// This is a brief description of the pass.
    return "Add DumpTensor ops after quantizable ops";
  }

  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<TF::TensorFlowDialect>();
    registry.insert<quant::QuantizationDialect>();
    registry.insert<quantfork::QuantizationForkDialect>();
  }

 private:
  void runOnOperation() override;

  Option<DebuggerType> debugger_type_{

    }
  }

  TF_DeleteTensor(analytical_tensor);
  delete[] danalytical;
}

Model BuildGradModel(Model forward, GradientRegistry registry) {
  return [forward_model = std::move(forward),
          grad_registry = std::move(registry)](
             AbstractContext* ctx,
             absl::Span<AbstractTensorHandle* const> inputs,
             absl::Span<AbstractTensorHandle*> outputs) -> Status {

    xla::CompileOnlyClient* client,
    XlaCompiler::CompilationResult* compilation_result) {
  mlir::DialectRegistry registry;
  mlir::RegisterAllTensorFlowDialects(registry);
  mlir::mhlo::registerAllMhloDialects(registry);
  mlir::MLIRContext context(registry);

  mlir::OwningOpRef<mlir::ModuleOp> mlir_module;
  TF_RETURN_IF_ERROR(DeserializeMlirModule(mlir_computation.mlir_module,

class GraphOptPass
    : public mlir::PassWrapper<GraphOptPass,
                               mlir::OperationPass<mlir::ModuleOp>> {
  void getDependentDialects(mlir::DialectRegistry& registry) const override {
    mlir::RegisterAllTensorFlowDialects(registry);
  }

 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(GraphOptPass)

  explicit GraphOptPass(std::vector<tensorflow::GraphOptimizationPass*> passes)

  }
  StringRef getDescription() const final {
    return "Legalize TF_XlaCallModule Op to stablehlo";
  }
  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<stablehlo::StablehloDialect, vhlo::VhloDialect,
                    quant::QuantizationDialect, shape::ShapeDialect>();
  }

  void runOnOperation() override {

// passes. Currently, per-channel quantization only supports 1D results.
class PreprocessOpPass
    : public PassWrapper<PreprocessOpPass, OperationPass<ModuleOp>> {
  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect, QuantizationDialect,
                    quantfork::QuantizationForkDialect>();
  }

 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(PreprocessOpPass)

    : public impl::ReduceWhileOperandsPassBase<ReduceWhileOperandsPass> {
 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(ReduceWhileOperandsPass)

  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<TFL::TensorFlowLiteDialect, TF::TensorFlowDialect>();
  }
  void runOnOperation() override;
};

LogicalResult FindImplicityProducers(
    const std::vector<uint64_t> &explicitly_consumed_ids,

  StringRef getDescription() const final {
    return "Fallback TF-Quantization-unsupported ops to TFLite Flex ops.";
  }

  void getDependentDialects(DialectRegistry &registry) const override {
    registry.insert<TFL::TensorFlowLiteDialect>();
  }

 private:
  // The mode of TF Quantization, might indicate different users/devices.
  Option<std::string> mode_{*this, "mode",

            "Lower threshold of the constant size for unfreezing. Constants "
            "smaller than this value will not be converted to variables."));
  }

  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect,
                    tf_saved_model::TensorFlowSavedModelDialect>();
  }

  // Lower-bound threshold for the size of the constant in bytes. Constants

    // It is a terrible idea to have local MLIR contexts so we need to
    // register extensions here, again.
    mlir::DialectRegistry registry;
    registry.insert<mlir::func::FuncDialect>();
    mlir::func::registerAllExtensions(registry);
    xla_call_module_context_.appendDialectRegistry(registry);

    auto l = tensorflow::XlaCallModuleLoader::Create(
        &xla_call_module_context_, op.getVersion(), op.getModule().str(),