text: #include <Python.h>  // NOLINT(build/include_alpha)
  
  // Produce deprecation warnings (needs to come before arrayobject.h inclusion).
  #define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION
  
  #include <boost/make_shared.hpp>
  #include <boost/python.hpp>
  #include <boost/python/raw_function.hpp>
  #include <boost/python/suite/indexing/vector_indexing_suite.hpp>
  #include <numpy/arrayobject.h>
  
  // these need to be included after boost on OS X
  #include <string>  // NOLINT(build/include_order)
  #include <vector>  // NOLINT(build/include_order)
  #include <fstream>  // NOLINT
  
  #include "caffe/caffe.hpp"
  #include "caffe/layers/memory_data_layer.hpp"
  #include "caffe/layers/python_layer.hpp"
  #include "caffe/sgd_solvers.hpp"
  
  // Temporary solution for numpy < 1.7 versions: old macro, no promises.
  // You're strongly advised to upgrade to >= 1.7.
  #ifndef NPY_ARRAY_C_CONTIGUOUS
  #define NPY_ARRAY_C_CONTIGUOUS NPY_C_CONTIGUOUS
  #define PyArray_SetBaseObject(arr, x) (PyArray_BASE(arr) = (x))
  #endif
  
  /* Fix to avoid registration warnings in pycaffe (#3960) */
  #define BP_REGISTER_SHARED_PTR_TO_PYTHON(PTR) do { \
    const boost::python::type_info info = \
      boost::python::type_id<shared_ptr<PTR > >(); \
    const boost::python::converter::registration* reg = \
      boost::python::converter::registry::query(info); \
    if (reg == NULL) { \
      bp::register_ptr_to_python<shared_ptr<PTR > >(); \
    } else if ((*reg).m_to_python == NULL) { \
      bp::register_ptr_to_python<shared_ptr<PTR > >(); \
    } \
  } while (0)
  
  namespace bp = boost::python;
  
  namespace caffe {
  
  // For Python, for now, we'll just always use float as the type.
  typedef float Dtype;
  const int NPY_DTYPE = NPY_FLOAT32;
  
  // Selecting mode.
  void set_mode_cpu() { Caffe::set_mode(Caffe::CPU); }
  void set_mode_gpu() { Caffe::set_mode(Caffe::GPU); }
  
  void InitLog() {
    ::google::InitGoogleLogging("");
    ::google::InstallFailureSignalHandler();
  }
  void InitLogLevel(int level) {
    FLAGS_minloglevel = level;
    InitLog();
  }
  void InitLogLevelPipe(int level, bool stderr) {
    FLAGS_minloglevel = level;
    FLAGS_logtostderr = stderr;
    InitLog();
  }
  void Log(const string& s) {
    LOG(INFO) << s;
  }
  
  void set_random_seed(unsigned int seed) { Caffe::set_random_seed(seed); }
  
  // For convenience, check that input files can be opened, and raise an
  // exception that boost will send to Python if not (caffe could still crash
  // later if the input files are disturbed before they are actually used, but
  // this saves frustration in most cases).
  static void CheckFile(const string& filename) {
      std::ifstream f(filename.c_str());
      if (!f.good()) {
        f.close();
        throw std::runtime_error("Could not open file " + filename);
      }
      f.close();
  }
  
  void CheckContiguousArray(PyArrayObject* arr, string name,
      int channels, int height, int width) {
    if (!(PyArray_FLAGS(arr) & NPY_ARRAY_C_CONTIGUOUS)) {
      throw std::runtime_error(name + " must be C contiguous");
    }
    if (PyArray_NDIM(arr) != 4) {
      throw std::runtime_error(name + " must be 4-d");
    }
    if (PyArray_TYPE(arr) != NPY_FLOAT32) {
      throw std::runtime_error(name + " must be float32");
    }
    if (PyArray_DIMS(arr)[1] != channels) {
      throw std::runtime_error(name + " has wrong number of channels");
    }
    if (PyArray_DIMS(arr)[2] != height) {
      throw std::runtime_error(name + " has wrong height");
    }
    if (PyArray_DIMS(arr)[3] != width) {
      throw std::runtime_error(name + " has wrong width");
    }
  }
  
  // Net constructor
  shared_ptr<Net<Dtype> > Net_Init(string network_file, int phase,
      const int level, const bp::object& stages,
      const bp::object& weights) {
    CheckFile(network_file);
  
