class_docstr: Profiler which relies on native Pytorch profiling. Current setting of the profiler
  captures traces, memory footprint and other info that could be read via TensorBoard.
class_docstr_tok: ["Profiler","which","relies","on","native","Pytorch","profiling",".","Current","setting","of","the","profiler","captures","traces",",","memory","footprint","and","other","info","that","could","be","read","via","TensorBoard","."]
code: def __enter__(self):
      torch.cuda.synchronize()
      self.hta.__enter__()
  
imports: import logging
  import os
  import queue
  import socket
  import weakref
  from dataclasses import dataclass
  from pathlib import Path
  from typing import Any, List, Optional, Sequence, Tuple
  import torch.cuda.memory
  import torch.cuda.nvtx
  import torch.nn
  import torch.profiler
  import torch.utils.hooks
label_desc: Use image node_id 2 for calling the PyTorchProfiler obj's underlying member method code with example usage: obj.__enter__() without return types