class_docstr: This class defines global attributes that are set upon instantiation of
  :class:`environment.Environment`. All attributes contained herein are class variables (not
  instance variables) because the expectation is for the attributes of this class to be set only
  once, then referenced by operations that may be executed after instantiating a
  :class:`environment.Environment`. This allows functions to be called or classes to be initiated
  without passing a reference to the currently active Environment, because they check the
  attributes of this class, instead
  
  Attributes
  ----------
  Env: None
      This is set to "self" in :meth:`environment.Environment.__init__`. This fact allows other
      modules to check if :attr:`settings.G.Env` is None. If None, a
      :class:`environment.Environment` has not yet been instantiated. If not None, any attributes
      or methods of the instantiated Env may be called
  save_transformed_predictions: False
      Declares format in which a model's predictions should be saved, with regard to
      :class:`feature_engineering.FeatureEngineer` transformations. If no transformation of the
      target variable takes place (either through :class:`feature_engineering.FeatureEngineer`,
      :class:`feature_engineering.EngineerStep`, or otherwise), then this setting can be ignored.
  
      If `save_transformed_predictions` is True, and target transformation does occur, then
      experiment predictions are saved in the same form as the transformed target, which is the
      form returned directly by a fitted model's `predict` method. For example, if target data is
      label-encoded, and an :class:`feature_engineering.EngineerStep` is used to one-hot encode
      the target, then one-hot-encoded predictions will be saved.
  
      Conversely, if `save_transformed_predictions` is False (default), and target transformation
      does occur, then experiment predictions are saved in the inverted form of the transformed
      target, which is the same form as the original target data. Continuing the example of
      label-encoded target data, and an :class:`feature_engineering.EngineerStep` to one-hot
      encode the target, in this case, label-encoded predictions will be saved.
  priority_callbacks: Tuple
      Intended for internal use only. The contents of this tuple are inserted at the front of an
      Experiment's list of callback bases via :class:`experiment_core.ExperimentMeta`, ahead of
      even the Experiment's original base classes. This is used primarily for testing callbacks,
      but it can also be used if you absolutely need a callback to be placed before the
      Experiment's other ancestors in its MRO
  log_: print
      ...
  debug_: print
      ...
  warn_: print
      ...
  import_hooks: List
      ...
  sentinel_registry: List
      ...
docstr: Set in :meth:`environment.Environment.initialize_reporting` to the updated version of
  :meth:`reporting.ReportingHandler.debug`
docstr_tok: ["Set","in",":","meth",":","`","environment.Environment.initialize_reporting","`","to","the","updated","version","of",":","meth",":","`","reporting.ReportingHandler.debug","`"]
code: def debug(content, *args, **kwargs):
      """Set in :meth:`environment.Environment.initialize_reporting` to the updated version of
      :meth:`reporting.ReportingHandler.debug`"""
      print(content, *args, **kwargs)
  
file_path: hyperparameter_hunter/hyperparameter_hunter/settings.py
imports: import os.path
  import warnings
label_desc: Use image node_id 2 for calling the G obj's underlying member method code with example usage: obj.debug(content) without return types