code: def evaluate(predictions, gts, num_classes):
      hist = np.zeros((num_classes, num_classes))
      for lp, lt in zip(predictions, gts):
          hist += _fast_hist(lp.flatten(), lt.flatten(), num_classes)
      # axis 0: gt, axis 1: prediction
      acc = np.diag(hist).sum() / hist.sum()
      acc_cls = np.diag(hist) / hist.sum(axis=1)
      acc_cls = np.nanmean(acc_cls)
      iu = np.diag(hist) / (
          hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist)
      )
      mean_iu = np.nanmean(iu)
      freq = hist.sum(axis=1) / hist.sum()
      fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
      return acc, acc_cls, mean_iu, fwavacc
  
imports: import os
  from math import ceil
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate(predictions, gts, num_classes) and returns: acc,  acc_cls,  mean_iu,  fwavacc