class_docstr: Base class for the recurrent layers
  
  Parameters
  ----------
  n_units : int
      Number of hidden units in the layer.
  
  only_return_final : bool
      If ``True``, only return the final sequential output
      (e.g. for tasks where a single target value for the entire
      sequence is desired). In this case, Tensorfow makes an
      optimization which saves memory. Defaults to ``True``.
  
  {BaseLayer.name}
code: def __init__(self, n_units, only_return_final=True, name=None):
      super(BaseRNNLayer, self).__init__(name=name)
      self.only_return_final = only_return_final
      self.n_units = n_units
  
imports: import types
  import tensorflow
  from neupy import init
  from neupy.utils import tensorflow_session
  from neupy.exceptions import LayerConnectionError
  from neupy.core.properties import IntProperty, Property, NumberProperty, ParameterProperty
  from .base import BaseLayer
label_desc: Use image node_id 1 to create a new BaseRNNLayer object from inherited base classes: BaseLayer with example: obj = BaseRNNLayer(n_units, only_return_final, name)