class_docstr: Callback to reset the weights between active learning steps.
  
  Args:
      weights (dict): State dict of the model.
  
  Notes:
      The weight should be deep copied beforehand.
docstr: Will reset the module to its initial weights.
docstr_tok: ["Will","reset","the","module","to","its","initial","weights","."]
code: def on_train_start(self, trainer, module):
      """Will reset the module to its initial weights."""
      module.load_state_dict(self.weights)
      trainer.fit_loop.current_epoch = 0
  
imports: import sys
  import types
  import warnings
  from collections.abc import Sequence
  from typing import Dict, Any, Optional
  import numpy
  import structlog
  import torch
  from pytorch_lightning import Trainer, Callback, LightningDataModule, LightningModule
  from pytorch_lightning.accelerators import CUDAAccelerator
  from torch.utils.data import DataLoader
  from tqdm import tqdm
  from baal.active import ActiveLearningDataset
  from baal.active.heuristics import heuristics
  from baal.modelwrapper import mc_inference
  from baal.utils.cuda_utils import to_cuda
  from baal.utils.iterutils import map_on_tensor
label_desc: Use image node_id 2 for calling the ResetCallback obj's underlying member method code with example usage: obj.on_train_start(trainer, module) without return types