class_docstr: Sequence-to-sequence base class.
class_docstr_tok: ["Sequence-to-sequence","base","class","."]
docstr: Build a multi-layer RNN cell that can be used by encoder.
docstr_tok: ["Build","a","multi-layer","RNN","cell","that","can","be","used","by","encoder","."]
code: def _build_encoder_cell(
      self, hparams, num_layers, num_residual_layers, base_gpu=0
  ):
      """Build a multi-layer RNN cell that can be used by encoder."""
  
      return model_helper.create_rnn_cell(
          unit_type=hparams.unit_type,
          num_units=self.num_units,
          num_layers=num_layers,
          num_residual_layers=num_residual_layers,
          forget_bias=hparams.forget_bias,
          dropout=hparams.dropout,
          num_gpus=hparams.num_gpus,
          mode=self.mode,
          base_gpu=base_gpu,
          single_cell_fn=self.single_cell_fn,
      )
  
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 13 for calling the BaseModel obj's underlying member method code with example usage: obj._build_encoder_cell(hparams, num_layers, num_residual_layers, base_gpu) and returns: model_helper