code: def forward_language(
      self,
      texts,
      norm=False,
      with_token_embed=True,
      with_cls_embed=True,
  ):
      x = self.lang_encoder(*texts)
      hidden_x = x["last_hidden_state"]
  
      class_embed = None
      if with_cls_embed:
          class_embed = hidden_x[
              torch.arange(hidden_x.size(0)), texts[0].argmax(dim=-1)
          ]
  
          class_embed = class_embed @ self.lang_proj
          if norm:
              class_embed = class_embed / (
                  class_embed.norm(dim=-1, keepdim=True) + 1e-7
              )
  
      hidden_embed = None
      if with_token_embed:
          hidden_embed = hidden_x @ self.lang_proj
          if norm:
              hidden_embed = hidden_embed / (
                  hidden_embed.norm(dim=-1, keepdim=True) + 1e-7
              )
  
      return class_embed, hidden_embed
  
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 6 for calling the LanguageEncoder obj's underlying member method code with example usage: obj.forward_language(texts, norm, with_token_embed, with_cls_embed) and returns: class_embed,  hidden_embed