSYmkJ1irHjUOFwOt5hO5JUFBFR0lrmgAdYqY13IP9qviMbYdnEAYdPMypNqwi0t12KjGDG8eypiVyuVE6frWQNXaWMou7qlwcXLfmEa8XZQKWgQ/K3ti4K+WbLaXJpgMp1eWI80bcZv7tTC54uO4DnYiy8/nACQtBjGsWx5R1P1JYv7n1W7lFsqb2F6uuT0YsE00ybCQcNr2uz0CsFStgoM1Dr705toJWf8SmObi4IZtRDe8NEbffKpuoC+Uqpq8D4mHPGOqGaU7YLwUxCJLYiC01dDh+Nkfk4XL0sBdnVWR83TcbbUPKsgQV6v2NK5drjuGYONTXowaxxjTqvP2eQbw/91dcOMEOEI8jSc+rnMOAuBdicFf2cX6R4DlhbwLafd/ncV+2denyopZIZdrLYSZymkpaz7y5RnRvrE/WItGu0g+oxJQOFDnq4NMbaftf3Cp8CFlsAzZZGLafX2IvXcrYANVxawj+HY0jSFvaQ9k4jaCMKs5Bg4Nu1gJQ0MIRTveXX44PKE+/aCKBgXS7yg8Owd1DZCrWP9xMCGZMNLEIoYkOBxfx8wEPoZqTCClDIOiVPp0PppEawKQ0J1xJz4SNUn5yiq6l/9ZhPI+UTmi+b91RbzDxCT8i3SVMhzEUoNNOvoKspVXv4kiFm6DORyZz2862GGiC2aMBdfYy0kerj1sCv+KN8Qgya58vDuNAFi2tI7ZpOqgISHzDEJIlJj6BcBmnOeoWZDnQmlES+Rq+pGRi78YVYQ9l2HViuf/uMwcRCrv/yZOwP0WsSONAFK59agNRs7lSMIyFBEgk6y45kJBuZL0P4xuacaf+I8ugQW+x/+z36z6hal7mBCkSNTaNYrC2zc6yA6r0Kj3wxkMeUQXUZ2xHHz++qgs+qB4J9Quv5bw//2r91nKnadVSW4cjfVkQ6Ah60TB8cSOG7gQAVp4ayc9LHCGqYKBdqU7eBuAfDPmF6oKl686n4w4OnqmL/AKk7d17OboPQCthAT4hhcoJlwAQNH82NSMpBdvK7tVABUUQi0uTL1oRUU5OgwKJHrNZKxvZZ1h22rF12BfRGtwgpWpunn6RhJjzO52G/Gck3Ul2Scw9LVDLmYJBDM3CJrHIEUS/9FhdyXB/GdgytWxFmjjXPB+metDON1Kv4uEDUaR3HTVf8Z8CsMR8dSQgJ/FX/9b77ZRuaki3xyivlqlliwy4RKs81c9YG+uDyZRwg2u/oAKVCnM9iZegY7teyQRtIwERWiAGzk60EmIwnVs+bJmYC5ZSw/ky0OFmUvPdhllkxPS6ijJcbYCjtJOElBDPuGiIkR26GeJWRs0Xrp60bWpfRsDU0Y2Wk6Ry2Ge50UG2l88IffX1zbwexwunVjYyP5v9ufOyQ7RIGtXnUgzt7SIHDe1NpIxmPMZBvxM/dI0hHfFkTz808BS4UW2N2DOhWFeyqAy6O9OdEJhOupc8fuxXoliLCySmKH9bbhbTw4EwGdinYftk3RYuXOswPh6ODRaL62WuJkvo5Pvz7Wk0R0G7eLMLjV3RSSfUa7/3fIdIv4wnr2KiR9PSLqLjAkCIE+ZJxAa6w0ttHBAqeJ1PkeQY6Mro64C+6dxj3C+bPDpnN1Wt3oK8bCKlUgqOuhEvmyvCT+Wr/ovKa/OKczg/6iuYQ2qoZ+sm/9F13DbO89IyuEwRYv9xOiKct4JA2C64VRQEU94l+5nIiAXyQfYqdi6V5qniHncV5egb0hlSsMv0cYJp3RHxD962ERcMD8Kcre/HjQSR7iJRea57s7mFlZJ75bzcjQIOUf160O7+sv0G2sMTF5ReQnyMrghdM0dwsI4UZRhspmlE8XamQdh9xbzXzqWIqtp/u9ShdB20CYC0Q7nuNPWuuIfDLuyl8vX4nNH3gM1JBOVAQcp63LKBgyjShgMqP2vhETggOJFBgCkIgJPWKKebouoe58mPF42XclBcBR4mTRtKN6LqQw/mPoB7QB9EhgkckWkIBGXO0Lm64k9AH1R7v331IQZ+bXl0udmDYJU/k+yTJbtGhjuaYg+WDoIUiuwo6x3rwDDEhzBQ8QY66hKAHNDpxbUv033hqe4kbzL6JIynd24J0LsToBtr6nzvEEXrSEDSbR4TbITvCtoUnCgeYIsKKJjeBMx0Wze5w9E127nFxCXJ3NvOmNzsZ7+gm6lHlAesww466Y/0lxHal/M+fEAWGmJI4mvSbahFm+6WeFRFR4J63fUCCTjMn3ZC1Nn8KmS2vHosK2EfXzbvB9qmkgbm9pv5FAb+qKXvhh7ay5+XdYo04s7jyP77Qy2ArDQ879//1msMAI1jx7I/tvteN2u4HZkXGlR8o2OQiD6GKGCPxbvRDmYjpfT3xE5hf8xeTYW87aLaDF2PXytDAt8D5mpJV11pE4O1MldvmlVF7ZDmweP5t2Ph7RYUdkTkkMeTuFJYKUTA5W6D8mBZocMxdKdpAvmL80A7SOhMidSzrmLgNY3jHdWgtOg8k71xMFJHq5SXPUowy0sT7R0LDziTpAaidQXClJjp/G/F4R0BDM7vTk9UKorzzWENsNkgRpsGKoWgKbiR84KOIkNq1x5alnRYC4iHwajkDaFoQjUwoV9et11Ik0vUgirJBrOR+AaJ28wT7dKvhrX24D24ct9keLw08UzVDRCEuqwwwx7y7kM9qZ4c7nl7vZHCSwH28ak7qQN5uxBvnVPqmzVfgax5Pp0WPoaynUF0LgF2Lwn3nVVNA+lJ9YeNObtPVm8V/svEX4PNu86n86zRXQ/F7vdS3un2XyFAoldFA1YZqA9HMDhl2gz7OhLHjhqHH5zP7ssU5BSbQGCjymI989M2AvRjg5yUGZ3RIKV2enArtOWghEG586s3RbCbGGLwoKwjv96lEzf1sqaJNm22af9mO5Dm0b7Yay54e7zfrFmOfvSd4Uen8e6OS0B3OQ7Oqruubh0SUE0Yjga6hIBdynuIK61h72Adjg5k0ctG0cNgbF5XMqAYek6BmviDerNSABY0RwK0HKSS70drjkImPOOmtx5xCwUp+razVyYby7rK6YTQHlwCBGgWcipOZ7q7Yi0Tg8PjAlEStWuI0UG/x9drn71NWbWlnu5WTh7rAULHf5B61r/SjoXYWMw4oF31vPWwN3mZgMK6kdDMsN6ujE/CJ7nXmQIoT+MkaaT9lTheGIDjpjp5jyf7RPDhl7zjsj55ws3vzVBDePEr1xm5daE7R4nSDakMhX5gXzE0kkq5ho7cM9tAyE9QONry/BPFNWhRXigfQm+3UctTIlrRl3Xn40UhkQZZapqJAZBIZeqaiLSoykIaPuxL1cDp64OVQP1E1n70/q1WD1La4Jynm5ZwpKzNj/B+dednkQauLtRqg+peS8PSc6+qwJ7q8ov7d+YKsl7Czg0jfdHGqMHB7Eoy4AsHiPkUGsHcxpLfBSw9Oq27r2vsnS/HFzyA0mtpFu8GlEd+fhSzELhZ1+tIGULjxFhi5xM4afU6R+E6AnEMRZhKUcNpI4+ZuLUO4zpMWQeOdWhMEkZrcNxxHE9vhRxQAvoareKl1caaEXEyoTaZpcYuBW9Dym6KzeCry09BrwmM9v+iGKob2Av4micbaADxDnWTtLaKRRRR4Tpmv2y5lPVuKpvmydzUTTxbonn1xhybJ26T3XjshXNjXX4fKuoeAcOtTYNmJFZZXgea4iy0rAFtFJV35CR+OWKI/Hb6cPbsQuJUWKGztB4jzi/KwFzsEAWYdeCxqQrtiNqWrFXqZJReoCGR5ao8ZLFHhyNhoT0ZtryTMKnhwCHClegre6ZltAeis69TRqZKFUPXvC03JGc/rk2n7+B36vlyp0qU46p4Gm0JHDhYMNAFYacIymXmM5CCFQgZryO5jU64uLEsdZ9w8XPBGv9mY0uHOnRtZx2QNnqO/e5OdhBWWp2yQEZnihE50V0MlINKU1QgfVs3zHnAwpcG9caBO9jbsBX/HX8R1q3yNcWLXOaZjJNd4aMVtBz5uazSRPK+rZ6e+x4COEQI4Wqj1ISH0x2UqMsrmSE4HUsNS5GaCZGvyk25NwyhMjzIwkN59eUrHg1K/gJ3zlpLkeF6q5M/ubju6spAB3jPM3tkmiqf+Bmc8YakcVIlUlBkRkHPwklT9iv36nbqzpEshbMdL3T+ZS8TaR9u+upjr0769gDQ/AHRFRRZTBHJs8U5ZW+z8WHhi0XM4Wkr1BatHnMwIj90n/hU0ZHPZFh3KmpnUVElkds2rdRA2Tms53Q6ceUuSuv3YSrcKfVLCloRed37hOumEJsWOBkIC8F/WVS1FqqBrxGtEO8TR3tBjKutdeVGrCmBDzhWdLA7MAYH+dJJvmkA+zRw/iDbn9V+EaCgSB20Ik29M8cbigdg9fFqlcFmMbT7O3Lv25ZDvxr+2HTTu1pGnzZVhYYg7H7KhfxKR+zyOWYi/rh1NzEEDa8p3vf4qOKxEb06GCWSUDuYz37YBjrcrBecErtR4jBVHT+dTsO+Xzl5isiPStb2cg4USOb3usTtOifg7MGpWkVfgb0AsMBPWxs1RmqU6eIzXSCo2Oti7/GQxLx0FP5/IH+7IQSUVrkTT8rvnL7PJ5iR+5f6YetomZ9T1v4Q8mBQYmpW69hVDRSh+GqO+rNnle2YLoDVp45IzFiNpB2vvOsr5ckkpudIxpba+Hpr/AYfJQpIK1mnekCE3cihV96Mi9fZJ9wRRmIFJJl/mMb9k22lA75D69Kmi4KatQKe+DQOp7u75XRaL3Rd1YXdLKgOiPOHU+pwXX0fd7ZNKgRYXkpv+zLl7wEkAnlPWW4aBNnlpasjs5QpFUEEpprQI6bR4b5NJ9a8rUnz8DiOXP9ZjCmvm0uZUlMJ28U/3GveofBrddrpDr35pTU4dNtJYElXYT5lZICADgGBxF0q7SJpNDL25W1tZdvXXXKARUhmcDcEdvQ07Rcx0kbkxAIppM1HHcKgoUS032Wiwu7SeNR/11qtRvxwlhhkK7UdqEzpaSFWzGcFs1BN+oCRQ/prxX1SonYLVCbmI6m9iZj51X4llrToQcJu0XMQVY/4sC6Bb28RoxNodSv/RGpovv0C+7Y9ecGERvoYKb+znC7dagmvROc+r2s7Dzd0Z83S2hxk4TuuodiWirR3Q192PLAh5JOm/WZODVs9mLFBbOMScLbGdTE2cbKPA/HhaSd65vqhDlT8dzapTMhdXvViEbiezaTtPJdxGK87pROCgTLkMJOTQHJuF5AGNo3LtEzzrMi7XtcrHZrkWsGw06wcI3zo2gsiBLeH3oFmZV8APDSyQOBEmDLUx47G7IY1I5Usdkc4z6CRcdo4iXSjBUBPF7UHjk6qvQ2UBg+F88wz1fzWgSlkqzmjlSiLGfGIPNwmov2Z7i9MKDHHypx8fMGPoJcSeN7SD6+Ejsy4Pwlstw4xbDWhTMiXzXN75KxsShJSB4/Lz7q2eg54WKpgsI7t2p/7TttsHJPvJZGCoqkWzp/gyljurrDxOmRCll6yV5pUTOHy0zzu87DP87aQXiyesY1Pb6AIbx+cYlezgnFQVNAM+OcW6nq1urILDd1OMlowow7Y4MgS+66BR0HtTC1ExlzludpwZUDapspAiQn5I2jL1b2M7lh8I38GVd+g48vgic4iursgpggng9dle911LL0AanR1IaO4CQf1GFx+EQAUPPb7wTBqHHkv3zzsmyAnZd5lQWLRrLyoRy8qZmrdyYlfVRFazFaSVVKnHIqPclPDC+V6OjgbpG64fNC8g3HcT/sggRz+fMqAcG9db0/agAA+4mGW2kvEkJdlYf/YylY6kquFSSc5MXjhYfhjdvRmHM/Dymy6xZ5ONCEKdcB57c9BYHyVgbzjJlFU9KCq/EfVMribNeWiDt26eAQAWfs8T8s8oaetT5Oin+i4Yeh5IQ/Hc7Al6cIqYpm4hbzUxftWXLuFu8NB9eA6xP3obIT4CVg36kpq0ZdzkPZ9Y7ylRD/6oOxXqiYwQQyU1IrqDCkqD4GJvQC2vhL/aNahLt5GlWUnqomlh5sBRh6bG+LXdnnC+jON80gMYJ6nfWPpmhB5vVwvFAAmiTKlk560GU90MA9kmgT5tTljZvn96tLsz7e9U1ameSDWoPFyHzt+1KBZ+w0pp3MpM+aFjHg4LpC0L1T+ec6hodo9iJMH9oXqs3FF0wLCSv2L9GBdJlc3up21hyQ0a1Sl/qznjoUsKhk4IB/duwGAukktFUnVS2xvGHklqUCrQPNRSOJyiWJAspa6AoLn5pXnFoyHADirsJ3GzYtgNST1x4n4PB+tgo6r8QaniQqZLzEFHjAloFIULklydNRxWAQ0Xb8x8idu2jA/If8moPaCPAxGu+OL9X8p5j1RkNKLtGHL3bSBJCdXKWFunMR2cAf1x4FU6UD6beCVQ91PC6UfNa1BbF6b+tt2nCeNNLWfdeiSfP+X2nDahEbGAzk7ldiLULNMrXfMZrwxJVt3VPtxW3G592LUjSwifnvbhnVf1m0cZwbvbWSuy2ercNE5YmGi1JAamWvHilPY6knylExAttNYjPDKMNyKI8DEcSOECwsftNET8tIeVepa9q+R72NtqfltYnChaeJbxxhwQCFkgXtmroYDp/2tAlsLbX6YLn9pewcK7HeCVCfPbXcIGeab0R7jkvNZxGNkEil8iNkvFc/RbdXSTP1RmLBs4kHbcb3d0ekLbnFTBywws+seWw5MbdamRECUtvYWmJIQV9YgSUpNAJbikd+Tc/+gD/lnG+f343U9n9+sT/f7dZjurut53NdpWHbK/dpO8zjv43W7SBEyekXkF1WriLVtUQRak7e9jAidf+N8gI8yc3GMTodl1jgOhMBR/ADm2aH4N9hus0C2yhY96U4xbQiNfTehV9ppNyD//vKd2212hDU7aeeo7aLZZlNXr/+K/y3XObq63BeALIeosfwsJ05mEpzJhutWB7S4DUF9eZhN7HxPwsAIQbsDpsFspwjiciSs7B3inTHSzHv9hcT2LRJByqPbEd9JGrMI031bTvPH799+/nu/+fHbX3z0re8N//V///jJd69kvZqI+c7L9W4eTvMy3+/D8ny+EMoTJq/LsE3bOk+0wiv9R6Iq/aCISCkoavfS7EOOw5nkDGDaVY5uH/mIjXXpx2jDDP2o8iTelEVspSSgY6pk8xJ5uoaJEa9CYd2p7rlWaODrNVRpsANXludQU6waSG0vuAfmgLTP1AkcDelw7P5LMoSm40tCENSFYPcvgUA6m7YrHxjkluGCc0Y+gDbfWQ0QNosglqlaYWx2a++qz6kDQSb5mhF1hNnAv5k8WmnIMEyEvJ3HE+ZaLRnpeYOF2s7DMK/7f/rRX/7swy/+jw9/+e2ffPXFj/742/effLRRaCUl+Y3T8z6s92/m7/zS+kv/p+27318XVoGn5XmiU254XtdlWvb5xEyVqca2zdN4otMWyCUrHl6qc7lvo2Ex2TY4gnkbp2EWDuweQvNwmf5tu1skqbzcwW2djKF/jtu3FOQ48filC3g4rlzeIDcUvUQFevpBURH6Y0ijBQUQkeIIsgtwwMfG6NriWOoY3SiBF4C9+nUbx2lhBZhFsVDbPFDP9hSh0m1UzdRNQ8M59f28WQ6P0hlphZoYEG840w8zSuNK1xXu4PlwQGASWtqKYRYXvAYHshWcVTFXqAnpucv95cNX707T8Ev/h2X+5IdffVivX9ODK52bvW/b/bhfx/3y+V8NP/7jT5a7RVIMh2kb5w/7+EwZBW/m7/yt7Zf/z9MP/nfT/f0qnH+ctn1eThQPctkJbSc6cInenOkglWHex207U3DFPD5fB4rEstKUHfW2mltiQtm2ke67H9qMlgpd6kCyLbM9b1gDFOgJKPebyb/6ajG0DDOOBU3CU+VETcOdPBoAw5vjgCrEJdLmiK9VuJQldeBWDLNYe/71dLoj+3NTVK3LjrrzrJr6LbUKfsoGz97ze39/Q3DFhIHMuF+4dic2KF0lMd6wVj4b2S/iN9jB0PUaSSB8pBDHEe/7uD09vXn8+jvD+jS9+dEP3nw0f7W8++JhX0kA2rZx3NgqPczDeLdeh+t12CbXI+849Z+Q+e3Ppp//2ZvTPQ1tGrnpcZvn8ZNvz9/54d0P/pvzt793HYcrFWIf5nm8Lss8jstEMVin5+mySUWOhryZPmgCR5JHdBM6xh4j8y23oygVyJaqaPLqq5Wju6h3Q6JuIQxmBA01774gR7ZJ0YWDg0+lgy03L5tNJxRBwZ0RiIqMsvmqCgbGaXOd4tRaXrCa+JkLAkWjzc2OxOI5sGqrybIZrFJ4zFu5ujaegWxE3IMnYXiSKqQ3U/gxOn21FQ+iEm+PDJu4qgZprOPTfP/J+7ff//yzd5frwzB9fXf3p9/7/n+7X37w5Rf7unI0JSdhc3AWeU6naZs5TGsjXdfLypLGfH68Pj1MG+UJqug8rNfPhm2ahuX00XJPDv1xHqZlHdbn+W5+88n9d37p80/+92/ffPK973+80KBtvRq86sgtLEFEHrVHWRyJxhI1IjErSX3E6BPYiR7fS+2BGHT8oAvJPTw0eE6I5NB0s8kOqcsxGZWpZjbWebsdmx5S0R+EHr6iX6xtCQji848oBEsJab/XZHrvYhyuQo0S08eOl+lwju3OdmwvKeyxHVg/EHL3Uqw1TiuPQZYFhWoTiIBDOSpY4UgL6FcHz0KftuXx6fzH/+L7b3/24bR/Md5N4/71/Zs//d4P/vvr9svv367rlWITY8wrQ7uk8VLaMPuQXT9hO/RMD4l5g0rz7Pu87fvjZd0ex2Eel2XYtnVfSXeepvWzv/54+eiH23V4orKyIimEmaC3B3YYvcoXocoe7aO1IkZuXyLNdAoxBbfnuLES5RBYAbbiynR7n5AbASLDrzfF4A6+ZVW1jhuB9DhfvUruwGjQhhLOcx94roE1L/OyLGDCgBS1Wx32fknHjoD/3YfVijxHuHnzsBu3BZTtC9uKW2XAuNhEgOypNV0WbwCk9FjMDt+Fv/qPHQXId7aNcZBryZ238d1nD88f/tbTNkzz/sm393F6fzf/6fd/eDfs3/nqi3W7MucMb/w0Mh6zf4jsUDoRigKhcu1q+ab+Vvozkd47LdsyX4ZpnkYKGiGnESP7vn10PQ934+ef3HlNLGel7SIni8eLV2GQYh3VKy13/Rjvdx45FDb7xoxsW+u9EKJAuipU3pw4SiEvDC+Zm270Cb+DMz93AVKh/Mg6mZivJk5gkJ8P0Bc6gloOzTRCUfQ+O205usNXA34GnUOjJVLtdgWTI7b9sTuMvX1VYEH4h4FGFh7Dmmh/U41IERtJ5WWDEpehGff9dPmwXT98RIam6w+H8zSQvLtf969Ppz//9vf+u3369vsvxu0MGhmdRzQTPuuK0SfT9FmOJxFdwibIYTQMKy8d6c37um8jc9p9JQbOQbLjdbje/ZCObMfQqqTTQNlYRWGHmLp4Wt26FchsKeLgaLQ1RBM5KvIljuwvlesgtqlnEO7KqP2y5tk+0xEFbkqTJd7zFVUvbUX5k2JBalLXW1sjlxG7jwiBK6bfJD52Pq0eOZe6aIk5NNXuX0ccATSw10Ayh466zBRElmIjhywDu9TaBFfSq+n/erwY/J4DFugvHWsUrl9NUZBEQGa/Ig9t8zDOy/p+vZ4pDXjbPhqG6fyBeOmbby3r5X5cP//4W8Owf+fD22m9TBpA6WTCalVGVqBBief887byyd28RzZ7MnKxwTkgYh4oFjoxmzTLHJOJhxynp4RZ8IdcEUFdSsZ+bV2w7rjXUWj39vC6AfyeE+T71H+/y5xx6Fn+jdvlQ/OaybohjOMyBoXs0gTQar1FjUNwvGd11+xClJUtQurpdCIFWDRoV9adMPcF6Xbw8aeLvdhqXmL/98j8gBwVKQQKz2XX8EOlTJks7tCPx8WFIC0it7auPVWmrWI5PCnZfyJB6kglReS6Dj/+z9PDu9NA1Wi2ff9oH36wfdjX531evj+sw3x6Wu7WN5988vQ1BWlRvvC2sG3CjpazbICEUW760POPIjoB/3WhZr0OlzOJ0ImixRY5gYqVF1oPYoE+2xpgfTHJZ0ZVCyjMXmu72A9R87xBiH4NpHrLvA/4PfG8Ir9Vvpy+hof1Bb7a5fKIj9hO6eWQaiB/aJhSTDD7LOUUSw67nbh6joe93Zjo4c1s1XC4t1nVN/dWY8pMsfksQIkGpCoYIYsuc0hVQALR90I76hzaBKOUKeiBWs4eI07GwIBTFpCYnJ/2uy8+fzo/f8TtEbbtw5vr/oPtedufvkVxGs/jPL+d3mx339quj3frZR8JfSW9Mw1TkICiMnaJngwDR9olUMEYi4hSk3B9kcLuxULoUnXLx5D4GqG3UbVbIi1x1a/o34Qiy444LA6Vr0JLAFEdK25ovg0HyL8cECC8k0h+hWdUutMggbxlkS9/7vJHC5nl9pvASUvqoousz7ME94iQZgYs35JmJ0HQaZWWyjwP6xlJUx3Cmr4U+52rZrgIXYKFI0iP3P5aCVAKLdP19LdcMszSpzFtjk8m6ZqXlBjQtF++3s+fX7fzfr3YiTXDsu8fKZsiiP9o26/L+YvT/Y9O3/3l/cMvXc8XFr711Dlpja7ok9IdKD6aqgJk1SP+iugtC7cNw3m7cEUO4QC4RxU1mi98I+mSoDU5KMswuMwOeYIjDa3EMcrf20dr2aM3rEbAMI5ePfhtv+U3atQL2bRoqdemLo5qFYBEXbbcHW89ryxc6WrbVzQYR4p/5pO7C55CimtQkZ7oHLPBFqy3unIV9I7SF+xHh4jxKNgrc3mVwhNFxH9bfN3jb6+kI3D3gE3/hs5L8Gj4MSjSBAuPlNs+3D9dhvPzuHImgi3vLGf22sl/0zB/63qZp+359PGPPvpkfJx+eH0aqeYFy02qNUpnyi9D9sPow6S6u0tCPRLbToQjFEcUu22rjxlXA5F7s1QyJsnmz+ipvit1EzdbUXfHpG3hdAJOgcwH5vNverXyRuJQYYiDYSEI9gaewP1Q2IBNytMQ5NGtrfRJB0Bn/y7sTQhisx/N7Zb0gboHTgGFp2Z2t3zCFU/ym04WDgYrj+DvWDAtXt3Ts4mfxIeK/43abNgbn333rD7OOKzX8/U63P/0Pw1PX52ocIoglNuRNFmaYH4lR+/Hl+G/Gh9/dv/RT8c348P2g/WyD9tMIRpp01P9u1gCHYoVFIrNkXUj+jHS0Sq2hz0MgJCcdqXzHfb2wh6JCMIasKi+GXrDjR1nNR00DmAlWwRj8gk6UiccLJDTudKQWhQuQ2resakHuKS2HUSPORQ3oB78boeB+qW+DsHJNC0nDq/zW+o2vaUU9OfW9YFj4ZRMvwx9Wr5aOvWo46TFgtID0nxGvw5ldzeGH6hIV8ZjPHxI7sDvSZwOkIx/dFKhh0uXUvhimqn8xlefPl0eTwTb0JEdZCfNTwOVormyfeuXp6fPlo9+8uZ+eN5/eDlzXgLlJMBp7KwGO2hb0LtnlLkkpT+wNew08vY7H+tDTyhwZePSQ7gG2MQ4jBsnOGe8S9RTdyIKjfe5aaY/hXgg9B8OqUXvRJcPeiz6n60jBCEm/buujj9/A5Gy4S0x+DocCCGXi4TnaRZrFkwromVuK5+BMV3mnDb+UHWJkRasa5Awz9BeKo+kBP5KjBOB2Fuao4Je1B4sBAUSG/JaGzESvlMn4trKNO7r+vzVer1QDhH7b+1Veiv8xpRrMFy37XoZ7obh+3fDdnf/o+F+2/YfrpeZ2ZYoxA08cli0kG0QKCTQY7OityysUzoSmS47kI0LbJ+tMU1V7D2bkFysLFIKy71IqcEYN2reHTnugJ1kSIhFL1gUe1oPmE2O40NGnTmPDajOBivb1HXxhl4wr/UmUzqJn/aBK+dQcmgSq7GrAt3eck+D7+7orScamaqhiPg1z8hrgtSlBEaJSlzyVcgUmgqbiceG+OPYDs/qZxmBG3EB59kna3A+svWKPLrzMl0f9vWJTiOkwCzKEBJmSVxVm4zsY04OXq8Xqj73/WG+zB/99d0ynj/8rfW8jTul84s2rAWyPDVWq7cgqKhnScdDXINPcqH8JKYEjpWSUlx2StG2qtMVYHv+ICmI1Tch5K2EJrrqdiuH6hkEjareSggFoMLEBNchh4wH1N4AGocfEvOClBxEoEzvgKv5QYBoO4vYKjFiMfZKkrexiKQ/tqe5ZlLsQxtfpijd+cTob0HCESOunBe7ttknrDsgsXvTQmyGMsZjHLas+RR6pb/6JCkAmeOVh/vT09PT06N4X13IkVIbyo49roEfGAdC1O1yuR/2Xz7tn57e/Hj8eD4PP9zO60jH3zCuh11HCkjr/ZgXGBJNpCfv7Eix0PGM86neZihRwTXzQ71x8St0shLs56saLc19aCjIERYZZy5SoYyqvnEDj9rXSxe3X+9ZClBcaBlJEnvTj0XkQCaPRusgbFb0SRMKdym5RH/maWavRIcavaqAWR5SvoP2kUwZs2OnF7ZVUbrF8XKB+Bpb1Ql7BkY8FiE5sNcM9t0Y1kDeIscbJqsSDLF2RDK3YX0afvHn14evFzb4U2wzjZcDLBVAU3wXBTmLJLzv2+Vyt6/zcPl0fvPT5W657t9dzytRBcVhMolBCQEYqTC4EL5YXh+3cbisG8W+l3VBABOhIoI265o7wZSGy22GOvCD90yq8jmqysJyo26VTqbF92FoRdPcbyGNSVmIYi9AepYpb9IN0Pd7HjOfQEMYD5qHYsFePZuKelP5djJgcWoLHj+NK5NXQE1c2VfQkQzwt7a8UA1rb5YOQEHQqRTcTfvfWUTQY719lOJiGGODvQ0o1z5AzMMei+orLmJrmbXay/P19IufXC/Xu5GKQkpfBTBtEKFQS/YCxTFv62l7/sFpe1je/HxersP27fW68TmDZCBzfC06XkUrUUf5PKaJ6kI79Ocwn/QxyEq7z6YpgE4CuqX5kAy/j1kCsKMSYnUAX8ig+mH/TesHP9xAXYzybFzQWZHHcSFLhY+xL3G30UbLVvAzHq8ssVccvEGH2lCiqRRDqzqr0dQ0aGHepfhXhuaG7uUUfOBuyMjqqt0UZeLQrHaF0ngTdUGK1xfA9zQqGFxIxXBcg45UhF+3Trditp+NTkFYX18/vJsHSkvS9bXa7eZuog62EK2NrYhKtA3jdbvbzsu6Pp/uP51Ol2H6wXplFZbDsOBwdwt4ivi6WF1+mkpeMgLbWtapF1nYPqSal5UsJuePEiJj/yqHoCyVgj+kWduEBmldnuuOMpmJEyG9yVdv1QOrFxYaedVbFYTBFJfNdp1x5VXyuyoRcezk3d28cGEzF7KsyWA7BaRhYZA190bR4geaLm9hZ6VW+hnM+dFKV6wy01LqyuwzMM3BGG47KDYKIXUEDMCYFpWWkxocfyO3YBjW6Xm73n14/3h54MNGBDVJ7CVTFMVRWXCznXVhhEGoJhUDIF2aTV/Lvn1E5X/vPp1O2zD+0nqh+ulS76pRQWF6RCm4NobShXkODlwWxobRakamZ7dqD+6qgBp5szGPvye46C+tEhpkP6NmN7+0MuBkX26f/eY1F6GfF5HeALBLAuFL5b0py9o4KJ67LIgwjuPd/f3p7k4SRZrUAc1uPxqZ/NufxEGYm2LPTbNT+RWEXiDusYfIV+PR0k6OpkBosDb3MgyUE5Biwl2UVDvTaSQp5Y7bsk3jFz9aLw/zsFFePXtuJTlBlsZyfzXeSEQ7aU/8BLK6VGd5G4bL+q3h/H4ZfracxmH4/uXM1MDNSgEixJ+joLt2JwfGbjunGbYDh2EUoxbIcsFqC4dPfLFaqjvJ4A6d0VpmjoUTN0CWFL3UdGLaTrTHF9hF78otdYWBBm2t5R5Dgt97Z9/VqcXZy/swL8vdm3uqvBGHTmGLrydL5jXtBQPY/ByvxqNN1Dy0dtTIRJNUFDJ4QpeQBeOGiJNeWz323poYkQQA/ja+3CyQqJ4Wkq+thxMIY6CCm9twvuynz/7m4fz8HR1aIi4WECgn3suhvXR5vTnHZolNPDPl/S7lE42fzsu07d9ZLyfw9fBBh54XBcltzBNHKg9A35a+AVVPcFeJreBw/FtjzPV1BCbT8+WvV4vIPtPMEFr1G6m4fogSd0V48igWP9Y0eknAGr/dZKtHUwRJoZLt1BKKQEiQdGxJIMcfKjTr13GaKHNwWnZSxDgeoC5BHT287WyvML+OBiXDSLRR/4UTDI4oZmMcTsQTMCg3H+8giXRUNJTrY+mY99tHAAqwj8TfNW0mNgKVYVVC6b/TfHr46vHhc1KA+SBCcx1xvbcky7O2CyUYOdkouJ55+8b1uk77/r19/zDfv52Xfdi/u125cBafaqJ+KBdgndpKI1pEmpMZOAiym3rXYYERgd2J60Wo03XRQRvfciyGyMtjlmF7ICMsN4EHdCSALlz5Ntdf6yzbX3UZxbfX+RU7hiCE0lEIyXD0GATKATAEYcBa+PM8n04kdK0sRhUERWoXarCpRXnMlWinZoAToeru0pZiRaajGVsCm3zjAPM9qt1QWwsEySILJcv1q1q1YK8rjEDZkJ7AzPDbI67WlCiLjiDz8DKMd8tlHZ+fTuZY8UOPPL1OizcG6ujBSFF1TtEA+Mi231+ub/b96XT6+rTs1/G72zps2zRMdHLKyBlK3kA0xZGXhObMgXuY6OQOq0FgwF8H+4JE6wIo8dLD2MCr5n4pR4sOEvdU8Kav9K1FLJAWXrgaJ1aX9wrkZZ5XHxlbb2YIyeISj078wFeUWNWfId5dq9IuRbXH5e5EpzFjSGBTnyz4qaNSswAuiHRUmg6CN3WV9JkkRtXX8gICH0XGkLlmxv/YFFjsbHAaQkyA6DNFKlglVHosbPlInfKH+KmZRd/L0/CLv9wf3y/bVQ3XiKn8goRe6UnBKjurmQL3gVFfhaCZjlfZp8v6ZhiG0/T2dFqv83e2M+U2acYZk2ljgKpCa5gjDdGMWC2EA78D+BteVmidC+nUPLZLZ2SUwZ4+Ri7UJot2ZtvlRBwF2bZqTUdnLYcbfZOrP2HsKD+FbtxeSHZsEf8x64PYQeWALHlgocwFCsJFRtOK7J1qCAc3EHtMgc9I7yPvkkz5mDgpitAHtrSErnlDmzVBt61OLSH40HFipWKxiOEYn6hxRaDtI1nznpmzruP5eb/74tN1PX8iJzAH3ZRIr0TQuOgkH8MpBmq2NDpKWGaP3KFv121Yxu0742U4jW/nZdhP310vG3NXwlh1I2kPjMPsNN6oRibrwOjr9InkBYe6YNmHlwQYmAY/Ok2jnBIs5zuZOVNezGWpktkYSwTEzRYEYKhp/BFz2J2PCluRQ9ShIYXsB/0JFNTv/YjjmuKP6GxjKKk+Zh9USwbd1lNyGAqmeV5OfBSOeOYgcq1vQbhFloqlAlalu7Xl5Zh0MCJkdlkNxmUxpGh/Ko/FTTW0JxsVREeO7eBsLijVmCJXmXwN3sBGXLQep9P6ODx+ceKgCxsFZAEqOJFRcePy73R7I6Cng8g8EoZh3nUGtmZxCel9uFJM1eW7+7Cfxi/nZRq271HuoflexezMrc5al0cKZY6ajYQcsbPXqNwUZpbRKntv6Psyz9d5vl6Ln8gJLxg1GmeI3u4wlAOpIe3CzWCCG8/0XoqzC2unRoblsxfmAUduQBR+66ycy9IuTWjZIQ662ZeF6saqQtLILR3zd//HYm4OqntAxV59pXnWSBRXYkNm8tEUY2PCpiIoxIMGX3bxd2hWsbdEQXvCH77VWy4YxDpc9vH09Lg/fhg3Kt4cv5sobvmRVF5HKs/OwltZ3FVzk01TXqWDGxz9ecMv275cLt/et2lZLuPwPI73YeZgam7dchAYqeIbHZXG0VLDjSsJr6bKdnWqokQKDHK1cWLCCv8iHSodN4ZjUo+7aJOA3UoIfREgy7IdyaoKeo5z9bk8Q9Qe1LziCHewXCal9FfWNTKZawhuZlQ2x4QUrduYDpLtik5dOKydefNyutsQosSCcjvZ0ASI4Na1bke1TVwQFGXsl5bfdymHCMBoMd+DGjbBa/Jjx1GcPmRreemaFJh9WFeKr/j0L66PX9EpC1qlUd61M9BMLLHp6RQhZsJ22+BH0p1kv9mbILRiW67Dt/btcZm/nubTtn1nuxK1NsEPrJ309SoldYwLJntCOncP81zamTZal9vMpd+JQu6nnetVF1bRuZKh0xeyhZaCZTJ2mYkzme5YEx7j+1lLznTagM6lMqH4tibovUuv15r0MEtoV+94rU4p26+HWvBjy3I3zyQuHZ0sk5alyhbxu0XSJUqLs8wutpY1djbGf8xp9FbNC/bG6J5SkDANZCzKQkolsFVmH4Km+c6Uh01rK+30imbh3Kyw7OPz/skXP//w/PhtCcIy2dnw0kzKYA3mGtJyoqCZ4Yx/aWkt494U48HkhxNvhyth8XTax+vd/HYihfgT5uIahxX1mWh4131cmrG3rMUXXXlEI+vkZwFuZKumaZop6kCygrHIw3grPAqJzdEFY89bpN9aRPWfK6r1TK0+Nqdq6J6MdrrneUil/chBxjVOSZsicjv2WtVg+kmszXd3d3f39yqW+3OwcvoBiv4cHIbQdzjkuaZF6cwrGtN/QtXsLSBSinK8Q2mnNB6u8jQBhxVXSUrcaditYRFQ7oi4k45tE5dBaOoyzY8P6/ndTGeMaXk7sxcoZkpFZwlbUmyLKUHkpnm3PcrTSkHr3mos17YP5yt5Gu6Wt4Sh25vpuu/bvWYf2lpOVBKEEFj06dsyGHgTE9U9wi9TDbm6PNVsWvhMU66uE1uaeDfMM/XrP9fI0N6VIFvZZsNLpboBdGrvButu0UNe1/nGEIH0dwfUwxhUElyGt1N4JNRGfqMPy7K8+eiecga5HkO3D5ROb2FoFpraUSvGdGghLkU4Z5qNSpCLSg52EyFCSH1SS12Sm1C9Z2zfj2dcQlV0yTurGSvMRdQJZ5Y31/eXp4eFj0jgInLqxBYjk5iXjIJpZ2TKsoNvDBeqnKGvqFjFhmXTL8dpPO3b6XLZl+XTYfpkuv/WsN6tFxqAatT02jO5kdzkbriI8lZhgx110fRWpNxpV/i0iJm9H+N4pfx+dlUznBr0g07apyNjVsoQ0KJfsBT0CetNMpVEy7bhOpL2SzNiK2FfLbVMACQTXx6LjRXXkVQAJPvzvEz3b+7neU6eqMPl6WCd34ggCTCkV+QEI7QjeJecFrmntZIdOB4DcPFLyCIZfrDBauVET/hQSVMaleG+eeN6aF9OgpIFkqjl9Tx8+hf7+y+X7TpbUxYCLf9pwIncElOTnJaC68cGEUjstcQjGLQ7W9k1te3zNp5GOpHl3TB8Pr/5r4f5W+vzxqbrZRgv+37PHDjtoH503lrSq7UXy1BoALiCugZkjjOdHbNM00TlObZlW1f6zzIN6QByOyOzEmM4aqErajVXV/WFbwWVLDisImoRsIxEVYWzTxX6nMG85pQhOo7zslBFHBJKVqZrVtrFA3PneaakhdMdCH3dCceU+7V5M4gDbdv7niF/8njFQ4l2u1Z//k2cLv6GmN+nrlkYzV+HqiqEGpZYLoBOctOX6VYbgDpcz5ft7u3fnJ+fPjEGKYZJTayHNxh2VeybGIP9HAPDdii9EUOTYphh0BRbHbmL9v35cr3btx8s4+fX4SfT6b85bZ+sT+s2zlTQYfjOtEoopZ4PnWC5W6QQFs/SGkzvAoUTPjjhH8mUNRIm83C37brK/9bVj59KsqWtshIRwKYqR5tS2OhK/itUXjCI55+goaT7laBEx5LMU1p4Q5GlxOZZe0oOKCLy7m4WBN6W9XK5rETVJHxDYjbevHlzOp1o3QJJso3euVwQuLQIhc12dWO0XtYZJWUC3ogWXSEM3yrGC1RqcuT4SUYm/Myj8BgG5CYKpUOaYlieG8EgZlRmn4aVolI4U3BZPwwPb4dtZbcPxTaaEQsSDDLgSCUdcd/LydgWMi0+Jp2PHlMIQWK801KjjvGa1c7hSpmG35vOXw3bz+fll+e7+/0yDhQp8sWJrFyJ+YKsmv61yaJu278KGkWwftS35QQp8okwVp/JAya6vynA8U5qujBUNA4D/lWaHKbjbjNJGsty3w2RGzc6LFK2i45NEJzrQEh67UjFnKUeHaeQyfqM60hGSDYa3L+5vyO/kYxC5zn2UDdN9ni5Oj9UjtasShY2POsoETsnVhbFnQWoxGQ1iKMKKCBCBzbZVlY0zA8f7JMxukrx8++t8dlCJwSkruN1HZaHp+H9477SQWZzqh6qEVJ6OBW3IxaeoJsR8U0u4tCZZWk8nNpcqFSmlmM/1CDCD1zpSPH9NE7f3y/vt+Ev7j76W9P8X50fpus+bqQSx5LUsLxOoksJA6i/t/CO/EpK0oeJgOpJkKFg5TMpwArslDbKt2fJB2K0g1VWNEyiFgzJNe9Unguic9rhRw5GD6S81aSLMjRb2Kz3wIe2T+Qcpz3Vk0HJWX5iqfqe95p+laVJi3vrEBrHnB7J6Vsbi/B4PHtvFrMXc2SeGkK89lEWUMvupPZNvCpxIPIZ+LlXzDIMi5H4G857W74bx1qkh4+JOeWLbMPlMvz8z/aHrxaqzkj1q9TUrPKzTJ0z9bUQB5uEkyRG1izXjEp/UNxIlJiASckNlqBj8jxdSfN6M18/2/c/P337O/v0Znv/7fOFXIutAFonk9Y+y61ZOU2GgOAd9jhEEWpQyDSQe3O6iO8EFHsLDEkbI00XEQs+9cS9AJF2glkOu3m1cmMAc0R+guDSgofToomsAXKsoAsn4z6raZyPHKSAW4e4qHtZBeUUh1IQEqpDI99ux5XqjeHK5BuFU3VEgMN3M5Xs6Ga3diAIgpu1AwSHyrTbthBj0xwkpK3FKe2BPqzDsnz92ZfnDx+510iKuqaiQPpd1lxOXeCMQtULyRZmEoEis/YrQC5ZK8o+Ez1iMXulA5MGUq5WEsm/Nz3/zXT37+++89/t+/J0JQ7s3O5YYoS59W4h6HQ+usANSO25hWylThVhrNWevgZkGlLZWiuNPJ4lShFBQ6oGJGtmZrVBFVRyGNze6BFKn4I7tIWwDRWnaVqkHDfgfc6Ptt3wbwaDDegfEp8+LJtUW/KXsrzSDS/tdtP0nte7gY7SZK1GgK14WZ0oCps5eccAscsjbaeumZqa1tyHBEY3JPKd7fr8/t35en6z71qZOchQnIeSu8xxSJDSKwVqlQNIFXgrUsYY7mDGgCFeCnEOcxsbnVK83z8//2Aa391/8u9P3/6l73JwDzBQg1fNeruNz6+5aq3i/Ju6vVC6OXjSpoXrfkS2D8n5sUbdvOIBnyJ9qUCMqJVle7fK2rOpJUz+pUqSbNAz6wZTME9/1Tr7SKHSAMcec0LRvSj8cDv2GRGyMTV3yGb5jsJnIwkllph+zVoQSM4gpWUDZWWPUZoYLD9yJUdsDCbPLjFzjaSzAjP8QGillDUwUw7D+flhskBgH55kBIq46NGyhtI6LhB76MBByeaJ5RcMRado2lp2JHFSoVXFk//Gcd2//fR4v1/e3X33b053bIX2LfVsCfv/Tc/p0RUygKtWhRW6dp1Yp3aaOwR+AHuLz1RI6g84AYbDwW2S4c7hAA/PVIz/gRoWddASA3ABhB8m0xVTVY9Lc9sPBGQhWPnEDuicez9iNYXSgccx4g2bVTokeC2S3nrcF7QgXUUf/ewae8Ln/gDsIyaQBdTs7QklLt4jimBgyeFcpAfSaCcyVjx+2B8el408uCwV80kjznC5ipWdVCbH9lqR6JidcwCLAyJmrv5/3X0Jy2glmLBb6jGX477P+z5f99P6eHreh/tvqx+469w/ls5M7O67GYpq7G+YxOjwHFtnC9siVF3ygwscAUEebDTwVDeBu3OFsdSYviICIFhzkqN20RHfFDIoOYFyirjcgg+U4vOc57btNYMNUmVWhWTXQW09P+/NHmJvJrvl54PNiW9uvHhhx4Av4/SSpSMce0iZlY7Ckzs2nBctmdyEYmoQne9pkqbsM+fgksC6Xoe/+avh6es308blqdS7q2IjMOtiETPXrg/F7dXqH3LLhHJ8LYAX78YY+QsVkZXTWwzVT/vy8Xr+5MMX5EaCtWhLeHdWfmzsBmURjyxbiK7+okRzBA8xJdk/9TYr61fJwhGMqsHVnnSZbtp+FOUORDxFGsvFLaq0LnpwxHBdCoKR72hZWHRLQQPtgG0ut9RIi6EF9SntQm4MDguu3ZQLBWx8KGhZZqb4GRuMM0ZiGijtF1UfIABWx4003lQJ/NoTPcr5CXiCBNDuPuNJI+fzs8fh3WcSwkHRC2E/QUkH6nwxZoJkEQOTvDTQF9JOeJ4gLpJTZCEWqnOxPWwZp7uRDnRfrk+SXxqb5Zw40acsEd6aeWHgWvJaCUk5nlB+W+VYc82o0tzCzEMQLByYgko4nwm22UFVD8U4oN7FvA0D1TUJuRahDpYM5mUETnfLRehJDjMSBFY1GASUOpgbYr7oj30oRBhKb8Tvla3JAz0btf7SkutULlhjgXJPMY5KMvIi6z8eE1KGbJwX3yya/w4cDArTHTKYrqzuSUbM46dpvHw9PH52N17HbSP8cRxGBwljFIvEyom10p1pUwacntSSLF5xGktMRAmUVm/XRnVx5mFch/G671eS8e++WHYyh7LM4KXmymRx1nl7jkkYrlGSMxsCzHFIxIOxOkWYJ7F0baUNCU7cVnZIXRL5LWJmJs1yM1CvB8/+hE1Klhyt7PIDnL62T+O0LFTRinP0dcrBtMvK+MfeOjdGvB6Nyy8gBoaX7wjHc1f5eyPBlji22kCyNu/90+nq6AsahwEzng312dZR0FhVX0B+4Y7QeN18kPklhGod131YHj8MDx9mrqaAIGjik4cPFtLRqxcHgzbIAgJklhYlhcE5tSyerqJVFZFjmSgcdx3YD+z/93ynimoxY8/I73ob0sqnlOIOakkqM51dmORO06RKw70rB3xWr0YChQStpbkGHtsxZ7rm1Gc4pnn61YKo5CzfE9XEIa1BDoCVF1qpsk9dbl1Q7T5PDr1m6So3K7tqPjcEXBeix8vSZPC33sI7OQxDTOm80Qd8Tcd2WoC9RRSokpmygb1A5DiP676tl8vy+V/uT1/NVBoyCvhIoonXbpfYSBlgxEi7UCIFORzxK3IAOeClZDE75H6RB1h4luxiQ9Cd3MIUGTZRtqElXls8X4MJqmPpF1PBD3GqLpH9AQbpcgXrwOSjrkfQezxH4Eq3Lkeh6Af3W/jsAhjKhEDWIsEvwr+U1rSSJXKDcPbz+8uJ0zmksIHboYFedXE1jzPRmlZBjZErq/RFB6GvGPabF9sRlHVF0zy+fjj6w5/zb6DtJjLKfaIg7VixgyiBJLqva97qO49gmNbLMLz9xfN6eTPN+1UrUmF8mcRmSPik4rYl95osLid+2ylnJiaa9UsLfTnHhYkpj5XaAYtLx/wLh2TS5/O6Pw+TFHb302htSgc6Wf50sCBod0WA02LxVYfU4ou1CYGbuN+yZIxBbcAgRtEbrWrD5Q5CW0bsMCjAH0UJPA8lJLc4KV6pEMVvLCd+iVOvsj2+JUS45e2Qaqp6XgNkPLlKRn0K61GFIPgaoT0Qqce3e7SyEiPjMqB4G73MlD52ttGl+VLEclnDOasdOOhXbbleJqLO07TsH4andyNFP7FuxKUlqUAlN+zYC+fYBPyQxdiz6KL+eUQGOU+DOu/CEIysa0AApzRRJT0mKjy51SK2uJgOnQ+MrbWcIJzE3+QCPAZwr4HuXI5L07PAXNmVkZKSFw+DCG3QkbcsY0eXicfL0TxeJgoEWU9PuwJvR/jCkJVoMfs9TQvlHrXe3E7RvrB7mW0OkLHKpYecs6JTSPP9qzVDdX5F9gjKH8470dIOj3OEbUt6gpm3DCSfUNqRn/fOViOl7okYimT+o1SdnIfhdHd5Xh8e9pUiGaUQhmZqW2q+gULMnpA2ZBwNOnEbLi4ux+442CZFLURrqjlJg1+HaSO0HcReZUclDSdKSBLrWqIKRojENAwy1u3rgJfgr5n2Md2knGA5jNGCt6vCYohbFEVs049+8LH7PBroOAL2eKM6Ma22PkymmKF5kFrGWT1i9oYcg0HmK0oe5PgNDQTn9+psoQ+Tu+J7g5BWE6KD/irUNJiObCvYZYqe6ehPhzBQHPk67oMeK5/qcm/E0htwFzR7byUvXzvg7T5O/1Yr38jbFGNDpXvX4e2Pzg/vhfVFt8xuFcSsP/Xxgpir2Q4NMAZK2T0v7iJ/2PCsu2H5xjqhleuBqAXR4iv5ZAbj5FghIAlDhyyrR3978pR/R9lNdRguzYHVKbtkMqho203QvHzL1b+yw92h9cbeqp1wlSB4Ewel0IE7nXQ7KXlwOZ0g4gp+PrhueJCOJ57fhseMO5VgJBQWes1rZJXz2w49ryjSDBC/Ys2A3tzKth7M0GjTmD3M/mgTzefdl4Ru5FjAEbb9+jwun3/+dD5/rLzQKrPzX6sKJXxWJCy17NZRarKCAUyZClSw8EzDsEaqmEnfJqpGuq3bzocm0S9comW7Wy+L2aoBXHH3GyAq/DA9lBa8x+ziZfV3ScZy7+oDhcsLCFi6TvAUtBJbnMy0ZcOjr/hi2qDDSSMMxHpB0Fu8aQrNOJ5OxH71MEElufIRlB5orcWRujhJ7Q0vhNxJKpD92y4oFCmoaIIDqovSXggwjbPdh/UKaqSr3AgOObYkAGzoKkw9dgLepzK3UKHDiLFf3g1Pn52Gddw3RhlFQT6EW/eLijMr3/PAKs2oNIVW6Z35+2M5TRaBSHoyWKsoRBq3esPUAEpWEyxAq3RmO48sLugKKd8y10zDPxNiaIRGJmGxLi1fEBU4abL7DfyNl+og2i5vNtF75Ii+eKcGhvCu/23ksgRbYExX8XlfTsvptLAVJBGJUDvC8t9RGnvKXsdC130rYLa/RqmRxBsMkWDa7bYmmtCXWTpt94fisYiAgX3rRVDwIXAxnMmhaCj6RLMV2etq0BSu43XbTw8Pw3vzAIfpmRaEcVhslIbKFmsOLFPDdDTpiFvWWlnSjiEyB1PrdERuYyas/jQ7cJicRgv/xCd1iMtpX1gsoFhoUd9ExCpZzi2MGztzIMw0+vhVT+fxc2WI6mx8snn19R2jZEMv9gOpHuKEDlrp8an0GUP2svky/p9Pf+PHAoyp1tc4LqcTlcVuJuUVQoHF9wbaWYzmFsJ5XcimZ0AE5y5tTrBlbOPUkfKlZjOe3xht6zToMoFWWIhnqvA15NfDZVBxvuXYPiY9yGza1/VKgQlvfzw8fnXHHmATmgKTJYDDWGpyXmDSAo5JJDSOrCWrshwgCsupjIzx0HQscDFN4A+cRi4nu2+UUb5xXWjdZdj7Kqv7nxub9Ao+GLMS+rVtrAPrAnU80M0FBjsw37sgYr/1Ebzb0qFdGuAicdqWMOldgHIPPdznZT4tcqARdBmiE96CuKLE2F5Yk36cxuHT6Z+42+VOwOvCOdh5vXOF2/nmQIB6947QSc9aQpXaCXVU9THDK+TIyTDWZRXiruUjCM7Xafjysw/n50/Au+y+Tg6oIBuVx4ZpYfdg/wrgIERErpHxbAJ7NIZK9RU6UJQfZXYdFRlcbN63baVSVDsX36HCh5LMgO6oIggVbVdGFcUPXUxJWxcNVClLH+FnCH31VJd47BihahdKjTUrM/FfX8t4A+BlfBnsuzyh/NwcQBBTlh0loZmTf2cY9pFW0CVdx/QMzB/RaoHLY8KUtgiCuCoJtWaFUUufFi7Vk5cqX1Qs8vMWu4PxzQgc7jJjID3KpHaQ8W/FrGnfB7+rXVcDL6bxbngcHt9yJIdir59XZiUj1ZfjsAcji6iAkFHF9MXYyOYoFsU9rDptCtdu10aMFTPx55iQbRqG67pSaWhJPt7oBEO0p/ccczcWPSH8C69EsyaB+VkNuPq3ejeUaT2WHujVMVMfNWup4QcddYAoA5cSAw97MxqqATnbtk3zfHd3x7WvYObRgqFtGTPw4YZnxBKkUZXliLstv0ydmQjfW6HA6rR1jWsoRhqdwZhSHBU2LffDgJT4JgrteWLWK9C8MY8dQcIbT0y4oLs6YMaByj/O8+U8PDxM63bdtzkX6zJhhK1NfJvioqx3ZZuhGoNhQzIUObWfAYRkZua6NgF+UDi9GTnp1gQuKJ0yl9aZhmmj0tBXrkop1Zs7qHtEsxKTKwywBceaEWAvUIlo5sBqxUmOjnYYaOt28lefOsTJnpDwMqu/cWXdyCi0bRkP9v7ujqo6pxhs0ES6pXuP6Al+ATMsmh8wA6MONZOkjOapoEen8/Qujjg4Y6NBI86UB6oanLgu/gTuq0w/4+tYuHojPUCniW4qv4fc03FmZLluw9ufXd5/Ne1XUlaNsFiwkfiQLLQm1UEy379kIznCe+6oHJ6iHU5cx1GTBA2SDJTkXZGzLVjJDSZ8MsM+UwjFfh5WNa7UcMbONqbddRUOjmopV0pLaFGNanRRoYOOO/RmKZ/jAj37cWpPLQqYv7f1AqViSkIRMSv6GHhX6KIKk3KGIEAJ/XM63d2/oQON/Dz4vHj6HGJDRtT+GQLQVPwLTKuDXdY4BDi0q4cc99al8N6JE/Ff90NMrrGNCS1LF/aKRjMo09MPiaCkMuudC8vWpocaYWIdnp/H4d0Xj0+PMycuKPOTVgJSYlJqBTTjU3NJDWUcLgVhTaTHMneXfbFZkgDnBfLYb0VMmz/rqQ5hP97m/fy968NCJ1aaQBCKQjKuNiKUDz0PVR9NXCWUNCRWDAEbH/yt1qjSmudOpzgx/4AOcNwpebfn7LjxHYSFY9gNGVgSvgx9+X/DPpHDYd0mzhihev7Lcv/mzbycbtQACYPHEUkq34wbVHbXoY6K5wmJi9wIL6QQLzvqstu4yzjAObqGZTv/KAhayxIPu4ibe1cKMLq6917Mr1skcqF9PhWoaUcS7sJI/HZdn7imjh1bBSOI8gk+p2SKpMvcP8myEXYmU/c1o0UbEvOVyqLs790nriMgLXNFAZoNHw5Mzzzv09fznSQzGE859mcUlezw6qcMWRMwG5af1zxxh4gQKxoGD/RDnnHSY5wydVatOwdjLkE0ejtORAhLN+QfTOM40+koC1dwvtvpsJh124d5HClvkE+DwrEnRcOHK8Os8g96bEB0hBH21YAmOKl3HYqd5U4m1/UtEzr0e9FyfeCNZhYEOVdnrVvQBTNwAqdncMpiZlO8jHUrdA7eUPsy48jlaXj4QAgkh4MKY4xImUps/bQUHReUxoxgWPndSuhZLJc4h6kXQWyVS6UnjuvYCIejiKizrXEf5mG4H8ftTAYtnfCtnSxQ114ZRo1K850cvRFNkw+p3k/VQVpIbEuDHIgDTkacgPkX2HU871C3p7vJ+bvSUAkspwrtyzLRGcjjONyxWY46maZpmCQGvrtO6VZDnHK4S2AL3jIqE09H8Em3F5t+rnV5m7RhRzUOLQlG2m+PT7aTi2Ef1WVSiO+O53DE+43HogKsDbd4IfjO03q9+/B2ePhAURNBKdywxF+iMqYWmkSCFES35DcyiTbhzbKQuAWBJogAcSZKZGQb9lnK5jkUa1LUeBmnL5cNysrC9KEK6msTx3WUtlz+AJoKLZKDPpICbEnIWXIHyCgSgWZ3FHks1wOOsQXhgEVpxVWQo2DB05u5oIytI5XwphpXVJyIT1fgmrGxKrUmV5QdacSRZildDEGorFzJ4BR4YJ5XeSAahgbSCLHhRPW7Yau4VEdNWeHRIi+UQYSgA8dPG1GNB7sFogd43i3eODzT15tUOBueVofdtuk6zF/+dH1+f/LKTqp1B/a7+zaf8U3O4cgmiN2VE8EFpa0UgHEBCY0kqBF6EYEA9EkKXNph0aEZa7lZcloP98Siuak4pgUuVP11tFGdHh5KC+NktWlGntVy5uJDyqUfG/BsRJ8wQQQy5VI6sKsJ33qsCRScw7nHBO00ULstFiyuEctHKVjwnE+0dOjmjjqpOmXMQ0rTKvLia65Ipdpf6DY5bNrHQnFo3r351fcj14iqracijjVrqtdBIuK72akyKawwUNrRVC6GxGEn8/PD0zq8/ezx+bxME1WQ5bRezyvypiximTOQuGnBIsz1kKysKVxrEXWkzxpg8Yl2pG+zLcrQ24Q9c/j6RPhcWl4weo2zgtFen6g8UHaII7steaXAskNxmyM45CSn2m6h/Om1KkN3ejdkTnQwfDudffR/m18t0BVwWDdAt43POLJfXEJAK3iHS+TVqUsZunvO/DHi7+8ejdwlmsTIymYoe+mSkw6TxAfBNNWsl/9rn1wnBMtG900V/xUW8/EiDTInok+XO3Sc96L4IWuWZXWL32XMkliieZlOAx1EeOKCU678pbWxEw+kk7BQh7hgp7RzDxQBoiNSjTzIOr8wMeteNypCLaKyCGpSxdKiC3RGXFaWK93xcvLXiUIptV6ICRmGBX2CCeKu702kVRzqmyBSsWWQwtHcIqfjrl01HcP1Sibkmxeu9YNmE2+FjVOrL9iurKQzHRFKdujUmx3z2o5DYCIfJI3SSoLKbLZNaHyMB4X4AiWsPn438wzf/MoLGZgJU957r/DKVdwLrLY6UvE72Ldzi+EK2PU5FFAU8trQoXZbRnMAE7Och/lbl8v+9GHeqOrGxEHLgoxqeTbG6C8HqBulkNNSdFChM7qYKLxQnxGEn6d5ZybMFl33TQuPNaHVzDXkSWaUW+g4NTJUcySWqVdo+ikEE5hzKhtmBbg6AiNqtggtEoOFzQDOB5+xez1UPeLuHQxV4uipudgVPlDsuvW5QDwfNLNfiCpt0QE19tQUDLYzlT4PrCgyHoamNUogSFCFDye6eSxWZVKSZwNGiAAPJDV+XAP2XcALM7pMZcWoHijvXHi1Xq7S4nRa7oGl89kuzMgyE7PkEI4frw/vCZWsPqQKsLBTUCA2pddVF4Nuv7oynG4ZHospTCRpTk9YryQRmzImlIVPY+J+rJjWvg9XZsLkRBbiEyUCbpLjA4EqBWUJyYHAb+ArMQManIRQ+v8LH8zhDRh0gZU4KwyUq0t0U9G8GL1HQ8Yw/TMCRFI3xmHm+A3FjUT54p3OkRm9K2S7TvwGPAbBPrZKhbX23xWVrKEUBXtLXEn6IQ+itO4/uORg1A6xqL7YE8adRae5OQ1orl1+ZNjoECO5XwdaxsiF4ubn8zC8/WJd14VjLYqCwzuvERfbPvJ/JMRafIVYPVJJGeGlJge5SKWIKAMXQy7dnJZ1nCj4y1ZJRGVBZ8cfFqF5onR/WpaRK2MZIYWCzO06qa3BhpHFG08PLoQ18YbgG6QDp1pAYfKJ9Ta3SKPyNcMq32VCYHpOSFTiCt2HFiB3IJ+bC0C3hKwJcmiz1QI1/tSKvqbidXy0urV1Jk3fB+a+fBIALk2AcLfVll1ktprIka2ll4aMFpwDFwW/bpSRZnwSiH9MoxknxkebtOd7OvbCORxMLdGti/tan8Nbv3w9PL2lFELGk9W5NBBnlNKyB1jjw2QweoQ3lG81YSJ0/ZhE7MC8jpRCcaIjeJhYiDhjkBn/cij1sK0jhXocbW+znCmrKpHkXgvwWl41L7alkmjhWgUbtR5YN9Ivc3f9rYR3diWHm/eC9JYQy2SLpBDKAnRZgUB5F8d/i8H2Qnciw1RnZgG63Utj6irEdV/IWxhYlB7ucD7gLD3qoFDdeRG/ArkEdA6qikGLcseYWPDOEWlHF0Vl8TuLDkDMEHne9vnhcXj4sG8kykr2LtCjVNOp2KWrIK9RuEDWDMRFgLP8O33InbwcTTDv43RlrkBfmd9K8rAYvd25IfizrZrV5FiOWAJqredbVYUrb0/agiTpoWGSfG7Im3yO2BRKON78C2yqN6QYGTYpUcHIUDuv4RdgTjpP1lVYh2wiAgx/cV87yYuyMCivZGrxTahkcwEJqY69iBmPTSu129J/hTcescqWjnnc8pFEk1ARB5xFXfzXnbGKKaPd74UiA3UwDGjJP6t0p22d3/10f3w/bJdhJ5NSmK+MbrBXKRyuAe/ZBSvZvDJxOuBQI8CFIigqiEDuiC2Wqp0xlkovjSMXrzW5XClVQJUEVHOSKp8/ah7OsoYKQ2XvjyDJ2WHRdXK74XnzEgTdFfUl6fJHeLz9fONCmf2QDkDQvw6hSmD8MzmAPYnKnnWYzI2rkHWAq+W5zMhjgzsPA800gglSqdF8/xk/gAHAOus7xK2J0EqRe5UBQ9dl0To6WeZQcrO3KVkb9RkV48nesPuGzdhoeYP0wGzmp9vw9DwM7774+nzmxF8/bk+y6UFkztzY5msfQNJyrmhv2vm+kCTjGySvcf122ueJzgDer3TigYwRLaiRI8QIz4qc4XCzcrFmL15mwwIst7HJf1Frj80AEYUWliy3gsVG1bKp2cRTAbiONPNw+RwzrQpCphwImybF4+PTTAowSnE+qsqjhD5iOhOSNYiO6c3BQBPRMTchQXoN9ttQoBpNlvpCyDWo5DsdNhWTytiT+9J1AlYNEvlNGHpBpkKx0O40wlVnAxza1ewbuAIgSr/O07x+WB/e3g/buK2Mw1hUOQl+hdnqLf6WpRgXwoMCgmLmw9TdU/KlRdvJAm2R2GEIhJWkia0U+sEnM8RwGgRokr/6D5a30tP5Ny5N4Ka2MB7FCRNxGLUNtfK/gwshpWOSSeKYHuXStB4WC5ws2DHEkztPwzxLofwO+CVjhXfQgiEMNgNKhoQQVILhJ3dyfiONCMz/LyxgHPPSrAeYIlNoaV3tMnJvMIWNFt0YhqrumdcY7b2t0RnFC4/CX+uZy8aNA5Vxn+/WbTg/beuFslG4apVZBYyyifkXsriFOBgQ+N4YXQh2TxxSTLYh4BhMyAMaiGGrIfbnq5TdUahL68iJj6xLcxJztRI1V0f8ec1y9R0HXMhuW8lrjcERri9X78itgK6bI2jpCJZBD9S51XoyEbsTadzZA6xV2pv3DESxvEnSTnBYRX1GlbB5E0iBRW1CM0bt04BU+D5aq0QEeg9FLJCAG6udVbAJ7FQW01eTTV5ou492emDouezp9N144Rb86YfSEReb1EM5KeV2XYd3P7t+/W64nlWAtjCMzDQFx8LgZ0CE4mvoq4lx+9B0DdX/JGy6qJny4MQBV+qGBAQ12zkbyzdKgMwIY5/lvDGHcfzZqxgEs0rCIURE4mpaK5xFqJ7pBHJ9ni3EOYrCBbjUDeusVx47jhIoZe4XNkZEbmO8vnAURCl5wPUsvaCyNta0DJa3GGFSB9zwcDV8RY5zl/NOSV9HbTl5aR7o2t3QnR9ygd9PG+KbJcNN0s8x2uFb0abxH/kRrKh76ctocivNxJNChtmDPw6UVT+t13F++9m+XU/jRCfPaXq9oKnGOIeKGHExISTKahhZ8UAr6bQVuUKI0SAvGbT0GkFSafkFzXTvw2e5sR84O8RAso1lL8E+AbhZesnaU0rd823m84At+rWsc3MhJ2r3O4aX9wkxFJ6qnfi5NUedYq8xkmmaFzkYHWOzbUValtzDjpvX63SU9vkDdcMOdjzA+QNsatkjfqooejs+vo4ea2vvmRYkRgJF2I37uQDSk2Ha4QWUImhmuWe+vh8+fH7SohfCgR3xsXqjwpd7PpMH030rQdO1xpU4/uIAByoK76YsfbpyYDEs2xOiDwdTV/cwtQE6sCNuXYIGOjLioYaUo79D0jHWJGlIm1sWsPdovwMKrSSdobUonAdNpOXVu324K1oecFRyIM1SKbb2IiPAaSVy6sv4QlGEdjClr9bimh5uSJc9c7Ao9XakXgIVRrwDiG4HkHYA45mj8f5Y2qosaUYJiQx+RozBzK1iNInxKzxhiqexbhQ48fj18OHrfV0pwCqDvdIYZXqxJu0GJHAUuY0CqlVOlspVIYxgHJRVgkYuh6uomU9AeFS05mPYxHaNK9653JsHda68wmaSlksEgyFqzItPY9iKI7QIYGVpjseVn3vNY52J6crltMjMWGMc9P95nqdxNldhoSI4mBa1cqzdzZnkWy0TO3r1ADk6TSZg7V+QS3OUSZxTH9GF0NvQ6pILJlwVa/2m8nYpzqJAt9+SbeBhw+0AJk7hoQoc67xdh69+uj1/GParVDoW/y0GzMB03PPs1YT9P+RrPFE+IYWZVRaeRUtmzJZADu4uvMfuqBPC0eQzUl8c40EfJKHfTG3BakEOiNXokGoQRJMIrj8DL5LzKPg4US8g0qBuXy4qEn5+B0SkKil0r457sGd/bzuyeczzIjlIIIDj+qQ5vTga+OAgd/vNl5OIOrb1JO0jr4v0jmJTR4YmYkNPWK1yQcagHLB+QNdKkqitQpY5mqnhFAb7q9mc9WkjAW4pFnF229d1uFynN+++eDyfPyEHCUnBGGqIVkhLftACwjI75cfC2302WrKyVB51kOcfdXkiFs2DcvPS5BgAnaU5hKVEFspIZW2yXN6NW4pE9hzUhbAQ7VpsSyZITiCzRS8oQoeyIuj3x9tcB6U32/lGhI+RcK22rVUoD0jZ4QgyhbY3DqT3pskbjxW9qbc/ilqHoiY2hNyiCD4RboQb6OtT0oBMzsQwr7Jxbq5t56IIAsKeIUp/1/nC8z3jillkGhHC03oeHt4RH5bSsX5SiQ81DZLuUOoDVK6T4EqVKuFSgxmYt0INaXJdZKH83PB8OmkIv1o4i29ysYGd6jnFcePed1Z/YUj6r97uiI/N87IcUYZEY9MOJWa1++KUU0KEbQkStgMQj1G2oeP5TSylB0NR6grrQgg8cd0cHUhhxbVBgENduSSltPJkXjwbUVf2aB4vMIrPgw2/dRpUAwJ05WVisme8eQqHIX6Pzhizlhs45LuTFUnn3+Cx6PGEMQ9IJyy+A+8I5icEmVKA52k+v387PD/Q2XMU9BT2K4v5iBo/uo+GhaKWarkcYufWtDFNOHBNlUvxW3mJ6SprOaxYCAweuBsPAXJyKY/qucthlT2Oe/NGIdztum5HFN43pPOytnDIswDrym4lVn94FfBomvBWCH+5gJ2gs61X03ZOX8C7dZzlkYOJ1UjXuIk8rJLbynpSI/pGI3X5+EJfBxbaoehoScKFbGSpciNcyi6CBuUeX1qePonYpXY0doZh0gp0UsadD/f76OFheHoY1ysXaXNU8XXSKDVWUE3dMAE6hAgPX3A5CMqKu98MAzJFCvaHtF48UFhNJWQQE14Q/nBdXjKSUUUO+9oTJvurlyjyLUEwN0iShsZhNTwfuVTOTGzY/KEVN5jWTeHULxTtkmXw4GHayImT+CuIHT5fOzII6gyzdN71YWZXXpQjzby67/su21bHFtAJsjSuCXLE1GV30Bnb69rqr4WlZndxP1WwN/KhGUBwrrwQBmYUPkkZAdfr8PXfDE8f5n01vBdtgP7PybrBDCHuizxGbpcOQgbH03Hogn4jRql2K15ArVDnC2UteOS18XD/oDlMUBbRsyQnc1F14n2NxBcGc9suAx0Ep7GZR4Rp7gKyopuGoJn0U9fiXy4jALbS5o5X+UgDiW80AOOR3aUqdrVmZ2dBbjfaNN/n4u1sUDqCUNtEihy7NHy+jrKIz/lW+dpFoOArgYTOEwzHix26nw/Vzq48cljrxKBqxGASI4lI5MpbabjbcHnahi++eH6+kCxMSqWUzYkChQ046kdhyPiDcmPgBoI5BisyMA/SimgtwW3sxku1Otjizwr8ms3EfmBQGoDW4+gaxEaVL+AgkT/UuuIoRa6jWUFVCVX+ASgbDiBE4hc4P5LeeCndyha+Jt4s/gqSUAQlF5G1E7mQZ+XX/bWQEbW3mqqV5QsUt+E+RNW/pAjExviw7JjpGK1Ifd2lPeKoeVCHrDVYoBeKLFtlN9ISORmFQAJ9vYvCzdcxb7LLt7EQJeGZbU/r85enx8/n4TrsksCXJ2RSqUMbio4SGO1lkKHMlT5DbsadnEDMrkP0RRTxcu64sFhCxA2GcLiM1qzft2GRww4hkSjb/buLlS8hMHnXu2RbDOschgW6jzXRafWgOfip4otPOpcg7JHvgHKFJzZJhDQaKlkU3GUH0jwvYcLA1puyFPgRqAQGPLfzbuWdOokXWLrPC8TcXpZe0N8EdgfG/cQPkaf4mA/H24y4pn0dvng0U9b9pPzFHreaZsBz39C2YRgu2/W8v/nwODw9jnTEz24ZAgIJUpKKIy1lyDBmPiJ44Lqzth6wROzczS5kZZrBePW3nFoMa+racUoCK0YNPsiHndmcrPgK92KVv8qdgvzxgMaNMvuhpRJbn4kpzQs3cU9ud6AMZ1q0q7Zd/5LJVcvhvFWWIU53dIpKFAZq+pUP3dh0jIlPw+h65srA8eTm4kvJZjsUXo9wqBJP4FzYcFZicXLpe874rEiVx9Aw4vKDkMkAjfqsxTm8QMd2/ceCQHw0wuq4Gs2wX/fzw/DZXw2PX43blcrTDCsXgdaSOkqa6hBswnilapWo3OrGEQkwQzqKdhoLUqAuww9KsaIJ6+ztoJYJyGqHVmYgzfhadygGVh41iZ5PY4B8bxsKrlGPLhe084jSDnglIlupTbbYZlEFxo7AE95ash8s8xJql8fFt5PFPkrb/BGIn0NKBtq6HeBQqKDtm9tZNMQfzKHFxxIrg7fcEF2CjIpPGVsryaeuqxc63zMgM4yY0AyUyFJ2crPNNAaDRqzE0LqZTWvmwKf1Mg1ff/50eaCcQjGDdwRDnERaTLVBROSU/8b8mc8TFF5dtHuok5R686/2ukxEwqY5gNEsSOJ2lomQQOhWGdu+NO1qyHA1zv5iKHaEELdoyPJzIcrlqVYNxpEd092SA9u7Gj5ne3z0u92UeNuRIijh+PU+g2qncOPqEKreGc04vBQvlWlnRthOpDbaGIQG+hQqIWtUytJOw5diNfIESvGK9Llo/Hnd3Px9IBfCbMfcLByunbKSAGD1VKvT9vXw+O5upQhKqJiftxYyjlJ2ole4lyIb/Br62H1UakQO0qOtg9EtTcCEOziZU7OOEbjEqEGfuRxHWZkwHbcSM/xwLPo1UhT9wKd544FmlbbmKbjhpkOwenv6OlxqpZMkp3rYFdbJ5Ft0fIqWwYL+Cr/WHwplrUMoYoAtBXJ7HU5tBCS7/nTjBTWCelvasG/fAXq+bDc//rknIDtbK5IaYG8RCG52YjBfpL8drRreDeIGL4hsK4fBLsN4N18vw+OHcaUYLHeRmZYrYKsBy4gKIhJbZyEgIC2DjbOoS6neE4FMmWYm14w1qY5f07RHs4c5geIf/HTCsowo5jVUwgfHpFLBBPS0rqNJEpFy/h3y6ZJX3DAHeeVmIs43uDL2JhXEes7ylO68P+PxOUh0jWno+SoatFriHPQtnV1fMsABtkFVdYk7G4gJdw2SoJp2EA8RPN7Pk+k3CLy329sNchtLDY2llvOrCWHzrTEyZPMDQQhNhNZt3Pbh3c/Wh/fTtnJwfoSVaKAi2YcwLNpVYo5ALvtk9+MELU3vrTTcgvZEE9RQPz4YLbJ67UR4akBOuJQmuFPORvQpjlzUThlJsANj49BrtV6EVqm9drTG9DonWvJ5SKn+VrDZZqM6ddwO0LXeBkNf/wWcQ34NoqvsfS+Ur/HQGf0PJLwgtT3y2KJCjxdCeqbwgzzsBJc2ZLe9RigYFjkpBbgK0/RXQkLPFeeS713LnAMydha2QWZXidFWm7JwYkmyMHPIlgfgzQmpnQwXvX3Y2QP89u35cuYa7WHxKlsjGqxXlAy8aMUo4Yw6aAvZ4HnB2ymrL8RsNYH4gcZ6H70D/DIc1CXBmnS4WRLZuhpcEIYYed0wi1g9wDHOQ9r00Aproktsb7PUrjBg4TIprre81bB24KHpH3fWIk3iI+GoKr6c9Zw85vgBdC1TV6u+eKAjd4cNnN41sMytUkJsjL3fOGqChbTUdwrLjqGnDcB2IYzgYA/zUFEAQRJQCZp7xJoyJh1Iwux7cSZGzYYwOlJ67n79MDy9nYariitWoHLn1F1fatNUlW039ARQRmUeBwwSw1xTleAtH6RFZVWDHkBURG7p6sl+W2CHLv02LFQg0vW9Em2OYmEjIsXeA4gVGIEG9m3lA0WD/wJIIpR78DwKRnk0qf++pSTNoVEmKxJllpHFT6F5E8dgOTsLX5K9psddpL6N30CNFWPK+nMPl5sJ2CTzsh4Iv+064JvIXlTOshVCxG5EpOb9BM9V5CqXKmyZzB0EZrq40ZuL5CcIPLUrt/NyR4MW8WCYaKrqmeTAu6fn4fFJarNlCz16Segu2ahhIu0J1RLQKg/oicLIIpDcAKWRRbFVgftwxAmI9K6e5kUZd+LA8o7Rq2ZNDM86aOA74e37hunyiTltpCLyKx225hGiDdtFctySYeQthyzGv6VO2qt7M+fCOoUUcYVtWJz4IeS6GWThQjZtYaKWS4OcpieI8F0xQHnjtomFfnUFlo6yHclsTbJpT1DRuUtBtCzuZKGxSfGJXYgfAPCLO6MMxOW3V1Aea9AqhA5l55RkWqdGwLUYxzjslLdwnd79eHj6atqluqMvskriJb7c9dUyPzmB1KmzeWdd+VHs2DMH1Yw7AHVpA8pvWV/MMdy5KZDBBS51DBtlI4WVu3sZIiSiky+jeF5SuOz1aAxY7/SbiTXruPyLh/dgkE1jr7paVbv8ZCeJWmE9tWZyRRd5wRx08pXN1SZWJcW06ajjbdMNSbiIHA5063YqmICmDx8uGYi0SfktsoXeSQ77SNNDIbCvP3lWLb7uQdp2FmFZAG3b5t0xrCA8jO7Kttmbkokz8RFMz5dpePf5++dHKqrjC4Dryp/0iLNayF0/eP5xPu2lGt3tXAXTpWMFDa59IkL/1e1Sypfrckj5DiE1hFLrvjSHGKtlsoC1TqsRkfNruvggbcstcUJLBFYXuTokuAoD/T6Pf+yN9aaTNjsNjfeyEDVyDlKjLBth5W3gvGqe/DybuDWZwKE8GUaUuSXKrE7J+iTVVPTwxzvHyFmzqiQ3zDrJKok25JXysaEainPPbBgKH5bWQHbKWkPZC5y4T69IO5HmmszOQ1nD8NPa2FyJoZqE64fh6fNpPY9yNjb4Kn1BjACbXM6QG/U15NGsanh5SFw/hyk1XIuxLqgPwwmLayOZeYvgrYsno2RmG95mEdfJjZQ2PolZrWnFdWzYtyg44GqHAa1tmlSidDnK2Drkt43fDDvhF892fjngI4ksx7/ahqtJmqrYSRFZnyVOeN/JtsWebvJIbPuqJxeKeBXlCitzB0zDeeMKuQmmHSOulInHlUDVAKLedE1G6vQkzxRp6Ii+HBBHZwYOvTAW+5OQULVAtJF5Bmy9Rm+5YyPwN81VSsUrxnWbPnq6DI8P2+V83feZRRHz3BRWpiw5OSUUp/SGlvCRcwmcSJQcYNUkHes08Cs36CsAUoNr8LbnHMhL17QP8zhe993Ko1aQgE2pzsJEyS0xJ/T4sqmyKxLAYRhemwKrUN2YV1wd7MhTSViDM+u2Il9iPnoCjSbxy5TFaKAHOLPtfeQKhNOyDJztsG3rNJyn5TRdSGeJ2AAIvaxWuYOxHI44yIhxooratxcQMT5pqJndNsdzIHDlnlKgmMsA2CwvVhkpytJqrUt2x8TMwqLsr++OAcC2AoSC7MpJflRE6uufX778YlqvHPgsZV89z1XdM964lsHiB+UchqgOGSYy7ayYpLSItXpnQDzBjHClv7D+EPTlT0rMc8TNcjTJPA5LT14+vlLYpJLXtsBOceZQHTtNqdJ6X+lp/9blFv3ROTCUkR3h8AH7OaITRs91uuJFchpr4qOIFNeVgmpPw/p0flpXyitd+NSGaZ6//e1p3bbrvqkXyuUxW6JD7EXgbgWLI0UGJZqjlpsu2oXLoSnxnCEPnHsLw0nvYulZLGiZQ81aUt4dtA8GWm4YxZDHlJbF/uHtO6/T/bvPz09P95Rao1qeFZkwauZoyV0C8pF5T6uxS8+mB5dgLDOXuHwq39Tlw9UCRHDgv1CRsVe+O9i5yXIS+zGc5kk4MAgJIUQK4WkjA1G2jcG1SBzCvkRBF8PmEbp1Vz8YFvRulRNehcUtHQhdV8ZfXouDcFT9lQK+4yiH1+3DRtrUlUqx7PP+6Rdf/Y9//1f/45/9yZdfvBsu23T/0S//8Jf/H//3/9s2kJxzWlzsUkQuwa4N/LnulFE4X5XjyU1gmY1MiKbh+NY2bjUTu6tHtleBucRdcSK2PXAnuEorMqE7FUfld7ypuok7AoSPrw4rGOU4j/s6PL7d1+tMh4NJSVcJ46BDPe2leogGqOKWrWq4pIJVBK46MusdoLdW1VIxzmUy8+O4F0otpsLiRd6TuCxmGyYcUoKNgGYSi+1jh/NFhEOf0nfEWTZecR7/LcW2pQr4U+UPrtwYYYuwnQPYPWZbvZnEIssZjmJIDO8KYfG63U33z+vjw/Xhq795+Hu/8g/+9R//4ZcfvuQzIYdl/Orh4f0X56dv33/MAgj5KpwNN2OpQ3DvyBFFyuuMj0Boh1oodEa4AbFIIQcoxMIADYAN8Ywb1ZFYmw792rje0Qoyep5y4hRGgQsxc2Gl2bHOYuydBdGG7UxCd8xQYOJ0eRge38vxAhAqI8MRN48HL3QiYDBO0b1NHkGdS2m3hnmkYZFN6O27y9fMATFpHV+4Z+XTuCDGADToOuQBuOLe2b8WOfwrYy8G99WtyGcZHLQCRCx3raNGue4Gd1eMzxJPoV/BCqQQJcfhqPmQMJHo0XkfLx8eHrfHhw8ffuVXf/1f/dEffvXhPaWAj9O4DPd3pyvJzuSKmEj1chtkIGfLmoxmO9YwN0VK2meY6UZSQcvPSQM/2q5kzLJaR52HS/BHdzgJ55OkFFjSmULDPDoMBvTGMdijqDxVzNY12fbnbbv/+svh8XGZBkpN5wxgbD2GBCwhuzZVLxU+qbjYWyBnwjByNA6kVS1ptZxC7BIAyjJe3lWcwkMcrQL9peCNcLD5OuVyQHUzTa2IyTPUH20DrrDzAmunebDzoj8aY2yskrF0YKoIxEncIzgE2zyYb2pQDpmkifcup/vHhy+3p/Uv/vrPfuUf/NaPf/Kz58uZzlanc9s2Lj67TOv1uj5P87emVSxdHYTqTAe1R9uCMgtXsY6kjaAE+vzL6+iN9GwOCU/K3U7waih0ZWb+u/oqIihSeu4PE6lqYjd75o3FJxaFfd0vNWzjlXMYzg/TtFEQB3kILNbdoFat12FuS2eleYiIFJT1LELZN1dxQ69V/C+6KJSMteiNrspjz+gxwiZoC12nP4sAQz58L21FKGHqVezU6McJhuFMl5kS+Us9xaTmePmkpqGGThcoumV/w+7yMyb3AP6WWJ5wqfMphGKAJvMUOw3WeVmv1/E6/rs/+Y9/9zd+/Sc/+RnFmLGZmQ4NJoGD9OZ5GJ/eP23fUuG5FvPLi5zGan9bz0iBZlwHvR9Libyks0pJbU73X8T2zgMmGzU7AvvmlD7YpTyBz5QWQRttZf4xVkOiGfQUSwiAtnN+aSunbRvPz8Pdl58/Xs4fjRNtIfckJmitYiVRsQyT/LJQwqDtWgorUpcCYlnQ0iHR6e9837chKc55GaGYgz7pfBbETOU5wGaUqaBfGdBMyVeWTm5tqtqskD1rzEjOYoHLJJE8nzTjeDJBcPYnwhga+Apdwr4Xu78tlcpCMX/JEBEpep4mPlBluXuzb+u07v/23//b/+Hv/d2//tFPaDvFxU6MV1qXqJWNROxhZxE6bYktbTMDENhQuIT/MMTo9nZ8gysLnNB0Kxc2bzUzidIWINX4ZoZkZzCT5Ud4ARRMKB5aOMFQgsQ49Yc5kqYBWfVGKslwWqb9YX16SyXsRJuUQ3ZZapKgShmzsFAPBPDuBBupDJWBtIQHlJQje9izhUG6rAzSq3ZkMqppEMpWZQkAAdT1S1XaBLcTd2/3DD13oGNX2TdvgU5DCtlBhFojGQudTXseuuAtYMx6dPR5LCDoVyR+5qmHogyW9j3MFAA90vlUFK0xPo/j/vx8ebr889//J3/vH/6DTz9/Ny9yzso6TqTlSsElgap1277++mvC9tOJ7f8vmt09Uf+GK6juT3cJbl+JEOpylFeT4ReIXIn16rTdekGyHOgIYlLtkecr0Wf825nmHp2JaBmDVWpHhPU0jPcfr+v1+XnfrIydOQPdp+FxSN24Bp1j4ok2gojNqFFCyhnM+VTX0GTsjrDZdG9pGa5/TBNUiExM171+SoTQdqQkB+aHhDYZXdj+rHUEulhe6L3TBRVSerCSSHyh9KkHGFVqq+QLR1EhF2DoBO95muflNJ1mKUK4z/PT5Xm4bH/4r/7g7//Gb3z69h0JzFahj1z8RKk4ZHTdtnUbxunh6VFPxAqq0YHIMuqyKs3cceDOzVzy77wP+XfRjnEMgZma14aNOVzqvqgi5j15VnAYPpx7pHhrP1ugESTrdEVy8/jkMKXrGxnQh0L6vSypvc1l3KdtuDwPn/0v24evx21d4xQVxfNcZ0O5oh2YguCSLjM+Z7DXXhXQvIg0hnTr4pgxXo4a5A8SIOQLmnPlzSEizH1dNwobkiYdpg6Ax+jf2A+eql/0YdmvNAhVVA4pKdx3t9lt1pXgIRGa7oS8vXQclpk6YIrTLmV39+E8TOvzOlynf/I7v/13fv1XPn/35bjMnrDEFH+zmgWbuemn56cnDrQcNoimtL6wMHodZM6iDNBslsEgOfDeEbIauSxmrnNl4tysa2YN4eCK8eOStu/bnCTuVDtELtSGqjjBRUeOvoW0f/Su8iHvMFyjb+t0fhruPv/0cj2fRjnKCA1UAg5q9odWpQ32FfNn1TnNW2duW8PJwDrj4DZRpETG8ZMKnRinAlbkFiJSKcOnbjZLXwb9MQuYQjZgmkmDzHEBiMHeKmuDzt+7GTjNZqeNSd9xJRLeguG7UOoSr2XEgw9NTnEh7jShGmfzPM7jOG3LNC6n+33Y79bpX/6z3/nbv/Z3f/7u84nSFVZRDojtrpwsyZ/WbeV4aIrvuKwXtaJYAkmKzyjzh3qeIBY5vWxpHuwTbJ+zIXvfDIZJDe1rGT1ERtnFP98+8+RAckKK4A36lCP3XfgP5u5E1chGTNhxVoACJTeBTc7n98PT2/tp41C5WBgcmwzCTzaTauEiD27Rrvj2gsdS+Wir24FA5ZPkLCLVzL3DTGn4SV6AqDnHj9i5wfEwCwj0H9fEMtOdxmRa2yijvrQpYWFCm7h07/ovPn1wOR1ybtHMEcBHKVgVOrvCdA4MSkKJ47ORCrNdTeN+P58u+7ReqOrBP/qtf/j//fW///m7L6fltHlUHOkHLDYLEpNutQ3jvO3juu3ny9U5jgXVmZW0RgRDQEVaaOR9FrTTX0EExAOhBXjpMavsN3t4VaeRh8oWOPYgozI/ME/Dk96zO1uRkgcajPC3R5lYDN6m63De5vv3H4bLw7wTrTU4r4BgRds8ax+rt1vIpIclGyQCN/HzOqT9so4evBEs1xRwPz3DOTduaEIA1vl04Sgbib9BV8CVdEjtJnbRJJ2FZStkkYcwE/dHY9ZAeSB4du9yctmTw9KEYwXSLS24gsqcxzvLtc0U9z5er+f7dfztf/Jbf/8f/PrnX325nBaWRySqmcv1SgbSul6v121bx2lYRjJ+jeO8XS8sOwfjMfQFCc9deAi2Lvfi1vmGtuU56ip1Vy21WJAo57Ad0Vl/c29TQDXeStorZAI+xWEhTYGBrGgmNy+CNuzl3pumHCxGr7P9hYJp1n2928fpqx8Pz++361WT+y0QWjrD6qeILi2lq6piDoRmG7WMWfdJjhTN1roki3iJiKgoz7tMjsuoced6hEhzKrBLIEcpFOt2uGarcejlDcNh2101iXEdHZNyYu8RVsJWXkvhI/vpjMMVn1vxlzkBKkJJjZspQNFR57s4goZlmZZ9PK/7sEz7Nvz6r/2Pv/qPf/Pdh/fzfOL2tDKBOovIanW9XC/rlUyb8zBRvC0FW27np/O6rsM0DVQMzDlSOXO3ScqAFYU7wQl68e4HC2Moa5gQht+0RI6EVnkGWGMS5xRRywhh0aWbDv5DBioaY9urcSYnduy3Kn3b5aZSSGx6HMbzvl8frt/+6vP3T0+f8P5Notfxm+7b8z7QluDNlJIwYkt3FI1RmMUB9T8nQI05VxcDViN+lAAgCxy2KjcS32tLtIgJFpJccS/ApFNj29HUhF37nHXPIc+jZySVdRq7OYVgMIuXNKEHBLamAD8aFtTFXygCb7NrC6puEXOYZwLmbd2v+0Zfnobf+ke/+Zu/9z+9e36Y72ZJDBNyIwZnlp3Xy/VKlme6s9Gh0dMyT/P1cn1+vgi94NiOSCJVJAQ5o9m7PG8FqlsCSUrF1n3LZMG0HAGyiJs3WGwb7RiXGlu1r3U7JB1+8Pa0kQeMAW171gIoaai57oeCh9mHKCx9P+10jP1+fRie371RVJNClOooxoKN6FQtcrvf9CitpMvYIRYmlyREVbsTrGOjomqQlBMkq+tthD4lGBudpZMZtMsQyyIKseCPcbsk9WbETxRLpmEmnN52tdJ5onnBAirqR2ExkOpiTYBcV7HF9Ntob9tXztmdpnnY1u183h+/fnv/Zrk/vfmV3/y1f/zPf+fh8YlEYp4F/0vcVfzb67peLpf1eqWKmxRHKT5kuuZ5enh84K+cUClEA+L9juuT9OaRVq2VYZOTqm3I9l8bzICXn818DfrK7aXl9HWuiRoVjg7kpRcSjAoVieMhB4WPYndVyu1hGNM0LPfnL4fz43a9DlKcHPWzTCkDkeN7sqVa2JYyXtwkZFOK4Z4Xw0gnMfEcUK8ZpUWwUb9NQlhFIK29AVtBZ+3ZuTyqwoIwjIsa9N3dsyZZldXX59WadRQ+2MYq4w71v6bhoPEKBKvmRqI0NjjfFqaSVPKKCq2QVPV4fn54eJj27Sc//sU//v0/+PO/+vPHy4XEasJxzUsiTN229Uomq+v1cuWMQrlmlsDJnURQMjw+PuwTRYDwKxJoKeNSazgYdXCyRpv249Xo2Bjz72X7oBV/tcMJA/y7OJvuG6RpmkfHLNQfeuokHjFa3cXtarvC98Y0Q0Ent0SxirQO133++qfX918N62Uk4Vljn8LzjzxOG4yICNN49P+KK8yGxQBsJxWagbIuqmvX+XyWRhVKEKx6kJUTsUEEbWVGujRIYRsRUjVKK9kCGll30YpPJY7S7llZCsDeIMmowx1AVmPAMjnMBYtScoLPc+ZKwPzjNK7zfLps0/PT+X5889d//ee/8fv/5K9+9uP9ct0590gqY9FWsb1qZW8R260Yj5kV02pSyKXGdAz7/vj0+Pj8TJ7giWiEQzwO3cdU6iJUXhbabznxxwl2WtAABWECzq2OiGRzH7Cl5w7oCVXN/uR3WjyGGfeSB0HWyDbtynDhUg1UM5ZFhZufzuMnX35xXS/3iowYTxERI1A9NPRGc/jGSrG86rii1NcniQes+ehMSU5QauYGl9+t4CnYaAR0LWE2qIGVj9wXjpLCDIUE/k4XmzWCrzhS89ZLPCrDPthMb4dktKyguV0faEUnCYzq2FHQX6FAzfGN490yT+Nyvq4fHh6HYf+rv/irf/i7v/uTT3+6b9dpnjY6e5IeXknUZqn5SnmC67oZ+goKbyRdq2h95RyX9en5+fp4IUvDaV4vnhQdi4FjO1qNYnE2U9+hbBNtKhlPPYABrG/KPo5EbT1DSQ5IW+F77htxhNWyJz5ZQQuQb3MyXbff3WInExBISxOlIA3z/jScv5iHK2MCUW1fQVlQP7VEUQXQPFWg6S002qiyqVlmpMqF5pRaDA/MXwh7MCkoOo7n2miuspwprpW8to2zkXS5UKz10GB/3xeuSntFoXIDq4cu+IGcBSuDbXdl3sMre0JCDC2mMX3IVZGUv0ICMwVOzfM4LfOwT49PD+O6/tl//p//8e/98x//4udkw9+Glc6TIAa9b/t1pf+Jt9f8RoG9FlMr1UdIlh725ac//WzY1+fHZy5KS4ZpDqCRwg/pApb6koX5gHkn07T5FiFDKGKDo6tkro4jy6HoE3DsptcwRGQjejZF1L3pHB0Bg+/8lglH9qvtQs1KMLf6hPjvvA1P63D34XF4/7BerrOWdtKjTEjZCeoShFXmjcZeECPCMwz59yokhw/GvkpQjo2Q0pUkThPWOT64+O4L45ZYkHftGTY+L7q9gp8hQvuCdcLsdK2Vh2fUCQMDq5gaS2p1E0G5apAuQ0CM5wjaYwlh4+qlE8JjBRl7KQj8ui3zsq3r07qeLw/X9fxnf/7nv/VP/+mnX3w2zqS6Xoer2GuprtXluq7k5lUFmJGWvuqZrSJsKCnlb4T6T0+Pj9cnKt0iNcFCJ6lbleHHjcq2NS1hhwmm5QB9JdkigRvjDrRSANpg4q2Gb6SWTMm5nYThswSUiDH4pueyxMfpjcaARxyw/yYDpvI5wzzsy/sfr8/vOYdhk4y/JMtatdbwE5hH1uJIkpxsTl2A1eIFskKkkA/oP5rxqs5Gb8aZK2W5sBmmLJToNowUloDSc4tVbu9Fuoq70GqlkgFvBwIXhLstRuMIhMIiAQkctH8rzHQs1ma+Uwziu9fLdduuZJQ6nc5Pz+fL85/8+//wu//iD3/22WfLNEx8MJ0UE7ySnWrdCHsZX/mvY7CFqYgWov+QoYt04PG6Xs4PZ3lMQnxmy8x2kQfXrXXwZgAHsREIcrt67VcnHOGUgBWNtoPPKG/tC8n2dEjJ3RH0DIx1cih84Rev+R6dOycz0XbnH8AUgHnX+0a1UPZhvFzH+y8//frp8SPOM6EQduaXlIkSUk8YRRM/t8XPIZvhBHGe6GbJAuURpJEXC8kleEVBAs76K2K1dsEeTxKhnS13FhmHH98BNTDUTBdW4UojsEpcPp6V1+kJJ1dc07jVhRD48oLI4DzcVswrYLOLd5qnu3FZLtfr+/OHP/kP/+Ff/Mt/9Teff7pdL1w/Y5KjjFeL0FArs4U76zfHXVlbO/hpImVHC2mdGeHpBICdrNBeklDD8kpwR6Y/zlw6pyg5tske3KCKojPpgsbdvvvZ2U5OVzgUgpwqQhwoVtWDh3qD77B07TI8OfFUM4pR/LEuCAQOyphouU/L3flxf3g7rVcLjdpLloIjMCUdOtEIiqK0DIeXB21RUeahNdU3Edg8I0BWo0jFU+yUwfMdXcDljijPZtsplNKlAB1sDxiQ/quylIusxhjF3enIG5FziLZJ4Giu8iv4Tu2+qHdQsssseVndskFSaQ3OnSTeOCx3pJPOy93jw+P7t1/+mz/8w7/+qx/99Gc/X4eVzcdEo7dtu14vlwuJzhKkETgcgjMgsaeGMZJu6/XudEcKs8eCq6/OjohsptyTqkFZOcJPU2ScmZagyLA/v7Ds2bKADxpYoSyW+g8RLtvDbde6Gmz3kzTSjCuYbNFIh5hcTJurN7P/ndyup7vL++3xaVovfJC3RNToTMibhJFlOSsXPbE2Fhde2hXMwobZmOUoI2/IEnrroLE5T8dxJdWq3EQonXCKjY1Y9lIYtW+xY59pO4HgxaoUSh4SblRn00BIc3Z0cMhK1okRGkPy8Ia8DAbf4rBYcuZO47JftvfXy7a/f//lV//mj/7Nv/6jP/rFp59dLhc5f1BCqig440KBkIq9zHvlH0lCcm3X/lUqSuxX4pv5+8rh0FJYWsM4vbCsryX6hl55ycT0MLx+bKMDTXMfWGTDKvHj2D1cNm1NJqC4m1V9FTkSyHyazKFQhr87uUisccjCmFaEo/KClMdwOU+f/i/bhy/ngaLiOIJCMhYynUM24WwaR58y+7JCE4NLQRyOcCr2y3ZBYTmocY2Q3TM1xP0Qu0SZl7ORUjJgbSB7JNPx3Mg5fI9sgU00R1NHuyUFE0MUzyPoXW30kWrNYzmtm2sK7FRpbqSQjHUat2l/+nD5/T/4gz/4l3/47st3BNBil2b19Xol9JVQDUfbIjaH7Uq5lOZCiGpCUVl3NJ7r5SxWBi3GG4CeOKKXQIUJdXHs0E6fiG6K9jeyXBq7RS9EbCsR2gHebTNZbDbYRLpiYKZBRbm76EODf6thxicuzbiyOTrnwcMcfDDTtA130/svzueHeyqdI9OIFqVLqcrmFTraeBGP95ClRHDPR3Vr3kJKfYM6KxRUC9hixV4ypYt4gCgibXKtOZikCgz9J6GUUbqgkVaDjBcFIKwgKTsboEXCxZGb1hi42O9boAnP5I+VivsuelqRPUVWZ7a5T8vz5XK+PL3/8Pzbv/M7/+Lf/Kv377+m+t60f8JbNU7DHERuq1IPb3DeZLwKHdUoNa/PtpEKPWzTyMwdxxpZSN+U+SIwhTiXfQHBMXP4mcMuMjHDidpN+HMLviWFynpvdzSNuHwwIy+iPUK96JDq4619ths/BJUItwrZEadtHZ7e7uuFc1VUNJThWnamfRYjRCCrOkOZ17U7hNjrfpdMLMMtoKvpg3QUEq9STqWIc4qCQ/dWlTNXh6UNeSjwZMjbJZs41NQJcybVgtE5Vjagh5edpaq/NRDn9gsxRehJZNO4bsOVQiyGeZvXYX56Wr96/vr911/99u/8/h/92z/+6uE9nRsqRJIRlcMjxeFr7Ne4rjl70x8FBZgXm5Q4YXwc5pFSGradCs3iPIr0hpY2xLFilIZIP5MdDzAfBZ30CfM98yYjswRKG7mO+gPuet5HpCvlwsiO/Eoy2ZZHMlc39S9biXdPwTf1VdgomQz3eZin7fownR8oUp2ksOAfgKxOCQWR1ERkbSnom57j8k0HXFVr8s1xepNju/DwNjlea9iJnco4XLh3Tmnst8lFYE+Jh1KigOftu1gsPONAArNNhk7FiZSxXCendo6qu2StokCJG66lwDIuGiZSiXdeTwYUgzPpQhvppfPdh6/ebh8uf/GjP/+t3/ndn/zs5+fLE0VbkF7M5Jq5LQVXEfKyy7crNofqC+WvHXnFAzhZuhI1SzV1HWawAlmeQzVttODRW5jOwz3E1B8Aex1jM0aiuST95c4Kp8HhtqyxCOA9QtxYiUBby0QC5NCCQ6M+EN4lw4ALOZKW8/Pw8DxZTmuU7/IjCfkHP/jH4jTMGodWbtByXcoWe5JUnHFzm2ur2qmindW1S8RSeTSVeHEchvRPeVTeUPGbCQDL6kRfKZAjeHcDIHXVGzSDoaTNQ/3QXkkMpL0KZYXWE7HAHn11zWplqhEv6rpeSWyex3Wct4fn07r/uz/9T7/yj37j088+p72dOTKKjgYl0ZgCNfj/ZHZmyTkMVigr21lYStmS9kFVLGXw0ziS91jCcKa5iCc3VIUcc9t9IJpoo5rar2VTvYu+tQm67G5Saa2ARkHZLrUvwwB2j/L4jdfih71YU8EVN0/Ddb1u58e7n//Z9vR+ppjKUf0BUErOsQxz8oOLMTxZ2IaRV0VUl+wj8tn4ry0SoBbjbZpbmmE2GviaiINKFNEi/k5svlrHDUVoTLO1dnFHwciOvWsEUuGfzLtChsAxpw0ybt/hvS9fSWnS/GeNwqF/Z+LG43T3+P79so5/9Zd/8bd/7dc+fff2/m7hjEA6NJAGKsquSM/8iRA4hObMgHVPbLNsDXC1xn2bpmXf1nGe7u5Oy0JiTolXq8BqCwUkrGf8y2t0/LP1k89Py/SxIJn9cOBj6FITbDpJ1xHDm3i1iJmdpCIXUaAv1TDbZ3ucpWIDk/any3T39rP35/Mn4zJsF+HAJs9zoX6O1hLs8iiQsBoZA9aQGUzW7cka4jSNUfn28JQ9PiLV4vP/YKOkiqUTDJc+YK+VS1PBtkzT8xUretvC1KwwVzKpBcfsWeeWEH6BuNgFyJDUlFrk2mFiiFcL08qbQSf2Tpf9tF9INP6T//Q//w9/7+9+9sXb0/09h5hs40j5gyo1k9a7ieIrvFdLAWlJ6xCYDXF74E97QB4KkuFnanwchrs3bxZWyRnFwb2fouUtLw0dTLEoImInC6Q9VowXab1662eL5ZJofhcIdJWyLLgCdZiOPKam4ZDE09622Ou7iZpwO/YQN7DzXfPYkRYyy+Jj+5bpdH3aHr+c9tV4oh694A4d8cSoUStVHDRAi/hxEteatbWEiOzSLQilUdcVYsTOBmK5xaVU+cbSNXhQWoVSVoocK8id8xqVYWgCI6Kyck/MFRdQ5LLmHkHavQpTluTGgsz5exPgk9SgkGwpOXNaKRpjunt+fL+cx//07/747/zar//i3ed3d3d8+DYNUjjvuovkTCowF7VSybnovfY/36E8fNgSVsKpD5Kd93FZ7hxNhdY3vCPWKR9kBsueSd7wIuNVqKlIGHuajvtRzAXaEfjtkrZDU0PLIdLepcmelOGyZtleB2xMnCiSiKmWIZ8ayo8uOZo+xYV89/m6Lx9dH9fHp4VKEooZ0ovSKVuxqjo6AljdqHzsFixNs0uvKCV0MRbNGFBlNuga0i5xOKFvxhl2zk8EO4QNmwqn0nmow0LOqB4QdEJqwj4QY221JuFdK5n9+o3FoyCEOg6nC6DUjW+Nj85PwmAnEIehj9v2vM3jel4v1z/8g3/1K7/1D969fz/Pd2y335g9UpzJlZguM15KEqS/EdosOrzFZyiOIf/NpmONbuPzGeZ5uvLBWdNp/vjjj+nmxinf5gtFF6l6R4GhQiGhNPm8IKgzdTz3qqZhIkSnOGGJgHCe6nub99C/tpxaK/VYP8lQGzZtUcNSl5VmudxpbB+qeLUkaxSCAsUiOZaGgv2XeRh+/qfz47txpyDKldIKU9UBT1NjJ7DKsc6SkIbE4SlawAnK1wB98ujlkK5YHjYc5sMQs/Shs42CpbHE2rdXAfTEZP9vHFbKRqoRmLeMTN5su5jKgPmz8DDiclFROrCzX0QBKX5qNHNlIx2WpUNhc9YaWyqG635d5/l+nrb1cf2nv/9Pf+u3f/vd48OynMgqTEFRk1SxIg8tRzoK9hJfthKTKjGrzos1gYoCH4smB6DJXVKAz+eR6m/s96eZbIZSt8FLYVqueWhs4EmpC1uQCCXXWNPAiiZ954ZvB7kxgOLYZbRGDfKGmGXaK0wEQ48KuE7vEuI3dAdJjL0IAG2nM8Obe7glLEhROt3G6zYtf/Ozh6cPH7NESNUJ5Sxv4LXO6SThXtwmEBCNTDVgEYrke1VEfYhfCrILM7M5gWCEofDQWQh40EDZVk0WGsZxmVSqdv5QAShdvVBp7EYuT9MRix+7mTQIO8angq9RFS5/AigMCSB6I/KcIU7DCdVAjj7O+VlOH3385v7rr7/8zf/pN/7RP/ntx8fzvAidopBGScS/UJikBkeK4iuHKqiJMkVH2s7B1xBT7YNcTLXI5rzu27DOb771yT4u1/X6hurhTURBTBy7dRmxSIk4TWh/V10E/wdKOXmb2u9qrkkDqB4mvV+NSjpUIDRYXzzTu0L3C+BkZLVnY4Mtra+Zy6hUlk8qoAIcIyWUDden/fndRPHtFEU7bqtVP7R0fQ848lAKmIIlDJelpjuew+QD8xp0YBXIUcSwBr7O5Oz0n/i+F8o0yayoa0YFXaccR4LssK55eoJX1GmFrgN4iFFai9Czi4pB/bBwtdW+ETmklZMzxCfZfdzYGURnl4zrvO9vPvrW/f388PbDr//qr/7uP/8XD5fzslDMt+DaeqUadJeVIp2F16aMfERdpYEoQQMst5qsHFRHdgXeFeK25//r/+W//eSTu3lm0jCNpItxSFgWFJsFzHOtarHe6tgJkpNWWJOhgDu7DIng8PognChFFeUTJbswvuGgwfKTcS/2OYt6hkXdFcDpOa/3majMPNiIApKYBZ/36zwtz0/D49NE5RNWLetrwqgRI2ce2hrwPUFpO8XEKZQkfsXC5UnbtkgBw1IlHUgZCi+H+45bG/nJcvqL9cT5wHx+PNYlS4y+wskh3oKIo0JoSE/AKjUqPAOwk2sSgRVkgPzHsOAn8eeRN4iNP+t6f/fmzen+zXfv33729v/zt//OP/vX/+zD4zPVzRkFacZ1Xc/nMyUYqZtX4zQkYkPw1MMSEV/tK0iYZvoLsUcLw/N3sV+Pw3y37Ndxu1C96IFEad82KHKEQGo+bNDsAo1CIq2b0tp3APGUMPqTMQ/5M+a+Ms2IzvMeuLkzyyNFwM0AA0ZsjVnK0KRmuoL2Qa5t2w1doNyMH83Cjn3Sii6Pw1/+u+HDu3E7cxlKdf8yrZcQ31h4VZXMkgyx2KjHitRqOo/UxzHhsVgniV1Avqb5R5I0bpuhw1CPa9ItaZU27lbz2FA6Zqqybuuy8cgo8s+EQuGIgD1BsONOwLg+BsxboV19HxhEV4Q/g0/zs4HPK4gj9O+UjvslMkGV5Chz6HxZ1/N1+tb1+e2H/+f/6//9b//43379+Ewbtq7jNO/jtO5Xwl1JD9QYK0XhbKsChRfG3dMrAoMtxYg+L5T1OxHbH7aPPvn2styRSEcLzNmmok9oo9CWxe4Fqy9ddimn0tqsWCFTKgjlYa2ZsWXGmBx86HmE7Uc2HD3LijSFoJPxz/86XTCKsbf225wa6UHE2US2G3l1IxYR69Pw9tOvnx++xSFxdAanab/ucXTu6v1YwFnQMleYnEU7EtoyKVZ5mQ5dxCTWaDXZkEB102BjpYY7ZERwAB89Y6cWG0sNJkea2sJWd2LDFigL9hRb8Cx6B7FEKco3hktNabSLc5DguTp9uA1iX9pV/GQU3gkF4QEzehKG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right shoe grasp and box geometry","python":"import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\n for u,v in [(220,25),(220,65),(220,130)]:\n z=float(a[v,u])*.001\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base',im.header.stamp)\n poses={s:(await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}))['pose'] for s in ['left','right']}\n finish({'points':pts,'tcp':poses})\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":271,"output":314,"cacheRead":41472,"cacheWrite":0,"reasoning":81,"totalTokens":42057,"cost":{"input":0.00271,"output":0.015700000000000002,"cacheRead":0.041471999999999995,"cacheWrite":0,"total":0.059882}},"stopReason":"toolUse","timestamp":1793224434589,"responseId":"resp_0b5218887c37459e016ae26ef3314c819f9bc7686ef49ed48b","rawStopReason":"completed","durationMs":10954,"thinkingLevel":"high"}} +{"type":"message","id":"fed2c8ad","parentId":"056c2182","timestamp":"2026-10-28T21:54:07.704Z","message":{"role":"toolResult","toolCallId":"call_6aa81a8a753445fa80dbc6b1887899b6|fc_0b5218887c37459e016ae26efab2b0819fa0c67090db3118df","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224446.993372814] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": {\"(220, 25)\":[0.33912656027719457,-0.06270945464371132,0.699152728908267,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 65)\":[0.34366378336074566,-0.0970220567701838,0.7184123702623861,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 130)\":[0.369180866644661,-0.185229663768754,0.6765957202615971,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145]},\n \"tcp\": {\"left\":[-0.1764191407332653,-0.039818355705945996,0.8221104464598411,-0.5187487037504172,0.46380859131529467,0.5349435100330139,0.4791834868324896],\"right\":[0.3142798555779541,-0.07013205333858658,1.0078345324518556,0.5420236400350569,-0.471363514483094,-0.5270262727561408,-0.45416970251444916]}\n }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":2160,"timestamp":1793224447704}} +{"type":"message","id":"4a6886aa","parentId":"fed2c8ad","timestamp":"2026-10-28T21:54:16.362Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Checking right-shoe grip height**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae26f024910819fb337fbc95e31b89d\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking right-shoe grip 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proven grasp pose","python":"import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n q=[.1836943,2.449954,1.6583215,-.7642399,.0036793,.2217132]\n p=helpers.joint_model('right_arm').fk(q)\n from scipy.spatial.transform import Rotation as R\n p[:3]=(p[:3]+R.from_quat(p[3:]).apply([.12,0,0])).tolist()\n p=await helpers.transform_pose(io,p,'right_arm/base','world/base')\n finish(p)\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":606,"output":248,"cacheRead":41856,"cacheWrite":0,"reasoning":79,"totalTokens":42710,"cost":{"input":0.00606,"output":0.012400000000000001,"cacheRead":0.041856,"cacheWrite":0,"total":0.060315999999999995}},"stopReason":"toolUse","timestamp":1793224447729,"responseId":"resp_0b5218887c37459e016ae26f00433c819f977e698badfc6c1b","rawStopReason":"completed","durationMs":8633,"thinkingLevel":"high"}} 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shoe**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae26f0e60c0819fa0431e2b03c64f08\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Positioning for right 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right grasp and survey near side","args":{"goals":{"right":{"pose":[0.344,-0.09,0.82,-0.5,0.5,0.5,0.5]},"left":{"pose":[0.12,-0.28,0.98,0,0.70710678,0,0.70710678]}},"grips":{"left":1,"right":1},"duration_s":1.2,"cameras":["left_camera","right_camera","head_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":221,"output":316,"cacheRead":42624,"cacheWrite":0,"reasoning":177,"totalTokens":43161,"cost":{"input":0.00221,"output":0.0158,"cacheRead":0.042623999999999995,"cacheWrite":0,"total":0.06063399999999999}},"stopReason":"toolUse","timestamp":1793224458902,"responseId":"resp_0b5218887c37459e016ae26f0bb138819fa4906a6545521303","rawStopReason":"completed","durationMs":13789,"thinkingLevel":"high"}} +{"type":"message","id":"9c169656","parentId":"ef0b2d37","timestamp":"2026-10-28T21:54:35.336Z","message":{"role":"toolResult","toolCallId":"call_d443b9d4d47142c481adf1611a60a24e|fc_0b5218887c37459e016ae26f15b024819faa44c5f948ba02ae","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224474.085063475] [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_segment.py\\\", line 41, 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_segment.py\\\", line 29, in main\\n end=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=410, elapsed_ms=100.172, position_error_m=0.004569528253270413, rotation_error_rad=0.00035777856102559653\"\n}"}],"details":{"kinex":{"title":"exec (Python)","isError":true}},"isError":true,"durationMs":2645,"timestamp":1793224475336}} +{"type":"message","id":"c2cf6866","parentId":"9c169656","timestamp":"2026-10-28T21:54:41.260Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Reading solver defaults**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae26f1e4e6c819fa8ca5e62e85431e1\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Reading solver 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+{"type":"message","id":"64693190","parentId":"c2cf6866","timestamp":"2026-10-28T21:54:41.285Z","message":{"role":"toolResult","toolCallId":"call_1898ea06c50445418f6c0a4b5d2cbd22|fc_0b5218887c37459e016ae26f211c70819fb513db13c372e3a5","toolName":"grep","content":[{"type":"text","text":"kinematics.py:15: DEFAULTS = {\"position_tolerance_m\": 1e-4, \"rotation_tolerance_rad\": 1e-3, \"max_iterations\": 200, \"damping\": .01}\nkinematics.py:50: DEFAULT_BUDGET = {\"max_attempts\": 5, \"attempt_time_s\": 0.02, \"max_time_s\": 0.1}\nkinematics.py:53: def budget_options(options):\nkinematics.py:54: search = options.get(\"budget\", {})\nkinematics.py:56: raise ValueError(\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\")\nkinematics.py:58: attempts = result[\"max_attempts\"]\nkinematics.py:60: raise ValueError(\"IK budget.max_attempts must be a positive integer\")\nkinematics.py:61: for key in (\"attempt_time_s\", \"max_time_s\"):\nkinematics.py:62: if number(result[key], f\"IK budget.{key}\") <= 0:\nkinematics.py:63: raise ValueError(f\"IK budget.{key} must be positive\")\nkinematics.py:127: \"\"\"Select the nearest verified solution within the existing search budget.\"\"\"\nkinematics.py:128: settings = budget_options(options)\nkinematics.py:131: deadline = started + settings[\"max_time_s\"]\nkinematics.py:134: for seed in seeds(joints, targets, original, settings[\"max_attempts\"]):\nkinematics.py:153: reason = \"time budget\" if monotonic() >= deadline else \"attempt budget\"\nkinematics.py:204: for _ in range(options[\"max_iterations\"]):\nkinematics.py:222: positions by name, within the search budget.\"\"\"\nkinematics.py:225: iterations = options[\"max_iterations\"]\nkinematics.py:227: raise ValueError(\"IK max_iterations must be a positive integer\")"}],"isError":false,"durationMs":23,"timestamp":1793224481285}} +{"type":"message","id":"297d3ddb","parentId":"64693190","timestamp":"2026-10-28T21:54:45.527Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_79c3154ed59947fd8987bd6bbc0d8430|fc_0b5218887c37459e016ae26f24856c819fb3f53b874a8c03d4","name":"edit","arguments":{"path":".kinex/tools/cartesian_segment.py","edits":[{"oldText":"end=model.ik(local,q)","newText":"end=model.ik(local,q,budget={'max_attempts':12,'attempt_time_s':0.1,'max_time_s':1.2})"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":575,"output":75,"cacheRead":43776,"cacheWrite":0,"reasoning":0,"totalTokens":44426,"cost":{"input":0.005750000000000001,"output":0.0037500000000000003,"cacheRead":0.043775999999999995,"cacheWrite":0,"total":0.053276}},"stopReason":"toolUse","timestamp":1793224481314,"responseId":"resp_0b5218887c37459e016ae26f21dcc8819fbe2375104e86b82e","rawStopReason":"completed","durationMs":4213,"thinkingLevel":"high"}} +{"type":"message","id":"ada27295","parentId":"297d3ddb","timestamp":"2026-10-28T21:54:45.533Z","message":{"role":"toolResult","toolCallId":"call_79c3154ed59947fd8987bd6bbc0d8430|fc_0b5218887c37459e016ae26f24856c819fb3f53b874a8c03d4","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py."}],"details":{"diff":" ...\n 25 from scipy.spatial.transform import Rotation\n 26 off=Rotation.from_quat(target[3:]).apply([.12,0,0])\n 27 target[:3]=[a-b for a,b in zip(target[:3],off)]\n 28 local=await helpers.transform_pose(io,target,'world/base',arm+'/base')\n-29 end=model.ik(local,q)\n+29 end=model.ik(local,q,budget={'max_attempts':12,'attempt_time_s':0.1,'max_time_s':1.2})\n 30 commands.append((arm+'.joint_target',{'joint_names':model.names,'points':[\n 31 {'positions':q,'time_from_start':duration(.04)},\n 32 {'positions':end,'time_from_start':duration(seconds)}]}))\n 33 for side,value in opts.get('grips',{}).items():\n ...","patch":"--- .kinex/tools/cartesian_segment.py\n+++ .kinex/tools/cartesian_segment.py\n@@ -25,9 +25,9 @@\n from scipy.spatial.transform import Rotation\n off=Rotation.from_quat(target[3:]).apply([.12,0,0])\n target[:3]=[a-b for a,b in zip(target[:3],off)]\n local=await helpers.transform_pose(io,target,'world/base',arm+'/base')\n- end=model.ik(local,q)\n+ end=model.ik(local,q,budget={'max_attempts':12,'attempt_time_s':0.1,'max_time_s':1.2})\n commands.append((arm+'.joint_target',{'joint_names':model.names,'points':[\n {'positions':q,'time_from_start':duration(.04)},\n {'positions':end,'time_from_start':duration(seconds)}]}))\n for side,value in opts.get('grips',{}).items():\n","firstChangedLine":29},"isError":false,"durationMs":5,"timestamp":1793224485533}} +{"type":"message","id":"f1234d2f","parentId":"ada27295","timestamp":"2026-10-28T21:54:50.313Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_e06b39882ea4416382986dcd89c24d40|fc_0b5218887c37459e016ae26f284e64819fa920b9f3e8cc677d","name":"exec","arguments":{"file":".kinex/tools/cartesian_segment.py","description":"align right grasp and inspect near side","args":{"goals":{"right":{"pose":[0.344,-0.09,0.82,-0.5,0.5,0.5,0.5]},"left":{"pose":[0.04,-0.25,0.94,0,0.70710678,0,0.70710678]}},"grips":{"left":1,"right":1},"duration_s":1.2,"cameras":["left_camera","right_camera","head_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":165,"output":137,"cacheRead":44288,"cacheWrite":0,"reasoning":0,"totalTokens":44590,"cost":{"input":0.0016500000000000002,"output":0.00685,"cacheRead":0.044288,"cacheWrite":0,"total":0.052788}},"stopReason":"toolUse","timestamp":1793224485553,"responseId":"resp_0b5218887c37459e016ae26f262e84819f8d2272576d484d6d","rawStopReason":"completed","durationMs":4760,"thinkingLevel":"high"}} +{"type":"message","id":"5469803c","parentId":"f1234d2f","timestamp":"2026-10-28T21:54:59.252Z","message":{"role":"toolResult","toolCallId":"call_e06b39882ea4416382986dcd89c24d40|fc_0b5218887c37459e016ae26f284e64819fa920b9f3e8cc677d","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224491.855424014] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.007556676864624,1.9853966236114502,1.7999980449676514,-1.3873368501663208,-0.0012377145467326045,0.3198549747467041],\"left_gripper\":[1.0055662935430354],\"right_arm\":[-0.11214446276426315,1.8662967681884766,1.3592849969863892,-1.0733658075332642,0.0006734680500812829,-0.12759973108768463],\"right_gripper\":[0.9959410239349712]},\n \"file\": [\"/tmp/kinex-observation-oewipf9p.png\",\"/tmp/kinex-observation-n29q4c9q.png\",\"/tmp/kinex-observation-bxtc_9a1.png\"]\n 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"},{"type":"image","mimeType":"image/png","data":"iVBORw0KGgoAAAANSUhEUgAAAUAAAADwCAIAAAD+Tyo8AAEAAElEQVR4nKT9aa8k65YehsUcOeyhqs50z53Z7DYpmwYlywZkCPDv8id/8b+xDJCWKMiGRdAwQAmCBZMySUike7jddz5DVe0pM2M2njW9630jctdpOu+5tffOjIx4pzU/a618epiK4zmb5mwsszLL8P8xy5ZsybMsy5aFfs+ynP7FK89y+gi/Ltmc4988l08XeRUZvZll2TzjHjndKc/mLM+yJcd7eZEveZ7nRZHlRVbgs2yesmVe8iIvcnoQHo6P8Bu/lmzWZywL3TXPC9wm4+eVeZYVdDmNc57pFnormYtOhGdHw8ZTs2le6N1F7pcXWVaUWV7iymXOZp1khhnky4xx5sU0jv3Qj8M4TeMy01PwTUyenoD/0wjoYXmOOdFwadnyAuMvaCEWTI4myOObs3yZpmGauq679MM8T/TAvCyKoigrrCBWp8B3s6Io5iVblnHMi2Wece8SI1/mZc7zbhyHcchO/b/6t//2P/0Hf1oeb5t6d6jm33z7MI9LUVRFkU3j2A3zvIzzvEzjWDcNvj1NGE2BZ1Z1lRcl77Y8mYabYWOx57KeWD/aA1pqug5fWDBvjIj+4aPDc8XbRVHk9BT6Mt10mvKimOclK/JhnPJlyTDKAVMvSizTjBWiXcvwqZxMrKw+ecEu4k+aBI4W/cyWIs+rqizqpsiWqqllM2jpqrIsC/q0qrNlLKq6Odb9y+np8XR//ybftSXWu+T9oV/oeNih4h1cZroAu0vj4yHwebPjTCcQI6RfeXH4VjRszEopr1xkWtOyLGO5jHNeF1WRPy9ln3dtlg/ZVIF6aeXl5Omi5KBnWnA8iqlBCWA26uJv4IELhqtUizXmxeTFpX3EkcbdZcdH/kmUDNKQCePhE5Oc3Ao3p//ocUylPFv6V7+oj2NCAinpEHWpeIR6J/p/KYRuQwVB5Didsjd8PbM2YnYTfTJm2ViA+Yz5gpXiKzM+r/xNPcz4PqiX+QtNYOGb0KTxoSyWHu2lKJalWMpyHvOlyOYln/GMpVgmsE5s6pLPc54vOC50frCC2IJ8os+XvMyyeimbMi8Px8/ffLm/OcxlUecTiAjsal4KYojFUlXZMuEDohHauzIDmyCuioPLp0roU1YSHFv4K1OvLLD9QWeE6JzZKeYORqBcTd7MMxoHnyJcUyxzPmPKSz7N9K0lL6osK5jN5bTvpTyENtFYgj2Il9LxcJpEQT+IkeZYPqIzOoJ5Ab47Z9k0ldnSlCRbhrEpa0icpcqHLMtnOsPECli24Vb0i7BsJhp8rOfWjYDOEXNpOebyHh0VmpSsDZ9b/DPzuSAWVOb1kh3y7LJUWVUuLwUO0VzJkeNbMjnxmAKBBlp1bMT9hS8WdC6Z//mv4BfiwMQXZfeNUzLZ4bvhzLvb0oWyTTw/2mO7Md2HRV3hvr4eYRiY/C7UJRxBVzRQrJ+yLD5JehGgOJw4AfMyzdgGmhmISuQ+75quAnE0Hm/BPEIOL51t5VM0GOGdYIXQY+ZpnjPQqY4HOgC+hlNMAyFxzzsmS0NPo7+zZVzmYs5+9/13bQH63C15MU95XS/TTFxknkh0gOMvEMVVXelqkYaT42TL6iRrK3qMEp09VUajB1fmrdstYokXiXeCuAALMFbvaIp5SarJuMzzUjLh6TMX6EFYbNP/wprSBoXl0kv4bIjWxk+ETsEbzmOCumAMCurDPGfDkjVtXeXT1DXFIcOQiGh17sLk9RDyTurcbAzu+BHjZSmoZ8OdsXDoZMFxrFk68G6NYHXZPgMBF3mTdax8Jic8eqDbL7eBzIKFTlkBZZnAeyh8xN0oTEkOaHiAI8eUPfgp+Zm6z/gsqCqtEts0BbmxaDF2aJhny+9YIb6DU7Td1ekAlIUWOWlLtG98aOhEiCig1eGHCF2Z3uA0Kj3ZjkmwjpHnRVmSoYGzKgxdxTQTDkR3ED1BbTJrA/SezUVeN+VS3+7yvMQJXpZhBFvgfeAL+Z40IFVeWOvxXEVVQbox7TduxifSL61bL7NxksMZfiWlQRk+s51lWaqyLIqy7/sJRgFrKCLtMhVTdh/MyZ0mEepBxosE08HI53Z8lOb1a9hZ4hcFSYU8q4rdH/74/ic/2cPWNNYT7h4ezHaYCgQ306Ab6umzP2VdwccioRkukyHjN1xTZl1ewUoEV/PfWNOtJ6iIfoM5bEtkailrPP4qT4XpI9aPtIfyE1m5Dcc8uZuSpxuqmCV+NcL3g1QUnuz2RG2FMHXPVUzx84yVlFGcIDJn9WFrRrQ5bXt50o2fzsKdt0kHJ0xBdc5gC5hpz9RNxDAtU7Hk3dAvRTENy1IuRVl++PhMR0YGqkwH9qGYX6Loi/7jdWOj9mg3wnSFdbJG4t+yf4Jd5C/gkdNjYekXRVVW5+kyz0uhR8Dke25Kumyf+Bqit2Rv5W35TxU2/sesVBXb+FyNV1GEsnEchv6w3w3zUNBYwjldk7HK12g7jb/JcvtpiPiLxI/yMnIh6CILN1wWuC7KCu4rKGKm7l07bhHfSBVoI9VoM1WXcLNasav108Luhgd6Ee1M3O3bqFPkygMCOdpNbYvFSI+m7vSgYMnGT4Dfgaw7ZeyOHPgWfjOjjY1235Yn0THIERW0NdEbRFtWzZkpOJo1S2BxjCzTXNTDNO6LFu4yeBrzvh+d1SivaZrgIgqjU+oyfeLq8obJ2xm2U2knnlfRM0FxGzobBwIdSkBRlmVeltM8sSUyz3MBnwDrlEVEw2EAgab5+SZPhDhNuVg2NAWmYpLTwW0BDWbK7u6OL5d+HMe2bsOKJHewd0RDTj5zem0QvnLQTJteizK/D7xlsNqyvJJFnGcyLYwmzA0QBNDVXQsLsfrYOLadkOjoOve1Pm/1/cDiV0/cvPDaEP3Xwwm8dnU0ojXdOhWXDpt32jvRuLoZjyTd0xVNiHUX6BeXFPA1GZXKh6Ikp5MJOq5+Dlc0uZanqixZ3BDpJ8/GB+M4NU2jWyej9XIyXdigVztNTPV/XRF931ZMTRa2HxwPEbOGPQtFUeKkznNRluygp9gCSdpCRmWWlB+f2ReeyMR3yNq60jMPSuME4hdUi8wsFrIh6qad624k55Q4aKP9TFcmUqmc5qAPst1c3YK9AJF+67c4z/OyLJayYjdVctSM2bt3VHv07+WbFwRNy/7enNsVQWwbvpIn+n46uFdutpqC+QmjKUbyfnV7VVcTHiSSjcM0GmJSUpLzLFE4Jkj5v7nRZSL6+WqysSG0TKBAjorJkZvZFWsvucmKjmWHQQ9lnrdNjeNK+igCEuyocKsHzi77KxqIatDJix0Gbuk3DoTQWHRwbCXVSaBsiacBviLvLEtT19M4LXNGDnC4YsdRpHGWrlekBEbUyyEUc5pnopnXdV3VlYSu6FMKm8m6+hHKChTl2HcFx023z0qydWuB5D7fPOHCvaNPorGoRsdeigoHj9mJRi/cE2LFOB4eezbSEcaHbkOyXJ+U1yg+SY4bX18PYEvubsl5+1LgbxuzSMwE8bIQd6YdN54W88MQeX5F2r+iXYRwAmI1TkESJqBHTQepgoFpObAYhIzgyZ52bVOV8LtCC2PuoaYJh9vEl0uHWrV/mZczlpWEgg7FttqWOBRfTqTMKKszR2e6Jvy4uqov3cclm8uynElxnKaxKGrdl9wvGYeCg/vQ2UCBm9AIqrpqmoZidxjABB887gaXLlxhc7kgzqybLuSdLXNZN7//47c/P9zI8ZfgTzQn+T1SfB33d44adrYkW799TqIzysMiJ5+4KUThib+9Etyrx5Bf+5PktEGMG1R0dciv/+n122j6q/tve5TSt67ypJiXsVeQnMkWz+NQ3Wpq/kBdf5HKtHUVeYJYKBSIPSk33tZQAscUZYUpclogN6Z+med2tyurMpuXkjASObnQ+eTzFAoOo3CYTL1AKsS8bamMSV09UIjVdUkMIgxPOJijpch85YuDYJXTXUJtzrpLBwuiKKslK6r86emxLCE2s2jLPOkkooVvjskUZVk3dVki0ns6nacR7IDE7TzDy52XZbnb7/a7tqgRpoHnkKJZEklblnGYqrKaaPUIp+H51OYJW4uUlTJrBBGWRlzyYZnVQR+sZOIpsIGZluWYOSdYavjSOU2Eiec5wq7XQw9RYdsdZT2fDCyHyTledlXqeiHrnhVf6sa8oRZeuXMymKAv8z+62IzRWKl4V7zRrz0nfMSSNPJvpGx2pSoxR9b/wwWdZ0tVV9M8V7CbIGYmgOTIJ8TwCnrANI6iUHKg2kh5rZ3pQHRv3AgNw+HhR9HxC2M34bH2yNZ1PU1T33dtWwNOVmRVCZN4HMc6r8h7rh5lZRvJaipbJK9ymVdt1fd9/9wv8LQzyiLMCabQOI/PY993tzc3bbvD2hARA+RDj9rf3z09P3ddt9/vHIkk9JfK3R+21Zsfi4W8YWjTu5WPQgRAQ1jb5LSIt2aDAjSOF33FB2Oc7Whzdi6ST87Ix30+8VpRYThaqtE6tfOHqu1hJKafmQYr0E6vwyjbXJtr18jZRa7dmwsZpcHr7LRDFloS5dhWUcxALrLit7/7ph/mbFyKGiDL7x/OzL0JCSIbPk5TycAGM2w1VJpawt63HoSp/fSixs4GvxGdbok8O/NFBfjctPX5cqEQXT5PI+LhWVaX1TxN4EMLxcUD/7BwMDnZ3QKWRV7WVVWXT8/Pl1NXVWVdVwUDQkR/AmKW7e+c3HgPD493d1mx32f5UjDODMYm/Fh13Qzj0C51kZM7UCcZ+KiddgupxgcnOQxekKnvzXEiT7/eb00MpWK9j8atpr8+zI5jdLA9sw9PtefGin+qjwb3hf/6NcXjb0Gyf4vXtaPO5zEoIa/wSoPEmSTS9wUo4lTNbepKb+jCleZpMco30J3EWiS8SvvFeuuGlaMymO8JFFVVFHlNCI4S+icwzoEkVVLOE5DAwT0bokGRH5olOztrQ5heg7TG/e0LMlr1Sws0YcMXwRheDHhesqpqnvoTgaNJj6UwUlVVl76r5rmoKsbBuyNv8liM9gXRlaLd76Z5/ubb76ZhbNumrioeG0GaQwCdcF0iypYsO53PZVk0BHUK275MZVVMwK8xKsrBdVMtQ8G/iWT+lDVlQiYmkFS54BBiBQeMZ1g+fBGG5HbPWzZOnIUQs208HzVxXjhtJejpojltaZzBlgncLNXBNunwlYVRkeX8USLBDJUZ1ASDiSXfdzqhXmknmJmiv483Sl9lRgGmImjqKOBNCGpKHPAHzunyWwshSRQyl2Wcs2V89/YeyMBpYr2QMSdY5mDPL3BfkXQKfH7lhDZhayfESMhUDpOlJhuS+OzqMIczuixzDfxZ+XJ6IScxuX4BQZZD1vd92zQBNcR8kyhNnPuAmGVVVba7Xd/3D4+PeZbt93vYzwgvF2VVcdqEKTY4j6zt0C0HIDfGAo40wMEZBpzlWV2VMJznBekFHBzyPDyZzvpYvkbDUTh0Qx2TIeji53klqUbKNdVuCrA8XmoFmqluHWCQ8lW4sDnEF49aYEmSWmERi3yDGmJ/rVmZFC6MYVdXDQzHHeK3/eLygIImkTiNN31J0VcDQIVmi0PFQVobgW5suNGWsPEfsWVi3lr9iJMTgJZjHTG4rla2TZhmosfzJ/OSTVm+P+zyaVmmIatrqIRExyZgpwm4DvLDOi9yAEGG3efd81qC3wMR5vRmAeU0gByDBUAea7NfRbWj3zhE1rbV8/Nz313apoZfDclZc16VWZmXRXnpL33f73Z78LVgX0Tyr93t27Z9en56eX7KsqxtW6ZYBmogP6Ms9Xua3oanQBAT+nU+X85VVU4TQtHktcJ9y7LK+gsR8GYkJt6cbRBEehb0R7S18TEMeq/MkkgLuoREnRSta9NxX+PEv8gQ8oJYlY7wLm9eSDfQFzNe48r8m+UOOHi0nVQSDXqkhG4iGegPmqnw3n7wrD0CebG+u2KIuiVrNrFeUeYubiFERfSW7xajiXFHNvwwQl1syjES2lXQQzqbGJzu06Wi8PIyQcAgzJmRIo1TaSAQUsuncTLxqz4Rz2acTUXKJoOSw5pKWJqQF/wb4fw4QlOWZVVVlikANCLRpQhvl3DHaR15UX777XfIGwELwLfgdZvzpq6mflzm5Xw+F2XZ1HXkQKPzXGR51dZzNv/x229Op5fDfrdrd7xcxEpIMxQGpBloog0j40soeSmmacT/xnlpdBmHrszLh49P+5ubWNx62/JvJXX1w08YW+plFj4l5wMZSBEOPjhKwvnT/Bb1r5pa5Whty1ljiBd5huQkmjqsM1fFKmFQylEkH4b2OzCC15YiXkR3BENKl6FkglK3MiOdCbNW8L2tooDJfK0GfCp+FJk5QUkKktP0c0trjCg/VfL5IIUR6/AVMULEVZWNQp1F2SVNYhkRYrW8uiiZx0wYKAECpKKJs8doXoYRL2Mws75M4x8oY5miOJDJ+UybCkAFsRMLcNAZbZvmcr6cTy/H456TiXkd5mnOi6Ju6+ySI7+570XBpgxB5BgD5g+Y9ziOzy8v8zzf3Bx3TYuRGMTKgaE1iYKVUEN5CjSrKIpz1zVt7Y9FuWuaupimKc/AO1bnLTlDbpuunIVPHJHNq5gYCyQzGJxOaTjAh1xgTkPHqgkr0N2+sT0UTY+VBZH5RMoloxTcqXEf6OZDMIDn045TFpjqwfyd60p1/sP9YmJPuXy5SDtKZLteoGZJyF7z+rqs21UOHCPmwm0jwLMG7R1Pcv7O5L62Zqoch50FrrgqgQlEBHiep5GBWCr8FgZRckq9Y1Sx9SSTAsEA3pgtwzBM05QtCyhT6iswr6VnQxpDIRshyGaCVS11U0u+BH1eMkQFOyxaTNXW7x8es2ypSiiJNk8yUZHlXNXVPIyXrqvrOpfM4oWg0+WUTVmZf/jmQ1Hmx8OhrlvOlI42zmvtmryi4GXWzki5yiGECco5Z/BF03PKvKoQkBOIi0Swk533h8ap0GsKibYo+mpqJQWfpIq3hSRwIFq75xJF0CSYKA5H84s4OKixGJdREWu0JpicTDGWa6a0cMKVHUDFKeDE54IdIedsy+LVKPDqE2ODkc7q19qiaEwdwbe1Wly+yBx7kVw07p4+emuozmsVFiokgYh+pMUrLFaSOtP88fEKdVhgFBxY4Ilmgcb0zcoVIiWi+5YAFnrStfvLpCRtl0TnOM0dXLVlQamLUFBRVIPqAIRQ8FKipEnWNPW8gNoRyB2GEmUvKi4+QLgIhhHwTYppxlBREwOKFyWpaxEI1ClY5qZpLqiAMnZ9l7eoNZBVkgdfVNX7Dx+WZTrsb5uqMTMpzEUZkulYhiSRrWATmOJr+TKj1AoI2PlUcQQjlWHroFwj1k+/IsNldQ9nFyIOLFzVLnDi0YLjRp5MYmqSeXcJfxYrpWkYNEarhtCds+XcWzITXRtxVdDjxSoMPDRY0Zz17O668doSiSoxo7/8TWzVgtGt2bnsBrKrlMwiLfaV8ah1EVQeWQ2mYMkHjOCBK2iX+fKFH2uE2lYf+fp5jkwGsvREWqpnTMX8jFoyTodUn49tBmRlThlC4zT2XYc6PhNwiJK37O0rC4Dp7Iui2LUtyAGkN47DpW5qmKyc+2RMqqimMXt+eTrsd1wQRx4v9jPeapo6m5cXekHgN6icUYD7lB8fP3aXy+fvPmPEle6K2mnqFA9aRtDm1HehIhnvwWHWzcsBogOLhxdzQSjuXjeO/H6RtyW8EYlU7/i4fkiVVesaaeoWVSUqGAmoVmysZIiY5WmHYx3uFmrbuGnwr8FCfoXviL4iYsKkXnqRYwlm0AWsr448Mtvlq1YXSPMG7L+Ub4WHud8jj/DVObjMFV3LZJvWv6/XxK1j+FPsSUItszcrBQZ4qhYK1lCCpgawHYraRMMA+ixKkiuqlJNsIQgDdEKzgc1rKMc7CAWAGvu+v5zP0GhnFASivF3n14/nx1o3IxbFm1XXh/1hnuen5+dxHAFpHAFrxBDmZbdrX56f8nxpdzv2qPlsRPGIFnm7b6uqnqfldDqhMAGxkW7oHx4edm1TlDBTxX0jNmMIZjiWK0MMWoOlLtD1hFSj/7kYITCtcEhsCsqtI7OiUud52qCPMLpEl/Sigk4FaaXpMYpf7Nk3+0f/0fQXUyDjHB8TBRuzCVMKvm/b9cg/qyMw17c+xp10C33Zf/wlL8jtrPu/Y6JJtOm172pboZGRay2w4PYNo4lThAKJr9ZGvuIZN4vVV5bRSnTo2GxLYC3OnG9E6XjkMOIZF3nW9wNbcfIwCgLTNVRgLZqxFOWTbSiy8+XUdd0CwQuMlJm7qlKudl7PKor1EJ2x+nZ/dw8v0fk8DP04TsM4TvPUtG3TtC8vT02783pNSO/TONO8zLd3x7Zp5nnuhv7Sd/0wPDw+3d7c7HcHs+5VjfI+jCjYqEeBp62ETn4AM2QIKc1rSqpAkc3TyMW1HO7nmsW1sctXtUN3mSyjC/zrjMSZBEBoiECqgsrJcPxfIBZ3MAOtOPKNnhseHg6a8vPUF8RanOqjer/omR7h7oSuE7hBGPuFiRzbPtExXT5ekcANnb/ITffaSqeswM9u8wsbF7xyueXlhzH5un6xCqPrJlEcpn1aWoRwSOWDLFmyYYBJHHafZCkZnNAOqYadUCYMUNbXCLvfdZeh6+YRgRytYcQrKqLa9sIyHMMZohfT8DiPeZ7d392N09T1/ThNA2zNZXc4PD4+FkXeUE1MC52FrGdGgJGutuTZm/v7ugLicp6XpqlRU7IomroiIEd4eMy5zQukEQRXnEekMB8JiaCQFsP2Gn3/zZu7eezlIIW901KqQVZHWnr00sd86rVSf+NwDRvjymhf0fD8aBycMmS5Jq8toZHQkGdYMgg9p6YIqvdVt3Lr8Ms7Gr2O8Nd+1C66be9bjo2zWa9YBZuL4sCCcQJddE26DMna2KKsiNpqKm1K4XRwKm+sNKj/CmMhJAiaLcPoHbNQoKcJiQ3BBxCdQCmQcem7cUB1OVf7zWMsxUGhWonfl9QZQtIYT7y7vZknOIqWbGnq8uZ4e7lc6rpGSFaCUOYsl3Xm2xUFcrOqtmrbpshRSKTru+PxUNdVwKhHbO/68nmBEv4NtrC4vbhozzhk7e7hwyPCeoE5vEqL7jSFpY1/fPLsRIn2vKcUSQ90xYc4YKlWstXpzIFuvVXh1ej40f5lCldY2+B0sdR0e9f9okMw79KGUru1fJs8xosPm4NoUTrqoLdfexaP1KwnW4rAldbfTe6UrJZ8V2F+yGJ3hfc+oQ9okr3m0StUGL8BTgwcYY6cm4mLlNINWTGDC5oUR0X5anVcQQhfLpdpBAaYrcAQJY4mb7ZEsFvMV6P7Llop1xVo2ub29mYaxjzLdvv2dHoZujM52xjDExR5Hiw7cQj5ueSoNjsfDvuqLJd5en5+OZ9fqFwf80mnk+hCh61PqEHEZ5I7KVJ5HKmCjUadsrzYH3YUoHacNTlNr1J0oLjVZdeoxSmwwdxk8KiJnPRgrIhwRTMRXaQnMch2d3mIIukRsY03MqYyi8q847ITStAJWUfjNEVYL3KaXKTwbK1qKHAWeO9VXSdeNAfACjqEXRd/y34z7XLjQl4qwU3E4MYrg5ELJCpNzqWsQAQYtRUXFKYpQckIbnI6HeppykplFeUhecwb32zJsoFeRMyU6y+HbwPQ7M0csctiZdGwX/BeUen2XbvPMwSK9zf3AC0LyFHo1Slo0bYt9C/hurN9u2PuQ9yGIt4ehiJjjddH6dTfLpwc97U8R+K0GiPM2VDbZCE7wl8bje+VV0pXYUOvsWavvXp/D8f//FmLiDCS5o5ZeZ7jfbzXX8HltZ6KEBq7WwNlqldInIDiik0J2iyuMEbPE9joCzP0Wow6v/zBZ7ZGlUQFv61hjOsCOLogZsdbpLaxThvEy+OlLxPz90u81nHkkS6dRM8azaBYst/97g/DSEpyUYI8RInR2shAVi6qQYsaZprjvCyIGJkOa07dIHAVWLAaUiTrHAtiAgOyKoMU3bX1Yd/e3d5fzi9t25r4jRLQY+BObpbwPEMe5lyigEtoUZmj9PUJIJ/XHbxuzEpMcLFTSZ26qgD35tizDDIVJtEOx5IucroGWRwDJ/w62toppXMIB2VlyZOl+5lITAkjia7sCDe5XfhjO9qZ3jcsp66cuExFh7EVdayFNhxHEJ0KCoqykw/O+GicCCC0KlX+GSNAJiDqk7lrRAfh42ne9uCKiAN6a3vH3CxaWEeD55vCKXp7/WmcYSu3d07cRJIE7k/HKhXfrOGRtluWFWmWLJWRQMplliV/jgGR0tWEfc5mSOSIIbPB6caoBTdEr/XYA/wlYBRO4vGzkr4OITwCjGOe1031p/+z/+B0PnWXrrmpuJaqqObGS/zKUALJouXHliw7HI6n0znP8mEYqAKeBeT1SabB5+uldi1M3Lra3nZ9P887RUIgjt40reR3ej1/43WVX0d/GI+LPosHz/gHhz5BupUFQmwmTgrLN1/xjq9wP7EYCkN7jRlSMHJGdJ8gASptAoDQYGrsO4FCCDEi8YuIGrS3iTF5+8zkhwUYHGNUnSoUhyc9rkJF9RA/NDU3hoAZ6FQZK2HuwsDC051x7PCa6+1NKpKGfk0bp8FwIlvIO/EV5QtAyBfUg2YLBRmFvpoVyzHOj1UpK3fX4jimZtgknIZBQDn60PFf1YFXlQDACySNhZ47jFPV1Ie7u9/9/rcVmh7IZKX+ayokwggLdXpPE4pmVVWV5TmUhbKU2n3u67qt1gIi4Np0m0Sn4inzfCW/MmBaqOEW+hSxNc7JERvyKdrQ118rjVeR+p5j2n5E35J0wuAj2L6/YgpX5Ll1CANLiWw9/4YjXSFgrkpEiVuqMUfWsgHoIeq44LfibS0GG2lsQShHI1tbB6YIWx8qvgyJ7xUKF9YV4+/jgEHQepkoHIWHJFp5ay0z45dnfCLQwkehmA5kafS1SMcKZZRUcQr5nainUxULdykjFAdRfGguwEEXyQ6SrPywV2FLw1RZsWEiXInH+MwlOZ68PYJ8Iih+Pi//i//wP+6eTx8/fKhrgB8NWi68SUsoOhivpjPS0zD6adrvdqfLZb/bD+NQMhKTq4j5mK5JDqeQS1aDWuyqRmsaj0oDWXTMB+VBZvjtwTK29nS1M15euKUK+TXhbc8u3T9eiOgBR2SfLR7ntQiEoBw6UiVNBiWapNNI1nQdavG4o5ocR23MZ/iCkAZhQEpmivaFuFeDjsvAcE7xDbr6xnoLHt08EnQgKDKB+khFWTcNskkTlD9/U+rdyJYHOybuXLCOAkWBdSurEZ92BUUpWW+dFbdY9i00C1RPEvW5YfABZ/0QlliR1dxEj5OUYI6mvh8eACxnzStS6tJpMZUBEaT9oIz6HQ5VfAWBEBU+nY/T+OaLd1mW/Zt//S9RMafmFnO6NOFouhxUCwBmdDK5ZMc0oo7HPC1wxRX9MGTZsmt31PLGKeIus1XJ2CeH60am/MZNHOs71VUxTENVN2Z8rQ7WlsakYn1jBxWTmyxduGx9gvKicqhuJ2O3R6GrmVCzH4ilO1xRnW0Z/ZcCiCmBChoTFDkWuudcdUfIid+sAMBsNs1IstYhNleZB40EEL1pJnfpuGvbsqwZT+/FPOrT2CwsMJdvnIfECRD/dMaQHjj/ZXYIXE+lVHbgOxGYBBH8EAxZUnSI7wiSF9eBpIMPx3UvIRori2IqigmRp0hD0HQ8ffo64p0MkSNAsnL4Zpnn9e74y7/7Z7/+iz+/nM9v376jjqHCtkyZscKI9mXtN5g5xGs5L8t+vzufL7t2fzqfFyROkl6tAXXGYGrbBbUBjHxXXNarIg5fCY95VZWXnhrxckmdlYDdeqWQ6NgYTl0Y15bRLickVrAMVicqpurgTlTUwlXVQbXSTUZj9/YX2PPXcWkLPkcXa8qa6NGbfDWYaG6oTsd2c7Hva+Ne/t00yiwbh+Fy6QbkxGndb41PudU3BhQ8C+F8X30lUMvwdQExOwFkTj2vwrvvxiUkaehwGJToZ1ZXJeoDTEgmcA/gLAZhDQTD4h5PdlOkClFdKC2O5UB1ChkJZkWkfaWBcSVDCm5zT+C3X3z2h9//4Ve/+ov7uzdSTy8Y2Ja9mwZuQ+POTPaIeiYXdVW1TTMM/fFwPJ3Pl75Tr7mthyhR8ekVMaZgH7Fcwhok20XnJGhcCefyulB8+l4hmu2PaFzruxhNChMW083mZadDauPa22nFHLnZtojR/gR8c3e09JnBm24sNz6Owd4KN+XGmlwUhRPNQthEGYtfwQCi0emptyoYQ54dJRB+vWTJULFxGAlCbCEs1SDsT5Oga6/mJ7iz+9jUET2sBOjXUxIlBnulI95il7oOGfr4+IyoB/VgEzCTyGYJBFJvHV0Nvxjmnb4GybYFCzLTMjzkLhaPY7QmEQj15C2rsmr+zb/613XZNHWDmj7GNGNXuOMpqRmT8QWUdljkRds0DP08Ho5d13c9MI8sdi1zO7LuDDAg9fNDAFOz46hgpdOFguC20xLZVVubur1yYcO3v3blbWtJxwC71XY4+3f9vEDdq0+jgHQQT/5la6OmI4eONKBB/itgeIMEdkJHBI5YVPxUEQtpm49ArcFsUmiS+88TcjybRLKTRrqM4zCNyIMPPIG7frKVH1l8ulafoN7glEjW2ZhvqJFx9cvrvRCCFr/Usry5uWtqNMPOKDGYkcyBFJDTRylyhI8nSqBwOM0SMlwc8vo42hNn8DuT3p6varUecllKvqKuariZ2vp3v/nN+fnpeLxF8WrX3lF1H785iftJXybhJWJW7FHIDsW02rZ9OZ17pGFR9FtYsXhVRJkyAlIRTIVE6D8CS/JTrbgvCT5ClYubw531VO90Ou2VLVRtcGVuRL87YRbfmZo4b9w6eisQT3p7EZ3e2fCazi4WgE5cgDP8EfudZQldLlDEf7T0Ky8+CjH4mX3SBIkk19qM57X0d2F7P3AL4H6GvocZVTTM00U+m8FoXMvFil4b1qdSQVmB9OaDMaZPsXfLTEcgN8+LsUNvea4dG5f7hFtLkxfE68wZymaME85JmhpL815eHpHiAukRbYgnofSnw9Ff6Kig1doyffzu6XR62e/3FrAVr5lyb1O6ZUZKQtqeLJPAtfFZXFu0LZzPM2JLqCp5uVyGYTgcDhXKd1TMIwJW3JzZhADRUmF4oaAATZzwamScc20Frsgn3V6vmKvmThbP6acOZ7J7qwPhcFDKNqgih5Jh1OJF6UtktTO9XRxB/4lssNVz5Y7+TaVS+bKzO/SXmLRc7T+vRoYYt0qR4JKPKHTtvzObzVnCQXgkS2lih26KqjBTWS21BjUkXSjcOTgG/zaOxTXb06UmiKCYWz44uGE3R7g04SUC56iqU3cps2kecBbg2rFEYUjluUa6Es2SVVwVsHwrVOOpSoJ/bOV7Xpmfj9UYN+Geg/3Qf//hfVu3ZV62h4aSF+Rb4taVPoHhnsxRQqguVzUojhYyhgSK9DRDfwaSFKGpruvO5/Nut6+ooA/fbwL+ANnIl+5y6S7jOOZZMc0T6L5pdrtd27RNgzK0kfMB6DZqXTFRcdloBzbE3zXqNTkYqfQbF6771MhlURRLrvImkA8mGRdwz41GG/SoxMpIhiz3CIeel90ZBArkccNwji59VJzVmczPzz55fvSrkoRTepPz6BIJhIMR4ISRelrjjQeozb1CMEu+JQGvV4LtfrAW9GA2y16l9b6qLBSGHPMudTBYxVRCMV+msSVVuSrLwfngLMjMgAzrXWtyuizypqrg/wpANmk9rdupIXT6VcrbRBvACRXzOE39+fT48FjWFTq55PmRJDBcG55gY+ltPrzVCi7JGeR0DMhN6jw4DDB5pFMMTXnq+1EgJFTemcG287Rr6qWuUaBv6Od57s+nvjsfjrdU6aPhygdhcE3djGOsnSr+YvPl5adt1UZgWA0nt5UbdxJuuAgBa415R6QbnoI1Vi/SXh3sRT5YzcVTmU6I11XiMDaRIGrC7stxYm2NWsXGi+MYiPt2kFOODjcoX4EipowYjsQJV8UOcdQ0GH5qlQe+FWpk84A2C2wlHQ/dakpSkE4dKURRQl6YCp8Cxx1CXoRL9sSo8xwAQxSRy/KqKHoNzrFPidzMFnFmTk7QS626V1VFOSBkxgq2MCnPqZ2SZCFF2TB6TdN0uVw+PjyMw9iiHmyBZtmHA7Vrojo1kfDwt9WjoT3TxGm3GADbYAZ6eujjqihK1OIinkubwo0p+DFBZUYdTErUIgVhHFuqsTl04/hyeq7qsq7KgWpuyoCmKSuKrp+qNs/GKaPa1xuvRLtOVKaEOlWNdIS1bV9J1SuSclITywrZeBNmSxPw51lJ2oFGHAw4wn0or5ejRqdTAVeUo0D55IqjcDXegk3lgvc2wJVmrN/WYax4QiDfRHvxvIiHzLNarbnMRzxtLBuD4IvK06VLFhpR2DBF4/Gj8dqPLIHoe5G5wpfHmDt7eUVbSVSQMuTqoVWnLZFj74DNppZqUErLQyFmW1fLMDDQRlXXEF4yFiorSkE+OhJAgZxP5w8PqFa12x0OhwO6qwHJVB72e2o1GPzoepRCMXI+pOagDqpbHtiGYRTkW6bP4gvUw039obQMBNyZ5zzPmoZTl6CVwGSmQU8T4JmXYXh4euoul6ZpxnEnJVwJNiJ3RxUwwiNHfks7c2sp6pz5qQYb7V/88jLEVFa8iIC5toEpx9GDvV7rh+eE08rCVF7ob4WOcqgbridUiJb7sgYDMo7G8OHzstyRrlW5DTNzKkwMr99YnQDDU3+OMLVk4XSx7BoWa+zp4CrEctsA0I8eSPIrZOZHo0jVjGTPPqVury5y7CrAYwzhtixLP4xt1ZAPiRqG66WQUUwwUtpKIZxKptzRsKWeYFSkUQPARsy8KSSa+f+AxQ5Th1f/8nKegLJoj8cjWh7M6MuEauvAt2GTgYbk/ik6eE/QkndLaVmMmuduXiW3gJGACgUWSYwTJYbdNfcGT5CTYNANmHqswEIpUZ6a6ZeTKHj2LfVq/PDw0PfDPKMfjTCJec6Kkjov6dnbUKT0mKY7dmULt8StV7sj/Yv9PXlehYTA9T1VDdp6dJSibUQQm68GuGS2Oc9D79P62Pqw8+YILxR2CPtnipEoh5bfH7GcRGWW0xApd9tKzZXVdVwhsCzKtrGKLRr1jVH7Nmgfwr6KhXTtdRK8toRy47J9ka4QbYcsgbH4oEBTNBknXyHkkbEpglckaoAxqONMCImBTaSRwpSFSczXFjk8Qd1wOl/QzWBGvK2/9AQjQTei3a4hJBj1AayKhjqMVeQQVme1dGPyRR1YwSUTVXOW+UQ53E7BqHgBiSoER8hQlrCqkV3MBF7AHQWvFZUQKuoKvR0oMUaidRQ7ounm6AJxezxcLt3p0o3j5NSiIqPGKtMwQwYHl/TqGEUnR6nkOtd2IlYucduUJKxwOqGvOhnr0OG4hearMYZAWc/KY+WcOCrcqXA+8dAou9IPxqD8os6Jgq6ad0R+iSqZLpiC7+QWaf+pSM1xSsa1V4LuZ5ll5q/JXnMVb9suCTlHc/+EJuWSCd2F0XamMD2DVzANF1l2f3vTo3mKOMdFkTSCydMJCwH7gEGGaBMEF6BZ+ZjloIMle3p5+e777xdyZe92bYX4bnXc7/lLBSeQoWAzpBy7F6iAdCAbOSqKDVNj2IqQk8wMuRpyde5wNgQUCVAh4D9x97Js8VQoe8Mwo7IG4F+7pq5reM6oJjwPIHQbZauYClTnNcnyEfW6Run8IFtCvAuZSamWGvPZVR+DeIOXv42GFZEAfZnCSI49RMaT/5Eq9ElgSBh3On6j4Ug/FoM01utF5NuXNRzCbhK9SxxU2Dr0MQtLvUTy4KRHqh/vatmcWqokGzplsuBdD26DX35qs9bWuHufkqBNAuhmiLNtwwpmnlxQ4JbSBiD/XrruXlI2pLMuo7fZovF4tHAU7OaGgafSsHyTYZnef3z4/vv3wzDc3d7e3N+U0otTsNeUYwwZzKFXUcSIRkX8EtHx2F03BzFuiyyf0ItNNBxbETaJx2lYBlSip4cS6oTyNUjKF3VdUxfVDOi5y5kiZ2UFOEquyagaS1IeHNXtEm7CMDbclL1d4q1CTJg7TswQxUET9acgNjvNg+EV4fjH+nzkn9K9kU7I3jnTOJxI8P4IGYDn8EFHTp6Z6Kz8q/ofYi3PaMwD5FM9UpT0iKg2oWKBcOTOhrRcXeIUiMDYIsMgXkQf7DOfEKfR2rO21j26w4qOvdG6NUy3eHJsI0e3fTEKIoWPdO4FqbIV1WrkTEmfDMAiOeLRXPqA0zbikdGcp2zJz13/F3/9N5fTab/fffnZ25vbm6qqDUSps+WSLxqdJu0UfmDpIS4cQ/zPcjCsDgk1BQwihHqvodA6A2iytm5ZYk/TnFeUDkXsIc/mfMku42mcxiUDhddQ/ClvxxJRBM2p/FihayRfKR7GBh65+nZtgziwl1poXgw1hHVuiVz7XQlW6Oq1FslJANCvdnQ81ndbqDuhXhnbVGLTOuHjr1GYz9bZdN8KiQNgw7DCtDpMdCJsuO5ABwr2SeLSx85CRqtpRbfeEmZufmpOJ+r56y9zqZjBFg/DUhbdshtRbKGJr5jFQZsJVWvUKbz5xeT+AlES9Z97AUKcEX0go4F8Tgq+4kIxUiINN6GCbUrnoWk3bIeyzJ9PL3/117/r++6zN3f3d3eHmyN1RVhZIzILsuFp57lVqviS4swLRcRINZwcWnBJ7v65u/QIODXAY7Djs6BuDjndZp7GYZhK0tAZOjrN1PYJ0Kkim7OR/kRp3YJyFamKLi+krC3tF/uzwrvyCTKcUECHdGbh5AS7xqzROjhRSFMtUP6ILbfravXma3VGiP7yoqhCw7vIHxS9Np6UOIiCfRlkCdOyoZLA6UtUyw+litZKRTDfwp8qgd3URfpv2I5OSDLNeGtEBh6HLrVPW7xUkSIlbxvvCoVg4jwGpykYoCFd940FTU0nMQGdVRpnL+rF68BF9JzQgIZQDdQ3E+VkQcxw5IgFqTGjJYeBx6JelWbrL6L5Q6Q55920/OWvfzcM/d1x//btm/1uT8DmAClnj5LxFI44TNnU9z0qY5YFKriPAwpiUZ8GPh4Qy1KlodBKt+ix3XV9ni9NjRaB8zSPI7cFBqUNYz90A+dFoFjSNJKXjXqDU6czAcDk2TJMUoFc2nrDFyBWhDpxqaZ9gdxKVaapzwuuujnuNZlBTBrKbRkJjjXn1LAmPoNrJSx2N6/7R2uq7BZhb6HcaPOkpI7Tv1P97IqXO26Bcg24oRQjAFd6cT1RJ7CUOMINgxM6Ut68LhCKhkoVGL8U7tnWci68Garu2DcsjHb1RcfAVVqxDxDQ0A9dIY5A+4lx76cZDTj4rd1EBNtvxqHdbmWjJ8NVDiAK7VyM01hVze9++81Pv/5ynOFGxlG2JSAHI6sW1tIlJANJrshS4k75b37/x2Weqmy+2TUlWqVwki0jQcLyCEEz5LDMlxqJfvyEaYYTe0EtaLxm6XkATxOs8QKtQ0/n8zgO8JlRB9GhHyS8U+T90C1ohjplyMwFFnJ/2EuzcrRdWip4riCRo1EBDIMWyXxUc8Ge8dHEcnXD8N33H/p+aHftbrfjmk3cBxxAsXEyiwT/EMBLTmB0/iMhcnWXtqTiq0pgpB/baa0CqkmrsIi+moCP7S6i3cUh4tRGTZ5JNJNEDDelyIZCHJApCpjQfAG0trQERyelvT7M04mrqEW+2i2puKkNsXJpdEVpPonm4Ykq9WmpYraxQBsrF+kJKh/Eg+Dzrq5YTv42wnrKIl/u39xfTk+oUZfXeB+RV2nBx1YfrTGHx3weFj8DQq4o8/MwTT1SsvJsRt/sluJBUjlMl1NdJlpPhQepNRgJC4X1Q0RqQSwKqCd0V6FiA6iDMlLnwRbGJ76Jsl6SA44KrxUK2UAmVzWpzeqAJjaiGeL0i9T/0CQnMUDUaLVEFGR6TPMzBasvXZeXRdeBjOu6apo6L2F4z71YAUEBNMFwTXolry1fY0KafEJXAje60Bs13OA7nCffmEet47AxQUyn5lzkWYkfa9Fk3+Yl+nasRus34g4GPAQ+FT6r2H9LdQkjs2sLucVukiCZ3EpvHNomEstVO9ggHaIDbeF0k+HFn7kV9plLLgVAG4u6McncVhq0meXubeNpaG42L/2EgnZ5XtZNPYwMiuQFTaodusMi6bsQfUVZvv/mfX8+T8P47v62qmrQj6k4iTPBZqRHx/FtqfVLiAw4sRDWqeppQrvQsqz2uxY+47pita2sKraZGb7G+jO/uKiDxL0SPioIGpftQoZx1/dQ44cJ5XuKcpqmkrR6KnePVJWuH6qq/vjwcDwe5myp6waDqUYBbqrXyKVD+X33kZzrVG30EqvR/jevS4a/1JgihWWhonZOkYpPhu2GnifTIUVMh0YaG6NIHy2YO/bPr9RmuVhJJulxFqG6VuiIZGFMZvs7R8woqLepnFsPJXBc2ztzYquDlQVNIKyYPdB4Eu+zV8W9/c0iKhqFhJzZnDU3tEaQtgZtJ8iHyPK8qoomzy/T1JZ5VTf9cBZJS+YNBYZU6/YLqkYyoo5wA5Xnvqur8nDY5yFHWMudBoNpywCSzQuKTAFYJx4vIZ+pqCBWa4h1mMRatsXCAwp1tgha5tIUg86o2yC80XgI86Is7/rh6em5KPN5Wi6XDjWD0FetHsep72Co9/1TXddd//Gz/B15iwoCMFCjai5MCVyIQgBsgtFGXqNeez8+c7EP+JXvqpOfLHpnTqddsfVuGsEwQe5hUsnxiYkm9Xg5NHIyejtm7g2PnYh+buCN9X3PUOJQWDwuR7UGvoxn4k3RgJ5WGedyYhQLzX8E+IgfV+A54VkOCK02ubtPYFeRuRHVPYr7DUUM0Q6wSkbCCFZ1XhZzNxR3