return
				}
				z.BucketStatuses[za0001] = za0002
			}
		case "tb":
			z.TotBuckets, err = dc.ReadInt()
			if err != nil {
				err = msgp.WrapError(err, "TotBuckets")
				return
			}
		case "cst":
			err = z.TargetReplicationResyncStatus.DecodeMsg(dc)
			if err != nil {
				err = msgp.WrapError(err, "TargetReplicationResyncStatus")
				return
			}
		default:
			err = dc.Skip()
			if err != nil {

// not contributing to the dimentionality. This will run if the rank>6
// Pattern will convert-
// %0 = "tfl.transpose"(%arg0, %cst) : (tensor<56x8x56x1x1x1x7xf32>, tensor<7xi32>) -> tensor<1x1x8x56x56x7x1xf32>
// to-
// %0 = "tfl.reshape"(%arg0, %cst) : (tensor<56x8x56x1x1x1x7xf32>, tensor<4xi32>) -> tensor<56x8x56x7xf32>
// %1 = "tfl.transpose"(%0, %cst_0) : (tensor<56x8x56x7xf32>, tensor<4xi32>) -> tensor<8x56x56x7xf32>

func.func @invalid_non_mhlo() -> ( tensor<128x32x4xcomplex<f32>> {mhlo.sharding = ""}) {
  // expected-error @+1 {{Could not legalize op: tf.Const}}
  %cst = "tf.Const"() {value = dense<(1.000000e+00,-1.000000e+00)> : tensor<128x32x4xcomplex<f32>>} : () -> tensor<128x32x4xcomplex<f32>>
  return %cst : tensor<128x32x4xcomplex<f32>>
}

// -----

func.func @invalid_mixed_mhlo() -> (tensor<8x64x128xcomplex<f32>> {mhlo.sharding = ""}) {

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1399 : i32}} {

// CHECK-LABEL: stateless_op
func.func @stateless_op() -> tensor<i32> {
  // CHECK: %cst = "tf.Const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
  %0 = "tf.StatelessRandomGetAlg"() {device = ""} : () -> tensor<i32>
  return %0 : tensor<i32>
}

  %index = arith.constant 0 : index
  %cst = arith.constant 1 : i32
  %eq = arith.cmpi eq, %n, %cst : i32
  %v1 = tfr.get_element %values[%index] : (!tfr.tensor_list, index) -> !tfr.tensor
  %temp = tfr.call @tf__my_expand_dims(%v1, %axis) : (!tfr.tensor, i32) -> !tfr.tensor
  %res = scf.if %eq -> !tfr.tensor {
    scf.yield %temp : !tfr.tensor
  } else {
    %step = arith.index_cast %cst : i32 to index

// CHECK-NEXT: "func.return"([[res]]) : (tensor<*xf32>) -> ()


// -----

// CHECK-LABEL: same_scale_test
func.func @same_scale_test(%arg0: tensor<*xf32>) -> tensor<*xf32> {
  %cst = arith.constant dense<[-1, 144]> : tensor<2xi32>
  %0 = "quantfork.qcast"(%arg0) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>

// CHECK-LABEL: func private @merge_stateless_merged_if_0_0_else
// CHECK-SAME: ([[x:%.*]]: tensor<i32>, [[y:%.*]]: tensor<i32>)
// CHECK-DAG: [[cst:%.*]] = "tf.Const"() <{value = dense<1> : tensor<i32>}>
// CHECK-DAG: [[cst_0:%.*]] = "tf.Const"() <{value = dense<2> : tensor<i32>}>
// CHECK: [[r0:%.*]] = "tf.AddV2"([[x]], [[cst]])
// CHECK: [[r1:%.*]] = "tf.AddV2"([[y]], [[r0]])
// CHECK: [[r2:%.*]] = "tf.AddV2"([[x]], [[cst_0]])

    %0 = "tf.PartitionedCall"(%arg0, %cst, %cst_0) {config = "", config_proto = "", executor_type = "", f = @composite_conv2d_with_bias_and_relu6_fn_2} : (tensor<1x2x2x3xf32>, tensor<2x2x3x2xf32>, tensor<2xf32>) -> tensor<*xf32> loc(callsite("test@conv"("Conv2D") at "QuantizationUnit(\12\06Conv2D\1a\04conv)"))

    return %3 : tensor<6x5xf32>
  }
  // CHECK: %[[CST:.*]] = "tf.Const"()
  // CHECK-NEXT: %[[CALL1:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[CST]])
  // CHECK-SAME: _entry_function = @composite_dot_general_fn_1
  // CHECK-SAME: _tfl_quant_trait = "fully_quantizable"
  // CHECK-NOT: "tf.XlaCallModule"

    %cst = "tf.Const"() {value = dense<3.00000000e-1> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
    %0 = "quantfork.stats"(%arg0) {layerStats = dense<[6.00000000e-6, 9.00000000e-1]> : tensor<2xf32>} : (tensor<1x2xf32>) -> tensor<1x2xf32>