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
}

          }
        }
      }
    }
    node_def {
      name: "add_1"
      op: "Add"
      input: "while_loop_counter"
      input: "add_1/y:output:0"
      attr {
        key: "T"
        value {
          type: DT_INT32
        }
      }
    }
    ret {
      key: "output"
      value: "add_1:z:0"
    }
    ret {
      key: "output_1"

  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"}},
             {});
}

  EXPECT_EQ(child.GetUniqueNameForOp("add"), "child/add");
  EXPECT_EQ(child.GetUniqueNameForOp("add"), "child/add_1");
  EXPECT_EQ(child.GetUniqueNameForOp("mul"), "child/mul");

  Scope child_1 = root.NewSubScope("child");
  EXPECT_EQ(child_1.GetUniqueNameForOp("add"), "child_1/add");
  EXPECT_EQ(child_1.GetUniqueNameForOp("add"), "child_1/add_1");
  EXPECT_EQ(child_1.GetUniqueNameForOp("mul"), "child_1/mul");

      (StableHLO_DynamicBroadcastInDimOp
        $bias,
        (Shape_ShapeOfOp $conv_1), $_, $_, $_)),
    (StableHLO_DynamicBroadcastInDimOp
      (StableHLO_ConstantOp $cst),
      (Shape_ShapeOfOp $add_1), $_, $_, $_)),
  (LiftAsTFXlaCallModule<"composite_conv_with_bias_and_relu_dynamic_fn">
    (ArgumentList $lhs, $rhs, $bias),
    (ResultList $res),
    (NamedAttributeList

bfbfbf bebebe bdbdbd bcbcbc bbbbbb bababa b9b9b9 b8b8b8 b7b7b7 b6b6b6 b5b5b5 b4b4b4 b3b3b3 b2b2b2 b1b1b1 b0b0b0 afafaf aeaeae adadad acacac ababab aaaaaa a9a9a9 a8a8a8 a7a7a7 a6a6a6 a5a5a5 a4a4a4 a3a3a3 a2a2a2 a1a1a1 a0a0a0 
9f9f9f 9e9e9e 9d9d9d 9c9c9c 9b9b9b 9a9a9a 999999 989898 979797 969696 959595 949494 939393 929292 919191 909090 8f8f8f 8e8e8e 8d8d8d 8c8c8c 8b8b8b 8a8a8a 898989 888888 878787 868686 858585 848484 838383 828282 818181 808080 

      CHECK-NEXT: %{{.*}} = arith.addi %x, %y : i64
      CHECK-NEXT: %[[sub_1:.*]] = arith.subi %x, %y : i64
      CHECK-NEXT: %[[add_1:.*]] = arith.addi %[[sub_1]], %x : i64
      CHECK-NEXT: %[[cst:.*]] = arith.constant 1 : i64
      CHECK-NEXT: %{{.*}} = arith.addi %[[add_1]], %[[cst]] : i64
      CHECK-NEXT: %{{.*}} = arith.cmpf "ugt", %x1, %y1 : f32
      CHECK-NEXT: %{{.*}} = arith.addf %x1, %y1 : f32

  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "_class"
    value {
      list {
        s: "loc:@training/SGD/gradients/AddN_1"
      }
    }
  }
}
node {
  name: "loss_1/fc1000/kernel/Regularizer/Square"
  op: "Square"
  input: "loss_1/fc1000/kernel/Regularizer/Square/ReadVariableOp"

  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "add_1_1/add"
  op: "Add"
  input: "bn2b_branch2c_1/FusedBatchNormV2"
  input: "activation_3_1/Relu"
  device: "/job:localhost/replica:0/task:0/device:GPU:0"
  attr {
    key: "T"
    value {