EXPECT_EQ(arg_node->op_def().name(), "_Arg");
  EXPECT_EQ(arg_node->attrs().FindByString("T")->type(), DT_FLOAT);

  const Node* retval_node = node_name_index.at("_retval0");
  EXPECT_EQ(retval_node->op_def().name(), "_Retval");
  EXPECT_EQ(retval_node->attrs().FindByString("T")->type(), DT_FLOAT);

  EXPECT_EQ(identity_node->num_outputs(), 1);
  EXPECT_EQ(retval_node->num_inputs(), 1);

    func.func @main(%arg0: tensor<3x3x10xbf16>, %arg1: tensor<3xi32>) -> tensor<1x?x4xbf16> attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2", outputs = "_retval0"}} {
      %cst = "tf.Const"() {value = dense<[1, -1, 4]> : tensor<3xi32>} : () -> tensor<3xi32>

//
// CHECK:      node {
// CHECK-NEXT:   name: "rets_0"
// CHECK-NEXT:   op: "_Retval"
// CHECK-NOT:    device
// CHECK:        attr {
// CHECK:          key: "index"
// CHECK-NEXT:     value {
// CHECK-NEXT:       i: 0
//
// CHECK:      node {
// CHECK-NEXT:   name: "rets_1"
// CHECK-NEXT:   op: "_Retval"
// CHECK:        device: "/CPU:1"
// CHECK:        attr {
// CHECK:          key: "index"

// CHECK-SAME: %[[STATE:.*]]: tensor<2xui64>
func.func @xla_rng_bit_generator(%arg0: tensor<2xui64>) -> (tensor<2xui64>, tensor<10x12xui32>) attributes {tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2", outputs = "_retval0,_retval1"}} {
  // CHECK-NEXT: %0 = mhlo.constant dense<[10, 12]> : tensor<2xi32>
  %cst = "tf.Const"() {value = dense<[10, 12]> : tensor<2xi32>} : () -> tensor<2xi32>
  // CHECK-NEXT: %1 = mhlo.constant dense<3> : tensor<i32>

    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "ret1"
  op: "_Retval"
  input: "arg1"
  attr {
    key: "T"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "index"
    value {
      i: 1
    }
  }
}
node {
  name: "ret2"
  op: "_Retval"
  input: "arg2"
  attr {
    key: "T"
    value {
      type: DT_BOOL
    }
  }

    StringPiece a_name(a->name());
    StringPiece b_name(b->name());
    return std::tie(a_is_resource, a_name) < std::tie(b_is_resource, b_name);
  });

  // Sorts the retvals by name so the order is deterministic.
  std::sort(retvals.begin(), retvals.end(),
            [](Node* a, Node* b) { return a->name() < b->name(); });

  // Computes the permutation to produce the correct argument order, and update
  // the argument indices.

        type: DT_INT32
      }
    }
  }
}
node {
  name: "rets_0"
  op: "_Retval"
  input: "identity:0"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "rets_1"
  op: "_Retval"
  input: "identity:1"
  device: "/CPU:1"
  attr {
    key: "T"
    value {

  // CHECK-LABEL: @const_input_required
  func.func @const_input_required(%arg0: tensor<10xf64>) -> tensor<?xf64> attributes {tf.entry_function = {control_outputs = "", inputs = "_arg0,_arg1,_arg2,_arg3", outputs = "_retval0"}} {
    %cst = "tf.Const"() {value = dense<6> : tensor<1xi32>} : () -> tensor<1xi32>
    %cst_0 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>

  {
    tensorflow::Scope s = tensorflow::Scope::NewRootScope();
    Output arg = ops::_Arg(s.WithOpName("arg"), DT_INT32, 0);
    Output identity = ops::Identity(s.WithOpName("identity_true_fn"), arg);
    ops::_Retval retval(s.WithOpName("retval"), identity, 0);
    std::unique_ptr<Graph> g(new Graph(OpRegistry::Global()));
    TF_CHECK_OK(s.ToGraph(g.get()));
    auto node_name_image = g->BuildNodeNameIndex();

node {
  name: "arg"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "retval"
  op: "_Retval"
  input: "arg"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
versions {
  producer: 511