code: def _hist_bin_scott(x, range):
      """
      Scott histogram bin estimator.
  
      The binwidth is proportional to the standard deviation of the data
      and inversely proportional to the cube root of data size
      (asymptotically optimal).
  
      Parameters
      ----------
      x : array_like
          Input data that is to be histogrammed, trimmed to range. May not
          be empty.
  
      Returns
      -------
      h : An estimate of the optimal bin width for the given data.
      """
      del range  # unused
      return (24.0 * np.pi**0.5 / x.size) ** (1.0 / 3.0) * np.std(x)
  
imports: import contextlib
  import functools
  import operator
  import warnings
  import numpy
  from numpy._core import overrides
label_desc: Use image node_id 5 for calling a global function with example usage: _hist_bin_scott(x, range) and returns: unknown