def forward(self, inputs: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
        """
        Args:
            inputs : [ batch_size x inp_channels x horizon ]
            t : [ batch_size x embed_dim ]

        returns:
            out : [ batch_size x out_channels x horizon ]
        """
        t = self.time_emb_act(t)
        t = self.time_emb(t)
        out = self.conv_in(inputs) + rearrange_dims(t)
        out = self.conv_out(out)
        return out + self.residual_conv(inputs)

        Args:
            inputs : [ batch_size x inp_channels x horizon ]
            t : [ batch_size x embed_dim ]

        returns:
            out : [ batch_size x out_channels x horizon ]
        
src/diffusers/models/resnet.py