code: def _hist_bin_fd(x, range):
      """
      The Freedman-Diaconis histogram bin estimator.
  
      The Freedman-Diaconis rule uses interquartile range (IQR) to
      estimate binwidth. It is considered a variation of the Scott rule
      with more robustness as the IQR is less affected by outliers than
      the standard deviation. However, the IQR depends on fewer points
      than the standard deviation, so it is less accurate, especially for
      long tailed distributions.
  
      If the IQR is 0, this function returns 0 for the bin width.
      Binwidth is 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
      iqr = np.subtract(*np.percentile(x, [75, 25]))
      return 2.0 * iqr * x.size ** (-1.0 / 3.0)
  
imports: import contextlib
  import functools
  import operator
  import warnings
  import numpy
  from numpy._core import overrides
label_desc: Use image node_id 8 for calling a global function with example usage: _hist_bin_fd(x, range) and returns: unknown