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# Source code for torch.ao.nn.intrinsic.qat.modules.linear_relu

    import torch
    import torch.ao.nn.qat as nnqat
    import torch.ao.nn.intrinsic as nni
    import torch.nn.functional as F

    class LinearReLU(nnqat.Linear, nni._FusedModule):
        r"""
        A LinearReLU module fused from Linear and ReLU modules, attached with
        FakeQuantize modules for weight, used in
        quantization aware training.

        We adopt the same interface as :class:`torch.nn.Linear`.

        Similar to `torch.ao.nn.intrinsic.LinearReLU`, with FakeQuantize modules initialized to
        default.

        Attributes:
            weight: fake quant module for weight

        Examples::

            >>> # xdoctest: +SKIP
            >>> m = nn.qat.LinearReLU(20, 30)
            >>> input = torch.randn(128, 20)
            >>> output = m(input)
            >>> print(output.size())
            torch.Size([128, 30])
        """
        _FLOAT_MODULE = nni.LinearReLU

        def __init__(self, in_features, out_features, bias=True,
                     qconfig=None):
            super().__init__(in_features, out_features, bias, qconfig)

        def forward(self, input):
            return F.relu(F.linear(input, self.weight_fake_quant(self.weight), self.bias))

        @classmethod
        def from_float(cls, mod):
            return super().from_float(mod)

        def to_float(self):
            linear = torch.nn.Linear(self.in_features, self.out_features, self.bias is not None)
            linear.weight = torch.nn.Parameter(self.weight.detach())
            if self.bias is not None:
                linear.bias = torch.nn.Parameter(self.bias.detach())
            relu = torch.nn.ReLU()
            return torch.ao.nn.intrinsic.LinearReLU(linear, relu)

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