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
      vocab_size: int,
      text_attention_probs_dropout_prob: float,
      text_hidden_act: str,
      text_hidden_dropout_prob: float,
      text_hidden_size: int,
      text_initializer_range: float,
      text_intermediate_size: int,
      text_max_position_embeddings: int,
      text_num_attention_heads: int,
      text_num_hidden_layers: int,
      text_type_vocab_size: int,
      tokenizer=_tokenizer,
  ):
      super().__init__()
  
      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.bert_config = BertConfig(
          vocab_size_or_config_json_file=vocab_size,
          hidden_size=text_hidden_size,
          num_hidden_layers=text_num_hidden_layers,
          num_attention_heads=text_num_attention_heads,
          intermediate_size=text_intermediate_size,
          hidden_act=text_hidden_act,
          hidden_dropout_prob=text_hidden_dropout_prob,
          attention_probs_dropout_prob=text_attention_probs_dropout_prob,
          max_position_embeddings=text_max_position_embeddings,
          type_vocab_size=text_type_vocab_size,
          initializer_range=text_initializer_range,
          layer_norm_eps=1e-12,
      )
      self.bert = BertModel(self.bert_config)
  
      text_projection = self.create_parameter(
          [text_hidden_size, embed_dim]
      )
      self.add_parameter("text_projection", text_projection)
      logit_scale = self.create_parameter([1])
      self.add_parameter("logit_scale", logit_scale)
  
      self.tokenizer = tokenizer
  
imports: from collections import OrderedDict
  from typing import Tuple
  from typing import Union
  import numpy
  import paddle
  import paddle.nn.functional
  from disco_diffusion_cnclip_vitb16.cn_clip.clip import _tokenizer
  from disco_diffusion_cnclip_vitb16.cn_clip.clip.configuration_bert import BertConfig
  from disco_diffusion_cnclip_vitb16.cn_clip.clip.modeling_bert import BertModel
  from paddle import nn
  from paddle.nn import MultiHeadAttention
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, vocab_size, text_attention_probs_dropout_prob, text_hidden_act, text_hidden_dropout_prob, text_hidden_size, text_initializer_range, text_intermediate_size, text_max_position_embeddings, text_num_attention_heads, text_num_hidden_layers, text_type_vocab_size, tokenizer)