


import torch.nn.quantized as nnq
      >>> m = nnq.ConvTranspose2d(16, 33, 3, stride=2)
      >>> # non-square kernels and unequal stride and with padding
      >>> m = nnq.ConvTranspose2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))
      >>> input = torch.randn(20, 16, 50, 100)
      >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
      >>> output = m(q_input)
      >>> # exact output size can be also specified as an argument
      >>> input = torch.randn(1, 16, 12, 12)
      >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, dtype=torch.quint8)
      >>> downsample = nnq.Conv2d(16, 16, 3, stride=2, padding=1)
      >>> upsample = nnq.ConvTranspose2d(16, 16, 3, stride=2, padding=1)
      >>> h = downsample(q_input)
      >>> h.size()
      torch.Size([1, 16, 6, 6])
      >>> output = upsample(h, output_size=input.size())
      >>> output.size()
      torch.Size([1, 16, 12, 12])


