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.

references/script_3.py

@@ -0,0 +1,23 @@
+# Install PyTorch and other dependencies
+import subprocess
+import sys
+
+def install_package(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+try:
+    import torch
+    print("PyTorch already installed")
+except ImportError:
+    print("Installing PyTorch...")
+    install_package("torch")
+    install_package("torchvision")
+
+try:
+    import numpy
+    print("NumPy already installed")
+except ImportError:
+    print("Installing NumPy...")
+    install_package("numpy")
+
+print("All packages ready!")