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: Build an array of indexes from a list or tuple of index tuples.
  
  Given an index object containing tuples of ``(start, stop)`` indexes
  ``_build_range`` will return an array that concatenate all elements
  between each ``start`` and ``stop`` number.
  
  Examples
  --------
  Single slice (convex slicing)
  
  >>> from mlens.index.base import BaseIndex
  >>> BaseIndex._build_range((0, 6))
  array([0, 1, 2, 3, 4, 5])
  
  Several slices (non-convex slicing)
  
  >>> from mlens.index.base import BaseIndex
  >>> BaseIndex._build_range([(0, 2), (4, 6)])
  array([0, 1, 4, 5])
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code: def _build_range(idx):
      """Build an array of indexes from a list or tuple of index tuples.
  
      Given an index object containing tuples of ``(start, stop)`` indexes
      ``_build_range`` will return an array that concatenate all elements
      between each ``start`` and ``stop`` number.
  
      Examples
      --------
      Single slice (convex slicing)
  
      >>> from mlens.index.base import BaseIndex
      >>> BaseIndex._build_range((0, 6))
      array([0, 1, 2, 3, 4, 5])
  
      Several slices (non-convex slicing)
  
      >>> from mlens.index.base import BaseIndex
      >>> BaseIndex._build_range([(0, 2), (4, 6)])
      array([0, 1, 4, 5])
      """
      if isinstance(idx[0], tuple):
          return np.hstack([np.arange(t0, t1) for t0, t1 in idx])
      return np.arange(idx[0], idx[1])
  
imports: from __future__ import division
  from abc import abstractmethod
  import numpy
  from ..externals.sklearn.base import BaseEstimator
label_desc: Use image node_id 6 for calling the BaseIndex obj's underlying member method code with example usage: obj._build_range(idx) and returns: np, np