class UpBlock1DNoSkip(nn.Module):
    def __init__(self, in_channels: int, out_channels: int, mid_channels: Optional[int] = None):
        super().__init__()
        mid_channels = in_channels if mid_channels is None else mid_channels

        resnets = [
            ResConvBlock(2 * in_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, mid_channels),
            ResConvBlock(mid_channels, mid_channels, out_channels, is_last=True),
        ]

        self.resnets = nn.ModuleList(resnets)

    def forward(
        self,
        hidden_states: torch.Tensor,
        res_hidden_states_tuple: Tuple[torch.Tensor, ...],
        temb: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        res_hidden_states = res_hidden_states_tuple[-1]
        hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1)

        for resnet in self.resnets:
            hidden_states = resnet(hidden_states)

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