code: def forward(
      self,
      ctx_embeddings: torch.Tensor,
      ctx_begin_pos: list,
      input_ids: Optional[torch.LongTensor] = None,
      position_ids: Optional[torch.LongTensor] = None,
      inputs_embeds: Optional[torch.FloatTensor] = None,
  ) -> torch.Tensor:
      if ctx_embeddings is None:
          ctx_len = 0
      else:
          ctx_len = ctx_embeddings.shape[1]
  
      seq_length = (
          input_ids.shape[-1]
          if input_ids is not None
          else inputs_embeds.shape[-2]
      ) + ctx_len
  
      if position_ids is None:
          position_ids = self.position_ids[:, :seq_length]
  
      if inputs_embeds is None:
          inputs_embeds = self.token_embedding(input_ids)
  
          # for each input embeddings, add the ctx embeddings at the correct position
          input_embeds_ctx = []
          bsz = inputs_embeds.shape[0]
  
          if ctx_embeddings is not None:
              for i in range(bsz):
                  cbp = ctx_begin_pos[i]
  
                  prefix = inputs_embeds[i, :cbp]
                  # remove the special token embedding
                  suffix = inputs_embeds[i, cbp:]
  
                  input_embeds_ctx.append(
                      torch.cat(
                          [prefix, ctx_embeddings[i], suffix], dim=0
                      )
                  )
  
              inputs_embeds = torch.stack(input_embeds_ctx, dim=0)
  
      position_embeddings = self.position_embedding(position_ids)
      embeddings = inputs_embeds + position_embeddings
  
      return embeddings
  
file_path: llmga/llmga/diffusers/src/diffusers/pipelines/blip_diffusion/modeling_ctx_clip.py
imports: from typing import Optional, Tuple, Union
  import torch
  from torch import nn
  from transformers import CLIPPreTrainedModel
  from transformers.modeling_outputs import BaseModelOutputWithPooling
  from transformers.models.clip.configuration_clip import CLIPTextConfig
  from transformers.models.clip.modeling_clip import CLIPEncoder, _expand_mask
label_desc: Use image node_id 2 for calling the ContextCLIPTextEmbeddings obj's underlying member method code with example usage: obj.forward(ctx_embeddings, ctx_begin_pos, input_ids, position_ids, inputs_embeds) and returns: embeddings