

def __init__(self, inputs, outputs, updates=[]):
    assert (type(inputs) in {list, tuple}), 'Input to a TensorFlow backend function should be a list or tuple.'
    assert (type(outputs) in {list, tuple}), 'Output to a TensorFlow backend function should be a list or tuple.'
    assert (type(updates) in {list, tuple}), 'Updates in a TensorFlow backend function should be a list or tuple.'
    self.inputs = list(inputs)
    self.outputs = list(outputs)
    with tf.control_dependencies(self.outputs):
        outputs.updates = [tf.assign(p, new_p) for (p, new_p) in updates]

dataset/ETHPy150Open fchollet/keras/keras/backend/tensorflow_backend.py Function.__init__/VarMisuse@97/176 `self`->`outputs`