class_docstr: Wrapper that support ensembles with the same architecture.
  
  Args:
      model (nn.Module): A Model.
      criterion (Callable): Loss function
  
  Notes:
      If you're looking to use ensembles for non-deep models, see our sklearn tutorial:
       baal.readthedocs.io/en/latest/notebooks/sklearn_tutorial.html
docstr: Clear the list of saved checkpoints.
docstr_tok: ["Clear","the","list","of","saved","checkpoints","."]
code: def clear_checkpoints(self):
      """
      Clear the list of saved checkpoints.
      """
      self._weights.clear()
  
file_path: baal/baal/ensemble.py
imports: from copy import deepcopy
  from typing import Dict, List, OrderedDict
  import torch
  from torch import nn, Tensor
  from baal import ModelWrapper
  from baal.modelwrapper import _stack_preds
  from baal.utils.cuda_utils import to_cuda
label_desc: Use image node_id 3 for calling the EnsembleModelWrapper obj's underlying member method code with example usage: obj.clear_checkpoints() without return types