}
  }
  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);

// Creates an MLIRContext with the extensions required for quantization are
// registered.
inline std::unique_ptr<MLIRContext> CreateMlirContextForQuantization() {
  DialectRegistry registry{};
  func::registerAllExtensions(registry);
  return std::make_unique<MLIRContext>(registry);
}

}  // namespace mlir::quant::stablehlo

### 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

  std::vector<AbstractTensorHandle*> outputs(1);
  GradientRegistry registry;
  s = RegisterGradients(&registry);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto tape = std::make_unique<Tape>(/*persistent=*/false);
  s = Execute(check_numerics_op.get(), ctx.get(), absl::MakeSpan(outputs),
              &num_retvals, &forward_op, tape.get(), registry);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  string read_message;

          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();

_CalibrationMethod = (
    stablehlo_quant_config_pb2.CalibrationOptions.CalibrationMethod
)
_REGISTRY = {}


def _implements(calib_method: _CalibrationMethod):
  def decorator(cls):
    assert calib_method not in _REGISTRY
    _REGISTRY[calib_method] = cls
    return cls

  return decorator


class _CalibrationAlgorithmBase(abc.ABC):

  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 {