code: def config_context(
      *,
      assume_finite=None,
      working_memory=None,
      print_changed_only=None,
      display=None,
      pairwise_dist_chunk_size=None,
      enable_cython_pairwise_dist=None,
      array_api_dispatch=None,
      transform_output=None,
      enable_metadata_routing=None,
      skip_parameter_validation=None,
  ):
      """Context manager for global scikit-learn configuration.
  
      Parameters
      ----------
      assume_finite : bool, default=None
          If True, validation for finiteness will be skipped,
          saving time, but leading to potential crashes. If
          False, validation for finiteness will be performed,
          avoiding error. If None, the existing value won't change.
          The default value is False.
  
      working_memory : int, default=None
          If set, scikit-learn will attempt to limit the size of temporary arrays
          to this number of MiB (per job when parallelised), often saving both
          computation time and memory on expensive operations that can be
          performed in chunks. If None, the existing value won't change.
          The default value is 1024.
  
      print_changed_only : bool, default=None
          If True, only the parameters that were set to non-default
          values will be printed when printing an estimator. For example,
          ``print(SVC())`` while True will only print 'SVC()', but would print
          'SVC(C=1.0, cache_size=200, ...)' with all the non-changed parameters
          when False. If None, the existing value won't change.
          The default value is True.
  
          .. versionchanged:: 0.23
             Default changed from False to True.
  
      display : {'text', 'diagram'}, default=None
          If 'diagram', estimators will be displayed as a diagram in a Jupyter
          lab or notebook context. If 'text', estimators will be displayed as
          text. If None, the existing value won't change.
          The default value is 'diagram'.
  
          .. versionadded:: 0.23
  
      pairwise_dist_chunk_size : int, default=None
          The number of row vectors per chunk for the accelerated pairwise-
          distances reduction backend. Default is 256 (suitable for most of
          modern laptops' caches and architectures).
  
          Intended for easier benchmarking and testing of scikit-learn internals.
          End users are not expected to benefit from customizing this configuration
          setting.
  
          .. versionadded:: 1.1
  
      enable_cython_pairwise_dist : bool, default=None
          Use the accelerated pairwise-distances reduction backend when
          possible. Global default: True.
  
          Intended for easier benchmarking and testing of scikit-learn internals.
          End users are not expected to benefit from customizing this configuration
          setting.
  
          .. versionadded:: 1.1
  
      array_api_dispatch : bool, default=None
          Use Array API dispatching when inputs follow the Array API standard.
          Default is False.
  
          See the :ref:`User Guide <array_api>` for more details.
  
          .. versionadded:: 1.2
  
      transform_output : str, default=None
          Configure output of `transform` and `fit_transform`.
  
          See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
          for an example on how to use the API.
  
          - `"default"`: Default output format of a transformer
          - `"pandas"`: DataFrame output
          - `"polars"`: Polars output
          - `None`: Transform configuration is unchanged
  
          .. versionadded:: 1.2
          .. versionadded:: 1.4
              `"polars"` option was added.
  
      enable_metadata_routing : bool, default=None
          Enable metadata routing. By default this feature is disabled.
  
          Refer to :ref:`metadata routing user guide <metadata_routing>` for more
          details.
  
          - `True`: Metadata routing is enabled
          - `False`: Metadata routing is disabled, use the old syntax.
          - `None`: Configuration is unchanged
  
          .. versionadded:: 1.3
  
      skip_parameter_validation : bool, default=None
          If `True`, disable the validation of the hyper-parameters' types and values in
          the fit method of estimators and for arguments passed to public helper
          functions. It can save time in some situations but can lead to low level
          crashes and exceptions with confusing error messages.
  
          Note that for data parameters, such as `X` and `y`, only type validation is
          skipped but validation with `check_array` will continue to run.
  
          .. versionadded:: 1.3
  
      Yields
      ------
      None.
  
      See Also
      --------
      set_config : Set global scikit-learn configuration.
      get_config : Retrieve current values of the global configuration.
  
      Notes
      -----
      All settings, not just those presently modified, will be returned to
      their previous values when the context manager is exited.
  
      Examples
      --------
      >>> import sklearn
      >>> from sklearn.utils.validation import assert_all_finite
      >>> with sklearn.config_context(assume_finite=True):
      ...     assert_all_finite([float('nan')])
      >>> with sklearn.config_context(assume_finite=True):
      ...     with sklearn.config_context(assume_finite=False):
      ...         assert_all_finite([float('nan')])
      Traceback (most recent call last):
      ...
      ValueError: Input contains NaN...
      """
      old_config = get_config()
      set_config(
          assume_finite=assume_finite,
          working_memory=working_memory,
          print_changed_only=print_changed_only,
          display=display,
          pairwise_dist_chunk_size=pairwise_dist_chunk_size,
          enable_cython_pairwise_dist=enable_cython_pairwise_dist,
          array_api_dispatch=array_api_dispatch,
          transform_output=transform_output,
          enable_metadata_routing=enable_metadata_routing,
          skip_parameter_validation=skip_parameter_validation,
      )
  
      try:
          yield
      finally:
          set_config(**old_config)
  
imports: import os
  import threading
  from contextlib import contextmanager
label_desc: Use image node_id 4 for calling a global function with example usage: config_context() without return types