code: def __init__(self, config: CLIPTextConfig):
      super().__init__(config)
      self.text_model = ContextCLIPTextTransformer(config)
      # Initialize weights and apply final processing
      self.post_init()
  
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 1 to create a new ContextCLIPTextModel object from inherited base classes: CLIPPreTrainedModel with example: obj = ContextCLIPTextModel(config)