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 _step(self) -> None:
      if self.is_training:
          self._num_training_step += 1
      elif self.is_validating:
          assert self._start_action_name is not None
          if self._start_action_name.endswith("on_fit_start"):
              if self._num_training_step > 0:
                  self._num_validation_step += 1
          else:
              self._num_validation_step += 1
      elif self.is_testing:
          self._num_test_step += 1
      elif self.is_predicting:
          self._num_predict_step += 1
  
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 10 for calling the ScheduleWrapper obj's underlying member method code with example usage: obj._step() without return types