class_docstr: Wrapper created to ease the training/testing/loading.
  
  Args:
      model (nn.Module): The model to optimize.
      criterion (Callable): A loss function.
      replicate_in_memory (bool): Replicate in memory optional.
class_docstr_tok: ["Wrapper","created","to","ease","the","training\/testing\/loading",".","Args",":","model","(","nn.Module",")",":","The","model","to","optimize",".","criterion","(","Callable",")",":","A","loss","function",".","replicate_in_memory","(","bool",")",":","Replicate","in","memory","optional","."]
docstr: Load the model with `state_dict`.
docstr_tok: ["Load","the","model","with","`","state_dict","`","."]
code: def load_state_dict(self, state_dict, strict=True):
      """Load the model with `state_dict`."""
      self.model.load_state_dict(state_dict, strict=strict)
  
imports: import sys
  from collections import defaultdict
  from collections.abc import Sequence
  from copy import deepcopy
  from typing import Callable, Optional
  import numpy
  import structlog
  import torch
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  from torch.utils.data.dataloader import default_collate
  from tqdm import tqdm
  from baal.metrics.mixin import MetricMixin
  from baal.utils.array_utils import stack_in_memory
  from baal.active.dataset.base import Dataset
  from baal.utils.cuda_utils import to_cuda
  from baal.utils.iterutils import map_on_tensor
  from baal.utils.metrics import Loss
  from baal.utils.warnings import raise_warnings_cache_replicated
label_desc: Use image node_id 12 for calling the ModelWrapper obj's underlying member method code with example usage: obj.load_state_dict(state_dict, strict) without return types