code: def __init__(
      self,
      embed_dim: int,
      image_resolution: int,
      vision_layers: Union[Tuple[int, int, int, int], int],
      vision_width: int,
      vision_patch_size: int,
      context_length: int,
      vocab_size: int,
      transformer_width: int,
      transformer_heads: int,
      transformer_layers: int,
  ):
      super().__init__()
      self.context_length = context_length
  
      vision_heads = vision_width // 64
      self.visual = VisionTransformer(
          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 = paddle.nn.Embedding(
          vocab_size, transformer_width
      )
  
      self.positional_embedding = self.create_parameter(
          shape=(self.context_length, transformer_width),
          default_initializer=zeros_,
      )
      self.add_parameter(
          "positional_embedding", self.positional_embedding
      )
  
      self.ln_final = paddle.nn.LayerNorm(transformer_width)
  
      self.text_projection = self.create_parameter(
          shape=(transformer_width, embed_dim),
          default_initializer=zeros_,
      )
      self.add_parameter("text_projection", self.text_projection)
  
imports: from collections import OrderedDict
  from typing import Tuple, Union
  import numpy
  import paddle
  from paddle.nn.initializer import Constant
  from paddle.vision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
label_desc: Use image node_id 1 to create a new CLIP object from inherited base classes: paddle.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)