Add WiFi DensePose implementation and results

- 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.

.roo/rules-code/tool_guidelines_index.md

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+# Tool Usage Guidelines Index
+
+To prevent common errors when using tools, refer to these detailed guidelines:
+
+## File Operations
+- [File Operations Guidelines](.roo/rules-code/file_operations.md) - Guidelines for read_file, write_to_file, and list_files
+
+## Code Editing
+- [Code Editing Guidelines](.roo/rules-code/code_editing.md) - Guidelines for apply_diff
+- [Search and Replace Guidelines](.roo/rules-code/search_replace.md) - Guidelines for search_and_replace
+- [Insert Content Guidelines](.roo/rules-code/insert_content.md) - Guidelines for insert_content
+
+## Common Error Prevention
+- [apply_diff Error Prevention](.roo/rules-code/apply_diff_guidelines.md) - Specific guidelines to prevent errors with apply_diff
+
+## Key Points to Remember:
+1. Always include all required parameters for each tool
+2. Verify file existence before attempting modifications
+3. For apply_diff, never include literal diff markers in code examples
+4. For search_and_replace, always include both search and replace parameters
+5. For write_to_file, always include the line_count parameter
+6. For insert_content, always include valid start_line and content in operations array