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)

        # Avoid Nans
        x_mu += self.eps

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

        density = -(-T.sum(x * T.log(x_mu), axis=-1, keepdims=True))
        return density
dataset/ETHPy150Open ml-lab/auxiliary-deep-generative-models/lasagne_extensions/layers/variationallayer.py/MultinomialLogDensityLayer.get_output_for/swapped operands of `>`