code: def train_batch(cls, model, batch, state=None):
      # Forward pass through the network.
      model_inputs = model.arrange_model_inputs(batch)
      model_context = model.arrange_model_context(batch)
      targets = model.arrange_targets(batch)
      model_outputs = model(*model_inputs)
  
      # Add stage to context.
      if state:
          if model_context is None:
              model_context = {
                  "stage": state.stage,
                  "epoch": state.epoch,
              }
          else:
              model_context["stage"] = state.stage
              model_context["epoch"] = state.epoch
  
      # Compute loss and predictions.
      loss, loss_dict = model.get_loss(
          model_outputs, targets, model_context
      )
      predictions, scores = model.get_pred(
          model_outputs, context=model_context
      )
  
      # Pack results and return them.
      metric_data = (
          predictions,
          targets,
          scores,
          (loss, loss_dict),
          model_inputs,
      )
      return loss, metric_data
  
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 5 for calling the NARSeq2SeqModel obj's underlying member method code with example usage: obj.train_batch(cls, model, batch, state) and returns: loss,  metric_data