code: def mean_iou(self, out_16_13=False):
      MIoU = np.diag(self.confusion_matrix) / (
          np.sum(self.confusion_matrix, axis=1)
          + np.sum(self.confusion_matrix, axis=0)
          - np.diag(self.confusion_matrix)
      )
      if self.synthia:
          MIoU_16 = np.nanmean(MIoU[: self.ignore_index])
          MIoU_13 = np.nanmean(MIoU[synthia_set_16_to_13])
          return MIoU_16, MIoU_13
      if out_16_13:
          MIoU_16 = np.nanmean(MIoU[synthia_set_16])
          MIoU_13 = np.nanmean(MIoU[synthia_set_13])
          return MIoU_16, MIoU_13
      MIoU = np.nanmean(MIoU[: self.ignore_index])
  
      return MIoU
  
imports: import os
  import time
  import tqdm
  import numpy
  from PIL import Image
  import paddle
  import paddle.nn.functional
  from paddleseg.utils import metrics, TimeAverager, calculate_eta, logger, progbar
  import datasets
  from utils import save_imgs
label_desc: Use image node_id 4 for calling the Eval obj's underlying member method code with example usage: obj.mean_iou(out_16_13) and returns: MIoU, MIoU_16,  MIoU_13, MIoU_16,  MIoU_13