class_docstr: Tweak of :class:`joblib.Parallel` that propagates the scikit-learn configuration.
  
  This subclass of :class:`joblib.Parallel` ensures that the active configuration
  (thread-local) of scikit-learn is propagated to the parallel workers for the
  duration of the execution of the parallel tasks.
  
  The API does not change and you can refer to :class:`joblib.Parallel`
  documentation for more details.
  
  .. versionadded:: 1.3
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docstr: Dispatch the tasks and return the results.
  
  Parameters
  ----------
  iterable : iterable
      Iterable containing tuples of (delayed_function, args, kwargs) that should
      be consumed.
  
  Returns
  -------
  results : list
      List of results of the tasks.
docstr_tok: ["Dispatch","the","tasks","and","return","the","results",".","Parameters","--","--","--","--","--","iterable",":","iterable","Iterable","containing","tuples","of","(","delayed_function",",","args",",","kwargs",")","that","should","be","consumed",".","Returns","--","--","--","-","results",":","list","List","of","results","of","the","tasks","."]
code: def __call__(self, iterable):
      """Dispatch the tasks and return the results.
  
      Parameters
      ----------
      iterable : iterable
          Iterable containing tuples of (delayed_function, args, kwargs) that should
          be consumed.
  
      Returns
      -------
      results : list
          List of results of the tasks.
      """
      # Capture the thread-local scikit-learn configuration at the time
      # Parallel.__call__ is issued since the tasks can be dispatched
      # in a different thread depending on the backend and on the value of
      # pre_dispatch and n_jobs.
      config = get_config()
      iterable_with_config = (
          (_with_config(delayed_func, config), args, kwargs)
          for delayed_func, args, kwargs in iterable
      )
      return super().__call__(iterable_with_config)
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/utils/parallel.py
imports: import functools
  import warnings
  from functools import update_wrapper
  import joblib
  from .._config import config_context, get_config
label_desc: Use image node_id 1 for calling the Parallel obj's underlying member method code with example usage: obj.__call__(iterable) and returns: super