code: def _default_optimizer(self, base_lr):
      if self.fine_tuning_arguments is not None:
          params = _build_layer_specific_params(
              self.model, self.lr, self.fine_tuning_arguments
          )
      else:
          params = [
              param
              for param in self.model.parameters()
              if param.requires_grad
          ]
  
      return params, AdamW(params, lr=base_lr, eps=1e-8)
  
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 5 for calling the TransformerBasedClassification obj's underlying member method code with example usage: obj._default_optimizer(base_lr) and returns: params,  AdamW