'target_opset': quant_opts_pb2.UNIFORM_QUANTIZED,
          'input_shape_dynamic': False,
          'enable_per_channel_quantization': False,
      },
      {
          'testcase_name': 'dilation_with_bias_and_bn_and_relu6_to_uq',
          'activation_fn': nn_ops.relu6,
          'has_bias': True,
          'has_batch_norm': True,
          'target_opset': quant_opts_pb2.UNIFORM_QUANTIZED,

      *this, "target-opset", llvm::cl::init(OpSet::UNIFORM_QUANTIZED),
      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};

          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};
};

llvm::StringRef InsertQuantizedFunctionsPass::GetFunctionLibrary(
    QuantMethod quantization_method, OpSet op_set) {

// RUN: tf-quant-opt %s -quant-insert-quantized-functions='quantization-method=drq' | FileCheck %s
// RUN: tf-quant-opt %s -quant-insert-quantized-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' | FileCheck --check-prefix=UQ-CHECK %s

// Empty module
module {
  func.func @simple_fn(%arg0: tensor<*xf32>) -> tensor<*xf32> {
    func.return %arg0 : tensor<*xf32>
  }
}

// CHECK-NOT: func private @internal_calculate_quant_params

          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};

  Option<int64_t> min_num_elements_for_weights_{
      *this, "min-num-elements-for-weights", llvm::cl::init(0),

// RUN: tf-quant-opt %s -quant-insert-quantized-functions | FileCheck %s
// RUN: tf-quant-opt %s -quant-insert-quantized-functions='quantization-method=ptq target-opset=UNIFORM_QUANTIZED' --mlir-print-ir-after-all | FileCheck --check-prefix=UQ-CHECK %s

// Empty module
module {
  func.func @simple_fn(%arg0: tensor<*xf32>) -> tensor<*xf32> {
    func.return %arg0 : tensor<*xf32>
  }
}

// CHECK-NOT: func private @internal_rescale_fn

          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};

  Option<bool> enable_per_channel_quantization_{
      *this, "enable-per-channel-quantization", llvm::cl::init(false),

// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -quant-quantize-composite-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -symbol-dce | FileCheck %s

module {
  // TODO(b/260020937): Support transpose_a, transpose_b for matmul.
  func.func @matmul(%arg0: tensor<2x12xf32>) -> (tensor<*xf32>) {

    std::optional<const absl::string_view> mlir_dump_file_prefix) {
  pm.addNestedPass<mlir::func::FuncOp>(
      mlir::quant::CreateConvertFakeQuantToQdqPass());
  if (quantization_options.op_set() == OpSet::UNIFORM_QUANTIZED) {
    pm.addNestedPass<mlir::func::FuncOp>(
        mlir::TF::CreateUnrollBatchMatMulPassPass());
  }
  pm.addPass(mlir::TF::CreateTFShapeInferencePass());

          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};
};

// Check if given indices in `val1` has same number of elements as given
// indices in `val2`.