func.return %1 : tensor<2x2xf32>
  }

  // CHECK-LABEL: dynamic_update_slice
  // CHECK-SAME: (%[[ARG0:.*]]: tensor<3x4xi32>, %[[ARG1:.*]]: tensor<2x2xi32>, %[[ARG2:.*]]: tensor<2xi32>
  func.func @dynamic_update_slice(%arg0: tensor<3x4xi32>, %arg1: tensor<2x2xi32>, %arg2: tensor<2xi32>) -> tensor<3x4xi32> {

    // CHECK: %[[SLICE0:.*]] = "mhlo.slice"(%[[ARG2]])
    // CHECK-DAG-SAME: start_indices = dense<0> : tensor<1xi64>

  %weight_zps = "tf.Const"() { value = dense<3> : tensor<i32> } : () -> tensor<i32>

  // CHECK: "tf.AddV2"
  // CHECK: mhlo.constant
  // CHECK-SAME{LITERAL}: dense<[[1, 2], [3, 4]]> : tensor<2x2xi8>
  %0 = "tf.AddV2"(%input, %input) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>

  // CHECK: "mhlo.dot"
  // CHECK-SAME: (tensor<?x?xf32>, tensor<2x2x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<?x?xf32>

  // CHECK: %[[OPT_BARRIER:.*]]:2 = mhlo.optimization_barrier %arg0, %arg1
  // CHECK-NEXT: return %[[OPT_BARRIER]]#0, %[[OPT_BARRIER]]#1
  %0, %1 = "tf.XlaOptimizationBarrier"(%arg0, %arg1) : (tensor<4x4xf32>, tensor<3x4xi32>) -> (tensor<4x4xf32>, tensor<3x4xi32>)
  func.return %0, %1 : tensor<4x4xf32>, tensor<3x4xi32>
}

// CHECK-LABEL: @ifRegion

func.func @valid_unranked_inputs_on_reshape(%arg0: tensor<3x4xi32>, %arg1: tensor<*xi32>) -> tensor<3x4xi32> {
  // CHECK: "tfl.reshape"(%arg0, %arg1)
  %0 = "tfl.reshape"(%arg0, %arg1) : (tensor<3x4xi32>, tensor<*xi32>) -> tensor<3x4xi32>
  func.return %0 : tensor<3x4xi32>
}

// -----

// CHECK-LABEL: valid_one_dynamic_dim_on_reshape
func.func @valid_one_dynamic_dim_on_reshape(%arg0: tensor<3x4xi32>) -> tensor<1x3x4xi32> {

// `tfl.slice` when stride is 1.

func.func @slice(
    %arg0: tensor<3x4x!quant.uniform<i8:f32, 2.000000e+00:-1>>
  ) -> tensor<2x2x!quant.uniform<i8:f32, 2.000000e+00:-1>> {
  %0 = "stablehlo.slice"(%arg0) {
    start_indices = array<i64: 1, 2>,
    limit_indices = array<i64: 3, 4>,
    strides = array<i64: 1, 1>
  } : (
    tensor<3x4x!quant.uniform<i8:f32, 2.000000e+00:-1>>

}

func.func @where(%arg0: tensor<3x5xi1>) -> tensor<?x2xi64> {
  %0 = "tf.Where"(%arg0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
  func.return %0 : tensor<?x2xi64>
  // CHECK-LABEL: where
  // CHECK: "tfl.where"(%arg0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
}

func.func @floor_mod(%arg0: tensor<5xf32>, %arg1: tensor<5xf32>) -> tensor<5xf32> {

func.func @DequantizeAndQuantize() -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>> {
  %cst = "tfl.pseudo_qconst"() {qtype = tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>, value = dense<-1> : tensor<2x2xi8>} : () -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>
  %0 = "tfl.dequantize"(%cst) : (tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>) -> tensor<2x2xf32>

}

// Returns a RankedTensorType which is similar to `input_type` but replaces the
// dimension size of `dim` with `dim_size`.  For example,
// `SubstituteRankedTensorTypeDimSize(tensor<3x4xi32>, 1, 2)` returns
// `tensor<3x2xi32>`.
static RankedTensorType SubstituteRankedTensorTypeDimSize(
    RankedTensorType input_type, int64_t dim, int64_t dim_size) {
  auto shape = input_type.getShape().vec();

  %cst_3 = arith.constant dense<[1, 0]> : tensor<2xi32>
  %79 = "tfl.pseudo_qconst"() {qtype = tensor<3x2x!quant.uniform<i8<-127:127>:f32:0, {2.378620e-03,2.848260e-03,2.545190e-03}>>, value = dense<10> : tensor<3x2xi8>} : () -> tensor<3x2x!quant.uniform<i8<-127:127>:f32:0, {2.378620e-03,2.848260e-03,2.545190e-03}>>