Update 02_setup.md

Author: pareenaverma

content/learning-paths/mobile-graphics-and-gaming/onnx/02_setup.md

@@ -24,7 +24,7 @@ Depending on the hardware you use you follow different installation paths
 1. Linux (Arm64). In the console type:
 ```console
 sudo apt update
-sudo apt install -y python3 python3-venv python3-pip build-essential libopenblas-dev
+sudo apt install -y python3 python3-venv python3-pip build-essential libopenblas-dev libgl1 libglib2.0-0
 ```
 
 2. macOS (Apple Sillicon):
@@ -127,4 +127,4 @@ The 01_Init.py script serves as a quick end-to-end validation of your ONNX envir
 After export, the script immediately loads the ONNX model with ONNX Runtime and executes a forward pass using the CPU execution provider. This verifies that the installation of ONNX, ONNX Runtime, and PyTorch is correct and that models can flow seamlessly from definition to inference. By printing the output tensor’s shape and the active execution provider, the script demonstrates that the toolchain is fully functional on your Arm64 device, giving you a solid baseline before moving on to more advanced models and optimizations.
 
 ## Summary
-You now have a fully functional ONNX development environment on Arm64. Python and all required packages are installed, and you successfully exported a small PyTorch model to ONNX using the new Dynamo exporter, ensuring forward compatibility. Running the model with ONNX Runtime confirmed that inference works end-to-end with the CPU execution provider, proving that your toolchain is correctly configured. With this foundation in place, the next step is to build and export a more complete model and run it on Arm64 hardware to establish baseline performance before applying optimizations.
+You now have a fully functional ONNX development environment on Arm64. Python and all required packages are installed, and you successfully exported a small PyTorch model to ONNX using the new Dynamo exporter, ensuring forward compatibility. Running the model with ONNX Runtime confirmed that inference works end-to-end with the CPU execution provider, proving that your toolchain is correctly configured. With this foundation in place, the next step is to build and export a more complete model and run it on Arm64 hardware to establish baseline performance before applying optimizations.