code: def style_normalization_activations(
      pre_name="transformer", post_name="StyleNorm", alpha=1.0
  ):
      """Returns scope name and depths of the style normalization activations.
  
      Args:
        pre_name: string. Prepends this name to the scope names.
        post_name: string. Appends this name to the scope names.
        alpha: float. Width multiplier to reduce the number of filters used in the
          model and slim it down.. Defaults to 1.0, which results
          in the hyper-parameters used in the published paper.
  
      Returns:
        string. Scope names of the activations of the transformer network which are
            used to apply style normalization.
        int[]. Depths of the activations of the transformer network which are used
            to apply style normalization.
      """
  
      scope_names = [
          "residual/residual1/conv1",
          "residual/residual1/conv2",
          "residual/residual2/conv1",
          "residual/residual2/conv2",
          "residual/residual3/conv1",
          "residual/residual3/conv2",
          "residual/residual4/conv1",
          "residual/residual4/conv2",
          "residual/residual5/conv1",
          "residual/residual5/conv2",
          "expand/conv1/conv",
          "expand/conv2/conv",
          "expand/conv3/conv",
      ]
      scope_names = [
          "{}/{}/{}".format(pre_name, name, post_name)
          for name in scope_names
      ]
      # 10 convolution layers of 'residual/residual*/conv*' have the same depth.
      depths = [int(alpha * 128)] * 10 + [
          int(alpha * 64),
          int(alpha * 32),
          3,
      ]
  
      return scope_names, depths
  
imports: from magenta.models.image_stylization import model
  import tensorflow.compat.v1
  import tf_slim
label_desc: Use image node_id 2 for calling a global function with example usage: style_normalization_activations(pre_name, post_name, alpha) and returns: scope_names,  depths