return std::vector<double>(vec.begin(), vec.end());
  }

  return std::vector<double>();
}

// Handles the case when the DenseElementsAttr doesn't exist, and when it
// doesn't returns a vector of length `default_size` all with the same value
// `default_value`.
template <typename T>
static inline std::vector<T> GetOptionalVector(

                  std::vector<Output>* grad_outputs) {
  grad_outputs->push_back(Mul(scope, op.input(0), grad_inputs[0]));
  return scope.status();
}
REGISTER_GRADIENT_OP("L2Loss", L2LossGrad);

Status BiasAddGradHelper(const Scope& scope, const Operation& op,
                         const std::vector<Output>& grad_inputs,
                         std::vector<Output>* grad_outputs) {
  string data_format;

    *deviceMgrPtr = nullptr;
    return OkStatus();
  }

  Status Run(const std::vector<std::pair<std::string, Tensor>>& inputs,
             const std::vector<std::string>& output_names,
             const std::vector<std::string>& target_nodes,
             std::vector<Tensor>* outputs) override {
    tensorflow::RunMetadata run_metadata;

		[6]*argField{ap_VecSpReg_10_10_6_9, ap_Reg_11_15, ap_Reg_16_20}},
	{LXVRBX, 0xfc0007fe00000000, 0x7c00001a00000000, 0x0, // Load VSX Vector Rightmost Byte Indexed X-form (lxvrbx XT,RA,RB)
		[6]*argField{ap_VecSReg_31_31_6_10, ap_Reg_11_15, ap_Reg_16_20}},

	PT_VR9                  = 83  // Vector register 9
	PT_VR10                 = 84  // Vector register 10
	PT_VR11                 = 85  // Vector register 11
	PT_VR12                 = 86  // Vector register 12
	PT_VR13                 = 87  // Vector register 13
	PT_VR14                 = 88  // Vector register 14
	PT_VR15                 = 89  // Vector register 15
	PT_VR16                 = 90  // Vector register 16

  // indices
  // in the idx map, the value is a pair of offset and size
  absl::flat_hash_map<std::pair<std::vector<int32_t>, std::vector<int32_t>>,
                      std::vector<uint8_t>>
      custom_op_data_map_;
  absl::flat_hash_map<std::pair<std::vector<int32_t>, std::vector<int32_t>>,
                      std::pair<uint64_t, uint64_t>>
      custom_op_idx_map_;

      const std::vector<bool>& reachable_nodes,
      const std::unordered_set<int>& output_nodes) const;

  const Scope& scope_;
  const ops::GradOpRegistry* registry_;
  const std::vector<Output>& outputs_;
  const std::vector<Output>& inputs_;
  const std::vector<Output>& grad_inputs_;
  std::vector<Output>* grad_outputs_;

  ::tensorflow::Status ListDevices(
      std::vector<tensorflow::DeviceAttributes>* response) override;

  ::tensorflow::Status LocalDeviceManager(
      const tensorflow::DeviceMgr** deviceMgrPtr) override;

  ::tensorflow::Status Run(
      const std::vector<std::pair<std::string, ::tensorflow::Tensor>>& inputs,
      const std::vector<std::string>& output_names,
      const std::vector<std::string>& target_nodes,

	"encoding/gob"
	"fmt"
	"log"
)

// The Vector type has unexported fields, which the package cannot access.
// We therefore write a BinaryMarshal/BinaryUnmarshal method pair to allow us
// to send and receive the type with the gob package. These interfaces are
// defined in the "encoding" package.
// We could equivalently use the locally defined GobEncode/GobDecoder
// interfaces.
type Vector struct {
	x, y, z int
}

    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);

// Compiles a serialized MLIR module into XLA HLO, generates all accompanying