"i64, ellipsis_mask = 0 : i64, end_mask = 11 : i64, new_axis_mask = "
          "4 : i64, shrink_axis_mask = 0 : i64}> {device = \"\"} : "
          "(tensor<*xf32>, tensor<4xi32>, tensor<4xi32>, tensor<4xi32>) "
          "-> tensor<*xf32>\nError code: ERROR_NEEDS_FLEX_OPS",
          ConverterErrorData::ERROR_NEEDS_FLEX_OPS, "tf.StridedSlice",
          mlir::FileLineColLoc::get(input_file_id, 4, 10))),
      1);

    // Use identity op to avoid the weight being constant-folded.
    %identity = "tf.Identity"(%input) : (tensor<*xi8>) -> tensor<*xi8>
    %input_i32 = "tf.Cast"(%identity) : (tensor<*xi8>) -> tensor<*xi32>
    %output = "tf.Sub"(%input_i32, %zp) : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32>
    %cast = "tf.Cast"(%output) : (tensor<*xi32>) -> tensor<*xf32>

      %mul = "tf.Mul"(%sub, %arg1) : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
      tf_executor.yield %sub, %mul : tensor<*xi32>, tensor<*xi32>
    }
    %island3 = tf_executor.island {
      %sub = "tf.Sub"(%island1#0, %island2#0) : (tensor<*xi32>, tensor<*xi32>) -> tensor<*xi32>
      %res = "tf.Print"(%sub) { message = "sub result" } : (tensor<*xi32>) -> (tensor<*xi32>)

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.DoesntExist"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      func.return %0 : tensor<1xi32>
    }
  })";

absl::StatusOr<XlaCompiler::CompilationResult> CompileMlirModule(
    const char* module_str) {
  MlirToHloArgs mlir_to_hlo_args;

func.func @side_effect_free_user() -> tensor<2xi32> {
  // CHECK: [[handle:%.*]] = "tf.VarHandleOp"
  // CHECK-SAME: shared_name = "x"
  // CHECK: [[value0:%.*]] = "tf.ReadVariableOp"([[handle]])
  // CHECK: [[value1:%.*]] = "tf.ReadVariableOp"([[handle]])
  // CHECK: "tf.ConcatV2"([[value1]], [[value0]]
  // CHECK-SAME: (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<2xi32>

module {
func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>
  func.return %3 : tensor<2x1x2xf32>
}
}

//CHECK: module attributes

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      func.return %0 : tensor<1xi32>
    }
  })";

absl::StatusOr<std::string> CompileMlirModule(bool compile_to_xla_hlo,
                                              const char* module_str) {

module {
func.func @main(%arg0: tensor<21x32x32x128xf32>, %arg1: tensor<2xi32>) -> tensor<1x64x64x128xf32> {
  %0 = "tf.ResizeBilinear"(%arg0, %arg1) {align_corners = false, device = "", half_pixel_centers = true} : (tensor<21x32x32x128xf32>, tensor<2xi32>) -> tensor<1x64x64x128xf32>
  func.return %0 : tensor<1x64x64x128xf32>
}

}

// CHECK-LABEL: conv2d_backprop_input_with_add
func.func @conv2d_backprop_input_with_add(%arg0: tensor<4xi32>, %arg1: tensor<3x3x1x32xf32>, %arg2: tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32> {
  %0 = "tf.Conv2DBackpropInput"(%arg0, %arg1, %arg2) {strides = [1, 2, 2, 1], padding="SAME", dilations=[1, 1, 1, 1]}: (tensor<4xi32>, tensor<3x3x1x32xf32>, tensor<15x14x14x32xf32>) -> tensor<15x28x28x1xf32>

  %zps_0 = "tf.Const"() { value = dense<[3, 4]> : tensor<2xi32> } : () -> tensor<2xi32>
  %scales_1 = "tf.Const"() { value = dense<[3.0, 4.0]> : tensor<2xf32> } : () -> tensor<2xf32>
  %zps_1 = "tf.Const"() { value = dense<[5, 6]> : tensor<2xi32> } : () -> tensor<2xi32>