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 forward(
      self,
      word_seq_embed: Tuple[torch.Tensor, torch.Tensor],
      word_lengths: torch.Tensor,
      seq_lengths: torch.Tensor,
      *args,
  ) -> List[torch.Tensor]:
      (word_embed, seq_embed) = word_seq_embed
  
      (bsz, max_num_sen, max_seq_len, dim) = seq_embed.size()
      rep = self.sen_rep(
          seq_embed.view(bsz * max_num_sen, max_seq_len, dim)
      )
      sentence_reps = rep.view(
          bsz, max_num_sen, self.sen_representation_dim
      )
      seq_out = self.seq_rep(embedded_tokens=sentence_reps)
  
      bsz, max_seq_len, dim = word_embed.size()
      seq_rep_expand = seq_out.view(bsz, 1, -1).repeat(
          1, max_seq_len, 1
      )
      new_embed = torch.cat([seq_rep_expand, word_embed], 2)
      return self.joint_rep(new_embed, word_lengths)
  
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 2 for calling the ContextualIntentSlotRepresentation obj's underlying member method code with example usage: obj.forward(word_seq_embed, word_lengths, seq_lengths) and returns: self