code: def test_fusion_with_add_broadcastable_0(self):
      @mb.program(input_specs=[mb.TensorSpec(shape=(1, 2, 5, 5))])
      def prog(x):
          x1 = mb.transpose(x=x, perm=[0, 2, 3, 1])
          x2 = mb.relu(x=x)
          x2 = mb.transpose(x=x2, perm=[0, 2, 3, 1])
          x3 = mb.add(x=x1, y=x2)
          y = mb.transpose(x=x3, perm=[0, 3, 1, 2])
          return y
  
      prev_prog, prev_block, block = apply_pass_and_basic_check(
          prog, "common::reduce_transposes"
      )
      self.assertEqual(
          get_op_types_in_program(prev_prog),
          ["transpose", "relu", "transpose", "add", "transpose"],
      )
      self.assertEqual(get_op_types_in_program(prog), ["relu", "add"])
  
      assert (
          block.find_ops(op_type="relu")[0].inputs["x"]
          == block.inputs["x"]
      )
      assert (
          block.find_ops(op_type="add")[0].inputs["x"]
          == block.inputs["x"]
      )
      assert (
          block.find_ops(op_type="add")[0].inputs["y"]
          == block.find_ops(op_type="relu")[0].outputs[0]
      )
  
      assert_model_is_valid(
          prog,
          {"x": (1, 2, 5, 5)},
          expected_output_shapes={block.outputs[0].name: (1, 2, 5, 5)},
      )
  
imports: from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.testing_utils import assert_op_count_match, assert_model_is_valid, get_op_types_in_program, apply_pass_and_basic_check
  import unittest
  import pytest
  import numpy
label_desc: Use image node_id 19 for calling the TransposeOptimizationPass obj's underlying member method code with example usage: obj.test_fusion_with_add_broadcastable_0() and returns: y