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","."]
docstr: Returns a dictionary of hyperparameters with default values.
  
  .. code-block:: python
  
      {
          "name": "rnn_encoder"
      }
docstr_tok: ["Returns","a","dictionary","of","hyperparameters","with","default","values",".","..","code-block",":",":","python","{","``","name","''",":","``","rnn_encoder","''","}"]
code: def default_hparams():
      """Returns a dictionary of hyperparameters with default values.
  
      .. code-block:: python
  
          {
              "name": "rnn_encoder"
          }
      """
      return {"name": "rnn_encoder"}
  
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 2 for calling the RNNEncoderBase obj's underlying member method code with example usage: obj.default_hparams() and returns: dict