class_docstr: Like :class:`SimpleTrainer`, but uses PyTorch's native automatic mixed precision
  in the training loop.
class_docstr_tok: ["Like",":","class",":","`","SimpleTrainer","`",",","but","uses","PyTorch","'s","native","automatic","mixed","precision","in","the","training","loop","."]
code: def load_state_dict(self, state_dict):
      super().load_state_dict(state_dict)
      self.grad_scaler.load_state_dict(state_dict["grad_scaler"])
  
imports: import concurrent.futures
  import logging
  import numpy
  import time
  import weakref
  from typing import List, Mapping, Optional
  import torch
  from torch.nn.parallel import DataParallel, DistributedDataParallel
  import detectron2.utils.comm
  from detectron2.utils.events import EventStorage, get_event_storage
  from detectron2.utils.logger import _log_api_usage
label_desc: Use image node_id 4 for calling the AMPTrainer obj's underlying member method code with example usage: obj.load_state_dict(state_dict) without return types