text: import ctypes
  import torch
  
  from ._utils import _dummy_type
  
  
  if not hasattr(torch._C, '_CudaStreamBase'):
      # Define dummy base classes
      torch._C.__dict__['_CudaStreamBase'] = _dummy_type('_CudaStreamBase')
      torch._C.__dict__['_CudaEventBase'] = _dummy_type('_CudaEventBase')
  
  class Stream(torch._C._CudaStreamBase):
      r"""Wrapper around a CUDA stream.
  
      A CUDA stream is a linear sequence of execution that belongs to a specific
      device, independent from other streams.  See :ref:`cuda-semantics` for
      details.
  
      Arguments:
          device(torch.device or int, optional): a device on which to allocate
              the stream. If :attr:`device` is ``None`` (default) or a negative
              integer, this will use the current device.
          priority(int, optional): priority of the stream. Lower numbers
                                   represent higher priorities.
      """
  
      def __new__(cls, device=None, priority=0, **kwargs):
          with torch.cuda.device(device):
              return super(Stream, cls).__new__(cls, priority=priority, **kwargs)
  
      def wait_event(self, event):
          r"""Makes all future work submitted to the stream wait for an event.
  
          Arguments:
              event (Event): an event to wait for.
  
          .. note:: This is a wrapper around ``cudaStreamWaitEvent()``: see
             `CUDA Stream documentation`_ for more info.
  
             This function returns without waiting for :attr:`event`: only future
             operations are affected.
  
          .. _CUDA Stream documentation:
             http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html
          """
          event.wait(self)
  
      def wait_stream(self, stream):
          r"""Synchronizes with another stream.
  
          All future work submitted to this stream will wait until all kernels
          submitted to a given stream at the time of call complete.
  
          Arguments:
              stream (Stream): a stream to synchronize.
  
          .. note:: This function returns without waiting for currently enqueued
             kernels in :attr:`stream`: only future operations are affected.
          """
          self.wait_event(stream.record_event())
  
      def record_event(self, event=None):
          r"""Records an event.
  
          Arguments:
              event (Event, optional): event to record. If not given, a new one
                  will be allocated.
  
          Returns:
              Recorded event.
          """
          if event is None:
              event = Event()
          event.record(self)
          return event
  
      def query(self):
          r"""Checks if all the work submitted has been completed.
  
          Returns:
              A boolean indicating if all kernels in this stream are completed."""
          return super(Stream, self).query()
  
      def synchronize(self):
          r"""Wait for all the kernels in this stream to complete.
  
          .. note:: This is a wrapper around ``cudaStreamSynchronize()``: see
             `CUDA Stream documentation`_ for more info.
          """
          super(Stream, self).synchronize()
  
      @property
      def _as_parameter_(self):
          return ctypes.c_void_p(self.cuda_stream)
  
      def __eq__(self, o):
          if isinstance(o, Stream):
              return super(Stream, self).__eq__(o)
          return False
  
      def __hash__(self):
          return hash((self.cuda_stream, self.device))
  
      def __repr__(self):
          return ('<torch.cuda.Stream device={0} cuda_stream={1:#x}>'
                  .format(self.device, self.cuda_stream))
  
  
  class Event(torch._C._CudaEventBase):
      r"""Wrapper around a CUDA event.
  
      CUDA events are synchronization markers that can be used to monitor the
      device's progress, to accurately measure timing, and to synchronize CUDA
      streams.
  
      The underlying CUDA events are lazily initialized when the event is first
      recorded or exported to another process. After creation, only streams on the
      same device may record the event. However, streams on any device can wait on
      the event.
  
      Arguments:
          enable_timing (bool, optional): indicates if the event should measure time
              (default: ``False``)
          blocking (bool, optional): if ``True``, :meth:`wait` will be blocking (default: ``False``)
          interprocess (bool): if ``True``, the event can be shared between processes
              (default: ``False``)
  
      .. _CUDA Event Documentation:
         https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html
      """
  
      def __new__(cls, enable_timing=False, blocking=False, interprocess=False):
          return super(Event, cls).__new__(
              cls,
              enable_timing=enable_timing, blocking=blocking, interprocess=interprocess)
  
      @classmethod
      def from_ipc_handle(cls, device, handle):
          r"""Reconstruct an event from an IPC handle on the given device."""
          return super(Event, cls).from_ipc_handle(device, handle)
  
      def record(self, stream=None):
          r"""Records the event in a given stream.
  
          Uses ``torch.cuda.current_stream()`` if no stream is specified. The
          stream's device must match the event's device."""
          if stream is None:
              stream = torch.cuda.current_stream()
          super(Event, self).record(stream)
  
      def wait(self, stream=None):
          r"""Makes all future work submitted to the given stream wait for this
          event.
  
          Use ``torch.cuda.current_stream()`` if no stream is specified."""
          if stream is None:
              stream = torch.cuda.current_stream()
          super(Event, self).wait(stream)
  
      def query(self):
          r"""Checks if all work currently captured by event has completed.
  
          Returns:
              A boolean indicating if all work currently captured by event has
              completed.
          """
          return super(Event, self).query()
  
      def elapsed_time(self, end_event):
          r"""Returns the time elapsed in milliseconds after the event was
          recorded and before the end_event was recorded.
          """
          return super(Event, self).elapsed_time(end_event)
  
      def synchronize(self):
          r"""Waits for the event to complete.
  
          Waits until the completion of all work currently captured in this event.
          This prevents the CPU thread from proceeding until the event completes.
  
           .. note:: This is a wrapper around ``cudaEventSynchronize()``: see
              `CUDA Event documentation`_ for more info.
          """
          super(Event, self).synchronize()
  
      def ipc_handle(self):
          r"""Returns an IPC handle of this event. If not recorded yet, the event
          will use the current device. """
          return super(Event, self).ipc_handle()
  
      @property
      def _as_parameter_(self):
          return ctypes.c_void_p(self.cuda_event)
  
      def __repr__(self):
          if self.cuda_event:
              return '<torch.cuda.Event {0:#x}>'.format(self._as_parameter_.value)
          else:
              return '<torch.cuda.Event uninitialized>'
  