    // Convert stages from list to vector
    vector<string> stages_vector;
    if (!stages.is_none()) {
      for (int i = 0; i < len(stages); i++) {
        stages_vector.push_back(bp::extract<string>(stages[i]));
      }
    }
  
    // Initialize net
    shared_ptr<Net<Dtype> > net(new Net<Dtype>(network_file,
          static_cast<Phase>(phase), level, &stages_vector));
  
    // Load weights
    if (!weights.is_none()) {
      std::string weights_file_str = bp::extract<std::string>(weights);
      CheckFile(weights_file_str);
      net->CopyTrainedLayersFrom(weights_file_str);
    }
  
    return net;
  }
  
  // Legacy Net construct-and-load convenience constructor
  shared_ptr<Net<Dtype> > Net_Init_Load(
      string param_file, string pretrained_param_file, int phase) {
    LOG(WARNING) << "DEPRECATION WARNING - deprecated use of Python interface";
    LOG(WARNING) << "Use this instead (with the named \"weights\""
      << " parameter):";
    LOG(WARNING) << "Net('" << param_file << "', " << phase
      << ", weights='" << pretrained_param_file << "')";
    CheckFile(param_file);
    CheckFile(pretrained_param_file);
  
    shared_ptr<Net<Dtype> > net(new Net<Dtype>(param_file,
        static_cast<Phase>(phase)));
    net->CopyTrainedLayersFrom(pretrained_param_file);
    return net;
  }
  
  void Net_Save(const Net<Dtype>& net, string filename) {
    NetParameter net_param;
    net.ToProto(&net_param, false);
    WriteProtoToBinaryFile(net_param, filename.c_str());
  }
  
  void Net_SaveHDF5(const Net<Dtype>& net, string filename) {
    net.ToHDF5(filename);
  }
  
  void Net_LoadHDF5(Net<Dtype>* net, string filename) {
    net->CopyTrainedLayersFromHDF5(filename.c_str());
  }
  
  void Net_SetInputArrays(Net<Dtype>* net, bp::object data_obj,
      bp::object labels_obj) {
    // check that this network has an input MemoryDataLayer
    shared_ptr<MemoryDataLayer<Dtype> > md_layer =
      boost::dynamic_pointer_cast<MemoryDataLayer<Dtype> >(net->layers()[0]);
    if (!md_layer) {
      throw std::runtime_error("set_input_arrays may only be called if the"
          " first layer is a MemoryDataLayer");
    }
  
    // check that we were passed appropriately-sized contiguous memory
    PyArrayObject* data_arr =
        reinterpret_cast<PyArrayObject*>(data_obj.ptr());
    PyArrayObject* labels_arr =
        reinterpret_cast<PyArrayObject*>(labels_obj.ptr());
    CheckContiguousArray(data_arr, "data array", md_layer->channels(),
        md_layer->height(), md_layer->width());
    CheckContiguousArray(labels_arr, "labels array", 1, 1, 1);
    if (PyArray_DIMS(data_arr)[0] != PyArray_DIMS(labels_arr)[0]) {
      throw std::runtime_error("data and labels must have the same first"
          " dimension");
    }
    if (PyArray_DIMS(data_arr)[0] % md_layer->batch_size() != 0) {
      throw std::runtime_error("first dimensions of input arrays must be a"
          " multiple of batch size");
    }
  
    md_layer->Reset(static_cast<Dtype*>(PyArray_DATA(data_arr)),
        static_cast<Dtype*>(PyArray_DATA(labels_arr)),
        PyArray_DIMS(data_arr)[0]);
  }
  
  Solver<Dtype>* GetSolverFromFile(const string& filename) {
    SolverParameter param;
    ReadSolverParamsFromTextFileOrDie(filename, &param);
    return SolverRegistry<Dtype>::CreateSolver(param);
  }
  
  struct NdarrayConverterGenerator {
    template <typename T> struct apply;
  };
  
  template <>
  struct NdarrayConverterGenerator::apply<Dtype*> {
    struct type {
      PyObject* operator() (Dtype* data) const {
        // Just store the data pointer, and add the shape information in postcall.
        return PyArray_SimpleNewFromData(0, NULL, NPY_DTYPE, data);
      }
      const PyTypeObject* get_pytype() {
        return &PyArray_Type;
      }
    };
  };
  
