code: def mean_iou(cm: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
      """Computes the mean intersection-over-union, given a confusion matrix.
  
      Args:
        cm: array_like; [num_classes, num_classes] confusion matrix.
  
      Returns:
        Scalar mean intersection-over-union score.
      """
      # TODO(mjlm): Check the mean IoU computation for correctness (end to end).
      # Based on experimental/brain/off_the_grid/lib/metrics.py:
  
      sum_over_row = np.sum(cm, axis=0)
      sum_over_col = np.sum(cm, axis=1)
      true_positives = np.diag(cm)
  
      # sum_over_row + sum_over_col =
      #     2 * true_positives + false_positives + false_negatives.
      denominator = sum_over_row + sum_over_col - true_positives
  
      # The mean is only computed over classes that appear in the
      # label or prediction tensor. If the denominator is 0, we need to
      # ignore the class.
      iou_per_class = true_positives / denominator
      return (
          np.nan_to_num(np.nanmean(iou_per_class)),
          np.nan_to_num(iou_per_class),
      )
  
imports: import functools
  from typing import Optional, Any, Tuple, Union
  import flax.linen
  import jax
  import jax.numpy
  import numpy
label_desc: Use image node_id 25 for calling a global function with example usage: mean_iou(cm) and returns: np,  np