class_docstr: Enum for the current running stage.
  
  This stage complements :class:`TrainerFn` by specifying the current running stage for each function.
  More than one running stage value can be set while a :class:`TrainerFn` is running:
  
      - ``TrainerFn.FITTING`` - ``RunningStage.{SANITY_CHECKING,TRAINING,VALIDATING}``
      - ``TrainerFn.VALIDATING`` - ``RunningStage.VALIDATING``
      - ``TrainerFn.TESTING`` - ``RunningStage.TESTING``
      - ``TrainerFn.PREDICTING`` - ``RunningStage.PREDICTING``
class_docstr_tok: ["Enum","for","the","current","running","stage",".","This","stage","complements",":","class",":","`","TrainerFn","`","by","specifying","the","current","running","stage","for","each","function",".","More","than","one","running","stage","value","can","be","set","while","a",":","class",":","`","TrainerFn","`","is","running",":","-","``","TrainerFn.FITTING","``","-","``","RunningStage",".","{","SANITY_CHECKING",",","TRAINING",",","VALIDATING","}","``","-","``","TrainerFn.VALIDATING","``","-","``","RunningStage.VALIDATING","``","-","``","TrainerFn.TESTING","``","-","``","RunningStage.TESTING","``","-","``","TrainerFn.PREDICTING","``","-","``","RunningStage.PREDICTING","``"]
code: def evaluating(self) -> bool:
      return self in (
          self.VALIDATING,
          self.TESTING,
          self.SANITY_CHECKING,
      )
  
file_path: pytorch-lightning/src/lightning/pytorch/trainer/states.py
imports: from dataclasses import dataclass
  from typing import Optional
  from lightning.pytorch.utilities.enums import LightningEnum
label_desc: Use image node_id 1 for calling the RunningStage obj's underlying member method code with example usage: obj.evaluating() and returns: unknown