code: def total_intersect_and_union(
      results,
      gt_seg_maps,
      num_labels,
      ignore_index: bool,
      label_map: Optional[Dict[int, int]] = None,
      reduce_labels: bool = False,
  ):
      """Calculate Total Intersection and Union, by calculating `intersect_and_union` for each (predicted, ground truth) pair.
  
      Args:
          results (`ndarray`):
              List of prediction segmentation maps, each of shape (height, width).
          gt_seg_maps (`ndarray`):
              List of ground truth segmentation maps, each of shape (height, width).
          num_labels (`int`):
              Number of categories.
          ignore_index (`int`):
              Index that will be ignored during evaluation.
          label_map (`dict`, *optional*):
              Mapping old labels to new labels. The parameter will work only when label is str.
          reduce_labels (`bool`, *optional*, defaults to `False`):
              Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
              and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.
  
       Returns:
           total_area_intersect (`ndarray`):
              The intersection of prediction and ground truth histogram on all classes.
           total_area_union (`ndarray`):
              The union of prediction and ground truth histogram on all classes.
           total_area_pred_label (`ndarray`):
              The prediction histogram on all classes.
           total_area_label (`ndarray`):
              The ground truth histogram on all classes.
      """
      total_area_intersect = np.zeros((num_labels,), dtype=np.float64)
      total_area_union = np.zeros((num_labels,), dtype=np.float64)
      total_area_pred_label = np.zeros((num_labels,), dtype=np.float64)
      total_area_label = np.zeros((num_labels,), dtype=np.float64)
      for result, gt_seg_map in zip(results, gt_seg_maps):
          (
              area_intersect,
              area_union,
              area_pred_label,
              area_label,
          ) = intersect_and_union(
              result,
              gt_seg_map,
              num_labels,
              ignore_index,
              label_map,
              reduce_labels,
          )
          total_area_intersect += area_intersect
          total_area_union += area_union
          total_area_pred_label += area_pred_label
          total_area_label += area_label
      return (
          total_area_intersect,
          total_area_union,
          total_area_pred_label,
          total_area_label,
      )
  
file_path: evaluate/metrics/mean_iou/mean_iou.py
imports: from typing import Dict, Optional
  import datasets
  import numpy
  import evaluate
label_desc: Use image node_id 2 for calling a global function with example usage: total_intersect_and_union(results, gt_seg_maps, num_labels, ignore_index, label_map, reduce_labels) and returns: total_area_intersect,  total_area_union,  total_area_pred_label,  total_area_label