// CHECK-DAG: %[[CST1:.*]] = stablehlo.constant dense<0> : tensor<i32>
// CHECK-DAG: %[[CST2:.*]] = stablehlo.constant dense<2.000000e+00> : tensor<1x1x2xf32>
// CHECK: %[[RES:.*]] = stablehlo.dynamic_update_slice %arg0, %[[CST2]], %[[CST0]], %[[CST1]], %[[CST1]] : (tensor<2x1x2xf32>, tensor<1x1x2xf32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<2x1x2xf32>

//CHECK-DAG:    %[[c1:.+]] = stablehlo.constant dense<1> : tensor<i32>
//CHECK-DAG:    %[[c2:.+]] = stablehlo.constant dense<0> : tensor<i32>
//CHECK-NEXT:   %[[c3:.+]] = stablehlo.dynamic_update_slice %arg0, %[[c0]], %[[c1]], %[[c2]], %[[c2]] : (tensor<2x1x2xf32>, tensor<1x1x2xf32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<2x1x2xf32>
//CHECK-NEXT:   return %[[c3]] : tensor<2x1x2xf32>

//CHECK-DAG:    %[[c1:.+]] = stablehlo.constant dense<1> : tensor<i32>
//CHECK-DAG:    %[[c2:.+]] = stablehlo.constant dense<0> : tensor<i32>
//CHECK-NEXT:    %[[c3:.+]] = stablehlo.dynamic_update_slice %arg0, %[[c0]], %[[c1]], %[[c2]], %[[c2]] : (tensor<2x1x2xf32>, tensor<1x1x2xf32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<2x1x2xf32>
//CHECK-NEXT:    return %[[c3:.+]] : tensor<2x1x2xf32>
//CHECK-NEXT:  }

  %0 = arith.constant dense<0> : tensor<3xi32>
  %1 = "tfl.dynamic_update_slice"(%arg0, %arg1, %0) : (tensor<2x7x14xf32>, tensor<2x7x7xf32>, tensor<3xi32>) -> tensor<2x7x14xf32>
  // CHECK: "tfl.dynamic_update_slice"
  func.return %1 : tensor<2x7x14xf32>
}

// CHECK-LABEL: OptimizeTranposeWithRank7orMoreEffectiveRank6

    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-NEXT:}

func.func @dynamic_update_slice(%arg0: tensor<4x4xi64>, %arg1: tensor<2x3xi64>, %arg2: tensor<i64>, %arg3: tensor<i64>) -> tensor<4x4xi64> {
  %0 = "vhlo.dynamic_update_slice_v1"(%arg0, %arg1, %arg2, %arg3) : (tensor<4x4xi64>, tensor<2x3xi64>, tensor<i64>, tensor<i64>) -> tensor<4x4xi64>
  return %0 : tensor<4x4xi64>
}

  // CHECK-DAG: [[RESHAPE3:%.+]] = mhlo.reshape [[SLICE3]]
  // CHECK-DAG: [[UPDATE1:%.+]] = mhlo.dynamic_update_slice %arg0, [[SLICE4]], [[RESHAPE1]], [[CST]], [[CST]]
  // CHECK-DAG: [[UPDATE2:%.+]] = mhlo.dynamic_update_slice [[UPDATE1]], [[SLICE5]], [[RESHAPE2]], [[CST]], [[CST]]
  // CHECK-DAG: [[UPDATE3:%.+]] = mhlo.dynamic_update_slice [[UPDATE2]], [[SLICE6]], [[RESHAPE3]], [[CST]], [[CST]]

  READ_VARIABLE = 143,
  ASSIGN_VARIABLE = 144,
  BROADCAST_ARGS = 145,
  RANDOM_STANDARD_NORMAL = 146,
  BUCKETIZE = 147,
  RANDOM_UNIFORM = 148,
  MULTINOMIAL = 149,
  GELU = 150,
  DYNAMIC_UPDATE_SLICE = 151,
  RELU_0_TO_1 = 152,
  UNSORTED_SEGMENT_PROD = 153,
  UNSORTED_SEGMENT_MAX = 154,
  UNSORTED_SEGMENT_SUM = 155,
  ATAN2 = 156,
  UNSORTED_SEGMENT_MIN = 157,
  SIGN = 158,
  BITCAST = 159,

}

func.func @dynamic_update_slice(%arg0: tensor<4x5xi32>, %arg1: tensor<1x5xi32>, %arg2: tensor<2xi32>) -> tensor<4x5xi32> {
  %0 = "tf.XlaDynamicUpdateSlice"(%arg0, %arg1, %arg2) : (tensor<4x5xi32>, tensor<1x5xi32>, tensor<2xi32>) -> tensor<4x5xi32>
  func.return %0 : tensor<4x5xi32>

// CHECK-LABEL:dynamic_update_slice

func.func @convert_dynamic_update_slice(%arg0: tensor<28x1x100xf32>, %arg1: tensor<1x1x100xf32>, %arg2: tensor<i32>, %arg3: tensor<i32>, %arg4: tensor<i32>) -> tensor<28x1x100xf32> {
  %0 = "mhlo.dynamic_update_slice"(%arg0, %arg1, %arg2, %arg3, %arg4) : (tensor<28x1x100xf32>, tensor<1x1x100xf32>, tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<28x1x100xf32>
  func.return %0 : tensor<28x1x100xf32>
}