also, this:
`model = Model(input_size, output_size)
if torch.cuda.device_count() > 1:
print(“Let’s use”, torch.cuda.device_count(), “GPUs!”)
dim = 0 [30, xxx] -> [10, …], [10, …], [10, …] on 3 GPUs
model = nn.DataParallel(model)
model.to(device)`
outputs:
  (fc): Linear(in_features=5, out_features=2, bias=True)
)

where “Model()” is:
    # Our model

    def __init__(self, input_size, output_size):
        super(Model, self).__init__()
        self.fc = nn.Linear(input_size, output_size)

    def forward(self, input):
        output = self.fc(input)
        print("\tIn Model: input size", input.size(),
              "output size", output.size())

        return output
type or paste code here