  struct NdarrayCallPolicies : public bp::default_call_policies {
    typedef NdarrayConverterGenerator result_converter;
    PyObject* postcall(PyObject* pyargs, PyObject* result) {
      bp::object pyblob = bp::extract<bp::tuple>(pyargs)()[0];
      shared_ptr<Blob<Dtype> > blob =
        bp::extract<shared_ptr<Blob<Dtype> > >(pyblob);
      // Free the temporary pointer-holding array, and construct a new one with
      // the shape information from the blob.
      void* data = PyArray_DATA(reinterpret_cast<PyArrayObject*>(result));
      Py_DECREF(result);
      const int num_axes = blob->num_axes();
      vector<npy_intp> dims(blob->shape().begin(), blob->shape().end());
      PyObject *arr_obj = PyArray_SimpleNewFromData(num_axes, dims.data(),
                                                    NPY_FLOAT32, data);
      // SetBaseObject steals a ref, so we need to INCREF.
      Py_INCREF(pyblob.ptr());
      PyArray_SetBaseObject(reinterpret_cast<PyArrayObject*>(arr_obj),
          pyblob.ptr());
      return arr_obj;
    }
  };
  
  bp::object Blob_Reshape(bp::tuple args, bp::dict kwargs) {
    if (bp::len(kwargs) > 0) {
      throw std::runtime_error("Blob.reshape takes no kwargs");
    }
    Blob<Dtype>* self = bp::extract<Blob<Dtype>*>(args[0]);
    vector<int> shape(bp::len(args) - 1);
    for (int i = 1; i < bp::len(args); ++i) {
      shape[i - 1] = bp::extract<int>(args[i]);
    }
    self->Reshape(shape);
    // We need to explicitly return None to use bp::raw_function.
    return bp::object();
  }
  
  bp::object BlobVec_add_blob(bp::tuple args, bp::dict kwargs) {
    if (bp::len(kwargs) > 0) {
      throw std::runtime_error("BlobVec.add_blob takes no kwargs");
    }
    typedef vector<shared_ptr<Blob<Dtype> > > BlobVec;
    BlobVec* self = bp::extract<BlobVec*>(args[0]);
    vector<int> shape(bp::len(args) - 1);
    for (int i = 1; i < bp::len(args); ++i) {
      shape[i - 1] = bp::extract<int>(args[i]);
    }
    self->push_back(shared_ptr<Blob<Dtype> >(new Blob<Dtype>(shape)));
    // We need to explicitly return None to use bp::raw_function.
    return bp::object();
  }
  
  template<typename Dtype>
  class SolverCallback: public Solver<Dtype>::Callback {
   protected:
    bp::object on_start_, on_gradients_ready_;
  
   public:
    SolverCallback(bp::object on_start, bp::object on_gradients_ready)
      : on_start_(on_start), on_gradients_ready_(on_gradients_ready) { }
    virtual void on_gradients_ready() {
      on_gradients_ready_();
    }
    virtual void on_start() {
      on_start_();
    }
  };
  template<typename Dtype>
  void Solver_add_callback(Solver<Dtype> * solver, bp::object on_start,
    bp::object on_gradients_ready) {
    solver->add_callback(new SolverCallback<Dtype>(on_start, on_gradients_ready));
  }
  
  // Seems boost cannot call the base method directly
  void Solver_add_nccl(Solver<Dtype>* solver
  #ifdef USE_NCCL
    , NCCL<Dtype>* nccl
  #endif
  ) {
  #ifdef USE_NCCL
    solver->add_callback(nccl);
  #endif
  }
  
  void share_weights(Solver<Dtype>* solver, Net<Dtype>* net) {
    net->ShareTrainedLayersWith(solver->net().get());
  }
  
  template<typename Dtype>
  class NetCallback: public Net<Dtype>::Callback {
   public:
    explicit NetCallback(bp::object run) : run_(run) {}
  
   protected:
    virtual void run(int layer) {
      run_(layer);
    }
    bp::object run_;
  };
  void Net_before_forward(Net<Dtype>* net, bp::object run) {
    net->add_before_forward(new NetCallback<Dtype>(run));
  }
  void Net_after_forward(Net<Dtype>* net, bp::object run) {
    net->add_after_forward(new NetCallback<Dtype>(run));
  }
  void Net_before_backward(Net<Dtype>* net, bp::object run) {
    net->add_before_backward(new NetCallback<Dtype>(run));
  }
  void Net_after_backward(Net<Dtype>* net, bp::object run) {
    net->add_after_backward(new NetCallback<Dtype>(run));
  }
  
  void Net_add_nccl(Net<Dtype>* net
  #ifdef USE_NCCL
    , NCCL<Dtype>* nccl
  #endif
  ) {
  #ifdef USE_NCCL
    net->add_after_backward(nccl);
  #endif
  }
  #ifndef USE_NCCL
  template<typename Dtype>
  class NCCL {
   public:
    NCCL(shared_ptr<Solver<Dtype> > solver, const string& uid) {}
  };
  #endif
  
  bool HasNCCL() {
  #ifdef USE_NCCL
    return true;
  #else
    return false;
  #endif
  }
  
