class_docstr: Wraps a Keras RNN/LSTM/GRU layer to make network state more consistent.
code: def trainable_weights(self):
      if not self.trainable:
          return []
      return self._layer.trainable_weights
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  from tf_agents.utils import common
label_desc: Use image node_id 9 for calling the RNNWrapper obj's underlying member method code with example usage: obj.trainable_weights() and returns: self, list