code: def __init__(self, config: Blip2VisionConfig):
      super().__init__()
      self.config = config
      self.embed_dim = config.hidden_size
      self.image_size = config.image_size
      self.patch_size = config.patch_size
  
      self.class_embedding = nn.Parameter(
          torch.randn(1, 1, self.embed_dim)
      )
  
      self.patch_embedding = nn.Conv2d(
          in_channels=3,
          out_channels=self.embed_dim,
          kernel_size=self.patch_size,
          stride=self.patch_size,
          bias=False,
      )
  
      self.num_patches = (self.image_size // self.patch_size) ** 2
      self.num_positions = self.num_patches + 1
  
      self.position_embedding = nn.Parameter(
          torch.randn(1, self.num_positions, self.embed_dim)
      )
  
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 Blip2VisionEmbeddings object from inherited base classes: nn with example: obj = Blip2VisionEmbeddings(config)