custom_legalization_passes);

// Compiles a Graph from TF to HLO and adds the resulting HLO to the
// XlaBuilder. This function adds HLO to a larger HLO computation, so
// HLO-level inputs are supplied, and HLO-level outputs are produced.
// xla_params is the HLO-level inputs and returns is the HLO-level outputs.
// If unconditionally_use_output_shapes is true then the unregistered

                                            RewritePatternSet* patterns);

/// Converts the provided Operation as well as all nested operations into HLO
/// dialect using the conversion patterns registered by the HLO dialect. When
/// allow_partial_conversion is false, emits an error if there is any operation
/// that can't be legalized.
/// When `tf2xla_fallback_device_type` is not `None`, also uses legalization

      compilation_result.get());

  if (combined_bridge_status.ok()) {
    VLOG(1) << "Successfully compiled MLIR computation to XLA HLO using "
               "Combined MLIR and XlaBuilder Bridge.";

    DumpHloCompilationResult("legalize_tf_combined_bridge.hlo",
                             compilation_result.get())
        .IgnoreError();
    return *compilation_result;
  }

};

// Converts tf.RandomShuffle op into a series of XLA HLO ops.
//
// tf.RandomShuffle shuffles tensors along the first dimension. If the input
// tensor's rank is 1, then it is translated into HLO sort op(s) according to
// indices randomly generated via HLO rng_uniform ops. Otherwise, it is
// translated into an HLO while op to first emulate shuffling indices using
// HLO dynamic_slice and dynamic_update_slice ops, then finally HLO gather

config.name = 'MLIR ' + os.path.basename(config.mlir_test_dir)

config.test_format = lit.formats.ShTest(not llvm_config.use_lit_shell)

# suffixes: A list of file extensions to treat as test files.
config.suffixes = ['.cc', '.hlo', '.json', '.mlir', '.pbtxt', '.py']

# test_source_root: The root path where tests are located.
config.test_source_root = config.mlir_test_dir

# test_exec_root: The root path where tests should be run.

  // Whether to enable the hlo/stablehlo to tf conversion. This also supports
  // the case where a saved model contains both TF module and serialized
  // StableHLO module.
  bool enable_hlo_to_tf_conversion = false;
  // Whether to disable the direct hlo/stablehlo to Tensorflow Lite conversion.
  //
  // This prevents from directly converting from HLO to TFLite without going

  if (tf2xla_fallback_device_type) {
    // Add TF->HLO legalization patterns via TF2XLA fallback.
    PopulateLegalizeTfWithTf2XlaPatterns(tf2xla_fallback_device_type.value(),
                                         patterns, context, converter,
                                         prefer_tf2xla);
  }

  // Populate with CHLO->HLO lowerings to account for TF ops legalized to
  // CHLO first.

#include "tensorflow/compiler/mlir/tensorflow/transforms/mlprogram.h"

namespace tensorflow {

void RegisterMlProgramPasses() {
  mlir::registerPassPipeline(
      "tf-lower-to-mlprogram-and-hlo", "Lower TF to ml_program + mhlo",
      [](mlir::OpPassManager& pm, llvm::StringRef options,
         llvm::function_ref<mlir::LogicalResult(const llvm::Twine&)>
             errorHandler) {

}

void RegisterConvertMlirToXlaHloPipelineWithDefaults() {
  static mlir::PassPipelineRegistration<> pipeline(
      "tf-to-hlo-pipeline",
      "Convert TF dialect to HLO dialect (used for compilation in bridge).",
      [](mlir::OpPassManager& pm) {
        tensorflow::CreateConvertMlirToXlaHloPipeline(

#include "tensorflow/compiler/tf2xla/layout_util.h"
#include "tensorflow/compiler/tf2xla/xla_compiler.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/client/compile_only_client.h"
#include "xla/hlo/ir/hlo_input_output_alias_config.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"
#include "xla/shape.h"
#include "xla/shape_util.h"
#include "xla/status_macros.h"
#include "tensorflow/core/framework/function.h"