// CHECK-LABEL: func @merge_same_device_variables
// CHECK-SAME: %[[ARG_0:.*]]: tensor<*x!tf_type.resource<tensor<32xf32>>>
// CHECK-SAME: %[[ARG_1:.*]]: tensor<*x!tf_type.resource<tensor<64xf32>>>
// CHECK-SAME: %[[ARG_2:.*]]: tensor<*x!tf_type.resource<tensor<16xf32>>>
func.func @merge_same_device_variables(

// CHECK:       [[RES_2:%.*]] = "tf.Mul"([[RES_1]], [[CST_1]])
// CHECK:       tf.WhileRegion
// CHECK:       ^bb0
// CHECK:       tf.OpA
// CHECK:       ^bb0([[ARG_2:%[a-zA-Z0-9_]+]]
// CHECK-SAME:  [[ARG_3:%[a-zA-Z0-9_]+]]: tensor<i32>)
// CHECK-NEXT:  [[RES_3:%.*]] = "tf.AddV2"([[ARG_2]], [[RES_1]])
// CHECK-NEXT:  [[RES_4:%.*]] = "tf.Div"([[ARG_3]], [[RES_2]])
// CHECK:       "tf.Yield"([[RES_3]], [[RES_4]])

        %arg110: !tfrt_fallback.tf_tensor, %arg111: !tfrt_fallback.tf_tensor, %arg112: !tfrt_fallback.tf_tensor)
          -> (!tfrt.chain, !tfrt_fallback.tf_tensor, !tfrt_fallback.tf_tensor, !tfrt_fallback.tf_tensor, !tfrt_fallback.tf_tensor, !tfrt_fallback.tf_tensor, !tfrt_fallback.tf_tensor, !tfrt_fallback.tf_tensor,

// CHECK: %[[ADDV2:.*]] = "tf.AddV2"(%[[CST_0]], %[[CST_0]])

// -----

// CHECK-LABEL: @duplicate_const_for_shape_determining_operand_at_idx_2
// CHECK-SAME: (%[[ARG_0:.*]]: tensor<16x4xf32>, %[[ARG_1:.*]]: tensor<16xi32>)
func.func private @duplicate_const_for_shape_determining_operand_at_idx_2(%arg0: tensor<16x4xf32>, %arg1: tensor<16xi32>) -> tensor<16xf32> {

                                                int argument_index,
                                                TaggedValue& args_in,
                                                TArgsOut... args) {
    Handle h(std::move(args_in.tuple()[argument_index]));
    tensorflow::StatusOr<TSignatureArg> x = Cast<TSignatureArg>(std::move(h));
    if (!x.ok())
      return absl::InvalidArgumentError(

// CHECK-EMPTY:
// CHECK-NEXT:       },
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 1, 4 ],
// CHECK-NEXT:       buffer: 13,
// CHECK-NEXT:       name: "arg12",
// CHECK-NEXT:       quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:       },
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 4 ],
// CHECK-NEXT:       buffer: 14,

// CHECK-EMPTY:
// CHECK-NEXT:       },
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 1, 4 ],
// CHECK-NEXT:       buffer: 13,
// CHECK-NEXT:       name: "arg12",
// CHECK-NEXT:       quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:       },
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 4 ],
// CHECK-NEXT:       buffer: 14,

<-127:127>:f32, 0.035427000373601913>>, %arg9: tensor<2048x!quant.uniform<i32:f32, 4.2675782196965883E-7>>, %arg10: tensor<2048x!quant.uniform<i32:f32, 1.0742187583900886E-7>>, %arg11: tensor<2048x!quant.uniform<i32:f32, 1.6406249869760359E-7>>, %arg12: tensor<2048x!quant.uniform<i32:f32, 1.523437447303877E-7>>, %arg13: tensor<640x2048x!quant.uniform<i8<-127:127>:f32, 0.021174000576138496>>, %arg14: tensor<640x!quant.uniform<i32:f32, 1.601389680352559E-4>>, %arg15: tensor<2048x!quant.uniform<i16:f32,...

// the decodeArg function.
type instArg uint8

const (
	_ instArg = iota
	arg_APSR
	arg_FPSCR
	arg_Dn_half
	arg_R1_0
	arg_R1_12
	arg_R2_0
	arg_R2_12
	arg_R_0
	arg_R_12
	arg_R_12_nzcv
	arg_R_16
	arg_R_16_WB
	arg_R_8
	arg_R_rotate
	arg_R_shift_R
	arg_R_shift_imm
	arg_SP
	arg_Sd
	arg_Sd_Dd
	arg_Dd_Sd
	arg_Sm
	arg_Sm_Dm
	arg_Sn
	arg_Sn_Dn

// `stablehlo.convolution`. The weight constants is transposed.

// CHECK-LABEL: nchw_conv_with_nonconst_bias_add
// CHECK-SAME: %[[ARG_0:.+]]: tensor<1x2x5x5xf32>
// CHECK-SAME: %[[ARG_1:.+]]: tensor<1x4x5x5xf32>
func.func @nchw_conv_with_nonconst_bias_add(%arg0: tensor<1x2x5x5xf32>, %arg1: tensor<1x4x5x5xf32>) -> tensor<1x4x5x5xf32> {
  %0 = stablehlo.constant dense<2.000000e+00> : tensor<4x2x3x3xf32>