GeBROJSSU8Uwm4gcnIhgMxJ4Q6Kuoetv7u/rusaCU4dunZnEi9nNYDF8+zio/pxWT5Tr5oyGZvgGxXIQDlL+QleK6eoXNdcC9OyTW8kf/pKs3kKVCc7n7vRyLpvq5vY2L/JpnIuqpsgwnth3PTpU1PB0DaTND8MALZpaIIzz2KhfXfWN5HC9KnXXSnMq7qK/3YnZuCcXLgMB2/quXloOUhc/xmG5AQt9iu1kimc0L7lQj/sVPpNsQNDlDe9no0+pwr4SWFmoESrXmZ7tZuHdVxu47sS9p7Qn+oeWHQpx1Q0+GlkpsYq3elx0QEPhLRE23knmHhMBXr16YoQsy1XUNeXZzOQayhHQpYNIvbDRoySq8EYILlSh5ZUn67KqymyaAIommYkM4QWiiHNkXQBBmaMmtHNxa4PVKUsP3C2kIrkh80WKftLCsgaQz9k4D5UlAt8WiucHCrRqmpbf/O4Px8O+KbIpnyyHS9I8yHCu62oahrbd1WQeDePw+Pj85v5239Zd3x10W3h5hemFZvdeCK9eqZhybmf//9WXPD7Y426xHbrA6TNF3wjnIzK9IkrmN1JuFOGaTOQr+3oFdBL0PktHU3h8JKOcrR+La78Q9Inz7zqIgRH4qjJvNIFo0YUP6c39MqgAcmwhEqgRDYf7BK1PJx/xaINBGIvQEHisW/Iam6Fp5BNsElw/Djc3R+Chp3m/q09PJsOgI4492Xi4I0dMLOKO75OgxQMQMWJYM08LgV3WK4tZ22WEYDVADoT3l6xpy9fkFQgk6v7FWNnuNh+yq7evtphzTObmow+uGQlf2WJqxL4cwKjwgSU2c0YKWxBFUbRNuyB7CQpzWSx5WQ6IVg1VUQ3dkJ7xlCdfk8AxzuaHKszrJ4lcEueFltRZe0SEwazGFIdelR3w7fzDQpFsZYq61JvTSpYlAKK0PoeTfiqmvF1vkcuNebMHSkcc6laFucWaaLApdA2WDbiT5JdzSoY+y45hsjWpjn590yLQtqqFkkzEcSrL9rUsLo8kSWg4pAvhPmVVIpW9badlPtble0riN0nI6QIm5i0SSo9nHgpvMeGBASFmXAN6ixFKBAgIqgg7L+j0O04Tmo8URUnpBtQNVE0xRm3Sc0mHx/sCAmFnMoVv5C81ep3uJT8XM8VCaf0IYMOF6AR2QznceT63TXs+XcoCGoRWEJJNI14kKUtyCuivuq7Haarrpuu0JzCBVJ1y+6rgTV5eB4zxHdfPRSSTpB0FCX748cNxUbG2cbC2nmF+mFh0O3nonmtqjmdGrmS9RXvkIqEB0ymd/Ffytnr0ydRFMzG6ifhQ4DMrAIW8Hwa/MgJMlmLtXO0kHl1wiSmGwglHv0Lx0lrMdetFmTPUsCL7Ia8NiI9OmuRaN0yoqJPlBCAytkfUxfhBc3AwB0D8RjqhLBlyjMiWRhqfVHkMSivHjOZunAaq+9f1p9P5XKEJWNY01a5tCGKBhgZt20wQhDPRvUR+iJo4IBTVz7LVp39YUgqvyZxCyJgNUasNTB1a4QmM7c3d7aW7GIiQPxJUtJZAkUfqBWTqY1lAwOYQA2BsIUyo1CZZHZZNook2N9qlHyKL3Xqz948AYtxRzBlfiTiWPxLQ1Wuji4WPHmPKDgHcBUhwbRwVLkuzeHzg2AM+gmsxasTj6fTK0ok+HnPNhEpDer5/26VL8gCYqdOuBUR4BPGKANcWXzHvKZuC6fIGpi4RbCqmhmAL5J4dWj5ngVO6HPGUs7vYRJ5nb9/cz9kFcVVjCjjlWQFYLwvhMIFgKZCOQzQJSCNto2TRmf46IN17zor8/fuHsiwPNzftrr30XUPJSSVqViznrl/m6Xw+Pz69PDw9l8vyZ3/3l7vjbd3UUh+OkE9mEgem7yKkKMozTiyxc+4KITRkyoewMYhJQwRRx6VlWZq6Or3MCFDReaSbquDV/uiR34O2iptySNVwldjWWyY6gEF1jQ9WOEhK3inlbIQVXnmxb5Faq0hkKMqZCwkMa93vB6gJkS5udqpU5xWUbuR50Sotq9u4Y+70d6vkqZaV85nYbX0tPn+/hMjtan+h5/xaREHpy5K4deAhJuRqRAjSUrS/KPDN7nJzzZl2kbjxiUnA5TSh9pL6Y8NB1XUN5yxY9mIkq6uNno6qy/PcX7rlnuEa4lYCbyVBMs5L62cvi0wPmOdsApWyVxZFZHjiiOWisWTXo1LH+XIehjHP83EYUHZr6Nu26S4jEoYAli6Kqmn3x88++2Kcpg8fP1b7Q4nSOnw0pMWBRVdtjb2aVRQF0buEcJgRsq6j1U7V8mfp68UJHN2IUpUV+d04JqSIDjEZdOK24Pwet+6iAJKY6QSFUSzgpjWY0mi0u8n5u6b8OuXR6YwKEwaUUlQh86J5nVqeu0m4ekyilIdwi0jA6W0J8W6RuWh74hEnVmS0i0wo1pIotiIcCURsVM0Dwxz4x8qtTXOzJVhFZjVvkq0q+HJCjI1y8vwNvZEvq3VVVYoa6akWN0/zMCCFlWu7q3GQ3sEp8rLyfg/1/C1zMQNRlC0FMhM1cksuWMpPR9fCqBWkcAj5oUUwqLoycgkYugih2KNeFLoczcuy27eMtR6nqW3aPEMhHk4hInt+ImdRWVd106Jys9UM4PubESDryoJRspSCFzSUgco9pkitbPUO6DqKNY9kpKLY73cwBzjbiTBecnRC1hRtJEWxpO4KNTHmmggSB4YNzOuU6nDp+bMDYm+mpzs+Kxu3ipViF/CGF1rFrH4hhUJEzY4cpQeJ6eo8uUX059F9wji38OfmyUuDxAksUXhAZG/HU01X0co/6TMtSplugd0/mOVyHw5XmC6MujKUHKPrZUa9WFixOLVd8+t9JVYtkY15GIdhQE9QB9oTt6GdAZPIQTQbqQvTwjpR/aK5zIrzBZSC1DyqjSXdgJF0D6qjgCcqRfnV4/0jmDInC0EYZRXygbIsu/Q9XNAESWubhtGMMA6zuW1Q7CIoIOQqw7eXpVyWuqrJkg4N3YKzUetuSd9eY9uJYF4ixUad75qqTUUn3XmgoDXVwQo5ZHobcXQhf1D8Ygy/7PvhcKDKBPwNUDX136PCAM5vY+vuf09omxczPq/u0xVv9tr5hsSGO0K9kBGFp69ILd1QFXxpUV3I1c1MmQkxnUh0eu0g9n9HpmJMVjGjinZTcJdx5MhJbBbimvK6lseJa4kFZHiq1O+Xf8MambdaVDK3ZMFfGK9cECtuTtB4oa9OYPnxuB3jUzZgLZqiO/i157sjKwgJSaiLy6n5uMs8UapYCbIssgwcSVuKRPdjPAVqsnM/0mwBLJFr1nEQFcI8xHNBACoDqXA8v6ljNh+wBMmsNgELQE32CnlvRDrsW1NX4sITn6aJuTGcLOTuxropB6CGabQb7GrnTHzKAOAao7KhKLZFpScXpBpxJfdpGA+HPaOwtFEzh7mWBS75Gd4DM4heU4RXL6+c+khQOHFes3Q/BZKHX1DPEzGx7VBS4Ob6fnrIrr3i4XgqgPiVvbIpBGxHAIVE35MnqpHo8vhcw7uE3a3Bx9FiWLUJfpBlzqXz29KJ+A+SvTCNRJd2HF1/CzwjGY1aliqF114P7kKG0k1hW2TzonskByElYj0e7KHB8V7yelqyAal8SM8mmBakZJ4XHXBIwnmMYWk9c1rqsuSsgmkY53oeuTjhNBHGg7eG3XrkLKaoElM215TlbAQmW1RvJUeaFZ3lllesGXGqvTxVshgpg8r0CnIdLxK+FfEJdwFVupqonSQV/YBqMKKSJu7IvkDrjUf1vVAnYBLBm9GC85WiYb17c0/yfCbYIjJBKPZKoeNxLguuaWNK69ZrTZterlw/pd4ojbRoUsjQ5WpZABmjSi1We8pdKskcicRVoPLmCEIMwn4myqGV5lDnTlILO4T8LCZvFYZVaWYhZ4m4AZQYLUBYrPCewz3IqdSnKud3CsmydYcwTragwLRRKW7mAocCvxUR7wNJgaKi3U5JV9wyVABdmjCFWIpVq2biiheIMYx+T4x62QIoc4Rt3ry9H7K5qaqbm/3Dw7PY5lAI8/Ol02x+hr+EOBPfZ56oSTgd7m++f78/HskkxlpQxbpo+QN4nFqZCpYbYg1NEtmqBxNk21ZqCYCwQ+81bSOstVyFzFhJHpF10FECouwl0zA3fwD3g/sADALlOOgRLLGRgYyLMRWGtfRUX56kN2Jm1J8Bj9ztdmhhoQFyMFPO6h+H82X47tv3P/7pT6mwwDX8Rnp+jAo2/oyvCsklgfd71iwkQckM4sqIqNdLYSWnxC1tWtWmUi0jN0gcX4j9kCxL5rGCzTEN3XMr9a1qLx4NHKkrisk/VCK9uoLhhkI53hkkKCHOqXPVMTdNFpWWUgZoRnnDkcOeBWCGFbrtlUg/cy62hHr9avlOeRYSx8ioB6gUrw+AUMfrnMVrPFC9ZBF/MoqXPvZLnp0u/eGQV2U5zeA7bEwUWT4M1GqAUQx+21Qgc0V4yumb7m5vni/dvt0xlbKxyakNqkoFXqxNtr2nDr+MA+gN0hDaDLMC7zpYBq4lm2Xn00lDZy6bLuP8R2NSUtkWYS5Y3zhqy7xcLme0gCgLCp9NHNbq+mEYeiqdJTW76gqTo2wnzrwq0VWFMp8EerIsT6fzAdV5+8enMyUyogwXebM3qMARkv7ldc2IVFbXaqQqOr9KVEawlTCvTS4g8s1ldCYjdFdvUo8XimbHoU5KvSxwzYTKjl7/c/Xs7Cf5QDUUrITtEDiffsVz2FxZxVcHL6hzb/olN/uagrQgYmq2AhouS2TqNc2utGIrCsT3klcworK6dshVflKenSQhKZ/Tx/t9eIVnpZ+ysb8sMxWOQYP5eUFHbMU4SDIe+jZI30JZEQ06k+ykmHRZVdO8lBPk4TiNyPXLUGoFU1avE+mYxJX0tiJmqcBNUUIdpSof5Gci7ZqHy60SuAe59BnDwpJ1zcFYNaJyqa+JpCVjF1VVR1E9erVtM1JfBaQMUkmYeQaYJFsO8wI//6Xrd7uG8SSkLQv5mGVENTigFg3jcOqox93Q00p6EPEmBftDtn0qr2xjsICu3Qm1AW2DfMq7XBKNJNES4pClMlbvsF7JUjoMxD5R/SyDI5TLrURyjoctvkePTzCBrwaEYOXSCfL+bqQrx8EWTo83GziKFLmyeCFIJG+E9VOQBVVHltGggP2ANsLTbrdDvg6PhJv62XalFpH+JKdKmFugLduhre10ar127QswmQBLEaccNdqbUOIS1pMrG8c+1WkYqxbFa+Cq8BKAjeglg8Aie3WaxsOu7YahP5/rmsq+LQUHahimxhRFAlQU5nxW9P8894hZzSW1FwoAEu7hytZyqCeAgpIFOVy9T3MJpYc0rSHPF6o9zQfNqu7XdUViXExldq8xlJI86mAiw9gjG6kf2E0GSUOlQIiMqdIOSXaoDMSQ1I1DAzQ/hZ7R6Di6D6OwpMdOxdLWEAUhvBjFHcI5hTEQNMlIZ/Yqp/wZdHHvGgwDj0JRzgkSGXz8xaIssbXUZMq+rflPFvxgH1XIRbEfLJM8x0s1Xf8StS5+Q0FE1ifX1DvJf4t6MrrnMxdRMDLpuuQoDnwBKtYwDnXdtE1L5yAKNRgDlSr2zsbReDWMTG5IG326tp2clyLaq+QiMzuIKaH6xTzWXAtG1HQc06quvvn+/c9+8iNN0PMquAwCKEqVg8uMshgT2c8ouSpwEaqY55yyE1mb1F4Mvl+uscNdiOD6nud2B/wIF8Wj4ht0wKioFZElCm7xEvoocWEFbC1A7C6wLCheZXJNw6kGFLcCngHqZoMO5Fqg+1GT5W3LNeXNbOZVmqfl5eV8s9+jUi9HQrk9lixzQizrLXjllX4akbJMLME9yX6TBN7oZOTuFeyuteAIZpI/Z05z9qJThiJUpz0k3awV5Kov8URq91e5kfblNmN2Yz2sr5YNaU3UAbds9G2Wu1+8tEeEyjOKQCzLAB/WxN2xzaHOnH6azuMwtE3TNHVZVsh9XatXsTkkj6R2UbbmLu90k0BNu0jnqagTDTpQFJNoshr6vq5bxf5jEKDAqn55esom8jOF24WmlLB+s7nMi0FSHzjnFnIMxdO50Bs6GMgSS7CJZWy+IKg9jnXdNHVTVGU/DNzR83w6odj6rl2WDHU46oqHTgVotV57OCKSjrioD0+0PrWwFa4jx8UrtuwHcNWQuEQ2UXRZVKj5JfkQ8F3R5KcJm1stMBQ+u79HdApBN8N+hb5i26+VybYFxAkMIL7WW1rbO1/p4qal4XRfeZY+xmLVbiJA1eqLgWfE4SiFXrDg4zZRuB7xjOt8SmViondYl6StVABHF34AV1/MSlQQx73T8hX9ugYybDnzQhtCkm/BxhIuoNYinpFedSWogR0lHhkz8alY8Y5ZHvY1NwpPkJvSA57Z7qTEimp0KJFelpe+v62PwH85Vs/QJ8otwkSmcVBOha+TDsIJHihVwc2TBmrw1zQNaRPU0oh7/tX9pShQoXJeyqrct22D4lMo3rwsqF/D7nwGhFgcXRMqHGQ+51E7x5jYRebidP1oVE0P5ePd7spHZVFRsEosc5Rdwy3ISmKtMx+GgUCUHH5NhK4TVR6TFPtfrp5xw5eE45Scww1pT1hoeaJBEUy7dzBBM6pMpgeIQkR4jlcm5ydIOa2Jbr/oQZWbyp6IAyEsgnnWwy09PQQp76jWql1YaMht+HpJrR6Aecqie0ZzQVgUL0IpeBmgZjSfHKqfWICRk02V8qDAm2wrOA1oy8Jn6EkiuP1FriTJFjZeAAlcSWNZmqbhhg9S/C3P6qb9q1//9n/+p3+yUJGdANZXZRothbQLCZc0r6tqnFEydplh/Od5MUwIxpIhML08D+hsSn9ymIfahZdNUdYtymLUTQPvFxGsGk0k1s2ZyZvCIAqHXZnV4GLzVzvFmFPQKJlhNQHBLpWwtSS4Ob14CajQATE67T26ZGVe9PO8K6uqGxEq5+VH9Njsuc1TEtwp3qqVMTj/QuAEXijoDf2tvDsGWmwF7sgdMlUrDYzfTkJAIIo1Zanlcsydlvk62sO0ZBEGOEbMGxPT31rWMHqDObBT2e2FQsOJO0B5TnjmaiiiXinvinhT/BJTO4CL9AaYupRBlMJQgdb1IBlEj71FZhimkGm3S+Zxl/xBi3Xr4VAHle2S6DNpXeHIPhNxFB4yDNOAKsqZdb3ljSnrangcH59P9/d3nMcvZ4/EFncBvfQDMyamuAJdC1HEalmWc3eZ52W32/V9T9BoNCUFchoOaozneDhAg6YqNuRGIkqYANI2eLnk1qtxoFk/1s5GZj3DBmXGaT5zg9JQfkQ4DBbwsC9IvWtT35QsDdAVhOs8z5dLd6672+ORahjQHvBZxAydDphoRvHxT/zCfoPULAsQH38k+KtWKNi+g8glrw9thh4Ki16YpalErOpbqHmkA3F39WwmBXqnuF3WVfS76hvzqnJsDYQtsHcFnqXj9fCkaBUiRSBsrYL6ExZgu5dU+nCD0lwocYCu8iN0WcwphZxSQj14nY/vZzyG9UOG++kt1dhjQg3roidGd95VIfDHho6cmea6ZT0puIe2fkJYRUKOhMlajoebv/ibX//Zn/zieDjIQ7UzIEP/i6Kk+K3FL3DLkqj5sD9IFmCelwgwwUACLqIom6ZBrSlWwbVTEvcuZ6gWyu6VFQC+joGT6A76lDB78mBn5FoyL0tUyzoUANFi3dItBQ0WpTC4VqJX+aeko/ZPkZEOQtbdOE1PL881FceiDkmEpOTogwSuXV5c5LBIaXGtS4ectfgARrsYETHPCm+gWc4ykUJF5yfKe41amgS9xNQae76cE7ZqbR3DoQxXsdrC4fZABeHsu5xsO3+KBzOD0NswpohH1JtMXUWUPDEKTzulV+8XhqtsJVa5LQIdWJFb8OCNkHvTuFgZ0yYH7oZBtbHbMNKVUYLxEVBRrHVQ0n3ecJAED5QNCWd3zmHftXX1nPdUXVYGWmSoLNf0zXcfPmTLvGtb9aDhqeO4dAy9UI4aODphoXl5R7L8hxFWA7ViKWv2xsOrLDRjZZwpSwTJfaZqSQ6Qrn4oIyRQLUKwmmJCL5WhImqE7NXRRDWDhFp5F6j0AKvMHE2OMTLs2C6Q+UBR6aXMoUE8PT3v2rYqxc3BWE6PIZBv24lxzjd7MflFZps/EKrNe7eVmWZCw3oq52WppnGY5gIO9mBxurQtZQca3uXGUebd9NFT2WQG4bGvWFJKfJVvdqEQ0U2obS8clGu2rF1Rqr4RyMedWM/uoEQrE4nPemyzu732i6Wb7t0l8RBSAvJnxnQ8BeukYw+ni1yyIlX0MSuOI7gwrTmx+RIuEmvMoYqJS23nq+VMCJKcDSZgT+bp0OzKohqHkbMVcSXl6h8Ph5fTKZuXN8cDVdEosxxRltOpG6BHFDm6nVhnDBFa/N84TufzaUAjT1i8VVE2ux2cyeSp1Ea+wuZVrmJlpGui9MDjg2KnVkxcy/hDmKfQ+6A7OcM8lDnkGQStIn0ohYpuhsIjAJOpOcOYE6qMyXRO22OsQMteZmA+hMcEiosGwyeWEx589o7TceOTFuui8Wvj3G90ERa8r+w9P6AakX+dobhmrBFRLTJTzXWDzNNFi2nSWhTYIJ8kXCG8QB8spf9IikujHd8HwpvOQSCZ+HbxTou3EvUyajGcXKeAC9v5JC0EdNranx3sBC1jEt63OcVbpU3u5R1Dl4mM4b7yAUQRbBA5mobvNbmy2uEwpw2by5RCPyj9HVEfpLUKRjEviwxRW6IscfaWZZW1zW6Y5g+nC/l1IDl5twpK5X14+f6uvoP2WAW2yQl8fd+BouhVlVXTNmzfcqciZ9KJ/KPZqvygj8ncoFRNzlsXRJQGiFigCCA348WDhAzuojhrzJRpNkxyYLe4txqr4+Rx1oYyov4bu6N6P3qgKoAxR9TTQk4Ilk6rmiT74o/yaoeiohNXXmyZbTfD01vQQyoE7lBqQUo0BI+y9G+TWkE+8Bp4ld5PdQ6tZRce5lVfudJ4lVqNkQbh5L2zQD2iSK+Rd13RVavS4ALbIsLDmLZIITGMV1CaeDBuDJpy6pQH1VfjTZJlIbkB9w114pqScIG0AqOAS7iXe22bR7E97MnVJh/jbkDCeb70l2FqpMgReYBlTqpwonY0W6diKdA9eGzI+JmnMkOFSs/yGMJxPp+nea7KirFo0u5E49lWDUebJ2FFOCeRne/ULYl8ZpwdErx/pk7J3XKZD+VRKFyZv8+6EXIbJkwHSE/ymQ3jmPfI2aibJl+ymnqmiTFNnRmKMm/qBvmVVbkspeR7U/JTKIopalQJAxTrwPU9tNSKLX84615ve2Urk0PnKkxENlrY9grQUKJTrcBitrQYtSYI+HxFJyXEWXwutX+Ss/DC0I0SRBEwGzp4yWSYIZwTDqtZAAq+ce9HDuMlsR+ckex+WGvkYK5vrnHQsW0/IGfYIkhqHoiy5/mCzGah9rahD2coMWHVrTR4IbBDbSCelgzzgDlbI/uN3TerGJtUoWFc9DBOS7G8ub/57vuBs3YoTU62sq4BKhanRdCihN+UbT0MQ93szMORo2YFmmkPAwItVVlWTU1xZXRF8YLDqFFqZBCsQoIRqgOj5Y+mnHFHo1DFnqDRHBYqKANZBkCnlfhOXVV1vsCjXpXV5XTaHfeUnkDoa1I9Hp+eMsSiu48PH4uyaKr68emxO3e///0ff/r111/96Mt6t9NokxTc4r2YpuWpO98cD6qVcChct8HrkU5b29y2H/pagS3UysaDCUrpT4ZRhTI4o1iBsfobBaboCq45DuCErZ4gKfXmDl90wPlXfWY0bgsV+MGqJuGPc+DTKuFiozB5baaC2fWOil0TQ942LkyxGmma1cSwT/WiwAsK7wAH7zxnkhuzCk13MF/O1vCCsL/+CnZxGg+gNpHVOA6HqmVVOrKtOW9MObNo8kzDRGhFXnbnl8PNXWz5wzg8nc7AgTcNdO8KiC42cYPglRaiXBceXybFBHIY2VGU3EM5UoQn5Xa0GgfjNkio+EEx9aqqMyjnBUxT/IteTVXVIGGG9IiqrOY3dzP1QxbhzorTsjw+Py1Z1u52RZ63u/3N7e0yZz/72U8BuSIViT1hfIrnBZ1OkRdRVvvjEfE25FBTv4sQQFr7cK6dLdufH6JLxyLMf7gssMuDtcyRXb8h8TPXodIob8XeVGQPP8SU2TjUEpmmFt/wxqs9Wh8f8RCzTBK/svfiW+TbHD6xY0CkuYj+66xRCknqXVjYWTYF98xxFrfKfFWtyTck0gUeoaJgvY7AtMpM6UFcrUakiox+CyR3ZZAm6LSojy5VonpIwbe8H4cDZCD67qqJr2RP4Rz4akIEUUMGedG2u8vl5Ot+86zPZ1AvOqRomTiBW5IE5Fo7XLuDuRj7CiY0HKQ/s6kbutPz+f7+DoKxqatqT9kTpGYvM2f2wtFG6cc5e4ulT6ga2Mt06bq2abMFBjb0azjPUM9Kz1i+37cv55fzZSBeAC5TZOWUTWUNo4LyIDhrEkAsGjdYVz/0AKuhLTBZE9OM91GxTDqlf5okV59/6gtmPHhmL4pmXiwVaR6q0pjAdbCsIKMSx6j3R+gvTtNzgdVIqY7sQ48jlWRtKTgs9Uvi+TnZ6/5NSdibpKpQWPRMwqwB5+vJXJ+hqrUz+22t49EQo7agkhQqCPxPI3YKni8LCqWAgLkNgj1PBkiZAJahns7ay16Jhax3fONE8N1UkrLST/s/jciPY2sTuCJTnDRq6jHlfsBVWaCYjnMUZ9lyulyofh2hiOkyFq2gXpaYWv2Z40DcJB0dPfsuH+B1yZb8Jz/7cflV0bTNQmkPLPQ5zqwWPR7GtvdC4lfdWaHzDIYhjnU64CXYA/lldZGa+kBoE54OteomNARVzlRVH+CIuoaWiiynqobFu6CkAYZEpicCYNLrfRMIlG/uiJfLn5DAnngcQ9YDiQbfjhpcLMUDKVRz9M8S0Gng6Pq+iw5G769U+UgiGvoLu8jmiujt7giGZ6m4djP69AKEqccTiRfUcy57dnJ/MRel8QeLHwY4kvMlNCpUFsLOm7yoIFKQNKtiA8l9WgGMJRxkMBtfks0QByj1t+CNWH9qv4S2daprBPWFYIDzhPqv97f751M39qF2vJp/pj87rQbpshh8VdbqbReaJACGEAnvDSwF7cMwjMOcZeVcwm+ETEaCT+fZt99/mIbh3Zv7r7/6+rMvvjgcGiRLDf3E5olFBkBO1BKaENR+vwoJIAkII+h7XrZwF25aN6o2mbVN01b1iHpgzFAoIETWtVXqspmwDYwaPeSoQ72euq7bpkQPxfQIeXsk3raUXLfp2u2f+0VSUkxH4p3FgNRjrnJZb8eAlWgMQSj7RwYIix6btdae5hsoiQd92LzZEQQ5nqcHEWrVZVEMYivUPSJGMlxdPC/FI2BFpOp75ZwNoChmr7LddSJVjsbuVtIG6TwVoae2hITVCcsTkraidOocWsgtsVsJvz/JPGl/YguCeQyFsrJyhLu4ADmv8reMilXaBzUMIaKmkmIZ9K2n52eGlQYzCN0A0YB3IfgENfkln7dWkM/R5rPfNe3nP/rqxz/5yds3b6qqOp9P49gz0MqyBK0cHmloQLC6ZV+UQ6rmFvYx+E9FwaUsIky/KA679tI2z6ezoayl+ahYSK7UGglnh7WG9B3msVrq56fT2zd3VI0+sTujCH103uJPlrV3NhzKoBsGpTXcDU4KEHBklSZswONdvEGcFnsMgmsjfC1U5BRbFSxOKzDbkZmfyHdjLc6ii06nQQjWL0e8nu62rpJffVl2T7wbt2fBUFCghUpGKKdTddhIRc4dqBf/1y5EfLGsIJMqw3qMlwU4oKNe+8UzeVc2K9LwQ/TMX01D5O5h3dDvoBPU1DMFPhtVe4KDLTpkDLgDWmOUBB1SZ8+XrqxK5GzQaigsj5ooWWZRyZ1EOc9Ybv/ycto17ZvP3rS7loI9gHDBcGV/lfUYDDslqxycZ7lCN/zeEMeaUeAusuTY5WTVAjiwxI0XggqpzMUMBxPFrLcjztS0WT6Pw4hmwgHZ7ikkPmJbx+4HvOsNpvAy3ACAHBrAtW/4k7sZRzbmzx46Zl2J3bslEJObGHczK9t3lJQPY/BGcjtvdhjUJFkAXz3JqQ12qy0L2Evw+Iar5SCCBOKO/pLy4WoumHMBuh+cH5TZb+EGKeQgyYOcWUq9RSj8TnkeSi7J0MIypwpCyvdd9c+QguZE7ZyhBsW+bY6H3fnca31VoRBXEMWvEdEsnX1qUIIqOZyPZaKXp6Q580JhQQ9i4AS5haZpevfZ2/1uh9akSLEY5mmiyvHCGoKCxfkLgiRTOGQmZ8+VozCJwDaN5QCJLk4mM1nEWbZvd5STzNnNbFHHPlB2VVLWiiSBsTejRAmhosiON3uJJlyj3vVr4wLbszUceNu0NtZciLvTmarSH0bb0prWxPUjgUAgy43zoJXmve7tDpCKWcdEt2Kt7pr4+miKq4nHX470cf0ZqSSpZh7nem1e5iyZLfYIjZB6HdB/BC9zXM/GAfKlAAf7Wcgl6/AnlrYqkCBC/DqscTz5sCmrCQdBQzu1ZTuooSiMs8z7fprH6dDUVh6EHxCwYMlCs3FL1eHIImA2DtoTc0wpVNmPxVG1toXabPM87fa7XdvWZV2VOYCXQ8dl5ZWfauXt0EVBcwBJIi66HqqUGPBPBqyliXiHuPIzVQskXnV7OHz27t2O6vKRfk3qg3R61nVSxq31wzD3JcvOl36c5ufnMzdnXUeQ1ueSN2MlZNSHv61kbkgwA1+QBNZ9jio1J+pypHoHLhsMk2TUhiVwpm0qShz8IBys7QWIZ7UOW0VGsNu+cMaTO/iGyZExYJOK6dnRsOE+6J8SkcZyEIGp3dYUd8SD5J7RRMDcGAqOUW5Q4HI+bFChJInqIJv2gfcKxp6sKCcp5lghZVzsFBBfUV267r5tURaHIjmJo8/9EmqvZwW8UCBgGu/LywsFfkFbgC5x8QJ5hOnyKv2Fjkl/bqHCU2XIYkSK4xjN3otQdjqEmi3ZJIELyfTi+XEeIA+VWh9KyqdGNnNY5E7raaqqbWqkZk3TOE9oMUNnF+Aq6nxKpYN54mCqJXVIK/LieDgMI1xZgFJz9a2rzcl0YzfOoifvzWivM1/it8WvFtnQjsnSFIMPIFIS5ELzGThRoBIthIs8QaxMSkeiqthdYUO66oYNlmNmrN3hr1Yn3ivGyaLx57zCTtu++nLFg1hHrZsa8Byq4azNxILEKvKiptJYRL4SR2Fns0Q41FLgQBo5S7ieTtj1eGM3zJRok20ZYrXCkXOwO6iLdXG+nG9uJgHjRawr/GU4LOkPXFYI9tp6owVJv9u1MIylAJWkqZYEsvelSNhBNWX56XS6ORyOh0NTNYA9ookM1RlgIp2dM5Ve1AgBdbiHEYCv3CnHgtKwVg/kYycEmOQYVyjVA+zUbgdWxciaqqrI5J6fHx9fqKwPovRVvdu1TdsywyVVA5KfwmyYwjRNuxbQrr4/VTVhxq6cqxhwbGcmJvPkWMbrH4jG3rHfSRXhGJfhvdcb75/hDMbkFPnAkTszaclmIb0EmruRQKCcw+HXV4qE3GClWycUHI5gqIatD9F5rWdlR9aRw3rtKRpRAzEoWURSnwA9B7gRgbTwoppYXKfYnMwCnVWqY7WZS8UHKRmt7ietq2T1fLaSwqJ1FVXXxcfTko/zeDi0Ty8dQxbcnnklShHtZA8igx+YE0ziuNu///iB40PalBHb/PL8cnt3S56foBwxPfdjn+f5/d3tfr8vC4jfGUyQup5wL1W6lMu+ns8nrWInsr2maFymnKlYtH6I7aObsQSKkbQ0vZxO/LWiLJu2ybL8cjk/Pz9dLl3FWK56mkeUo97v9yhjRqo+etyQyVAWLQrxLVOel+dztwCJ7DyErsBNOEOrvfk0AGulAaVBXm5yQ10iAj4j6OyCf4+ypJL7RrxDVR47c3pKQ1kJw6kEZ7zxAyuLYoUgQ4dvfYLMII5rmaFnOX1p56SNFVm/r4CLMK2IBfj1do51J8ioBbZUSwNxlBRhKaqybXYAFTB6iGsm2AQsKCNlIVm8AKrr3PVXSddzFmvRsr7GcCuOvXu/Mqma7aEblwbYfeQDs6GZrJlXALmSM2fwc0GUghqdsS/XFdnKUMRuGmvUavZPxqcPHx/u7+/u39zhU0SJEfhVlMkyzygYMA7j0/NzhkJZVPGDS+kDNBY4ca5rpU26Q2and6aooiB1PvhFheyXoetujofD4cANX+Z5HvoedvI0tvsDSe4SQMppqmsEHYqiGMeprKkTDcfRIiPuU9RyjXyv6tj+zWDDsvJLYSTAYqWqk/pP5ewkMM+IzvUi7540ER95Ba2Cu2aBquNbVzlEW5TPrLAavm9IOjM9E2FcwY8UU3IEZnFXKE0m9zZz39OwwVoMSGjbZ/msaM1Q5VVTo6ys1cES15HpiMEZoQonWlcHAKYQb/BkbZv0W6/4kMjSycOcC4Qj/XO2XIZxt2tCn770NlGIPF/y8+XStDXXvaUyq8XtzS1rzlwniv1JZVkM/QC0YkW8jXN6Ge+9THd3NzfHm6Is++5yuVzQY4kiruMwvJxOAIGgFXDNgBkrvaRgC2mYoIuS7p31SQvbJAkZrk0pvZq6+PjxUiB3StKndi0XYJj7yxnBtktXFBDXWKtpzqkKWlPVP/nRl8tMvSzUBjO35Ktm2HqDooNt8fb1S+WkiDvUxALKDLUGtRQJI3u1BLsBEO0h1gCHT6rd0Y3FRqh1wlRLt1qpATaahHGCjp+AKoIRHWRh2DbVOHUZry5esCsTUvAUHNPr2i7xXiK9kaHI7VWWsH4JZ7sxoAgDqbo93KOkeOv6xQZ5OBrm7nVjTKcZ+94SYlSew/ou7Qu6NBTUBZe+cZ1H0MCeHh6//NGXVikyQ+f7epxHhmLxLbgE5DzPXd/tK2TeUfI8GZYLchU/f/t2GMauu4BOCAFOxQDO4ziiiEeFYtHG8Lh/Gh/AOPSZ6xlh1o3fuFSTFuWTxSaPuTgZFYwJs+bm5vbj02m3g1lL1csQ4C7R9k8KR48lSgK1bYOwNmX2E0psrurydDp/hpBUye3YNhiJ55lhd9L9CgrllUvSHZCrciRx5FW5ABAmgFLbed82zz8/qI4OtmZWm3AGEYRcFE1EFqFZOIagTIEKuHEJc3/Y4prtQeQrqon/8lok39JTXZhkNHWvgIdbJ6nTq9/lITGniJmAooAEtUCFLBiR6wss6rT9DcUSQOEsrp8YcICBCON9i09KZG2EP8IsYtCNXSjkBxjwtPR9/9n9zbcfHgdSZaOvuWXkdiZ5VQATGqFg9OBIBxSc/qbdcVIxF7BkI6LvujzP/s7Pf16iynr28PH9MvXTtAw9eqmWBToSMTxGEoE1gcmWLHSgyHQmqs5I9IrCtuopDFXhaQDSBsu4bVXWh/0B/itYvEBG8HPIMYYsJLIOMHiqTIIvX9Bc6lAU+cvLhRLLqk1Y+mof3NmL4XNeSqz0zDgEH32yVChgXSKBgNK5CN+nM1gdfO0paG+KFi1UrIvN6ydhJBeKFKyCU5MlsQsuRWrrGKho7SljAzgIp0DEmmMamms75uxwvDKrBHwZ8FrBubZaPGe7kT4a6syFeJn4FRi0QV3OJI9d1iogkZWGuTCqKiPUpsWQCaLYO0eUp2a3PJuyMlL5I6RKWBfJ0OMfRZZ33XjcEV5qGGO1g0WsaKCMP9nt9qqMinZrqjdrZaz1NnXNNaiQQkxC4nw+393ewidclg+Pjx8fP87j2DYltUzIQboKa8ZjpYizShHbIPp0ZrsD2Gx3lVgfkrMox45KWAIrwm2WXFkyaquyHI/HcZpI7dZ2LIgYgN1UZV1WDedoE/3ikh21krA6uKSE6mZtGHrO+lq9Fx+utewNJp8Suj+3GeCpGBdi0Th93JXKqNM9g39IRkjwEusF1qBE9DJ93/qiCLv3R0/Mx7lcqE0r1RzeYDOOGrfEz/pCJxxFmRSYa7xC3uxwQCG/AU79FeI1b4wrFmK8nEOOlEbOTfy0LFHARsuRCnaKnnhunMfcJFh3fvNkoYPKscWS13NYv3ggWoCBxoDuJ+htjUgWl1PnYI98QRkwf3ecprKuCD7KiZSa2U6RbfQN4rJvUuBBKJLc8iDscejmcZgndMa8nM91We7be0rqU26s/MJyKpwPTJq2UmvhRWF8EKqqtDhHgzfnGX00UQc6uvk4jpxLCDIo4cDjFjABgaKty6qsJIiWfMi4aLWl19aRMtuVXeOl07Xt+SEOatuVBckMPGUU9/V+na0EAdOT14M20RgAqp4JRL4wD/R1rhVXcyWeq0j28O4a/hiVy73igV61fQi3SVAuq2vCioVfIw99MB0KSihHHYrVQLSohWqawfqQNAakEZpUXw0mUeKjd/1GhJmGIFwkhFlMBihiKOveTHDJUH1Dh0UL6SXq8iDgtD1CCwuR+UjvTsuYVXVTNMAbsv3Kqc51A7WE4qrzy8sLQaXL2/ubkMpr1oy1X5Ie3g6eQ49jZleQVOeJszTmYQXBqBUzhCRRdHpCrzIkdeZdP5DIl/QSAWsE94DC0aWGihQVyLPscNghoZ/SKql4NivwLp4UdUpJdmpF2a9RbYKFUrVbT3QlbMRBL7yWzrsfdQdKb+4FFDNqrU2rF6moYq3QxKBrpadetajylrthclI9fxMCNF02Jrp4xunY01Vbr2O4KGhuIr/iMJdIYPJwsK7o7qgqP4ncgFCNKZHL6HiZ70ykTTU5AUVHw1aXffCfmPofxLgBxbmcWZb3w/DZ7e67p36gVimmwIRYHW3Gy8up2dX8VXJE4xr4n07nGrAVVMngq7vLBesBjlYhOj4MS1HO6MRbt7u2LKqmqiEUqf5a6J7gVV8BaFB/Q87/lyo/3FXUlo/8xoSEUdKz+EdYgIIizPBQoIxBUdeNAEEFKWnRFhEcqrPLCeWl4Fap/NyqhvOPT0BY5pXm++rr9WvcKVjDbJas8mLCbXbK6fVcBLNcCTc6R+G9VPUNY/GkpKILmicbM4pQX2uNzph2KQqxiLyiW6fT2Fyi1xcynGV3G408k7QQHA83qvcITSEZo1p5DyEVNUdG6hROKezbEj8aqZ2YFFllz/Vj3HBah/iyMmkWBKfz2BykUDN3M0ikCFV1zJcJTYa1ziZuMM/LpbvsqHwsAi3sMJ7nXYvGSLBX0QUCgPC6brg0h6ldjHjhYWn1A5mlAcvxJHGtOaVu4UOsiX7sEJXyvdIXw6pGii0nFbio3ysjnCn7WqqQBUCT2ie24mo3K1xHfR8FN3AztWnTXfjJV4gcrU6gk+q6xaE4e7ageMLqS3EUX6gpIkc1Q50YdF+3BmiyjHpg1iiU8AiNwod3RRt0hKPajZluqrWoeRlJ/q2Zrd42ppteHAV+4wXcYGyEniXvFTWlNaXBQ58kVUDa2Cr1o0kv0HlUHVTPyUr/SodhZyog2la6g2jn6n90fvawTLpPkrM+5/VIsEE0+wVBEFArKPQimBbUr5AEeep5tgx9z24jrlmJklQoLiKOASqqXJOTtLRqWKbesKeAuZnMSxxUUlOWi0IrRSnTkqLqWQB4a6SYO4OSQi3AEPoGosr0G3EX+pzd0UHyyrpyOYVQys9zDT5qQOnQuUZa0jihsI0i2DaP3iel8KcuCRaGIwP8H+lg0hcwSLxwBKKz6hTpCEeVjuPqGxv6KW+h5qwrxQatJw6haWGQIHtSPueB2Ztr4IMNqYSLQhOJBmE6qR5/lQasz8N9RdVYgttbmJfaMN7gN7OGyWZGZikEF/l4hB+EsjqRPzEIYWcwbOBT3LUCblSbxQtuV5YQ7tkpW7pphAtKwQJe62ASGiltg7vsKp0tfX9pqmqaJ+7KSeUKJGsP/gDKgpZ6GFIXkw1q7R5qmQ+S7sZloeWUceVeHgGHdqkxt+oz3ElJPeF8jqjEF+xk8oGLl0t7ktGRZ5vXK8w6RU3oF9+EoEj0m7wizBu4t5m0L7DTaCfTWcVOGP3/90rU1xzlOwO7v3b/8LHJKlf91hvvzvcS6dFb7MFHVbUqCyd9hQt4X+OHi2uTrrF1/QEqi2ve6GaVGPV2gdb5ZYYRbGEdR8gjIpmKIJwEGfRp8l0Rxl5IqkIhFEpdiiB+LRjiunpLdMv5kDaMEPsltZ/D1oj4D75rT5iii+JQnrqpRvly+a4qsyJ8iqL48PDIQnXibB3qZj6PIwpec8q+OiRRF45aqaz4iuTrcfEpwnWAr1Czv9DuVKzXAFgL1MqTmomIzOAyDxZ/B241AJXdBhLQGtVAZ/wvWwoqw4XP67q+dN2MHk4M8hCvNoFkpdBCMaOmGZ6IkMm8UFuxIkOg2FpXe6dGvA+flMKvBJPWRzt8TnX1rfBq+NiclOYdSjxcgYYdSs/Offi6FadYPdsrdOpHc7P2TTRFjVYiEB4eHmQuoVdkrwdSr5cjUGhkf2uikCOn8H/LH2Pr13rtaUkLI9sIPsJUaxW22aHKCErrcRjo3SSkuRRTNcMFCNNNCnqyNUqLmDHtj516XvKyqm/25eNpGENrJo2T0x4VeV7vCSPFy18U3aWj7t6ooo6iX6hcxQTK/sliq26GGr3kw53HaeG2b2RahygQDcCLX8fJAbQgXZe6E6nTSrEIOXzi0vKLsyYxjBI8qFwq0CBV3CPMXFGM8/Tdt9+1NcozHNGCEGhnakp4vpy7tqnRo72ltkgUfwL6mwY4zfl37x+Pd2+UGJIjphqvT1rdfLnygJG72S1W4N7OyUJlZQP9eqrRExyLPn1cOBdBktnnzp3ny9LJpgUnaNARCUhSlvNM7d25yLjooWvx7SIi1hguHOKVGh/NYWPpZOI6NYed0I+9ZygQj73FBLwyOXR6CUiHCyPooNEBfByo/L+Vdl8NMI2C8ZppcFsYSeCs0bcVfeE1/qA1+Ub3AtTIx3mQxiNczkb3ah7ncezf3hyGESl4XN0qz5a6roD1H0a4oAmApgw1sGXm0rraspqC8SAFeUalKwC2WF8mh7zwIsn6Vcm8aFfRQkLE0suCXaEC3lIVhxkUV6KTMgBoYOzSg8tympePHz4AAbbfo7Eb0NclR492u910M8EjgF6EE5nNAExQlWkY1eiNCrlNDZakn0ZyVOOzsvWhLEnqZwyb7SVMcGrRRCGB/beSlKXoBEXP1IOwsm+NxDUTOHYpm54qJ5sPDX4h84kKiE3Bm0POB+t05YSkEQs5W6DLaJ7T2giPRxquCFPyBVq3LBUHEYt7IxKncoir+OmeCcQva64H9POIXGJFMUQb6dI04z5mq0esWVRg/tHfUcqWZ3zkYMUynU9jUdRIbS/KaUGP37zIh3Gex/6nP/7i9rj/y1/9+tL1DVo3wJ3bNO2p76Fbaun2xAsXwKE2EKYyjcewR4pxelLqgFibNwwCNebFBNSPTJ9rbPACojqsVswhO1laB1IJTu7mQDY2PYkLGECETwNyHjOEo1VbULWcGcqIcTYEzuECSYAPAgciTV2gjgMCvpadXtjE70YXhfr9Wyp0fK37bcngPgskbg4TH55QfuYoOkhsQ4qLtNTK67rkYnjFj1cxHcGc8oyatLdVXVPlA8vqhqbUQ8VUgK4/CGrAeZ/lZmTr6qJcXarkg8SK02XQHCMljaCKu5/OERWbdZwcx72/jWEFjTU0NpLbbAeqtyjXEIcBcsKj0NCR+lgsXqBm07x0U1GMXd3sM8ihksqNwKL88U++uL097uoacmlBJUZWaeuG8ZKgB+kYFkS7tDCzjTaWFNq201g5I58tW0GGScM1OZBMSpLXq3dbZPFEDjOgzrKRuREpz5KTcCzNWD3h+FrbookM+7D5xpNm+SKIXRTD0KNaCKUTiiNdlT4qbIg8Dc6NEivSQHfkMvRic/vFN7x6JJ3yajyZ+dICS9zpzXGlcFXeLc9XZaCj2ZWsVus5FCE175xLj4/OnJh05JOEnSKnVQG2BaXIIkck8pXF3M561FjuX8D2xDPaWJe1G04lUhhjoELrMRJpxnECaroHzvhkhsnFHSQBWCp0agZIYJRMgEE/Tpi46qVr3OQaymcVTcIXAjkrlJRs3GLJ6nzpAeyfpHTuNM37XX1zs+caUz/6/PO//u3viIThCwLQqiiAKNDsH7ag2DWlQC5zNtlogxrEjAAlcrhdgzYa5RaFFr4R+mS8lJWeX6T+HmNXVUqrI539YdoUmDuhCTxzXvb7vSaBkTms4RX2jbEFgOypqs6zfJzG0/mCDDMqgl/XgRfQSCymmTgogpn56svBT1Yf2SEMiQryLGRRuHYFV1/R4U7I2rTUYNgmOqwedSHUtapvkViiGxP8ytGQ0uWWQaw6fZLwGa4hHhwlmwIrzCVojirGmRysCnJMLabFGnFETokgWPxz3Hjj56uLAPhnTuFQ38WaUKP10sK3gc28sm/6qWxT0JmUmnmRQ8asWhMQQXmNDDmqfYPM9uEXP/4JJbvgu/sdDvFIUGQyXeY3b94+Pz3WOOhSNp0rSCkFiRR2YbggA5gmLReaegIClMVOK4v3Ul1I8+KxrbsIkSE50Uq94zncn1UFMhzPXG6BgDbUxxw/SxKtqByAnNqcTXexcq2IBx/EoiwqKiDX92T/5zlMZRqN9B+Vtdvajs0+oZ8QyldetgLK9ZD1aGWKk93X4yr6QHK2lHK3Xb8G5ti4q+k/wTxYMQMzZ6VOfpRYZx40R8PitLR0ZTdW7wJwWJ4N51AEhvH+9/BxyJG2iwyUIcQl723kpQR9hFUrUp+nJISosNpQrNSnQ4mDWztjhC9FRmZasygah7vQFVLQylUUgimLlhrhornmnGXv3t42LQK8yNiHu6J0ZxtEUpXl8XjTdx3fSfJDI2eBopmDp1OwVmRqkkssz5+fn5um2e33fAcVwjJQdoVQ7UjyMGcG1FLfs/JvhpkUeTnBLco1VKmzMbJMgHal5A2sPPea6/tuzCa2aKdlKiiXm/QkCQdLAj2ovISDehm7oW9QTxPUC87mVWDZqmjZN8E569PxiZdzw/N8tTcSqXZRa9BwRy+pkpcaZxsoqAjUF4jfn7RQhzG4xEzWB3XevWlE4H3uobJOXNvOsbm15eiM8S37Nl2Da3q3/0roTJwsUApklLfId0oAXjNi0lPgjkUoN2Ry9NqeOzvDxhp4iXN2BMiCqXtIvqEzMY1dWdRIcRymt28+Ew1P47T3tzfffXzkezFGEaHUy4V3AJmR0C3ZM28hcQJU8MmwwbCSjdZFeCHJ1lRxreDBEGkuFUbhYoZeiruFu6aZxssFBeAJm8ZpHs/ny/l8KbLscDwcd/t9287T1NRV0x6oSwMG2jTN5XT6/v0HtAmHOUDSHpwjOKs5NgyPQIaeLP04PDw+dd34k6+/IBStpC7RzriChHIiIkL61OuVq2JtnMaG9GXqkhc0d6+x6YVJ9pNQ/LpfreuTa4Z1Kh1iGggC15GUCEINQ6iWG8bmwlN810hKBu4XgYVNh3Q+OL1drFisV9HrCCad5dy79nDJd1cNoeKgrSUg6S2DjUMXJ5aspzxb8TAvN6mgw2xpR8E0M++JAPrtwyXPu3483O77y7hrqqapwyqimsZyf3vz7ccHhsDyg8uiPBwOQ9+zjkowJW4guuRTtuTT+eXlfLp88fk7QryIgcpcBl5jEv1oFyrVJCl+RBhKe4bavrq6hZiNXLQIpef6njF9zB6XaW6rev/mTVkVx+Ph7vaubdFMlFxTYBn90PfTWBZogypxPUV6mcj1DgUEkBB5ylDHuqrqogaTEowL96i0o7RNhGuny8bWrD4M58o2TN0amk4Ytb9OXleiqBEVOE3MY/xE+3Y3MJSWk8P60yReApoKaaJqNpmpLBdEF5vHxBFHNBv7lXlGrHbES7n5ifcK+p9h3v4GRrHeaJeoiGYgOXkaaZ6Rb03PbTgjsUGwGqeNxkWQPJk7Fzer/jJgiJDp/va+qYsPj8PXX33GWqtbt6VqXNtuhQLVdfX09LRvd4qmY+gT4i6U7Tzf3hylp5smLYiqBRV07PuuaVDzdZIxQoiKHYEEYPgyC4C0BTVWEA4Z9eUoIFQgyalCMjbKTbZ5tlxOl9v7O/IbZ/tdu2tbkC75jWkMMzqTNQBysCItg+FGRybkHf4E0rkkb0uGnG9GfNE6ODsorTCY6p1X2Wr41MuqsIvRIRfn/gwbOPFg+UuDCHZ3inV8d4CcMZXCOOKD5uIAftTOpRRGowVVkhFF51UeJDI3nesrK+WWKjUBsivWa8yRgsaQlOZ1mnu8uKq2EgiL2sVHa7YatkE+wkex8hOtsJK2w08oDYcKBLpkYVqiS0ize/ISdSiSghyNA3BX7D9S6x1FoAAdZYyyMaYcNaUkHiDhee4kWOSX5/MyLeVuN82ztHPQYyUwjKLY73fLtOyatqQ673lRztpFYRhQ55VMZYoVU/JHRn1Dd1SeuqbgFhXcorrNu7Yqq/vbO9yKmlJVqA1KritpJoz/gZ5Row6P5mJd4nNRTJwmIoeGSdJZiTkez3KGMwPepNB8YuOk+f26qhfZ7sVl2a5dp90JP8USIpoS9/BKSQjixw5POFEbyrkGa4PGkVjMylkYo2XXqP6QKpegBEnluwapTFdkRV9xMl7AvGzZAkYDcaL11loGNLd5WbjkBVcRMr4dmR/XX4xoDm60iCTNDvGLteJnwQQ1+1fuxrYocM5LOYzzfr/bNbXKagnt8nRqVEUfKKsnLMbd7e04DKzvknQSO3Loe9Sd0oIuFo+lGBEkpwCjs+zm5ha+Ia6DTySiTc+t5iy1VCJ6zkRgcoU6slRJWSCa5RY2CGFAKnP8NjxbSsCxzkyOZUJxhtI8Xg66H4zbEKMP+1chv5JqIWl43+uGV8nwlZfxyteCw3RBYSW81nd47f5b1GZaml51RW/1rwgccv155ECktLuIPtwRVWekB4e8Ni+v55ppmRqcdtiDXzlQuN4nSJJgy3v+5rxQzL41h4FCHQzHt566tnBWYGHDqErdWw7Y4E7PFifxxnn0m8dZhHhw0V36H331rqQsJc0HkHXOsuz+9vjdhwemQVZ8pDI2lgueOQ4fLMt8OXU3Nzfw61KiImsf0os0lGPgvEL0/qNqG1TqkYK9VCRfegXjEUCdF43y7Uz9sdI9kI+/dETUGoPMHmRsBrXiuC+KcXEUWEuWM30iuisqh+fhgplTRZSG1A9ju/b8J4ZVskGvENmrVrTJFDYiJRvJImxOEVYZGp8Adx93aqKRJgJh6zthDBuXxX8TI1DFJwJoeOVdKMhlLCT66BWVU5jxxnMdiYSfmtXBalWizVwpKL++JeejDUAnUiE15y1eYWQ2nFNbBtJq0qk+7i0XeeKGw0XBDKT4ZLc3h7bh2kC4gijVvI4L2o/U9enSOVaq1jCXI12Wru/6YWDwA9OelQqz0qUGixXhmRVVibpiUiY+R/EAVruhuoc8S3P2UjMXJ1y1A6QUjrUSUHiEQMNEV0YtkIIZKFxujJU2k8O1nVfZZPmMtChVTn0bluzD4/OPjjdZ2dIakZqx7U9am6fbx+TqN90JINUnr+KUBD0QqvFv6pzq4JRz9Soned2ylIeZ2uhVwHCJ2h3iK+CvqCET7ObIkRoU7dUEEmswYRfpigVNWU6Bz1lISTioBWaLOgeVKaGUgYT2K4xATMhtG3wX/+3RmZ94mas/cut5NcKFtXV+gklC8bgRmirXz1DSYeOvLIv72+MZBGzzZrt+ypf5fD4vWY7ATVOXVJyd8oKt3WhyuiSARg3fCB9iDZakJTeDlMM3bOsX7cnADN4omd6STzglivV5o3UNiXE2uiAJhED0+HBeIid1MMgTOQwcOKJbf/z4fHd3O85ZNg5ZtRcmn8Qe/n1V6Y2XM9a4qEulUOiNK7ds11SDTe6cWFiJghz5VE3Ft7rxV0r1ibzgCo/mQ3chV9H5LNxoRVmiW3gJ9SntZSW+rNyAKA7eXIhzMROT060UDwLmFjonoWIF7DgIGhe73qDJOPM4UqQt+GciIlrveFLqn1jNPCZqcXAWcz6XWYU6ISx6ba62SAUau3EMRXvnQoT2w3A6vRRVvTsc4cGyzsh6RBRo5WciXQXBLWBXcglIpikGa0pDUQWHabWfRZLu7ZhQczVo46RAEyISibwFl3VX+pUGK3wk+E5VXQsQhYAlhLaSgBjqUmbLMALZIrnKKDGLW5BxjijZ+XLa7/ZZCSd1fMDMEOOfkef0yiu1TzeMIam0PhMBa19MIy8RVNuPCVajp0ZvHjrkvkL2vALsD1sqwPwMzPbQzbF6zOEJ7HXQCL6jY8+r/Og38Q8R/a07uAbqjWxHsb7E7rKWu+5QiHBT/Z7CmJQ9g5JxaOWBuAgntMieJNgtPsV616QD7GpbQ5nJZAWifYrXXe7kCnYw3AJhUkqjxX96V2ubIoEI4A/RN4hSQKkRYT/0Xd8dDkf4dsn1GKpji4sicHYprGsaGFLzCNmCF7BUAWDOpeeoa9w4jMOE+mGzrow1PSsA4FY5qqlLDWqfV4yj5JKDTY3C8QwRoQpfAm2V3CLlC2Q2C6PJMxTik/imbkKRF7tdy36/9x+ff3JzmxW7DfL0ylzY1nT30r3c+FtVM0OlcndC06llX3+AV3pT8XTYSHvXkBmuUmXKX0JajF1hoWpN+NHa/JoAzFcy0Qpf1ULeMkk3q3gBVIRv+ruM5oPV4z/id3XxVD0zkZ8otjoXXXfKfSMn7VxV5fF4nHYtmDwOreRaSWKNZcd4r5k/Oxam29gUD9kKH9i7gVGbQc/l3W1+ll0ANw5h9o3EA0sRplTXFcbP7Gmen56eDvs9EnTo9py7wzY/QqjBxHAKvWwEHkvOY7RcJl+XugRIgacWLUN36djWpmIfC39fGIR2fpinuR96Pg8VATbQhSxbyrpCS4Wh37XtkuU15f1SDeCC0F0B7QjThvzS6h0TbAntCLdeF/xvWZYsmcdhzsYpq6ZsKbl6ejg3/z7Kc5BeGy+pbku9kaTKgT/VkZ7qzsUVZIS/sdeH3bt8JFzUyG7l/b+SfqhnyN2C29qrIsWDkVpKBIURMyfKW9OvGuWGU5d0PthYH//oUAZQs238kgj/CJOx5YnEMKKF48i5+7TkFCBFcWJC6IGAKaOF0+LQqh7p41xTLhngFgRupc+EGF7QwINVtJ61Zre4LSYRZD4f5R5iUFiifFNXQzdIvAfV7Yb65nbOFjRTpcx+1vSgNFOrTtMmQ+4zKWqcOWB1EewMIn2Xcrb6vkchAS3XbvXr5okq0ZNJwgn9qCgwz5SZT30h0cMZwKmqqvMiv6HDQ+U68OXHBzjSOTGjrEqyazgRg4ZMti5LCdKlyb2uAA92aHXdcNhjBKfTeV7Ox9u3V9MD3I/rrw2QbMQNgvIL81/SCUXZNTNFiTG5cRCpW3eXM24Ja6k+Z5f7IlxbZ06/INyP/qHDTtVD6EMmWLBhrZ+meqiXwkaifknTw7+xhjqT8HcQ6Fp2ONAqo3dlwUyrYBEjJ3hZ0HyeekmzMm0YH/xLqTAkZ/hcUDtp6s2z2sqwoGFXAjAhYltm2iQQkWSRg+IVYwdYr+B2WTxUn0DJJeqAoKjgpmaJAIOQqMgQF7qOVvPAnqb30f2UPyV6yyUv8f44DU9Pz32H5qMVyUxWy5m7lWUFe5nZDT2C40dcWBNl+qi50TCODScMk20MWkZLMMAQ97v9OI1/+OM31DWNRq4IBraDcWsAt5SVSlFvRAF5JDV7yLPl/cPTZ/e3yInF3wG4tjpbr9Dw69qv5BOa7EMUWpSBCKmwYSb90Ef8oGiKgOOUiwiCTTQ0J1BUNcX/yX6pR1tFZo98LEIpx1j6Kw27v1I13n2qzMFBz3mxrBu2KcTiBgiMMIht7dcoWfvs7R/HseeaFYKh5MpsXp9x2SDksc3JTmafjSUEGDBIv+iWesX2Y8aavhn/ERQHdQKI2lRSWi9l2zAN27lkNCR3q5TIChXVpeqw2kXLeLh5nqPnB8clxJ1U1iBVHBCQJeu7/unpqSjLm5sbzvjhj3lLpgkucrakuKIYm1p8a21FLp9SChHgk+g2iG5M6NNNGKGirpq7+9tvv/1uvz9wKItbpqpBA1qdqRoWFTYIOas8FIk2o6cslRDwAdmtWOqrIvgqeacuHVVQyAZ2Sx1teXzAnKH6qae5c3VtuKHMmn9ohOWXg8vStVzKmkBzqDxMg2f6RdjN+Pc6Rpw8f8My3PAGBWyVi7Ea0Yb76hUU5FAAlVmTpuxN6O+u1SHCN83bFlhWKOIsnebEoxnNJFBwaHP12it1TVxZDTGhjKuyXEPZ4egxzpBSPwCboQV1uKSEQwWVKsJFaTh6khXc4BSoPAd+A/0BSUvNs/zl5dR1l127qyh7kWtQhpqvdKe6tMYIcAvapLygD5AOngaljl+6jnqGVyREIYdvb2+R1bNMlw4VQokYmUpZ3sJJMU4j+cMY4oXS2RT3ylo6i/M0XbrhOAER6CxG1+NPZcCqwpn79Yrz2PseFMWPfwGlvNJoaQ3JCDiDyETc+q4LhwXV3Kwyt5H+eYGgtQe4Ru2yfCmzsgK7yRBl1Nt45Tm84vsqRj0gBpIPk9dKTEQpfdFXmNjMGR2pqqpqAZpLQPwo59nFXnkvrHqEqvwKAHINmWVlw0glpX3DulpL4w3TJzImhOvwH8aN8MaESjbcScwKF9DMwWG6flCWh6QFiCluh4KKxRbj92dZZuu4rTyI84QufZ+X9dLBC7WDk48loZ0kygEkFaZC6j/0dotBSG8FZS1yyp0NoXXH8Cew3hDjaBmBBMmb237oAbEmNDUFffvdbk9muXQhrKiOOjIoFhSgZZV/nueb44ELbz08vHyGrsnu1IsaoAci2sSgwK2U3k8FjtkFsGREEgp03dr6q+9t3DN+bMhYiARO9AUng51zyfuHDCcH/Hwp5c5y8dkK+4+5VnSO5eg4bL9NaLVEtuB+gJZ0rsQa7sAKG5eTjajX2Xo4YK5tqmUyXvGfCXUwf3Lau5VZjyuarLOw/V6k/HH9rEgbUTQkbYSa1P04FeUoSAbDdOtSFHk+oCoyfadAQRSOu6R7LasRIBzrYqWMSy7KAgiQJb85HgX+RfU0wp1cxt9ColHKAGmDFXMx0TMQwZ6zueS6GQykkZAG2Ew/DxSAQi5kgxBTPk3j5XyhDsY5+b3GCk2epcU1m/fUARwwLHZ68+ghlMtqmPquO7e7m5D2F9hULEQTDTe6ar1d4fuhtzQdDyJgt5QuQS1WeYJS7997/ZUQqBteUOUTPdqBt5R+9XlcVIFyPiTf+tNwpETy+MkFFd7UCzcwo7O4uE4Ib8HvVDBGgUIfqebjzA32tKrO41ZvxTEYaOHy9sQtsMwZl4xJa4Fqg5lXJ2/OjU/o2iIjxKacccSzDy9D9jzsDrvjvt5R01ArVcOjYU+beGkpc9AqY6mia+grE4+C8vJqNdX3QzxtGMa3b9qSVHF28HHdOnIaMVBbbPFZHPpS2k62FbeUMlpybAhQNY4DX1JQVzTCUzMkHdpyP/dDVdVlcXo6fXj/vkJ9XORXcM4EeqG5eoBlWXV9R1ixpqrKXdtwgXp2pP3x99/87Oe7nJIN/dqubN8Qo7i2Nf4kbmiKZLmxE8vv3tVXrDluKGWrvyM55/2inifEtxVjKdZ1AxMjXwH6oFE3uwB925pk5JGLZY38YgZaWCtNkHMiMxTM4ooUbPGBcin1lPNmgooWLyJ7ZEzq6qaZFenVB1NO5SCK3ogaFEsOQVRuO0Ku2DO2S+kKrJfC/iaGwUo8G8FkPpany+Xl5fmr+yMy+1VVQyV3SRIi/C+MWF8m1pidwTjUfnBrbgHoiSBcS5bf395VJTmZ6VKiTSwgRL1m2MMg4dJbmXUPtgSF0JOaPynI2K4KNOLkLoqXl24ch6IsUdeKwRzLcu66tqrRaX4EHowFBucw4lnMowk/XZZFQ11JeSZcNZoeXTR1A/D3MiPXIj3DyytuqTWTTXZGrF73IYRImVfSgsbApIk09M6cV1+raM3GODYN9xUES11XajHZR/KCw1KoN0QEXXVyuz5hSbZa/nEqJSQnRvF5ySiRdSEmOQF6CNtTUc4aHwC9WBRu/hJ+cj4rr074UNiNY1NBC9CmfFK7UudC7uzVil5f6/Tdq9snyjFvvpUEQWAWJx/iaZxg6RXl06V/1zRooUg+u2XOOrjW1Q0ATDEXfAQZz9NUIk6jg2B9t0CuggI/lbKpQeE4jl3fo8wiqHfJStwBlasoa5CaITBTK5ZsVL6dU+BX9MpxwFJbmV4S5r0UoMyym+Oxbfc3N4fd7nA4HAHnQkm7/nQ5c01y1JTOsv1h36OoCHmn68YyNwK8mh3OaAju0Ef0IfG6JeuzbEJ/5Phob+6NAY6ub6rdI6F3Ig4IktCpcH17MV63gkIhWuwHGLXliyvVxROJgRq6CmL4aknA6MFMPFTAXXmVtJii+t2xRsw0YOwk1KqxTRBJH6bJqwJ4D48vyHaqlYc9I3UZFhGVYXLQWnVRBBagzhqK5dIiEvkJjWsgOa7T4xFSUa7QRnhsrQCpCA9rHWsxW7ZPvOuGuOapoOXdApcU+6TmLHv/eLo9HKqKFCCSmqfzxd+A8M7U+i/YDCyoTM2zerAwPFnx5fI3Q9+jRQuMTGDVigntTlBTfUI2P+chUrApBB0y8EfgyglazjleaifSPqAlIsr1LH3ffXx4rIqXy+VIpWRx9KcZIeum3fEwhmmqJ1TeeXN39+HDh6kRIRoWUivlgVSlDDVrWBZYwvnve23S5OjEKGJz6eN9S3c11eniv5BOaI9IttJBOiJH4hVu4dQFn2awVVzA5aNH31RIlYo14z1yG8RHCWEefLLWPz4IGk38tgkHFGJ4npKzKkJMV9zkRYWjkTxellUqRpgG4lgRVOXbmbmwIWHYhRHZxyHlPrhm19a2SG2rn5BYUUE92N7bsAFh1QNLC/pNTPqqHMD7jRlDnmJcZZG3dfPdw9OP3t0LmgWHdZCt4Qbh1PRsniF7qYG99CJToGi+TEtRoAsakQMENV/JWmh36fq+77t+rqa8LPsB/iTGXVCQBtmIrDOPyOQC0YY5cTVNaSao7jJSkEoqGMCglN1uh9AR7yy1+eUKO+AFZdkNQ9NUb968ef/hvSYVIycZh0TrBpRLyb3E+Y2Z4NnDMJE6Jkk3YDzcZiJlvK/YulsbmGy4kw38Ii+0illVgvmLMSQx5drGYf0N48Pijc9ro94yzH0yWPqJWFc8JKfT+Ad5maNSQCYoq+BH5LzTGrsOoSIX6tAmA2EpVTgHELGBmXnxZopwcM2NFSO1hRO715Qx4zxSkoZSYD+hNW+tpN+9AF1MP/ajiYeHBkRLiaT2QcZPiImXUze9gX2INmEAcZMopfWlICoXWJXEPaaxcZgo+Y60a3p2UaKrUEOmiDiQF6Ro3d7c/PG7b//43XdfvHs7L9npfAH0ioiWXPnQBWChTiOKwxJDXXRHpfIr6WfMX7XuTRaB6bm5OP26LKUWRcmqqmrquusu85Ld3t0dj8eKwsSSk8wvqYBp8BmpgwVdfRzqqST1P2+aEgATQqRJiH8VNfq0ReoNudXOeneSdicMbpq1ZRZ/9So7yX+IOfZKdVx+rsGLIwKWz/CrNFIONB6G7MBNgThdHQ1iytF3VMTHxnvyt48Dy2kJstT1OnAQS9UMGVYrXC9Mwmu5wfKIV4ORT9yBdmMhX3s5lWxDh3bvBhvMf6T8jkrAypA5aS9vmvrldDnumgypBAGwIYtJbtj+fG53O+05ht5KBGtA3fYMsOQG5ZkpAMMWyESNfJumPp/PL6fz8+PT27vbrh8RoSvQJfByOVEnsRI1PXIQG8rkYGyzjZ4Fr81G/pQWpvyTTFQpKCAGGhL6yVuGCnV0z3mZmro+3t5SHqUJIXF+s3q2FIzpQvVc/mSelwGegnIpirap2OhQMISTZLrAsUDcpOVtYRyHjPBFdnZbNgh9pnMPCqzfd/4nacbkhxL+0d6iXsePHqEnyP3pI6qJw9gNJKBq5Ka2fVvZ1MHyDJRr/24mJW0vpAu5OTGuSCBbNANhsf/ZZzZ5PWfNKIOKJO356F9fNvP1UW7wW+/gjC9frVJE4aQPK2abkTMM5s/ef3g6/ugz2SvdLvEIUPWpjmoyUySmXuAZmtnlC8LDBZzBqtFayc0FZ3zz5u7333x7Pl9eXs5AcaC0Oi5q2kZEpwHWzKE5O5wNDYHNbvtFpK7IXSZdd97IHcVhXGgVVZkv0OqPN7fn0wnpzdHu0lcRDKC0KtgK5OZYQL79MFRvUCIvX/LudG7bfVk2wQ+TqqfReqf+idfRlm7HGInF7gpHeFsCIXqaAF2v845AkM5XI+ZouCasSvSpYwDx0+1pWw+XWlTqOWfniR7e2Px1FvBa1/C9OmNJFojX7O1k/lJPRnuiEgmbwzTYnPpYQ82LPh4Gw2WSYfFbQpIb68ZCb72hDw6rqDajTyZxRpMFrLXjPREsTqtaI4SZKCvpu1CAfqy6M3dRevv2HrAkdl0hu5+qPbc7YENLllyBZWqROgCqUS82z3eHw9PD47wsN/tDTn2G6emcqwD5yfvASUI5tFhY6NaMGe5GJ9vVCpaqpnETyYAjl0RVabBAGQJIM6zFayl9nx33J9woG+6Ab88YJSqHjRNs5GUZuuGOQztrs8n+UH1aT3oqKMN525LSpjoKkMNqZOnHG19YmdDrw+OxJ+GTQJEbI4hZQ6DUZD5xzwVpnbZxR1mOVGiZ3Jeyd8xXNhhdpFQqXfkbbbv9zaGlmgFXdYCNGJQExU3pbWUi/AW5yLK4yAsCQ44dIt740X9TDraxpEJ16QKt7R9+Vz2p4nBD6+2Fy6bz3sLbn5fFx6eXd9WxIvSyyTjmmDj1LQ4xBVBnNCjFYRcRiH7Y8WJaM0GuHf3jr7788N13jFwDLpo7pImoRPKQi0thiKA9wW9rfRxdH3ORWF5VFNhQEW17qng5yJuyrqsR7Y5RvD0kZGnhL3XVCDALYXq0Rng5nw8k0rtLpwuixPWakqcHxNB+6zxg4zZRvBgTJy/0Bmwqbmxjb9pWG9tzJyM52jE004ypsBKrGURfj54l2LtV7flt7TLSwFOQpbiMjNfoEofC5leJPz75xgQ1TBIHCjgaacB6baXplRANv/p5657TGEFAlAOdNHm+Puf0gmvnJtKxvGGgwRjxpgUUKNlubPaeB6QBIb+2rl2wiyvOid1HGjiWFGVoqJ3fMIxlLecthPk1YMC1G5qm/uyzd0i15+LRAGTh3sHzFByKggEWiz3gK1n2iah0mpZ8pOB6JW+aP1gVlZ7P84zbUHC6m7pjhLARn3ZmHb+gqQApR93JKY8SmpMF03jdrFK3bb07oukepFsTwD5O95dJSs6UwQei76rDJr3lximPr3AQisgHk+jE7jYWwIveTab7Aw+njjE8JBZCMczKGIf7dU0mK2ln4JeQgrxyNqsDPGglfjX56vBUOh92CzB16RImOmSkrKxXKJlztBJ2y9RSEd6bGDN6hcM/hpmVCJYXKKk5TTAgWULKf2prUmqBpTTz9I0elHa4pqw8SM5gnt/c3HVo0dDAD0xRd8relco8qhvLeuSqi9vYOQJJ5aC5kizoSjIE6aCLKHadEwNgmSAoZVlU1O9AYn/yqODXMAPLlB5ubpwhIxJE3DTNjDp3ISFLD9v6eMfwqVggeeU0pU0xE4B6IfQtDTIyPK8JxORkvH5VdJ0d1ChE6ydjy8RDppTx+Kw6QjTCW6uzq284fKadYkNdGeE5VpEygddm6DQeJTMGS6vjWWB44dEpIQeKUp4iYxPUgsWRoxSmjaV+5U0DMnrl2xsEurT64qaZUcUlQTJneYaWf0VW1eU8jdyx0FaYlUsc62nuup7tBKsmGzRQvb9mnolAbne7fhhAZkS0XF/Wxwo551hP+6LLZutnx93aZVD9QKpFRq0egNXSYF8gspK83txwrqd23kKxLoEgRv+J1stQ+GGcXl7OXM9DQ54hxhkfpetOqugEXtlXjb7wMltBNZ39xi2CzI1Yivvd629SBz3hHyuGEISdlaeQI6Uq26t5gtfYy/p9VXCDq8gHfJ38evVlI1kp7XHsSi8nW4jz1Mwgi+V2NLGUFyrRSMXKTZBqPLjNF5/z1/lQpKo4WSpcMi5CxsOZc6Cyprxo6ppLREV9JemfeUYlql27C517Q5BfikKbMutyrfJd2zK6itqjmcS2EbJuTE1SMi4cL2gvdj7ydqM8JfdumKe+61+eTy8vJ0buUUSZIdjSBlUtBdTiydHNfPn44ZGCXqYkyUG1gj5CxAJ9Fe93WUjoW7Tyre1JzNs0xuQ/3dxVz2ppNZDMoKyVNltPtYPrpahru394mm1R8qjIzAvfSi5LYtNavTDNnlcm7OKtlm8Zz9szFOd+S/mB+9LWk8LENqqLGUJKwqTucfwdxC2bGqqblFlUsAYXMZJp8+iFvXi4NL/YqkxU5ivm7ideYSsjRSuapdMLIfwBnMI30KDMiwfKwl2Geb67O7z/5ptlRnFZbZAuO4DQLloxEJqaJJaldrvyOnD9minKorshrjf0Q7VDQ23GvRtmXetCUvXpAjcBTDrLP3z8eHs8zhkgIgjhEsAWJbuaelmWmsrWMbS1Hy9DUbQUmqJO3wgjhcOHr4E6z5fLkbJ8gw4knwOWYkKHrUwunsUOrZyQlVzGWjK2I+/Rq/1S/CuhdLdTWoMDC8FOhcj6CY66dIevCI2AtUxPheqmr0gPVbtUvxYtmt/1t3KtDq/pinoyrxgS2WaTxiuDC37lqD6H13Kig7X16LKEO7NHBwacCifQ7JZsNURjVT+NrgrXqhIyThKbvezUEYSFSJJbwpc8u4nXKOim4rEHIjTwSAZUcO7C0E/7Q1XtdpyC5Mu1KNvCG5TMwz54lb0epeN/5cAPZGvZdd1ut+N5S80xqvDOXVlZ7k2oIE8abFl89fm7AoYrlctCVxZkj0vsmEYHlzjp5JQCiV5q9L54rRk9S15/gEd3ze7Dhw/3d2/KUiRckC4U0gqnySm0xE2GeWl6RIX7VpbuVX15UzhEF4nfO75Sk3m4P7Bl5LiRpqdi87xEfGX9vSuyO5CZ097DRaKdlK9J/2Q0iSKi/W+SMQYBuaELx0sU/asBICsV5UJNTJDGHpPF46xDwHQ4XTW2fMO8Yu6gUi72W13DmzjFNX4lxs36Cr9KbsbBFcclF8cCOXdwIxvcEzmxeT4PKJGbQ7hRIXZ2CnMrUCp2QUkmlGrnEK/2REJwMCUo1ks7FWZLdrl0t3cEeRIjgJDlxAqmeSQxjqLVe5TsIK8V0yOjHRWmI+vIc6fGogzDrnLIdubgtDNWM4ICz3l+f//m//uXf36+oOwO1451q28d3kL1D20fQpY/IJrz88vTzf1bOs+JKI3J9Qrxro3YyAzSvOw548Lu3F0i5t+2bval5I5ekiSx5vhQXRG/K6i3nVusaT4DiKt1cD6tKyYPccJbAUl/m1fySPElr02GT98abhjUb6eUGqZ2mrXLlLInsKEVanDowwO2JCFKWTK9oYPBJsxPro0VqESY29Tk+/OclfnS7vbZDFChsDL6lyueny5dXWXH/f7D43MoR0gAkGGEj5qlJQhE4IzaEHA9KNa6aFWKPG/aBqmFyKQXtCT1QkcHwBqg5RbuV8sHywik7HQSNsGEzigbiV3eFlQxu5tSISUzge+A1CIS3NM0Pj0/Hfc7hK814SEdvaWoUJIVl4UHLmuau4+nL79ER4i1urP58raeUySv2cYiQ5C8EfR7ho7Ix68ol/EtP00aXo7Yc+g/O7VGCOqGdsELk6tbo4lSatzTVlqpG44ZnH5kDjMXe8+C384tiWqB6nwNXrG0qwtqiyOzFAWKXU6UOJYixYF9QU6263DDBUbh4U07uN6dHi2c09W2sCtrJc9WiImH3Enc2CyYq0uevVz6Ocvu745U6ih4duYZJeMoMZ76KtlCBNi07Z4qh2ZRSrGrqu97QkhQY1HEnMt92+53KPUMaJRnYgv3IZZ4glSRl2IdSv8APLI+L9Xy5XNud8jwD8j25dJ1fd9NEzqkPjw+wuTlkgymvKl5IcV9iOYnapczDtwzhyuHzMOIavIxGNONOj6HfjPEWgrf2qIxde9RVUrvOlGpuFKat8xc9+Fr6MtEaw6fB3hy6JDJzb+4IAJXe9QlCy4S+36iVkSPtsps6+GGn7F6k9oKr3IwvsAUQqHv9fqgu0fdzON0AS7S2pmpsIg8/2wWE4IjUeT5VmZDa8GasM9RCmeMRYvJdmMHwzlR3xI1GKN6VOU4nalgrOJi6d8CSfA1UcVSFsjs5aLq5D1akGbIif6QXpEGnYgiltJiGtP/pgnGKkd9yLwlLGOQSbRiHuc9cwF68Q9x9qYONLiIAeSkfGImaEyN9HfLFuNf0ClmGrJ8+fyzzz48PCCSyTWgJ8hlVhFMWdRFwxxpwOoNQEo/SLqq6B1DPL2yBeb/3Oz9s/ViAYLuhML7bRdZVkSWsZppW7ZubGXKexHqajXOcCOxJkzPFRQh1UCEISztrLcVr1fnubbXA+m/Urk6TrnaumGEKv0Byj1jcesa7bBH+F20250Lbun9nWuLnKipju69jdfPhNzSn4bXNTgfA5A3QllcjohYSYSgHZDSCwW5LC79pUHLXqLAeb5Qan5TV/DZSkKQBoSCchNgVOE0wK6bLpcLB3hULXRaENRloKEYnZFzWIvg44zvoIbEUouIkgolvxuJjfNUcRKVOsZVlNJlpEnVMI+ncegPh8PpdJrRLQWPn8ZxBkuj3GWR2HxOqfcaI0bEDCamJn3PVEqlRtnGLiZ1xq8eLRVWbFtRe9FkG8Xa2rJ7f0jRduMkyWBd6MB/EGhXWsuajw0ckzJC1nZf+krIe+PdK2P1w5C5b1Xui1iGeke2b+gOqPsuoLxoNUK1I8RBF6DPq7FTDgMnFQcLIkZEBUsoUhjcSKNCCp8C5NouiYKucgnKJ4CB2ibIwqIBRnHY7x+fnijZCLigLENzI+oXM1M2Ly/qBrKWGb0DcfNH+f3N3ePHj1SeaiiKinLyTdcWxhcAaosgqMicRSpu13dZXnAAaRyhX98ej6iw2YMsDV4DCCQdSQlrcZUGSu69XM5tXTZlCa/yjIDTTOWx8mFEtZ2iZBYs+rigOGVFphkth3WUFDyJ1Nm1qup2M1x3VUgnlAgnlgIvNItHzxbf1Qkevsy8dhuPf8VjFOcax1q6UoSyIJZAEMIF924V/uTY1ZWg7habi+jxWrQpmohjLQmdRxesHnDtRQg+6rdAPhXtzxK8r4FJ84YTAJpki2prUjxke+gbepHbOrFar0w6NYD9nVgJnSFUOXjkY1pLNp8u3W5X3h3bP1DxKpY/1DCpvpzPl366OR5t/bjHr+yygRjtqQb3zXNUvUFHsu54OFD3QHiz4WglRib+bf3eol6uLMtOp/MAFBdiSN1z1zZoHdjU9UT9k+qyokaKQrHQhKQvTxZK8I/jMmdtsxuG8fb2BtYBSpKIxANBIucIbm9dV6EaGRiBz8pyx+5382DZydXvbDsjXteUUilFTIzKi0R+hZXeu3HPDd1YMvhCI9HoinCGovulntFAEUbB0jksRTMnrgGvGEhGTezBF9K49uT15NJfdWkMVLepQduxdPNXpzKlnmqxKyZfqlYjQA5NZ+WaUZxTYH5fvtnKltp4XReyQf+ROnQhdmCMma+SU0apCFVxvD2e+xOEsPNVkKqaP58v93e7ps137c5jWeq6fn5+oWYmsnIijhTSnyp4EU9dSrRAKp8enw6H436H8nEobsFjlipaEvucyXLlliwslpAGSIkQqPhRInW3QFLmhLpmVISdHkQNkARcBevdzPSacNfDMN7kN7umRUwrWUKw4MA6RK9XZw4jWMZxoswpdXIqmScM3++mjH516Pwr8YYxe6A4sD8ZdvMQfxHvha10pKlFT7h6eMIBCbvsWM6WvsBmIJXQsaaEil1KpiQKy+o8WEJX0NfCMJPhy8EW5Ji7zn0aPdXP5Mo62BwZjsA9tXrBIagITqmSiZtMKaqrxD6tdTQt5cdbKknEZUKmsjjDJJwpfodkxgoytopjGn40GifkcMHOG1RA55Y3Wh55GPr9HonBrIz77ebDLOmcHERddVw87PfPL0/Pz89N01KRWS39qbXHuFbCQh5gWY9l4TK/7HSQiJWSuk6D3mdBbzZAHOLgjKWqanDZBJ9ccBbygQr+AahIppHCcFAFAe4bdXQkmxcsoU9y4/iVmJG8DUBi8dFfH2tvAa7vwAi31RO8nhwprP7PLWLfIAVpFUXodeVj/tLYQaMMKmJrrzwisedjDeaHrq1TXzYtF08/JQoRNyOBHCSC4a+ThTP3KRkPnNAfGLyOPDl0fv+87WMWtsuG9AbZ5svCcNQnBdTFKa+O6tjDzPQEiXR/d/vt+/fshsZlJXLq2ABWfYk5hvOdm9Ckxu2Oj+HT483x4eGDmFLk3w7ltbkWu2rRuXEuZCAhdTHkEhB3DAI2ToYK0BL2YosoAVFS6Ryq3YPKXpoSJjF6ze6knAqqqqWqsrLkaUQ/B+I1U9g8n1C47UKJzLTXXhL0J65hkSjTcrWSlL9tdMP4JPk8x1j0bJpbYQ/XV6zOoOwT1BgYkEH3iweyxpFEhzSSVUYp0ZVyB3XQyH6kd0l+MQpJFOdopbXGGrs6mqYexgrllN33ArabQ4t0RiauLe43J3DBKzu8fnPFoFIOJVNy/i5FhHC1VF4aEWYo0eEMFJrROC31srQtYM8adEXXz8+/+Pz08gKkgeA/SQuJxqb9s90QLVh8d3v78ubt7e0tF7sReI/ioEWw8UtjzIxt5ilw4RwtTalXOXLXr+pQBEvFbm6KfpADEfeZiUUJQpDsYTbjiYRKKo47UdcIXmDKs/AR12DPuSCy35SN39NXTCtmfOcgYJ8gY3u4tdPWQTi+5XVrMnwSy/JAROl3Y2kmCCTu67cwoo0VzNc94am6adxpS9llrc/N1JN/UMCv1eJbqSARCwhyXf5ANUYWwkjHCwGUQEBqX9FDSRAQWF/ji9bF181IF3WDy8a7Hth/vDAxTxBqRsiUS0FSaz5AkVlZi3Y+78apWZYdwYwJQifxem5NoiuskVYuC2eyV8NpLrhEVxNu+csvv0Q5WFt8dYxrewax8SQv0ZGn1PcJgpnV7yCp9cB7GpZttgrD1MWHas/iSpL21OOMlXihQrqSNgn4DU6fQM139mPMo3xoq2tMdL1TfsMSR9R1ygYBKx41UTwTzAYf8ygW4A99OqIQF1rXTrOEp41mapZpZHvB16Cw8AAGAkRbEFiRLumLHkSFLkxTigYcmdLJQF5T731jEHMwrWAvqdoqIgO/S6PjZQBYQ85UEu+Bpkpsi0SBl75pNouurpuiTGZLubHAsA92JxOKtX52GmXTuEzVUiFKZAY5xzznl9P59naPLKK6vvQDgSU4WlvcHI5918NgoOr7yAaSsloAGPZ916JWlsIN3BQY/NjUDRuyJkL9PoYDmrsShVZohU9HqCvPMjaU0ZA2wvQ9B0vXjEQOfKEhRcZwTkLnE6coLANCnNdzgZVBbfoBCJAsy7pLX+VFfzkfDjd5oUVIVick4fVrCRd0wavf1HRCBjauFdEtULAW1FndMxVwoU/D6jCpCrJxbkI0Jf7SPI3DUi0zF3dP6F5uZYgP85U4jTmUuwjk5scX6MJpGcnM47fDN+Ov6mFhiza5KxLHuc0i1DFh7YJl1gFyTxOcDT5tTv312ouNwWUgJcsS1jWENKIF31BLeCgEUqD8OJR0byvKHWcdWrxQSNPPhxEXZGXeNE03DExqfF+pgQMJPnPzJBsjp3kYjXm2o4qspB9SChHLSoFP8Cxs3Hl6vK28jB4zVR1xaNjctRieagBMYYJs1kWlhoPAhPVzf+m7EtWB0OesQEMi2jMqG4SmilytnEGnaAZeDv307Tcf3r37Si0SWPlmoemBWq6HCmICEOUrvRrphKxNCNZTS4z7AnLuMIQTtFqySK29IvPDkq/GsVZFHTGoKcmdqQqYWFnMQeNyQ6snOgbrr0nHsdWw7W/7CszJUFBuXcRjWVXFCEQWsU3nVjeJwsV0CMghAH/W2qR30VVEqRtHwKb61UgiOMklHJ7RqKjVo0AKPHUrshI7bNHRq5RK5xkl7mjshO7JXVlKoCCwKNQGTjEsHIwV3LnQmNZyJgpjqiVmZ5kGgeFQc7vc1EAlf3JuUcs1eL64HqXyeo4dcRMeqpSr4BB+nFzEGQLIryJGm2XLgJZLUNT7uT+/9MUyttUe1brKooLdIB0ZkP5d1dM0oqROXedF1jRlUYGLUfl79V9vHJfrByneTb9/Nm3kUHL1nsSC2yg1bpsd83vNSRCqV9U6qA3RmD413te8c7RDKHqQLSUOhpWrUG3cG4LX1yZWnSPx9CpY6Ye95Hz4BQytznl2aLNUTgN1XeGqy6oiUgV0dkFTeTTK/eHu74yj5uSbZKmildeligNrQX92U7e3/XJY922xdlF4PS/KYZxQkq5YUO2JSrASNZGdSEHrClCPUP2DZ/RyOt3c3FSUNE+SVIZGd/DsQ7OppOEU17FUl7IepQRWuti3yeWmGQwStKM2K9pXgXKM8onrvIuvyyPLwCTZW0bFOag0B0x6ilFRKtLY//4P3/6///t/8fbu8Muf//x492a3b+umgZ6fo049v1CDU9rfUr+1fqhqDv5rlR935swIzbcwRPEe+W85iwdQSr2pEVx08tySBdfxii6TOIerXB6f61hj9loef8tZOYlTLbobR+9L2qEg+qUi4WsUqOmJyeg3LozG/O/xCq7p1BjlFWybGrC/fmBpRg4dUpY10MiSEKaXmIEM8/Vi3Tuvg+WwPS8jXw0kua0NEf9E+TG9dl6yYR7btkHjhctIrdrKvK6lyA7dmbKG2WbWjm95XkEWsfZsPU4C+tl5CWUuKktEqDCYh2nYcHoigl04dg7VbmRKM6UHFZN1YKA0/LyomhpagKQZIt1PmoMuOWDqBKRGi2MKHEwT8iKRF7VMj09P//bf/Ns/+7t/Z576Dx8/9OP42eefgbGiTSGdRGE7tQxnml9Ol5eXy919Sytkjq90d2K91AnR+IJA+HoH8pQAuh1Mpas2bTgg6Tm5cnqvxIlWRJHSODHhINiDSyu9DTf+02ZxCnXnIGrsW4yEjihrDj/h4CrG438o4Qbtdc2wtl9W3LQq0Jx2Gidq/KXR3zA5kaEIggaMjqiwEjcNTsVNTrn9eD/28Lj0SJkRSbouLQv6iVG2e9+Pu13Z1NUwzqRElg/PL59XtxxNJRQHvgZFdJqoNTbRssCNFfus//nDJ9URgE9b8rmYciIL9Qf7cdOAufp9Bq1Vc4lY0hZ53g94MeQDkpzWt2katDIFWVNqEfR7igyRs1iD1ZLLAxYwIqUIQ5ugd/zv/tP/5Pn0/PT8+PH9x+5y7pHu3841MZliRtqVuMhKziWbx/xvfvPrXxT5zd0ddZaiuJwTsT9EkrhPNzd2IQLmIH18Ck0q0h8xmOS1blvhFKxGEL6pMjNSEvQv96bXF5yKHBxg4KLBsBFMjGkJm5kXkcq8GqD+Gozr116x32/N81LKUO8WESjXVZ47ymhhI0kbukoyGPfiNOacep2isWnhkFR3SD11W9O2UvPqvfVpr/iBhn5FC7dbWY8ThO/nn9399g/fLxQLA+gZUCnUc9eTmaMw1Yw6b8xkhWqd+NVk3TAl4lhSqU7FNYs1C/ioI8gJ3FH7PxqpDyP4IjmaGe2BYaFtCnuRtcYlOalQdLYf+rqCOkw1eCgDjpYFrWEW1B05vZzP51M2jfu27fu2oTIgFP6lugUlB4ipIJeoBwKinMbx17/+9c9++pO7uzcEhcFJ3TJPg+z15/3KoTNKJR0n5r/xmYjuHmvmqWIYtztaKY6xAh3ZbtHAr33Dn0JzNPNDZ7j1Pe1IpYUtaX9tXV75KFqL/FPfiyh2pS4JiklOLDS6qjJgVghlWsjXDEYCZbnokVOYvNmhqxsDA3lM0VvxAPVRNgEJvHA4CO8R3iibxilv0By4Qov6+qdff/Hb338z5cArkZtGsiMJerH0XUceHnhJ5aGqPWvWfVArGNKsWyaAECZIAWOwsc1slXFRdkDoxR9BnwECW337VOyKfWBlUdTke4ApbiWm6RfOWxrGoZum/X5v1bOAnUYrCYj58+V0Ob18/vkXbdvc3d6RyVBT8aAB1QYWkudUo1m7BghfnIfhr//mb37+s+zu7k5IzdpNBq/8J8WvbU0iVBBG4vPjjaSw97L5sTzyQlRd5NHTNttP24KvdFo3SDeA2J7zUoQvdJ0aJH5HzJvD76qrumw8FyZzlte1ldNZKF7YVG2/RGsmsfVytnw4dPyTPJ0Vlz/ld0ifwIO4BIwCGFd3DAONXQcb8zFnz1pl886UlBVJR3UyRgXwv0zcJqhFou9SV+VXn7/97uPzMIwvlw4tC9UhlC/5+dId93vuEuzGIuBuOKjZW6bVocd5uFy6/nLOlqxu9nXTVKSfM6VNHCfhfeXkHzl1C8lS7j/KrNAWBi+ZmcEZySopmcSojiSXp0VwvqyoiMBYafPEeUF7cb5XPi/Pz8/v3r5rqra8qanQCmJKnEfsm7bQU6GPF0gfBkirnLPf/u53Sz7f3bwhqEuyC/br1nmMAFz2BDO5Fo5ohcNg5yOKzXkmsNps4/CR4A0pNNFX4iBG3L5wZVWuwRZ2EJhFqyjSiD0D4MRqFKXLGEW6ND+E74WzH/jkD7WQrz4lZEZyFGJi16U+wsD2gVGKTWzmgzczNonzivHwStmUhMVK9KqAZqg1QtGMgZpoFnXFSTm7/e5tXnzz/uHj40tbFuM4om0DVYrsh/6430uXQPVQMNW50v5kBhEE4un56S/+8lfd+fLl55/d3N8fDzdL05SVIJl5dILo8CcrD0q1HVxEi4x81fdrtE1wSJK9MiJSDMjhT82+a4p0wL/FrCHPiroq5wn5FVwhhJqe4X/S/JAq/Kjj3vFEbpOOQiDzMo6/+c3v/uSXdd3uOS+KQdU+lLC1kYm4Na1MXog5s7axbb5tbfHWoVeyixTp/G+rrqqdGxFvZDqLUDRNQFmHYW31EneTZBqhxGQ0pViD2DIRwm8bnVVffTGLsT/EvJPitmhpWcKhzudNkoBjCnQLv7l4QUe2ddnGmvLhMuXFvbcasCq8ACOhYRAWFsmyQ99WihQuEWHaH9rbft89X6Z+WJB8wpn82fPL6d39ParDUXcVmTTdlOES5lyeRnS4n+bs3f3b5X5qdy1COPDjzkgFUqsiafMjDrzFEp3MTMY/1MUXBWLJja07Zc0x8ILrSS0GCtqZxs7+0QKGC+nb5eHm5rMvv+j7U7vbcTk9rllL6BKrh6mKKvsvtJDuAvAR6vsVS3a69DfwgwMRvLJgIgvV7RkN0UtRdyaoP7C1DPAfbSpc28bqFTDP6lFbEoG1k0gT3STuWGmPS2PJLFVScY0z9qdsHmOPcN5meq+8HX34txbIMTdlVkPAShyoaeJ0PLuAGt9oxnoIlsX5h5G64rT6yCkQ0Xei96csIfAXceqziot/2qbO5qmFN4jz4MW//Pln9++L/EKIh7rEl4ahf3h4nL76SntTWHMZbYqiVXYsO7+tmsPx2LZ107bIvycC4REpNsuV11kdsIKulrwgxXON4zSOI5qzoAoP6+5MXZxwjdg034MAM+ApnMJB5QHIxbVkiHvn5fF4iwgyXFwiLqheDKGjWT9Xu51GZ1hswrTlOXxd4/j73/3h5z//WYmOh1zAcrUJW8fOLDm/U+bI53TCQJvR0Yi1X+Nj2zsfnYmNl0d/+FJN/GHkZRFJsYnPN0+ue9sPVVSmgO64PqaN6Kn9uRLM4Uaf9E1vPmalIckcCmo5z817rCYnw/HpfGtVRy9W04oO8lminqge/unx+rTQoHuZCcndFUgW9uOI0ZKXiQxUXFFUxedfvH3/zXvExhBEhdy9u7kxVLO28eEjrVOT8UJJhhcIEdQdPPNFNSxoCO6AHAZ9ZCnu+U/O5MNMS2q4S9cTBEoxRIrUIU1/nJoWYXVZHk6w0qoDfCvAPchJRleRLkQnqq6qjkr2kLyGGGadQCq3o12DSO+A1Wc/GaVkkn99Kcvl1J93yx6wVNEfXVzBm6kJ6ckuezknqyFY6PC+92ZtnWz/l255ms/3KWG8KWhlRYO+s3qQjjyWMBscS8jAX5BKJN1GeWMV/12t7tXXD6CP7fwTI0dC2FbjMGrJWCEdxg5JSFaPrXHbSJ1UxSmY0WFfkmGaBiL/OJSi5118oo0PAGKBHtbTsEzZPKCnWdHUJMF4qZe3b98+Pb2QYgpC+PrLL+umou5G5PWlEA0Xj7NQPJmjyO5Q/OZcllVd1fOQlYhcsRYfSFYYgA0uVqi4vDvcY92UzVNRFG1TzhMGME7Tvm2rqnl4fCBXdFHVKK/AWjOb2Vz8js0XdTRIKmSRF3WD+FkxIsGIKpZbJhVjS/nIzYrwjRRapmXkBo/T7373h9vDTbGTcqtpTDDdLjP83NuqFfFGJwSc3if6PVFdwgVWrsF0Hvcd73JwB8U9LXr+ctUrZ3Py2TMr2tMiNFZ0JzEllT2t9VCX1BfBmPPrDET5eISOiqR3qthEKi8DD/FWXZVTXfVdKOAigc1QK8MVTXF3417HW8lHMeuNSN4BCoJ6FVF4oh/JQc2ycV7aCv0SQHsgadKKqTRjXZU/++nXf/zm/TQteV7f3t6w5NH8H+qgwF4oySjUrvJSoWpBW2C4iIo2b0T51G+HgSadqxfbfAJOTdPD40tbFV999qaoqJ3CgrBw0aMxw93Nfh66h9PL+w+nS9eN41iWxWG/2x32+92ubVqkQGrseV4mLgkPOqZY2DiOFXituEw1SdHWjOlYs/a0YLCNn3HsFD4epRC8lKBaGYR2RsJxinfLKUlEwMumsudI7VUxJA2cto5PXAtGhuSp1x/0gBje+lR5QCSYt3R4naIZJzbONUOTDwwGFcgtLOpawsZ3CY7zKMXt1ZfYCPw7KwTg8dM8jX2vXlp1owbtaUWoamhdczFE89e3E4iq/kj5VFBVqDIZ99sjxIl0KmBLmDqcEEZhIYdcVcKVRbYlcP2sebMiLP16lQtTpSsKGqDPPYRJhfCMhK84oCo9lzSxzOub2pd0IccyaiSM2ds3N5+/fdOCEouxn/q+o0rR+f5w/Ozzd8fD4fZw+Pxy/vjw+Ifvv3//4ePpfHl4eOr7HkV8Dvsbeh2Px3bXtACK5kVJDdYU+hle7ISTs29qvhgNCFW5lE1UIyVvF9SKDpXj23afV6j1FZTuoNbrBlgF8ESTdruPGmDuWKxk1Q96hXKHr5uHArxwQlB3whnHqSYbjzjV4f1xjrRdV2bk6pB4yEoe6eM2v2MfrVVir7nEt9vIzlAuGX1SAlzZoGxiHuqDUzUhSLR0TI4FrJWm1O6JjZyw4ImKEoarvMk8v0wtfD07c9C2V0tqsLNLqnYwXylm5LjzYKyPBYddnLXOoAbK9ANVCENgKIi4uCwKJGMNVoJUgRyHqWnqn3/9dVPXdVOU5CGuynoYxo8PH+dxyPfV8Xg87Nq6LOq22e+bd2/ufvrjrx4fn/7w7fe/+eMfv3v/sb/0Hz4+fPj4UJRFUzVtU3/27s1XX35VtzCaqcIkMisRtAHEy9pNiqUrYE9UpZ/6YWTFnFcfGBIAwcSPUBTlr371m//g7++bcu9gAaa0pjHV1dnxu4zlqNIjK17OYIzHh+ZqXWhFOyQPZVermXvpUbs6tmtiXw6umnGxsu4q8L7WRdkEjNMFVnLKLYj9/spybpL9FvFuXCx+Uyr+Pk01OoMJgXN7wBAj8+zarcZKgVbFzEclI17ujeMoQSIZJEd8tEMIqq5TDQrx18BZxNVe+SSPnHco3EGqRYVWwFoCw8kQqZco+b9cFJ3QX1IYwI1aKmDo73AXLxCbb764raty19ZFmddVWRXMWYoiq/btrjjsSqpTWXFLYej7sFN2TVO/ffvu/vanX735d3/517/5/R8fnjG5acm6ri/y7NJ137z/Pl+Wz9693WX7ZZ677kIF+huAtaX3ImcikiZMBNtd+rzMh66fYNvjrbqkwvCcuYUmlbgalbKoUqMhpZyU8v1fXzmh8j7VxAqGBH8pKI/xGfM62dZhdmzEX2Ao26sk5U5PvmnCe6fAFmFrdILPg7MRtaLJFkNLXfNbg9oW4T4DyH3BqmrL15O7bQlRVU1oVtwJra8G13bU6vQaPm/d4DdePt35jWVarqo2aQ1eN1Xu8sLhXU61g6Y4zUu1UKohNRAl4+7D++dxQgbCNANCDD2S4iVOY6cd0XUKbm6te2MMh7KCJKbA4Vzu980FqKqyur+7uTke6qpoqqJtasCnKq20Q0pukWfHA6AXfEMqpMHtQ8mKBVQDFV7ub9/+x//g5n/59//etMyX7vzh4fnXv/vmj999GMb5vqyHvnv//kNTPx8PB2QXTlNJuRCQxtRNhiSt4bTncZyaohz6Iaf2Tv00NmW126FDWlWiOiDSKivUmja0oLDI1w3Va7biklUC8rEyorqC3gZMfJVrHW11juJXiur4pH7+CZPby/toSlo1XH63vDwdn7cfbHKvvHzZkJQPbusHQZO/hrlYcwNSKMQXxzgf0x51VhFlXtXg/Ueui/hKW9+ceXILKU4RCmgSJezrFnBiXWNgj6fl0o9odDZnQzdw3iM1NptyZOppA2+LG6+ezEauuaqo6i7QSzn6kwAUhTrLlNk3jfOubb549+aw39V1BarFv4BcGsfTwA7u11ScY8TngZstmfNY2TDAV7t9A5nf7Pa3t28/e/vut7//9tv3H5pd1fWXfFram6aqq6aucQ+U0qVauuOEZFzRlOHW64d+Wsaun+/fvsnyopvm/+af/tefvX3zd37xi8PumLWIRXEzVOrcyMU8wpZqsPeHv6iwu8BKva9k+2j498yw+gGkEInl6PprduanUgvswys6thRmtQT4WNP+dOLCpx+8/frU4m8az/bDFFwcyrI8K8MJslVyJe1id9tN8JVk50U28qem7tc26iFGuw6pk5dlW2V79Fssvvv43KEFYbQC1F50YCkHE57KSgUCVSe3gkfDo5RtgYZReoUcvuMwXLr+fD5XVfXVu7d1Xb253+8boLUKNqmJNFVx1IQQUh24/IFmQFP+BNWHl5RdwlvSF6iGguZwHI+Hz97eZXl+8+aubdpv/vDH8/nUNnXT7rrLi6QKghmAHVAhP3yvyItxGPpzh+jUsuwPu3/6X/3f/tv/5r/7+kdf7ff7X/74p0uJNnAL5y1aJ0W3cOGPTY68Pk7s0DcV+rW99YH+yOyMH6IlhdIjsWFJv/ZSA9ereNERtN/SWEh0taySe95r7dI3X387fqhwZoWSJLdKVZdUk0HePF5lUbZNy8fXL6qV72b2mWhBVs8pGpJQzMasXfEnb+bafezRPFDtHpQVI1VyL8ryw8OpG+CpEses9jCnjkQw46XDJ7mUWZGmqiIq4Z2iZ047Gau0GQZG+uV0enx6qIrs3d3xR1++++qLd7u20Tp3C4JHEzp7ChIGQAtuNipoaIOA0GOCc5NHrWU8MiAnNZyVZ9nd7e3tzaHK8rubmwpp2+PlcqnQ2kpuy/knkz6VDX6q2181bTtP0//47/6nP//zf9f351/95V89fP9+Gi6owELVZ5V52G4Z44mtIj29bmPjk0U/4SVfGWghkmLXXnNeuZr+V2Mo6y9upOFFBZvzH5aZy0825MmmA1uH5Kd9TdGPBxU04Stf8QC4jYFFOgeLgEifXbtzqPYFnXmkCjfDMDASS7Mu4+e4ZgYiKTU957oxFVkAfpTpN/whkRRADu8U0zw+X8bPUNZrHvqBSsUJlQTAH7UsMFtd0wrUHlFSl9VQUITZCoQiLeZp+vjw0PdDOee//NmPj+gODJfNNI45KkQisVExCBbFUSmfI4Ve0kwtzmMXoQhfARgk+gfysDRwx1RRlsfD/uV8gVOMvE2n82m/O0joSJgEkTHyDSlWgLpD1e3dnWRJTct46adhLMvydL4M01TNcyOuDIZzaon44Pw1Br0pZlTTjrdIm51uXO/a6zod1Lhm2ps4EpPpb2sZsKWap58kqVdrEepcN9IwfN390KS5/HmldYPnH/b0eOpXR5wM+vrV0UpE1GueECWHBlGRmsceqmNc2eFklfyfXsxe/4ou0Mo+FRijepHhWZ2z87l//+1T3w3CNzTWw1OYIAelWZG6n4OCIEdfirN7m0blkZRll5oeX767/Xt/9vPjAQEiSqYYkPELkDOJXe6bQNqwSP9IcbOMLhHxjJS3ws9SLEAXlj6h1M482+12cEZk2b5t5zm7nC+yPySAuVw5Z1wIclMZB00g++zdmwLtKIFFe3x+Bi9oYABP04yWi7ytScllp+auz46pYckuUcDNCmlu325ju5MHfPqD6NytrzJmsRahYQLXB7PJIrYfEH1nRe3KCKTyS9o7ZntWKvfsBv6pm79fuZWeeEbIo1ArEuINQxDtnwq9K2u+9eZG8cqt60K0Jvk6EnMqRELHkagI8gvEyB9z/TctKhdQVKpiK6VRvj3LrxjzqutHrxqI67Guy10DpkFl0tG2d5pHyhZaoLvO2px7tbnMJgRyEcLZXPQHVjohyQINhy0ijsTtv3e7usyWm8Metdqpxi6qzLL+EFAdNggGlsByGKf5++8+3hxvvvryq3Z//Mu//NXz8yPzF4qKgIgX9mN9as+iNwMcLyhI7HlfqeDqvLx+f9V2kk1Or4lNrZWcujbmTXLVGW/7rlKDXP6KQ8VXHvfKn6+/tvV6/59BMkQXpW9t+Jt0ewT6S3Wz6rpEfwPJO/URB6eGbw5kwwb3vNAJ9PBLIrfXsl7BkIAlUkhTk2BF7nAbNoRnVecOL1AMNa0fprHrzn03UO1Xll2BIqTWLjWCO537qeupGW85TZDGkvaNhyj1LvTE+Cg6vJRNJxToY5WC+5KpHGaktv7BNSVRQ6umj4q2RavDeUa9jnEktTt6EMXZwo4g9+inP/7yP/qH/+CLLz/7k1/+5B/8/T99e3eLLEIaYpFl5+48oW+DK5V97djlWzqcU/fhhda5RUnGAhJb7aszR6Nokz8oV4RNElMNR/Dq4LkkhfsaW3lbycbmhPc2QtxN68pzXnGGhytem1XCFf2baheuSy1sckgt7Mb4/pzgwahyTAUToS1Jly0tprwKr2viLN8vJBEq9fp6RjrNmFkmCm2Yiea3T7AeK7ieUbk6pAkVJSMio6pXLHY4J+njw+Mfv/3u8eHh7c3dj77+qoGOSs0ECVHtHd/0R3E5n+/2O4CPp3nKF6nvTldq59nCDNwwBZatxu9oUbQCtTJHMBTKGRQSIiyGckfpxEQ1rI7HAzT6soK3ahrvbu7Op5OawFyGgZ9IaQwUfC6R+AHF9u/93T/7+ouvnk7PbbO7u7sRtNaStdX+t7/55vbuvtnvcHHkEbh2BMOeJW9bMsOG+sxFaQTq7ACP12nUnRe7WJ4c0VY6qFc0TJ/CEODAvhxpdJtA6vzUCJ+1Od50RdK8vNeHFz063C2ikgTX6r1i60vZMUOCABkCdTXO9eU02sx18oaBD/N1VQ3CrKLlWA/36mSc3q4fcJ5Rni/tDo2zo4J0+l1Li+WMWArM4u9vv/u+ag8/+vrHP/riS6rcKCknQQaoH4ujYlCQp6lB2XLKIgByk9tq+y3JWcFhLzCLz2Bf+E7EHGg2PYii2jMtYSg8SyoEcUBougwFzfOlbqrDbjfe3j0+PrT7pijLaZzIPBZDnaNS4AvmhCvhjSyK8s39m/v7e5b2wzyVKEEnlQOggTDUDpG2LZtFKef16HCAUqqT0Ku7cvKCkzOl4dj8i06i++s1S3JF6+lJunrn9A6ealdvR4J/y4FmPlSPl/90MuHf5hUTeqgc7ZI4GINEPh4WdnnRtu00TmPfqfHFuxHSWLxYjZ60RbCpPvWJIRtPEGEGeZiVbVMuI9d356vo9BIIAxmz1FOCGQBbhi/d6V/+6/+h6y4//tGPf/mzX+zbPUq9c+QJOH8ZjeZRYtGp3GiG3uF0J6o9xPWx5IH+3E4kERlfEnpDyDWilJitaO5qLvvOCV/nS7ff7ygpI8+oGZKU88iWtq2ybHc+n2v0x8yKshq6i+wD7xbc2Vzhg7P8dQzCKNg1pvwFuGhyGKA80Si5HTypLc3nFW7LUQjKS9zY+Suu7PTeSfQx0uVT08RfErliHK07iygd1OY4Nmla27Ve/9bGa/WNYL+uJ/HKK3LI6I0sIB1dt3pDfLZ0IuEaonTUsqx2yGWthdSDNHe3vD6+xCRP5htbwa/MhxtmUpc+qLzcpk+/aEZZuJv8xWrr5XT+/W/+5ld//ef/7P/xT7/77puKGrJQNlM0LPYqidOOFqNG/RnqSIRqehxs0uy8MLSCCt9wglSo3aH/yoiCF8teZNBz2UrqHYHfuMcCZVVRTRDKNdrvkXHYNm3X9fe3dwI3UaU7uMF1vRSoYfJLvGXq1UNvIG6ays0kqMT1leXXRk7JhsgiZyi5JcpytGmuxDBf7CktULkaGdshpegVLo/I3SeXB4EpxrjX37ZJLJ1UuMPGmf0Br1ifjV6vUPLah57Yw/G3nPhYzYLXmfp5BmWPQYM5lSzeEKjbEaKrk/dIGOfWVj+aGsVaHJqUZvaKsEd3zoB8Um1RDQP8n7MC+ZBaS9CqQW2NcZyfH176y/D9h48d6reiP47NO/jiNKIGiYrOacBjUed7IfawVmbT5kS6ol0HOSvkyd4CgfUoqtMlVzA7rGpgitntI01SwBa4OxuK3d0ej7vDfprntq1rKkOr8SMlQov3GWrEYgQcQpcSeFTVoCiQNU2gUZ/KutpEx/iNNblThrG5c5ce0dTfFL2csRFDpgJILpBmKqK3DpfzkK+V8MABVtO8qmgoBSdfDLk1P4C4f9gltgV2yyv+eXP5qK8pRDiSiXLZJvoOVTCtkJ7OLQuDpPP5OZ8Yn7s0/dhkaPIb0bAyWatlle13rTm2rNgjDwx8hy5mXzQ9EY6iv/nt7+pm31T7sqgeHh77oYOFqxM0y93kNrt2D/vd8bhvGgY7S+YeXxhyEzOhWXLXawV4qeHD+RFRwTe3FQIc4F+JohBbVgktjdG4ndOSTU1T7ff7tm2XItvtDwzFCosYkTG1Bud1E2S+DIh8dpyi1Pz+t98ohCRl9xs7GD4NZiw/LC7bq5epANywbe0vuw/PYPMIfUIWXHmxm994ezyylPCuhYATdeAHjuEVv1WQElef5LtKBQyD/hf6igRZm9wwFcscWISaCLaPbteRNq5fjBxVEVJEzxC1Y4njwJ5iV1wu0o35T/pd4cPsrzHsF8d359P5THl2Ipd5utM4f/X556fzy5TNXd/VVUWNxrh7mSnMFj8ORxolIAuuekPCMKyLWRv04kQu+s2KXWoFgSjTMtLWY7mVA/yGvAizkKUUkGr6eZntDg3KdjTILuYbM6LDssWIqCUuHKLQtq5cGIxKYU458OHMJQIM+9prTdPOewK7fXV8Vt9L9n7r6rW5lWCYlCO456UKYKpFXLEnP/H79jh/iLyNbxK0e4P4mARc2bU6l/WIPKDNrIhYvw8CLsASzYzjg1CWZUu4DjFlwr1TkPUm4/uhsI5kJ3QYpguopzOwDFHECE+IZiJyhMn1zIHtJfvZj776X/3D/7Cp2x99/eMvvvy8bsCO6rqGnakdJr2vk2mgaRr0WPWBZlchVuVoHmBeC+rRcRMzmOvsC3Q8Lyh2brVsE4lcOTIkbIERnSV1XcyLfIdehNW+aQ77/f5w4LhRCAbLKRFMGCM8VCrL6IEDJZw2sJwVCpsIoNIZjGsdbu2jsg3MM7QqTvbVppgWdkzqv6bOqRWfWDWBieW58K0wsuulpF1AUy6OQ8EhviLPEU3rqiNZwyyfTICM/UQOSrZdHNoxfFX2HOg3UqQjrhUkKscVuE+JCyLjAlRhQ8b/oMlgYeZhWPbo2Ibx66OLtV1iyO2I/M8EI88O/uGyyEZugCKIXk3Jp6aeWdb1fVlSyTtuK1FU/9v/zX/y9/70T4uyPt4d2rKR6tQOPh2S0tGvDHzgeHugRrtBjzGWpQmKBVu5XEfSCoOgjOu8oKvgPIFNUAgqaIpasMLMFNR5N9eZ30vSrUuqJbRQ3bLjfj8MQ3FosrL87rvv0FdlRuyXjXa45cWRrXFoifehog6BTLnEAZAeX3/907KqqW2hD5r7/UwFoKYMOkMiyyrp9RDHjZx0jNSzzeMeS63oyW44EY2lqjkb/X71oieE79pv2m7EnuA00/UtIvYQ9A83eitaulq0rWmGG0TvbgTIxRUUf8C63hpZ6heKv+Pvho4Idd13HVU0doc+ZggJzXqPh3CDKxOKl0RCWyLHQj18bgpJFGPKCGKqS5kV0zhSkl/157/+/d/52Y8boBsAtueT9fbNOzJT6T7kkZUHOc+BlonCbdH7QPzLdPxDNjHrxmbxSqNoabFQoAbl0PXQ3jHWnq9VY14aRcSmBtz+qm4LZ2c1nTgCkT/9tt/vJlQsKA75/sdff/3tt9+eL2cgSjMtY82Uijlo3UllBSBdikuVRTGN8/6wn4YRnYDEOIhOctiTVAQqIRL34zYk0XGLJIKqhTFT3tz89VuRi/Oqnf0DXjGDMB/p6oZbX/MXu4PrhxWwRNGXr4/cf9M07ETcpYO8qlu4tMeQOyaRfVGWTU+FJQx5oqWhghffdQbwc3xl7FeCFS4E7rNM1dK0EhnhGeSKRVSTEIgokZsXxfcfnpFKRWWkSKOe+mmYUNxLagOoEElMFvmTUVBN2wDDiBQArzkHmsiV4tTgJZOVS14SiO2w3zdN8/zygjw+c0S7PWEGANk4zehHOo2XrkeFfX2Sq6YJUquq6ubmSD7F4u7m7he/+MXxeJst6ELI2Gz+R/ChCv9SzWWZpzGf86qof/HLXyKsQD7wWHRc05fDBWYzsauM+gjFx1AOH6kCqw2PlOLUZn3F07NB368Zas6W3ohfGnkkZnB4V2YUuWacBL4aEkpt02gkia8hyPuQDeLRAuHnSuOI7uE+YWLRKjDrb1QV6rMA0iBCRLD0ihvYnlE07/UYRAqqPacEagvt3DpKPFLWwgxAKpU6jTlqSs1ZPt/fHuclHweAOkDB4v0g6UwmZdhE54o2/HJRlof9/rDb1U2j/T4Dg7KeRgs1eOHvc6kdwB2HYZ7muqkZUE6UiRwmf5AC2kD9DuQmo64LWUYYqRCNEvagdEwsFMUMljw77PZ/55e/+PJHXxVl1Q8jWAAqkaB+CPM14XSqsZN5VL377F3d1OzEXpczS/YmeEsSB5IuWRUBsPRQOoXZ9Klt/XR9GleuK7PTrI6HCI1PoMReKZ2xwa5Yr/afG8YrsYO97p3ozWF+enQDW0j040R2rMYUbw0bL4Y0jALvDEKPbqrRFCektDRQhZBwPaASKq7nwnEBLxdcGCmZyiOTkH+0gJ5yNetdtzAIYY73MvRQh031JKt+GJbTVLbNP/wHf+90eclI7klBHtJyOdFHnWFhujQHNcoX1JU8HPYowsrp0Aah1TAxD2aelxFFqqTyjs9kYiTnuevef/iYZ9lhD/GvtdiTvcZEqZcSqfbUNDTgzO0Is0cIBF00dXm6XPJiKXb1vmi++vyLuq7//C//sh9GdCqdi3lCH0foSmgMQY4xHndZcRyuouwJQbJEldSSHdHnr98W0CmSGWLr3XueYl7gG1V6Wt3WZr1layxCXDNbI7pOyFroKrkiJqbUgkjuqC6hwIbWJBlxqLUa7KlJyIWPm33JWGFiuFh/k0CnrgB2PFejZi+m4rsB2TCNiFtSkFJLtNhDV8n/6TZ65hYG5KRTkI6GKXJRV8IuBoHIdWrg9Z2ObTPMQw+iAhXNGVL/qE+YNI/U5EE5gf6B5q2dKQ3ocDhwzhG/Gf41AIeKg3Ec2L0XnA3sfSqK/W7/9h6Rnn3bMMyzWsgadk18o8XWd6j+qzcodHB0aqgv4dJdLrt239TNPE/9AEv7w4f3NzfH3W7XoEyG0/yQUEDFKJflMpyquqkqCE7qjxSfPyczVxufMmSWyxr40n286gp24PjtorNBsm6dG//kQCnLNTIN3MJ4RuyKXpcmcKsQU6fpls5ZHYVj4+/GZPBqBS3yGlO/TL+KQYzz/Zn5Gno50t6EINJACuIZXBKd2wXrtUDWgn6rubKSMb4gSVgOnnCs4wRllX6E1fCarCdmOssQj6jlJPKP8Q9SbMyYAHpgz9P+5qYcuhG1cFCkikpZKN9Sbq7RB+rNTp9QsMcqY0CYH3Y7NM1mytb0JKcVstQixxFAURR24jaEWlsA6UHUdLtp6n4Y+mEoyzkrC/irIWtD9MhYcdgVBGlZNga8j50+zqw67PePT88oNFuWfd9/fP9QlcVh115O56HvAfnYtdRPBQYFOlr0PQrZlsX9519QirdZ32F/nLjd0pLs6Gggj78PvhVQ6fLpWiBeD/Gk16nmvSH19fxE5uj216/Y0jFtrL6c/hrW3nSCDWG9OZJ4zHY7bXXL/1HtBepQxz1CQgcjep520HR34P4aV9XcNPDGWAh2gXJZSHJbovjxVEzoHu3IcxNpFVOvXSFEZwzFang6SBbdkIs/IQ5NnYR45lIIi4pHGr3nC0ktjg+gHL2cMVUfufROWA7GMVk5CzkW81zW1fFwxN+UT2CeJ69TsGqeByeTzME1OaAcoQwZA+8/PhTZsj8eblHwvUQeUmp7Eg8OfeolB8/2hBcBFryykqqq9rvdy+WSFSWbvMebY11V50tHvY6fp2k6HCm2nM95iYzitmmaZg8vHqc8pQcgSKrN/bM/nbWF/1ecO35dyoRnhOeJI/6Vy9fy2T7hY5omCNmPVSrR6/r19hNWf5van+rJ9kpMWPndpD4LHaVQ8S3C5Vmwci7F0SikEAR+gSZdYuGt7ipbYCan3pfdl6YmWNI4Ieux+NSzlpD2yEgLQAubcaJJiUg2hKtqhFaOTomXy9wo6JIT1QMJIT9O75DDWaUJC7I29N9EZjlP3+Eimd4YWixHlQNUXNRR8A9z1tT13Ztb2Ah9T0apbVosA8ipPGsRjPgs4dM8m8gvVTy/nP7wx2/ef/gwjMOf/t0/+cnXX39296aouPcFd05UXmxqIMeo5XHaWzFHKex+GjKu2J4XdVUX+XA6naZ5aeoSCsj+0LbtSHn/Hz9+fH5+ye+Qh2EtVPqub5FgLM4F6fK2cVSjN9en0vYTMFuOQgdP1nY6bPwckxTXCOt1gnv1Uzt8uoBb03D6vBMnG/eNL9x6+LVlU4tVE9jkEeJdFgg5tZkcSHcr0PAO+PtpWajeDNB3C0oZT8vMyqSQR8J4Qz0lYv2UnGLjML2OGgjPhKQl1gEknrTEo95FunbBkFvWurGSJ/MaLWlh3wpx4Ni0YZpgAUuiaX849OfTOIyerNgjPkrhRb4PNNWBMucKtDI38SgUy9Kd0xDxY5x+/rOvP//sXbZkpxd8i6vVuNpR4gc3oi9ogK7WM0tMQvmXCGv9/g/f/vVf/sWHjw/zvDx+fPruZ9/9r/+jf/jm/o0svPQSNKKAQ2EtHYXW87wqKqrtA9lO2lD+9PRSVFXT1s/P3b49ZHlbzlNTN1VZfv/9hw/fv//s3TuK/8FOb1BbusxR+YA4hZPE5pqwH58Uq8ze4u6E6Rn28n2bPBRpZx7E63rpFdJJ9TxlH7rZKjW1gYyX0K/PcP2sDUtVLNQNLue0YbVW+W+pfYSzNI9TUTVFPp77rujzYrdHJOF0Gov5fn+XLVnXD1mVFWXN9R8c3tdBFoIaKhqbSF4lYI0Wz5PUkICftizLnsSCmODmIjbBKssX7i/1T0MlWI1XBsXAxVxlKXj+yiOQ0SpKmDXcE6cIjbSp6667EPZDdpGLqRJlQHGICFiipigRP4zj5/f3x91uoZ5o2m/UcKtGUtKclOk+IyUaXdc0J56xTyht2w8vQ/fNt3+siqIuyzFfuv7yu9//4ZuffH13e4v8QfVmK95t4zjx7IOEKNC4ihoyMg1S78Ki2O+PXJMfb4zLlFXtbv/2Xfb0/PTx8fGInKps1+7KIru5RWlL7X2dCODoZG4YQ17bVqZWkRmz0V8nPc7R71GlqSvC+oeovVuKtIqBuOX3DybVDaXDuJUnR7s0yN8r9nHk5qOCpeJ3Kca8h2y9vP/j+//q//5f/n/+5b96eDydTs9Llt3cvPk//h/+9+/evpnnqtIqakK3pqEFJG2QmwSvCS8TgbICdGyYxigfHYUaOX/AHUf/dVVnLMrrq85Z7l6Y75rTEb9ibRYecMYklgR3FnNeKlqwSVaWZw79wp9MBQBI1NIS4E2jXyqPjkuHeXj//funx5fjnzZUHBvAD3Zf2yIE2UDKM6cSFTq1aZwuXUc9eMHmyrzsu36ex2/ffzw9PtR1WbftMowEISmeH08jfFqUm5nOlgs2O91FfRYhWGOhfWjqCAvjirI4Hm648F4BzxWWqml371p442qkYjVtuxun6Xh3K8hMC0qkNY38UU+OcdgduwhQSlUQt0khfS8xuXQI+ScJaZOy4sEG384nX9fZhhjogZEFqoxN60gemw6jwizoVea4IssJZReyfMoRSqmypXv45vRf/hf/13/+3/3zjx8fcULG82XOns+Xj5fLPdosFGVFirHQ4gqz5WK+QtPsM9Fqh3aILXlONwwvhjoRwtZCPaYtm3mtBObowZsWIeZ5ZVFDt3lNtWUi5DcUdikycxwG0g/RRnjsphxVryCn5qWEcsFZPGxC58vL08tvf/ebrCjqqnn7+du8KKZ5gmLDBWMt5ZDlrWGnNPVoIfV+GKHM98NAgCn42443lCuRF+eXx8fnp58cbu7u7sq6bNr9uzdvWB/2mbZyNsmnoT4HjgPoCpiaopTMWwP5z7X4LNzArssyr/KqKivK3EDvpnmavvzqqyIvIX4ZABnpzU7KyGp6Ynal4YLJjheEfko4trWhmaPES8Qai+jFnvjDPNWRD9p19/Ricc139LfY8o395S7y7D1hnm+YJpyO1d0sELqrAy4iDJUYF0Tj5+I0zYdd3X94+Sf/+T/5r//ZP316eqqbpqqLpajarBzR/gtGI5adm4Nl6BnNirGeHR6lrYEQNiBFlFbD9Wls5xy0QApHGb5CxS4giMa4zDOmufXyl6YCCj2mix+zZ9W8Le5BUp3Eo995ydoAWHDsBwRmx6mHqVjkyzhWbZ3Dl2MchAus5o/vn371q19V6PpZ393e7Nt66kc0HUOTTrRbCPa5bYRMCKxgoZXlJmMlWIDYg8BTIi49t/vjL//kz5YF/T7rukU9zaI6HNpd24IHiSQPbFF1aeKFqg2JLePUV0YoQm9nfV3BcNK2gbaLAdxVXREjQVs18BeqpyU6ix5Re2mFJN9PQQ/finpo1NCLLIyUhP/za6f82mvt1UqxFE4o8pmKZMEPfCVKt8/+WWnNkfRXz7n2LV8HSH2CT9I2HTFHoo5lgCZWohX8Mj1+//R/+s/+s3/6z/7Z08sLqWTLtIw59Cli70NXUxIMqYvaMzPgca1FqoxPfAjsAuIQjuaVasw17Cer4FzoUFUoaSiaqEfmngorJZ44+90trbkb9B2vxNolnJs+T1Rqj5sm0SEfhjGbs93u8Hx6wu+sfBdFBXwScI4lA/CpMG0/DL/53W/O3fnmeFyW+e397TCMj+NLXuR93/HoOKGXk4Gt0KTKCsE7WrVHS8ydJvRkyiac8BbF8SmlqShBTtTygnMb3AYH/62rUk31jGgnxdPGkTOvFXDYGoo5LGqKZnGwQmQSqtKitWk19eOPvvyyreoFQDTCZvG2JaZoIpP9BuqfBtxiVQQNvnHeLCHVHdrwb2wkuV+UbJNTEzSBbT/TD3hzRdVOqL7CSzybkBq03kfh4B1217QvixOGGhUtIQrHAc7mvPn4/nHIhvnc/eN//I/+n//tP+/6vm5qbjJJZ6hiHE/fDeMwVSVahORqcUUZ3m7Qwe7SCujBjtUQQdAEDJJp0ToSSgEDm66iOgdTRdlSurwEkiKkBFCxEFKor8DxmxKuVG3lSnWhIAlQQKdZhuHx6RmlrAA5KZqmIXnILciIGebFNM9/8ze/vpwvt7e3P/ryKyCTsuyP37+vq6YosnmcOV2YUMfI5JGpimsLPjGxFhZhMQRCBjQNFa0q5NqKRlpaSzjwO3jv4Ukgz33IhWDHV1BpJaKmWq3ydHbC8x8AR5dl1mRZ34/zSNw9y54en0ZME3QLKCVdPE/ZOE3NrmWM5gq0Htl7Gwf6VREnrv1VWDn+zhVJfNXz4ehs++UvSCXnqwI5prXN4cW2boqXdCBXGbT+IQsbAZvB3akKGZltc14+nx7P4+kPv/3df/F/+Sf/7s//7WVA2tqyzFTSJR+nKc+LcVmqonw5XxD4YUAB+1sCKIPVP+VxGpYVknFhWn5DsuXtesl9ZYNGmbQtbLLOOtVXALfCvtWOEPluCVJBIEu7CmYp+L84m6lceVnkS33qLn13oTpedQn3DWrxUa5SRmuDBNu+63/z298+PDz84mc/u3tzX5f1NA55kY3znM9TCdoEhecwbLMSVinC3qrFI5mWinkQJkP8Zyj9R2ovl48WjUvha9wUhrITOX6uW6wqs7eVPBBa+25xcWv1QHKn8BFGwvj+48ff//6PaAuMKvCc5kjTrrUcH+nYX33xRVs1AdejjjmnK68PfiJ27UL9Mg0fuWnbZHZVyH1S493+5msM4vqN/bDTIxhkiteF7IdjISluRE1FpSqrGOaLTXDcYJ6WLJ/KvKj2N4/PL8tS/uav/ub//I//0Z//1V8U0qVZAEri40WgBRVmXk4n7Hm+jMtSzHO5UFaNhPC9g8GBn+xvvUiyWEiR9GGt4FamQyUg3YDqiJWwOCU5YZqRK9TOhj5JIQd+OaEH7/aH+dJNw8Dvsmfq0l3ef/9+t28pNNrmOZzVywgTFA6AaRqGfFqWb7/5tmmrH//0691hP0/LuAxo8EsD5QYJPC1uZsKQLU3fN50ZRDhOsH75VJCOjFK1lrHA6yYtSK3lAmO35wV5+jJvNCYkZcKh1c0/z6Y+2p2O8KQviMDnVdEN48enh1/9zW8/PHxcZqQuF9PcUPFbrsKHsC8g0/kI9jW3uxb9WUiV0LVN8fC8zpHbKlENg4bA72J6kPLSycMUs3gPU81WwdBregtsPKUydzaS8cZKrDdSUxke6+lOHIVnrKzgDZ5ga6JuIVOkBCUgPibtNVkW8zhMU1lePj71U/c//Q//4z/6J//4r379V3CrklHL5hmULkoHLfK5LOByfDmfgiSzPiBuGKwQujQC3gHxPxP221nhMlK7h4Lh+JT5OPL6WISdXBlC6WeynGadmxQLwp9ooCqqvC6XYQD4Ecim7OPz03fvv6dWA3O7O4DtUW1NzAV279R1XT8MddPc3d9TqSm4dqSmJJmPNXJ/gZ/kIVLdAII0h5iiOeJCPlOuETSx0EMWLhcPUKIH2Uv2OxM219ESwJmrl+TuKX59ONKzuSrroiqHcT6/XP7lv/hX3334Li+gZ8CuhnFdFWgvSroztH9QMnk0snkY2rYxJ+Qraqn/yPFN5s7ubRf5Jy80vNoanLLFWmm2wanrzpU/Fvy1hMFv6wXBfeMGvWG8XX1d0Q/CmbPpOG164+5Bf1ZKZvsTjkws+Ah0Vbn/+PA8TMO/+O//X//4P/8nf/2bX3M5Mj3aBJMiQ3CcJnSRJcXvfLlw/3cKhWLviqyYFuiFGpeSRgDGdwN6XsmRKjkEXhSok5C75CPKYVman1pq+ASbdrXYkflgjNhZHI6fCDnIQRZfnB6pAqUFkMi3lNnDy/Pj8zNOMaixhOEA+0PCZ5xUVAMO3KLEJK4Bio2rWBDD4orOFCsC5IvLTWnYXDQbiZNxVY+ZbNrCpfbjSVzlWYrmhfxHIm0Yx/+/yr78OZLjOrOqsq5uoBsDzj2cESmJNCWtHBu7EbsR6x92f9g/1bLkVYharkTSFA/dJmUFZYuSbR20ZIvHHJgB0AD6qCOrauN77+VV3RjaHRIGbFRXZ2Xmy3d973uShBLKWKDKY1cgiYu7XkMbG2WPSqOuVSopJpO6aU+Oz/7hl7/+9NNP4yTZ25tm0LeGhBYWM6WwSPcSTTVM7a7Vz959ljaBnDAOan2pKG9vdT+jYkSb1jm14upJuANAjWTMHfOXe6VyUO+UQOsp2wPFv/HTAJzj7ws+LrgW/4G9m7rgTNiPxI3YeECmLsWij5IkrRu9XF9UXfXBL379nddeu//oPqiesCPJHBoohk9iR62mdQwmY6VU1jQNbXPgZomyHCHYWJsnN6kXb9heeYAp7bJ5aT+bYuIu8DpVkqCbtkSyLLzH/p/Ldo0NZvy6AC7ggNgyoq1oopcyp/+iWvUO1FNQTknddRerNbYrQk7Iv3DE2EBOh2RQ1tng7R4zopjd0QFKGzhqbHJgvrtBKwTzucJemoEyTyw3WKEOjtRBykstUDManBSk9zviD2BFhaivjrsUsTekYQcykySwzP4tBeGoLRUdKQMqjduqrZvm9Pzs+Pj4o48/Pj46Xa2A0pnuT2Z7M6SJ4gFrjXwzD4/s5zRT6CqMQ6rT6GCR5YUGGAtnn0Cg/n2i62/4cLEkFxmjnDBcMh8LeYkMhora85cu+U7ZR1tXhH7aLsmUo2WXuxxIoC/5DtjKi2fdXH+cfqp43EMWUai2a4cGYDtVLuvN0He/ePdnr3z3b+4/egSeU3OdcVM5dNy1usU6oREWQhq6bcA+YSw6ViAoUhfGlnGJvSUz9Jn6WdH6iUiLK0FWM4X9hlaflDLl+idvUkSuRysSOLgjY9qLxHqr4J9wdK4h59nrWjPgZN00SRIj20mpUVdyZAw2Owkcd2oI/pklcZnlqBdCwXDUom13UsRp2zRx16WTAtFbYI+7qIuHNIHwUUIJ2BXNp1wckcmNBBVmAvpPYF+Yj66B3sYadFR+Ebe6BV98VNdojtg0bVNVy82mqqvVat3UzdHjxy3e3LSUzZ5MJmhBVha9HpbLTd20Qxxl4KkHmJkBrSSWRg2DwRt2NDxhdoB1de/uvSzNrNT9h0GFbgmD+JX0nOoHzn1vJyDMlt8ymo0oWpsrUO+XtMN2zur2+WC3mcEM+bb/Tq65S3Crl3xtSA0sqsooJ7e9LYSJED8II2eT6Wa97Pvm/b/7+be/+9rp4jTNEI3mYAsh9Q18gjxXkKnoLk37LEnKsoRixP3QSt5SQjNxKSUDSf5hH3M9jozO9OwwQGhTL+Plwuj0YTZzleZF2UVDPTQUZSGAhAnMWn/XtEkwNWLeanhBhMDmstMrhfOeSIoPqeK92X4HyCLqNYR7ju0IsbZDTU92LmSv6zdNnSbq57/4xcXi7PPPP5dl6QBOKTQKenR8fLHeqFa/9NUv3z68sj+/UpRZXkx01E6HfKmHCX7vImSMkMeC/KJeL9KMvu6Ri4Yyp5TSEMf10J0eP9w0dde0m4vVw8ePhiRanp3ff/SorRt0IYbFDntepWme5XkBAY3juGpqsNhN98oe8afZbD+OhrNFR4cIxarAdUmal5O9rIvREZZ+T+K+wz7Jy1xScpGkEsPXKNbke3/2As97DUxq7LrUgoAusV13EL6NCN/5piPX9/LXKCB86SWjjI7/VQ77tuOWnjK3TIPWifS+WTrYyrljwkwaZZ+TvIjns81qebw4+cnbb3/vnR88PjkpypwQ+YzgIXSkZGwl7NRqTSSjxJjR1LpqepS1E97QkBUYATYjRMiaPT3ojYRKYLmIx6OBsied59hLr8AEbT5Iv21Wfds0QsgsjTgtL7yvdLejEG7W7BbxCxJtT91gFVhOZSe5CLkNDpqvDj4IlU19vc4uVo8fPvzBD390sTynuJeU9mVpqqO+UNndDz74yksvzuYHRZk+OV11rf7f/+sv+jy9fuWZu7fu6l6fnjxe1g1X/sZAQ8ebdbVaXSzOTpMkvX//4z/88Y9Pjs7rur56eAAGnCEqCnVwZZYX6WZdnyzOn3vuuSIvuRNSksUIOxHQhIqcpJFaniFNkxVZkRRZqpYXFyy0XEzCMfOUCbWI6oi6SECSMTldfefWbfQxDHdeuEtHomt36w6Z8F9WryGBHmDFvMM+OJKf/hqrxEtF2Q9fuw2y46meciNrFbs3RrcQb1cgOqNJcqaCKdURTQlwHrrVqlWtk8XJ8cnpj975/utvvr1er/I861okM3l7ChGjAS2zBBOYQdzdi/XFqlpFyaDyNEZDdqONie3BwgR42NL4mXH5EHqkOhn+I3w5FnvlTBQ436zpMuI3RMRW67ppsf84n0FKUdxRu+beye5bz9bt3pp2TsJwjMb2H6KAEfZ9OjQNzQmn0oJyJvOE9uBAMpiYNFSWZ3lRICpNJbbczCCJE61TqNO6Pjs7OV4cn62Wx0+ePHp83Nbtj3764yLN/sd/+6//6atffXx69M4P3r04O0dmCt3ueTHiqzeeeeHFLx7M5uvl8sGnD5tmuH3z+t7+3sGVA6WSybRUKrk4W2w2Ta7KSTEx/dLgTouFzpmgOIk7NBlPWB6pvxwQXn2fxkBHZlkmqWj6FPQxaH3oTfLxO92naVFMJrxyJihgT9OdLuNONRpmAI2U8u4GtcCoXMvd7OmAitFXeMLh8QvZ/GPQP8kf7647jXT+aGjWMNzWulb2zbaxZc5Wg/B1tjCRwsNcPML823mWExFwW1frN15/9fs/+PFqvcpUpjUwknQngNeJ/7BHz2eqJZD90w+gEKdv0pv4g3/4DREmSZsCsFrwMCg96RWM216UeE+pZCgH8qZRPSOFhB7NP1cBeUYPbqnStNybYh1XMfhcGX2wG5zOcaNdCzFeDzFg2HDgDW6gYVKSnqQpeZxi7Y82kTGlBW5h4NPQX1me3Ll7Z76/37WNbtGMEe4qWN8Qlxu6frG4+N2Hf2hqvV6vCb4KANeqHxbr1aePH/3jB7/+9P79erNR8DMR6qYe4EleZl3bDINOs+zFL7wwoPYWPirFh5O8ADFG3XTxxfruvVuEkerpjMOycbCdbbXF2Xk5mZCVjlw/gNag1CffitjqEKYyrQz5uCQWa9bbVGfatfc+9zwzZpl9u93g3rN5PkPa3Cdtwo+hpGNWyn+Hi82RcPOJUJC848JS/uwemn8EhVe49E9g842u2PalPbizR6ThDDrLvWAzXbLzEpQYgfIiKVfnVZT3qk++8Vd//ZN3f7Jar5IYZAuctOHmHR0Q+xBY4lKRF9Gd41BE5bpSVVPFq8VGtwXAdrC0pOuHHCh8jPghCf4huQXYwJzqY+uUZM4pe877WoZLbgsSZ8k+anCqzQYMxwCg2AiUcSksBcU2ZjqYX9foQGZYdik2NDFSmoOFi2OpbNAGGOwiSToWwS3kYzh2EHXwMvo+atbNbDZfr1ZRVAO7zM9Mibd+ACc7GpGAqLkGPe0ARqtCqaZu8xSADYCPI+KbgibqkigpsvzzX3zuysFsAOZriBUsfI5EpxkODZXEy+Xy/PR8PpvTs1ArM1zCtDw2YQb8CcKNFg1CJwCSYThiqNOcVdcmc0SJX3YlCBMQwfDmlKRQVTtDWkBCIh3eUjxdir0l4xoZ5AJklMFxYC/fKXuurt1tPyty2zFMlqydLIxhdsrWpfGDXv4s9i9hJetuX9n8Yp9G0qU0oWSnEjMO+oWk8Fr69g+/+tV3X//ehx/+S9VUChoGFqmwy/QDeXHkxnk/2YTmhY4UYlbUAb6rNpu+nODG3EUWhwQlVFiDScWYod9mX5ixky7WLM9GaAJsEWtRb8UdEaYFcXSBpIVuG7zahloGMhwhMGTCGqWQI9K7NxIrlEM1Lrlbxzga9qbTerOqKtyaKjXkbDW+MId40Eebuopw6A4L0rbtF55/9ubNqxcXZ8998fkyV3meVVXz5PFx02oQIdS1StPJdLI4PxcxoHP0N//02xdfeL4sp0WRg4m5QXOTru+zNPnP/+WlmzdvkGwkJOOcO6bRqzSJhk1T/ea3/9LV+stffpH3g+WD91uiOd5wBMOwtnylpopi4tVKxQnm4BUpYC6xpIC0iofo9i1o+IAt1FdFvjY1gZggarx7x5s7cQMZNqGxZAJ92Xnx1muM57G6bqei9LfZZ9QbGr34lGTw1vuhyca+7fgisyDM0s1fQJlM9u+wEVUxWV9cFF3223/+4Ovf/ManD49Q7pvEyOoTlJdllMxmEduOfoBBhtlPYeLh29jMhaxlqtE6RsE3rGw3B67mnplkvFwXpVhFeXpBPK8IwYbdxMSV6DX/owZkUrOsZP+hH+q2rjabqqqbtmHVyTcJ4g5+PaNpFMbHnuWDIp3ZtVoqgtiIgEFBaaF+6JEuk+gc54FFfvnDEm6geB6sVA3Q1aapnr1zdz6/8ud//qXTk5MHD4+Grr994wZ6HMDA6Zab9dHREw5YkiLHuVdtlg8fPUyT+HP37jw8erI4Od2glF8plQNw3UPrSnRJMuBwZYdoqNb1H/7131bL9VdeeiErMrP8hhXPlvHyWWfgGYKApudp2oabf5P+Fe+XsRuoRaPbwAOCudHned6hSpmCjcbDC0JK1q/cTcj/lFcQhaQ0kmexmisuUb+XC6EXHAnedAr6KaPcOnY+E9HhQf/svyY65fnk/CfSckxJJ39Cpp46Tqk+0nHSbWrVdb98/2df+9a3Hh09BnQOug69fTjLT9KLfUWSyz9F+zK7OpX+2qOhI1ilrquaQW621s3HOnLcyyAHRW1RcwGUDTtYhVv2ka8i4GevTtjoSGKQQzIyy6blVLctUp3rNfX7Yr+BR+vUb1Dpbwm0WXwhEIQ2Q7oIBZJM0QGBRIwHJXID9I8lanFoGcvEbruHQSyypGm0jtSLf/aFjz7+U5Zmd27d7IdhcXqm22aIqGo67uf7++tNTcFeZJjQ9GjQVdNUTfPCFz9/sVkVk3KzWjVo6ouPHD85vX39hsqBaxRnih5Ka6Tom6a+fvXqvc99DqhGhoYEKGlJWMRxfHx6olQKWgKyn3E6A9CN1STiM9T4stByDEvMaT4M6GC7fesmaHFdv6WtHsV2Tbc00E4VbGZVfpE1JtVLMW6PnUAW1PsK/+Z8H7ulPruE37PuLxmYwwbu+ugWBCMYTpBmCkXa7G1+xzOOBOqEcAPhdlTZVpu9JP/1L3/59Ze/fXRyjEpXUxcawwDtNOlcDlzB9eX/pACWieBLkz6xoocoL4oIVXFM+8rujx2OAd2yLqebehEpop4JAhPB43mzY7WvX17lFGg/oF9RnMRZkadFXu7t1XVTVVUD6nW0uSS+AE5iCw6Ey2544kySk+rm2AtkvCMH2gnsiD4/Bfp99xrZV2+5fcwmW7FkW9LxQKY+lPd8NtdN9/HHn9649syNa9enk+lisdhsNnVd9xUKSApwWWQ662GFg4ExjYfk6NHj527fK4u9siwmk0lVtRxyPTlZaN1O9/czlbVdy6ECmDMIP/RZmk6uTs00wi2yMiy9U8QLhv5MM+sG0B2GSHfU3BiJInRFJaSGAJ+5+yF3n4hTNXRaobqUF2hn/+1gE1/2hr/8IcbdWNx0WEvJ4laMa+eNjTSZGwu/2CVBNHvfbR/9Mit/+yOh8IaxuMDhdirYngcegxyj+qgYpY8obAksXR9nQ9cVSfTeT37wtW9982SxiFWKM5TOTl5+QOqMuDJFhtW9Vm9JhIJiyGR3YbugIU/bOgVrRiiMUERHjmAzwjUUhbUMG94z8Gy7x9g9gcKRJkXBpq2r+NYm6ZwoVU7KLM/QKwj1GR2SWxKVEtEl04IAIYRkhOZN4jzLymkJFlVT+sNYYVBepUl5a7I4ebJZLikWCEvSbjIxUrlBNokw3tNy4PRdN5tfUUm6WJz0XTOfHWRFdnBwUJbFeoWy/qjvqJv2BBPUaZIGpFuX5xcD2DbyK1cOi6JIEdRSeZrduXNTZanWOJWqtuXSxx5UnipPizil0qgenhGXKHG3I2kFThVOMvVEzKUoatU0qL4YhqiuKt22e9NpRn0VFDB1FIwW9QvLIk2gddsG9oIpqNgWC7ufxxrJvhkAfu3fmX7UEt0LfICQWKMiGV8uRgO4NAw1um5b23pYnx1//0yLeddfx9kxXxUFARqOpkI9ipWX9AnCjNOmqqZR+u67f/uX3/w/xxeLPMs4VsxZD2pTpYFwYiPauMKkisHIbANJNo3O+qqPIxUlbY2Gd9xFFyUH0gfWlb2Y3JNIr6AnxjCZrXSZ16jAezuQ4SAq4r2gS0FumvZDIQYFfzuX4yUEZyHNjL/QpCVxXE6n+7N5ksCTJIfCVDBywjlT+/v71WrFuauwtswkvejmaapgv7BiSlQ0RGVR5GWmT/rlcoXGQllWFAUadql0OpkMfX/renq2OFPJSV3XFDPs+7hbni+atjrIDmezmcpyVYDJ9YXPf+Hw2pViMtFan5xePDp6fOvW9b3pJIlVnpcKpWNUN+Z6kpr8ltXBzBNIq89HlW5a0HeQs7BerYcomkzKAvUYLLaB8UynXax1e/fus1Q9ydSiFqE+Kjx/+tbe+Z4tfpHb0bIRkMPfCWHfrZGofaacmQ/9B2otvDu7ZK7vp7sv9jBkzlze8cAjOAgpvI4g6ppw7mh+pfK+jzqtv//Dd77xyrfOLy7SLCc5IsY6OLCEjESmyLCrUbCKdhFUgjND6GePqjp2DCXEDxwf+ZzSlMDWAtIRIQFsy6AmsYztM86PNpkvHB2xo8JD88NzcX3yDhvJwp5TKDQQMwqXcEltlMmJS1s5ywq0oib9iZJ7RIoMnV0cFXmxt79//PgImpzAjRRyMF0vpQG9RHqRjCFYf5rnTd0mKj44PDx+/ETrLk7AgtW2NWXa+XDDUO4+e6ssy02jz88WdV3pfijTrO7iTdUsThaHVw8PDvav7E91U//rxx+tzlb1ponj5MaNa3lelMWEfQCeCkrRC7E2JogLEKQ4Qiz945NjISoy/jOTxZ6fX6AmqShAFMI+gOdDsyTT6R+l1HmQMw4ek9m4UMeDtX6mTIk0mFCVdxSwBg6FwRODsGQhCBrJHb3e4Jd9rf2vreu8C7h62URCR2a5sS6Cxig2l2GagdnqhO3B9NQxgVpsdWAJzst1UzVn9U9//NZ3Xn/9bLNMs1QDCcCYxqEludWtafoqYGeBTrLWlAcwDqdgJ2jKFFiguzRN63WlW81IKSI9RZCUVS1M5w6xLmcneI/quSq+JWTiz9s+hjel2+6JxbXYWwqJpYcvDZKRxmXnDYp93OkUHbw5Titqile/77v9vVmeFVVdmUydZcDzkPRkdUdxlGVZH/UMGG429VdefGl5evrk+AQU8G1bbboGhRnUXkn3bdslKir3ptlkmJTp8fFJmqkvvfBn1Wb93j//05OTk9UGrUwUde6dHxw8e+3G3WdvZHsl8n+oljIhDwnveeJiKjPE1TcmL6L43D10gPSmGaq7u15vNusCdO2SQGJTS1BbUsGYdH178/oN6kYOcy9cULsxgzHwQCzpj+slFYLPzYlsV0xKyYdEiN3ZbRoFwcaRLE/lBFnfbXvb6nEv/7ElVe6DptLPA3/JVjVvyBOFOaLQMhCUgkSfRC1KtBF7cBiSDEm8NkqyRLWb6p233vzum68uV6soVkzIAN3bgdqXjWfr99qcD/vApqGn5Vgz2x47m7vyRE3TZFla1ZUdMQW3qFSGUFkswMJcfsnUeHV+3rwbg8y70h5qPqXI7qbvRu624yeGISR0Qhg/CIuEIvOiu5BAEsShUsmtWzdPjh49ePJQqHDEUDXN9wQGQliZOI7zvG7rLE8n0/J8dXH92vWbt2+3Wp9fnB0dPbl//wglAElydnaqAXWmnBARkjRt3Q3JnWdvzmbT9brVaGgy3d87vH714Natq3t7xd58H2iWIW473TRt3VIEkZbVglnsJvdsZ9tuHPh237xFRQQJ6Ga9aer64OozplLfBPgkkYR/W9o36GnGJpZRN555tEMHG9UUjzSVWXe/n6bDxBrBx09ybMYCNrK2RpLjG7mhjedJtnyeOeiDe41/33ovlFFvP3lDCErlnK1srQZ+QmQO6TBUKNSOewXayKHeLPRr333ljXfePm82KapGgMjheGNDGEYJUpGJ6wkwya6wNRs0jfldiEV1N+REa04QZfSV5iGBTosSwvTfmoxFoP98y3h3yMPOnhPo7YScOYDHDnSYD7SZAAtG83mHhctOKKRtDIH+Q9OxZh1HpJa6bshSFQFgqLJkujeLnjwi/1+mXihsOFBEWH8QwSLOFykN8T+YzY+OH18sL/o+Umm2v3/whecnLzz/HKu29boCzWXXtRrtPImZB7CNPMuq9aZQ2f/8i//OCCkp6zOFG7RAadWD60dS1oHaNXtfZFdkkLwHUFIzOzzH65DfpmOr3QCqMp1OKeAsz8Zy67gO+v7O7dtpmoF8eIus3du51r6xISsf7bx79e2lIt1M+0XvudYql6RqnZm4LVmX2fBBNsELvJhAqq/bQ1eSXTrv/Ar674boL493YMt2pO/tEEbugcwbVN+r5ekiLoakaV/9zuvvvPvDqm4InyBD5GwhJXvZ/SLt61UMOmXsni5YJik6opQDEPZxvKlgVbLxTBFYPBCFVHFG+IdzYDo7B56F0jOyL4srWCfIMW75fzG3Fgynu1do8njbxB3hplpZvAauTyYsChU4d13fRDotSvHijTstYsF9nAgqDC58uInSnHd/Nr//4MHedO/KM4cPHzzQbafSPFNxUaaZSufzGQLI3GuCWcYY60apLEZQdlRZjWJAihij2reLULYL38d0pfBUj1NsVveaLDD/cbm6INGQerEkRgQ+L4rF4ixJ4ABT8IoAzx61AMDbcdxTEE4YGaRfolvIYCCuvNoNbeRA+S/TC9Czn80HQR5oEuzGO/JXb3Q/J3fOtLZ3D/bB9oe2/bdLxmq2oX1ay0Qini+P18yBseTtOSB+tDQZofVImmGo2+Uw9GcPT1955Tu/+/1vN1pjjqkLEdHCoDcPdSLzi4s86XWtEtyxYw4lkzGgNjnMxsbxzAbJDGJ0A5RwANNMh9Sy1gAJ2OUIjOgRWX1oCY3mNHgntLUlPhGYR97qjUKX2660d1b6lrwY5dwRItKofUa3+eTmrZuffPKnzWYFOWdImuAKE6CHkfbFvm971MQjJt91WV7s7e31iEVP86xYr9eR7qsu1n1XFtTLE6trYFymRpKdd1Z+itod9H1fVRWAFjCf6Ihwek2cCt+hNBUaxnr2YJQcV5cpx+9gVukRgl6VBYLPlMo2gi8QaWCv2hYRk4g+a7453OQCmnQT7M210UchHse1oTX5Bdny5onYBkQxg4RfbGt4e2uJylziefpD2KUewsHv+MMlsE/nyIsJGOwkt93NdnS1yFIMz52j4c+An1nlRVttsij9058+fPm1V3/3h98TaTCFteg413CZUBBP+V0xlR07hk/U7GASNAAfiU2ml1Qe0E9CR3DGRXaEtPjlJmWUoTF707NGLCjdm51dVJPelNllv2Tyw9m2dEeXKXTZSS7t6P9NKgp5iUwjxSHKc3Xl8HBxfgZblmLyUiLMUC5sdpxv8p5KolanaXwwn62rVa6K2Xz/5OyEcrZJ0/YtItVgtyB21pQKhogdDl/HOBPYMS0sJoTzq6Yld0eUH1ML+O6ldS4NptawZokWxf1RD8W0N6ZsG7iMVK2Xq6aurz3zDBHlwiYgw9tWIsEc6Jrm3r17WZaaLjfBFvVTBJ4+Djwb7wP2mtADNtvMXsF146nA6tkJ23nue8bdpbvHXewQnv6TeDcMomSje/Hoxk75thHiEYAGz0c8r5QM6uj0ToY4bdt6GPrf/+Pv/+93XvnowUdSRsmdOYjIqmkbsr5cqY/03rJ4ZZ+t2b0YkchrYWwyHMdSqdC2OgcrMhdyyHTAsyL1bkF2FoRs7rltmXidWLzDa2zHuGPUWSvbw3V+TLg0wRte2ing6DEpVFJyYp9SznNoOr2/P8+zFIh/bH7UgFiQmKmYEsuIjc8oiq9fu/nJ0SdKxdO9PRRskeEbRwicte1QwWauBARGhYGGMRZEeg1ZypE8GjVNltEZBm1n2jhT2nq/Qo/nqV8YBVmOaB1AYlDyYOdQSQ0/aCjLgo8fXmWvkCFuNXIJCtJr2qb4NG2eugoWdbx3gxj56COjv9il73tCYtkTP1zb8PuDtQ4RUeMdMNoOMjgvOB6Mx6kZ6zXsEN7gSns2MQRKfHsw/NLfOtTlgkc7zc5WK9327//sZ9976+1PH94HUsj4G30EIhdUtLSNJmghW7+2ft5Kr+0iv/XyF8fksRBrpdr3PpqVE8ZFMG00l5DwFximHImfuzv6S+BZV6MiXocxG5MHuxM6mDSGiRiE1rZ+Hsc1rM1tmiJbx00uIqC4a64U9bduXT96eOWTTz4p9yZkZkprUCMxpNiAX2UZi1vdzPfmOVoxqDwDZJI7WpBXy0SRxCpNUuk1RAAAD+BJREFUyLeodYlTnjqEu2MvqCz9wrwyvWAejAxzfaYkyOSnqGJi56e4N4x+FPqkeZ7nJ8eLaIizPLenDyeR5ONAnw3Xbt2WRJQ9Y+wa8oReJiR2hNvi68dDXOrBHk5iT8E+McRHNihl/I4wSunBmcPTYCzj8j3c1MTYhJfgpl2t/3b5hOfGed187KUWyS83IoM4ieKUSlMrrZtq2TbR3//8719/843T05M0pXJWYoCNKL4FSKFu4PqaprmSI5JWZpIAdpir0DCQ5xQdLAk94bYc+jTNp/t7Pdh6jCSAIJk7kFGQwxyZHp+4/ScwmUdBLD8AZT+xtRO27Bt3J48Zcxw2G5vMIbRGgmp8IKDuxmT3qK5BXTm4cnp6Qi4IONcN/YBFiwPOiTKvGDEC3bT9XlRmZdt35WQyn83Pz88SBX0OmivZbbxLOW/nDC8iSQ+Ct85ScPUD3kpZ+RUhDJCUXvpapTlxXZFtkedgw2rbJkWPCQJQwh2QKiRW4GyupERY60VrnMraNiV3vEIj2RT3uggwG/bjjzA+3LEi2vTC9v0vgT56/4w1sU8vPMpc7gJ0eIyv/jO5Rsgu/G/DViIq3takq2EXxyoD9qpLfva3P37tjTcXZ6cZ4vtCnE5YqwG6l/4n3aqk14EVY+MB26SRDaDZ8Ym8OCsaOHgc3mRkxv2kAKqdSFSTwbDkoOGgYZ8ZKTxvhsy8jGV5S4wD0bSpqF2hqtEJHLogo9e2FSDS67LLsukoFg0GmabvZvN5rJJ6XeVFLtNIDIH2a1wjBcJ16a4pi7JenpPwH9TVBvSRpmE5H6K8/2ygUn4zbmrkniiwN0O5DgTY1Uiarg1cRETSCKhpkaV8BOVZvlyu6qp+5vDAS/8a+BWlkijdiORWSlVZzi41XqMvxGFWNDQ13cw7d9OaY75o+J9BsaSz0cRb8y8ZIfSssPgqwfrPBqnpDfYpHXnc7T3p3oq4hMdsoJVt3bUsM7QBsZN2UZ/HgDK//ebbb7z19qpaoT0copMd07b3PboLNi2we1R7y/JrjWbbmtfHJ8luDZswOpdAolM9WM6HHk6a1l0GJwoOFaeMuKyBwcrsz8mXBLJmEWW+TevcYDtbowjF1nYw3bc9dR1c/7R1CWbe/PSseAFJ2miX5FauXrt+cHDYo5cK0y5zSBB4FeKidVV73OGwJerW8+X5MABmvD/fv7hYoWgxTsiDjpF9k2iyZ3PQiqUqge+5Qwyc8TzCPJgKfovDMGEorkOi+FxO7XyZ8Swv8tXFMur7vckkBcmwe8nH6AFNLYRbNjOCbW0YuimBHtwi0Ah2mmH02NKUTGfrHEkmwLfBde9l8iW0i6xV78TIoIBsoG/r887O852CLW1Mp6/bJvZty7PImQCbiSfSFjhX8JjySTbfn3aNfvnll99463sXy4tUpUR7BckFgoJEt2kbTTFMy2PDxjJD/DkSLezM4kuYjqiuya0hlOYRSSsWq2QYdEjhEFObwuswELmMLAt5xZaW7JIws5kl9/3yuwBZvekM7STbW8+t1+7XON5lf4ix7YZnu56xO8kMYDSGJIp71R9ePeyHodrULO6uBETIZQT0z9O+Wi5VBjLHOI6yNJtOJugnZOLERt3Zwh6GeEnmVlj7+GWSK26XGMd7JL3hy32cJRtDyUHLrlC5VZRlWTd1mqqyLKlL2fjFAYJr16+Rxy8B+a0p3dLKwfbnI8l7h4vI/A3hHcD+JmFxF55yhrkKYNXZvb5PZkmpd3i9/kD53rYGw2uQuQMnZDw2W+Lo38v6MqaNiWcHWeMNbJLwaLtet02k53lRn9ff+ubLP3rvvaZp8hLECB1gT1Q9AHycgCWl/7v5YrfTvICzLxbjR5d+Kfbskr5G7JIQJk+V0xIwAzTWAaMqk5UGXdrkjBpLbmAaCurMg6d6rl9owWz9G062ha+bTWE8gm3PKfQTfJk2K2xOJU6AS2dA9bl7zz28f3+zWrNyppJnnYB4XVGihimV0aeQS4r7aMjQwhdFwkWWToqcl4HuDMvWhDpMCJ1eVCICKmebL7Bjcy6o1SvWgt5Wv6BTEpQE9SWFCKcq7YckB6lkobs+pUZtghy1n+PUFk5nJJa4u6pJgNv9YQ5tY/jvUKC7XsMOEbBvOpXNT4YMO0XggtyU6V5md5qVvvH8eN9od97uAQXDsVxezuS3z+3JhD1AzZ34uGGMHvfTGECmHKl0EuVFlidnpxdf/8uv/fTv3quaJlVpU9dcUMTGMVXzQg9zlQLvDfbWpLA+LKbcGQiyvzuIgDgWAvqAOaWp7cYE/bqEvofOG9s51ECsdmbDxp6MFSKLX3F/Ca7yMO2jybfyaz9n7ilhqNAd9yKa3gcN+b4cXEEIkRVzXE73bt68/dG//bGq6smkhFHU6oTAk9jf5CWiHEKpoe/RapRK9lSidBwXWTFFCyKk5XmIhOUwTpoc+KI7qepQdcSJ6UKlwcHmHtZAIUxSiwNYRgfzVpeiItIVSZwUWYH8YtvOJkWe5xJ6dpYoJfqjeLVaXZnPET833bS2D9FLRMLI5dafnd0wetPzXq2BBRwNc56Z5Dc1XBSJ8gTYu/d4LxjaAbv3t4In3lnAiXM5SAQb7MJEAVmxp6qMt2DuQz4kq1+05lC6qlWu1iebr/31N37xwS8bVNJHaBvAPOI2GcSSyogNqftFOJqrFkwwb8u73P0y5rxRv10P6CxJZTL07Utfen5vDzz/rfA4UgKJS/kRZzWEsd4BZmKII/l1RFWSwQlW9DJ3K1iR0d+f8mjBR/w96Png8sPoNOmem2BPN11z5erVh4/uMxUjcNTDkOhWoQwsiajHKnfrBdhDqfV6OZ/N0zStwB2ZlmVeVjmXWwfHjMczz2R5XLXb47jvpCrOErQEGAmrhm0CWGr4DeGeDWmx9Mq5UZbTum6jrp/NZkXOsQyCYYnxQImyrivLAgw+XDi4e159jlTO5FnqIUG27oRKBatsl1qejj5EtZop0ebSdrIj4Hx7MGVhICT4DyvK5rKtlzOVrfzbYgSmY/ZQus64NPkATyEbY4hOTGLf1XGeR13TJ/2v33/3rdde+/R0dbY8h6oF1Aq1uyK3rIGJC1bYcCjsZnBWNlfkSW/wvGa8W89qnAvciwuSkhjWwLXr1/fiKcbRMbewlMuzFe1NlKvLsPCI4Ai22CwvguQPwu9Lt2U1BLi1IETt3vZuESyby7LSKtuZMeMTM1JmjZt9RV13/ZlrD/Znj9dHm7rO4d8mbdwwQ7rQrtJZmiQJwryrFWzUPB+WiASXeTmdIrNHCydmobd3reLivukDGY/0jsnVmblz0V8nvUZyOQPEhUSoYDZZXQvVYJb2s9PzJIlAG0AVwLYVofW9O9qBaaqwG3cZnw4kaENV5gA057WtIAw+PYZS81N5K8AGugKDTJ8QyYktGpL0rX8Tt1WDXe01vfPMdG/0W1aEG7C53oRgg+RX+O1y6podTf1HyGnVQ5rmXVf3m/bD3374l9/89oMnR/GgUIQNNwnwZoFn9FZ6EcaylrItVbCKx58u96S2VMfbFv51vJzSEoc8tCiKDq9eY8hf33YJutVTyoFCsjb1bdvS+kHIMHTv58m8fLSdf4t8G+lhK+Nb9FreebwVafF+pcYpdu9xOEsOXzaZXUNQuRnMKbDmqujw8Pri5LTrhq7TXKHUNWmXUgcJHie3YkjiMs865IHLLE17rVUC2qmiQOSiaWBvWx/LIbyZobAHKU8K+Ic1pnakQT0ktL9mTFVPCC9GXxqx5G+CSd93q3o9P5iVk1KBVprlnesHqTg0io4eHh/OZlx+5Ksed+TL/eTkoWFyU0szOncIh9LFwh4O3QTj8Qdmu4yHKO3SWqkc7d4UN0MPctG+6In35r7bGnzump1BLYO1NqMIfGangWwdg/1Q+Jx88FKpAOwm7Pgu0lXT/u5Xv/r6X339wYMjlad66BJqlcNkU9ZKRTkBoiicg7WnG0Vy6fCmrw/mzxUt2nyjNQkcKIBMgQRVcjl640ZKxf2AMvRnru1PSuJhYq8KupgOEm8Ogso9a4d4+CY/MmAmw82fk+cdOZ9gMWTuA7/WaaiwqsoulGOE99fTbDrKuwS9zxh+FfVRf+e5e48e36+eHK/Wm2lZdARU7EFEmSYpWonRAczVJklVbdBoJc83rQZpa5YW4GSFk9PqljoG253j5dzZW4kRFTMIZAMRMNvX6l7nPHOzGW40oYC7YnJ2gUWzIoUxn1ebqtd6/8oMJVLEv8FcWhKNRjR0yJS6eeMae+s2nud3qLfgATsqGGicxw7jUj7EwJhFownnc5T8f6oM6yMVp1kaNUNBhOWdyoeulX4Axm6Xc81sN5c6t+CVALXm6NjEKTHHtRmIh02Q3WgvIjFjhSwRNAOck3EPCAtxKDJJ2jRRRdbq/qc/fu/tv3n1/uI8KZTWbdU0oD+lFZVIFRnIYAXnliimv4FdZpMWYlpJ2Y0yBiLJ4YsN8MuBJZjPE83tFHiw6BwGkrep1sXe/ryYxd0kS4t4aLskiTT+nsaqI+ozwc3aYkHutMbhAekayFMtRxzeIbPf+sN2jPIUXgWgUbMmYGZ1tYHz2yf3hZy/icseBSNDQSQ59E0oycRGgBUF4aPXshci3euhRXRqMtmfTNdJrOqmgttviDwRdiF9Rsg07MVNVc8mEyJoJflJwPEDuzoDGkQP4EWRZhTWd6JnUMQgieYpksK3rS3YZBG8qm++sfGdEGVfmij0NHSixY3l8I4akvV6laHbQ5lFmeoRKKKMoAKfWsfJv77I8kk2jRG1lJoIGYHZXc549gE5FleKVhUmv+KXdFtcgawXV8RIjBQTmMfppunLLImbFPCGrGhUqVRHrTDBHUWhFjTCELynw/dbo9aVKHLxo+GTsaUuXMlJB4Y7Y5wCF33L+QITCYSbSO6pT+lHNfl8VFKrmwFN4/p2VVT5+++//9b/e/X85FG7XEfx0KIHx9B2aKvBQX64wUKgLnoDtSOki52HgYFw82U2W0wDQUm5UEmidDOhdJswBBpDlDJGcayw7qCDiJouPSyIv1Dljc513/aUpdJRh/mlbUoJaG5MQvdDw/mIUsYS7BycuHLbahIeAhNLDlICsExnado/mab2VBIko/ejo6aW3yYMJRAu4kGflEUX5WzoldlXpLH2dM2gsHUBBUbbCMJuEBUUdG3cP/v559d6valOq7bJyFruugxxPirTi3Ac055BsVFTN7VK0iRNhx61BCpB0/AcyGTetIxUp2EkTGyG+c+ygiJhMIGogExAOA7kiuwts2HKg/j9FHHWag3bCMMjfU7Ue4DcxnHTAVNQTosYrYlxQJMYDX0yJAptPLpmOJjtz+Y5kEPCgi9HqTDzSwid+2BaNSHRY0o/mcUmPeHpRQbME16VtD6h9PE/LHUao7S6mIJ6et39fxVQamLQdqC+AAAAAElFTkSuQmCC"},{"type":"image","mimeType":"image/png","data":"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+{"type":"message","id":"0633ed76","parentId":"1c836c4a","timestamp":"2026-10-28T21:55:18.074Z","message":{"role":"toolResult","toolCallId":"call_803c44e50f5049f9a6b17c9f83345063|fc_0b5218887c37459e016ae26f3c7bbc819fb55e9269c34f262c","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224513.394437567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.421788215637207,2.089327096939087,1.8129761219024658,-1.295375943183899,0.0016572437016293406,0.11011556535959244],\"left_gripper\":[1.0041607076471502],\"right_arm\":[-0.11952149868011475,2.052452564239502,1.1140962839126587,-0.6284643411636353,0.0016702237771824002,-0.11548030376434326],\"right_gripper\":[0.997136029330167]},\n \"file\": [\"/tmp/kinex-observation-ocl_jdlh.png\",\"/tmp/kinex-observation-yq84hi1q.png\"]\n 