return false;
        }
        Output output = (Output) o;
        return Objects.equal(path, output.path);
    }

    @Override
    public int hashCode() {
        return Objects.hashCode(path);
    }

    @Override
    public String toString() {
        return "Output{path='" + path + "'}";
    }

zirain <******@****.***> 1703405786 +0800

Jacek Ewertowski <******@****.***> 1701261434 +0100

  // all the outputs of the main function are actually the final outputs.
  // main_func:
  //  %output1 = call @subgraph_1...
  //   ...
  //  %output2 = call @subgraph_m...
  //   ...
  //  %outputn = call @subgraph_k...
  //  return %output1, output2, ..., outputn.
  fn.walk([&](func::ReturnOp return_op) {
    for (auto output : return_op.getOperands()) {

overlapping-outputs-input-comparison.png...

   *
   *   A =[[0, 1],[1, 0]]
   *   B =[[1, 0],[0, 1]]
   *
   *   output1, output2, output3 = two_adds_and_mm(A, B)
   *
   * We expect outputs:
   *
   *   output1 = [[1, 1],[1, 1]]
   *   output2 = [[2, 0],[0, 2]]
   *   output3 = [[2, 2],[2, 2]]
   *
   */

  // Build eager context.
  TFE_ContextOptions* opts = TFE_NewContextOptions();

        }
    }

    private void onlyTestClass1In(String output) {
        assert output.contains('TestClass1.testMethod')
        assert !output.contains('TestClass2.testMethod')
    }

    private void onlyTestClass2In(String output) {
        assert output.contains('TestClass2.testMethod')
        assert !output.contains('TestClass1.testMethod')
    }

        then:
        output1.toString().contains(ARG_1)
        output1.toString().contains(ARG_2)

        when:
        String output2 = runBuild { launcher -> launcher.addArguments(["-P$ARG_1" as String]).addArguments(["-P$ARG_2" as String]) }

        then:
        output2.toString().contains(ARG_1)

  func.func @serving_default(%arg0: tensor<1x10xf32> {tf_saved_model.index_path = ["x"]}) ->
    (tensor<1x10xf32> {tf_saved_model.index_path = ["output_0"]}, tensor<1x10xi64> {tf_saved_model.index_path = ["output_1"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_x:0", outputs = "StatefulPartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {

    }
    tf_executor.fetch %outputs#0, %outputs#1 : tensor<i1>, tensor<i32>
  }
  return
}

// CHECK: [[OUTPUT_0:%.*]], {{%.*}} = tf_executor.island wraps "tf.opA"() {_parallel_execution_ids = "p0:0"}
// CHECK: [[OUTPUT_1:%.*]], {{%.*}} = tf_executor.island wraps "tf.opB"([[OUTPUT_0:%.*]]) {_parallel_execution_ids = "p0:0"}