class DownBlock1DNoSkip(nn.Module):
    def __init__(self, out_channels: int, in_channels: int, mid_channels: Optional[int] = None):
        super().__init__()
        mid_channels = out_channels if mid_channels is None else mid_channels

        resnets = [
            ResConvBlock(in_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, out_channels),
        ]

        self.resnets = nn.ModuleList(resnets)

    def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
        hidden_states = torch.cat([hidden_states, temb], dim=1)
        for resnet in self.resnets:
            hidden_states = resnet(hidden_states)

        return hidden_states, (hidden_states,)
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/unets/unet_1d_blocks.py