class_docstr: Grid of parameters with a discrete number of values for each.
  
  .. deprecated:: 0.18
      This module will be removed in 0.20.
      Use :class:`sklearn.model_selection.ParameterGrid` instead.
  
  Can be used to iterate over parameter value combinations with the
  Python built-in function iter.
  
  Read more in the :ref:`User Guide <grid_search>`.
  
  Parameters
  ----------
  param_grid : dict of string to sequence, or sequence of such
      The parameter grid to explore, as a dictionary mapping estimator
      parameters to sequences of allowed values.
  
      An empty dict signifies default parameters.
  
      A sequence of dicts signifies a sequence of grids to search, and is
      useful to avoid exploring parameter combinations that make no sense
      or have no effect. See the examples below.
  
  Examples
  --------
  >>> from sklearn.grid_search import ParameterGrid
  >>> param_grid = {'a': [1, 2], 'b': [True, False]}
  >>> list(ParameterGrid(param_grid)) == (
  ...    [{'a': 1, 'b': True}, {'a': 1, 'b': False},
  ...     {'a': 2, 'b': True}, {'a': 2, 'b': False}])
  True
  
  >>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}]
  >>> list(ParameterGrid(grid)) == [{'kernel': 'linear'},
  ...                               {'kernel': 'rbf', 'gamma': 1},
  ...                               {'kernel': 'rbf', 'gamma': 10}]
  True
  >>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
  True
  
  See also
  --------
  :class:`GridSearchCV`:
      uses ``ParameterGrid`` to perform a full parallelized parameter search.
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docstr: Get the parameters that would be ``ind``th in iteration
  
  Parameters
  ----------
  ind : int
      The iteration index
  
  Returns
  -------
  params : dict of string to any
      Equal to list(self)[ind]
code: def __getitem__(self, ind):
      """Get the parameters that would be ``ind``th in iteration
  
      Parameters
      ----------
      ind : int
          The iteration index
  
      Returns
      -------
      params : dict of string to any
          Equal to list(self)[ind]
      """
      # This is used to make discrete sampling without replacement memory
      # efficient.
      for sub_grid in self.param_grid:
          # XXX: could memoize information used here
          if not sub_grid:
              if ind == 0:
                  return {}
              else:
                  ind -= 1
                  continue
  
          # Reverse so most frequent cycling parameter comes first
          keys, values_lists = zip(*sorted(sub_grid.items())[::-1])
          sizes = [len(v_list) for v_list in values_lists]
          total = np.product(sizes)
  
          if ind >= total:
              # Try the next grid
              ind -= total
          else:
              out = {}
              for key, v_list, n in zip(keys, values_lists, sizes):
                  ind, offset = divmod(ind, n)
                  out[key] = v_list[offset]
              return out
  
      raise IndexError("ParameterGrid index out of range")
  
imports: from __future__ import print_function
  from abc import ABCMeta, abstractmethod
  from collections import Mapping, namedtuple, Sized
  from functools import partial, reduce
  from itertools import product
  import operator
  import warnings
  import numpy
  from .base import BaseEstimator, is_classifier, clone
  from .base import MetaEstimatorMixin
  from .cross_validation import check_cv
  from .cross_validation import _fit_and_score
  from .externals.joblib import Parallel, delayed
  from .externals import six
  from .utils import check_random_state
  from .utils.random import sample_without_replacement
  from .utils.validation import _num_samples, indexable
  from .utils.metaestimators import if_delegate_has_method
  from .metrics.scorer import check_scoring
  from .exceptions import ChangedBehaviorWarning
label_desc: Use image node_id 4 for calling the ParameterGrid obj's underlying member method code with example usage: obj.__getitem__(ind) and returns: out, dict