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.
code: def __init__(
      self,
      doc_output: ClassificationOutputLayer,
      word_output: WordTaggingOutputLayer,
  ) -> None:
      super().__init__()
      self.doc_output = doc_output
      self.word_output = word_output
      log_class_usage(__class__)
  
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 2 to create a new IntentSlotOutputLayer object from inherited base classes: OutputLayerBase with example: obj = IntentSlotOutputLayer(doc_output, word_output)