class_docstr: Output layer for joint intent classification and slot-filling models.
  Intent classification is a document classification problem and slot filling
  is a word tagging problem. Thus terms these can be used interchangeably in the
  documentation.
  
  Args:
      doc_output (ClassificationOutputLayer): Output layer for intent
          classification task. See
          :class:`~pytext.models.output_layers.ClassificationOutputLayer` for
          details.
      word_output (WordTaggingOutputLayer): Output layer for slot filling task.
          See :class:`~pytext.models.output_layers.WordTaggingOutputLayer` for
          details.
  
  Attributes:
      doc_output (type): Output layer for intent classification task.
      word_output (type): Output layer for slot filling task.
docstr: Exports the intent slot output layer to Caffe2.
  See `OutputLayerBase.export_to_caffe2()` for details.
docstr_tok: ["Exports","the","intent","slot","output","layer","to","Caffe2",".","See","`","OutputLayerBase.export_to_caffe2","(",")","`","for","details","."]
code: def export_to_caffe2(
      self,
      workspace: core.workspace,
      init_net: core.Net,
      predict_net: core.Net,
      model_out: List[torch.Tensor],
      doc_out_name: str,
      word_out_name: str,
  ) -> List[core.BlobReference]:
      """
      Exports the intent slot output layer to Caffe2.
      See `OutputLayerBase.export_to_caffe2()` for details.
      """
      return self.doc_output.export_to_caffe2(
          workspace, init_net, predict_net, model_out[0], doc_out_name
      ) + self.word_output.export_to_caffe2(
          workspace, init_net, predict_net, model_out[1], word_out_name
      )
  
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  import torch.nn
  from caffe2.python import core
  from pytext.common.constants import DatasetFieldName
  from pytext.data.utils import Vocabulary
  from pytext.models.module import create_module
  from pytext.utils.usage import log_class_usage
  from torch import jit
  from .doc_classification_output_layer import ClassificationOutputLayer
  from .output_layer_base import OutputLayerBase
  from .utils import query_word_reprs
  from .word_tagging_output_layer import CRFOutputLayer, WordTaggingOutputLayer
label_desc: Use image node_id 5 for calling the IntentSlotOutputLayer obj's underlying member method code with example usage: obj.export_to_caffe2(workspace, init_net, predict_net, model_out, doc_out_name, word_out_name) and returns: unknown