Method __init__

src/diffusers/models/resnet.py:404–416

(
        self,
        inp_channels: int,
        out_channels: int,
        kernel_size: int | tuple[int, int],
        n_groups: int = 8,
        activation: str = "mish",
    )

    """

    def __init__(
        self,
        inp_channels: int,
        out_channels: int,
        kernel_size: int | tuple[int, int],
        n_groups: int = 8,
        activation: str = "mish",
    ):
        super().__init__()

        self.conv1d = nn.Conv1d(inp_channels, out_channels, kernel_size, padding=kernel_size // 2)
        self.group_norm = nn.GroupNorm(n_groups, out_channels)
        self.mish = get_activation(activation)

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        intermediate_repr = self.conv1d(inputs)