let description = [{
    Fixed point shape refinement pass that utilizes the shape functions
    registered on ops using the InferTypeOpInterface as well as by bridging to
    the TensorFlow op registry's shape functions. This is an interprocedural
    pass that propagates information across function calls/control flow
    operations where possible (the GuaranteeAllFuncsOneUsePass is often run

}

void LoadImporterDialects(mlir::MLIRContext& context) {
  // Load dialects involved in the conversion
  mlir::DialectRegistry registry;
  mlir::RegisterAllTensorFlowDialectsImpl(registry, false);
  context.appendDialectRegistry(registry);
  for (llvm::StringRef name : registry.getDialectNames())
    context.getOrLoadDialect(name);
}

absl::StatusOr<std::string> GetDenseTensorNameFromTensorInfo(

    }
  }
  return false;
}

struct EmbeddingPipeliningPass
    : public ::impl::EmbeddingPipeliningPassBase<EmbeddingPipeliningPass> {
  void getDependentDialects(mlir::DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect>();
  }

  void runOnOperation() override;
};

bool UseEmbeddingPipelining(ModuleOp& module) {
  // Enable automated pipelining pass unless:

        *registry, InferSparseSegmentOpCost<mlir::TF::SparseSegmentMeanOp>);
    RegisterCostFunction<mlir::TF::SparseSegmentSqrtNOp>(
        *registry, InferSparseSegmentOpCost<mlir::TF::SparseSegmentSqrtNOp>);
    RegisterCostFunction<mlir::TF::LookupTableFindV2Op>(
        *registry, InferLookupTableFindV2Cost);
    return registry;
  }();
  return *registry;
}

}  // namespace

                                        TF_Status* status) {
  RegisterPasses();
  mlir::DialectRegistry registry;
  mlir::RegisterAllTensorFlowDialects(registry);
  mlir::stablehlo::registerAllDialects(registry);
  registry.insert<mlir::shape::ShapeDialect>();
  mlir::MLIRContext context(registry);
  mlir::OwningOpRef<mlir::ModuleOp> module;
  {
    mlir::StatusScopedDiagnosticHandler diagnostic_handler(&context);

### TPU Enhancements

*   Adds 3D mesh support in TPU configurations ops.
*   Added TPU code for `FTRL` with `multiply_linear_by_lr`.
*   Silently adds a new file system registry at `gstpu`.
*   Support `restartType` in cloud tpu client.
*   Depend on a specific version of google-api-python-client.
*   Fixes apiclient import.

### Tracing and Debugging

          mlir::registerPassManagerCLOptions();
          mlir::DialectRegistry registry;
          registry.insert<mlir::BuiltinDialect>();
          registry.insert<mlir::arith::ArithDialect>();
          registry.insert<mlir::func::FuncDialect>();
          registry.insert<mlir::TFL::TensorFlowLiteDialect>();
          return registry;
        }()) {
    context_.loadAllAvailableDialects();

  absl::flat_hash_set<std::string> denylisted_mlir_op_names;
  for (auto& entry : denylisted_ops) {
    denylisted_mlir_op_names.insert(TfLiteToMlir(entry));
  }

  DialectRegistry registry;
  MLIRContext context(registry);
  StatusScopedDiagnosticHandler statusHandler(&context,
                                              /*propagate=*/true);

  // Import input_model to a MLIR module

class LowerGlobalsToMlProgram
    : public impl::LowerGlobalsToMlProgramPassBase<LowerGlobalsToMlProgram> {
 public:
  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<mlir::tf_saved_model::TensorFlowSavedModelDialect,
                    ml_program::MLProgramDialect>();
  }

  void runOnOperation() override {

// quantization case.
class PrepareQuantizeDRQPass
    : public PassWrapper<PrepareQuantizeDRQPass, OperationPass<ModuleOp>> {
  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect, ::mlir::quant::QuantizationDialect,
                    ::mlir::quantfork::QuantizationForkDialect>();
  }

 public: