class_docstr: ## CLIP Text Embedder
class_docstr_tok: ["#","#","CLIP","Text","Embedder"]
docstr: :param version: is the model version
  :param device: is the device
  :param max_length: is the max length of the tokenized prompt
docstr_tok: [":","param","version",":","is","the","model","version",":","param","device",":","is","the","device",":","param","max_length",":","is","the","max","length","of","the","tokenized","prompt"]
code: def __init__(
      self,
      version: str = "openai/clip-vit-large-patch14",
      device="cuda:0",
      max_length: int = 77,
  ):
      """
      :param version: is the model version
      :param device: is the device
      :param max_length: is the max length of the tokenized prompt
      """
      super().__init__()
      # Load the tokenizer
      self.tokenizer = CLIPTokenizer.from_pretrained(version)
      # Load the CLIP transformer
      self.transformer = CLIPTextModel.from_pretrained(version).eval()
  
      self.device = device
      self.max_length = max_length
  
imports: from typing import List
  from torch import nn
  from transformers import CLIPTokenizer, CLIPTextModel
label_desc: Use image node_id 1 to create a new CLIPTextEmbedder object from inherited base classes: nn with example: obj = CLIPTextEmbedder(version, device, max_length)