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/code_editing.md

@@ -0,0 +1,32 @@
+# Code Editing Guidelines
+
+## apply_diff
+```xml
+<apply_diff>
+  <path>File path here</path>
+  <diff>
+    <<<<<<< SEARCH
+    Original code
+    =======
+    Updated code
+    >>>>>>> REPLACE
+  </diff>
+</apply_diff>
+```
+
+### Required Parameters:
+- `path`: The file path to modify
+- `diff`: The diff block containing search and replace content
+
+### Common Errors to Avoid:
+- Incomplete diff blocks (missing SEARCH or REPLACE markers)
+- Including literal diff markers in code examples
+- Nesting diff blocks inside other diff blocks
+- Using incorrect diff marker syntax
+- Including backticks inside diff blocks when showing code examples
+
+### Best Practices:
+- Always verify the file exists before applying diffs
+- Ensure exact text matching for the search block
+- Use read_file first to confirm content before modifying
+- Keep diff blocks simple and focused on specific changes