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"},{"type":"image","mimeType":"image/png","data":"iVBORw0KGgoAAAANSUhEUgAAAUAAAADwCAIAAAD+Tyo8AADyBklEQVR4nKT9iZZsWVYYCN7ZBnd/L4aMhEQtpKqu7ipRq3v1Wv0F+tsqqQuJQYyJJFRAgUAqJCFYNCQiZyIi3+iDmd2x1573PueYv5e0ZYY/d7Nr956zz57H+vv/3x8dby9bU1dTV9fLvNRNNa/rtm7w/2pb62pbq7qu67au8N+6aehHVW/wqrZtq5u1ggtX/Gvb1nVZ5mkZx3GcpnVb17rCL9VN01Z101Zb29Rt09Kb21bBC27Ij6Df8f1t2bZ6q+A9+ggurDZ8+LpVKz6zhTs1sCb8Dly3VSt8GbbSVOuKq67qpmoauCkuu6o2ehR8VOET4TPYSgOvtsH74rPWjZ9LF8F68A34a6thMfou3BH/t23VNM0Ag3Fa5pnWW9ewAPiMgLcuK7w2eBfvCfcA4CK8N9gB7rFpGgICPI6BscEn68bfxUdWG95xWXDndE7b1vfdze7419/563ZZ/uf/8f/aHA9N18Fx1HWzrhU8feva9uF8/smbx3mZVzhyXAuBC26PD8F9bri3elvwuNd1q6tqhTfpYXgN7AD/XqsWjpiOjqCE+4a94vFv8KsgEgGBcaChXwnw/DuABZcAF9d0CLC8bWWw4Jf4QXQkNSLJujIiN/VaNcs0Xi7TNI3bunVdSyhNB93WVdN2gKhVBeh7OV3m6eEyvn/zfryMw6578eLu7uXLw+E4dH3XbHAsTbNu2wxgr5ZqWZbLeBmXeW7bod8f1/MFbt22TQs/AE/hB6+TsFpOFNaBOMQUwbgJCFZV69Ig+OembdZumZa1nbqXt9W4r5rHalvHaoWNrxUc3wboTxhJiE/IA49FDG62tQLCIliu9HaDgGOQ19taw1s10PeGgN2aagUKapoW99LWTQsYyeiP5AQX0n6QaAjXGbf5f3yUFd2cCUcOG46VVgOnUW/w+AoYCEIMAMSgYfAJxeGT8E1ChaquW9gZEixtix6FF8FziZSRjnhFdB/4DmANAKZZq2atWoJH1WxbA+wOb0j8agPgNnW9IlkQbCvgPXgvfA/PtYIbEhbT6uEvoGA56a2BW8EfSP1NhZgOS1rbqm/7H/7gy3rrXtwdq11bt3Wzzc2GDIq4Et7k8elSb1ULuAvHD4SH7AK3BpcBDLatRtbOeMCQQF5BNE4goh0Cm0acoafwKdIbxI3wCDagcPwGLFn4ObMkZiV6rnQKtKANFyIX4A0JPAhVxp+VeSKwkqbaqhbEUbf0IALmaVrWeavatkEaqzvi9HAeQHV1uxuGpr6pq+r2+NA2p9PT33399bv7+7u7m9vbu/2w69sOiR8ePM/zNM/rtizTcrO/WZa5upx3PYgqgDViO3JwFVm4cAQR0i6ctaEinyzKomqjIycKb6upOlaHatet1Tzdw1l2a7dtK5zHRk8AmNK9EOsZesL3AJmIfuk6wmWWUgAtgAC9C2TGn+EBIrNGagUKJrQlTsAcFcU1IIdQtWNJSn5y+ojbdbJl+pSIbjOCIDJ3t/P31vviHeXoRewiWuEj7UJkmoiWhGcCL1x1BUSJ31+rbV6RIphSSJ6IBBfK5wWriJI1IdUw/yQooa5A1I8MDL+p/wGERTAR1VX1ZVzWZnk6n771jVugKLwJHPUMPBtYVlUtVX26XBaUmIAAChw5cSIY1MyIZhkUCGMCBXNPkq74WWPXyQ/FXP2bWBrBUkQ1g4hfvBfhNSJga+MNdFuTvBXzYYKXnT5/pW2BflHso8YB925rRH06dLwMlcS2a/th2PVN33fdvmvOl9Pj4+Pp8eH94X4/7PeHQ9v1tPetWruh2+92Q9+t84RqYQ2kC8pcy0qUnj+LItHllAySPfP+YDukCjV1tQCP2abd0rVVvdv16wVUJNIrmR69IEc1UmlDDlOAIVeyzivqjkhxEc8ARkQU0rq2pmtIL1d8ZgXaH3dgyLYmFruomTnqM/yQq5iqkRUo448Em7yAf9H6Sc8TVDKUYdIQkvNPJQxCnYKxChS2DRU4xGMkOGE1bjMKWZFUxOT0qX7BCmflWgY/JlnTRUkxWsd5muamrl/cvej6neg4ps7W1TZO8zwDswb5Kk8ipEENCW4FtMt6EQnkBj8FBuBQkNektOxewqFVWyFGKyt1EGCmz0sUDSC536ZszqOF8DRW7PgCgZ0wJLCR2rbq+6qul2UBRAV9BggBbQvT96q6WZem363Hauv6dj/u9rvL0/k0L8v5clqrdbffD/3ucNh3Xbut83Iep3rru64dhrbr2rYlQq5BBUtRRtm2bJKFrscOupbWvoAohlusVVOf665a16Zq0MpEQUUoLJqL2z8fKn8ivEIPRH+HF8IBtCsw2Aw5lV0yejMOMZwczzCVtoADDqFZNjnB639zrM1kZkI8yc29hFeNVnbnb+W5AGlPAexsFMsbqHc6LdJeJL7924x9KkxE7tq9zQJIl+/xlZQA+qdqO7Bbuq69/fQODDw+BOAtAsrmzdu367I2XZvwOqEiIF42dmRpKv4VOKIdip1jBC2I6bRcvjF/xhqdO34PEJHIBpVwJgJmd8JC7+ZhERVK0bVr2qYDcp2beVkWVEZAcG8bKE9oGKNOC0y92Q+7rmn7vl93y+E43Y6367o2dd31/W7YgTY+z5fzeduWvh/6vh+GAW1pJF20CwU3VLayJpkIFrEY3ZkrRjq4ocnRdGjekyPCSZZcTkUeLxqbqpX8AOMhwMNaMr5JS07JkFxTKjcTKvfLTonHn2JYHd/DHTGLb1HHUq2MnGVoWWTPVEQkeBaWxWLGr9MgY3cBbFCp6cGYvRjr2UoOH6Du4o80I16BqnxFbBbQe6rdbrcu84vbfTf04BgAsxD9PqLK1HUzzguZjcwQTYMWj5tT7ElZZueVmr/6NUSADOSGhR67WLjS7wIzkc9RdrhrIrOt5V5OyIvehEqE2hgObkibDWIpulcb8PwtRMhgtMLO0IUnfiZylDZrt3ZLtxsGopx1XafLeZon5Ahdvz/s9nuQusQA4Cc7ST14lM4c5QQyUc+PIZuhFHOmbVu6Fe115DvqTFLJ7m4lfCsgIJliim6qzNFf5FEjpUHQklVTYnXgB8tx2raQKkxOkU2ItkARUQtxwl3ZFGr1Vwzh7Hv63Xi1V3CCxJV/caPiBxNRo6ZwXDffwmwf+8D9qYoh79uLebOixGxeq22ou+9+94en8+UffesfrMsKQgckJQJfuNAETmtEWDFZVGSqTS4GklmgpD+jr0vUMjaDWS3JRSmTUJSMTKh2uogk4UQiCxYppi8Vbxkfk+M2+1IiBsy61LUJ1DZX9YpkrLcA3iQmPhAiCNUVyHhdyKKgrRyqqkMrt2nJZSmeZQ+EFJ0jncnKdY/MeMILz4xpH5bdoXOd8KBEBe43px+6P8nh6KQLgwjFWtO0vCCi6w1NLHGGQBiis/XaIxM+HHZn8AjWAt+ff08gonCJjhExq9zNTG/2yoan/5TY5ZEeeJ5gqxpiGGJEppsp/xkp2GsC8RkeHU0+eeuGOG/br9W2TPNxt18hagJ+NXLbCLzq+4eHZd1IkDrYstDl4BH/6z2ZeiKMBCRIDM7KXQRpVK/ecs3Lc6nwT5Sz7v2a9sxiIj2dACf3MhGMOwGchGgQfNDV/VLXQJxoEpOjVJ9O8QDwdtXgsQ4SEsWsMtSEchUYYYXub8dTbO8RpxJyZF7fLRAtFMOrAL4CSDK5IVIYf3iBKn5/81CIFcUkjGKZkSZ9VEH+FrVGBUDpCwXUzy4K1nNCvRnOKF2KeuY/DGY4xzly6tVTDusyT29xz9n1vBqnGzJJyR8A2m2d16Vb5roFgdt3HWHeAsF1cngwZj6eLhJlFtU1eZrDCQgzUHyVw1wRtxzLdlsgZGJSECCXzyLI3sLBku8/GDsFcvGrtj/BkeoEA7MTMFExswEijn23LsB2OXAjDjz7AkRJyNgPm5QYeclqEMl5bV+qqjpZcgXf3VVbVXUFsVuAgPcRJijrT4OVUtZN0LKoIFKPOSG6VJEVzKnCJpIHJ9sQd0+iXzpweMozH1uQUf677hz9M1K566WgRM9K+k9yawt76F1cLojnqaxzBirN+Gmk4sCe2dHjcBpJeF0A9hVk5oCTo2mrtu8gsIVKAa0FIn7rOi0L+kjdYvBXjnU7+iVmrVDixAmvkLnwkEPScAgFYLmf9m2vSCjiqYu+crwtpeCSWHfWhYecXk3pHOjeBQJeyTUNiUCSEEGx6sKiTYcPKOF/s92JsEv2kH3H/vIfy33oaLqgYsadqk+Ioo05RPJwiFGzLQBAAPHehJMqaoR7uluKehtpKxEFAoZUpY7h45QYc8ssvnTn6tLxj/HqXequVzPZFFxOuSKvsKgvKoVltdE/5V8mY6MQVILP7qgLBYdMtTVdW6/bJy+O/X5oSRajXqD652WaOaJurNrbJBlM6Bw4lU3TEtQ4S1bPOFgyiP1fJVES7masSxByM/opcwbFbQQ8qQqcI5c8h7yZ4pDbNvDngaGLrI4xoaykx8UZxTkGrxFXL2VSesPbG4/nL6b+TN0zXgNOuMTb5W/nvyT+kQzNjKSC6oDJGC1Ert1bLh0EnTuYI8hfTa0cBZUm/ehNrr4yHJBcEv9hikbP6SCl8wqBLZIH4dnKmE2lNVMnrouv+vCOWJN4ZqXuK25DSFx/+4MfTtP82e2xb7qaMgpNH4QVPjw+WYKbHYgLyNCxoR7lOQ3rT84K1zNOFpJtKtuLOx7JaXtuv7memrwc39dwgeRglt2e4iLmfGFwR3X0wgRIusZl2Lg8G6ZSXj3nUnDKgNut2RqSrejXmOCIfsV9OaijvE4O53lKTL+un+jPjIGVgFiDLx61aNP9CDTe8Sv0SU7UK+twVxc2aZ9FfdXfWD4Ud4z6e4NjSxKUDVZ6uV1T8A0aYI2OCechN6DAcSXLT3xCsnH/uPT3wIYUdAl/NuOaE++qdtgN0zju236d17ZlQSrsoKmq5ul0UeGA6EbpJnZoPl5AOA4yHDLc/bk4unCe6nhAOSSS3fCJiYmSepvtSkcXW2Ar9rCoGZUuCi8nYVlhBq0EcLjVvHhO1I8bsSxD+dsUeq/aP8doksO2baQS06ubELiSvHJBsoBsXp16dvPB6JTnYklAW9egxnFmAjNpTO20vABvk7jnWlBZICELFF+LA0K2AllWwrhYZ78CT05pDBk8z208DUHx3tm9BSeCvpAMjdXMLCk0+uTMW2jk4dTD9GOmYU1jg6qDrd5e3B0XoEN1q6H7EBzSROoctpWMbw6Ps7QSdUHVCRDGrGUHluXw2OnMmiTLC7QEHtlzFAFO32KuEFTPgvSuPQ4bnNIr5TaWgCJKmNpylsmJa4IcJ/J7cfoqZ4alS5DtlWiF1n/9K/psH3ixzaRPY+ZPRNQZ3wt8JWVeYVVFj68xD02zYAaGb0KgmTM69FixksbDXLHYXB8pHBKK8kdl5kIiVMUaCeZJBGNGSO5ChW9IqlC9PEvusiWhdKWoaJBw4ngogrB0KyEd27BXC+Iv5sqD5zTVchnHbWvujgeqXGClF/GxqZvzCAYwamKqCBro3D1V7WYl13sfiSZJPvG9r+xFBJQaGo6sAh/zrmLZscvO8P9mr6AZeJ2TlhYVB0M3E26WV8ExbRXMku6tHNSdWlCvTdAENhXX5aI/UfQZ6yuxK74Q7M+o2SeUntBp1BwCtikDdenikEyJhgRW5HFiHykhUJTBd9FgRqYe2i+mYGfqNVX58O8ZY3Sh3+DCv0YrkaxK2l/QA/XhoqPzAlX1xnJBVTMKS7+yeY9MjiUiTji5fYXgWdGD+omtOt7c1F2733UV1H9RPj3rxXj37d39PeOaX5UX/uSw8hnYOZ906JeGwiQqrnAwj0pB77C78TciDxWDg3wPW0r3ehjOzvWfMeMRFLD7OZ6oTIUhLykxkuwh5X9MH5yvpOqHfWBsPthiaswZdFmsm4IV2UL2spVyMrr7JDXlPvpFiKWHjelHTVV3HeSUoc8KHVaMCKSmaI5D8lLczrDaAPDMCiOg+L2SAip/OxbgZUeJedEfSQlFyQfASIFOzARKBXGvbIqx3V/GWmwwlhNeowSlhQJU/FnV3//+j86n8fNPX47LinXenFYsIdn6dB6dMGVDW8jY393WR84s8XiZgqJXJHw216YcwMT3U9T1MoDqZd6bX6kbR/mnx4Li/Vx2hnw9uDOT5Dez4+TwPSEHH7MegJGzvOHWltjtKUpkKy44nqhyPEpZj5dpjpSv9inYIV7x5roceGLbdFz8z6zR0AFyKbEgPUq1cIwm120BqfZ45aMIET0mPqlndK/i1mTTtvWMbiPToFsJ2TFj834qxj13ku5nVvbgzjruMBGVehDkAwUbpmrqftcvM5SzY14C0ydFgKke3/ivJy0vIc1hQVlcHv+LCxKpJfhOEIu8KeJMzs9ERjqHWno4+BK5wb/F2ybpT/JuUtFYyqFxh4OfI4N3lCHPyFQr+c1qxN3Tg3Jla4o8r3T43mtE9eUs/5WXJF+xO+Yc8jkhKJYqZjtjLYZ+mb3qwrSck9bECrdcyOGRvIIOwrIzRScRKEGh+sDabbHZRSxplMau3Ya0UGimwdiUbsIfYhQtiB85cYdvxpP3KMl1dFy4u6xr1zUdZF912DkAOiiwjw1TCJ9Ol6tuTv6XOY1dpamC+ESiUVYCJcsuXaEhl1Fz2NGzkQe/uLjOza0quczjT9C2vKBRVmcXBNvBFW8wSJzLzv71Bn8JWxX9PHJJoK68y+RUMi1O7lN3HxFy8yszD1TBPVsXjoySWzhdldNo/ToDxKIA/MCRJpjvTpCDfqhaJRavS7sQXKM3fOLKtXCDqiVFkeOPUZAfddZ4sXFTvjJk6snXA6fONY58fXZfux4APU/zinVysMAWGkeIeQjK1/uHR3dnB6tgZziJVkOLoyDQyEwlqBDYncRNcvhy2P2UL4mqyv4qV/nvE9lVh/GJk1Z2lkPRKh8DLyA9kkNBDnESCcxvJ+mKtluuz40fE8qwe0jX7Jcboi0uIVdNUEo4sdhWqrkXGZr7q3gWCfuF9i5Q0CzMxrl1SItmjY7XUNCfldlm8kg4W6aTJHwhBbX9Xo4zXnnRUq98BcUm6qfUKwNNfEg0FxbiOGmQN25vsu9c2ujz0/f8jUKhP/YlI+kAbSK6tu66qoJ+TaD1yGmP4+TBzGjG6M2sViWxaMDOjevNUf+WA7QuOVdky1AufZYgfnx3s2866KlVpVgioSbHGMPCC5akgEMOz0yg/FW0eDKBnWw1EfKJOpK5DeUPi8FiTgXHZwLnfG51yUqvHYYYjdzfIOonwgs1LOkX75Sk57mFQcIpJqqyFhbmcj1TZcmcRuEI00Iju55XF01FEw6+uC/fhXp9zU66AsV8sxnXCmly4gVftw3SJoGBVvvjrkUCVn6xVRWyFy0gEimTSGDVU3Tj0iUrrDDScOCTLjwsWVbXNxc2JX9GjOD6ag/8rUSTis7O3ZzSvDsu5wH0K5MTjG559cT6BadSLjIU/yhmhebKlqXGCNc12lIjCfO01YfmmUWaW5J8PYIpvYxa8Jg+zDBMLlP4K/ZfzTO1Y8rZpq7AH6b6/otrL+V9078FU6VAnGVgWAqXhY1KekD+DgPKYC4UITzCh44MHH6R6l6R5o7LujR1fXd3U23bJy+Px30P0TtoGondG/DO4zRDDQ4fOK/C8MZbaF7xN/VV/rWwih6J+W6u6EIZ+ITK7Ikx51HjdNFbs6UrTO8TDNeAb5EJ5msqyzHXgCzdmLnTHR2a0hSzJV2UKdnJVTB5fCIVD5okel6Vb+LKHUs7S78hdaN8uqY98rPBxYWmh3NDP28hJWLVq/uSgxulas7EnlV+Uu3PHpI5PtIXCzB5Bm2Wwi1e9kZLiv5R48D7pp/lk0Ea2rWQlUsYhqr80A4//vLrddluDzfdQl0yq3WBXhx0+dMZMyi120vJsEvywcik9xo0rTeKwKhhl1+Z2I7Q5PtlMTgmHNUhtyuJrcI4yhKs9B114Rs2uICv16SSvGUfFC5v27Kk7Vt6ZeLhckw0agveZ4L/QQod9MPKitG9zM8ONqiXcR1OibUEULheuuDmphFreuyr5XulMv0arMmfn3z6wYz/NPQTXJAZwXM8LLLrgiIWNFvj6+zvxTtd4yP6ncQ3FbwBpW0EFUFZJn5Y103XblXT74Z1HDEOX3VtB1wT6pPYMHh4fKKr+YZehvndIh/eqNkd5F9S7kBQY3x6Q8CZGHt09qg+ILObxImood1EqKantIX6igimzNRQRnqNeaR+GHeXK5hlUsplSrvgTjEZyindCcKHUxVyDbaYhQrBieVDSymPL9w5Z5yB3RpeqbKBpdLY2Ev7G8q+hTICR5K7GMiLio5Tat3bbMWoP0wyEuISwzui3RjY3GmnGUMKQb1GETJ9oRNriSgW5BQvIGF+7vvu2hKCxkhDACvmb87LBL3zoe8oUF0r9cEstep6RmmM3R8lp8wZvboLfUNKEA0H0oNzpxAgRmpCOCf7xU7Z5akEAnWOhuy5tehlHhqO8rJXQb0pLovvGvxI1zDRf4kglJmDKZ/StUQ/SeoDolvG4+dbc4euFK5qTkaIXNeKMqlRWCp2ry/5I4KBEj/3jOsZg14PJKHVwsu1evEyPEWOK/dRBdfE0jOhYNdgTAGjf5TSH+J+0i9S9/RUkfcGlyo80GwSnz3N07zM87JCW+K2bbuWbF/n4YM+MlJnZ33TtU5BViQBGtqQPe660yKs0S3QUVbMofAJPnKJv3vwrz332hzyP3/ldZTiJxY+d4LYY6qu0PpghWTxXPT6T6KQTZFGOpqWNHBol5E6j1KW70JgMfvMPSYu1J7Gvjps7021DZx6bThEqUBSoer2EO0UVa+eBbqqokqtBcGY3CBSmR2O01H9lwPv16/57ZNU2baFg8AuzylbyYeRrLR+433+RgJBnN8Czx6Rgtet7gfwYG0rxJCofKSBTsga4kollippDBzMOmbil0krAXSpoSUlHNmHHGgTSojal98k7UW1HJeznNqvm/uSmL1eRGjydLAhAw8voD9TqksxTYBk4kdFkCaw2hQJi88yEKw7f7bla+1owivQMNIWSGAsebN6i3Abd1YMwA+bqPoNJlVfAMwyF1U20X1IYHi4KozK9052kz45moh2tUnO8lr5OiOsFKJ+9oIiX/Q92coxySzlmuFBuaffcCRD0miqJ6slHoFDZBRuMPkGOrVvM54xvLVgBJiQ7Hy5JKBIpKC2s6PMq6QzgWqvXm90iQRZwEh3LP4RUSk8BQrrzl7xLae2bcmHKUuy9y1fJyJWKij8UIysxiOTvIoGAQK8Wxn3EgVouA9lG7ElETZb5u+KfwSFbcVW8a6YMoVU/jMYpnyZuVJt8yG8Sc38XGydOnZIzrT/tnpk9dZx+T+tyAp7CS1y0o+fvTNzHuHjvvwoUQ9V39Q86NSOKlCzCQt3R3qfb84zh669POLSHYaun5fl7vZg/J2VYdCF7h9PuRYuIx0cmigVB34ke2HiduBLmE0K41xex4udImn/L1iF7sS2Z59mNzHFIrvS3epqCnrxCZrl7jdjDah0j4q4VzKMrkrB9F6JrGEq+lAANj7mujWqxBbvx+3thZ45+0pQisKS2R1NsJXX8sxL8SRILdWsy18K0QIXqHCywiyx6HVK7ihfocQ93yWUP5EnOPafASCBupH2czonLYnocIUelNuy3h53DagDC5bgqw1UT/MM3ulMT5HWlJkIi4w+Y1jCapy2mN43Um2quDoeLqpZ0G89K9eki8o+8+jhCTJKtuSqsH735WuC3ML94ZNk8pojFRPH8QHeBZkUPoV7eK9NuhwMAEomVuoxuAKpdKPxnfxSXiinYtFgHQa1GyiIXZr8Iuj3kk6Qh4nypRUW6wvGP8AFnJ3vv5Hio9NCnLS0RsrKcePdw57UDZbpPWXNSX71lllqZOLvNBixAeY4DPumhYIw7JPKvZow04MabmQKThLUJSKiBqs5ZK9ZdH6VWijjcTgY8JliRZyAOs1lyG8ksJXzZGLpcmpbOg0yupUj8cWb2A2cWhmzRXzvnMx0MljFAytwwxQOyZfoX3MNap3QT6+ZJih97ROSt1hRCFKA2KpLD6eUMIZk2dLzHoTcc/0xCw2KR+maZ+5YonzvLQ8797iYi83IpJ7bhBBkRtq55uhuKjoOvgdB4GXd48QtKvxijZeLwNRTWhApvASNonGbfk7ISTw65t13mqTZjnFHGTBDFmvcNbfmegYI1bWkd1uNUaelmV1P/7hyEzGJnFx3G4A/aIIoD0EqqrS+mFSO3yxnfRj/lpqkCI+4KOwmQDNU3S3jtaqNPrM1rzc5JdF9SgErKus3hMaOL9yfEiWw84d8GLofT8IZ9+cfZqEmdSz2Pe9DvPLolEnaufKwc1LIUm8ng8HJ8eK+zPenH7OY9JfT4kUEMPXUf/u3P5inFYf9orOfxuSiIox9ZG2uk2hC3kFJajQ/hxxyfF92zJgvKPAT0dNTxSnoHfIvE0SCmM6pYrt75rVZzNxaccT4vf7xnAZW+kDuqWuxE9N7Q8PtZrcbIOTuBrTF5zMjVPadl+8a9ytwrOLd4D8awRs/DwdQ8Nxf27XtS9emwNcBWK6QwjUXSjD5uY5Kjt+kz6Z0hGfpTd8p6TaOVxco1y1PIgU+eS6oamQvFFpDpTkbz2Cm3FqVJuXUabKa6bw2Qrvuur4TMFeSD8ddfs/jSI5E9Zsavnue4cQPZuII2BO56qZHBz0xoqhLSjRMEVYeqbcA9OdIeIs0pXAI1Bq19hzr01uqDl1SHWTB7Hmsq6rv+77r+QM5BJdPDm58s58dkInhJHiSgCDNJmJOjK7gazsokVAp5yu8cS0hlCeLM/r4ECH9TsOvDd2jBSE79AHoZ6GfAELPIeP01zYaPnEiz+xVT81RBPGGqWimsBr3SNuQNYfIjYj0gddXSiwRlapt2/a7foPoL3Q2ohXR5PIa6vjPPnxTeIByVGxMydVeibmeaBzZ2Sf+o5QnBcAUtpc4pvg21+CwXRNbz30/reXzL+mMkFOXOyoRUHXdtd0w9Di+Xttuu6cmip4ygoyerv2WbkSsT5p8XbzYra+0f+UhGSqkv3O/e+n+wU/We8BdQcWz27Kmpn6usptc4BtOoWg1PC/Pr74E0o6NCE25aJoPJym5s3M93k2vlxum4ZfyEtKXFe2GXcpWAWINTlSDyl+svONEdGEU4MFa0aBNc3hcJMv4KfUyU5Uv0f2MVp1zgjhaKlWuVml7FMs5s67kg5G+LSLgNemd6K6umDU+Q0VF+tTUH8L2LBmJFrxw1WlF33W2GuOIUYJFEzV+mVKjJJalwfsCLZReebTAve0/AfJtMZ+Dqhdkz6RjsN2S6U4+GJg+JVRbytfVaVSET+HP/BUaFfrVGHiSVboV0t2JURHzds3oUmMk89nngjk8IoFM8mJlWNoJcr9AjQvJrDWahoH2cC7rs5p2/JtECmdcZvBTfHOGWVTCU0+UkWgIn0bxXjKanX6arNteyckLcvlXtgUr/9F/c52q/AzeHB03lFgvC1V9QSoeZ8I7WeP0Fel65OlVBVGIrDPaJFku8i9Wluj7icTNMu0tWYxFY8HUSWHFnhLsy4E6nOlRHkcTIWZPzLkEffR8RrGBTT2P+fKyqz19peqGS8v5IGPDod6B0DzwvSxPVhBuoY/Ol6SRWjK+BaTGeeu6WnByL6deabxwo6Ii4DL+vD1tUG97lR6+g4cLCiVJs/j4YELXZbFqeot9WBTMxdMvyYv05ZUCk4EJP4iedGcH8ccF8LtXwsQpTViJlupYlgXhH5ZVAE5h9WV904Bmd9pwdGBR13DUlBGpfB6kvV9n+l3sBY059c7J7uBJObLhLs+dTfmv0sXxirLs8pjl/04oJnnreh2DhijlP71H6GyMB3998aqsJ7TuzN2wKRH3mr0Dg6p9rJ0bUNbVOE1in0sevUN3omRl65Rlk8VF9CpdZUyxFf924VvKKErAi9VfV8DzTMivvvqJ3N/7X5658Opnzmkv3V1wMjpav9DGiDIcEWyUu3bN+eKYYUJsV7+Qb6dq0p5rhWueUSWu2Zi+/kHEQ9PAkCgaixy5yZVnfJzp6jmt+1YucItPUawPorewU3U76O+2R/dk6V4gW3eJsOzh86lvH5Dk4WOvOzGSx8eLsOG/mqrl+v6G/0/ra57OI/XtEnep07vcMC46OM7odmI3Kk+cCqva/9WN+UTUVNwlCrQXkeFwP6j4VFGH/Ijrsy9/4C2n1WpGUlVBrVfXykiWwMvKERyBe7LDq8q+ab3pcnDuSTAcAj7m+ovXSzOiKGu1fKO6hl02LdOv5inifdDlk+j3H4J/IhWvfCuzAtz2o+QtADHFhaBjp08S5VA3Uyqe+rCMyJKA8tzaMoWopr5VG0y0Wbdd328LeLMkdwYAQI041LUvlWGiV7C/hfumrOtC7hmJMfux9KkZ7XUCOxyXMG7a9tX954pzyUK73oejclddt+3ShyazXiK79LfIS10gSaltu91uT06sTBHHTTO4Pct1EEuWrlmJ7gvyi92NDqrx5h1OSsx3amn06QQlXJU/nfCZ6cfwBkwbRQms7ey4m46huevtIltIFmN/FlBAji3woytfzz6u3WMF+o5iP6zspjcVV6QXbsWXyXTPT67ZXkXKdZUTDPK+65qtuhmGeiWVmrsHQrfqZQU6vGII4Flxvw0qLbbiuA8QXlyVnasGoK6IxhSTCx+XH7MlSwga6MfYYPFGObIk7mbVV2T+oDynaZphN9BV3EfJuaPd7cIJf8C/rRVduSki2i3pVvnSy1LqasJdcgRXDhn9WF3f903TSiEwp0CbeiUcS/XuD6rRXt8ypPyIVDmvc0e0UEek3Dv3hsvzyjo5eYtM8sR2WRK58F2w9NElYn5uJ/4zK8EAXFvWZdnte1DtMDosfkoaRKa+iGSeCP/DeVg4wtnVIdQfBUPXQMLCbQn03Kt4xNuHVS6tEnZi+ZlX9hhPWx/Bp0060qRCfbOG+UHdfr+nCY/UIgpCLk1Djd8K9pmF3QplI1ef7eWwJMRBp+8A2qgVRR1TNhuW8jHP5VfT1H3fD13XoPOM8vvoPwg2KvWG76eokyEcCwjmJPJxXlHzAbdFct3zk8SNXxTyhtSvyuIw+ClS+CWGSbJDdzbKsHnT5gtL1D20SMHnv1XDrmfXv+Xyhoahsl7ttu8XAi4ZNxq7nH9hPEY1wy3bUNy8LNrDiOHusa8YOU01vi0/lfxXf0GmwwXoXecBTisLp4Jw6fq+7gesIgEFB/3PEDbFiJ3tXmWUF8hpBrPGea6vgoIKlFhBU4sk9OV9ox+L72pMfehC3GvbtsMANFw3kOIDe0XqXbjeLVX4fU1SzlCEaD3mx/iwFQd91E7cb9dleLDRUveNlv37dWdZQongTG4VYebgof8ZIrlAM7hPIC8X0q+++70fXhbIf6SuST511RRBWQktVwWyI0AY4c3mrzr6/KRVlbJxd65/Rip+LNMgo84oOyxQeEVGCCF7vSFSVzgvA7eqVEk27jN1EUoURXV2wxwsDblBTHiZl7btaMByPFdfmeXqeT4C3ZKAEJXUo9mdiFjTZp+5Yfz7Wc4VfcSgb0DeKIz+rnT1y7LMy8zrCqMs8oSw51iFl0LuG0GARwaqHZXl86i3lh4mAtp7PByjpbtIUPZKXVpc7zUuE2Wylxl6RpqAgzQMOX1g3+4OQ1VTJIn1ZWwNyhNuHPobhXifIqeEuBoGfXLgIyQSCyp2Zm/EhmTpDYt/ZsIy/Ta/FD0cqBO9KGcxBQaSXuztMn2Qtwa0PcJut6M+rwv4CzGvY12bFry2+cHSJl2KoXiT4suZJCqT/bQ4/owzsRLfwMf4KtxAF/0n4rAPFDrIQDxp6Lu+B0+pfL6tC/R/0S5N6n+LxnAu6XMbPrVYEwR5ZktX2OBVaLijTRCG7uZaXrNrt3z/iCbhNmor2eIKAsnxR0iObSuIuKMoBjcDMmpWDKYJZoySgAs6QrgXkvm6VusmMzUSXpLU5+vKPK6aGpWE4OImP6QcbdcdxnUuSz4qf+C5p3hqcmpukZuQylLX9X4PbU/OpxPlv1ED1qZuh77nfCukeme0xDnq2WIdtuf6GIOBoGdzAwu8p4xVud/Sr8NsmiKcsAgGONNuvx92uxa6nQrzh7YRIbUsKD62q6TVbsI80m8mz8+MVkcPpW+VdqLSufgYbmhhIlw5OQvNK7Sa/EqAQOhccfE7yhLQbFsNUhMTV6kDFpSq1i0N0IJWWONo1eBOlptwkV9otCI5ZjKNJOb+cOro9WMPItzt1TSHhEQcvw4VEk4bdWi3ybuea6dMXUTKFcnr1pnlLgXG6aBOymPdNP1+DwQM8TkbVtw0zeFwIFPx+iOvoWrIMgihUM4j4R3CvBzXRjRSjsdtucLt6lrRX2BWwrFVZ4MfwNd7QLK2bS/ny7LMjBfrsi3slMr0OlofZTOFm6sSK/vzWyku0M0o9NTvFYhsC/G9K1vmYxXbkjTWcAmnY11TnNK3mbjs/tFx5C7jP6H1MxaoQi0hDgcG3ZkmMTd1/Xg6W2dE6rERh+aBxCYP9IL6s9TE0VXpzpMmFCXWlKnFEbncfXXr/J4jUaIaP8gv+Ra+GNfo12B66pVZME+T8uPb6dEkKKFvbdvW9zjis6oeHh4wJRo4KLkMD8d987aZ50Xq8Bwrc1M63eqSx/j/0neJFqwayU1TyEvys925pyZ5JPlP/i1B5Aq6ju/2+9u728Ph0LcdKB6UBEFDv32dduH5uhszks0nq76vRPU2pEjCEG6lBc00viJDjeqAPFYfUgBDelv/cRJusTCMyuQiEOTOELnVqDBOLuMaXXQKQLLuPGPk0J2P+wE0L0+CGgYdtiDtUX3v0sIRay//bHkGrKhNoGbpHbPPwJ2XqPet/TNSuCSeYr+CnAsFKBRf5CB0xpIiUr3fgwFcVdU0XqjZOlfdbdvQd13XYW/Qj1AQS1q6+0v5T+BM4Cgz1HCCxKbY69YytVVHrOl7ZerNVyiPA6cL5Bw0kPzNVR3B76NFbNpL2lBZWAgV2ri+oYHRO2nrPzCIFtp/XOsLnb0EIU0XlTwm+T0KUw+qgrxP/Qr5FbmnS4xZJ35QeaZuUtA3WJ4Gd8d0aIWHQlB+MCzgunVdYyNj26oyRfXiOIDwh5n8Eje3rZuntn0QygXa5CfUhe8GFYYgk8wI+Hu8FNmFk0q5R103t3d3VVWNl8v5dLk57ClyUiOoq6o+7o9v37xpsSONAkAJxNNiSQfWjx3j1Aga+jK6ZO8/par+IQAqALTdToAKCFtiWm1TwwgQ8L00M0WW0B+APgItgCi0z01W4FU0IWzBHkbToLv5MGqWsu/w7kM7t3J3pF6FtzcB/PpFRlcf9fI0nrrCHPUKZlN8jqDatJA2Q/3rpL1zmSJESsPf5IxpJW3OIZYQKKpLYiY4mibGH7l/rn7TQXgYeLHsLgqqq51EEmXYFK1LCkrpfTl98rdfx+7nHLrwatv25nhTVdWPfvjjatvAp4MOw62q5mVeluXmBj4FvklDUGyAd6oZl0Bkl+l//jsw8socYpw+p4wWh4UreK6MJc+3XACTcW93AtLEiTgZMCjIKB26rtc8LYgSgy9lMdoDpq34xhJb8clFKoU0Cc8EfzPl1c8Uw6XUOdGxbR125xCUby06Cg1dd5ZCkovi0kQ/QMYyvT1OmRZW4b7m9Sb8BVgzkh4w/aZecNI3q491A55+cL0wjLxRYSkc5D+nNjoS1bOaflEgEoQTqCOObs97HlLhI3/55ahmnKqtlq+w0RmYmOXzc8dV5lhOpNm9CzgdFSj9QTXtdT30PbmgX736SQcD5cjbTEG45enp9MnLT3fDfh7HFvwRUZuPfxSAmfK1xOsELouOHMN6sepTBgFVGgqvFLkjrqWHqGxG4MBqEFZwcAUPzPBpoQ8bvA/T9FaIEC8z6NhM1oxqXmB6GRfeURLVBOz0K8kJZTqqKWxXTtjU52Sv3lpOpYkB5LoQDjcu8R/5JDQIgbXi1Mmmqoauh/HeqBOT04S8/HkAJlGdIIMy5BxEADtPIh6bTYj3LqgECaIoz4DghIV+wOl0+VdEFm0OZxPO6RZacFGVFBFhmOGiAmrJQgFX9sdD24Ma+/jw2GHmhpAD6JXn83l7ub28e/HVV192G1xW6jgV1+dUnhLJMRtkim1E3GtKcskoScNRfhPZrzEYlb4n8yUjVkr2MWjNHNXAxzVN3XYNZF/uhn63G4YdJGJCDgj357EAd9QiEzD71PJ4gTBx992iVZAWu0a12qWx+eNOsprylV0h30wpznfGn2YfE29btw2GmlUbpFKCf4FLAmFVjehV8rVUHZeDoKbhBpHQny0o9Zn4qiPvYoQu7FTW7M9SXX/k4PL3tN+TcV+VCmPG31zSSyTmucbSqVpTVBI4EEIr325ub6BzwrSczydIX2UNDlPi6nocx6fHx7vbO0oXpk40Dmfl5h63EsSIYso7TshVhio0Y4xqKF5buapC58jjd8v6eDyqIKdVdqvaY+tmBVtdyqB8dPXWtF3fiRxmmPCocPS6UnUE8agQZGJhka/8w6+ynIrQCXBQBiTWDtfeliEhGvsHnp+NDzZF0T3epUxi7ULd1U1XNwvW5dPEhqQ/u3E+rk7D3wCMOHlUky9Z5ClG86pFOKuyyvjrLCUBi1qawVGTywtGAA3AiTwyQSoYzyszqZno6MldE8nHPpErGGorMbXRfcrzgJoWwijvHx9O53HdFnIZ0DLpK+u8nJ5On376Wdf1ywIXEDZodJ2B5s+QH1AQ1H4vRL1tXQcn1rVNf8yrICCSezilVkCf3IL6RmsMRviCnCN2teQRh1YHsfB/69osMG9AsseZevTJ5FriLlF+kaoiJlJCBYdfZ2aEpMKICUjGk9TPgzO6SRx/zRX5aC8zTJxFoEqGECGI3Q2cC1R4BviE4eAGzl2XI3ySlVGc5Y1ckTMvr+lxEVzKGxOnjynSyFTxaxk4I82IduxjVXRtaN+t/v+6KCnCfUNQRb9cFrCp4hykk0/Lrquq60EVPI3jV19+2ai8kQtQ2m6n8/nzpj7s94+PD9vaVtCUxhb4QQITviXMyQGUboCJHG6fAT628p/uJT7n/PRzZL/y7ailKQ932nUDRjB0HQJWSLTctM08g+vPGfboi6NmxuhtcqkVeoW4yHJqU+7hm6b6VXl68piVhK5SyJe2LcpOXq9ENwrvJzaD8ClN9JgrTOiDeaILTq6BKMa4LMquqDDfpZl4cEsfUZoIm2XgOmEaJ2iWNql8x/kfDS+dwusoPahMSfGEGQs1yVBmJrmlS9Mn7IGpuE8lNocqruAlvImOBBHAVTUMQ9M2w9bdv3srVp1JH+CVXTtN4+np6bg/PD4+oBBuN6BhxxBkhpZ7jmcu0VgIohErGcMKr/CkjNXGHZYc1DR2wV2bAkaP0qW0JjaJ/u5UrmRjnGaA7mjwGqAKDVIYRtG7hC49cXY0x+XSrQqGQLqQuFXMXIzLZNmvUzQSBh6fG5RPExQJGAMLSbKWt/CTq2Gqqq3ry7Z1pIZiNjRZKOM4oUy2tcRsNnT70uoZq0TTM2MzykWNwRgNh/XHU0RGWujW4DymdqyFcwh3dCHZKovP2HocpAONCBsIkXynGeZErCepk4D2+/1abaen88P7h/2uEwBA/il0qFyXequ7tn14uN/voV8HmnroSKQMZj4D3TnCpP64YDQuea3AW5mq2cpcIyiv3PKDEtqWd+1WioYYv9QZPnJ7y5xM+VQkOYwnd5AVgqYeymTKtZI2oXJonrTShaYlbPGChIFHvuSW+RzUvNc4bWwVxzxH9peJ/HAV2wZQwgDZOZBGCRYXTPuWwpd6nGbfRCdTeHk1WJzNVpYkFSkIQo5NmsVx3egyic2FSUzEyVe8mDe2Xl0tTqxMPmnFhHES+9eHkOktzVCRsrFkrXI1ycg0CAlVBE172O0v4/j61et5njAC7Oo1Oe8NqsHgl3Ud+gECKW4QmMzc4adqbEzoujBjyYkjdgFxkubVV8xm1OLa1J8b1d1U2noxk0LKjb0VGeLbYxUewIgW+0qwkxW7T6PVpw0D8uYmQYZcUeML8SRmBuYr8tLat2t3MofVO1cj6TErsEq/vIR/GPlceYlTA27Z1E2PyW3YZA1WiylA8Ok0p0MD6CXzXYl6AdsQQXFSl19VZHJeDvti/KjzZZsI74ijTLi8uvNF1rpKWWflJ+hWS8WdxAs8QTveHYyAoN07kaCJMqrGB3YvSFB1PQxjeHx4vH//DtKQoO468DOw8NZ1niaMGs/DsKOwqLWDdg/4QNXpFVDiZIZiROyauZ/AwQGxLI29siKQTPqgBN6g5BZJjNigSGT+jwlXM3XFbcqtChQtvBLN2+C7Wv+2ZNVaOBSWqE8PN3P3pH91TZ5X+GQlL3ZMqYjlxVEu+Q/8JS4dnmcRb+s8L2/evYNK4KbZwEPA/IOGEqpv2RGhygFupUOFxag/a+VLeDA9MvRfTDTrKxglXMA9nxegLMDtMbQ5tJ5fEnS0tluW1y49bPSBztTSNUgmZAxSeaL3+rKpCgEE236325b16fHp/v3b3W4QZuflBAB7npcRKsDq3TA0DRQ24KxmDpzE83Wo4FZTZIWKAB1vhg16l3/ly/RDrpEiY1GiGqoGiKmZ4XMqXRaQp3RRQZLk7EyJcTfURGjfgIidqJotYbm4FqKSe+cPEL1U/7wuB+PtQ307KqXkTbnOYGOCi8EseUqAnSxRocE9JptpWtqmW7YRughiWht1F+A15gnJ/m6QwbugCzp4oFWusEbJ7DGowGWlIQeTsNWgLNOimLULrMzqD1TtsyBrUfuTVMTMQx6tZ5V8+QBiFiXXtqFZac3x9vY8jffv7qdxujkeyYQzIYzxJCzsrM6XM8iUptkN+9PpaYFoEylFvEnbrH+Q/Zq51gw84MQqTNq5LsuTAXQKkeLxBbIPlQTuOtqtGRuUo5brr36TkRYkUZI4OYcrpABWsFYtN/mStEamH/EM3VrDHvgty8svFEt6+W7UG/WApEw7IW15EuczXUWnYBVYqAF1ZsjSRWc91wT61GVXHphwBJrMsm4blBAzcabRsDRQJO9kKoO2ntYzLyXE00cqFEPBKEtez6YS/zW+4I2Vf5L0smQgt6q880whlmnP9e8GhgdnOux2w+7w/v7+9PhQ11WLhhtjsqEOH3Vd1ePl0g/94XCYpssyoycCfI0I5KSCJSFG61UWVyh0g0V8kd+n90k8r5GU7DkhPc2JkgiHguLpaIcCldW6+qxRPYeg8TCBEkvRlmxcgsNLwAhIcoiuuCbVebKV2V6MND0pO0HuMgp9epjZOqU7W9lDgTozqPHTMGKCIRmXLC3zffHn2jb1ZVrbDvJflnWDKhGnnhKdI5Ok2Jo6hoEPkv4pQeD0hQkWtrIAlnTxGbsOfvkAETkO1o6NBfrWdtTYIjK7xkkTdgRQ9At3RikDJegyWPVoEw6a+mCCpgMf3tzdzOvy8PT49Pgw9DJVlItFjH1BNjCgADTJ2iao1dntdqfTad3WeYEZDl3Xa7q5W1nQqZ389dKE46JcTujzujLsLWzFCQn34GcpImjRHmwhHMdWaVELiM5+WytvgEIJIec5ZkC6/RlQMtEbpX2iw+tVll3JZ6Z5V7R8LATYlrgXUl/UeijeO9lsCoUcxC5njRG3XrBHYD3QLBtaL484k0wNLQPhW7BwwygAtoNRH2iUUsikY+VPijP+6vTcTCSbEshYoCxKmAgn2knXDpzLRi4gomT+QT5zrmnr2r6FTroNyUTuyBelSkieYcPC8TVlI7HiykGgaZr97nA+nx/e32/bNgyD6hDO6SUJ4qhvY3nY3NYwRnge52me66GZYZASlENwAYzA1CKgxN7dDxc5BBfFtnGCdYpL9tX6GoIFNDOWi44NK3r23zNJ5o5TfHCicehYMIKVytn0qQz3gIQkmjxdyx1SDhv5WnGDhqH+GWKuaW6TOOcCPxHYSbq9aev8ZLPbLLad51QSVSXoLwvJ9TyNAwMuT1BOuPN459P3TPaLv06EHL/kYqfUWmZrMeEnQE7OX7BHsyAJRQgkvmSJhC6SLrU3pDo0clKREKPQIFG1JdMuWLKG8xbpJPq+Px6Ox+Nxt9u1MEmAEv791v3GnBLhT5uTiUJRBjU5gBLcrq/r9nS6Pz89CaOI9VdmZ/DtefIotNSHflLTPG0L9EuZMbUDqncYLDqIyoRbps3jFZQZs1ZdUEurj31lubkeOk7hcEnUjke4Rwaowj+SIslTbbllkgvBeIDnKpllF7FRqDzVvAv2TL9ag1qiGMZ0R3oLXBItYr+OkAyf+zZWWbT1yiup5Iq/5n6uIPTYs0K7ZHJsobrNyR/rcGJWZGwoxZ5eajrMT2BDmP90CXpsScfQCd8/U3yCTii/GFNYK6TbCUQTdUajl0w54A0QnVJVGmXdbupRWKEj5DiO9/cPb9v3N8fDyxcvbo43w9DjQB+ecR5LCDgFiOhbP0Cer5yTQCbWIZJX1/fTPJ1P58v5BBX8MO7L0vnUGiAlx7vhVE9qmnaa5103wHYghmwGi1YUsQkf08Vo2SSslmV9PJ8sE8sILC1eSNHPdB791PmzKJohl4q+yDRcZBGyUsoflh5+CdN0yaxGvZZ1R95rsQZU+VG5Io/y5edXsS7xbTiC1oArxP2k66q4KkIxkq018WbLdPWEVDPI+HBySslJIiXjpkhSDsIBWmAduXjjqRqnVJciwTRooyNIZqXNpqOrwimswlkpOWNWaeIQkA0O8VeBFTdO0+VywZmJMNCgrXEIHkfFJOBDUhdajaCQcgJ5oYoWyjc7Hqd5nqZxnqZ3b99ezpeb2+PxcICcR6JhTIZEXY38ffQeRtfEOyInxzD14giVm2bYD/cQ/73H7F2o4Dfnh+PWuH3W9djwwKW3XTNsu/HpYVnWFgJLc1M3wzBQUVbo1OLQUMol+YJlWe8fn06XE/StLbzEt/5sjFhFgpPy3s5QNuszebzUVMowxZDtGg5nGQ6nrRMy+9CnF3kKVi6qPgtLYfN50aobRi7DEp28PkqUFGnWexrpius7pChJrNOD5adTeDycMqVKYcn+J2j9AKoaoSaZh6yWMWkTtxTDmRMRiUqAKGSasEaKApBcKmSYtOixJbVY7NSkeQ8e5jhNDw8Pp/O5qqrDftgfDl3XAZ6KDeyFkSUIuNnFGw7i1fq0bdt2QNI7UMCRsp8en5Z52R/2+92+H8hjJCt3S/PBR1pfsVYJvL7w6t+9e396eup00BcjnxMyqW3BuW3zOjdNu9sN50s3jVO9g/bd4zRBe8e2xfCdM0i9BoWyg/lpUz88PN2/v98fD1cIOD742U+z83KfqiJvYtZfmyuFQsFUyRFg54R9eFeV7/ghc1gnosjVIZl0Xn33yoSTpMYkiJlS/JOdJf7UaW22Og5l81AxVcHYz8YGXwbF5FejmwJ4meqs8T81IsZBDG09LxMggkt4YUZGoa2oXFkEhxcK8xycRu6X4tJTrrig9Rhy3LdYP+SEzfdPj6enU1M3L1+8PO73XQtTfkSsMtOk8rP4bKfMb9r8B62uZZ2XeZ7megETZ2shG3lb12mCDPB1XvbbYegHisEmC2fXqVTzOnVD0ICM9g1mA83LchnPyzxiboYfeS+5+U6Vc49A7w5226/a7ng43D88LNPcdDCvc5zGfb1vGi6FTXVVbc+PS3l4fHz39l1TNcfD3lcjFV5pul+BltNcgPz8hCsLRDJyE+IQCRmZmS5B7xN4frocDCSwzpcJWVXOyfJpJHOfmqleT6akkWA4i6ZpWzCruOsqn3SqrUhKn0xSZakHlmViL17bSrGeWuMbVi+rIKIKIlTyZmyhQ13pcDwKmOsifp17nddl+yWfnw4T9eSifk3LBEg0CQ2B5uVeMiyNUjVP59Pj01NVVS9ffnJzOPZtu27rOI1o4nLYHEe6pD1o9EFswVa4HvHNYQYupcAvM5AykfMESvUyrZd1Xpf9fj8M0I28gX67VNUhfZvN5St39vUtCBHwPx8OoO6P47atLcwElhxWx8XVvNDVU6gOlQu48TSNHfTi2U/jhCoPGC/tPA09NLhkDlNM/Knrx4fHN2/fNFX16Scvh65BCWyi5oqwdSSU22r5r6VyQolXuhCPx5Eg+AztGZDsQLAckmsrBTYAI4jVCOAac4aHSBtEMo0cAhL4ZpKS75qxmbppYaJED2EKPnQzwQ11gS2wt5U7f5VXnGoYWYcs6++U+cES2mUpwaIEW6gBrrB71JkU5J1yN1W5yKrPuq4dNAKwjrLsR5RsGctv46/jDYucnXGangjPHKfpdDqP47Tb7W6ON30HEdQJYqLwIv2G9p0rW6ZOinK/sRbC2NbidzsQST32U4NuTNM8X8ZxmqZlWc7n83iZDsflcDx0WIfXwJhV9pqoWq7hIL8NWkwPCdD9w/R0OZ3B/4yjxXw3ERXChtgmskQHqZtlXaplPux327ZdJmiXtW3b6XLBflLQ5co0oHjPy+Xy/t37vu2Ox30/dE2DYSTB/IgVJTS7JqtTzFO5FONA5nky+9e+SuTpmuDIV7xBrQ27nKz3qiI4CUEnAQ+7qVxNBTU52nWRRAFCVE1ZTqCVWJYza1Vmw2Q2NIHw7Pl7krAakJW1dMR+bGcTAaN2TvVBqWvNNJ2x4P5IzAQYHwfyE+JAkEm/LG0PZYVoIXPNgrtbrLLhaWaYS+4oyMnYiBzMAf2GiJiFc7pdbFs9TuNlvDRN8/LFXb8beEIl5AUD9YqV6A0cc5VnppOSLr0kFU6OnMJHG86zBUE34bPHy9PpNL57Oy/zzQ04qDHdmzPozc6zl+0DKhOWdX84TPM8Xi7TOB72O9RUWLRG2OSJToznyk/B+Ty0h/3+Mo1VXfddf0a/9n5/gNY80Rok6p/X7el0rurqgMWJMEanaToBkUosAccVYfzRLz4DX6V5RRM3BKRlsLuRYSOePYfdlGaa+H1FToCe45qhUpygWRtQKeU5UfEVae81S7dQxgqwMHGUBM37A/5QkIrk/yA5rAxJFu1Dn/67nNBwbQyfW4wlYomvx5xpSE84mnBe1raup3XB1kwkhusk7JIwAkuYwKil7h1/cZ0eJcPIY1jJYrJ0OBLdl8tlmsah64Z+B3EXiInO1giJbBr6tpGCrTYFCIO6EoHAbic5U/gO+DtQsYLxBeDZ7vph6Nru/v79m9evpsvlxcuXx+MB5baA0vNFRQTh1P3QH+9evHv/9nw6gf8Z/Q2Ia9xYrs4cUCmKyBWwuhYCSMOw3+92l3GsOmgPAL/U9c3h6OPwfLrQYevy8PgwtP3QD+KnxFxov25HFCFVx1FPDsr0zYxnu7WUvq2/k0SEirZ5gQxA/URURGa8XG9EPg5jFiBWV/Dpq02D7VPRbYGRAjKTJH9QEoGCBHPrcmYM/UQvETN+tlOCgHAmAolfZEkm8YojStQEFeDlvUgSfVk+iedGi0fqPZ3Ou76H/Af1iEidphKh0Jd/DGOx6n6JMBK4McTjKYuqrXfk4BqMaDqdzgtqjEMHPdjWZRZzV5QVXoPaz8KwXU5DQahspphqwFI8hEH/B4rp2sPQHcAI3v3kzav3D+8v0/j5p5/c3tziDEGuxfQQJ3uVzqNp6pvb23mdz6fTm1evKPtC+mYrT0tsfyMnZyDTW1RFu81g9/YLIPza9129qy/ncwdDlY70DQXosm2P4FGfDze3XdsuC7yzrQtIYB8avKYkK5IUBbNfou9FYep/ZKOGd/g1SXplQxedEFWDxr3W/DnoiKtPer5w2IN9p+IExmXwmAt2DDQ1TA6ygl4uHTaMC+ia/JkUuGPVrvIVGhOntckSjG6aCnxdTNGO0aSPChryc69AwfrTF6a1zfatLz4/PT6e5nHh7rDwg2KoCBG7kRwQcRlkN+ylZU6nXixHnGYWOXFpyjmvCB86L8t4uWzbctiD6whL6dDFTPFYp3AaUMTj61ieuCxiQL/0chqUqL4qxZZl7dr25u62HbpXb16//smb6Xz+5hdfvHj5sm7aqm7WBsAVX9ghDKDaHfaH+9P5CTrjzIfDLRirmG1P/1c27lIyIud2FhBZ+2A+IJx7IEhIu4W0ynV7wgaXuwEdWgTKpj49nU7n02G/6/tedH7omIQ2cDFeoSkgDlHsk6Dui7hWB7FXJeiiRDsxqgmPpjE+NBMTmjlFwR/EDTN49CzBq2sasHyIPJ0SKCyeG0EYGmjSYI4JqQahfS71UMUaL7Fdcjjm3Sbksuy9gtWr72cEq14D5fyi/rPGsUEoAjxZHYg78rXClwBbhJvpzbSLPSUb85vCphKY+N+Cm8nSFMQKwk0sK3iBm6buux1YfTA8B5+h9rEFxWP3Zs2/833tA4tVHWJjLV0Pzmxxp7hIGsQ8z23b7vf7zz/7vKm7V69+8uXXr9e6efHyZUe5xUgtRHiyRoBFP+yrul2X5f79+74HbZxvKtFyh+/eSklMJadogZcdoEW1F13XLRi0H4ZhXdfT+QzmWgelCgi69fHxsanrw25P3fNQxYZfO2qqHoOhYnqkqhxD0UL/9rGoo/6YhRUTH/MfuXMPIxPsBN04XaY6hy1MfzTnFuJqS9suWNiBG4ZqOAr5qocJ9gsCHdGS9CSKAkmqQCr4vKeHSJFkuXB4EeTUyJq2IYKJ+UYMgZSq7oovddKnr8TmZJbnNDQiHBxK1m7r9nCBBAm5bING0Q6/NKfCx4DJ0e/krnsw8yVnV+iK5TR4mgJGdMAxhRMhwHxBXuzC5qKuGRPwyR6yTXN/O04jlqq6LTZPzmVwK3ICWszz0rbbYbevP2+2unr96vXXP/l62dZPX77sKK3KpjKKj6pubm5v26Efz5dxvNwcDtCxHHvoSIaUE6/So86LMMeaeC+kmGNFAvVnh1piCAG0zdAPp/MJCh7IzAbrdxqn8bjfAVNmVgeP35qtS6at+aX4pyYH5hDJhYq9nE1aV5denk1y/qiTYyrB3Dna6UjCIHUJw2G2GAFEewb8dehkljZtBGbIKMdOYILprChKc0C/NPEohZoShQyTL1/rAwnOKGbNCvXp0E8/AaaJIVfflsC6bICo7aXCGhcJ1mXTdeP9LHwPLkEVOoN/XAcFu4O6ETw7govBKBJJyBY2bH+ZIaeCpfLqBiYY7YaT1uKGwtatSsgeHmR/lRbEOxPB67DwG7D1FnSBqqp3u+HTTz+ptu3d23dfffn1Oi0vXtwe9geUwexRw5Kyreu7u09fni/nv/vR99u66QcIJVIZoH9mFLYOZi4Z2vxcLIXYKYjGdDvO8wYL243TCMpCh2Pa6/pyuXTQD2BwmWRI/q4vdHkCRU694gXJ0nySa4PVWqZi+75cTDeP1rimQeRTL0ybrtZ1hjT3pR4l/4lpn1sds3xqauzxBi/wJVMCrZP2qhB57JK2S5riz1hON1Vvs3jB+UVIoOW6zuntQWme0wTQ0XAxZC4eiuZ3EPhmKAmg7iQUnoG3V5SHXiz42Le2m5IsUa9Gy5mnaMCQohxGCjjAUAiIv2IvW32KfMmdr/pj/U4tI1UP13EQsWdsUh28nPPeG5/F6WdCc3ULZtpc7fru5csXTd1++fXX0IcIrbf9YQ+Ejg9pN/AdfPHpJ599/o2/+LM/e3q4/+TTz4ZhRyNNvBKhuC7o66b5eAVPtyi2jDhWAUx9B3MLQMk/HqYL0HDTdmvVTuO4x+Iqv1Hg020NvTDtXCMNB56fajiWpaKNssrDGAiYWTDA9KLEuio836U25muTJGCI7FBpEFaZLRuYxG3HzT5JWUnX57lj3KmXr0TXnPJBeqYUdnOLfn+YfLGrNQ8cIezB+B6nOsWIbwGO9G8Kg+Slo97AUYmmEkIlsfeVLNlPwyRsdvKHXoqgSKssbiGsO2sjA0FW+ymWk5lPDhweFUg0qbz3SplaDxXlNhluKvPXII5XVzihHh7TtA2utdrvdt2n3bTOb968Op1O2JtuG/Y79FHBTbqu/4c///PLNP3oB98/3twcj0flAx5ItkXNG/Bb894GEWiOsWEaKXhtIVIJVb5Nu/UdTxrCm+6GXqGnXkdIXHG26/UzSz8JqKNgu5KeYOIp3sHNqaFdKD4Un2xqrqjbaqkRYWlpUtNsy3o+n+um3u/31dbD5NI2NZwIinm+mrS6yPctlE1qu9S7kQ/BWY68fsq7Fh+/brisZ4nfXqeElOFokirC28kxIkIqFuctW2KF6jbosIksms2CSOJpj+SEAWhpAbSewMLdFZu2kbmh3Sm805hQPJsoo/oSnYrK1ShPzKOcfLLxeWr/fpUbcbkqa7atonxYkHt9/8Xnn02X8/np6QmVrLWqdlAmCDmen7z8rO6HVz/+8vR0/vTlC/ShMD/Xwn96nqZ9qG/BiDjoTmYlSxInrL9r2/Fyabuh74Z1W4euhzm7SNhQTYUuL76H3BbbyqqweaYr/QdeNu7+Wb4dbDffHdlK0hIh61qqZM0jmY1pOyrmehRZwjy3eRzH02kF5ydmcVCtnCiOdFMhmCCExXlmT1L9S7HHpRaT/HSFYPK1lEvH5HbTQy30WgSa2Ux2I8IDq0zkC2md2I9jg+aymDLBjAfmlV09GVfKL+KD2RITfrGBs/q94Pdlm3EuhgsgQzM912ib6bMUAXIt3+O+5UMmicTFt/mcOWVYXtt2lCMxW6HvdUMPEWxv2A3f/OKbTdtBrOZ8Pp8v8zRvK7S8+Zn/y7eqqvqPf/LHGPDABC/+vlNH7WhDe1jtWGuMNgEl57LVgKOQB9qN0wXKG2ncbgPOSEoudG4X2SCyKspVERS4dsIfoGobrvAh8i/3nlX655wwk8K6/YL6rMKKkV86LpLqRU3GJkiiG6HTIheHh68q+8g3LqgmL8x7Fc2Pvsmp/2ZZ5RDLQjFkTquzR+TC1eGFuUA2NT2GcuQEYK3LvHQ9pNUOHfTFIseodh6Nkpv3wvRPI4UzxpPDRzr8GlpBI40V6oDi151L1lnqaorrOavwkOOWTG09Nk8xjnlW5l0weokIxEuMlhiY68h94Ftt0764u/vii28u63q+jJAzPU3LPL948eLu7sX3vvu9d+/f3d7d1RCntCcJ9nyE0GMPg2/0wGDXrWOpMEiey+VC8XielrYCZ6Rm3b5+s4EiFXBec4LhtaO6+qY/zaiP/T1fUafjm2bW77M3MDuUU5G3DfomgBzmou2ihRd1rXTkgH4paEQ8iY1pT6gx7MZyo12AXBtKZVNK3TeTu6U3Tn5Jvg1rmsd56Afy72nzCtmEUwN8zwmfAZo7zUOIzC28tnbwWkij3/CZBEHpFWJSpV78soJNucYmEjbchV6emFlBYqLRi0SV06/weEv6rK7A1fzFN7843t5C+GuGtgBt237xM99ct+0v/+LPX97d7foO/XXBWUaKSnQiRrXfZ8AY4yPb1okCdgRWKGzBhw/jDmHSCPyJQYQF5VtgGDKnXeOhxVfpgyLf+anu8PzLoOSLIQq3Na3fWC4lJfB0H0gGulzO4zhyvULBkeP+DeFvrcKkf1NuonzLdathrsPFhBY1fWYL+d9BkQxL5VWFf0XMKEbB/CNsvAShX3ad82hvKSXyvMh9l4v4zD/h/ENCw1r5KhFjHB0CPRape1uAbuBrKmDdwTmNOHgH2DVedNi5ZLqtiLby5QSKaj6ZLkDVFIguTdN882e/wFGgkE2wVtXNyxcvP/vsy7/7+vT0eHs8Qh2vmRQe8rojTX+PJJWZViKSWdGgry3QixCDnU2zLFRm2FDx427YoSVoliTOXlqrau20RXzoVSa8pAgfbhSQjagtmjbmOLl6UfoN2X8Iq4YbOdAYf2UdkELpofxtXbdxnNpupLNxkJQIT8jHpQ8FWymjlwokTHpGLRoz6jCdEn+jD0Wvk8D9NRM3AqZ47M9yQHcLUs15Q+M4YdEy5y3qyZuOEh5GAoBbjvs9KqRiPad9EdxW2WxePoLygtFNyKP+CrA3AUEE4lig8ZXcSNm8p0p+tSaYwf2g1iTpKBXmnRwPx9vj8WG/qxtoFvmzPwfW71/82X+GBA+oh6DiQbdH7/iQp1shTQihy1VJGqMmAcgwIEgRadsJ2t5NWEzVD323rbtpGtdlQUOOvB8QAm2gYlRyHSIu6O11xwFQ2mqidECeBSaRPXcLZ+ykNwrJz16juobIXsciuoMpZzQxnXKU14p7r4DqxKWGWoYtTmSRtJYlp0fF6daaCk3OnkSam3uDzgWO3djAR70SR9OVvwr2qf+sbVpo6yhuRW3CaljjZ765fFbZpvlZAvo7h6G3VAojd7zmHA5JLI6oNSXWsZe6ql2k3ip/2yqJ5itpULwvlJ/RHX3NBmTzrds4TU9PpxnmWkC/9c+/+PybP/uzb97dv3vz6gjlQV4zs1+cVWY0FGjaAZtjIDoeJrBvgDx6W+H9rm0W6O11meYJa4S7YcBmQZyvJNVwTQsF/ZZOVaCQsGp/kc9S9Qen2URMG4mY5uvSxJkQBUyCDOmFAYbh/Kl8BHkfJJ3td/UImZbEh632Wg7ObUoV9uB/4b9Mj5bkBk1yYJFv8kdDJ5AVxQYl4Y27rBzJRcBlKKpZzvb+FT5GhVzgU+26E7qo53liUUOp4KEmQY6L1UCMGFOhlxKplXIayJInavMDyVgJ+XnJi+9zPavU1T4HgcnJVkYjelCbX1oiC4SRxu4q7jGaTwqptfNy//4eCvqa6rDb//f/w/+tqqo//8//CaI6PTVvFV6mM2pdLDOYChyHVvAZuwpSwXE65o9U6FFBeUPf95cRGndA9eBuNy/zebwsMJ+9qbHoYqthRDbkQl97ldurX+GvMRNSyFGX7nkNU7iiZBquK968+LsdmPJy6lyEEIa6DaichnAalDvs+mGArHq9VFoqscHs9fJQcSPBKZ9zRSMUiZg9sYX0ARO9LBCiypwhVcliUU2Sv2YOVqVNFwkUxRG0PvQtwd4hvxfksTaCDS5dPihMesReM1Jamy0yyTwmhV2En7tMDJnSlFb+plo88hxBHsX+ENx1nNUcEXVidkTSMJ1Iwr855hARsmlZN6fTaR4vVVXd3N69/PTT82V+9/b1izuM/UK5kkvVMc5BpBqFC88XKxxnVLhlI5wuaGnVkMvRdjN2FJmXZTd00AtmGM7ncwNVF7z/ZoPRKpDSnYFaN6vvF1VhW4yA1cm3AjZGShVZnV/pvp8EPYIMkdC5iQlVTehiUKRhsPLaNO3QAwG3nOsrMUHraCjeFadealINjr5Rm0bew7bCLI+zdHxOnwa9iLsrunb5peFlZWaZvmnnkegpAheYbNRUn3368nQ+w/wOIEtBEwcw+k5wkcrAVsKogvMtnAP+kHsmmoyzRp1xZqVflqGtNQvJLtlJa4fi8MJdVBfaBBdQ1DSNiNk6RqnB4qB1mpFRd9/41s9UVfW9v/1v1bZhqVt7JdGYN+poQwXydi1fxWxD/yb3FGDFABxUIIG6Cazheeh3Qz9MOwi0c9dBCINVC41WCUEzuqXmwWgrxSJU4i8hMVRETipmQpZxklMlIGERGBYlNXoGHRO+1ohbTBW5A+a9Ni2cDvVshOgaZTUb2+ReUBgXijvjPFXIvMMZJUyW4vaCzGpV25MTZu8XE3wwJXJ8vWa8ZChTPAfyaoTGAsC/oQZ4huhsj8LDPdi7K617pTtCl9LrW297Q5JVbmnfYV9SjUPktfPTWOISLzOZ26JEoElszB9TCBgVbqLJpkScuEwSTNzctzniWK0jjoT44pvf/Cf/0y+czufvfuev9lg/4IbX8fJdiz8Tx6YamFodziymf4cVUVGTboQ8r9Cps1kv53Pf94f9oe+6HfTBG6mSEFPdF074pFXoiJ0sQ9n0wCuiOKE7pzkHnTghaWMdmUVdlODZO4XsrFTHtpprFxCKgIPMQmxvxriotM3Kio8biLtbw9Qu34JZizonraLQIa2tUtU7rla+pgSFtiEObl6JM9rkTKwVfKvjPFMZFg4QYvwQzVtwWHZCcSDSGqxYgHFNRshlLEUyNygdmgp4uT2AxxZTOj1jCnDzXEXKZWSJOuUgOeAtXQ6JVTut9HNbhSdJ8opDE8vdfv9P/udfqKrqv/yff7rM0+H2lichpDcnMksfzcDyOKNS3iYOqdEVRu4IjxNooVMaLOFpPp1O+93+eLyZ5nnr2m3hG64Vdw1ST1bokpBtXTOJBIe8fq0QScpW0pRb9w1/pgnLKKtvJpWL6ma2YLnWd5tTNdGSGpyfKkT5+AMZF06XStcP4nTGE/Rjs+tI0HPwzgMhzejMM7GybeiC8j+80JSqKRjh0w7TLJMIfBDMBIQmhzpiE9DYMZilGQ7doGZtaCWDKrxk1kthF4ZPjjn6nbtkRE30SEFVG6lqcvYV6s2+J15I7MK7LMvP/3f/6OWnn37vu9/90Y++31KjDEUJViGuBXidyhglhLlOvBLJFV/JoVqMHiK9qBtSjvTT06nr+tubm67rsfQdcgOh7t30nLiQsiVhKpIdQNgHegMS0o4vhnSaXuuQh1Mq3RvJupzmEcWtZGRqFo7eWWdHK+3Gm6sJjHfNI1aer4m9m0TuZQn2ZXLqKrE7sBZcDgWA0ycpNEUr97fLvL5YhotTwXAdHGJJcTrqxtj5yfDVAd0eJkJYc5wAj+j+7pYqtmXBUX9gjMvSnZ1my54sBUxkfz7DUl6JOV/OCw5sSZUDPMN5Wfb74//9f/qF12/e/sV//a81VrNhiT/nX5mzTk2FTMzoAiz6GTRWxix3DiZPTEwoDaMPErN4m6fT4zxN+93+cDj0u6Fum6XaZlAaqau622FB9hl7S0BZOB2nJibASzTk4r4zVi0SEj9KRBnRukdjr+rLhRKolOmyznlpEtDRhnICD2bHYLEZLXybuoq67xmRMxgxtTi+Z8Tn9vzcK6GnkIypS4vgJEDc3R03iBRCeRrk3HE3LMN9pRYFh1Q8pwzDJdPA9o2Pwgg6uLMXgx5sDnc8OE2NtXqDKJaDVaAfuGHBOeS25I/4eUGQSPCWqiCrqvrmz/3cu/v3f/Od7zy8e38D7V27BgeXedS3hEzGTHVQxkdKjryqet5QyGPGHux0rFSSTE8a+m5d5tPpsa7rw+HQNtCNYDf0+65rdATRFQmQwtzx5Oe+UQLwdals3/GswmBxjc1d/c3CKvZ/afsUGKNcbrHkfJxjdj1ZiCLHTQgHVJMpB3w0edKfaFTEX8pJS+lCE9AxPRvHVCVvWZbT+dyR+i8lWvnItsgOgyrrL6I9E5nicrXYANptQ8IMbDOKFjH8vIkQygAMteUpwi7c5p6BCYep1YtwJa/SdpLejqXqNmMD7d3htun77/zVX/+37/xV17V9P/ihgfkZGLMqqqy8X0fYlk3v9QCfn28+QDozmo8DCdvAiJvzGVL6h2GAhvjbVrdtf9iTzlRaZgHgHks/qnrheYB+7Mtn66bfNfmjvyXXkfVH0gcywmNiiUOmqI0rVagTUtCDR9FSLb/tMqUEmsSB3mD26+iq0s1kGqu7k1cMArk57cQSTES/3S7zdF5mbJfB/dypPtkpU6bDsstX7yH4RA+iJFLvLVAJjL0NqH2FW7DPOXLkWE4yKzU6ZT+CM4jSb4qZWEuSp60pR9AyqnFUEAIQy9b3w+H25gff/8H3v/u34/m83+3RNKD4mz8K+d3d22WI+9CoXBnAllm8yXGqp1Vn0uEJQpuOulmm6Xx62rbtxYsXXds1UIAhTUaz+/lyXQVI8kv5lWjjV17PMEsFTSHHRLQ+df2apA2Z8TFvSsEsgjFZiBS7q69ayMI9jUiYM+JwgoH6n3wKn9hKCgRy8ETmUFIpci7AyKBKRZHUPbTVf9bUdd82n93eNNsC1efYvdQaskT5ygWOVuymGOFq3yKSSCidIYJBctaik+VL00D3jv/UqfHifC1ksxTuq7pL5ViYMZ3cpEmLv6QrOAP17sWLh4eH73/vb9+8frXvd7Aj6EflLafE52wkkqgeJkwT8aJuTodrbj3WRU82RP4FJFFB+Menx8s4Dof9J59+CpVKXQ8ulpBNl72uAf35108hYK+8ONMtC4QjQvjTKzw05KgqHKXFuT2CTWAfB6V/HOXbYvTuhjjuPvny7U65iym9PCR4uv2YVzKheglipRKPBFa91WuLs8F4/AeHooMQ0aFjlngla/Xp0AX46pK5VxmObZOU8sAErvAqVwIuU1Ed3LwjsCgxTNHa5BuOA5r6ZDxenBpK0y4Ztt8Np8vlb77zN69+8pMOZn9C72VpNx4OiCW95RCk2yq4J0Ngw/5VtHQZvqGMxH8df4EKs3ke3717O45QqPzi5YttXskLHT3jGY0qW/MG4ceox///krE3V41oy5c6UGulm0V+g63hvpWQUsFOtY1oIr4FKlzYwuXKOy7hcv11ffZfkBeanm1xrmRLkYF7bVZuo/gJdNX2r9+doHHMukHxNw2S1zU4M4JkqfagiVTul61o54QfmcGUqSar0u19wFPijbRg1iSvnE2bCg0v5cNOC/OkF9gzmZf8tbqp2sP+8PVPXr17/65ru2G320H3Vu5ql6pO3EBDKidMPzH+WzQUjEVrBq77tMi0Q949WWE4uO/89Pj61dd1VX366WeHwy1Pj5EVmn/eq3T+z7Ls0/c/RjqXNMWrUSn7tRQC9M+X3SqPhHHv+P/OJmGARqlJxJrLcG1tYn+pqszfM3WFh1Dn5SWuPt4CqfZZCrfUcipyyJCLq7aCx2nZeN22HfT+h1OHOARMTcJuEu42Lq+NlGcd7G0swkmAyL4TinOjrkWh8SFhMVv8nYjfmMTR/m2RIjyjMZB5OFU5b3dkJCzF+YfoRSQM5ZYPp6d3799V23ZzPB5wWBGOFwsGsLlF2dzWHRWZCxNrSiNqn0UlxdSfbNuSY2B/9m33/t2br7/+auj6z7/4BjTvZyikrQauckJP2/kSP0jCufqYv3+lejZ+IZVglrAII7g7rCWkgdw6fcUlJcihKFWZd0u0dETudGX8RYOpfWLlspQlQ/cx5wY/trCzSBsKx5L5kH1FH+/SeGjcVr2tzTpvDVStVD1IYOXAliLp2QrPQbsOeQ8ErjzGNHz0uGDPAJ8bm5y06xHL348VHnwzD4gsJdDAFLSPyheQyCdin2oNh+uxQCmwTd3N9fb2/cPjwwMUAOH8M2r4DNdrkqhjIrI3f2KpguyWURDK7hq5IOwhxW5NO2Cpj7lyX375437YffHF5x1WRvq084/GmA/S2d/7pRxeys6i5zjlHW6dnFYD7aCw8EgVaPmcIeE6/fsfbkOSea8aijd7XFSZOwyGz7QZAWE3f/6RyVaFXelFWT2gbEomtglCYF73WsO8GQhyLj1l5Ls7KS8RRFO/naUrc96lVv85UHDSrMCIE80lXYbzMgXGmluaaF5ieeS8yMmkbKP4W0rBmwelslaNElCljyEDl3TP2/rV119/+Xdfbuty3B37thuGIc23jWRmvi/7k9mid6F5ssx/093HN6+Xb9Fy5TvYsmP5u69+3FaQC709gzmJiEt+T9fjhJF9k4pV3KUfR/LOr6uTt+KG0w4Odrrg+6USBkPOcL1RmM+/UuEQtSI31YNZihPf1Gk4ltTIdtlnnR6LFvheq9Ysw8OxGQNAXKT0/iCWAk1h7iCVl/VUDPm779oCZO4I46EgaPjY3rCx5dKfDycetA0MIqTuimq1iTKtAllVXislkt6ARg1+lfaRAk+FYJ2BJEEul4esliUd27pt0zw/nU739/fbOh+PN00NczvJ0FCmrwygKGWdKpqVshhAFWl90WHS3EQvzZBQxbJOpqfJmFBVV717/45awDAonGZf8CkUs7AcratjUT6z/elF2R+FV/p2KnP0ttczokkABY+zwNAyyFNisPuWgo9kpGnoSCcZqCXN5+1Wp2PoXO/efEfObAquF2cP4T+5gpQhqxME2JejphENmHDhQkQqUoI4U7Gpl6RSUzUJQg/H3DkazKHLFGwhyMb3LcjjdHv0P0Vn0QrMVtwKx+/xVlp24/8lrEq02U7zfP94//XXX5+eno7HW+jh2ja73YAdqZywfTZNIj81txtBhFK1pL9HzM63O+dgseATDsFutmqal06hJNwtMS6uPLwgBp8RKImktNuWjiBViPKPAw+wWxI8RMksapoR1KQoXKl7wtqsYE/ZklXNll0R9Sob1ltKsJq7M0RwXAHBM3B8TmDHTMUGCLjp2+4yTftmSBQLW59ABmxCSVkxe4PmAStVOCPb74Ne4OhuamxjnPWbk5EIWq7GHcNipRcvqC79Fp6rZdqVLEpsazkFr/ApicCicCzxZbq8e//+9ds359PTYX/s2nZdoOXDMPSQrxYp1v5IOsX7l5Ot8RdNFCNXvzrs7M62E/PIFV+enVLPhW3dlo73HvtR+KN6zi8VdRiDYvGi3Fx55qYfrVyqEuu0LE7q0OI+vW3ilHpmRcLSxOuT254Ud/E5NqbdaZt0ls8U6oz3jtvNeK2P6/G/1ylbBByr9VTd1zZbP3Q4EFm34W/DKqyWGUZxo7zJ1kTKcR6Zp+tRraO6RW/F6tDtCPy4awmtGKoIR9SjSnqDFApe68C3pVsK7onNMByCd56mt+/gf49PD3e3L/oeek1VVbXbwew/mhjtVBE1GGIbBreLvBQ5oZmUIzkb2mvWGReIekv4FcBL3TQ7nprrex8lL/0kSOWUT/gzyj/4aV4fQ7pJVXEq+FSZjRETXU40M5RnWEae3IVSHeKjpckkcwn2/DjlJSc9T5KqIWR+klBPcw0weqgpp/ZcHO+1QB8SLElaN2gBTraT585Z8IuyMkRZo+ckAxMSKWHdGLA/CY4jhhJGqgu27XmZ4yg036FXqLSzUWJSBpyqRfbEwSBO1tEprtU8Q4ead+/fff3V1+fz6eWLl0MPQ0yg22NTH44HnBhqCfR8B887VAq4N67QTaocOsVQervkVpH84ML8iEPxUtRztq0TBSXwrqBsulr7VHQl6y0kGKUb+ljBWngliSTuITksEhcrI4JJiyhP4+acXudkeFBQaK+sAUqcyN+FIYiWJxbrhCf5n3JgIkSEtVtfKr5t0AJEAY3g0V8Rc0grBpWRIGF1yrq1FGhZvRZTgPS2SQ0nVejoA1Shmw5yC7DTsl1Oynggv/wAHAKnFf7i9PJRAmVZXthJ+yA7KwqnbdM0Pp1PX3719ZvXr+u6/uzzz3b9HiaxTdNWbZi+gRmU2MQ0GkQeHbzlY0SuHxjR+Fy0cDNVLdi76TsVZGiZKGuJdgTHiu32TCBZTYncOwjf5ygwD0UpqBPVvyQ9VXBkn6S3s4+DDm/O3lAJmBv1tFk6fyPp3NMQ4J0sSJvpm86JUPKRZPJ6Et5BhxSvu4uoT5lawpdEMSh96FcW3tYuyiQEl5lGNnKwB6cEx4h/FFuJtpX+LRiSt5Ll9N0NmxfBMHXzLtur5M/xfVtyYUt/WgjZAuSRxGQ3BlJRqOZ1OZ1P7+/v37x+c39/fzjsX7x4set30G4KZrEtTV3dHm+o9Ff3kp15xnNUmQifFbJmLUFDkLaoRvnQv2ItQsIsM+aEdrPNh5FKdqpHnvQ8wnJ8d5zkzLSrkh5+edXP0WsOwrh7ZQS0GJ/YLfSVep9dumOiHzhekCxIT8u1xTNk4V06n1aFff+TxjvK42UVzjq0RXiCD5t3Vj2mUlxRtyVjQUsfMb1jdp3OqcuONbekXsPcVbbw8skS4V0jOggmre3WwZRgnL5sqmzgBBJ5Y1XFaZRCr6QlMoi8Cm+dXLdEl44LpAHJ4zTdP9y/f//u3fv31bp98vLlcX+o2wabPUK/4W1bO0ighN5X0jco2VwmB5Omt2JnewkaDF75Ir+pYpf2nkTOMr+sBYmYern9GrnVcT7wR6RPxTt6lTt8VfA+qJQq5AuN/b32qRSQK3cllEpSLKwkQ7MsXH8qgoksOUme1MImltVhe4HBZJwmzFywIKlnNNY3vYBwgv2OqbocovzFrWEdsN3vPlYsniP2KgHKcAWVigRSQZL6qRDPd9ayvOHYjjo+5Yyp/rndGpALdbXg8E60iJP2b3JjR9JE/25PjN1Wp6TfS7OhalWhVI7DbKN5eTqf3r+7f//+flmnXd/vhh3NWFhhZXhMCzRYvru96Xro7hgEgjmj86hfALyxHPwv6tt0zB6vPFcLGjhf7rQAhknEWO7WyEmzNVarOpj61uNe67T4wnMJRXq1MaLn7d5cl1bsVZS5pomE7crerNOUlNSbmu2Yu6B6MFwTwkn8nuUN8JrEO6RZXzZPwhNIoH/9g7WjBCacNR8e5aa32aWaFCIau34Kj6auWGsN3c/avt2WOdmJTyC6VhKfM9DghbENYcJ9C45cUNdhTjPNHoLjM1e8sBjZunvfTHBpSZkkNgceVanOzZfV1bqs5/H0+HR6fHx6eHycp7nvu+PhFsd0wO1hzB91wV6Waqv2+/3x5oYmQrENIfFfh4Jir14zS7mtjcAjyxySclfzcfB7LkOE1WVBKNm8oClXzaHhRiWc2GrHCFi05BQ/gjkSHTrXXhZX/vDFeeCZ2HIqrrxJ5IV2tmAmUCwySNT5rPZIhHRGrrKBiKH6pvOMBg5gyZUi2LC1IPeFdmKHtnxd0PLqEkilua1JJyoOILEGgBYw/YGLXXEA3/Z0ggocslCluWapX1Yq6H2kjv1plnPpdEM8Hsw+3xoYbQpDsqE527pBo1T/JLLRnSVkOo/rVB4yWF1KnAioinpWLDD+anw6nZ6enh6hfdRcVXXfD/vbPQaGgLX5/E6c3dEOu+F4OPYwMUtcIsmQJz4oYyM+jqaVM/xBWC3bYYohqn5ga2PLzKdFYeogTReV1kdwehhBUKqDmAJMLpzmCfR/7NRhXuiPcxH7pJpAO1e+7jSjoAUYGlyRbcm7cdx56Qp6y7RIE3oFjZMuZkYvnyo1sBacphSa2BGpimpbklRNe5OZIzQbQZIbdEG2g6sAUOMio2OnsaUfm3pBBUbgFkbaxaPf9b3UDtL3ja/w/Viky6npkgshfoerjgnz0ZI/C6f9rNDbeANpvIB7muSwAE87LCDM8XfOPzXsl0ksxlWhby7meG/zPF1wnC/On5zgfcyjPRywIQ7mhW0If9kY1qjha4Cy3x5DR6wDCTsizqxH6lQ4n3BuuX3XsritkBUq82HCEU5K2Q09aexQ4tsD0eI8ehpLT5OAgFpxyhm4IZf5crmMI8wcX9dqnqZ6qi+nM84MXk0C569ncMtfEI4vwcBUHU24vDNpXKlKvEVhOS6wlV4u3k9BxybAU3l8UFCc5BbrTrQpny6j66GKEJrMYHa4rxvh/yBKsUAyY7pv+oqoiw5TPL9IBy8Egno+N5rUOaKOuV6WFZqzbVXXA6LIDm3tTo0oMOEyoJMn6rEE24/iOngKkFmC80vEp88uNIzDMrOjyCb0NMcuVTCHAKBHI72w2TVHuvAr1PqL0hmWpm7ruoFCfKyq0F6C2M8APsIurEAr2GwDabiHsSQNd7ZmgLEl4nqkJw6NFCy+gRj3oBPMw5tC1wxoYoU/B5hzBHVylJvFhcnbPF5gq9O0Arbwi/wX8zyt66KxANjqPI/wJl4Ds5FEOSu+bHH+TdfHyNl2wYqLEfvsduljTHJc/dQ9LV2vG/LIeISnXGZByZYKwUR6Wyoxw421SRlPJ5SYK2taRHhs6hD5AIqBp0Qe7rT6ghddTcBITOV+sA7utKjkAnRTblVLg5yMNYgO6YYy1L6LvWMhPO9CnQUGs0x54q7hLtLjXE8kkYGKneID2iESIozGnZblfLk8nU6n8+lyOl/GEXgftGXmjVGJKPSCwglOKES7Xd/RHHvyn2d+qArUUrgYYIEV4WAJE4lz2SA63nzBbYowBjbKXWOAWbzDHRFCAAedwxrrDUoOthY94iOoCedHzICEDtTgBQemNMMYwgn9aqKcBgElVXHC4rHVHqwdRqvUkMhhNtMzWrTj/d7qyV9O1gbOnt+YfQ/PpnrnX0xCUZ6+GNfiIaj8dKaUXSL2iWRTKVUpASvg5Ha8Qcp5TyLv5kwm2wylCCkX8rz62rygkP9eUGZMw0+AKL8kp8c3QV6Ov0OOBYWmqV2pDIhBVwq57sXP5GwF/KrqHToQzMNER3lIWEiB7lbHLAZjBIiqEMcBA6O6XM6v3719f39/f/+wLtCd53g8fPLpy8N+P4DOAG0o1PuiSdSuxmsjsuG8Mhpkjk4eonP4zxrQqxcXLWOvZ/ncC5ZRpFMx/mrITRPEXVULgU7qWwQfYQ1tN56Bx6j0oYaxy7LO8zyOML0MZSxJBGqRz3Ne8fKVHS64IgixwxBw8bI4FfqD+rISZkDZ8kUMBf95lkTrbnGNgjPNnBEpv9C8AeKZ95FgH4jgd4RwvBnjL/FMoMSTpdT4OsMDrW+eMRHqylWRlp/XVK7cgT8Lyk80araqXra1qzo0xskap9wsvpIVvrpehLT8vYIykOGIPh+9RAlbFMbNyhC68/D3eVnGeXo6g8Pp/v7x3fv38zgd9vuf++bPvnhxt9vvkWyB2pBLihvQ6IT7oKyiaVGZoHVLwHA09tGlgdjmgpLeI1I05WqbSkqRUavKW/4hR0/zHNiUp1siWrABQePPN+yHYmoCqQ/QZnBZ18s44rlggZEoJ4n4FRajEMYQOx4HBOycL/YKhgT0cUNeriBW8PwY9pUuLztqrr7ctWmcy2SccCxu+GZRYr/H7Ldg6sa9JBqhnBIZS9i2xpgBLgcsO9CQwCGRMQ+VbldszuytaAp7uLqof4AxKw/UMGMDO7FlX2V6lKYHAWw5tyyfM37F5E0B6HLLLD+EuRkBZb2M0+Pp9Prdm/unh8t5POz2//Af/IOb480AHVSglTpJSqDetgEqxf/jq1E9vQVLfmtQ66/BhRw8wlqRn9p9+JHMmbRoQp4fphZJbK8nTF8OFANRYGaT7czAJNXcRDoHkAT/eMYAxNv6tpvbZV2mcYKUDDAQLPxL52o0mzpY6Fzqjs7MVxQmx5P94fmyfqv0CqdeIlHJR9FYbdIsKv018Hcn0c0IcGdA90uUWt25/sxESu7blR0oO+R0NufeknQcGsOB0gZdMHDA1+7v2c+zOnNwX2UvPVC7jVEUeIok5xG9Km3TTNLK0IwRPICVGLsqJ5Z+bel7osyoKRXjeWwAS/2RSTGIvo7T9HQ+v37z7tWb1/M8397effPnvrEfhr6F/2EQBabgip+NxTc8Ct5g1VLjUMSza50zyQa/t9hU4cXl4Z6kt5Jlp3tjgK9niGjIVLU0vocCAnaKrETKwvFjSWUTRRpYBkahvUGJnYkxZl5X2+l8qqtqNwzVMECESRATtsXd9Vjds75spD2smIklvXai1pSgrxVIeKb/rPCMBFnI6chSk0zCZrzhmUHk4ZYi3XhsN6NjkR2VZa7StkkQAYOPUSUzXjQIgSS8goE3z0lnGW+Le+aWrCFPFjB4JaawZsY6xuuNdukovJEfeKuqtq4nVJlJShKo4GLsgex7dvk8EvUQ8Cp09rxfoak4VItkkNmWbZqm+8eHL79+9f7d+5vb44vPv3HYH/seQ58tjG7WOSZi3/EWW+jMIOpUpMnKQjnaBUcqcawNkzqeZBCdhOk9AnoG6bqT8OBKd1Yuio0jhUk/8FROjRNFI9E2Akq/LEfJoIDx66B6bOM4gbt9W3fDjn3oxHLXiuorDAnEEieWBXFg58aSJTq3RESkkF1yjXoJyCVzqfAVUwmEg0SQkVnwARNdgqN2C8ztlUrX7OVi7+mdNI5kS4grV5RhEcZdbFhRJ1sPU22BgBXVRWPhCNYzEfD0yfpQ26n/1KkA0VmNSiYcJDrC0Vqrq/0wnKbROZtMzLuqDFPRCPY2gDkmt0T00PHldDX09EE//Hoex7f377/88qtpmr/x6acwrHoYdkPXDRBegRQGcK9Rv3gUOVi/JXoZHmPsgBJafGz8PqkPjvk7fSGFonoTzci0J3pF1VfHu5C/+qoc9rIKJqoe44Riiu/iRI8EEl63oe/3uz16H2AgzrIuHTWUR9cBCGgSQdpl1p4Hf2MY6QrSeF6foY9gZUrV1+4WrnG3Sew7D2oOcxhWWdcB2kFimbn7+4GdzlsaVfS4bG+SCocPW/CJcFLsZx4QRjYKToL4hbqfqLQn9YH68A96sIqfOT1cMu2S6A7VEtbVMi3rQIZ6vd8N9aPfErtMGiroRakAcprBlsY9fapGlkNnlE2SfV23cV7O4/T6/bsf/uiHu6b75OYWuqYP+2G/2wEFg9ZMWQykaqp7UDcIzYAoJ4n93943uYlDC6eTKYEnXY5ERMcYHoVh1RUvK1dFRqYc64MSS1kyUXgovI73QIJrVG0Gixfc7YsErAS/YFYGD97Z7QbkdMDxR4ifLRSFQtdcU3OTajB/+KUGckMegIKJewWdjO0mTuXn5ImPLOl3TJxlCOvoMtiJ1vi1wApUheH7SdfIqKQ7H1haEh8yj67sXT93fZTpAE14IQHPGJF3HCHCz+lDLmL+3OuqtsNMOcEt+QrefJq2BRtTQv03NXC2CiVIH8aekrAdHIcr6Cv5eVcIOTVLzEjEv2i27XkcX799++WPvxyq+hbMXWhrPHT9AMYuxHUpC4kb/5II1tZzJOhwEj0Y6JT1SSYnmwkVJUysG1r1nBenug74r5XwHGcQGBnKiJYk+KHeZtUlpQyc7XKfSUakC9Fp6vtZ1x30xsOqynU7n8/LstQwJaPpkLAhqxTyTJuu74e+H4f+6ek0Xsa2aeZ5GedxfJrWdRsGqL7YDdCmCxzWTdN34CAAFZ3mqSCK8+itDCfMZDPbN6RCJcpJijr5m3rQqSBKrN/iTUrYHetUCpdS+MEEZFhv+r3SkK1nXnBuKDF04j0zKZS+SL3qwUysCW8Eu+WrOE0WGwLfZe3Z6yDqrpMN1VDPsKzL0AB6IZ1iNhRxbkwyAfxDdwnEM0zMmtWjXXLlOaIR5bCUrAPin/ePj19/9XVXbQdIuYCK/xY671RtC/4bQEdMsRDaiiEdM1eqpobFM/FQirBQVyVVlYyPbOLiDx4OynnF0B7bSgb4A2JdnDhP7lCpZTMKppxxbcYp62tID0fmMwz98fambptpgoSNt2/fr9OC04v6tgVKrtt6f9h3XT+N4zSN1G6hadtduxuG/h4qHzEbC/4boXnIOE7DNMGoiGnd79YVGpvV3VA1a7UgbqPW0elB+wMKIi4WPxrdRjoLaks5JlVU0r0e/HHxJPM8eDehpGLqdlgCOwe+1ov48K37LTc7E8amqXI+D1odBli1D6lXEyYA+rVm8jGqF0oNz2aOeUnrNQmz0BjbOUmDyRdRDAIePSA6GZjIXjg/TC058L9B1iKGnJ5RCbylqJENZ5DU5nae37x6u87zvu8Q20EhXpZpgWbGOzLheBSEG7wuhpU3qXnDTHz11lbY9aOirKZtrSGQCpsCoYveD8JvDpBiajX8BfkSZGBSeQPwdyRh4mvwPHTQG6NiMUsTHokB8EBPNc3apt3h4O2bu5vxMr599er9u3fzPHddt+uGu7ubw83N48Pj/cP9eDkv0wxJ2/MErIuc4g0EkOq6nqZpW5eh6477PfSc3LZpmrd1OZ2eLpdz33b73e5wOOxwcATuE1aPmVgWJygcmnkcKX8nu6xU8/YMBoRyDvmR1s5lQjjm/yci3CiU1sOM2beV5YTrRGhfS96UlUq6spq/qcxBrHdAQf15XcDy5PjNR3Akt5hQ4uQgYWAI72UxKhOdulWWhuiTRw9Ri6kdWj/HXyAGRN6ZhOGww0vjS3IU/nf/fxRo61Y9ns7n82mHWjvoJvNagU8VTDpqYcnzlPg+0ptNXQzK5XSFfI5EkrIBMNiFyFaE/7rOmDoNpifkUcBUWUpaXJZlmkZ0TwDhgRbfgPfbB5loJCCoq22H1SCSpYHp2xpBhmeTw61pj7e3Lz958fT48IPvfe/+/X3ft8fDASz8pp2muT2P4GFbttdfv6ZddUN/2B/QHdB1XbfcHF83Tb2uw9Dv93tKF6VcepotSinf0zwvjw/LsoCnGvAOpASEkZzNF5uUiG+XJERJ7sq1glhmG8UrnGgPSOdJPU+rDM9KQ9RuNfhm6p61ET11oqOq093fvyz9pYOd95uwkSTxRa3l4tzJaQZtzXf5LMAjVepdSNm0iWuJL/77McnZGpyw7oGOYKi1w4Q8TOgH/dlZP4yYRCJa+egUWeEG3o2RBAVYKxU4U4VCtb2/f+iautsaUnohnWS/g/nUoFVylpQerqWdxxay8qlfDgrKCKQasyerBoZySB467IaSjrk+gPbGlRFA8DgvmngXZokA7+GHzcu6LaNumscFiyVOt9wqEJLTNL9+/6b9XnV6OrV1e9wf+oG6W0IDo8vlskLXnqZrmhGesu72u5cvP3n5ycvbm5t5nsZxHLruZr/fXtyBU6BtyR8HZ1VDDimJEkr5IvYEN8RgOZSIahsk0fm0q4jDcYGjn7gQjRX3S9EOVtx0h+LoItcMnZTz/1wRak64JsWt3mqxRbjQt7GRTGtwlmAkR0d1hu7btkIbiGXmWgY+azNtczZR5obJcnN1o2SKFAAiJuG2bjNm2+KI863vuulywRimq2XA6CXGNagxgJtB4RUkcyRSuE3WHnUG6mUzjiNPEAPtdW6bZo+944YBKnIotZDLJFzoNYGF/kqfmzEuTHgzkBkWISWCHtq2Tb9BthKVMmHkBhR5clwbG3ZBQXBgsGObZrmL9YuSGWEIRUJQvYh9eZq67qYOiiqqBuoEwULhAjT4bwF2OfQDuKHaZt2q29vbzz77bLcfLuPl4f7+4eFxuYxNC8BZZujS1XYd5lWiECZloW3qrt02iBmRggA1kqg1Q3o72kaGOEpXJBdjrLAAWi/jLIvAi8JEvgSULiNiSZV3h1f+ZpDuyXj4lKM7ytTQerhCAZGbyxqskAgCaTuUfWXVp4pVEUXsbkl2E/8RLZB0szEdJtWvNfzlGBgm5kLvNmDq61LVd7eH0+VCoo6WJjuBJ6KPVFtFkEWoPXOZtpOHO6RQQNYTggJDU9UKeDkebm93A/yvgwJ6zrYxhxPekj3kLkTlRbSBypl1dRylZrBXTMQFgeyjqAEMREXFmLQrDFKh3YM/iLDp/+Iq0/1j0gU8EloFLnO1LkhZQL0UA6NlzOvKmjexi3ldBsh2poYh67o+Pjy8ezufz1B0tS4LbKFtOmgrT6kfXIoI5Cr+eY0eVVu1wC3BiGibqgM6vpKTHwVQphMXmK//otK+uRavIF75nfSC7fmvpoq2T+pwpp4TK7YLNxPPWboFb7l7j6pPNFYJahXWdOrMH/ZdOpPJlhnUjEykChWWQBtDOhk1q2w0HRuRHKNaC/TI2LauAxeIVoJZgyX8MsQ81FsQbqaZwI6lSWGTXGFqE6aRIi+DTJKlrevdbg81sRQKISaz4mRwiBFRkjCpi9zBhsVAYL7so+M21JXWFJgL2nIhifvy8A1UsTF9ER6IVIHCmAqi8P+YVAUGJ9rK1ApIHGwUoIamBJzUOM9NtbVQl0+05SJguAS0jrHJLZox8zTS94a+n8f5YXvYFnA4DG1XtT271tdlG9RUseRMzhjFeibJV8EKrRV6dHRJnDZDF+GEHse8iAhoV1Dl/EXPeJm9aiS3u6KFF76aIraRMTJCqfwsvUz8KQaGmUrJ7szkkmpa+jJYTDPXr7IMiugnTTnUyxcgJtJC/yiWOmSrqsvcgG9v/RvrDauR0FFCnmgqKPe6Fc4QhkxprH9MWa4uwVEH/R1ymZRfN5jU3CyS1rI/QEwTZW+Yu+vcisKAqP8UFzih01lysmKwWbmTthHUYK4mAPCZUoQKEH6tVzAeoLyibqkFn2jKUjMhdY/sykbjE7NimdnB/7Euk9oBKMsCZoRdN3iuLeVtUFak1lFRggfAARxkZLYQD4HHaXYIJzLQwvCdxeWr0IGB/KWWOmYtxRMTsGoKnWvMahTtVWNDB4yxeKoxtdxJxRJuxm9xgC9iW+mVIhwdM3cVapOMuXA//qap+WGgb1ygeXg0dZJEAdYOgnrkRb31bZQn+amm6SvGc7ONJQ4+93mWr+LuyPUsWJM0g5q3rVTeDg0ZqREySTucMFg3zQQVVDhJqDBAz2IIIvoCK9IEbEJlCJNi25t6W3fgcgXfFV3LBQ/qfCChqvU2SsOI/JLcn5hBMrlKG2E7eOgJEe627ELHsC/pSw1MPzcYibimBXFRB0pGOFTxHmlKft0PXGJANQUcTq87apFDBU8+ws3aDMhMrohSbYngj33qQi4ZTYilKi7yR1OjXq6X4LIq6shRQg3Xe0DPiI/NaPbZgnwVBMnb0f3rzz/PzMj0y4TFpLJCiJLBBswTfkUXQ6ITY5YOxUdYyTJPgKNeS45Q9sV51tKrnQiY60Il4dnkr+dNyZySaKGUlY5n7Y4ImSQXnC7BFEIg4BmQjEKfUDfPLTthnSCPce9N08zTLM1MnG4czi+sgSlOJC/GJvHGIB+QgMF9VQ/D0LYQ8lgrTKhQkLrxUi6DWHraiOok3EPVQTTQa9UVDKFsggNhtgTGucJSo/eQKJ6k3Errtm1ruVifaJaTPLBBD3YIwKRGigfTYGRVnznp0bmUaFMtyWqU0toqizsIaYK31DuTFb66f1RKB7cAcoKO+zXEIymKgvQMBUMKL47Clr+cDLKSf8TrIBzlWrF85sQRZcvdTwHEFblV3abeSkmW0yWRqcPak0g8YqN8jWg0WqdNhgtlE4D8hWfpQsrlU3Gd3vqWn841HvhNGRjefaM2N4OPeK6kws/TSvkl67Yd9/uHhwdMh5Dvi2CglAVff2Qqmp4A05GpI36doGFC3ynMBF7Xall6SJ7E0bvI+aDHLTVbpArfAlwkOAfLlzbmpbyvSiN9zhhgu4Z9AKzj867wbpx9w2eEESnlpNrnDJRbQq8Na6ZQ+8XQrtq6omuZgm9/BkuTzGRJC5O3mQFtWJGIYF830IxYPHAnMCJfylGXjBdnR2AmlqvR93hx5fUxbeANJM9dlPzqa8CyK+IKgmTOn8V5B9oEYlmlB5L7lkQFhe2yNaLdUehQKLCvzhN90Lwsl8tlHkeSJNzmU1ZXoN6SAA3vOyWQ/1HpJsZccitV/rXDgvQv5mC3soe63jB/Aepbqm3b7Yb372G31ucBfidlG0wxTC9MzaWYPm68j/mfamzs2duwIh9+6/q+a3sqJ5AMYpnsLYqyB4tTr42CC2i5sVqanb2PLokfoNENBfMaaTrr4kWPRjsTkjoq7DMASciIRarTm0RU6yix5vgzUnetqKuUqqi6sAh/xl4W1Fy8wMXP/A3klc3iyvXcfTP1+IMU+VNc6HsJlzZC4GVIlq4IqM9SMfzFJEymD7hDk7JgqzDlUIFr4iEhT1JxMFFPbBgqV9jWaZrGy2WZZh2Gbv0uCohGrJnMkitsyUWOottaUcEQJBxLvJ+mVqrFs67I5NFcIimwG3qraZfQEDm31gWYUYsNwx38ne8tXVjcI/7SNs2AacCk+LVk/ToIU/iG8JklrG8nrkKKPM25L/oqb68CrYfCYOXzRAvG9kVs8ulwphfkeEnWpDIraR7NWxZpqDfN1hFkMrNi761kwtfVumEXyuWEP1qoQD2tWMzgQmmED1bRHGm4SDfX4FiAa2oMKmbmiOD16+zlpW/5Y2dFaW8XLzKCdR7DMqyv+bG2skc2rpS5EI8kknGpV7FOUW1RtYee01/EFnQPLUMw+9RdBKtDxiOcCa2qqlqnedq1u3Wt+gG8weu6QkKurIi6UGE69zz0kM8k9wxNrTMGTVLO1RejoIB8jV3fNM0ENesDVlDofTRLjuYeep1TDsifEkl3y4oOmJS8jFRdcznhmvJ0r/vwmpPAeyxb4qaz7ss2K1F6biX9ZzONRRand1c7mfujYI465WhIHZs2gsLJrYKZjgds0BOrqIJcfYXMS0ygC0cX7+bTJZL7BBnixp/5YJCqNinzKCw5vC+aSPjc+kuYMMl4psAm2gDUiokfQG4IrtcU5LZ+jnkmYBl6uk75XrqTOrNsDakUE9T6yzDFv43eq7Uap2nodsu6dhBHbNZ5hVw9voDnNJB1QJ4Vjpkka0r3pgquAAhpE0r2QXNuTytI4K2hxK+oA0ndoqEOJzYGGCL5QfyHu1nJu5v+mv9TIGwdzBxgJJay4bF8zHv1I1HIrFK3m7bech2weGXB5LG5DxrqkZg660nQeh8sYBjPykqy4wqGmNoxiwTzum6YCx3x6jr1kh5lZn52ZQo21z2AhUHJ2ZoVCbtVKHz9CvIHZZZQfEzycb7lSP8K23QfomZhKjzkzUGOgobmZNUpXMzEIlvGczNj0uo4lcPPul/rd5yHSYs4BF0icIioZErY5Twd9/DHWq37/f7p4dFkj3ZequsJJpKK68rpbF5Aqn5qS3cj6sDHi/WrXdNg6ye2qR2Nk3WiTiOoo/AUy9FkBRcEfqh9GwdgUjTYEl3KvX0lAS/VefXcJf2bacYEdeyMEfK4TfhkEU+md6N2d5L0LWnUL30+LFjpCif86Uq3GaxGEpAbJoVexWrYBOXZukB4+PFyXfJOKhrtRonIxB9l1lFQOZMetWUvVjkZQjfp/ohadAyARtq0Tm91Xfddt/XLiqWdbCmKj9pJCtMiBPZxW44ZOmMtqpFBvBSgVGBpekR69nU1Q53p2NTdggm64Nb0/lfGsXqcxsDLAnzMjrDPo1bLSQgzVMPW64rl53QTnJOQKuBaSImXac51VdXsmxFxx30EKeDlrcrKDjrgUwoujTAxYTo9zJEu443L3STyFA8SpmX51h7uLhKjcDm24jq2wismHIEJeZ/J1YyFR1yvwKMrxNhULZJTOykeiCp0Gce3q2qf2RAJcQYbIm4ufzmUMOouaJipklsg5/L9M725ZFemqzCmkxG/7ha9gVW7te0A0cANGsdMFlXW2wVWlm+ABTFf4TJEeF0JcLM/sg3TIg11WbZJUQIup1nnue7qbW26viOUFUMIXX24u/EyYY2kEonjNVccFoYDmPhEfWzbBibv8iA9utq6u4t/gqN0jn8ZxOBjbNAL6Nw0bd9XTQu6DysodfEkhQWkAkQkv4oaayDiUEPVWz2llM17CzQ92vg/uje7vYPg1SZfFCBaIMKHlIvVqFhCxdVToS7Hlk9RpXXbOrViPRwKaJMrmc++grDO7/mhbxlZX8Xgazld7uKoqzotUERGdoOE6q/dX7lpP9Trul9P21xBEZJcLjWuLhnIdqUl634fqi9lW024ju0pqkCFr1FnKgU/mlrLPEEeXwXd7cxvLxYOlfjNWIgA0RMbqalx8fAQ90y1QgC1MNcZ6nC7rhdvrtA6FxfTfxoaZaVG1BFrvYCVsTAfFXr0jCPlIUItIoV/KkMTm9DNNyQfNweMcuNNOnt7c62Y8GcEYPF1FtYFzIsUTEMg0YTH5kvMXLgyCkUuky7He3lWFOUAc8dLxhyny/HEtg26YjNXjOzKIVJUVmxPYe1u0q5Y6WH3jLPPxpcVitkbRdr/WFGcf00zvoIWW3bRqHPJc27FOLhN33fzDOF0zb6j3hP6NeW9qTJu2rh7UrrBROH02GZFIxyciqeWxElAs62rcVz2eyCbtoXmTDyrQQ6ua9qlgWToaV0HJBzfKUy77fi0AR+jiYkyWAzQ9zWSm5PWcR8+4ctruQ5MJBQb9K5B/R46erg6p9bRRjR/2IL1LLwc+8mDR76NjCxCu2KbRmUmr4lsPSFOQyONF8mPx7BJFFcIWeQp9puEW0uRsgZ7OfOXMxRCdz7vlsXdYCXDBlnZ3kDmrdU/tdbqcgajw+nDgjLBhNL7P/3LWS+GIqZnxl0p+yIgOePGEbbn9eHfGjqq7Ju2pSpvzIPQckNmDYRV+NPacBdTaJJ9qxprbmWxiry2QsGjdGGeSTAZbOMEq+yh1gBn5AkWo78TfoB3HYoBl77XQY9RIRJsNxrW6gz3C4xiatq1qnb7vQQqQ98OS5ZX3ZZyolge0PgzyHibQZJ35PifF5jNB63ucAxnQ+mE6O6GEmP21gllsvJjlm3gfOgOi85jRwSimVgXEpNJGs03tZxPHPt+YJkwzuXQURdOQ9Hl6fhfyaK0bEq5Kiqv0gRTExQg45yqIsPRe+u+gFfPmmUxyhQu+LD0DR4dr9YaEViU3z66amkXRJ7soUQ2JVvBQKIzvoyYBBZN2w51rfWkxEQXKlNTLsx1bShSMu3+Omwi28it+PDi+HViahANU1ho3mAez66q+wYGIqBt6UiRsgWa5jSOh8NRh3zFJzioemA5LzNkPUHVRLNsaz/0PtzlkVFlDJ3jBnVQwl2R3pd1PZ8vECyhzu+YjQiFinU9jiOokViW07Qj/OhabhTPTVl5zAlX+K3S312Bgm9QAwNxFUdoEvmnuiZ3MFG/EiMIFw1Tz0BuAyJt2EJLafR5SrUwZV2ZfFayDfav5oMqA6A3mw1Gq4TC8oAgpUKFohXkNvxxhFp+BYEXn8iDfkqL+aBgNw3f0XDB2uQ/o6Yvktmu9DVZqrnSyCqoL0OUMj+2OiuoCQtmqE/YuEOVtOc2kEXerpgTRh1utWwEu61BZtS8zIRnLfR8MUOU94HF4xeYcC+les5ELELXMxZpTo36BQ8iajQyIpklOjyBkhZ4hhOlvdIv+D344tPpDM1T13XoAduxFS1Q6LZu0zhS/sz5fJ6mqarrAT6EIdpd30ETW6B55LqorJLSTSDHomjK9JR5z5yTEV2ZvCm8yEFZmJedvyroOBUR672oeEi4Pud5cVNNN69NE0uVrUUpqTq4WwtjDjix+Jwki/aDL3+NiYXn84s+fFOL6knWTZEhXpU9ifVqxk9Kne5O1++VaM1b4YfcwNG3xgOpy7I05ucPOVCA6Nk04+WMzopnAJfoFWkSRPhTrdCwPK89iOSnxleES33fXc6TBE28ZY79NKgcXwxSl46VLDPRDthcWLcVmqp2hGBYmURKgGAfmt88I53WtkKCKvfTJlf2uqwjVFljA4qqbjvwNVAXm91ugF7KPTBNaM66O4ClvUG22f3TI1bwIo/AFkJt12IqE+pDFVAvaOPTDE3koMnPnlrtuRivk0di9yaYY9LN83fCCO7J7pt50EkAEqDLylr6hBidM7kSRHAxCzWI4BPoiVU+GWP/wdenH+W59X//VyH7INw7GOXZnNIStasfkH0MCZStqjW9kX+uPjwltFDWuxV0Skoy4iCN+MuwAm1d0Y86t9AxtCzWDFUcV+NHKVZ5XdQUH8+DkzCKemM4r2Pbjsf949MjCDtqFC4OoLZpqLh51/bzBqI4qFZZtCcoN6wlgtCDouAaio6p0oFoGHQAHMSEqrYhLMzLxQAKTzNYV+zOu8Kkgnk51LAkbv2KdbMws6Cpl3mG3pHUNQo20vfD0A87sj/hlthr+enpdL5cID27bU+Xy/uHh2mamrr59JMXX3zxRfWy6XsuDLQsjTB+2MLCliKRCRQVoXwD5XkS32bPM/lK1KklbELoMncQG2n7nDCwIKBnoLR+YW0g5+vXCDTSVi4fnzXtko9dfpJ3ZcZH2XOu6PaelNwkGspc9c8Nuoa3je0uEfHj8xwDSVkPo7Qlu5Z09DAjwD/RS/zgM1UVxS3RcMMtNc0YDGwKXbUrtEm8gYElx8OgnWCsYTDPxW7uHx8Pn7x0WT2iwjltJxRNWkoGvKBqsAGyrBso5dTkfMMoFh3Ub5EctzoLFjLcKKEB2q5Ce9cGpkYg32rbaoXOjXDZvCwDatoUh2/Qhu+heHEg+rlczuuw3L0Aj926zuN5bNp+XNbT+e0ynT9/+RK92Zrd5Z35gbs7551Dj4xeWIHBMibgg6A4MMPnNnlqJ/jEA+cP11onHaakji0vuZirbFROKLQfES285Xh54gC6SqT5yM9nXynhSrzQsfbgQMmVY0ek2lNJ7uFtmvxehbUU3okRYoK/b48Tv22P49JWZZGRe8Qbxg3pr4WjibJfarcc/PzjnEq4rTTxD1u0uURfrwEDGj09PG0vX2j4RDvO4EW0eZ0QlcIEroDmy8t0uZCsAIMT2oHAnES2ufHG1PaV8VnCahu2wSNdGmc38JhuaM+A7TLqpVmneZygI1zXdSDvqYaDmzZSky2Q87vdXqaDVXW9v7ltPmvan5nnr1999d3vfm9/PGJTHYoJa8EpcUIPNs/UP8LnImnLSYUDteHZuPEGVapLFFDUSBP9vhAgTT1jN8O6QEsdNeRT9cis+IgZ0SPvIoCZGusSSf9+L/E9uUhIImDjsxxjlKi+5E5o8o7bh4E2UoOzOmQZLp2JwZ4SlrIQSSfwWrs+kFo0SfTdEl8LpV1JVCNggpn55L0QNc89MpjxKkHqalm2eZ37aodt0qBPYtPD2CTVVjBxapuhYeLagH7KLgkalRboNaxYNi8RWOoZB2ME2JenIQSGwIRTHJ0jD6kUe0RiwyieNkqNGV0dLkVQ4ek9VSyyyoqEp3ImxOp4BARWTmx913/64tPuvweX1/540/U9sYlAtS7Z2QEWLXmjAcMF+yb85F5ZWs9HedXw1QbbdHp26IIU8kONEUOrNPrDGvgiLXUUAX4qqSmbcjcvksXVm5qT02F7QJKSZCq/EGccq7I0G9GgSxyUP/BJ6/yvu5kOiNLQvuYT2ZocsaU2TPD0cNGpVPrFfXsrIK7Rc1S5k+NYnHxUqLrxSjm4coCucPD4vELb5HlZmgqnctEGEW/btp2q7Xy53PY36wrz4/EG2oiWjkhRzCMwsymMa0Jxcd/0JICBNcimoTMrWLkQdmtwaFPUH4AcMSJNSj2FhSBBhgcn4DzFlt5kvyETuUoV5kZSSytnSnff2rrp2n6rwa2NkxCs84pTfBR+8jVxCFogSeWm1erC3zCpjEaOS0SXFGEYDTFP0JXWOEQQ8GLxSU8Ge7wkxVozBFgPe9LxoRENr/hHlQic8eN+4SJ5Qc5EzsRXllpr1Z7hcmP0OVm7oHpUi/UrWoMVNBNqfSwXuCuD5BInmL0TQ9TucRpMFP3gCsSQP/NzghofTKx4c0uCkL8ETxSQjEckK7n60RdUiGSg/iJPJwiubtv26csXkBFBfl8BNZU3d8PuB3/3FfqiwMfFYCrEvBz18mp4OtEG4w6hNXSImaAPHCdIgY0rKMnUJhJNiEIGnVhvZE6sRgrvMKKk7/JYXXJoi5tXDpGbPXMNFLzTgIONvkgC0wY0KRvQuKwUlgv9xGC+UxNwShw7okGbgJ1iascEOj80u4cgIkcVqeGV3lOIx6V1KFDYX60ue4EUVSMJgiZatCBxllP1/OsjTGo7cqcSu4cWLg/hyGQhSp+uNs/kaqhHV1xOOVHh/tF01pVqZ7/E/nPPLu9V9Bxp4iExQLgVtWT0anJJDPvtOdWncDSZL5BkEWjBWwVRXrz/HibTmqAidzFDCf1G5/P59uawwLQnq8+JB+XPS5RkUjDEHytem3VeoCa5B58vepJr6I1FefmYT6LOP9R+0QbmvqzU69yUWZTntUwkY2rTDgZiLZgHxAwo8GaBGqvNN9R3JUlyweS0jj8i0QvaghYhKtbQjTAjC37QQXOVAj08pGGLnBNl0UssF04RaW7SqYZ6YN4mrdSRMaN4GoH8kFYcf4lCs/SVQv+3NHIT5bELwHEgVv3nqVNWVGGLHPlVJV0O3aPsUk/tqsaICeSFT0yaEJTgz41+LQ/StSa07H4DQMpNP/AKPEmysYTxMAKr83Oc53G6tNhUDvq1wdACHnPHbB6UwHo/DG/u74+HPc5hYQyXpiRS+BrNBRWhFbqSML1R2itW4L95++7d7d1d30ElF5RyoS2LB4FZFrIDHKGgcXXNSPcbJu2ZN0iBVZHcgTFz4ogfHSwaME1+UeIMrFHQjDvbyefGnrm2V0hX2aWpGjxpjQoUKILEyggDWmo7nEHCmMJ/WN5lIAiiaEzjgr7QDlG8uPWo4Vjv9XyrojQIKrJ5L58tbLAlROzIrvOx9OhbS67nmdZqLqie5vVCPUmWJvRRIVYsJGLYmxqdCgznedFPMEFHs/oKukbCgg0kKQzEt1YCoHAuOywqOMIsR8jHWtoWfv/kxd37pydp+kUOIVGku/bN+/vPbu9ubw7Tiv15uV9PWKfgMz1E9MC6nueZNX1JHcFmbRUM7Gt7kH/UDIEbZGHHOL9llr3kiRRXV8CnWiWWRlwcsJinu/AtJpDg2XGPO1WWLVJhZMrZVWpMGaC1eazc19mxzm6k05YyI5zd4vvHyyxmJ8k9AonkxW/wJnkMEr7bgP6ydQl+emHgJ3LytpJnuO+kGPfM61reiL9B8UHyeaJoxitdF0PvJ+CzsjPQn3WC9iJjpXibT4jYJpToyE2xTT4zc2mpk+2EdUK/aGssSAUPqQPNe9RFIUqHNIffVcvXBysDVwxW9R9Jo9ke70/D3c2GHSrn9+/37QDVwCGUC16ipqrfPz3eHHfSjYJ3bNCOaj0REBEb1iCpVs6ukePhcBmn3W6vFi9+Cef9imLinBrRLMHwEBkdQYeq1DFPso/iQZgx4hBA/3MdFzg/TJ/hM6dEZVVSYIIgb4OdmFhB7Da11Az4CiSoUA0VVjVbfTppEA5tCIV0mK0OZqB8aW20gzWf4IdvtnqGeWcOijaPxiOAxx+vZsge9SQtbBORMcNsvVXR753T5AdVyRBE8naLaqrs3UDxoRoQo7M2NGJ/pXxRR8az9xVNVfZ18hZ0+gYclPZds5rvvAGF9BGTU87bqvoNZV/Ga50uZBzICWhJzPXDYg3aOJagmdca0p6gTB6yIgBXqL8qVrcIYOrdsP/x1z/57O6m3+9IEkhela5FlSW1vuB/ONZsqZtWRv2tLSLnfjc8nk7jZdztdypZWDcPJoiSR5L1piBN4GaD17hCAc4J2+haX2jWS6REETzjlFjh8VWSGx2v13wpUWdUncbRYhLJZTEnMxQ40D1P0yTjxhnp2WcdUzdp6Y5doL6D7Y4XGITKLjlCYHA94rqhHliTAD6U31BSEgPOCVrJatLYcnh5rVKlRkDnECZxX0yCsUWaV8yW6VPYZ1zGTApLI3CSn1N67pJa48hfuIDWm9gqMGdonrBIjxSe4Ft2mOYUfM7To+Mo6NjlzWRqh77h1At/l+jt01koaG42zTpvy7a0OKGhhTkj0EoWY6qoZpMLGCahdft+//7x9Nlul/OieDaudBiaQsMItbptKedZ3at1U7+4vb2MlwbmjUrqYppsbc4w5hbMGXw6Gj3Jw4JomK0gMomle4dfNvt2YRIvkQHSFiuobg+BK4rxJ4LYTyAwOWTFRYR4AATi7ay36AAH3ntshyd1Y3yoJBtoMXPFnjDRxiEQNW0L5ELHE0nNupjh53Emtcd0a6S6O+It0nBgc14R8H6zXDFN3yEqza4TyAK4aBA6wcr6V/GJBDnnN28Qd6aDZYbRfsmaWtdptCMUgc3KjA6O8JjEA2STVJ0AEdeDLUQCsoCAtwFMeCmzYTiL/scKyVbX87J10Ht9/ZnPPnn1/j1GLlQm0tRraJM4DMPX797dHA8DyMw4Ek3Ozsfp6AUtaqF7HhjbPVcyUFykPgxDV1encYK2d7AAbGhLIpEhZVlkMuuRzFnnx8E+q7UpcrQSOlmcmkLRmBUKthkueJTkgSBI2IRg2AOmZLr70C+m9JJew6tKHK+ca2Ft6RA3JP5VoxPf6UexI5cPW2uVIY8il6cTt4OPGwjbo08bvOmQyBEzxQxhAkJ5AstfXtR4/6zRwQeU4ICS6XI+cLlTm/VjE2pUrUK5AMxO5NuG37zwcE+7ifKmYJUx5gCf7fq6GSE2EVKcA/uLVqKcsLISCbk4BPXGvNZDunuITIgAURvKrcOGWGm4c+va+jIuQwft7bq+XZa1a9mLS3oBaXFrtcK86fH88HT6bD/wQmJqEOkinveRXIVWHNhNFrJHJFhFKuYw9FvTPcGYYp5JX1crZUCKuSdzCV1w2FtHBI41pGYHUKhnASePBRnH+XDihqB4Ff40l4IZCZJ9xRqOHKoycrHwvZsf/qPWOKxkyDgKbjhQVTD9HMmbtQ/V3BX3TMLQaFSFIIWMYNRpD6NVBBs+qMdFykq0HVPiC/B8lhavCOirMacCD2HLJ5CX6eSSDlC6nxFBzNxWlUguU+POZWmKdZabMqWtWK9gi1GwC8yZQPFb3t8bisLC75JjIUvnuJm7W2z/hpgEhVF1ByX0VNQCnV/hVuxm0e72FZSnt23/gy+/ujns94c9D7xU6Cl4vL6JLYvJOIRBaj3IPW4ciyS9rNUw9Gu1jdME4gWdhTBzHrsjyC5ECVFlVdokq+ZaC9g4lGN6N3MgkWx0BEz9nNSCpLVCxw+oTxS9Q3xpLkahZ83C2iOhpFlpRgp9TLeFibN4A9CiqfmGWWkEImAuzoTlbXmnjoCCPFFSuAxHCHMXsaWOtxjDyXg0jzZXEJfa2j0VovKb71Pr1dRUThfw3uHdB/iAE0L+Xcmzs5YGqQLqObw93PRkMUn0I9GpEI402pV60RZYAQFLqU3QSZUzhf9VCASbJvwuIs3kizWggE7LGLyhNflBDaLuLpDL2HVooH5yd/vweIZcZ50xQnowutrrpumb7uHxCSRnyMmKlS2OVZE5PbTN4zLXu568qTwvF51L6zJ3HbQHuYwX7iBN2EusVjoDsLgMzeRojZJkzDmOdvxa1cR8hY2aUgkbKayg4brJWR4CcpUJYY7tWBmIJn3xL9AzBJpMkipMxb+gZShvJGLoNIzt7DPFSScWZbMO6mIdwoBvX8sQ6OXD8jj8aY7q4hfKcd9E0cs/tZFiwvUiqmdUre4rfRsbLVI0KLYNiBquOCnDiKu0UtopYuEtnxHC1SSOZKUdinzD2RtGw2m82fbi/Ly5vk+eUPXlcH8NaSHOQRO3dgvVIDCmcet32GOghRFZYAfyijnLhBaABm3z1Zs3x8PhcHOE0Q2Uiupkka7HRlBXVd/AABdwE+BwU1ocXU/0vNsNExYVgWLdtGg3L01NVquZuKL+m3NdqxRqbjijS/Efi1Zg7ZUhw4QBAr94g4hhLkPqRBpI8qJQMrcBlzxHuPEEqW3yqM15spB/4lRkHuIqIoXypIP+lmCIHqqp+jKNgRG7hWFrpEIjW2RQiTHmNnEl5PvTvOIKA/noavP76tgnp0ma6aW+fHuGxfeUnBoo9U63I4qmbip6HdWyFNhJVE+34jZCDVtYW2MMNQWLlh8VFpGcXusvQyDVUoqS2glq0F2vJCxzf1NnZTc4hGFpsfyohVsvEkL1Gjm9uq65nMevfvL6Wz1U287rjG2mVBI7/dmhG7bspHQNzMSSiS2W9lxVfQfXNNC7owbWAJ4zbMBR1U2FyxL3cqykdgMvwhFyY3XpO2uHhG+CNUmlg6DUOwq3WgU3FV0zqqXHpHSSxN89o9fQhoGN40RiBXNqtmpJUiFDNYZykLY5Bail8mtCNASSmgbKpGHIlRnkPAUoQ3crZ3UC4RqeFTVCU3/cNR+Q85lCr6TLwtiqg4R8DaJ0EcaPAAl8Jly4eb5VYxJGxsqNZSSXgIhS50mFFilqirKzSlSMqqVkidD8X4B3sjTRDguXqFDllaoPXAW/l5PuBlgJ1IyXqdsPTV1/8uL29f2TgNcCdJr017btw8PDV183X3zxjV3fYUWNc+lLlj8/CAUFJVRjn2koSAKTWFUSMIthRDAEsWrIdWihEB8MY7ayoW2qijXVK9FfjO1xKtdimw0F8zVq8hYjP/qjMTLGwQhqYk9tbjbg8BKolXbM2gSJCVQSMWhagjB0tRwpSKbpWazxoQBvsBhaZLKaOpx34TK8IxLacbFCxaFHSYjGdn5UzFBfpyXhSeWe1xm65WhibynJKnmQQMh65OhtSYlOE+j8I0LuhXfm8DlDUxdkbx+woH1z7JiWqr8SNaIU87ardECWBdjykqYrPI0erFPpdeoCWg4wKnPSgzVnTVidqAnWqdzUCPWpmmrPQGXDEcaOTtNEdT0yA1DraPmpWnhQN+3bd/dV2332yd2u535Mmgvpd0se+q7v67Ybx7HvIGnX35MFF/asqMEgX7dlbnEsuClbojfygXDdPRAEdwaqHRtz2VDyljqjINRHGdpkLGKPSngMedHEMSeZVuKSckkglESAZYBinYi3wWY8kduZiqPI9SJd7LgMi9OtsgzdTAl0zFrIV1myJqvC7GJv+m6p4aXYco1y7YvPkLchZXqF34UTJOnOxKgpaYb2Qx1OckMoKQWEYDdG6j/LMY7PNGVlLntDM10ZxJLxBh4LJAtrIxqgJuEfnWBF4koFlyFFurkiuGJmZom/yaothmpVl+LHI/G1VPW4LO0693WPrCW2AuTVwT9t283ztFXNm3f3l3F6eXu8uzn2PTa1R8EqeKcdx+BuTVOP5/Nwe0sSTtkJCSFUlsHyxS686BJvMeuDjgOTFyxNFbXNtYU5Z9aCroZuIS5cy9JdEdgMNXQ6qx+Yq/nwPKnBfYvmMdhcMhuQMlqE2YHjWn0ompqrz5BWhg1xQ83iBG4D4XZ0B4gJSxqDL5VLo3OM1aqBabxKjD9sXaxznDOcTX4po1R+SXItbzf1VQbN0r6QmNesN5kh4BVmT5Be2TBocCE4U7bLaShezwt1q1O6V2WAyMxrpWweQeWM9wN6rkC8xNnxvqAzbDUBvJKi52qe9wTQOZFjbgCODOEXzYWnuQtsV1YrGMNd1/Z9t0wLqaf6OOlQT8vGur+tOp8u4/n89Pj08sXt8XhsWios4mAJ1d8uUPcLBDONE7Z0pviH55HcXYN6WKgjF68kMsYcT5FazCjWBfLqyFivNZIkSRTotJQgu41o4fm66DljUKN/mE1QSRNrQMlmQUrnftXqIhZMMUramzYXYAnM58DJGTWrAJIsFsdAc4K3Y79qiMkV7jezlLEjR45yH5SnV17ef+NCy5ZVz58l+l9w3CavJDwb7hCA62kCrTvSx1h/Di2jTNbLu9a6LXAbpTrVaaKHisZ8s72npoj7SbczI8GequwW3/DWcrr9q++WxbXowY6xS5hFz1bUUoLG2nZYeo7Dyp2jXr5OWLhigGfBIkRUPZb1/uHx9HQ6HPa3N8fDbg9+UZ7yBhgLlevz3HX9WfoE65xl1fFbiB81kC28zDBvAT6hDCvxMLlUQ3EVWBPGSnNOsF8fZg4jk8HUWTJckU6hSzz5hDdoltlRNiNnMlfNaTzhyCh+A9EGDW16jmZZouQXcgMDjfRltPZNLSYJbr5lGh6HTYIoT5vTtXn9puwq4VpxlGChZB+I8GL1HvpCb8WCgGew59oro3rPHJx6HpEwkUUxL8TResRZJgSfW6d7JYjDOQF8OSXbFI+8RkCkVyEvmW1suWtqUXCV9jJjOgS6A7ndlfyq2p9JbCZ8lmi+XlW35hich5uIUtty5n3IJ1WbFiTd9dgiUX2+qsGnvN/WF8f9/eN5WaBQFpUK/zyg4a5tVRenj+dluX94un962g07aOlKIV0Q++s8zdM4azqFeNjEfSD0XLfdgDPLTqcTdK3bWrCIydMKQrHVwk6cY9NCHd6CAeUNPwHTHTzPLqECIsow0BzqCKD9BQhmVMe6rkXnF4w4x3SoaVnmpmmx3gBSw4Go8dQhQQy7dRASePc3Ao6yxjDsgE1/SALbacuBmW7PlqBgLnd6j8qX50yODP18HDNS0M+H1UjqPfQehg+9PiikU+3UVpBgnQrTZzT04kfx3po9haUweABQAMNXBO1HBcCHBH60KyyW75zai5uHZHOXVKPFb2gKkVyG1essXmRCnxat5jAMXorAYiMI9D66FZ/OIy4edZ2Jx44cVOu8NvsamklajpMHCx+dQIFDNQTKbdtO58vT+QImH0qkdV1abOC2VZDGcBqn3V6mMTmwYnUulLa2XXc4HsfLOE0XmLqwLCM2Z8c5B5AJS2TULAs30EGSbVHSUtUtOZDp9lS/d7mM1PIS8aHbMNvsMkOna7RT22kGHQGyxZa1aWfoX0sacF1v3YrXozSFUakNjnngimUSn6AXM0H65BY+BXGrGVG6g4K7olNLESoQsP3O33LHzVYNiqUVCvpNaWSd5MOY7THISfr0K+IWuk7rHwgjPXth6tZyvkrKf6b+4dluEgHmszuck6jwirqn3AZ7SnCjHeuvzCaEEpUKV8vZ43c4imuka9r0h6CTmRZWgqi0SX8IILxzmSU9LWittvM0DXueOepLZCQtQrL53Fg2254Jjm2eqXqOK+qgvVbXv3rz9lvf+lnVDZymQ4okJjw07X6/gxRLyBGb22k8IwXW29o2UN6ENYrsy9kqzE+U/RAb1ZCtNHbHdKh1W7D55ooFUkR4Kzre6TJeB8xzgc5GDVZrgWaPGpwEXanxFTmWWTvhJl1a360kp+JX/rG22C7VtaHmPtTsQI07x/zVfnPFcXy4MrcFR6skkV1Do1S3zV902GbuOj5D/+rIV4esDjODLawpwoVHmfbCqBk8pfEidCeS8uOuuBasYusseSPdpZOBRr+SmEMZzViWrlGMwsApLVRJtIEAavma1PQGn0QylSL1q2caQ2m/+hir6aCNTAsMbYKIDgdqfUhMhLhmZVJdPEt5IW99qPTuQDcRSODxMi44nYgfzr1oXNojvo8eKDJh236/O96sIzZxxEERK5ThNHULowgbaHnbYMU85hvLKECe8CnqNHeEnpeZXMt0Apj4pVMAt7bpBuAb3QAj1DBygaat4KTOVVLHMjXSpLaYyq+FXl1NoDsK7pzkuapaU5ybaeATag9xJcMOiVXDr+TECuf+EQ4spXEL96fooSv3qUZl2V784vWPgrMq+ZevoukC2k8j3EY8OldVh5LcU0M1XEZBJG1+SEH7rLRRzVKzIDTBKyZW8kkSE0h5SBZiiJ+K4NVyziAjPUGqm0C3jh6VbWuh/+vdYf/u8UzJTDp2TJIHHfiJr6i8Fo1DlCDLSoV0kaoCa3ia4Egwr4YfrgFxtdRRmmGTOyAcoPkaBik2MIiesRysTpHtKxEsjlGihVAAVkcA4iColRtJUwjHUjLh5qBb991+v9vv98g4WKDyYuAn1My7ZGjnVNN2PyDj5RsaaTTUiizclD/at3bdiCaaS50SLBSdFngQVFnD5ArTu8S3kOLGcypwqYop3oELK0yZK0gmXaVl9SavJGob2JulEHDQntxXxHL5kdoMQQSgsxndYhTcCXEHA53VbgncE9Wpfp0aqW47athoQq2WhzuRG79gYZfsDITfeyUo/diBKo+w60fUp2St6/O03O6IwYlaGB9rpoaxATNJFMXpgaovUIBmvFwgLgBmG7iIKJyTMTutJSLHLej2TVP32HudkrqqeV5xnMqmaY1Epbw2juJqBQL6oKlPNRecgG6MEYqmxVrxvt/tdl3Xc3dozalyISrDC82jEsasbFUc1CELQ+WriVLTOFwfXGl2Z5o3f4ctRefIxB0DAYOZgiq0yIVEHjxHuKWXyOIM29OrQv5GSIDULJzitwMRR9EmsgUjbeBwSGKn6Qqi/RhROxmHkeqx4abKDrS+1K5LYWeEbW7VdBRzkqJxXSOx1lHhKulczn8I3jipK1aNq7VF/yksfEWmLr0SUx3A2Q6KM6maLgYOPIXdYRv0ktgNu7dv3+32B5g3CI7fBme8QTYvR4g9ViAUoN86lAHo8uHVdS0Mc4Dp3hW1mWKFkhaElfyaaGzlBE3DU0k5O5nbQTdNMwy7rodleIbtVUWrEJRKZYWq83jwMsmgViBp5aB6bFicqQUjuhiPKwwOLdm0ULNmnVOSJ3Vl42qkyBgSRM11yg96pZJ4lPLsGEU1z7R6kEtadLBQc/3UPwcC68itdbaFydhspVJwatk0YXOBHeLinLnMeqV2OuKEEedmjW3oEtmE0WNtzpZI6WiDeCmsMC1KWwW8sBsHb6krNOPX5AlegHGQedsuyywF+labqMlZsTljYHRRbRSxJO91bffu/h0P56OKhXlppmno4UVt2YPg4WgQSVOeTgoEujRb1chwvwpyUOBEMLiFshoLmDEPYF2pKR9tA+YKg2GFXipUtdHOgsASJWAprVKRsEpgSX7mC1wuJLdGUktVM9E0JCmFFWzzCyooD2QcEa9bbBcYBLEnHk7MBm/FsnRWwFxA8eSvaIZqPCoO+/MCK0pNJ92Za7gbKhqYbsiaZQlj5WtGm3xB28F8Z9egNBGHgrOKkEVGZBgewjPe9yNtFgD23EWtYDrHvevQE9K8CwmxV19eOw2Syu+sSN8Wb6R1iPCPwwT1VpdxbanlMGX7S98mpWHfd0rd8swFqejYmoKwvxheTTNv2zRPqnuQkjtN07auHfilSDqSA4g1A60r4laCkjjFdU01twegHAs8LfI+ESqgsY35GjgAjRIxdQwwbIr+EvCQTEPfmLdamf3lpiwNRSNJHFRkk3oKXe3UrZ9o7zx0E1j7K3WOeOU7xP6Rj2BPLJjtJvxCGWZIlsmQ0eOUkWCJCqRvdcoHPL6VpHv6BP/wwOMD6tFvKH4h/MuoGmoAzS+v4ujqU8t/hBYPiDRQum3M0YpT3ZNdx3x8NVUF+h8201L/vbKakAdVglFQ/XmJ0gM6eSV6v9cofHa3PQ+Lp/vdoVmfTiNnQrHPnPzMhkx1KmWDBHbyGgEgts44TofdjpzYwj4g8rzhXG9KkBTDUmSUuIa4JyFYf9JsFV+ipjLlV9hDj9soiVOKY2UcB6IhGIIeTr65bh6yQ+V0pteID9RtN6Inw4PdkcriJM6kkVVdBafjUtDcQo4uKuzuTEwRu3nATTETXWldzXZPx2aTp5btFTVa1EcGiXKl9HL9m0ETUDCR6glVGeZY1U0Nro5+GCQnVSGp6VqcksyMj6uD9AisCYdfmzpXta0j/oBcPwhu0Jgwtg/4nuRLYAHn4CRUSrJnvZL95mET2X5uq3vFJmpIvmCGICD5/aZGCFZaKAeua+vJdc0xWWH6vxM4ClmPdU5TYWDV0D1vnpfz+fLi9maaIVil/IMTnpetJsVVmJEmisngMngucUwQViSKa74PVCtaFRARMEaDKBLBHIMlryD8ym2WnClnLduZghDC5Nli8goaJgPBGU7ScECUagGTL6ty2MsZZNS7w5zdin3OEvNJWiSuYTIDjrxg7hUzga5izof+dqsLdqg3hh0SmqTJE3Xyl/fGEcjRNMLaSBC/kiujLE7aCJo/QZm2SSXHPZxXLXEKB74GI+ShHH4JPa34+1o5qKLZvaSXuMqswnbjhFJCwHhZwg2ThxjoMX2J2wtj7D+A3sX4sOwB1t7U/X4bl3Va2IgxbQ9/OJ4kjEJ5vd3fYyokFtd11zSXy5lEKqsbIsclA5KliY0zl4oB4opIujxMGIofGywPEAaOe8IKNMzj4Xm/krlNayPKV4EJFOxaqOikhQSc0r1f+bGzZ/hIDaR6gZYXSwl+OlCC7kIlUdZgPKPhRLth1sYZKRAHxqHLpvQUtWWfCuLkkMqy4rc8INQBl72K6Jvfw6mTJMKCBEYvBQyKlJpY0TlSD7JmluitJToqSpN0/KVoY3JCGirZlmWG/HvT8Uxaal6Lso9sv3wOVEj6IYBc4WcB6kF/MYhpgTtFZaT9v79xsJfxywuMAa0XQjKdjRgMAv8U1gJCeD7QOKUAVEPXzuMFOZuqqZItLrEXRE32x7KljHg6Q19zJmnstlVDHGpV11ANE55aGFgAQMVmzFCERSF1bgCiuS5i4qDb3Sc5YxESpZqQeaMbcMSrGCFFIxEUHjORR1ilpRhhEoOQEmLKJsDe7ThBibsaBWdNhkDQCwGOF9vKcrcVw4koMTxFZSIr/pOhmA/J6LBtQ4JUquSSxulnRpQKT3m1bdsPkEajXSjoSr92kTWueZ1/Vi78c24jz4Seg9hun+k3aLwa4FU/uKqnbMuJaMzJxwFLQBXUgKioXnupQhDqoLjunN2Ohc1Zjsk69M06Q22QpB/ZF64aQiaFTIfjFeBTB3A8g9+FC/VU0RZtWe6ijXe4Ky0PBsMpaGQrYpBqUd4EPT2qFRL7MXg1czNXyI3Upt6srFlbZx7SYC4tQjTyfDPQhbdYs2+HColjyW3BZX04fx8+geaSq9iRTD5KR1EhnOrarEE4hAETDJoSkA0Mn0PA/EqWBeNlxrv9RdeEsB57qnRf08SfH3umfJ0JB/9s2xaiEVgO5m/rs3av4J2qmlrvrzLbmaYhmwJ9pzDldZLwo1cg45MsciN3kjNTCW1Ldetyx8BPTmk2YbEFwBf6H4jrJKmOVoebJYi2TbdtF7Gg3TFLrpiCDymUSwjEHcoO45DsQSYr5CxDdMcZpNzjnmFvA2/YaiW9mn7FIUPWEr+mCnvSk1mUYXUEBkjVlShsVUuADMoyZkVa92CvVu0KRpqqJ0LRHBjqUmBowtfpy8wsRWMz97NTTeAVhifrLfBj3wIhniMF1WB10JEj4HL+Sgg3oYTwNf9ZcjPjyu55VvwW+ftVCk4EBzRt6TqoYht6V8duIihR7dJlslZtP935RJC5u6zQ7hdcKa4Doz3RxoEw/aecUDR5cyJFoPlaaXdb5U3xrfirqvCaRCXJHpZKKVIozj5TRZtcnHXdsrJNQ8qEtXmVUuGlHxA1Sow8eL4gQN+22zhyp0tVIplUVWvkT6SbHI3n5K548zTTMHJcFMWEoFgJ0qraFsk4qLno2eb6XFcwkqpdudUrhgbnS9NO8uwOUZLNV2zDuimJ1GmNQs6aNM73oci4Uq2mlbNri5YQCky58wHhJPeF9hpA+nIsx8tw3XWStHBNtOrWyw8hVcUJvQhkoWzrMwtP7ikJrlfqNS7Hh1XUhEtbK7wiOyLTe4GQB7ifQxsXZs2uMlCzG8lRKoRpColLA4jaSYzH4Jci3/b8kp1P6aKdCmEWBNcgaHgiqvai0/IVECJbN7BIIIupricxzNytCfF9iEH1X8ZdQ2CaqrfMS6ojsaWj8wZYvPJtIGGQe6/Pyzrz8ARW7GuOFyEl22YFU8T1VTr3WHmm27fMDTKOnPdJ/bviDLMwt0vbNE8xczL3JcFjadFnoHK/GMHLM31yfUBlujeOddaQ39VawhIMhIfHhICUGhK8S5egdnF4W6ckxmeKWKOTaft+v98z9UY5mNiMxcUzbJ2Sa17f0tdoasG6LlgZs2iUkpAGqs2CBARJgVEK4e+2B3YMQStE8BSl6yyEiwq+bPdRuk7PWJ3+m2hhGgDWJAN7H9j74XAYt2keqfshN1t3EUkDQOJo0FONHLCZl6XqQLDDUHlvW4S5ZgQcVUsh2WielxGaA8CAYuixXGOWJUeNGgYq8ldL0uA6IUm3FuNf3sB55Q6opDaT8BTSFBgal9aUD4tD8xfUTeMo2QFKYOPMZpOYqF2IpqGaixoKHnXkbFVppFxo4QUuYFDyaj5j4PqM3uD2Doh7TT8Nqkx6gTFVkhmIcJDa2u32u74fVHm0zPOSSuB5miJ4wml8RZxYO34DmMI2z9M0QjIgBiFxOdiZRdRTcidQSgdW5lFTEHNy+xpda3DmTO8CmItNs8PutCIiZ6WMmi64aZ9mHgKWBlDkzk0sIG8CAjMydUUx0jRHl92iFpvdlgJB28LtZFQq2lw+F3QG0mKHDTl2xgk0HijzQl0Z5K0cY4NyGMsiiJYpKKRtwJ1qLLSrYseiIiGC6JI6TBrz2aMuwFtVZ76L+jCxk/uNTHGlXPcEh2TyFnVlchaKrtXCJAxKaRuKih244YmAhXO4lBTvS7qOPKloSJXWlP37KI2+kd7eaQGCKarq1DBHp+37HeSg9xThTDKt08kIeTJY5qtjfdI5bHVGLv0g2EDsFwtT5TKubuUeR1wTA1/BxkwAXxxy7XrWGmDMrSFqbyq0whKfe5ltl1jD4U6OhZab1FtWBsZLIZsYHISAjlTIHshfu3PKjbgiKwtIsuiDknzquSr6eylRT20NFHLzAhlaJOU4I2PdGpjjBHkaLbbsJ2lLv+GwBb2ph539InF66crsnk3uOOdLIQHLIwLdbrWVhjcJLetRk6NSHV7mQ7rELL91nZ8iB0WQEGZpdgs+jvSijurRrQTZ8jlS2ywhxmfNx9KLGUPBzvUyIbwyly6GE7sei79CcXQ0xPwcK74vXxKfF0z6kMtkmc2inq8wDw7Snqm1oBQbwwQi8rI0bd3DuLBqXhewSnCQNvloye1Li5V6NakvsIwlXXFQVkR06EIz7SXvUhkBG5SRxKFtZXHG8ZDcmm1rlmkD9y6oPEQh5BA2fdsCdcb6wgwiycPDKdXz1DVQqYt9D0haIkWpBHPSA8bazzS8d52mEaqCW2i3JZ2qdLguvmT8lZKBIr6jBA9B00MdljCKULxNWLYN+/QMX6NElssmd+DNMOmaBCI6FDRg7Y5ajJAOD7oaKx+OMjjjA3p3iCYMX6CpMFsF0QIxZ1QCh/7hCd07QHzY1DTxqgHJolS/6glPZQo0Z+l3A3QVFArzuqszQdN7cQ+/AGRZmKddDZsE3om351HudA0aX021zlvTdFW7nsZxfBz7qplW6PUCzd/aroEmb1TiRo3IUfEhS0XUsCxWnxoflKMmSrQFIz6ecxZfaYRcqwjM4sMyoGatoDUso6Q1UDN65w5rMiHTElf1KbDZqu764enxcdk2YL2JySV+e7YBSZ2B+WfjBGM422EYdrsdJjZry0dm6JTuLLcrDIrmTGihNNVE1CLWqjT0HHOsVkYxmIGrdOBLFxIW6cUkY0sEspjrsiJOq+V8a8QOqd/AL6GHjidH0inQIEWwmoE79qxC8+NF11dad4u6+rpOvf7TPJTpuFnUotM3OLG47vthtxsgVc7MA3tIcvut1KLRNLrSgqXTi65NWjZSuhs0WAIlmWQMcMGlmqrmcOj+7gdf/vIv/6vvfOc7p6fTOC0vXr74p//0n/6//h//zyNgbdt0zTouCeERIknQ1NZsT3e+2rAD6ajkYRhkbE6c2QFK5IjRz+X9CLMF5bk6HnfLDK1wYPxm29aQ+O1vYeETswNU03RykEZiguqxzOo/dsdrq8MG1FXTtdu8nZ+e5mWlcvuOKszMsczpd41vasM0w3LY6Vui6jrXEBeDOZ5OB6JhWKr4STBKUrrM2mTvl2IL4y6dsc6DjNlLmLwsKT1c2ojmoGSQQDd8MPdlLJ90jHcdBrArENA6eqGdrpw0qGScSQ5fDAlbumqyBkz7xWlZuclLWbCuP1OitxP2YkXYMIDsdZqwD0d66g1OGfos5FyJRzIYyPKPD4soLrKjH5uVgnEL2Qj9Ni1Ds7z67lf/6//yi3/xnb94ejrj1PTl4fH9b/7Ov/4n/+R/vKkPbQVjwyQNELPnZQyPQaWYWRVNJ1uR+l0Sz36ibmT3C3fmTZoGypSrzTMhSQDLA1pIq+RKPKijEtd78MKLEW/mmjtvVn9wrtgK1QhdQ9OqbDVSRUjYXK/r8vD+fpnnFy9fArbTfLrwHfMe1OpDVU9VJnJ0oFpw4QqlmepJWRWiBOi13Lgfk7FVKtD2aIpq8F+Rqox6vkamakz2lDkh1FyeHW/bsl4uZ8zqo3tAoa/VUWlPc3ExcPED5KZBs0zso8lTcrysipDJxUS0TiOCpMiYqIgxJ4x/FRvKZyzIs9u2aztoOkZ9NtSW9kcqPNCv4FkkZuYn6ob4b8WAZt0vuijmERKB+qbp+v3Tw9NUnc6v3v2zX/rV//rXfz7PMNcHhleC3tk+Pd5P07jfD9haFXlk00KSH7Yj1mIfZ3sR1aQQjOaLu+jq3hLTJP9QzEENHah9aGQBYFlXaJjc1M1MrdYDBgQjRO/tosuKQBInRaRcwKwFpyvN5NIsE/b6bFvXtvMyv379dpzmb3z2Kc0vYynrhv6ax2KTBufmZ2bWzIEvW6a0BCVmoOLXNGKWrZyLHeU49NkCtRXj0TgaosNxijSKhWeAUNxWfuEkE7HW0dVGDXchyIw5KC2kcAMLWM9nKPPAvDJooCuNFIi1uA6+CE00mGEUBlQjuTYS9spEaWx9lVFk4fuFVxTZURinOrOEcKA7ITgvBqBenqPOS7B6S88DFB2T7Vw1BJh0iR2rM4JLM6lsF9nlVkHhUY9dEU/j6XR5+vO//PPf+Z3f/cGPfwTZueitnBeYIdIOIHTrjSNI6LVmyUAqEpthpjiUE8Yi61RVzcAUqEe3Y4nOXkanmy9YHP4syFBf1xa6ycHvEFAKjjU9yZBdFGIq9gtsuanreZ6giQZNg2P3WUC0rutevX5dVdvnn3/GzmfJiGamKkpwaFGyxXQWuSU3FXHFQkJdrIpbGIxug7fCCjOiGJbPwHrQH940zWWat207vjje3N724EnlWDT3jxa6sxZXou5Lzx6ugsQwdg0B7boahh4r2yDQrXJF87MDG2IFEZCyb0HQd+Df11IGNhR0XkD5ZWpzkqN53SA2Kiq9fOBW1fy6qjro9DnQ9A1zRLgnGYwS4Rt+FfUwyRGPNCENKoQbmw4N2VfQ/nud9kPXtsPb+/dPj9N//JP/9O3f+9c/+vJLHOEBdTGQUr+1G9RFwYACwns0oakiRlQ19VlQNhwtwmXKOozWjj+BPzmLxYyOAL7UJq6v6eU+4UKZgQhS8L717bAO63ICKxg6tUORj+jQ7kgNgbxF7KFPaImDHZoGyoWwkICVLtJZm6o+X85t2xyOd5TZwE57emLQzKzkEF6kghskRLXwv1ozUJsEzF+QTGmclwQyFqPNUtixLfM473f744ub/fGwLsDbESUxRIi3EW3DNo69fdTbzPxa60lkaZjmBiOphq694EgKbrtFPgNfJ5WcFwZDqF09XaBowTF42ZFLfcisruvEmtKRzupzJCgi3b/B/aWoDQqmSaLLyjKa9NYhe8cOMqw1WZHTl+RxJgr5i8GWwmUvUHg0N3W164an8/LmzevH08N/+S9/8Zu/++2vXn+Fo77YzbBgb1GYqIN1XuMImX8wLYgymTBw51QZhfBzcCzpE8XaBbd9Z+Y8a0Vk7jw+VXtz3aAtM/SmoaprUIJmnrQtxX/q2HFgDfVTuhBqJLdCQyxpJaWSGzs8rdv6eHrq+166RXHurmmBcYMbPpmSxGQekazCFRixLav1e9aQxVmDon8hU63mZUW5ttQwc3x++eLlN77xDTCRWJ+ylB8ccWSaKfVrdn2hETmcuahVJBSYo3yy3dBfYNfE2Dmmbc1/wvGQ4U399ViPdzqHZnNIurtU6TjlOT2a53DJJIt/N0gJPzkc/mzbbtf30CQJ4/X28KgpCw3nT44dj/VNxQJ9Sw/bTlDNYlZ4l3kB6m2r3dCvVXt+eHsaH//4T/743/7+H371k6/Jm1U10Dlx3YDU67rpIDIHDRPPlxPHElHBJhya12WoW4Ct9pVNVIVoDGTGvDL5NDlLzATHJhNzuNR0R8ePWuxNP0M2tIAGIT3TaTYvapg+TV/TCL0Z7zoBS9ECoiSWInnOLX7fqnp8euyHQcr6Rcg7bPFrFNVko97STTb5Qr+g26aIrhET1xfwIojcp3k6n8/TPHdte3t7e3N7czje3N3edjR+EZq526gCmq7gqjZYu49wR2kcDAbN9UQCritM6R9WqLUCMwXf5O4/Fk52Dn74FSPI0JSPn+KZCPniQ/JAMaHa3pId+WEihiQGLus4FPRmQi3ox9R1u90e6/KFg7r7Og036tuKfw5t3eHkFp/szys9sjS0ZLiBC9Rbgu+wHZf6/vHdu3ev/8O//+Pf+8M/+smrr6m5tsx1prISaBQONt6yVE01jSO7X4nfY5SK0uQx35LbKOhArFTBdyiYB9rCXty7TrT7PhNZ3lVIXrNDIM7NjYFqGCpf1zjBBDRKUGZltsh1e8W2E1Qm0RnRAnSdjqXmZruMl6HvHWuAN7mFVTwjJlzxN1eEHa65Iik9mteAmm5dg9OWU+xEGxX/CX2xri+Xy5t379Zl/fyzz+5ub15++snhcOTRKQiKFl1ZMtJB0pYt/cwAIFCywDCXZpJ3y4lXyhId+n4GaUrCmS/g1oyWUG+ZIyIRKJWS4/DU1cnsSmPzQQRcFbxivao0E3bju87EfSJ8cYwCOqv6Dvp3uyYn4d4+ZbbwivHekGvjbxanqGiwgDN5BKkp7tt0VVN1WzecHh5Op/O//6M/+d//j//jq6++XrZ5BaxGBYHyDmCaFhaa1zCZpFrreZqxUVMHSh71SKYsWQnwE/5tz1Bv0aa1DQlheFs4bO46gELtnsAlRuVoemBVrbuhq5YRhooxRaVQ9VG38Dw1sPGKZV3aru15MgN7e1ANBQqe5+W4H3AWGXxFCnq9LOA+cXRvMFXq1GhAjKWsT271uFUwwRyTQcGVjonTYkOToU/CG3nq09OprZovvvHpt771c4fjTde3qCQvlPoEDWslei8vLR/M9+9RnviDkKz41ijNltwibQfG8LLyDFQbTyqFHQwAVb/Y3tw62KX66DmdRp4qITyHFOITED0G15AEWZXsNalJT8sbq6RCtB0oyxgjwyQ5pyXIpV5wlLL+C8gTi4PVDHAdKnhwr6kFJLTxcLAwpO4asHnOa3U6vXt7/+4Pf+8P/t0f/sGr16+wNaL0NELw02AtmvON7n2QzOfzeV7nqu43kMyYi4TdiZk3CQrnlmpUMswxkSiECpDElRCh4qKinheIfeRPTQ1Ce1eqAojLwcgLDCxB+qhPAqafFIp0C/AZ1/yFDeYkoeIhOckaN0Y0pNOXcXHaqChOkQUE0yrNSuUG5UtrHwHMwZy89dt0Le3D8rEqqDqgHVwu4/FwfHHXvvz0k27oSZOiwmPS+kU5ZZJTp4qr4EjwlVWvwnnRNzjsRR2tG7AZZ2ysL2aAYXEm0dR/AKmUgG/SWIyvo4orad6PHhjBci6HU+A5g8C7AY1yOdDHfldKBsNEE+iWT3TLKWKKUKp2B/xI157YeP6PgNW8dtePkSsgMB+GVQSdkQKNSyGoC62Kq74bT6fT+fHffvvbv//7f/T6zWtiMeBHtQQPGqcFvuoFWpwAT5y39TJeeC3oy6JqHrAhSehrxzznSjIFw1Ov0LDZM86A9LpOeKUhJDMwMn6ntRu6Dp7jSyMwaZIXe03xhW3I9XrpDh3EkBfmjPIrDBKH6WRUf8fkyWYjnAeMWeZZU2FXV1L4TAwgx2d1ht3BmsUoietY/YAZuCS7WEVjClu37XQ63x1vj8dDP/SoEYw4tJizr6sKUlA4VdEdgkfOiLBpEZkQbpCn7lgwbAxajmsYnnBmI1DtJdF02JUchZEYBGZLiX5Jz9ZeJ4kQULrLPxZnv2QpcrveFscHkjojQOQfXg0vv9QDmESSwzXxX6/Sc7yBE0s1aIUZMJQTvzU9aI3d4zhW09PrN29/+zd/63f/3e89PNzDh8DtLItQxDW0VcCuTVXXLk0NscRxmqj7BIZAG8hCJAAjNmj8UZO6ndfAH5vLWTBvfsKhEqpOIKGZKp4eEgSR2hwmKGXGrG2iG47IBAhhQTYk7QP5Bi5BuHBeaOWOXddrKNOLLaIKcBli10miOF08hzm1S5idG72sUpK4tGYEU58W7edO6EbbUNlNBAOVFhO0KIRsP2pLjH3ziIabpoEeLMR3rNekieKrosTK+/AvvkBa/rpryKgFAqamWbxUsZsVtoGZwUcdlc2IRe5sfLcKzyiyV5TualDJSrmnAxIs55c5FUG+JahjnE2X4VxR15aVAij+rYTinJsWMedyMekh0cEg2aZrT/M0beO7V29/+Zd/6ff/8N/fPzxgeAMSKZW9SycyHBYCJjCXmDV1NW7QRILVD2oZgXo2W2GWG2tdACxi6Dan+pbjVQF3FdppkoVZTsoZzXzyclk98HQT1BSQeMTFBZKTNY4GEsHJSUpxSFcZJwp4KjFFiYcukbv9nhQQPgi8kn1CVX06nzkohAEb/Db6U2llZtlpxv7GekzIeZfO3Ko9S/WSuVGc0ovnDsr27d3tbg+Z14SXbddCoyZIQ0DTd1KiRQ1BUSa1JGzekWKyBK2YTsUCpoxovoi764Fsk/MiHzS7kESeioHJM8CahrtS2tHr/83AYHUv0RDEQDZJQnKR3EGkJtPL1345tcjQ0hTxmF6jfNqh7HOxK2cs+gCk3YX/DdqitJhrQAYtYDUdHt88nOdTU1W/9Iu/9Ad//EePT09t06LPWTraUqEZ0i5E/qWhAkV6MdtuneYJlGrJrtWOLYRRpHHSL6rZq0r5zN488HSzAaoWPshgJZU3yhvkO46rS7iUFV/WjYH86IYUugQKlgYYeTqPwxQR5ms1TcvtyxfUDzW/qGrqed2WadzvBno811mb7iC6HHwKPRk3Df3KElCcYrcN/oZT8djSFsWST5+pcpqm47Ef9gM2xFs7iIbs9ocDdEps6mVZLtWFXFZcoqTPcx4gn5MtyCUfOAy3D5nfySeoQ7OBx1UM5szyBpFxfeiJRQglDXJF0dBQMv8X8IF4hzqomGbpN/oSzm4kVi2XJYdrOkxE0TQFLCJH0CwTLAgCy0Zbyh3UQ4OJUcIc6Qk4kLYGNbjtt3l+eHr6q7/6T7/2m7/zvb/9wXkeSbFpWpipRbfELr4seeFbEKIAptyq8xMSp8eO5OCKnTcorWdd262l8DoxPwwkGoKltpOxoFiI6wnP3FQ56buzL33i/xAVgIWbjnuChL1hGE+T1Aaj9QPuYnEzBXlrFl5iuI7z1A+DUwucerFB54Cu607n027oamhMjU3m8QGqq2jtE8U1apOoLEtExrnZ7uqdY3TgNpSCJLSQbZoXmHpWg2nQVtUwDPv9fjcMmCixqYZFxx2lbtA6TN7E0+R6ULCpiOBwyDEFKiW6Bjk/3Vav5HGSdA5uVKAKBzyKexlgXTW4gFmk89BFVpFE3UUHk3iUORmZe3p4ihdaDdZIZoXbMRfcqQRLLtF0MIgeq2hv+7cYgtagw38mgAbtUG0LmofF7rNt7ffHy+P9/Dj99V//+T/7l7/0d199XS8IAK4PZYkE1ItyF+LuUNWFUraiEiXgXCCAq20cx7VawHeAqbxYGwZJlzhXDlVoFNfQbSbEb4Kf/aonILcgch557YtsntgNgg9GSrXMeQ9dUGoQhQ3n5lEfOajMcmsICSjCS80sQgbUdzwNUxLDSb5jOwQoEemaun58Ot3e3mqGD5ZTk5vL4UOaBF/jRdRcFnkp78S0S5WYvGNZI6oD22636/ueMGQ3DDc3x8N+T9YvNOWaZvRxELOmkkczGrxlQjDzrnzuz0H5AKzKwmcYTeMNUffqDlpronOBKFo/lT7aUvCEb0FUHocZ9P1AQxDBucQ5x1QVgVYrGa7ExiCQzQJX2oBJ7ovgT4ZVTr/5SPQqachF32nxS3FEUKrLsOCTxmbYbYG1nqlu58u0XtY//7P/9M9+8Re//upVU7dVizN7AISsbaHHGZskIv3S3Hf46VxDM7QWX+fzCS6Zp7oFryahqvXslg4DqgW4nfnWKmUYmfgItm4GIg8Oi6WVHQauklWSOYwalx6G6E4QFyNsJEveJPeVRQrcsRgAZrVgbwPQUDCjgzFeLPDmeDi+f7g3iMitBa0Z1ZxbY5W+mmrcSIyBnGzOaRyxQbkT6sxt03Ygt/uuv729OR6PwzCgzgXm8eVyGceR6hmkj5mTwp5NyXNccJhOUpvs86BDbM5n0V7Om64r8vjTXrV/OAGIO2Jv4KZZobJmbba1O94cKHZLfYbUKUpuJAk9K3FqiFcVYxW5gsEhwYkvS5D0pyDoqy/VnUof+XdJUdF+b2Z4kFIBB1h1w/np6bAOf/qnf/LP/8W//Oonr9Dymfm0qD0xki+AbsWxZtickswhSNjA/BkwwLZtGA6Xy+PD+QR0jXGIje1nLUVGRQBan0GnlOcI1X3kKC4Wh6kHueB+TH0OChJ3bHp3Zxbr+G8pAmpBj+AsBOrmutWQ24TB3yskzAiHpQrLetjvwVYjxVw8EiyDZSFt31NSKudXsIgjFZkqAbFFoBFFLTX0YWcg0tXLm+EeUzU14tiqx6en2+ORqiyOx+Pdi7vj4YiZz+t4xjDi+TQvMAZVe0DL/kLcPJS4hpxdwX/nzZKcDiuIEm8VMyJXyMBqCjlaaKz5ui5Qzt+23eFwkHYeyBQ5XmA6VVB2jetHJYyOOfFxOzp271xNxffal4H+yuvaR6WsTw1CoOmLz1jgLFYwI9Z63Jp1nNtp/f3f/53/5Zf/xevXb5q2gwbi8C1QFJFoYXQcgg9SViHegWAUTyTV1nBQHzNUh+/89ffbpp3nta0W8OLS3QhIlLOlLIiNvOCb8ltKu145973+WdSgnYBMg5fev+0exaqgKtBq57V9B/VukBwtFhWjI39fI6+5LMKxw5fDYY/BSDazlbpd4JabdmKGOS9dB2qLHDJHyWZpl5g/6VIaXcm9hRuDDsRpSzjhDNguvLHf7W5vbw+HI3RCquvzZTqdnp6eHnGIMUpA43ROObdfRC031KPVswlslfCoM3O3PrgCCxjdgZqPC5FrnucLqAAsR7hjdgVsFYqZUIX2uRPWYiXHGgJngmdXUI8UaEUKZ2eFE84MXPf7805nD8+i+9lr06SqENoCbFoUg32/ni/9qf79f/tv/vmv/sqXb18PbYvBSO58gPFdivMSAVvQSJNpcd4W4AHV3FSA5uv56em0zocW+ktAiyyccoWmEHTBwxpXVjOvgVCBFeozfDxJtUrnDjAQO125EFlyAlrzOOiELSjg/KrgLweuh383DdhWbVtR0xkiaUAi53wgwiRorxAEfvnijqOmaPyrr8OjPyU9jePU9+ABJN2N5IqzXGERjVREbcSdV1gRd59XvkXX0KqkekmzETnagzmb27ru9/vj8QDGMCYHTst8AvJ9hHgi4oMVj7ncZ2NDpLEEp6NEy8QxY+EtCFXMoAW7kLLYHLIvajaL/5uhmTGYbDQnESrPZ+hMDh05wG4OrnUXNHTuCMMq32o6vFyozpPxcxLXqVGZrphi5HN3CFaIze0xMSNJFFi6S92J27qelqoZ1/nh9Fu/8du/9Nu/9u7hqW0wME7D2inDGQYCkPIC3kjRYXSejX8U6MsNUCxWJsEgkQkHhGHjXwYd535wSV3Ya1SLU9h70nPvSD5eAU7sIwqnkDDEOOgsZB0kuRIw9I9sAaRZUDfRFbpmPRWCEkYIjBAQL5E8VaI4ZtYgfLq+X+aZu9VhLx28j1GHjVCouKc6hudBPYcwsmOIJqKxH61TpVXZrS/jZZ7mm/3ueDjClA/Mu1qr9Xw53T/cw6dgLoHnWVUXatznveLOgPTGHW7TTaeWJEb0a0NmCGhx5MTSOimFJWfQEL4h3lHRJcWAemiRDxwTGi5ElI+SK6fAqySlB5cgSWaX5Z/FS8L3rww7s/zYTHcvrFI6mPHs73Zt13qc1ss0tufmd779m7/67d98d3nqhh6yyQFMyPugtJtolikYfue8Dwvjq4seTLiuo2QucPlAebYUq2OSFru+sUOAq6IrvdwARUeAecYPW6tX7hLu5kBt7uHkm559CLKSbgv/AvHypFxUJYi6JE3AHxnnKAkmrzAObkJN1fKwXGaGZHJgtvV+vz89nrhnFGt9mJ3k9qADmFZUa3hqIbZlDSllLhGKUsdURJKQXrf1fD7dHA8vX74A6sXqWpB583Z6ehrPFzCW5mlZOSeHd8e7ZDwjA40gKj4Tsr/U2WeKEi59pbgrpkhwohk6+FMGKpWSBOVupdb45LfbthaKPTdoasfe9lADVEYL5S125NEWtqvC3ybOE3Txct5nihhFhmKbhPjVARS/oVhN/hKxE8HqaOtt2fbdcVubtb4s0/Ib3/61X/yNXzudL21LaXdYBixqssR5w+xHqXKxFgyaDCmlOtDbiRzVcAyoBWmDBag3kXoNi3R4JuWEr39DP/aBUEXswArDAbk3zBB28wg1N8u7Y5LDBKNga/p+HseGiqmAWiieRHXvHDyJ3fr4mFasQ4IyhtjQQJUYSmWlzQz9sOygwRj3ysKporoU9NTwYlceDwmBX6ikhbABK67SJMvLYUUvnaMBNYxVVR8OxxcvXoLbGZvjLOs6jZfT09M8TcTDvW7MW9RAm7VaYY5BPbWA1cOQR26KRTkRYrxjcaKMaNSuvBxXDy45Ts8AbyH4a7YNeCDjzLZuXUPNxVK1LLxXZO9OTWHVPXig3eu6lHFkZxZFrkPmqB3+VU9pWKlkO5PZwQpsU83rOq/VdP9U79rqYfnffvH/8zu/97unaer6Dp1U2JaQfMwybZlzrIiGySgRrBP0l/pZGgyPQT3oKILdJ9j97WhCqgi9Rhe35ZRl7uTvP78ibpnQjJrpYlNgPJyVhCPI9KxEq5EWtlRmincB1XZbZ1DktOsLJlqpz0IVaW0aP0/jBkLM1EPVm82FwVACZ8xuGLCi0KYfOFezBI/prLZqXhYwA8FF3PFThW9ZfJsSaiRiR3nO87I+PDy8uL27u707Hm/6AToWt007jdP9+3vwPIPlic5naXrArnn6D8rLIIKhNAvFT0iTHfqWoJUM/ijwSU0hkfdCzpYULksRgeVuixjDPaFxAe0jCq0anHR8RtGK2BMcwB9jvqa6tXOMll7XPdic5GIMQWoVsOE9pl5A9GGB6tzz5X69TD/+y7/5lV//nf/8538GM86p2gaZNnBfnEIrdLuY2hwHuTICmgCkPDScYIgm03kcz5czXYtRd94k4SW3R1LT/BkwWWYWnW5SCKZMUHr4s0+b1Xv+oARX77f0UOYfOvZM2eu6QGeSbd4mxLgGGzJCMMw1BRA/lshVWZuVzWoEiVRPDqOYG0MKYCwOJT4o3yJKDe91W9GJi7zV8S1zHuT1uvjp+fzU1u1+t3vx4u5wPGDr63bZlof3D+/fvp2my3iZoE0wcQVKN1ExK05ODePDwPG1ajoRudKtkjBR7WD+PqQXqC4En0lCuMWv2REtfi+wzLGbD0GHkwiqrVOfutCCHqATs0YpzDVNyF9PAfqQZeYvUldDwR7OX+EqnysrGjPnrkgvYnS+QBh+nMZq3r7zl//tl3/tV/7mB3+7co4R+fMhPWOCUVqSN2cCWLyBUgvqsE0MOl4+9+ao6mqeod8v1CHVC+hmLSeO+PQS2Q5vKFqqBaIL/FHcypZpqPfT+7rqdQcx+00NEO+L8R8rJnAlOijMUFwFJgikfnPyHg5j5Jne5oWR3ApIOyBxaVo/bSF4Xz3JC8K75Ck0TzHXn/XnikCPDYwgqzwUBsXYP5fomeWx4Fl3XXtzc7y7vRmGYVu2ru9Op/P7N28u57PL8rGET+LizNJpRBYP6UCdCx1gBBJOQ06h7iK/tDct1xe9XLRpVhwIXmCAbFjggcvCr8LPpVqxqZ3XSG3F12zgBNOuXRuwxfTFqwl/qeV3xXuVf00hI3QsydtbXU3YFqPv2t3+5vL+flnW//p//un/9su/8oO/+yEkylH0ELk3WKzob7Qwkdi8Wq6kYQiRS9bugA0h8vJj3S8Ek6GDIWVaAypIAzQqAHCsMAt6MV/0U4bidj90NIC8qi86xTbyCUWUNPXFPY+sdGYQImGpnpdyK9qmXajliOXr4IfMl8CBN809zTTDHlZWIKcKtM9IM6EslXa6LpnsogWDFVIO+Mapm7NJ2iCryftAYXh65jiOAyZm397e7PYHdPECUT/cv7+cL2Qv4VQrXRTIALK5NQm+w47lEHqgfh8kb7WCiJ0eIebE61Ewc5olF5nqyp0B6I4Fv9tAojgNGQD4ggT2ialF1r9dlZuGd15oxz/ir2YLJpSfW9/OZmLSVLNcVGa/RyFkTh2j9mt13dXtstXnx6dlXv7Lf/zP/+xf/tKXX38JVenUiQRNqXlZRiVdPJvgs7J0Da8GyplQaqnGNjg7a8bL6nWmEdXAVnFlGHCX6vNEXMYC8AAQzZzKWZyp4u5aJ+hZRTDfrDN1vCmmUkaVCXlXvBvQThxq/FUPA7WzaUDDwW2zmiuBJYqvYh/WraX2iTIO2XRRWp6riRMK1cxIfNMS1jSqCk9A6xeOCzPn6Lz0OKLyLLTFM9pmCE3f7A53ty+2rTqfT3WzXabz+3fv1xlKB2Ud1IKDQq+gnJF7fBj6ph64HalrIKnZLZLoZ/aEng9JbOaymvnITQ0MkVQC2zfF+8mSBIVyR4n6mV6c/Jbn8QQDiheZIFj6CilBWp0bLzHclBuzmihBAIIMe3G9w1Mpm5TG5QLVfOBFGIbz02m+zP/hj/7wl3/t17//wx8Mu5664xMSAfVSX23Kr1Lxa/25fWDEVm36p3VmxEJRLGwgE5zYPhdZkidNG/lw/zEbnJG8Mkh6KesEJ1t85ZcNu0rOy3mT1J9vwC0o0/AmsiZUG0W14Nm8EBCWGl3RuUHbnJfL5bxMI6rZTA2qR6r9aEmX7FvW7AYz+mA91HGJ1kw6Vg1PQ14MjwabBQDNjcTprlSXx9U2SGzQa3ScLufxv/tHP/+NLz4/n8/LOu+GfpmXp9NjD7XbKiXIzw1qM/adb2EuDG0Yr4B2JdHLiHqWADcySlEDBKIum01krhmkFkAx+qGULnabUb0Wj7jQp5mtrQFIjzoJNkWji9ZckLvh24mhlt9VS2TlLVVTgwGVW2z8H3S3mdcOlbvzPK/gjbj86b//01/59V//wY9/2EBDe5CH4HZmrxXYvZIwqbLX1GaLbEZVM4n+IH7DN7qmmcex4lk42McQqghpeo02MfStrhPq8wzUYCgcKgWXWb8l9wHjeJj0Tp+avahkYZd4VZR7XXFT5WHoqwX6RcvUhAamB2MzGEzsllwf7PZ3uUAiRDtACgcW0NDNTYkRCcumh/LM6C+klaBnSKYoKTBqdDDB8KwBOyKK4Ull1zQRQqpiMUlunheo/r35mW9+8x//4398A5VP8I3huP+7H/7ocj7Vw86GfoghxxMCuY6DS6KDDHNCjNvNEtSsJET4pg3WlIIWCVWYZZpZj+qHlipi1jVt2pA4YzI7NwQkXaJIQDVmqyl2JQglXy7fJGb46m9Ry/ZGOqGVCY4Kxn1CcXYLA1DatWvWUz0u2+/+m3/zG7/+W19+/XXTI29maQoR3mkW8pX/qdVrebrqtUqkr8CGgYmBPQgCD8OCwp9aukC6Iao5OPKbdDzqC2dAN3z8qJe11PFM0xruGGdwiKDajCabeg+kqej+kITkVKeF2Ga3rCMSCAoUTNAif5+tBEkYxhtU665DHAPXF/qruaiaH2VS2FIKOerO4Hc2JJ+BaR0bvdm07YAjo6EgESs66X3qtoZMDMTwNE+np6fdbnj5ySc3x+Mnn7wY+v709LjMS9203//u937y1VfQoB9DQ2w207FSv3XOLJHm0woejxWOnKQlCElO27KCmhmAZXinZ+WOQWiG++KqNK5hNAvWlEQtzJ2q3M8BTGrNHJt29pWtwFNwzJ/2DF7+KYqAAlYbzmrHZJSplDGxwHSTtmrO87wt2+Pj8m//9e/82m/8xts3byooRkXtFjN4wGUxL5O6rcThbIxfM4lEvcv1Bl2eC8mDBB7RObnre3SncYoCuMQXjLkE72ROtuEgHJyvakN2Ss6M8cpQPjbNAZqJLmPiHPpkexZvjjTaUjsd8kpjTjdScDMjfvP0CcyuqHoc3rUuK7TjxIAeH5m5E5g9sos1+h7ISFILAfJhIIFd7YKaejgO0EucWuGYjk3CnMcfVPXlMp3H093d3T/8Bz+/2+/G8bKs25s3b8+np2max2l68+pVU2/7/Z4idTSXTESdTUdUPaI4WUh7WPkeNOJgRT7ueg34AxWtxdGwmakpU6braTg4pYz7wy6IXTN6+d5sXDtEc66Vq/gVbap0G+FSpU+rHUmu1E0JtVG2Mmao1dW+barm6f7yW7/+q7/17d9+9ebVfr/HhCqKGEIUAROlgHrBNUHpzawx+//E7k2p1zimt1NYwQIxAM7J/a6HwOAClQysIkr2pb9R3JvxcvorBEbEfxAgKFeaEC0AVEMqek4SW/VGjV+E6YCmHYIgo9Fvqr2CWwvusu/7Hfhj2eG+bJAKftkNEK+Bomjm+kRcNvpLt6nxVfRCGANlT61Yj2CCY4UDWJ8NJZlj0oT0suO4qeQFNFXXdjAjZry8uLu7OR5fv/rJOEHHyZcv7mBY2eUyTeP5dJ6nqR+g3wBo3ZhChRQsSm6WJ0hvYv9qcwsJxbESTzsU94gRIWsdEjVX5JLGHSbIOdNSJKwE1AXZwEfL1rjEvLKoYUY6Ea0UvoWXxLV4nVeKU3VLyRNc/zXtocuQ4GxJ9pQwJmBt4PFwO10g72d8XP7VL//qb377t9+9f9ftdhDR4SAt9NOgkBFFfM1p5UnWNywrBEnFspQ0Gckb4Y75UCNeV/uha7qmnqp6w14t5NMKuy/bOo4zM4PI0234QF0DQUcRecCeby6NyzSDoKQTaXCbP+K7yZa3/W44TyMMfAMbrG52Q3cYehbYULmD9FOP49w21ds3b2b0QlMzbXFVsXgloUX8Fw8CfYkwSk4G1aPijb/T8AucqUvfatSn6BVw7kdDqRE062hd15vjod7W0+MTcOxpOhwOyzq3zUDxDJwYCuBs62aA1gWShm3HbEAXRxFUZ7Eyr5/4Su10BG48EIvhGxVrXp/voKRtOdXRZ0QBwgIA5BiuqoxOq3JZdi4vL2rIcY2ux7QPe8TWi94cCJu0X/WhGjhQahH4UjwMB53MS728e1zqaX2c/vn/+ou/+wf/7uHpadjtp3nSu5Hdi+VZ0GDBq2xO7nqV2bwsbrtCPjqBXL0aECecq63ZD8e6H6gwEYwxLDp1aGblLAECme3h9GefX+QVJIdBydGEb7i35Qd1yAhby3fpTDHE1gXqUDFjsK/7m+O+g+4w4DqEbn7LBO2y8Ntt1/W7oV7W8/19Ayq09OQWFzSaFpiVAdkRzEyncaRhy1BRSDugKhKazURticmjvECLD0R7UJ9ayKir4TvgPoQI1jzPDcxkqJcZ2E3btP0Abeu2Zbmczo9dtx4AD6ZpAlUcU+imaRrAc0H9mdln7qamaT9MUyAM1poWoNk1ymUTY9lOV9gMDy4kIGkOqZ5EYLTepOpW3yYkVW1dmVL5fNWydl/yH9kiWUgQh7LPIgVnLEGxNISpOUDOpsAGM3yWteuHZT1XS/2Xf/ln/+q3vv1Xf/3flmUB6p2gsQYF4tHnjKWV2E/D0a5Rr/eLCneP/mc3P0gjXJy2ifUjoJaP51/4hf/3DtIXFhwe0/JUPE7noUxjFay2TX+2iTYdcx8dgzbnX0mBDnD0GHeNbs1xwqF6y5qGra7V2qLtu+v6m8N+N6CVuzYLTALHthYUB0Iirdv69pbq4zlnBgNpkjlNiaUIN+S/0whFS/Xti9u+G1AGAqwmfI3zBDGsZYYJL5DUjzWbxIR0cto0tV07Xi7LspzO52madsOw2w3QLq7r26Y9PZ1If6+r8fHxoYK6ZvCxUdSpqevxcoGjhAACt3Qnr6eox9Q10h8SFafycaFGTWwdm73ICMcsYKFfVwEhiibLI39WGDAW1S3IlxXCSK5DWKZT+dBtqkVHnTL+ZVLWVGdGR0WfwoZUM/YSzxyl4g2olYogZxHSQceqrcBlMvzhf/iDf/Gr//IHP/zRVjV936GvAiAidfgLpkqC1YuuZm6RogUxltpnMCjDXfvd68rFq0FtTeub29v1vE7DtIBw6CjzzWk34h1KIGBbd3pPMFK8k8K7KoLP4qpU9WAuPDT9ro9xipbYLDAFe90dDsf9ALkp2DYMEguabakXmqPCT6mr/X4HJb6gKC0tNFKkxbNnXjJVtwlSXaG53DDsoJkrhWAROSHrZltxCND/r64r243iCqJ9+97umbENWR7y/y+RsrwACYogSgKKFBTlT4gDFl7GM71F59Ryq8cmisDAuNt9u/Y6dUr0Q1PlxppNmPoSNkalgKP7RRzUHIDAGNqcN/1CXhQCrFj4oD6mrutIs5n7UlJpuVF1aGfMOoAyTjYzVGZWW2sVpMOzvdrP020mUil+UB6uffYaEYW4KSK3guqsiqjmWRfpA0tmoq7/tBv0cBI1PoNWSU9EIEZwfm37DlNH62GujUOoyMTnqEMmAfwHVg2SLLVpPOa5//ufd9/++N37fy87QjXuDwdCaOBrxzobSNdrs2iBtyTUTeILWjtgFT/rwygbkW9XoJyDgiOlL548Rd+oWaZxXsimCsAQcVrB9K5s4Do40bOnaDxqSU507iGO7vSlWTUkLAcJoK2TVsMJB7zHJZJ2576/uNj1QP+PmE/iG4IzGvC1xJ8JFG2p7brt2dntgF2NFfVtR88rAwkHZ5IzFiO0aQaldqXxloESn7XVBJVnPk4TfCbATfCTbZvBxtOmru/LzGV/5kBAIiXviK5bF52RZotxObdQsgUl+Bt09ad5WECmA09MwhVVjhBfatpkShJITORvjEvTSE98BryGwR79mV2Lr8ZkIGq1whDlkpgHluoeC1yeNlXnEgx6jL3jV7GlVPNb0da1X3bHFTLuz7ljNw3xqOx0YEgTzH3uNss8NMv45o+33794dvnfZdl0GJqfRoUc42vX3FqzithIg1h5qO/391R19QjrQ4gtY4W59dvtl1993QI/2DLv1oCH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right gripper on shoe","args":{"grips":{"right":0},"duration_s":0.28,"cameras":["right_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":658,"output":107,"cacheRead":45184,"cacheWrite":0,"reasoning":49,"totalTokens":45949,"cost":{"input":0.006580000000000001,"output":0.005350000000000001,"cacheRead":0.045183999999999995,"cacheWrite":0,"total":0.057114}},"stopReason":"toolUse","timestamp":1793224518104,"responseId":"resp_0b5218887c37459e016ae26f46b6b0819fb4ad5a91439be7c9","rawStopReason":"completed","durationMs":7735,"thinkingLevel":"high"}} +{"type":"message","id":"85d47354","parentId":"483cc450","timestamp":"2026-10-28T21:55:28.887Z","message":{"role":"toolResult","toolCallId":"call_49dadea9fd994b87b00486b57cfbacfa|fc_0b5218887c37459e016ae26f4c61a0819f9c85904c6cc992dc","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224527.355764551] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5191649198532104,2.114835500717163,1.817434310913086,-1.2732782363891602,0.0029058295767754316,0.061535708606243134],\"left_gripper\":[0.998820808800784],\"right_arm\":[-0.12290065735578537,2.0888092517852783,1.0617690086364746,-0.5257982015609741,0.002643134444952011,-0.11187005788087845],\"right_gripper\":[0.8180002976547588]},\n \"file\": [\"/tmp/kinex-observation-bbmzia4v.