code: def __init__(
      self,
      embed_dim: int,
      # vision
      image_resolution: int,
      vision_layers: Union[Tuple[int, int, int, int], int],
      vision_width: int,
      vision_patch_size: int,
      # text
      context_length: int,
      vocab_size: int,
      transformer_width: int,
      transformer_heads: int,
      transformer_layers: int,
  ):
      super().__init__()
  
      self.context_length = context_length
  
      if isinstance(vision_layers, (tuple, list)):
          vision_heads = vision_width * 32 // 64
          self.visual = ModifiedResNet(
              layers=vision_layers,
              output_dim=embed_dim,
              heads=vision_heads,
              input_resolution=image_resolution,
              width=vision_width,
          )
      else:
          vision_heads = vision_width // 64
          self.visual = VisualTransformer(
              input_resolution=image_resolution,
              patch_size=vision_patch_size,
              width=vision_width,
              layers=vision_layers,
              heads=vision_heads,
              output_dim=embed_dim,
          )
  
      self.transformer = Transformer(
          width=transformer_width,
          layers=transformer_layers,
          heads=transformer_heads,
          attn_mask=self.build_attention_mask(),
      )
  
      self.vocab_size = vocab_size
      self.token_embedding = nn.Embedding(vocab_size, transformer_width)
      self.positional_embedding = nn.Parameter(
          torch.empty(self.context_length, transformer_width)
      )
      self.ln_final = LayerNorm(transformer_width)
  
      self.text_projection = nn.Parameter(
          torch.empty(transformer_width, embed_dim)
      )
      self.logit_scale = nn.Parameter(torch.ones([]))
  
      self.initialize_parameters()
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 1 to create a new CLIP object from inherited base classes: nn with example: obj = CLIP(embed_dim, image_resolution, vision_layers, vision_width, vision_patch_size, context_length, vocab_size, transformer_width, transformer_heads, transformer_layers)