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: Called when loading a model checkpoint, use to reload state.
  
  Args:
      trainer: the current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
      pl_module: the current :class:`~lightning.pytorch.core.LightningModule` instance.
      checkpoint: the full checkpoint dictionary that got loaded by the Trainer.
docstr_tok: ["Called","when","loading","a","model","checkpoint",",","use","to","reload","state",".","Args",":","trainer",":","the","current",":","class",":","`","~lightning.pytorch.trainer.trainer.Trainer","`","instance",".","pl_module",":","the","current",":","class",":","`","~lightning.pytorch.core.LightningModule","`","instance",".","checkpoint",":","the","full","checkpoint","dictionary","that","got","loaded","by","the","Trainer","."]
code: def on_load_checkpoint(
      self,
      trainer: "pl.Trainer",
      pl_module: "pl.LightningModule",
      checkpoint: Dict[str, Any],
  ) -> None:
      r"""Called when loading a model checkpoint, use to reload state.
  
      Args:
          trainer: the current :class:`~lightning.pytorch.trainer.trainer.Trainer` instance.
          pl_module: the current :class:`~lightning.pytorch.core.LightningModule` instance.
          checkpoint: the full checkpoint dictionary that got loaded by the Trainer.
  
      """
  
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 38 for calling the Callback obj's underlying member method code with example usage: obj.on_load_checkpoint(trainer, pl_module, checkpoint) without return types