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"e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/run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5487600564956665,2.125786781311035,1.8208038806915283,-1.263669729232788,0.00247021927498281,0.044266678392887115],\"left_gripper\":[0.9992801709608599],\"right_arm\":[-0.12398703396320343,1.8660025596618652,1.3495227098464966,-1.042709231376648,0.0013285059249028563,-0.11811474710702896],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-0idhe3ah.png\",\"/tmp/kinex-observation-v16xz1o9.png\"]\n 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"},{"type":"image","mimeType":"image/png","data":"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[rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5512616634368896,2.1280250549316406,1.8211685419082642,-1.2633064985275269,0.0024033302906900644,0.043757226318120956],\"left_gripper\":[0.9994734796610746],\"right_arm\":[-0.10064993053674698,2.227466106414795,1.3507928848266602,-0.6901805996894836,0.0034024433698505163,-0.07860491424798965],\"right_gripper\":[0.9747358587655155]},\n \"file\": [\"/tmp/kinex-observation-0me27shi.png\"]\n 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GstsDGfqElLIKYIWYSMDleYGnZOkc7pZ4rZ+j9shk1HEPQOuYQ5pvKT/V1adOcqeVnmlfYRCokfgrUc4w9QIJLRY00DRywikHJ2soW+6oiP+ztWUdJ8TJt3tYePuLgS9HpG11hyinQw7P1es0ihcyUjUUWA8c9jXuRFeX7nIah58lqfCRRtuBWTYbzzG0op4gUi9KL6pS48m0hZpUgKnxceLHU7PC4f62tk7G/ENK2JMrEMSyDQmM/LcWx3ozYQBDGPaNRQSnZ0IpVV5P4pldJn0EEeUIefM5bG8rd859pCG+VRySxgUpMeca7yofLbc1boIkxzeklUivH6TU5IBV19RuKCTYiaMs31yt8hsQF0JxVtnYw0/Ou6rnGRFqmHS6qxAWVGpFQDQ6HRQh8+63MjZfCEgwGfE2q3fWJ43cJm+kP8QRbZmFrmYKaVWiypwlV5LFeJ0hDCQGLAwaT6DbQ470tORtnU5C15h4Vn2f9ZmNqVzjAX51SbcB/e9bbAzQf3vaoZ2GlBNL1G8y/eNlfZSMcRjCivZrK80eu7gxTbjlcADMfMVcO82SGmFU9aB1UL4DCvmBjuSWLK/f1KieqpVj5x15n9Z6uPtr4rHDEbyNalcEWP7dx7/osgmg0Rij3uYjP3xrTv8hkN8+/VjCuz719U+PjK1B5BZuMqCQpjt2K7lms3oHKXpkCsnke6xVpo/i0lhOKGTSxGwSXhhj4DKx98ob1KmIYZRYjliLwddiD90K4tIo/WrpRqvplpQ/yNAKfjZY2SFdWUAVdLSNZkscl86mk3mLtvEoYvB5Zma2U7jH+Xm5Ciz7ifl45u22RtCFEOGH1nTEVoH015RAg0fhhudnVSBtssPz4KvgVXCgH7rUZr0NSzdTbtLqa03O5XeEXLESt6go6AVtXUJ/K8dE+EYFeqEyW4aYEuGeQSH6M0VciMVL1lA3SXCxXJ+AuOCST115u5qwwSzuvBQLu+Ig6uTmDNyiUK7kp9bcJ/y5mt4hULTUkxL6OCdurv57w0boTKvjgNjSWIhNS2ljyBTgYW/GWd8epYXsJ1/9qiwSte4ycK4G+KmVAdaByxemLJ2ToUr56+soiYItOxn4nFH6S7lyVaFrPJjS3skjuBBLmWQRe+u8usiQZGw7ZWXmsWj8PfEBk9CzSMu+zBaPIuKUYrDHlFENaLPYp3EIZPz54Dkaqw76RFWrEt2DFwE1RdknLg5lsMquGHOVc65uSkgaXqN94TXHAuW581hRN2qzpqiTREkASIPbFjOurtnI8T+9tWbG3VqMAJGBcZikX04gzq2jTlryuk1kTRTar8dReeEsa1HLKUnPQgmG5RHU/e6FcmLAvNPwx23lHxVr8Ew3wkBLOVh/YvpJhm7GMlWusXqATFKxHffWhxvwR+aOsqI1/xZeEKnrj6405P+9r8OPUr9haW0G6n+Zqz7xwD6oVPEF/wOiMEfCNkbutcQzqGrbqjQc0LVVcQ7UriGsTl1zUMDL4ms25nenspN7RxsBmy/1lNb8iAo2q0w0j+YG99GhRcqoljjW2ylsRFLwNqXjLjZXJ/NbEzSGpgdS1GLR5MM+GvyLeoy2pJiXq+tApQv0ZswgiW3yGmx3koOSONXrkqOcrL8UrjV7dAlpJJQJeRdEgBm0626pDV5DT69f6VBUH1zGeQ5byvrchNSSG9liCkIUUAl/CB89Uq3T+kMbLki9tHXeTKML10JaZjAwQMaxkUdy+1IQBeybUE0oMASXjxsS3eLTrS9UrQLvafn3r8/xB1iLSHWCQ81sq5yL81H9raQ25IuaOlL9U3PPa1Za4U52Ta8/GQ4g5m89UVOlZ79kcIKIFam3oyUHX1oP1DTjohqAKtoJaJCxMrhvkCiazMWccOVl5ti+ckUMV91oQvZWa1Ujt2Jiqk5hcqSnVEbKhjNm02M0V/pNMGeUX2QwVyq51XSpl1WvVQWX9CTWvQWa5W0Cv8kQdO3CmGwOmuZRABwfUyrk3+/mGf6mGz9ZdmTqCYlVuCGbmQGbJ0xj91DG0v9gCEEBdqLz8nBH9wvAGPCqDnGquhRkutqHbvuK7GDZVGG8Q+gQyT4tnzm/tT7FeKQumfGCKUGzxHP0g4WquBeaWqCgRnp41laH4rCiK8xyBRHuOoS23rVBvqFSAiwV4Q2paMfuCGJdT2vw4BRNX9z6VfiYzbafxbEdtN99WdpMobtsQO5OG8w2rvoi2U+lh1VXXoeI/rLyL3yUZvBay0LJq9GUII86l2rHnEQEd7zmhozl32yVnh7dC74g7vsnGVhqLeaQoFb9bB2NyBTXBvxqW/thnCqUUtd9dtrp5ggE5irEW/6/GHqlULJghNizMaYIUicZaYQE+FjwIWFJKy4VgubEzTRJeXRtSMvfnwuml1xhPg50D1rHNfkry5DuIX1dcfmsE8EKjKNZWbwzyn8Le1jahcgLrW68fVUMk3HzlN7yQv2yzBN6iUCvTnEV4yuJ023XxTadmHJgaG/V9PzlGtGsW2mdmS4o2cVYtzUxyhd3VsBhAIOFmFmyw11lRUi+yBSBao0CwgqunL69k/OcMdpjPE5fPDp/b2D5eXvGSvqL2DV5hooaWHGxXzCgIfcm+2pVVNhYgT4G5EdNtNheaJKLSaKaEyP9RL0vJNDMVFm8HDpuLUPty18bkqtdvr7b9YXmkpUTVvruvbYFJGILpNBSW9MFVmUH1iagD3U/c41Rq0kUN00ZhLU39sC88vtcdUhVSwRzixLb2ov2tpUXpO6EwVkSP2/Tao0O1+sau4BzL6TxJ3ouAh6hsUrHsDdqBUrBbGGMwS2bHckVVNYoM/P66KLiNE2iSnExLC0q4Cbpbu2NTVTWgcaBQJCl/CmMiCG6E9uYFtq8yy6x5R1CJYn+eUHcSFW58XH/ommaAomfPev26J1+nFE2bNw9ThBi1PFbyutrLgm8NHdWZZ0mzKgbZRvZqAfiVx3BUkmLA5xY/daIdxVhxIg7lxditY90SJPwj5BQyneeKcBnmc1x2AS5bIkNti9iefP11izbU7KdZ+UW5oQ+fTeCaRwa3b2Pv9vTK+fdBcqJz51M8zEZJbCr927LINgMArAvjNSGgeEutjsWOlcsrVwE76kYs7eCeoiA32AXUv1BMtQ+l6ZkNnWeV5p8hP8/Of/WGgwA8JpPwK33LFEaS7b/chSssEN+5gREF1qiSZ2tpWyKKuPvWHTHtSuPbPMNmTtzz3MyVxNoaOd9tr8jxdxKdvGVszp95/yzp3R1djU1wK3PKnq+oFiHWejXjn55JFfJVUUDVFTNJVSR/BvstR8p/B96VMZgVibHPIfax7yfvO2Y10bXqftj3gj3WZW3oGqzjdxK5dQrXSMr2IkuQCwrEcnTfcR82p7gNh2pbXgRKdp2+XpnclVCZ/LZKrCgEWScubRGxHD03E67mjAQ4ZlnFONQw0pBy0fuw/XD7MYu8w87VrQCgpDE03rJFRZuvpctnrKq/0/drsod2plRwreAlB5qUskqqe93S0FD8ygErSVmBbxriTkmkpMuvmuzXZbpSfds10DqiPL2AKUL1tnKkej749ZUr9ohd1slNLHITNul05GiTs1wtIlrJJ0nmSuzdpi3uGQtRvHKMfSbR8GlZyfRrInJ8Yu6KbREZpPzy+edLhvhGXNjzpMv4vlTWW7NShIiz7BkzNphEsTjHyAIlnmD9ucJqmlL6e4NQAM62xdIgMxUe50I7eotkMHDoFMtCWtRO5WMvqF76jvonWLpU5jCZK0uPm5vUDsdvnbliv8ksIYA12Edjlo0hN0B3mwq1rItPXpVsmlXyJ4Tb+kto5vq8MACTvIsVPVP2bgz4bLx+5p31hkDHu+rWwqLRbRS+rP4y/RgEd0gGiVScDOKlPubOkGdepVKU8Kk4+kLDtLiwUkaRDEKJfOTG3ORG0rGMxjv5w/1HISL3GTOUUseyDmLIYXKMiyLb6IlXGZMUFXw9X9ADjlPn33g4HF+JogeNK2cCVF+G19sTk3DxLkvkW2das+dKri7TSa/vS8M62JTqQ+f0gZBFPMl4wnKfX9ikibWE+fxrIzrlucP1+WehdsFYxS7H/a5rPHv6bcG/DUwGhE0+VplUWyIH/EZW5yijS30FV6kLbaUuQilL8Uml/aSwvAgnpEu5QwsiGc/Sbkf8Nb6iYPq4BhH6OSfYcTefTZJD3H3c3Oa2HF+UTytZfHVyDX7aepfvVxNBG4EkyXrUnDNYcdoSdDnk5nWVGbdm0RR6OF3smSNcu1oJmRkWkHl2aiErkR6Za7lF6Rj8IJ/ExW+4kAhXbQjZ7fFUFkhFruupaA6DxxevQ1SpCUZscnR5JAW0qGqssqy1SUpBO+AhbqJCoGFb6spiZ8QWYenTOoN6Wym6el0hhu3nrx1qBpT2nSAFV8K2htxkAaDCNyhg4MGuTwmvFU60+tmUE3U4bpkG7M31m54D9gUzz9Sr2iJzhSSW0qXRcnrO01N4xm75HJ4INduSepovuvLe2oXhAjVl8GvGI3Fec/80J7HxtlqmZ9F8tHC6llUhKyDA8CCgYXtnoOy7BVQCvd2sgVBPHhqnPa8+QxOpG08qTKUDTLpaMUT5Vy1sxzE2+n8Xwop3cCsl6SZVTG95gtXjOLKWeqZXKMYzi2CUIFGCGJieisQgvvJLqq//a6800rqdFlcLUt9kI9pVoZwRyl8RN6VMl9IJn7dSmDACsknnwzBKo8P470lCYNaoGgrg/NyYoP96MFbRZgzl81bqd36rA0trfc2pu3Mf5GZY5CYEAxBW72suALldNYXWo5jqiKLnNt62yHVRrbuef2OYkGOffM2T15MCZjHfltDyV57DswWFxAlKUahmC0+Gg5QzbX+dTxczk1igNh0NMN0QYYsaA6pZ+JD4pgB9WnMB4oi4iUSmeYlAbxlJDekkCRtXtgLe3cy8s7CDtNon5maiAQyDQ5ZT1Ak4R4M5X7s2E1mvQfv2fDepVPsV+fn66cIkbmBgOtWzJrb1wq0vt9SSTUnuuSL15qPQq9Zfp2bVHKeyNbP2LbpbWXNI39v/wjip0VTUXrQIngIc0y+ac0oPceqo2rG8awM08XxyTdWc8wfhcPOCWFdIVeRHtmdr6IORl04TNg+gXkgd4Fi8pRS/k7dCpcK2mihh/03G9qT36JsAaAEnVwJaoAqrOwE0Km6LYFaR1XbfNyou15hJY55rKa+0EgX+q+Vq2SHEM1D9JJyzpGuNtETH9BbQRTWXahz7XpubaG7uxCK1BnbCvdWOFEup9pBjQyzsVeI67EbrP+F4tN3nDqcA8VW2VBqHVGDuM9oMU5bualBmpf0qZCOaud6G3gLpN7593hcb6UvPuDU9kf0jTw51bWAgbE/KAJkuBrBsYT1AUBkB0STsRZZwQR6B5+VPV3imGtB/tpD5m5KRKKDahIRStt98CUw6y8bx4QbptzKyhk90JhoLne2dxes2F6Q/mVlIiemoGwV2Ym+O4SSqIQSXo16ZhPR0qT4Pu6MW3Wwee/uVoGa3L8MU91tsFJZp2Dbi/c+Kn0B4ax+/mVifib1NuxeO3/ZaVWNoKHRrvu1xQ8PKb8+eIBgEh98kdf40/lutdmOS16wcT1ztesbXx9nYVljzEyqGiz38hwRRGs0UdTJ4pT/SiI4qGD0jaKQ18M+h78dxxErkm77Mq4vOAArylz7KXc5y9YMCBrauhuZXPGM20O1h1mfrlu07rzlA7BZY+eZqUC59ajvTL0/uT37aakWlIZKnqnwqn8eVWuHga7zq9Sn2q3FXHxCT96603efteD7DUmgrIkyfFns2TkZ3tGH7S1XNkRXzT4p3REcxa5MsiTZmX9TZ2WIsMo7EDRMCe9ZSdbsd5RU7RKUsVP4XKUxJbZIKotI+fCMXNd2tFlcMVd5ov5enh+ylYsjXVtdQdMr3PRFdkGlq8yrB/Em/7FPEBb9rVse27xJp3cYW/hJK0peJA8UwXpc7ptrbTfCOjWltmRuqLdi4NkoqbvHh0NL0/8n6+zQnq6ajIcswRq81sVqVLmRzzGTS1MPVSGW1GNgQrYNQ9n88lfYovadh+6lNOfa113IwP1xOTNisb8UfIF99qnpuPV35rVa33OBeKi2tWWwnMlfvu5axYJuecH1b+mrq3g0xpDUHffQ5ti142OS9Bm9uPJ/eWY9dZJpdu7/LS6tzuxOMgFHmSUTqo1QIwblWWyh3sm2yvbrbKWK1zJmWd8KDUDlDEzQpIpqalWSflodhJdTeFvg9Htp6HSL5wRzEWj7N4r86hfsaGKCStlfe8vWBH7JgiJa9jGjemr5t83ZESE1ptrlZ0vVaUJT+quakJ2r/IauGB54lvznQAilNveyujWJH+IR54DkC/FP72mK3KFQ1WGo0E+mFYxSvtF8K4ahpNyqnvG0RMMtLk8dj5RS7u1Zxk4ZZQHVgWjwb89UYDhiNOfLWEWTrw6ZGhxEAkiYBswjpZvMqGs/IBrka0wQ0zioUH0ZuGOXwneZcDVEzRpjtpp7Q+OxpJEo3Z1cfRJK0ax2YPtc/oSI8Oa9NiXLjG5hIQSq3b2yMDPi1oafXr8clt6+k5PZFrPGTSwKUb4/5xJXluPyVGFDKACFzhjs3a6V3Gk3dIqu8m2KCBhFEBFGqSgkOguvhQoAfTp9XLNPD8wiMTo8bsGJ0a0UakrbaDhVeu26WNmcugPC/5horcSFHzoI0UL3V2XZDg6/+v3FVJhT9I8aPd+ZzrgaqaVu1tGvTyFS8DJcqZPCtIXBJ6f5iHzYF/ivkf/OJzW+qCfT541r2qRnzVVnyKSSuQtM34KFBIupptN+OLEy9KUL7Wf+N4e1X743khS2gLKG7cXPwCWrGgrDy5cBFeiTUoqho6KzG32XVswxZRZn298WdqSy77L3YoXMsW6saVHGm8FneSJe2sijeyrCpTqDFzzPQ8XY9uzCWD0TEGhMYy3kg2OZZVWmP24I3pCNuIWN60HY5SRKtGx18alNcC4hTVmh5z1NSb/fUVyUfAGuZHE51dy6ytTGuFAwN42x3pdJDW7uV7ktXqB94gINFSxkRz20omwzIvFVbarKbAskiEHdgT1KaRZEo6GgH+dL+Qk/3LXiVleOyWlzCfzWp0O5tRyit23GPsNrCUJ20P9/UFhihglrd2HxxvjZcq83Zw/l5BYZN6CpAFhPOcMD4Ib2Ine37YmtOndxKT9naq5T0doTUhtEOQ1Q3r35DRqjktjpLSRcHWYeVAG/pUUWoY2xFW3D3ka9PPPN0+CuiqhhbpRVweSvnJVFzs2TUNcKwRbU3lUQy0HG3NHVyxTSB8TYGLMfJv2XWbSJu9DssiXclkSN1CgKsf2FfwvI9VvKojAjFYgJb0898CB7xK1hkqwBV9bri46dEvXSWV2xIzafirwZrab4fqUPj9iJeKsS8BJBVbYJEYPQFCYnWYgQfxwVKP/DtuRfShE+0WSZdJtmgQLF+v1NJUC3GZWdJ3VooCa0WraKtgHGmTGyGiXTJsWVp0vULgAG/daQq5DvesnEaczWABsa7udyFsYZ8ua3jRX3Za5FTNUZvoCluWPaYOMqHRpGrKGxCLBwE4E9AhG7bUz4an0AiiRsNK8tHG4SrPd/ke9Xo8Gsu6FrgRHqmMy1Rv+ZP5fY8Xaa9enF/5ZYiInG7EiEsPn2QdZQ07yTNZDGterZ+eS3BeW9QGDFLPuIChj/5kDiQciomgW9J64dp13Vc5Hbh9SaSBK1ChIEvtCZZ8f6MlWUwij5OVGdJyw4hcVNE2PgcvgOMkl/BaBhzh5NVPfXKoJXQXH2Y3X9NPSDKOybCWd+VOX/zr/pp3biQCRrLiPtK/MR/3etVjAX2jk15LsU4xDv7J2v99OWmbeDLlm+93styuHLngksXdCidk9NDZ1FGsT0SBV6fsLealrZDyquRjoSMwJzfYBJHjvQohZAk4iRSxPePw0iSeaAhSLshwKbN8iMvUTnxBJRFE77ZnSGftzFqM4xCbPtWtcsEl2JDUQxhXcE36FkvK3dKvsezM/ZeXk9w2idvNStLXRC/eBKpE0rf3+RCySJ/Xkt4NX+rv8RneELbD/X6MbpBKjYKfRCa77wmUrU+x/pNxR2t5WZwATE9AULOGbGkX5afDRUU+jixh6tS6oe5ZEZ7IlDU1TmM+b6WhUKrx2EIr3SWVnU/3YiNzdCam2dvCmOPVdNww5aZvdrektZoDqFhTIe0MHhJ8VA11LXM2uLjYnX1Dl+Lzb2KxE4pC1EASVsIRU9cyiyM5YD5JvHc61GZ5ZuuBM+WH287gYsFFKDc6aNWVqgaw4KY5B4MBMUFJnxq8mfYhq31XP9241aFRkxP9IWwaYm0XP9U2TlPGkVrgQGrOpVeVZs8mzEta98Pu90OaiJGsELtYg4jY7RM2ybIZhe3bEgcJ+rkVAPY9z6JIDgVgUlPXbt0tj7hxjCbAz6FSM9BNKR95rEr2HcwBSQbrcFTpBLcA5kqSd+DxV/1irf9o8iMFOnM0wlB1Ha7fbOB1b0/V7wrelyrqRjEjADcohpMOwysubItclg/94zYgcLgXtJ9rfhaEw/qaUY1sa4wfhxnE6hN6hThspt206CNGhoPlRBe/J1UWQsCzwFl9O28QGHdhBs17S0otb/MIwHyU3n74AXlF/22WtUcDT+CESsYZx3JYh7rNcBA4I43Eb+o85KlhMb72guHGV6nZE9L2C0O9gSj9kVfA/nYnDX9H2bkvqFQaRqyddmQu87QqESk1KW6Xlxz2qVkG58XSbb4DssSgv6BFuDs95Jh2qClb5yg3RwgB3XHmuBJBu+RS22pPGMLqO8uvdgYvmsLhMQWU+R77qioOGghz+n5ciOR5tYb297yxvrS30WG6+ZVebzl9ivkMNpQPeN6OscNuMwm9sYn22y6/VSWyp5k98Bjt9eH8bER85yF3bTi3n9PUinGS9tP9LwzOGMdqyRp5IUVvzs+wPTi23rlSU5rfGk1tVJ1N5FQuFgVvtWwmHRgLgaLlN5DKlJMczmVelnxGSPw2JHPCsSiCOwDriFvuoomblZyGiCjzTNZocOjtFHUL+hqFntCTMxvrAhTe9grQZHbV4ogAfZQzbdc+1OjdkgXMk8Fu2gRWr9Jc1yZrFSkK9IyBEJYZO82UUtyRaG5F127ZSDTX+Omesrd5gszuUy46IViVAFDSC3Hjv+HzG7360ehC1Rk5toGOhNJ8Rc2NBuwLBEYufzarTPrwOj5iaiq58FPWqiiK1WopCtRUb2KMCxj89ffBF5fYLalxBTIXWzUNfYIz+Z7mwJ+HhJQp3HL9mIwFOXKVOL2NokIw6MhQnNMsRj0T77PNdtG8o7aHvytBURfW0XztQVDKwSb0nSG8YDXUKBrHjqwRB8TUA9GTQNXcdbOtAGFfQzk9v78dT05Ldml0RAIVSaW1p8VwaJH2I+6qhsJhIotaT3etTUbT9/k9kvDsggVuGYYNo/rtRcW4oNGL1ln06wR1BOto3Eb0nxdZsrYJJOFRvInvEXcT1KMawOK2iuTvurX6UtmqE9w7G2/T90YHJXnIOkGupCHrQXMSlk0DdeK562rb0HamJk5tZyjMdiNvOZW/rO+sS/mo+Zc52cxcEjbEP9mTCHtInuioqigE2h3/NgxpzxNmLD7I91/hgS7zRWKJoPB20MbLTZF8oCnANgnORXMthX/F1s0DsM8Dv08dP3S0iuiK3ko2OgExyXSZtppu65AhXUsFjpGd1y16Me08Otkv7yewbaLpxuRHtc3tYFuWPuvNZECKTBBrbwPIU//rCLsDbFz/22wOSdXAyRlF8uP6g2ADDXZlBGraAfAzkDz9KSqczkGzT7sygsnHakG4PcHpPYXQpEm+cWibUEWk//qiBfg0rEmhQVzfHCSj6O9iz2+hoL0+ywgghLm21NNR8KynjmwwrsRpWatnieuBFYkQ0/TNM/zMPczq9xG1sG9lMr/ODNIGEdTMcxN01m7ZZ6XeaYMydgNLQQSixXALE5aKXqEhmwvrmD68tEVlmkvaoYzVeeuO55Yfwp5uLLrBu9F2Cp/+BQtTuEHagrcOPEC8yXyLMLPikAGrA1f5QCnqDmYPyI8yH95E6N4OcjTInzxm+BA0yuhO675H7c2JZEdVEUj7jBNVghSQRCb1ARLuaRc2syAHf5DKBKDUtktyajZOJAEKqGUpkCl9txXriio3NoLeu04TbtlWWZKG7KNVr1A1d9sYpGZeaaiTtQkGViWjrQsy/l8EX/VYiEHLQTxgEujhFdFzdaXEnh1JYGhuUP5PH0B5fOtuER9RIj2NXRsJ+y1bmd7Pe9Pfttah4ImuMXGy0q8GglKGYMDB9F3aSfN38iRyP+AvAZ22A02kSTe+L1DL206Ut6cQ5jzP+QfZnExg5sTJgtzF10oUdEyhQHoCmxYvePwRbDyUnBOkFLurbU7w1XFm0k0Hai9aE3Fr0Lqk4yBS/UMw7Tbcdb9ejlfcmVoz76v1gS7hAeBX4oRYV3X8+lEsaDTxGlQlBjpSZJ4hPqbUI8QY8o5+zsKfMAY8meqGo3d0jznDbq3of1Vml76qpUZc1U8MLa5tkSC6v1Ja4x5Noif8zYi1Ryjy1F/zIOxg73x71iYLAFjLpR1Q0Qrq3qIl4RsEgffmSrN/xoq4haZlqGqa7AORWxLYE2BzDoJ8BdgDJeXtuGiMBiWyPTWaQhaEWBDi41zXKxpmHxmLiSz/obtQC3TU+24hvUXb36K1oD03ZOMPu4Oe9JX13W+zDGC8mEmd0CzswLoFuokjQVArd08z8vpeJkv4zgN47CowpCEDa8HpYYm0Bf9RU+F/G4xza0dk8PInG1z51ovwWxy+PK6EWsTc5No6gTcBa1+mx8AKYsdM8SLZal7YJ7ny3zRtnPO2WIDMHDVXDZ9N7K7MeRbxpDgNfxhTsQRC6bLJj1F3us7XIC0/6k5ykpOCZVZyC4DYjWpfOZupQpTKwmNWutRXUOMKuMwMoTJjaOUaJZBmMjwwvUfs7T68nErlMmbZN46OzUSsTAbUkvBYrmGJIvQBR8Pbs0KTwEOKZ2hghU8byIPY384dF13XB8vl9nEMQ2uSkcFY8c7ZF8sx82TOvhclq4b1nkmwOnPpu1L70WJ+7a+pyK68xnxhngw7FURKO+FrsiVVrP6FBY3kGXCfmH4jMSktXuJ1zXkVEBVh5f4dlM0ACAyPExiiMkGBc2B3yu5ocgk5SJl8+V8IpLKRRJ9a3QdLQokL7h07KswU7P1nUTAdvu+kUYYZc0Qqf5GmUOu72TRiFbjEI1/y7L2/bwsnsSrvjcSGFiGnM3GZT0DCJ/JzjNN0064hjF2P51lQ3ZCaTqYM5DERK2yGTF+NaWhJxE6kp7iVJwcZgGmqHDTgP5EXAiHx6m/IfCd7x6WZWGqEe/jlYN0xCMUvEtNU26fYglKsidUkgmtxkUc07Ds7CS8hP5PcWL0Q77CPS5QAidT4tyWqNqQeP1c/csM/hWF3Pojz8Ppu2Y9VgJwSyJWZE2rDqNek7LU2CtLcC2VWRuXSKH/wzmViNtQoIM+uheF88IQY9tQDrbLXqiPjkdYx5RaNgbjCAU2Bg8eV+iS++IlXN2NwFKKj1NsK7krZcd5ifzoPM+n02kap/3hMHH8f6s5UsNulrU0FLTDtabnEUkWvgL4yYRmaoQzRnw8wlw+wZy0VX6r8KrC7zgON4ebblkfHx7XlSguGrQw/wuX60IPFB6KOZmA7QKSRyAQ1ZBD0ImaOMAcmhB4mqZu2hEW8yw8jRCNKrprruFAgYYCmvEstrPSlTgo4BimFH5g025aiN14H3wE2YKGEQmH4awU95K/x5e5LYlvvt8EZssxKWzMQO3KIqMMAEE64L9k2lOKB1VjcNAOCXeaJRwgRs6I993gSn3xPjFycHRUrwkNKHpQ0bjDgW05Lcs8Xw6Hm/1+3ylX2N4pXP2mgBW/OAcuS4bxC4RaWCBHOrzyFIOJJG51/Ur25Gkcb25u+q4/nY7zPEv6MWycy84u4YiyYzgBjpxMgcVKgQCIDjwpO6brnZdu6Zdh6JeZrv3+ME3j0I+ab7e1iqCRT0E3AE2xT+0zbZV8w+QweLSY0uZcn3VVTLV4R+2MrqfiKdHC9URNdQXY9IXk/6jIiemCIBzb4CqXoRlbxwMBvoeJuKwusOx4CzABEynEUggzU7TWTAILAGR8VuVMZg58b57nh4f7C6HxYRwnj9tNsnJ+XwUPtsEgoVgdLLfmomTM8icDOFel3KLrSWZpnKLz/ZiRnCM84uA/DuPh5jBO4/l8vlzI2pG3MYhvtlQqK29MUb0odjtEOwe1BfST/6SYBzm45mV/oP/15IsKOhFnHEbj/rkYfKWlaskRharnQbOWXK27MYOnLHBXZpp/M7lqi0XYvpSfuKTHMvSqcbtq6WBrNM8xCQiIMD5UIb+bdBKYFS9L3LpP4r8zSUdUGQBnAYxFWEFi0ApI3gfTDGZCjowVm9wjesG6rsfTcV3X/Z4g3KKTA2ecwzScnSh5OSJrSgFlBYnTxeiV0g7RFNe+n8wJ5gyhlKd1A5MnDr8zA7rRry04Yb2f1jZN04VY4GWZ2d7HVkFTRtTbI/hZtpMqB7WNLaSrOIgAEAxGEEvjup4WmsN8e3tLhFNPw0dGGU5hIJayFSee84w38b104tjThQmjsY0Fd29Ql6fFYYOXau7yL9B8FLLlVcrQIvJacJUc/qwdmrBroRUiBsULzKWQeVE9DyUmyDxBaK4JWwfgaMu3WQQdJv6pzh59Rl1fKxtTBBAFCkWl1xgG6+CthWvEZBXKnIq65/7MVqVpHMbIqCr21/k/hLXoQF5ImPnOwJZuLhsbe6LMiu8nKzgFcrAhvWRd+kfb4lyL4wBG1+CW38pWrHHYrZMQN6nRLrVi/WIEkzDM5BBuhT37nkDSVeJqBn72pXkG12WdHx8fu75/8eIlB6wVwWGufTWEzOdchdlVoDEooehfm7WsQG/FMc1MK0Dl5X2eOaMqrNiIs+1gZoSKc5UCEw+u63qZLyfmP1xNSfc44m9sTSCWI10Ehm9vMxNo8mnhhFFu6Modk1H4o9TJwCRG4+ECtQOJxmxGNqYpXqTL3DNrQTeutKcmQ50gtkOLdrcn9zSjMiXj5ZSutIQkTMRfar8ReJW9tBvC8SQ/GJYkkEPWUggqfwWpDPQ53ccWYOnp8NyYHHYdy68ixhj6so4qwZKyg/zYAGXfa7iFM4RqogkcXcsW3qDe/eXx4bHrOsLhYWSXHuBdKzpu03Vcka8mBW5vHkpWOHTxt59T+a7Wb0V8L37pP50ripMtyZqaFygEVoUiEV/0yMT2PF/O58s8i5lQn+Mq/6IMB0f1X9J8Shu1c2nE3So+wCOq+kQPcp1gJz++d+qpcuOnjCJWEPL0qhdy4SwfCgVWe40Ir2KFhoqoMSnCKJFBxKs0dGMmHxEWtrYtCe6gok0ctEGCGPYDfmPqHH/YL91kcWgmECAGf5N2AhXmuzm6oWUHI7B18eyHUZoMT5Og8eVyWS4XYdCh3OS8jaBfus0FbMc7BO6NiAmY8l/z+nh/1zMftkyUPHi50Cw/o0slvbV8Dpk7FifMjCnt4JXiqUi9r16lAGEwg1jln1mYhKVaK/b6nhj4i/KjUue6kmokvXssgUYsOXJvMG2DErCBG9JW7rViwhVCwgaiMQqdRBTIJTzTTdJEcWB9ukbmqIrGUjCd4gpYTF1mZcHu77Dc4UixcRQ9X868izfT1JH4G5WcC0nfdlPDOznhk+Ja124gJ6nIypT0vp5xo8xUo0L2upAfWEmMZROXRHrreo5UWfjZQW0L4A2iuXKjCK4SImi82+3Z2kQxPsyUpZqsoB4QNYBGYykRmEury95zXmXYp1ls6u7v7rq+v729NQtihV5bCzWnbBbnCsG3sWWmuoC90t65tdOh4DXPAOdTD9EwCINk0nUSuM7yD/8Uvy7BiWxn6DHRQENNPlqERW5W7JW/GRQFYZTAoVaME4JCahvzB7lBwaQPimK/qgVZNHOK0TA9NozKBUGSEdn5a2+XdDdpftCRT1ijLZP9OwOHCgTzcl4v3Xpc9+u02w0j9xu6doEpwddu5WG1AVjrgaEfJF5MIrHyhj6DrD+JvUHm/W/XlNOvoZz4QJpU3A/duI7j0O12Qielp1kSs4UJcGBHtkeqjK0nGXbD2Hy3pZgven18uB+HYb8/uDz7HAZnOogJAM/cQXlHaUtShPbdkLXUcVJ2oXsN3Jsum4ZwipK9ISTzUIZV1leWeWUcjhBXUe2CHHpR0qgPpA52xVY9QFoXM5LkKEzLTHJxadjR5DtAFTRFaR89FWtl9iFYu50yLoJ0Q9YcLW2vVNnZBJBQeSEeRbU+sxaIiTqogZG2mWRGChyedpNoqmn5LhGaoh/EALZIswizVKKWMxHXB6kq1U0DCdJB0kv968p11a2Sap7HTzHkJNNQiprz+2wuWu6AzHFUrlbIqp8OYzHz52X2QzIDpMFFBMQiqUirlL24XC7H4+PEAVsan+tjwPRSXAlqZTocECn8tHxvuecaD5yjY2NDYAbJfZqcmtXC3OsSZkrFVzEc839ECAU6nOaBCUppU56+I1LRttYR1oOVt+SJgqcm8C0tD0Z7bUKkmBN0L6Rmnc9nsiSb6A5RAeYf0g7YdYlyh8CyrKxLDUawQlnVZdIpUDUJczSRBY8t10T/lnmhWS3rbjKBAF6YQkQ0rjgWLNsvEdf4jPIHjT4JBV6ykUK6AagJebdxheC/icYunOT5P5MEKPkTXHHLhLrSzXw37TgUlN0YrDKTqL1cSAS0CWikgaXm1Rese1lOp9Nu2h0ON2ZFrFbtLjdQzTwwJ21fXnKzJkZjL+Jo8+Ts3SJahH2qDP7SE/M0aLf3mdRyYX3konzJeEEELXtKly+zsOpbmKpyXX2quaaG+p0UGYtXr5+O2CNQk5ylKrgudOgUw0yyO1VONk8QygssVfhnRZ7yagoC/0/8YPrSwF6UBQIhyNtD/5EoyxqsmAIoyYZrpNN+21JCXVeBzyUMGNq91NSyjB2urrbYzkvhZjtXKpdhyQy+thSbsgVxtd+19sPa8+ghr5VhUP2KZ9O7mPTwaJnzib2O+PM4riubvtT6NV8uZ5Ox89CQ9eL7pq9Zu+UyH4/HiUvjwhJCRDBDrFLDcHKlqTc2rgWjjTVvEzSbalmdJQUMlMfGMCji8YVMxReylLLk5z4M8y5WmozJqGq4B2HYTFQsYCuZ1K+qWVeEK1BCA1k8HRej0Lx4v60bzE7GWxdpUhl5viJHuAYg9nNJrwBymy3fLibowaov2Pak9GHqnU66FKHtZvpkHBZQHyDOyWOHof0uwoMqSpQdNZBk3OjtC9xaZUruTogQVMDSNhqXt9cE9sqHxVuuPhMUEhRp2Ywoai4JIhzrvFKKyLIuh/PlcjqdyJRNtDB6sQIuOha7vEhezfPlPE7sy0vyTyIFuruVkQFloXSz0p+ik1XJsRNhq3ojF7eBjIpKlsAfJ+WspNyeWTAR5YNus0KkTg4acREWpBpY6wELWBsOhEJr5ahQKzTAfD2m6Sk62tHD7qWGvCJVuSAbeAmWjHBsrfwPl3XQ3tGKZuLzkf3UcCY/SqYXmtNqS2FZW+ImjWc67bD5G/ZklpU0QM5OZ7MeidV+hx+Y97xM8GL3Efsl87VaClMdIUzSkaTkhdxIaWnOKRFDnhb/0lSy4w6Wtvlse6RE3oCphydHHdppCDLFDxTjPO6IlT48PJyOrPEb9gS91aMKEsqEXewQC6WqggpRKDKuaBWqXHuVdgAlf65OMek925fjDqZd+Tdu5Lswy0Wja1iwgKnDXge0aCiGUUuNDkwab0HEAa9sa5Ct2+G4XlltFtTMkNgemKFjt2sGYQ2X77kllzxrXBvCHy2OmlxK2mzX7hck8XxAjbXClUXwHcvLFtrSakunHMKKhGTYRV7tSRV4rFSGkqrZCFjDXjoRdGYuG9JTLHRhbwjjBUYG4qtq6KqEJ7Ox4Z7D09lO41BbFnjZeGkBEeVAxmmHYdjvdqQan85zt6S6DTkFSrdW5UkJUFg76lVuUtDmVTIvtWlXu6Gh+2puKBhwnQ9oamCMmhQbVx2V+GjuOgHYmTSIs9AgRDCTGIKXpDeiYu9kxTAWEjSTmOugYkXg4jBQhJBWtlBqI1J8Yw9iUoqFyoT9+zD+WLzUalI1GyxEE+VHGIt4UrlIdUgR7KSJgk2ki7Hl0gN7LUDaZDPWj0VyF5MZGsZx0cZeDQND+wUxvi3a0mdcRnYoGXtIw8ZadZ9IB7ZddydAOe6T7pT21zLp4phi3iXpvzq8SBMtudvAKFTkiJSk9OOBgsuHnmpg11EeTkl8EiIyiRe0gPGMlyhCFWJzEroxuKAmheYF75uiWJFEFnm/FqQnFIfvIpozL6fz6Xw5cwSbcx2TQz1ERUXdWF+2g9pqE/YqrOJNhlUqiQtP0wg3zN4Ta1fjSQ2VDdRkwxr1uCRlqL8QLikKiy6TdMiFhPZh3O2kAjkrr8syS9sOFO9FMBZc5wDFYe7OY0ed6KnB37IeTxR9MfTDy9cv2WxCW+l7tEqiP79Cdy7x3ThzkHO9ioibCKxqj59nOuoQ5cR62nD/6gGWpietiYXUpGmWgdcGEMQ8qu/L+RVXOCmCqhTD+K9oJqkYnv0bsSC2heokXKkOSrL6Wedy996WYivR5nmZJ0TSyljVXlXeA7MbyjIgRrB6yvZse+OB+SZc4KSZmQIaL6fz6XI+m51T2mC4JVX/AXeSgZ2jskZnl9ibBGbwhyU8NRiSdPdQHMWHx1FDUv9mIFRT85JLiGIwFFFwXikWgkbgXBcKtmUSPPTScEtS7Cm5bT/tht06L5eHu6PUueGRhQxQsRZno1I/nBe2jOMw7W9EP5IOnf04UkTENFG+OI++LuRTZNPB5XI+f/7tT2laHFS0zhqEzMK4ckLdHwjklMDUJDKKjJOsxAh7Kp/JwWWPiUnsIvUDOyY/MJvC2zJyiy+2KENG1zbtKMhAsg35CMVnLcZfQnghWFtYqK1AIEyyoDziTF/mgh/M3kwzIsHFxgO7ypOsxNAWQeHvdPeN/1UCUvMKo4mXZ1AgoS2UAAyCtPP5Qo5HEjOskaxZbH03VIYOClBY0rIU4J+r4U1pe2u2uhZLh2Olm1wpxIhWCnUndkQVkHppiDMOI9vGOfCQZ0EMV+NGRsbDMxVdGCXRRiIXhgsHRa7zPIyEvV3X35/Oj+8eOD+m3+1GSgKir0DW4frJIoVJfXTyV0xWOE0lA8kwWR1IxE1+Ol8eH0+Pp9N8uTwej8M6Lsv8+vWr05Ei5zmZjt1TDEVUsMPiHgRUQpkDuMGMEAAmhANWz9NzIn3wSuxZNTwydZOysjjWJp9x9ty+o6H2VUJlYWP1gTdlcCVLSOjhRcI2YqGtOXuk30DhCngXhmeVHL7KF9RQ58BpmCamBMnXTiOBGdtaqvWVWkVxBpmY6gBspFLMVeeQBjNYORR3g5hpqZCCY9dc9uoLL1EhWodMGIKalnSk2lFS7I1zgOeVA4FH8ZGu3bxolZuH0+NuT7VK+7Wfpt1+v1+7Xo1t88q2W8oO2g2ckccLYrl4WeeVePg4LSM1pmTP/7x0l9sbGmSaqLzKxA19OB2XkRg6m8fmcuqCxVr57suLNMCbKP40vNgfXr68EUWc8tdP8/F0uj8dl2U9PZ5fvNjP55M4McNKDbE4nKmTstnUO1AJtAnCMAkx20cb2tdAOMwJ/emmamiATRcBSxyGv5vImIydSeeMSV7xPWteS5v9lw8hOrlEyMG5IWKDTBKmEVcvsUw2TlzTKEovXu4YUOxnS1eG72tiCE6DeM6NqCQZUvre+TSfzwTZEgAM4RSeHeqSc308WXBLX0IOm4GcArr+T0vGqqtW8gS4gNGyntdlmsb9fjftduNh9/WPv2Sc6Idh17NSedi/ZO6nkqcEgJHBn/CPEJ4qlRMiMs+kWqPKFQlbF1X9eQRZ6SKKpTiFZaGLhL5zBHyo/bDhgM/OBoUKSbCPKKr0Svu4n6b+sNu/eHnzZpnn8/zx/vHu8jiOu4eHByIf47gsFxE9kv/JyHKDQVUw67zKyKQa5IsDKw6rWxeqALLFU2tcbUjD3SYPKTS6EsqNLj2xNKOcScwtXpYXlTEpmD/SAK8hXRT1D4elqF/VasMmFd8qE0qIZ2IuzvNJNTrzQv3DHavLMpOF6nw+nc7LfAmiYmYVX19yr7IAlgMOqn1q7WYuQRdFK2a1Ji4c5U+fLPMycYH9x+PjYbi9nLvl8XJ5955xQCZ/HkfCTwpTHTpCUU551/JlXDXNfNdSN26lyFhyXedYKP45mz1/JXy18jcu8uXmrPjdpsriYaRB4YPEcd1Z0gAoNp+S7LtP96/fdC+X4/L24e7LH3+93+9INpu518/KteapMRhHVVZMSd+C26znKwYs7yRqU7XcXwxAkQcEvqpAjv5pHN76rikBwkZkqMlsBpnwxpsrHChF5waZcsg2vqr++wzneUgJMJLP/ZvK7QJyZLG35jHKTpfq4bxx9SKcRCwdhdaezqfT8XzmoBSx6qlpVP2ZTjfy4kM9SPKwK2QuZxpJS5bnNBnNvGONjwYmprnrd+N0f3f/eLxMhJeHZR5F2PnkxYuJDb1ALSOiUPdfyl8MlDdHSp5+Q2qvvpwb5Cnx0NhF25YI66ALIaSU1dx1Blsfsm3huNZdUaOD7SJvFjN3WV2/drub6TsvPn19c/v+4eHh6+M49mcqUjnOtCAFtnCMVTDSuKI2NYKGBfyBXiwjUq1bdiNd4wzOJq+zDrjdASbPrdSG0yMbq6o1QUAoE+zgfWl/TEWLmpYiRxsyJ6oFR18y+HoVUvrHccN9yNW97X3YWGk+X1PYLhyefTqRe4h8JKqjmpMiI5nDsU3KjGVNl1c8xR+DsOaF1C0RSfw55KcZB2afwzROw+Pp+OWXX3f98OLmhtI/13U3Dbc30/6wEymXrOPkFlJ7eARxK6aYWVa6WHYX5/XWCEZRM0K268Ogy3BaBQUDlIB1e51ibTiuVDNDqdrAxqJ3mZRAkjPZmPqFbW50/4sXhxcvbn7i9Sc/+PFX794/vryZKc2eHQMixRkpV90Y5Ym4eDLk4JLorcLF3DSciKfcOXAb3HxZcKFZBj6rfquuOryhmBScQj1U+WgAcOMG3zQbShI7KEI1NrVYDVbBUoNxRCoIrEXpocK5W+z3k1vRomOqeqlJlKd5JhsVYe9MNQA5gCjYqIkI2gtAq/uZOOZrwa21lejf6MiD1oOwRkvIJ+M26bIMyku3vn337nIh/XW/Pwzdup/Gm8N+tx9Zex36kW1XlAivBEAEBQ7Cjrrq9ipsQoIb5LiYjjWUoHw+ayah3hZPiofkjY//0DmYRHFwTkumcN8TJRJuzMWo6AZqrcc5SLtd/9d/7qdevrz9yx98d0dmt0t/c9MPzC2aQRS1nMuOaLEDVqCcUUOthkqBKJADpcEnr6v35BLO1e1XmFvzNvwEEsJbioVV2HAwVM6uwiZFlkbnKpweaL1guDKyXMi5/tqIvygU5QCJJ+Slmu1qmCfhLlmWCXkJhRdSrjRRPpRaVbOlCFNM1+DSA6JVXAg/eGVRwTm4Mq1ln8R52w9kJ16X3TC+ff+BWh8N4363G7r15esXLw570gu1/Aa5S7sLNzKIF0JSTWQZgjxlGA101emUM9Z0GCFHd7Cf7s0Ga4Bhqj+jEWPpecQP5CE+MMScLbzOgdV3MgOQdU7kovk733pzc7P7kz//ix2pERoXtpAXrapZnU7dGS0ecQEp/qvWtRMRnbKRKgkwEeZveEX/YxuqfaFAl48Ob4lhM7sDLpIXiHzQ68pDyKoqesAB0GyvNF/xWMyYxdQ2GW15l8WhRt3yAtEN31jRg0DHZSG/7vlMAZGctStKYHCDpInITJP4pOsxuEV7Vli6nP2qDK6xjhQARSEXbEAyoZzDMIZhPa1f3X3c7fY3nOr26vXL/X6yZlRRnyYYWoFygF4gC6RdKRil4/mG51IF46jVCAGcroPE23lAnF+CuzRtBhXY1cjrMEv1wlWr3O/DZQW6ceg/eXX7v/9b/5sPd8fvfv/7h8NMukTRxr3BkE0C1poCFX8LigdHbjsjDb6fVrDzu/Jl4SH5G4fsNrttmFxk48r36S+AwR4vlO6AQZK9Rsu6cJAQOBY4QEc6VrEbk10yHEek4jaBsZ5ZwNOzqVpLRoLV5LAQoxzi2uVIZkJdsYSX9TSKtda6A7wiFCYLZMkhd7aD2j2KIGheVwpo6ob5QgHkY999uLs/Uwuq9eZmv+u6F4fdm09eEUqbYcsFW6BpzrFCZK5SZpKEnLQTT1eBdnyFyGW/9Om8sZyDj1ZcKmfB9rvzprjNboIkJFM3lFSRa0sio9d+mRcyUk/7/rP+0P/0T/3FF1+8fn1zs7vpaFeZOEJ/zPrEzFgQH7emHhxYTs3iBVtX8g4VnCZvqm0gzu9pYI8hk3iaDwjepx8VYWalDGucLULO6Jeh72eTNVy87qnNCvn9KW6P5ET6UPSQiL6NzLtq/tsOOKOIiFHFr2HJkeBryvvjWEi2M6uwGRkoJk00Ri5UoGBxGOSiJht0bAfsi3o9dAuFjPfneT5fzkPXX07n4/lIfiNy4Q6vX968evXycNjvpkE8PPaqXNLA7E8myLg8UMEDEGKZYD5C36ZiX+OhDn/WBXo9EKChN4SIUlXX9xEMyiOCEUmTNumi56n4HSn5hFPT1O2WV29ufm78qT//7l/Ot8ur29tuGDk9poSaaIirgTOVwIezAaTT6vKcjdRyjBUrSfJscW9G1RDgmrdm4pJMoy3+jeBYX1keLSdRYBdTyqC4/B1Vz6Py9+ST3O25wsMyM0Wl24ZhBBhyIQz23MXSVoJ2zYMTY/CpSwTQPLO56qSd9WAdIHDq5O1D/80rJNqIQRxRfFOXgkZrQHSeojsXKl275Xw5HR+Px9OFqBl7jV69unnz5tXLm9txx7G3bKCCqYU+pBtWMFiPcE0zr+oMhaDb2j/TCkqNpLdVmIfQxQ19R4YDOIdqe9E/AeeYOx4Y6qgiKjICm7hk9Mu5W4kxfOvz19P48//fP/0LKnh6c0M1Q8QxbA1R0dtsnUTLRbt/OtF/cSEO/ToMZIWu5/vk5fYAFEViT9zetPWo31uYAePXkhSV2NoIjHDfnlmjYE7qNTW3qTMeipGXdJaew2EXcoeI1DppZSwbOyvdyfbzjO3yh2LKIjlTeC8VHjifqVFWBntXE0qKukVd0wXSpe84gIyHEKr5T6T2bu3Pp8vj8TzPK0knXffq1e3nP/HJ7WHPMczEpXMVgBQ4UVBvpFTFcQVlrvLywFXb2kqzY3R5+S6HiKAFIfGZBzdyyH3gLCcFuTexWWEzimpolLwWrqUCi5wOMV/OtILPPnn53/zNv/HFj76+e3h8dXPT9dOyzkw4C47nBaErcbRSNT3lgTI0JB8YZC813Gx6fBKSbX1diSPlrfmmkgak2564EqMuwMDuiIrhdpgmJmjCp+yFiMyWisk9nMkrp9EGtinokHRiXopzm1ufrJ5i9blQrPzj+Xyy0Rsj1aBsOQZ+77O0c0/JsecIFbSa7Hzp+uHl7c2Hj/cf7h8oU2/od7vh809fv3z18sXtXuwCUmxMg8ILWRNOonxv/gWkriCALssUotX2qpqGAZct7QCgQRrMDGvY6puqMOFS7pbsIzhW0K0kzajXZGLxM11mEmBev9xfLm/+/Ls/oHjtZZ52+5mzXAWezEeQjI9pH91xApI2VlawmljPAIDtgMssF5kWs3Fn0ihr8yxMtrBPlpourrJkjv6d0CMvBIY7ogOZKVo1SztvM+o0Y7BNzY7itfndtZcr7ZQxLZKcj5TxcuJcUy10nIhS9V6zhib5Vc/+yRO06RhNW/t14HyfbpoOy7J874c/Op3OFO6yLi9u9z/5rU9fvLjZ7/ZSRkt2z2iQmPx0L1LXeezMl8y/xTmbjAsHBujrKovfGkInUv++INyautPYPS3wVXSVt6i7qpc9WKwjVV6QPBpnqlNWFBOOM+BbGEFHwuHd1L9+dfO//us/e3ycv37/tu8v/TiRc0EF5mJFnhqBSSYlswNa15MIHU4yA4a2sRmxppRd87/mp7hi5rEn1fhQiVetwy5uSCddCZkJk7XImLQ/Mn8G1LfLmOOG36irVqzWraPSJw5WHndV1cudKQj2Uofo4+OFsde/DjeRU3o+Q1iwyrxVaGct7RiWA5mLJrcyjctl6ZfD7vDh7v6r9+/NmLW+fHn7k9/+5PbmQNYZkvoKyQurvWBHbqCjuJD4JtS+Bsmua0/l4ygW2wNmG/1C6QLK5NpR6ZcFgcV8MtQFzOiZ+I0ZAuMIvOsZG0ikuCUzhpH70gzTze71bjxM8/u78ZFKJl72+8OyrBzUxa/gyoy5djdsJqStu5YoZ7ByMkNxuUso73DO2YkdK8E7RfwAV0D9oqXStlSk+iqiOAqYhRM1CmlbEWzWAFgXq3HFOcUoPKaKbEVSmjzpHGTjCnE41+zlxndktTpezmcnuI5g4CtNFfBKx3fskFqX636txsXM66CLs43hHMB1WX/09qu7+we25lEK4Oefvvr8s09uDgQbmLFeFCJyGc/c3j67HHeRTUCxgbZ9sA47tlI6huM260V53F0sPlT+iCtSs4hunLwaDHFxRpb1lczFTrNx5ZyRC3yEOa9aU0DGni/rqet3w26//vzP/tSf/MX37x/vd8O0kkHB1OHYoCCD1fYEmLttfe0oEquWXyvkyVH91eY2EQ+tCKXgk38HL1vjhsLGbTE0tRzgE8VQBh9A7Q5GO33b/QzMwgWDRcizEdli6cgDilq/xR5FfJcUzePSl9L2yZHLfEYAAk02jqeQTUnlXpg3TKVlZa+mP+/G3d3d3bu7j4/nE0nR/bQ/jJ9++vLTNy9vDnsTkaWwluU8lKEROBnkwSpsqgDjgci2VQkbkegCiQaHSUGZN3ajd75lBMo+c/u7pZg5yTGbmIMVakZASRqJl+4IBEJOmVJcM0tiswzwLsNKicrdcv7pb//kH/3xH+935xc3Eh0ossKyrqOVDzGzdzD+lKMRGhPLPpxOWHbn2LgQp3yU7auQ+ur9T0b7lr3Hc1w35nHt1eD9Yf1EokzFahUiqoJkadHWJ3WCFmWEpefSSase1bK7Z/1MJWfDXqqE4xFBEYOC+kyAbrGHIUcEe/C9lKG00KpsImfUW8tcStZb+w/3Hx9Oj+fzpV/W3e7w5vWLz968uLk97CiYWSIE7ShU59AlxO6GedCrO9rkYOdEohSnJxRXjCnDb0BG9cPyuP3Fa9IOVG7F8wk7n31OzT2QMjvrS74hs2hmlmuT0h6xuAuCT24Xkfq2Xn23JzVkmedx3HWvXo7/q7/21/7jn/7Jtz/75OWLl6anmUSMZ6nZH4JFgbYq85gthCKxrmFhgVnPMnZW292iAQmyG+C/5fVvx0M9OalxGBaCS+IqC5A0qPMWER5htM6+TP9YfxQfVFeW8SRSkoKcucY6G38ZnczFlVdZjYrpBzCmnyqYPQy4omq0oC/n7l0ozGv9cHf/cHzkBNZ+P+4+e/Pys0/f3NxM08ieXp2vzciJYWEmBk5s75X8AUlc0G8gyko/8N2BrGOTRPCu4kFoeZXG64q0ASQB2cACL5MuJRZqB1owwmXoHnWF+oYrikoGkU+Y5Bzx7VLqEjvcl+4igReffrL/a9/5yf/yp3/xN37+Z6bDgUvtacktDSJSgSGJNCU0kqpN8gR1J0ygU2FHjGM3XEd4HKUh4j73CoJcQPPTNCS/xIisetq96pX8o3DmbqL8cGpp6l9lhQVAyrlmW+JYO8lSoG4g0vvPgmlrdbCxniICreA+8lswsGITtGM6Zd7O5/Xh/v40n6T08n6cPv/80299/ma3o+4kRgUStvoP/R/oeM4+5SvZTxNXwKgf4nBB1mARIfwCHU2UvPpj9QnXIm7IMIHqSuuMxxkd97pfWCbcEFedZihH6ettNLOVcIHBee66mWqKwLzkiLkHVdcPy8/87Le6rjuvy05SRtghcZmXkSsZM0UxVzDwq1Li4B2XqpQo6MbSY674yTfgwe1Yjn7zDpQVUUosIPVaBzYUNQD7zTCRNh6IW4QKw1AhU+U3IRnEdyaLDzwishwFbCynMxWxksLTCtfFdjxtZahPMv+qtF8eJbs75c1YqlW/DPf3H0/SxnbtdsP4nW99/vlnb3Z7yktDX6T+CzpslPmLUpdm0A/ruu2xH4IzQ/0Z9UGCR6KqVG1I0+ZpRSy6Qmhp1XyFD0r/rzlLoqcngp+JBEZbTZeHohD6hOM+WZgonL2fp3ledku37qgkj3VPn5d5N0zL+fGnvvXpf/qTP9+9eNPvKXaIyl0Ol2WhbGvXPdX6nKxAPjPVBdQKDYZ0NIpuXZtcMKtrxQi4eQ1pUCcSGFjRnWrYDf245D6qgYlMieqW0ggLzLJ6HT5f5AsgLocMnWfhgAkmG/+IYjaoyYn1eHfxEQGuphJJHSw3q7F2/4X+ic5dUnhuWd++fXte2NM7L7tx/Pa3P//80ze73bSQRTqkb5mw5xeFC1i1W91BNsm56oZVqwPwIAzMGbwvVnlZwRxgu0vsLdhLh0s252C9UbEsJP643PqpUpBSaqS2I4vDKPaeNLVhmHsqqXNetKv1zaGTzC3ZwWWmprnrfvzf/W//5ru70+nhQaJpKfnMU85BeokJea9S0JUkEiuDQRt7TbJIHtM2bCXrFGwisHhES0CN9MuGBbbYs/rNxT2y1SLnKAMJmU2qLrEi6j22BD4s6mbToR0EYHtyvjSpREf91kz+qt1+avjJAzVJnH3hHAG/0GPSAsNSGb0nd8by1Vdfz90yTARl02781uefffrpm2miIq+K6orvJj8znQlV2LDZrS5SBd3rVcjCUOk1OHRdN68JtE7EYTQaZYNsdhCvMAiQDjSQlz9j80rkgF0tigNkSlEZ3ITKqZWbI3OnZb1o4Fr3eDqd58uL5ebmZk/JhXM305dT1x0Pr99Mx+Np7NezUgjpJ6CacLoyWkPEhlgszJhqMvfW1WIEV25vyp0o1lY8uzV0weZ8P6tYBqCHfpAAStF0AwiMwhtibi3EJewFslMb2tpr4dKk58ssXl+YYThIDP7AV9XaEzRYJEhGpqXMTeR20rRHgpuPH+/mfuFapN009p999snrT1+S3qvWFq8CbwisTRFcbA5ZleMvV9LlucS5+U41Ia4CPS0T72dTEmxXZJztKY1XU3AVCi4Tgh0s35iHbwfTRcNRBPkUDh0vBItDg0HEFCjibxyWlSrFcyUinv/S3T8c52VlA/9A9v2ZO2+dzp9+/uYwfXz39v5CHSmogi6XDKAsmrDqo1hjfnCF7qGfyExWi6iZDaOTAgwHDWElW5waiFA9cZ0EmKOweFWOFnGBwrG7SBBzJgu4K8xXmtktJeaGCwlHtS9tC53oFfNCW4wsmtjv6TjPszrGNJaiBBWtbSqfeJsYWIQrN8Z+YQMzmBI0MPukTu/r8OO3X5/nE5VcWijH6vPPPnnz6aubw16RT9mqabaGwrJRbJvSN4otgRpYU1P1lTsZiIIg1CfiPOU/Nn67Sl62gAJ26JGYJp+hYuHBc/GQqlo9Yn8+CVNNoTdtKXg7JUmyUJGgqkYA/z7CNFwxBuDj0IyJ/B1kfdDsEGIe3cMjAcCLm8NumpaODM7z+dKfzreH3eP+sJzOcgjSjI7JYpxr7IrLv1aZTU1l2Y9ox5Y9xAWtq+If650sUakiXumMigtOsHzOOEE5jG1xy3Zt0USWbGTH0rCUiakyvT2b6ivVZ3vukuvLyUbULoQkVfDOIGW1pZkposwXcOzNG6yPBX9QXUHWMJJe/70vvugGEszWmYJ0P/v801evb292O6kIyX2ig/VGOUXfOM3p5dQlYrzL6TJ74oehPEdyi4QXbEnJXEhBBRplniDyegMw9PM4b8hlbl+SXBB/pfhnr8eZOHtLdUFURwUQqE1h6BSJv+9Jy6VPpXuDjnU8n5d1fXlLSLyQULYMU7/v19ef3Czv1/OZkrG5Rj/XH6sX5fxMtGFWw7i5mfGtmK3uUcWZU9WcBl41cfoJx4/ShuuieLES8MrVZlz7BeIETOV0B3wYZkSoNg0ZSoFnLlcinIvQxeqguoN+e+FcX6opaZLPhuxiHgRYiU8lSbM2QDj54fXyF0HBMk/9OE0Tp51R8cf5cvmJb7355PWL28Peo28pEV1rmTO/CPbm1EuKapPcfHw8XqSyrDLeVTLpEuMNM1Z5MraKTLT8nWF1KFYefcCTG2rNEef9pkahSkAJzsJctR6gjgCEO8lhiSEHeMCq4kMuXkl9Xmhyi2q2Mqfz5fLx/qG7vdlzRdh+HC+neRz7/WHk2md8bgu1oTADZHHF1jGwEAJD7E8JUwpQWJAgjwUszh7BDzL0o1EqyzLVPFEtqtG7JDbtZ4E48rQMXi0sMIxGHiGIyFupmNV7Cih1K74/xFroQp6jC9VhD4JhD2YnZWxF4Y1C6Ri2NZmwbAr6nDTumef5B199n9LNu3FZ59dvXnzyCWEvl44Mt4O3wsSFGivm6m0rOZAfSZHjNBr5itPT6Y8coJa3pRC/tJB1CmfN8nIIpoXcgxsMJq81nrUxPWjbbq0Cb80JorlEwerjawccD4yr2JnclvMg3I9GsX8UWy4+DvePUyuZ5cP944vb7kXf73aTtIzaT/26n46nk/S8ooxEbhAjeAKMQk9LXjH27G1OdmXfZ4W3irU2+O6VawvxvtEgNZfbektreIXKYMCctGlBCcxYpMUsVG9M49STK6Q8s/gbOiorV+Mzl3TWFiQxXHJXbk29NYsCFIublCBxjM64G/rpMB3vH++W+fLq9atv/QShLzX40p6fJjJ7T8HQ7GQs7aN7PJ2PDycxdrmubPqlOZhc1lbW6r7govADsjQPhdI3eohjseIo6YHEq08nAmJKtJn1Z+xg5ZwiLwP/9aMoeKrpwaVsVFD9PF1uusY1TkhIEX8bt5JZOaLj/d0d1XAahsPNgfLyu27YcTEP9inJtukO604ruVDGykNqOuF1B1gWE0p99jryNbULGOAJvA1p/kp9j4y8jkPOj1yoS5xButKJeAN9sKs5GzqX7ZAKppH3xkwNy7JS1CT14GtNtuK8bqtzoNJN8tkAcAOQobAonsYLlac7r2/v3q6k4navXhy+9a3PXr04iNVKROBFAqcwYNkxiQv7EO9duoeH4/F0poYhVvtKj0MMLSKAU7ivFO41O5bFjjsHVBzLwqqXr9NtQfppe7RZ1m/NiIR7B4GgYZoL9E46bQ6HV8abBLB0zlHTE+BBiZUWGAUaJqGy5GPnpqeaT8GPf7h/GIfxsNt3I2VBUL38/Xg+k7XCqBmoCyYkggFXWqu4kaGAQodTezJDa60kP8VC4jZfYqG/pMNoFSKrgRbMCbDjaR2e6aNeDt4Uy7VRBgRBV6hOZKED45QKFp2CuAQG126huEnSfoNrKC0tczTKJYFLF6aAbKmObzDUkECRfu2/fPcVYxlVXf+Jb39Gyb3cu4M6NbJqJPGVkSzpqqkKaP28rPf3x4fHo6SCcAttDbDmW7Wm29AP5L3kwgCMwWZFDNESS3YhpSuBDuTEAihB3K8wsMu6SaZ3YS/Q+1RMQWpY4jK+DkOSZfIlBITAnRm5njUVNxkWpn9E1Mm4Jer8OqzvPn48HPa3L245PmTZj8PlRLE+ZHZbhpXrSxv3CaLkdGnpLR8Yd+upC+efsBhjE/GBzLUR7q6/pgwf90CLGNg4dDFiTUvEjyRmQS20zf/ncpCSEmSc06rWFjQDq0FAyFUGPs1pEZPPOlPkFVdYNuw1yIzGkz7vzJPT5hhdcbBrb6CUbiKqPE4UUb9SuMa6rp999uZ2f6BVS59Va6VplhV/OkCRKqEu68e7++PpIiarCI1mE6nczD5I+oJ+WZeeqqyO2jVbxZ/YqiDKgJIbMRPuE6kpY9zTwYZA6fW0HzyEWSbdx+3itf3Pu6F7qIBDcta1I8YRPVi5U62OAp7GldousFe4o4QaKp3FTuNx6ZZ37z/2Q7/f7+aBGlkfbpbT6bL0VKC75yAZdKIWxgIKpUTGGnwwA0b1WfiZcb8AoJGw5ju+2WVgHpEfhfG/nFYxYYl9g/wv1Rw0EkpRWk39CBY+c1ihSpX5XUUUoEpNknh0vpD2i9uUYp8rci1/lfuPpQZCRql2SaBrWbim2vrDH/5o2I392n/y5tXrVy+maUcRQtTqXu3Nnoqs3MA5P/uHloXKQT8+noZxBF054NMUYMEJaxSoSKD9XsIAFJzJ4pez88CWBMFavg9e/iLdWRhtVty6skhoBGnHqkPa8hjuHGEVU0pQbOEqULjYvnWICIsou3NNy2e6SVoHqcbEh/n39Twv7z/cvXh5c3t7O5JIvKxTd+F5es5jS4Fgpxv1BwZjQEDJ06jmqmVBHhC8njVQPW5GUGRVxS14W/G90WNT0vBeQCc6QM4LWqhQeTF/E6Y1JAljUPGeVJ5T6aDkDVLkBqcThJYtX1c2TTMLFXpJsZkgSANjwl1eVgqH77put6OIXIq4evOS2qAMw7ij/rvc/ISjoqErteus9At1Zujff/z48PBIcRrSTgqcjYn3SfA46yOs5TFgiiJsNNf2Lu2YZYoo4y/Wkv1k8jjY4Sv46JDKVkqHNbqiZfC6udkFasiNCGo788bBex/1iJV3icFporEDTdlH7yMnGWrrUdZTluPx1Pf9ftrtpqnrRoqvmSeKkyFDtDDyoqBNbBhz4GL2LaRrOoXrT/4qXLb1rq33JlIN4li6sbWcLKDKU1rAUA2oTaIgQB2CZTVBFQ4LJkynQk3JuDO9oG9D58W5pCihYBGpYjpyIXRfhJliWXvKWPzihz+cKNN7/fyz17e3FILLDQUpaopj9TgR2a1MFr64ciz+ZV7fvf/wcDxStV31L9HXIhWjnoCMj76hDHYCTDHUUHsHz2zVfTbmWxpDvSW5AlXenhRX42YwR+MVPs1ELl4Rur66ZKPuvBIiRzyNQ0t0HEIoLHsbfnitJnyx/mctW40+uxWG+qJxz+F+WAla7h8fKRWb85b6dd3353vK9z1YPZICbIwDDQMVdlfd8pshYENfBl5Z4KDKPOs3Gc3GTBNyY03LylU/C6Ma2xMBUFPp2RzLXNi4RkOJcGOwReRFrAbDLoRWi5BNUf6ceeQGHWiHEFMrZuoTXjcjOGyQnAjjg8zdOg0Td8CkF755efvJm5ccyDHsdpQNQwo5yxtpMu5O7vvLsr599/7xdBLeK6390EmOwVTmkVQmrK0MxYzl0BSIqYTS+xCnMwwROXE+X3rMwOxVYdPusyZXDGNkUPCW+4eLR8eLndmk4QIK6QZl04+sRSTb9bTpaBUaFvID745rXlGlRz5iKZpMgMuyfLi7P+wPL17e8KoWbvRIxY3F75mhxVg6h1KC+XzzQj+OFzGrbireUezptfGvfpluEP9PqXkiBci6jAUlctSgOQCDUMoh8ZlGo4tSd49JuFkS3h5OX3uYMwdJ+1XpzQNX01oK2afOg3OpNTya9k+EgjrjYj/P0PV/+b3vDhSN2//kT35GeNt1E7Ff8kygz4zcixLEAAaYr9++Px3PIwly1oPHBw8/i2VPRwKXzkLIo8Txk707nYajfUXKE3kSSUciw5SgAf5X5t41SSclr7JSZh7dramcxSXxxyaHKYMvwrJDfpaIOgq1kGa+vlIMYfGsIeTUlrykyR2M+GS8pxRgFozeffgwTuPNzY6GWM79sO87rn3HRi8gECp+SyhlxrZremuBNt/oQsMSbHF8W6OMP5KEaG33lWRPqxDUmrJ8yMUvVP7hDvCKe/qTTxcyZmCtTMI9a7varZRALjdRg5bLRWYauqZQPRNWr+5gI/KrjNyFFwrAzctMFSXZ+9wN6+efvbk57PiVFE45DP1lJnu79LxXA57Pi/MJP3y8P5/O1Kpbyz5h8yOXOcxdjVDt+qxpG6F0FMQ+iJgQ1Sj9msLNjPGjmGscM426ujSGEhTYkdTrCrquc1z7E9C3dAYBUYZ9V5ZLiEwWSu387e2bIy3akFd+5abg6ulQ/GUWS6SVwmMohfgy3909TNM0jWSC5LgPbn8Ol1BMi73RihxPsk89xitAV6HgNYl8g+HWQOo0DTkwTLFy2W7NDqNo3aKq9hPrGUkSDaiWWGMq1cdXGA6hyXO35CaqWqeuKSU0GMpmJL61M0nTtpuLPcVKpm4RoghHcol97/vf2427YVjffPJi2u3I6TtOwzgs3MBFj10zV4yeMKjd3T3cf7zrh9E7Yhs7St2FYbet+zCothJEJPFH8mt4QECXiHPw0vCosUIuMrp8klDcRDW4Quq3O72HK0fFgTReytA2gFEICwKD9EUb3XVikaZFqFZZ29ivi9gc6Me5I8RC5H7lopwwxkP26+PpeHu+GYfdNA4XDoIVSVHeWCxx4YocItshx2nWjfMroVM88rQY3BwhJuQ/ynq0ct5ByTdfdIVmeEqqEUqNTANgxRSceIoxulyyf4+VKpXsdlRRZaHCSME3XMwFzT1W0Zhw9H9tb5Feosh167wu+363SrfUof/WZ6+pg6DBDysI5PB2TPGWfDyj4XS6fPx43/Vk5bIdCZTIYmoi9kbKlJhaGBaAvxcld29VaL2Z54JG4MK7WbRD509Y1ik2Fs8hxQmpO1QQ110KQbrAXzPORWhmAbnmM5Ym3/3MWquI1ch+7V/KAdE8EMm+ZtWWXE1931EbH1Z252X9+v2H7s3rl7fT0M99N3IgQVVKx85IspH8VApCW1wQJ5NuusIEG58VxHIjngvgN21Z9Xs4HbdfaUJjqu/iIqEd5dZzbvENFHeqWHnoqIAo9yiLXp62QtlhY35F8aXWyvAv3DU0CllfgXXt/vy7f3l782JdzoebHdVUIxM4qb8M51Q8w7FEa6NyQu/pPH/19p10gyIN2fCuIKGBEH0STYjLRjS4z1ys5zh3bnyoI2QCEWtyrRd8zvwbNa/SD0zytcmsYAADjA0FOokPKHIDxVDLVuLrPiGM4mgIohbPvV7oIEiwlngZM1qHhYtwuCfrgCJydGERWXoUJX2Zl7v7x6E/3N6O/djPlIeKNEXc7yqNWXOzkKeuXM58mspm+wHEvlxLVi22xW0Fc69AvC6ZwCNURT/1nNC4xXRQ7K1gDlRIU2Kv5pkozlvVD0IpOKoUBTAwtxPDGHJbYApyX+SqOaYomIRZO7RUp2Vg79Y5LVQKjWDmsNuN0zB15E8kJ9BIYVFUSM11cqu6p6/lKiXv7+4ktVTU9ejXphigQTtpx7FYRPaBg8fUlVjfstBGUuaT/SYo5PEkeKDitzaU1C1ZvcRPzBbHQyTFnQf5pvIAB2mwP2H9toRkMTI44LvVVsW1hkI3djWahW0OOhgoQdvoKcneJndLTNvj8bjb9fsdORXGbqI6TGa8zVMmP7AqMACjTwrQxQ1PYr49CXe77agY4YnXVzKlo1QLh+NGj94LIuI23kIsE4ENTQNmwnGF1uiyuRoMhCKmg/HBU+2ygcEWWnBaRNP0BQAU8mMD17lbxnn43g//cpim0/H0yecv9jc3I7l+yfi8LsvlfJFJe20qYgaMhW/ffzwezyo583DafTCmW3JXNROkMvdxo2837qjtjDJgLPuejgc+igbfulkWDVZg6ZrUZPsAI0XKy0uQmG0yTjWGAGKbgbsQCeN4IPdKR5aWwSpS23sXw+FxGMj6vHJJI9JjiNbSV1yfbFm7u/vjfppeUpluEqbwdeaPpxE1GwmEjBCTMTYC5OSneW1ebmMLfZvb3+cBN8lD1tadrLR9FWY8NIuyr8xoe8QibdV1hnlZnSXDXm/PQdeg8Yqm8prUDoTPvO+xwmDSJrY5G/PbnAnh9CkovqfoKzr+y+X85vXrm8MtVc9hfJUq1GwicUZJMDYO48Px9PBwhAw+70iAG6zEscbkILVa003m6HNNCGzMj37jWArgdrFKjzRWbQOJajCfJMp0hYobZDCoQBM4PSxFzMK8SqTlYeAGeaEsChRjBSER56HAlPQ7c1VYZmUlNQayPvtq2LFsSZ5U+2hdHs+nG/ItdENPKYcu2rv1jMr3FL5qtKniDGET0HAFEFimIeYr2K4SEVC5r9m0nrzSdopSxB8DT3dngFgPqGON7a+ZQxHeILCqQN5guh76I7zXEvFMrHAlsoJ3pIiJ5wMeF4JgIq/uPTFkYDfD/macxvF0On/7W5+8/uQ1hT3vCBbI70uFuGIN7sO8zMvX7z6opVzFZg5rLnY0/i3sB76MoD5hK2VvsLwwtjV8N2J8Ct0UzTB6BAaozihdrxbemjSS1afZo1oTM6ygCd0HHEBKk9DEezB8xebnpqQJoKOvnPY8cUThpDc3U1sYjNYmom5IzIQDLNiRL1UC+3nuP3x4fLE77A/L3F/6nvJSsIOm4DBwYGeKCRGdmIEYWcBV8cTGFQRNj6gp4Dxhi2qNikjSHtJzskj3UGEsjFISoqMVPXUKeuxYigdwKrhM6B8GgH76JgikVDhT4cucax0mqlc7o3EBL++R6unE7qd1+t4Pftj1/el0+uzN62mk0odiIuE6PmZ+l9eLG6Prv3z34TKTLcUBtom6ra0WDAoFgsvHxzPgvg191u1JmMGIiQLyTGLyvH0SfuEfKP7WJsc+TCKJSRqGgjzhKG2kWKSXte+E1lltoVpIBP92FqNBofDDUuVZfJSehu2x1AyGAxWRDb2bLBfcdLQnEalbLut5ng/ddD7d7XavKRZJt5PzuTl+RNIJBeDUnIvMMliPrh3xKxmIMKjoGZfR7at31HQTPWIi+6S5Bu0F25BTajXrS+y9gKCkz0g6A11sRBBlTRm2xNkkZmh52xQezMeFkTiR2ISer3SBA9YAwnEs0uL01rwHYT2K4K9hGGcqGzB/8vLm5csbc7hyhJkaf0wcsCDA93f39w+P46CpLCCa50AzOG6riKE7D5Z590ihtZfv5IBVx15BXcqlcwdN3OzMA2xbGVMgbyiDZVdIgmWaHFBSlCOigp57CQQFpWyG82AoclZDLAJYCa4W0GIbyOSTxGQhd2s3Mx+mQEpGcdmfcaJyKaT0MMZ/uHu8Pew5jp1CviwyJbZgAsu46HEJJdLWZHW5lrKfxTT9wFvXFXyGN8qm4zniEHZOxdzN0a6orLca5HLfk36YKS3dzaGcA0A1lK06kRtZGBIl+k0yNqkKocWExDET7gOliph+m1fQhZZEkr71Vbm6wnIYp6jNa9+dLpef+/Tzw24nYsDArS5V1mZPv8fgni/L26/fm1lIgxZd829xNpxSuH6dKji/9QGgXYOaiwVtjbPZ2anGEzicTYka+OWbkBwQiLp9JnWZP4NKlYtpYBvEBA3J+RTabz4Um4YBdJHSYityhUDTRjjBiFVdM19bwojKJt06kkOZRGlK8Jrn0/my34/c+mwqa5WqG0nzIZN0jFbYdI5tjRUgLG1cUP+EfTgqsNXrPLkYOH71YQoiE0STfhMbQRBWMLKwqnihNE1pL2KgQtmA8zhQL2Y5VykOr0jKUMmoO46BrHYM0UrTpxJst9xJFSC1UV7Mv6g4gyfA4w9Dv7x8eTv2/Xq5fP7ZJyQMUJMUIthEXvh2h3UByq++fjdTH61JDM6JpoTHw2qxwpFkDaIIIeWEAbjR2CWnNkTqgJJeCW2yUErsOYhoWB63s92SyaxP845UNB9xMwABh7VjVPtkciem6Fl4Rdb6zd8PXkBGMoIZMjQLyWTJfeVIEO9TQ7L0RPYtLmm0fvn2w0988mK36yjLn5ss4ZlQsHsU9Ez7xCBlH13Hrc1v/MRbjX6N5JWqVyidYZAszisTlGorDQrNOuJAT7435xbKEUUFnC/UsNdi4EJY689iFwnCbqyYZC0KP6Z0H2VxDrUqgWsuQzXH5MzO8rn/rgws74168vWPtV93/fjD735v6Ppvfecnbm4Py7xQRhqFVUrkhtmV+ZGhHx4eTw+PjxIyCbwqMY3slHY5UU1KSceLpSQ50hmtcxXfGQ9CV6WsKI6O+oN/Eb0DsUpU+NKUBWxVKoeV5sEVMb1tG6vcPmMm1mKLyzCYPWa6F9V7s6QKJgGS8mZNZrIuouRY0kQupyRUr5J8S8SE5+Vw6LmOR5gb5AXSWqXAoXonQKGo9iAzwriCszbWVrygwdNhMht6ZN2zNUU1yi9RTkjKNwnnVJJva+G6GUwMc34b3903563QQzvvtS3kPWYQylha7438DN+AoklSVkwhznslAi8p7GTwWLru9Hj86Z/6NkkK60J9BtdOkxnBjSVk6Ou37yl6SmvQtbar2EpDSJOHUwAMmwucREKLD/6cY6MZMMFA6lqlOVwsKK1pLrasK9uE6GgB8NElDSMLLA2+k0bQ3Xcao6n/cKpqUDAcVvIbQl8Cc19IyOpeWdEpHWdKU664ZUBwYuFCcZMk9M0d1yBmWZqro3Tr2/cfb/bjRMW8d1KiiXPPuLUKiJ9OcIEF2/pDIFC2WIqr7c3Cs2jIvu3big9kh54pXW+pksYYyStHUfYKDMH8TJWIJh/G/6NqrBmOnJH6CxJAJDoqH4ZbemsdlbxY/qoljDX9kSprrOvcvXzzpvuwm/vl9rDv+m7k+gHU504ttyEWjuPw1dcfKNd3JP+FIqRXfCj3MVqKQeaDAbTCQQQoQa05x4SiZJ6VGYc7izfmD9QUlwO2HFUSdVzxv0rjcBwtAhvsXzGqWZxazB/Ek4IWh9u3SHiGqds0Ra8oTl2NCFLTiYwzlEJmsaZiqOZY+o7DO7p+uczH83xzGGZxTIVqzTqwWERsRehsbGzzttAaHzWiqcJFVbPrTZJZUIi0h/W7DbQaArZjHDrUq1mwOOeRhIWKmiybsYRERWHQFKHlpT/cDtWmX9mE458gs1FyT+5fitAZv/je98/L+urmcHvYiYzB9ISiOEPi5xrFp9Pl3YcPar42M7ISbE8f8RZH0CJCa9+Bd9PnF4YSMEQ7E0tlFH1RgBRyWO53zV1GWyoESvwtI/8KenOCY5BjknkaKXLklgoOW4QPDBgvdvEvKyFmxQD0AZQIuc8rrVBDQzpMSoMgQZrLluM0uYTAsvZfv79/dfuC8g4tkZvhtJ+0XChY+0pQst180syMAWVaxShtVr55m6OKm6QmyHjD5qPearHF1CKFQN2jEfXeoDcZmHCZdhqRCYqPi+M+oA0Ytmx0kHRAUxscmHW4d5ygK5VhMXiYpt3x4f7hdPmbf+07436i5DKeiVSfQBvv0A9fvv16XnhiwIASiCvNgVfj5IQaRBh5VOINpdwkHVN9TezB2C4VJTAk2TlDLk3TOF07GiCla/vGphzWOk+vOi60nZMQ2CDnb4PYDHm35ROISThk/9hKk+d00/RrCMQIRY1Lecoukp/ISlaSNM0DDRMVWSEmvMxnarm0jGID4yTwhTgwHAKMHTtxBW0zmtSCbq3qPlkIp9jujbfW97uon4kRiDOWmoliQvxMZncjyZh4U89LvrP8RBcqxTyhZctsaA++yNPelGLgDwgT9HRAtqHN85k03XW+fXmzLss47vqOWksj7+Von+F0nu/uHqMHAtYFivyNahpIO0LEEEDEEFFbi/Beb03qkS7Odlz2hu33evLp4yyhKe0LJS59t6Z5a3qsDNNquYyjIhjQTrh5OOCaBflsjPQhlBSb8pANJg4x1ZMNCxApxiJAD6TgsUit+953VCurG+6Pl0921BZHAHNZurmTBt9KeUvUK4Bpg9Q1P08tV0GEbe3k5rqevjLRcdNI2rJUGtEiq+JhIK0FEQOIrVbBXj2lRxztofqxedLNJ+yAEAK0TSXZChPLzZeJ9UHpRczql/n2xW0/9i9v9mPfc0nDkcUwQWCn8TSVL79+K4UjvY6V75HGcuMDwR6c6JpaEGG+/p1yHeS99p1xZmNJ4TXNRx7Ru8FZAbFMcwNB3m9bW+p0ocRVcOUHmqeiFa/YISihZqAWo1DuszZBVn+iyp5O0Nbv4kueLSUZ6h9EgFlkIuzkaI+hH8iy1Q1fvn3/8vZm2g2nhQO1+Cwt5+mpwMatMEXnZsEAr15FekeV7REfpmLO7cvRthjMASp4lnyukq7gWHOVocy4oB2fyf+xW7VyYLYGoyJkHgJgXTE7pMwuu+iTYRCDuFe5TRMxQIf793/0H5dL95Off7I77Hb7A9VzT4muChyn8+Xh8cRRk5IzmMQmQx/XJyInyGZiYRChGHruQjBPt9xqxLMPhgcAuyRv9i3IkB/xbMpIQQSKxlar3mr3x8hyv9UzUniAcpSxeg3P9vlhwE+Efoau7JHu8Jy9JRz46o7QMdDGHRZv/EazULleKBfttiIMFKDAHkE6+YXSDIeOEhVd1Gz0RmrrEE8Ke+kRHKDBVpLEe/UVT7BlCKZtTA0fVd0z6nJk6VlueJaJOJ7XKbgNS7RN9Ruj70QsqgCkLS8vrLacvStR9maKrVv6Sz/MD8fXL1/tKXdBB5bSi26P67vu7fuPaC8t3ZrwBigYA4geXRT8TrM1qKPbzbaaMAh1OnzzS+m3YghJCvDfzDFf7hVapztdF6rULl+kSZjIEBKPw4D7qmR/rCdoWW/YI7JMJ7KaO+Li8NgUGxQhJ+pl52OVM6eWhKQBS+F3fvkyLOTRn1dyHBA9+vHX729evOjGSWoX9dyjFKEYXgYLx9Shjesp/v1NZGNHyqde+1QADgoq+o8BRLyq4FdmEEgkKP8TTFokaWW5ITwKJODbK6kJDjdoUItWFe4StmdQ15Zlt9u9evXy7Y++3N3erD0dMPcr4+jOuLs7z8vDkRsLIhVqS0phUUVGlTIHJO+Od8jKqulqU4CH40KOltw4n7RCXbNOM5p/OpWtxuizL6fAGjxlwTiowoMIavFjUqsEOG9sRAjQZoIOszPQmULMR5vMQhnBeUP0Man2vhLKuqzHsc9zv/SXC0t0wzwvp/O5p0jZaRzGbhEEhiJUjeupVKNtLuq2n7/KdUUaf55POE/FkM7EXaOUnoNQPZi1Oz8J+p+Fw8iQjL3hK0yEH4BDBLDEzH2pXogiYv4AwcMDKxYaqtIw/uCLH82n83d+8jPpw8QUxOrmmPGm78ev376XHA2BSxOYE7kIR4uhCWB51o41h0EjiFw0RBRTjd0UVitibdGTSTmPt0O1YPAWqVkj9iyj1GreuuK4khKP64kjULdgCnlGBp6+NFaCDa5SMxZDQqDG+XyBOGnoS7L/Ml0RxWvoBu25ztFF0qRnvpDHaKQQjy9+9OXnn322270cdlQFXpIZ4AALvbJ9Be+P5ZW/F2VRypUhefyvvtCwAOKgOW3Cpxb1XNR5UGVZlReoPQpgKoPzZ5z6pR1cIXxYg3k0Zqq1/GofCqot/9ZyGPvxuRz4NIwf7r/+az/982M/jBPFYK3zrDZYO8/LvJ5OZwogjYzB0MjhCMw1KS8EE17ImnKLBpNoSp+XsGyavQx95TOzFwOAOMY6kmVnu2shNX+xp9YgtuVmxgfwbbBIlILCvB/qUbjB3KdpYdxOcZDOOifN6gacrW2wx+nGYYQpjt0LjMPUWJgNWlztjrqAU4mk4XKel/N54ZiOYVgp7E5nDpz2Ootz7LiGe6Vf4ptx4uv26KZ+ntxAxR12gtTIVpqv5iUi2rtFQtpBOva6sMWCjrGUQteTm1SctgcgcOd5K47YX9Q5zXdPytB5Pp8Wajv8+tXrnqKsqGm0WphcLuwHqncl7LA82kRQgHVCDc7QDlEINDVD7Vn2KKbxmpM4ohkipiGDtA6PMzGO6lKx9Ri0SVkACARqrD5yUvYqmRpAJXP1clLFSXnvEu8UaHkOupnJh5NZm+rGFR22fVOhRH6VoEoKzOJgHUk5UyMXmbK4HN4ynC/n/YFa5+2mHbVWeaZMWjhxs8mqvjtRp7/C9Y19SvicH2gxFAUbkpGR6iHUKwarMXhO6FEuFqhJWyzdSN6wOf74TDRXzutHRIUKlQNwXeGCAH5RLtkEtbByWlNerok27IbhcHvDOSrD5ULZVFwHXHFlXbv7h8eu08BJGzNzs+TVAPk5QkbS5iiJ0wSAGNZYu60p4ZHdhPZeSySpUCmcvvYLerxiGiA19XGThx37lbEy85SWeRWpFRyNiykgoMev2T1Q8TbwvFmEiytFMQYjMzdsXbXshLS2FDWbigfPs8ThvP9wf9i/HIZpmWZtL1qhZwyeLMbQS+pJHbUkYz7If8VVm/DyKzz0FxWaNMA0Tbtpd+FanYWSJzdAYQWsIkGtpmRQ2VQNPuVmCNaXPVRmNmFKjVYMVdhaC6hw/O4MQJGQJZ19+q4/UMpC97M//9NUhJKLT164SLRnLozj8PHhKFVfDXysGGnxWphKTNTCLf3cUFZtGA/RjFT3u0xojLIrPJse8jhLU/uD4sT5r0H3PJgk73T1qjSH6svweGmakEdTFnaymLOeDoas1JTC5gsVFINNW5yX6CZLRx5hDtaXISTNhhRiepVk3czn+Xi63x0OMxV2N0ZRW/fKVT+BfqiHNAC2+ex1fN54xMqDg7U1UZoGYseN4zgcDvvuCNUqQHhU7stYS9kCbBPS9hZWf4NoodY4ig5C+gbVbLhJJymk0Q7XMQeapJSpXYnwmMuiBvW+m/78z7/Xd8vttz+nhORp5MB4jWFW8bUf3r3/SKlrmbM29gVIRwL+2mfqWr4VxvcTkbB9sGLjG0vwMsxJTBuJKdjEZCaxD8Uaen8LSN2ovjfW7NlT2cWkDwN2eQeHHAtgakGKPY0Cl4m1m+hgmZgWJa6l1JKhQdbIZWGE1lKDR4q343LSxCm4Gf04UrmsL796++LlJ92FrdBuEJCZFCsu/zYgrC4ERryhnVZ3BXXRatG4zZmsrh7FNdiPen5m/t/v92zgmSUFfaHUHeq6qWEbjIDLSrl5lmnOVmfeH4/ylW9Grp5Q1hMKoqzTd0mriUcx6Y2Z+4rlvUM/rv26nM47KmXXD1QYTXuUyxCUukDpzWT1gPigEC1iUOtLLzAaNppQ/uMIuPqHwnHSNhQuQ1CMiVen6EgfrDzQeHMPXHMvlPm12kLkky3fU8MYhuxYiIlRZ0U5Lqzkpav8FVnxBZuF4bDe73RPihBKXSHLAWsA+CLaL/FhRemZD1I60w/90o/jPF8eT8ddRzGWop9vFZnDpW5C2NWrUeH1iQfyv2latWwWd6Wy5PUV9udh2O33A8uiiqEc7BJVxZ1FSqyC7DlGztAW0/9oHO7E6bsjFZVc9lNdui1VYUaN1kqJLCL5x9RApxmUKLH242G6Oz6+ePlCYv+WlVsxCbPgt3719Tt6TKqgZaOVycRd4SLD9BMUA2A5kv1m8eRmK/Dco9ZJRuph1xSR7aPC+VMY/qwWcIh4oc6XV6nkx2fxt049m8Ht1fw+T5HypHt5Oo2bJGF+NuLZkoPZSSNUBQuSjuKWuDbEgUQx0QxS3EVNnqcI+JHn9Od//uc//XN/wwI5nmXDUtLbtHr5YTwHXbfeVj/79LyCF6col+uPUMsR8ebKlnMufIwRyaEKrI7BUhWACSE/zMTZzlCmEhoy+5cE7FskJ9yX66YEokzAUMHr0czzT37+miv5cA1qJuwOj5fLcj5fbE0IcsD/5CUgxWdx2cNRUj+ZUAXdbOXt/UqHaMTXQnS0Dm1pyA3K5kIBblbyTBfXGrNLH9cQmQhjoSwj4SpfYzGiMd4VFuECDPiM4uykwWmemVMnFavF6MxUm5vCUmKSVImnu2emobtpdzofHy4fPZmhyefK6V25suR0/ZYkmF2/cK32CagM8TVmkDReXAJJMt6A7mBx7EILLe6S/iHbAk9cgiW13n5bx+CQY7YgbU0CahmWYqD5TRKtl4E4RHZZlku/dJ9//hmRIS5/ZvEbTMcHajXIxTq0MpcM5wEiCn/eBSm8XTFbgXIvkiyDsK4QaCyBIZQMxS1LjbRkvChaygS+QjcLvxu+zScNwr9hT5id6QIJMqvPsdMlbMiGpE/0R0Ywp2N4n+kf8ZhAjGUn+Ui2JheiI6DAq5NjWWH3/TPHMCKzEOJLRTetfkfS3478TVqR44pVGSWSUowOAXDj4XKkK7e6YLUha6qUA7JQApUMCvAmvAusEVEF1lNzSjOFFaTxYFdtY5XNyrUgR3ZpTZfXsHiYh08tLL/w07oypYShMPrzJgz7ly/7t18fbvYkS3N/9wSia//x/sEqUoP1FKOFjRcpepbHgoCrFF5EDkAIwhmpxYuSsFVqg2JX6RTwNEinSxq5HnIqRokRJmE4s9biawxbZAz5Qq5yGtsdV+nRK43xoTq3CjSzTB1We/suahVYEW3/FmQWLZynhdrkWzIpLGRzkBp4S8+R0aLH8KjT7nD39QfOX8mrqxedqQ/gmG9Asy9RbWYov2xsbPP+ooDKNgMveXt7NGQ34aPx/CEnSlaM1SX0tUJ+Qa5WPwNBX5TvoqZFnjHQR3QRhtshXt/3u376/vd+cDnPO+4DPU4jxeUsiwQ5Dv1wZnXeaIQv0jmBzc9IPZyThh3J58J/LXJD3dsgRGm6DCY9RGWPiO0At3JDgs+H6p648ujiVj2tXMNuTWykOAnMH0C6WVSjLNvKwEmG3MACkMZbZNeSa0KJXwQSV/kdMbyncwLJ09PgPmhiDycpa6Z3kgFTw4WGYeyttmhzt+zvGq0zvUbrBKygjWRbGH3tKqbXnifwuJr3Fs8iiY6zRQYdslqIZui3QoADMU99tS5+A7gUb7a5JBad6KN5MnRccu6Pfb9M42E6fng3HW6n3Z54PWGvGtyGYfj44YNVKvaVGKEJREH2EIWNlRorN4OT9OAWS5RdWHKGliixNaFPhuaa0RKkcJfPEWlzEar4xevwbdSwXrOE79PKXFQ+BJDH0BotyZAlJgnauczzhZIKVH8S5cSdTGkFBTMCColfa6ia00cvkCdsSLoYgocS4gHp1MklsEostJGvp4IznlZdvxF2NkXqLdwrbriiqF+JLGvI5wAskRQYXjrAaVGalLdptz3Tlf114VlITpVGoc9aMceA7iSYOQvruSbSulx++jufSQMVtqDPWk2AG1Z+uHugcqTlBhiHNRQ13DXgwmQ+vGKrhX0RREkji5JU9xVCIhwjMIOqpy+EG92b7q9OrWCjRURaXC15oYXfx4Rv9U1WMzgNZ2VlmeN26+UyPx6P5/NZooAol14KNuDyUH4PRuFtL0RUltn4OqNCURhccEHqcCL7C8+GsgzFqrDMS9ddur27kZAVNBGgLVzX9+BJPP2Ec6ggliEc1ftRDr/1knzq/hH8LPoD8y/i7vP7uGx6VsutCpZ0YV64dGtE73lNNLIgsj+pDW7AnMq11BgMU6cIEmq5sV4urz7/jGkznSOfveZHnc4zHTFKETG2Ya95OewlZlRmGJL6xIn9+g+J/tZUuzy+GRAKExLSaEdh7wOxSaydFhjku6Ie4avpDZ0BjD+X5Q17cWh9kRUVrFE0Bt4Azaufl/XxdPzw8ePx8XR7ezuNrmyD4O7ibwo9LZwimSblQ5eKRgGeKIGo6E6/UW4w1wOmqh09lZftKRIrw/SmI+d5huPrDHD7MvBPNUl8mNL5nplcYw76b1La7Ev2tFpWcJgG+74bp3E37bg/AxufIstX0VSQmiLKF2qZfaIKF2KINTMj+nBbWg+C29b2JeMoRAcQy1+WcTfO57O0kxVRVsiVTPerd2+9EQxATWFDNexFOiv+Ks+CtW1xmiVDqkOF4/pir2NoHTk8Z0mWl3uAAcPpaOF4d8IAWSm2T52q+VoDN5xQoC4CgqhLNHgiNnt14Zi193w+f/hwd3//OE2EKYq+9jBKZlkVsnna717l00yVPh2znaW0aadEYCOTeXFJZI2SXrv5MkxbbZyfuK7gKObmWcnSJIOXz+aVV6Tffvf1txlyS2Qo3wXKR7B9KbzQd/00jruJrHpYYsjKTRjqiQ5ExQEFyZNihfV69Gs56arwWrFclT4S/7I5m47EpWS728OeiIbYKMm7QCEcYm9b1u6RunVTmkbsKbgjMTqyxGH9MDEkm5qX7BOrVd5tSABycA3rsC80w1eTNidruuZkzlHgxrhLSRPWcMuAoI5gBDEVtjJUAPBGz+RfFpKc7x8fH4/Hvut21CLL6AP6CIIGBRzYAsAF4YU7PHepJIBuPrCaCciYGUolWpWSDXmeE0UQcVE7HS7H/dWrK6/nK5p45BX22vnX8hRgP46UmPHm7FwDaseYoXyjERpeHUw/S+MXB1aGXgDlj6o9HnBYv71q5tgWJnyq0oqB7BZ//J//5PHxdEP2Cwng0E6KQz/cH8/cudt1iKB3SV7H9GZjWt6ST5Op0LVij3rARhNQ1obJN3Yv5LIcKxcYZ1ul0M9q4el8Hvth2lnBIJyNLaWLHbTlpuaEhV3bpY+0JTCcyiDLQrUvjqfTuixUFNJm0OZzoKBtYRB4KwLkPblapg7Z2h4mo3s2pEhAN2R0xHAC/TecQX7MbobdlqvLWMZtwEzfozIDX7aeLo2aT0gOWSE0fxEwdOWrFFWs4UNw+sWLTVe0EEnjTiiIKQbXmkCx5swKqzeCW1OeH6epn4d5ILfg4eZGC67YZNauf/fhIyr2KhXHn87gshmLx1fVVzLXHOmNAkX+r88u7Trye/8AbsalwtfARz3CT98sMHqZlwtbu3f7Pav55BGFN66xuyo48Igeqmh4XcS6WXhZLT7Zbqzr6XI5n8/zPE8TR5xbAE9IkCqXeAJYrM185yX0K2eVvGKIZk0WgWATtifhkpcyd8NKqrg0x1wmcA22MRiBEV0EkPSJPC6vA1zv+lzTFbb9Z0Wsa9LQLi3qG1nxfrAag3zMFixWLHEKIeeUFrHsR7XRdJOUAeMEC9ni+oUBCiYbUnvn5bSs6+Ww6/uJo5ylZ6U4Nrr+eL5IflRahKCxTdPqtIWQLB1GNfVUULPa/mg26NHjjdPxQkUQOpyyjGA/0geo2XjcpThBh8v5/Hh5XNf1cLjhMm9WciBEpLUtwPqCnZGlmp4hz6FWJ7rPNI1Lt5wu52WZd+NusCuiBwB/6+3K/YnzyYYKsOXUqQzqOkVVglgOIuFZXqAqH0Tdpi0PFcZ2BIKQKnNpOWePeU9Qrj9LHaDG0PwFijwaYJ7Ep1BG4C2tDSpJgPiMKCpiGrHsjtzhtv2AsqxH4sCeNVaWPkAOW5Y6KFWOxJ+VX0ggp1gjl5/5me/MnL2gNibWeh9OZ6peSDBmhnQl50U9jmBNWgePCxUEDvhaw4wX5mRUL4S4gGLoh52Ih1mc0dUjzyMg+e5JpD8xl/nC5VSH4bIsdw8P/Tjsdjspm5Di23rcLxToghYUMIRLsRkp4yNOS2S8f3h4PD0epYfgwP2fh2HUAHi7HHfi/DHAqgQQF5rBCJ70tQAz1+WUrxrULBxOS91hKYySfCFaid6jkLYSeQuCnoEt4pCrp5tFHVo4j5vcvCUk1ibZgmerycsXwE3hMWW//W43cXsaW6PxrODiXhC6QMCQF5X6WvH4kpMDf25OErfB+abJQCTXdeTkmtfdzYF6vRupkGE/3N2H2g37hu7/XJHYrVqUZQrbocBkxfr8Ed/8tDzpO5e2v3Ai+06a5gnIV8ov4oC9XKir9eVMGZHDQK6B8/n89ddfPT7cs9ho1uJVosHkM9EBbEzdxw1gK3YdJs+C8vB4PH348JHi3saJuhZR1ujEPdzVPSH810PqMXNDUcnQG84EaAXsVroKsAJfl5ktiP/ORNnYhEmpjkABkT6GlzreF4KDfZOIaJJOdFMaLFbPPWhlWBRw141u+g9uhSwWTmdzsVKzYmSFJFX1bR2fyiG7iXr2wQkE0ZGTcMu0s1kuuo3kDEJ2QchsaAwVHBWTtDqOUQ6DK9iSHDjSWVIVaNLYtWgX+6vHx+MpvdChBUy7jr8O6pKKYNOnGnWGY8LJ0zTsXMMuZl3b6pP3KaSkHI8NdfwOTJRQBfLPzcfz+XQ5n85nLrO5jONwe3N7Op6++vKrh8dHMaepNbyDACXz6PmuXrsibSjZ/teO6uDfUzD5st/vhnEYh4EquJIAjV0QSow1vMWkEDNZgXEUXt+akgrKijm53YKzFelHT1RrvlwomSE0Oj1pU7rKd2xxN68JWsJPxqZqrErAKKhlGD1UEbPP604++lDVuAIpXtpDWyaV9hv2h4MQ12BXehImKbl8EzGJydkCMBMkEZdU7911AEs7Q1Vh12kcb3c34zDtKB6I5QXeGok2OV+s6TGiG0zap+EmHPXoWjQfR/myNAKJNlxJyNzcMpydg8YPhQ2lTLcPO02UQwe65qe5xrOUaXW5nE8nCf5l59gyd+tuN7588er9h3cfPrzv+n5PmThe29d8SNA92N4Rt5SCt8e1GC3iQgjjMi939/fH4/Hm5kYk9mEc6f/akLtqYuJN8hzUAGjBZA9TSRMrlD+V7lQFCCLj/EFNWefLhc5/6BiBtSKLHrngsx89TtWX6wF4lYhdT/fKlRYI0Wit+2Ia6e+kAflXTh8Bx5IVxT4WMYnlZ648xIQtWmNbDHB+XgKQuXM1rDKCLENuVGQOgaDgVddddxgtNnRDN/7lX3x/7qZuJu8oV4pgDW3oHo4nkTYFuny9qnBxZFgU+JJQbZEhtC+tfc1xv1p3kiW02Rgs+E+MZ3mGoq0Nwdh3wVC1rBgL3JkIhWwxu7V5hyk+lANXaIZj1/c3N4d5eXk+nd6/e3t7+/Lm5na3Y20VTKu+/GTlEktAubVKyVxiGsZxWdYP9/cf7+/n+XJzOIzjEM1WOPw52uqAoRgAUVz2Ua/aWTG47K4GPLpJ2sO9AoNVVNWN5JQV6numywhADwtm1Uiqfq8jcz2pZAKu4ykMJ0JAzy8prjZi13CvL7V3G40pNUPbLjry3Y5FJLJQ0G1Suy6Z1R3Lsqhfv1ms2X6bCyC4yGLBmTqhcdhgjKc1jP1ynnmOJCiMA5XXNPgbvvz63bKsVBvL34jQ69KZtdyR7oHcCZysRG56UxDUgNt+FgHbtTknJ8o0Jdstb0ggabA4d84a4CeGpfimFSeol+J+tz+tpwuxXkoh2JNre+2H/vb2tu/7u7u78/ntus7j8HocuTpFBgRXTMFVBiQW49CUafU0TtffPdy9ff9uOZ9FbB7IURdHyRYSgKYgaGl1GFBVh3sVuIDRTdbswaMEoMZ0BehaXmLtpxC6XWcBflNZmUN1xDFrTEdsj+OMygS41fFbyBD+7qSr6qa0+VWNVH5q7uSP1ygcjUNP1mdJCwBaBnpLQK0PmhwZVopGjMVk6Kj8FQpLjR1KwUOlT9kWIIXOSKLcDfPdTGlk46jlPljmPV0uBG2FZApkkYtJq8FHO2dxNDc9Ytqk1CiQ0C4qEsDRSNSjNtiNxpbqBnGKVCm3Jwne+brvQ6kKyugSeKS1oMhHNizTdLmcaIL9vC5jz82Bpml3c0Ne3vu7+7fv3g3DePviloVbMQ/r4WRDfw4bKFLKzfazLOvpdLy/v1+Z85PNitIEaDc83xnNy8iHFZosSMPtz94NSz4oA1yAB2a/jlI0MlAlzpYgqCOl6TL1IyGwlVxCn3gCvmdwyG8QJb0VwdXiqFtBEBt9JJ8zZkasYRipKyfHiDOPUvyMBiIoEsaLFRd0U52rshAnaorIXGEiwXsafBj+cnsxiNmShrrb78d+JolyGHrKR1nHfjidL/NMLlOwmlgdLTdeagwAGTBXKgHPlfwMfSy9gJUI+t3i9jifEWyK5uow68DAFgSkzy7safSf7bPAO4QHw6mZZUGJDnmkh/1ud7lMp/lEDSeWeVhGryJIfLjrv/rqq6++/urz/rObmxcDx+BoQ2ZRG2pOV240CJprd74c7x8fLpdTRzYzsjiP4ygh35ATkVKK1NQpKgfvCIh7GkNPPVLcXJegEQW8StcyPqZNnDw+hSQehgNOuRFVeVLru+ciFoMElWzs/FN/gnCgiy7GaJEDZ5Z02mXL8BaGp6tUiUGorqxkTOwnorVSXEINsC4vBqKl38SModubUNMwXrUlnRH0JUx6TUV5chYDE2Ht9qcB9HRs3/n2pxM1cqdM73UhgZOaD67dQHpidoYX26DCs9gvl3mZpUSAiNMhbgjHsVAlY+PmxhWbGZS1l07UaL1y7DALBxi6vGCA745m0qVPGFGn/f5mvlAKLhU0Hy4sdRCxmMbp9sXLz7vhq69+/KMf/fjTTz978erljhmmEVzlZBBLgzQ4pSkNXX++zB/v7z9++LAsl4kCNyfySUykEg+j9uuVU1U/kgZ18FpIO2bSacZ9VcTIUrxcZpp80pECKAxUee2qDQk3t/r0SocBWDIfpdupM4N2FLCEMr8fRZ80iB5tMgzAEaKIC3PPukpDjC6l7/rr4grhDQX+ckCIegFBnH4d+v6wJ3sm8SUXiFHfa6jeTHmXiwYhG78Tl1qQaxE1bYI6RaOSVZnXNDvXIwwTdFPYcrEe2BsplSEF8T7cPzjp8LNQ0zGXL5XI3pmBSVZKPzhGwt053IMn6JQ3mbCs1ywxyrYq7kFch3AssKhAsqv8XmYIGbCgcE0pXus673bTZb8/X+jiyoHC2mjO4zC8ev1ynPovvvjiRz/60Zvj8c3rN/v9TmyPbJaPxEwftqwRz2s7zecPd/cfPryfL+fdbndzcyvsd5wmCt1gTZhxVimzPCcHkILt2BQnxk1JVjufzxe2yeFpb0iZcoOgrnX30DUYSULWZIe0zh0xH3neP/RJYbh/Ilx+OhmFG789dTWxN3V5qwTkSjIq1XLAAy9+GJ8jcSE6u9unNnQQrxGDuhXTsr5UIWGubV6EEJFt4i5fN/YDlVXf51wrPN/LaHA8nl6+3O0PVNeaJzWs3XDhkoX2oiSXRoQxMwRPBmTfL+u4oSpLwTRbtxucvKgqtAJCoc/EQ1vQUk4erDKWPhfU3KT3ENPokoTNcRxvbg7n8+l0PJ5PZ3qSkIpWvSzzOI2vXr7svvNTP/7Rj7/+6uvH4/GTN28ONwcOmCIa55PVM5PJhjRLnx2Pp4+Pd4/3D6zyDIfdYTftyCXB5b7ZssmBHBLi4x1krVJg5F4oxRXHHskkzKVHMV5rhXiVr+2BttzqBheIlwk7pDiR1o4q+UvRrHHSLrcZ/Xzlhd684eTBc9242sy6GNck0KQgNC5fL37fuhcx3WUHw5Gh3+13PReFQxYD1lZ7NA9IpT1N3nbewcKYp/5xlJQoAImoAA1EnFefX43sVoZL3dLr+Xw53LwappENxTRPkgRMLkXfGb5JLFfS6H1ZF9KYqVQ43SLis6hQiRqaTuaCiBLEHAOoqo4fqpnOy8BW7fBn+2zwZhtvmXMioGhdPHYAk/fodp6X8+kkLyLGyKWD5svSTeOr16/Gcfjxj7/88PHD3f3dm1dvXr18uTvsKTCWrfTU+SZttMoC53l+OD7ePzxQxtHxuK7rixcv9nRRrTESjZUOKNkhgzh7bmayHrC0IdXCfVxLpRZ7nOcVEyZb7IyfjbG/mhsLRa9CVtMdEm9K7JdjoQOjkH4r836Cl4bPIN1bK6IbEnP+FtheAY71ME3LVYVr+tOpfwxI2u+03+94N1yASZnA/gOYjK5anan5bSnAMA1Qb2MqyIaP1fgrNS4n6iG6H4dxf3Mg0XeZSRMd+rtHinkg+S6Sk2P6YgqxyB3VgKmwk4KQIp2KeaLZ+rOgxvvkpIutp89gDJODA2gsqeyJi6AaxR0iaJEw5OhG1zRNNzc3y7xQn1R59diN5FTql5n8aq9fvbw5HN6+e//FFz/83g++f3t7++r1q1evXr28vR2HkfqEeSURNT525/Pl7v7u8fiwXObj8TiOw6vXbz5988kNxfNEkLNBjQZPzPPldDpzipJ2Vwiaa+2C0wF4V9LCNOGbBMo/PCtbVEqn8h0Ra61YqHVCpxoVZAARymrgKz65Zo4q72rQE7wjCcGZgvs2XH9NVrACiBLbkxso+Hk3shFen8gZ0SDXOjNTlcqdIxrAxz3XC6lf9henId9dmX8xR402U2QhxPn+D394nhcKQqKKAmRi6frhwx2FB5PJVM4NiqupY9X6zYv3SCRpssGYgG9TimaLViE+Rc46kEY2Oh4RHID6zDhHgow8ENcv4VyO01GjLo45AxR7SHbj+OL25u5+OR5P2lydwsLpupwv3TIcDoef+s63b24PP/jih+/fvf/qq6/u7+4ZiV/tqXUG4STZw2ay159O5/v7h+PxkfZnvuz3h08++eQTUqFJMQFLYJIpLpfL6Xg6nY5hLnH0lShfL0nryOdpacoywjpUW4QbDK4w35oySO4lNm5KaPHkHhOIBEw7uSVOFswQbg+YeJqFN2/KawofW0bR5mj6i+to6gUuLyq+sduxMV4T4lFyB/HWXYf2KSlgmtceIqZO1KIUpDpKGXik8mLBtUKNgo8NsxQwSJ7s+nHoLzPZYLW/yUIs+ExdRfPe6e4Z/VIWrEyDirBD9BLSzdh1K9CyocGkA1DErLKyzXWBYVBhIAXZWnEeQCd2x1FkT/nAw7v37x6Pj4fu0O30hoFzlboTlZt79eLVz//s7uObN19//fbj3d3XX3/98Ph4czjsdvuuW8+Xy+l0vJzPpyPl6JOoPE77m1evX7958+r1NI26CSZ8qDXCWLDEeIqFHm0WNms4PItjS2KYrR+IZhXaq5hdFUUwg2QIVcpkyRApxnehtH6Mtu9XsW+DcuQPwsOgn6OhOL5oS8NA2Z9wJjUnB/KycSThndRZd8/sl4UgD3cuQRDLt4P/sqhWYc4D3UUpiWZYDxU/xUC4NfVkuQV2ZCL0yE1j5+U8TqTaEdnhsTj4IdvYnbjYSyVfR8P92Y8YUQEWFOnKr2yfV7Gpd7uyUBgwe+aKFLjNp+XlU4sKip6pGwsv6tVwS+eF8oEPbz759N3bt+fTmb6hWjIjefK7gV01ZHoYB7Js7Sayfr378PHx4f7x/l584PN84Xjy6fb25pZMzdO4G1/cvri9vaWQSSW8Rvwg2l5mLH4jSHgyVNRTDv2pYBd5gQW/wr9tABSmoQVNccTa5paL2hmpAPkx6dfgtkQ5fYMTZmLeYH7VI0ZX8qfVgy2JoHFPIYWAJiw/qPbVRKZGYU9eQsdUOu1wUS5Y1T7bLG8GmCQQXQl7Cf21iTrjQ+3FArP3X5nfUk2srlv3FDApUYyUNzOzpQeCR70JY0rREaSW7rIWJ4ayEsAeFOKMqnzRtTTFVAbfwJ03vdHs+e4MCfEkRdtp5fJqP6JyFAm78zzf3ByWT17ffbg7nY7ruleNhh1MLJqbjXbcvX79Zn+4uZyorsaF84qnYeCU74lrjVJP1t1ud7i58RzSCpLAr8UFtykD7EJUwClzyIgN5ch9AlB0u4brQoEt5Zj4K4nWQto55mbSsoZxQymd5y1NYz8hH7cup/D1Z0+yefPGg2jiHvs8AIawFgNT7tGerEFKporCKJ4JgpqKzUHLBXNME1kjQ0yAVGFqiGTtoR2Da9EkJln+XUoAgglUCnieKHhj7ldSfIZhePfhI0fn8PcRMK/hAOYgUq+vCNEaKODRPSFrRYiY1X8SAQCyeCQGKEeYJjgI4cpzI4rEMQgzwJ3JyWwBJPYo+eo5xf/mcLOu3eP9w4V0h560IScokt3BTtxd1x32B0lUnEn0nSlOi326EtMsfiIOe/VIpayV27nqBGXgfriwU6iSEVEXSR81LM3OX1Fkhi2CzbSgAhw0dpA+l6qUKlerR6QoQr6NqTBUiuV64sopHIWnRXHH5C0UvovypPAcKuCt2YY9ldgvlwh1c0oq4Zhe0FBS6KylKQHkvUjwADMCowWameh8x1kqciMZFkQxB4oUc2Ib0vfr55++oWBIqg5NmPHx471NwdtqRS0hwSGN5WVXMHdWNXHRrMGxcMmgzJFSKLzY/xs4iHuIpoEkbpt+WXsBERGMOEBAiFNsDkijPKG+P51OFJmirRJIQhGpXYpmEekgwZg8OD2VXo216qsl+yzJtfmwdZqaYSAI7DQlQ4qBYBxsOBDdloHg2MYS1GMCs8t7HVxkflPja3e7G2zYh4X4HCIbzCFjlm9AkuAT/1Qne+oWK8qi9XqCEt1PXJkq+qtkDiLckvGZwg4rrTciZgy6Krx2DUUaNHvOAgqWVHRamYenZgYMOoHPpbeqeac5eZR2//L2wFXjtd+oRlwbdHtzWi374+2LrHqDTFJClJWP4M7GqTqEIQmL6tDl/cUHKvbqakM39liGa7Ibmv7STzHqS1DpnjLIRgrRYuGC+9ZrM06o8aYL0P4VWt9Rz8tzvM0vW9Z0tCMJ8moRlEWiSt4npANOxPLtBUaC2lvhqikc9ccGAmbE8hGKBHREKuSCUSg//lRZzAl03dexInTwaRLe/XPYq3C1pZS0aiidGOKifjlInKu6vrFbXImyeWs97EANNRyCZZnxFrll1WfkMMyOYy5Wj6xx4VMb+IE+WC/FAV5qVpAreKSiHDqgtSaKGcIGquq7SMABC8/Uad6+17blFnuQxVwP67BYdBSZazqeGDEfAe8LoiAEpl5tJY8OGrcoFXVEhfBKvOOOHDwcIMqRraifh0uLLzWqBb3T8BvL/MiCoS04DBiS+2VVBBoiKooTyvJK6eSJqxwEX+Afe01LIdGMwJrGBs9m8aZo5JrkHdsKfAWAKU6hSUzw3qbTWSO067WE9G29V9MNVaxaNw3jbrf3Qhb9M2kgLMuAx6RpD2NG+Z2NnuaGULxSQYCD4UHkch3TcbVwJ+m7KWiBec/+sB9IezfGrpKNOtqA8tmbNXufE/cBfN0NFrvj7J4RWIKH+kEFU1sk22KM17i3A6oMaxxvwHDa0OBoLm5uCJPRhcoLf2BFDV/o2A/DblinUTqtBbfM9otyLunwoSlRBvGa04gBDFstoqqAWGtRHE/LjU5RYj45rKAGVIC+RetC8+yam1kC08bvydjt6UdJl2nyXh0IhW7/FahxhLmUlCZJ8PF9OhIiVNMwUYkjytmB97q/GLdvi2bqnCw9RfNY1YesXYPd8hvkSDCZ6lZxwJQMoqcskQq+/ijgn9gU1QjoKJVhmoaOO5iNw3A8n5d1GQY5QUVa1GnN78sdOYSmuJQEd2O8nqE9Wzap7iHFIsZEVXCQfS6MV7pWrpxppx8aRqCEp83IDpidw7hrOtqwqAMhdEDQjDXplKGJGda1yV7cZCaqi6csKHSYATvOHFGFaCk9BDBqz1n5BXidImPh38aFSuylvFTwxxZW5uKhYsOB1/3SkRvpWQy+pbeE2Fx7v+R7lcXMKeHfxJrT0eYRfDsTVNqjyG78g7Rzuntrt44jFXmgMFoyEcf9OpJCkR4KjxN+3eRGE2FU3flhqLD6WQQSnIhyIRRmAFY5QYg3h/XxnkBHOiNDQGih1HcgOgUVUnYbv3Tq+7fv78om97HBUb7OfqGoD8MCY8BYOtLt0Vw/j2bEebnDQG5zqufTU8CZOjVZjEj123TTQ880K4sxZPkapECcc/VrKGIFx0BLSu9AbzIwV0Opu4tXwqjhsLO/QI0NPiYPsOmaMi1CqPJptjodusBTWGCLO6oHim9sxR4kLcfF+03u8HrMpqwLw+ZChRvz0sUkIdst3GXblMYgIce4hJqdPHnwbFsTWq35q5RgIsIzlq200m5B7VzjUb4RIp9JR975yjmLIQUNS0VfxbywcNciCSjktG+ohgYWZ5cTEiKWzmMmtxx8whGQ5Ons+v54pthgDPyD6gJefFLqcLD9WQPzpWuiXflB5Z/OZbVDYz9c2FclIdfsfbEOubxs8i0ZJXRupBndgb1xPqnwdoFeSs8QVyOyz4R/rRy6ujpjBjM79QCzRNdq4CpIfgN9kUipEqTuR6i56iuD9TS4CUAnbABswqZSYV+7n0vFm3UqG9xm/obyVfoifZDkvcCvZN90N2U4ILPwvDFxFCZB8aiJK77eqwlRySsK29hLQqk+koqMZ7qOIGRtCKxEmY7qBU1dICRwIzej9BvjMsKHcTevy2U5k/GI5FGKQl+kubpvqxO4hmogv6gdl2ra9ZzgRjxgZFcze4A5ac7YQMi4FoFFEjT/XyOrgCWbJ1jMbCpQOw0z/Zqybyl9T4zWVhcaEmStbqdU19Psdm/laKEcnBhkkjRqzsaS4zCqbtVNzmVx26sypSoDE9wjtVWhsDo5WNTvgXwO+h+XhgZBG+EHPZ9bl+sWsXp8YMMxDG8L1yTZ3ImkciRWHqPx68Y2IiXJahsUpS3jGIoRErReX3jmxymKyDkkPMO9Ugh5dxQrZ/GtVuWmQtqMQeplB7boFElK16j3271/K7XxIU13Wc6nx+Pj4zj058vpvCy7Ydqvu45KtEiFKfZCVcTbKIdlItpKJePv5uXt3ePjOJG2N/b9SNDC2QygjBrnVg4rdc/ZukPzUgS2glh6V2Csm/xxY+UdhP8mFhMz5/88m8h4Ap2/RjUxLrNIK9UcpSlJVGZ1Dql7p4ZUT0w0HhURbjEnVQ2VoPZQiKEGGlMPQqCx08qWdTBOl0/rY3r0rAe5QJZPTieB1YWUV9haoeJdel+OddwCyfyFUNJhnCwyp31nYzuaX0e8dQLMDazd4Lgbd/cbqEwSWk4M8VsEdDhikry+EQthpN3FpYYX1KUQG9TNrcZWNFRJA6S4NiM14aLIgeXdVx9+4zf+6R/9h/9wT8niH5elf/3i0//r/+X//O2f/U43rxMHFcjrnKXr3jm8wHuFUQ7d8MMvv1rO68047UjP4xJQ0tfb7SmxEW58Vo1d1u6ifwjQFt5gfRtSWIehFrfwMA+Y1XhR61FwVGUqlpnJBCrSZLSCllakcW1CjboSiAFdbMzs5aRBlRVzAeQCAZ0KlKCO+E6aZhB76wRBNVYMS4EIE+T+njtJn0o4R1j1zaaAJjuVaniEitFWSoNgPbzI9l5r/WGcmm6opWlTTagyHjaDcRO+jQaUgksO0LiKvfVHjinN+9OSgd/EPZFgwJZCKn++Z8m5SHYPiPPCMBmR66nLYWO7Zpmr0EGy8CzdME3zfP/hh3e//Ou//i/+4PdOJ8q0P85Hanry8e7t8eFbl3kcd6bFGY93d44Fqwh9Ma5C/4zjuFz6w4sXH9++n0hiWty/zBXVij21uA8zyXLpHJ6+9iIP3BXHixW9UxxGDc3+CbMFspcIQWSaYTbfWCHHKdvp9WwfML3ZsFjEbj4jE4w8IsaqGTib90RdIwG9MzovXxRcRNFVjR3B4xwcLB4iWbNkBgrgSCS8zSerCdLWVZZa8E+U1wtIBc1NmXXKQECeDHZwDCFL65DAfl95jTbOTTNzKjMEMhIr6G1iL8rzPgCuvWEFdBHZBR+rYBjmeT0XamTD3XT2+/00jswvTJuFaALvWxEZmnnrY2mwZbJALjTlTHwdO2r5uSyXj1/d/epv/Ob/9Hv/4uPd/bQ/yGO7aaJMtIfzcpl3017tK2aNAzVvk6pJIEdHob/rjsLpCR5P58u6LkNH3diccapEbJxXCumokcleYiUMo4WoVdUxmRpXHNW7IsUNOJvAteKttUWUvASp+EeWa3sEkB5ESsdJOQ9zy4FkHjqMZFirIhS8ti9ENJCRYL7+gBreoH2zeX9cUIjK3uCVcuHfuAFWYsqsTG9HYLfZFapTju8MKhwuJBeKUrCGaHBE4NiLeCW4LfTt5g0b5kSYYr0I3NjYjI0LWa/vSBWsrXnjXGVyt2ejsxRG4QyepG8k1adk7ZkyJfOH0W3W39idQ7MY5rVfh/PleP5w/o3f/K3f+b1/8eHubjftSN0dnHTPx/ORKoXThFS3yxZMVbgbSgnlkiu9kOAvhs3h6/dvvdNnkkkMPYVcUz1HqXgK8nkw4fjAw0KSUUIoHwTeyjRhizBSySYCdIkMLcgRdbWQyuXthDygzZ08buX2UxFyYPsDUgJsGV4ub7jtWoazarDwvJUo4nSHJAdgBpZVE/SlBPCA/Azn5zFZGdjwzbYa3FTgKqA8FNArdJpyOZzaFLCe+FE1aQV4C8ixJKEmcnwDRbgRjnXd4cTvFwMoYy8xXs4b4cYF4IhNWwVSgguC4czRbyzEywBbGdfQrTPh4rCsj1xx/eHx8Vd+7dd+87d/6+F4GjlUk/RUCg/i3kPLejlf1nkd9/1FqygpGNkEqm1wcxK/ehqm6Xb37uu3IqoOXXd3/xDVMAEt7SToMYmCNo+LNBEN3djDPD1xyHl+QJ4yL4tDCDmvUV0/SXqxNCQKpXZqgOzFLFQM1/QqcaybhBXnaIRiRWebO59NqHVBxAwCCkuqhjtooQhHRgVKz6x4f5lJGSGohq55E2L5th1ue5Oj951OgJ62CT/347FN8ThgzQf2bUC+sOl99mPwtxV8rYmH+BRIE3XoYKJVtX1QWU2wMSouOe04w3M/TZwyop3mg3eA4dkoj3ddDabuJ+f74T/NDa066zKKID0sp7vll3/5V3/jd/75/fFIEfbzTNnmu/26rLtxdz6f+q7jmHuJGWIm3FQT/IezND0OCqUch+W/+Vs/p7HmZERhC1agAkzYzMvkiDYR0bkrqLtJbM6mnhJg8nEapZfNivsNthX2zfITiFqds4dl8Vo1OKqRLGAZwx4EgnJ8H3iDxlgUb6WrsHL1EF20wDdCF5irzJNibgvW51lFKM+sNP7p2WlAqIXu29GC/c0LFKlnGQAjigvEDoYxVqkN90YK1uMLCFxzufKKmIsiXLk2uAd/yXwuPRpiQJGDVW6WSJgLVwDfT1zgisrNDNLcBwWGokWNehUEqKy1plBINtjCarKOo8k9/dBN40AZ5bvp/P70D/7hP/yN3/lnx+N5GKfLTLarfkem4m7hpifrer6cj0fqMc9V0/38vSVLKQcCbBqxJlpB1RgfjpfduO9GBmcK6ojAQlydIOhCrRs8rDgE5YjidhxuNHt00c8nCMZSg8fCeePFNCxVMzChhA+AG7cuOPd20PXieXIjcLyY5BoIaOuJFauDyj6iJmqrO6VBIbDkFrRexdplMK5FBIBqMjNSvwaoptZjCZeg9g5AvcKGwggKM7IFUvxUWupQJFZuqWtVTMG6jpR06+qf/S360JtPFdILTt+PxpxyPTPd/W5/oMZ8UvREKkbiwxVB96ArrAANUUxJDBSDhLUmptJow9qv3Jjk3Y/vf+F//IV/+Qf/6tJRwZfT5SyWXXmaUH0cpmk8n0mEpvY+liRshc5RTo5D8t8FGYgUrMv7j8eXL15L9XixyTl8ozDmRikpL+5uKvcZCW1zPu34EPprmgIegMMBpqJFpRyTcxI7s1TUOI5CcKs4DuimtWJhJ2LKYYfAAcw3mWudAkk7P6vuHKYBoNOqzTa0NufzDhfxWSlc5ifV48Br06KBNgLyLTNza+yOmalte0DN88Adbm6WOGAh+sDzCZO2p5w87xiDBqBRDNW4KuwFpUa/GXsKsdrvD+O0o9gkC5BXO6tNudCRXJVyMCnVBE/yizOle9hVRGEYU3949/brY3e8f//wS7/0q3/wb37/4XTq+4FqjXLspKvfPNlFmh6SOYeDSczyjTyjiCvDrdXDnMb+k5vXPDDhrXQfC8uSUXKvwyX1zigTwU1Q4fG1v6QuECJKlQYoQIZSqSvebl5RL3ZSVZ1f+mI8VvU5lhGX0Cvh3XbJHUB6qU0WDXD+mzrANIOW/mTVI8uV5n/SbEEYG+EAXso/qay/Pd0iOcV0MuCXxnO4vb+KES7ic7IoB3K4Ac3uko0O/Xu9yk7zHjsLNXuDOTc9LDBL5JsUzDiho69WYOfaCIS9VL97J9E/gq0uOSeJvSQHeeOQyDif97lIHBEXY7jM8+O8fLx7d3e8+y9/9me/+Zu//Wd//qfHy2Xo+gsJq2QipWlxepCUUl/Py8CurOPldCb+fEN7LkqlWSEaAKqIqJI/txi7dPPYLbuFytGzYczDeiw8OJ7m46Lqk6wlrTORFWO4NnyjbgmCTyGM4E0AlMZOLPyvIcJ4ek4ys+Ca5YS2sLoxAf1PZZAuC7qIueVYqlsNzIfB/IH3O21FIPA7nP5kWd7+K9sg+Iuhirb7rpDYZecSKhC2R/a8e8a5PgH52SmZIQzRsKs1EWqimcJhQVzKlftPkD9ChChkiSw5uK9DtXcqTbbfHVjhtbAeHzpL523lPXbFt9eFyDLxi4XyvutnYmbr7u7j+/nc/fF//ONf/LVf+5O/+LNBDUlsTKA2OtSpdF1I6Z2pSsTCbYdphx9Pp56G0Dc2VSacIQgPREDmrtuPVL2MAZAkau4cHwJiaLhC6agLIfVegJoFLn5eUdfgbFzwK5hHOtWWagsarQiCLtO4WSa/X+kYoGpQUjsP+DT2EMaySSJhSo0Q7UGKDB+pIJHoTzEEUoqIKtm60MSyRTJM4NU/tJ0ZCqa6nNrUE9K5Ub+W0ilnKUas1iSveWefvtWteUB4E61MiAJ7vDaoCGnuIxd7HKmYCtmrSDRNodfAbJHX2ib55xGDkcHIhDaOUhRok+xPsgbNx8vl/cePyzL/l//8X/6Hf/LL3/3iu5TdS+UuOCZYc3PGZV2pdP/lPM8zD9eP1G+2P50oDEN8OQXN15fbmTnqYiBA33fz5URJSPz3+UJCdAQV6Sa5f0iVDUoFhF31g1Ad2N7diKCEGCXnpuITjrPNZw2TLtRo8BaosTl7NyyzEhIri5GdwhtBEcafUppWnIdvg7MeUWuJEq/LQL0O+cRS4wj3GIKgjEJiW0aopd6gN6CWWoxavllYnzu946usGkNRdPVBqODFSgNX5AAil5Ap2kJuXDlWK6GjuyCS1Ty5c9rD6vHo4sRvx2kJVA2WaspwLkxm4e1R0qYqWwM/Yv2s74MoyVSReeZk2N2H+6/nef53//pf/8N/8ivf/f73x4lKMFGmMbemlUO4zPOZW2+ILZrq30hP1249HU8Xag5yInVdEy1zwrMcs2kcWilApU2hxlLTi+jG3f29Hm5z8WQ/IweShE/qLdEquH0hSzGQMMIQN2GzOGSbEKamGwioLkOBlOdB4IkYoDLuwioa15NteCN8v0u8QZvzqvGTg2GkouhlpgBOTs5MsMCWAS+/A6/YEPEB2YNLoEIQZDjhUo3KSBqt3FN+lf/rS2TfGKcTFswoXgWRJM2T976R8AFqUbpz/H+nrk4WVX1OOrd+JA598n9yr/RxGrnT46iom07MLPkFOBR6AQh87pQoib13xuZfdzvy9d4fzx/u3j+cjn/0b/6Xf/Qrv/q9H/2AqlJpC28Skd29RLjr7JcxbhomKQn78e6Ohh7HYR0pFUkhJ/lTHWPDKqShfZZObEG79w+P0vNSdtwByYPapH6dhxwSqeYnnfdCHKX9CZvlFNxBBWih/Glxk8bcasDIEFpsdFbxwvuU+IXTkFrIdkbQO9Ab9Hl0ucMjpI3owc19P5IhQXy7pfyY0xsV+purVFC1dSITlUWBBB2hobUWV/8B70Pvmp+whoMOLEKDuRBnFsaphpZbHXfec9tFoX0aKFfO1uNZw9QoMyWZlBosU0iVlKFQGTVFVAHBBhxoz6tVNQQ00bAMy7RJfxyW80xS68f7h2Xp/t3//K//ya/80+998YWkxhAVYYm+63puukuV1c7cypZqnXLvbGrSwC0ahu7wZ3/6vT15idmwFDZBmK5Feed8WK+CRxSQQzMJGufL3Gtda3nEeKuwJoZRjU60JmKFFJ3grATf9EumzwUMmOirVLsQ/4oBUTYGQl7KcMXzGk9qQIsxB2FBL1SA2FKiCBqGRaZIDnLm3kK6ZS7pYG5xMQjOpME5kUnjDDAUGmK6XC2B52p9JJbnenoJK6TrkUNTstuAjpotx7pNFqXSvPKPvcwJGN5n4mBn8aPqjQBY0CaSpjd5nhmZfSYqXsXJ09KZ1YeDA8yqpEvUVw2aLr6pWofZDEyP6TUc/7j083pahsfTh8ty+jd/+G/+8S//2hc//CF34u1IDuDjFnnsMl+8F7t0AGQPDoflcTV2YsjLeuzWHaehSWUMZ58p2rck44rt7IJeOciMalJCzH+ZFq3eBVaAA2JQKAr3c6EM4xkWHyGHBm0N3NEIeyUVNUGjeXCKF+DX2Ti48JTiTavBewOH3aRn1E3OnY5Hi0uiFTtZ5MJXUlxBfV2KS4J2yQcrvtggdHCGJSpXsod1qBFBkkJLJu/lo/wQrI8KRKX7DuUC1yAsvsDXSYYnk1qMoiT0FfjUGHGijoyzwzCNpFhC4GnYIuBf3EaUDlK9JYTbamc87UWAmJvCE+talhPZkw83H+/vjsfTH/zuv/jFX/n1v/zii/1+4lR+FUW5Zjo3/GDUlZ+C0kLfhdie6dvzbrd/fDy+pBSlVDUpZCzAClMbpXaoL6OnNnvM1oXA1iko9C0VFtD2ZbHBLgBp5hGEVuZtcXiBQd0aHriGkiX/2/KDBPe0xZjs3apYabVSs3wQtwSmWQN7M7bRFaQdyRkIkD10eLAatHqYlo9UoWQCJd9AAD+Dxy2W4fNImUViDQyK1qKaNrv8KxijzSwyDNNC9lLVt4rHYO7pUFNdRysmbl8pHbB6cFqtJ8LO5CtJBJVeF9LJnJHWyi2loLUaTltlLHHHsmu5KqkCGa+xWuq9wTMc+/7Sdcf7D8fH87/63X/1D3/pV7/48odU04MdslwcUuIkVkFcriuu/+gXslrRUsVMTe5YziuwehtgZshyWiUkCoHhqMiBezJIHQEvZYhstIcISig6D2BYETMg/olOg+yaeEdWpWDiPn6U2gOTtH6P9fr0CSTL5SnjVsS5u0TRk4sgADTsSTpzr0zijTi4HDy1tvDBNcIjlOeEkFHuF+3EZmcs5lv+VctVfkqGb007WSGTxecugfCTmsDD8ULaWsfCxPFcMFbHzj882kgT1LyNZkoTw6O2FHFaae2qiGo/HbPxRQgnuK3tBSq4G6kMol3e7OFvBmuUE9j11PhvuYyH2/PD3en+9E9/7dd+87d+5wc/+tF0w6Y+aW/AFXEIBuySjruMvJHrY7lxBGG0zStFUzqJzHylIlDCu0XFFa7XU0IS2wW0OFCZV6rMlRs3sWTF1U+Fh3i2LQMOBNWn8MNyIiB3pwNQ5lkrnIn1tvCxEXSsgnFNVzZO10LOjBKuMUoljDgGSwSlyZ8m03npPg9sdMdcMPwSnwOlW7NrCA4gO/Tt+52SF+9K4N4qMstTWVcKPOBqgx6aortpSgUUZ9R/4tg9ZdILxmFOrRZE0lbMhKiCulbZRqcZPewwCgZXWSwnadywyPhpyLlFxoB4SmQyJR6QvLvcL5fufDp+fPyNX//Nf/bbv/3+7v2LF7fU1J5amUm9ZGJxbrgSecwRV3PXiDx6/reUZaWu8D2z8YQ0bUBPBmDhaCyskIrB9qxWqVL+fSENnn3ZsV4QofOOmNMCN7OICvR3FA+20S2o5ZVGqi5VplSMJDQZQqOTItugkXl2xvF9PdlirdjtygG/zAWlvIW1bN+aemNGFRluP+1oG+wGsgyD77TgwqMbVcglK7SUBbe2Va5zGpPWP9XQyUFJqjRwMW2pr2bo5+zXfM0qvQmbjXoKOKe8K8hN6vtMgQiAKu0hjtvpGNLORsCAQ5qgH/db3e36eXz8ePyVX/nV3/rt3/n6/bvDfi+KrQhi1F6XZWVuysONeaQjgGUG6H3dyko8G6uIY1OTrdPxkXJhuHIxrLlQFEJwxHQ4Lq9Omcd9z2GbrrFJdgTI0JzIsRKe86uppYMcl/fyKqlDCXpmv8kuXmhh2eIkYRp0mGwblDXFTrXp7AnKd/p5Gh8srFcIHqufegpwdL1WZWQXBaG0aDwOQ2OlcAO36A7R5sH+YlEjGqw7JpzIi9waAg8ig4Fwlc9PSyJXyLpOqnaSPsxlz13rsswHz9WnShBSb4r9sVGoNQKf81uUwQLttzToWEBIXRX7jSOSJzxiiKfo8OJ82w4Q3BqWzwnidWy08gGaz7j2d/N513d37+9+8Rd/6Xf+5b+4v/s4jRPFRFLuAHNWti+LzYpszpLjy8jtOWzMoz0HSKq7Sx2O9Xg6LcsyXqPUpdRqEVHr2FNR+n4YJchZMCHSNqIMFgdgs/DD8djazUxCX8K4AP1c6sJmHhqv22mb3+IKLsz0WTy2WQE4aN2BpGYHyTIvTlv5c10okz6LSAJTWQFAXnhT/gDTSjjerHZ1eRURJpnebulw7QHyQlBYKLhTw41lhKDBkqWy/jSOUz/1I/1CaKnf1RvltYYiSwEmZdxbhW5/S8EkizCVmHv6JQ1tbAMlZ3Ckoe8rPi/puQcWmx7FUnPHIRnd0I+Xfh3Hw8e3d7/wC3/v9//wDz8+PuzGab5cVuqddREVV6ssE+eVdlrCfJEBRzCjQRt3NmDX7Ol0Jio5Dpy0pLufjtFdSkKgdJCV6OTajf0sAR2Xy0VcCL563SGKu9J8YwkH5WhQsm5IcwU+XBe3InizgDCjDnr2bjwyI1dYAHN4QLpABMejjiNXop74kNmGeAYuVraYnclRfX3MAEeG8tqXUMFIYSFUGZEu62gOtDOCnT0U5Q06nLw/cUbgcK9QuyAWIH+VOo8ikRA86qyzTtOOIiWoHE30DXJWqjUrEO9MEAocMn8zxIjYfkFWgh+576W9qDSEQvQA6DHxA7i7j4RwZeWay4dh2xkgB9Jm++lw/+5u6ebluPzdf/ALv/8//8Hj8Uh9nAlvXck19658tAj2usPIytPYH0ZTzDDHbPPxfIIDQfBG+y+WINPFUEogN0MSB/LpfFY3mKk7vqUswAudHakUO4MlufsFgzmYbVmkm5BZZtNxwJ+Y+Rs0IvYblbnQ7TB6wcDQ4kmQlYJgLr4K5+fib3KUKwAqQDmitbugMKh40B+i0WggtDlKucKupm1jAFHJbCIt0sqSWOyC684JKzLf6Fu4bHfquDBTl7y1c1otpUROHwUshIoy7aYdeU66kUwsGP2FxwX/FdppoRA4vw88yXiXRERYYxGnkfVb12SqI83OJocn2Q1McTNopwmzlLn2S3fqx/V0fjie/vN//ne/8uu/9Wd/8d3745Gbe1EFVG1rYNdMXmK3PDv6mjQGNl2DSfX4ERdWjXnZWRffzLzQxmShVUaxOXeQQ8OY5j6eToU9xz2oMiNtdALMSPIKx3FcuUSmQjSqLZBorRve6g5gH4MZ0AXtCjDSY0VGDZBjKGFfOfHlBXpXiJ+aAQcJDnqBgO6l8yBVgyUiPo7LmeiwJz+H3NGSpXEzis6o0OlE/8nGMwHFYm+U/KIUi9+qvKhcKQ3YKC9FueLjuAxUOtxaB0EwhvyEmYLqWPPDWlKKP5uSlsdiYahIxaWBgZbiHvyRQj4smpkPmIuQCAnVjBpRFMbDi8v7D/tl/OM//g9/7x//4+/94IfdhWRQOVryFbHHkLy+4t4ltis4LV2zxd7spuLi8JRgS9glYRfFY2kdI2XTIf+a3APUM/pk8d5MEynA69qd54VLj+QIdL5NHJ5c3kBAhApwqQ2RRXEKTBUEloeoZif9AJZfaC4NC1N9AiXxLaSdAMZ0sMHT0DoLtjMbmddveRRmY3OC7FdJRcyvJplZiuhswSFGpQhsd/KPIr9JX5TflIOiAx/4L5h5RcX8U8eNFJtYNU0u/lL5wmS6MDlNWNqzBCmfJ84s4xmQMP/apD9YZrJRwWoLez8MoXtVbwCKqCCbmfTsvNcIndQWl8z4WRTTsT+tw8P7u/V8+nf/7t/+7b/zd3/4ox/31OOdLFMC2GxsFm7LBiyO0VAUDu03uC4IhkgAVVQV1XTsR9VKDT1QfohDoUiMZKBRvyVLyBfKpEm8QqFPowapJAjdQMgrB85NEs3nPu128moO2KKwj35xSFLlUi1KVmc07z4wYJfCSVzx6m0OnSBLbaiLLkQD3BX0whz1CuOhtwrarEk8rPISRIy2MvMyHVYYyZQwX+aONiMFVCSJLmYIFuGNhSQpI4tIm7GiSd53PUJvcx+tb2lihLr5/eQ1V9KLN6aZr5BWYNgQkLHYmNyeNRQmbVq/xpSlYm1grShhwFq9+7/p7EPDU58bsRpSg3jAcXf8ePeyu/33//4//e2/9/d/8KMfj0N/mc8SRkVNdRlLDVVF6hT09QuSTvOOOX0z7YGheeFipRT5Nq6zl8XySCXP9+Dgew8O4ov4RTSGV2UsWIULRIy/0itXZEVKQrJUJOE7+sC0o8ldLtS5jMPwOBrP+xAI3Kiq4jYt2F/sDmd4nl0glv5jNkNQmo3Ehwaq35jN2jqEhWE6YCIBgSmqLo2aR80dn2J5JLJHWScyC+nxO8yn4/l8Xm9udGkZl5w/hCQAmmqBnjmuIAQEu0xlysofsr8qJB7fBxzfQ7RN3Oy7lSOxPB4S46DsfZtM3qaRBN36WrfuDS0Nyv/G9H0iLaOfHbodon8uNmbz2CsdWZbuwvxyGPrzOq6P530//n/+8Pf/27/7//rBl1+uXU8ZgCI5r4i+IjhrgCRc2dpcHysUEfbAc0qKHEcyGUp/WbD85XoYiu0umFoPLyZCSpWU5oo6KCX8BF4lqlwEDfP4q0t6cSRm6/QqTFiguZecKgowjFKy5tjPUnWhOoHHN4mzlutQwEB6XJVQ2EH/4skyigCWK8COoyMvREwX2THCOsY4kl+AMret1GSbfzf4mNKkhG/FIyhxwmclYJuVo6GjRHyYCmNc2dukBe1VQM8PC/VGWigOySQNWEV4XnHPwC7V5I1bMkYrftlUDWBmojgWw+XIDjS4+HFrAVHGAP+e4Z7S8CiNh8Tp3eHy4eHVevP7/+p3//u///e/ePvlOA0zpwFKohnlFS3kNxLqHeyXUVr/SszXhDufL/hepZ0CFWFa1sPNgesRjMsw6FlIew8JcDR35Xw5SzfQtllIU+MC8k0Wsaw5icGeiWyTaOwlHJiLC8Lyb9TulNImRSlkyw5j8UV0fj+pFDZnUJtEHWhD6RPdDMNKQqaFE7dudpAzwTELHQ4Jq7u6kC0I9lL8ORc20nYKbj6iiLZ+OJ5Ol8tFOnggNHocCrg2Mncrrag55bLUzz0isza+KglzY6s3jLTAvnBNii9/5MlzqQjyLywr18Ris6SlR4qgYYIdesh1H91R4rwgtz5AoKvQuxBUAHPD8WaxLmazT0QknbjTXeG6DHCqiolgQf/OBMgUQXHuh/nh2K/rv/y93/7v/4e/++Xbt30/iVOXU4siK0G6eCvm2n/AebeYQ5gkle1ZLG43DDeHW2raQJ1BB5VSpRqfCLoRfL9QMgUYtLkwF5tPo6NPgTKyXpoe9/Ux/5/4gZUamI/V0r+sQD1ZsCgeT3XkgRImeMGFdy9qHib7NVSeCjMVyHy5rgKYok3Q9bTS8hb/O1XPwW9W+2FWWxO8Tf+Q7qpcdEEXosyLFI3hdLmczicRSyy+BcZVF6rhbZKiEQ6DCOVatMVk87Iic4LOieZpvg33czj0yfjaGprKrJEhYxyGHft9J2pArZRdUAergaFy6ysqMMe3OT5R2S1Nv+K9cDnPVKxzVEheqgQ/uIfuO5PoFKFravulGAgOOpv2y/Hx5rz/vd/73V/4xX/04w/vqZ8g8d4zCc0amSE2K8dUlJn10zRtIV0GOWCMUR4schwZlqbx5mZPm99TBQ+/xa2c7va2JA8IjYnVexCobQIaAHh6zG0SqPg7JEfZjGODFAUS1t1JaQJKdR7GhSp7SdCZGNozEKeITPWRWiRaCAolW3WN2f/eYr7ApYyym6hdBsv3dUE4wtGwgYodkiJkLVvGlyAdzM7ny2HPflVnta6t191jTBDBX/0X53G1RAwehfQh1WZY1/PpdCJpXhmJBPCFFKygYKLgSkWeZs4qnzmCg2odctscynImcyXmzmd3osgptj051d+kXs+g0UOzT2sKWpif9TyTWIz32nJgJ53ve0S4T1HyhmbOdZ6G/tKN3eWyPp5/85/+xi/88j98e/d+GcbLMpO8ulJsBiflMxNmm4f2wC4Yb3UIhWYnEYrS2NpwkL6hCMpxPBz2I/UqHlbIEsqn2XTYqBmDK4N5aEhKBYAq7StlK2mcJdMzyYuyGnD+Rq4GRUFiIz8pJeAJpkmbkoIFigCy/qJQTa0HGS4xmSkUxIaE3IpnqS5wtsEeezy1704HUrm/ngWSC8fScQyMfiYHw0msrDZczufLqR8OEi2Dwk02PgPCFlS15LoNNuyTxQO+zPPD4+PjkQT5wLDskXO6aVvKeTecFTf3HPTejezZ5zYgpl95eW/PMYQNBeaLnfuydtuQpcHKENgMA7TPz7SJ6pbM4fH82NZjWz0Q7x2m3Xo835ym3/r1f/r3/8kvvTvej/vdfL6cz+dlvkgZHAVU57bKfgOBy7lV4omqdMaU7fDJzjvP8+6w3x8Ow8gV7QD+0giAFhImJIemoc8i+WppIQ5MF88N76eHFlFgLARzuWCotWP4kGUOQ99Pw0jmWcFhKw0JAkQ/jhYB7oTM8hYca71GQOsoK4YEKgZioO9pAw7SUG51jgiuLjhGyqWbF8o5kXBW0YLZK6/SqPSzlEpm4zh19InYD3R/TW/IS1JMUtIZFeMM41ozNzMxhNw8nk73Dw/H40mOicUm06dEjzWK4aKDEnxXhyzadOKOrtLrXnVhsCE12WayGcgM9WW+v/iNm/chTwXI0tabkucN9skdlA2ruIzN+QR0PrthnDmO4vJ4/Af/+H/8xV//J4/ny25/uMyEuuuiCYFmbBbOG93sKuRtciDDIm9a4A3a2fJAGMieWymnySSF4h390TCTOIxo1Immv0ijBbElMV5bWwaAYfMhcPM0qNIaPicX95E1cphlR0ybjT7Cqi03ntVtLU0rs4EzNIuLxZA6Frn+U29W2HtSRQ6X0qr8SIxyauhfUtggmQNAtyP2e7mcd9yqkpYB1riJS5weT0cykcyA51qKSLPXESvdVl16Zn0z7fXZmWkOQdHJu/U0nx+Px4f7x2WZ2WPPr/NBnDy4FgWuhLiD1SxWl4aJ+zOotaiwf9f0sICA+gL9yH7BbhwW7ObEDfBDFTsIC1FboBZo7K5L12EYoBsmdpRQTP/l8Ufnf/RL/+Cf/e7vPnTL7uZwOh1P7AP0Sjih6Xpib8RJLu1l9rUNzkyNbn8ysY3ktrXb78ZxRyyPOnTrZx4vHy6FXFIRoScKkbNbOIJIRAgWSVtkRdlJrSiQ65GYj9mrMbNjlHrALD31UJOerBKmJUmR2rsb4klgI4I2e5agkjGTh0LudHnTijSbxMo/OGUj6b0GJ7oH2WMRvp/eAYVnJDYdZq1U3Jf7ZlkalrYjpM0/7Pf39/dqNGIK68qq1ixLaq5GOdWpOIVtiieKxCQsapdlOZ6O94+PM3XzGPaumSc8U5uetruW4qOGui5Kq/mT10X2x1wiPTQY/zQH7tlX6H41zoyrycIU0tEQIi3nT+UCPHenYBb7k+ekUkkESlsdTDrpcVnnftjtpo9fvP87f+fv/c4f/M6yrONuf6ZUIqk/t1KyAgQ7h+3KNeBkC3Yia9wslWRMZ6ASL3O1bl2GbrrZ3+5eHFRLjnoaEDdoo0BpBpWcRJowTTen9aqPWfzXFBZLJMv2BHaq8HPoP1J4VhRCshKsJJXw05z8PKtvQjIonWCByczROpwVwqnR6OHCIIotXEUCgovUnxZhKUIJoxoQtDaw1ocxqpe3scYUCwU7Xy7dQhxVFqkYQP+RInM4HG5ubhSHl3mgUkWqWVAUm1vuHUhdenUpMqFeqIihEJndZFkoBeWBOMdJjkmEAkAHVAvpP83btZoYcpuHlWCeJhd2F2pmMqomLbvNzghhMcvWFV8kjgmw4ztdaQ5YDqootlg9bhTFyYga76h/CUUbLsO4PwwPP/rwt/+7v/0H//pfH+d5GobT8Tifz3SwZPKjf1xq1sc9HFH/SiJHAZDOefJyQ64iGCVdaprnx7/+N37m5maitkYmfbQ1PsuaFULiRxAJzkZAJLtGKrezYYN9SOa4go3DE1GzVmR5+B3iQhpHjuRgHCaP+HKxXzgUVFsuc8RLTBiw17yn3rXJvogSe7BZcvRh8HM4ZBeuY2lQMrbqY/hqB3VIIwqLd0OMuhIGLrggb1M/3ErhtK9evDg+PnIJUT5wQigLS8z17NIe6ifJmWr6gzOdMAss63o8n+8e7onx9uwBiqYiLhbYT6dqITPgYYJLVbePOzOMVmXLHOOpv+JWgSMjh2mN8WeqBB3qmvO0etAKnjds17b2LDZRRQLqGLQMZMm5HX703S/+H//tf/f//qN/KxBPiQnLLBxY+TCHSTK7kToWKam3XJTDaBBYj3dHuSVCgsUszW734cWrV8NpnM/Lys0KG0OnY9HYDIFR2WaCMnHemn4G4qaqWVpAmyOzVF+VuBifr7/WokHUasCvkYr27JPkWrms/0va89JxXnTELTkltYAzNWoK7hvWmp6ovCZiITRAXXKDiM3YQCLuyhHMrBcYSw5XVSxmNdcAtVygVYhDlVpjcGTOYbfb7aX9XaiU8pquW25uDi9evPh4d0frQ0B2YVug2P0JzpNTlh3gsse0GjERUvLw+DCfL+Mw7EmPmjzpQOzhbLXQmB5IBsQUXVWYIsSc1sufz+u0zP2FOg/p/d7TkeZoMVuZB0W4SpIhPGDNdRkEs2TaCKuHx7BuYKsznSgfHgCplFdt5/Nlvr9cumE5vJ7e/fD+//5/+3/+0X/69yIRzucTeYlWJswiQZuv3HbcykkKBXa67hKCuqgD89KUzbkDupZ+wcW0ljefvOmGbl7nbu64LQuXh7WTIehz+ZQg0cRlVcg0TYzObLSKWG5QMJZN2d3KX0y1ZDFU/g28NUzTgJl4rf1/Xi8XcpBL2U1OySLfWrdSTrIU60bbivF2g2gvNE/18cm+QohlFZWs+LDuujWPIIxi4uOlDhkQpToROvCCFGmg82pmxrlfSPJal3E39SRFLPv9dLM/HPZ7jrtSouPo0vfjbtq9ebM8Hh8v87ybNC5VPU5utXJkC1ItUoFae1MQsOtBJLbQHI6n0/39fb+uN4eb/W4SrdVFAZuJ6bDNFPYImYeWa1rmmkXo7kC7upzXfiJDhuhEgiqGZVYMLHTVwqXk94doYMstdXS3kVjEFAjCRuG8WKBpQWqUsRwYCe92GkicgzJ4p+Hli/F4v/tX/+z3fuXXfv37f/m903leR8o7YbAUQ/NMiGzitso4wjeEvck+QiKkx5olrV+NsR7Jj3Z+2UKO1xuH5XIe+/WTT1/e3E6Cjy62CulxO7dgLR+8yKhsPZJjEksSr5WKtlttDY6z8mB3co901HiPsZ3aGBLAMh6YaMAt6rPtGux2rA2eLqSriXuNC+TR4BTIzVggQSbBoDDs2xQvw2RFZXGsO6WURtv0PVnNFopxYamE+wzIeg2GtK6IbncwKBtrEf3BkHxZ1xc3Lz751md/+b0f3H38+PLm9uZwQ509rCinF5XhwuNEjD67+aSfhh/84AuapFSMYu3U1U+0AwcYiGnTDCYaK8WzkgSYmR0cx/P5eDyNw3h7e9jtyAsRShkUuEFrsyu6oTS58xjkWXVor8NMUN9PO2kh240dxZ2Zcje7v89kVbQfoftea15GI6UoG4fVc5nUW9iOWafMnIUxqPy3ejmFagquM2eS9Br6H+MZHcfaL3e79XZ3++LTT/6n3/nV3/7n//xPvvuX3XK59Mt8PF247/bFea+WX6cjE2IpVSVDxTDlDNcqUG8aGCRHey6IEEpOLhgvy7Kn+K5HckUe9ze3r3evp+PhsLuRkAmOTGd3kvjt+C8VCGcKU+bDZsVAooQu83K+9DfktaU4ZtboOGDHlMR1HbtuWoeBRxJqJ+UxKWKD8blfFpWuGeJERiVi5rnOsjnU/4WSESeqYCtVqCWOU7GK21eYL5bhmTxR4r5y4BTHq8lXHqRqNe8J7jnXoJ+7y0A+A81O0tw4cDgyFmmNPgd9sYaPXJXNHcrr2r169ebFi0+m8cf7cbzZ3+6nfc+oKlU6JVaFLLcjNYWfxuEnvvUTf+Nvvfqj//C/fP3jr6jsGFETasPlZY81VBykKrXjqRTACeKXs6eIk9BCvdNIjF/X5XZP3TSJ/JEDkRiNC5teg9lO0H3bEaRiST4KqCKT0zgUGHwZuv0yzdPp8eHmsL/sXuzHrt93I1mBTL4jCYyjzIyAKXRzonj4/OxbVyPc1KT5EZIF64q96wlcztY9NSE7sUInCirtvdRzssWYaYc4qWzFuevX093uePjP//Hf/Mt/9jt/9md/On68e3c8URrBshICD5Pl9QrTI6GVQJrfrrlBAT9oR9T9AwAV2iIyavg+1YLaD/3SzwMVju7m44XKQa83t+O0nL++/zjdvDi83I0Dp0ZRtWLTVQjZGNeY460Lu4xZnD1fCI3ndblblmk5s9w2nFfqvUSlAriLFMHOSCGxZ66dRSSPxxqWjtOaRsrGopYNhMoSbC3vJ1JBu0wUjv6l3EKO5epJbxTzFUftMTOk4xqYEbOpTIWf9XJZqN8rNK9CbimyOpcDGRbq7KnUoqMeveTK7Pp+IYjk//P9TCo0sYRkQ64FpGVG7GiUfA7S7oiBkWWh43o5fnh7Xufb25tlWMY9zZtEVyFA7OOlt43jtN913fLh4e4nf+5n/k//x//DH/7+H56P1MN5x6VWGYO104Ags4ClBsxwQDsd/rKcLueT1zhjeGdxj+qSzss6cc1IFodYmTAPmEsSnHVCozEUsA7PfXkkGi68FaYpKtIT6E/dfthd1v8fo5vLPsVEg6IAAAAASUVORK5CYII="}],"details":{"kinex":{"title":"exec 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projection**\"}],\"content\":[],\"encrypted_content\":\"gAAAAABq4m-JoCkYp2wXgqhvK6wluBBio6bGCtyKnjyTwXXfHG63-t4AA7G9e3Dr72iKtygwLZrDMZR-iPhfZ-MvlBOfLUpy4YyzERn2ifK131jbqWF96Z0jPtsqmJ6yNjbTc3uVmXCR5XNJzB_Glo3JqtP-TTwcRvOI6SMVtd1CIRHhPxTwRXRiGq_FzWOAPw3qFSH24m_tQfhazZQdM6b740YBNGfzlL8a97VfTSYov_ftdm_PQ8mg2izxhSvw_xBuh0G7DVpIAl3s3ItYCh1Vsdl7wB_2NEJXfkq1P_1vSIwUWP8z6t73SQlmqYZhbe-4p_PGYa2mmaYTzGGX_g4Lm_OCpNbk9zMK39Gw9ysCLThq9p2pqFVqi8LXGDoUhzPIeOQ124pa2x_xRa-HLkVOQKcG-AVgmass6CyJIQLYdUZ1bJTarz98sMv1r0XdAAVULWmdnGYnjKe4qq-TOoAhV4pI237B6V9bZ4BDSAY5C0O7ISeFzLCp3BXFdT_glWJyi2VAcKqOn9HoeXeAZjyLHk1FAlsOf3jCsELs_XBsyrtB43uAs6zPs1KlF7a_XAnGp4xflnqRAQQOY7xvTj2CMojhrWJEsxg7UjqVgHArNOu8SqMkw29CrcpTLyMz_8vxcbJ2yJaBo5J6CankyFcpF2emeIXtnFELpUapgNFy0XxP1gm7iUDIUiiGJceW1RfvfuHPPbfgUitFYlBQ0SCZZEm-U_HTtMzBW0tjRwbGv_76C44oUk6IARvYvmE10P5Bkh1nGB6rQv4HbsIGsTCwenKmIOqBftULNhjaQqHgG9Y30U3d1YJHLXeY_wU0M04lsrZASTsDccDAuVZV8FbsJG2m-LCMCUKGdIzeLFONrwCIgeuUWgWB12trRDdjaKSgpaWzD6xU3HQQcT-NU4mM8sDlQhz2UNQa-DjngxY_ICSFp-k3cZIEIrAw1aMFs9Jnyli1sf7ek1S4OTBezsek_ICzGFucBYoMmHKDDzY3bCvyMVc240X9hvPIb7HO5WPKMCRMMnwGHFsdLXP2O-AyD1-9wAKEztWWLq8kzB5XEzK8CWms3x48hdIGyfnU5OqPUsuPIA4q30mHnqOOr5FvIHuPJdNE79zGy4hKkExbdycYcsTc1vW-CGGjxyPvO9tAyHCavaUw5cTEaEPLI9L-LIvTMGfQgoNG6S9gedmpNDduJoKjqsiP54HGhh4bauYjLeYJxMWOYs2KIA7mHPepb5pWJMgPu3OKXmDrO8mJ0-BAxP5t86BRh3W6eQj8f1_zXVt_GTVyoQFFPGHBNKBiVc-Jg8KEsqedwapZygVvZmt_WdZ9PXcvPuiWnjWy7rN6FymyaL1cruOrU8r44fcdFhFXgoIesBxkNa_-qTlDR5VoHvC_chlg8IefYhRpUkvK73PWguaFIZSyjgDAiOLPOQYkFson7wMXSuRjPWmTov3E0TkwYZIY_ARBYzx8PYvbcDDiyTqACsnnIR3tVSzGCknAKdLp5GLzW7ya2fYi6Ucv2N-ZXoGQYEyMNoJrzfHh6gfsB-NyYjP-aIcU3qhjEqWKffnladrfDPr4G6wGBR60XYbCeEI0YDSRSsxR7XOKjHkOGdLVkN7_BwyVx6amqNxl61ba5hyxc5xvq6UvhZErp9g69QhmJ6UUfbiKColExxWTPsT71W6V-9ZghsZC1IVkgcIpwkV4AeuxfVUO6aEvdab6kEKHJLvIEdOMQ2IIeAgQ3T_WayKGyoK_kMZ0ZP6Brldj0SCn3cnm7W5V-VxiCF2WcEESPjGL1VR0i8pfrp_Sarm3m7x6hwRx590kA5_XI1cXmraRYjBnfba59CqN0gE46mIsDWXR8TXCM2GiDO84-g_Ey1HpD88VU-HBDIunF7schbuAsK_nZNuoVmiON_H1Hc2uiZc4Kl8dCc2D_rH1pP27UISHnu2YQpyuyrnT4mvV5OqBLqh5TW_WwApJrZGJDg6RruMXqo2gtXSuiEvJkfiLOIdC08h3VzCVSV_fCFhDn3dELiOzfAZTiJ7TvEGtaXo81Rgef-vbQerpFq0SQd4TYd2P8UzLhaAk5MsLJSU1wSWUfXe4qWOsx0t6G213MK5TFIP3gz5bi2bdjfsM1HSc9t9RwO6GCXXGdVyJGpU7oFfbRg4Yk04NgIIlDFSgMnnou4EmMSG-Us1XGmroS5Uj3Is5yO-TR6BNpRh6F-EECnveiocLeR3rKMJgrBbcmf9uFfvg4OIEpCSmpzmj8QV29MkQnMyf7IrZHxu91brOQtJ_cU70_axdI7KQqSIP_LtsAhfO8iKcNUcWZpEcy94rc-zWO7NU03GTF20VrTS6gHbx5yKu-q-w6sEqhDrVUAdM6axOPVC6iIK2wrV1jIxOmDX25dXwWpq_vFaxvsT9rcuDXRTqFremiIhVsj54Y8uSJh--vvFFrtX58yjVtpVRKyCjEKYkmySI2Mw7WFS6RwMRmUY_2ALB0ZN36AJM7dTLruu82uQIAp6EWrFTXmrz8IYVIClZvJYb30Sf9BVlbVBQkl3_xGZEz6oa6D-p_ZhILPPzvJLtNQ13n8woyJ65\"}"},{"type":"toolCall","id":"call_1f2454817820440fa30d581a8b7bb57c|fc_0b5218887c37459e016ae26f8897d0819f865d0760571e0e3f","name":"exec","arguments":{"description":"compare tool center with shoe surface","python":"import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n rel=await helpers.relative_pose(io,{'frame':'right_gripper:tcp'},{'frame':'right_camera:base'})\n tcp=await helpers.relative_pose(io,{'frame':'right_gripper:tcp'},{'frame':'world:base'})\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\n for u,v in [(220,190),(280,170),(280,220)]:\n z=float(a[v,u])*.001\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base')\n finish({'rel':rel,'tcp':tcp,'points':pts})\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":416,"output":327,"cacheRead":47488,"cacheWrite":0,"reasoning":81,"totalTokens":48231,"cost":{"input":0.0041600000000000005,"output":0.01635,"cacheRead":0.047487999999999995,"cacheWrite":0,"total":0.067998}},"stopReason":"toolUse","timestamp":1793224577012,"responseId":"resp_0b5218887c37459e016ae26f81a794819fba053be63c527e80","rawStopReason":"completed","durationMs":10885,"thinkingLevel":"high"}} +{"type":"message","id":"e94429a4","parentId":"644b4f6d","timestamp":"2026-10-28T21:56:30.087Z","message":{"role":"toolResult","toolCallId":"call_1f2454817820440fa30d581a8b7bb57c|fc_0b5218887c37459e016ae26f8897d0819f865d0760571e0e3f","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224589.387025232] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"rel\": {\"pose\":[0.031999988504280985,0.04039803789401142,0.07136523029414477,0.5893679914824401,-0.5893679004513149,0.3906986006032956,0.3906986716386298],\"timestamp\":0},\n \"tcp\": {\"pose\":[0.33640272493657036,-0.02633167088001903,0.6917798048149151,-0.49874693434276596,0.5009926290810037,0.49905228115506656,0.5012032539440671],\"timestamp\":15.92},\n \"points\": {\"(220, 190)\":[0.323961443569907,-0.027427434548870908,0.6438170953602051,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 170)\":[0.3420592549054097,-0.019472769566128686,0.6447653287773406,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 220)\":[0.3453272239232121,-0.0403433584497698,0.642715433205389,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303]}\n }\n}"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":2190,"timestamp":1793224590087}} 