code: def _visit_materialize_op(self, op):
      # this is the catch all category of ops
      # these are the "not-lazy-transpose-pass-through" kind of ops
      # output hypothetical_value is same as the vars
      for out_var in op.outputs:
          self.var_to_hypothetical_value[out_var] = HypotheticalValue(
              out_var
          )
  
      # check for the inputs
      # if there is a lazy transpose hypothetical value as an input,
      # all the transpose ops it hold,
      # need to be materialized here now, i.e., we should update "transpose_op_to_materialize_ops"
      for input_var in _get_input_vars(op):
          input_hypothetical_value = self.var_to_hypothetical_value[
              input_var
          ]
          if isinstance(
              input_hypothetical_value, LazyTransposeHypotheticalValue
          ):
              all_lazy_transpose_ops = (
                  input_hypothetical_value.transpose_ops
              )
              for transpose_op in all_lazy_transpose_ops:
                  self.transpose_op_to_materialize_ops[
                      transpose_op
                  ].append((op, input_var))
  
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 4 for calling the TransposeOptimization obj's underlying member method code with example usage: obj._visit_materialize_op(op) without return types