code: def maybe_load_pretrained(
      cls, component, checkpoint: Optional[str] = None
  ):
      if checkpoint is None:
          return component
  
      _load = checkpoint_utils.load_pretrained_component_from_model
      try:
          return _load(component, checkpoint)
      except RuntimeError as e:
          logger.warning(e)
          return _load(component, checkpoint, strict=False)
  
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 5 for calling the XMTransformerModel obj's underlying member method code with example usage: obj.maybe_load_pretrained(cls, component, checkpoint) and returns: component, _load, _load