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"},{"type":"image","mimeType":"image/png","data":"iVBORw0KGgoAAAANSUhEUgAAAUAAAADwCAIAAAD+Tyo8AAEAAElEQVR4nKz9d5QuyXUfCGaYzPxMuVf1XHsD770hbDdAgCBB0C5EiSIpkaJEacVdarSU2Tmjs2dmV0e7R9Jo9Meuzmi4opZyQ4oUSQAEQQIk4U1bNLrRjbZotHu+3ivzmTQRsefeGzYzv3qvKSXQ9aq+L01kRFz/u/eyb3/lG+vTtWJa5JplIl+2ddtUSjcmY4UQo9FEcpEZwxiXuSjzgk2nQoosEyYzmTEZY4ZlzBjGs4zxjLGMDq2NVlobxhjnIstYptss40a1jPMsM3ApzzKRZ5nOuMiaJoPP7bVaG7hCa6WUgecYLmVmDOfcZFlmWKZVMRlnuchUlokMfrKsrWohRMZ5s1jmo0IbkynNi0I1LecZL6TR2eLgsByNRCkzA5dUy5oGWeQ5lyzTOIC2rRfzYm1jPpsfHMy4zDlnGc/G01Fm9HJZHe7vZwLHw3gxKpezWds0k+m0ns+LcmS4aJbVSBYTKRaHu03TlCIXedE0dWuMUq2ua56ZfFRkeVnXldEqU1oKLjjnujFMmIzB4Bn8zzAOE2UyHCbjUsCHShujWWa0yYxqNUwJ5/A9LIDJDKxIljHGBGM6Y0ZrDS+cGVgvxuCUDL83OKEZZ5mB+WfMGHg0rAB+AadyA8sF/+HUw0/6gzPGhWQcHk1rCgOCh+DDaXj2bFxxuBjWH97MaPiHcW04y/BpHP7GR8KrMdpOOBY8Gd4en62Nhh1gGD0WdiC9HJyW4TPwA84yZRi9Kn6NLwQXcZgG3L8GLsEPcQI5ZzD5MG4YEu4RmJQsg88ZXIG3gulxM8A5g5FzGgNslQw+gVEbRa+NMwNLhm+B00/PpSfge+HvQGm47PRudqrhUlgpvDXPGsYzJXSjWm7k5mQi19iIm3JdFnxssjUYJ9M1F6PRiMk84zgTmgGlMQM3gNfDQdGChMlxM5exTAgmLLXhELPM5PCvFnQOXA7DpV2aZVJmWaa0slODb2EEZ4ILeBluYBPD8uC7AunDwOAmymiGFG+Aq3BgJcU4h2VVmknOmGYctzPu4XJSwnRnmWlbpVSzrMrRWEjBJTOtWlZAz1VdrU+nl/auzA4PMqWbpVocHo4mY9NuZloXRTkuijZjpZTz2ayat/PZQVU1IpdCyu2dY8v54lA3PGtbzrI8Z3Vd1UvZVMBkpChzmQmpgZ0x3TRZ2+JexvWEeRUaXxU2Kwdiw8UGYkYiBVqEzzi9PW4ty/n8YiBrtUuC2xAJlXYuzT1ue3cGXqaQQ1imbHcSTi/8D/kD7WW7zkRptDWRwmi9aJ8TKdNNwnBwh+KzNWxjYgTwdiQCgBSI9+DmsG9Nw3cbOTPK3919mtnN5Xdg9AVdHDYqTRBtLkfU7ht6WyBd/2wckDvJ7mQcKrEovNDOMw6VuBY+TWuQO47P0T+WA7hbOqbnOY6jHxwiLjg+l+bZcSjYAZlgXAitRllRZcwcLLQE9oNrIYCbcy5LWlg42/6/MzluddKPwpyG+Ux+0bT93CLQBLvxw0ZGwUPriRIFeIffMfRE2DEGubVdE/fmwJi1UkDKxtAGN4YJgRyRuC1uY7qP0kq1LVAsPnIyHhNhC8YO54vlYtkChbeTstg/nC2b5tjWVlkUuq50xloNe7quapCfJKSEMDCzMJH7ewcbG+tr08lsNtve2ZlduFS2FfBAzQTPNOMaxopiEqZCAV8Gvm2Jz24vGIx/uzDVVirbk61AimbdLbX9zF5kt6nd65Yg3H5y54enBaKJpjeRY/6J7takPaW3cXIeXsreIQzcLrm7Ay1lGFtnvyW/uh8sXO2f5+5CPC8ajhV29CC6KOYGXpL0XjjcIJogO3vw046C5KaneffydqNHbxM/x482ku52xZJXtvNK8o5pBkobqFZFJrMc9wvqAKCglcKpGn4zOPL0O4p+MYGdWMXKzz0x3KDL2JNw04Ji4d/DviRxNHi81WzDdrOPBDXCcke6Kd4GTiE1TINOqZTCU+F7BfJVG62FEECZJIncsFA5b40xdQNKjpQSr9eqbZum1koVeV43Td20mdYPP/b4bbfcbIxeLhaqaTQo5rooi2VVz2azclRKWVZNY1QjZaFVezg7aNtmuZjPl0uWZdvrk+ZKNW9a4DGiENxIGC3oxpmUvNWohOHrCA4CFgdJCpYXAUGW4gQ5VZlOstNi/wxzbq+2GxllT0SapBha4RoYQkQS8YZKaMitPKnhJIho0fxOicnUyuVk90TcpSMfnDD1FO7VCvc9yUC7mtHj/CjsS9OZHRr228vTeVBdwptboR3enjhr2O/uJpH4DNcQ9Tp9pjuBQW8NojaaLDe/QaGyy+emzqkUouAtk4pnRkjg6QZUN7vC9qYowuhqO9ZoYROpQE+lvRXYoxsujZOUf6BgT+bIuVHy2QFamwzFJd0CTgfiBRoF7RrMAb8nw4Q7jQUp3RiD9KuAmlu0DRNLDBU/u+TWmFFGA72qVivdVvVyudRZlktRcHb61Km1yXg+W4CBZEDItwqEqVJq72A+Be19sXdlbz6fqba9cP7CqChGhVzf2Nw5frxp2qUyTBbZolofTWrGlTLNbK7BYmCjYiPTrJES1L1GMa0FymJUuCxTdCP1k+4URprOeH/Au1k+FcslRyVeD7MCImHQiQ6HimDY5enh7LZoWzp9OGLk8Z6NFLLwTk5r7apuPW6Bn0QqspV8buEzvweTUTpGH13lZXfMP0hy2s2bvG2wFtIx4f4LMxbI0XkWwjRH1znm6JWDRPFPGG40TOsHccIs0mqAMlqdMYncHjY12N0x77VeC8eqA0/yTw0yIF1mMtmCFPaKEw2H0wQGEqLT3avjcz3zor+ILNFx4/RFq06EFbVkDHo0Smv0GaDarrxR6FinXUyDzh2WMaVUtVjCNZlu23axXGqlZF5srK9xwV9520bVVLu7l7UAdtMqVTdtq1rVtKNRYYw+f+HihTNnMyElz5aLRZ6LF85dGh/Oa62K0Xg8Hp88efJr33n0iaefeO7cudneYd20o+n4xNb2297w2o21aVkWJ9YmWk5NLmFYMApnZMZbIRYwfhlwM1hxHLH2cDh+7RXsmBg6uqolzdiwsbsyoWS3TZJHdbZ5dxTuH7t00cf2tVItM9pgbh/bXe1sw6GRZwmVuztFs9JhSB2deVBdjm+RaJbxCUGz96MJ5kTyI5ZxHbMkndV0tSMtxHJ2dE6YTAqRg2EaVNZwRyJ+vzO8zRWrSO6usf2VCmHvcohUIxIAljRTDyd8ToLWEzMRsDP24tf27NM+B/wt5EWFwaD31n8VnGb+TI0qNxe8VS1DHwQo0U0DqjgQNnizFvMFONTB7jDS8iFQb9cnI8PEpFWXzr2wf+XKvGmu29rQqp3m4HIvRqOz588xyYoiv+XmG5977vn/+F9+7/LlC8u6bup2WVXkhf3kH/9hzuTa2vqH3vmWV7/uDa9/2UtkMamZ4GjTKANat5NTQfVzs7mCWrxDxk8SnmtXy69hbOp6zSvZOuGOlmjcI738S89IdnP8e9+AHhx650O3pZLd5jd+107Ien/3STAMZ2gMqcrsfxvUKeJ3TeRXxFciE7zDJoIgje7mVqijSoSJi4ZtVXpYNQnbHHzynbehc9Onx09Mdk9wxcUS1L2RVQP8C1qBGugz0Cm51x0J0j1TzcY7OukzcEunbgwkdXITcnBgw0EiWaFSTb4vZXTbNMZkUgqecaUbJoQV9HimlGKxXO5evmxGxZhzwQUYEwrcvnBC0y5my92Dg62NrUrpG05ft742/dbDD126eGln69gO4weHB4uD2eGVg8ujvefPnLvxxPHrb7rh2ee/V9WN0WBCk86vmamaajavf+MTnyn/6HPvfOtbfuqHfuTWm66vDC94JsAjDYPGGEfk5HG+IXIipOaaNzcD3/QbOHbfOrK1tmTn8548sYx8aJf0rk5/j+RTQnSJ8jy4a2PtM+gM0ZtmPQW/rxN4hbN3ydXfJ2jdyZmdsXQEb3IDK/zSR6XLED4l4iXaCkqPt6QC8wkrC0IFPsWgxMAwopnv/mk95Pa3WE5GxOcGZs1OS9tOAvsrQ6zAfea3l/MMOIeXjWqEqCL8jszAmYQh/AFbn2KjHE1LCDiiwmyMANIuuDHzqtEGLU/BVdOCG7ttBWNSsLYBqbN75txu225tb29sbGQcb9q2i8XSZHqtKM6cO/vdxx79whe/8Mxzzy6ruqrmZV7sHD9x/PjOzrETh5cvs9MnJddGiOtOn8qlXFYQw+OoJhutGcR2TSZAz62r+vNf+trd9z/woz/8kXe9/e23X38Lh1gSR/O8xdCpUx/sGzo3zsDOCXvOhgtQkU53rbNuBvZxsuEcccVq5MCDgmkS+X+j4ESgpSC+0qEMHn1xmThrs6seXbneVX4TdT1V3pPXWj1FQ8zFXtsZfWLbdniRF4QdzSPlIJ1wGcvkyhHE058E3hCggUKElDUrTTAgmJzuSNkHw+wNKO7p6DnSOSLbINCtHUyIS0YsjDRMgiig8ZtpBZFkOw6EAkghMZJk2rYRGMYpylw1qlbthswWTBmVZXnBOa/Aqm2INQgut3emnMtXvPwV58+d39vfY0IIULabttWN0VI1lRZf/uLnv/Slr5y7eE43LSjtnFWsOXvu3O7upfnygel48tz557ZObqytb45GI4UCXgBcgyvdovAkFxr6xjLDhTjYn/36f/rtP/6zL/3Sz/2Vd73t9VIBL1FZYdoWoAfkXAoOrXR/OM0s2TZDG9S78fr6YUdY9aWzW6terCcmdCs/wj2cs3X4GBCGiSWY7ParCd7e0dP7nfc9NgpoNrzdl9ygqycP8kvPt44cj9cmOnRoB+EIgORVrAD3XpmsXtgR5IvtneMkdzQ2cCGBC1YpoBHyJjvqDLYZxRIiAyso/G6gRGz2OyRMhBqEt0xACe421qfmxA8plVYIuWdgcNe6qyFqI3OZMSMyLnPDxSjns8NFq0zJ2Wy5fO755x5/9Mn33fEBlrXT6Xh2eEiAEK11KWQ5GRejcpyPFsvllctXGt2WUi4ODw+XVav1yY3N3/30H/7xZz997sy5um0RjGQE5zhCkJetanNZjCfFxsbaz/30X11bX/u1X/u1g9mc4vfAKZDz4Q8crc6E4IwLrXW1rNc21v/Sx37oh9//vmNbJ/RoZHSrlAbEmbM4vPT1HlZr5cfR3nQ3piQXr2xEt84z2GfmQzvJ7javZ/fOCA7jriodrXh/ww8IvtT3El9r4ruEF4/+c2cnE7OSzGJrODkx9d0PTE9wMCf3OYqsrcDq3zL865gl3Zr2l4sNkQRepZPbe5NPqW1bpFvYSohwdL4VD3Nx7CWyid1b2cklFdAjdXCzOYgcyfL44U54B2QRozgyxHIMhLJR/oIjSgLeExUD3da1QTpiho3yvNVqLERjjFq2L7xw6YnHHnn8e08/dP8DTzz91JXLl3/17/79K3sXt7e2gTzIYAZNG4PLXDLFWoZO6SwDAmrbWVWDb9novcXi63d99fkzZ0AFFoLQdYik4xhnalGpb5cLppr9//yf//MdH3iftQq8f86vEDxNMISoNU3NRSby/OK5S/+ff/cfLswOf/ZjP3w8224Z00KAym2DwJZDRzThVa9h6g1mSLTtOlJ31dExpQaJK75tPIgBonb7akA/jv8IIiCVx53TGIXC0buego66VEr71G3N9E5dZhI9lJiiP2eIWPrKbXKsnOCegnvEDdKbUFSWewIOro5EU8DgDVAFOoA0YwwDKC0hdcmDhNLAxRlt1MlvUKsf+rBiQNf5WfLwUusEs8odKgckaSy2DsFWgLZAtBVQDCCcMeIiJGz6WvOSM5bDqU1lrlzaO3P27D333/38888+/eTT333ye5f3LgsOuGndNI3Wp0+dOn7jdcW4BIhVpsBMBn+8nQ3OWKvapoWY8LKtgG/h0BaLg7Jc/+wff/qJx57QLQBFKPYMOBjGlNJ1XQOARErnD2eXdi999ctfycAW5oCFBiBXRzDAT2CPwCAzwdl0rWwb9Yk/+Izg2Y/c+eEbTlwPoA8L2EIvHc5Mb2vExmJXFETYGbd7ot0bb5IhFTKRtyufmmzGFepfHP50lJHotwPu3cHx4EHOnyg86Pea/7Y3L73PwgsGl3wHsdJBmUVTkmgA9KHXEgZDe+Hl+rSbBudjUzvwGKtcA8cCRH50nX8ubFWMdDakjyJakGmlYHcSSwreyjRsAZ7heFjx78449YMl2Upgb69OIqcgVAgCYOFPJnmmNeD9UY8UQtStznlem0Yy8EM1dX3x4u7Fs2e+/LUvPvrkdy+dvXDp0u7h4b5C4L+C2JDh8Lpog0ohdfYTH//4bbffahC74bR6mip4NoWSIQMB7M9Mct40oJ1PR+OHHvnOn/zZF2aH+8VoRJ5ra1jaULRl2WgrwEvVTf3C2XN5XmIGgltiwJVDngBdTg5wytaAAQhTynFbtb/3yT9UOvvpH/mhGyfXzRuwzo1uo+AcQYmOZuKpDIu2eWfTdG3R1HBOdlY4I/0y1mgtMxy2cO3pRABDEmZ4YF3LvX9EgOMOv4mIrSOoY9/RwEyu4DiRY3glw4kgb+H6lazJeikjFuKQcvEDnN0JZyMBOz01vq3Walktm7axa66NbgCrpLXiQjqHk3eJenMqRmzH9hHJz/BekNeALhyK67j4kYVgoOii/2fgtc3R/2vMhJf7B0vGTSnLar64PNv9xr1f/u6zz1w4c/aZ771w5cqVulq0bVO3yusHHIGWEAgSAbOpTHZs88QrX/c6rhBByrlpCePZ8cWAXaqUEhJAGpDYwLjko2ef+94zLzwHdjXKTGtHoC3AshaFLtmSgKeHd8R8LNTwlRTSGffWJQcALw15K3gfjpaAgsCA1FxmTa0+/Zk/WZ9MP/7hj6yXI1MUCtKVFPmznM44sCXseia7nTZDIksig8XvmFT8XMvRJ+0k1tn9tztiu2cTBcBcW9THROkKnWeEEPrwHSIe4pzD4XEpPLn/6DiK3nu53plWaPaG3jliD/QRb+54JaCA7WS5xBJ7H3x1wAJDIh4gHbWG7KWmrhljOSQYYr6fgzZ5c8cjvXBj2TiHJe0ww3BAooACDRX5g+CcCZFpSDaEEI+UTGdCt0owYUAoVW3TXtk92NX1l7/21eeefe7i2fPffeqZS7sXa1XrzEAECDFVYEkCzZIK7NRhSv3AieRcKNOqRn3g/Xee2NhoqmptfYpjsjhN1ACUxEA1vIA2TVNNy5IbU5t6PjvMjPnqV75qGqBqAnXQnJK6L6WU6JF2GS7AF4o8Bx+2FFoLBTlPlTY6z3PBwcWGPAAgJWSPEAPTTKu6ApNf5LP92e/83qdOnDj2/e98F29Uk0nGDWReWSALrSYmyPnBDG2W2FHq94Fbl5R+gigKWngnkrtSvV19xNf2dOcht3fPzBi8ofG6XNeA9kjA9A4pUzvy3t1r0vB6N8678u3dVF+FKfa5gnt2zAA8jiMDCezAFcnDIBcIhYYCwtUIRoZwpFKccQnhjwT3SUJAe8FvcQdEMzayC7vShh8MIqAgObEoC57pGgQ7yCDF2Vo5rpYLTCfOD67sX76w+/X7v3bm/JnHHnns0vnd2XyvasAJbtqmBZmleQYkD2qxFGAbY5gLTUXtVHPIOw3sDDO9NjaOvfKNryvH46LIWZYB+go+JgdZov/jPYGmtTFXDq6ITH3+S1964exZSpoFLIZlmzAHHBP7hBQ4hRjFxduhjw3iUAj0giympqlzmZdgfRfwaCeHKV1LU/KFdU2b0STf39v7N//ud05ub7/21pdAmiEwOA3x7eAkCtlpXqDaXRNtoSBmk7111K4a1gyjX47alKnJNEBL6aO8zk6DOsIwiHF9GUHAEzuh+1t/VMFt7vTc1U/rG/P2dIehHvQMBPL209x5xDWrOCGjIFgoeDfAQseODQ9zZVJMJmMDIY0lOFVxk1FygzaKIfjAGRlOSfdQKtjXDJH/QAKIb8QkfE3ZQkozMGi1YQVns6oaj3LIB2rVcjF/6pnn/ssf/P4LZ8+ee+78/pW95XKhMwP5QU0DwrCtUCEFKQ3eI8oBhyRseD6AyjLM2YttMztTziDigH5637vf+6qXvrSUBZeCzFQC2/osSUp/QNPBcJmD1t1CYrDU4vnnzy3rmoS2FSOUpWEJ2GrpYK1jTjsltoNW3PL5YmGM3tzYaOoaYsJctDYEBXdSKId9Wjy56UHtzpQYFRfPnvn8F++67fqbTuxs1krxTKA/OyQGrNgZ/V+P1Ebdr24jJEHnSEDY/JXODew8pg9OcDoplKIzZEuG1666Z4nC3B1oKmljRKr7zA025QAxV+t4DHpWgrP0o2F0YRrX9BZHGfYB+RiYrx0houcHHHIgNsrxWHJxKNjB4SFl6sKek9yDjj34y4lxIMwMAk5URgPO4YjxZ5K1YASChccgy4dxXu7vLfbm+/d/8+5nX3j+2We+9/yzz128eLmq5uA8g1z7VkqZl7mqAfMgBK9V3SoFVqICWqHaEvTY4Ff0uP1Eu7dxU2An2oxGa697yxvWphMOedW2ggDFsGJUmYJnsaqqGGO6rZVqquVyfXPn3LmzbVPjRgWQI1GPK4ZheTNAuxAEBm8OlrCGwFLLmqYuyxL8aThEwGBzDqo1ynmbmmfVcuu/BMA2yGGdl/nnvviFV73iZT/6Ax8umOZGLA14KLw/ccgI7ixrvPG622ZYs033ZseXm1B19Eeiw3TvHyvoK/lI5wbxMHtPWH3YYhhDvM2+Z/+NbUwupBYNKcf9F7SrF5FEUHncLQZeLDxzgOUkp3ZcHe5ixOc7DHOka9tiCEyK6do6F/Li7iXVAJAY8H0kfZS2VRQgwATXtm1LVU5ADOJZGsw/0RoFBTJUNt+bXbp47lsP3vfs2XNPf/d7Z58/u39wBfcxqJYg46ksD0gyzspCAKofRgQmsi1fAoo9CDoqYpIkdMe/h1QlvwCGgUWgmuZd73v3K171sjzPKXECI8nWgewC1PY/znndNFhjB3KLj+9s3P/Nbz7z/AsKKuBgnSC/xTG+A+U/EOBCNWUE56NyBPFzBdgM4hQ2lA4WCajNaJ2jXw2DdcRO3EgcZAoUNZ0JOV/Uv/Gff/u1L3v5zbfebFqFsSS7Lfx2S1BzXartC8cwYdGGWyFB0i19Ve2683EPCtW5tduhR97cfdUhY2M1gp6KkVoK0eta50jntlYoJddFOkOqhvTeJWjJPqErvX24f3qDcJuVojjcO+I5KIEJFGAVBZeF4IKwlJxXjkbHd06cO/s8aaAI6MfEnRqAgZIMQY7yiGdVtTTajItS8uJwfnhp//yfffXPHn3iiee/98LlS7t1XQGqEXcxQosImYALKGBWtc6g6hOVdEJF0suy8WjUUv6QE3de7g3k5mCwK8yc8wRMJ8U73vuO48eOYVQWHMIgYAPVxMBuyF1o20bmueSiBTE4OnfufFXXNCQ7V070YkYmerPdw6WUo9FoNp9f2d9XbbuRbwjJwbGPTkGw28F4By26aRqwO2zaMxjcMAOY5g91UrjAWiZqOi72r+ze952HbrvlBmAAEFeLYFg4E37n+iD7UKps2HwJxYZgoNuBXrlJNpKf12TCo43qUsSSpYgTEzuCOR5R+DYqfeMf6Mfc+SawXVyLKDdgFT341BgbiXNSznPxaCuFofZv5wojJIEer5/TogzqJOlTOjI4+WqYL8LekASuopJXFLyhabbCCL6E6mFFnm/vnNy/chmimuBE5SYDRZBp0WRcShBkIhOXzp2/74G7DxaLZ5986jvfeXLvyt6ymimtq7bFMJDhQJyUfAz5AzQQpRVYtRhosU/26TcORw0lsEDZlDQwl+Vs91vCvaP5JionnR+EozGnrrvhFa96RWYrC8H3FH+2LMOzOhCFmoG1D04pIUQ1b2oINV8EhCbZurEoggJ3eDlW23G2NKubplFtVVVgAtR1kedQCUQbCGiDXgFeLgi2K4VhLgPi2tWjI5eCj0/BkJjOlPndT/zhu9/yjq3NdWUMqDZgZhOEs7vw8UZJCScVwY4VxY7qoA92dL9IvHVhkuHXxBUY778eQYZTwuaPUAY+F9JzAEcwPTKOR+Nfa+ARgcVFBqBPMfB3DJasHaxTdoYmt2eURBpvMPCiu6VTGifQ+0hOP9sjANhtXSCTyaap4VPMo3VvbqEELvMvUwz+J7iYTjd03WZFNpsvIWG1UYvDg3u/dc/z555/9plnnv7u87sXztdNrbOsWS5bW5ISUIKFlC1KGBR34FzNoIYUkSi+PpxsMXH0ZBC1LksZRFAGirQUQgGFYN2KOOfBp0K417DVR6iADqYnaKO40R/7wY8dXztW8gIVUASF2PzGRHd1ZUCg+Bj6zbVaQgGdy5cvg+1gV82rV1hsC1IO6T52CrMsa1r4HzrYAAazBOGqIdQkpcBwOmhBAoqSNTXIYagi4nQLp/UCVedFwZmg9MmLuxee/953j73h9VRdzOPhkqWnDRPp+EmEom/R4X+R1HQ6S4/UYg4Z4MyDVnV8/2BvdLV6fN3EwdQJibjhdyp6hSGmz7FbvuttClptZwISFtAZdwx+duk0Q1i0YaHaDW91vkqeZt/fO2tsLl5aqCs5HV21RjZtiwRMZT4jIRalz/tcvjarF/Xi93/vk2deOPPod75z7sz52ewQoE6ZgWgTIpDAyUTCkbQ7lH0IwsdR2QJulIljEYX0E/c2YbDCZIJYw7dQGOYF69fmYdh3QDdPYD00AU6pta8CNWHB7c1On77p5W94jSxLBFeEnMTYf0UuYKVbYQDCAdXqUA6ipa8AF43CObo//MLBKw5hYeQvga44WsLoNQd7HtOhrEmPCjBU520AetLUdWVTk7AUgU0WcdkFxgBmExApCPh86nvPvv6Nb2JScN3CPEPdTaD8JPdkYKvFxU+ineNE6cD29OmoHZM0Eoj+gyHhnxBmuo0jMzXWg70+7PNiu+I7dQpHx5Ca6X54JWQgXByZ/s7ucMU7u6wP5WJPke7Eq2NfkuUZ8fTEeTv94gjJXSMWGgPMgqYgUWeDGK4r5xvSTl0sxUpklmWjsjRL88A93/zmQ9/a37sCjpVCZq0CeYL6oN26JAXRs4VYSFsVMrJa4Q8QSiiwCFFolU4HpbauXQJTIEirwfLUwsZWA3MkN1DEcuxTPAKbM45Oo/ZDH/7+YzsbWZaVoxLMzhCmjsoK4LSSQKZ6r2AeYHynhozfKlp4LAVsw8AsF2JZAfI06E9AsVCdt6obuiQvcioLQpMAVS3rSkECRhPVJkD7waPuKQIHZfDAt4cxb/EHX/zy93/gzs2t9XZp0JsW9mmcttkBKTry9vsj2ppRFrkl/cgdQ9I8kSSBSfTEVufvVNhEuy/e8l76BFiudTIE7FjkvUkfxpJfzUri6JF9lyt1MABdKe9Bh9ECR7MXkXxk0Tqqi2Cp3hnVnbCgSsfHAPbVloyDihSqBUvWIuO9SenUbKcMIplpk+Visrn+kQ988Bt3fUO1SpYF1FGU0tp/UHaGSnl7YKXXDEK4hQZvC1uHUDopxlSADlNffQaI0+7IH0uOWxg0mOEYf7ECHYM6KKLRorb7ESJPKIVPnrzhTW9565iVOUZ3SYoCVgIlf1RcwKKx0CusAbmVmQbq2EHB3Qjbk0B/QLSCmgBoEBaDOoC8IOYFZUDAk23AoIYCA2y+XCyXFWaJwBdQP4QymG1ultsjTplGyDTjHCr/HhzuzWazY9tbgCuziVAJBwpbg1SYZB9HSmPYtvYSJ0DijRfEjpcrQVp6Aj9CCCdl5PokHv6hFI1ELejWfjn6MOnqxPK/8/5JIYTYUuhqCEMDj+Jhbqqd0pC47+JLYnfN8Midj84Osus/jFYlyGZ0YiGswl9Lhes7NVQIGghabFFsnj4FHQaMkbYMOP5B4sMh+vz6+llwFm+UmhQxFdq1AWEdnurteCvgCd8MaUAmA3nH+Qj9YkyKgnHNISvPAA4UnL2k3hqWqXr5gTvuuPGG66CCO+QDkG+EyNV2IfBPJeQYdYcgNx4W68B8IxqWq15No8WHkwFMKfuIxELjmXNWt+SIAi1DSnC1swz0bdVCZojGaLMFssL2tYWwndvckjCiQ8kNB25ApdqD+YIpqCVSq5ZBtR8HhPPr7D3vTnbFgaSwk/0VHqEXKcauLEoq59y10bkdz23kCbNo/mSfxtPt7+3kfAfs4T3jHREcnRKfHbmUEhe294M7X3ho4tBVS4PivVq9Dbp559MBPzU9pkfSyUxFXznKcZOU6BbuDaj7hs4k5eVlkFhAdh3qnrZUsZs1fF10mWppzOmdHfS1emMV7oQ5xlaHswxk2PyOMA/kRsYiFba+Pz00PRMNYyiGbi1VLnJACkPM2WTZZlv90s7pxyfFvVrP5gsIsGL1K2AmnKMmrJRm2xvH3/DONwuZI0AKPqf3pHo8Tlmwi+KNSPB+C1FDAqBqVKONmUwmRS4bgIS7UBYNlXMIg2NeUdjSrqcG7QWgXgJ1tPVs70pTV8tlhc4sArSGmkMhrdW76NDiQGi2ydGKWFbLWtWsIUwlJ0eAdQSGFfc7Mdokcb0s+yedFlQ11t3UicbB+okyA3sy7L+ekt01IqPK1FagrRKg6fNM34K0PlE3iEjNjS53FkNgTDH/jmYqVVs7UtefH+vYYYQegeC9X/1p6sr3pFCyj4KGbenfkTRWjTWx4CK7xbz3GW1Si64l/CTgHUhSsbwotFIgYGwJSNpx5BJigzXToqT/sHiEXqAiEs7mJF0YUhdBzmiUKuAOo2QkCI3mSt3JxOvF5LfU4pmM/8h4ckNlHq0PmqJAjzdXFYATsfcLVe4QmW7f9/733f6SW8flCBrFOJBn5HQOLlULB80ySHvWinoyARPQarFYbO9sTaejqlq6VF+03AjPgh17aJXpZlwAQwLV15oPoP5Lzg8P9hbzw2XdZMbkAMYONf9I6hPBWjhIWGZMm0Rpj02PoMZAziWoP6jwe9ddR8p5+5G+djayj/kH1h9tslDlMJGeqVTqoybDVo/lyoAsCxkuA1LMbm+XnWslaEr1zhTLUHP1+qk/i97Nv3eqB5P+FarVxvI2ekgomGUfFlGoL0/t1H43rs6LWHHWn6XoLQOYLnXAx8jnSLrblUU6BiilpU+SwF6VwtkLZiiEfVmja6n5OM8xaAnALL9LyAVt/bq2Cxl28PFJSXgiVQJATBV1t8oAXMU5eLLJEdW0WBFDsoxPx+Nbb7vt1OlTjGVf+LM/a5tGiKJua5mZG3X+RiaW9fw9051vq/bbQlVNw6VtUBJtE9VqM52sv+r1rxdKMK2L8QjrYwE8kxAsNs8xOH9sCVuqOpIzXhYFKKuVFHl+/PjJUpZ5XtTgAwNcJ3njwEWMiCsrkHFHYS1LIDOPtuRIfsu2PZjPJQfZCyyMZTmkJXl3QNgztP2cpgPMD9IiJdjL02kJhWc5me82l9i71Jw7xPe+8HLJ+UkifS2Jb/bgWitUv96RRIgit25/81qSdAMbRkemFBd5yt3Hrhq7U+D94NMd4FTvbtUaT8hWUnVVc9yz3XePpyaS5j33WuBBiQ7S90YN3dyf656eAigtHVu2DGZsuMDpbxRIoYe51FwmMw47NAcIhMxzqmnjdgDChkL8hgjbV1H0IAIEcljoIsgT8jm3TQuF1KHpIWuNGZXFu9/17p/8sR87cXxn+/jJcmvzK3/yp1/98pdUC41OGs4+WS1Pqfo9x7ZfNppscPnEiO23kKfPpISeRgpyfIlR6AxgzO/+vne99CW3lUXBCwkRGoWVpVwBH6c807RgdyXVUjaTFGw8mcyXMylFLmUxKmaHc1mMcingJPJTAyISolyz2QxQ0Jgh5LFiaBRb2SghX6o9OJzpRreN4gUEhAx0chH5WFItHr96ARwX9pcbrmFS5rwYqaqFBRSQX0KRP9KFvDIaG15Bq4xbPvhfom2UaJdWvxrQAHu0kFBAhylEv7nKqUfcy9+ld3H0faIHR47zFFsWHOx9Z5bzEXiDfuCkq4yyN644O7Ob49d1QfRd+PHI405oCc/Fj+ztDECoEJ4bdWGkreCeTjl6wGa4bjkEGx9//LtOvIRltrWaHKDbInjtF7C/qbixreTWKrDWUAqb1pTjiRzLejnHKjlMNeotb3rbbTffnq8Vjzz6RNs0X/vSl9sGnd6QScwrzh8y7GWqvX08/sLl/UeOTwGM0iooJNe29v0gfwgYztpk4x3v/77trS0IyTLALbomLYkG7aaF8KOaCdmqZjJZyyBjEVBbsijbtj198tTuLTfuX7nQcK5Aa8ZMJiYgwNTUeZ7bNXBmPoagsLot4p5zmRetyiaTY207Xy79uU3bQlqj31Q+vQ2LWiNGDVaxAEwn4Nfe+prX0VzIDJYEg8auFgrObCxSgmMlEi5ew+xsRa8YhtGkOy0RBz0cpJt76+pLoZmBkyQ722+9yJaMxt8jAndZloa2hjxXw5eylZ/i3FtAUeQrSHJ+U7U4PeLyc11NJhR461yS4Pnd4zxIgZ4VemF5/4yVwNiFANqPIBLA23CWhfocUypoLjjPs+zZ556hbKSoaLOzUWxGjW9WhzXico6VLKFtic25M0bIQhTF4nD/I9//4Z//xV+85+67/+3/79f39/fBjwyxXj5bXvmX//xfPf6dx5fLqm2a5XIhGHt5Lr+DOvY3m8P3thNZt+fW8/miBbc0AErIl26NdQBINM0d77/j5a94aZkDopkxU7dN8LGEHe6R39SDSXMO+AooOTBfgrGPkWpoAlfwkSjzfLwhyz0FycnkXoL0Ruwta+sNuTXB2I9B2CRQMGCtsKDXaDzOOK8qoGFAp9YVdpkFu9fTsFOK0P0NWjp83xozma6/4pUvH8kSCu8gNh3MFWsI0tq7WHTYNvQrLb4vQZo6coPAWiVuI0p2DCCwnTisHCi/s2e7cijyGZNqQOcfkZwbqQqGEhi6d0/oIdZDwilDAo9QmyiTdf+UZLqS0cX80VmLJDMTR0C01cJtfWjGPSHSXrze4zLO/fp5KQ9xYCxXqhG4iGUZ3UT6F7P1n0GKNqw2fM3cd999qOxRsis4kP0zaGykTdN4QElsasnyZVNhDAW02ze86U3/13/wPzz53af+0f/wD7Xk47XJqCjGYyjsagDu3463Nr77vRfOnbk0Gm++5nW3nDnzwpOPP/YSnv/Viv1uLr+uqjdN17danS0PX3V88m2tzmdcG0jNozbKKOcBOLW+Pn3Hu9454iMJVbAQR+286h5lbX2/qIVaDDRElwWiqgA4hnkXMEmFKKUQJ49v33TbLY89/GhRlA3kDwHFQkKzRPFrne/wGCGFqrB4JlbtoaeBe18ZIcV6vsYZgKXJHdICHsYG08KCU18UDrcS2BV5czp92+te/8bXvHIsCsSXYDVLxkClIe91x+KLC/w6X0hHwiZ04YnDAltpK/TkbHKyx05FH/pNGZ8ZqWz+2pgiw4mhHJyLbqaP99Epk44jsBX/u1/zziukloStEhi71Xx/pSSu1ectLu4SyjHZ+t3habGR7gMBwZ5IXL/pL95W6BgqFvcDEhgEF2pftu8sPdxlRKIJh4SamZ31jU/9wSfuvf9+1TqfpyuphlsTziG8JMEY6qYq8nJ7fesXf+mXlvPF408+8fnPf3E5g/a5W8c2+dMgu9bXNy/tXzmYzRbzBRangnSkIs/f89Y3veSW20aT8c4Np/79v/rXzzz91KOL2R/IyeMa6mK+buN4Kybfms3etzCv35r+2fwQ+mQ7SdqCAQ8jvP70Da96zWvK8YhxTH+nqcJgD0WVXaDTOaVxiiHuxFpOIGQgfEm1sMvReLq+sXtpr231bbfd9vRzz2TQP5Q0FEh4sCUPqGMwilzXigreVAo5mo5brQ739w/n84xlW1ubFy5ewgm0xXSonXG8qngTuKPIBRdye+vYO9/42vVi3DSQJoWNGFtsaR8hCHrE65ITU59KX+FMQiUeipXYs9HWp33vRmsLOtjvPYPoSKvuLylDSLAR1l6P0E3p6YbeKRFz1tEeRuZLXiWqgD8htQjon8QZHYRfNMqEgt3M2MyIBDMZq80DHYuDf5q2ouU5zgCxnNwh6rq6N25nBHLQ5bi/XVQoMBPa6I1qZau2jm0/8OAjUJxVcmpc4gtNQ6CnbWUB3mMcBgB9x0X5K7/yK69+9Su2j58wKvvoj/4oa9Rn/uSPx5MpF+BoFkJsrE03JlOJMVuXvGDGRV5pM97aqBYH995931fvvWdR1bXKvsihMeCHRHl9rf80u/xkc3h6fPz7uPyzRd0CfBjlJwlTyAVkH/zAB2FsCtLucFDRstia0mnhFovFAsMVyjorXYCTHPNx0RdVCDEdladPnriytw9xZFwPkviYDEzrgNAvzqDsBvI1Eo1CiLZtRmW5fvr0o48/bkC062NbW7uXd8lEaVH9RsJ3rW5QGceCtRC+nhTlX/zYR1/5ipexvESbBoqcuK7JMJi4iGlnl7kQ04AA9Tsm4f8+auK3oIc52j8icW7Br16t8RTkd2bHIoz3shNRHQK1TpT4YfYWwdvEUiXVY4jCyKPaIsmZbrmD9ZtQcvg7WOYxDwiE2LEHiNz7PgjPS/rBa/ecoCqQ0I+iT+myWagALJEki8iZNFadJFXMeaWY1tB7fnN983d+//fuue8+ZZRAQD55W5EwAGo8Hk1lzmezGew2DtDBH/2Jn3jTW964vrH927/5W+fPvfDa17324ccfy7Jsa2vr4ODg2echwVgImefFC2fP1k2DdaFAx85l/uz3nvy3//5/f/DBB+fLBXUGLEYjzXneqncVo7NCPKibWWM+3xyw3YvLcQmQbhKiZI4xPhqPbrr9lszoZVWNyhIiYVj3GTRtS3tu39ndEEBi2N9Mrm2sCZmD9xvrbzTNIsvY2vr0pptumK6tz+ezhx56kC5TgIXkWreoBNucpBpSfy3xjcoRYJ6b9tKlS1j3rs3zfDafb21uTqfTw9khmMoM0CBSQnKS6zsBK0AFyUY6+/jHPvbmV78yL8fQji2D/AqDfkHrC3HxxKiuQTDwBgy5zuHAhXbzRVs6ucJu474wDcqt27fBDu8+K7W/h5IDBrf48GHoyzhc6RbUjzWu++CcNQOjiu8ZK+7Ji8ZR7IgVWXYRI6gSN3nykgP2RfS5YwXOQraIDP8wInLNNeQDRxzOKlhuBAQkgCBJy7Q6dmLrrrvv+95zzwIaEfMKNBSXBDQfoJ/a6u//o//x0u65X/vXv9Y0bZEXWumf/umf2dne+Xt/71cffOgBY/SXvvp1yl9nQhzMZrmUSuk8L3irqvmMEL3YhIzJYrp3cPFbD3xbM377y152/oUzkEXAeVkWTVOfr9VjWXWgTVvmd9eLdjQGV63vCI6eqCwDkc51ppfLGkQf2KIAOXYav5uuqPVOXHnBaCn42nRtNp9DuzMpiqJkfMaknK5vbm1vjSZXLly8yDmEf2mWycp1pGRDaxgZhgwMY/R8Pofs36raP9xvMe8fmiQ17bgsZ7MZtFxzWxnGj44vNH1FBXEn9eEfvPMdb3pDkY9UqzKekbymxbOJ0Va+hrQI36k77Jc+/VpNK35/d0a/O0+iiPb2o5vFEJZJnh4VWIiHErevjKALIdrV9U6HEcdHeIOEVvuC3cJsnOaxii2E3IAV33pNJbl/5DyOhHmiO1/bkTzcOpL9FNi347a5WSgZ5VV2q6DbkkJ6a23tE5/6xH333VvX9SgvFOBvQXdEpBEgHq674ebXvenNe88+t75x7MrlS0qZD374I5vHjv3+7//uI48+Mj9cCMF+9q//wrGNzX/+v/yzTJv16WQ+m3Mu1jbW81G5rKvRaKQzqJ9+Zf9yVqrr1o7/03/6T0Zr+fqxnf/3/+t//spd31jW9XI2r7T+dQ6yTUOteV5BBdpWYkiVVBVKXYJwlG5m1eJwsVwvR61qMI0xaGXWcHcbnOi4Vaap26qqx5NSouyF0lvoptpY32CCHTTNcn+2tTEZT6YP3PdNKXOolI0Wcg6ISNv0hPyoELXSIFGlFG3dFBIyD2bLRV03a2trdV2Px+OMZePRuMjzg8ND6sJGMBhwWwPEWTdMt23z2le88mMf+dDG5lYFLgKBOG3QJkJlWRt2dn46VOn9snc12FS4JEQdgqbWq5Oc5VXTGCTkv7GkEkg4udL37+ju1BB6Co9IFdQeYUYNmbL0ThYh5fXmlIOFrm4dVjAsbldSXchp6X3R11zsx0FTDi+aVgnw/8bEG0F2A8SWlshm1IYd7UQviiB6fbZsGtWo7Y2Ne++657kXni+KwqIeLfABIsLGtO+/8wOZWr7tfd/3zne9XRZQOvr2l950bGfjrru+vlwu8rIwLHv1q1/12te8GqZXyP39PaVAduVQpKLhnJ86fcMdd37wLW9/e17k08lo8/SJtc21ds7u/dJ9L5w/jx1dINdHcK5y2VLhC204SCPntaRkXhJDAPhvdi9dWi4rA9LKmgfWTHZv4O0LqvkO5a9blQm2DuSat1g4EloIGwN1+JSe7+4ypaqqvrh7+eR1p4syd90VgGCEzYuEwYzKERWdNIBbrrXJjp88CXgVKBAt9/cP8iKHuDFiTm648UaJFnI5GpFtg6FqiAs0Sp06eeoXPv7xrY1NbtgIEaNQMhcLcnqbmYwdau9iQW/RRgy6hi1jEm0Z+sR1qXJqu88yCt+6/UNBsvhDVx3F3cSOBwuv+P5XrtZ99/A71v/f7W2b+R3f3P9tf8/CzUOFlu72D3dIzk4vs0wwnBmRS+9WoRhMMgee1PzX9p7J1KejTe7n/ojWbGjATqVGJBY6u2wFnVhDIm4Kn+qt8dqnP/snX7/73qauQC5h0AlqzXBIkdPKrI2P3fyS25lhTz/33Jtf99ovfv5L8/nBdDL+7Cc/9dxzZ1pQAFspmGJmf38vy7LJdJJxcTifIWhRNbPlP/wH//Dy4f7Jkzf84ac/8ZUvfeG+u+9p9g7ue/jbTz/x7P7BZbA/21pKwC5InamqBvkLiXo2A5Hyip1fEvgOEHKrn3zye8c2j5W5kDxf21zPsfcfCV+rebj8R8Sf6bqtuODrk8loNMkEAy80arIQLMpMWZTHNrdaravFcvfSJSjxtVgSl0ctxEaAgdqlrXSFdflwdgXb39s7f/Gi1qBLl2UJgrcoeMaWy2XGsuPHjy8x2bgo8qZuIHqFDvGTm9t/4y/9hTe8/lWcF1gfC76AlESXUePKIAWjx3mOY1nR4fE9cELq5RxuypMayYmhHanFgfLtBg/Sr+u8ceuVfpwMMhlfpPLGNoPpXB/fhFz8sQUcvFk9fNNRKnNXLianh1GGU+KSbJ1Rdb1j/uQ4PSYoLomOEKDlOHxE6kelHNLxAn1y1Zy+/dS9v/7N58+cgdI2No/X2nhtA0HkO9//thuvP1UrdXDu4sd/6i/+7if/8OFHvt20+bu+7z2/+du/c+YsXx7WUoy3to5dOXcuBwyRllJMplOATyp92/UnLy+qs8+/cO9d9917zz111f7L//lfTsajZVUzDfgpUjRExhH6hDWnsV5ljKnCVFqXgGhTqsSXv/rFYzsb03EhZV6URT4eO8i364RkxTVkY3HMFhqtjSfT9TrTUrO2VZgHBVNRq8Ywvrlx7GB2uEBHHZeirmvbsoWixmiKQO49FbgDOQnwybLImTbPPvscVNXBtgzTtSmVueIiL0ss2MFYnuecsfF4dPHSrtamLCXP+I9+9KNvefNbhCih65kxbQMF8anoiXP82uiXVzQtG3dNWxMbzMN+g6cn3WhB5Yx8oAO72Vu8ibjr7NTu9u48pRPqddAtZzmmj+2RT5bs6lQNjly9PpEm6obo3MLRhzF1EUUNJVr03i6isq4u7F1J0WTR59537gmzZyJEzMB+5/xmsZDFZAar9VmPV+RN1Jq3qt0s88/8yWe+9JUvV3VdFgXUarTRbshVEAASZre89KbbTp965NuPHyyrzdPHPvwDH3ji0e9c3j1/WFVvf/PbHvnO4xtrm3/z//TLt7z01q9+4U9FlpVFfnx75/yZM21dP/zYd/7xv3j04e88eubMhYPD/VxwbVQuedPUYA0KoRGeDFFWxkimKexS7CNkDoxm08Rc9iy88OH+wRf+9PPbG5uyGI/G01FeCAnOLeexBlXUOpwYFzLf2jpWTieH+/tVo8cFADPAcc2hGZFhYtlUminAblfzYgQ5DdO16XJZ+fKRyPLAbCY/NmdsuayKcpSx7IUzZ4UQTdsuF8vjJ09MJxMsuwVzD3Whjdk5vrOcL0zGgNFc2YfURKXueN97PvC+75vm47ZVHCxiGDNWokVODF6IyCvscPlxRaF4ZyQUEAlZX8iB8smCYPS52/FWDl77gQ1teuwhfoA/q+90JghUMtyImax6XA+n6baCI+xITQgZC7aMf/RqgZAcLTocsLMm3bU9fcZd4jTeOCwUqynxVV1JGd0/5kMkVNMIICUEOp6E7UVdRwW/E+yiCexd1GydPHXPV+969tlnwEmDMKfoyWCHjYrJa1/9xrX17T/67KdHW8dPXL9zcufU1tbOM09/78FHvvOBH/zIjS+57eYbbrr+pTd/8rd+5w8+/Uezw/lT333yv/z2bz7y6COciT/45Kcm43FVL9vGcJE1BH6yVaAJIwYgCQi91DWUjwbnuSspEplBrhKfHT5scshC1LsXL545e/7m225fVvPDOeC9hETEou2gBsA5SJMHz3rWNK2oal3XgAWB/ALIlMKS9pCVmKm2qpv5ojqcLxZXruxfvgy1AfyyUW4G6s+ZhnI5UoidY9tC8L29vYzxg9lsPl9sHtscjUYIe8P5Rm4opGzrRmbQXKZZVJNC7M2bW267/S/++I+dPLZTN4pnCjtPOBPXhfv8Foodxh5jEwK0nrqDoxh5XbK5+oQSlMxE9+vo0J4KfD5BMgy332JpHo/WC+KouxNtXWeJH1FGjlni9wBmO9DgMQsO+fBkn8fgzvaQ4EgF9vlcHWxprNXGOeGd6XC+PKcTJPpyygdcOmFw50WTmN4WlzoKMVBRDWrwk0wPxIG5GRXj3//sH3/lrnvaps4LsMGCXkWwWqXe9X3vuPWmk/c98I0HH35098pdH/7AHe+/872f+exnPvOZTxej8s4773z57a+YjuXv/sff/MQnP/XCmecLmf/pH3+uEEXdLLG2LK+XFRT14SjdfSuyoO+YoshHo9ELe3tglOa5hetbeWN3BQ2NnCcgWiGmDG+6rOrPffazL7n91q216f7+vsnMZDyleuhW00Q1XEDyA2QpQaWbuhW51AB5VAKqYqkGmqcBaly39ezK5bu+/o1vfvNbh/ND4Cx5DihylIqYaQz3hRtmzKh2US2Vas/v7grBl8vlxsb6+toaGsYWY8QYuPGgfiWUhmZFXrCC780Pju8c/5Vf+PmbrruuacFvh2F3Eu0Og5IadlZ3SmVYVDfVkrPtZZHSUNAtY43UqTcBenm1AmxOX/QizS9hUAFs6MapuInTxg45jDfWg9PtGYk2FhFVON2xmli2JjdwLWBdHk5y80ChNGifcBw9OlxE9zHXYjaHKQjPSrQO+s6BIodmmQAahFiGihwpBMbrTNAtkGXHjm099Z2nnnn2GW7bVYebosUJN7j1FbeviY1zz5y9snelbepvPfTQy1/+yve+//2f/8KXPv3JT37tS1+89dZbW22e/u7Th4ew47HboKnqGhKcSJvBcDMANkm+eGwPjoWizQcHh9BGyCbQY+JrN34RzxWDbD+IACkpS8bYxfOXrjt1UsoxiHLMZ6ZbcMGMAa8ylzzPARsF7RSm4+3t7eWi2j84UABjaWcHB7sXL3z1a1+/575vnjtzdrFcVE2T50JCiFc0pgEdG+GTcGM0btu2ZkI0ul0sKsGhKeGoLKeTKRTTUa58l8mqqkIctazrGlSOqj55fOfKZflzf+3nXv+612HXFXh978z08G27oLTSib0WXFiOjoODg0CzCa0F5S8RmPZOLiAbQxIGcRc+8ThR2Z1W51hPEjt1QizEQaIlTEVFqpPHUo+588OV0c2dcdWJkVkXZlLQ2d3O1fSJUAEp94hI1WOrQtq1h39GmnV8dfxv4K0JT4rt2eggbI/1WeN3Ii+g02CopWFvCE06G9Oytq3b+d333LNYVEUuDUDnncMWzuJ13XIhX/fyV22Nx9+8/5u61dyYJ5986ht339MsFiMpW5YdzmYPP/ztFoCNvCxzRCkCsAlBGzFvi6LqKb4btrUx+/sHWOMCGxFCYg5IOSrf5TYdvFmrMC5bq/FkLPJia239Hd/3tltuveXmG29em07qllV1vayW43JsIDgEktIwAQVGlOZ5Ds3TVGaKUV1rwbP5weXZsnr60ce+cvddDz308HKxWIJLHQrxFgAaBbGJrZ/ADc45K3KpdUH1iMrxRotlrw4ODsfjSTkqoWspnGBbN8AlDLIU2ratoEgPL/Li5OlTuxcu/IUf+/GPve+Orcl6DW0vAGNJfrIUkO8jVl52DjD8UC8X/fT23MANnDcnUjaTLRfFUe21oYhnrObZzyM5jIpU7GZ21Uv8lqcKRF4wOcvZPt6CfMlIskVU3AbGjzJ8v2A2udizVeWdNw+rpTp6tfEXRzhRsVTn2fEyLDUUqFCcE7bpYMLYfHFTusTD053BnTAoZwSF7PGYxoPvLWIgTrvGsouAVeLg5/RoyuAaM0yp5vja5H/7X//XJ773hMghEwCMUwJJI5eBIkxMv/kNb1nfGH/5rs995+kns5LJtvjSFz//ja9+ZTwazxcLKEcu4FqQroIK6Fhi82jfMBeB1/rXRsbDIdeHCrtH5gi+tk0yBkQ/1CBowLs7nk5PHz/9vg++d2N789h0qxyXSvGcMyioMSqNhiRhKGXBcsgdgCp9cs+Y/UsXslEp+HixuGKKYvfchS9/+fPffvjRc2fOnDl3Ye9gr21qSAqChk0gMEGpxZ5JVDuaUkKEyLlUuZRa6cVyodp2NlsUZUn1ABS2NcMUa+jSSrJaKbasDvOiHJe5yczh4cHLXvayX/m7/93mpMwUq93WDyFGu0EiP6onZeedcRFSu4tcNkJcNTwSl0H2Ba8V1c33mzJsVv+Z9eLbO0cU71aPdmwUdKLK2JHNGGJdjod76WPtfEvcfn9HFlycMmwimoh1aTrNVlCyQrcrqGOCIVeKj1d7ee3d4/6C4OcLIsTr0tGPDuTPv1TUBdNz1GS+g3loF4WK2NlxQo1/CgNxECOWfxHkCHNaoflHxupaP/DAQ/WykkJiINSNHv8FSQI9r9svf+0rjz/85KWLVxiWjpFctk2zgD6G2EWBZCwVtXKBSuov3Fl4v17hKZQqoBQvyrW1KTY5BXc0lVDHtD8YKWNic319urFx+uTpt7/7zce2t45vnlrbWCvAamcnT26PNzYPD/cvX9xtGq0EhzAWMzLnspT7u7vffOjb9z726NMPPXL7Dafe/O73HFw4e9c3H3ri8ad2L19aLBboDEdVPM+BTzRtY5oCDwm6L2AkwVlOOErVNE1TLStjzHg0WmKx+zyHotmYq4w5EoBehmJfqEZAqFlwMc5lURZa652tnV/97/7OxvEtXTVaN8xAWS3PVn2lKp835oHQQe4EO9BBHhxcp+dLjqnOEVFqEEcqX+x68fK20xU+VgPjvIKwtJHh2CsaGZxlHf04XNQzNFnye6KIpmawxfpEZmtnBsJzvFlK1gPxgUT7SRN4fRyXNAyrmie+KXs/P65oMMlQgn0RLYW7j1VSyd+CZrLM2SMPf8fXUsxMJgpRV+B+3plu/vpv/PrvfOJ3ZosF9RKNq73CXgI/DRREXCtHrYHOn6gx2Ro8KKvBMwXRpkjHGIaXhvVKElisIxI5DRMIknZlrtDHJkf5aLI2efktt9z5gfetbW+sT7bFiAleTMrR+ub65tr6ZDIdFyPBJZdQT1k17bPnzx4c7n7v6RfOnr1w991fvfvue6GvolENUF1Wq5ZnWdW0mWqphYQxmQJYF6gfLnMLmAcU2C2LArMNsQMDiNNJWS6bZjGfk6aPzb4h0p6DxBZQrgD1fwJOQ9ICrhvwAiG2NjaKcvIP/u7/5Z3vfGueTwT0wYDKPk5doe5Hkf0b9N/hifWKa1izSAr5beX2SSoF3JUhW91K0WgPelnToYZYdIVtmdBI+qD0j/7d+uql+2F69nhgO25kvRBWSFHsO0+cWE2eGMd5IrntpH3vDfqvHQEsu9n/6RG8FQP8JTZZiDPIAtuLukQH7IoCG0RmWTku773/m/sHszLPW6gDkbAIqGxMHleWLZoa1HAqjmfPcQkDTk70Xqqnejk3aXgGKlPUSwVKzDUNVGmv23I0WltfO3X8xFve+oZjO8dPnz6+Nd0q18fj0WS9nGxubUzXJqPJRGInIeg/SLmAyuSSK9O+7JUv/+Ln/+T/8Y//8cWLFzXmHJYSGo41batbnXEoFQSQLwhBwfigWRG8PZXRtZwO7QJd1zX0LpRQjw6pkVGdZ8eHQfZqEPmgiZgsW1ZVURToQ0chDJU9wUCDTH1l1qbrH/sLP/F973/72IwyaAHVgFMPuUAUwwwWlwNdJ5qYX/Zg1wad1kvoiK9HuqaTG4EleLW8s4ccyMFaeolY7/9qd0ws+rz+68v3p1WIe0WzEjkbyWq2Ikbc2VghIuz8Q+lYExpPpEnPkdzvYBprMyvoPpa1q7zLkSVjy9nYb2Kt37IefCFtFFMKKxLbOYPiDiQ3T+1s/X//7a899cwTWIjDOyKiMj0hAubLwVFt2sSM7c1Usgpu+h2BY5FKpwdBBRlszAv9vlSt1jc2RtPJyZ0Tb33nm04cP3nD6RuPHZvOZqooxfp0evL6U1sbW2VRCngJrhDKD1hLGzZFFpBlcgzvuzPeBAyaZDKfZCxranCqgV/Nxw0poxPnDgpA5hzyLqFoRoMtFIDsDCUwu/qPpBurBkrzESaMWjGANx+T/aWUVVVpreV0SpBp8gcJIw3jW2uT9935gY9+6P0FH+N+B5Ml7FLv5Uw4YSjPQgHQjtcysnB7Bh/+Fu7obxOhFwZzADuGcyr/hnq8Rk9LiKoveT3fCXLIM6SwmSJTwJUQSuR2HOeJLFh/m0GhFoVoo/IbaU/Q+JbudrhAaUe4+JK4jseg6h90++APT3Ku47npLaTJAFSEYSQK66PyazSUSlWG33vf/QcHh5LAw1EOj5UCZPLZ1bdfRHGIeLISGym2iVxfcYc1gGQA6DlCKQeQQqH0+vr61vaxG05d/+53vXV9c7uQo3yclcVka2OzkMX29nj7+MbW2kZZjrDKrXPVAANAUwGHQjUqoVSdkhnTr3n9m974hjfcfc83lk2r6oZM6wYgIrqpm9F4NJIj0EWC04NSnbBTIbrToRws1o5HYrY15QCvRQVw8MGUWFHKQmAW5HK5hBAa5lJg5R9o0AgobmZGo/K6m27+6Y//hVPHjvNMZHmma6qjb1cqaLnRLvEC0pfRSvaK9wL5GG6URDRkxsR7JNJf3Z+pVbqCH6BpHpPD0UdCp2GjdjTTmHoDUjDYWln/3M7QoiOOWUXMwjle7K9e0rkbxcpN6PcWpmsFdwiMsTOKUI+5O5NelLsFTOrk+TNs4UIoMw5p7q4SiYK6c9vj9X/z67/xxHefIodWqlFE3kGLXExexs1RmJR4Xn3owv5J46P0fyQTCHlWajyejPLxZDo9vXP6PXd83/bxrel4czKVuZmy3Gxsrh8/eXJrfV0KWXBsvg3lYwEVCtLSIiRd3wxn9yM1iIzLTNRZw9/ypjfedfddCMgEaHGroIId/Nc2atY2TY0AjZx6GlF2Taj5ig0Hie9QKVygQjDNIbmK1H4SwlTck0rVYjdzZ34ADwAEdavVSObbG1t/55f/9g233lAvasm1aeAaRHsDJ4vAr2nLEbcoXUJZRThO/UstvqBj9s73Si+h3GLtz5FBABsFn+SgU3rQNO7xkt6O6XKOmI4iyk3CM36P9yYivFC6Pd1Qgv+vf769o1dCfcMaryhdC8tyD4vAMt1piNENVhOIIV12P3tSJBMRhyUwsmqyfDr61rcf3Ds4EExQqUpKWcM6T9jJEq+g5kMD0zTYMdHPtoWiWnkLDiStGtNCWqwcbWxsnto5/o73vmNzbX28tjYSo9FGIVm+sbW+uba5ubG9tjWajCcFkwAew9aj5JrFxn3IVZBk/POs29MVSwYcw7Li5abEApFt0wAVtdCAG2xg6DwGbvkWOpkBFjLLTJ4XRQFxIEqRwyBQqw06rsCNDA/WWMIWOiRBWToyXFH0i5yKZQFnwREC3AorsFd1xfOCZebU8RN/+5f/j698w+uyRhXFOGsrqErtYBs9/tzRgIPe6vJKE19+T1ntb6Xkn2TB+q6UvmyNZHOqBqyS8zGS4qjzhnQFj9iPH8VWv0vv1qt1DyuSnKIT15D3MjusRzB4rTMiMQM6T0yeEFFBekrQeVIQRHR1IF0fHYYt5/g6g9yETG2Np7/xb//tY48/ismo2MSevK+g00ERPGN0XVUtghCsNdhRIOI8qDhY6OaPHDMaorYKIreTtbX19etPX/+e971ne2dzY+2YLFu15IKJyVqxtXPs+Mnrtjc2iqIQGTQwo8bGZJ66vsXBL2ob9cWz5tM6oY5zKyZSLRffuu9e4iN121R1XdcNFQOBpttoXGF9dmzSi6X52oYXZUEzSHFFbG5iSdpyS/wOg9XkOcd4Hab9LasKA79yMV/SHaC+vDbr69Of/D/8xHvf857N0ToG5hrTKuQLVujZ/ZqYVokcC9qQ30VD+/Ro2RCqrUW1aY883w0j/tGXu8O3SpXTwdFZaystehO3M+rc2cTkdRTf8GOKiGTIx7ryraM/w0NpkzsNI/ILxkwlCfGteoB1JQ46unpjgS0L2bWQH8s4bxTUFi/F+NsPP3hwsCdd3XaIWdpQh6zrar5YQv8wLEDhylDaqXHcKyrub10XsKMhMYCxFhqBw5xtTNc3j23fcPr677vjXdvHNo+vn87HWbVcSp6NpxvjU+sndo5vb6+trU0gaRljsVg+TmErL2oj6jigZxOhnq8LIfihGdZmjSx42y7+4X//P9770EOUtwTlpLWRuQQ8Ns+9EwvCPVSGFlcGO5BhDW0DnUSjVHSKh6Mx7A4qoM0FlnHP5eFsTjFZyEbCNqXkjhaM33brre1isTZdU1qLnLO6bhEYQKX14mQAX32t000ohQuFD6152T3viMMt1BFdEwYvOULErySoTqlKL/DcKGOOf7QG4Y8VLjc6fKZD9En4NQ7x9A2VIBFTQvcqQECX2H/7lo57mUQRTr7q8N6etyJWehwnwB0hsQ+84gz2z8m1jd/9xO8+/OhjuB8hUmJDxowViIVaLBYHh4eQjk/1K51qmtj46Jci6qF0e4yeKmgFqvQGwPk3rz993Z0f/MCx7a2N0YaRtci4bheqLafT9ZMnT5w6eXxaTkDvLKBIDUA4QX5JSkV2HA5fxPaV6Lrvgr1utwBHHpVlkv2Lf/KvvvDFr1/a3WWAzZLr6+uUlNS07WIBNesIHoHqBcS5QSNGJZgoKi8AS43dzlqsCQ0ZSFBkA+JVaCpTZS+kXin44WyOdeogeIQ1c6GX93QCoJTRdPSmN7xRZ+zw4GA8mQjN27oG1xcrHCtOtktwTh2xVVfQ6qrU+f6JA/dLP+1UyrIbcliDPEKkBdkd7NZINXZAnpAwtGKo7Oj3iWR1qvhH44iJbmC45Ou5+tT13q9jBwxJ4DCS5Btfn8Gr8O6LqCMdAU0kKIpcY2sPnk8n99x375X9vVxC1StSdwmeQSWLoWYVIBMwsc1uC8dZXEwSRA1HbD/2UanrZlJO1ibb28e3j+9sv/uOd22sr22unRiNZV0vC56tb+7kYryxsbZ1fHNtPCmgYCrUogNNFSkE5B0oovAgQFBYMiZg/vCG86qSzaWB9QGHXbNYPvDAw1d2L4MP2UCAh+aUcz4qyyLPq6qC1kgauit5oU4V2Rk2N0I/OUcXNAN4h9ZY0R1tXVsZNxsVUCxHaX04X5DFC7o0ZfEib9NGSc7vfM97t7eOnb1wfndv97ZjW4f7+wD54LzBWqFOiYyQLSnmNHFhdndFwF2Gj47abEl0PoVQJbf1BDu4DWNSS4BVRzzZG0Epb0jHPviKV3urZFDRkCLC999F2u7w4XN+7H8x1HPlA90zo7DrtRwJGztCm0YNF+QGCODcHN+Y/sff+g/feuRBwtl73DlGOU2jmgzrOZUlJPcADQPIz9awJRceuGogbtyqTKlGrU3Xphsb1586fef337m9sbNz/JhghRYVpKU3Sjdma2Pr1HWnTm6fkFwWRS7KHGLY2E3Ik5aISncSv/Bgs6jDwNAcejA4HKBv67rNx9PXvPxlX/nKV5u6Ga+Nc4zNwj2BusDaL8sSO3VSzSAKr3IpoYicLddM1XPgHMiNFkLkUOIPZgbldgawbQNdXWoQp9bgJ2aXS5mjWyvT+k1vfsvG2vTipYtK6U/+3idO33Dja171ys3trfW82ASrAYtdehyt1y7CvrOxaruQ1h3dCTh0JuSIPRPp6oMkandMEj8Io4k14jgLxdHFiic7Ths6QCRR3WsxAztHX1h3xHRspaZXOkfvEOty5/ggc6qC2EKy0RT0xzRg53ejS9fEjiwPQiQi6r8SPKiZLgH7U9x/74O7u5chDtxSfWN7BXl0lGpHZYmlGyGrhugDFE2sV4HZDapdtmtra9s7x288dd3773z38Z2dzc0TouTLRQ3dP/hyOp2W+WR7a2Nne2symeZCZhrLXWCDXXA0Ya0tzHeKDAKbc+KGn3L9CDoUXtPNM4kTnQnJoEuZvO3W205ed/rMmeeFENPpBEvqKUgbQs8zbTrqMBjs3JBsQwY++LqKstjY2Nzf28M0QLsXCdyEItwyNapMQBx4PBqNIF9CvOZVrzp58pSBFMJGZ9n5Cxeee+HMfffcvb6+/ua3vPWOd79nvLWeIQ9x7sbQ7TmSaw5TE/ZcJxwUgsBX2xwR6C/+1PKDwBZCALOjcAaK6wrHQXYSOJJ9IQed+W90sG6Pws7rDpipyffu2viO/tK4b3B6xjXZ60lUKDzNT3DnRXq3jgcPvZGUbHW9IYvP/OnnHn70EegZDR2M7M0Ia2FNWtcGHjRG3JyAKGJZo2qtzcb6xtb29i033vTeO963vb29s3mCgU+mMarSlZiOJ+PJ6Nj21s6xrVJCe84cCtZALi7iqSGeFFi2p5Q4JuWk7tV5c0/5gquaplWmqg//4l/+mb3l8r7773/owYcWswPJIYWoMIXJTN3ULSTtowPJiRUheJ7nVQ1ZCt7nTISyv79njIKupVj3B1oEY5Euiv12E9URWXlsY3P75InjJ09ljM3m0G1Ct0oKDqpNnl/Z2//sn3zuHW9+03h9TBVBwp5ZsUu65p197TjQQlUcVk3byj3nHb6RnRa3K121CEMjWnUksYvuLbqPuVYCyTqbvuu1D4bCwGP7Mjy56wpXcgLVjD5PYufh01j4pI9aPW3R60TTA2EhVmWq2dm+8b5vfvPixQtFnpMCSa8KmiI8paU2X1AIGoM3ZAHmLC9G4xPHj99y480/9NHv39jeWhtttlBXo23VQmZiurE5yvOtjY2tzY2iHEEOHgCWbAogepewZZfVkG13bPt0rFflLXpvvK+Mzydr1ZlGcLlxw3PG21r90t/6qxn7W3/wqU/+y3/xv7xw5kwLNTMhEA0NDIWERqvU1gyvxJ5j+ags66qihCQIMiEWmiYfNGlsF04+aqvMpusGHixQxsWJUyfny4VS+nB2CBa1ELPZbGtzA5sZ8+V8Nl3bAF8a+NmpemZcmd16FIOm3NtocVZ9eHZn1pILB3csknuS6u43t2eK/chRHE30+mUXhRxSCKNTUKit0Pzj33qaNYsXPHEXDbybw1+s4AR9QotMi5iCB49EfrowQOcVrw6XcEnJRxnMNmUAgJMs05Awngv5uc/90Tcf/GbT1GUxyjIACdoAElZhwhpxOHjowsBGo3E+mtx4+tS73vueYye2b7n+tp2t442aLatquTxsapUxuX5s/YbTp3a2toTMEcggUHrDBII7x2cDByU5vJPNyXaQ7lj+JKjBoCHHKc/dpfDsjguTZ1gRt86yMrv91pfdfNtLLu3uNvM5lN7CujkUIqIuo5RmpBX0HIa6GwL6npBN22Jzb5ulactbOUvUdfoKe84VnZCCb21tzF6YPff8c2vr61ApEwr0ZUWeQ1NyjT0udNsgmsxmscbxCu+4TSRhZ3G9PRpLrx6hdf4OQcurSc6QxnCtTqSuKt3Zmk6/imSbF5B9PjVkSWYpmawyQv3v9r+roDqOJNTun52TLaY8HF57HOC4SZpHyvsdF3JI44DpDrJbSr5+7Hj5m/f9pzNYNVYpgArhJiYcPqTggJqr28l4srW5fcPpG+74wLtOnT65vXmCj0TWCNVUewfnMp2V0+na2vaJnZ1jm5ujvAA4IcRtsZAENjdAkFKsAiRigSrr2NcKTkwaMDV/dK8S2tKkH7qw89DS2GAxpOYqo/b3X/XaV77+lS9/5KFvzesKup9R/zJI3cB/W6WWC8b4dDplGfYKdzVs7Wz6EAjSmxOQYSXwN2v+wm9QDUAezhatUgcHhxubm4Bmw1ZsFy5dlEI0kwnLGMQC8gL7tlLNSurY0udJwxCrDoz5xRxElX30UsAqeHs7Wrw+GbzY57od2pG2MSNOtYejzVc2pE4MXXCVcUbDGQCapmqA79xrM4CtCRiFZlwdpCir1td2jyL8MYQjWECJZ985Lx3iRl5/6sS9X//ik08+3jQVRFqwMhU+TIFx2+jNjc3p+vp1p06/673vOH3q9A2nbhGlapZaNTVIZCNlUW5unTx1+sT2+ga2AYIfUAlWY603DhHmMJRoVNH0WnMrWgH73n2Tb6jMZ5xqETYd3Y4kRuDOAMuQWNYnO3H85GSytjebaQHVgqAlKbTfhnsAENqA1qBUC7YoAqfi3RCMF8ruCMy9q/cRR0ERDnAuzNkEUqmxhh5jrGF1MZ7UVbW9uTXK87XpWmZEZrDjL+JFO1MWL3q6r5I/r5mUI3/owDVDQiN5ywGPT3Jy0Dr/PBJv+J4vklGYq45g1XHV4I9Pqwvi2B5AAbZYuM8GsmFXzNwlJKMvp4MdPkmIDT4z3kxe32NMzmaLz//JF7/z2GPYUQQw961S43Iynqxv7+zceOr0HR963/axzcn4WDGWOc+h94hh5WhUlnI8nhw/trO1uVnmpYeV2TQ3xOtHqrJbJntKT8Gw9oXj8P31HGDHYZJ9QSN3xyQQSpEKZwvCTMkSCkFe/7Kb17c2Ll65BHgsrBFNgg8QlJhkMZ5OCONBuUo+OJxYlIF8IwUt5eBccCjfAa1UAGsNaSRoEmtMgZD5WEiAqR0ezMbjCTwUI1W28VF4oUhvjkVhmNwQJenPb9B7VoFtOw6lYblD2tDV1OejR9C9ZQhGDnzrbtAdzpHhKZP9Nzki/74f7NH2s2u4BXZX0wA5uVz7SJgSCNDW66ISORiEpeqj9rvwmrEZ4pRPh8Bj8vGnH//y176yf7hfFlD7fLK+fv3J0+9637vX1zdOnji5tb4FYK3W1G01O6zBE8T0iRMnrztxcmtjrZyUhSwA1YjwSLirqwAjXIkAN4hIRHaWP1mlxOMQbykvVHteGnqRgQ0Q72UnIZ3JpWAaRnJ6cufkhXNndQukFCSRrUCiF/NZAMq5wFriffG1qlK9Lxb4CLFGBAgyWqgKhOqxbhWHWlngu9YaauzlZfG6V7+mqhZCjqhZoSvgHM9UrH+kz0r+Gt5gbl6GvvcbPy6w7ntU+9Nik3nVA4c1TuscCB+nPq++UzZReY56pXAcpZP09t2RdO79CR020rmj6+XmYaDUMQQy28DsstzW06OvYGb1Q5+EDsFLwOsKmUFeHQnklUPzUya/+uXPXz7YO7594uW33n7HB99/7PjWqWPXibGslhrBgeCibWolC3l8a/uGU9cd29oa5wDrx7oymMCEsSWW4JMjSHfsGfXE0JsRa1rGDiv8kdQrj34OEbIvKx6pd1E7jfi+nEuzrN7xjrd98U8++9RTjy0hGamm4jWQM9SCLp3CD+Mk1R5BRaDNmHTpMmv5YyOGw8N9QpIrpZq2LqCOpQBkC+QPitFkdPzEcYqwg+5s7ftAWKk5SCcMb8J4aMnfAxraUIQiWqZVGvzRxxAS60htdLVEjVSM6NQX40EzRw+y86GPnPXlxACoNeE9kYUV16CydXy8AWyBwFZGWdLApglAVTIDzA+DvBrHKRMNJH4nJkdz8Ut/7W9sbE5vuO7mcq1sqkYqo+ZLuAHTjcrXx5vb129tH9tcW1sTozFchZ3hKZ0vEpKeYsOow8946EPZp4FDpevSWQCbkOk+7HgofYMCR992kqxvIaIq8BLlMEHX3Xi9zMs8X0ChPHwv6OYChTpd+8KoBHlcl5EN8WHPGy17tRlK6MYH/Ck148aAE1IsdI0BxVxDsCoHu/f09SdFDqaHIKR5tyaV9WBkVzksIxtgXqa3FiupMWEQ8Yu7BKQul+he3neJ9UaRnBCp8B2rtaNIXiP1+iNR3652xDvKJb0H6ZpypEhUuHVHAAAWpgrf+cSq/hsmFc6w8mELewP77Dqlr+OrTTR2+dYPvUtMBJ831bxZHM7gUggCbW1sbEJROOgLVoCFJnK4jOrCQmde1wWPqkTFr5XU5EpLnKRjH7hk5V6w9yOBFBfoGjjsfMV+Hke+lFyE4VV66G233TZdX98/3BcCTQU8T3OOVaO9PyJlUkGFiZMJvQkQqqTb+mjgDQDXHuO8gvYLmVIamzxhwQBbIUtlCgLRp0+czg3XynAsykU7oiN3E4NxlX45oCSbrpISJ5cOHgnyMJHBsVtweBEGtIa+iztashX36hoMkf54jYcnrZ4GlR72LcOXziucTs7QVHmPa4A8p+LM4tfSe7tfwmzZdFgQnIpDKhEq0uElBg65WO6dnBw/bPRiMZPl6PrT150+fd10Ms6LHBr8kRz31iTdxHuVuxZGV6VNuaXbQP2hrA5sdu4X1vlIXuoVFEsC/ubwfJsw3NYVq6p3v/f9f/yZP7pw4azMixa4J1dMYYkcm03paTisUfxyqdPZNYD1v4InLC/yUTlq27qqamhWDIFf0JwZy5pGjUfFaLLGOb/++us/9oM/VI5LRGVjc5mQwp5obRETDn+ZlUsQf05iPZqnlPUOKBbRvZLg74shofgBXS2NyqMm5ibd3gFnk+ntvNXVjyHbIzli3hhYvtd2r+ERyc2GOKNbwE6WYmoTuFFio0wopUjFwxFW3HlIxE8NwCLHs4U6tn3sZS99ydbWVllMMgj/OCIgAJaVL1YI2ut9ceoX8a4xGx3aakd5Ko4Wu8k8rDrHd9OA+2oIw8osu/WWG77y9Xw0MfUcBCNwLS4y6E9G7Qttq+zEfxaU6ugvu4KhjbPg4HxmGRR/VlpBt/GqKUflddffcOf737M+mbSZmIxGr3/9a7fWpidPncjLkSjHGTa4AIRp9NZdLdizEcei3PwN+o4TOuwhhGLFrnPpAIrixVGP00wGL3XrFSmb+GMVf36RinMWij9d3YsXpM2Q8hJ+6yxH/EWQvdQ3h+g59C4afJ9eATJqu4B4XkjF0+Cw6T0/8DP5mle+fDqejspSsMJJc/S4UhEdrwbHZLGKhFJ5F71p58JBLeQaTRIPU1t1/opBhb9JAjMuBUdc1vGtk0LmY4lJlaqFepPU9QKbn8B0QEvtTugrouFIs/bd2Mn0pWI6TVMvlwtIFTZM5oXJ2Jve+MZf+sW/PtrcNNWCFaVezlULlaeBUzRgAjmURBxsHogP+RFEvaRDt2c3ef2Z8lqQ3S5Hzl7X5dVxC1+Foq7ppOGLerr9i0vJNfbdV5ivnaH56if2q0HFNby31YhT0k5d3AlmzV3dM/+jhfJiwDbCoZb1xKStGWX17KCVccZP7JyaTqeASCazEPrVQk/NxOHvRdsR69FXj5LX9i9+Ncoj8FJy084TVge77Q3ighnpXYgMLSwG6s6rw/qH7vzIzrHtTJsih+ajkPDs70T9cGxfwHBjUpKpWaBTlyzl0m9Yv11QAS0h5Xg8Jl2aZdlkPAVIeKbNYnE4n9cH+5C6hOnKAMvU0BgYEdZ+pMmkdzZUYPpuB3T0evuNG3R/aa6ZLMIArmEVr+G46i0GJMGLuTdzNxniYn3TuvdVl7F5rbR/WJ4da/kdM2NwlrsCMdqxtpcV0W//2jAMaK+HQ8MyjsRH+vG4xJnRMcfdGQk36jwuoHhXrpvnbldzFwYgStxUqnuS7U8fceLk7S0Hgyxfxqbs+pMnn3rqKaxcZbDbEXiJvU6cXBQphA7jxXqMg2A2QMBQEa/IZ7O5Urpu2rXpGuNssVy0usn0mBuJRQOBoG2oyfo7nMFCj4zK63RnOVVxbLH21I0XXzTEgW20eeWMh2lLH/3iyLgb4QvXX6vxda00bPqDG0SJxg+Porjdr+K/j35hzzLsRralaaJmFB2yStYw3q5QScJX30im2xcWtJ9CV4Fkdvx+HZjcVcOPDbQjDNBrXvGrn9Vrcm4virWVqI6Uu23qEwcV13DN5Ghne6NWdVVVY6imAe0UXLkAW0bUdkHtbUHXnNqB4zxABmvZGWMW83ldC9W0s/kCu15AxgJi0HMmBW+xwjZWFou1soR88YarEQjWJexCHuGMCDWx0v0XfTHsiz6aaF4kSQ1d3LXxVz8oBUv07Wnjf4sxuan5tCIPOPosuqTnd0p10ki1TQbihKez1ZK3HX67WBd3xE5qdCojAmvyiEUJbqpB7rhqqgb4ULRrBu9wVYpMzZHBJ3XXOYYOJiDM/sPCNvbNqpy3CbItuChPnjghOW+wKHTGjMT6ewAqo5Yq6HcHo7g3Q8HmDQKYgY8Zq/PMF4uqqkZFgclV4FfQjZLjEeBtQD6DpwrhpmmV/NBbK2q50Gm5Gl6OlBH8MCXNKEdryLfV/+LIRMX/yuMoAXoNwvXahLeJrIqOxkDMd8VzYlXXXpqKvs794vMG7BJAN2EVcVwa50hbpXx3YLhR5qxlBULEXkCnVTkHGPVGugbN1l+/amlfrIuw/5TwR7CnV3owV98nzkAnfhz1VqMfDgDpus7ectstGPJjddNwztY2pwy6H1dAdaFBXfIkz+Vt+zG0e4FOIfDG9q/stW27qCqo0aM01NLC2BtyBb04PFgslsW0xLK9EAtOintFfsu4+unw3DuRncpnuyeiSjUxdxsQRO5xASQ0sCAD0/0iCD5msqnGt1LymyGsTPpZFpswqbDwekxka3bM0hclWY54rXA29uFqIZHB9xk+6l69QJ6ffURlkSsalLshlQPgDETAfbPI/XP0Gxz9/gP+88E5SwzJ/ilHcwbiP0hkfTMmyJbI2eoCmQyivjlaqtPxdHtza+9gvwYQq7py5cqoLLHAjjdKSaOOzV07XGoy7vpNQ8RoPp8vlwsp8vFoBN2PoAM4NLkQUuZlUVf13v4h5FiOCqGhOD4sD8SMIiE8oJQMuXScFT60TJ7BpFdEyLKEoDondsSNdY6kaVD2DlerHY+38/S6mntc7Ti6wiw7Yvz0eW8uOijV4WspUH2V2nJ+MrSG1Je2JU/kNfI039cqfTCIcQgHQyqbTUtLgvi0YaDe6nAl4CNcjSGjKP2/n4sQdEkSEpy070+A7ZBkf4mOzpAHjgG1cnjwwbr0v0DWiMmq9vvu/ODLXv5SmctRORJcLOv6cDar6gqr/jmV22UmuCQwKgKNQKrMNKqtoEJ8s7t7eW9vH8DNtj19JqB+kCzyUghRjApl+AMPfmexqDIGLZRB4/JzhmkNhJR2B4UTHJbETXvyfj3dznvenPfZTqN7FaeDDV4cy8Y06yrcPtwsXHlt3uyhMhRHMwDn2191PxOcRt03sRenIQn7LknqWLTPkjbAXjj4tptDY3e7XWuoRAO1m7DegzvBrZtH5kYgXVeo0S9vckCROUQQYHXWXg4fvgKUUFyNhu1a7VdDsR/5bZLDsiIKfm3KVPTNwLr6O1EGYcLaLLekdufQPIFnrGnrvJSveMXLvn7PvZNJNp/Pq+XSleZkXOZZXTdt4zr1woGtg+EuVQ2ur7Zp5otF27RUxRJ6lOZQFJ5D8R2sRQt9z3SRlwazNZtm+fzZZ6+77njGC6MqpVqq3KtcvcsIgYwBZco383Y2GXVYrd6brk75AuANkaxH3yaWz2q9Mfo4trZXzPLQDY/SSY+i72uh6V4VkGgrmo6yOjRy/0vQZtLGD8NH6jnr6IOd39u2gf6y2CpghfnctfFoieKU7zgcoLUGYZ5luQG/DHSghrqoFhNMy46tVQYG03m6Nyeu+sLpHx034OAdOgj7sDbsagZYdHmYgaDfuVUdzDPEXyD9F7s0ZtmNN52WApqklUUBOcAAWjZAmkBW0KyISgLZ/Gx0OlOtDOjxAlSaC0jmBAwVUQsWh4bqf2U5Wi6XRutFtRAtN8BWqwsXL1RVXa6Nsgaq9kBXG2hQDCjOQMCRNCUiptK2+BxKTrRNUB0Wi6rXU74aUrzrkZlUePX62jUkFQ1MnNuEnawU2lfdJTV/Th9Jcofgpknt6Phw7xfz+0HvSbCCojOjb1zRsZ7B7Y+Ec3jyRtOXrNYOS+tsYbe0SRpD5wkejQM3xH5aRso8yyHka8MOVmYjaAG2zwqW8ec/khoZ135RkKsJ8+wRdv/C+LdUnUoK90a7DirdgXcJTIxJsZkXo73DK9WiBh8xxIFkVdUgFY0RQpJPGoUw4amBlNBJKGBTj63HmorglGUhpWhbYA3N/j5YMliMlsp9CMZ3r+xXbVNi2Q0sHE96s1e9XBUHLDMAfSMB4o4gTwB3Sci2kNhbzfZfBHqmNm8ZE41uhVFC5Na0tpbyNdLP0ZTmIUuRcybIqNVuiCOPPpc2K/dSyMtj0fWW5CLSTRWK6IojkY2dIXkij0Rkan441wDBH6OiOZFnpsMofRelwUFEYQX/FOiahTogRjMEljknx46tHLd68gJLvbYjtYuu9cz+JclMOjuua8QfcW+LhPDgqBDqcZahnXdlRCb1Qn3ojvffctONkzLPi7xumvl8wTI2HU9GZQmFY23Xb0j4qpu2buqmAdr2nRNy6GJYFnRA4Q2QvWQrS2yzwjnHatFYIJ6xulrCKoBuDkSLtamxChF4P/zgDdOaacUz1mrdQtvgpmqrqlnWbdW0VQt1dzIteFYwNmK8KFheMuhZJbAAPYpzB4QN/w7McPQ3co7IbI59B77LY7xiAzcKdwy3eHHiN/nTdAfTvZehFQ3DDk8O1VJSozcbGmsQHvaaVaKkI4cdx030QPf4Drgwne94xD1HpWOHlj2QT8RaS3gmh+6EK1nk1SBRA9apH8o18N3OPMbPjLdEPHmD5vPwmAe1J/eRwyyT/4A1mcoaLs2tN938yMMPC8YnY6ikU1UVdCcrcigIj1lM1FXVVa3OmlblkLJF7iqr6FKXw/F4DGqz0VxAH5YMauUrL7aAwDOWA9wdENfQzKUFVR0BAFC5l0xdJGzDCl6i5VNKwYQ0CmoEgthnQrVKcGxwnGFyM8jx6uyl8yMmtk+cIESZwCI+XfuoY+mkn7tS8p05TnDFvekPKTwrDKZ+5lO4x1WE/orFN9HnK3BWqTKdytJ0DlKLbLWt624YxzXoA2q5HbTICL7sNmXEC+kzH/NzAf14TB6mCz9Ad8P8JCjtIpxCyikOPCzYPA104OtuvMlpiWoxTNvDH6Y36bxA/GtfN/IYMt+4Giw+r4FENS49KwxKeOiKia2h6nw6feObX/upP/rMslpOx+PjOzt7BwfLxWKxXAjORuU4HxV6dphlhfcZUh9QJ9sw/wEIGvTY/f39jGVlUXLOQLCiLm2dSxkDGV2WrTElIuaaplEt1qml4SoFUlrmSulKtaxpL1ye3//Qg4sF1O1lDIra1lVT101VV/v7+81y2TRNdeXKuYPDvYPDIi9++Ad+8Od//meObWy3qrYtyuOMyo512ZvuxHwdoopr+CyYd8kOir6Ny+usuPiIT8JuZ/Efnry6l4ZnDfCu6LTVL5hu8OgpbssBsE9xKJBExNqxg53SFyOOwuPigh12U4eRBElMSlw8iEEgR5RGEQU/eyO/lmNl+sU1HEdaUoFxQxAVzT8bUKAtC3EzqjoQVBO3j7AEEKYjgAqNcw+ty2684SYJhXXbxXKpoUMFVznUvlNQMENlLdIVquEQGMQ0fVwpSF7CZQM71jUfhfJG0LW0qi25Y9yZBiOlGBUjprlqssViqVpwpiHGg2NhW6g+LXMJXSON2NzY+MZnv/yvf/3f7O7tLZdLnQEbpqgWPBLrGVEEQmNZL8n4fd+69x3fef2bXvLK8vjJQd+erR6yei3ijX1V63lIsA7gGNMrXrQDbcVDmTdF3WZPRfpAzPmI13Y0lcia2J3lGRPJjKB5g3EEHXeggTZi6Qce2csGWAGQjcWZz5GwekEcdofNKGE38yEPwrA5uvrw9kHgVKGIXmA3R2cDBiMj9YF1V4E2MBbWUJC/g1XqMTikNCtyJqHMs5sU6zXOoF8RVAIKmgsQGlBO2QKhjkfFZG1tb7aPkZ5aStkqNRqPpBENhOch2/BgNoNmSBTcAVSz9ehii0JZFmBDQ2NHliml6qbG7sqI4XTxH2QWKs/aDOrEQ+FCKL4BRe0E9r7QyrAW+sBkWrWcKzOvylKeuXC+qWt8EbOslwadZ/BsKN4N5buh/rxqwcOVF3ffe/93/+HTf+9v//JHf/hjeVkQEDSaY5+aH/ZlcHv31yTQRrIYR6hSkfw94ugvbafOyMojkUtZdL809Y4NcKBV0r1r2traJd3opn+/KPUosoyhJB1sJVj3aE5teomjYUcCVg1PuyX335Qehd4q21HePxO3YN8guHYBO3Ry9D7pHHkD61qYb6wA+xe0vNaaCyB7BMRw8kKORmJSZjk3UrAiV4umPqzVss0aoxrTLmulap0peN08hws10DP8n9QfzjPdvO4t73jZrbdJbMiQZbyBqoJqMZtXyxroAgDMQLYoWaF6ILRNgmfD/235SCiI10CnFvgH+9HQNCflb3ndqLrBxsdNBTnDKMNV0y7my0ZDjXmQxBkK/aqpdLYH2cJQORPDxJRZQXVpRZlDeY+igBZtWAKRKegD1Vy5svev//2/e/rZZysspofBKSydm8xxZ9emrqLe4Q2VI5atS7qea1/DYRn7oFOlfwQDMxzE0tNjWH9I1cLhhzi0SnxukEypgWfTCTngBmRRljm4Em3lmv4jPQdKECYDvgpXepaihxCotNVaPBZFrsjZ7r9LZ16jsoAJli+Yo5EkdxIg4WTXwp7jdyWW5ZQKY1oGzUrqxUG1aFWjlvO6zpplU+nD6uHHH6mVFpJXdb1cLBfVcr63P1s0Uoq///d+OefrxrTALGFnG/DwqjbTTDD9mle/8q7771EZRHeYAT8xOoehuq8WQmldlqPCNygO+55AWhwcUcS8Iy3INwKFX6BdMKtb9duf+sRtt9942y23mBa6olVSTgTjBdOVYjlvG8OatmWFYNKwbDafYZeMhhDVvICqoMBAIIJlF2FjfR3+hC6RpgXeYC7u7n7vhedf8oqXZBDSBi093iCRqEz0w4FVSQzDYIwcsYRJoadEK3UPSt0pvVYkXfhEdyzRSFhXQA5uZidIB50t/SPqxZU+ekVOdWSYYNlxaJDAwPBynWj7I3Ev2mF6kUJBCr2NZVA6DCyxLXTnKnJ0Cd/+MaRU28keTMnyLvQVAHe7iLGJ1Dsj8NShjUHZHWiBwpkikwwG/5v/+2994667rlzZb2tomcIyrmpoFgqeBAPRMwivYzk+o9mxnWPnLlw+uTHmuZYFlOazb6OBisW4vPmWm6XIl6ZGSxlbMaKhC+7ntrHFoSOngGeTLpezEzOx1pHdsqg7FFIs58tHHn34v/+//U9/9Wf/8nWnrhMs259Vulpc2N+dV20hOcSfl8ulMvWyOpgdPPXU99bXJuO28AvJGfRAp4Q1FK1WfYQKl9CiFeyww8P5C8+fgeQKwNNiFWBnUPi6qYPOwc6i+V+HVj2lu96eGFrIjsIYyox2tm+qIPRyCXtqOusMmc6MdbfQWbonRGN/W1z/8apHTI++vwoUxQB1jRprASFDgmpSWiAeftcUjvyM2HTa1UTEDgGoNdO7Iq9Ip83fo/tbNK3J0IcmbejoP8JK5j7vcL/EjJtqVIB8w84kBc+4zDLxu7/5O5/54z87d/YMFRQB8Qf9DtE0wA5Hy2XVajAyNWrLxZLPD64UO6e15HWbFTAfQJFinPNWZ7W5dO5CWeaLJSM3BK6FQ33blhN2GuxGjPhvxyoL39Bk24wALUAbz/b3D5eH1T/5Z/9cQp8Xo0Dd1kKwxhiODZw4FPSAqj4Ahm1Vm9EOcGomuq5C3zm3ONbwtyhsfXlvV7cVZ9LmKHbSS1brQGxI5F4Lliomsk5F0uAgibwiqwGAvY0d+Y0SojURvfWkS4c9pdTucgm6L52MOVEl+4PsZyE47mARsGg3UbVJl/Ya9lDyot4tEclnqhQtJEB4kGB9RRiQT1BBdmARkuF3PvlzmMdu3oOHoqv3r7iQfD528wDAVDBZlJluMD1DZFrd/81vnTt3Xkh0CKsMZCVSMHq1DOx8dAkigglCqc2s/dM//OITtz21aBsO5W7a+bw6PJzX9bKqq93d3Scff7I1CqrquJoIdhEtJfsOEJ2qqXFTwk5+mSuORD4MaEcINQMlF4AGMdBYHPxYaIijf9uW6vcLSVYQUb+fHqrRBSWBqPad03Jdfh11RTMXL5xbzuvxSDZKQ4eqIcA57YVe6Nz/6x2gIQDSW7SgLEcChXZZmIjYQ5UQzSq35uCngVz9Y67e6X7g3lYSJd8MbEYXJwxbeXVzcP9NhOAA+WCDF7howzaKN1Ic/wtRP/DSKgFmFlm/vmOD3YgUB+7ghXvaydBor86OOyzrKnD27rayB9SgxgrXuYbIimo+9enfP3vxspovzpw998QT321a8BBnmgHyG6xVaIdIspIyKuFmbauhPJU6MNmv/YffgPpyyBOU0XWjGGQdwCxg4ocuxgXMN1W4QUFsMR+e9Tt1NXkt744IelvHKQEEJ7kERR/gXJgdATqFzSQEZQn9pViQEHUJNHeQ00KvJmd6kr5OjTGcQLLNjOF83CY0xPaFcxdms4PJ+noGzdyG64gNVd5ZsYKDy96RJOGXgBOmTwIr6NzsqG3WH22s5a44BkXFn/MI+Ork/pY8Q1THFSEODMVfZY3Yo4/B7/FGlJ2GSalpXif+ThK4x8J6v616t6GBrNTIr/mIYgGgJ3CVtQJSDsp/8j/937/w1S/vH87raqlUgzFz8DXRrvVJOUgDQd8ljARlIFQVxHgpw5N0UeKVeAcg26auywKsTZgvKlDZmY/A/ZPVHZiOyMxCujZlnrdNzThv2ybPcwBLg0lvPdVYCilkVCoIUtvyA5RNBulRLhvJZJBpbFV8QLxDajG+CJj7CAUZPfjgd5bgG2gJMz2UFb5igXzuQPxVwth9cZOhl3WbOsROVuwmuy5+2VftiP7weoNd6b3pbdIVxNSTrV63CdZ4UONDJY1u6abe45CAXe7g4JmdETkCpS5yGhv9WHxI/FIkgXuv0bt1vIReH47OPzpWniBlVi2Q00djivDUArJGZXLy5MOPPPDww88//zyAfJFE6TTkTAaCMbTr7GFPIO+NhU+ivwclF9zVeglAHJN40Bg5YhJwFDlnvM2okbdLMfIqfQorjuSIV4ailyAfNGjBTORy/3B/WTdFWUIXF86g8DYGjbEpIlVTsqXnXaarbWbncL02hZQLAe45xqCPDmM2ZdQZZVxkdVNpVTfN0qYQOziWWwnbJm94VVZstUiTXK19JhLbI95WbIHVlZCGKSIK7ZsVz0+H8l8vhNPHR4jp1JTyz/RFkfACMoEEZ6DqBQ0k1d/CcJMvnO+FcsQzmVIw7uiUgDtTcgTbHLjg6A+j4QaIRXxRZLnHJIHYYirRamrz/HNn6rZFDAPWmKOUKzT0XcjOfko0TDeE/YMdxl0ECqk7Ikyie0vEmO8nhMjLkitue8xZZZyG5grwdl/CjtypV24SXWHaTEP+MFTYkblRuq4BpEW9EG16d+DmTi8PUJzI040aPgD30KunGEDEqBg9IEORJ7GMG55tba5LBlqEyMFVFmW/Wbs8WeqrrWTkULKUEVsU/YsGSKfDFlb7r47aRhGTNJ0H9XfWi6Pf2CG2kvydkeQ9deF5dum7gtzifuLBrVJf3Y9QCJzaHWKEEgObgWXAmUNeaP+EVUfwzXl5dPQ8pbrbqkxLvzCxm87ucJjY2eKAiIlcP9Yr7EnRCSvUMy2VhrexeEa0di2nC53DfM9zxCyZFvp9L7mQvr69IiM0QBAjzTByREbOtzCJ1J0QMop1CziQvJjPF9AomHAdDMAoPivM1xKN0vZcZq8zKtwsQniBzsR26iCN6VspRVkUeVm89nVvnG6ftLk6yHSoPEwiQsJcDC5078NYgAwc6e27hnFyc2/QDX4/dFj5E0tBE5OxW4D0/BUDHXB22bEP6t2umkZ4iC8/6Qwdu0c6zg/qcNdxPkWWSHfX2Mu918MmBoOOlUFowl8MxRvkn8dKDZI5sGD/10rFK1ye2FIJTwn3dJYgRn1pjfcunx+P87quWwQPW7K0qqc1ebH1moVNxMlv7pkOpOpLsqeDVcha8Q68bRqOjgN3s7jLmV8kFxhw28eZR+5/4BUHaQm5+ozNF4vD2aHWBvKFpcQmwICdJOHoHgbdIyA5DnqzYPwJEo8RkK3RqUbUCw0MHTKLsfF4RLqbarH8gMnW19b/zz//cxtoC9CS2VUKiUbJQgwbOav3xpEWaz/3qGddBnMyTGkS5xrS7P2lsTqwWvoPi5ZEx+8yG9fcItVuSUdC7QY0GfqKSh4R0F1wJmVuk8PjXGmMIEohWg5AIO/+thzTxzacwHWjj2gJxRGYjaBFoxptOTlVpRxQm6+NCcff+iyfo03dwfKlg7cPCANmdAsTYbKnn3v2YDarqlobk0PNGiqQYbOoUU6haCUt1CN4I4vfTpcliwC+sMmcDiUCnmFI10NVWfcNgEgQR6FMWldfgYmkPJXorpuqaevxZLqczY3WgLJuFZaxxJILBujTBqGEgMxfw/Iyl7lUmDwIjnbAQULg1/cyB997BtnFQkIoATNF8XYQ95Za6Xe95323vPSlrYFO4gYT0Kw2brkuMo3e/k5cFnYLrlhGz8lD27N4Bd36HXWP6F6RijOwC8mAjwzAqx3s2g1gP/rQhjZlE37rQOARjCCIyePCUrwAprHlLCuxMXf3vahQClRhYLCRkxUIMzSQQRXeHLY/Jo3DPqFH0Ji0djZwvAQvwvIPEiktW3xkD+i+TrWaWWNVTZgqkannnj87m80tqoKyb8iwi+QfZfpat7OXhcEqQYcQFqcJvmmEaFKxKyvDsbYguMUwEZci8WGN7TChE6st+I6neNL1wpcanEGAqqm2ThzXAA8T+WiEedngwRICUiCma2vHNtaKcjIejzbW1tc3NiaTSQ7Ya5FlPGfqc5//4iOPPYYKEyDACcFO+jDW1gG8B5lZmeB5WWbGvPn1b/j7f+tvivX1Zv9AachSgix/6utmM6HtW3TSd2I1aOWqpKI7Itfugl59HwVzJLkk4eF24UJb+6vdlF3jw3uD6ThiQs4veUEAWQW1F7BwSkhxsyDfpq4BrowInKCcdnzRNtDk6m54O3Gg4m6XmWKQVFEtJa81+bKyHQpeqVKteHOnUiV5wuYayD51Q8S0TUE2/EUURdbqF154AQpZIP5RMA49/AjgAqFUi3dJVgOwWRgHtrXhbFoIZPmoTIhcCGhMAS5CHAJkGzU1wLy0mI7Xi3LKWDtHw9sWE6M0T9cSGVOMLPdAT7hlIdTTEOtjmVarjIu3veltH//4jx6bbh7bXh/JEQI2gNiklKNSFowXXNRasJxLzlleyBZ06yprtje3P/MHn/rc57+ErIDq5QFMr2kayW0DVCkEVAaBoDIscC6yIi+fe+757ZM7UJoLIN+UtOHr7iZul9UmavTxIBCro4n2roy8GvEuoZ0Rk3i8JeOPg6adbot0IGwlu7nqDl717u5TS5wkMcj9AmhzG4lHBS2Y4DD/VV0LWQis1eArddBJVM407jXtS/zSeRFfCjQZWypYtqWF3Qo2EdGGllEqcURXfw7u5afCXMVySo6O58FZab1pZEotL13ebSDDjkPGjgBNAikYu3L7+3RwaoiFoU6fo3IMSUdarY2nN91008lTJ9fWN4syL0dwFHk+WRuvTaf5+tRcUW9/42v2W8WF+cwnPvm5r3z+/LldrZWUQDMM6tUAMVBqAXAEKnWHCi2iJoU2baNbKfIbT1731re+7Vf/1t8qxjkzeVlCli9hajT2doGFgWJoqkAgaAaAskwIpkVeN2Z2UC8rhYkoEhgw5kKA5WNTkQG+QkYU+bSgdo9kddXMi3rRmlGzAM4DCQ7YZdEC4WPpMKxuRR/1VeA+4Q4RdiC+iI6C47v33KTqQLSFoqSJmCAd7o0NKHt/XvzGAErF/XCRSQREWkbuVOEI4d+20KGncLZA6DEYmtT5c628M0fOZWSWwn1go2C6GqimDkrZD2Wtev8hHjz0xt01XkXS3k3VvQsJVHsxdCoCaacWy0owYSRTkC7YQGBTQ9qQA4mSHLXmimP39sllLk8c28yLgguxc2zrjjve9QMf/shtt72Cc1MIKM4MPj3JwS0v82zGMj5XPBdl8bZ3vPN1v/e7f/S5z9x/zz2H+wvma8k5nwYCuYC1Qvk5AfnFgFvMy1tPX/ey21/xV37q49ffcOv6emHyHBJUGqUaA0XpsBw0kCsANqwjhBkQ9YyxBvqPKy6y6WTtzPmzi6qCHAYIGqPGQa2bcIcYA5uGVH2IImcaio8S9JIBoNRW3qQ0DLc0sYFIyv+wBO0u5VGfdfdFUAuHKgK4zLIUPJFqzT1dckhemw759mX0NbpogyXcf9dQuDl8TuWTOnlUxuiqWoKcxCXwQRJbsGVwxOFaQuoOzBe5sgzy+jprhCI2DjtOvngPVvr9tUyJm4fhUzrYw8D0yePiYGgqO339DfuzujX1888+01QN+PTQwwsy2Qd1vYvVGsPwDRUD0ywDL2FeHOwfPv7wUz/8A3kzz0bTHDKE4QDIFxDTcomzw3H3w2c/9mM//iM/9uOf+u3f+frX737u/LnLly5evrLbNI1u6wYqOlPlD6NrtT7dHI3Ha1tbr3npy37h5356+9gJKcysbhYt4KsZ8E+4qdYcfIqQP4zvaivP2UAFFBgB5Ztp1eh8fgCdDdWoLKtqCVFfTE/z9jbB5f0bkyQrywK8oRrgkxgTJlr3eopJIkeD9qoLsqVIfloy56Tryo6+ztzfMHHT+t7DaXhJ7QG/j0JYpbuB0sOr65FGd01H/9Rg6AdF2X6Oq45SAzpIYyAfhSL6/7WqqlDA22nRAbptHxdUFL/7V+Os7cwAOK8xmoNGCR/l5MRC8TPwNslU/7lALUe4sq7teixFkOu6kaO1f/r//MetlA998/5f+Tu/enn3smlbKpNMibYUjUPz0zqQUKGFWpAEwiA/reBCZeb5M2e+9+zTO9u3Cgik0bThfeAH2ZZYOaOt2raVecmV/JGf+Ikf+YmPXrq4P98//MY37nn+4tnzzz27WFZAHywr1icnyu2P3nmHXJebm1vjciNnzUI3TSZyaG4GIC9aZGeJY6DIqQkWHOKc82BqZVml6nIj3927IjibjCdt25BDzQFF8b1j8nUck4i2rZpsnUiYYChUBCBiqZYywoLbO1kDNAq3pqTch08TtUSaOev92kcpxr9E3jT/Ip52Y1eQ3xneoW5i06+3f1KDsL8d7es4O3bgDnYMMHNEJU45dC2BYbGw+q8FDdlfXITIOVF9b410tIF6ve7QNfId33X9Monho9PSAjn6YIwo8aRvtXaMoJg7X6WDTWdyj2YJlmlhLgfk2K1PNiDVqIYyMgobx0CmA4ho0g4j/CSYl+RVBoFHpmlTt3qK+57L/fnhAw8/8sqXv3ljY5IZYemePD2EPwVrByN4jGdtDVprBa6zrfWtnWLrpo+dznKmlgfLWhmu2syI0WisyqY+2F+oDOoyLyBZQUAMFhBYlo8juWG6QhSjjraQiyhDcqTU26O1b3zt67uXLuaFFEKiHw5QzmA9kxkW9oRNYHMrDvulWkI1DpLv6Plz+RKdRYhoqQO4ScyqSB71nUMDC5/I1kgr9Bpwh1os1UaswA0pdC6IhbPjPqY34KNreMWXpDxlWAbbVtPQBJbwOMHtFE1Dqkk4d6aTuaihkTpoc5LIa5VonuzI4HT0l62wRn+uTieM7Q5rbh8BaU0/uLrZkUYOB833qC8yBT4b1Uhjzj73DAXiwLeKbf2wjpyfU7L34sbclleBkxmEMRTW4Vzcf/+Dd773uWM7J8o648L5mW1UivzLRmOoCOYakcyZ0iLLWt6qutE1VhWA/uiFaXWzWzXZHJkJE+MC85FxiOC1dNpSBKHwtlUw2+O5RkyHaseSTy9e3HVOEIfj9vvDT6XNvMRdjU0StTY1Ws4oglUmORnaDjXW2R+eAONkvcFVi69OfY3hl0TorTgGv+/jPdJb9QG4WWyABuzhkBkfbtUJmBLjGoplx2MlPyFnaL4EYC0trGty4wE8VgEMKHb4nhDNYPpFIvIaDowYutNdPz2bB04KZDxj3v2T1mR2pvSAfR1+ObJgXVej7p8WaU7BHPJtoBiXULNztpxDqwTOR6MSgrE4UxbUjAnBHk7sDQ+dMSmgbXdd1wXikGVRXjh//tvfefS2W16R51MhHdKNkFxWWvoREOgREamA4sQmKFDVHeaxoXA+dG6AdZI2YQjy8hFiQrLdvZd3vYR/fBsNnEUkZw2e6qzgWT1fLiAyIaFQXo/hOQSPNRnoF3CugBZtmvkSMzZweAjPQk06Aeo6zhxac1JDqbAdVm8q5zbuUPW1kO8Qw/bAGyudu2TY2S5B12T+77SGT/dsqwCt2qB9UyIo8LYODcTSOdcMEuOCBRvNk2Ou1AqHMPzUEAc/BtKtXb27NDsslWYBktXxMAN2AOxfbKuHEY+opE73tQedw9ciWlefc3WAh9es3E/X140Q0U3TLBY10C0UYXXGRsQ1ogxCDMs4lQVbjUHpRqU1XGl025qvff3e97/rA5PxGCtGQWCWbNTOmDAhSmFSFzZKwhopSNGZxr5KVgb6kcOzsdpWRLJJETGvgSGHsYCSSKXGJogm5/rChbOkQlLmQ+JSAr3AVVeJmR8lHWbZcrnMwPAGJlI3LcTAIG06kQku54JKeLid4Jc7WXtfTNC/FesVJ+/hQpKZ9Js+QijEEeaIhlfygGhrp0dSYzPVh6/mSh0+otWjQaMNIwRXjMM8Wrs0+LdiGrJ6FOThA9KGVAOALihFvTjieXZ6Q/zOvneTL3UOjACKKGI4w8YgGHSjjgcQP7/3Rv/VDqkwL0edFms3NiGHEFnG6NFo1Cio0YYnoS/HJhaGZGDPb4lu7TxQbTEQRbCVuclEIc6eefbxpx5Zm07XxZSVEN9FPxZYml4goeXisNleDUXqjdmnS/d0HSS9kEzf3rO36JeOquYMAZ7JkTy/uwvpoNBZDR3tLpfQEQ+5hCHMC3UsqS8E9GMBEVC1TauhkiUUyGwbTFzErAmaUi+xw7Nd+0NfTztJi4vXzW4p90dkVSXKnlvIYM4kkiZAf7x15uJpVxHiYTbN0Bfd6+0mGBJA/szk/BW/U7AdcwODpdtNSHKIW6dmOz8WRFQMIOBFW1MDNKfzB1GfMDqbgWaryjKA/RQQByWaxWAE98kMnfnqf3K0bjzwsp1veibG4OVh4rtgNLsPinI6GWN5V6yBbhMZQrJ0yKL1v7pPCQyHV2GflCybzQ8ee+KxV7/iFWvZFMvwRPvUeubtrTUAiulxjvXSZuvupGCoDM5O7KiNZLO9wO4k8IVLY9qM8/O7u5yZpm4sLC0OSOANiB6j0rsA8mrbRopJUy8hWq4NpDeAk15BxjTRB9IpJWt1y+BElpyXhUmKJNIoPpc6nCKqzOPx3eZJOonHWmHQO+lFLI+NqhF1amnFR+AUaSoFW02Y3U9XHbETwt3VOZTDYyiPU7R2KoHX21n3CeNRAg30nURkpWuPETXrcnd10x69X7S41vTGjnYS8AMiXANFGhhgCbE7YRzHTmcweemjidBpU8On9D71tBpyIYbPoZp89McYG47N57PEqece69IKaZHdfUn/wwlt2wbanWjFFFjM9973rfe8451bmyehD7CVqkFLQ4LBlgtIulgVAXEwHHa/E7qx4e35ZmyOeT2r70IKBoO3zqCQgGlUU2XT68+deSFTqlYNZWoE37Uj49BZhwJLmWnR9h6P1uWoaIyBJmtQZ4sw5ejew64w8Xt6XukGGsISzoMa5oWw43FytMsXc/hvL9xtnAugJVG8izRtB/LwZrdvO+J6SfbL0DjyjU2TrEenXeETG1jRlCcnDG52x64SzgSVxaVsBag1yLsQwmObEDoEvjNJ0I+pbZIZ3oyaXfbqeCUvErE6qkQJXT6QcbgChjZ5B1vnrczO7b5QImGOOo4wTgbPjN8lYs+pRYDuXpR+f+mnfubTf/Qn9x3ugyRFnyAxcadbxiZxVNwXZxQrW4J9qpTOIFswf+H55x5+5Imbr7sdkgIg+R1UaGurEwoMKxJRwq0vlREEl+MUPr+KHNjJNumozJG8i70fJNbhT4j1NCNZfOuub5y5cMlkrKkaXyHI9Ve0c0fFw6FGEOFjIeGYHRuv/+iPfOy2W28phIT+hgjjsmjt0K0v0JjF4dqG6JGBTgkTbiEsIiQVx8GAdR8EzdK6x0N7cvqVaNXbfDbDwmvTXrP0qkZPeGe9TIAskj/RvAea8PlvyT1WbMz0nBCYpgFwALGLRrUanKk2C46ivmRlUS9LegfYOTj9gKMiSEJoWRgxm0EImJXrVg7bD4gB0hnQ3CwAof4rbVw/J537pHLVrtvQs2L/ViS/rMFGQkrpbFK8+lWvfuCRhzKo3uxkg9910a7oPIiEMIKyFOGsWGZm88Mvfu0rb33bm64bX5+LAioAUg09aISA/RCoPLdt62gd4pGWRcUro0LuPqVhgHN5RSO+R3hJwgRAU1FcGg79Q81oPL50+TL1QPIIPkSckDSCMruG0U7ia2trr3zlK37qoz/8lre97fh0qwWWhylLkXbnvarRpHlp2GE3dt4cP/R07bV+t7xBJ4xLL+PfyGXBd0tKH+Zykg5PeBWvL9lWx6SRRhPjSWfAoWCojorbxPEZzuKN1UJ7iVeNhoK/Cc403M0nHmBfOzwov8+N0vokfAk2O2VQ38W6yK0e5xKS4tWPGJcfPuWiagWFvhnPOY8rIFnuCkXtVlFvxxmx4qBpOUqz9myyk+2Urkb37FShgDdBTUzVmRjdeONxCeWfnS0Bm9rC+oOISNYf6MobkHXdjMqR7WnC+XPPfu+Z5797fPtUzpWRhkETR/D4awgcQ8zI4yV62Fe3FaLt5Ty8XijYOHBcrzAylZP5tQFJo0CY5nxvNqvqen9vX0GXCcFbu0dQS8DcQQYtJCBaXfJJOXn9K1/3y3/7r58+cV0h5drahlKNch2ng4/cMnCsIRAvW9jeg6sTC7Y4Au02Hv3qvalkfThJTCBvaEIH3ASgKITW1lRLsIPyD6a2ZcPU5yoeQyBB5uNnLD4CQwz1U8hpkb5XhPmKNk5Iq/Bj8luXviH3ss1hoRmxQteRvPPFasDnYtKrs3MscaZeQTcqa1HYhHMS61jRDv7IC1eaxxbKQieWMxoH6PRaYOB+C3t/2tAZtr7eUXfv7ZuuSoR4fIz0njp5487O9uXdixhusUFg8LI7t5PVCIOfAEE0xAJBqYFwlMhLNEbE3t6VBx789qtue025vokB8lYZ2Pxt05DOk4qiaEd3ikoFz5W/Io73dvUK+jwpWetA4KCUcbM3O5gvF4tqAdpxA7WjOcUkAI4FqVBMq1opobOXnrr1b/7CX37jG9+6tjaZN5oboVVbg18DAERWbMdCzY7Hx0Lc63X0oHg9XLx4wL6MX8Fp4NEbI8jVtoDVDEgZXbKuP3M8Y44oHUv02d6eLtIJzBzhgFhHVy16mTAKZ+uqR97yIR+LVUcTO3hgCtJgPVoqUtp6MJjGYhVBLx0dvyAvF23MDvcfILnIFej+QfNQmwbjiGAM29wbK1TkcM5QL2ZwtWM1ra90P6+QvJGO4CokuDPR956J7MMf++gjTz72H//T3qVLu9ooCG7bOCVmW6FW46IX1LXd8i1yQjMpm6YtlMKMeWh7cM/d93//u987Hq8BtKvIdQO1AqiwtEtCcQwzWYSgSfpAxcBr+rdyxcSjdw3eLocxQPQnOpMXBzPVVBh5BmUNmleWeWZ020A0URuRC7Y+mr79zW/91b/2iyduOqXMqK2r8aTAGD90l0GhR+U6E5UwIa94+iPdNHqBEKikCgbuNVxcOrJfvJ/O/hUYh9UcEZtN1UC8uREujrQEB6Px44l0/WRyGdwIKvu5rgWsYdA3SkLjOwLE+3h3GGK8hKkCNXh0CYJDUIdzbvP7MdLo5yGN2Ht12Bc6jvmoVzQTPSwy64k7gtcQikwYI6BPQbDTfEK/9zu4IV5F/A6CpP3F1+bEGjyik30VRSpT575SWSZ++a//4umd6+5/6N6nn376mWef3d+9QsVhYfmaBoxXl8BJ6+W7UoDiYXSrVaMajmlZxXhy/tL5Bx5/7OTJG8H8ZRkQua2sFY/Fjq4rRu3+8JpqWpCSXBsx/x9i7661g32SVq2B2g+A2URFvs3zHOtBNAKq4hW6aTama8e3tn/2Z37mve9/75pkUkwz0+bjUgqOxgSUKwllg9w4OwsSdlAAacR6JpFHYp6t0v8T/dvNUdyaiGrA2WiC3eZo2iQKvKcydE/GJmrMgsLvrCt+MG4PioniUshc5pmAqoEhPpveyS5bb02689NZbhcQhpCSEKDUYTxTK3KJkOLgUq8SEnNY5uC/8jbDEblIFtoA3WiVZgrT4bG5XxoHjk26lDd23qA3o9E3Xaz7NSjiHZYY/gwqKprBkFsA6CJhRDn9qZ/9Cx/Pfmxvd/61L3/tgYceePTRR5564vHFbJHngvCL2NK3QQAkyW5AcGE6rW45wCoZY9AcVAjVqM9+9ktvf/3bjh1bb5sWYsBU1DKGrqfUZw2VaIN2cgFSUeQXofOhpQzMTLJeFxghVEiDjECoaIWOKM6zerEsy3w0Kngu1/jaG1/7+p//mZ+95eUvHbdZW1etWQKGgwlwu2HTBmp3GovHgWQXF4qN5j/wyn5rd7e+A0w5sXjSCz3F05wFl7D1KsTZRxSdG9oWQ3ub+UF7Q9fJO4zbwwMKUEKwJpjjKYny45Y14RU9r/UKMgA1PUcvHTmfM5PVTQ1APV923E+qK6TjH0hCJUxlh3qtHEjkBQIMnTMVMs6UQ2INUn6sVA+sSO+agXm/mgae2DZhYhLGGBgCOOTglRqKcuc8Gx1bH/3QRz/6Ax/5wYOD6guf/6MvfvlPH3rood0Llw3PirxQRjU1dF/BqpxI1sj7ddvWmLQJRWd0ubG2fmX34pXDve2dTUqSsOwZqkl5PStkxXgrLbxFRxVK5JE9iTZ+d9YCpNLhkLEql2o0gh8xVCRyraCSez6aSFkcG0//+l/52fe++x2jybhRvNWNyaGZE9i7UEUIIxlOcYv9msn+8bx29fr0UojcYqy6xC1cHKyOXQT+eS4TMJUd/Z0VCrh3Q0rx5DM/UCdfiZChPxaCwHNy+0Wos4GtejX0V3j/oGHR3EBRJqPAOSdyqOa7WELBCUi5iW4dE5JzhYVYkg1DRfFhP/XxYL0YwXAUQHTkUZI2QFb7367gh4nu7Z1bnahJn8J7H4bFio0XpD2L/jNZtczAzgEAM9dimssf/tjHPvTDP3LXF7541333PPqdh5579tm6qqu8BnJom7paNg2YK5RRqdq2qiptTJGX05Fp2uXu5d1bb7pJMjBsrCrspy7x3nSKb8YbLICJonjqqllDPKadfOoHbwkffI6QkA/F/JVWkoucixLamI9fc/ttv/zX/sZLXnV7bXJKp8T2qXgX1xOKjDSaLEPBhyBQAi/3r9KLoEZGbx+b4wAX/V0RGiN3XHM+nBnNQfAf+H+78s6BU9LNFe0bk1zmuKB7A5tS1kIYxxR5DkCIlMVaxnyVnmfu6cnAvUJhpwmyPKGQmcmKfITZqNj21Y0rHXuAeMUu964CnDw8GCfOFkOAsZXAsRc64Xer9d/BT1fYeIMXJrHxTpMDP0eD4sq54A1jUHOCPmjyUZbl+aTO7vj+O959xx2zw9lXv/BH9z5w/1OPP/HCC2erqllKCepN3UIxDSpqiZVZ5/PDkeSyKGZ7B7ZFi6UoWxAvJTwPXLDTaXmn06Ajmdbx7ceIHh+0SCWObTAL1bYEh5Z04JGrKyPkkrNj040f/7Ef//AH7rjpxE7WFoCekxCioOpgFtBCLZ78/nAZwtFkx4ZWgGnEiw9bPxpSPHp6KQRjuNS0nvIUAXyTuhOxiWERS/aaSKt3FXtxpMnqxwVtYs0MjpBSYN8oHj04irHEKBiMPqAH31qmmbqmhxEVqU4YnxgAGei80JCaCsXJJEQxXB3ymDB8UcqQIj4kb9PHhAF4bkAPj7zQgyrxNSgV0Vxd8xemx/qucuf0ex+SxfIzKGOwq4KuQPK0DVdiwviHP/jBt7/73XsX9z/3Z5976NvfOvP0U+cuXVmIiguuGkhLAk21zRbLapcdbKxNz50/29StKDjILXx1Cx+KljiVAI6C/eHpIIJJe47lObgXK7G/K5wLvRoYV4bJXLctVAPTenvn+E//+I99/w//yE5RMp21AjS0RmGQyNfKdU9xnitKRk5oqDPd3sU2OOlOVAU4kHe1Oh05rIgN3dlrI2nlVTxPov1tFfl70k1h3ySEllZI3iyyWWhnu4iw1SawmhEgSmJFIEQQeria+N7Djcr9tDqJCME6rcFHA8hlCGYFLdqGf8N8+Fc6isIC54v/ciIZqdep0F1Bkd7iWo5V+vbqBlrxQ1ZwnqB4OTsCDYAALHR10cC/jM8iQ1ctGTON1rksTuwc/8mf/ImP/eiPf/2Lf3r3/fc/+NC3Lly4VLFF1rRQqBnhqfPlkhv9zJln68ViUpY0t9SCyDqWHc+MYBkDKmRwQ3WYTryv3Swl2mNsL1IGqRQsl8BCinxnY/Onf+wnP/zB9x6fbHO2AFSpAQiHbpWtx2WlCdUT8raude24XWO9nB7aMbiGyRLYpGh7N3KcuOR/17xg2B5yDOmo7RnDSAJxxsEjq9wk1kqQ7iYwvmTMFPrDur0gdwmjZk2VJPIevbXDUa0aa3Bed97Bsi0SpLZcJCooobt3yg7JjRanPMQ6w4Ah6X/v5Dtj528ojHzEFB+hQvdfMR1BgtC8djmejrwPaQt2mWPobmuRNgLGLQAgqRyGhjoUmR4zWYjsjjvv/OCHP/q5z/3Rl7/xlW8/+ODe5ctVXVFVVoA9LPnDDz2lmhr0LUz2xY4k3uEfZ7rZbhepZUjl4oGPpPMcxu2Nr0R+OOWbag7TjaD2b6PWJuuTTLPx5Jd/4ec++OEPj8dbuRAtZAlChXjKrMCmgzBMBPtQfS6qB0Fb1+rQkTcinuJVUYbAUWPBEeR3F8MVaJyQV+5K+1TXy9btQvv+A7aeU+FjeFa0sTxbTPqEZcGUif6GqdFMcM2hrTpHiWi9Riv24yA2KmbFHeEchu3aXLlCtzQZyP11Ok9do9BGSoOOMzApHuYfLgrefErot1cnE/Rij34AKZ7PgZOjJ0efxRcMSO6Q/WK1EBI0TjxiH0KI8aIfn7C2cC700QaQjlIf/tAHPvShH/jd3/nPX7n7609/98kru3tN0+QGYnrV8kDpmWDHJFbDc1guD+0K6uRgEByyRriAxFx8OqUOR+sf9QaMVEwfNiYPKeNM5nmTmZIVt5664fpTN//4z/z0Rz50x3q5kTHQmPXScGhqjDXrwY5HdAolWuCakgoNSD/Iz3CdPiJHVkrG0aIH5ExQ1BID3dkBkUykCQ6BIL+3wjVupmzSCbVXS5iBI6q4to01AmJeYc1uN4RQPcYyVRLaqL9QzXAqs0J9pKB7YyewPHQcBQ8citPGziJ3FjXOtHZUgANaThbnolgxZ+PsUVZe5xGdZxEs3z2LEhohdOJGGVYrfqEVRnL86qku1J+BZC76n3Vmami6EC0e9KbIH0fqIyjEy6pqGvBOecYp7GzqRikObsJxJowy4rd+8z/ddd83nnrkoSuHSynzrY2T/+yf/qOX3Az9hACIhW4sl+IeSaRIGMT2YVmWRV44ewtQF1gDyQf24wKSkVhDIrOfYLX3clwyZvLp5rnHnjx76eJLX/vS6cbxrNaZUW09X+ztc2MabWoKcYc+qTgiyNCCu0khi9FY5DkW7YhNbS/5IyeafRmHuQpL2rEMU4XVFkYOX6dbJrL+Hd/wDb/7m8nXBgn2LAEV3S2I6i26w+KODc2/fTuq9wmeC+yz6nZ5LmVelJiLRxkg7Fp0TDuGzqf9FMdUR3SmLmydelktl0sIRtsJCDXxkkmMYWI2LJx4WgKD9daZY8fQQBM0MWYwvhrOj55xbV2OutcOk2g8N0fP3SCfjNJi/B0ixwg+soWaO3PQMcP2CbMGfdJwnyKQJc/zrCjL/+1f/9oXvnHX5fNn3vGmt//ln/vJm667HXIYQg8b2187wjr69Y1kMmPjUVmWI1J5KBFUaYxCtw3uwxht6T0sVhmk4pu5zDGYCAUoZSahwHveZOVGVqusqlSj66ZSTQMlqBvwptvOjFF8kNoaYr8VWYzHxai0m8aZYt78CnaXf6MQQono0UW5wnrTjRzg33kT/dtEZBsrtYly2MG8uGlxbIw+cazJZXG6NrvOQrBGJqJOLUnHmRt+xQA+DphKWx2yh5cIb9bbpO52AUieXBzW3s2g49SQ7l/X9WKxaDHS4cOQCGG3uYFOCBGO226M1CeaRvNo5q2jw2p9WLIQZ8vpwJEE9lrREccRenKsP628sHv90SrOEM48ELDWGpgeWraRAeqKZ+H00DpSu2QBBWd4LstZU/+H3/ydUzub73jnm7c3tgk8jOsN5OGz0bsOp0jScM7Ho7IoxzgW11jcGNU0dVW5bsaglNuBoT1Got0mzYLfSmbUYM3wXPCmVfk4x8YNddZo3SrszKiYhmQMaLKKSPp4MD65R3BejEajyTSHLsdYzs7Vkw4JOx0qs4qol4SJSuSNYresqKJiogIxDkw6xk5rTgx7Cneypbds3Q2B6xTlH3iuZPeGnTH4moNZK23xYMxhcKp48A5RAxKHErVEPsA80rcMppstVjY0WLcPXNaVnRpbfQlnr66b5WLZNg0ouT59GkeOjeZoUzot0aanpFZjMGQCDyZ+bSsfQX0dS7p+ifyAXrT/6kUc8e0jZN5VTe/gD+5o9qhyY8GwNgPqDcAL68V0m0lBGWjMWMFsNuhvqOYbMv9LP/kRw/loWgB80aZ+EqI+1iWTxFI3JJuzbkuGeF8Afg7dBzGcgFWs3Z5GiDZVOcQdCBvQ3o6wBgjGAPW3gQIbHMmfa90gp6WeltbTk/rSvKIPpA11+DRUKXA59AT08AWkqTBGWHMroC1cqqtspoUS7D/gNgugbw3lSlyicrT9Ak2Fi3venC6f8AG8BGaF43cSiMG3QBxu8KnFZxffB9/9TToFF+ILwoPcfK44bLUW4ordPEWHdqTp9s2raWsQUI62iz8P3gVT65L8KSsMrGvCqdCO0eAaAQQvEG2IAcQTH7sZ/SBjq+fFHB0N/eozteLyyNSyo4PM6ZYS3wMppU+h/QkVL2CWJMtMDu14zVK3OeO8KIFUkKUlAjcOIvgd7IiUJglm3rFVd5FTQUFQCEHhDcdrfJ87ClTZe0L2KEc5DU5l6JykcL9SsS4BzcAp1crfifJ9aDlD326sv5cZA64brHeL7ZcchNbGk6LMSHJkB7wUNQ7xZBjZC7GKTIkiwQNFvWhc5NndaaDiMl097KtxyK3Ymx05pWFSsO4gN9BPB15FQSyNW2+F86vFWyRJw/CqR7+DdzKgIwrVWNeH1e5dZ7s4uuuUFPsg92j3G/BX5/2z4wTxA5kQmMLh1D/Lw9z/IrQL/AtSQDMJgVMCHTlDMgZgB83c03A63yuPVYp3mMxYczvqRj23iJfWVjUjvQN6gzYtNT2ItR+7yfElQtqHNzYgPRVw6KLIYU2BPDjMbkSzru9J5HlyL+NNWdAeHeype7j8G97VNEy6WUgbxTfCJqhArvClyAQUXmotnNt7RZzS6/ALVtrgvsLYCXZiw6YMRPUs6r3kvT7eKnACw1XAskI48jyE8QbdLl5aL9ddLn5S8KBrFTtFxcURHAm5ghbUzovKnmEiC9TMR8QoUC60udNOFyGMfDCuHROh1GNQNanyW7qZ060V6zHJQtqNlPiAiQyJhoMKTYEkVyPEGuVUgNI/I2idiUPe38NbRHFpAi9NKPeZGlLTXEFFDpwfJxginuG5l02svQYv/Is4fACjh8R2HscOZXsbPYL12BEyKNoOiXdO/e0IXkf+9M5JmrH1djvrmGo1OgUw3nmxlLPGFspfKttNcAG7zLGaHUq0JkNxI482hP1dgUjBDCQ4mSPYO2Os1VCcHUvqoQIO6jGIPIdnIX0DissqxTNd8LzEdGe4m7W9iW85MAPlyRILpJlAXcymRtHQLaqL0NQaUIk2LzPS6khu42bGTqYwG7QLyT3jX9vCXxGJjw+hTE9sFIOdX1CRsZ2uoCSK4yahe2NGFfu0hAqEAkaJmk+EIqXXsTvXBtVskXW8OfGNoRLwYcsF/cGvfxQYsyaKBYz4a62DOIKmWoLzK+xdfE6Mov8j2I9drYSelEZncQWxP47dghwkcOAg0b826k4mlhfrsYLeedc/xxEhxLtHHxUTJiupcEjfgfbcNPROsboVKfvBixBbRKTQeJiVQ4UHNSy2FmIatDcjAgbEBJadstq3g+/TC2IXYXsv/y7o2HDjihD7dhdSXS4N4hf4AjRL0tAbgtw1EEZyJrUtcGF7pgF6ISsmUwmubKGrGmgFmwlbCezB0pbuoMAmUr5zuSKExcK2kOwRXoS0gBLGihysiBNNtdunlEth7QpgNpb0vVkIQfIWciyg8j6qGpZvYoEYIQyIVqXbFnA4nGdcYt86uLcGhE4L0W1ZGAGCKMxfrK7iX6jMIyQWdR9X7z5ir4Gqopfw3CbRpzs70ZXi8CZRRPhWAcSdTSzQRiPd5qWnkzfakajva+e9p11bMZ5hKjYCuB3GpNNCnLbk/FxWi3eoNdvXzhZVcQp6eOewpYdRZy8GjrUCnTtwP1fdSTc1hmoi/cEPIzhQYqYbHMlRaKoz9vCN11btKzplBqjH+qGISsFShdrqEFcHzxOehKWgrL+WnFQoFe2+83wpiH0kWvAHobyCW7UaOgtTfR/s1w1EjbfFjk4QFdNQQVZzkQterm8wxsGjV6scvGFIlpSGBTIciFNC0jQHHzfkHzJFW8oFOTCH2jqlDPpMUYe35eyxhSNlCUCbC6gXQfW2sfkj9MwAfUQyBkUkMCYOlT3BCwAdn6hEoAIHIpgrmhcjAJ7hRsRW5zVHeIoShZDQz5m1ZBxyoF8FmfRGZfWilZLlsoDCYOSB9mWmbKHIyJh0Om8oH+BVrIAqiX0mkUchqM8W8IeGr+WFjnp9RIn2Bul1MJ/+Hv4H3YT36uZGfg1vCwSmEAklsuiAfWqlJTImC1txNNwprIALTrNPjj8qKuhHEI8jdRGHIxnqVeHWaRJL+MTp9ZYn2ZNUq6jaSKK7ulvH+KcwCeaISuuJ4D4iikYxDXIj29Ae9GBrodgcgedh9xnNdGNMCcBlmXEjSIYBUwY1uDVMmgooFg3erByB9VtAWcxM5FnbzDUbmZaLRpQ8q3XbamkaAKWAXGVcQD1KyURlWKmrWuRQBW2aF4K1C4hnl4pXEucIKbXKWMm4EpnUzVyMJszMmRgxBWlYHGRgy/MROuKp+i6UzSsnUohW502jC7WkgBEA9rmBPnEA+oIantzUuq0Zk00+ldLgPcwSHtpmpiG9pBa8yIAGa62KVtWc65blBSvzkql6AaGyJaRooNarZTYes7yUaqmag6pi0CM9K3IO9zHKLJcNlCk9JnjLS650kedSgheyhVLMVk2IUF4DalBkoifsPhiq/f3po9ORzI91PvtYVM2kBM+IZd0REWAogTRhxxhicrBymgC7iURxHMfpM9qQF5ru4RBgER3RZZGWMYRNObIt8dDGj1wZ8YT535y225k5T8ZJ5FPrGvL1wZLqqbh0l17QwC9LFD5x36C565WLYeK1XkGLKaD1wgKgPBeZ3MiWSxhYs8jUEqqHGCVH40yUYJHWIEzaBoQyqItaQUakHGUb06xVGThYIYKYzRuzqKpiNCpHUHlpXGbZtFgCfMOYtmEjwWql1TSX2XgyLgrwS7U1WyyKfMQzc1AVExCchZZqMT9oTbYuxZyLPFPCMLCOOZP1jLdYaEuKoigyUQCap1GqyhaGQx0DlR1UegsqIU6yUpqDhazbjEkuCsm5ripuNLjXc1lVasJzuT5dLpYw3bJYHOyrK/ttWWaZKbJW8MKUE5Cks4ocZZIXUgKX2quWTDVZyZsGCvRNoTxNJoscKvOp0ix5NVs2y0Yrs4B3hsDaoWaQBj4qR0VZ8JxxORZ6aQ4bZZqsAIbJuCLrBXv4xZWiUI0OwVabl+G+XbXagXasEQCsMLjMon1FXhbvl6PuSFhwx0EvvEmKzrmuXLPjsG6anp3ubHsfG85sWVmXYRJVdYkcL/ELBgdAAPQP4cuOOgb6y/Zewv+V2AKRlegUf9AToYEIiK/YO+vHFLwKgw9093EepWTmuu/lsQaOgimHz62agqKZ5x7d/9R/mjat3DqWCTY/nE8yk8kcWiYAieqslNrkpWlUq8T6lp7PF/t7C6Mm65t1rYpRPgHyyhdLDbBPAYnOh5lZVg2fz2S9FBnLy7xu2jwXY9Ne4SPW1nPMSppkqiqn5ewgywtdHcyn27wcTerZnhG6PqzkGFI9sLveCBus5sVkVC/nqjlczC6zkZKjiWkLwcaCLbFem+F5Wx1eFvlWs9gvprkQI272lNjUum6r3DQqH8+bRctllelRLhY8n+Z8vqh1U0tl1kfighnpesHb6nKrbmLLptwqjK5kzkRRN826Xk6K8bRaLLgomSmk3Mnaw6yc6KWSstJ8f76Q2oyKcS2yrVEhNFNNVZWjkc7mmolmMVNMr22x5qCpFpvv+pHstltkDtMH0TiMa1F6B4k5MoQZ09DcKLhkwyJbX7gnpOBktPaVq9SDMHvn3nOKaWolOmwv9aYO3k8bZk9bk4f97X8EoehHFP30jinoweNQxGE/p9vXYbD9Vs/+mxzXghMxV/kGizdDuZzosxDsSJ7S4bNehicMIbqwp1GF8kVO9DqTEScFFDutm5zn2WPPfOuR5y+en9c8H5e5AQ/SeDNbHjDdSikrxVqlRCElUw3k5AvA6kpZQyaUbgm4ALgPpoQ07bLJGDcgKadFbqp6VrVZDikTrKk3Wr0jTWXUfsuqvMjHZZ1JBq2QoBNjq/m6bterPQ1yAHIPCyFGEjpzQXFsKIEN1uwBL6dt3QDIVEjBAGUtRlO1FEwoVTNulpAxITUXZaYw0t1Kk6lig5tqTxcsaxomM5EJZeSolOR/0k2jpcilrlsmszZjGhxUvGZFLqBrm6qWqKRrxIXWigsI4nEuTVubHMqZYSRM5rxkQmueaVDGM8ZLXSP58XmrWqPzPDeiZMvD23XzxltefvKVrzCCt40CC5PwLN7VThUx4RPNTM4lAFxCjMCKV9wI3k3nVFnXH8sW+ocoLm4CEPBUzda6o+zZXrGzlW6jbNTEUOv6gIfw/xFm3u9pyp2F36EuNFuhy/qAiKfbSKjF3KBHWVel8J4t8uJv4qulUOgodiUGr5UPBPee4uBLzgPcfe8wzMiJESCoVKgWAdXIAFCBB2Zb5NkSFIJHxWSOXb5zEMpMzQ90o0FbzXLGjRSyAf+NqCAyZMaCsWVTG71cNjlnshgRgLOpazChZC6NzoS5qJQRzEwLzjMhcpWXl0XxPVWD+0rkhmV1VUMtiIJrcDGB++RSU+f5mItR2y6UaDPGsL0dr9HqYIZBufgsk8WkaaEjMUaijNIsB3rMRDExDPoC2ColbcOwTY/MDPQF0sVobVwv5nANmNVctayp1agoK1OMSgELU0A3PujiDB6XptGNYKxRS85YWYjaZKCNy2leGNVAjJth4W/GS6gKCx5WrVpoTdAqSMsoJ4XWbLGooU82Z01rJhk7nNVVm2Vr45dcef54XWtZRHYorahdUyJkQlgiOfM4KED0iR3gdWroWs8uVcnB5vLY/xlih4yJXOZQf9CCaigtOxKvcQZDvP3jXqQ9uFjPaeRteUd34ImEx0BOfydJJtnpiTvI3osu8BZgj2Nc+9HxJwRFtneT2Ch1DA2rrEKJZI+77UxT2qWya9aGCFyqyNCiR5f5+U6Aqhg+Ig06wJt12x4cXKjrmYZ0P2LJzt0mWZPppqmllGDSMSaY5FJCipRRTLICiZBlZtlWAvBYBqBi0AjVLFstoEwdRk1bXWDZTZUZqLmJ8WuUnwYiWdxU4MltqZiIEFyOx8pAlZdMgsO6lbzIR0qzZrkoJW81UDXLC6WavMjrqoal4FkNliyQOkWeOGN1o/IiR9EN5jv2j2D5eFK3TS7z2XwGrEi1k6215bJq2hZ62QOuDOkEBzNbNvC+UDsKotAV+MgAI660qqsYGcVEBlGtOqvJ0a8AMSdrY5plA0JPQuQLpl2YSmvFTF4Wl+eLye6z88X+aHqM+sTGTmEMXtsNwrm0MT+qIebCLuhIpyNK8qLYuMNeIX0j+IpsYJjhttDlaIzpihS6Il3b+qwtUoBUgTQXxwkDu+sGdqH7HbOoXJV7CDoA98yEZiwXkja+L5/kKSnZ6R7P4bWREEl6UTpxSmGdvwZN6UGGgOYNCN+m8Xjg7kWhk9/A5T6DNOFNg+8S1A4vkdH5LKBouGfPMKOqqdpFrXStpcxarAcAAgTitohJwLQYiPdATwXMN4c63UJkhjV1ywpUqW2wFUKg4DmG0puigUsgBEJhZqjTryC4o6H4HrB4Y2zpFg3ty+0KgouKYySnhfokbQMKJ8+yplpCjAbQ8xC8oXYdOeb5UKILVJZmDKo6QRlqiAxBV3SBCC2FjRUg8K4YE/PDWVODCqDbdlyONIdXYBpQ3LbwYMZarcp8BE0hgWdBwAkDy1ixC0tYW4vPrQQWKVSNQT8UhRs0FadH1ZE6JVvOq7XJRow3PL/M6nOPflfuN7JU0KNOhqYtmKTpME8Qs8d8YV+2xOYkYgYp/h9CVjG2LS5fj39DBomt+oFxLzBwhByVkL5vbWFncFIGFefYBr4X5PBdX5Ld57YZ4cdwbpxEJ4NNN1W1t7ePKB9EykatIr1kjyW8MyIC8boTgq6ZepiufsSYuqMv6Tjh7C/QJrSuqch/5/uOYRH+SltcdcbjJzFKaY/lcLCEqTVOQN6iooXsl7VNezhbzIsRr5dYgB5XOnplIFZmFKAtALdkAQy4GZpGSQlKtdFGcI76EdI5RIU51KMn6AQmb1OoFoDAusW+odg0DOgL/F4AVkKcRwY9GEFkUn0CKFiPOiTsX2aaFhy2LZAidFGDVg5ESBqIE9pCQUgMxDC1UVcgdMGWNpyD/aqyogDIJgSAGavbNocOtqLFCJLGADaiR1nTNlVVwUYkDmQ1W2p1YyfYpwmRXHT7jGqoU7AZRsFFXoO1YTthMUicFMtGN0WxzHlZ72uzg7X7g1PJu26JluB9QndVJ2BtIJvcy/YKPEDxobAfqdS2TkNEEkrpqqqkEMgfoZWrVycIPyY4tLUKAZzYxkuNxUBjBNCh1fDYHsw0n+3vH84XSmle5FAonSp/oFLjARQOPu0fl6RHJIHvLqk4w3/gSKJNR4vrYS+4V7PB+VHXisTvqmv7ffJWPSryM0aqQPxqgSkihgayE6jYhQvtI5BQK2H4/t7+PhSEJZEI9GkRQ3CAeUV9ihhomApb+NiiClopEFwCOs+GhzKmsEq73w3d6bR+E1hs6j4ugTyB4K2DDUULNc1TWoOzCfRWjn1rwWyH0BVKRaBD7FkNxIFYJvLm2Hwb9NwQBpJkAeZKNRzCJC354wGJXkMZbl9iBjDqWKAEiuMisjNReDpBDFvKyjaDJsuV3AyU3gEjh4qitkyNwfIbYKVrlatsra2y+a5pb84EpH/ZXthO1FD1ZpC9UFw23rrWORnqIvht44wnK6MR2eJQV1HFCWy+rLBTTwhLoOSmVtIEFPBFomOYcORhjv/nTA8kcZvxok29mO3vHS6qumoa0L8ao0DVQJg35qM6hTDEv/sbf1DaH3n4ofY8Y+6TlJZi0yD+LjKYUXeucdtHXio/uEiYBjJO2V/sco6AX0TsccGI4JC0EQbIwXTAKwTaY745xsKNEYzvH1Z7FWaMSVEvAcCQAu6wbDwArVDYkOpLHs62FXkOmSXWJgOakTKnJrSo2YEvCJMeHCoOTFQMJ1KLWnoEoYS0ljmYrOh4xSovVh+wui0SC2wTuzdt6zKsb4xuK+gu7xrPU0o95SiRJKQwptatEOB4Bv7FRat0LqFdHsJAwQK2gG7UwyXo5CjiIoRw5OBxtd0Ij+m+zQTBoq3I9VBm0o8FdI1GbUDmz2fNzYsDUQiwNyBe5veLq5UNhEGNVu1b2SVBVZxqI9igTIRPt/astXwDXMrbaNY8JVwauCq8fHNqO8h8Ts6aEHbsiWBfXilOFXHV/s1iMdu/vL9cLhswnzC7FJgUeAHRmU4R7464+m+SukCjTYhkwNhMQ+r+wt4AcK/XVQU2mHOM+0SWSGimuomfVMdNUvL0ZO0Q+v2XdwUdbbt0guRF/ZcA3KvaImtUfTDD3QAFA+OGeFGE2XN86O1SFHALaCDAFdAqpNyQMkxJ220LwsRtbVLMyLVCOQMUioQzqfk7KrTUv1Yoo2DDInyOOANigLDpLBKr1ReRqsiV4GcdyAM6aJL5Ziu2QRUEAGFSX3Xsl96CYEfUBOju5NuhyYESfFDFiBpcg0lPbWI8n409ljb6EkShVSKhqDOoM8HdiDRmKZiBh8xg10o1r2u++yzXdcZHpPDa24dcLyW4IvWUFoESdV1Zk7DHLJjYb06X9hvtTKuXO3kE9fdjKKS3J2ltUt3Te60iIGeyQSkEBvoFNlLRbVMd7O/PFwtU9IG/2rwxDQoX3R3J2Lb28BZ8TBTBlBvQnDuU1+3itVphjuI5gWoi5T16Kys42qoCx4lzu3luGT2gp6gnbMKXT++MJOIg3nbqincoVQOqoBWRNh0U31iYTAjZZu1hkymI/jiNJtqiQUMnkwuaBYCSjK8iRKP0SHKFMSnLhlE3y/GJ3tsCKUsQNEUnGEHb6QGuJRK4ZiHwg7UrMaiMiFJgOIC+clkQBDkk8UzYYfhTQx9TUhIpUEFVnVzPBdTN8R+qVwLIJz+BSFXQ5lUrLljdQCoFKSySs6aq6K2osW1GrikS7qRyuzWm9XFGZLIBnEeHEOEG+r+B6SGE4bu8LC6eBZ5Ylsumoqm2UG2kFOq0kpNhG9whriAP5RiTw8uldtKsYvCYCNgq/D6hw69vnNZvLZ4otOLqGgVqTejBViO0LjciQdChBGuqNtOH80Uzn80V1mYCzo4Th2njEC23eaHR/o8xKrHR26OOlcfqbOjeieGXvsD3c+WMkxb7oQACMdW0g2EVgtVWyY2M3Oh0J2gHUbIdL5d37EG3u1xa9mgbj/qfatnU2fZGszhs6hb8VJSl6PLRbeFb7K5pq0mgBwdiPgoaTJCaiGFt+2Jg8hGdYI0RojyfDKMUNF6htsfwZ9vahcJUPPrIQF9hCGtSf2Y8C5oeO0mHrhsMjgAd4APAaW01C/oSa19aVkGPcl+iJ4m4A2BPNMRmwTmHij2c0wJMA3YcvCnmadoMWVtNPi8kWaAdhK5vkk7i0aIjrOCNVs4A7TUtyH2eF3vj8tH77t0/0KqGjCVsMCrhh5C5wL66qFVj3rzC3BJKCcGYAuMAWaXIoC9U5lgSua5cAteABxTUBGqmZvuBJnajrRGdKt4+IIuZ276FgLVuWt0qXS8Xy/MvnGFGL2cLWB96BrE43CSAW3XsYBV2wpuWHljdS7iNK2UPJDPEyOdUNvZKbEfPjISgoyhor9g2EMQn0z7xNPSEvGXp3nTt6c8DT+39FcwTSLApAKYHpaZ9Oqitda4zLdRoNPnuww8slgs0JN13kTZmw4IMtGJKYaK7w6Jw4S0sx3RgnOB/svoCprJbiYuOE9VmUioF8SqyhKEilfM5qaahGEYDESmIeGFnSgX2dMT70PsCmKmmbYtcwsZpIZYyKhmgwiiuSeAHpCYoVQslMSj/BTotQ5F5UrNBNEPAk2JRaC/zoijn87ksC2raDO+ITcAIUOF80FGh0bC57TJYN77bV6SV0k4UIGFFo5bTkYQ+lNtTeXBp1FxWZsJ0xqVTXPFa9NE6XYF8Ai62b/UnTHEkdQT+H4nTaMPYcTrgrd3fxNFmi7ky4AWwHczdKWBfIfTC6fQBLGTRfBH1qLatVaN0wxEqcvrkpsyRXaL7gHGQH8hWgG35wu6rBKYbA5lygUY66USxdt2hhY7kS1/9KDHdo2x8NywRFlUSjosrJKPq4tQ8FsdVrO0/vF9ExTtSIFUL2Tn5UZ2v0nJBsjt024I1Vi+X2EldLJa14ALqY9rlREMM9zG1g6BdUtfQwps0UhK2rs6DTa+jtilwOsaBwArFJl0kvSUXSLqIn0KVG3V6BAMBp9MVoKbzTMqqakBvdTgcEm7kLUc/NMgQMoQho68GRxzZvZRBzAVvVVuAiwookGQ8a6vRqEBcFCYnoWaEQSWaJGg/BWAvjOvkUoKSggyUwkBQ6w9VbpgBy7NClykv9buBDQtRzHJm5osFlgfQgsmm4Wd4ef3isqxPyTEAHMimoLWleizOR4ZfkUfOuT/ivRzc4l5mhrI+zl0TuaJIulSLRbOsCuhPWMoixyzM1MkS5y6GkC0c5His6wZ8zG0D0oIXjC2zQly4dFAjfpOeY4skOIYWFXYfOELt7JgYYk6Z2sbd365yJGLc8yb/iMixBdABKNFK2IZ4rulkn8zrcNukmiVi3PuPnGfJSWT83XEDm27pSxhbpDO2co4XMvZTYgK8arXZ5E0GKXVwpjI1yQuHn+lMK3zRtg29OAR+kWAE53UD/cWtV8eB3agWFj0QKdS17XAmE3SablsJCDDb2Lj1eGDVYki6BTlPxe5dAQmKGxlM60U0CAQkgk5jgaIII4Gi8rZpMrSJR8yhlMgIMKZN1cgA/gFZhoiIhFpcJi/LxaIuiwKtB1CwabV5qL8LKpUUQoEpYR1pVncehgWSWsNqnTVKcwklOwCxpqqx4OzgcnYayhvWyJuwXqDFKhOeiZRZiqrDF7brChT/JNPDVZt2W4tMhY7N6nste88GqWJK1YAd17nWeVninsFQMLj4bKGSQEf0phh9Vq1a1lVd1wD9gUmWZy48d/stJzkX0CMNahswrTAbRRDAw5ZP8c3N4t0VS7AYrdGTqMNHzBE6dxs6JX6n4MNLjQ0Le0bx60nADAGbk2NorFHcKA71B6vF39lpr1DYHxH0NBJn0foAMFaawQ6BVZ1pQCMaqM5FRV9JHlk1xkY6vShwuZi4vUCs0WuAewn0MIyI+iqUfv4VsmGaLkT4U5YRlr1GsaZ0Bih/TNACRR1mD9x+oVOAwRoA6EoTXCpEiVBTH6xAQFRO56N6DHEh2wMMtjQgwFzdXaoygFVwGOM5EgPPQFwzKFTWInQFy9yjAc04pKBDJ2Mqb4UO7VwC7NmpM2Dte5JJKsza8hbAgUe5PGwrphWXAgEtUKzjHGMv3T3fzOa8CXU5CG1qs08QP2eLSxNMmaAdViUJZfJ93Q0Lien7Qzu1X2jz4j6A3iBNkwmBcHn4hAru+qqCzj+HtRMU9JpfLKsG2vpQuEtXul02S3xXcoKEDi1UyoFGPlsu4+6EMblek5248gj9cIbuFsnQKI/PU2+0YI5mwARosNz6cLksP6/p6AdHGwQn/UhsgY63i5Q5rLphF8Basj7S4a4zJit4nu3vN/M51rjD2CwW3gg1ae0l2I2pF+6jylAtoiBg0wDPxvJXls37MTu9ggSUrSOtSNwqbRpAVkX185Dxo8ZokSfeP0SBFtda3roZ7H92xLCBUYxgCUhb6B7SeaD0NFbaJGwjFhMKdqZkrIYLsUQOmtHgGEf4Cuoz1vjHfWmkzGG7W4XLOgfdPrId1Xwg0K0eIDRV2yIWBXRhDGnx3cZMdy9erg/H+cR6EK2qi5MAvKEV0P4GCxNh9BSDSqSWBqZlPRy+aU3kYY53S/gZeXWoyiA5JE0uWY6mOoOUMAwOhpZstJNb1c7m87oG48JWK4PAQTMdlY1qRkpgJN/2pifSBXPFZLPZTBajvgT229wbAh1ScOpqx2kce33ilD3vwUlJKC6LMRA5SpRzdJ62UHQlPDMaVeygcmrNUB1xSyrhi1DbLlHdPfegAuKQEgC4YXvnqO9WpA9gt9K2VnsgeVAMQqlTEC9d7YOyBfw7+s4PXmv3mwe8k6Cfg3aNuyPWxX3ZAdcukWxaaGZtveTEcVL/XbgWq8n5ISEkhqAl+AnKQ+/ngXCxDwjDeCh0hLSLjmeIVFHFdWglA5uyyRAmBGW6sdYdYV7ckD0PpKI6ID/J72fXqMOinWnk/4JboanAUAkmtKVqjcz58/d9oXjzx1QBjmgbIEKdCgMwEP1WHFkSKlWIMKdKZgG95P38xJyIy3gma/dHSBAebpiEvYKhTiqhBqC4GZT7gnE4dzOYJQBqAOwa1pOxqBLc5KoVgHluoeCD95aSpoZdF+FdcOEooT/S0GLyiKs30qfxRogS77qWsDUS3U7vWBDxXo4O4l5RYMmJXwUlr7DVakjrS2RnSiCOMlNNPhBMzCUihGp6WO8kMmfEXQXx6x/kCJrSY0SRCT4/aJoW9SUwwxxgPvJ4uEEENRo/JQvWswTazWQVx4ZW/53ddqP0dSR730HTVsNF5xYFYC0OCwcfbmv95V4l8ZNrP7fN7gl9TRYvw6ARTou9CMQ4EjDYDi1DT1tAcNo2pbE+TOY0Gv8hnyzeStGKBlFAe4JkJaNAilaGQ3JWk5ff5fKOrNlo9lV2gnqaYbwITwfVU1IxQQqIMYVj9koQLTah0xxDtCAPnAfUsx0yKvauuD01VIgRY8iovpH2Tngst4FxdjB70c8jFmjU58+d39mcNk2j8hzXNIK34CxIzK+GxDCby+r0rc7zO7t6wMhMOKVXwzr6aPAxpfeJVFng87wXRCDXXI3IDQITDSxypIq6YXRs6dDuw28fd2FsyiTeaW82ocnky4tbK8UqQmBHopCEGlGaT/cO0VYEcAyoeErlUnit1IX6XKFg568JiBk7K4HvIcTZLpA1tPBXJEXbepFUXTqfUIeu/GDKysh8hWQ0DJeAVkkGV+hr52w5QuFa7Zrkka/5RZPmLVQQnjgSAbBcDo4ohFXCTgSFEa0AONP2anV7w+rydlsmEPpIJ4uNf8f6iC+gQz6jEBp4uRlgzhdCHmR6rV3KfDQuhc5YrdoaGY8ARxItpd+3CDx1+ftowfjM/BD+9fkL2rbUsskGwdsZUYJViKh8M6U9uca3EMOwWQ3WO4neBOxlSQhOF5pv6nYJGWMjyPKCCp5hyrRWZVYAATO2Pp3MFhWEBXBTRv0BupMYyaxOpCay7pz55KIsgYA903WUGpxIEYdA64lmODxPA2pyuVySq8O1Eelq3kmL1WCZxJ4+R+Vp7yjHRVJ4tnVVWt3MpnQRbtYnooSHo1OBXLX8/9/Ym+7KllxnYhGxpzzDrbmKLA4aqDYlwnLTLauFbgHd7j+Gfxkw4BfwU/kN/AD+aXgADMMNo+m2WnS3WpBIUWSRNdetO52TmXuIML41xYqdeYtKFqvOyZO5h9ix5m99qxw///rIKCFSWFteB+RsDAFQfTDpP2A0AjLEmwMTtotMTrjwszDwnhna2WOx1AjKRgoHYjJMuTFurDOKNBdnqIOgITHh5uUUuE4EwMPAdJDaHU+tUWJGZNmGAeXosszndUtU5RICTMvy0SpR2kwXQyyJ1M746elGsWSZ3yo6YBzl34zOD/VTIvHSsrMEwrvjuV8epqEfpwEuNmhNyGuQngIBtduF2XQp5ud3SE4nl/ID8RTyXB7CkGlB0m0m3j/cid0PVHckI0yhrUw/UmA27kNjFnG7GdiHiIxA49w45lDdQO9QUiumeNN3N2++SRZYyoq+TtvGrnu764WAkfeydQw1ozbYG3XncXgLLV15AbWTEDqkMCXFt+UVcDzqg2nNY30162c+oXK3N9WlxvMvF5pILW+b5dfYV3gW/Eua3MBsgVbC4e13jo9PqXbELQq0V5Un3vSV+r6q69AdmCkBXRELXHavjqW57lrHxKYj007WBZGn5oToU8zz6ruXeDivhLSc1hK0oNaqGG/AxhlthkZ/yCVfqlqTWSY4LrX6c4tfPi9nhMHoQuT+1JrEFBFknllohCZkoT0qLBYmJqJVtH9EjYgmH2IYeOqjI5vpKKG8Lms/9Z/0/feXmWY89mjtIxptabS3gi6pTUlQs/61EoNRW2vQS1dXVT/3dVoo4kih7UYw8qrHi0aK20ZVb85SD+YP++o00QowlFL4a3sebKyajXuz6BPoP+2NOMjfxk7k3JLLIrtnIK9KdO0F6brXrba7opQ0AS4No/QnzTxXMTWbX7VWdf7ClVNrq5j7NKtdfcu8aukKEBfB+IO0kOvq+D7OkVoK2tnW7eUXf/frm+7ICU+prFL7DyV9baaRtNo5xwDKdlt7mscNxiYHtKyKiIIMF+kQKwBtRvZy8SdBd0qhmPeMMEGIwZf6N6Foa61GiQJw6A1Njupa8xHYSohVJci82Gc0NnCqGtiVjjhmGb3A6oha0tVXEABbs3mrNysPyK9xTRE6SU4hT9t6Qrs/6Nzs3vgMfRxf5tS/+nwFweDAjK6RiLZVCdSAUbEAbjSpEONYL7L1vzfhLUcGDZGNNUlx/zJgnAOVfqt94anrHAY76eEjaJjF2UdaY+5DDCEcDuPL4wwqBXJRKY/IjjgSc0TsvgsgG3H1719gsNV19oOmHcSxydz4I1bplQ3lkpNUk+Ac58p8sYpzlyyXd1Ych6BduyYJ7B2n3tvOh9rrZ59Ub1o8birD8oxRH3/zQ+VP5pLmrUxrf/z661clnVZqGeQCy4ZMZDf0ssZ0Cs2lyx5lCnLLQfJ1c9TlSvB6kfwrN/dxRknGGqhZkQou6wjuWRGfm5psWRqdd9vMcRPAdCTAmXlPbDaV+ATZ27ksNIAV7mRG7g23kMl6S9iskS6bvh1xokv0u1jH70AvMc6j5ftbUk9jsyWhxJEOOUQ55e5ZKU8++9vn6wLkPKlNc/LotNqRWa9D9pZkrmryg+SHSgYuFOD6hrpPF/lnbsRn2I8zv3ID0pBcwxYvahQS01mobgdoLTdgw9lRa5JSeng8jm8CQAnkTM54EL/95eTu2l+9m+fW+zW1ZO85NAFyhUZg+y8Lhh1Vl8rz/b3usIoTdNetBlljvuoA2HtyybU3nz8luALLTuzqR7omZDNzd3//WOLDCjIqTgQTpQ51qkrzId21/ON8dI247TrwItIlyaBwmMZ8LzRJiBUACqEb1EeGzUe/OIslNog0yJF9ZhJHtlB0SjbVlmHlXj9q2UFLg5pbmK7UgW0DMA/kb1DP3Lb1TARWZLzjsgBgjzLBgslysC27xGddO5UV7rwRpSWZBWaaswZdcVbdRZJGhE+ApkQe2GKiFySk6EtZS/l8iD//i//Ql1skuagM1GxJ3y3OxHakAKSezP0MlbmjefFfrE3P73n5R5mGdVJHE5pxyo357/ze93kkMgjoRuli9+LVkV1o7RykVHpMp+OZnCCxEWgHcanQ1wtctXSOfUzOfCU2Nc3ujuBKQ3rd/kCqAAAt20o5S+nI3OF6t66W+9vanvRMRoGkb+ub5s67KBN7nMfcilKpHoTN0+TYjX1cxFlDt5U1ZKSdzzr/F1tozV2AdC1aD+U+SOEp48hTNxaTqNBexTPqJVHLjehUB6K122TCCaqgI9vZEGeCS67VupMHjzIrf429ROk2pDug5DGlcskxRhZ9Jd+bwc8U9NaNDp8QjcBrXuauHzThhfJIieH29m5dHzCvlRGKPPCI+vKkhm0PX6AZgjBHuVniR3N/2J+XjhDJExMEO6QyrfMce5tJU7gKwFsLAJj1cTg8PD6f8kMK9wuSbU7c3GasqCErfJiXTgRGBIwTo89H966afrVOSdPDMLKLJ93ttiJuEJCCmIA2dV+T4JPjZ4pA3nnzrS+efQ5PCL2mmbouM4qTBAzdtjICEoNyA5obdE7pBXijEcELV0cdD30wtYxbtZKqu32D8P6g9R6lg5na5Sy1LRbM1YEsFeQKx/tguzr9dvamkmTRrxdOytZQtFnbSayAUk9sdSmg+GA61m2ZH5YZDwYQApAvc8yX74fUTeOrJfd5BQYiZ3A3ozjIWh9ywU7mGMsWwMaaOkjvu+vSL2ErqS/bFLbU5QkKIa5EgAOmijSMGZQUp/P5+Xk+x/ib/glyMv1UugG5HeRs46HkeV3nmB/TTQHloxIh0DD0Lq6HVEa6y8fQzwPGGli2FnKchsM0MEfHeQFlHfiaVVHcdMNtl+7Wx2U+h76f7qZl7EENh+YoSpxSQKFjv6Vok0oZifRniXHpOmxSzvlxtpw74Ol/CXmmTJPYcaDvzPPPQAWI3HhklIzkCQroXrcFwJCcl1dfbXfvlo66jkx3u1bTOsbA9qEW6Lmmza459VJWpcgflDx/3b7+KOpe2ifbjBKRJhESRhIthAkTF00+jZQITYJkx6FL8f5mOs8zb8FhGJ5+/ezb77zF2UVMoLrSh3NFuq57r/7dCmFyakolLe7NoFl1FxBaWC3FMf1NDqtqskYPF7S2HhJg/nBTGdAMuWE4pGFOlSD3aHKekc2C1XZcTO+iZvEMtzN6ONGOQynRAO43ZDJQyNh6bCVMyqSDzYQ7RluGzWWh05wpSGMgx2NIz5eFNSupZfY2YdkxVGHoY1m7ElE0XOZtyOWwLef58TSHse86MDms8wKXmJB5POUjdCtSo7EjUg7Kp6Y053QkID8whkBfoKuIMmvYR6jNjP05x9MjhrmUAPwAZdkBj+u6NAzD88elLGuPws30xniL4yhgg58lL7wQJNO2XXJB8dZcz5p+tmqgWGLJ/TGbdurKdihnxZcFNkgKt+IIPJS5S+H55/2H/4g6RSi1Ttx0PiFzmc6xGAeypbZUPaDGIpmxu4ZkIp4kcI+ZznJ/J9GXapYKCzJbXpKo/D6MQwxhpdRmyeUGCjQBOkq+/vl02vC8wrpuKL63d1IP1kTZZtJak+lTh6LZvJdr37gQ/n13YrWHEmsickSjGQ3JsgKMBZPVuTPGeQnBda00+PGlAc31GXGwkuk6DS3Okh/X3dJTGiqLLggBaMFUofPxfDqdKWIU16TrB8A5SpzPIIDMlEKsSTuu4Ii76tVo6YkYGRMYNFnKqCFhZgp53vAsgc5LU5nGx9Py6vyqjMNwO91MNzmjd6/LuUO3ExXCCGPETS0SauW4xriAyoeSkPyokGCFAWYyD4qjEw1exvi4YYADOA5oG7q9mWI8LPM6DEDjxtubnNfh5nA4TOiyYudTEkOoaQmMxLQxbWSfU2TUFtG3yS9WlyB1ggNt2/Yq9swzw18buPdWymSQnKHEr7theP5x2OaeUDTEuyriZ57XNY+wMaStVa2FEycn1+weUy2sS+5BE6JZx+oLciaZ4aictGaAB43G5VVBL/M4oHkLNmDkXiu4MwsY9CnVP/S/+fzL77z/ThxR5Zaorh3u54SyXluttexfWhtx9tcyfzXl6O/Xv1u1ri4R1Cpppsp9YX8WaaYYjtVvm8lkrll+Gb7WI/Vq+cGa/rXdmQmMuDbDWj1dJxmgQwgDP6Wb01aWR/RtshaH2cFuRR2bWoKEppjBVYbsktiFuVq4wUhceWSDJfldL51WhSAEXIG2jhaedkhDrJEDjaEfBigC7GACwJAtYTwMw/e4LMxaClnTmHuQORYwyqZI4EZmlhQNSfxUNBuIont0DqLhKYNhqFu7vhu6blkXtAz3uALkTrnyJDqxPmROIMOXV3XJz6FpfLMMBD1qKqBBq7zsxtTNcaHRpNHcWNPAMZbheSr900+POd+kcQsbsfCKvmw3yqXk8pm1Fu+HYdespRqlmjNpih401JFw1ET3Kh8yHAPPo2WuMVobeDF9N8+g9rW76Icul/TRbz7/0Q++D+KjVA7jMM9IrQOG1fXnsP76y6doCOfyMdImWmXUK/GYmAuXYy/VnCnTnX6Zvro2JEGoHpxUVpvMVJoBXh2xGV9GrAKgEG3p35dueJkKfRHn+vO48zqVywNuBelLK8EBgBzL2omlqg6agdiv6/xqXoEFRpMs7QOmvAEvjeYq1LkggI5v7rZ9pJt3pybtLjWXboacy0Wx6zuQyGO+ZhkGdOfRHF3SI8DXkvEjjh60ZRJhmtSfBBIIuuoFgo0wgAAOSEhRI6vgB9dcwHJBdAIvXrzKOd3cjvO6gYOi68YBQisN1ZQpY+UrfjGdghIwVIklr4AbJHytW+Afzb5hUyxUdFs/pAwYs+RuQhVGUGLD9xh+FZZf/eTfvPfn/33p1g4hvXlw1qG028TOu5P1VxFGgEp4WXYqSZ20XnHTVqaYMKb+VCSNRFmsFGXsqEyGIGWGmId612Q1clxoYO7jepYs9LbeHIaHR8T3fL5pnDDFFtOk1GAFIhLe39l+w/ucsBZvW/t6pVgsu00yBObwOm/d5NjrXxpTL5V0/U47DV2+489nyOOrTk+btjapMAHiRnOU64T3oJKq1fOaLiGUck5xKWEIYX4BOkr2/dTlzyOm+NZlkEMaU4RqZ5cIlICiwvS4Wi0bQYEH+k0ubCRsiFRiBrKCCJDRIH5CTE2N4OhO5Lo0I7QA8gMXDvMGIHnLRPUdImS8iXFDMY3koPIQrxEMz1SY2Zh2rwuFSLnI2A59PM3LzThMQ09kdbw3hfhWueCYLkuxOoSS4ijZnklNMyngUqVbGx1ZGowKp9B9cx2O3t5yeXU4rA9Pb+ND6W+F4kQyDZp49FuoYanzQswJZfRDEvc+kViSOAHy1OxwbvkmLwXSCYZ3KRdVSbERhVgE/mvJlNPcEN0zX5fsVzyS/J/83u/+6qOfYXINBBVKf+y647oxNpCIewmMxVhzCv89BrS6GjXZ03T5VgyTk0MnT7JItauvghmd+FfZcqKmsNtasDWn3I01vyii12Np2bF9262m6QjzMnCbXBihcYFUu+ULvoiWlAdPuyu2PMSty9uPv/j6J8cZyQbMTOR5xeWGekaYqZ03IySI0w5WkzSL2zL6V03hch5Wa7F0Hu8LJrugkibRiGMUNh4ul3pWoommocQ0dJwOh59COc2o9ZIc8kgmRLDncbkZx7tD/3A8E+k0PboUeWL1mpFGZlDh4/FMwKO4ncvpvD15MjC5DzGnWCaEue/MGAEqbDhu9+D2E4LsSeuCCL2P/b2YnSSHl8Q53CxhS/chjmsuA0dSGibXnXk1EpTNoWEUTadSXKqES3D6FFPAh9Ue3QjHCtEqOLAdgLlxATlHSqA7GniR80xMvTyFlAfNkJqM0zCW3D99+uK9996OPZzqt+4OAEfowRDbJxrwLWQtrfT6dXXQZttHjXFrDqDZRBWNeuVN1s4Z3isRiYYflCrnPn5/YQ4+JO9b2tNWrXoEcrF6turOXBDcdSkN/cD9OkbjV8Og3cPmXBBKmd15i6/WMDLRpDoAGWPmB2KREX+ZG/Sd+d2l0XVLqgyLoNp11DS82HCij5VC29gPmMxArcvADAzdfJ4p/N5SSsPYT6jf0uTSglGimGvax2HkLheajYbOhAFolASmuDfuJqaYQCdenzAxAi21+N/UI7TsOkxmmaYRR0uYjnpGY7o+ER68SdBrRvYyW1jOeRz684Kgr6bg9/pW+jG0iqcp+wRaPqLUVXIvzilQ1NOP/ZCW97bunT/+p6G/C5jUJFQEqktkfAxppBqqOBSAumNs0tG7BYuriRcbr+1NkY6FkT8Lmd6FC8hPjv5LOwJeMxq4OhDwgQ1cSE6ISm09b6fjXD757Kt33nozg6B7hUMQ05xXIxUpXaRuJHUjLYGs3vzuSlX0HHzVr3ub3nNVYH6cAg/gTh1nYb106Yqot4zEo6i/akCd3bIVrwVeST3rh2uFoBFCS4BacwxwgQm2l2I8/othp/Sr3nOXoiaZPIxGuu36u7Cm0K8Ml2XiamratIqZEblosGdull9Hg5E0u1ltsvRJWdzIloFzURK1kSdZ1rysG00V7A+wxuE8z/3QMdtPANPSuaNJpdM4hBiXZUHP3RiOp+O2zS9enjFyjTtZkWmmrCr37GDGYJpnjOrtuu40Lxj1mfN8Xo/HBfTK5K+uGOwmmWjkbYgMh3M8aOvhoW/mv0m7hWthILoZ9i19h+mhi3OMM2NlksZBJYzDBHK/tN6u67P+/snpcZxG5tq93Ka2rvulF1utAYpIgeQdOBnAn6yps2bfisGz/Wih0WXkzD4/ktAg34Fe4nQ7p6ZDCj/43e9+/vHfn7e5CzcLxjuX+5vh/Ej+HPWsgg2XjYMv0Fq3DmONmruvSaerMrwLR52vonp23zHXQNl9PoAqCJRxkdxplR5RdztEolYdm14Lkd4riYsq9ApBJ/wqU6gLeWPNPKv9lxACx+M4ljZcH8K8bYdpnCDKSDEy8BAOOQTYeRcWGClpXHMp3g60DdSSu7aELVSA9BVrkCdbGWnqkJd1PT4sPAj3eJpDLMsC5DKRnqKHBRSfKypgqQyH24FAijO9iVzY0JfDYTrPjwBoFeStELZBiBktCD+Ri97MyHc+n6nDj0YEUVc8W61xAKHsutAMZx4yjC3bI21vs7icLpY1EqwW0ylYOwQ+soV0LpnDgChqDYftiBNv6PrYxdt1Tre3mP5EMFQCrmgI58M+n9OqUYzBmuRbtkvUaVMXyUmLfsbctjbz0sRu0rRkQT67UcYcLY+SUGj9mEo3/uzvfvOjH/5+6uF64P4IXMff7CKc/Jrf0bto5PDyx6vMA7zhvZPsZeviDz4caVx3+zshbBei7PMrbCKg9b+qRx2fEq2hy+3WH7xxdnKFDdr3CHnYrzZ/hntZtdvAWr1F+3I9hPtZ0dvVESiZUjt8eDIgeBgMk7TCNXvFVQlZYsfnCG2H0xPWaFh6wWUpNNqQviQbLgpWFnHez8uK+AwjswEGCENaZ1i2HsVi7PPlfOqGcRz7DcmVuU/dfF4CoM7ztmH6EQodI5ptCZmMLvm+i2Of5hlHBtlgKOd5TVATc0zjgGRYApqM5BaZIGoJWrcNuAWa2OKx5V5y7VHRXGLpkbNG6ITSIagDaJAxv9gnJFBmSkSQGdL9fUpxmZfSD2hRlbJAlV6TMs2bC1Oo3+Z+06homxnye9m2lCld32zm8r7O2ZTp0fph1lW1BgsUxxZTGAAlAFs3M3v3qbsZ++O8oFyJ6A0xsDqKVZT3ps02lI5NrslSF5Zyzmr/TLzM7stwJmbaBeKeHkzEtmIb6Sa1JH0dLa6utrj1Br1qlYij068qSHwd/nCXOibvl3F4/G9LU1krg+Lv5ULlaNCWW+omGiBSNvafkT3ixaTxJeSA8XZz+NPL2Tiu8igSbJ62OGZVexmQDdBKbhZnErmY4jSNDKg+nc8AV4TQ92jE3zYge2KI4zDQkEXc07ws85pnONUBaaoYw9jHCLbHcRphr+EPb8OASLjE7rysNzfj0PePpzOGa8V4M00xpPN8ShHwoLVLI+W7qM2Q0KKEmloJlgO0lzminNirW1/YhtibUj4Ft/vYqEo5pwBa2A1bRtdaT3O+csm323nkGatMZ2WUI35HNGKqYxKtzFGTXgKjlQ3sgEq7KNi0gv31mgzVCxAxVeGnCWX6PLm5iih83v/Wh7/4xc+Oy3w/HNacuz5NlNpYMmoAQM3yFkOTiw/wXKa3brcLaVRgE3fSuJqSX6k2+myrPm3dSCZoER/4ti7zmSvAjHDgzU+aSqS3JqisZcghXpqz2Vmc+24bo0O9BHgE7Shgy1hBZowiUW9cwzW7DVpHFE43qP6yyVhO2oV0uVbPv0R+qv/IwYpm5lyhSCf3oIJK1WQZXGSxubjikRrI0QaYSx5o6OTYY2jCcHeTSzydT6hHE9kNXinc3oC1mBHIfd8NSJuXEYNj0nmZRwheN01vIlFCtFU0G5H64bq0DGhrOJ9n7QTCcGOMMiMQSQd4VJhhiqmarsRiGFY+DjTijG/KNplQk9mi8vNlAkf3RC2kwRGxth3eHYaunFdjlOnH8RETR8uCG2LaPZTtDFjXiJ7RgdtoLLHSqjybzK7bPWY63T6u0irhr7VfX5RfdMKi7TDKXtOgLHXuSE/FOEJj/vqjT//wB79T+rilMg7p7jCcXp4KioXAWqKbxNVnfPLJtP2FAFfsYwUc01zZKx6xF1qvAzRdUE0R6wOm1aUxuUh+KHmyuc/cHeeWo2bDvAOjR7b36+ekG43EFWheRIZcbzEPoFp2AyJUE08FAMAgCaSMzAxXCdl41EZ5jU3UDdYUpmxcjgl1ARgvpc6Geuh61+qBaD6UFS8BvCgXC8mRvzNCuPSEcUQNCQFnAuX66UQIqg53m+oe64d+GBHTwjvte1BqUhhLQGoi5aRRDAQCK/OyjOPAPeCULQMYi/37lLrDzWEcJzBCb1vf99yLJLMSwTKbjsczwyWpQK01WlIDNK3CsuscNVR54A3Iw5YlNZWhLSkfRkE56cfDdHhrSEM+MTOvTpao2YUr6RBnb92e9cq2CWndu3JkX6lwUqJ95TvogsVpzm9EEEezP2jguxgL8HUGpC7Kup7meUoDkjM9JaNDnmeoLbQT0s6VZ+mdd8P01qKNsedV+08KlAGMAOtbbUZuqonomwWo6q3SZmj1WL0IPHbz1RUKr9pbnok5Ot6BMYnlBnfvttdIHdt6QGMA+59i3JoQSREE/rE75CeYbVBa2dBpFMJaAITiIZri+CrArV62MDewVNQFVT6qGu3bRQjTjfYb8j1otx3neOPtYcI7VF3pum6eF/4wsQpssMac8JQJ9XxATuNS4RPflclj/UBBI3X+E5sxLo4SfB3Q8yhyhnVF1gAoZQrM4E/33ZP72xABw0YEAbB2QpA2MA0tbr7vu7u7w7qgFkVHpa5DJqPFJrZJSPx0dKiF8n6JZgPIvIvoCMkxxXk+w50f+pUCxRjKU9RmTl1AyxfRlQkglf07vzlp6aWVwjHVVTF2WR8dxWAWW9zgprlfm5zEBBvtnUObVemuuS1ahK7rQHDPnDqU8c6YDhu//a0PPv7lR7/86JMf/O53Mb+CFNZbt4fPX75aQ0Qzg1BmMk2Hhl2OBPRCDe3zXOrq+ZeP2ncXblNLdKvWv4llRISWDnKX3NNdXQHTJnod1NZKT8iL1+6aqzUUfoMO6D/O4zDeqLmz2lzhj+CDKb41GsabU+rnh7SdxUAo6yh5N+Ixqesv+qXSrCpqRVMqPmCxUEkvXrv8RKrVRPddP44wuPOCGi7hBCRT0KMDAeKKDr8BbaTkQlM7sqSAxEfhy6PZSx3A3Ir1xFSkFcO+meYW+GrA63FSoBfJ3FNJA/lofT5CPk7ApG5DK0ZacznP8zQO5IjjaDwRSoptYnaYa6PaXJnRmmJGhwT/xvVlGYMwL+vNYaJNlbaSzy+PIYTp/CKCkGOCj0ToL4lya2ji0oQcURsPi9rUGvqqLKsTv/cnHRqaizccFvnZ8dfFyN4hnCncHwwhlMwtmcYcuz7103R6fDgjnQGoAUqefXz/yd3T5y95PrD2prvQt/63EUKzSvWlIcw15+TKyxcN1Hfxa0J7oBswISpvGbVINLqK/KpPKccxB9cfwxvzuvTucuEWwvb2NG2Q5mrYo7I7q562kXnIUR0LrLj8GD8yjI9rmAPVJwPaR8iisjMhwKuqebSX1W8D627VC3BfItPBLA9ZO85ZhoWZTWMKShjBmQRgipg2UorjNGCE+FZ66j2i+cZGxdjg8qX7jwevYA4hNhLwWahcMHesUKUywSpVYVEDZ4YQoqREIhAdhZr45ZyZoPbLQCgFUB/b5hHYuy0nGnv1igRKrAsjv5WenKYNPj9QTH3fw1mgKeixlHMc7h4eTomyzxHNF8rt49abtaIxe6gvWEPidsfXnys5rr3HCRznTpo0Nx65unfydEV31KGJRIGCe1G+QWLfhJPywQfvfvLJ9uvPvxwP093tAQizDsaCcPDWP63iJSvla5T71Iv5Mrrt/d904fVQWjixV7WlJIRyAcj20AgE3ANM4zjR2BuBcVgA7OJfHsUqg9b9tblVqhpHTkxYq2kcp3FkBvZ6G7rqxqzpYNX17mx5hJcZQ1XS2C23Dy8yyQYZGMbJ8AmV0NS8hzbNb5ZZFke3irf21f+2CUK0bMxuR7VfXClpJUjxzc2EaHTsp3EYKcgf+o6KQRhxLISJGpL4fBKnndALwW1vqMqwWJKo0bSUwzQi3RUjJvFNw2EahwGRGSScJycy2x5XeonMDUNLyY0iYlRqgmX2GXjm0iBLGMSuhTzVXIZR/DLKtSflG0npYDILhQNjiucQP5+6v/+Lf7ueAFIH8hEqqdL3SKrVQ/bax15/2Jktv6/k8yL+9Vn6z8vfNXYT10nlVj9o7i57K8jIjPzEUHATGEwfv/Pt98+n5aNff3o+L7DRgNzF9958opQ6Nfi9Gq9Xu+TyQxd3ta8hiWPtREoUXq1+1nw37Q8G+gghA7IaYNXhqTt1m4lzK2yvzvbXB34tZuV6ed/32G7gB3S92j7mrQ+0hs3meYnrS6Virjtg4Np5S29P/eNnL1Kfw5mtRCz5hoAy1vh2GZVYSAtVqqptj0ivJJmKYyNVzdfHnipRN0s8ieINMlWgakCQSHK4kqEkh5fsHQkbYD90h6wUCSFgaVnSpgVTXQQmIQIADUriF1bkpUXppK5DbVL98BjI0yYnt94UqTa46AKuQs2W2Z6YC44TWIbccKAf2R44r1K+DsOwzPO25WEaGO4ZE3Dsp7W8Gvt+fdqHhzUdcPHUCK1hqDRgSwqm+pgWqYhHbRmonVS2abBacWr3vROK2lLoszbNJtUkCR4W2kxUjTIslxGoYVhv758s5+PpPN/dTgs10tASesG7Ymx3L0sytVrJLqGtcza3o2Ff85ZYKAZBkf2ladT8AJGW3Ah8q6lZxwsnQczFFYpLLrtNaobiCCTKE4BvW6RXBxY00ltrRLv3/Q3zZaBa2uXxZuh/+dM8P99mahLD6C20BIAax1vbVnzNc6ZxfrsUP3+g2keHK8FLcTs8SFU+0fUQDx/Rc66PS7sS82OD4AUdBl9bBMx42NgPsnx8/SuVsiAkaCkkCn76a+O960he6VuGhAvyRQcvcsdOTVJqGkP2jdK+8SflLkW5U+KNHwKjUDIhq8cRbBVQOH0XqFI9n5fT2KVnv8FEHmaeJvbGpk1f28R3YmQ7QIMzs8lSEXYVCds9But5ncT4BhuPNSI/WRZHUiDcbAgXlBM0wN/0HBp9+O57JZePfvPpq+M5g4ib6g3/0NDV7Qi//fy+9oUe+7htU+d8eo9agkVBzTlnnmllKd8j/ahyxySWwrMlzoyGxHoyv3h6JdjBcCR7dIpYFds6S5pW2ypETZ+wXoDmtWkBgfXpt/DFw1+O4/O1Pl7YFqrkCJGhv6DqJuriWFJLtZ+5JGJ/lUPOvBhToxqYSDxB6fSo+pATRuKm0n4Qj55ynmKQpYkJ2QALVtyF0SmpzQO+N0+7ZDg9wQ+QEzaPXyamEIJKWDKJ3ZER5mrwNJyV6FH79CgER5ULPVMSeCjYGR+jvmNuVEaFN1F3FOPnUodUdt7im/d3/Xj47PZb5aNf3AzxtvSp5JG0i8DplJulumbOwbSUhzD6cYzaZnj2RvmK+F6XqYaiUl1xtwd1NxA6FYGk1UeIoaH0ALlta/7kky+2hUbJAon12tc3m2L1uAU2reai8TGsn6dxHb1Jrpu3wX/hB2J1p4ke2qdsgtPQB/h+Rl2XGnZUQxdAtt0jcvMq0EuU+0EsoIuCtDFYJR+kihK9d+H8sPzml796dVrmPqWwgguR6AhkzKfJq6uINcsoYUVNVEtixwTKUJeCyJJjmf5kP1YLcyj9qDhxkNnnjohBKCtCrVb4YAeArQFh5WlI5GMPh9OKaHLCZmIeH6oJc4ir0y515oI1RJLDzLeIUnPfoc1wB2DiwZvk3gJcTd4WbdoEICG1qHPnA4+hWuZVyF4xwJ7UD5CVOOc0DPOWH06nlHN/GP79tj7+n//zP/79P357vBm+88M+n5Cd4yhARyHXB7zHUdpgD5dfkuSyRF0WATagAslB+w2zS8q00WjdXN4IisBwL/LQ9xixlXmaXHj/3XeePXtxfHx8PM+3w7Qtxhhz5VX+wenkuul4IDonQKnAIJrUv9TeWmdOK718Zox4oqEqnPaUDHGd1MrwV8dap6rssvxD1waDg0SLpVgah0lW8TIb57NWItRCxEERHYZMdWE7/uv/7T989vOPCw2/4+G5kqsyzcKTSZya2GWPOAQS6eQZUV53SbrYWAwNJ2ZqIZGfrCZb+C4ZO8Uglb7HD4JXkdWHI03ZLInS9bywAPYzn4JMAS6hixErCRur95cLeHTo8y59IwqJ74gb42vXnepkrqjx2uuOoKsl4Ad8SNIcuAtGkJEvQMna/jCOlGxHuyLqZyHcTNOMKU3bl8PwVyH99H/8H8a//J/mx08NbGtavrqOtn9M5fv0U+tFS+ikO8PKmo0jeuFM7/x086ft5K3IMb+SuEW4ay4nb2Dtjql79+23Ywm//NWvHx5OaMyu1/faC2iUyMUO19qlhOESTVUppn+0z5z3hO2MfULbTodq2EKdpfx4mQ1NPRyh/JbxT/oIlFdSxdkcY6YdQgFDvMyrq3z1x+ZW7dBWMYjLVraQhqdPP318/orBEMwhqUmJ+rwlXy6EelourZkxK2eJXmvTe3oZ8je7RG1OpPwQJakslcwnldKukBWLPTQ/WblHhAVGTqwjGf2h+FZoNgiki20mFYcQkjAtBOeo6m2Zj0yrZpzmjWuhSBl+LlRI5lRbrE0mAHWOhwN6JPi2gApGlDiCen7NQI8VdC8ylgOsIfP28vbm8/Tws//7/3r6MJ+OsMD2cPVs0m3n5MxlLi1f7fMhzvGu22u3jy6BQ/a+9xOtasb07bShVSVrikMvhCdMoDYQyhpLmoZ5WT/61cen8/wNLnS7n6+D0HR/q8uh/Ne24VqZ0K1s/l/9syYHaYvllWaaoU3AkkptdlmxaM3y2z6wgVSwGF1CXh7O8y6QkSPKbO1L38ZIvfmwmgMna4wkM3hqIMSlL+vjAndGtI21f9vNGYLDOeJa4xS75CqdTDohGCR/xYwi5VlvNtyYss080UnkUFaJnXgUgnBmRKGFx2k7H56yTdzyzkBzN79IkWEu1OFMFcMwyA5TBYswaRToot/IQoUG0q8Efe5mqD/YJxYEAsvwUJ00RLSS8Hn7NB3GgDYpypsjLOznFXwiQwQ940k434jDa8lLGD4Zn6T54f54Wg73PQ1VEfI2y4IK7MLAeY0pEWhHxUvTFRp5o78T3sFtp2HlnlcKNe9Cqx8pLmSF8pjOVqYeHZCp5ecS3n333dNMAIkZ+L/XeMu/zYP2NyFfcLvwil3V72hW1X1IZYP/A0oa6SBV30Q7qt2nG5SLCrd5KHwm1JSHcRqGgcPR/V1pBkF2EIWG/HClCan+UPOIzJ+2YSxtWGmqSNjWJRJllHBkqr7RdKuFwHVZNFdlhkoCdgF+1/6HpsYtLGh+VSCkZEjZallOmt5H1ge+K4eOjL5olCldg1pGKsfTJwXcJ4lgBgpI6CNBMUb4cBhcRkAvGTVN7QVomdD+E+pNEYeahwjqttA8tvhj3C4vEi53z1gdzVLTPaPvgouACZ48m/sZ/YzsTaAXCnTYYMBft9vbF9sWz1/zqD8T0cZqNl6/hWEWk3mBs4RzW9Klra8fu+LK7n3c+jhN+EV4eWtpZCjGmLkmTLD5fB++90GX4m9+8zFZ4F1uoREL55lfeem4Rltvvay6v77h5Q4sl0Aan9rKVwn7zGFtrqJqQnVpq5/ANo26UFFj4EYFajmQHvCLe7Et3eicCqLW4MdUJWGOS5dDV8oUj+HxWe76sLhn3OSNDaxTr57NnfQwqEXcJUatSu7oONyN7q9ZiBmAodXlR3EO3NqEWMR4lBRm1ozuMBrVsAMsHrpY0YBKE90FmiUoMmbePFSnCP+IL/ZdWrdEfeYsouiqEU+aXFci1qV23hTANS90JRpLUNGKLD9bXWYOQ2skr7Z05DNhMA1tinFd8zgMUokgKtJhHMUP79GHAT6wfB5Dt54fxx7dF0beYxGQCJ8mYu1BWS7K9f8q6FmbibQtXx+JRZL2/KpZcjGykbDZltZBVrLXtNe7EhbIoGoQRkeQ6mOKK9B/wKyfmf9yJ5V+Z3+zDKrCbAShPcbFn9p3HKiFkXA01sxYOPS26v3SP4qqcaGvy4tIMW0cqUudKh+6xGr8r1ykmcZvmhNVp7MSBVneunh/F8rjQ4IqFArEAqoESwL5rEc9jtO+Pp9Ve63Usa7FOA2KrARTAW1if8w+Vr+XxwQTYcveNyJRq0UjzVKRaZX8WWT8Fok32EVQiGJ5VgT5QCzQqSNIquR45Gq5+4K6guVeJd/Nk/iUnVKHNrh9xE1KPL9bniqNe1PSb9DeUgZuAOQW0LNt24ZxOtzcYYYTyQjqATl+1vfjwxcHYhA3D2y3X5vYyVnVmhdt8LSNmnXpiOabVdlfbPq6vejLzPUrh9fEhbmQRJEnKUWpwycA7lPfvfHmW+eFGvprQ89vi3XbS2nt1fVE1/UPXzWC/AkY3wUzMag5RQvcjRBb6rmurj8sESwiaUkdssA2ipS4B1U9VP636dDmujSE2aPScS3w3rptvJnCRz8NDy9O2wHdIxI3WeZYnFpH52LLhfcrIFmvSoEw6jdW2old/4icwGSRtbb5rriELMgn62xQNL5/FmT9QH2OzVLAssZ7ncwgNU2RZKK3njv2BHYfygKOd4KCKVeBHwDMlFddl5YCai5qRQaNMZtBJuUvaNatlUCZJ0aXBI5cpv8QV4Zal8lv0YGi8dWDgeQBXzmfT5TJizmDoAvfzONjSdPjl+g2iRgoZoUj+pfvApT/qgfvQVqyOZqoXmd567+s7NQOXHH1ploi0h/Ee+D7lMckDVhaACU/EpUCkO1rxwKw8LHE+36c3v8dcqG1bbGdvvual1R2LVMkS9HurCZgdf1ZdU0uxJ1t2kb4OPhfmlPmv1XwmUB7aszrftcmUkB2CbTBE3Ft7q8eSmPaRpNY/CVq17co1tSTuARYyeNWbuclLcdf34bjc0gjegnl1lIJoK0Rt0u4HjxSUpxqhVjVJkc+v3rsasF1HYxJhhi+xWCTbaIkkyBEmV5GDsc9QookNJ9AFk26iTjhzAz0MJt0bh5jyKloTijS9OC+61Zktpkslgq5AfVbdM+TSycuIV8J/tQTLBa45NoRZL6FbntGa/FSkCjiuBx4E+UVUZRKeyhyhX0XVzTQcvo2n89HLaRTGaxPx/PyNEzp+auwrf0w4nucYFQVxnvZ146qiDf5Nu2bseREHeO3SzPWDnDv6FXSgFaScgbYwb5cP84bvcXSW/IIErxuqKz0AHLItrROU5OyK47kLjNa9Uvjdtv/mxTVpW6Qpam/owCwrtbrozdjCWizv7a83m+xRBFIIWh4PLehuIBDKgf6+aaLqtJ1NGhPbkM1eKY6V5jGSSPKQh+/XDAqkMyF8FVBluy4RsPilsNksu5hZ+UtxtJUKDpPZPOpSSbzxVklIrEkqKOaX1yiJJncjrp4adziESQWZvDgGr5GjfU4ZQ1QFIDSAijZyG6CLx4YDw7FyRVSbdXRvB+YVU10089EZEtyC/o5emEgiyowfjbs+msRqIoDvILTCXh5vT3CjSDXzBzLcc1L3306dv/xL3/6O3/64v6t+zQOmZu+JV9spkRap+wJN4bTZLrikvykEV898fvGxW1OEKz7X2khSCc5QCQXaKT+IfkRLbuyTWZDgFF1HQZn6JO0WPKKq+YfeE2qXdSPLz+8CzJcWVzux7YLmyYM/ZDJKHUWma6rq8H6k7L3JTdI2heuM8ZPWpR8EYo0t6PXaghqubEaLzbehDzMEsOQwJccvnzxCNvLMR238EFx6OReeyw+Ay3+hW4B631xobA7s6xzfYpi4qz5lleQN6734NWVlYPIqCehsKzuo2Sg6Uh+dJ5FxbzJ+eK4j4/hJPxJbmEUUhipNlNB0RIY/F0ZOCIag2HSxEdP33HehegP8pgtbd50epKzk26mwqTNvKXJlaBPMl8nxOM4THk+3T1+mgv51Xvwzx4/sNu7Drtc8zEGGmretnq221wtanj/4t59Sd6pYAuYQvoB2IumO6sZD4bH4V4BjxU7ZG1G5vlfWk41hJfRbt2ILvXmf9h3NttOdu43nAmEVbtOrtqO4BVC1X4OLkAMEsTtzFvZ6wvdS/IIrlyjFQ1cRlCusrGNcskoJW1hKOHZq2UDkysbKk63blvsugHjCIXzRkMq1xho6fsa8uoIAnuHgRE8opbhknb/jKDSLUJ+sso3lYJs2Lxvb2JoCeX5ZNSN+gMCxpQQje+dv5sxSQWH0OwYggVtMAp96rZSFo6i6e8y6pBvTRqPZFslNByR3Nr+lpYGsGiqqhWrk6AAIeHgF1AgnvDdcfYoSbc+H4fGlFuVEq9UMBQqx3A8P05ryR0Uj6VMd0USqz1XV1csfbPlnKR43S5HcH6wliG8O81U4ZJfYPIomRSFKEhykzL7nTmqeJWIUBQ5OXUDRJ6Qq6dOq1a6Gh2yf3P34V0QUF0Pb3XbA+zOE2vtd+Gh3nqZNd/fSK+TR3U+lZGDuCmoIctOIR/Xxb4U2uZKnPq6aFQ0lcwuXglLjNO2hceHI41LkTiZxxxkEI1rzdZCIDtWjalt5YQciuu+qpYsZvCpLCGXVXUMRltJVUqpllLEggezTD5LY0MJaIFKzQaHVnPBteP5LDxgEbPLgerDSxh8kEelshCJb48Za/iVZ6mIfWDmIAUiMHkYXxjxSwCXgQZ1HSYpnLxWzOJiGyc1layM9/G24j0wUYuS4CAef6PLy6Ev65ji8SvKYEXMuxeMTMtetduVnhBrhxVU//diPxsJilR9rEDgXTh7roBGUpO0sDJQBtTjAlg3J3DHctc0hjNAG8lTwzd69BlWkIkWKq4Sgbz+VcuV1/+4WyB5Q2ErcjLCTq7U/avwP28z9Rj1OKbj60VEsAAAXqeYDbXke1VpX3ExgDkRjt36wlNX/SFMBHkryxpmgLZXmjugAoz/KEOVVjL3Kr/2DPAvomxt/ijnpGv8rK41HUu689hs8rQ7Tr8TUSyTUZslcbVojaE1hyQ189br9mk2Wkx5m0tH0PdUp+V2JA5OtW9B+N8pHgGggg+jPRUdqOSFRp+2m/SrU6WYK0Y8/4ouV6ZzaosGdw7nnAeals5++DR0D8cFDNi0y333IcFLcQ8vQukevg7nuR8PYCTZye1l27uFqJJOkJW3rbyr+MrKGXxYm+ecG75XBpR0gwPNcRC8ApoRW90H3glkxLXLNqUVqRDkD6R7KyzypPeb+wJJ5SzhlbzWld+dvTbah90pPNwSrg5NvVQyERMv833l54tL1cgXu3YaUPaVQVfixbiP1Qt0kXRd5n2hykNtdktDChgEqrHrwnpeQM5ojrnYELgBDoUiPqC7O0keieW2wFj/qKe2hg+FUUuwxUVcBjMqe4bIMydFdAUN+FZ9YytT6AOQZsQWNyZKjDcVPxnwRoIOgNCXBFNl42eQbC4dpy4RehX0OswNQEdANxJD5FkBSV8VV3mddhMfh1eztm8Rr0AGQzVfVWKgKBQWLn8jDNYCs6aLkHPa0tdLGl8+X2kCK41kZDoGSwN49PLFA+eFcVvHHCi/A3k0HPVB0tMwPk2L/qpqcbteim5qt3kn+CK+QGy5GExetATJ2lKNCWwVJFb3215KHW/PbjdfmredNa7IiQok3MUebH5noK8ciq3JBV6ixivAQf5PxG4jIebrhAJp/HPf9mmxnUfdpuCrxmiRd3pV5EbiD/P8kilRQUIs6VTL5hocy8TPHUiXSk2iRKA+YtCkhfor7HlW7Af19xEbM6yZPFyN3tT0tvqYXb0qwTzl2NaBDZ6lkwxUz8fUrl50XTkUnnoGSrhJjwbuNLi44OZaWpBRCQUFfyT+EIrzX80tcK6Kgcbt8lIXD4eRf0nULNMRAT3q1UTLivoTEzsS8x7ONqVf/rv/J29hXbXLt9meene7upGc0MmsGseqFfVZiyqRSnvda97cV/y13JnmrqQTTYkYDM8v3X3S4Ke6Ei9eQ/6mTiVtrdulfx9+y8vnf2oLRmPEvQ11uxlj7Of5fJ4N8rq/DJNgS7jIvjJSCmQyB5BN8FBWLjxUeNpVD3oXg15er7P95oirUIITAHQbXZnDw7OVEcttIFUzcdJJKK5jhRp7oI7Ko3m2tnWaLaAWnpvpqV2w0e3KBiOfqSrfhJCOak6IJTabGpd5IJIIlJyNnYb1CeVPNbllA4zFKaBCLBkkRKqECDH9pB2mvOlrRVjjP24wFkisCpd0tPTUKswJupWGIenBarGFuv2FmXVN41+PT54C6PkYMWCVsdmMct9r8PZ1xVE29XopIF5FykJ7FXzp1db9p9etKsykl5txiYmfBqDRLGjdf5T2IFyc1iTlSdWs+v6f5j6v4MTcvr/ugre/SWyw0Qwk4jpzX2k7N/w6yS7gf/R0vF2E+NdxyniQtHlA2rHgn4+qTAt1m7crVIvVA/4pcZhuw+E+lOWcgQM2syFQCgmEmh40faIXp3Cntbirrkb1ctQ3kd4jABVt73ohrEZd/bBazK2b018INwYJkkxS3/ocBelBHTJW8eKqE2fIBSJkOWHKIXFhian5+eMwvEQBrRvPxphxocR5a7IOdGVSKMb1UIGB+iVItyQAldJCI9mYgAkTeXiiChFKhZJPQ3eTl8PxSzAkeqPqXbELSfXav0mm+A/UelTzJNVU15/b0FsfoeWxa2nJC7oYZrzARcId15LbIHQ6gbQ0E++AAgAAL7BJREFUJG/PsNvHV3+Td77Bbu8/1RbJ+N4hvTS1eAdsq7Hj5fGc2HFFASqfkBv8Z8SGtVJ99eIuxUZYKtpIzDvvosc4LgVnabqN4Sf/+/riy+PGVDo7J9yiSHtYtVRj7pMoQn0yrvHcrsMX1eS4FXZRXXUp0jKYW+MlHs1XadR4y+xCFOt+cbuytlibLwPRo+FDLH5cIENiSauPHBUrRzdOqn3I9ckKVtxcZda/1SOQVdCkkPolfEeCkiHQJfHQr3lL1PdvLEPM7cisSSTIWwrh+TSup6/XbWZURHU7X79hvRBeumZmn8zNa4ufZmKcrvBKgK6Tca9enVrahhsl/OOFBeYaoDR1KGdDk9/RPbIPGvdy1ASjArFtTXJjxvXZue/L7shbnsn8igKuMaa/Z1uU1rBz9x8jZTE1k8qG6h+qbOtnW/23x346HSqEW/pD9U2qP41VXkoOpzWE+W+mPMdB8s90FOOCqoVWl03y4n0tZDAfqjHAcnlNdUMa/HRWsJT3W6+Da1DqWml4XZlL6B/rwWguRvvaqvdVUDoyW8oHrZPoeYwgX4N8Rm5I8RU4DmGPUDNPKc3rxqqHx1moB0A3o5VAFmE6glgaJRviVjv6LHU1IqTWdLCXviHE45bHh6chr6lkpLH4n70T6RSrexC2W7xardLBAlUbDCV9XeE011QFRz/kFWtrjL8E3tpK/qFpdcpjMfyDZWfdImh2rK5weR6NuMw0uPOov37x4F+v3fZqDIy+67pIH7aoRY+leL1JF30ncSQxEsI40B8Fk6x5zZ2XvL+Y3a23D9Qeksk3LwR1lwb0D5b0FVWveQIhyzCjlOuBGbcsolPBGxUgZ06uefiWJlUhr+6YHtOFToraqvfE0/2aAp8FJXqLFLg2dSNncap7bYaxYo6k2ifLK1koViYcE3FdUBdUa5/sBidMn0GKCwUqyC2HcrwoTNEoLUeVpFDSGqIItMWluEjO0TXJuvOvOH6IL7a+e/k8B5ryAhp+91C9em32gPuPRVLXN6Pgu9wQn3rX11w6uimiy9GMo20Ai4HrroDlZV4bYufG0ojCxUJBY+438bV9vtMlrMP0ul6DVNQhJroP1DerDnRel6VkHqUl+Qf73/WXi8nF1jBFU9eLoSHjoNPFrn7fHoeuvntUjWX2bqzrXWTdQKALtNeeCzGWCkSKy3tosjFn1QDMtizcGyC/mAfkhGinvXbiJaIuxeLGVsujUd+Nl91vDvuuO0nj69XD+EdPHxfiDm7XkJKt0pTVFG5NuRsPO5O6s3WCeBIQhCM7yuUjB8nMHhYIS0arFlRqPo5S/zXi6zFQvbr6ljTPdDSqMKU0hV/+m5+U46msW7+tDWjZHrDTbJcrX2M/PkkrzATRFKlSPhZZWPPsqimUqArMuDRvXUyXolbZ+dPbUTPEoYEyrhiXFASYl917py7024eOLhC82GS1EOVBGMau4Lw/3lwbGgdBHCtVrQvZqrerZ6/+rIZxTHdKt6EVI5Ihb3/N924iGrNXrzP1bRJJr4Crd8ROBADlumZ0o4qMy3bONDKA9zwBNOrle/fYrXBFlfjAxbskNR9sGeYqZfQfBTDtVpwNuOeml5SUnW2vA8RHFp3ND5TVIqB/BRMB2dKpyGgZWba4NjrR/TMGwQ5DKJQNA27R7cjVL70GQ3QbApttr3qTrBssjxnovFyUro9HxIjGC0NhxFMZ/3q4ebnNZX6xxA5js0GIZNh/01b7Ndj96LycJncltohAJuLder9UBLLubNtsSEINI9GYyGHshDWdrLkT5gMFA5ksPMdnXHHeKeAmdfOal692VW+D5e3ia5fWXSdi5A1D7MQeVLGvt+t2rUqzq2qopyQ0qpS3oPJsTes0lmQ3dVnDgCsqt/kmfcZ5P/ZTDg+P59BBCVXuHUqWmvOkRsTcXqlhmF+6XxwnaA5XsfuMcK3oRRtWwHKsNUm/S6/UZGd9VGaZq7RWfiT+nDjSjanlB8dXWngqmZCioRORePlBbgMeHBDf2fqCNXbdTueZysIAe1SNZQG2GAIalc5UAYxN405JrUhF2tf16WGGm7YsUB8pOCvX9TGE2229Wb4Ghgt/ow6+i8izNTPemOx0fpXcGrQyswbTa5j1dE6+3958Z5hfgUkqg5TT7BpkQTg0qVeZiMCA+EBlT1qDyDdIan1dlesqujskjW0Q3T/mpERrXSAqUEo+cfBocZVu/wvbrTOiah+OcDjJzsMMemAMWpO7M+3ukVy+LQFJ/ZuJGYsx5cmg8mn8YzifzyEsGybr8hAz8bDQsoMDMI2iJJCUalGySa7u11yUD2ncisknGrEXms06Ztgpepatmr+yQ7m9IqTzWjo2PI9bwIpkFPwG/cwgFhkKLXwFMn5BNx9+YWwLjKHm4vljkppWkDP/jTtz2B3jtJDVgXW6txDE17C+MDUHGAsrtkCTfRvlI8hK9y/G6fjia8IzaQux2/hXlWmzoS82vJoWZ4UFbVdleC9Zu4QR9W8TZA2N624HyP6vKUT+FuZR4MPYQuRGHdDwceUir712V/KaP3gMYot1anLs4M2Z57wxR4KMYG4OtquEOgnWfUhqiHPxCvCmBKe4rO2mV4V1cdU7zWp/lHswC1OdLUbnr6fzGTSmDy/mZZ3Jhdbogvp4aHaR6BiTHH3Glgu2Fat5gRr26GWqVbfr1HKgOsdmMGVP6x6zXeDu2EaEuXtXh15FuK46fdqQmxq8UWjXbsTqZ4hnh4e6LDSjuGDYt0WdDDIFG46URTjHjP2YSyD4NH7jmFnBkuaXk2h2goVKQuIDV1zK4eY5udE7sWwd9tua5gfQcvAMJneTegONDO8WsakKV2fQm1bKrlFxq+a19lFoPbo65BEM1zRszvapfELUQQWT4ib7gcYT0FTg1G3DQHOfHDTYyUmrWvYCbv6Av1kVWffZxmcV+1OWZQZvDg830JpjPaT/+rUksQWHOrYHXXso4Evm05lRu6Tr+fzmQdlPF9GDH8hGv2+lnJaYl35+WIihiTM7NX1hxTXNBe/iTAFEaLBoBVi/WasfJlboYi84V4HnQMmu0YOLeaRPcq+FPbjqG7XKkR+FfE3/iCyUHtp8Sos35aIlCUCOtKt9mzHihBaNnwS8g/NM3LxFwAz8iTLQHAFZh5nELPz5Kpf0SuKQWWywDzakmhjjQ5eWF1/k7cStBoIlvpq9s5Cpbh7n2Oz3T5VhW3aX7OTOZNkT7YlI0TO9Pg2qcqOa3TNSUk+bdw+O5JFnxcD89sLD67lF3M23O6X9W63z6RttxGB6vmFGxW4g3hxwcCN6YWfGsxw7aqHd8OAmCKEQHtl1OM7rKg/ePmbrdiGibd5K0Vcu89soJl0ZK6kDn4CJg9P09m0I51fc0onuLtET/NA4b2N2T/8jfpVMnDajy666ZyeWb1bCYQY8OHvpsUD0oGvIWmN760KqdylLIG6kXZ5cjKS3pB3R7LqItGDLuA8Dgs0clBKt6fbNJSD6Vc2s/bOC3PBdHXxN1OcA2tkCig38zK5LdeQkyVFv0Hdrl+qgaCum/kZLl2PozuN4+/Lpgoi6Azt6y6mhu61FzO1YN2xsSiMHVLR1NSi63kzpYcyGqcM1qmtfdSDja5HMGjg3b/Q6RmyiLJX0A7GC9sQtCHJPcL9V36BxY+tueq1z3bjRVzzsXU+leA2lIPU8L8hPegXz2lIUy3PVLvZpob8IyGbLTHP5hvHz2aOpxseJr+5v1UT7W2P2ULt8taJIeY/xjfFw/Kv/7+bxxYJRRNVeKWWrjV6jcYHN86Z/XxS6REqFdql5GFVLVspXXkx1wbXzmDcLUEo6w8EpKN5ZpjFrAM7DTVmquN4qiWUNyY33VLqm3JSgxmkXs69aqpaXlJ2D/kVBsWxeMdEbqDUYgEpfoiICla4sBjGJrKo34EWyzjg63ury2GwF+xDLMH5+e/PxT//6/X+2nvoFA6Ec2Laub6va91tC47Zr5kz2myQDKOUgEBRqxb5IQ7pv8WDUoR8wBQptMWIu6uga4UNRFwaF8wAJxkCd/gpTo3ytNWT7V8OPceVdNSsOgEs/EHYSqSaKhGUwjnmBer2tCnG+AY8h4B8pQKKsM0lvJUmoHM5trNYs3JVfd3+ohlBosVj+AAAOce3SDaYS5jn2oazkdZqV0/yFPFBDyFEnLfk8/oHqzBNZNRaU2pyk6T9jt1QdJpQ0RNdInYXAd5KryX9GlUu+aXDJml0RYDA+3NGTEv9tFyyYC2+2VRp+mYSQ/QiZFWFuH7X6pq3gKfMfEGJI+G/hgce/0V7g9AI3LurHZGopk3hIwsA9pyixA//CfiHLsByEKWzX8vzm5rG8LPOrebtnALF2bFXX43Izq/HVCxJ/8krGq0qMiy8MluF9e1ff1BPw6MxxGMq2FAC65Sh6ODWI1qIE13PbsO16jY1ss/itvFN2u0u2j79OyPX6zEkD6xU6f/mmquWtGtxsXv2XJE6EiKLWUskXChsTQDSWytEAfmNmsVXC+5fMVdFD1RYI2IoIPp0+/byPG9FCMkaflIsICABhDA8MQKrQJKFEtRaJ8QTHn2JXMHascYfN3dYXteAxqJB2M9Q0VIYkeRPOBe44QlZyt2EhGkehnSb8AyVZalWADgEHE76QdPyZYTbfHe8wMQc5t0xVSe4cz4lgzStdG2qq1Z/mnYzKgOxmJWtjJVzBQDi3NSHVZShhDeTuSgVOv+kAi4VT4nw6pcKUmpWx+aJZOPapxNOzcvt+GNgEO6za9Z1Rd0L1fm2UYfW6TfK936ShBwb0KYFKI1HGsMY9mvCLSx5IJTMQqbqwpu8pdaVdhaSGekb+7Z2HGpBdFV3TW3qDzQq4KML9zkWIdUbfr+CCNOe5N9JCc2xLKUEOe1fiUhN2Rz0719/qNNvrmUVap73eZS1H14kquhH8rZW8LSFMcT6BlUOStJaG5kodZm1tkGu0v4Hrp5a7XL831oE6PXWuknrSppTYfNN0RSJbRZs6yg9oiCoFnIw4e4bJo49xPwOfjKpqXCqPFGI18FKmtlQgZlWcvOU5MaQgCqw9BBhdBCuxMDG/pJDj8EPClMAZxCp5wyP2gBVpQ+aIzoRYDQd799ShADey7gLSUPwOj2KxtJn1awQSTxuSTeN80dLPaRopg5U85+7lcLM8vuw+QOa3QoHExDrGyau7RaMGCblVhjUqFuE0xjhZYYkZyENJ3aUY6dLx/UgovK25bBwM6/Ov+RThPF808OstTm68Oid+r3tJvFfjtWvhg8UMtO7rus5APivZ0TefoLHuoi74HjgQII9L2EZd3kI2H+uG2kugiSh3zJ2m8e7/PpNevSc6/wrodQqnh4dlIyUoNylcMAxsoM62lXgHwZZKfXasYoiWpkopJuo1QIpGk/IxmWUC6SJEhQVGXWfLsPoAcSuNJpI9BPJvTNYVgwWPQViVudCKk9AcHkFHUJuB7T+CN9Z+N/VH1cKKAmSf2NCizOyElkOSOPbKzYfi5j75HgcNzEAqx1I1QUlpEvg6NUTUio13NHIsvJqGANwKueu2jqxcceunmNLp5Yhou4OT6tOe/N32v/ZiZaY+eg2I/UctYhQaYvIkvV/O7j0Vsew08gfWWOxIj+MABm0eiozN1PBvccPZOAx5XuB0YcCAAiw8nKzZvL9Fpuuo7euf0iwhQ682Ui3WXKejZpw9t9zizi9xUTA3g9ubIhXNqlQvob0sy3F7keY1rc3AKueiLqxUwPsVPzGDzhdfPcz4oadRQBBp8kWRfaSsONwnzE8it1MgrnQiHfZTFYMwrZlbIM/C7oEGFRC8V4h64GcpGyttDvJomaCQRhzkrUSAWsRZpUEHutvVCZNmQ7dCEk7VRgy39mIBqxyKb6T1JSPJY6LJylwpsQVvM93NvKQc0nMiU6gq1eZgLRndxTufE390h0whVhhCIkyXevCawSU1SXuspDjE4SHH4eWzkACdR54P8Y1eiF+D9qUeu24G/77+SayGKk816jX9TFAWutDdaXQ1cLGUsU4Yxzdu61pWzbVbzkEvl+tJiRhGQOx+sc0vCkrfZI6/0Y5q0ErRL9LPeB7GGfkPOEdjgHVzM35DyzZ7yCPrvAujyv+u+t6e3Del6tR6u2ANpi6nNJQuvHz4elmPW18wTgucqbi3BMN33PICpjtSkky3ph4kDVXglKJEo2ySRVQEpOCznSxqsPk0oY33LAXDdOnshAilK98LRb6bprBYQsgBEEkQ1DgxzCHjS96Mgplox7NYUIgufoLgzAN0F529GhDydpgpOlNPQt5Kp99lhENF5mvGXvCfzNWhjhL5ABzm414y8+rSFa+MWNCaJOHeMrvL7IDy4xe2P1435kwnnTUfy7Jtn/6///b7/+S/Xbs+9QzLtmxXDcidcn0NdqC6JY7ask1smUao8ZkGBU4LsIOv3iU5/sD2j8O6DOQxbbWE6LYx7SQ4SFuJcP9sT9s17CLg192CXUc1paqF5VAqvUg+zwtPXaih+c5fdBJlmnR3JuBgCRzvUrWictzKyV78LWGATrtp4uYrH5O/mM6kztU4lT7M55S6A4BFHXfYweqVOA7lGNO8ha3HsBgKgInejUJTcqc1Z8R4I1crpJRy9ZqE2ZlrQin0IPIR+8M2m8cFA95E48V4tIp1+letzclj7Ho6Faxb2GikEIb8cpkxkiTLPuD4khLcMRKdLAsKkTvro7WsOKCSaNMrGEWF/GgGoWwCmJF9QR4FLJlkIyzhp5e6PuKTFQhizqASBnF/ouR2jL0nyEQWzqBTKQL33YFBOSKcIF5uFKVLnEP4+Gb6g/W4bi+X+EZfmKByb6n8026dIbcHdyUo+0UyraYMxJtmbeCrqrtzilUhjUq6uhunaUESRQZUWAgjAaTS7KSyAYTJvJ6Kl+MyGntLlshtnNyLy7a/VTVgzq/ELGDBRfQrJIzf0CnBuQeLIcxDoMNQGzRPPNIsS6P7LOhyAbQZcVun5qguC6E/SdhoRlH+rvmXDF0+liU+//pPyvqfxnOX4yv0L+QFApAHTLjMQQiYkOJNYUEnD0rfOPYQA+UocI8rjTJEOhFp48TNOXyDayljKCeyn4cczgWD6Fd0qGP33YR8it0hlDPEVu4KMwRTXAJ0MxEwwkhtpQwkIDkkKlSEl2VIpENyCHexnAO407nmY3R5NwE8I8fSjSmsqZ+Qikl9FEWAZxDDiCxaSGVLEB9gHI8l9nl9WdJIz3lFfi13JRzxXQwiFqJpQBzAadyH+DyOd7G8iuOIgSt5zphKg3sxlamkIkSKDGvAtxbgVkDfUeBCQ0yxMmkK5Yg7zVvueqibeA7lsKU83R0wg3xZ1txNXPFooV2egXIvYNdkW4yVBWMiNb4vRIJM9YC9wfcbT7BLVEXvujROI6LNx2PIW4OP4owHMfjFOa1YwF36SSs5bVzrjrB7S7d4le+mhCyxDWdzNINj0uXjZlpN97uFyrJOtDBMsiTxYU34mYxeKR2ZX+Sl2jkmHpNuyQp9NLbSVv0jozemWNb1Ecq+e7MH3el7OSE8oMHfCDK3rUPklbJAXNHCttEoK2xo8gMxe57Q3KB9o2yFFF2kmZaqBF0CIp/efqNsQ4oLXQZMbsmPa4YBBq0qNTCKd5zuoQ7yUPIizRRwWxZkrcmxX7bDdu6orz2hMWcbtrKkYWS6uVh6DFoICy6+v4PrHlM5g60XRdk8QtTRjN/H0qfSYxzvhjwc6snYT2Pa+m0IuKSwEXlOifGQQwpbj6vdOqgVnpbGsUGZUrijOQugmMRYuIU2roTvSM8JZJZupoMOoBk8BV+iqi477kPYziWNKbxBrEK55GGDVswhrHl++biN4JqFyrRiVGOEJaDdybBFEK3hvZTGBolQp9jB++DYxwM67KyC3qmoBZAQpDQdpnXbltNJDK/sQs0yYHxfKvOKzgZuo9k1RXJs2RTNqwyYiF1Ks/nFTYvQuqxaMmxQRq2L31StmtBWHEqevgPcgfWFSuArBrlqQQdGc1eup64mePdqNEC58gcKZvvYdXfT7ftvP/9NKDMGe49hXkGPoAgxik9CyCscRnhGHfmDMfTYyNQoA2a8kBPhyATBSNUGUF5QLQQF5BXblXs+kYuh3HCOHc3tm+AFMj2SRL4SzCAXHLYJu5ZiXESmfCQGfsAXXWE8IRVL6TCfA+felq10sQxpOJUu5DzkfIKjDa/5VYyH2Od+QuMPpBFq6AyBpfsIaYjUXJ4QlS2QWx7iDLgs1+Ro7ARnlCiWBRU+lhO0Ohxz5+0QoHQYT01+uTT3U9oQORvKvYPfLXZ9Ru4QeQGGN4chDpy64luOmMmypLDleMrned2Wri/vfPBuf7eGZeCYmWLuNkSrgliDyl0Q6k2U/adWiDnr1jh+nEiQBIZLhNHRzMGVgwkbQUqHw1S2bVkW3a5iKXkjdqkrXeT+YA9e8XfT6qZrG75e4759ym4ORfR1Be0bH9P87UaGVBw98ENeXKUnAeios5R7ta9c5t6E1/cu4vmqRTx1lne9X3fvvHxbzMvw1hQPcw+zylzi1BhJ/YYshATulUqujD4Vvhx8gMLVLoLylDsqOX5ibzBQ3NkFYDQieapUpIGdpm44QQt2ASAQDiYYVMio7FyGDXKc53XOBXoa03nBCMUxJ8k45Uy6AqlADEldkDmGFUKX4NGHjX8KYRsKh+9pwVBzJOZiCmPEvD+a1QA0JNIAW5g5IIMKEvAFQ0u4TUszQxylZWvpj+RvP4a4FkGUQbxz7nJZoaUo5c4bhPJu+FfsAENhFnuk6YluhvBhXYgDrfIWsb5LGd65PRzf/M77/+K/Pt3cp7gWBCJ+aHSbg1Gf+GJn1c97L9hyDXzEplBk81CJhk8SuG1wVtM1glaTOTuYEzRN0Gbo2+OcoxOcmNaIR9nEAM1GNb7fxmN3aa8Lmd7ZUMapL9QoJLkm5zi3mqcewYWlFZVIiRw40OoPmx/sqN6uePp6se1fWpmsEYDqXbkqq3K5J0V5HYDzxvLj//L9X/7ti6cfvQjngHIRWrvMmSIwOxUGOD/rYvkUwkA8bDS5mBvhtH1M5pfJQkT4gTysFy/KQuN9TioJaorAGAa+YWnkOVh934MCal5SDD0EDYnNUrpVnDmUI8gVpWxnASiUZ4J21EvL/eL4T+wiuvPzkE6UDpMkGekXLvQoZld9SE42CYqLMwdUX2OZ5txYH0vMa49cl4xpoc+vPMarl98RaHRUvUYLtuWQkFDY6EoolUwLDsGGUpPzUrYPt/BWii9Pa/7R7716+/fvusJ5OzVlF1FXGxVfbO12u+9+2aOfvLWlBy7XdXEYE2lNtnD6rh9QFiYfTeRBaJGp+aNfgjJ0aA+K38pOxmqeqOJXLj3n1hZKY+QGaIl+S9Ay6pk44+dOqdUva3djwBy2IyjRkG1pFnsfsF/m2+SN5tauvkSDyo8myvXBCNVzKOG0rLfvju+9l599DnB0SBElbqliSlxD/rAeRU4rU4RN4NhCCUsHNXioXhaF3rAdyfOgWi5JM2Wtc0Q9v+NHS2vdseqIaYsrFZBSj7hvpfMPZAlTCd2WgQzhniK2t+zW0hFiSRD1LaWx5JDWLc/9umzwYKU0xogKbshG/E8LQcklfYK8tZihTsHQmUwnR2g5xpmc4wRrTBgPWiRuz1z1oVHXq2xFrW3QGknrr25vAoVzgWLlrukcB2BKw3R7//4f/vH55hZKjVnTpGxrOO5qg71wea+uMQMGslXMl8t37reVVJAEIN3mr9yTlXiwGhmiwgJEGqggsyr8V8BwYkK2v/Jc0J1rWV7jdu/MX4nBnZTUza6uPFImK2gKaxpOk+1yxrZdcHdbtYmPJ8TA/DpPfYf0VCVhpYaLC92T9zm90lyBQ5dYIGAuDtUk41aW0i2HNz4Y098NCZN3sHWAgqvQTsqLqjaTu6lo7qpXTGEpZJYHCzNmobGutbGxJC44AQcdltShmES2COEf5GLNcQTmKJSUNywdEzinnlmZqSEoLjSaW66xANrA/ps0vqQQ5tNpukUoRlFQV8pS1i2lfjoMXd9tM2FJZdZaQpseV58l4aQwEyIp4YYP2W/USkp1Nwo3TIMpnkYtAgalMIWO5RllRKk+Fu5hohSCpjc5kogrKaN1PR0f1+/+8/8qvvsHSHDB9ycQhzrJjU53hWGHTWzKxfX56jP1e2c/f9cnRrWgpl0Uzd6/zLhQ9gE1yDJQdC9NBHJl3EHdU5nP5qKajDkZcVfesD3I32ta6iJ+5OoRcKpu9dsA97q3W5WqciQFJsVtP7r7lgUcFxbYr1Grx91FtN/wCDFBktX1KGhlXtYwnX7458Mvfl6+eMwRORyqZMLd4EIcZZXYGzLAKl9sVdXmcsOZhkVCmMqbkJOwNkgb/iZdU6aqK4Z94tB9GfuAviiM2I1b7tEdce7Wh7g+lNB3RG1OmABgxtYt5ERzJORpwqNDdov2F5JThIBkyEfewinkEdEP/G/WXl2Zh7WU5S5Ph4gB58u2nhFtQ72AjpOoqXGdCIktncOyRjqhZ5CzYAQ4xqZbdg09rLX4WpAH54y0MIHLYkbRleK121al5e9Rk0pdH0o355tvv3f/gz+a+zQQ8lCalLUr05tW52Ua2telU+U5SBW6lb0aGVZLZ09YgWIu572P95pJlO6PPHVEyBmojU/2Bywal5Eudu7F7m9EwqemtU3DXZb+iNOtNDH0OqxRVFLz3Orp2O8SJcn9U6pBdut25dTO5W3DEnfdF364/92IcLSFz12hKJgUxlzO90/6+9v4pdADoqpCtrO2LvmJf3qJdA/A1knuWSjRuctBkUYsV/QP5Eoy1Px08IW1vykpdusWUj9Cu/WAqZbzQqFsSGUNyP6MORD6fAmhz3FYQeu2DnldGCTCTOySOwX8QaabEtl6wPzoLsFOUuDNGrLr1hgPXey/9ztPb9/+1pdf9q++SI8vUF4t3VY2BMZc9msh6sK5b1Eg90JxxUJ8YV5ZNi82G5sfV426oGPUoy6uo0CdWM4vI0u3bFteH09b/r0f/7Pw7vfj0CMtr9StIpk+dPQ79Eo9SbauuETet27DXd3BPjfKDZU6LeCahdn553pQ+ndHVaMNLwWaIkE6lNQLN0S1QW3weCG1zjVtgFL6bwkimYWUyJCssF2zOLaEHr+1vx29E0YQycTkqh6u3XwjxO5uLqS+fblcmhTApKW6+YP/Ni4MAWOc8pvf/vaLj/9uXs9E5YRcL/ukOizb6zdtqqyXohka3Z0wpJRl7WmnEo4Ixo0bEDS6prLte98pd2+uz56fTw8pbmBqmMaYhmWJX5/zeDri0QKftaGOhTNs03ocY1rgB3c5p247jzkfuztgueE/9yJJNOFgK3DFO2A3aDFQQAUAI8bh0MU3w4vy0c/6P//vjn/yL7unH6d//+/6z36xPbwEzzeAW1r+qapfDsPOMAsz+xec1fPZQi7tCCyJbLUqYwEpcJSRax2yZUFAfLF1YelLPqzn/sPvD9/70WkLw5g3JIJlV/lWqd1+MP4J67VqfnZ5ofpLmwWrGS09iwKeKeffhtk7GIUeV/11AuP1Xbd2Ka6CHaXhzJAIioEdXYIXhEpkZrauhtB+N9f33d+IIRLmt/ERWrdRD77XDhr2CbAF4DI4hmaBK+Crmvv9v+1a3K8Nvc5VJ7t+S3+SK9SASbcR0jjb1nfjwx/82fTzn81f/Yqp0sRI0D7jjJREU9rJxDaEDi5YKMK1yqgc3uuAvqzzEMuYQLxAdRRqotG9PpZTevrL89fD7Tzfp24dxlOZujgs/Rv99771/N0fzGkab4dTf9uFdUz9mGM+f33+5G/T508JB5GHdT4fHx/OxzKvj9vcRaC+StnGbX0VhhEVpb5HDE/jWhOKxiHEseQ1xSn1j3E4lsP9NDzrhvjhP37y/h/e/NW/Tn/zF+WL32xlhntHRrIngKVtp5aqWjpz2oDHxIIJW2qqhAyvRJ9cWi9qC6zRh/gMiFkurCgQx+1Fmt79z/5Ffvt3Yt+tXHyXhFKVv5a6RZ7yRTxou/jqu+0HnJmSfkbdPkwt5pve7TLab9dEES0Amt+7rg+J9oLsX4QY1MzAhXOFc7hI0bvKOz+/NUneuPIRMaLVsatrpxX/2mqrxjqa6qrtYjJb0cv8zmG2ZbDWp+Zd/52rYbTGPN4ratHUknmT+EIZmpBCXe7ePbz95sNTnv3FbqknndCbEo4sGasnrHfcvY0yCkqynNpd8VfUZ+d1GYUEmXKOiihGbYkSZqWsM9rvthXqLY/rY3r1+fDZT7fu9s0Quun+q+HNw7och/vu7W/lP/ovth//N2l6ctricTuv8VjOxwQ0RH+PEupaHk/bco7DOjx/iI+fh68+Cc+ed6gOkw3f1ritS9gOS3c83Ax/8qdPfueP8hvvd+uSwrzc3ZZ/8q/CcAg/+V/741MqlKPyz5Ug243WuVZhR+4Jkb5jTaGrrMEFGykjbuR8Vebl4GCNwFgapWBO0rbl+64bP/zu/e//0dp3A1D/vY3z0PNa7xM745q+3G8dp72rVVNj5Elk7D8+X+O7kniaTKUCcd9we7gVB3mfB6xQIQ5XCaAbYDOo2LvGz+agrZNxDWTmN7zZaWJvYPPbrIQPFSwg9AvlfBO5CZ6PSsZJp+Z68rn9Xeq9CoeCA0zWgvF1H/ra+3rRpDaaRAZd3kZIgTRut29/azv88tnx+SLPRZxwpufg2p80y4jpIQtDDSXUJ7iFsi2oj5XYw9GgNG0f+jDnTVB4ZSMksKwLTTAli80UkFvsgBnJKW+oxzy+WkN+vq1jOR/SelO+Wh4+efHpXw0l3ITwZtnOsX+Zpod06N59e/zRPw3TW18jBx3CzW0Zx+nN7w2HP06xj3OYC8pP1B1JteKY01Bup22ebo5l6LbzlIDjisv8mKb4wQ+2w5M38mlZTkCNomRsvpVWBDUzIPS6Fmi0G5/dQlczFQthYESW7SwuDhW0pF4lwK8S1mdp/ODH/zy98320D5rVl/QYH9TwSxd7qMlC61stPr4mZHYO5D7bql9SdePaDATq4sTNCbHqPd6CQp6L7CZVBruupNivmfKX5PZdO6sVVVrMijGJ6zt14gpg+6BMZormRsada3w1KK2roZENxqJ2g0yQqCJ7oVPaXJ4RIboIw71a4+1TDe49iYKVL9J793QFBMXt8tp1h/WH//L2409efPIAPBKBGvjLyYoBGk5XNBFLNULbbgO/EXoPQ84JFPHUcITWIMzWJYSmzkuR5C0mXEfSwQy2mMNaSo/WH6SRptiHJYYpDSnmBZe8hrC98fjF47yekfw5PW5xDd0h9OGzbvnZTyYExHfvhfkxDg/d3XDzZPj9P4o//PE6vHnqUoc2gLJ1uLLQlTV029b1D3PsyjJ2W0LTQ1fW2xfPlp/+H+Pj1+tyjtDd4IJkSeRJWgKTKmjY4CCCdZyx9Ksf5EMWZaitYHnxwvxTS1IBlgMkssBT173xwfdvv/ujOW/DwPXtpktTHlLrEu4DwWaLiOrZRWZWhVaXuSqjKtpC4KAH1Xiqlf1qQetluPBUqKF5DDP/Cq+aoqvKB9Zu9bqhZRyzHN7Ln+gKoeonVhTi1yP2J3MhrWZe/Qlhpt0JmAgk95mBZAREE+oa2Ymrv1HvtH0G/rCKq6ihiTX0y2JIaOWQHNYoXCP2qn/ExMOKDuv2cPPkcH8T0AgU1ojmm+o4CxqJLXFVTeIPQ7Z5NgycWTguZcU0a6bqx66EjEsFl2QV4TV3yBFIiQKf0uclr7kHPiLlOJawzrF02NlotRtC3FYw0R/T3e0wPIapxNBvL2IaUBfOay75/vh8jnO35be2svTd+tXfvPjp//JhyQ+HJx9sp/WtD3K6BafX/f304feWdz5Yb2+HoTs8Pl8//fn8yVfjunbzHJ5/uW3PqacIgT0LKmdBaIsVMD4JQj3Cf6kzFmvKlWkHtAlSUjHkVVfqdcGvBCDSKuWB8jeGnO9LWp68f/un/2p9+/2uA1hFqlVCbla3gpegq7lOixrNNzSxd/i+/RZuTbLZEnFBHN3jzvjXSzLPxRjHqQ0JLyHsjRFD/QCnB+ZWqAub7X9hwMz8qASKLiLHnkDyJLbo3IQ8a/lX77cmn/3i+DJNdSIo+CHiAU7uWIX/QjqvLMP+fV6QvR0WRhoVz/oxVdF1VVviCgEOwIVLyIaOh/n2nfffur/7+uFFKKkrGJia0Rybd7xfLqxm0lQimkHfg6xqB/gR8UBRSoIBSTaFRbmYGHhpJcnYxy3kBa5sGmYh7KHOS97rJXT9IYzz3fF4uEmPx8c4DljdvJFmASs96ZgEoe/jAJ7c9cnxaYqnm/PznJavT6/eydurvr8L6+kX49pNt6kbQtevZSqP47oc+1sEBakMEcAu95yV/IzQVFAospnTCv1Dg9Kl1s8fEAQLmRlbKRnzp5tfOkk7DT95rCE72CWEOSzPys17//mfrd/54YBQhRAoLHqNJ6koPwHaVAdPdayRajao/d1H7eNm07WAxhZVrIRpqMaumztRa8fqrNk1iomhtCgLcEG4hRJ/3vp4s0J+ZxA3oU2mdep3/qmr/mjrL7Mi0tw6trs030ToCncBvcUG5A3lNtrw+khnGqM/nedikYWXh8iPSV0sfxRvMfUdD9tqZLOWJtT4Kp08ayXj/laf2AcN7AWSFd7WfhzOf/hn6e/+4+26HrslnUrCvKS148SWq10TCaTE82LmhbCRWoUB5IADjcmlrCC465BVBrXLyx0CO6n9AGhRWHNeiaNuGxgpDXaO3AMtS436Jd/FEvol5rnLCyhFyrKB8DEsJfU4HXruGL2H7ggkn9LcvXmbxhTXN/J2l+aVXIywzuP5hP/msMQuD/02AOzXo4eRy2Bs5ySERLME1YBx/UyqQYFWisMxHahDAreAPLe4PkkgO/ScgZdWvcftQ9y+T7nAsmB/IyBklgQS73QXu/jdD9/9Rz967CbKOnMoLQbC4T7qD/x4BAdatYZPX6k/plv1ClhCvoNPClOkqVvdO7bjuJhVT6B7z1ui9rdAjJRd6ZFeQKPblmJ36FFSJOhB6HviWuRAQ4lK2mEVLnkk+F3mUuXJk9iuxCAq8qvMm972yp3yPqw0wnqt6uEiPsSDxAQO5qLhdaZwilgrBAJWlZYN7+Jcba1BSt6rzqCsd+JCAtPDHLKqi2/Ksk7HEU4moofmEnU/l6/G985TWj5/GJ9Mt7mcH8/Ib0lvAM9RId2jHWXoUCVJJgoa62HARuypk4nzzhHxK24G/jBAWnhzBdyCgZVEqUozP5lfPlF1OuTjvK6lO2CUZjrg2RCZ75qmroRlvE1w2Lu0YFbQHPsc00IhSwnlDuQD5ykNuRuQsILuGLd0PJG5DqU/DH2aQg7rsuWum4buNhMZwNqlZZ37tEzbdkKzBjkoEFTCgLGLEUuHvnBE+luKA5oHgdejjivkuqlZctoQiSAjjyoU2YceRV1qjyLHpI9cTgkr2EzS2A8RNY8tlrUv+Rhv7378Z/nw4aGf4GeUbmNUOgXi0E7eBayiLXQ9FazMlF2CtZEv1UpTLUeYXySIaN3VeiCmK7J8HQ3pZh1UzaNLf1445TV4YP6S4abEF/NxK0N61m/Hh+H+PveHVJY0soNcViwbc58xqFWvi6v8fJ1ocgOEbuN+hYy8BTwkQm2L8DOZaMPOxm4FvahuZV42B0RcJkPfK4boIBWKPcn2k0niUC4Q36QyidoKkOcqnZeSbSLlyu68lXDoJNSxJpF1hu/L8HcBJzOegC8cjfP6BAwrzB3rpR/P6fjtcf70nemNj15teSzL/Pa6ftJN6KfZiNsJzYFhSAWzqCPmZwA7DckJmSCY8GKhlWkqJymvLYahxCOICqnyD4pzVAy4CwG2Bq537vOaxkNYT2/l/Nl4e0fV6SWOOW1PlofPbp4czuCLXkt6Ky/P0/bGfP776fDd5eHZzRtTmqZ8PE437zy+/MU0vvt4+mx8EkqeiPYyx/WNZf10euPdXMLwxvk8jh0uaRoPL59/+XwM39vys/F+yPllV76zzPmtd6avzw/j/TovhwyKAlosoqlE5EtaDPsYybe3SnkG2x6GvB2xsH2fpi5vZyAW+jXFu3V7WTp4Eyl+WLaPY/9WXr9O5f1t+aS7uY0pd+G9dfllmgjbPfCArNuwfvHyfP/G9O633k1j6W6gQkBhhv8bFFdIvjYiJ1MpI9edc4RK+cfSyTzbDOWUKRDsd1IpWsdz0+aihIdRWlNBUQwgY2Clv4eHolJjpSSHEM8oyYNB9phsFG4VO9A8BY7wqsdtO0xpOS0vl/8fAw3S8q4grPQAAAAASUVORK5CYII="}],"details":{"kinex":{"title":"exec 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recovered shoe forefoot from wider view","python":"import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\n for u,v in [(200,80),(200,120),(200,180),(230,120)]:\n z=float(a[v,u])*.001\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base')\n finish(pts)\nasyncio.run(main())"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":623,"output":203,"cacheRead":49280,"cacheWrite":0,"reasoning":19,"totalTokens":50106,"cost":{"input":0.00623,"output":0.010150000000000001,"cacheRead":0.04928,"cacheWrite":0,"total":0.06566}},"stopReason":"toolUse","timestamp":1793224631778,"responseId":"resp_0b5218887c37459e016ae26fb880b0819f8a54ca6a70a39867","rawStopReason":"completed","durationMs":8724,"thinkingLevel":"high"}} 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[1793224655.360224304] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2415974736213684,2.1075475215911865,1.6001873016357422,-1.0607448816299438,-0.01720820739865303,-0.6539632678031921],\"left_gripper\":[1.006791507655924],\"right_arm\":[-0.09445098042488098,2.2661819458007812,1.34000563621521,-0.6393835544586182,0.003709397278726101,-0.07148103415966034],\"right_gripper\":[0.9967559142546221]},\n \"file\": [\"/tmp/kinex-observation-b541py7r.png\"]\n 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"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":7102,"timestamp":1793224660893}} +{"type":"message","id":"e20fd735","parentId":"a2352307","timestamp":"2026-10-28T21:57:44.080Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_0038c08a3bf74a2fb94a8541cabe26db|fc_0b5218887c37459e016ae26fd771f0819fb3933d0d6868e282","name":"exec","arguments":{"file":".kinex/tools/cartesian_segment.py","description":"secure forefoot grasp","args":{"grips":{"right":0},"duration_s":0.4,"cameras":["right_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":22452,"output":54,"cacheRead":28544,"cacheWrite":0,"reasoning":0,"totalTokens":51050,"cost":{"input":0.22452000000000003,"output":0.0027,"cacheRead":0.028544,"cacheWrite":0,"total":0.25576400000000005}},"stopReason":"toolUse","timestamp":1793224660938,"responseId":"resp_0b5218887c37459e016ae26fd593d8819f96b6d2b6ca800969","rawStopReason":"completed","durationMs":3142,"thinkingLevel":"high"}} +{"type":"message","id":"6c9d803f","parentId":"e20fd735","timestamp":"2026-10-28T21:57:48.001Z","message":{"role":"toolResult","toolCallId":"call_0038c08a3bf74a2fb94a8541cabe26db|fc_0b5218887c37459e016ae26fd771f0819fb3933d0d6868e282","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224665.686067778] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2397918999195099,2.106983184814453,1.599143147468567,-1.0602302551269531,-0.017222831025719643,-0.6552022099494934],\"left_gripper\":[1.0052635290405967],\"right_arm\":[-0.09369406849145889,2.304854154586792,1.2598658800125122,-0.5211624503135681,0.00392327643930912,-0.07036155462265015],\"right_gripper\":[0.2204216422343796]},\n \"file\": [\"/tmp/kinex-observation-duv2kayc.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749706149101257,2.10660719871521,1.5987578630447388,-1.0601166486740112,-0.017234427854418755,-0.655457079410553],\"left_gripper\":[1.0032016147266736],\"right_arm\":[-0.09411819279193878,2.071403980255127,1.4278910160064697,-0.9290177822113037,0.0019836677238345146,-0.07476944476366043],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-itxcfdys.png\",\"/tmp/kinex-observation-z0_kr8cf.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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"},{"type":"image","mimeType":"image/png","data":"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"}],"details":{"kinex":{"title":"exec 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was displaced during grasp attempt; stop motion before further inspection."}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":1748,"output":48,"cacheRead":50560,"cacheWrite":0,"reasoning":15,"totalTokens":52356,"cost":{"input":0.017480000000000002,"output":0.0024000000000000002,"cacheRead":0.05056,"cacheWrite":0,"total":0.07044}},"stopReason":"toolUse","timestamp":1793224700887,"responseId":"resp_0b5218887c37459e016ae26ffdb0e4819f8906bdd82ef8ab5b","rawStopReason":"completed","durationMs":7557,"thinkingLevel":"high"}} +{"type":"message","id":"5a30abce","parentId":"172c381d","timestamp":"2026-10-28T21:58:28.541Z","message":{"role":"toolResult","toolCallId":"call_af13ee3ea5cc4286990e46befd9552ca|fc_0b5218887c37459e016ae2700445d4819f866a87f83acd87ad","toolName":"device_stop","content":[{"type":"text","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"}],"details":{"kinex":{"title":"stop (Shoe was displaced during grasp attempt; stop motion before further inspection.)","isError":false}},"isError":false,"durationMs":96,"timestamp":1793224708541}} +{"type":"message","id":"cd41a609","parentId":"5a30abce","timestamp":"2026-10-28T21:58:43.618Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Checking the right shoe**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae27009656c819f839feeef3880521e\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Checking the right 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shoes slipped during placement and recovery, so I can’t confirm completion. I’m stopping the attempt and leaving both grippers open.","textSignature":"{\"v\":1,\"id\":\"msg_0b5218887c37459e016ae27011a1b8819fbd60fcf06f258943\",\"phase\":\"commentary\"}"},{"type":"toolCall","id":"call_a839ec63918748f7900267391ba467f8|fc_0b5218887c37459e016ae27012f578819f923e53216d95c7b9","name":"exec","arguments":{"file":".kinex/tools/cartesian_segment.py","description":"leave both grippers open and verify","args":{"grips":{"left":1,"right":1},"duration_s":0.4,"cameras":["head_camera"]}}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":194,"output":297,"cacheRead":52224,"cacheWrite":0,"reasoning":200,"totalTokens":52715,"cost":{"input":0.00194,"output":0.01485,"cacheRead":0.052224,"cacheWrite":0,"total":0.06901399999999999}},"stopReason":"toolUse","timestamp":1793224708587,"responseId":"resp_0b5218887c37459e016ae2700541c0819f93d956fbc93b62e2","rawStopReason":"completed","durationMs":15031,"thinkingLevel":"high"}} +{"type":"message","id":"9c30e840","parentId":"cd41a609","timestamp":"2026-10-28T21:58:47.503Z","message":{"role":"toolResult","toolCallId":"call_a839ec63918748f7900267391ba467f8|fc_0b5218887c37459e016ae27012f578819f923e53216d95c7b9","toolName":"exec","content":[{"type":"text","text":"{\n \"output\": \"[INFO] [1793224725.282645542] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749713599681854,2.10660719871521,1.5987579822540283,-1.06011962890625,-0.017240097746253014,-0.6554499268531799],\"left_gripper\":[1.0024560825391249],\"right_arm\":[-0.09411459416151047,2.0698814392089844,1.4282174110412598,-0.929033637046814,0.001976696541532874,-0.07476774603128433],\"right_gripper\":[0.8706039027734237]},\n \"file\": [\"/tmp/kinex-observation-rljb8q34.png\"]\n }\n}"},{"type":"image","mimeType":"image/png","data":"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/run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749713599681854,2.10660719871521,1.5987579822540283,-1.06011962890625,-0.017239799723029137,-0.6554496884346008],\"left_gripper\":[1.0018847584724426],\"right_arm\":[-0.09411459416151047,2.0705742835998535,1.4280035495758057,-0.929033637046814,0.001982568996027112,-0.07476842403411865],\"right_gripper\":[0.9748014916073192]},\n \"file\": [\"/tmp/kinex-observation-u51xe342.png\"]\n 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"}],"details":{"kinex":{"title":"exec (Python)"}},"isError":false,"durationMs":4666,"timestamp":1793224739921}} +{"type":"message","id":"7b1145bb","parentId":"bec6c641","timestamp":"2026-10-28T21:59:09.908Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"**Adding motion timing logs**","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae270280720819fafc87efef469b7e6\",\"summary\":[{\"type\":\"summary_text\",\"text\":\"**Adding motion timing 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read(io,d+'.joint_state')).position) for d in ['left_arm','left_gripper','right_arm','right_gripper']}","newText":"states={}\n for d in ['left_arm','left_gripper','right_arm','right_gripper']:\n state=await read(io,d+'.joint_state')\n states[d]={'position':list(state.position),'velocity':list(state.velocity),\n 'stamp_s':state.header.stamp.sec+state.header.stamp.nanosec*1e-9}"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":437,"output":209,"cacheRead":52992,"cacheWrite":0,"reasoning":56,"totalTokens":53638,"cost":{"input":0.004370000000000001,"output":0.010450000000000001,"cacheRead":0.052992,"cacheWrite":0,"total":0.067812}},"stopReason":"toolUse","timestamp":1793224739982,"responseId":"resp_0b5218887c37459e016ae27024abfc819f99c5416bb5dc80a1","rawStopReason":"completed","durationMs":9926,"thinkingLevel":"high"}} +{"type":"message","id":"fb0ee13a","parentId":"7b1145bb","timestamp":"2026-10-28T21:59:09.916Z","message":{"role":"toolResult","toolCallId":"call_3a291b8a1c42424b943bc3cd7928342b|fc_0b5218887c37459e016ae2702b4898819faa7716f28cc4c00f","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py."}],"details":{"diff":" ...\n 33 for side,value in opts.get('grips',{}).items():\n 34 commands.append((side+'_gripper.target',{'data':value}))\n 35 await asyncio.gather(*(io.publish(op,payload) for op,payload in commands))\n 36 await io.request('world.wait',duration_s=seconds)\n-37 states={d:list((await read(io,d+'.joint_state')).position) for d in ['left_arm','left_gripper','right_arm','right_gripper']}\n+37 states={}\n+38 for d in ['left_arm','left_gripper','right_arm','right_gripper']:\n+39 state=await read(io,d+'.joint_state')\n+40 states[d]={'position':list(state.position),'velocity':list(state.velocity),\n+41 'stamp_s':state.header.stamp.sec+state.header.stamp.nanosec*1e-9}\n 38 paths=[helpers.save_image(await read(io,c+'.image')) for c in opts.get('cameras',['head_camera'])]\n 39 finish({'measured':states},file=paths)\n 40 \n 41 asyncio.run(main())","patch":"--- .kinex/tools/cartesian_segment.py\n+++ .kinex/tools/cartesian_segment.py\n@@ -33,9 +33,13 @@\n for side,value in opts.get('grips',{}).items():\n commands.append((side+'_gripper.target',{'data':value}))\n await asyncio.gather(*(io.publish(op,payload) for op,payload in commands))\n await io.request('world.wait',duration_s=seconds)\n- states={d:list((await read(io,d+'.joint_state')).position) for d in ['left_arm','left_gripper','right_arm','right_gripper']}\n+ states={}\n+ for d in ['left_arm','left_gripper','right_arm','right_gripper']:\n+ state=await read(io,d+'.joint_state')\n+ states[d]={'position':list(state.position),'velocity':list(state.velocity),\n+ 'stamp_s':state.header.stamp.sec+state.header.stamp.nanosec*1e-9}\n paths=[helpers.save_image(await read(io,c+'.image')) for c in opts.get('cameras',['head_camera'])]\n finish({'measured':states},file=paths)\n \n asyncio.run(main())\n","firstChangedLine":37},"isError":false,"durationMs":7,"timestamp":1793224749916}} +{"type":"message","id":"d96ef9da","parentId":"fb0ee13a","timestamp":"2026-10-28T21:59:35.070Z","message":{"role":"assistant","content":[{"type":"toolCall","id":"call_59053b706fc6404abf3467e748120ce4|fc_0b5218887c37459e016ae27030c860819fa45609d2accdf2f0","name":"write","arguments":{"path":".kinex/memos/dual_shoes.md","content":"# RoboTwin dual shoes\n\n## Interfaces and tools\n- Read refreshed profiles each episode. Stepped world, native 25 Hz (0.04 s/frame), 600 frames. Only world.wait advances time; reads do not.\n- `tools/dual_shoes_policy.py` captures head/left/right RGB in required order, calls helpers.run_policy at 25 Hz, returns fresh head image and measured joint feedback. Current policy returns 16 actions per inference even if max_actions=32. Task instruction embedded; override via args.task.\n- `tools/cartesian_segment.py` makes bounded world-TCP IK/joint segments with optional gripper targets, and returns cameras plus measured positions/velocities/stamps. Inputs: goals.{left,right}.{pose|delta|joints}, grips.{left,right} in native opening units, duration_s, cameras. It does NOT perform collision planning or certify arrival. The TCP offset is 0.12 m along flange +x for these profiles. All goals are planned before publication. Recheck calibration before reuse elsewhere.\n- Both grippers use 0 closed, 1 open; policy uses -1 open/+1 closed and helper converts. Closed empty feedback may bottom at ~0.18, not zero. A shoe can stall grip near .82, but that alone does not establish holding.\n\n## Important limitations\n- Policy achieved initial bilateral pickup but failed first placement: shoe slid toward near/right box edge, then vanished from head view. Continuing inference after loss produced wrist rotation and empty closing, not recovery. Stop and reassess instead of blindly extending policy.\n- Manual recovery was not successful. Depth-based targets require renewed perception after EVERY contact; shoes slide readily. Do not reuse previous attempt's object coordinates.\n- A 0.8–1.2 s trajectory endpoint can still have substantial tracking lag; gripper close while descending can displace the shoe. Inspect velocities and position error before grasping. Verify retention by camera during a small test lift before transport.\n- TCP +x approaches; fingers close along local y. Do not infer safe finger clearance solely from TCP height or simplified URDF geometry.\n- device_stop holds measured gripper opening, which may relax an object grasp; when intentionally holding an object, preserve closing target unless stopping for a fault.\n- Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\n"}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":161,"output":544,"cacheRead":53504,"cacheWrite":0,"reasoning":0,"totalTokens":54209,"cost":{"input":0.00161,"output":0.027200000000000002,"cacheRead":0.053503999999999996,"cacheWrite":0,"total":0.082314}},"stopReason":"toolUse","timestamp":1793224749952,"responseId":"resp_0b5218887c37459e016ae2702e932c819fb6fecee747c7b018","rawStopReason":"completed","durationMs":25118,"thinkingLevel":"high"}} +{"type":"message","id":"50837b9a","parentId":"d96ef9da","timestamp":"2026-10-28T21:59:35.076Z","message":{"role":"toolResult","toolCallId":"call_59053b706fc6404abf3467e748120ce4|fc_0b5218887c37459e016ae27030c860819fa45609d2accdf2f0","toolName":"write","content":[{"type":"text","text":"Successfully wrote to .kinex/memos/dual_shoes.md"}],"isError":false,"durationMs":6,"timestamp":1793224775076}} +{"type":"message","id":"33b28444","parentId":"50837b9a","timestamp":"2026-10-28T21:59:43.482Z","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","thinkingSignature":"{\"type\":\"reasoning\",\"id\":\"rs_0b5218887c37459e016ae2704b3b18819f810b88127844bcae\",\"summary\":[],\"content\":[],\"encrypted_content\":\"gAAAAABq4nBNHHpgH2lG6krupn97j19B1ewPzaugxcCVBboDGRMv07Y4Oti3qwFZJWScjGm7l6R8bX-2Lfg16fvh6DHCohjdQPqagZkUKOoHZjlt0SbvGQ9J6nQiAtqOqeEmgfK15Pnq4ZUz5DbGDHPIW2nuCq8NgO2eQ7HC6vQRK4WrAHjmhqjWZtrapfUa-eGTNRMGP60iWG95j9Tu0tu0cagCn_VwFa-yjBc_6rHAEJ8v0TMT49YMqEESNkykughhiudgWLvRMIAze5L-gNzPoqKPhBEkc0dZzNyCL3qIGcAdTY_Odc7FTTBNiC_9kknzB8wJMuQiOBFtECBnBa7ygRx19YfAv1_jXmbfNEOxAL0ZdGwygoo2LL7uJYs4VQ6m2YjP_keUt0-7smWgSgo6r1FSlgXAr2wcOt3G7YYN0PuZVFdvPQlSl_KxNTnNZS0ynmcqQsCUAgP5FzfW7wgiReQdrYF7eh7jfGDp1iVmK7XKeYBBmFwZkUFIG9riXngTzMo1bAE8X2rBKkiwcuHxDjLF1uX4yHOJ54Zi7VggY6e4K9ep5XjmcWir-2JXDHKr3sq9wvTN_spOuQBE2Fl5TWxS_Eb-sCCltyGwCE_HwAZ_P-MD6G3BH30PXmmyhVktQte5u9BDkIla-dhNj-x3OQaTK4xFFMxfYD8lxozTJwamkQGIEMKK-4kZLGAkXG5lPExOH1jgmJFc4E7prUb8IRTLUgWGXtiAc9vuWXgdAhDvNTvg61OfDZWwwnGd44QVDIr-iuCrbo_PqMB9sgiiw5iFYYsYXxEGbojZSvIUHUvZPloVu7gidDV5az39PaDNvJmyIxF5fmkV8r0xV7wWVy-hbDIzmcX7FiTJqI4GJKl9Oqxs3fTwthZ1ckGGIlIMR1XZzYBM2X6gPVvPptAP3fpdT0bf5Ee-lUPhgbLaJhvAruQkVKC9aq8U9jp-HCLJgVlN6mz6tDg4_p5mkZYMmGYDs6seaD5Xe89A1kj90bt2NENL3Grsef0KJeKYH1myZH6T5YmXp3waZweREe0DrSW0JVOxg5R0WWMHhKkoq7s6DRk6ePgPcCQIdekjNYGxAeKM6uEJvsyRvrnzArnGxsXMojz5sLcz6XKgDo73zW-G5Xl6qB5tTk8bRA2G9YwlHfNMaSdKAKl3pzKT1GqThWY1Pc3kkGceY_UxxNMfGDJfKnric4iowBQ8OJw2DwC-m1ImvCkSg5rLS640J-iL1oAQ1x67eoPEE2MBJ1_GLwjgxK5Efg6odK3FBTPHZYGcPfwWz9H6-92Ps2UYIgCVcGxbs7hjaZGT5_ufQ9kqadK9x1r7NFAOBJGSv2yJssSPxgYfPZcN5zHMJg8gWGiHN9Qs09CmMNYSO5-YOteiWFaP7p2f4IYH5qFxQ-GxBYxy5POcQvTpYXJkKqttrHegln8TE8jXaMLdo1W2Jwhr4eQtGrsmv-C0KAR9nFgDkPQckuStb9v-e_v789WfO2V6igmjdEscYbwSflYlYTglTbywgIKR6Lx-0ckm3o_URhodqJOFYBsjhE8eK1h62qcZ463x_2wVQxOMJ7gUQ6OWrN3pqnWQM070_yxXuAySKQhqEk6FkfclHtas3MOG7p2-Zziw5vn9YJimnVflFX-i3hRH_fprYjLEo9pGwuvOP5EnUIJUWAS3mks7EfC-eTGyB5d7I1t3HEWAwYpnVq7EmKeDkUz9NgWeuOKXVhySOYrbuhruVsElDKvlX1gI4n0_OrAaGcJmgWeLpsn20XzEUDbcTBXkBwOxy64eUler_QRRYrzaoXeyBYQXLDhG8mWvv9qlZ67qmhWTvpKBs1tH2NNwRmSLNJrtd54rOOQ10XCHgY4sH85olQezmZQBrEjB-J8_51wsJfBGakWlzq-0E10_vjpRB-oqxD1vmcCoNiiuRIOw9P67h0oth1i4fndNUejhuRhdbMGd9HijzK-wjeTBHsuN9hr6Ar1vTwYL-RBn75AjUEom0dp2i1Dqr3BayR8e9qOURfovPPV-GdU9oobZC7drzk4=\"}"},{"type":"toolCall","id":"call_150f2ee852e34f54b4bbfcc5e2fd70c2|fc_0b5218887c37459e016ae2704da438819fb542e886d3a11931","name":"edit","arguments":{"path":".kinex/memos/dual_shoes.md","edits":[{"oldText":"- Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\n","newText":"- Finalization: command both openings to 1, allow settling, and verify feedback. Report placement uncertainty honestly; the world interface exposes no native verdict.\n"}]}}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":216,"output":148,"cacheRead":54016,"cacheWrite":0,"reasoning":35,"totalTokens":54380,"cost":{"input":0.00216,"output":0.0074,"cacheRead":0.054015999999999995,"cacheWrite":0,"total":0.063576}},"stopReason":"toolUse","timestamp":1793224775101,"responseId":"resp_0b5218887c37459e016ae27047dd80819f9feaabc54be081a0","rawStopReason":"completed","durationMs":8381,"thinkingLevel":"high"}} +{"type":"message","id":"511c9210","parentId":"33b28444","timestamp":"2026-10-28T21:59:43.487Z","message":{"role":"toolResult","toolCallId":"call_150f2ee852e34f54b4bbfcc5e2fd70c2|fc_0b5218887c37459e016ae2704da438819fb542e886d3a11931","toolName":"edit","content":[{"type":"text","text":"Successfully replaced 1 block(s) in .kinex/memos/dual_shoes.md."}],"details":{"diff":" ...\n 11 - Manual recovery was not successful. Depth-based targets require renewed perception after EVERY contact; shoes slide readily. Do not reuse previous attempt's object coordinates.\n 12 - A 0.8–1.2 s trajectory endpoint can still have substantial tracking lag; gripper close while descending can displace the shoe. Inspect velocities and position error before grasping. Verify retention by camera during a small test lift before transport.\n 13 - TCP +x approaches; fingers close along local y. Do not infer safe finger clearance solely from TCP height or simplified URDF geometry.\n 14 - device_stop holds measured gripper opening, which may relax an object grasp; when intentionally holding an object, preserve closing target unless stopping for a fault.\n-15 - Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\n+15 - Finalization: command both openings to 1, allow settling, and verify feedback. Report placement uncertainty honestly; the world interface exposes no native verdict.","patch":"--- .kinex/memos/dual_shoes.md\n+++ .kinex/memos/dual_shoes.md\n@@ -11,5 +11,5 @@\n - Manual recovery was not successful. Depth-based targets require renewed perception after EVERY contact; shoes slide readily. Do not reuse previous attempt's object coordinates.\n - A 0.8–1.2 s trajectory endpoint can still have substantial tracking lag; gripper close while descending can displace the shoe. Inspect velocities and position error before grasping. Verify retention by camera during a small test lift before transport.\n - TCP +x approaches; fingers close along local y. Do not infer safe finger clearance solely from TCP height or simplified URDF geometry.\n - device_stop holds measured gripper opening, which may relax an object grasp; when intentionally holding an object, preserve closing target unless stopping for a fault.\n-- Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\n+- Finalization: command both openings to 1, allow settling, and verify feedback. Report placement uncertainty honestly; the world interface exposes no native verdict.\n","firstChangedLine":15},"isError":false,"durationMs":4,"timestamp":1793224783487}} +{"type":"message","id":"b3f508d5","parentId":"511c9210","timestamp":"2026-10-28T21:59:47.604Z","message":{"role":"assistant","content":[{"type":"text","text":"Task not completed: the shoes slipped and were not placed in the shoebox. Both arms are held, and both grippers are open.","textSignature":"{\"v\":1,\"id\":\"msg_0b5218887c37459e016ae270529b9c819f8e15f783f04792a2\",\"phase\":\"final_answer\"}"}],"api":"openai-codex-responses","provider":"openai-codex","model":"gpt-6-astra","usage":{"input":265,"output":32,"cacheRead":54144,"cacheWrite":0,"reasoning":0,"totalTokens":54441,"cost":{"input":0.00265,"output":0.0016,"cacheRead":0.054144,"cacheWrite":0,"total":0.058394}},"stopReason":"stop","timestamp":1793224783515,"responseId":"resp_0b5218887c37459e016ae270502d00819fbeccafd7cea35653","rawStopReason":"completed","durationMs":4089,"thinkingLevel":"high"}} +{"type":"custom","customType":"kinex-active-time","data":{"milliseconds":809160.1628569999},"id":"3459ffee","parentId":"b3f508d5","timestamp":"2026-10-28T21:59:47.870Z"} diff --git a/experiments/robotwin-native/kinex_policy-2/terminal.jpg b/experiments/robotwin-native/kinex_policy-2/terminal.jpg new file mode 100644 index 0000000000000000000000000000000000000000..5695d280ea848e9413914189263716ab44acd249 Binary files /dev/null and b/experiments/robotwin-native/kinex_policy-2/terminal.jpg differ diff --git a/experiments/robotwin-native/kinex_policy-2/tool-calls.json b/experiments/robotwin-native/kinex_policy-2/tool-calls.json new file mode 100644 index 0000000000000000000000000000000000000000..4d2713801fd28e65f6af42d3ed5c8c35b2579823 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/tool-calls.json @@ -0,0 +1,1108 @@ +[ + { + "name": "ls", + "input": { + "path": "/workspace" + }, + "output": ".kinex/\nharness.md\ninstruction.md", + "call_id": "call_2be0a7f253ee4abf86ce531d7ad43b28|fc_0b5218887c37459e016ae26d2d0c78819f8cc39f5feaf7fdcb", + "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_d9fc0acab16d4ca2a46a36ae77694e1a|fc_0b5218887c37459e016ae26d2fc904819f835d2d1c802765b8", + "is_error": false + }, + { + "name": "find", + "input": { + "pattern": "**/*", + "path": "/workspace/.kinex", + "limit": 160 + }, + "output": "devices/\ndevices/head_camera/\ndevices/head_camera/profile.yaml\ndevices/left_camera/\ndevices/left_camera/profile.yaml\ndevices/policy/\ndevices/policy/configuration.json\ndevices/policy/env.sh\ndevices/policy/node.py\ndevices/policy/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/build/kinex_interfaces/rosidl_generator_cpp/\npython/ros/build/kinex_interfaces/rosidl_generator_cpp/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/include/kinex_interfaces/kinex_interfaces/srv/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__builder.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__description.c\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__rosidl_typesupport_fastrtps_cpp.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__rosidl_typesupport_introspection_c.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__rosidl_typesupport_introspection_cpp.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/json__struct.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__functions.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__rosidl_typesupport_introspection_cpp.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__type_support.c\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__type_support.cpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/lookup_transform__type_support.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/wait__description.c\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/wait__struct.hpp\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/wait__type_support.c\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/detail/wait__type_support.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/json.h\npython/ros/install/kinex_interfaces/include/kinex_interfaces/kinex_interfaces/srv/lookup_transform.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/\npyth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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/\n\n[160 results limit reached. Use limit=320 for more, or refine pattern]", + "call_id": "call_07a13441eb1747dbbbee6d0001fc549b|fc_0b5218887c37459e016ae26d323c0c819f940920553b103914", + "is_error": false + }, + { + "name": "bash", + "input": { + "command": "ls -la .kinex/devices; ls -la .kinex/tools .kinex/skills .kinex/memos", + "timeout": null + }, + "output": "total 44\ndrwxr-xr-x 11 agent agent 4096 Oct 28 21:45 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 head_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_arm\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_gripper\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:45 policy\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_arm\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_gripper\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 world\n.kinex/memos:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n\n.kinex/skills:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n\n.kinex/tools:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n", + "call_id": "call_c9d4800136cc4be1a80e7f4eec97898d|fc_0b5218887c37459e016ae26d356528819f85f584a5574f324d", + "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_6705f0d2e2394a30bb581ea44d385871|fc_0b5218887c37459e016ae26d3831c4819fa634b638d90788be", + "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_e58691f7e16147eab395daf95a28a0c9|fc_0b5218887c37459e016ae26d3b4940819fae10e30592a8b3f2", + "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_60f5baa90b4a45b085b550c6542a738b|fc_0b5218887c37459e016ae26d3b4944819f8262615e431e6507", + "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_12bf679f95494399bb47080d1beebdc7|fc_0b5218887c37459e016ae26d3b4944819fae385c7f7afc6f23", + "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_88760df5480447ddb8fcb3567c44240e|fc_0b5218887c37459e016ae26d3b4944819f9deac9b84c192193", + "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_03371d20a1964430b350f091a1a0b420|fc_0b5218887c37459e016ae26d3b4944819fa8b82bd928c268a3", + "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_b9931bd73ee64dbc89d5daad25d34ad8|fc_0b5218887c37459e016ae26d3b4944819fa63c5ea7c8a37119", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/policy/profile.yaml", + "offset": null, + "limit": null + }, + "output": "name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n absolute ALOHA joint actions. Use the native run_policy tool with command order\n left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n created_at: '2026-10-28T21:45:05.294658+00:00'\n updated_at: '2026-10-28T21:45:05.294658+00:00'\ncompute:\n index: 0\n kind: cuda\nartifacts: []\nruntime:\n domain_id: 83\n nodes:\n - entry: node.py\n factory: create\n overlays:\n - env.sh\n ready_timeout_s: 600\noperations:\n infer:\n description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n transport: service\n direction: input\n endpoint: /kinex/policy/infer\n type: kinex_interfaces/srv/Json\n timeout_s: 120\n", + "call_id": "call_dc800044efb24048a2da7659cbdf024d|fc_0b5218887c37459e016ae26d3b4944819f8b44321c1a8b9031", + "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_aca37b9173a049859c26b1a1c7afedc7|fc_0b5218887c37459e016ae26d3cf1fc819fbb5c0783e4554891", + "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_2cbb4865d07c4343a74eba4b21d3e0a0|fc_0b5218887c37459e016ae26d3efb30819f8bd7f5954aee7123", + "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_c3f15cd1c50341aeafa8ee8bde74be5f|fc_0b5218887c37459e016ae26d3efb30819f90097de3aae79959", + "is_error": false + }, + { + "name": "exec", + "input": { + "python": "from kinex import runtime, finish\nfinish({n:runtime.operations(n) for n in ['world','left_arm','left_gripper','head_camera','policy']})", + "description": "inspect operation schemas" + }, + "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 \"policy\": {\"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}}\n }\n}", + "call_id": "call_efb801f592ff478d9cd43e403a7c5552|fc_0b5218887c37459e016ae26d3efb30819f9f12804ad88a7c4b", + "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" + }, + { + "deviceID": "left_camera", + "action": "load" + }, + { + "deviceID": "right_camera", + "action": "load" + }, + { + "deviceID": "policy", + "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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}", + "call_id": "call_990c26cdcea84c32a82583ddc2e7e432|fc_0b5218887c37459e016ae26d4248ec819fa2ee0f4e25c3c532", + "is_error": false + }, + { + "name": "read", + "input": { + "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/policy.py", + "offset": 1, + "limit": 260 + }, + "output": "\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n max_actions=None, inference_args=None):\n \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n model serves a Json \"infer\" operation taking obs (and task) and returning\n actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n entries in flat-vector split order; named action maps use device names.\n space \"device\" takes absolute joint values in URDF order, or end-effector\n values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n tip_link, frozen at the observation stamp. max_actions caps the horizon and\n action_hz paces actions on the world clock. Joint trajectories submit\n asynchronously; end-effector targets are paced by world waits before\n returning. Returns actions_submitted, duration_s and ik_failure\n (device, action_index, error) or None.\n\n Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n \"\"\"\n import asyncio\n from kinex.communication import duration\n from kinex.helpers.frames import IDENTITY, transform_pose\n from kinex.lib.geometry import invert_rt, rt_to_pose\n plans = [_commanded(entry) for entry in command]\n if len({entry[\"name\"] for entry in plans}) != len(plans):\n raise ValueError(\"command must name each device once\")\n wait = f\"{world}.wait\"\n if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n options = inference_args or {}\n if set(options) & {\"obs\", \"task\"}:\n raise ValueError(\"inference_args cannot override obs or task\")\n data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n if not actions:\n raise ValueError(\"infer must return nonempty actions\")\n actions = actions[:max_actions or len(actions)]\n split = [_share(action, plans) for action in actions]\n count, failure, sequences = len(split), None, {}\n for entry in plans:\n name, spec = entry[\"name\"], entry[\"spec\"]\n samples = [item[name] for item in split[:count]]\n if spec.get(\"space\", \"device\") != \"device\":\n stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n try:\n state = await stream.next(timeout_s=10)\n finally:\n stream.close()\n joints = entry[\"model\"]\n seed = measured_positions(state, joints.joints)\n base = f\"{name}/base\"\n ik_frame = f\"{name}/{joints.tip}\"\n target_frame = spec.get(\"target_frame\", ik_frame)\n reference = spec.get(\"frame\", base)\n # target_frame is an attached tool frame. Freeze its offset at the\n # observation stamp while resolving the policy's Cartesian chunk;\n # URDF FK of the measured joints places the tip.\n offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n converted = []\n for sample in samples:\n if spec[\"space\"] == \"pose_delta\":\n value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n current = value[\"translation\"] + value[\"rotation\"]\n else:\n current = sample\n ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n try:\n seed = joints.ik(goal, seed)\n except ValueError as error:\n failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n break\n converted.append(seed)\n samples = converted\n count = min(count, len(samples))\n sequences[name] = samples\n if count == 0:\n return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n seconds = count / number(action_hz, \"action_hz\")\n prepared, scalars = [], []\n for entry in plans:\n name, prof = entry[\"name\"], entry[\"profile\"]\n samples = sequences[name][:count]\n if entry[\"model\"]:\n joint_command(prof, samples, entry[\"model\"].joints)\n fields = {\"joint_names\": entry[\"model\"].names,\n \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n for index, value in enumerate(samples)]}\n operation = f\"{name}.joint_target\"\n else:\n target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n targets = [target(prof, value[0]) for value in samples]\n fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n scalars.append((operation, targets))\n prepared.append((operation, fields))\n\n await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n # End-effector targets persist. Timing belongs to this workflow and the world,\n # never a device-side duration or a background worker.\n if scalars:\n for index in range(count):\n if index:\n await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n for operation, values in scalars))\n await io.request(wait, duration_s=seconds / count)\n return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n name = entry[\"device\"]\n prof = profile(name)\n kind = prof.get(\"type\")\n if kind not in END_EFFECTORS | {\"joint\"}:\n raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n model = None if kind in END_EFFECTORS else JointModel(prof)\n # A gripper or suction tool takes one command value whatever it reports back.\n width = _cartesian_width(entry, prof)\n return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n \"\"\"One action, split across the commanded devices in order.\"\"\"\n if isinstance(action, dict):\n missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n if missing:\n raise ValueError(f\"action names no value for {', '.join(missing)}\")\n return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n shares, cursor = {}, 0\n for entry in plan:\n shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n cursor += entry[\"width\"]\n return shares\n\n\ndef _cartesian_width(entry, prof):\n space = entry.get(\"space\", \"device\")\n if space not in {\"device\", \"pose\", \"pose_delta\"}:\n raise ValueError(f\"unknown policy space: {space}\")\n if space == \"device\":\n if \"frame\" in entry or \"target_frame\" in entry:\n raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n return None\n if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n action = vector(action, 6, \"pose_delta\")\n angle = math.hypot(*action[3:])\n scale = math.sin(angle / 2) / angle if angle else 0.5\n rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n value = number(value, \"action\")\n if not -1 <= value <= 1:\n raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n return value\n\n\ndef _grip(profile, value):\n \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n grip = profile[\"grip\"]\n if not isinstance(grip, dict):\n raise ValueError(\"the gripper has no declared grip joint\")\n return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n value = _action(value)\n if profile[\"vacuum\"] == \"binary\":\n return 1.0 if value > 0 else 0.0\n if profile[\"vacuum\"] != \"proportional\":\n raise ValueError(\"the suction device declares no vacuum control\")\n return (value + 1) / 2\n", + "call_id": "call_a1a74d8fbb94481d864fe661fa79d7c2|fc_0b5218887c37459e016ae26d9272e8819f8d056301e2d7cbef", + "is_error": false + }, + { + "name": "device", + "input": { + "deviceID": "head_camera", + "action": "image" + }, + "output": "{\n \"deviceID\": \"head_camera\",\n \"timestamp\": 0,\n \"frame_id\": \"head_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-uo5653ej.png\"\n}", + "call_id": "call_490e6b1f64b34e829277af881f08870a|fc_0b5218887c37459e016ae26d971ec0819fa660db6d9d5e632f", + "is_error": false + }, + { + "name": "write", + "input": { + "path": ".kinex/tools/dual_shoes_policy.py", + "content": "\"\"\"Run one bounded, freshly observed RoboTwin policy segment; no lifecycle changes.\"\"\"\nimport asyncio\nfrom kinex import args, communication, finish, helpers\n\nTASK = ('use both arms to pick up the two shoes on the table and put them flat in the shoebox, '\n 'with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. '\n 'Leave both grippers open')\nCAMERAS = ['head_camera', 'left_camera', 'right_camera']\nDEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper']\n\nasync def read_one(io, op):\n with io.subscribe(op, depth=1) as stream:\n return await stream.next(timeout_s=10)\n\nasync def main():\n opts = args()\n async with communication() as io:\n images = await asyncio.gather(*(read_one(io, c+'.image') for c in CAMERAS))\n paths = [helpers.save_image(im) for im in images]\n result = await helpers.run_policy(io, 'policy', {'images': paths},\n [{'device': d} for d in DEVICES], task=opts.get('task', TASK),\n world='world', action_hz=25, max_actions=opts.get('max_actions', 32))\n states = await asyncio.gather(*(read_one(io, d+'.joint_state') for d in DEVICES))\n image = await read_one(io, 'head_camera.image')\n path = helpers.save_image(image)\n result['measured'] = {d: {'position': list(s.position), 'velocity': list(s.velocity)}\n for d,s in zip(DEVICES,states)}\n result['time_s'] = image.header.stamp.sec + image.header.stamp.nanosec * 1e-9\n finish(result, file=[path])\n\nasyncio.run(main())\n" + }, + "output": "Successfully wrote to .kinex/tools/dual_shoes_policy.py", + "call_id": "call_dd58d395e4f74264a0b4ffc91c55c48f|fc_0b5218887c37459e016ae26d9a2314819f9adac34e651ab34c", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "approach shoes with both arms", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224100.950625034] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.06924128532409668,0.620575487613678,0.4789740741252899,-0.28832128643989563,-0.006016099359840155,-0.20582163333892822],\"velocity\":[-0.14827853441238403,1.9776692390441895,1.3301373720169067,-0.7481668591499329,-0.00023766420781612396,-0.4732377231121063]},\"left_gripper\":{\"position\":[0.9972223205999895],\"velocity\":[-0.05145943300290541]},\"right_arm\":{\"position\":[0.059656575322151184,0.6675826907157898,0.5648283958435059,-0.336734414100647,-0.0007643997087143362,0.10298626869916916],\"velocity\":[0.16773490607738495,2.116530418395996,1.5773547887802124,-0.8487532138824463,0.0016799001023173332,0.25821131467819214]},\"right_gripper\":{\"position\":[0.9918896691365676],\"velocity\":[0.10728505016727881]}},\n \"time_s\": 0.64,\n \"file\": [\"/tmp/kinex-observation-bzj6q2qb.png\"]\n }\n}", + "call_id": "call_c3ca42f54ce14bb69448ec81c32b61f3|fc_0b5218887c37459e016ae26da289e4819f901815286edd1deb", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "continue shoe approach", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224110.743159302] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.1642390340566635,1.7305915355682373,1.2296197414398193,-0.7650421261787415,-0.0084557319059968,-0.500076949596405],\"velocity\":[-0.12782171368598938,1.6054855585098267,1.0615302324295044,-0.5280291438102722,-0.0035553264897316694,-0.3419665992259979]},\"left_gripper\":{\"position\":[1.0039003437215632],\"velocity\":[-0.005440424535084854]},\"right_arm\":{\"position\":[0.16377829015254974,1.8440415859222412,1.4337947368621826,-0.8591420650482178,0.0009951238753274083,0.2373320609331131],\"velocity\":[0.13659954071044922,1.7306162118911743,1.2370691299438477,-0.5968512892723083,0.00857531651854515,0.10719234496355057]},\"right_gripper\":{\"position\":[0.9957916059277274],\"velocity\":[0.056688948957757515]}},\n \"time_s\": 1.28,\n \"file\": [\"/tmp/kinex-observation-v9kc6wpn.png\"]\n }\n}", + "call_id": "call_eb7bbc8d52dd470db2efab960ff4563f|fc_0b5218887c37459e016ae26daca2d4819f97de120bedc61c03", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "lower toward shoe grasps", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224119.654208670] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.2097044140100479,2.1003403663635254,1.4922027587890625,-0.9227323532104492,-0.012336636893451214,-0.6160438060760498],\"velocity\":[-0.007071767468005419,0.18269474804401398,0.0708129033446312,-0.004062924534082413,-0.0012147556990385056,-0.0011546239256858826]},\"left_gripper\":{\"position\":[1.0023565834218806],\"velocity\":[-0.03414872864430601]},\"right_arm\":{\"position\":[0.18805797398090363,2.2374651432037354,1.7380448579788208,-1.0270179510116577,0.0015673550078645349,0.25923529267311096],\"velocity\":[0.004303688183426857,0.19159452617168427,0.09309601783752441,0.0003027580678462982,-0.0012894750107079744,0.001212606206536293]},\"right_gripper\":{\"position\":[0.9993701875209808],\"velocity\":[0.01965627201240171]}},\n \"time_s\": 1.92,\n \"file\": [\"/tmp/kinex-observation-iacietm2.png\"]\n }\n}", + "call_id": "call_86e8e48b96e54af6b996d3b046fa0821|fc_0b5218887c37459e016ae26db5a2c0819fa362a29018a7c0d6", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "align grippers over shoes", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224130.042645948] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.2132786065340042,2.2452938556671143,1.4555960893630981,-0.7601785659790039,-0.010994759388267994,-0.6320369839668274],\"velocity\":[-0.006282667629420757,0.41175612807273865,-0.1620422899723053,0.4812483489513397,0.005704400595277548,-0.0010892432183027267]},\"left_gripper\":{\"position\":[1.0031733025204053],\"velocity\":[-0.05907964977351102]},\"right_arm\":{\"position\":[0.18439644575119019,2.3707516193389893,1.7036454677581787,-0.8743376731872559,0.0015077369753271341,0.23946310579776764],\"velocity\":[-0.0019623737316578627,0.3756299912929535,-0.1430189311504364,0.43219083547592163,0.0005497047677636147,0.0012198584154248238]},\"right_gripper\":{\"position\":[0.9994854005900297],\"velocity\":[0.010186451783573086]}},\n \"time_s\": 2.56,\n \"file\": [\"/tmp/kinex-observation-bs7tom9w.png\"]\n }\n}", + "call_id": "call_0253188029244684b1a6b0ec2945e160|fc_0b5218887c37459e016ae26dc00fd0819fa64eab19154a02c2", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "grasp the two shoes", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224138.539332553] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.2147672325372696,2.32867169380188,1.4041498899459839,-0.6313791871070862,-0.00845736637711525,-0.606859564781189],\"velocity\":[0.006417857948690653,0.021585844457149506,-0.0001902952790260315,-0.0025077760219573975,-0.002072217408567667,0.00007816869765520096]},\"left_gripper\":{\"position\":[0.8222877681255341],\"velocity\":[0.03745188428597017]},\"right_arm\":{\"position\":[0.18369433283805847,2.449954032897949,1.658321499824524,-0.7642399072647095,0.0036793008912354708,0.22171318531036377],\"velocity\":[-0.0033897748216986656,0.0317833349108696,0.027323372662067413,0.00080084428191185,0.011743731796741486,0.0016810968518257141]},\"right_gripper\":{\"position\":[0.8141168518499895],\"velocity\":[0.17688775604421442]}},\n \"time_s\": 3.2,\n \"file\": [\"/tmp/kinex-observation-ik8vfitb.png\"]\n }\n}", + "call_id": "call_8482002b0ed94ae6982a7b825604fde8|fc_0b5218887c37459e016ae26dc8756c819f9c98f58a80389a33", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "secure both shoe grasps", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224149.569556886] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.22005799412727356,2.278794527053833,1.4649302959442139,-0.7562634348869324,-0.0094643235206604,-0.6023668050765991],\"velocity\":[-0.0010886157397180796,-0.41600582003593445,0.30070409178733826,-0.6667615175247192,-0.0043848371133208275,0.001474970020353794]},\"left_gripper\":{\"position\":[0.8442490317604758],\"velocity\":[0.05986423400992697]},\"right_arm\":{\"position\":[0.18073023855686188,2.4141697883605957,1.729750633239746,-0.8848705887794495,0.00425342982634902,0.21443425118923187],\"velocity\":[-0.0021621412597596645,-0.35253795981407166,0.3380318582057953,-0.6683180928230286,0.002519281581044197,-0.0016803189646452665]},\"right_gripper\":{\"position\":[0.8242001289671118],\"velocity\":[-0.007260924162851138]}},\n \"time_s\": 3.84,\n \"file\": [\"/tmp/kinex-observation-pf35xytz.png\"]\n }\n}", + "call_id": "call_6d1c78a466674a9dacafadb3056a52cb|fc_0b5218887c37459e016ae26dd3a528819fa2033778fbf947fe", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "lift shoes clear of table", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224161.483818059] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.2337704449892044,2.106487512588501,1.5987350940704346,-1.0601452589035034,-0.017284579575061798,-0.655439555644989],\"velocity\":[-0.20414608716964722,-0.0711108148097992,-0.003488592803478241,-0.0486275851726532,-0.0066644977778196335,-0.30675390362739563]},\"left_gripper\":{\"position\":[0.8484432345086879],\"velocity\":[-0.03571745262227275]},\"right_arm\":{\"position\":[0.16433337330818176,2.2665646076202393,1.8794596195220947,-1.1764945983886719,0.0008793658344075084,0.19070172309875488],\"velocity\":[-0.11569812893867493,-0.1641731709241867,-0.04941084608435631,-0.07024315744638443,0.014065418392419815,-0.00043169036507606506]},\"right_gripper\":{\"position\":[0.8275782222097571],\"velocity\":[0.0033681344410235233]}},\n \"time_s\": 4.48,\n \"file\": [\"/tmp/kinex-observation-ys4yasbv.png\"]\n }\n}", + "call_id": "call_32b3ef515cdd4439a0408b7f1d48f7a2|fc_0b5218887c37459e016ae26ddf9abc819fae358eafe07b9ddf", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "rotate shoes toward box orientation", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224173.489829010] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.5958328247070312,2.0936496257781982,1.5494917631149292,-1.0726386308670044,-0.014319771900773048,-1.5304045677185059],\"velocity\":[-0.8067498207092285,0.005487743765115738,-0.12532629072666168,-0.005442067980766296,0.033786822110414505,-2.0603280067443848]},\"left_gripper\":{\"position\":[0.8458616760644047],\"velocity\":[-0.19374703141775998]},\"right_arm\":{\"position\":[0.04124896600842476,2.116722822189331,1.6913503408432007,-1.1479504108428955,0.00006936372665222734,0.08349316567182541],\"velocity\":[-0.31583401560783386,-0.21932876110076904,-0.32688185572624207,-0.0031804367899894714,-0.017355522140860558,-0.15398237109184265]},\"right_gripper\":{\"position\":[0.8272719356146726],\"velocity\":[-0.14341287314891815]}},\n \"time_s\": 5.12,\n \"file\": [\"/tmp/kinex-observation-1x49af4e.png\"]\n }\n}", + "call_id": "call_8319a9d3e899404289bc4c186e03f8df|fc_0b5218887c37459e016ae26deb2e20819f8bdc08ece1c26e19", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "position left shoe in near half of box", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224182.890409604] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n1793224182.892589 [83] dq.builtin: Remote secure participant f8544f26:920a72fd:9ebfedca:1c1 not allowed\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.8931366205215454,2.072211265563965,1.4770663976669312,-0.9812818169593811,0.00011473950144136325,-2.233191967010498],\"velocity\":[-0.3547639846801758,0.018884602934122086,-0.08769574016332626,0.07969250530004501,0.04081038385629654,-0.3869875371456146]},\"left_gripper\":{\"position\":[0.8453784720464187],\"velocity\":[-0.04491062665527517]},\"right_arm\":{\"position\":[-0.08277181535959244,2.0239410400390625,1.5335749387741089,-1.090429425239563,-0.004047530237585306,-0.042060717940330505],\"velocity\":[-0.07115635275840759,-0.04377933591604233,-0.06454810500144958,-0.005736846476793289,-0.0161428265273571,-0.04591057449579239]},\"right_gripper\":{\"position\":[0.8220208341425116],\"velocity\":[-0.06982283666729927]}},\n \"time_s\": 5.76,\n \"file\": [\"/tmp/kinex-observation-xkceikpd.png\"]\n }\n}", + "call_id": "call_542c0304cb4c45b1befb5bc9bd24c4fc|fc_0b5218887c37459e016ae26df4e304819f912cb1699f58f4f8", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "lower first shoe into shoebox", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224195.699174388] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9944702982902527,2.084920644760132,1.3925881385803223,-0.8199701905250549,0.009655511006712914,-2.410446882247925],\"velocity\":[0.10853226482868195,0.026367992162704468,-0.03144228085875511,0.00848725438117981,-0.02719578705728054,0.06114771217107773]},\"left_gripper\":{\"position\":[0.8887087458913977],\"velocity\":[0.9880326688289642]},\"right_arm\":{\"position\":[-0.13358038663864136,1.990712285041809,1.4891928434371948,-1.07383131980896,-0.003542549442499876,-0.09856189042329788],\"velocity\":[0.02053144760429859,0.003911168314516544,0.016390755772590637,-0.00030988454818725586,-0.006688009016215801,0.001631051767617464]},\"right_gripper\":{\"position\":[0.8224071806127375],\"velocity\":[0.01656769309192896]}},\n \"time_s\": 6.4,\n \"file\": [\"/tmp/kinex-observation-40hcihy1.png\"]\n }\n}", + "call_id": "call_81f75fc461924f4cbf0c368d19f8028d|fc_0b5218887c37459e016ae26e01cc00819f8e989012c262d14e", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "release first shoe and clear left gripper", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224210.329004551] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9979008436203003,2.105285406112671,1.3758498430252075,-0.8019817471504211,0.008222300559282303,-2.3118176460266113],\"velocity\":[0.08126653730869293,0.017701422795653343,0.006158708594739437,0.007415026426315308,-0.02043578214943409,0.0001324489712715149]},\"left_gripper\":{\"position\":[0.9908248457041654],\"velocity\":[0.3809970888224515]},\"right_arm\":{\"position\":[-0.13651666045188904,1.9356255531311035,1.4003468751907349,-1.0447499752044678,-0.007218408398330212,-0.10203051567077637],\"velocity\":[-0.0007513259770348668,-0.006915716454386711,-0.016261789947748184,0.00021159276366233826,0.0009361580014228821,0.00038932450115680695]},\"right_gripper\":{\"position\":[0.823714012449438],\"velocity\":[0.004685608754781159]}},\n \"time_s\": 7.04,\n \"file\": [\"/tmp/kinex-observation-ly14eio7.png\"]\n }\n}", + "call_id": "call_0649b195b81044a4afbb601d8fb84ce1|fc_0b5218887c37459e016ae26e10f394819fb3ad19d6e55c943f", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "withdraw left arm after release", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224225.085355440] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9609145522117615,2.103285789489746,1.3415217399597168,-0.9416824579238892,0.12990465760231018,0.26933538913726807],\"velocity\":[-0.012519998475909233,0.00815054215490818,-0.06386817991733551,0.14375868439674377,-0.025883832946419716,1.493956208229065]},\"left_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.4523076117038727]},\"right_arm\":{\"position\":[-0.14984078705310822,1.793803095817566,1.2386841773986816,-1.0167691707611084,-0.005433639511466026,-0.09398231655359268],\"velocity\":[-0.0026481677778065205,-0.0341680571436882,-0.0263235941529274,0.00112156942486763,0.0046378737315535545,0.0006861891597509384]},\"right_gripper\":{\"position\":[0.8233322040601211],\"velocity\":[0.006881222361698747]}},\n \"time_s\": 7.68,\n \"file\": [\"/tmp/kinex-observation-79svv1xn.png\"]\n }\n}", + "call_id": "call_4202a3d7c9064d15a5c12b694b321b80|fc_0b5218887c37459e016ae26e1d40d0819fbacb54419a1f481b", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "continue clearing box for second shoe", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224240.559793638] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.9466960430145264,2.0996274948120117,1.3347556591033936,-0.9375165104866028,0.1452205628156662,0.4871319532394409],\"velocity\":[-0.0024265546817332506,-0.006117640063166618,-0.03794396296143532,0.018360305577516556,0.21688298881053925,-0.12456168234348297]},\"left_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[0.13143813068216498]},\"right_arm\":{\"position\":[-0.16619357466697693,1.7558674812316895,1.1857863664627075,-1.0019826889038086,-0.005586357321590185,-0.12964484095573425],\"velocity\":[-0.0030482755973935127,-0.009106620214879513,-0.005098237190395594,0.0009567426750436425,0.005949969869107008,-0.00099231768399477]},\"right_gripper\":{\"position\":[0.8231995837254958],\"velocity\":[0.002671432924795557]}},\n \"time_s\": 8.32,\n \"file\": [\"/tmp/kinex-observation-k1r8fgfw.png\"]\n }\n}", + "call_id": "call_c17ed539302e4c37858cbad081c78a90|fc_0b5218887c37459e016ae26e2ca684819f8d673b25f6a61a1d", + "is_error": false + }, + { + "name": "devices", + "input": { + "calls": [ + { + "deviceID": "left_camera", + "action": "image" + }, + { + "deviceID": "right_camera", + "action": "image" + } + ] + }, + "output": "{\n \"results\": [\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":8.32,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-g35vn_3s.png\"},\n {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":8.32,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-s815sybc.png\"}\n ]\n}", + "call_id": "call_dcd57cf1e62d4c43afd1ddbe7f65778d|fc_0b5218887c37459e016ae26e3bfa90819fa911b02343b001f1", + "is_error": false + }, + { + "name": "device_stop", + "input": { + "reason": "First shoe placement is not visually confirmed; pause policy to inspect and recover." + }, + "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_bf129035ee174f919191145a4b6393c6|fc_0b5218887c37459e016ae26e48a838819f88aaafacfcb21b14", + "is_error": false + }, + { + "name": "read", + "input": { + "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py", + "offset": null, + "limit": null + }, + "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_542c0fcce2b948eebf6a714e6f1fb3da|fc_0b5218887c37459e016ae26e513fb8819fb2800ee5de3c1296", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "lift left gripper to inspect first shoe", + "python": "import asyncio\nfrom kinex import communication,helpers,finish\nfrom kinex.communication import duration\nasync def main():\n async with communication() as io:\n with io.subscribe('left_arm.joint_state') as s: st=await s.next(timeout_s=10)\n model=helpers.joint_model('left_arm'); q=list(st.position)\n p=await helpers.transform_pose(io,model.fk(q),'left_arm/base','world/base')\n p[2]+=0.16\n target=await helpers.transform_pose(io,p,'world/base','left_arm/base')\n q1=model.ik(target,q)\n await io.publish('left_gripper.target',{'data':1.0})\n await io.publish('left_arm.joint_target',{'joint_names':model.names,'points':[{'positions':q,'time_from_start':duration(0.04)},{'positions':q1,'time_from_start':duration(1.2)}]})\n await io.request('world.wait',duration_s=1.2)\n with io.subscribe('head_camera.image') as s: im=await s.next(timeout_s=10)\n with io.subscribe('left_arm.joint_state') as s: st=await s.next(timeout_s=10)\n finish({'position':list(st.position)},file=[helpers.save_image(im)])\nasyncio.run(main())", + "timeout_s": 120 + }, + "output": "{\n \"output\": \"[INFO] [1793224288.135853199] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"position\": [-0.949935257434845,1.990056037902832,1.6935598850250244,-1.411597728729248,0.14556893706321716,0.481610506772995],\n \"file\": [\"/tmp/kinex-observation-om1ylyes.png\"]\n }\n}", + "call_id": "call_f4dcde1faa784a6b96a77eea113080fb|fc_0b5218887c37459e016ae26e5af5ac819f8a81e257365d403e", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "reassess shoe placement from current views", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224307.245751174] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.769152045249939,1.8209655284881592,1.2780230045318604,-0.8482129573822021,0.0019667400047183037,0.5423021912574768],\"velocity\":[1.039013147354126,-1.9829304218292236,-1.5112085342407227,0.9554992914199829,-0.028640955686569214,-0.41967734694480896]},\"left_gripper\":{\"position\":[0.6473405157977885],\"velocity\":[1.9632707942615857]},\"right_arm\":{\"position\":[0.01240176148712635,1.8635932207107544,1.3279516696929932,-1.0523301362991333,-0.002492120722308755,-0.16756686568260193],\"velocity\":[0.9300477504730225,0.4109690189361572,0.5477051734924316,-0.11741548031568527,-0.05728531628847122,-0.034550949931144714]},\"right_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.06072843785990368]}},\n \"time_s\": 10.16,\n \"file\": [\"/tmp/kinex-observation-r9kvl_ew.png\"]\n }\n}", + "call_id": "call_4220e4d4b47841d8a82345b23d07d682|fc_0b5218887c37459e016ae26e71b410819fb13811e1859c27ae", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/dual_shoes_policy.py", + "description": "recover from lost grasp", + "timeout_s": 180 + }, + "output": "{\n \"output\": \"[INFO] [1793224326.069034591] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.7760891914367676,1.900067925453186,1.2369452714920044,-0.7888695597648621,0.012184467166662216,0.5715698003768921],\"velocity\":[0.5874661803245544,-1.2688400745391846,-1.1548991203308105,0.9930360913276672,-0.058601319789886475,-0.37943756580352783]},\"left_gripper\":{\"position\":[0.23286564004692165],\"velocity\":[3.2529833641919224]},\"right_arm\":{\"position\":[-0.04621698707342148,1.8618661165237427,1.3253315687179565,-1.070886492729187,0.0016111346194520593,-0.17026476562023163],\"velocity\":[0.5801291465759277,0.2907960116863251,0.4152977764606476,-0.12846273183822632,-0.05734775960445404,-0.23737221956253052]},\"right_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.08739067918875]}},\n \"time_s\": 10.8,\n \"file\": [\"/tmp/kinex-observation-2l0llko8.png\"]\n }\n}", + "call_id": "call_7f2ab1ec14b54db2a9ea98a47715c750|fc_0b5218887c37459e016ae26e8146b8819f86c4e28078c7626e", + "is_error": false + }, + { + "name": "device_stop", + "input": { + "reason": "Policy is no longer maintaining shoe grasps; hold arms for geometric inspection." + }, + "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_585641e182404aeea06e5b09b0738ecf|fc_0b5218887c37459e016ae26e9ba650819fa38d8b87a3636577", + "is_error": false + }, + { + "name": "devices", + "input": { + "calls": [ + { + "deviceID": "right_camera", + "action": "image" + }, + { + "deviceID": "left_camera", + "action": "image" + } + ] + }, + "output": "{\n \"results\": [\n {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":10.8,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dvobb360.png\"},\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":10.8,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-v8oio2c_.png\"}\n ]\n}", + "call_id": "call_1143c77fb8be4a6abb8b6e4575522c40|fc_0b5218887c37459e016ae26e9f1d08819f922f07fea76fd76c", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "inspect gripper poses and visible shoe depth", + "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n poses={s:await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}) for s in ['left','right']}\n frame=await helpers.camera_frame(io,'right_camera')\n finish({'poses':poses,'frame_keys':list(frame)})\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224359.682511338] [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 8, 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 7, in main\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/__init__.py\\\", line 98, in finish\\n result = plain(result)\\n ^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 12, in plain\\n return {key: plain(item) for key, item in value.items()}\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 14, in plain\\n return [plain(item) for item in value]\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 19, in plain\\n raise TypeError(\\\"images are not data; return them with finish(file=helpers.save_image(image))\\\")\\nTypeError: images are not data; return them with finish(file=helpers.save_image(image))\"\n}", + "call_id": "call_f659d0456d8948aaa2dd289b53c4e0b2|fc_0b5218887c37459e016ae26ea464a4819f92a7b7aa6895bff8", + "is_error": true + }, + { + "name": "read", + "input": { + "path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py", + "offset": 1, + "limit": 150 + }, + "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_bf701aaf00e144f99c654465d20fa7d1|fc_0b5218887c37459e016ae26eace220819fa5e3bf1ee2b54732", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "measure shoe surface coordinates", + "python": "import asyncio,numpy as np\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n poses={s:await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}) for s in ['left','right']}\n points={}\n for cam,pixels in [('head_camera',[(315,160),(175,200),(175,170)]),('right_camera',[(270,100),(270,180),(250,40)])]:\n im,dep=await helpers.camera_frame(io,cam); a=helpers.image_array(dep)\n for u,v in pixels:\n z=float(a[v,u])*.001\n p=[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1]\n points[f'{cam}:{u},{v}']=await helpers.transform_pose(io,p,cam+'/base','world/base',im.header.stamp)\n finish({'poses':poses,'points':points})\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224374.798465320] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"poses\": {\"left\":{\"pose\":[-0.04576036873904488,-0.1518396196357057,0.8105108280162003,-0.06397159671261682,0.6655944712893721,0.1116262315077717,0.735140271677748],\"timestamp\":10.8},\"right\":{\"pose\":[0.3158530877393279,-0.0837637020994616,0.8411548580346625,0.5416649392500444,-0.4740655883365559,-0.5247640136212739,-0.45440471118866305],\"timestamp\":10.8}},\n \"points\": {\"head_camera:315,160\":[0.3105280301800081,-0.047369169413844336,0.6644731372869409,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,200\":[0.0007164514080967438,-0.13173156963338484,0.6424513366211899,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,170\":[0.0025748496589661773,-0.06011766118549816,0.6424117683126856,-0.9486832972651138,0,0,0.3162277683730379],\"right_camera:270,100\":[0.3420385028964361,-0.0843225323833221,0.7120579827021101,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:270,180\":[0.3485558820624562,-0.12394106388230323,0.7274574502446138,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:250,40\":[0.3286115784257454,-0.057996926368941285,0.6946765327046098,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335]}\n }\n}", + "call_id": "call_29f165f826c5442a94f4599d8b7313e2|fc_0b5218887c37459e016ae26eb18030819f91651be63562500d", + "is_error": false + }, + { + "name": "read", + "input": { + "path": ".kinex/devices/right_gripper/gripper.urdf", + "offset": 1, + "limit": 180 + }, + "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_69cb68b39b0b434fb5973109b24a4381|fc_0b5218887c37459e016ae26ec31f00819fa910bf9279b5ea66", + "is_error": false + }, + { + "name": "write", + "input": { + "path": ".kinex/tools/cartesian_segment.py", + "content": "\"\"\"Bounded world-TCP moves using calibrated transforms and measured seeds.