def forward(
        self,
        hidden_states,
        encoder_hidden_states,
        timestep=None,
        attention_mask=None,
        cross_attention_kwargs=None,
        return_dict: bool = True,
    ):
        """
        Args:
            hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
                When continuous, `torch.Tensor` of shape `(batch size, channel, height, width)`): Input hidden_states.
            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep ( `torch.long`, *optional*):
                Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step.
            attention_mask (`torch.Tensor`, *optional*):
                Optional attention mask to be applied in Attention.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.

        Returns:
            [`~models.transformers.transformer_2d.Transformer2DModelOutput`] or `tuple`:
            [`~models.transformers.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a
            `tuple`. When returning a tuple, the first element is the sample tensor.
        """
        input_states = hidden_states

        encoded_states = []
        tokens_start = 0
        # attention_mask is not used yet
        for i in range(2):
            # for each of the two transformers, pass the corresponding condition tokens
            condition_state = encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
            transformer_index = self.transformer_index_for_condition[i]
            encoded_state = self.transformers[transformer_index](
                input_states,
                encoder_hidden_states=condition_state,
                timestep=timestep,
                cross_attention_kwargs=cross_attention_kwargs,
                return_dict=False,
            )[0]
            encoded_states.append(encoded_state - input_states)
            tokens_start += self.condition_lengths[i]

        output_states = encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
        output_states = output_states + input_states

        if not return_dict:
            return (output_states,)

        return Transformer2DModelOutput(sample=output_states)

        Args:
            hidden_states ( When discrete, `torch.LongTensor` of shape `(batch size, num latent pixels)`.
                When continuous, `torch.Tensor` of shape `(batch size, channel, height, width)`): Input hidden_states.
            encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*):
                Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                self-attention.
            timestep ( `torch.long`, *optional*):
                Optional timestep to be applied as an embedding in AdaLayerNorm's. Used to indicate denoising step.
            attention_mask (`torch.Tensor`, *optional*):
                Optional attention mask to be applied in Attention.
            cross_attention_kwargs (`dict`, *optional*):
                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                `self.processor` in
                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                tuple.

        Returns:
            [`~models.transformers.transformer_2d.Transformer2DModelOutput`] or `tuple`:
            [`~models.transformers.transformer_2d.Transformer2DModelOutput`] if `return_dict` is True, otherwise a
            `tuple`. When returning a tuple, the first element is the sample tensor.
        
src/diffusers/models/transformers/dual_transformer_2d.py