def METHOD_NAME(self, input: Tensor) -> Tensor:
    norm_w = self.normlize(self.branch_weights[:len(self.dilation_rates)])
    if len(self.dilation_rates) == 1:
        outputs = [
            nn.functional.conv2d(
                input,
                weight=self.op_layer.weight,
                bias=self.op_layer.bias,
                stride=self.op_layer.stride,
                padding=self.get_padding(self.dilation_rates[0]),
                dilation=self.dilation_rates[0],
                groups=self.op_layer.groups,
            )
        ]
    else:
        outputs = [
            nn.functional.conv2d(
                input,
                weight=self.op_layer.weight,
                bias=self.op_layer.bias,
                stride=self.op_layer.stride,
                padding=self.get_padding(r),
                dilation=r,
                groups=self.op_layer.groups,
            ) * norm_w[i] for i, r in enumerate(self.dilation_rates)
        ]
    output = outputs[0]
    for i in range(1, len(self.dilation_rates)):
        output += outputs[i]
    return output