class_docstr: Representation for a contextual intent slot model
  
  The inputs are two embeddings: word level embedding containing dictionary features,
  sequence (contexts) level embedding. See following diagram for the representation
  implementation that combines the two embeddings. Seq_representation is concatenated
  with word_embeddings.
  
  ::
  
      +-----------+
      | word_embed|--------------------------->+   +--------------------+
      +-----------+                            |   | doc_representation |
      +-----------+   +-------------------+    |-->+--------------------+
      | seq_embed |-->| seq_representation|--->+   | word_representation|
      +-----------+   +-------------------+        +--------------------+
                                                    joint_representation
code: def __init__(
      self, config: Config, embed_dim: Tuple[int, ...]
  ) -> None:
      super().__init__(config)
      assert len(embed_dim) == 2
      self.sen_rep = create_module(
          config.sen_representation, embed_dim=embed_dim[1]
      )
      self.sen_representation_dim = self.sen_rep.representation_dim
  
      self.seq_rep = create_module(
          config.seq_representation,
          embed_dim=self.sen_representation_dim,
      )
      self.seq_representation_dim = self.seq_rep.representation_dim
      self.joint_rep = create_module(
          config.joint_representation,
          embed_dim=embed_dim[0] + self.seq_representation_dim,
      )
      self.doc_representation_dim = (
          self.joint_rep.doc_representation_dim
      )
      self.word_representation_dim = (
          self.joint_rep.word_representation_dim
      )
      log_class_usage(__class__)
  
file_path: pytext/pytext/models/representations/contextual_intent_slot_rep.py
imports: from typing import List, Tuple, Union
  import torch
  from pytext.models.module import create_module
  from pytext.utils.usage import log_class_usage
  from .bilstm_doc_slot_attention import BiLSTMDocSlotAttention
  from .docnn import DocNNRepresentation
  from .jointcnn_rep import JointCNNRepresentation
  from .representation_base import RepresentationBase
label_desc: Use image node_id 1 to create a new ContextualIntentSlotRepresentation object from inherited base classes: RepresentationBase with example: obj = ContextualIntentSlotRepresentation(config, embed_dim)