code: def get_pred(self, model_outputs, context=None):
      predictions, scores = (None, None)
      if context:
          stage = context.get("stage", None)
          if stage and (
              (stage == Stage.TEST)
              or (
                  self.force_eval_predictions
                  and stage == Stage.EVAL
                  and (
                      context["epoch"] % self.generate_predictions_every
                      == 0
                  )
              )
          ):
              if self.sequence_generator is None:
                  assert not self.model.training
                  # During evaluation, quantize is set to False or it will
                  # create with issues in evaluating the model on gpu
                  self.sequence_generator = (
                      self.sequence_generator_builder(
                          self.model,
                          self.length_prediction_model,
                          False,
                      )
                  )
              _, model_input = model_outputs
              (
                  predictions,
                  scores,
              ) = self.sequence_generator.generate_hypo(model_input)
      return predictions, scores
  
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 4 for calling the NARSeq2SeqModel obj's underlying member method code with example usage: obj.get_pred(model_outputs, context) and returns: predictions,  scores