return absl::OkStatus();
  }

  TF_ASSIGN_OR_RETURN(
      auto hlo_module_config,
      xla::HloModule::CreateModuleConfigFromProto(
          compilation_result->computation->proto(), xla::DebugOptions()));

  TF_ASSIGN_OR_RETURN(
      std::unique_ptr<xla::HloModule> hlo_module,
      xla::HloModule::CreateFromProto(compilation_result->computation->proto(),
                                      hlo_module_config));

    // This is the argument used to refer to the pass in
    // the textual format (on the commandline for example).
    return "quant-convert-tf-xla-op-to-tf-op";
  }

  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Apply converting Tensorflow Xla ops to non-xla ops.";
  }

  void getDependentDialects(DialectRegistry& registry) const override {

#include "tensorflow/compiler/tf2xla/layout_util.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"
#include "xla/shape.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/profile_utils/cpu_utils.h"

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

#include "xla/client/local_client.h"

namespace tensorflow {
namespace {
std::vector<const xla::Shape*> GetShapePointers(
    absl::Span<const xla::Shape> shapes) {
  std::vector<const xla::Shape*> shape_ptrs;
  shape_ptrs.reserve(shapes.size());
  for (const auto& shape : shapes) {
    shape_ptrs.push_back(&shape);

    # Check the converted model in the XLA opset.
    quantization_options = quant_opts_pb2.QuantizationOptions(
        quantization_method=quant_opts_pb2.QuantizationMethod(
            preset_method=_PresetMethod.METHOD_STATIC_RANGE_INT8
        ),
        tags=tags,
        signature_keys=[signature_key],
        op_set=quant_opts_pb2.XLA,
        enable_two_input_tensors=not use_kernel,
    )

    "quant::QuantizationDialect",
    "shape::ShapeDialect",
    "sparse_tensor::SparseTensorDialect",
    "stablehlo::StablehloDialect"
  ];
}

def LegalizeTFCollective : Pass<"xla-legalize-tf-collective", "ModuleOp"> {
  let summary = "Legalize TF/XLA collective ops (TensorFlow dialect) to the HLO dialect";

  let constructor = "mlir::mhlo::CreateLegalizeTFCollectivePass()";

#include "tensorflow/compiler/mlir/tf2xla/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/transforms/rewriters.h"
#include "xla/mlir_hlo/mhlo/utils/type_conversion.h"

namespace mlir {
namespace odml {

class TFToMhloPass

        "@local_tsl//tsl/platform:status",
        "@local_xla//xla/stream_executor",
        "@local_xla//xla/stream_executor:executor_cache",
        "@local_xla//xla/stream_executor:host_memory_allocation",
        "@local_xla//xla/stream_executor:memory_allocation",
        "@local_xla//xla/stream_executor:platform",
        "@local_xla//xla/stream_executor:stream_executor_common",
    ],
)

cc_library(

::xla::cpu_function_runtime::BufferInfo(::xla::cpu_function_runtime::EncodedBufferInfo{386ULL, 1U, ~0U}),
::xla::cpu_function_runtime::BufferInfo(::xla::cpu_function_runtime::EncodedBufferInfo{5ULL, ~0U, ~0U}),
::xla::cpu_function_runtime::BufferInfo(::xla::cpu_function_runtime::EncodedBufferInfo{386ULL, 2U, ~0U}),
::xla::cpu_function_runtime::BufferInfo(::xla::cpu_function_runtime::EncodedBufferInfo{5ULL, ~0U, ~0U}),

#include "tensorflow/compiler/tf2xla/tf2xla_util.h"
#include "xla/cpu_function_runtime.h"
#include "xla/service/compiler.h"
#include "xla/service/cpu/buffer_info_util.h"
#include "xla/shape_util.h"
#include "xla/xla_data.pb.h"
#include "tensorflow/core/lib/core/errors.h"

namespace tensorflow {
namespace tfcompile {

namespace {

using BufferInfo = xla::cpu_function_runtime::BufferInfo;

bool IsAlpha(char c) {