code: def ensemble_prediction(
      data: torch.Tensor,
      model: nn.Module,
      weights: List[OrderedDict[str, Tensor]],
      cuda: bool,
  ):
      """
      Get the model's prediction on a batch.
  
      Args:
          data (Tensor): The model input.
          model (nn.Module): The model to use.
          weights (List[Dict]): List of all weights to use.
          cuda (bool): Use CUDA or not.
  
      Returns:
          Tensor, the loss computed from the criterion.
                  shape = {batch_size, nclass, n_iteration}.
      """
      with torch.no_grad():
          if cuda:
              data = to_cuda(data)
          res = []
          for w in weights:
              model.load_state_dict(w)
              res.append(model(data))
          out = _stack_preds(res)
  
          return out
  
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 for calling a global function with example usage: ensemble_prediction(data, model, weights, cuda) and returns: out