code: def standard_deviation(input, labels=None, index=None):
      """
      Calculate the standard deviation of the values of an N-D image array,
      optionally at specified sub-regions.
  
      Parameters
      ----------
      input : array_like
          N-D image data to process.
      labels : array_like, optional
          Labels to identify sub-regions in `input`.
          If not None, must be same shape as `input`.
      index : int or sequence of ints, optional
          `labels` to include in output. If None (default), all values where
          `labels` is non-zero are used.
  
      Returns
      -------
      standard_deviation : float or ndarray
          Values of standard deviation, for each sub-region if `labels` and
          `index` are specified.
  
      See Also
      --------
      label, variance, maximum, minimum, extrema
  
      Examples
      --------
      >>> import numpy as np
      >>> a = np.array([[1, 2, 0, 0],
      ...               [5, 3, 0, 4],
      ...               [0, 0, 0, 7],
      ...               [9, 3, 0, 0]])
      >>> from scipy import ndimage
      >>> ndimage.standard_deviation(a)
      2.7585095613392387
  
      Features to process can be specified using `labels` and `index`:
  
      >>> lbl, nlbl = ndimage.label(a)
      >>> ndimage.standard_deviation(a, lbl, index=np.arange(1, nlbl+1))
      array([ 1.479,  1.5  ,  3.   ])
  
      If no index is given, non-zero `labels` are processed:
  
      >>> ndimage.standard_deviation(a, lbl)
      2.4874685927665499
  
      """
      return numpy.sqrt(variance(input, labels, index))
  
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 11 for calling a global function with example usage: standard_deviation(input, labels, index) and returns: numpy