class_docstr: Arguments to enable and configure gradual unfreezing and discriminative learning rates as used
  in Universal Language Model Fine-tuning (ULMFiT) [HR18]_.
class_docstr_tok: ["Arguments","to","enable","and","configure","gradual","unfreezing","and","discriminative","learning","rates","as","used","in","Universal","Language","Model","Fine-tuning","(","ULMFiT",")","[","HR18","]","_","."]
code: def __init__(
      self,
      base_lr,
      layerwise_gradient_decay,
      gradual_unfreezing=-1,
      cut_fraction=0.1,
  ):
      if base_lr <= 0:
          raise ValueError(
              "FineTuningArguments: base_lr must be greater than zero"
          )
      if layerwise_gradient_decay:
          if not (
              0 < layerwise_gradient_decay < 1
              or layerwise_gradient_decay == -1
          ):
              raise ValueError(
                  "FineTuningArguments: valid values for layerwise_gradient_decay "
                  "are between 0 and 1 (or set it to -1 to disable it)"
              )
  
      self.base_lr = base_lr
      self.layerwise_gradient_decay = layerwise_gradient_decay
  
      self.gradual_unfreezing = gradual_unfreezing
      # deprecated: This will be removed in the next version
      _unused = cut_fraction
  
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 FineTuningArguments object from inherited base classes: object with example: obj = FineTuningArguments(base_lr, layerwise_gradient_decay, gradual_unfreezing, cut_fraction)