  #ifdef USE_NCCL
  bp::object NCCL_New_Uid() {
    std::string uid = NCCL<Dtype>::new_uid();
  #if PY_MAJOR_VERSION >= 3
    // Convert std::string to bytes so that Python does not
    // try to decode the string using the current locale.
  
    // Since boost 1.53 boost.python will convert str and bytes
    // to std::string but will convert std::string to str. Here we
    // force a bytes object to be returned. When this object
    // is passed back to the NCCL constructor boost.python will
    // correctly convert the bytes to std::string automatically
    PyObject* py_uid = PyBytes_FromString(uid.c_str());
    return bp::object(bp::handle<>(py_uid));
  #else
    // automatic conversion is correct for python 2.
    return bp::object(uid);
  #endif
  }
  #endif
  
  BOOST_PYTHON_MEMBER_FUNCTION_OVERLOADS(SolveOverloads, Solve, 0, 1);
  
  BOOST_PYTHON_MODULE(_caffe) {
    // below, we prepend an underscore to methods that will be replaced
    // in Python
  
    bp::scope().attr("__version__") = AS_STRING(CAFFE_VERSION);
  
    // Caffe utility functions
    bp::def("init_log", &InitLog);
    bp::def("init_log", &InitLogLevel);
    bp::def("init_log", &InitLogLevelPipe);
    bp::def("log", &Log);
    bp::def("has_nccl", &HasNCCL);
    bp::def("set_mode_cpu", &set_mode_cpu);
    bp::def("set_mode_gpu", &set_mode_gpu);
    bp::def("set_random_seed", &set_random_seed);
    bp::def("set_device", &Caffe::SetDevice);
    bp::def("solver_count", &Caffe::solver_count);
    bp::def("set_solver_count", &Caffe::set_solver_count);
    bp::def("solver_rank", &Caffe::solver_rank);
    bp::def("set_solver_rank", &Caffe::set_solver_rank);
    bp::def("set_multiprocess", &Caffe::set_multiprocess);
  
    bp::def("layer_type_list", &LayerRegistry<Dtype>::LayerTypeList);
  
    bp::class_<Net<Dtype>, shared_ptr<Net<Dtype> >, boost::noncopyable >("Net",
      bp::no_init)
      // Constructor
      .def("__init__", bp::make_constructor(&Net_Init,
            bp::default_call_policies(), (bp::arg("network_file"), "phase",
              bp::arg("level")=0, bp::arg("stages")=bp::object(),
              bp::arg("weights")=bp::object())))
      // Legacy constructor
      .def("__init__", bp::make_constructor(&Net_Init_Load))
      .def("_forward", &Net<Dtype>::ForwardFromTo)
      .def("_backward", &Net<Dtype>::BackwardFromTo)
      .def("reshape", &Net<Dtype>::Reshape)
      .def("clear_param_diffs", &Net<Dtype>::ClearParamDiffs)
      // The cast is to select a particular overload.
      .def("copy_from", static_cast<void (Net<Dtype>::*)(const string&)>(
          &Net<Dtype>::CopyTrainedLayersFrom))
      .def("share_with", &Net<Dtype>::ShareTrainedLayersWith)
      .add_property("_blob_loss_weights", bp::make_function(
          &Net<Dtype>::blob_loss_weights, bp::return_internal_reference<>()))
      .def("_bottom_ids", bp::make_function(&Net<Dtype>::bottom_ids,
          bp::return_value_policy<bp::copy_const_reference>()))
      .def("_top_ids", bp::make_function(&Net<Dtype>::top_ids,
          bp::return_value_policy<bp::copy_const_reference>()))
      .add_property("_blobs", bp::make_function(&Net<Dtype>::blobs,
          bp::return_internal_reference<>()))
      .add_property("layers", bp::make_function(&Net<Dtype>::layers,
          bp::return_internal_reference<>()))
      .add_property("_blob_names", bp::make_function(&Net<Dtype>::blob_names,
          bp::return_value_policy<bp::copy_const_reference>()))
      .add_property("_layer_names", bp::make_function(&Net<Dtype>::layer_names,
          bp::return_value_policy<bp::copy_const_reference>()))
      .add_property("_inputs", bp::make_function(&Net<Dtype>::input_blob_indices,
          bp::return_value_policy<bp::copy_const_reference>()))
      .add_property("_outputs",
          bp::make_function(&Net<Dtype>::output_blob_indices,
          bp::return_value_policy<bp::copy_const_reference>()))
      .def("_set_input_arrays", &Net_SetInputArrays,
          bp::with_custodian_and_ward<1, 2, bp::with_custodian_and_ward<1, 3> >())
      .def("save", &Net_Save)
      .def("save_hdf5", &Net_SaveHDF5)
      .def("load_hdf5", &Net_LoadHDF5)
      .def("before_forward", &Net_before_forward)
      .def("after_forward", &Net_after_forward)
      .def("before_backward", &Net_before_backward)
      .def("after_backward", &Net_after_backward)
      .def("after_backward", &Net_add_nccl);
    BP_REGISTER_SHARED_PTR_TO_PYTHON(Net<Dtype>);
  
