code: def _add_output_sinks(self):
      # We add a identity sink for all outputs.
      self.old_output_vars = {
          var: var.name for var in self.block.outputs
      }
      new_outputs = []
      output_sinks_var = {}
      for out_var in self.block.outputs:
          with self.block:
              if out_var not in output_sinks_var:
                  out_sink = mb.identity(x=out_var)
                  output_sinks_var[out_var] = out_sink
              else:
                  out_sink = output_sinks_var[out_var]
              new_outputs.append(out_sink)
              self.output_sink_ops.append(out_sink.op)
      self.block.set_outputs(new_outputs)
  
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 TransposeOptimization obj's underlying member method code with example usage: obj._add_output_sinks() without return types