code: def apply(
      module: Module,
      name: str,
      n_power_iterations: int,
      dim: int,
      eps: float,
  ) -> "SpectralNorm":
      for hook in module._forward_pre_hooks.values():
          if isinstance(hook, SpectralNorm) and hook.name == name:
              raise RuntimeError(
                  f"Cannot register two spectral_norm hooks on the same parameter {name}"
              )
  
      fn = SpectralNorm(name, n_power_iterations, dim, eps)
      weight = module._parameters[name]
      if weight is None:
          raise ValueError(
              f"`SpectralNorm` cannot be applied as parameter `{name}` is None"
          )
      if isinstance(weight, torch.nn.parameter.UninitializedParameter):
          raise ValueError(
              "The module passed to `SpectralNorm` can't have uninitialized parameters. "
              "Make sure to run the dummy forward before applying spectral normalization"
          )
  
      with torch.no_grad():
          weight_mat = fn.reshape_weight_to_matrix(weight)
  
          h, w = weight_mat.size()
          # randomly initialize `u` and `v`
          u = normalize(
              weight.new_empty(h).normal_(0, 1), dim=0, eps=fn.eps
          )
          v = normalize(
              weight.new_empty(w).normal_(0, 1), dim=0, eps=fn.eps
          )
  
      delattr(module, fn.name)
      module.register_parameter(fn.name + "_orig", weight)
      # We still need to assign weight back as fn.name because all sorts of
      # things may assume that it exists, e.g., when initializing weights.
      # However, we can't directly assign as it could be an nn.Parameter and
      # gets added as a parameter. Instead, we register weight.data as a plain
      # attribute.
      setattr(module, fn.name, weight.data)
      module.register_buffer(fn.name + "_u", u)
      module.register_buffer(fn.name + "_v", v)
  
      module.register_forward_pre_hook(fn)
      module._register_state_dict_hook(SpectralNormStateDictHook(fn))
      module._register_load_state_dict_pre_hook(
          SpectralNormLoadStateDictPreHook(fn)
      )
      return fn
  
file_path: pytorch/torch/nn/utils/spectral_norm.py
imports: import torch
  from torch.nn.functional import normalize
  from typing import Any, Optional, TypeVar
  from ..modules import Module
label_desc: Use image node_id 7 for calling the SpectralNorm obj's underlying member method code with example usage: obj.apply(module, name, n_power_iterations, dim, eps) and returns: fn