code: def forward(self, weight: torch.Tensor) -> torch.Tensor:
      if weight.ndim == 1:
          # Faster and more exact path, no need to approximate anything
          return F.normalize(weight, dim=0, eps=self.eps)
      else:
          weight_mat = self._reshape_weight_to_matrix(weight)
          if self.training:
              self._power_method(weight_mat, self.n_power_iterations)
          # See above on why we need to clone
          u = self._u.clone(memory_format=torch.contiguous_format)
          v = self._v.clone(memory_format=torch.contiguous_format)
          # The proper way of computing this should be through F.bilinear, but
          # it seems to have some efficiency issues:
          # https://github.com/pytorch/pytorch/issues/58093
          sigma = torch.dot(u, torch.mv(weight_mat, v))
          return weight / sigma
  
imports: from enum import Enum, auto
  import torch
  from torch import Tensor
  from ..utils import parametrize
  from ..modules import Module
  from ..None import functional
  from typing import Optional
label_desc: Use image node_id 4 for calling the _SpectralNorm obj's underlying member method code with example usage: obj.forward(weight) and returns: F, unknown