func.func @avoidPackToConcatenationOnUnknownRank(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

// CHECK-NOT: "tfl.reshape"
// CHECK: "tfl.pack"

// -----

func.func @squaredDifference(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {

module {
  // CHECK-LABEL: main
  func.func @main(%arg0: tensor<4xf32>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
    %0 = "tfl.squared_difference"(%arg0, %arg1) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
    func.return %0 : tensor<4xf32>
    // CHECK:  [[VAL_0:%.*]] = tfl.sub %arg0, %arg1 {fused_activation_function = "NONE"} : tensor<4xf32>

  %4:2 = "mhlo.reduce"(%arg0, %3, %0, %1) ({
  ^bb0(%arg1: tensor<f32>, %arg2: tensor<i32>, %arg3: tensor<f32>, %arg4: tensor<i32>):
    %7 = "mhlo.compare"(%arg1, %arg3) {comparison_direction = #mhlo<comparison_direction GT>} : (tensor<f32>, tensor<f32>) -> tensor<i1>
    %8 = "mhlo.compare"(%arg1, %arg1) {comparison_direction = #mhlo<comparison_direction NE>} : (tensor<f32>, tensor<f32>) -> tensor<i1>

  func.func @func_1_GPU_FLOAT(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<100xf32> attributes {tac.cost = 2.000000e+01 : f32, tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_1"} {
    %0 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU6", tac.device = "GPU"} : tensor<100xf32>
    func.return %0 : tensor<100xf32>
  }

// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu6_fn_1}>
// CHECK-SAME: {_tfl_quant_trait = "fully_quantizable"
// CHECK: %[[PARTITIONEDCALL_1:.*]] = "tf.PartitionedCall"(%arg0, %arg1, %[[CONST_0]])
// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu_fn_1}
// CHECK: %[[PARTITIONEDCALL_2:.*]] = "tf.PartitionedCall"(%arg0, %arg1, %[[CONST_0]])

			case floatKind:
				truth = arg1.Float() == arg.Float()
			case intKind:
				truth = arg1.Int() == arg.Int()
			case stringKind:
				truth = arg1.String() == arg.String()
			case uintKind:
				truth = arg1.Uint() == arg.Uint()
			default:
				if !canCompare(arg1, arg) {
					return false, fmt.Errorf("non-comparable types %s: %v, %s: %v", arg1, arg1.Type(), arg.Type(), arg)
				}

// CHECK-LABEL: hardcode_all
func.func @hardcode_all(%arg0: tensor<2x2xf32>, %arg1: tensor<2x1xf32>) -> tensor<2x2xf32> {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function="NONE"}: (tensor<2x2xf32>, tensor<2x1xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>

// CHECK: %[[q0:.*]] = "tfl.quantize"(%arg1) <{qtype = tensor<2x1x!quant.uniform<u8:f32, 0.0078431372549019607:128>>}>

	{name: "Copysign", argLength: 2}, // copy sign from arg0 to arg1

	// Float min/max implementation, if hardware is available.
	{name: "Min64F", argLength: 2}, // min(arg0,arg1)
	{name: "Min32F", argLength: 2}, // min(arg0,arg1)
	{name: "Max64F", argLength: 2}, // max(arg0,arg1)
	{name: "Max32F", argLength: 2}, // max(arg0,arg1)

	// 3-input opcode.
	// Fused-multiply-add, float64 only.

  func.return %0: tensor<?x?xui32>
}

// -----

// CHECK-LABEL: func @floordiv_int
func.func @floordiv_int(%arg0: tensor<?xi32>, %arg1: tensor<?xi32>) -> tensor<?xi32> {
  // CHECK-DAG: [[DIV:%.+]] = chlo.broadcast_divide %arg0, %arg1 : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>

    return %1 : tensor<1x3xf32>
  }

  func.func private @composite_dot_general_fn(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {
    %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    return %0 : tensor<1x3xf32>
  }
}