// This is a brief description of the pass.
    return "Quantize composite functions with QDQ input/outputs.";
  }

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

 private:
  void runOnOperation() override;

  //
  //  Arg  --> B  --> C  --> D --> Retval
  class Subgraph {
   public:
    // Creates a graph to build the subgraph in, if it doesn't already exist,
    // using the same op registry and versions as graph_in.
    Node* MakeNodeImage(const Graph* graph_in, Node* node);

    // Returns the graph the subgraph is being built in.
    Graph* GetGraph() const;

    }
  }
  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:

    const std::vector<std::string>& ordered_output_arrays,
    bool experimental_prune_unreachable_nodes_unconditionally,
    const bool disable_vhlo_to_stablehlo) {
  mlir::DialectRegistry registry;
  registry.insert<mlir::arith::ArithDialect, mlir::func::FuncDialect,
                  mlir::quant::QuantizationDialect,
                  mlir::quantfork::QuantizationForkDialect,

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