func.func private @not_quantize_singular_op_without_static_range_ptq(%arg: tensor<3x4x2xf32>) -> tensor<2x3x2x2xf32> attributes {tf._original_func_name = "main_0"} {
    %cst = "tf.Const"() {value = dense<1> : tensor<2x3x2xi32>} : () -> tensor<2x3x2xi32>
    %0 = "quantfork.stats"(%arg) {layerStats = dense<[4.00000000e-6, 9.80000000e-1]> : tensor<2xf32>} : (tensor<3x4x2xf32>) -> tensor<3x4x2xf32>

  constexpr absl::string_view kModuleGather = R"mlir(
    module {
      func.func @main() -> (tensor<2x3x2x2xf32>) {
        %0 = stablehlo.constant dense<1.0> : tensor<3x4x2xf32>
        %1 = stablehlo.constant dense<2> : tensor<2x3x2xi64>
        %2 = "stablehlo.gather"(%0, %1) {
          dimension_numbers = #stablehlo.gather<
            offset_dims = [2, 3],

// CHECK-LABEL: simple_strided_slice
func.func @simple_strided_slice(%input: tensor<4x8xf32>) -> tensor<3x2xf32> {
  // CHECK: %0 = mhlo.constant dense<[0, 1]> : tensor<2xi32>
  // CHECK-NEXT: %1 = mhlo.constant dense<[3, 7]> : tensor<2xi32>
  // CHECK-NEXT: %2 = mhlo.constant dense<[1, 3]> : tensor<2xi32>
  // CHECK-NEXT: %3 = "tf.StridedSlice"(%arg0, %0, %1, %2) : (tensor<4x8xf32>, tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<3x2xf32>

func.func @no_qdq_except_conv_and_dot_general(%arg0: tensor<2x3x2xi64>) -> tensor<2x3x2x2xf32> attributes {tf._original_func_name = "main_0"} {
  %cst = "tf.Const"() {value = dense<3.000000e-01> : tensor<3x4x2xf32>} : () -> tensor<3x4x2xf32>
  %0 = "tf.XlaCallModule"(%cst, %arg0) {
    Sout = [#tf_type.shape<1x3>], _entry_function = @composite_gather_fn,
    _original_entry_function = "composite_gather_fn", _quantization_method = "weight_only_ptq { }",

@testLstmWithInvalidInputsRankMatch(%arg0: tensor<1x4xf32>, %arg1: tensor<4x4xf32>, %arg2: tensor<4x4xf32>, %arg3: tensor<4x4xf32>, %arg4: tensor<4x4xf32>, %arg5: tensor<4x4xf32>, %arg6: tensor<4x4xf32>, %arg7: tensor<4x4xf32>, %arg8: tensor<4x4xf32>, %arg9: tensor<4xf32>, %arg10: tensor<4xf32>, %arg11: tensor<4xf32>, %arg12: tensor<1x4xf32>, %arg13: tensor<4xf32>, %arg14: tensor<4xf32>, %arg15: tensor<4xf32>, %arg16: tensor<4x4xf32>, %arg17: tensor<4xf32>, %arg18: tensor<3xf32>, %arg19: tensor<3xf32>,...

  func.return %0 : tensor<4x4x4xf32>

  // CHECK-DAG: %[[CST:.*]] = arith.constant dense<[0, 1, 0]> : tensor<3xi32>
  // CHECK-DAG: %[[CST_0:.*]] = arith.constant dense<0> : tensor<3xi32>
  // CHECK-DAG: %[[CST_1:.*]] = arith.constant dense<1> : tensor<3xi32>

  // expected-error @+1 {{op requires indices operand to have at least 1 dimension}}
  %0 = "tf.TensorScatterUpdate"(%tensor, %indices, %updates) : (tensor<4x4x4xf32>, tensor<i32>, tensor<4x4xf32>) -> tensor<4x4x4xf32>
  func.return %0 : tensor<4x4x4xf32>
}

// -----

func.func @tensor_scatter_update(%tensor: tensor<4x4x4xf32>, %indices: tensor<4x2xi32>, %updates: tensor<f32>) -> tensor<4x4x4xf32> {

  %0:3 = "tf.Split"(%arg0, %arg1) : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
  func.return %0#0 : tensor<1x4x3xf32>

  // CHECK-LABEL: split
  // CHECK: "tfl.split"(%arg0, %arg1) <{num_splits = 3 : i32}> : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
}

// -----

module {
func.func @my_composite_op_150(%arg0: tensor<4x4xf32>, %arg1: tensor<4x4xf32>, %arg2: tensor<4x4xf32>) -> (tensor<*xf32>, tensor<*xf32>) attributes {tf._implements = #tf_type.func<@my_composite_op, {example_option = 10 : i64, example_str = "value 1.01", tfl_fusable_op = true}>} {
  %0 = "tf.AddV2"(%arg0, %arg1) {device = ""} : (tensor<4x4xf32>, tensor<4x4xf32>) -> tensor<*xf32>