class_docstr: Sequence-to-sequence dynamic model.
  
  This class implements a multi-layer recurrent neural network as encoder,
  and a multi-layer recurrent neural network decoder.
class_docstr_tok: ["Sequence-to-sequence","dynamic","model",".","This","class","implements","a","multi-layer","recurrent","neural","network","as","encoder",",","and","a","multi-layer","recurrent","neural","network","decoder","."]
docstr: Build encoder from source.
docstr_tok: ["Build","encoder","from","source","."]
code: def _build_encoder(self, hparams):
      """Build encoder from source."""
      utils.print_out("# Build a basic encoder")
      return self._build_encoder_from_sequence(
          hparams,
          self.iterator.source,
          self.iterator.source_sequence_length,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import collections
  import tensorflow
  from tensorflow.contrib.model_pruning import get_masks, get_thresholds
  from tensorflow.contrib.model_pruning.python import pruning
  from tensorflow.contrib.model_pruning.python.layers import core_layers
  from .None import model_helper
  from .utils import misc_utils, iterator_utils
label_desc: Use image node_id 2 for calling the Model obj's underlying member method code with example usage: obj._build_encoder(hparams) and returns: self