docstr: Parameters
  ----------
  model : str
      Name of the transformer model. Will be passed into `AutoModel.from_pretrained()`.
  tokenizer : str, default=None
      Name of the tokenizer if deviating from the model name. Will be passed
      into `AutoTokenizer.from_pretrained()`.
  config : str, default=None
      Name of the config if deviating from the model name. Will be passed into
      `AutoConfig.from_pretrained()`.
  model_loading_strategy: ModelLoadingStrategy, default=ModelLoadingStrategy.DEFAULT
      Specifies if there should be attempts to download the model or if only local
      files should be used.
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code: def __init__(
      self,
      model,
      tokenizer=None,
      config=None,
      model_loading_strategy: ModelLoadingStrategy = ModelLoadingStrategy.DEFAULT,
  ):
      """
      Parameters
      ----------
      model : str
          Name of the transformer model. Will be passed into `AutoModel.from_pretrained()`.
      tokenizer : str, default=None
          Name of the tokenizer if deviating from the model name. Will be passed
          into `AutoTokenizer.from_pretrained()`.
      config : str, default=None
          Name of the config if deviating from the model name. Will be passed into
          `AutoConfig.from_pretrained()`.
      model_loading_strategy: ModelLoadingStrategy, default=ModelLoadingStrategy.DEFAULT
          Specifies if there should be attempts to download the model or if only local
          files should be used.
      """
      self.model = model
      self.tokenizer = tokenizer
      self.config = config
  
      if self.tokenizer is None:
          self.tokenizer = model
      if self.config is None:
          self.config = model
  
      self.model_loading_strategy = model_loading_strategy
  
imports: import datetime
  import logging
  import tempfile
  import warnings
  import numpy
  from functools import partial
  from small_text.classifiers.classification import EmbeddingMixin
  from small_text.integrations.pytorch.exceptions import PytorchNotFoundError
  from small_text.utils.classification import get_splits
  from small_text.utils.context import build_pbar_context
  from small_text.utils.data import check_training_data, list_length
  from small_text.utils.datetime import format_timedelta
  from small_text.utils.annotations import early_stopping_deprecation_warning, model_selection_deprecation_warning
  from small_text.utils.labels import get_num_labels
  from small_text.utils.logging import verbosity_logger, VERBOSITY_MORE_VERBOSE
  from small_text.utils.system import get_tmp_dir_base
label_desc: Use image node_id 1 to create a new TransformerModelArguments object from inherited base classes: object with example: obj = TransformerModelArguments(model, tokenizer, config, model_loading_strategy)