def simple_rnn(incoming, n_units, activation='sigmoid', dropout=None, bias=True, weights_init=None, return_seq=False, return_state=False, initial_state=None, dynamic=False, trainable=True, restore=True, reuse=False, scope=None, name='SimpleRNN'):
 cell = BasicRNNCell(n_units, activation=activation, bias=bias, weights_init=weights_init, trainable=trainable, restore=restore, reuse=reuse)
 x = _rnn_template(incoming, cell=cell, dropout=dropout, return_seq=return_seq, return_state=return_state, initial_state=initial_state, dynamic=dynamic, scope=scope, name=name)
 return x

def simple_rnn ( incoming , n_units , activation = 'sigmoid' , dropout = None , bias = True , weights_init = None , return_seq = False , return_state = False , initial_state = None , dynamic = False , trainable = True , restore = True , reuse = False , scope = None , name = 'SimpleRNN' ) : cell = BasicRNNCell ( n_units , activation = activation , bias = bias , weights_init = weights_init , trainable = trainable , restore = restore , reuse = reuse ) x = _rnn_template ( incoming , cell = cell , dropout = dropout , return_seq = return_seq , return_state = return_state , initial_state = initial_state , dynamic = dynamic , scope = scope , name = name ) return x
simple rnn .