    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
        """
        Apply the model to an input batch.
        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param context: conditioning plugged in via crossattn
        :param y: an [N] Tensor of labels, if class-conditional. an [N, extra_film_condition_dim] Tensor if film-embed conditional
        :return: an [N x C x ...] Tensor of outputs.
        """
        if not self.shape_reported:
            # print("The shape of UNet input is", x.size())
            self.shape_reported = True

        assert (y is not None) == (
            self.num_classes is not None or self.extra_film_condition_dim is not None
        ), "must specify y if and only if the model is class-conditional or film embedding conditional"
        hs = []
        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
        emb = self.time_embed(t_emb)

        if self.num_classes is not None:
            assert y.shape == (x.shape[0],)
            emb = emb + self.label_emb(y)

        if self.use_extra_film_by_addition:
            emb = emb + self.film_emb(y)
        elif self.use_extra_film_by_concat:
            emb = th.cat([emb, self.film_emb(y)], dim=-1)

        h = x.type(self.dtype)
        for module in self.input_blocks:
            h = module(h, emb, context)
            hs.append(h)
        h = self.middle_block(h, emb, context)
        for module in self.output_blocks:
            h = th.cat([h, hs.pop()], dim=1)
            h = module(h, emb, context)
        h = h.type(x.dtype)
        if self.predict_codebook_ids:
            return self.id_predictor(h)
        else:
            return self.out(h)
Apply the model to an input batch.
:param x: an [N x C x ...] Tensor of inputs.
:param timesteps: a 1-D batch of timesteps.
:param context: conditioning plugged in via crossattn
:param y: an [N] Tensor of labels, if class-conditional. an [N, extra_film_condition_dim] Tensor if film-embed conditional
:return: an [N x C x ...] Tensor of outputs.
models/tta/picoaudio/picoaudio/audioldm/latent_diffusion/openaimodel.py