def get_output_for(self, input, **kwargs):
        x_mu = input.pop(0)
        x = self.x if self.x is not None else input.pop(0)

        if x.ndim > x_mu.ndim:  # Check for sample dimensions.
            x = x.dimshuffle((0, 'x', 'x', 1))

        x_mu = T.clip(x_mu, self.eps, 1 - self.eps)
        density = T.sum(-T.nnet.binary_crossentropy(x_mu, x), axis=-1, keepdims=True)
        return density
dataset/ETHPy150Open ml-lab/auxiliary-deep-generative-models/lasagne_extensions/layers/variationallayer.py/BernoulliLogDensityLayer.get_output_for/swapped operands of `>`