class_docstr: Top level container for Callgrind results collected by Timer.
  
  Manipulation is generally done using the FunctionCounts class, which is
  obtained by calling `CallgrindStats.stats(...)`. Several convenience
  methods are provided as well; the most significant is
  `CallgrindStats.as_standardized()`.
class_docstr_tok: ["Top","level","container","for","Callgrind","results","collected","by","Timer",".","Manipulation","is","generally","done","using","the","FunctionCounts","class",",","which","is","obtained","by","calling","`","CallgrindStats.stats","(","...",")","`",".","Several","convenience","methods","are","provided","as","well",";","the","most","significant","is","`","CallgrindStats.as_standardized","(",")","`","."]
code: def __repr__(self) -> str:
      newline = "\n"  # `\` cannot appear in fstring code section.
      base_stats = self.baseline_exclusive_stats
      output = f"""
  {super().__repr__()}
  {self.task_spec.summarize()}
    {'':>25}All{'':>10}Noisy symbols removed
      Instructions: {self.counts(denoise=False):>12}{'':>15}{self.counts(denoise=True):>12}
      Baseline:     {base_stats.sum():>12}{'':>15}{base_stats.denoise().sum():>12}
  {self.number_per_run} runs per measurement, {self.task_spec.num_threads} thread{'s' if self.task_spec.num_threads > 1 else ''}
  """.strip()
      if not self.built_with_debug_symbols:
          output += textwrap.dedent(
              """
              Warning: PyTorch was not built with debug symbols.
                       Source information may be limited. Rebuild with
                       REL_WITH_DEB_INFO=1 for more detailed results."""
          )
      return output
  
imports: import collections
  import enum
  import dataclasses
  import itertools
  import os
  import pickle
  import re
  import shutil
  import subprocess
  import sys
  import textwrap
  from typing import cast, Any, Callable, DefaultDict, Dict, Iterator, List, NamedTuple, Optional, Tuple, Union, TYPE_CHECKING
  import torch
  from torch.utils.benchmark.utils import common, cpp_jit
  from torch.utils.benchmark.utils._stubs import CallgrindModuleType
  import operator
label_desc: Use image node_id 1 for calling the CallgrindStats obj's underlying member method code with example usage: obj.__repr__() and returns: output