data.h_in = op_type.getShape()[2];
  data.w_in = op_type.getShape()[3];

  std::vector<int32_t> kernel_size;
  GetI32VectorFromDenseI64CompositeAttr(composite_attrs, "kernel_size",
                                        &kernel_size);
  data.kh = kernel_size[0];
  data.kw = kernel_size[1];

  std::vector<int32_t> padding;
  GetI32VectorFromDenseI64CompositeAttr(composite_attrs, "padding", &padding);

==============================================================================*/

// Tests for ShapeInference.

#include "tensorflow/compiler/jit/shape_inference.h"

#include <map>
#include <memory>
#include <vector>

#include "tensorflow/cc/framework/ops.h"
#include "tensorflow/cc/framework/scope.h"
#include "tensorflow/cc/ops/array_ops.h"
#include "tensorflow/cc/ops/control_flow_ops_internal.h"

#ifndef TENSORFLOW_COMPILER_MLIR_QUANTIZATION_STABLEHLO_CC_CALIBRATION_COMPONENT_H_
#define TENSORFLOW_COMPILER_MLIR_QUANTIZATION_STABLEHLO_CC_CALIBRATION_COMPONENT_H_

#include <string>
#include <unordered_set>
#include <vector>

#include "absl/base/nullability.h"
#include "absl/container/flat_hash_map.h"
#include "absl/status/status.h"
#include "absl/status/statusor.h"
#include "absl/strings/string_view.h"

  // These fields are populated for a device in GetIdFor, *before* we give out a
  // DeviceId.
  std::vector<const XlaOpRegistry::DeviceRegistration*>
      id_to_compilation_device_;
  std::vector<std::unique_ptr<DeviceType>> id_to_device_type_;
  std::vector<string> names_;
  std::vector<bool> is_cpu_;
  std::vector<bool> is_gpu_;
};

}  // namespace jit

// Returns the DeviceType corresponding to 'device'.

	/* Vector instructions */

	/* Vector load */
	{as: ALVEBX, a1: C_XOREG, a6: C_VREG, type_: 45, size: 4}, /* vector load, x-form */

	/* Vector store */
	{as: ASTVEBX, a1: C_VREG, a6: C_XOREG, type_: 44, size: 4}, /* vector store, x-form */

	/* Vector logical */
	{as: AVAND, a1: C_VREG, a2: C_VREG, a6: C_VREG, type_: 82, size: 4}, /* vector and, vx-form */

static absl::StatusOr<std::vector<int>> RewriteWithArgs(
    mlir::ModuleOp module_op, llvm::ArrayRef<XlaArgument> args) {
  mlir::func::FuncOp main_fn =
      module_op.lookupSymbol<mlir::func::FuncOp>("main");
  std::vector<int> params;

  bool has_resource_args = false;
  auto builder = mlir::OpBuilder(main_fn.getBody());
  std::vector<int> args_to_erase;
  for (int idx = 0; idx < args.size(); idx++) {

limitations under the License.
==============================================================================*/

#include "tensorflow/compiler/jit/shape_inference.h"

#include <cstdint>
#include <map>
#include <vector>

#include "absl/log/log.h"
#include "absl/strings/str_cat.h"
#include "tensorflow/compiler/jit/shape_inference_helpers.h"
#include "tensorflow/core/common_runtime/shape_refiner.h"

#include "tensorflow/compiler/mlir/lite/sparsity/sparsify_model.h"

#include <stdint.h>

#include <cstdarg>
#include <map>
#include <memory>
#include <string>
#include <utility>
#include <vector>

#include <gmock/gmock.h>
#include <gtest/gtest.h>
#include "absl/status/status.h"
#include "flatbuffers/flatbuffer_builder.h"  // from @flatbuffers
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"

#ifndef TENSORFLOW_COMPILER_MLIR_QUANTIZATION_STABLEHLO_PASSES_PASSES_H_
#define TENSORFLOW_COMPILER_MLIR_QUANTIZATION_STABLEHLO_PASSES_PASSES_H_

#include <memory>
#include <string>
#include <vector>

#include "absl/status/statusor.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/Pass/Pass.h"  // from @llvm-project

  output->os() << result;
  output->keep();

  return output_filename;
}

absl::StatusOr<std::string> PywrapSavedModelToStablehlo(
    absl::string_view input_path,
    const std::vector<std::string>& exported_model_signatures,
    const std::vector<std::string>& tag_names,
    absl::string_view input_arg_shapes_str) {
  mlir::DialectRegistry registry;
  RegisterAllTensorFlowDialects(registry);
  mlir::MLIRContext context(registry);