def forward(self, id_embeds: torch.Tensor) -> torch.Tensor:
        """Forward pass.

        Args:
            id_embeds (torch.Tensor): Input Tensor (ID embeds).
        Returns:
            torch.Tensor: Output Tensor.
        """
        id_embeds = id_embeds.to(self.clip_embeds.dtype)
        id_embeds = self.proj(id_embeds)
        id_embeds = id_embeds.reshape(-1, self.num_tokens, self.embed_dim)
        id_embeds = self.norm(id_embeds)
        latents = id_embeds

        clip_embeds = self.proj_in(self.clip_embeds)
        x = clip_embeds.reshape(-1, clip_embeds.shape[2], clip_embeds.shape[3])

        for block in self.layers:
            residual = latents
            latents = block(x, latents, residual)

        latents = self.proj_out(latents)
        out = self.norm_out(latents)
        if self.shortcut:
            out = id_embeds + self.shortcut_scale * out
        return out
Forward pass.

        Args:
            id_embeds (torch.Tensor): Input Tensor (ID embeds).
        Returns:
            torch.Tensor: Output Tensor.
        
src/diffusers/models/embeddings.py