code: def mean(input, labels=None, index=None):
      """
      Calculate the mean of the values of an array at labels.
  
      Parameters
      ----------
      input : array_like
          Array on which to compute the mean of elements over distinct
          regions.
      labels : array_like, optional
          Array of labels of same shape, or broadcastable to the same shape as
          `input`. All elements sharing the same label form one region over
          which the mean of the elements is computed.
      index : int or sequence of ints, optional
          Labels of the objects over which the mean is to be computed.
          Default is None, in which case the mean for all values where label is
          greater than 0 is calculated.
  
      Returns
      -------
      out : list
          Sequence of same length as `index`, with the mean of the different
          regions labeled by the labels in `index`.
  
      See Also
      --------
      variance, standard_deviation, minimum, maximum, sum, label
  
      Examples
      --------
      >>> from scipy import ndimage
      >>> import numpy as np
      >>> a = np.arange(25).reshape((5,5))
      >>> labels = np.zeros_like(a)
      >>> labels[3:5,3:5] = 1
      >>> index = np.unique(labels)
      >>> labels
      array([[0, 0, 0, 0, 0],
             [0, 0, 0, 0, 0],
             [0, 0, 0, 0, 0],
             [0, 0, 0, 1, 1],
             [0, 0, 0, 1, 1]])
      >>> index
      array([0, 1])
      >>> ndimage.mean(a, labels=labels, index=index)
      [10.285714285714286, 21.0]
  
      """
  
      count, sum = _stats(input, labels, index)
      return sum / numpy.asanyarray(count).astype(numpy.float64)
  
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 9 for calling a global function with example usage: mean(input, labels, index) and returns: unknown