//   2) The `tf_saved_model` dialect requires that the arguments with
//   `tf_saved_model.index_path` attributes should precede those with
//   `tf_saved_model.bound_input` attributes. The init function of type
//   `kTfSavedModelInitializerRestoreType` usually has an argument with
//   `tf_saved_model.index_path`, whereas the init function of type
//   `kTfSavedModelInitializerInitType` may have arguments with

  func.func @main(%arg0: tensor<10x1x1024xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<10x1x3xf32> {tf_saved_model.index_path = ["output"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input_tensor:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {

#include "mlir/IR/UseDefLists.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/convert_tensor.h"
#include "tensorflow/core/common_runtime/device.h"
#include "tensorflow/core/common_runtime/device_mgr.h"

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/convert_tensor.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/session_utils.h"
#include "tensorflow/core/framework/resource_var.h"
#include "tensorflow/core/public/session.h"

namespace mlir {
namespace tf_saved_model {
namespace {

void InitializeVariable(TF::VarHandleOp var_handle_op,

  func.func @serving_default(%arg0: tensor<1x4xf32> {tf_saved_model.index_path = ["x"]}) -> (tensor<1x3xf32> {tf_saved_model.index_path = ["output"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_x:0", outputs = "PartitionedCall:0"}, tf_saved_model.exported_names = ["serving_default"]} {

            representative_datasets=[
                qc.RepresentativeDatasetConfig(
                    tf_record=qc.TfRecordFile(path=dataset_path)
                )
            ]
        ),
        tf_saved_model=qc.TfSavedModelConfig(tags=[tag_constants.SERVING]),
        pipeline_config=qc.PipelineConfig(
            merge_fusion_with_dequantize=merge_fusion_with_dequantize
        ),
    )

// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-quantize | FileCheck %s

// Test that hybrid quantized dot_general is produced when q/dq pair only exists
// for weight.

module attributes {tf_saved_model.semantics} {
  func.func private @quantize_dot_general_fn(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {
    %cst = stablehlo.constant dense<3.000000e-01> : tensor<2x3xf32>

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_dialect.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "tensorflow/core/ir/types/dialect.h"

#include "tensorflow/compiler/mlir/quantization/tensorflow/passes/manipulate_model_attr.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/translate/import_model.h"

namespace mlir {
namespace quant {
namespace {

constexpr StringRef kSharedNameAttr = "shared_name";

      return %2 : tensor<1x3xf32>
    }
  }
)mlir";

constexpr absl::string_view kModuleXlaCallModule = R"mlir(
  module {
    func.func @main(%arg0: tensor<?x2xf32> {tf_saved_model.index_path = ["input_tensor"]}) -> (tensor<?x2xf32>) {
      %0 = stablehlo.constant dense<[-0.211145893, -0.708605706]> : tensor<2xf32>