class_docstr: ## CLIP Text Embedder
class_docstr_tok: ["#","#","CLIP","Text","Embedder"]
docstr: :param prompts: are the list of prompts to embed
docstr_tok: [":","param","prompts",":","are","the","list","of","prompts","to","embed"]
code: def forward(self, prompts: List[str]):
      """
      :param prompts: are the list of prompts to embed
      """
      # Tokenize the prompts
      batch_encoding = self.tokenizer(
          prompts,
          truncation=True,
          max_length=self.max_length,
          return_length=True,
          return_overflowing_tokens=False,
          padding="max_length",
          return_tensors="pt",
      )
      # Get token ids
      tokens = batch_encoding["input_ids"].to(self.device)
      # Get CLIP embeddings
      return self.transformer(input_ids=tokens).last_hidden_state
  
imports: from typing import List
  from torch import nn
  from transformers import CLIPTokenizer, CLIPTextModel
label_desc: Use image node_id 2 for calling the CLIPTextEmbedder obj's underlying member method code with example usage: obj.forward(prompts) and returns: self