class_docstr: Base Index class.
  
  Specification of indexer-wide methods and attributes that we can always
  expect to find in any indexer. Helps to provide a uniform interface
  during parallel estimation.
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docstr: Method for storing array data.
  
  Parameters
  ----------
  X : array-like of shape [n_samples, optional]
      array to _collect dimension data from.
  
  y : array-like, optional
      label data
  
  job : str, optional
      optional job type data
  
  Returns
  -------
  instance :
      indexer with stores sample size data.
  
  Notes
  -----
  Fitting an indexer stores nothing that points to the array
  or memmap ``X``. Only the ``shape`` attribute of ``X`` is called.
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code: def fit(self, X, y=None, job=None):
      """Method for storing array data.
  
      Parameters
      ----------
      X : array-like of shape [n_samples, optional]
          array to _collect dimension data from.
  
      y : array-like, optional
          label data
  
      job : str, optional
          optional job type data
  
      Returns
      -------
      instance :
          indexer with stores sample size data.
  
      Notes
      -----
      Fitting an indexer stores nothing that points to the array
      or memmap ``X``. Only the ``shape`` attribute of ``X`` is called.
      """
  
imports: from __future__ import division
  from abc import abstractmethod
  import numpy
  from ..externals.sklearn.base import BaseEstimator
label_desc: Use image node_id 2 for calling the BaseIndex obj's underlying member method code with example usage: obj.fit(X, y, job) without return types