class_docstr: A unidirectional RNN encoder. Stacking should be performed as
  part of the cell.
  
  Args:
    cell: An instance of tf.contrib.rnn.RNNCell
    name: A name for the encoder
class_docstr_tok: ["A","unidirectional","RNN","encoder",".","Stacking","should","be","performed","as","part","of","the","cell",".","Args",":","cell",":","An","instance","of","tf.contrib.rnn.RNNCell","name",":","A","name","for","the","encoder"]
code: def __init__(self, params, mode, name="forward_rnn_encoder"):
      super(UnidirectionalRNNEncoder, self).__init__(params, mode, name)
      self.params["rnn_cell"] = _toggle_dropout(
          self.params["rnn_cell"], mode
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import tensorflow
  from tensorflow.contrib.rnn.python.ops import rnn
  from seq2seq.encoders.encoder import Encoder, EncoderOutput
  from seq2seq.training import utils
label_desc: Use image node_id 1 to create a new UnidirectionalRNNEncoder object from inherited base classes: Encoder with example: obj = UnidirectionalRNNEncoder(params, mode, name)