class_docstr: This class is used to override the schedule logic from the profiler and perform recording for both
  `training_step`, `validation_step`.
class_docstr_tok: ["This","class","is","used","to","override","the","schedule","logic","from","the","profiler","and","perform","recording","for","both","`","training_step","`",",","`","validation_step","`","."]
code: def is_training(self) -> bool:
      assert self._current_action is not None
      return self._current_action.endswith("training_step")
  
imports: import inspect
  import logging
  import os
  from functools import lru_cache, partial
  from pathlib import Path
  from typing import TYPE_CHECKING, Any, Callable, ContextManager, Dict, List, Optional, Type, Union
  import torch
  from torch import Tensor, nn
  from torch.autograd.profiler import EventList, record_function
  from torch.profiler import ProfilerAction, ProfilerActivity, tensorboard_trace_handler
  from torch.utils.hooks import RemovableHandle
  from typing_extensions import override
  from lightning.fabric.accelerators.cuda import is_cuda_available
  from lightning.pytorch.profilers.profiler import Profiler
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_warn
label_desc: Use image node_id 5 for calling the ScheduleWrapper obj's underlying member method code with example usage: obj.is_training() and returns: self