code: def __init__(self, config: Blip2VisionConfig):
      super().__init__(config)
      self.config = config
      embed_dim = config.hidden_size
      self.embeddings = Blip2VisionEmbeddings(config)
      self.pre_layernorm = nn.LayerNorm(
          embed_dim, eps=config.layer_norm_eps
      )
      self.encoder = Blip2Encoder(config)
      self.post_layernorm = nn.LayerNorm(
          embed_dim, eps=config.layer_norm_eps
      )
  
      self.post_init()
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from transformers import BertTokenizer
  from transformers.activations import QuickGELUActivation
  from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPooling, BaseModelOutputWithPoolingAndCrossAttentions
  from transformers.models.blip_2.configuration_blip_2 import Blip2Config, Blip2VisionConfig
  from transformers.models.blip_2.modeling_blip_2 import Blip2Encoder, Blip2PreTrainedModel, Blip2QFormerAttention, Blip2QFormerIntermediate, Blip2QFormerOutput
  from transformers.pytorch_utils import apply_chunking_to_forward
  from transformers.utils import logging, replace_return_docstrings
label_desc: Use image node_id 1 to create a new Blip2VisionModel object from inherited base classes: Blip2PreTrainedModel with example: obj = Blip2VisionModel(config)