code: def _compute_new_pad_values(self):
      pad_amounts = np.reshape(self.pad_var.val, [-1, 2])
      rank_diff = len(self.transpose_axes) - pad_amounts.shape[0]
      self.pad_amounts_new = copy.deepcopy(pad_amounts)
      # append "rank_diff" rows of zeros to the top
      self.pad_amounts_new = np.concatenate(
          (
              np.zeros((2 * rank_diff)).reshape(-1, 2),
              self.pad_amounts_new,
          )
      )
      self.pad_amounts_new = self.pad_amounts_new.astype(
          pad_amounts.dtype
      )
      pad_amounts = np.concatenate(
          (np.zeros((2 * rank_diff)).reshape(-1, 2), pad_amounts)
      )
      for i, axis in enumerate(self.transpose_axes):
          self.pad_amounts_new[axis][0] = pad_amounts[i][0]
          self.pad_amounts_new[axis][1] = pad_amounts[i][1]
      # get the top "rank_diff" rows
      top_rows = self.pad_amounts_new[:rank_diff, :]
      if not np.all(top_rows == 0):
          return False
      # cut "rank_diff" from the top
      self.pad_amounts_new = self.pad_amounts_new[rank_diff:, :]
      self.pad_amounts_new = self.pad_amounts_new.flatten()
      return True
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from coremltools.converters.mil.mil.passes.pass_registry import register_pass
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil.var import Var
  import logging
  import numpy
  import copy
  from collections import defaultdict
label_desc: Use image node_id 2 for calling the transform_pad obj's underlying member method code with example usage: obj._compute_new_pad_values() and returns: True, False