def __init__(self, input_shape, init_val=0, rand_state=np.random):
        super(BiasLayer, self).__init__(input_shape)
        # assert len(input_shape) == 3
        self.output_shape = np.array(input_shape)
        self.weights_ = np.ones(input_shape[-1]) * init_val
Bias layer.  For an input shape of [...] x N, this layer adds N bias terms.  E.g., for a 
dataset/ETHPy150Open bbabenko/simple_convnet/simple_convnet/convnet.py/BiasLayer.__init__