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/apply_diff_guidelines.md

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+# Preventing apply_diff Errors
+
+## CRITICAL: When using apply_diff, never include literal diff markers in your code examples
+
+## CORRECT FORMAT for apply_diff:
+```
+<apply_diff>
+  <path>file/path.js</path>
+  <diff>
+    <<<<<<< SEARCH
+    // Original code to find (exact match)
+    =======
+    // New code to replace with
+    >>>>>>> REPLACE
+  </diff>
+</apply_diff>
+```
+
+## COMMON ERRORS to AVOID:
+1. Including literal diff markers in code examples or comments
+2. Nesting diff blocks inside other diff blocks
+3. Using incomplete diff blocks (missing SEARCH or REPLACE markers)
+4. Using incorrect diff marker syntax
+5. Including backticks inside diff blocks when showing code examples
+
+## When showing code examples that contain diff syntax:
+- Escape the markers or use alternative syntax
+- Use HTML entities or alternative symbols
+- Use code block comments to indicate diff sections
+
+## SAFE ALTERNATIVE for showing diff examples:
+```
+// Example diff (DO NOT COPY DIRECTLY):
+// [SEARCH]
+// function oldCode() {}
+// [REPLACE]
+// function newCode() {}
+```
+
+## ALWAYS validate your diff blocks before executing apply_diff
+- Ensure exact text matching
+- Verify proper marker syntax
+- Check for balanced markers
+- Avoid nested markers