code: def freedman_diaconis_rule(a_batch: np.ndarray) -> int:
      """
      Freedman–Diaconis' rule to compute the number of bins.
  
      Parameters
      ----------
      a_batch: np.ndarray
          The batch of attributions to use in the calculation.
  
      Returns
      -------
          integer
      """
  
      iqr = np.percentile(a_batch, 75) - np.percentile(a_batch, 25)
      n = a_batch[0].ndim
      bin_width = 2 * iqr / np.power(n, 1 / 3)
  
      # Set a minimum value for bin_width to avoid division by very small numbers.
      min_bin_width = 1e-6
      bin_width = max(bin_width, min_bin_width)
  
      # Calculate number of bins based on bin width.
      n_bins = int((np.max(a_batch) - np.min(a_batch)) / bin_width)
  
      return n_bins
  
imports: import scipy
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: freedman_diaconis_rule(a_batch) and returns: n_bins