code: def forward(
      self,
      ctx_embeddings: torch.Tensor = None,
      ctx_begin_pos: list = None,
      input_ids: Optional[torch.Tensor] = None,
      attention_mask: Optional[torch.Tensor] = None,
      position_ids: Optional[torch.Tensor] = None,
      output_attentions: Optional[bool] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
  ) -> Union[Tuple, BaseModelOutputWithPooling]:
      return self.text_model(
          ctx_embeddings=ctx_embeddings,
          ctx_begin_pos=ctx_begin_pos,
          input_ids=input_ids,
          attention_mask=attention_mask,
          position_ids=position_ids,
          output_attentions=output_attentions,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
      )
  
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 ContextCLIPTextModel obj's underlying member method code with example usage: obj.forward(ctx_embeddings, ctx_begin_pos, input_ids, attention_mask, position_ids, output_attentions, output_hidden_states, return_dict) and returns: self