\"\"\"\nimport asyncio\nfrom kinex import args, communication, helpers, finish\nfrom kinex.communication import duration\n\nasync def read(io, op):\n with io.subscribe(op, depth=1) as stream:\n return await stream.next(timeout_s=10)\n\nasync def main():\n opts=args(); seconds=opts.get('duration_s',1.0)\n async with communication() as io:\n commands=[]\n for side,goal in opts.get('goals',{}).items():\n arm=side+'_arm'; model=helpers.joint_model(arm)\n state=await read(io,arm+'.joint_state'); q=list(state.position)\n if 'joints' in goal: end=goal['joints']\n else:\n current=await helpers.transform_pose(io,model.fk(q),arm+'/base','world/base')\n tcp=await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'})\n target=goal.get('pose',tcp['pose']).copy()\n if 'delta' in goal:\n target[:3]=[a+b for a,b in zip(target[:3],goal['delta'])]\n # TCP is 0.12 m along flange +x.\n from scipy.spatial.transform import Rotation\n off=Rotation.from_quat(target[3:]).apply([.12,0,0])\n target[:3]=[a-b for a,b in zip(target[:3],off)]\n local=await helpers.transform_pose(io,target,'world/base',arm+'/base')\n end=model.ik(local,q)\n commands.append((arm+'.joint_target',{'joint_names':model.names,'points':[\n {'positions':q,'time_from_start':duration(.04)},\n {'positions':end,'time_from_start':duration(seconds)}]}))\n for side,value in opts.get('grips',{}).items():\n commands.append((side+'_gripper.target',{'data':value}))\n await asyncio.gather(*(io.publish(op,payload) for op,payload in commands))\n await io.request('world.wait',duration_s=seconds)\n states={d:list((await read(io,d+'.joint_state')).position) for d in ['left_arm','left_gripper','right_arm','right_gripper']}\n paths=[helpers.save_image(await read(io,c+'.image')) for c in opts.get('cameras',['head_camera'])]\n finish({'measured':states},file=paths)\n\nasyncio.run(main())\n" + }, + "output": "Successfully wrote to .kinex/tools/cartesian_segment.py", + "call_id": "call_f214bab6552e4fac894e29af38414f0d|fc_0b5218887c37459e016ae26ecdef10819f94cee84862a9979c", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "clear arms and inspect displaced shoes", + "args": { + "goals": { + "right": { + "delta": [ + 0, + 0, + 0.2 + ] + }, + "left": { + "joints": [ + -0.23377, + 2.10649, + 1.59874, + -1.06015, + -0.01728, + -0.65544 + ] + } + }, + "grips": { + "left": 1, + "right": 1 + }, + "duration_s": 1, + "cameras": [ + "head_camera", + "right_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224412.462059980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.34138405323028564,2.0631067752838135,1.5276085138320923,-1.006088376045227,-0.01025577262043953,-0.4240250289440155],\"left_gripper\":[0.9917404543269764],\"right_arm\":[-0.0485311821103096,1.9509698152542114,2.119028091430664,-1.7710597515106201,0.0023190497886389494,-0.17672263085842133],\"right_gripper\":[0.984824080358852]},\n \"file\": [\"/tmp/kinex-observation-umnt4jud.png\",\"/tmp/kinex-observation-tqaq0u11.png\"]\n }\n}", + "call_id": "call_1dda0b3177e74e1891be115c85009f3c|fc_0b5218887c37459e016ae26ed8cfb0819f9b33fcffb6845c21", + "is_error": false + }, + { + "name": "device", + "input": { + "deviceID": "left_camera", + "action": "image" + }, + "output": "{\n \"deviceID\": \"left_camera\",\n \"timestamp\": 11.8,\n \"frame_id\": \"left_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-r0rh5wg1.png\"\n}", + "call_id": "call_154911b1bf974eb3b49061fda0895957|fc_0b5218887c37459e016ae26ef10960819f8bd1d9979b84a4ab", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "measure right shoe grasp and box geometry", + "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\n for u,v in [(220,25),(220,65),(220,130)]:\n z=float(a[v,u])*.001\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base',im.header.stamp)\n poses={s:(await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}))['pose'] for s in ['left','right']}\n finish({'points':pts,'tcp':poses})\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224446.993372814] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": {\"(220, 25)\":[0.33912656027719457,-0.06270945464371132,0.699152728908267,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 65)\":[0.34366378336074566,-0.0970220567701838,0.7184123702623861,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 130)\":[0.369180866644661,-0.185229663768754,0.6765957202615971,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145]},\n \"tcp\": {\"left\":[-0.1764191407332653,-0.039818355705945996,0.8221104464598411,-0.5187487037504172,0.46380859131529467,0.5349435100330139,0.4791834868324896],\"right\":[0.3142798555779541,-0.07013205333858658,1.0078345324518556,0.5420236400350569,-0.471363514483094,-0.5270262727561408,-0.45416970251444916]}\n }\n}", + "call_id": "call_6aa81a8a753445fa80dbc6b1887899b6|fc_0b5218887c37459e016ae26efab2b0819fa0c67090db3118df", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "derive proven grasp pose", + "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n q=[.1836943,2.449954,1.6583215,-.7642399,.0036793,.2217132]\n p=helpers.joint_model('right_arm').fk(q)\n from scipy.spatial.transform import Rotation as R\n p[:3]=(p[:3]+R.from_quat(p[3:]).apply([.12,0,0])).tolist()\n p=await helpers.transform_pose(io,p,'right_arm/base','world/base')\n finish(p)\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224457.863503532] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n 0.21555207762772288,\n 0.019347892345493667,\n 0.6978195099953525,\n -0.4912582223427763,\n 0.5016447242922935,\n 0.5008975819995253,\n 0.5060825445689198\n ]\n}", + "call_id": "call_45e0314da90347b3a18bf075d2d65a4b|fc_0b5218887c37459e016ae26f081b14819fa6d2a75446efd05d", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "align right grasp and survey near side", + "args": { + "goals": { + "right": { + "pose": [ + 0.344, + -0.09, + 0.82, + -0.5, + 0.5, + 0.5, + 0.5 + ] + }, + "left": { + "pose": [ + 0.12, + -0.28, + 0.98, + 0, + 0.70710678, + 0, + 0.70710678 + ] + } + }, + "grips": { + "left": 1, + "right": 1 + }, + "duration_s": 1.2, + "cameras": [ + "left_camera", + "right_camera", + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224474.085063475] [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_segment.py\\\", line 41, 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_segment.py\\\", line 29, in main\\n end=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=410, elapsed_ms=100.172, position_error_m=0.004569528253270413, rotation_error_rad=0.00035777856102559653\"\n}", + "call_id": "call_d443b9d4d47142c481adf1611a60a24e|fc_0b5218887c37459e016ae26f15b024819faa44c5f948ba02ae", + "is_error": true + }, + { + "name": "grep", + "input": { + "pattern": "DEFAULTS|max_|timeout|budget", + "path": "/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py", + "limit": 30 + }, + "output": "kinematics.py:15: DEFAULTS = {\"position_tolerance_m\": 1e-4, \"rotation_tolerance_rad\": 1e-3, \"max_iterations\": 200, \"damping\": .01}\nkinematics.py:50: DEFAULT_BUDGET = {\"max_attempts\": 5, \"attempt_time_s\": 0.02, \"max_time_s\": 0.1}\nkinematics.py:53: def budget_options(options):\nkinematics.py:54: search = options.get(\"budget\", {})\nkinematics.py:56: raise ValueError(\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\")\nkinematics.py:58: attempts = result[\"max_attempts\"]\nkinematics.py:60: raise ValueError(\"IK budget.max_attempts must be a positive integer\")\nkinematics.py:61: for key in (\"attempt_time_s\", \"max_time_s\"):\nkinematics.py:62: if number(result[key], f\"IK budget.{key}\") <= 0:\nkinematics.py:63: raise ValueError(f\"IK budget.{key} must be positive\")\nkinematics.py:127: \"\"\"Select the nearest verified solution within the existing search budget.\"\"\"\nkinematics.py:128: settings = budget_options(options)\nkinematics.py:131: deadline = started + settings[\"max_time_s\"]\nkinematics.py:134: for seed in seeds(joints, targets, original, settings[\"max_attempts\"]):\nkinematics.py:153: reason = \"time budget\" if monotonic() >= deadline else \"attempt budget\"\nkinematics.py:204: for _ in range(options[\"max_iterations\"]):\nkinematics.py:222: positions by name, within the search budget.\"\"\"\nkinematics.py:225: iterations = options[\"max_iterations\"]\nkinematics.py:227: raise ValueError(\"IK max_iterations must be a positive integer\")", + "call_id": "call_1898ea06c50445418f6c0a4b5d2cbd22|fc_0b5218887c37459e016ae26f211c70819fb513db13c372e3a5", + "is_error": false + }, + { + "name": "edit", + "input": { + "path": ".kinex/tools/cartesian_segment.py", + "edits": [ + { + "oldText": "end=model.ik(local,q)", + "newText": "end=model.ik(local,q,budget={'max_attempts':12,'attempt_time_s':0.1,'max_time_s':1.2})" + } + ] + }, + "output": "Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py.", + "call_id": "call_79c3154ed59947fd8987bd6bbc0d8430|fc_0b5218887c37459e016ae26f24856c819fb3f53b874a8c03d4", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "align right grasp and inspect near side", + "args": { + "goals": { + "right": { + "pose": [ + 0.344, + -0.09, + 0.82, + -0.5, + 0.5, + 0.5, + 0.5 + ] + }, + "left": { + "pose": [ + 0.04, + -0.25, + 0.94, + 0, + 0.70710678, + 0, + 0.70710678 + ] + } + }, + "grips": { + "left": 1, + "right": 1 + }, + "duration_s": 1.2, + "cameras": [ + "left_camera", + "right_camera", + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224491.855424014] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.007556676864624,1.9853966236114502,1.7999980449676514,-1.3873368501663208,-0.0012377145467326045,0.3198549747467041],\"left_gripper\":[1.0055662935430354],\"right_arm\":[-0.11214446276426315,1.8662967681884766,1.3592849969863892,-1.0733658075332642,0.0006734680500812829,-0.12759973108768463],\"right_gripper\":[0.9959410239349712]},\n \"file\": [\"/tmp/kinex-observation-oewipf9p.png\",\"/tmp/kinex-observation-n29q4c9q.png\",\"/tmp/kinex-observation-bxtc_9a1.png\"]\n }\n}", + "call_id": "call_e06b39882ea4416382986dcd89c24d40|fc_0b5218887c37459e016ae26f284e64819fa920b9f3e8cc677d", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "descend to right shoe and scan front edge", + "args": { + "goals": { + "right": { + "pose": [ + 0.344, + -0.09, + 0.699, + -0.5, + 0.5, + 0.5, + 0.5 + ] + }, + "left": { + "pose": [ + 0.12, + -0.4, + 0.87, + 0, + 0.70710678, + 0, + 0.70710678 + ] + } + }, + "duration_s": 0.8, + "cameras": [ + "left_camera", + "right_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224513.394437567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.421788215637207,2.089327096939087,1.8129761219024658,-1.295375943183899,0.0016572437016293406,0.11011556535959244],\"left_gripper\":[1.0041607076471502],\"right_arm\":[-0.11952149868011475,2.052452564239502,1.1140962839126587,-0.6284643411636353,0.0016702237771824002,-0.11548030376434326],\"right_gripper\":[0.997136029330167]},\n \"file\": [\"/tmp/kinex-observation-ocl_jdlh.png\",\"/tmp/kinex-observation-yq84hi1q.png\"]\n }\n}", + "call_id": "call_803c44e50f5049f9a6b17c9f83345063|fc_0b5218887c37459e016ae26f3c7bbc819fb55e9269c34f262c", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "close right gripper on shoe", + "args": { + "grips": { + "right": 0 + }, + "duration_s": 0.28, + "cameras": [ + "right_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224527.355764551] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5191649198532104,2.114835500717163,1.817434310913086,-1.2732782363891602,0.0029058295767754316,0.061535708606243134],\"left_gripper\":[0.998820808800784],\"right_arm\":[-0.12290065735578537,2.0888092517852783,1.0617690086364746,-0.5257982015609741,0.002643134444952011,-0.11187005788087845],\"right_gripper\":[0.8180002976547588]},\n \"file\": [\"/tmp/kinex-observation-bbmzia4v.png\"]\n }\n}", + "call_id": "call_49dadea9fd994b87b00486b57cfbacfa|fc_0b5218887c37459e016ae26f4c61a0819f9c85904c6cc992dc", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "lift recovered right shoe", + "args": { + "goals": { + "right": { + "delta": [ + 0, + 0, + 0.18 + ] + } + }, + "grips": { + "right": 0 + }, + "duration_s": 0.8, + "cameras": [ + "right_camera", + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224538.386130877] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5487600564956665,2.125786781311035,1.8208038806915283,-1.263669729232788,0.00247021927498281,0.044266678392887115],\"left_gripper\":[0.9992801709608599],\"right_arm\":[-0.12398703396320343,1.8660025596618652,1.3495227098464966,-1.042709231376648,0.0013285059249028563,-0.11811474710702896],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-0idhe3ah.png\",\"/tmp/kinex-observation-v16xz1o9.png\"]\n }\n}", + "call_id": "call_28fb1a41149546689e7c9d04594749ac|fc_0b5218887c37459e016ae26f5895b8819f8e960cac4bb244ef", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "reposition grasp to shoe forefoot", + "args": { + "goals": { + "right": { + "pose": [ + 0.333, + -0.025, + 0.678, + -0.5, + 0.5, + 0.5, + 0.5 + ] + } + }, + "grips": { + "right": 1 + }, + "duration_s": 0.8, + "cameras": [ + "right_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224561.581901781] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5512616634368896,2.1280250549316406,1.8211685419082642,-1.2633064985275269,0.0024033302906900644,0.043757226318120956],\"left_gripper\":[0.9994734796610746],\"right_arm\":[-0.10064993053674698,2.227466106414795,1.3507928848266602,-0.6901805996894836,0.0034024433698505163,-0.07860491424798965],\"right_gripper\":[0.9747358587655155]},\n \"file\": [\"/tmp/kinex-observation-0me27shi.png\"]\n }\n}", + "call_id": "call_dd4740028dd144718eada8f158e25bc7|fc_0b5218887c37459e016ae26f6fe5e8819f911ac0c1f3e27b47", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "settle forefoot approach", + "args": { + "duration_s": 0.24, + "cameras": [ + "right_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224574.797605845] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5518105030059814,2.1282575130462646,1.8211756944656372,-1.2633064985275269,0.0024045207537710667,0.04374692589044571],\"left_gripper\":[0.9995164898308841],\"right_arm\":[-0.09451431035995483,2.3065285682678223,1.3511804342269897,-0.614876925945282,0.003993599209934473,-0.06940840929746628],\"right_gripper\":[0.9861905845728788]},\n \"file\": [\"/tmp/kinex-observation-dlwemtpp.png\"]\n }\n}", + "call_id": "call_205739a9982d407a8acccbb7129b49eb|fc_0b5218887c37459e016ae26f7d0fa8819fbd31531d27c48a72", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "compare tool center with shoe surface", + "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n rel=await helpers.relative_pose(io,{'frame':'right_gripper:tcp'},{'frame':'right_camera:base'})\n tcp=await helpers.relative_pose(io,{'frame':'right_gripper:tcp'},{'frame':'world:base'})\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\n for u,v in [(220,190),(280,170),(280,220)]:\n z=float(a[v,u])*.001\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base')\n finish({'rel':rel,'tcp':tcp,'points':pts})\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224589.387025232] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"rel\": {\"pose\":[0.031999988504280985,0.04039803789401142,0.07136523029414477,0.5893679914824401,-0.5893679004513149,0.3906986006032956,0.3906986716386298],\"timestamp\":0},\n \"tcp\": {\"pose\":[0.33640272493657036,-0.02633167088001903,0.6917798048149151,-0.49874693434276596,0.5009926290810037,0.49905228115506656,0.5012032539440671],\"timestamp\":15.92},\n \"points\": {\"(220, 190)\":[0.323961443569907,-0.027427434548870908,0.6438170953602051,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 170)\":[0.3420592549054097,-0.019472769566128686,0.6447653287773406,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 220)\":[0.3453272239232121,-0.0403433584497698,0.642715433205389,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303]}\n }\n}", + "call_id": "call_1f2454817820440fa30d581a8b7bb57c|fc_0b5218887c37459e016ae26f8897d0819f865d0760571e0e3f", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "locate elevated shoe pixels from depth", + "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,dep=await helpers.camera_frame(io,'right_camera'); z=helpers.image_array(dep)*.001\n tr=(await helpers.relative_pose(io,{'frame':'right_camera:base'},{'frame':'world:base'}))['pose']; yy,xx=np.indices(z.shape)\n xyz=np.stack([(xx-159.5)*z/358.64218,(yy-119.5)*z/358.64218,z],-1)@R.from_quat(tr[3:]).as_matrix().T+tr[:3]\n mask=(xyz[:,:,2]>.66)&(xyz[:,:,2]<.78)&(xyz[:,:,0]>.25)\n pix=np.argwhere(mask); pts=xyz[mask]\n finish({'pixel_bounds':[pix.min(0),pix.max(0)] if len(pix) else None,'world_bounds':[pts.min(0),pts.max(0)] if len(pts) else None,'count':len(pts)})\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224610.029616113] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"pixel_bounds\": [[217,104],[239,163]],\n \"world_bounds\": [[0.2889084230314078,-0.032890889789939426,0.6600515195421714],[0.3053804617258074,-0.02516070082117358,0.6626498111904289]],\n \"count\": 86\n }\n}", + "call_id": "call_20a1bf8d69d64695b29c470a62ab6cdb|fc_0b5218887c37459e016ae26f9ce970819f9904f7258f62e640", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "widen right wrist view", + "args": { + "goals": { + "right": { + "delta": [ + 0, + 0, + 0.22 + ] + }, + "left": { + "joints": [ + -0.23377, + 2.10649, + 1.59874, + -1.06015, + -0.01728, + -0.65544 + ] + } + }, + "grips": { + "right": 1, + "left": 1 + }, + "duration_s": 1, + "cameras": [ + "right_camera", + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224626.173253423] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.4864189326763153,2.1139965057373047,1.6437352895736694,-1.1013754606246948,-0.012391126714646816,-0.5226401090621948],\"left_gripper\":[1.0006968649950894],\"right_arm\":[-0.09558303654193878,2.0816221237182617,1.7370487451553345,-1.2321414947509766,0.002054061973467469,-0.07567489147186279],\"right_gripper\":[0.9936021512204951]},\n \"file\": [\"/tmp/kinex-observation-nsqsy3z2.png\",\"/tmp/kinex-observation-46fp2462.png\"]\n }\n}", + "call_id": "call_d7c02ebd0c924b98a4290386fea210b7|fc_0b5218887c37459e016ae26fb04b24819f92f6f47a3457bbfd", + "is_error": false + }, + { + "name": "exec", + "input": { + "description": "measure recovered shoe forefoot from wider view", + "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\n for u,v in [(200,80),(200,120),(200,180),(230,120)]:\n z=float(a[v,u])*.001\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base')\n finish(pts)\nasyncio.run(main())" + }, + "output": "{\n \"output\": \"[INFO] [1793224642.187029003] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"(200, 80)\": [0.3319128077876595,-0.021384687358054988,0.6807880606684285,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(200, 120)\": [0.3320860201227272,-0.047179444335913354,0.6896982618039296,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(200, 180)\": [0.3313421415530356,-0.0816889616404346,0.7110633133544213,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(230, 120)\": [0.35305419668191745,-0.04801774041168197,0.6878528021042477,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695]\n }\n}", + "call_id": "call_09d533fa54c44e92bd7cf0e5f290f468|fc_0b5218887c37459e016ae26fbea500819fbfb0dff90c7e68e4", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "descend for firm forefoot grasp", + "args": { + "goals": { + "right": { + "pose": [ + 0.334, + -0.045, + 0.669, + -0.5, + 0.5, + 0.5, + 0.5 + ] + } + }, + "grips": { + "right": 1 + }, + "duration_s": 1, + "cameras": [ + "right_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224655.360224304] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2415974736213684,2.1075475215911865,1.6001873016357422,-1.0607448816299438,-0.01720820739865303,-0.6539632678031921],\"left_gripper\":[1.006791507655924],\"right_arm\":[-0.09445098042488098,2.2661819458007812,1.34000563621521,-0.6393835544586182,0.003709397278726101,-0.07148103415966034],\"right_gripper\":[0.9967559142546221]},\n \"file\": [\"/tmp/kinex-observation-b541py7r.png\"]\n }\n}", + "call_id": "call_ce07926c139047cfb864dc9ff816fe0f|fc_0b5218887c37459e016ae26fcd84a8819faf223c7ee94692b3", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "secure forefoot grasp", + "args": { + "grips": { + "right": 0 + }, + "duration_s": 0.4, + "cameras": [ + "right_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224665.686067778] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2397918999195099,2.106983184814453,1.599143147468567,-1.0602302551269531,-0.017222831025719643,-0.6552022099494934],\"left_gripper\":[1.0052635290405967],\"right_arm\":[-0.09369406849145889,2.304854154586792,1.2598658800125122,-0.5211624503135681,0.00392327643930912,-0.07036155462265015],\"right_gripper\":[0.2204216422343796]},\n \"file\": [\"/tmp/kinex-observation-duv2kayc.png\"]\n }\n}", + "call_id": "call_0038c08a3bf74a2fb94a8541cabe26db|fc_0b5218887c37459e016ae26fd771f0819fb3933d0d6868e282", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "check whether shoe follows lift", + "args": { + "goals": { + "right": { + "delta": [ + 0, + 0, + 0.16 + ] + } + }, + "grips": { + "right": 0 + }, + "duration_s": 0.8, + "cameras": [ + "right_camera", + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224695.262363581] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749706149101257,2.10660719871521,1.5987578630447388,-1.0601166486740112,-0.017234427854418755,-0.655457079410553],\"left_gripper\":[1.0032016147266736],\"right_arm\":[-0.09411819279193878,2.071403980255127,1.4278910160064697,-0.9290177822113037,0.0019836677238345146,-0.07476944476366043],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-itxcfdys.png\",\"/tmp/kinex-observation-z0_kr8cf.png\"]\n }\n}", + "call_id": "call_ccc1a9a553874c9ab5903da799735e1c|fc_0b5218887c37459e016ae26ff47bb4819f8d9d077cb0ff58dd", + "is_error": false + }, + { + "name": "device_stop", + "input": { + "reason": "Shoe was displaced during grasp attempt; stop motion before further inspection." + }, + "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_af13ee3ea5cc4286990e46befd9552ca|fc_0b5218887c37459e016ae2700445d4819f866a87f83acd87ad", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "leave both grippers open and verify", + "args": { + "grips": { + "left": 1, + "right": 1 + }, + "duration_s": 0.4, + "cameras": [ + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224725.282645542] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749713599681854,2.10660719871521,1.5987579822540283,-1.06011962890625,-0.017240097746253014,-0.6554499268531799],\"left_gripper\":[1.0024560825391249],\"right_arm\":[-0.09411459416151047,2.0698814392089844,1.4282174110412598,-0.929033637046814,0.001976696541532874,-0.07476774603128433],\"right_gripper\":[0.8706039027734237]},\n \"file\": [\"/tmp/kinex-observation-rljb8q34.png\"]\n }\n}", + "call_id": "call_a839ec63918748f7900267391ba467f8|fc_0b5218887c37459e016ae27012f578819f923e53216d95c7b9", + "is_error": false + }, + { + "name": "exec", + "input": { + "file": ".kinex/tools/cartesian_segment.py", + "description": "verify final open gripper positions", + "args": { + "grips": { + "left": 1, + "right": 1 + }, + "duration_s": 0.4, + "cameras": [ + "head_camera" + ] + } + }, + "output": "{\n \"output\": \"[INFO] [1793224736.756986163] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749713599681854,2.10660719871521,1.5987579822540283,-1.06011962890625,-0.017239799723029137,-0.6554496884346008],\"left_gripper\":[1.0018847584724426],\"right_arm\":[-0.09411459416151047,2.0705742835998535,1.4280035495758057,-0.929033637046814,0.001982568996027112,-0.07476842403411865],\"right_gripper\":[0.9748014916073192]},\n \"file\": [\"/tmp/kinex-observation-u51xe342.png\"]\n }\n}", + "call_id": "call_d7ec557521874c1294202cf117917e7d|fc_0b5218887c37459e016ae2701ca96c819faf99f1455deab1b2", + "is_error": false + }, + { + "name": "edit", + "input": { + "path": ".kinex/tools/cartesian_segment.py", + "edits": [ + { + "oldText": "states={d:list((await read(io,d+'.joint_state')).position) for d in ['left_arm','left_gripper','right_arm','right_gripper']}", + "newText": "states={}\n for d in ['left_arm','left_gripper','right_arm','right_gripper']:\n state=await read(io,d+'.joint_state')\n states[d]={'position':list(state.position),'velocity':list(state.velocity),\n 'stamp_s':state.header.stamp.sec+state.header.stamp.nanosec*1e-9}" + } + ] + }, + "output": "Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py.", + "call_id": "call_3a291b8a1c42424b943bc3cd7928342b|fc_0b5218887c37459e016ae2702b4898819faa7716f28cc4c00f", + "is_error": false + }, + { + "name": "write", + "input": { + "path": ".kinex/memos/dual_shoes.md", + "content": "# RoboTwin dual shoes\n\n## Interfaces and tools\n- Read refreshed profiles each episode. Stepped world, native 25 Hz (0.04 s/frame), 600 frames. Only world.wait advances time; reads do not.\n- `tools/dual_shoes_policy.py` captures head/left/right RGB in required order, calls helpers.run_policy at 25 Hz, returns fresh head image and measured joint feedback. Current policy returns 16 actions per inference even if max_actions=32. Task instruction embedded; override via args.task.\n- `tools/cartesian_segment.py` makes bounded world-TCP IK/joint segments with optional gripper targets, and returns cameras plus measured positions/velocities/stamps. Inputs: goals.{left,right}.{pose|delta|joints}, grips.{left,right} in native opening units, duration_s, cameras. It does NOT perform collision planning or certify arrival. The TCP offset is 0.12 m along flange +x for these profiles. All goals are planned before publication. Recheck calibration before reuse elsewhere.\n- Both grippers use 0 closed, 1 open; policy uses -1 open/+1 closed and helper converts. Closed empty feedback may bottom at ~0.18, not zero. A shoe can stall grip near .82, but that alone does not establish holding.\n\n## Important limitations\n- Policy achieved initial bilateral pickup but failed first placement: shoe slid toward near/right box edge, then vanished from head view. Continuing inference after loss produced wrist rotation and empty closing, not recovery. Stop and reassess instead of blindly extending policy.\n- Manual recovery was not successful. Depth-based targets require renewed perception after EVERY contact; shoes slide readily. Do not reuse previous attempt's object coordinates.\n- A 0.8–1.2 s trajectory endpoint can still have substantial tracking lag; gripper close while descending can displace the shoe. Inspect velocities and position error before grasping. Verify retention by camera during a small test lift before transport.\n- TCP +x approaches; fingers close along local y. Do not infer safe finger clearance solely from TCP height or simplified URDF geometry.\n- device_stop holds measured gripper opening, which may relax an object grasp; when intentionally holding an object, preserve closing target unless stopping for a fault.\n- Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\n" + }, + "output": "Successfully wrote to .kinex/memos/dual_shoes.md", + "call_id": "call_59053b706fc6404abf3467e748120ce4|fc_0b5218887c37459e016ae27030c860819fa45609d2accdf2f0", + "is_error": false + }, + { + "name": "edit", + "input": { + "path": ".kinex/memos/dual_shoes.md", + "edits": [ + { + "oldText": "- Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\n", + "newText": "- Finalization: command both openings to 1, allow settling, and verify feedback. Report placement uncertainty honestly; the world interface exposes no native verdict.\n" + } + ] + }, + "output": "Successfully replaced 1 block(s) in .kinex/memos/dual_shoes.md.", + "call_id": "call_150f2ee852e34f54b4bbfcc5e2fd70c2|fc_0b5218887c37459e016ae2704da438819fb542e886d3a11931", + "is_error": false + } +] diff --git a/experiments/robotwin-native/kinex_policy-2/trace.json b/experiments/robotwin-native/kinex_policy-2/trace.json new file mode 100644 index 0000000000000000000000000000000000000000..a7ac24fd92fe897ccb6da0180ed0aa341b2a32dd --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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": "01a1847a-64ba-730a-b6fb-21be31ad6e87", "timestamp": "2026-10-28T21:46:16.123Z", "cwd": "/workspace"}, "messages": [{"id": "20d7331f", "role": "system", "timestamp": "2026-10-28T21:46:18.709Z", "parts": [{"type": "text", "text": ""}]}, {"id": "c215ee7e", "role": "user", "timestamp": "2026-10-28T21:46:18.710Z", "parts": [{"type": "text", "text": "# Task: robotwin/place-dual-shoes-aloha-agilex\n\nuse both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers 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 600 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": "a9879048", "role": "context", "timestamp": "2026-10-28T21:46:18.710Z", "parts": [{"type": "text", "text": "\ncore.guides:\nDevice guide roots (nearest first): /workspace/.kinex/device, /tmp/kinex-25e2e270ec6943b1b9c010e7ee1c402d/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": "741ff487", "role": "assistant", "timestamp": "2026-10-28T21:46:21.491Z", "parts": [{"type": "tool", "callID": "call_2be0a7f253ee4abf86ce531d7ad43b28|fc_0b5218887c37459e016ae26d2d0c78819f8cc39f5feaf7fdcb", "name": "ls", "input": {"path": "/workspace"}, "output": ".kinex/\nharness.md\ninstruction.md", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11328, "outputTokens": 19, "cachedInputTokens": 0, "cacheReportedInputTokens": 11328, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11328, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 19}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "c4aeaddd", "role": "toolResult", "timestamp": "2026-10-28T21:46:21.499Z", "parts": [{"type": "text", "text": ".kinex/\nharness.md\ninstruction.md"}]}, {"id": "a0f31098", "role": "assistant", "timestamp": "2026-10-28T21:46:24.611Z", "parts": [{"type": "tool", "callID": "call_d9fc0acab16d4ca2a46a36ae77694e1a|fc_0b5218887c37459e016ae26d2fc904819f835d2d1c802765b8", "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": 11368, "outputTokens": 29, "cachedInputTokens": 11264, "cacheReportedInputTokens": 11368, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11368, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 29}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "e62f1e22", "role": "toolResult", "timestamp": "2026-10-28T21:46:24.617Z", "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": "4748d1f1", "role": "assistant", "timestamp": "2026-10-28T21:46:26.699Z", "parts": [{"type": "tool", "callID": "call_07a13441eb1747dbbbee6d0001fc549b|fc_0b5218887c37459e016ae26d323c0c819f940920553b103914", "name": "find", "input": {"pattern": "**/*", "path": "/workspace/.kinex", "limit": 160}, "output": 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results limit reached. Use limit=320 for more, or refine pattern]", "isError": false, "metadata": {}}], "usage": {"inputTokens": 11501, "outputTokens": 30, "cachedInputTokens": 11264, "cacheReportedInputTokens": 11501, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 11501, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 30}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "84d5c0bd", "role": "toolResult", "timestamp": "2026-10-28T21:46:26.726Z", "parts": [{"type": "text", "text": 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results limit reached. Use limit=320 for more, or refine pattern]"}]}, {"id": "312a0fda", "role": "assistant", "timestamp": "2026-10-28T21:46:30.011Z", "parts": [{"type": "tool", "callID": "call_c9d4800136cc4be1a80e7f4eec97898d|fc_0b5218887c37459e016ae26d356528819f85f584a5574f324d", "name": "bash", "input": {"command": "ls -la .kinex/devices; ls -la .kinex/tools .kinex/skills .kinex/memos", "timeout": null}, "output": "total 44\ndrwxr-xr-x 11 agent agent 4096 Oct 28 21:45 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 head_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_arm\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_gripper\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:45 policy\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_arm\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_gripper\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 world\n.kinex/memos:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n\n.kinex/skills:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n\n.kinex/tools:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 14555, "outputTokens": 47, "cachedInputTokens": 11392, "cacheReportedInputTokens": 14555, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 14555, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 47}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "fe633654", "role": "toolResult", "timestamp": "2026-10-28T21:46:30.031Z", "parts": [{"type": "text", "text": "total 44\ndrwxr-xr-x 11 agent agent 4096 Oct 28 21:45 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 head_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_arm\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 left_gripper\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:45 policy\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_arm\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_camera\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 right_gripper\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:14 world\n.kinex/memos:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n\n.kinex/skills:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n\n.kinex/tools:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Oct 28 21:17 .\ndrwxr-xr-x 1 agent agent 4096 Oct 28 21:46 ..\n"}]}, {"id": "7198fef0", "role": "assistant", "timestamp": "2026-10-28T21:46:35.380Z", "parts": [{"type": "tool", "callID": "call_6705f0d2e2394a30bb581ea44d385871|fc_0b5218887c37459e016ae26d3831c4819fa634b638d90788be", "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_e58691f7e16147eab395daf95a28a0c9|fc_0b5218887c37459e016ae26d3b4940819fae10e30592a8b3f2", "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_60f5baa90b4a45b085b550c6542a738b|fc_0b5218887c37459e016ae26d3b4944819f8262615e431e6507", "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_12bf679f95494399bb47080d1beebdc7|fc_0b5218887c37459e016ae26d3b4944819fae385c7f7afc6f23", "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_88760df5480447ddb8fcb3567c44240e|fc_0b5218887c37459e016ae26d3b4944819f9deac9b84c192193", "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_03371d20a1964430b350f091a1a0b420|fc_0b5218887c37459e016ae26d3b4944819fa8b82bd928c268a3", "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_b9931bd73ee64dbc89d5daad25d34ad8|fc_0b5218887c37459e016ae26d3b4944819fa63c5ea7c8a37119", "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": {}}, {"type": "tool", "callID": "call_dc800044efb24048a2da7659cbdf024d|fc_0b5218887c37459e016ae26d3b4944819f8b44321c1a8b9031", "name": "read", "input": {"path": ".kinex/devices/policy/profile.yaml", "offset": null, "limit": null}, "output": "name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n absolute ALOHA joint actions. Use the native run_policy tool with command order\n left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n created_at: '2026-10-28T21:45:05.294658+00:00'\n updated_at: '2026-10-28T21:45:05.294658+00:00'\ncompute:\n index: 0\n kind: cuda\nartifacts: []\nruntime:\n domain_id: 83\n nodes:\n - entry: node.py\n factory: create\n overlays:\n - env.sh\n ready_timeout_s: 600\noperations:\n infer:\n description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n transport: service\n direction: input\n endpoint: /kinex/policy/infer\n type: kinex_interfaces/srv/Json\n timeout_s: 120\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 15031, "outputTokens": 252, "cachedInputTokens": 14464, "cacheReportedInputTokens": 15031, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 15031, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 252}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "9674672c", "role": "toolResult", "timestamp": "2026-10-28T21:46:35.390Z", "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": "bbec448d", "role": "toolResult", "timestamp": "2026-10-28T21:46:35.390Z", "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": "431bc40d", "role": "toolResult", "timestamp": "2026-10-28T21:46:35.390Z", "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": "89bc21a1", "role": "toolResult", "timestamp": "2026-10-28T21:46:35.390Z", "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": "8c5f6959", "role": "toolResult", "timestamp": "2026-10-28T21:46:35.390Z", "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": "fbbe690e", "role": "toolResult", "timestamp": "2026-10-28T21:46:35.390Z", "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": "95028f0d", "role": "toolResult", "timestamp": "2026-10-28T21:46:35.390Z", "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": "ab261e44", "role": "toolResult", "timestamp": "2026-10-28T21:46:35.390Z", "parts": [{"type": "text", "text": "name: policy\ntype: model\nfamily: starvla\nloaded: false\ndescription: Frozen StarVLA Qwen3-VL-OFT RoboTwin model. Infer on three RGB images\n in head, left wrist, right wrist order and a task instruction. Returns a chunk of\n absolute ALOHA joint actions. Use the native run_policy tool with command order\n left_arm, left_gripper, right_arm, right_gripper and action_hz=25. The model makes\n one inference per request; it does not control the robot or decide when to re-observe.\nsource: default\nmethod: unknown\ncalibrated_at: null\nmeta:\n created_at: '2026-10-28T21:45:05.294658+00:00'\n updated_at: '2026-10-28T21:45:05.294658+00:00'\ncompute:\n index: 0\n kind: cuda\nartifacts: []\nruntime:\n domain_id: 83\n nodes:\n - entry: node.py\n factory: create\n overlays:\n - env.sh\n ready_timeout_s: 600\noperations:\n infer:\n description: 'Input: obs={images:[head RGB path,left wrist RGB path,right wrist\n RGB path]}, task: nonempty language. Output: actions[T,14] in left arm (6 radians),\n left grip (1), right arm (6 radians), right grip (1) order. Grip -1 releases;\n +1 closes. Also returns call_id and inference_s. All paths refer to this workspace.'\n transport: service\n direction: input\n endpoint: /kinex/policy/infer\n type: kinex_interfaces/srv/Json\n timeout_s: 120\n"}]}, {"id": "4b601a01", "role": "assistant", "timestamp": "2026-10-28T21:46:39.081Z", "parts": [{"type": "tool", "callID": "call_aca37b9173a049859c26b1a1c7afedc7|fc_0b5218887c37459e016ae26d3cf1fc819fbb5c0783e4554891", "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_2cbb4865d07c4343a74eba4b21d3e0a0|fc_0b5218887c37459e016ae26d3efb30819f8bd7f5954aee7123", "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", "isError": false, "metadata": {}}, {"type": "tool", "callID": "call_c3f15cd1c50341aeafa8ee8bde74be5f|fc_0b5218887c37459e016ae26d3efb30819f90097de3aae79959", "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": {}}, {"type": "tool", "callID": "call_efb801f592ff478d9cd43e403a7c5552|fc_0b5218887c37459e016ae26d3efb30819f9f12804ad88a7c4b", "name": "exec", "input": {"python": "from kinex import runtime, finish\nfinish({n:runtime.operations(n) for n in ['world','left_arm','left_gripper','head_camera','policy']})", "description": "inspect operation schemas"}, "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 \"policy\": {\"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}}\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 20140, "outputTokens": 167, "cachedInputTokens": 15104, "cacheReportedInputTokens": 20140, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 20140, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 167}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "0a300e73", "role": "toolResult", "timestamp": "2026-10-28T21:46:39.085Z", "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": "be498bfe", "role": "toolResult", "timestamp": "2026-10-28T21:46:39.087Z", "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. 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"}]}, {"id": "3e876673", "role": "toolResult", "timestamp": "2026-10-28T21:46:39.088Z", "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": "8d7b1094", "role": "toolResult", "timestamp": "2026-10-28T21:46:40.015Z", "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 \"policy\": {\"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}}\n }\n}"}]}, {"id": "9f951d64", "role": "assistant", "timestamp": "2026-10-28T21:46:43.425Z", "parts": [{"type": "tool", "callID": "call_990c26cdcea84c32a82583ddc2e7e432|fc_0b5218887c37459e016ae26d4248ec819fa2ee0f4e25c3c532", "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"}, {"deviceID": "left_camera", "action": "load"}, {"deviceID": "right_camera", "action": "load"}, {"deviceID": "policy", "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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 23684, "outputTokens": 107, "cachedInputTokens": 20224, "cacheReportedInputTokens": 23684, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 23684, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 107}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "135ab1df", "role": "toolResult", "timestamp": "2026-10-28T21:48:00.080Z", "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 {\"deviceID\":\"left_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"right_camera\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}},\n {\"deviceID\":\"policy\",\"action\":\"load\",\"ok\":true,\"data\":{\"ready\":true}}\n ]\n}"}]}, {"id": "36dd6397", "role": "assistant", "timestamp": "2026-10-28T21:48:02.891Z", "parts": [{"type": "tool", "callID": "call_a1a74d8fbb94481d864fe661fa79d7c2|fc_0b5218887c37459e016ae26d9272e8819f8d056301e2d7cbef", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/policy.py", "offset": 1, "limit": 260}, "output": "\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n max_actions=None, inference_args=None):\n \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n model serves a Json \"infer\" operation taking obs (and task) and returning\n actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n entries in flat-vector split order; named action maps use device names.\n space \"device\" takes absolute joint values in URDF order, or end-effector\n values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n tip_link, frozen at the observation stamp. max_actions caps the horizon and\n action_hz paces actions on the world clock. Joint trajectories submit\n asynchronously; end-effector targets are paced by world waits before\n returning. Returns actions_submitted, duration_s and ik_failure\n (device, action_index, error) or None.\n\n Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n \"\"\"\n import asyncio\n from kinex.communication import duration\n from kinex.helpers.frames import IDENTITY, transform_pose\n from kinex.lib.geometry import invert_rt, rt_to_pose\n plans = [_commanded(entry) for entry in command]\n if len({entry[\"name\"] for entry in plans}) != len(plans):\n raise ValueError(\"command must name each device once\")\n wait = f\"{world}.wait\"\n if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n options = inference_args or {}\n if set(options) & {\"obs\", \"task\"}:\n raise ValueError(\"inference_args cannot override obs or task\")\n data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n if not actions:\n raise ValueError(\"infer must return nonempty actions\")\n actions = actions[:max_actions or len(actions)]\n split = [_share(action, plans) for action in actions]\n count, failure, sequences = len(split), None, {}\n for entry in plans:\n name, spec = entry[\"name\"], entry[\"spec\"]\n samples = [item[name] for item in split[:count]]\n if spec.get(\"space\", \"device\") != \"device\":\n stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n try:\n state = await stream.next(timeout_s=10)\n finally:\n stream.close()\n joints = entry[\"model\"]\n seed = measured_positions(state, joints.joints)\n base = f\"{name}/base\"\n ik_frame = f\"{name}/{joints.tip}\"\n target_frame = spec.get(\"target_frame\", ik_frame)\n reference = spec.get(\"frame\", base)\n # target_frame is an attached tool frame. Freeze its offset at the\n # observation stamp while resolving the policy's Cartesian chunk;\n # URDF FK of the measured joints places the tip.\n offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n converted = []\n for sample in samples:\n if spec[\"space\"] == \"pose_delta\":\n value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n current = value[\"translation\"] + value[\"rotation\"]\n else:\n current = sample\n ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n try:\n seed = joints.ik(goal, seed)\n except ValueError as error:\n failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n break\n converted.append(seed)\n samples = converted\n count = min(count, len(samples))\n sequences[name] = samples\n if count == 0:\n return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n seconds = count / number(action_hz, \"action_hz\")\n prepared, scalars = [], []\n for entry in plans:\n name, prof = entry[\"name\"], entry[\"profile\"]\n samples = sequences[name][:count]\n if entry[\"model\"]:\n joint_command(prof, samples, entry[\"model\"].joints)\n fields = {\"joint_names\": entry[\"model\"].names,\n \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n for index, value in enumerate(samples)]}\n operation = f\"{name}.joint_target\"\n else:\n target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n targets = [target(prof, value[0]) for value in samples]\n fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n scalars.append((operation, targets))\n prepared.append((operation, fields))\n\n await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n # End-effector targets persist. Timing belongs to this workflow and the world,\n # never a device-side duration or a background worker.\n if scalars:\n for index in range(count):\n if index:\n await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n for operation, values in scalars))\n await io.request(wait, duration_s=seconds / count)\n return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n name = entry[\"device\"]\n prof = profile(name)\n kind = prof.get(\"type\")\n if kind not in END_EFFECTORS | {\"joint\"}:\n raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n model = None if kind in END_EFFECTORS else JointModel(prof)\n # A gripper or suction tool takes one command value whatever it reports back.\n width = _cartesian_width(entry, prof)\n return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n \"\"\"One action, split across the commanded devices in order.\"\"\"\n if isinstance(action, dict):\n missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n if missing:\n raise ValueError(f\"action names no value for {', '.join(missing)}\")\n return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n shares, cursor = {}, 0\n for entry in plan:\n shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n cursor += entry[\"width\"]\n return shares\n\n\ndef _cartesian_width(entry, prof):\n space = entry.get(\"space\", \"device\")\n if space not in {\"device\", \"pose\", \"pose_delta\"}:\n raise ValueError(f\"unknown policy space: {space}\")\n if space == \"device\":\n if \"frame\" in entry or \"target_frame\" in entry:\n raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n return None\n if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n action = vector(action, 6, \"pose_delta\")\n angle = math.hypot(*action[3:])\n scale = math.sin(angle / 2) / angle if angle else 0.5\n rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n value = number(value, \"action\")\n if not -1 <= value <= 1:\n raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n return value\n\n\ndef _grip(profile, value):\n \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n grip = profile[\"grip\"]\n if not isinstance(grip, dict):\n raise ValueError(\"the gripper has no declared grip joint\")\n return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n value = _action(value)\n if profile[\"vacuum\"] == \"binary\":\n return 1.0 if value > 0 else 0.0\n if profile[\"vacuum\"] != \"proportional\":\n raise ValueError(\"the suction device declares no vacuum control\")\n return (value + 1) / 2\n", "isError": false, "metadata": {}}], "usage": {"inputTokens": 24008, "outputTokens": 40, "cachedInputTokens": 23680, "cacheReportedInputTokens": 24008, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 24008, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 40}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "4fbbdcd9", "role": "toolResult", "timestamp": "2026-10-28T21:48:02.894Z", "parts": [{"type": "text", "text": "\"\"\"Policy execution: one inference turned into validated device commands.\"\"\"\n\nimport math\n\nfrom kinex import profile\nfrom kinex.lib.geometry import compose_rt, number, vector, xyzq_to_rt\nfrom kinex.lib.joints import JointModel, command as joint_command, measured_positions\n\nEND_EFFECTORS = {\"gripper\", \"suction\"}\n\n\nasync def run_policy(io, model, observation, command, task=None, world=\"world\", action_hz=10,\n max_actions=None, inference_args=None):\n \"\"\"Infer once and submit the valid action prefix to every commanded device.\n\n model serves a Json \"infer\" operation taking obs (and task) and returning\n actions or action. command lists {\"device\", \"space\", \"frame\", \"target_frame\"}\n entries in flat-vector split order; named action maps use device names.\n space \"device\" takes absolute joint values in URDF order, or end-effector\n values in [-1, 1] that release at -1 and grip at +1; \"pose\" (xyz + xyzw) and\n \"pose_delta\" (xyz + axis-angle) solve URDF IK for joint devices with a\n tip_link, frozen at the observation stamp. max_actions caps the horizon and\n action_hz paces actions on the world clock. Joint trajectories submit\n asynchronously; end-effector targets are paced by world waits before\n returning. Returns actions_submitted, duration_s and ik_failure\n (device, action_index, error) or None.\n\n Example: await helpers.run_policy(io, \"policy\", obs, [{\"device\": \"arm\"}, {\"device\": \"gripper\"}])\n \"\"\"\n import asyncio\n from kinex.communication import duration\n from kinex.helpers.frames import IDENTITY, transform_pose\n from kinex.lib.geometry import invert_rt, rt_to_pose\n plans = [_commanded(entry) for entry in command]\n if len({entry[\"name\"] for entry in plans}) != len(plans):\n raise ValueError(\"command must name each device once\")\n wait = f\"{world}.wait\"\n if any(entry[\"model\"] is None for entry in plans) and wait not in io.catalog:\n raise ValueError(\"end-effector policy sequences require a loaded world wait operation\")\n options = inference_args or {}\n if set(options) & {\"obs\", \"task\"}:\n raise ValueError(\"inference_args cannot override obs or task\")\n data = await io.request(f\"{model}.infer\", **options, obs=observation, **({\"task\": task} if task else {}))\n actions = data.get(\"actions\", [data[\"action\"]] if \"action\" in data else None)\n if not actions:\n raise ValueError(\"infer must return nonempty actions\")\n actions = actions[:max_actions or len(actions)]\n split = [_share(action, plans) for action in actions]\n count, failure, sequences = len(split), None, {}\n for entry in plans:\n name, spec = entry[\"name\"], entry[\"spec\"]\n samples = [item[name] for item in split[:count]]\n if spec.get(\"space\", \"device\") != \"device\":\n stream = io.subscribe(f\"{name}.joint_state\", depth=1)\n try:\n state = await stream.next(timeout_s=10)\n finally:\n stream.close()\n joints = entry[\"model\"]\n seed = measured_positions(state, joints.joints)\n base = f\"{name}/base\"\n ik_frame = f\"{name}/{joints.tip}\"\n target_frame = spec.get(\"target_frame\", ik_frame)\n reference = spec.get(\"frame\", base)\n # target_frame is an attached tool frame. Freeze its offset at the\n # observation stamp while resolving the policy's Cartesian chunk;\n # URDF FK of the measured joints places the tip.\n offset = await transform_pose(io, IDENTITY, ik_frame, target_frame, state.header.stamp)\n tool = rt_to_pose(compose_rt(xyzq_to_rt(joints.fk(seed)), invert_rt(xyzq_to_rt(offset))))\n current = await transform_pose(io, tool[\"translation\"] + tool[\"rotation\"], base, reference, state.header.stamp)\n converted = []\n for sample in samples:\n if spec[\"space\"] == \"pose_delta\":\n value = rt_to_pose(_delta_pose(xyzq_to_rt(current), sample))\n current = value[\"translation\"] + value[\"rotation\"]\n else:\n current = sample\n ik = rt_to_pose(compose_rt(xyzq_to_rt(current), xyzq_to_rt(offset)))\n goal = await transform_pose(io, ik[\"translation\"] + ik[\"rotation\"], reference, base, state.header.stamp)\n try:\n seed = joints.ik(goal, seed)\n except ValueError as error:\n failure = {\"device\": name, \"action_index\": len(converted), \"error\": str(error)}\n break\n converted.append(seed)\n samples = converted\n count = min(count, len(samples))\n sequences[name] = samples\n if count == 0:\n return {\"actions_submitted\": 0, \"duration_s\": 0, \"ik_failure\": failure}\n seconds = count / number(action_hz, \"action_hz\")\n prepared, scalars = [], []\n for entry in plans:\n name, prof = entry[\"name\"], entry[\"profile\"]\n samples = sequences[name][:count]\n if entry[\"model\"]:\n joint_command(prof, samples, entry[\"model\"].joints)\n fields = {\"joint_names\": entry[\"model\"].names,\n \"points\": [{\"positions\": value, \"time_from_start\": duration((index + 1) * seconds / count)}\n for index, value in enumerate(samples)]}\n operation = f\"{name}.joint_target\"\n else:\n target = _grip if prof[\"type\"] == \"gripper\" else _vacuum\n targets = [target(prof, value[0]) for value in samples]\n fields, operation = {\"data\": targets[0]}, f\"{name}.target\"\n scalars.append((operation, targets))\n prepared.append((operation, fields))\n\n await asyncio.gather(*(io.publish(operation, fields) for operation, fields in prepared))\n # End-effector targets persist. Timing belongs to this workflow and the world,\n # never a device-side duration or a background worker.\n if scalars:\n for index in range(count):\n if index:\n await asyncio.gather(*(io.publish(operation, {\"data\": values[index]})\n for operation, values in scalars))\n await io.request(wait, duration_s=seconds / count)\n return {\"actions_submitted\": count, \"duration_s\": seconds, \"ik_failure\": failure}\n\n\ndef _commanded(entry):\n \"\"\"One commanded device: its profile, joint model and share of an action vector.\"\"\"\n name = entry[\"device\"]\n prof = profile(name)\n kind = prof.get(\"type\")\n if kind not in END_EFFECTORS | {\"joint\"}:\n raise ValueError(f\"{name} is {kind}; run_policy commands joint, gripper or suction devices\")\n model = None if kind in END_EFFECTORS else JointModel(prof)\n # A gripper or suction tool takes one command value whatever it reports back.\n width = _cartesian_width(entry, prof)\n return {\"name\": name, \"profile\": prof, \"model\": model, \"spec\": entry,\n \"width\": width or (1 if kind in END_EFFECTORS else len(model.joints))}\n\n\ndef _share(action, plan):\n \"\"\"One action, split across the commanded devices in order.\"\"\"\n if isinstance(action, dict):\n missing = [e[\"name\"] for e in plan if e[\"name\"] not in action]\n if missing:\n raise ValueError(f\"action names no value for {', '.join(missing)}\")\n return {e[\"name\"]: vector(action[e[\"name\"]], e[\"width\"], f\"action for {e['name']}\") for e in plan}\n vector(action, sum(e[\"width\"] for e in plan), \"policy action\")\n shares, cursor = {}, 0\n for entry in plan:\n shares[entry[\"name\"]] = list(action[cursor:cursor + entry[\"width\"]])\n cursor += entry[\"width\"]\n return shares\n\n\ndef _cartesian_width(entry, prof):\n space = entry.get(\"space\", \"device\")\n if space not in {\"device\", \"pose\", \"pose_delta\"}:\n raise ValueError(f\"unknown policy space: {space}\")\n if space == \"device\":\n if \"frame\" in entry or \"target_frame\" in entry:\n raise ValueError(\"frame and target_frame require Cartesian policy actions\")\n return None\n if prof[\"type\"] != \"joint\" or not prof[\"model\"][\"tip_link\"]:\n raise ValueError(\"Cartesian policy actions require a joint device with a model tip_link\")\n return 7 if space == \"pose\" else 6\n\n\ndef _delta_pose(current, action):\n \"\"\"Translation and rotation increments use fixed reference-frame axes.\"\"\"\n action = vector(action, 6, \"pose_delta\")\n angle = math.hypot(*action[3:])\n scale = math.sin(angle / 2) / angle if angle else 0.5\n rotation = xyzq_to_rt([0, 0, 0, *[v * scale for v in action[3:]], math.cos(angle / 2)])\n return compose_rt(rotation, (current[0], [0, 0, 0]))[0], [a + b for a, b in zip(current[1], action[:3])]\n\n\ndef _action(value):\n value = number(value, \"action\")\n if not -1 <= value <= 1:\n raise ValueError(\"end-effector actions must be in [-1, 1]: -1 releases, +1 grips\")\n return value\n\n\ndef _grip(profile, value):\n \"\"\"Interpolate the grip joint from open to closed.\"\"\"\n grip = profile[\"grip\"]\n if not isinstance(grip, dict):\n raise ValueError(\"the gripper has no declared grip joint\")\n return grip[\"open\"] + (_action(value) + 1) / 2 * (grip[\"closed\"] - grip[\"open\"])\n\n\ndef _vacuum(profile, value):\n \"\"\"A vacuum level from release (0) to full vacuum (1); binary devices switch on above 0.\"\"\"\n value = _action(value)\n if profile[\"vacuum\"] == \"binary\":\n return 1.0 if value > 0 else 0.0\n if profile[\"vacuum\"] != \"proportional\":\n raise ValueError(\"the suction device declares no vacuum control\")\n return (value + 1) / 2\n"}]}, {"id": "2b4e4ea2", "role": "assistant", "timestamp": "2026-10-28T21:48:07.442Z", "parts": [{"type": "reasoning", "text": "**Saving reusable image script**"}, {"type": "text", "text": "I’ll use the dual-arm policy in short segments, checking the camera view and measured gripper states as it progresses."}, {"type": "tool", "callID": "call_490e6b1f64b34e829277af881f08870a|fc_0b5218887c37459e016ae26d971ec0819fa660db6d9d5e632f", "name": "device", "input": {"deviceID": "head_camera", "action": "image"}, "output": "{\n \"deviceID\": \"head_camera\",\n \"timestamp\": 0,\n \"frame_id\": \"head_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-uo5653ej.png\"\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 26338, "outputTokens": 75, "cachedInputTokens": 23936, "cacheReportedInputTokens": 26338, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 26338, "reasoningOutputTokens": 19, "reasoningReportedOutputTokens": 75}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "34917289", "role": "toolResult", "timestamp": "2026-10-28T21:48:07.729Z", "parts": [{"type": "text", "text": "{\n \"deviceID\": \"head_camera\",\n \"timestamp\": 0,\n \"frame_id\": \"head_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-uo5653ej.png\"\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "d27588f2", "role": "assistant", "timestamp": "2026-10-28T21:48:16.830Z", "parts": [{"type": "tool", "callID": "call_dd58d395e4f74264a0b4ffc91c55c48f|fc_0b5218887c37459e016ae26d9a2314819f9adac34e651ab34c", "name": "write", "input": {"path": ".kinex/tools/dual_shoes_policy.py", "content": "\"\"\"Run one bounded, freshly observed RoboTwin policy segment; no lifecycle changes.\"\"\"\nimport asyncio\nfrom kinex import args, communication, finish, helpers\n\nTASK = ('use both arms to pick up the two shoes on the table and put them flat in the shoebox, '\n 'with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. '\n 'Leave both grippers open')\nCAMERAS = ['head_camera', 'left_camera', 'right_camera']\nDEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper']\n\nasync def read_one(io, op):\n with io.subscribe(op, depth=1) as stream:\n return await stream.next(timeout_s=10)\n\nasync def main():\n opts = args()\n async with communication() as io:\n images = await asyncio.gather(*(read_one(io, c+'.image') for c in CAMERAS))\n paths = [helpers.save_image(im) for im in images]\n result = await helpers.run_policy(io, 'policy', {'images': paths},\n [{'device': d} for d in DEVICES], task=opts.get('task', TASK),\n world='world', action_hz=25, max_actions=opts.get('max_actions', 32))\n states = await asyncio.gather(*(read_one(io, d+'.joint_state') for d in DEVICES))\n image = await read_one(io, 'head_camera.image')\n path = helpers.save_image(image)\n result['measured'] = {d: {'position': list(s.position), 'velocity': list(s.velocity)}\n for d,s in zip(DEVICES,states)}\n result['time_s'] = image.header.stamp.sec + image.header.stamp.nanosec * 1e-9\n finish(result, file=[path])\n\nasyncio.run(main())\n"}, "output": "Successfully wrote to .kinex/tools/dual_shoes_policy.py", "isError": false, "metadata": {}}], "usage": {"inputTokens": 26580, "outputTokens": 465, "cachedInputTokens": 26240, "cacheReportedInputTokens": 26580, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 26580, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 465}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "d726f1ad", "role": "toolResult", "timestamp": "2026-10-28T21:48:16.834Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/tools/dual_shoes_policy.py"}]}, {"id": "6958f7ee", "role": "assistant", "timestamp": "2026-10-28T21:48:19.505Z", "parts": [{"type": "tool", "callID": "call_c3ca42f54ce14bb69448ec81c32b61f3|fc_0b5218887c37459e016ae26da289e4819f901815286edd1deb", "name": "exec", "input": {"file": ".kinex/tools/dual_shoes_policy.py", "description": "approach shoes with both arms", "timeout_s": 180}, "output": "{\n \"output\": \"[INFO] [1793224100.950625034] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.06924128532409668,0.620575487613678,0.4789740741252899,-0.28832128643989563,-0.006016099359840155,-0.20582163333892822],\"velocity\":[-0.14827853441238403,1.9776692390441895,1.3301373720169067,-0.7481668591499329,-0.00023766420781612396,-0.4732377231121063]},\"left_gripper\":{\"position\":[0.9972223205999895],\"velocity\":[-0.05145943300290541]},\"right_arm\":{\"position\":[0.059656575322151184,0.6675826907157898,0.5648283958435059,-0.336734414100647,-0.0007643997087143362,0.10298626869916916],\"velocity\":[0.16773490607738495,2.116530418395996,1.5773547887802124,-0.8487532138824463,0.0016799001023173332,0.25821131467819214]},\"right_gripper\":{\"position\":[0.9918896691365676],\"velocity\":[0.10728505016727881]}},\n \"time_s\": 0.64,\n \"file\": [\"/tmp/kinex-observation-bzj6q2qb.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 27068, "outputTokens": 40, "cachedInputTokens": 26880, "cacheReportedInputTokens": 27068, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 27068, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 40}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "a34c0da2", "role": "toolResult", "timestamp": "2026-10-28T21:48:26.651Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224100.950625034] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.06924128532409668,0.620575487613678,0.4789740741252899,-0.28832128643989563,-0.006016099359840155,-0.20582163333892822],\"velocity\":[-0.14827853441238403,1.9776692390441895,1.3301373720169067,-0.7481668591499329,-0.00023766420781612396,-0.4732377231121063]},\"left_gripper\":{\"position\":[0.9972223205999895],\"velocity\":[-0.05145943300290541]},\"right_arm\":{\"position\":[0.059656575322151184,0.6675826907157898,0.5648283958435059,-0.336734414100647,-0.0007643997087143362,0.10298626869916916],\"velocity\":[0.16773490607738495,2.116530418395996,1.5773547887802124,-0.8487532138824463,0.0016799001023173332,0.25821131467819214]},\"right_gripper\":{\"position\":[0.9918896691365676],\"velocity\":[0.10728505016727881]}},\n \"time_s\": 0.64,\n \"file\": [\"/tmp/kinex-observation-bzj6q2qb.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "8d5362b7", "role": "assistant", "timestamp": "2026-10-28T21:48:29.308Z", "parts": 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{\"left_arm\":{\"position\":[-0.9466960430145264,2.0996274948120117,1.3347556591033936,-0.9375165104866028,0.1452205628156662,0.4871319532394409],\"velocity\":[-0.0024265546817332506,-0.006117640063166618,-0.03794396296143532,0.018360305577516556,0.21688298881053925,-0.12456168234348297]},\"left_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[0.13143813068216498]},\"right_arm\":{\"position\":[-0.16619357466697693,1.7558674812316895,1.1857863664627075,-1.0019826889038086,-0.005586357321590185,-0.12964484095573425],\"velocity\":[-0.0030482755973935127,-0.009106620214879513,-0.005098237190395594,0.0009567426750436425,0.005949969869107008,-0.00099231768399477]},\"right_gripper\":{\"position\":[0.8231995837254958],\"velocity\":[0.002671432924795557]}},\n \"time_s\": 8.32,\n \"file\": [\"/tmp/kinex-observation-k1r8fgfw.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "5466ef65", "role": "assistant", "timestamp": "2026-10-28T21:50:54.269Z", "parts": [{"type": "reasoning", "text": "**Checking shoe location**"}, {"type": "tool", "callID": "call_dcd57cf1e62d4c43afd1ddbe7f65778d|fc_0b5218887c37459e016ae26e3bfa90819fa911b02343b001f1", "name": "devices", "input": {"calls": [{"deviceID": "left_camera", "action": "image"}, {"deviceID": "right_camera", "action": "image"}]}, "output": "{\n \"results\": [\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":8.32,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-g35vn_3s.png\"},\n {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":8.32,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-s815sybc.png\"}\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 34314, "outputTokens": 68, "cachedInputTokens": 33664, "cacheReportedInputTokens": 34314, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 34314, "reasoningOutputTokens": 29, "reasoningReportedOutputTokens": 68}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "45ab9995", "role": "toolResult", "timestamp": "2026-10-28T21:50:54.601Z", "parts": [{"type": "text", "text": "{\n \"results\": [\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":8.32,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-g35vn_3s.png\"},\n {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":8.32,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-s815sybc.png\"}\n ]\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "c395dcb2", "role": "assistant", "timestamp": "2026-10-28T21:51:04.818Z", "parts": [{"type": "reasoning", "text": "**Withdrawing left gripper**"}, {"type": "tool", "callID": "call_bf129035ee174f919191145a4b6393c6|fc_0b5218887c37459e016ae26e48a838819f88aaafacfcb21b14", "name": "device_stop", "input": {"reason": "First shoe placement is not visually confirmed; pause policy to inspect and recover."}, "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": 34705, "outputTokens": 102, "cachedInputTokens": 34304, "cacheReportedInputTokens": 34705, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 34705, "reasoningOutputTokens": 68, "reasoningReportedOutputTokens": 102}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "d82e271b", "role": "toolResult", "timestamp": "2026-10-28T21:51:04.954Z", "parts": [{"type": "text", "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"}]}, {"id": "82c8bad8", "role": "assistant", "timestamp": "2026-10-28T21:51:14.887Z", "parts": [{"type": "reasoning", "text": "**Repositioning the left arm**"}, {"type": "tool", "callID": "call_542c0fcce2b948eebf6a714e6f1fb3da|fc_0b5218887c37459e016ae26e513fb8819fb2800ee5de3c1296", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/kinematics.py", "offset": null, "limit": null}, "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": 34869, "outputTokens": 128, "cachedInputTokens": 34688, "cacheReportedInputTokens": 34869, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 34869, "reasoningOutputTokens": 84, "reasoningReportedOutputTokens": 128}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "cbfe32fa", "role": "toolResult", "timestamp": "2026-10-28T21:51:14.890Z", "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": "64fdbee4", "role": "assistant", "timestamp": "2026-10-28T21:51:26.652Z", "parts": [{"type": "reasoning", "text": ""}, {"type": "tool", "callID": "call_f4dcde1faa784a6b96a77eea113080fb|fc_0b5218887c37459e016ae26e5af5ac819f8a81e257365d403e", "name": "exec", "input": {"description": "lift left gripper to inspect first shoe", "python": "import asyncio\nfrom kinex import communication,helpers,finish\nfrom kinex.communication import duration\nasync def main():\n async with communication() as io:\n with io.subscribe('left_arm.joint_state') as s: st=await s.next(timeout_s=10)\n model=helpers.joint_model('left_arm'); q=list(st.position)\n p=await helpers.transform_pose(io,model.fk(q),'left_arm/base','world/base')\n p[2]+=0.16\n target=await helpers.transform_pose(io,p,'world/base','left_arm/base')\n q1=model.ik(target,q)\n await io.publish('left_gripper.target',{'data':1.0})\n await io.publish('left_arm.joint_target',{'joint_names':model.names,'points':[{'positions':q,'time_from_start':duration(0.04)},{'positions':q1,'time_from_start':duration(1.2)}]})\n await io.request('world.wait',duration_s=1.2)\n with io.subscribe('head_camera.image') as s: im=await s.next(timeout_s=10)\n with io.subscribe('left_arm.joint_state') as s: st=await s.next(timeout_s=10)\n finish({'position':list(st.position)},file=[helpers.save_image(im)])\nasyncio.run(main())", "timeout_s": 120}, "output": "{\n \"output\": \"[INFO] [1793224288.135853199] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"position\": [-0.949935257434845,1.990056037902832,1.6935598850250244,-1.411597728729248,0.14556893706321716,0.481610506772995],\n \"file\": [\"/tmp/kinex-observation-om1ylyes.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 35207, "outputTokens": 353, "cachedInputTokens": 34816, "cacheReportedInputTokens": 35207, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 35207, "reasoningOutputTokens": 21, "reasoningReportedOutputTokens": 353}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "8a586584", "role": "toolResult", "timestamp": "2026-10-28T21:51:36.959Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224288.135853199] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"position\": [-0.949935257434845,1.990056037902832,1.6935598850250244,-1.411597728729248,0.14556893706321716,0.481610506772995],\n \"file\": [\"/tmp/kinex-observation-om1ylyes.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "63496da8", "role": "assistant", "timestamp": "2026-10-28T21:51:45.839Z", "parts": [{"type": "reasoning", "text": "**Reaching for first shoe**"}, {"type": "tool", "callID": "call_4220e4d4b47841d8a82345b23d07d682|fc_0b5218887c37459e016ae26e71b410819fb13811e1859c27ae", "name": "exec", "input": {"file": ".kinex/tools/dual_shoes_policy.py", "description": "reassess shoe placement from current views", "timeout_s": 180}, "output": "{\n \"output\": \"[INFO] [1793224307.245751174] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.769152045249939,1.8209655284881592,1.2780230045318604,-0.8482129573822021,0.0019667400047183037,0.5423021912574768],\"velocity\":[1.039013147354126,-1.9829304218292236,-1.5112085342407227,0.9554992914199829,-0.028640955686569214,-0.41967734694480896]},\"left_gripper\":{\"position\":[0.6473405157977885],\"velocity\":[1.9632707942615857]},\"right_arm\":{\"position\":[0.01240176148712635,1.8635932207107544,1.3279516696929932,-1.0523301362991333,-0.002492120722308755,-0.16756686568260193],\"velocity\":[0.9300477504730225,0.4109690189361572,0.5477051734924316,-0.11741548031568527,-0.05728531628847122,-0.034550949931144714]},\"right_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.06072843785990368]}},\n \"time_s\": 10.16,\n \"file\": [\"/tmp/kinex-observation-r9kvl_ew.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 35789, "outputTokens": 120, "cachedInputTokens": 35456, "cacheReportedInputTokens": 35789, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 35789, "reasoningOutputTokens": 76, "reasoningReportedOutputTokens": 120}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "0cab8987", "role": "toolResult", "timestamp": "2026-10-28T21:51:52.090Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224307.245751174] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.769152045249939,1.8209655284881592,1.2780230045318604,-0.8482129573822021,0.0019667400047183037,0.5423021912574768],\"velocity\":[1.039013147354126,-1.9829304218292236,-1.5112085342407227,0.9554992914199829,-0.028640955686569214,-0.41967734694480896]},\"left_gripper\":{\"position\":[0.6473405157977885],\"velocity\":[1.9632707942615857]},\"right_arm\":{\"position\":[0.01240176148712635,1.8635932207107544,1.3279516696929932,-1.0523301362991333,-0.002492120722308755,-0.16756686568260193],\"velocity\":[0.9300477504730225,0.4109690189361572,0.5477051734924316,-0.11741548031568527,-0.05728531628847122,-0.034550949931144714]},\"right_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.06072843785990368]}},\n \"time_s\": 10.16,\n \"file\": [\"/tmp/kinex-observation-r9kvl_ew.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "1b795bda", "role": "assistant", "timestamp": "2026-10-28T21:52:04.535Z", "parts": [{"type": "reasoning", "text": "**Inspecting shoes at right**"}, {"type": "tool", "callID": "call_7f2ab1ec14b54db2a9ea98a47715c750|fc_0b5218887c37459e016ae26e8146b8819f86c4e28078c7626e", "name": "exec", "input": {"file": ".kinex/tools/dual_shoes_policy.py", "description": "recover from lost grasp", "timeout_s": 180}, "output": "{\n \"output\": \"[INFO] [1793224326.069034591] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.7760891914367676,1.900067925453186,1.2369452714920044,-0.7888695597648621,0.012184467166662216,0.5715698003768921],\"velocity\":[0.5874661803245544,-1.2688400745391846,-1.1548991203308105,0.9930360913276672,-0.058601319789886475,-0.37943756580352783]},\"left_gripper\":{\"position\":[0.23286564004692165],\"velocity\":[3.2529833641919224]},\"right_arm\":{\"position\":[-0.04621698707342148,1.8618661165237427,1.3253315687179565,-1.070886492729187,0.0016111346194520593,-0.17026476562023163],\"velocity\":[0.5801291465759277,0.2907960116863251,0.4152977764606476,-0.12846273183822632,-0.05734775960445404,-0.23737221956253052]},\"right_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.08739067918875]}},\n \"time_s\": 10.8,\n \"file\": [\"/tmp/kinex-observation-2l0llko8.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 36413, "outputTokens": 159, "cachedInputTokens": 35712, "cacheReportedInputTokens": 36413, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 36413, "reasoningOutputTokens": 119, "reasoningReportedOutputTokens": 159}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "9ec9a799", "role": "toolResult", "timestamp": "2026-10-28T21:52:12.246Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224326.069034591] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"actions_submitted\": 16,\n \"duration_s\": 0.64,\n \"ik_failure\": null,\n \"measured\": {\"left_arm\":{\"position\":[-0.7760891914367676,1.900067925453186,1.2369452714920044,-0.7888695597648621,0.012184467166662216,0.5715698003768921],\"velocity\":[0.5874661803245544,-1.2688400745391846,-1.1548991203308105,0.9930360913276672,-0.058601319789886475,-0.37943756580352783]},\"left_gripper\":{\"position\":[0.23286564004692165],\"velocity\":[3.2529833641919224]},\"right_arm\":{\"position\":[-0.04621698707342148,1.8618661165237427,1.3253315687179565,-1.070886492729187,0.0016111346194520593,-0.17026476562023163],\"velocity\":[0.5801291465759277,0.2907960116863251,0.4152977764606476,-0.12846273183822632,-0.05734775960445404,-0.23737221956253052]},\"right_gripper\":{\"position\":[0.18181818181818182],\"velocity\":[-0.08739067918875]}},\n \"time_s\": 10.8,\n \"file\": [\"/tmp/kinex-observation-2l0llko8.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "4b066c48", "role": "assistant", "timestamp": "2026-10-28T21:52:27.776Z", "parts": [{"type": "reasoning", "text": "**Positioning wrist cameras**"}, {"type": "tool", "callID": "call_585641e182404aeea06e5b09b0738ecf|fc_0b5218887c37459e016ae26e9ba650819fa38d8b87a3636577", "name": "device_stop", "input": {"reason": "Policy is no longer maintaining shoe grasps; hold arms for geometric inspection."