def __init__(self, img_input, beta=<NUM_LIT>):
super(TotalVariation, self).__init__()<EOL>self.name = "<STR_LIT>".format(beta)<EOL>self.img = img_input<EOL>self.beta = beta<EOL>
Total variation regularizer encourages blobbier and coherent image structures, akin to natural images.
        See `section 3.2.2` in
        [Visualizing deep convolutional neural networks using natural pre-images](https://arxiv.org/pdf/1512.02017v3.pdf)
        for details.

        Args:
            img_input: An image tensor of shape: `(samples, channels, image_dims...)` if `image_data_format=`channels_first`
                or `(samples, image_dims..., channels)` if `image_data_format=channels_last`.
            beta: Smaller values of beta give sharper but 'spikier' images.
                Values \(\in [1.5, 3.0]\) are recommended as a reasonable compromise. (Default value = 2.)