Update env-setup-5.md

Author: dominica-of

content/learning-paths/microcontrollers/introduction-to-tinyml-on-arm/env-setup-5.md

@@ -12,6 +12,7 @@ layout: "learningpathall"
 These instructions have been tested on:
 - A GCP Arm-based Tau T2A Virtual Machine instance Running Ubuntu 22.04 LTS.
 - Host machine with Ubuntu 24.04 on x86_64 architecture.
+- Windows Subsystem for Linux (WSL): Windows x86_64
 
 The host machine is where you will perform most of your development work, especially cross-compiling code for the target Arm devices.
 
@@ -23,15 +24,15 @@ If you want to use Arm Virtual Hardware the [Arm Virtual Hardware install guide]
 
 ## Setup on Host Machine
 1. Setup if you don't have access to the physical board: We would use the Corstone-300 FVP, it is pre-configured.
-2. Setup if you have access to the board: Skip to "Compilers" Section
+2. Setup if you have access to the board: Skip to **"Compilers"** Section
 
 
-### Corstone-300 FVP {#fvp} Setup for ExecuTorch
+### Corstone-300 FVP Setup for ExecuTorch
 For Arm Virtual Hardware users, the Corstone-300 FVP is pre-installed. 
 
-To install and set up the Corstone-300 FVP and ExecuTorch on your machine, refer to [Building and Running ExecuTorch with ARM Ethos-U Backend](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html)). Follow this tutorial till "Install the TOSA reference model" Section. It should be the last thing you do from this tutorial.
+To install and set up the Corstone-300 FVP and ExecuTorch on your machine, refer to [Building and Running ExecuTorch with ARM Ethos-U Backend](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html). Follow this tutorial till the **"Install the TOSA reference model"** Section. It should be the last thing you do from this tutorial.
 
-Since you already have the compiler installed from the above tutorial, skip to ## Install PyTorch.
+Since you already have the compiler installed from the above tutorial, skip to **"Install PyTorch"**.
 
 ### Compilers 
 
@@ -71,9 +72,9 @@ conda activate executorch
 ## Install Edge Impulse CLI
 1. Create an [Edge Impulse Account](https://studio.edgeimpulse.com/signup) if you do not have one 
 
-2. Install the CLI tools
+2. Install the CLI tools in your terminal
 
-Ensure you have Nodejs install 
+Ensure you have Nodejs installed
 
 ```console
 node -v 
@@ -123,3 +124,94 @@ Follow the prompts to log in.
 
 If successful, you should see your Grove - Vision AI Module V2 under 'Devices' in Edge Impulse.
 
+
+## Build a Simple PyTorch Model 
+With our Environment ready, we will create a simple program to test our setup. This example will define a simple feedforward neural network for a classification task. The model consists of 2 linear layers with ReLU activation in between. Create a file called simple_nn.py with the following code:
+
+```python
+import torch
+from torch.export import export
+from executorch.exir import to_edge
+
+# Define a simple Feedforward Neural Network
+class SimpleNN(torch.nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(SimpleNN, self).__init__()
+        self.fc1 = torch.nn.Linear(input_size, hidden_size)
+        self.relu = torch.nn.ReLU()
+        self.fc2 = torch.nn.Linear(hidden_size, output_size)
+        
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        return out
+
+# Create the model instance
+input_size = 10   # example input features size
+hidden_size = 5   # hidden layer size
+output_size = 2   # number of output classes
+
+model = SimpleNN(input_size, hidden_size, output_size)
+
+# Example input tensor (batch size 1, input size 10)
+x = torch.randn(1, input_size)
+
+# torch.export: Defines the program with the ATen operator set for SimpleNN.
+aten_dialect = export(model, (x,))
+
+# to_edge: Make optimizations for edge devices. This ensures the model runs efficiently on constrained hardware.
+edge_program = to_edge(aten_dialect)
+
+# to_executorch: Convert the graph to an ExecuTorch program
+executorch_program = edge_program.to_executorch()
+
+# Save the compiled .pte program
+with open("simple_nn.pte", "wb") as file:
+    file.write(executorch_program.buffer)
+
+print("Model successfully exported to simple_nn.pte")
+```
+
+Run it from your terminal:
+
+```console
+python3 simple_nn.py
+```
+
+If everything runs successfully, the output will be:
+```bash { output_lines = "1" }
+Model successfully exported to simple_nn.pte
+```
+Finally, the model is saved as a .pte file, which is the format used by ExecuTorch for deploying models to the edge.
+
+Now, we will run the ExecuTorch version, first run: 
+
+```console
+# Clean and configure the build system
+rm -rf cmake-out && mkdir cmake-out && cd cmake-out && cmake ..
+
+# Build the executor_runner target
+cmake --build cmake-out --target executor_runner -j9
+```
+
+You should see an output similar to:
+```bash { output_lines = "1" }
+[100%] Built target executor_runner
+```
+
+Now, run the executor_runner with the Model:
+```console
+./cmake-out/executor_runner --model_path simple_nn.pte
+```
+
+Expected Output: Since the model is a simple feedforward model, you can expect a tensor of shape [1, 2]
+
+```bash { output_lines = "1-3" }
+Input tensor shape: [1, 10]
+Output tensor shape: [1, 2]
+Inference output: tensor([[0.5432, -0.3145]]) #will vary due to random initialization
+```
+
+If the model execution completes successfully, you’ll see confirmation messages similar to those above, indicating successful loading, inference, and output tensor shapes.
+