class_docstr: Base class for creating bundled profilers.
  
  Contains functionality to manage configurations, orchestrate the execution of the profiling
  functions and collate the results. It's up for the subclasses to decide on which profiling
  functions to include, done so by assigning a list with said profiling functions to the
  class variable `_profilers`.
class_docstr_tok: ["Base","class","for","creating","bundled","profilers",".","Contains","functionality","to","manage","configurations",",","orchestrate","the","execution","of","the","profiling","functions","and","collate","the","results",".","It","'s","up","for","the","subclasses","to","decide","on","which","profiling","functions","to","include",",","done","so","by","assigning","a","list","with","said","profiling","functions","to","the","class","variable","`","_profilers","`","."]
docstr: Returns:
      dict: Bundled configurations (defaults), profiler name => configuration.
docstr_tok: ["Returns",":","dict",":","Bundled","configurations","(","defaults",")",",","profiler","name","=",">","configuration","."]
code: def get_config_defaults(cls, as_dict: bool = True) -> Dict[str, Any]:
      """
      Returns:
          dict: Bundled configurations (defaults), profiler name => configuration.
      """
      configs = {}
      for name, p in cls.get_profilers().items():
          cfg = p.config()
          configs[name] = asdict(cfg) if as_dict else cfg
      return configs
  
imports: from typing import Any, Optional, Union, Dict, List, Callable, Tuple, KeysView, ValuesView, ItemsView
  from dataclasses import dataclass, make_dataclass, field, asdict, is_dataclass
  from functools import update_wrapper
  from inspect import signature, Signature
  from datetime import datetime
  import warnings
  from typing_inspect import is_union_type, get_args
  import pandas
  from tslumen.scheduling import Scheduler
  from tslumen.misc import repr_html
label_desc: Use image node_id 2 for calling the BundledProfiler obj's underlying member method code with example usage: obj.get_config_defaults(cls, as_dict) and returns: configs