class_docstr: Hugging Face Transformers PostEncoder.
class_docstr_tok: ["Hugging","Face","Transformers","PostEncoder","."]
docstr: Initialize the module.
docstr_tok: ["Initialize","the","module","."]
code: def __init__(
      self,
      input_size: int,
      model_name_or_path: str,
      lang_token_id: int = -1,
  ):
      """Initialize the module."""
      assert check_argument_types()
      super().__init__()
  
      if not is_transformers_available:
          raise ImportError(
              "`transformers` is not available. Please install it via `pip install"
              " transformers` or `cd /path/to/espnet/tools && . ./activate_python.sh"
              " && ./installers/install_transformers.sh`."
          )
  
      model = AutoModel.from_pretrained(model_name_or_path)
  
      if hasattr(model, "encoder"):
          self.transformer = model.encoder
      else:
          self.transformer = model
  
      self.pretrained_params = copy.deepcopy(
          self.transformer.state_dict()
      )
  
      self.lang_token_id = lang_token_id
  
file_path: espnet/espnet2/asr/encoder/hugging_face_transformers_encoder.py
imports: import copy
  import logging
  from typing import Tuple
  import torch
  from typeguard import check_argument_types
  from espnet2.asr.encoder.abs_encoder import AbsEncoder
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
label_desc: Use image node_id 1 to create a new HuggingFaceTransformersEncoder object from inherited base classes: AbsEncoder with example: obj = HuggingFaceTransformersEncoder(input_size, model_name_or_path, lang_token_id)