code: def mean_pixel_accuracy(self, out_16_13=False):
      MPA = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(
          axis=1
      )
      if self.synthia:
          MPA_16 = np.nanmean(MPA[: self.ignore_index])
          MPA_13 = np.nanmean(MPA[synthia_set_16_to_13])
          return MPA_16, MPA_13
      if out_16_13:
          MPA_16 = np.nanmean(MPA[synthia_set_16])
          MPA_13 = np.nanmean(MPA[synthia_set_13])
          return MPA_16, MPA_13
      MPA = np.nanmean(MPA[: self.ignore_index])
  
      return MPA
  
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 3 for calling the Eval obj's underlying member method code with example usage: obj.mean_pixel_accuracy(out_16_13) and returns: MPA, MPA_16,  MPA_13, MPA_16,  MPA_13