class_docstr: Ensemble class that uses bagging for ensembling intent-slot models.
  
  Args:
      config (Config): Configuration object specifying all the
          parameters of BaggingIntentSlotEnsemble.
      models (List[Model]): List of intent-slot model objects.
  
  Attributes:
      use_crf (bool): Whether to use CRF for word tagging task.
      output_layer (IntentSlotOutputLayer): Output layer of intent-slot
          model responsible for computing loss and predictions.
code: def torchscriptify(self, tensorizers, traced_model):
      return self.models[0].torchscriptify(
          tensorizers,
          traced_model,
          merged_output_layer=self.output_layer
          if self.use_crf
          else None,
      )
  
file_path: pytext/pytext/models/ensembles/bagging_intent_slot_ensemble.py
imports: from typing import Dict, List, Tuple
  import torch
  from pytext.models.joint_model import IntentSlotModel
  from pytext.models.model import Model
  from pytext.models.output_layers import CRFOutputLayer
  from pytext.utils.usage import log_class_usage
  from .ensemble import EnsembleModel
label_desc: Use image node_id 4 for calling the BaggingIntentSlotEnsembleModel obj's underlying member method code with example usage: obj.torchscriptify(tensorizers, traced_model) and returns: self