def __init__(self, jitter=<NUM_LIT>):
super(Jitter, self).__init__()<EOL>self.jitter = np.array(utils.listify(jitter))<EOL>if np.any(jitter < <NUM_LIT:0.>):<EOL><INDENT>raise ValueError('<STR_LIT>')<EOL><DEDENT>self._processed = False<EOL>
Implements an input modifier that introduces random jitter in `pre`.
        Jitter has been shown to produce crisper activation maximization images.

        Args:
            jitter: The amount of jitter to apply, scalar or sequence.
                If a scalar, same jitter is applied to all image dims. If sequence, `jitter` should contain a value
                per image dim.

                A value between `[0., 1.]` is interpreted as a percentage of the image dimension. (Default value: 0.05)