code: def get_text_embeds(self, txts, name="grounding", norm=False):
      tokens = self.tokenizer(
          txts,
          padding="max_length",
          truncation=True,
          max_length=self.max_token_num,
          return_tensors="pt",
      )
      tokens = {key: value.cuda() for key, value in tokens.items()}
      class_emb, token_emb = self.forward_language(
          (tokens["input_ids"], tokens["attention_mask"]), norm=norm
      )
      ret = {
          "tokens": tokens,
          "token_emb": token_emb,
          "class_emb": class_emb,
      }
      setattr(self, "{}_token_embeddings".format(name), ret)
      return ret
  
imports: import os
  from collections import OrderedDict
  import torch
  from mmcv.cnn.bricks import DropPath
  from torch import nn
  from transformers import CLIPTokenizer
  from .utils import get_prompt_templates
label_desc: Use image node_id 3 for calling the LanguageEncoder obj's underlying member method code with example usage: obj.get_text_embeds(txts, name, norm) and returns: ret