class_docstr: Allows profiling in an easy-to-use manner. CudaEventRecorder can be used
  in a loop. When it is used in a loop (or when an event recorder is created
  multiple times with the same name), get_timings returns the statistics of the
  timings since the last reset. Note: in case the number of timings is greater than
  10,000, only the last 10,000 timings are used to calculate the statistics.
  
  Usage:
  >>> event_recorder1 = CudaEventRecorder('1')
  >>> # Sequence of events whose time is to be measured
  >>> event_recorder1.stop()
  >>> event_recorder2 = CudaEventRecorder('2')
  >>> # Sequence of events whose time is to be measured
  >>> event_recorder2.stop()
  >>> print(CudaEventRecorder.get_timings())
  
  Args:
      event_name (str): The name by which the cuda event is to be referred later on
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docstr: Returns the statistics of all the timings
docstr_tok: ["Returns","the","statistics","of","all","the","timings"]
code: def get_all_timings(cls) -> str:
      """Returns the statistics of all the timings"""
      return cls.get_common_timings(
          cls.all_event_recorders, "All timings"
      )
  
imports: from collections import defaultdict, deque
  from functools import partial
  import statistics
  from typing import ClassVar, Deque, Dict, Optional
  import torch
label_desc: Use image node_id 7 for calling the CudaEventRecorder obj's underlying member method code with example usage: obj.get_all_timings(cls) and returns: cls