def __init__(self, input_shape, init_val=0, rand_state=np.random):
        """
        Bias layer.  For an input shape of [...] x N, this layer adds N bias terms.  E.g., for a 
        convolutional layer with an output of shape WxHxC where C is the number of channels/filters,
        this layer will contain C bias terms, one for each filter.

        Parameters
        ----------
        input_shape : tuple of ints specifying shape of a single input
        init_val : float, value to initialize all weights with
        rand_state : a RandomState object

        """
        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
dataset/ETHPy150Open bbabenko/simple_convnet/simple_convnet/convnet.py/BiasLayer.__init__/original