code: def compute_normalization_fixed_point(
      activations: Tensor, t: float, num_iters: int
  ) -> Tensor:
      """Return the normalization value for each example (t > 1.0).
      Args:
        activations: A multi-dimensional tensor with last dimension `num_classes`.
        t: Temperature 2 (> 1.0 for tail heaviness).
        num_iters: Number of iterations to run the method.
      Return: A tensor of same shape as activation with the last dimension being 1.
      """
      mu, _ = torch.max(activations, -1, keepdim=True)
      normalized_activations_step_0 = activations - mu
  
      normalized_activations = normalized_activations_step_0
  
      for _ in range(num_iters):
          logt_partition = torch.sum(
              exp_t(normalized_activations, t), -1, keepdim=True
          )
          normalized_activations = (
              normalized_activations_step_0
              * logt_partition.pow(1.0 - t)
          )
  
      logt_partition = torch.sum(
          exp_t(normalized_activations, t), -1, keepdim=True
      )
      normalization_constants = -log_t(1.0 / logt_partition, t) + mu
  
      return normalization_constants
  
imports: from typing import Optional
  import torch
  from torch import nn, Tensor
label_desc: Use image node_id 3 for calling a global function with example usage: compute_normalization_fixed_point(activations, t, num_iters) and returns: normalization_constants