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
code: def __init__(self, model, criterion):
      super().__init__(model, criterion)
      self._weights = []
  
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 1 to create a new EnsembleModelWrapper object from inherited base classes: ModelWrapper with example: obj = EnsembleModelWrapper(model, criterion)