class_docstr: Base class for all RNN encoder classes to inherit.
  
  Args:
      hparams (dict or HParams, optional): Hyperparameters. Missing
          hyperparamerter will be set to default values. See
          :meth:`default_hparams` for the hyperparameter sturcture and
          default values.
class_docstr_tok: ["Base","class","for","all","RNN","encoder","classes","to","inherit",".","Args",":","hparams","(","dict","or","HParams",",","optional",")",":","Hyperparameters",".","Missing","hyperparamerter","will","be","set","to","default","values",".","See",":","meth",":","`","default_hparams","`","for","the","hyperparameter","sturcture","and","default","values","."]
code: def __init__(self, hparams=None):
      EncoderBase.__init__(self, hparams)
  
imports: import functools
  import numpy
  import tensorflow
  from tensorflow.contrib.framework import nest
  from texar.tf.modules.encoders.encoder_base import EncoderBase
  from texar.tf.modules.networks.conv_networks import _to_list
  from texar.tf.core import layers
  from texar.tf.utils.mode import is_train_mode
  from texar.tf.utils.shapes import mask_sequences
  from texar.tf.hyperparams import HParams
label_desc: Use image node_id 1 to create a new RNNEncoderBase object from inherited base classes: EncoderBase with example: obj = RNNEncoderBase(hparams)