adaptive integrate and (not yet fire) neuron in tensorflow

adaptiveIAF.py

class AdaptiveIAF(tf.nn.rnn_cell.RNNCell):
    def __init__(self, num_units, dt, reuse=False):
        self._dt = tf.constant(dt, dtype=tf.float32)
        self._num_units = num_units
        self._reuse = reuse
          
    @property
    def state_size(self):
          return (self._num_units, self._num_units)
        
    @property
    def output_size(self):
        return self._num_units
    
    def __call__(self, inputs, state, scope=None):
        
        v, w = state
        
        with vs.variable_scope(scope or 'AdaptiveIAF', reuse=self._reuse):
            
            a1 = vs.get_variable("a1", shape=(1,), dtype=tf.float32, initializer=tf.random_uniform_initializer(0, 1))
            a2 = vs.get_variable("a2", shape=(1,), dtype=tf.float32, initializer=tf.random_uniform_initializer(0, 1))
            a3 = vs.get_variable("a3", shape=(1,), dtype=tf.float32, initializer=tf.random_uniform_initializer(0, 1))
            a4 = vs.get_variable("a4", shape=(1,), dtype=tf.float32, initializer=tf.random_uniform_initializer(0, 1))

            new_v = v + self._dt * (a1 * v + a2 - w + inputs)
            new_w = w + self._dt * (a3 * v - a4 * w)
            
        if not self._reuse:
            self._reuse = True
            
        return new_v, (new_v, new_w)