assert(t, ADB, adb, "adb")
	bda := B.Difference(A, nil)
	assert(t, BDA, bda, "bda")

	Ap1 := treePlus1([]int32{1, 2, 3, 4})

	ada1_1 := A.Difference(Ap1, smaller)
	assert(t, A, ada1_1, "ada1_1")
	ada1_2 := Ap1.Difference(A, smaller)
	assert(t, A, ada1_2, "ada1_2")

}

type sstring struct {
	s string
}

func (s *sstring) String() string {
	return s.s
}

TEST(ScopeTest, BasicNames) {
  Scope root = Scope::NewRootScope();
  EXPECT_EQ(root.GetUniqueNameForOp("add"), "add");
  EXPECT_EQ(root.GetUniqueNameForOp("add"), "add_1");
  EXPECT_EQ(root.GetUniqueNameForOp("add"), "add_2");
  EXPECT_EQ(root.GetUniqueNameForOp("mul"), "mul");
}

TEST(ScopeTest, OpAndScopeNameCollision) {
  Scope root = Scope::NewRootScope();
  EXPECT_EQ(root.GetUniqueNameForOp("foo"), "foo");

  Run({{func_feed, Int32Tensor(3)}}, {{func_op, 0}, {func_op, 1}}, {12, 15});
  VerifyFDef({"add1_0", "add2_0"}, M({{"feed1"}, {"feed2"}, {"feed3"}}),
             M({{"add1"}, {"add2"}}),
             {{"feed1", "add1_0:0"},
              {"feed2", "add1_0:1"},
              {"add1_0:sum:0", "add2_0:0"},
              {"feed3", "add2_0:1"},
              {"add1_0:sum:0", "add1"},
              {"add2_0:sum:0", "add2"}},
             {});
}

// CHECK-NEXT:  [[ADD:%.*]] = "tf.AddV2"(%arg2, [[CST]])
// CHECK-NEXT:  [[TENSOR_MAP_INSERT_RESULT:%.*]] = "tf.TensorMapInsert"(%arg3, %arg2, %arg2)
// CHECK-NEXT:  [[ADD_2:%.*]] = "tf.AddV2"(%arg0, [[CST]])
// CHECK-NEXT:  return [[ADD_2]], %arg1, [[ADD]], [[TENSOR_MAP_INSERT_RESULT]]

func.func @otherVariantWhileCond(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<i32>, %arg3: tensor<!tf_type.variant>) -> tensor<i1> {

  %0 = "tf.OnesLike"(%arg0) : (tensor<*xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}

// CHECK-LABEL: func @addN_2
func.func @addN_2(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  // CHECK: %[[SUM0:.*]] = "tf.AddV2"(%arg0, %arg1)
  // return %[[SUM0]]
  %0 = "tf.AddN"(%arg0, %arg1) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@training/SGD/gradients/AddN_2"
      }
    }
  }
}
node {
  name: "loss_1/res5c_branch2c/kernel/Regularizer/Square"
  op: "Square"
  input: "loss_1/res5c_branch2c/kernel/Regularizer/Square/ReadVariableOp"

      f: 1.001e-05
    }
  }
  attr {
    key: "is_training"
    value {
      b: true
    }
  }
}
node {
  name: "training/LossScaleOptimizer/gradients/AddN_2"
  op: "AddN"
  input: "training/LossScaleOptimizer/gradients/loss_1/res5c_branch2c/kernel/Regularizer/Square_grad/Mul_1"
  input: "training/LossScaleOptimizer/gradients/res5c_branch2c_1/Conv2D/Cast_grad/Cast"