might become the name of Retval nodes as well (with an index suffix if there
  are multiple output tensors from one node). Since Retval nodes are not used in
  SavedModel, this function removes them and restore the names to the actual
  output tensors.

  Args:
    graph_def: the converted GraphDef.

  Returns:
    The GraphDef with Retval nodes removed and output tensor names restored.

  name: "arg"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "ret"
  op: "_Retval"
  input: "arg"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
versions {
  producer: 27
}

  TF_SetStatus(status, TF_OK, "");
  if (retvals[0] != nullptr) {
    TFE_DeleteTensorHandle(retvals[0]);
  }
  retvals[0] = nullptr;
  TFE_Execute(matmul2, &retvals[0], &num_retvals, status);
  EXPECT_EQ(TF_OK, TF_GetCode(status));
  TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status);
  EXPECT_EQ(TF_OK, TF_GetCode(status));
  TF_DeleteTensor(t);

    Output arg1 = ops::_Arg(s.WithOpName("arg1"), DT_RESOURCE, 1);
    Output arg2 = ops::_Arg(s.WithOpName("arg2"), DT_INT32, 2);
    auto ret0 = ops::_Retval(s.WithOpName("ret0"), arg1, 0);
    auto ret1 = ops::_Retval(s.WithOpName("ret1"), arg0, 1);
    auto ret2 = ops::_Retval(s.WithOpName("ret2"), arg2, 2);
    std::unique_ptr<Graph> g(new Graph(OpRegistry::Global()));
    TF_CHECK_OK(s.ToGraph(g.get()));

// CHECK-NEXT:   input: "serving_default_x"


// CHECK:        name: "PartitionedCall"
// CHECK-NEXT:   op: "_Retval"
// CHECK-NEXT:   input: "tf.MaxPoolWithArgmax:1"

// CHECK:        name: "PartitionedCall1"
// CHECK-NOT:    name: "PartitionedCall"
// CHECK-NEXT:   op: "_Retval"

    // CHECK: tf_executor.island wraps "tf._Retval"(%outputs_0) {T = !tf_type.resource, _mlir_name = "func_call", index = 0 : i64} : (tensor<*x!tf_type.resource>) -> ()
    %ctl_1 = _Retval(%test_func_name0) name("func_call") {T = !tf_type.resource, index = 0 : i64} : tensor<*x!tf_type.resource>
    // CHECK: tf_executor.fetch
  }

  input: "input"
}

library {
  function {
    signature {
      name: "func_name"
      input_arg {
        name: "arg0"
        type: DT_BOOL
      }
    }
    ret {
      key: "retval0"
      value: "arg0"
    }
    attr: {
      key: "_input_shapes"
      value: {
      }
    }
  }

    }
  }
  experimental_debug_info {
  }
}
node {
  name: "main"
  op: "_Retval"
  input: "StatefulIf"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "main1"
  op: "_Retval"
  input: "StatelessIf"
  attr {
    key: "T"
    value {
      type: DT_FLOAT

A. Unique TensorFlower <******@****.***> 1605121757 -0800

// CHECK-NEXT:       func {
// CHECK-NEXT:         name: "function0"
// CHECK:      node {
// CHECK-NEXT:   name: "rets_0_RetVal"
// CHECK-NEXT:   op: "_Retval"
// CHECK-NEXT:   input: "identity"
// CHECK:      node {
// CHECK-NEXT:   name: "rets_1_RetVal"
// CHECK-NEXT:   op: "_Retval"
// CHECK-NEXT:   input: "statefulpartitionedcall"
// CHECK:      library {
// CHECK-NEXT:   function {
// CHECK-NEXT:     signature {