class TFRDecomposeContext {
 public:
  // The entry function to get a decompose context. All the required passes have
  // been initialized.
  static absl::StatusOr<std::unique_ptr<TFRDecomposeContext>> Get(
      mlir::MLIRContext* mlir_ctx);

  // Constructor of the decompose context. To share the decompose library, the
  // whole decompose TFR function library is loaded.

 public:
  DeviceCompilerClient() = default;
  virtual ~DeviceCompilerClient() = default;

  // Compiles `result` (HLO) to an `ExecutableType` using `ClientType` and
  // returns it.
  virtual StatusOr<std::unique_ptr<ExecutableType>> BuildExecutable(
      const XlaCompiler::Options& options,
      const XlaCompiler::CompilationResult& result) = 0;

  // Serializes an available `executable` to string using `ClientType` and

  void UseOriginalFunctionNames(NodeDef& node_def);

  Status AddEdge(Operation* inst);

  absl::StatusOr<std::unique_ptr<NodeDef>> GetArgumentNode(
      BlockArgument arg, unsigned index, llvm::StringRef name);
  absl::StatusOr<std::unique_ptr<NodeDef>> GetReturnNode(FuncOp function,
                                                         Value operand,

  void UseOriginalFunctionNames(NodeDef& node_def);

  Status AddEdge(Operation* inst);

  absl::StatusOr<std::unique_ptr<NodeDef>> GetArgumentNode(
      BlockArgument arg, unsigned index, llvm::StringRef name);
  absl::StatusOr<std::unique_ptr<NodeDef>> GetReturnNode(FuncOp function,
                                                         Value operand,

#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/errors.h"

namespace tensorflow {

AbstractContext* BuildFunction(const char* fn_name) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_ExecutionContext* graph_ctx = TF_CreateFunction(fn_name, status.get());
  return unwrap(graph_ctx);
}

    const XlaShapeLayoutHelpers::ShapeDeterminationFns& shape_determination_fns,
    bool use_tuple_args, XlaCompiler::CompilationResult* compilation_result,
    std::vector<std::unique_ptr<mlir::Pass>>& custom_legalization_passes,
    const std::vector<TensorShape>& arg_shapes,
    std::vector<tpu::ShardingAndIndex>* arg_core_mapping,
    std::vector<std::vector<xla::Shape>>* per_core_arg_shapes);

  char* buf = nullptr;
  Py_ssize_t length;
  std::unique_ptr<PythonErrorReporter> error_reporter(new PythonErrorReporter);

  if (tflite::python_utils::ConvertFromPyString(data, &buf, &length) == -1) {
    PyErr_Format(PyExc_ValueError, "Failed to convert input PyObject");
    return nullptr;
  }
  std::unique_ptr<tflite::FlatBufferModel> model =
      tflite::FlatBufferModel::BuildFromBuffer(buf, length,

  NoOpPass() = default;

  llvm::StringRef getArgument() const final { return "no-op-pass"; }

  void runOnOperation() override {
    // Noop pass does nothing on the operation.
  }
};

std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>> CreateNoOpPass() {
  return std::make_unique<NoOpPass>();
}

class ParentPass
    : public mlir::PassWrapper<ParentPass,

    : public DeviceCompilerClient<xla::PjRtLoadedExecutable, xla::PjRtClient> {
 public:
  explicit PjRtDeviceCompilerClient(xla::PjRtClient* client)
      : client_(client) {}

  absl::StatusOr<std::unique_ptr<xla::PjRtLoadedExecutable>> BuildExecutable(
      const XlaCompiler::Options& options,
      const XlaCompiler::CompilationResult& result) override;

  // Returns a platform-specific serialization of `executable`. The

  Tape tape(/*persistent=*/false);
  tape.Watch(inputs[0]);  // Watch x.
  AbstractTensorHandle* exp_output;
  TF_RETURN_IF_ERROR(ops::Exp(ctx, inputs[0], &exp_output, "Exp"));
  std::unique_ptr<GradientFunction> gradient_function(
      new PassThroughGradientFunction);
  tape.RecordOperation(inputs, {exp_output}, gradient_function.release());
  TF_RETURN_IF_ERROR(tape.ComputeGradient(ctx,