code: def __init__(
      self,
      name: str = "weight",
      n_power_iterations: int = 1,
      dim: int = 0,
      eps: float = 1e-12,
  ) -> None:
      self.name = name
      self.dim = dim
      if n_power_iterations <= 0:
          raise ValueError(
              "Expected n_power_iterations to be positive, but "
              f"got n_power_iterations={n_power_iterations}"
          )
      self.n_power_iterations = n_power_iterations
      self.eps = eps
  
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 1 to create a new SpectralNorm object  with example: obj = SpectralNorm(name, n_power_iterations, dim, eps)