How to draw decision boundary for 2 layer Neural Network

I have a two layer Neural Network. Given the weights and biases predicted by Neural Network, how to draw the decision boundary on this dataset?
My Neural network is defined as follows:

class Classifier(nn.Module):
    def __init__(self):
        self.fc1 = nn.Linear(2,2)
        self.fc2 = nn.Linear(2,1)
    def forward(self,x):
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

list(model.parameters()) returns me

[Parameter containing:
 tensor([[-0.5128, -0.3776],
         [ 0.4611,  0.4625]], requires_grad=True), Parameter containing:
 tensor([ 0.6443, -3.6282], requires_grad=True), Parameter containing:
 tensor([[-0.1985,  2.8158]], requires_grad=True), Parameter containing:
 tensor([-5.4537], requires_grad=True)]

The Google Colab notebook is attached. You will find the complete code under Toy Dataset 3 predicting logits. Besides, I have drawn 1 layer neural network decision boundary as an example. Find it under Toy Dataset 2 predicting logits. For your ease, 1 layer neural network dataset and its decision boundary is attached here as well.
1 2

A naive way I can think of is to have a grid of points, make predictions for each of them, then draw line?