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# Source code for torch.ao.nn.qat.dynamic.modules.linear

    import torch

    __all__ = ["Linear"]

    class Linear(torch.ao.nn.qat.Linear):
        r"""
        A linear module attached with FakeQuantize modules for weight,
        used for dynamic quantization aware training.

        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 `torch.nn.Linear`, with FakeQuantize modules initialized to
        default.
        """

        def __init__(self, in_features, out_features, bias=True,
                     qconfig=None, device=None, dtype=None) -> None:
            super().__init__(in_features, out_features, bias, qconfig, device, dtype)
            if not torch.ao.quantization.qconfig._activation_is_memoryless(qconfig):
                raise ValueError(
                    "Dynamic QAT requires a memoryless observer." +
                    "This means a MovingAverage observer with averaging constant equal to 1"
                )

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