class_docstr: Base class inherited by all seq2seq model classes.
  
  .. document private functions
  .. automethod:: _build
class_docstr_tok: ["Base","class","inherited","by","all","seq2seq","model","classes",".","..","document","private","functions","..","automethod",":",":","_build"]
docstr: Computes the training loss.
          
docstr_tok: ["Computes","the","training","loss","."]
code: def get_loss(self, decoder_results, features, labels):
      """Computes the training loss."""
      return sequence_sparse_softmax_cross_entropy(
          labels=labels["target_text_ids"][:, 1:],
          logits=decoder_results["outputs"].logits,
          sequence_length=decoder_results["sequence_length"],
      )
  
imports: import tensorflow
  from texar.tf.models.model_base import ModelBase
  from texar.tf.losses.mle_losses import sequence_sparse_softmax_cross_entropy
  from texar.tf.data.data.paired_text_data import PairedTextData
  from texar.tf.core.optimization import get_train_op
  from texar.tf.hyperparams import HParams
  from texar.tf.utils import utils
  from texar.tf.utils.variables import collect_trainable_variables
label_desc: Use image node_id 8 for calling the Seq2seqBase obj's underlying member method code with example usage: obj.get_loss(decoder_results, features, labels) and returns: sequence_sparse_softmax_cross_entropy