    def _conv_block(self, input, filters, strides, batch_norm=True, count=None):
        """ Convolutional layer + Leaky ReLU + conditional BN. """
        
        x = Conv2D(
            filters,
            kernel_size=self.kernel_size,
            strides=strides,
            padding='same',
            name='Conv_{}'.format(count),
        )(input)
        x = LeakyReLU(alpha=0.2)(x)
        if batch_norm:
            x = BatchNormalization(momentum=0.8)(x)
        return x
Convolutional layer + Leaky ReLU + conditional BN.
ISR/models/discriminator.py