def __init__(self, img_input, p=<NUM_LIT>):
super(LPNorm, self).__init__()<EOL>if p < <NUM_LIT:1>:<EOL><INDENT>raise ValueError('<STR_LIT>')<EOL><DEDENT>self.name = "<STR_LIT>".format(p)<EOL>self.p = p<EOL>self.img = img_input<EOL>
Builds a L-p norm function. This regularizer encourages the intensity of pixels to stay bounded.
    i.e., prevents pixels from taking on very large values.

Args:
    img_input: 4D image input tensor to the model of shape: `(samples, channels, rows, cols)`
        if data_format='channels_first' or `(samples, rows, cols, channels)` if data_format='channels_last'.
    p: The pth norm to use. If p = float('inf'), infinity-norm will be used.