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/file_operations_guidelines.md

@@ -0,0 +1,26 @@
+# File Operations Guidelines
+
+## read_file
+```xml
+<read_file>
+  <path>File path here</path>
+</read_file>
+```
+
+### Required Parameters:
+- `path`: The file path to read
+
+### Common Errors to Avoid:
+- Attempting to read non-existent files
+- Using incorrect or relative paths
+- Missing the `path` parameter
+
+### Best Practices:
+- Always check if a file exists before attempting to modify it
+- Use `read_file` before `apply_diff` or `search_and_replace` to verify content
+- For large files, consider using start_line and end_line parameters to read specific sections
+
+## write_to_file
+```xml
+<write_to_file>
+  <path>File path here</path>