    bp::class_<Blob<Dtype>, shared_ptr<Blob<Dtype> >, boost::noncopyable>(
      "Blob", bp::no_init)
      .add_property("shape",
          bp::make_function(
              static_cast<const vector<int>& (Blob<Dtype>::*)() const>(
                  &Blob<Dtype>::shape),
              bp::return_value_policy<bp::copy_const_reference>()))
      .add_property("num",      &Blob<Dtype>::num)
      .add_property("channels", &Blob<Dtype>::channels)
      .add_property("height",   &Blob<Dtype>::height)
      .add_property("width",    &Blob<Dtype>::width)
      .add_property("count",    static_cast<int (Blob<Dtype>::*)() const>(
          &Blob<Dtype>::count))
      .def("reshape",           bp::raw_function(&Blob_Reshape))
  #ifndef CPU_ONLY
      .add_property("_gpu_data_ptr",
          reinterpret_cast<uintptr_t (Blob<Dtype>::*)()>(
            &Blob<Dtype>::mutable_gpu_data))
      .add_property("_gpu_diff_ptr",
          reinterpret_cast<uintptr_t (Blob<Dtype>::*)()>(
            &Blob<Dtype>::mutable_gpu_diff))
  #endif
      .add_property("data",     bp::make_function(&Blob<Dtype>::mutable_cpu_data,
            NdarrayCallPolicies()))
      .add_property("diff",     bp::make_function(&Blob<Dtype>::mutable_cpu_diff,
            NdarrayCallPolicies()));
    BP_REGISTER_SHARED_PTR_TO_PYTHON(Blob<Dtype>);
  
    bp::class_<Layer<Dtype>, shared_ptr<PythonLayer<Dtype> >,
      boost::noncopyable>("Layer", bp::init<const LayerParameter&>())
      .add_property("blobs", bp::make_function(&Layer<Dtype>::blobs,
            bp::return_internal_reference<>()))
      .def("setup", &Layer<Dtype>::LayerSetUp)
      .def("reshape", &Layer<Dtype>::Reshape)
      .add_property("type", bp::make_function(&Layer<Dtype>::type));
    BP_REGISTER_SHARED_PTR_TO_PYTHON(Layer<Dtype>);
  
    bp::class_<SolverParameter>("SolverParameter", bp::no_init)
      .add_property("max_iter", &SolverParameter::max_iter)
      .add_property("display", &SolverParameter::display)
      .add_property("layer_wise_reduce", &SolverParameter::layer_wise_reduce)
      .add_property("base_lr", &SolverParameter::base_lr,
             &SolverParameter::set_base_lr);
    bp::class_<LayerParameter>("LayerParameter", bp::no_init);
  
    bp::class_<Solver<Dtype>, shared_ptr<Solver<Dtype> >, boost::noncopyable>(
      "Solver", bp::no_init)
      .add_property("net", &Solver<Dtype>::net)
      .add_property("test_nets", bp::make_function(&Solver<Dtype>::test_nets,
            bp::return_internal_reference<>()))
      .add_property("iter", &Solver<Dtype>::iter)
      .def("add_callback", &Solver_add_callback<Dtype>)
      .def("add_callback", &Solver_add_nccl)
      .def("solve", static_cast<void (Solver<Dtype>::*)(const char*)>(
            &Solver<Dtype>::Solve), SolveOverloads())
      .def("step", &Solver<Dtype>::Step)
      .def("restore", &Solver<Dtype>::Restore)
      .def("snapshot", &Solver<Dtype>::Snapshot)
      .def("share_weights", &share_weights)
      .def("apply_update", &Solver<Dtype>::ApplyUpdate)
      .add_property("param", bp::make_function(&Solver<Dtype>::param,
                bp::return_internal_reference<>()));
    BP_REGISTER_SHARED_PTR_TO_PYTHON(Solver<Dtype>);
  
