f3c77b1750
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads. - Added a comprehensive training utility for the model, including loss functions and training steps. - Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
44 lines
1.2 KiB
Markdown
44 lines
1.2 KiB
Markdown
# Preventing apply_diff Errors
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## CRITICAL: When using apply_diff, never include literal diff markers in your code examples
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## CORRECT FORMAT for apply_diff:
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```
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<apply_diff>
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<path>file/path.js</path>
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<diff>
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<<<<<<< SEARCH
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// Original code to find (exact match)
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=======
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// New code to replace with
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>>>>>>> REPLACE
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</diff>
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</apply_diff>
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```
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## COMMON ERRORS to AVOID:
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1. Including literal diff markers in code examples or comments
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2. Nesting diff blocks inside other diff blocks
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3. Using incomplete diff blocks (missing SEARCH or REPLACE markers)
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4. Using incorrect diff marker syntax
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5. Including backticks inside diff blocks when showing code examples
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## When showing code examples that contain diff syntax:
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- Escape the markers or use alternative syntax
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- Use HTML entities or alternative symbols
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- Use code block comments to indicate diff sections
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## SAFE ALTERNATIVE for showing diff examples:
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```
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// Example diff (DO NOT COPY DIRECTLY):
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// [SEARCH]
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// function oldCode() {}
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// [REPLACE]
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// function newCode() {}
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```
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## ALWAYS validate your diff blocks before executing apply_diff
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- Ensure exact text matching
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- Verify proper marker syntax
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- Check for balanced markers
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- Avoid nested markers |