code: def _visit_transpose_op(self, op):
      input_var = op.inputs["x"]
      if op.inputs["perm"].val is None:
          self._visit_materialize_op(op)
          return
      perm = list(op.inputs["perm"].val)
      input_hypothetical_value = self.var_to_hypothetical_value[
          input_var
      ]
  
      """
          There are 3 cases to handle:
  
          1. input type == HypotheticalValue
          2. input type == LazyTransposeHypotheticalValue and this op is the transpose compliment of it
          3. input type == LazyTransposeHypotheticalValue and this op is NOT the transpose compliment of it
          """
  
      if isinstance(input_hypothetical_value, HypotheticalValue):
          # case 1
          # the input is not a lazy transpose.
          # Since the current node is a transpose, there are two sub-cases.
          #  a) It's a output node. We materialize it directly.
          #  b) It might get cancelled downstream, so make the output var's
          #     hypothetical_value a lazy transpose
          if op.outputs[0] in self.old_output_vars:
              self._visit_materialize_op(op)
          else:
              self.var_to_hypothetical_value[
                  op.outputs[0]
              ] = LazyTransposeHypotheticalValue(
                  input_hypothetical_value, set([op]), perm
              )
          return
  
      # input is a Lazy transpose hypothetical value. Lets first check whether the current
      # transpose cancels it or not
      do_cancel = _do_transposes_cancel(
          input_hypothetical_value.perm, perm
      )
      if do_cancel:
          # case 2
          # transposes cancel, so now the hypothetical_value of the output will
          # be same as the hypothetical value wrapped inside the upstream lazy transpose
          self.var_to_hypothetical_value[
              op.outputs[0]
          ] = input_hypothetical_value.wrapped_hypothetical_value
          # also update the dictionary "transpose_op_to_cancel_ops"
          all_lazy_transpose_ops = (
              input_hypothetical_value.transpose_ops
          )
          for transpose_op in all_lazy_transpose_ops:
              self.transpose_op_to_cancel_ops[transpose_op].append(op)
      else:
          # case 3
          # transposes don't cancel
          # this is same as a materialize op then
          self._visit_materialize_op(op)
  
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 6 for calling the TransposeOptimization obj's underlying member method code with example usage: obj._visit_transpose_op(op) without return types