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# Linear

class torch.ao.nn.quantized.Linear(in_features, out_features, bias_=True, dtype=torch.qint8)

  A quantized linear module with quantized tensor as inputs and outputs. We adopt the same interface as torch.nn.Linear, please see https://pytorch.org/docs/stable/nn.html#torch.nn.Linear for documentation.

  Similar to `Linear`, attributes will be randomly initialized at module creation time and will be overwritten later

  Variables:

    - weight (Tensor) – the non-learnable quantized weights of the module of shape (out_features,in_features)(\text{out\_features}, \text{in\_features})(out_features,in_features).

    - bias (Tensor) – the non-learnable bias of the module of shape (out_features)(\text{out\_features})(out_features). If `bias` is `True`, the values are initialized to zero.

    - scale – scale parameter of output Quantized Tensor, type: double

    - zero_point – zero_point parameter for output Quantized 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 Tensor

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