norm.
See Spectral Normalization for Generative Adversarial Networks .
Note:
 This function is implemented using the parametrization
 functionality in "register_parametrization()". It is a
 reimplementation of "torch.nn.utils.spectral_norm()".

Note:
 When this constraint is registered, the singular vectors
 associated to the largest singular value are estimated rather
 than sampled at random. These are then updated performing
 "n_power_iterations" of the power method whenever the tensor is
 accessed with the module on *training* mode.

Note:
 If the *_SpectralNorm* module, i.e.,
 *module.parametrization.weight[idx]*, is in training mode on
 removal, it will perform another power iteration. If you'd like
 to avoid this iteration, set the module to eval mode before its
 removal.

Parameters:
      * module (nn.Module) -- containing module
  * **name** (*str**, **optional*) -- name of weight parameter.
