class_docstr: Abstract base class used to build new callbacks.
  
  Subclass this class and override any of the relevant hooks
class_docstr_tok: ["Abstract","base","class","used","to","build","new","callbacks",".","Subclass","this","class","and","override","any","of","the","relevant","hooks"]
docstr: Identifier for the state of the callback.
  
  Used to store and retrieve a callback's state from the checkpoint dictionary by
  ``checkpoint["callbacks"][state_key]``. Implementations of a callback need to provide a unique state key if 1)
  the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.
docstr_tok: ["Identifier","for","the","state","of","the","callback",".","Used","to","store","and","retrieve","a","callback","'s","state","from","the","checkpoint","dictionary","by","``","checkpoint","[","``","callbacks","''","]","[","state_key","]","``",".","Implementations","of","a","callback","need","to","provide","a","unique","state","key","if","1",")","the","callback","has","state","and","2",")","it","is","desired","to","maintain","the","state","of","multiple","instances","of","that","callback","."]
code: def state_key(self) -> str:
      """Identifier for the state of the callback.
  
      Used to store and retrieve a callback's state from the checkpoint dictionary by
      ``checkpoint["callbacks"][state_key]``. Implementations of a callback need to provide a unique state key if 1)
      the callback has state and 2) it is desired to maintain the state of multiple instances of that callback.
  
      """
      return self.__class__.__qualname__
  
imports: from typing import Any, Dict, Type
  from torch import Tensor
  from torch.optim import Optimizer
  import lightning.pytorch
  from lightning.pytorch.utilities.types import STEP_OUTPUT
label_desc: Use image node_id 1 for calling the Callback obj's underlying member method code with example usage: obj.state_key() and returns: self