myCustomNeuralNet.py

class myCustomNeuralNet(nn.Module):
    def __init__(self):
        super().__init__()
        # Define all Layers Here
        self.lin1 = myCustomLinearLayer(784,10)
        
    def forward(self, x):
        # Connect the layer Outputs here to define the forward pass
        x = self.lin1(x)
        return x
x = torch.randn((100,784))
model = myCustomNeuralNet()
model(x).size()
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torch.Size([100, 10])