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"]
code: def _build_embedders(self):
      kwargs = {
          "vocab_size": self._src_vocab.size,
          "hparams": self._hparams.source_embedder_hparams.todict(),
      }
      self._src_embedder = utils.check_or_get_instance(
          self._hparams.source_embedder,
          kwargs,
          ["texar.tf.modules", "texar.tf.custom"],
      )
  
      if self._hparams.embedder_share:
          self._tgt_embedder = self._src_embedder
      else:
          kwargs = {
              "vocab_size": self._tgt_vocab.size,
          }
          if self._hparams.embedder_hparams_share:
              kwargs[
                  "hparams"
              ] = self._hparams.source_embedder_hparams.todict()
          else:
              kwargs[
                  "hparams"
              ] = self._hparams.target_embedder_hparams.todict()
          self._tgt_embedder = utils.check_or_get_instance(
              self._hparams.target_embedder,
              kwargs,
              ["texar.tf.modules", "texar.tf.custom"],
          )
  
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 4 for calling the Seq2seqBase obj's underlying member method code with example usage: obj._build_embedders() without return types