class_docstr: Parallel cross-validation engine.
  
  Minimal parallel processing engine. Similar to :class:`ParallelProcessing`,
  but offers less features, only fits the *callers* indexer, and excepts
  no task output.
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docstr: Process caller.
  
  Parameters
  ----------
  caller: iterable
      Iterable for evaluation job.s Expected caller is a
      :class:`Evaluator` instance.
  
  case: str
      evaluation case to run on the evaluator. One of
      ``'preprocess'`` and ``'evaluate'``.
  
  X: array-like of shape [n_samples, n_features]
      Input data
  
  y: array-like of shape [n_samples,], optional.
      targets. Required for fit, should not be passed to predict or
      transform jobs.
  
  path: str or dict, optional
      Custom estimation cache. Pass a string to force use of persistent
      cache on disk. Pass a ``dict`` for in-memory cache (requires
      ``backend != 'multiprocessing'``.
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code: def process(self, caller, case, X, y, path=None, **kwargs):
      """Process caller.
  
      Parameters
      ----------
      caller: iterable
          Iterable for evaluation job.s Expected caller is a
          :class:`Evaluator` instance.
  
      case: str
          evaluation case to run on the evaluator. One of
          ``'preprocess'`` and ``'evaluate'``.
  
      X: array-like of shape [n_samples, n_features]
          Input data
  
      y: array-like of shape [n_samples,], optional.
          targets. Required for fit, should not be passed to predict or
          transform jobs.
  
      path: str or dict, optional
          Custom estimation cache. Pass a string to force use of persistent
          cache on disk. Pass a ``dict`` for in-memory cache (requires
          ``backend != 'multiprocessing'``.
      """
      self._initialize(
          job="fit", X=X, y=y, path=path, split=False, stack=False
      )
      check_initialized(self)
  
      # Use context manager to ensure same parallel job during entire process
      tf = self.job.dir if not isinstance(self.job.dir, list) else None
      with Parallel(
          n_jobs=self.n_jobs,
          temp_folder=tf,
          max_nbytes=None,
          mmap_mode="w+",
          verbose=self.verbose,
          backend=self.backend,
      ) as parallel:
          caller.indexer.fit(
              self.job.predict_in, self.job.targets, self.job.job
          )
          caller(parallel, self.job.args(**kwargs), case)
  
imports: from __future__ import with_statement, division
  import gc
  import os
  import shutil
  import subprocess
  import tempfile
  import warnings
  from abc import ABCMeta, abstractmethod
  import numpy
  from scipy.sparse import issparse, hstack
  from ..None import config
  from ..externals.joblib import Parallel, dump, load
  from ..utils import check_initialized
  from ..utils.exceptions import ParallelProcessingError, ParallelProcessingWarning
  from ..externals.sklearn.validation import check_random_state
label_desc: Use image node_id 2 for calling the ParallelEvaluation obj's underlying member method code with example usage: obj.process(caller, case, X, y, path) without return types