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 = paddle.create_parameter(
          (self.context_length, transformer_width), "float32"
      )
      self.ln_final = nn.LayerNorm(transformer_width)
  
      self.text_projection = paddle.create_parameter(
          (transformer_width, embed_dim), "float32"
      )
      self.logit_scale = paddle.create_parameter((1,), "float32")
  
imports: from typing import Tuple
  from typing import Union
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import nn
  from .layers import AttentionPool2d
  from .layers import Bottleneck
  from .layers import MultiHeadAttention
  from .layers import ResidualAttentionBlock
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)