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/search_replace.md

@@ -0,0 +1,34 @@
+# Search and Replace Guidelines
+
+## search_and_replace
+```xml
+<search_and_replace>
+  <path>File path here</path>
+  <operations>
+    [{"search":"old_text","replace":"new_text","use_regex":true}]
+  </operations>
+</search_and_replace>
+```
+
+### Required Parameters:
+- `path`: The file path to modify
+- `operations`: JSON array of search and replace operations
+
+### Each Operation Must Include:
+- `search`: The text to search for (REQUIRED)
+- `replace`: The text to replace with (REQUIRED)
+- `use_regex`: Boolean indicating whether to use regex (optional, defaults to false)
+
+### Common Errors to Avoid:
+- Missing `search` parameter
+- Missing `replace` parameter
+- Invalid JSON format in operations array
+- Attempting to modify non-existent files
+- Malformed regex patterns when use_regex is true
+
+### Best Practices:
+- Always include both search and replace parameters
+- Verify the file exists before attempting to modify it
+- Use apply_diff for complex changes instead
+- Test regex patterns separately before using them
+- Escape special characters in regex patterns