code: def compute_percentiles(arrays_dict, percentiles):
      """Compute percentiles of dictionary of arrays.  Return results as dataframe.
      Parameters
      ----------
      arrays_dict : dict
          a dictionary where each key will be the output column label of a dataframe and
          value are a 2d array-like of shape (number of samples, prediction length) required to compute
          percentile(s)
      percentiles : list
          A sorted list of percentile(s) which will be used to specify percentiles needed to compute across various
          arrays
      Returns
      -------
      dict
          The percentiles across samples with columns for `50` aka median and all other percentiles
          specified in `percentiles`.
      """
  
      computed_dict = {}
      run_check = False
      prev_shape = None
      for k, v in arrays_dict.items():
          curr_shape = v.shape
          if len(curr_shape) != 2:
              raise ValueError(
                  "Input arrays_dict requires 2 dimensions: (number of samples, prediction length)."
                  " Please revise input. Your input:{}".format(
                      curr_shape
                  )
              )
          if run_check:
              if curr_shape[1] != prev_shape[1]:
                  raise ValueError(
                      "Input arrays has different lengths in second dimension. Please revise input."
                  )
          # run prediction consistency after first iteration
          run_check = True
          prev_shape = curr_shape
          # row = nth percentile, col = prediction length
          computed_array = np.percentile(v, percentiles, axis=0)
          columns = [
              k + "_" + str(p) if p != 50 else k for p in percentiles
          ]
          for idx in range(computed_array.shape[0]):
              computed_dict[columns[idx]] = computed_array[idx]
  
      return computed_dict
  
label_desc: Use image node_id 2 for calling a global function with example usage: compute_percentiles(arrays_dict, percentiles) and returns: computed_dict