code: def compute_normalization(activations, t, num_iters=5):
      """Compute normalization value for each example.
      Backward pass is implemented.
      Args:
        activations: A multi-dimensional tensor with last dimension `num_classes`.
        t: Temperature 2 (> 1.0 for tail heaviness, < 1.0 for finite support).
        num_iters: Number of iterations to run the method.
      Return: A tensor of same rank as activation with the last dimension being 1.
      """
      return ComputeNormalization.apply(activations, t, num_iters)
  
imports: from typing import Optional
  import torch
  from torch import nn, Tensor
label_desc: Use image node_id 5 for calling a global function with example usage: compute_normalization(activations, t, num_iters) and returns: ComputeNormalization