}, "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": 37077, "outputTokens": 106, "cachedInputTokens": 36352, "cacheReportedInputTokens": 37077, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 37077, "reasoningOutputTokens": 72, "reasoningReportedOutputTokens": 106}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "16a7f27b", "role": "toolResult", "timestamp": "2026-10-28T21:52:27.853Z", "parts": [{"type": "text", "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"}]}, {"id": "95190790", "role": "assistant", "timestamp": "2026-10-28T21:52:31.562Z", "parts": [{"type": "tool", "callID": "call_1143c77fb8be4a6abb8b6e4575522c40|fc_0b5218887c37459e016ae26e9f1d08819f922f07fea76fd76c", "name": "devices", "input": {"calls": [{"deviceID": "right_camera", "action": "image"}, {"deviceID": "left_camera", "action": "image"}]}, "output": "{\n \"results\": [\n {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":10.8,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dvobb360.png\"},\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":10.8,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-v8oio2c_.png\"}\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 37245, "outputTokens": 37, "cachedInputTokens": 36992, "cacheReportedInputTokens": 37245, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 37245, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 37}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "cfb787c6", "role": "toolResult", "timestamp": "2026-10-28T21:52:31.893Z", "parts": [{"type": "text", "text": "{\n \"results\": [\n {\"deviceID\":\"right_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":10.8,\"frame_id\":\"right_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-dvobb360.png\"},\n {\"deviceID\":\"left_camera\",\"action\":\"image\",\"ok\":true,\"data\":{\"timestamp\":10.8,\"frame_id\":\"left_camera/base\",\"width\":320,\"height\":240},\"file\":\"/tmp/kinex-observation-v8oio2c_.png\"}\n ]\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "676e1b73", "role": "assistant", "timestamp": "2026-10-28T21:52:38.162Z", "parts": [{"type": "reasoning", "text": "**Checking shoe position**"}, {"type": "tool", "callID": "call_f659d0456d8948aaa2dd289b53c4e0b2|fc_0b5218887c37459e016ae26ea464a4819f92a7b7aa6895bff8", "name": "exec", "input": {"description": "inspect gripper poses and visible shoe depth", "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n poses={s:await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}) for s in ['left','right']}\n frame=await helpers.camera_frame(io,'right_camera')\n finish({'poses':poses,'frame_keys':list(frame)})\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224359.682511338] [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 8, 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 7, in main\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/__init__.py\\\", line 98, in finish\\n result = plain(result)\\n ^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 12, in plain\\n return {key: plain(item) for key, item in value.items()}\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 14, in plain\\n return [plain(item) for item in value]\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 19, in plain\\n raise TypeError(\\\"images are not data; return them with finish(file=helpers.save_image(image))\\\")\\nTypeError: images are not data; return them with finish(file=helpers.save_image(image))\"\n}", "isError": true, "metadata": {}}], "usage": {"inputTokens": 37606, "outputTokens": 152, "cachedInputTokens": 37120, "cacheReportedInputTokens": 37606, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 37606, "reasoningOutputTokens": 22, "reasoningReportedOutputTokens": 152}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "779def20", "role": "toolResult", "timestamp": "2026-10-28T21:52:40.820Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224359.682511338] [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 8, 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 7, in main\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/__init__.py\\\", line 98, in finish\\n result = plain(result)\\n ^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 12, in plain\\n return {key: plain(item) for key, item in value.items()}\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 14, in plain\\n return [plain(item) for item in value]\\n ^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/plain.py\\\", line 19, in plain\\n raise TypeError(\\\"images are not data; return them with finish(file=helpers.save_image(image))\\\")\\nTypeError: images are not data; return them with finish(file=helpers.save_image(image))\"\n}"}]}, {"id": "18061e3f", "role": "assistant", "timestamp": "2026-10-28T21:52:45.319Z", "parts": [{"type": "tool", "callID": "call_bf701aaf00e144f99c654465d20fa7d1|fc_0b5218887c37459e016ae26eace220819fa5e3bf1ee2b54732", "name": "read", "input": {"path": "/opt/kinex/packages/core/assets/python/kinex/helpers/images.py", "offset": 1, "limit": 150}, "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": 38344, "outputTokens": 39, "cachedInputTokens": 37632, "cacheReportedInputTokens": 38344, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 38344, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 39}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "af46c091", "role": "toolResult", "timestamp": "2026-10-28T21:52:45.323Z", "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": "3d43df48", "role": "assistant", "timestamp": "2026-10-28T21:52:53.367Z", "parts": [{"type": "tool", "callID": "call_29f165f826c5442a94f4599d8b7313e2|fc_0b5218887c37459e016ae26eb18030819f91651be63562500d", "name": "exec", "input": {"description": "measure shoe surface coordinates", "python": "import asyncio,numpy as np\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n poses={s:await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}) for s in ['left','right']}\n points={}\n for cam,pixels in [('head_camera',[(315,160),(175,200),(175,170)]),('right_camera',[(270,100),(270,180),(250,40)])]:\n im,dep=await helpers.camera_frame(io,cam); a=helpers.image_array(dep)\n for u,v in pixels:\n z=float(a[v,u])*.001\n p=[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1]\n points[f'{cam}:{u},{v}']=await helpers.transform_pose(io,p,cam+'/base','world/base',im.header.stamp)\n finish({'poses':poses,'points':points})\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224374.798465320] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"poses\": {\"left\":{\"pose\":[-0.04576036873904488,-0.1518396196357057,0.8105108280162003,-0.06397159671261682,0.6655944712893721,0.1116262315077717,0.735140271677748],\"timestamp\":10.8},\"right\":{\"pose\":[0.3158530877393279,-0.0837637020994616,0.8411548580346625,0.5416649392500444,-0.4740655883365559,-0.5247640136212739,-0.45440471118866305],\"timestamp\":10.8}},\n \"points\": {\"head_camera:315,160\":[0.3105280301800081,-0.047369169413844336,0.6644731372869409,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,200\":[0.0007164514080967438,-0.13173156963338484,0.6424513366211899,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,170\":[0.0025748496589661773,-0.06011766118549816,0.6424117683126856,-0.9486832972651138,0,0,0.3162277683730379],\"right_camera:270,100\":[0.3420385028964361,-0.0843225323833221,0.7120579827021101,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:270,180\":[0.3485558820624562,-0.12394106388230323,0.7274574502446138,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:250,40\":[0.3286115784257454,-0.057996926368941285,0.6946765327046098,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335]}\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 38615, "outputTokens": 269, "cachedInputTokens": 38272, "cacheReportedInputTokens": 38615, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 38615, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 269}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "212cfb7d", "role": "toolResult", "timestamp": "2026-10-28T21:52:55.537Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224374.798465320] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"poses\": {\"left\":{\"pose\":[-0.04576036873904488,-0.1518396196357057,0.8105108280162003,-0.06397159671261682,0.6655944712893721,0.1116262315077717,0.735140271677748],\"timestamp\":10.8},\"right\":{\"pose\":[0.3158530877393279,-0.0837637020994616,0.8411548580346625,0.5416649392500444,-0.4740655883365559,-0.5247640136212739,-0.45440471118866305],\"timestamp\":10.8}},\n \"points\": {\"head_camera:315,160\":[0.3105280301800081,-0.047369169413844336,0.6644731372869409,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,200\":[0.0007164514080967438,-0.13173156963338484,0.6424513366211899,-0.9486832972651138,0,0,0.3162277683730379],\"head_camera:175,170\":[0.0025748496589661773,-0.06011766118549816,0.6424117683126856,-0.9486832972651138,0,0,0.3162277683730379],\"right_camera:270,100\":[0.3420385028964361,-0.0843225323833221,0.7120579827021101,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:270,180\":[0.3485558820624562,-0.12394106388230323,0.7274574502446138,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335],\"right_camera:250,40\":[0.3286115784257454,-0.057996926368941285,0.6946765327046098,0.9739351910892778,0.0678785002499696,0.012351526088398351,0.21607913495984335]}\n }\n}"}]}, {"id": "23c278ae", "role": "assistant", "timestamp": "2026-10-28T21:53:11.085Z", "parts": [{"type": "reasoning", "text": "**Repositioning camera toward shoe**\n\n**Preparing gripper alignment**"}, {"type": "tool", "callID": "call_69cb68b39b0b434fb5973109b24a4381|fc_0b5218887c37459e016ae26ec31f00819fa910bf9279b5ea66", "name": "read", "input": {"path": ".kinex/devices/right_gripper/gripper.urdf", "offset": 1, "limit": 180}, "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": {}}], "usage": {"inputTokens": 39478, "outputTokens": 238, "cachedInputTokens": 38784, "cacheReportedInputTokens": 39478, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 39478, "reasoningOutputTokens": 199, "reasoningReportedOutputTokens": 238}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "48632ce5", "role": "toolResult", "timestamp": "2026-10-28T21:53:11.087Z", "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": "e6e6cdab", "role": "assistant", "timestamp": "2026-10-28T21:53:24.544Z", "parts": [{"type": "reasoning", "text": "**Estimating shoe pose**"}, {"type": "tool", "callID": "call_f214bab6552e4fac894e29af38414f0d|fc_0b5218887c37459e016ae26ecdef10819f94cee84862a9979c", "name": "write", "input": {"path": ".kinex/tools/cartesian_segment.py", "content": "\"\"\"Bounded world-TCP moves using calibrated transforms and measured seeds.\"\"\"\nimport asyncio\nfrom kinex import args, communication, helpers, finish\nfrom kinex.communication import duration\n\nasync def read(io, op):\n with io.subscribe(op, depth=1) as stream:\n return await stream.next(timeout_s=10)\n\nasync def main():\n opts=args(); seconds=opts.get('duration_s',1.0)\n async with communication() as io:\n commands=[]\n for side,goal in opts.get('goals',{}).items():\n arm=side+'_arm'; model=helpers.joint_model(arm)\n state=await read(io,arm+'.joint_state'); q=list(state.position)\n if 'joints' in goal: end=goal['joints']\n else:\n current=await helpers.transform_pose(io,model.fk(q),arm+'/base','world/base')\n tcp=await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'})\n target=goal.get('pose',tcp['pose']).copy()\n if 'delta' in goal:\n target[:3]=[a+b for a,b in zip(target[:3],goal['delta'])]\n # TCP is 0.12 m along flange +x.\n from scipy.spatial.transform import Rotation\n off=Rotation.from_quat(target[3:]).apply([.12,0,0])\n target[:3]=[a-b for a,b in zip(target[:3],off)]\n local=await helpers.transform_pose(io,target,'world/base',arm+'/base')\n end=model.ik(local,q)\n commands.append((arm+'.joint_target',{'joint_names':model.names,'points':[\n {'positions':q,'time_from_start':duration(.04)},\n {'positions':end,'time_from_start':duration(seconds)}]}))\n for side,value in opts.get('grips',{}).items():\n commands.append((side+'_gripper.target',{'data':value}))\n await asyncio.gather(*(io.publish(op,payload) for op,payload in commands))\n await io.request('world.wait',duration_s=seconds)\n states={d:list((await read(io,d+'.joint_state')).position) for d in ['left_arm','left_gripper','right_arm','right_gripper']}\n paths=[helpers.save_image(await read(io,c+'.image')) for c in opts.get('cameras',['head_camera'])]\n finish({'measured':states},file=paths)\n\nasyncio.run(main())\n"}, "output": "Successfully wrote to .kinex/tools/cartesian_segment.py", "isError": false, "metadata": {}}], "usage": {"inputTokens": 40182, "outputTokens": 672, "cachedInputTokens": 39680, "cacheReportedInputTokens": 40182, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 40182, "reasoningOutputTokens": 53, "reasoningReportedOutputTokens": 672}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "d7ca3c2f", "role": "toolResult", "timestamp": "2026-10-28T21:53:24.547Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/tools/cartesian_segment.py"}]}, {"id": "bacd7459", "role": "assistant", "timestamp": "2026-10-28T21:53:30.996Z", "parts": [{"type": "tool", "callID": "call_1dda0b3177e74e1891be115c85009f3c|fc_0b5218887c37459e016ae26ed8cfb0819f9b33fcffb6845c21", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "clear arms and inspect displaced shoes", "args": {"goals": {"right": {"delta": [0, 0, 0.2]}, "left": {"joints": [-0.23377, 2.10649, 1.59874, -1.06015, -0.01728, -0.65544]}}, "grips": {"left": 1, "right": 1}, "duration_s": 1, "cameras": ["head_camera", "right_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224412.462059980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.34138405323028564,2.0631067752838135,1.5276085138320923,-1.006088376045227,-0.01025577262043953,-0.4240250289440155],\"left_gripper\":[0.9917404543269764],\"right_arm\":[-0.0485311821103096,1.9509698152542114,2.119028091430664,-1.7710597515106201,0.0023190497886389494,-0.17672263085842133],\"right_gripper\":[0.984824080358852]},\n \"file\": [\"/tmp/kinex-observation-umnt4jud.png\",\"/tmp/kinex-observation-tqaq0u11.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 40875, "outputTokens": 117, "cachedInputTokens": 40704, "cacheReportedInputTokens": 40875, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 40875, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 117}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "2335c3b2", "role": "toolResult", "timestamp": "2026-10-28T21:53:39.781Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224412.462059980] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.34138405323028564,2.0631067752838135,1.5276085138320923,-1.006088376045227,-0.01025577262043953,-0.4240250289440155],\"left_gripper\":[0.9917404543269764],\"right_arm\":[-0.0485311821103096,1.9509698152542114,2.119028091430664,-1.7710597515106201,0.0023190497886389494,-0.17672263085842133],\"right_gripper\":[0.984824080358852]},\n \"file\": [\"/tmp/kinex-observation-umnt4jud.png\",\"/tmp/kinex-observation-tqaq0u11.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "50af7cee", "role": "assistant", "timestamp": "2026-10-28T21:53:54.394Z", "parts": [{"type": "reasoning", "text": "**Scanning foreground for shoe**"}, {"type": "tool", "callID": "call_154911b1bf974eb3b49061fda0895957|fc_0b5218887c37459e016ae26ef10960819f8bd1d9979b84a4ab", "name": "device", "input": {"deviceID": "left_camera", "action": "image"}, "output": "{\n \"deviceID\": \"left_camera\",\n \"timestamp\": 11.8,\n \"frame_id\": \"left_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-r0rh5wg1.png\"\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 41422, "outputTokens": 151, "cachedInputTokens": 40832, "cacheReportedInputTokens": 41422, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 41422, "reasoningOutputTokens": 126, "reasoningReportedOutputTokens": 151}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "1202e4b6", "role": "toolResult", "timestamp": "2026-10-28T21:53:54.552Z", "parts": [{"type": "text", "text": "{\n \"deviceID\": \"left_camera\",\n \"timestamp\": 11.8,\n \"frame_id\": \"left_camera/base\",\n \"width\": 320,\n \"height\": 240,\n \"file\": \"/tmp/kinex-observation-r0rh5wg1.png\"\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "056c2182", "role": "assistant", "timestamp": "2026-10-28T21:54:05.543Z", "parts": [{"type": "reasoning", "text": "**Preparing right shoe grasp**"}, {"type": "tool", "callID": "call_6aa81a8a753445fa80dbc6b1887899b6|fc_0b5218887c37459e016ae26efab2b0819fa0c67090db3118df", "name": "exec", "input": {"description": "measure right shoe grasp and box geometry", "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\n for u,v in [(220,25),(220,65),(220,130)]:\n z=float(a[v,u])*.001\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base',im.header.stamp)\n poses={s:(await helpers.relative_pose(io,{'frame':s+'_gripper:tcp'},{'frame':'world:base'}))['pose'] for s in ['left','right']}\n finish({'points':pts,'tcp':poses})\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224446.993372814] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": {\"(220, 25)\":[0.33912656027719457,-0.06270945464371132,0.699152728908267,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 65)\":[0.34366378336074566,-0.0970220567701838,0.7184123702623861,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 130)\":[0.369180866644661,-0.185229663768754,0.6765957202615971,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145]},\n \"tcp\": {\"left\":[-0.1764191407332653,-0.039818355705945996,0.8221104464598411,-0.5187487037504172,0.46380859131529467,0.5349435100330139,0.4791834868324896],\"right\":[0.3142798555779541,-0.07013205333858658,1.0078345324518556,0.5420236400350569,-0.471363514483094,-0.5270262727561408,-0.45416970251444916]}\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 41743, "outputTokens": 314, "cachedInputTokens": 41472, "cacheReportedInputTokens": 41743, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 41743, "reasoningOutputTokens": 81, "reasoningReportedOutputTokens": 314}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "fed2c8ad", "role": "toolResult", "timestamp": "2026-10-28T21:54:07.704Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224446.993372814] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"points\": {\"(220, 25)\":[0.33912656027719457,-0.06270945464371132,0.699152728908267,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 65)\":[0.34366378336074566,-0.0970220567701838,0.7184123702623861,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145],\"(220, 130)\":[0.369180866644661,-0.185229663768754,0.6765957202615971,0.9742144348625146,0.07054615050136454,0.01317976945776365,0.2139059822258145]},\n \"tcp\": {\"left\":[-0.1764191407332653,-0.039818355705945996,0.8221104464598411,-0.5187487037504172,0.46380859131529467,0.5349435100330139,0.4791834868324896],\"right\":[0.3142798555779541,-0.07013205333858658,1.0078345324518556,0.5420236400350569,-0.471363514483094,-0.5270262727561408,-0.45416970251444916]}\n }\n}"}]}, {"id": "4a6886aa", "role": "assistant", "timestamp": "2026-10-28T21:54:16.362Z", "parts": [{"type": "reasoning", "text": "**Checking right-shoe grip height**"}, {"type": "tool", "callID": "call_45e0314da90347b3a18bf075d2d65a4b|fc_0b5218887c37459e016ae26f081b14819fa6d2a75446efd05d", "name": "exec", "input": {"description": "derive proven grasp pose", "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n q=[.1836943,2.449954,1.6583215,-.7642399,.0036793,.2217132]\n p=helpers.joint_model('right_arm').fk(q)\n from scipy.spatial.transform import Rotation as R\n p[:3]=(p[:3]+R.from_quat(p[3:]).apply([.12,0,0])).tolist()\n p=await helpers.transform_pose(io,p,'right_arm/base','world/base')\n finish(p)\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224457.863503532] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n 0.21555207762772288,\n 0.019347892345493667,\n 0.6978195099953525,\n -0.4912582223427763,\n 0.5016447242922935,\n 0.5008975819995253,\n 0.5060825445689198\n ]\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 42462, "outputTokens": 248, "cachedInputTokens": 41856, "cacheReportedInputTokens": 42462, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 42462, "reasoningOutputTokens": 79, "reasoningReportedOutputTokens": 248}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "d7d7d2b7", "role": "toolResult", "timestamp": "2026-10-28T21:54:18.870Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224457.863503532] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": [\n 0.21555207762772288,\n 0.019347892345493667,\n 0.6978195099953525,\n -0.4912582223427763,\n 0.5016447242922935,\n 0.5008975819995253,\n 0.5060825445689198\n ]\n}"}]}, {"id": "ef0b2d37", "role": "assistant", "timestamp": "2026-10-28T21:54:32.690Z", "parts": [{"type": "reasoning", "text": "**Positioning for right shoe**"}, {"type": "tool", "callID": "call_d443b9d4d47142c481adf1611a60a24e|fc_0b5218887c37459e016ae26f15b024819faa44c5f948ba02ae", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "align right grasp and survey near side", "args": {"goals": {"right": {"pose": [0.344, -0.09, 0.82, -0.5, 0.5, 0.5, 0.5]}, "left": {"pose": [0.12, -0.28, 0.98, 0, 0.70710678, 0, 0.70710678]}}, "grips": {"left": 1, "right": 1}, "duration_s": 1.2, "cameras": ["left_camera", "right_camera", "head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224474.085063475] [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_segment.py\\\", line 41, 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_segment.py\\\", line 29, in main\\n end=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=410, elapsed_ms=100.172, position_error_m=0.004569528253270413, rotation_error_rad=0.00035777856102559653\"\n}", "isError": true, "metadata": {}}], "usage": {"inputTokens": 42845, "outputTokens": 316, "cachedInputTokens": 42624, "cacheReportedInputTokens": 42845, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 42845, "reasoningOutputTokens": 177, "reasoningReportedOutputTokens": 316}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "9c169656", "role": "toolResult", "timestamp": "2026-10-28T21:54:35.336Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224474.085063475] [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_segment.py\\\", line 41, 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_segment.py\\\", line 29, in main\\n end=model.ik(local,q)\\n ^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/joints.py\\\", line 54, in ik\\n values.update(self.chain(link).ik(pose(target), values, {**DEFAULTS, **options}))\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 231, in ik\\n solved = search(self.joints, options, [target], [seed[joint[\\\"name\\\"]] for joint in self.joints],\\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\\n File \\\"/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py\\\", line 156, in search\\n raise ValueError(\\nValueError: IK search exhausted (time budget); seeds_attempted=5, iterations=410, elapsed_ms=100.172, position_error_m=0.004569528253270413, rotation_error_rad=0.00035777856102559653\"\n}"}]}, {"id": "c2cf6866", "role": "assistant", "timestamp": "2026-10-28T21:54:41.260Z", "parts": [{"type": "reasoning", "text": "**Reading solver defaults**"}, {"type": "tool", "callID": "call_1898ea06c50445418f6c0a4b5d2cbd22|fc_0b5218887c37459e016ae26f211c70819fb513db13c372e3a5", "name": "grep", "input": {"pattern": "DEFAULTS|max_|timeout|budget", "path": "/opt/kinex/packages/core/assets/python/kinex/lib/kinematics.py", "limit": 30}, "output": "kinematics.py:15: DEFAULTS = {\"position_tolerance_m\": 1e-4, \"rotation_tolerance_rad\": 1e-3, \"max_iterations\": 200, \"damping\": .01}\nkinematics.py:50: DEFAULT_BUDGET = {\"max_attempts\": 5, \"attempt_time_s\": 0.02, \"max_time_s\": 0.1}\nkinematics.py:53: def budget_options(options):\nkinematics.py:54: search = options.get(\"budget\", {})\nkinematics.py:56: raise ValueError(\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\")\nkinematics.py:58: attempts = result[\"max_attempts\"]\nkinematics.py:60: raise ValueError(\"IK budget.max_attempts must be a positive integer\")\nkinematics.py:61: for key in (\"attempt_time_s\", \"max_time_s\"):\nkinematics.py:62: if number(result[key], f\"IK budget.{key}\") <= 0:\nkinematics.py:63: raise ValueError(f\"IK budget.{key} must be positive\")\nkinematics.py:127: \"\"\"Select the nearest verified solution within the existing search budget.\"\"\"\nkinematics.py:128: settings = budget_options(options)\nkinematics.py:131: deadline = started + settings[\"max_time_s\"]\nkinematics.py:134: for seed in seeds(joints, targets, original, settings[\"max_attempts\"]):\nkinematics.py:153: reason = \"time budget\" if monotonic() >= deadline else \"attempt budget\"\nkinematics.py:204: for _ in range(options[\"max_iterations\"]):\nkinematics.py:222: positions by name, within the search budget.\"\"\"\nkinematics.py:225: iterations = options[\"max_iterations\"]\nkinematics.py:227: raise ValueError(\"IK max_iterations must be a positive integer\")", "isError": false, "metadata": {}}], "usage": {"inputTokens": 43844, "outputTokens": 71, "cachedInputTokens": 43008, "cacheReportedInputTokens": 43844, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 43844, "reasoningOutputTokens": 20, "reasoningReportedOutputTokens": 71}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "64693190", "role": "toolResult", "timestamp": "2026-10-28T21:54:41.285Z", "parts": [{"type": "text", "text": "kinematics.py:15: DEFAULTS = {\"position_tolerance_m\": 1e-4, \"rotation_tolerance_rad\": 1e-3, \"max_iterations\": 200, \"damping\": .01}\nkinematics.py:50: DEFAULT_BUDGET = {\"max_attempts\": 5, \"attempt_time_s\": 0.02, \"max_time_s\": 0.1}\nkinematics.py:53: def budget_options(options):\nkinematics.py:54: search = options.get(\"budget\", {})\nkinematics.py:56: raise ValueError(\"IK budget requires max_attempts, attempt_time_s and max_time_s options only\")\nkinematics.py:58: attempts = result[\"max_attempts\"]\nkinematics.py:60: raise ValueError(\"IK budget.max_attempts must be a positive integer\")\nkinematics.py:61: for key in (\"attempt_time_s\", \"max_time_s\"):\nkinematics.py:62: if number(result[key], f\"IK budget.{key}\") <= 0:\nkinematics.py:63: raise ValueError(f\"IK budget.{key} must be positive\")\nkinematics.py:127: \"\"\"Select the nearest verified solution within the existing search budget.\"\"\"\nkinematics.py:128: settings = budget_options(options)\nkinematics.py:131: deadline = started + settings[\"max_time_s\"]\nkinematics.py:134: for seed in seeds(joints, targets, original, settings[\"max_attempts\"]):\nkinematics.py:153: reason = \"time budget\" if monotonic() >= deadline else \"attempt budget\"\nkinematics.py:204: for _ in range(options[\"max_iterations\"]):\nkinematics.py:222: positions by name, within the search budget.\"\"\"\nkinematics.py:225: iterations = options[\"max_iterations\"]\nkinematics.py:227: raise ValueError(\"IK max_iterations must be a positive integer\")"}]}, {"id": "297d3ddb", "role": "assistant", "timestamp": "2026-10-28T21:54:45.527Z", "parts": [{"type": "tool", "callID": "call_79c3154ed59947fd8987bd6bbc0d8430|fc_0b5218887c37459e016ae26f24856c819fb3f53b874a8c03d4", "name": "edit", "input": {"path": ".kinex/tools/cartesian_segment.py", "edits": [{"oldText": "end=model.ik(local,q)", "newText": "end=model.ik(local,q,budget={'max_attempts':12,'attempt_time_s':0.1,'max_time_s':1.2})"}]}, "output": "Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py.", "isError": false, "metadata": {}}], "usage": {"inputTokens": 44351, "outputTokens": 75, "cachedInputTokens": 43776, "cacheReportedInputTokens": 44351, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 44351, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 75}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "ada27295", "role": "toolResult", "timestamp": "2026-10-28T21:54:45.533Z", "parts": [{"type": "text", "text": "Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py."}]}, {"id": "f1234d2f", "role": "assistant", "timestamp": "2026-10-28T21:54:50.313Z", "parts": [{"type": "tool", "callID": "call_e06b39882ea4416382986dcd89c24d40|fc_0b5218887c37459e016ae26f284e64819fa920b9f3e8cc677d", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "align right grasp and inspect near side", "args": {"goals": {"right": {"pose": [0.344, -0.09, 0.82, -0.5, 0.5, 0.5, 0.5]}, "left": {"pose": [0.04, -0.25, 0.94, 0, 0.70710678, 0, 0.70710678]}}, "grips": {"left": 1, "right": 1}, "duration_s": 1.2, "cameras": ["left_camera", "right_camera", "head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224491.855424014] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.007556676864624,1.9853966236114502,1.7999980449676514,-1.3873368501663208,-0.0012377145467326045,0.3198549747467041],\"left_gripper\":[1.0055662935430354],\"right_arm\":[-0.11214446276426315,1.8662967681884766,1.3592849969863892,-1.0733658075332642,0.0006734680500812829,-0.12759973108768463],\"right_gripper\":[0.9959410239349712]},\n \"file\": [\"/tmp/kinex-observation-oewipf9p.png\",\"/tmp/kinex-observation-n29q4c9q.png\",\"/tmp/kinex-observation-bxtc_9a1.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 44453, "outputTokens": 137, "cachedInputTokens": 44288, "cacheReportedInputTokens": 44453, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 44453, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 137}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "5469803c", "role": "toolResult", "timestamp": "2026-10-28T21:54:59.252Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224491.855424014] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.007556676864624,1.9853966236114502,1.7999980449676514,-1.3873368501663208,-0.0012377145467326045,0.3198549747467041],\"left_gripper\":[1.0055662935430354],\"right_arm\":[-0.11214446276426315,1.8662967681884766,1.3592849969863892,-1.0733658075332642,0.0006734680500812829,-0.12759973108768463],\"right_gripper\":[0.9959410239349712]},\n \"file\": [\"/tmp/kinex-observation-oewipf9p.png\",\"/tmp/kinex-observation-n29q4c9q.png\",\"/tmp/kinex-observation-bxtc_9a1.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "1c836c4a", "role": "assistant", "timestamp": "2026-10-28T21:55:12.031Z", "parts": [{"type": "reasoning", "text": "**Scanning for left shoe**\n\n**Lowering right arm**"}, {"type": "tool", "callID": "call_803c44e50f5049f9a6b17c9f83345063|fc_0b5218887c37459e016ae26f3c7bbc819fb55e9269c34f262c", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "descend to right shoe and scan front edge", "args": {"goals": {"right": {"pose": [0.344, -0.09, 0.699, -0.5, 0.5, 0.5, 0.5]}, "left": {"pose": [0.12, -0.4, 0.87, 0, 0.70710678, 0, 0.70710678]}}, "duration_s": 0.8, "cameras": ["left_camera", "right_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224513.394437567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.421788215637207,2.089327096939087,1.8129761219024658,-1.295375943183899,0.0016572437016293406,0.11011556535959244],\"left_gripper\":[1.0041607076471502],\"right_arm\":[-0.11952149868011475,2.052452564239502,1.1140962839126587,-0.6284643411636353,0.0016702237771824002,-0.11548030376434326],\"right_gripper\":[0.997136029330167]},\n \"file\": [\"/tmp/kinex-observation-ocl_jdlh.png\",\"/tmp/kinex-observation-yq84hi1q.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 45140, "outputTokens": 271, "cachedInputTokens": 44416, "cacheReportedInputTokens": 45140, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 45140, "reasoningOutputTokens": 144, "reasoningReportedOutputTokens": 271}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "0633ed76", "role": "toolResult", "timestamp": "2026-10-28T21:55:18.074Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224513.394437567] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.421788215637207,2.089327096939087,1.8129761219024658,-1.295375943183899,0.0016572437016293406,0.11011556535959244],\"left_gripper\":[1.0041607076471502],\"right_arm\":[-0.11952149868011475,2.052452564239502,1.1140962839126587,-0.6284643411636353,0.0016702237771824002,-0.11548030376434326],\"right_gripper\":[0.997136029330167]},\n \"file\": [\"/tmp/kinex-observation-ocl_jdlh.png\",\"/tmp/kinex-observation-yq84hi1q.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "483cc450", "role": "assistant", "timestamp": "2026-10-28T21:55:25.840Z", "parts": [{"type": "reasoning", "text": "**Grasping right shoe**"}, {"type": "tool", "callID": "call_49dadea9fd994b87b00486b57cfbacfa|fc_0b5218887c37459e016ae26f4c61a0819f9c85904c6cc992dc", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "close right gripper on shoe", "args": {"grips": {"right": 0}, "duration_s": 0.28, "cameras": ["right_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224527.355764551] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5191649198532104,2.114835500717163,1.817434310913086,-1.2732782363891602,0.0029058295767754316,0.061535708606243134],\"left_gripper\":[0.998820808800784],\"right_arm\":[-0.12290065735578537,2.0888092517852783,1.0617690086364746,-0.5257982015609741,0.002643134444952011,-0.11187005788087845],\"right_gripper\":[0.8180002976547588]},\n \"file\": [\"/tmp/kinex-observation-bbmzia4v.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 45842, "outputTokens": 107, "cachedInputTokens": 45184, "cacheReportedInputTokens": 45842, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 45842, "reasoningOutputTokens": 49, "reasoningReportedOutputTokens": 107}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "85d47354", "role": "toolResult", "timestamp": "2026-10-28T21:55:28.887Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224527.355764551] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5191649198532104,2.114835500717163,1.817434310913086,-1.2732782363891602,0.0029058295767754316,0.061535708606243134],\"left_gripper\":[0.998820808800784],\"right_arm\":[-0.12290065735578537,2.0888092517852783,1.0617690086364746,-0.5257982015609741,0.002643134444952011,-0.11187005788087845],\"right_gripper\":[0.8180002976547588]},\n \"file\": [\"/tmp/kinex-observation-bbmzia4v.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "7a85c529", "role": "assistant", "timestamp": "2026-10-28T21:55:36.807Z", "parts": [{"type": "reasoning", "text": "**Preparing pickup lift**"}, {"type": "tool", "callID": "call_28fb1a41149546689e7c9d04594749ac|fc_0b5218887c37459e016ae26f5895b8819f8e960cac4bb244ef", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "lift recovered right shoe", "args": {"goals": {"right": {"delta": [0, 0, 0.18]}}, "grips": {"right": 0}, "duration_s": 0.8, "cameras": ["right_camera", "head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224538.386130877] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5487600564956665,2.125786781311035,1.8208038806915283,-1.263669729232788,0.00247021927498281,0.044266678392887115],\"left_gripper\":[0.9992801709608599],\"right_arm\":[-0.12398703396320343,1.8660025596618652,1.3495227098464966,-1.042709231376648,0.0013285059249028563,-0.11811474710702896],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-0idhe3ah.png\",\"/tmp/kinex-observation-v16xz1o9.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 46269, "outputTokens": 119, "cachedInputTokens": 45824, "cacheReportedInputTokens": 46269, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 46269, "reasoningOutputTokens": 43, "reasoningReportedOutputTokens": 119}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "bf55277f", "role": "toolResult", "timestamp": "2026-10-28T21:55:43.381Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224538.386130877] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5487600564956665,2.125786781311035,1.8208038806915283,-1.263669729232788,0.00247021927498281,0.044266678392887115],\"left_gripper\":[0.9992801709608599],\"right_arm\":[-0.12398703396320343,1.8660025596618652,1.3495227098464966,-1.042709231376648,0.0013285059249028563,-0.11811474710702896],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-0idhe3ah.png\",\"/tmp/kinex-observation-v16xz1o9.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "affad71f", "role": "assistant", "timestamp": "2026-10-28T21:56:00.050Z", "parts": [{"type": "reasoning", "text": "**Lowering gripper toward shoe**\n\n**Lowering over shoe toe**"}, {"type": "tool", "callID": "call_dd4740028dd144718eada8f158e25bc7|fc_0b5218887c37459e016ae26f6fe5e8819f911ac0c1f3e27b47", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "reposition grasp to shoe forefoot", "args": {"goals": {"right": {"pose": [0.333, -0.025, 0.678, -0.5, 0.5, 0.5, 0.5]}}, "grips": {"right": 1}, "duration_s": 0.8, "cameras": ["right_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224561.581901781] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5512616634368896,2.1280250549316406,1.8211685419082642,-1.2633064985275269,0.0024033302906900644,0.043757226318120956],\"left_gripper\":[0.9994734796610746],\"right_arm\":[-0.10064993053674698,2.227466106414795,1.3507928848266602,-0.6901805996894836,0.0034024433698505163,-0.07860491424798965],\"right_gripper\":[0.9747358587655155]},\n \"file\": [\"/tmp/kinex-observation-0me27shi.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 46820, "outputTokens": 363, "cachedInputTokens": 46336, "cacheReportedInputTokens": 46820, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 46820, "reasoningOutputTokens": 267, "reasoningReportedOutputTokens": 363}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "eca65601", "role": "toolResult", "timestamp": "2026-10-28T21:56:06.136Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224561.581901781] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5512616634368896,2.1280250549316406,1.8211685419082642,-1.2633064985275269,0.0024033302906900644,0.043757226318120956],\"left_gripper\":[0.9994734796610746],\"right_arm\":[-0.10064993053674698,2.227466106414795,1.3507928848266602,-0.6901805996894836,0.0034024433698505163,-0.07860491424798965],\"right_gripper\":[0.9747358587655155]},\n \"file\": [\"/tmp/kinex-observation-0me27shi.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "c1b4da08", "role": "assistant", "timestamp": "2026-10-28T21:56:13.238Z", "parts": [{"type": "reasoning", "text": "**Waiting to close grasp**"}, {"type": "tool", "callID": "call_205739a9982d407a8acccbb7129b49eb|fc_0b5218887c37459e016ae26f7d0fa8819fbd31531d27c48a72", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "settle forefoot approach", "args": {"duration_s": 0.24, "cameras": ["right_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224574.797605845] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5518105030059814,2.1282575130462646,1.8211756944656372,-1.2633064985275269,0.0024045207537710667,0.04374692589044571],\"left_gripper\":[0.9995164898308841],\"right_arm\":[-0.09451431035995483,2.3065285682678223,1.3511804342269897,-0.614876925945282,0.003993599209934473,-0.06940840929746628],\"right_gripper\":[0.9861905845728788]},\n \"file\": [\"/tmp/kinex-observation-dlwemtpp.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 47506, "outputTokens": 77, "cachedInputTokens": 47104, "cacheReportedInputTokens": 47506, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 47506, "reasoningOutputTokens": 27, "reasoningReportedOutputTokens": 77}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "1194c8b6", "role": "toolResult", "timestamp": "2026-10-28T21:56:16.979Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224574.797605845] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-1.5518105030059814,2.1282575130462646,1.8211756944656372,-1.2633064985275269,0.0024045207537710667,0.04374692589044571],\"left_gripper\":[0.9995164898308841],\"right_arm\":[-0.09451431035995483,2.3065285682678223,1.3511804342269897,-0.614876925945282,0.003993599209934473,-0.06940840929746628],\"right_gripper\":[0.9861905845728788]},\n \"file\": [\"/tmp/kinex-observation-dlwemtpp.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "644b4f6d", "role": "assistant", "timestamp": "2026-10-28T21:56:27.896Z", "parts": [{"type": "reasoning", "text": "**Computing TCP camera projection**"}, {"type": "tool", "callID": "call_1f2454817820440fa30d581a8b7bb57c|fc_0b5218887c37459e016ae26f8897d0819f865d0760571e0e3f", "name": "exec", "input": {"description": "compare tool center with shoe surface", "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n rel=await helpers.relative_pose(io,{'frame':'right_gripper:tcp'},{'frame':'right_camera:base'})\n tcp=await helpers.relative_pose(io,{'frame':'right_gripper:tcp'},{'frame':'world:base'})\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\n for u,v in [(220,190),(280,170),(280,220)]:\n z=float(a[v,u])*.001\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base')\n finish({'rel':rel,'tcp':tcp,'points':pts})\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224589.387025232] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"rel\": {\"pose\":[0.031999988504280985,0.04039803789401142,0.07136523029414477,0.5893679914824401,-0.5893679004513149,0.3906986006032956,0.3906986716386298],\"timestamp\":0},\n \"tcp\": {\"pose\":[0.33640272493657036,-0.02633167088001903,0.6917798048149151,-0.49874693434276596,0.5009926290810037,0.49905228115506656,0.5012032539440671],\"timestamp\":15.92},\n \"points\": {\"(220, 190)\":[0.323961443569907,-0.027427434548870908,0.6438170953602051,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 170)\":[0.3420592549054097,-0.019472769566128686,0.6447653287773406,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 220)\":[0.3453272239232121,-0.0403433584497698,0.642715433205389,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303]}\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 47904, "outputTokens": 327, "cachedInputTokens": 47488, "cacheReportedInputTokens": 47904, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 47904, "reasoningOutputTokens": 81, "reasoningReportedOutputTokens": 327}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "e94429a4", "role": "toolResult", "timestamp": "2026-10-28T21:56:30.087Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224589.387025232] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"rel\": {\"pose\":[0.031999988504280985,0.04039803789401142,0.07136523029414477,0.5893679914824401,-0.5893679004513149,0.3906986006032956,0.3906986716386298],\"timestamp\":0},\n \"tcp\": {\"pose\":[0.33640272493657036,-0.02633167088001903,0.6917798048149151,-0.49874693434276596,0.5009926290810037,0.49905228115506656,0.5012032539440671],\"timestamp\":15.92},\n \"points\": {\"(220, 190)\":[0.323961443569907,-0.027427434548870908,0.6438170953602051,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 170)\":[0.3420592549054097,-0.019472769566128686,0.6447653287773406,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303],\"(280, 220)\":[0.3453272239232121,-0.0403433584497698,0.642715433205389,0.9801154340873507,-0.002145094267080949,-0.00048323939059973637,0.19841597948260303]}\n }\n}"}]}, {"id": "d063d515", "role": "assistant", "timestamp": "2026-10-28T21:56:47.844Z", "parts": [{"type": "reasoning", "text": "**Checking shoe depth mismatch**\n\n**Visualizing right-camera depth**"}, {"type": "tool", "callID": "call_20a1bf8d69d64695b29c470a62ab6cdb|fc_0b5218887c37459e016ae26f9ce970819f9904f7258f62e640", "name": "exec", "input": {"description": "locate elevated shoe pixels from depth", "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,dep=await helpers.camera_frame(io,'right_camera'); z=helpers.image_array(dep)*.001\n tr=(await helpers.relative_pose(io,{'frame':'right_camera:base'},{'frame':'world:base'}))['pose']; yy,xx=np.indices(z.shape)\n xyz=np.stack([(xx-159.5)*z/358.64218,(yy-119.5)*z/358.64218,z],-1)@R.from_quat(tr[3:]).as_matrix().T+tr[:3]\n mask=(xyz[:,:,2]>.66)&(xyz[:,:,2]<.78)&(xyz[:,:,0]>.25)\n pix=np.argwhere(mask); pts=xyz[mask]\n finish({'pixel_bounds':[pix.min(0),pix.max(0)] if len(pix) else None,'world_bounds':[pts.min(0),pts.max(0)] if len(pts) else None,'count':len(pts)})\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224610.029616113] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"pixel_bounds\": [[217,104],[239,163]],\n \"world_bounds\": [[0.2889084230314078,-0.032890889789939426,0.6600515195421714],[0.3053804617258074,-0.02516070082117358,0.6626498111904289]],\n \"count\": 86\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 48655, "outputTokens": 444, "cachedInputTokens": 48000, "cacheReportedInputTokens": 48655, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 48655, "reasoningOutputTokens": 158, "reasoningReportedOutputTokens": 444}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "03964626", "role": "toolResult", "timestamp": "2026-10-28T21:56:50.492Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224610.029616113] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"pixel_bounds\": [[217,104],[239,163]],\n \"world_bounds\": [[0.2889084230314078,-0.032890889789939426,0.6600515195421714],[0.3053804617258074,-0.02516070082117358,0.6626498111904289]],\n \"count\": 86\n }\n}"}]}, {"id": "a706de89", "role": "assistant", "timestamp": "2026-10-28T21:57:04.555Z", "parts": [{"type": "reasoning", "text": "**Raising the shoe**"}, {"type": "tool", "callID": "call_d7c02ebd0c924b98a4290386fea210b7|fc_0b5218887c37459e016ae26fb04b24819f92f6f47a3457bbfd", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "widen right wrist view", "args": {"goals": {"right": {"delta": [0, 0, 0.22]}, "left": {"joints": [-0.23377, 2.10649, 1.59874, -1.06015, -0.01728, -0.65544]}}, "grips": {"right": 1, "left": 1}, "duration_s": 1, "cameras": ["right_camera", "head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224626.173253423] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.4864189326763153,2.1139965057373047,1.6437352895736694,-1.1013754606246948,-0.012391126714646816,-0.5226401090621948],\"left_gripper\":[1.0006968649950894],\"right_arm\":[-0.09558303654193878,2.0816221237182617,1.7370487451553345,-1.2321414947509766,0.002054061973467469,-0.07567489147186279],\"right_gripper\":[0.9936021512204951]},\n \"file\": [\"/tmp/kinex-observation-nsqsy3z2.png\",\"/tmp/kinex-observation-46fp2462.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 49238, "outputTokens": 231, "cachedInputTokens": 48896, "cacheReportedInputTokens": 49238, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 49238, "reasoningOutputTokens": 115, "reasoningReportedOutputTokens": 231}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "7403242e", "role": "toolResult", "timestamp": "2026-10-28T21:57:11.739Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224626.173253423] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.4864189326763153,2.1139965057373047,1.6437352895736694,-1.1013754606246948,-0.012391126714646816,-0.5226401090621948],\"left_gripper\":[1.0006968649950894],\"right_arm\":[-0.09558303654193878,2.0816221237182617,1.7370487451553345,-1.2321414947509766,0.002054061973467469,-0.07567489147186279],\"right_gripper\":[0.9936021512204951]},\n \"file\": [\"/tmp/kinex-observation-nsqsy3z2.png\",\"/tmp/kinex-observation-46fp2462.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "5bb3bf42", "role": "assistant", "timestamp": "2026-10-28T21:57:20.502Z", "parts": [{"type": "reasoning", "text": "**Measuring right shoe toe**"}, {"type": "tool", "callID": "call_09d533fa54c44e92bd7cf0e5f290f468|fc_0b5218887c37459e016ae26fbea500819fbfb0dff90c7e68e4", "name": "exec", "input": {"description": "measure recovered shoe forefoot from wider view", "python": "import asyncio\nfrom kinex import communication,helpers,finish\nasync def main():\n async with communication() as io:\n im,d=await helpers.camera_frame(io,'right_camera'); a=helpers.image_array(d); pts={}\n for u,v in [(200,80),(200,120),(200,180),(230,120)]:\n z=float(a[v,u])*.001\n pts[str((u,v))]=await helpers.transform_pose(io,[(u-159.5)*z/358.64218,(v-119.5)*z/358.64218,z,0,0,0,1],'right_camera/base','world/base')\n finish(pts)\nasyncio.run(main())"}, "output": "{\n \"output\": \"[INFO] [1793224642.187029003] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"(200, 80)\": [0.3319128077876595,-0.021384687358054988,0.6807880606684285,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(200, 120)\": [0.3320860201227272,-0.047179444335913354,0.6896982618039296,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(200, 180)\": [0.3313421415530356,-0.0816889616404346,0.7110633133544213,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(230, 120)\": [0.35305419668191745,-0.04801774041168197,0.6878528021042477,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 49903, "outputTokens": 203, "cachedInputTokens": 49280, "cacheReportedInputTokens": 49903, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 49903, "reasoningOutputTokens": 19, "reasoningReportedOutputTokens": 203}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "55624b8a", "role": "toolResult", "timestamp": "2026-10-28T21:57:22.896Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224642.187029003] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"(200, 80)\": [0.3319128077876595,-0.021384687358054988,0.6807880606684285,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(200, 120)\": [0.3320860201227272,-0.047179444335913354,0.6896982618039296,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(200, 180)\": [0.3313421415530356,-0.0816889616404346,0.7110633133544213,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695],\n \"(230, 120)\": [0.35305419668191745,-0.04801774041168197,0.6878528021042477,0.9794699809940978,-0.0010291198589706435,-0.0004335844173035729,0.2015869768816695]\n }\n}"}]}, {"id": "a447d130", "role": "assistant", "timestamp": "2026-10-28T21:57:33.712Z", "parts": [{"type": "reasoning", "text": "**Lowering gripper toward shoe**"}, {"type": "tool", "callID": "call_ce07926c139047cfb864dc9ff816fe0f|fc_0b5218887c37459e016ae26fcd84a8819faf223c7ee94692b3", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "descend for firm forefoot grasp", "args": {"goals": {"right": {"pose": [0.334, -0.045, 0.669, -0.5, 0.5, 0.5, 0.5]}}, "grips": {"right": 1}, "duration_s": 1, "cameras": ["right_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224655.360224304] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2415974736213684,2.1075475215911865,1.6001873016357422,-1.0607448816299438,-0.01720820739865303,-0.6539632678031921],\"left_gripper\":[1.006791507655924],\"right_arm\":[-0.09445098042488098,2.2661819458007812,1.34000563621521,-0.6393835544586182,0.003709397278726101,-0.07148103415966034],\"right_gripper\":[0.9967559142546221]},\n \"file\": [\"/tmp/kinex-observation-b541py7r.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 50456, "outputTokens": 220, "cachedInputTokens": 50048, "cacheReportedInputTokens": 50456, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 50456, "reasoningOutputTokens": 126, "reasoningReportedOutputTokens": 220}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "a2352307", "role": "toolResult", "timestamp": "2026-10-28T21:57:40.894Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224655.360224304] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2415974736213684,2.1075475215911865,1.6001873016357422,-1.0607448816299438,-0.01720820739865303,-0.6539632678031921],\"left_gripper\":[1.006791507655924],\"right_arm\":[-0.09445098042488098,2.2661819458007812,1.34000563621521,-0.6393835544586182,0.003709397278726101,-0.07148103415966034],\"right_gripper\":[0.9967559142546221]},\n \"file\": [\"/tmp/kinex-observation-b541py7r.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "e20fd735", "role": "assistant", "timestamp": "2026-10-28T21:57:44.080Z", "parts": [{"type": "tool", "callID": "call_0038c08a3bf74a2fb94a8541cabe26db|fc_0b5218887c37459e016ae26fd771f0819fb3933d0d6868e282", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "secure forefoot grasp", "args": {"grips": {"right": 0}, "duration_s": 0.4, "cameras": ["right_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224665.686067778] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2397918999195099,2.106983184814453,1.599143147468567,-1.0602302551269531,-0.017222831025719643,-0.6552022099494934],\"left_gripper\":[1.0052635290405967],\"right_arm\":[-0.09369406849145889,2.304854154586792,1.2598658800125122,-0.5211624503135681,0.00392327643930912,-0.07036155462265015],\"right_gripper\":[0.2204216422343796]},\n \"file\": [\"/tmp/kinex-observation-duv2kayc.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 50996, "outputTokens": 54, "cachedInputTokens": 28544, "cacheReportedInputTokens": 50996, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 50996, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 54}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "6c9d803f", "role": "toolResult", "timestamp": "2026-10-28T21:57:48.001Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224665.686067778] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.2397918999195099,2.106983184814453,1.599143147468567,-1.0602302551269531,-0.017222831025719643,-0.6552022099494934],\"left_gripper\":[1.0052635290405967],\"right_arm\":[-0.09369406849145889,2.304854154586792,1.2598658800125122,-0.5211624503135681,0.00392327643930912,-0.07036155462265015],\"right_gripper\":[0.2204216422343796]},\n \"file\": [\"/tmp/kinex-observation-duv2kayc.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "4a7a1baa", "role": "assistant", "timestamp": "2026-10-28T21:58:13.809Z", "parts": [{"type": "reasoning", "text": "**Checking shoe grasp**\n\n**Leaving shoe open**\n\n**Inspecting shoe position**"}, {"type": "tool", "callID": "call_ccc1a9a553874c9ab5903da799735e1c|fc_0b5218887c37459e016ae26ff47bb4819f8d9d077cb0ff58dd", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "check whether shoe follows lift", "args": {"goals": {"right": {"delta": [0, 0, 0.16]}}, "grips": {"right": 0}, "duration_s": 0.8, "cameras": ["right_camera", "head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224695.262363581] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749706149101257,2.10660719871521,1.5987578630447388,-1.0601166486740112,-0.017234427854418755,-0.655457079410553],\"left_gripper\":[1.0032016147266736],\"right_arm\":[-0.09411819279193878,2.071403980255127,1.4278910160064697,-0.9290177822113037,0.0019836677238345146,-0.07476944476366043],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-itxcfdys.png\",\"/tmp/kinex-observation-z0_kr8cf.png\"]\n }\n}", "isError": false, "metadata": {}}], "usage": {"inputTokens": 51369, "outputTokens": 508, "cachedInputTokens": 50944, "cacheReportedInputTokens": 51369, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 51369, "reasoningOutputTokens": 431, "reasoningReportedOutputTokens": 508}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "1e357bae", "role": "toolResult", "timestamp": "2026-10-28T21:58:20.839Z", "parts": [{"type": "text", "text": "{\n \"output\": \"[INFO] [1793224695.262363581] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749706149101257,2.10660719871521,1.5987578630447388,-1.0601166486740112,-0.017234427854418755,-0.655457079410553],\"left_gripper\":[1.0032016147266736],\"right_arm\":[-0.09411819279193878,2.071403980255127,1.4278910160064697,-0.9290177822113037,0.0019836677238345146,-0.07476944476366043],\"right_gripper\":[0.18181818181818182]},\n \"file\": [\"/tmp/kinex-observation-itxcfdys.png\",\"/tmp/kinex-observation-z0_kr8cf.png\"]\n }\n}"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}, {"type": "image", "mime": "image/png", "source": "session.jsonl"}]}, {"id": "172c381d", "role": "assistant", "timestamp": "2026-10-28T21:58:28.445Z", "parts": [{"type": "reasoning", "text": "**Stopping after shoe contact**"}, {"type": "tool", "callID": "call_af13ee3ea5cc4286990e46befd9552ca|fc_0b5218887c37459e016ae2700445d4819f866a87f83acd87ad", "name": "device_stop", "input": {"reason": "Shoe was displaced during grasp attempt; stop motion before further inspection."}, "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": 52308, "outputTokens": 48, "cachedInputTokens": 50560, "cacheReportedInputTokens": 52308, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 52308, "reasoningOutputTokens": 15, "reasoningReportedOutputTokens": 48}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "5a30abce", "role": "toolResult", "timestamp": "2026-10-28T21:58:28.541Z", "parts": [{"type": "text", "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"}]}, {"id": "cd41a609", "role": "assistant", "timestamp": "2026-10-28T21:58:43.618Z", "parts": [{"type": "reasoning", "text": "**Checking the right shoe**"}, {"type": "text", "text": "The shoes slipped during placement and recovery, so I can’t confirm completion. I’m stopping the attempt and leaving both grippers open."}, {"type": "tool", "callID": "call_a839ec63918748f7900267391ba467f8|fc_0b5218887c37459e016ae27012f578819f923e53216d95c7b9", "name": "exec", "input": {"file": ".kinex/tools/cartesian_segment.py", "description": "leave both grippers open and verify", "args": {"grips": {"left": 1, "right": 1}, "duration_s": 0.4, "cameras": ["head_camera"]}}, "output": "{\n \"output\": \"[INFO] [1793224725.282645542] [rcl]: Found security directory: /run/rlebench/public/security/enclaves/agent\\n\",\n \"result\": {\n \"measured\": {\"left_arm\":[-0.23749713599681854,2.10660719871521,1.5987579822540283,-1.06011962890625,-0.017240097746253014,-0.6554499268531799],\"left_gripper\":[1.0024560825391249],\"right_arm\":[-0.09411459416151047,2.0698814392089844,1.4282174110412598,-0.929033637046814,0.001976696541532874,-0.07476774603128433],\"right_gripper\":[0.8706039027734237]},\n \"file\": [\"/tmp/kinex-observation-rljb8q34.png\"]\n }\n}", "isError": 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in ['left_arm','left_gripper','right_arm','right_gripper']}", "newText": "states={}\n for d in ['left_arm','left_gripper','right_arm','right_gripper']:\n state=await read(io,d+'.joint_state')\n states[d]={'position':list(state.position),'velocity':list(state.velocity),\n 'stamp_s':state.header.stamp.sec+state.header.stamp.nanosec*1e-9}"}]}, "output": "Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py.", "isError": false, "metadata": {}}], "usage": {"inputTokens": 53429, "outputTokens": 209, "cachedInputTokens": 52992, "cacheReportedInputTokens": 53429, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 53429, "reasoningOutputTokens": 56, "reasoningReportedOutputTokens": 209}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "fb0ee13a", "role": "toolResult", "timestamp": "2026-10-28T21:59:09.916Z", "parts": [{"type": "text", "text": "Successfully replaced 1 block(s) in .kinex/tools/cartesian_segment.py."}]}, {"id": "d96ef9da", "role": "assistant", "timestamp": "2026-10-28T21:59:35.070Z", "parts": [{"type": "tool", "callID": "call_59053b706fc6404abf3467e748120ce4|fc_0b5218887c37459e016ae27030c860819fa45609d2accdf2f0", "name": "write", "input": {"path": ".kinex/memos/dual_shoes.md", "content": "# RoboTwin dual shoes\n\n## Interfaces and tools\n- Read refreshed profiles each episode. Stepped world, native 25 Hz (0.04 s/frame), 600 frames. Only world.wait advances time; reads do not.\n- `tools/dual_shoes_policy.py` captures head/left/right RGB in required order, calls helpers.run_policy at 25 Hz, returns fresh head image and measured joint feedback. Current policy returns 16 actions per inference even if max_actions=32. Task instruction embedded; override via args.task.\n- `tools/cartesian_segment.py` makes bounded world-TCP IK/joint segments with optional gripper targets, and returns cameras plus measured positions/velocities/stamps. Inputs: goals.{left,right}.{pose|delta|joints}, grips.{left,right} in native opening units, duration_s, cameras. It does NOT perform collision planning or certify arrival. The TCP offset is 0.12 m along flange +x for these profiles. All goals are planned before publication. Recheck calibration before reuse elsewhere.\n- Both grippers use 0 closed, 1 open; policy uses -1 open/+1 closed and helper converts. Closed empty feedback may bottom at ~0.18, not zero. A shoe can stall grip near .82, but that alone does not establish holding.\n\n## Important limitations\n- Policy achieved initial bilateral pickup but failed first placement: shoe slid toward near/right box edge, then vanished from head view. Continuing inference after loss produced wrist rotation and empty closing, not recovery. Stop and reassess instead of blindly extending policy.\n- Manual recovery was not successful. Depth-based targets require renewed perception after EVERY contact; shoes slide readily. Do not reuse previous attempt's object coordinates.\n- A 0.8–1.2 s trajectory endpoint can still have substantial tracking lag; gripper close while descending can displace the shoe. Inspect velocities and position error before grasping. Verify retention by camera during a small test lift before transport.\n- TCP +x approaches; fingers close along local y. Do not infer safe finger clearance solely from TCP height or simplified URDF geometry.\n- device_stop holds measured gripper opening, which may relax an object grasp; when intentionally holding an object, preserve closing target unless stopping for a fault.\n- Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\n"}, "output": "Successfully wrote to .kinex/memos/dual_shoes.md", "isError": false, "metadata": {}}], "usage": {"inputTokens": 53665, "outputTokens": 544, "cachedInputTokens": 53504, "cacheReportedInputTokens": 53665, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 53665, "reasoningOutputTokens": 0, "reasoningReportedOutputTokens": 544}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "50837b9a", "role": "toolResult", "timestamp": "2026-10-28T21:59:35.076Z", "parts": [{"type": "text", "text": "Successfully wrote to .kinex/memos/dual_shoes.md"}]}, {"id": "33b28444", "role": "assistant", "timestamp": "2026-10-28T21:59:43.482Z", "parts": [{"type": "reasoning", "text": ""}, {"type": "tool", "callID": "call_150f2ee852e34f54b4bbfcc5e2fd70c2|fc_0b5218887c37459e016ae2704da438819fb542e886d3a11931", "name": "edit", "input": {"path": ".kinex/memos/dual_shoes.md", "edits": [{"oldText": "- Last observed attempt did not place either shoe in box. Arms were held, both grippers commanded open, measured ~1.002 / .975. No success claim or native verdict available.\n", "newText": "- Finalization: command both openings to 1, allow settling, and verify feedback. Report placement uncertainty honestly; the world interface exposes no native verdict.\n"}]}, "output": "Successfully replaced 1 block(s) in .kinex/memos/dual_shoes.md.", "isError": false, "metadata": {}}], "usage": {"inputTokens": 54232, "outputTokens": 148, "cachedInputTokens": 54016, "cacheReportedInputTokens": 54232, "cacheWriteInputTokens": 0, "cacheWriteReportedInputTokens": 54232, "reasoningOutputTokens": 35, "reasoningReportedOutputTokens": 148}, "provider": "openai-codex", "model": "gpt-6-astra", "stopReason": "toolUse"}, {"id": "511c9210", "role": "toolResult", "timestamp": "2026-10-28T21:59:43.487Z", "parts": [{"type": "text", "text": "Successfully replaced 1 block(s) in .kinex/memos/dual_shoes.md."}]}, {"id": "b3f508d5", "role": "assistant", "timestamp": "2026-10-28T21:59:47.604Z", "parts": [{"type": "text", "text": "Task not completed: the shoes slipped and were not placed in the shoebox. 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b/experiments/robotwin-native/kinex_policy-2/workspace.tar.gz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3753a4e59af6c1bb46ea13f0d95be18647ce22c828fb5b93c84d931447769544 +size 12463 diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/head_camera/profile.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/head_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..844dc67d987ffa1268fd4f604b8247c07b7122ee --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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: "2026-10-28T21:46:43.498Z" +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_policy-2/workspace/.kinex/devices/left_arm/arm.urdf b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_arm/arm.urdf new file mode 100644 index 0000000000000000000000000000000000000000..0679f698a073bf49a5e79246d115ce8506e14935 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_arm/arm.urdf @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_arm/links.py b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_arm/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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_policy-2/workspace/.kinex/devices/left_arm/profile.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_arm/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..ee0fcf64f17cd42dfe00ca34215a79153cd3db3f --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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: "2026-10-28T21:46:43.506Z" +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_policy-2/workspace/.kinex/devices/left_camera/profile.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..05957095a192a43b352c36ccf680b1e28a253fb2 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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: "2026-10-28T21:46:43.502Z" +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_policy-2/workspace/.kinex/devices/left_gripper/gripper.urdf b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_gripper/gripper.urdf new file mode 100644 index 0000000000000000000000000000000000000000..ffde595542f57b4e9ac0bbba73940f540888b67e --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_gripper/gripper.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_gripper/links.py b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_gripper/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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_policy-2/workspace/.kinex/devices/left_gripper/profile.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/left_gripper/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..f38494109b763bf320a3a9693bc0024b8070fe84 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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: "2026-10-28T21:46:43.507Z" +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_policy-2/workspace/.kinex/devices/policy/configuration.json b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/policy/configuration.json new file mode 100644 index 0000000000000000000000000000000000000000..ba81208dc1aae38fc91a422279774a7d96adf60b --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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": "/logs/agent/policy" +} \ No newline at end of file diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/policy/env.sh b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/policy/env.sh new file mode 100644 index 0000000000000000000000000000000000000000..edf16febfd76b8a4854d90c45b51f7d3ac192a43 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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/kinex_policy-2/workspace/.kinex/devices/policy/node.py b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/policy/node.py new file mode 100644 index 0000000000000000000000000000000000000000..8c51a97b60baa14f2c3e5a50e87e3d3bc461ea5e --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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/kinex_policy-2/workspace/.kinex/devices/policy/profile.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/policy/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2893546300c2b99f1db35b7dd49f22733fd33f13 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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: "2026-10-28T21:45:05.294658+00:00" + updated_at: "2026-10-28T21:46:43.501Z" +family: starvla +variant: null +implementation: null +artifacts: [] +compute: + kind: cuda + index: 0 +type: model diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_arm/arm.urdf b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_arm/arm.urdf new file mode 100644 index 0000000000000000000000000000000000000000..98d0eedec4a96aa88680315bd8abb3ec31107ea7 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_arm/arm.urdf @@ -0,0 +1,53 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_arm/links.py b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_arm/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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_policy-2/workspace/.kinex/devices/right_arm/profile.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_arm/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8605452f7e375a32f2a44707f7e87f40ca4373bd --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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: "2026-10-28T21:46:43.510Z" +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_policy-2/workspace/.kinex/devices/right_camera/profile.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_camera/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6a6dafcde9d1160e7cc30b92952573f182395b1f --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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: "2026-10-28T21:46:43.500Z" +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_policy-2/workspace/.kinex/devices/right_gripper/gripper.urdf b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_gripper/gripper.urdf new file mode 100644 index 0000000000000000000000000000000000000000..dfd1ec14f3c8085e398e7e1f5e81cb2af81e1f30 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_gripper/gripper.urdf @@ -0,0 +1,31 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_gripper/links.py b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_gripper/links.py new file mode 100644 index 0000000000000000000000000000000000000000..3c48cabf3056ab9cfcc1a812d09e0e27c4481b93 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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_policy-2/workspace/.kinex/devices/right_gripper/profile.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/right_gripper/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..233081efa85ef351a49e5ebb7cb4a048e425f30e --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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: "2026-10-28T21:46:43.511Z" +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_policy-2/workspace/.kinex/devices/world/profile.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/devices/world/profile.yaml new file mode 100644 index 0000000000000000000000000000000000000000..c14f75c1a5cd401b617a6d46000e33ed5e8bebd1 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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: "2026-10-28T21:46:43.497Z" +frames: + base: + parent: null + translation_m: null + rotation_xyzw: null +bounds: null +surfaces: [] +type: world diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/kinex.json b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/kinex.json new file mode 100644 index 0000000000000000000000000000000000000000..c0400a4af73fe4a6eb2ed538d2e3826327c5ba32 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/kinex.json @@ -0,0 +1,5 @@ +{ + "python": { + "provider": "ros-jazzy" + } +} diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/memos/dual_shoes.md b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/memos/dual_shoes.md new file mode 100644 index 0000000000000000000000000000000000000000..f8147ff7a68d9961b292267e01c420a6e6fc439e --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/memos/dual_shoes.md @@ -0,0 +1,15 @@ +# RoboTwin dual shoes + +## Interfaces and tools +- Read refreshed profiles each episode. Stepped world, native 25 Hz (0.04 s/frame), 600 frames. Only world.wait advances time; reads do not. +- `tools/dual_shoes_policy.py` captures head/left/right RGB in required order, calls helpers.run_policy at 25 Hz, returns fresh head image and measured joint feedback. Current policy returns 16 actions per inference even if max_actions=32. Task instruction embedded; override via args.task. +- `tools/cartesian_segment.py` makes bounded world-TCP IK/joint segments with optional gripper targets, and returns cameras plus measured positions/velocities/stamps. Inputs: goals.{left,right}.{pose|delta|joints}, grips.{left,right} in native opening units, duration_s, cameras. It does NOT perform collision planning or certify arrival. The TCP offset is 0.12 m along flange +x for these profiles. All goals are planned before publication. Recheck calibration before reuse elsewhere. +- Both grippers use 0 closed, 1 open; policy uses -1 open/+1 closed and helper converts. Closed empty feedback may bottom at ~0.18, not zero. A shoe can stall grip near .82, but that alone does not establish holding. + +## Important limitations +- Policy achieved initial bilateral pickup but failed first placement: shoe slid toward near/right box edge, then vanished from head view. Continuing inference after loss produced wrist rotation and empty closing, not recovery. Stop and reassess instead of blindly extending policy. +- Manual recovery was not successful. Depth-based targets require renewed perception after EVERY contact; shoes slide readily. Do not reuse previous attempt's object coordinates. +- A 0.8–1.2 s trajectory endpoint can still have substantial tracking lag; gripper close while descending can displace the shoe. Inspect velocities and position error before grasping. Verify retention by camera during a small test lift before transport. +- TCP +x approaches; fingers close along local y. Do not infer safe finger clearance solely from TCP height or simplified URDF geometry. +- device_stop holds measured gripper opening, which may relax an object grasp; when intentionally holding an object, preserve closing target unless stopping for a fault. +- Finalization: command both openings to 1, allow settling, and verify feedback. Report placement uncertainty honestly; the world interface exposes no native verdict. diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/tools/cartesian_segment.py b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/tools/cartesian_segment.py new file mode 100644 index 0000000000000000000000000000000000000000..6a4fac99d1700b327b13033c90e571f021ad9e38 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/tools/cartesian_segment.py @@ -0,0 +1,45 @@ +"""Bounded world-TCP moves using calibrated transforms and measured seeds.""" +import asyncio +from kinex import args, communication, helpers, finish +from kinex.communication import duration + +async def read(io, op): + with io.subscribe(op, depth=1) as stream: + return await stream.next(timeout_s=10) + +async def main(): + opts=args(); seconds=opts.get('duration_s',1.0) + async with communication() as io: + commands=[] + for side,goal in opts.get('goals',{}).items(): + arm=side+'_arm'; model=helpers.joint_model(arm) + state=await read(io,arm+'.joint_state'); q=list(state.position) + if 'joints' in goal: end=goal['joints'] + else: + current=await helpers.transform_pose(io,model.fk(q),arm+'/base','world/base') + tcp=await helpers.relative_pose(io,{'frame':side+'_gripper:tcp'},{'frame':'world:base'}) + target=goal.get('pose',tcp['pose']).copy() + if 'delta' in goal: + target[:3]=[a+b for a,b in zip(target[:3],goal['delta'])] + # TCP is 0.12 m along flange +x. + from scipy.spatial.transform import Rotation + off=Rotation.from_quat(target[3:]).apply([.12,0,0]) + target[:3]=[a-b for a,b in zip(target[:3],off)] + local=await helpers.transform_pose(io,target,'world/base',arm+'/base') + end=model.ik(local,q,budget={'max_attempts':12,'attempt_time_s':0.1,'max_time_s':1.2}) + commands.append((arm+'.joint_target',{'joint_names':model.names,'points':[ + {'positions':q,'time_from_start':duration(.04)}, + {'positions':end,'time_from_start':duration(seconds)}]})) + for side,value in opts.get('grips',{}).items(): + commands.append((side+'_gripper.target',{'data':value})) + await asyncio.gather(*(io.publish(op,payload) for op,payload in commands)) + await io.request('world.wait',duration_s=seconds) + states={} + for d in ['left_arm','left_gripper','right_arm','right_gripper']: + state=await read(io,d+'.joint_state') + states[d]={'position':list(state.position),'velocity':list(state.velocity), + 'stamp_s':state.header.stamp.sec+state.header.stamp.nanosec*1e-9} + paths=[helpers.save_image(await read(io,c+'.image')) for c in opts.get('cameras',['head_camera'])] + finish({'measured':states},file=paths) + +asyncio.run(main()) diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/tools/dual_shoes_policy.py b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/tools/dual_shoes_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..07d2e3453edef1a9167fdfa0c50b6b1f789acf78 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/tools/dual_shoes_policy.py @@ -0,0 +1,31 @@ +"""Run one bounded, freshly observed RoboTwin policy segment; no lifecycle changes.""" +import asyncio +from kinex import args, communication, finish, helpers + +TASK = ('use both arms to pick up the two shoes on the table and put them flat in the shoebox, ' + 'with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. ' + 'Leave both grippers open') +CAMERAS = ['head_camera', 'left_camera', 'right_camera'] +DEVICES = ['left_arm', 'left_gripper', 'right_arm', 'right_gripper'] + +async def read_one(io, op): + with io.subscribe(op, depth=1) as stream: + return await stream.next(timeout_s=10) + +async def main(): + opts = args() + async with communication() as io: + images = await asyncio.gather(*(read_one(io, c+'.image') for c in CAMERAS)) + paths = [helpers.save_image(im) for im in images] + result = await helpers.run_policy(io, 'policy', {'images': paths}, + [{'device': d} for d in DEVICES], task=opts.get('task', TASK), + world='world', action_hz=25, max_actions=opts.get('max_actions', 32)) + states = await asyncio.gather(*(read_one(io, d+'.joint_state') for d in DEVICES)) + image = await read_one(io, 'head_camera.image') + path = helpers.save_image(image) + result['measured'] = {d: {'position': list(s.position), 'velocity': list(s.velocity)} + for d,s in zip(DEVICES,states)} + result['time_s'] = image.header.stamp.sec + image.header.stamp.nanosec * 1e-9 + finish(result, file=[path]) + +asyncio.run(main()) diff --git a/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/workspace.yaml b/experiments/robotwin-native/kinex_policy-2/workspace/.kinex/workspace.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f4e663d629af8919ee84dc2cfeddd9204bea9c6 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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_policy-2/workspace/harness.md b/experiments/robotwin-native/kinex_policy-2/workspace/harness.md new file mode 100644 index 0000000000000000000000000000000000000000..f77861ba7febc66956580f9e840c24a340c5e35f --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/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_policy-2/workspace/instruction.md b/experiments/robotwin-native/kinex_policy-2/workspace/instruction.md new file mode 100644 index 0000000000000000000000000000000000000000..c11369ff0b46fa7f1916cac5f8d4d112bd43ccf6 --- /dev/null +++ b/experiments/robotwin-native/kinex_policy-2/workspace/instruction.md @@ -0,0 +1,45 @@ +# Task: robotwin/place-dual-shoes-aloha-agilex + +use both arms to pick up the two shoes on the table and put them flat in the shoebox, with the shoe tips pointing to the left and the shoe initially on the left nearer the robot. Leave both grippers 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 600 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 766bc239848bd1008fc6924dcf763bfc8654bb63..17f78a1465f82ad1d099054fc09969cd1a5b9296 100644 --- a/manifest.json +++ b/manifest.json @@ -1,8 +1,8 @@ { "files": { "app.js": { - "bytes": 9091, - "sha256": "dd0c7169f293a34830b4ce44f1d34b137d96412f00a1db0a6498b623af2416af" + "bytes": 9203, + "sha256": "f728ad1b18e40f65e1fc15a8f4d9c3b34df46e6684507bc25c6797eb2288e1c6" }, "README.md": { "bytes": 321, @@ -17,8 +17,8 @@ "sha256": "c787bda4dcd8b664d4844e72a39ac6d2855bbbdbbe4b58cb317488b30aee86e3" }, "data.json": { - "bytes": 4760, - "sha256": "3e0f91f983b4cf79037c1e217d33dff577a9870e810f015ca2ba50f33120c6e8" + "bytes": 5721, + "sha256": "9fb39890dbd731eb1eda337408569966a6b9b603fe705a90ba700de5f09b203c" }, "style.css": { "bytes": 5015, @@ -104,6 +104,86 @@ "bytes": 444, "sha256": "5a3694bfdd4fbeb4b1f47e125dde01703a7e004ec43f0bd6b47bbe7ba7c989e5" }, 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