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# linear

class torch.ao.nn.quantized.functional.linear(input, weight, bias=None, scale=None, zero_point=None)

  Applies a linear transformation to the incoming quantized data: y=x A T+b y = xA^T + b y=x A T+b. See `Linear`

  Note

  Current implementation packs weights on every call, which has penalty on performance. If you want to avoid the overhead, use `Linear`.

  Parameters:

    - input (Tensor) – Quantized input of type torch.quint8

    - weight (Tensor) – Quantized weight of type torch.qint8

    - bias (Tensor) – None or fp32 bias of type torch.float

    - scale (double) – output scale. If None, derived from the input scale

    - zero_point (python:long) – output zero point. If None, derived from the input zero_point

  Return type:

    Tensor

  Shape:

    - Input: (N,∗,i n_f e a t u r e s)(N, *, in\_features)(N,∗,in_f e a t u res) where * means any number of additional dimensions

    - Weight: (o u t_f e a t u r e s,i n_f e a t u r e s)(out\_features, in\_features)(o u t_f e a t u res,in_f e a t u res)

    - Bias: (o u t_f e a t u r e s)(out\_features)(o u t_f e a t u res)

    - Output: (N,∗,o u t_f e a t u r e s)(N, *, out\_features)(N,∗,o u t_f e a t u res)

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