}

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

 private:
  void runOnOperation() override;

  // Returns the function library for the given quantization method and opset
  // pair.
  llvm::StringRef GetFunctionLibrary(QuantMethod quantization_method,

    registry.insert<TF::TensorFlowDialect>();
  }

 public:
  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(MockSuccessPass)

  explicit MockSuccessPass() = default;

 private:
  void runOnOperation() override {
    getOperation().walk([](Operation* nestedOp) {
      nestedOp->emitError()
          << "Error at " << nestedOp->getName().getStringRef().str() << " op";
    });
  };
};

class ExtractTPUCopyWithDynamicShapeOpPass
    : public impl::ExtractTPUCopyWithDynamicShapeOpPassBase<
          ExtractTPUCopyWithDynamicShapeOpPass> {
  void runOnOperation() override;
};

// Finds op that created a given value. If the value is a BlockArgument, this
// returns the owner of the Block.
Operation* GetOpOfValue(Value value) {

  explicit LiftQuantizableSpotsAsFunctionsPass(
      QuantizationSpecs quantization_specs)
      : quantization_specs_(std::move(quantization_specs)) {}

 private:
  void runOnOperation() override;

  // No explicit quantization spec is specified by default. Implicitly this
  // means that all quantizable units will be identified and lifted.
  QuantizationSpecs quantization_specs_{};
};

class SplitIntoIslandPerOpPass
    : public impl::SplitIntoIslandPerOpPassBase<SplitIntoIslandPerOpPass> {
 public:
  void runOnOperation() override;
};

void SplitIntoIslandPerOpPass::runOnOperation() {
  func::FuncOp func = getOperation();

  if (func.isExternal()) {
    // Just ignore the op if this is an external func with no body.
    return;
  }

    this->fold_transpose_in_ops_ = fold_transpose_in_ops;
  }
  MoveTransposesPass(const MoveTransposesPass& pass) {}

  void runOnOperation() final;
};

using Permutation = SmallVector<int64_t, 4>;

void LayoutAssignmentPass::runOnOperation() {
  func::FuncOp func = getOperation();

  // Get runtime devices information from the closest parent module.
  RuntimeDevices devices;

  }

  // NOTE: The implementation is mostly copypasted from
  // third_party/tensorflow/compiler/mlir/tfrt/transforms/lower_saved_model.cc
  // with the original code trimmed and adapted as needed.
  void runOnOperation() override {
    auto module = getOperation();
    if (!tf_saved_model::HasTfSavedModelSemantics(module)) return;

    // Clean up functions from tf_saved_model attributes.
    OpBuilder builder(module);

    return "Inserts a new function that wraps a SaveV2 op. The SaveV2 op saves "
           "the values of the VarHandleOps that are found in the initializer "
           "function of 'restore_op' type.";
  }

  void runOnOperation() override;
};

// Finds `tf.AssignVariableOp(tf.VarHandleOp, tf.Const)` patterns and removes
// `tf.AssignVariableOp`s and `tf.Const`s. Collects and returns the

 public:
  explicit QuantizeWeightPass(
      QuantizationComponentSpec quantization_component_spec)
      : quantization_component_spec_(quantization_component_spec) {}

 private:
  void runOnOperation() override;
  QuantizationComponentSpec quantization_component_spec_;
};

// Collects quantizable target ops, then insert Q-DQ quantization patterns.
class QuantizeWeight : public OpRewritePattern<ConstantOp> {

  StringRef getArgument() const final { return "tfl-parse-stablehlo-ops"; }
  StringRef getDescription() const final {
    return "This pass will legalize TFLite custom Ops to StableHLO ops.";
  }

 private:
  void runOnOperation() override;

  void getDependentDialects(DialectRegistry& registry) const override {
    mlir::stablehlo::registerAllDialects(registry);
  }
  inline TFL::ConstBytesAttr CustomOption(OpBuilder* builder,