class_docstr: Base class for all kinds of trainers, inherits from Workflow.
docstr: Execute training based on Ignite Engine.
  If call this function multiple times, it will continuously run from the previous state.
docstr_tok: ["Execute","training","based","on","Ignite","Engine",".","If","call","this","function","multiple","times",",","it","will","continuously","run","from","the","previous","state","."]
code: def run(self) -> None:  # type: ignore[override]
      """
      Execute training based on Ignite Engine.
      If call this function multiple times, it will continuously run from the previous state.
  
      """
      self.scaler = torch.cuda.amp.GradScaler() if self.amp else None
      super().run()
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, Callable, Iterable, Sequence
  import torch
  from torch.optim.optimizer import Optimizer
  from torch.utils.data import DataLoader
  from monai.config import IgniteInfo
  from monai.engines.utils import IterationEvents, default_make_latent, default_metric_cmp_fn, default_prepare_batch
  from monai.engines.workflow import Workflow
  from monai.inferers import Inferer, SimpleInferer
  from monai.transforms import Transform
  from monai.utils import GanKeys, min_version, optional_import
  from monai.utils.enums import CommonKeys
  from monai.utils.enums import EngineStatsKeys
label_desc: Use image node_id 1 for calling the Trainer obj's underlying member method code with example usage: obj.run() without return types