code: def _visit_unary_like_op(self, op, input_var=None):
      # pass the input var's hypothetical_value to the output var's, since shape invariant ops do
      # not modify the incoming hypothetical_value
  
      if input_var is None:
          input_var = op.inputs["x"]
  
      if len(op.outputs) > 1:
          msg = (
              "transpose optimization pass: op '{}', of type = '{}', has multiple outputs, hence it"
              "cannot be handled like a unary op"
          )
          raise ValueError(msg.format(op.name, op.op_type))
      self.var_to_hypothetical_value[
          op.outputs[0]
      ] = self.var_to_hypothetical_value[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 3 for calling the TransposeOptimization obj's underlying member method code with example usage: obj._visit_unary_like_op(op, input_var) without return types