%21 = "tf.Cast"(%20) {Truncate = false} : (tensor<2x2xf32>) -> tensor<2x2xi8>
    %22 = "tf.Identity"(%21) {device = ""} : (tensor<2x2xi8>) -> tensor<2x2xi8>
    %23 = "tf.Identity"(%22) {device = ""} : (tensor<2x2xi8>) -> tensor<2x2xi8>
    %24 = "tf.Cast"(%23) : (tensor<2x2xi8>) -> tensor<2x2xi32>
    %25 = "tf.Sub"(%24, %cst_4) : (tensor<2x2xi32>, tensor<i32>) -> tensor<2x2xi32>

    return %5 : tensor<1x3xf32>
  }
// CHECK: %[[CONST:.+]] = stablehlo.constant() <{value = dense<127> : tensor<1x2xi8>}> : () -> tensor<1x2x!quant.uniform<i8:f32, {{.*}}>>
// CHECK: %[[CONST_0:.+]] = stablehlo.constant() <{value = dense<127> : tensor<2x3xi8>}> : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32:1, {{.*}}>>

// TF-DAG: %[[cc_0:.*]] = "tf.ConcatV2"(%[[pc_1]], %[[pc_5]], %[[cst_0]]) : (tensor<2x2xi8>, tensor<2x2xi8>, tensor<i32>) -> tensor<2x4xi8>
// TF-DAG: %[[pc_7:.*]] = "tf.PartitionedCall"(%[[cc_0]], %[[cst_6]], %[[cst_5]]) <{config = "", config_proto = "", executor_type = "", f = @dequantize_i8}
// TF-DAG: return %[[pc_7]]

  // CHECK-LABEL: roll_non_constant_shift
  // CHECK:  "tf.Roll"
}

func.func @scatter_nd_updates(%arg0: tensor<14xf32>, %arg1: tensor<1x1xi32>, %arg2: tensor<1xf32>) -> tensor<14xf32> {
  %0 = "tf.TensorScatterUpdate"(%arg0, %arg1, %arg2) : (tensor<14xf32>, tensor<1x1xi32>, tensor<1xf32>) -> tensor<14xf32>
  func.return %0 : tensor<14xf32>

  // CHECK-LABEL: scatter_nd_updates

func.func @scatter_nd_add(%arg0: tensor<7xi64>, %arg1: tensor<1x1xi32>, %arg2: tensor<1xi64>) -> tensor<7xi64> {
  %0 = "tf.TensorScatterAdd"(%arg0, %arg1, %arg2) : (tensor<7xi64>, tensor<1x1xi32>, tensor<1xi64>) -> tensor<7xi64>
  func.return %0 : tensor<7xi64>

  // CHECK-LABEL: scatter_nd_add
  // CHECK:  %[[GATHER:.*]] = "tf.GatherNd"(%arg0, %arg1) <{bad_indices_policy = ""}> : (tensor<7xi64>, tensor<1x1xi32>) -> tensor<1xi64>

  %24 = "quantfork.stats"(%23) {layerStats = dense<[-1.0, 2.0]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>
  func.return %24 : tensor<*xf32>

// CHECK-DAG: %[[input_0:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1x5x!quant.uniform<i8:f32, 0.010588235481112611:-15>>) -> tensor<1x1x5xf32>
// CHECK-DAG: %[[input_1:.*]] = "tfl.dequantize"({{.*}}) : (tensor<2x5x!quant.uniform<i8<-127:127>:f32, 0.011122002376346137>>) -> tensor<2x5xf32>

// CHECK-NEXT: %[[q0:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<1x1x!quant.uniform<u8:f32, 0.0024715415402954701:178>>}> {volatile} : (tensor<1x1xf32>) -> tensor<1x1x!quant.uniform<u8:f32, 0.0024715415402954701:178>>
// CHECK-NEXT: %[[r0q0:.*]] = "tfl.quantize"(%[[q0]]) <{qtype = tensor<1x1x!quant.uniform<u8:f32, 0.0026575490540149166:184>>}> : (tensor<1x1x!quant.uniform<u8:f32, 0.0024715415402954701:178>>) -> tensor<1x1x!quant.uniform<u8:f32, 0.0026575490540149166:184>>