Tensor, type: long
Examples:
  >>> m = nn.quantized.Linear(20, 30)
  >>> input = torch.randn(128, 20)
  >>> input = torch.quantize_per_tensor(input, 1.0, 0, torch.quint8)
  >>> output = m(input)
  >>> print(output.size())
  torch.Size([128, 30])

classmethod from_float(mod)
  Create a quantized module from an observed float module

  Parameters:
     **mod** (*Module*) -- a float module, either produced by
     torch.ao.quantization utilities or provided by the user

classmethod from_reference(ref_qlinear, output_scale, output_zero_point)
  Create a (fbgemm/qnnpack) quantized module from a reference
  quantized module

  Parameters:
     * **ref_qlinear** (*Module*) -- a reference quantized linear
       module, either produced by torch.ao.quantization utilities
       or provided by the user

     * **output_scale** (*float*) -- scale for output Tensor

     * **output_zero_point** (*int*) -- zero point for output