    bp::class_<SGDSolver<Dtype>, bp::bases<Solver<Dtype> >,
      shared_ptr<SGDSolver<Dtype> >, boost::noncopyable>(
          "SGDSolver", bp::init<string>())
          .add_property("lr", &SGDSolver<Dtype>::GetLearningRate);
    bp::class_<NesterovSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
      shared_ptr<NesterovSolver<Dtype> >, boost::noncopyable>(
          "NesterovSolver", bp::init<string>());
    bp::class_<AdaGradSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
      shared_ptr<AdaGradSolver<Dtype> >, boost::noncopyable>(
          "AdaGradSolver", bp::init<string>());
    bp::class_<RMSPropSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
      shared_ptr<RMSPropSolver<Dtype> >, boost::noncopyable>(
          "RMSPropSolver", bp::init<string>());
    bp::class_<AdaDeltaSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
      shared_ptr<AdaDeltaSolver<Dtype> >, boost::noncopyable>(
          "AdaDeltaSolver", bp::init<string>());
    bp::class_<AdamSolver<Dtype>, bp::bases<SGDSolver<Dtype> >,
      shared_ptr<AdamSolver<Dtype> >, boost::noncopyable>(
          "AdamSolver", bp::init<string>());
  
    bp::def("get_solver", &GetSolverFromFile,
        bp::return_value_policy<bp::manage_new_object>());
  
    // vector wrappers for all the vector types we use
    bp::class_<vector<shared_ptr<Blob<Dtype> > > >("BlobVec")
      .def(bp::vector_indexing_suite<vector<shared_ptr<Blob<Dtype> > >, true>())
      .def("add_blob", bp::raw_function(&BlobVec_add_blob));
    bp::class_<vector<Blob<Dtype>*> >("RawBlobVec")
      .def(bp::vector_indexing_suite<vector<Blob<Dtype>*>, true>());
    bp::class_<vector<shared_ptr<Layer<Dtype> > > >("LayerVec")
      .def(bp::vector_indexing_suite<vector<shared_ptr<Layer<Dtype> > >, true>());
    bp::class_<vector<string> >("StringVec")
      .def(bp::vector_indexing_suite<vector<string> >());
    bp::class_<vector<int> >("IntVec")
      .def(bp::vector_indexing_suite<vector<int> >());
    bp::class_<vector<Dtype> >("DtypeVec")
      .def(bp::vector_indexing_suite<vector<Dtype> >());
    bp::class_<vector<shared_ptr<Net<Dtype> > > >("NetVec")
      .def(bp::vector_indexing_suite<vector<shared_ptr<Net<Dtype> > >, true>());
    bp::class_<vector<bool> >("BoolVec")
      .def(bp::vector_indexing_suite<vector<bool> >());
  
    bp::class_<NCCL<Dtype>, shared_ptr<NCCL<Dtype> >,
      boost::noncopyable>("NCCL",
                          bp::init<shared_ptr<Solver<Dtype> >, const string&>())
  #ifdef USE_NCCL
      .def("new_uid", NCCL_New_Uid).staticmethod("new_uid")
      .def("bcast", &NCCL<Dtype>::Broadcast)
  #endif
      /* NOLINT_NEXT_LINE(whitespace/semicolon) */
    ;
    BP_REGISTER_SHARED_PTR_TO_PYTHON(NCCL<Dtype>);
  
    bp::class_<Timer, shared_ptr<Timer>, boost::noncopyable>(
      "Timer", bp::init<>())
      .def("start", &Timer::Start)
      .def("stop", &Timer::Stop)
      .add_property("ms", &Timer::MilliSeconds);
    BP_REGISTER_SHARED_PTR_TO_PYTHON(Timer);
  
    // boost python expects a void (missing) return value, while import_array
    // returns NULL for python3. import_array1() forces a void return value.
    import_array1();
  }
  
  }  // namespace caffe
  