def forward(self, hidden_state):
        """
        Forward pass through the residual unit.

        Args:
            hidden_state (`torch.Tensor` of shape `(batch_size, channels, time_steps)`):
                Input tensor .

        Returns:
            output_tensor (`torch.Tensor` of shape `(batch_size, channels, time_steps)`)
                Input tensor after passing through the residual unit.
        """
        output_tensor = hidden_state
        output_tensor = self.conv1(self.snake1(output_tensor))
        output_tensor = self.conv2(self.snake2(output_tensor))

        padding = (hidden_state.shape[-1] - output_tensor.shape[-1]) // 2
        if padding > 0:
            hidden_state = hidden_state[..., padding:-padding]
        output_tensor = hidden_state + output_tensor
        return output_tensor

        Forward pass through the residual unit.

        Args:
            hidden_state (`torch.Tensor` of shape `(batch_size, channels, time_steps)`):
                Input tensor .

        Returns:
            output_tensor (`torch.Tensor` of shape `(batch_size, channels, time_steps)`)
                Input tensor after passing through the residual unit.
        
src/diffusers/models/autoencoders/autoencoder_oobleck.py