code: def value_indices(arr, *, ignore_value=None):
      """
      Find indices of each distinct value in given array.
  
      Parameters
      ----------
      arr : ndarray of ints
          Array containing integer values.
      ignore_value : int, optional
          This value will be ignored in searching the `arr` array. If not
          given, all values found will be included in output. Default
          is None.
  
      Returns
      -------
      indices : dictionary
          A Python dictionary of array indices for each distinct value. The
          dictionary is keyed by the distinct values, the entries are array
          index tuples covering all occurrences of the value within the
          array.
  
          This dictionary can occupy significant memory, usually several times
          the size of the input array.
  
      See Also
      --------
      label, maximum, median, minimum_position, extrema, sum, mean, variance,
      standard_deviation, numpy.where, numpy.unique
  
      Notes
      -----
      For a small array with few distinct values, one might use
      `numpy.unique()` to find all possible values, and ``(arr == val)`` to
      locate each value within that array. However, for large arrays,
      with many distinct values, this can become extremely inefficient,
      as locating each value would require a new search through the entire
      array. Using this function, there is essentially one search, with
      the indices saved for all distinct values.
  
      This is useful when matching a categorical image (e.g. a segmentation
      or classification) to an associated image of other data, allowing
      any per-class statistic(s) to then be calculated. Provides a
      more flexible alternative to functions like ``scipy.ndimage.mean()``
      and ``scipy.ndimage.variance()``.
  
      Some other closely related functionality, with different strengths and
      weaknesses, can also be found in ``scipy.stats.binned_statistic()`` and
      the `scikit-image <https://scikit-image.org/>`_ function
      ``skimage.measure.regionprops()``.
  
      Note for IDL users: this provides functionality equivalent to IDL's
      REVERSE_INDICES option (as per the IDL documentation for the
      `HISTOGRAM <https://www.l3harrisgeospatial.com/docs/histogram.html>`_
      function).
  
      .. versionadded:: 1.10.0
  
      Examples
      --------
      >>> import numpy as np
      >>> from scipy import ndimage
      >>> a = np.zeros((6, 6), dtype=int)
      >>> a[2:4, 2:4] = 1
      >>> a[4, 4] = 1
      >>> a[:2, :3] = 2
      >>> a[0, 5] = 3
      >>> a
      array([[2, 2, 2, 0, 0, 3],
             [2, 2, 2, 0, 0, 0],
             [0, 0, 1, 1, 0, 0],
             [0, 0, 1, 1, 0, 0],
             [0, 0, 0, 0, 1, 0],
             [0, 0, 0, 0, 0, 0]])
      >>> val_indices = ndimage.value_indices(a)
  
      The dictionary `val_indices` will have an entry for each distinct
      value in the input array.
  
      >>> val_indices.keys()
      dict_keys([0, 1, 2, 3])
  
      The entry for each value is an index tuple, locating the elements
      with that value.
  
      >>> ndx1 = val_indices[1]
      >>> ndx1
      (array([2, 2, 3, 3, 4]), array([2, 3, 2, 3, 4]))
  
      This can be used to index into the original array, or any other
      array with the same shape.
  
      >>> a[ndx1]
      array([1, 1, 1, 1, 1])
  
      If the zeros were to be ignored, then the resulting dictionary
      would no longer have an entry for zero.
  
      >>> val_indices = ndimage.value_indices(a, ignore_value=0)
      >>> val_indices.keys()
      dict_keys([1, 2, 3])
  
      """
      # Cope with ignore_value being None, without too much extra complexity
      # in the C code. If not None, the value is passed in as a numpy array
      # with the same dtype as arr.
      ignore_value_arr = numpy.zeros((1,), dtype=arr.dtype)
      ignoreIsNone = ignore_value is None
      if not ignoreIsNone:
          ignore_value_arr[0] = ignore_value_arr.dtype.type(
              ignore_value
          )
  
      val_indices = _nd_image.value_indices(
          arr, ignoreIsNone, ignore_value_arr
      )
      return val_indices
  
imports: import numpy
  import numpy
  from .None import _ni_support
  from .None import _ni_label
  from .None import _nd_image
  from .None import _morphology
label_desc: Use image node_id 3 for calling a global function with example usage: value_indices(arr) and returns: val_indices