class_docstr: Classification model with sequence of utterances as input.
  It uses a docnn model (CNN or LSTM) to generate vector representation
  for each sequence, and then use an LSTM or BLSTM to capture the dynamics
  and produce labels for each sequence.
code: def arrange_model_inputs(self, tensor_dict):
      tokens, _, seq_lens = tensor_dict["tokens"]
      model_inputs = (tokens, seq_lens)
      if "dense" in tensor_dict:
          model_inputs += (tensor_dict["dense"],)
      return model_inputs
  
file_path: pytext/pytext/models/seq_models/seqnn.py
imports: from pytext.config import ConfigBase
  from pytext.data.tensorizers import SeqTokenTensorizer
  from pytext.models.decoders.mlp_decoder import MLPDecoder
  from pytext.models.doc_model import DocModel
  from pytext.models.model import Model
  from pytext.models.output_layers import ClassificationOutputLayer
  from pytext.models.representations.seq_rep import SeqRepresentation
label_desc: Use image node_id 1 for calling the SeqNNModel obj's underlying member method code with example usage: obj.arrange_model_inputs(tensor_dict) and returns: model_inputs