code: def text_embeddings(self, prompts: List[str]) -> torch.Tensor:
      tokens, lens = self.encode_prompts(prompts)
      z_t = self.text_encoder(tokens, lens)
      return z_t / (
          torch.linalg.norm(z_t, dim=-1, keepdim=True) + 1e-12
      )
  
file_path: glide-text2im/glide_text2im/clip/model_creation.py
imports: import os
  from functools import lru_cache
  from typing import Any, Callable, Dict, List, Optional, Tuple
  import attr
  import numpy
  import torch
  import torch.nn
  import yaml
  from glide_text2im.tokenizer.simple_tokenizer import SimpleTokenizer
  from .encoders import ImageEncoder, TextEncoder
label_desc: Use image node_id 2 for calling the CLIPModel obj's underlying member method code with example usage: obj.text_embeddings(prompts) and returns: unknown