def forward(self, x: torch.Tensor) -> torch.Tensor:
        r"""The forward method of the `DecoderTiny` class."""
        # Clamp.
        x = torch.tanh(x / 3) * 3

        if torch.is_grad_enabled() and self.gradient_checkpointing:
            x = self._gradient_checkpointing_func(self.layers, x)
        else:
            x = self.layers(x)

        # scale image from [0, 1] to [-1, 1] to match diffusers convention
        return x.mul(2).sub(1)
The forward method of the `DecoderTiny` class.
src/diffusers/models/autoencoders/vae.py