class_docstr: Container for manipulating Callgrind results.
  
  It supports:
      1) Addition and subtraction to combine or diff results.
      2) Tuple-like indexing.
      3) A `denoise` function which strips CPython calls which are known to
         be non-deterministic and quite noisy.
      4) Two higher order methods (`filter` and `transform`) for custom
         manipulation.
class_docstr_tok: ["Container","for","manipulating","Callgrind","results",".","It","supports",":","1",")","Addition","and","subtraction","to","combine","or","diff","results",".","2",")","Tuple-like","indexing",".","3",")","A","`","denoise","`","function","which","strips","CPython","calls","which","are","known","to","be","non-deterministic","and","quite","noisy",".","4",")","Two","higher","order","methods","(","`","filter","`","and","`","transform","`",")","for","custom","manipulation","."]
code: def __getitem__(
      self, item: Any
  ) -> Union[FunctionCount, "FunctionCounts"]:
      data: Union[
          FunctionCount, Tuple[FunctionCount, ...]
      ] = self._data[item]
      return (
          FunctionCounts(
              cast(Tuple[FunctionCount, ...], data),
              self.inclusive,
              truncate_rows=False,
          )
          if isinstance(data, tuple)
          else data
      )
  
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 3 for calling the FunctionCounts obj's underlying member method code with example usage: obj.__getitem__(item) and returns: unknown