class_docstr: Performs spectral normalization on the weights of a target layer.
  
  This wrapper controls the Lipschitz constant of the weights of a layer by
  constraining their spectral norm, which can stabilize the training of GANs.
  
  Args:
      layer: A `keras.layers.Layer` instance that
          has either a `kernel` (e.g. `Conv2D`, `Dense`...)
          or an `embeddings` attribute (`Embedding` layer).
      power_iterations: int, the number of iterations during normalization.
      **kwargs: Base wrapper keyword arguments.
  
  Examples:
  
  Wrap `keras.layers.Conv2D`:
  >>> x = np.random.rand(1, 10, 10, 1)
  >>> conv2d = SpectralNormalization(keras.layers.Conv2D(2, 2))
  >>> y = conv2d(x)
  >>> y.shape
  (1, 9, 9, 2)
  
  Wrap `keras.layers.Dense`:
  >>> x = np.random.rand(1, 10, 10, 1)
  >>> dense = SpectralNormalization(keras.layers.Dense(10))
  >>> y = dense(x)
  >>> y.shape
  (1, 10, 10, 10)
  
  Reference:
  
  - [Spectral Normalization for GAN](https://arxiv.org/abs/1802.05957).
code: def __init__(self, layer, power_iterations=1, **kwargs):
      super().__init__(layer, **kwargs)
      if power_iterations <= 0:
          raise ValueError(
              "`power_iterations` should be greater than zero. Received: "
              f"`power_iterations={power_iterations}`"
          )
      self.power_iterations = power_iterations
  
file_path: keras/keras/layers/normalization/spectral_normalization.py
imports: from keras import initializers
  from keras import ops
  from keras.api_export import keras_export
  from keras.layers import Wrapper
  from keras.layers.input_spec import InputSpec
  from keras.utils.numerical_utils import normalize
label_desc: Use image node_id 1 to create a new SpectralNormalization object from inherited base classes: Wrapper with example: obj = SpectralNormalization(layer, power_iterations)