code: def dice_coef_metric(pred, label):
      pred[pred >= 0.5] = 1.0
      pred[pred < 0.5] = 0.0
      intersection = 2.0 * (pred * label).sum()
      union = pred.sum() + label.sum()
      if pred.sum() == 0 and label.sum() == 0:
          return 1.0
      return intersection / union
  
imports: from __future__ import print_function
  from sklearn.model_selection import train_test_split
  import matplotlib.pyplot
  import albumentations
  import pandas
  import argparse
  import glob
  import os
  import torch
  import torch.nn
  import torch.optim
  import torch.utils.data
  from torchvision.utils import make_grid
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.learn.pytorch import Estimator
  from Unet import UNet
  from dataset import *
label_desc: Use image node_id 2 for calling a global function with example usage: dice_coef_metric(pred, label) and returns: unknown, int