code: def __init__(
      self,
      model: CNNModel,
      length_prediction_model: MaskedLengthPredictionModule,
      output_layer: NARSeq2SeqOutputLayer,
      src_vocab: Vocabulary,
      trg_vocab: Vocabulary,
      dictfeat_vocab: Vocabulary,
      tensorizer=None,
      generator_config=None,
      config: Config = None,
  ):
      super().__init__(
          model,
          output_layer,
          src_vocab,
          trg_vocab,
          dictfeat_vocab,
          generator_config=None,
      )
      self.quantize = generator_config.quantize
      self.length_prediction_model = length_prediction_model
      self.sequence_generator_builder = lambda model, length_prediction_model, quantize: create_module(
          generator_config,
          model,
          length_prediction_model,
          trg_vocab,
          quantize,
      )
      self.force_eval_predictions = (
          generator_config.force_eval_predictions
      )
      self.generate_predictions_every = (
          generator_config.generate_predictions_every
      )
      self.tensorizer = tensorizer
  
imports: from typing import Dict, List, Optional, Tuple, Union
  import torch
  from pytext.common.constants import Stage
  from pytext.data.masked_tensorizer import MaskedTokenTensorizer
  from pytext.data.tensorizers import GazetteerTensorizer, Tensorizer, TokenTensorizer
  from pytext.data.utils import Vocabulary
  from pytext.models.embeddings.scriptable_embedding_list import ScriptableEmbeddingList
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from .conv_model import CNNModel, DecoupledCNNModel
  from .mask_generator import MaskedSequenceGenerator
  from .nar_length import ConvLengthPredictionModule, MaskedLengthPredictionModule
  from .nar_output_layer import NARSeq2SeqOutputLayer
  from .seq2seq_model import Seq2SeqModel
label_desc: Use image node_id 1 to create a new NARSeq2SeqModel object from inherited base classes: Seq2SeqModel with example: obj = NARSeq2SeqModel(model, length_prediction_model, output_layer, src_vocab, trg_vocab, dictfeat_vocab, tensorizer, generator_config, config)