class_docstr: 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.
  
  Args:
      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
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docstr: Record 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.
docstr_tok: ["Record","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","."]
code: def record(self, stream=None):
      r"""Record 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().record(stream)
  
imports: import ctypes
  import torch
  from torch._streambase import _EventBase, _StreamBase
  from ._utils import _dummy_type
label_desc: Use image node_id 3 for calling the Event obj's underlying member method code with example usage: obj.record(stream) without return types