code: def from_pretrained(
      cls,
      model_name_or_path,
      checkpoint_file="model.pt",
      data_name_or_path=".",
      config_yaml="config.yaml",
      task="speech_to_text",
      generation_args=None,
      **kwargs,
  ):
      from fairseq import hub_utils
  
      x = hub_utils.from_pretrained(
          model_name_or_path,
          checkpoint_file,
          data_name_or_path,
          archive_map=cls.hub_models(),
          config_yaml=config_yaml,
          task=task,
          generation_args=generation_args,
          **kwargs,
      )
      return S2THubInterface(x["args"], x["task"], x["models"][0])
  
imports: import copy
  import logging
  from typing import Dict, List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
  from fairseq import checkpoint_utils, utils
  from fairseq.data.data_utils import lengths_to_padding_mask
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqLanguageModel, register_model, register_model_architecture
  from fairseq.models.speech_to_speech.modules.ctc_decoder import CTCDecoder
  from fairseq.models.speech_to_text.hub_interface import S2THubInterface
  from fairseq.models.transformer import Embedding, TransformerDecoder, TransformerModelBase
  from fairseq.models.wav2vec import Wav2VecEncoder
  from fairseq.modules.layer_norm import LayerNorm
label_desc: Use image node_id 2 for calling the XMTransformerModel obj's underlying member method code with example usage: obj.from_pretrained(cls, model_name_or_path, checkpoint_file, data_name_or_path, config_yaml, task, generation_args) and returns: S2THubInterface