Could you explain more about why you need to save the tensor in a list?
Does the following code do what you want?
def forward(self, inputs):
    x = self.conv(inputs)
    # combine features and output ('concat' is just an example) 
    x = torch.cat([x, inputs])  # supposing their dimensions match
    x = self.pool(x)
    x = self.fc(x)
    return x