def __init__()

in models.py [0:0]

• 27 lines of code
• 3 McCabe index (conditional complexity)

    def __init__(self, input_size, output_size, tds_groups, kernel_size, dropout):
        super(TDS, self).__init__()
        modules = []
        in_channels = input_size
        for tds_group in tds_groups:
            # add downsample layer:
            out_channels = input_size * tds_group["channels"]
            modules.extend(
                [
                    torch.nn.Conv1d(
                        in_channels=in_channels,
                        out_channels=out_channels,
                        kernel_size=kernel_size,
                        padding=kernel_size // 2,
                        stride=tds_group.get("stride", 2),
                    ),
                    torch.nn.ReLU(),
                    torch.nn.Dropout(dropout),
                    torch.nn.InstanceNorm1d(out_channels, affine=True),
                ]
            )
            for _ in range(tds_group["num_blocks"]):
                modules.append(
                    TDSBlock(tds_group["channels"], input_size, kernel_size, dropout)
                )
            in_channels = out_channels
        self.tds = torch.nn.Sequential(*modules)
        self.linear = torch.nn.Linear(in_channels, output_size)