code: def __init__(
      self,
      max_text_length: int = 77,
      vocab_size: int = 49408,
      text_embed_dim: int = 512,
      text_heads: int = 8,
      text_layers: int = 12,
      text_hidden_act: str = "quick_gelu",
      projection_dim: int = 512,
  ):
      super().__init__()
  
      self.text_model = TextTransformer(
          context_length=max_text_length,
          transformer_width=text_embed_dim,
          transformer_heads=text_heads,
          transformer_layers=text_layers,
          vocab_size=vocab_size,
          activation=text_hidden_act,
          normalize_before=True,
      )
  
      self.text_projection = paddle.create_parameter(
          (text_embed_dim, projection_dim), paddle.get_default_dtype()
      )
  
imports: import paddle
  import paddle.nn
  from paddlenlp.transformers.clip.modeling import TextTransformer
label_desc: Use image node_id 1 to create a new CLIPText object from inherited base classes: nn with example: obj = CLIPText(max_text_length, vocab_size, text_embed_dim, text_heads, text_layers, text_hidden_act, projection_dim)