code: def ensemble_predict(args):
      model = eval(args.model_name)(args.layers, num_filters=args.nf)
  
      checkpoints = [
          r"D:\data\salt\models\pseudo\UNetResNetV4_34\edge\best_5.pth",
          r"D:\data\salt\models\pseudo\UNetResNetV4_34\edge\best_6.pth",
          r"D:\data\salt\models\pseudo\UNetResNetV4_34\edge\best_8.pth",
          r"D:\data\salt\models\pseudo\UNetResNetV4_34\edge\best_9.pth",
      ]
      print(checkpoints)
  
      ensemble(args, model, checkpoints)
  
imports: import os
  import glob
  import argparse
  import numpy
  import torch
  import torch.optim
  import torch.nn.functional
  import settings
  from loader import get_test_loader, add_depth_channel
  from models import UNetResNetV4, UNetResNetV5, UNetResNetV6, UNet7, UNet8
  from postprocessing import crop_image, binarize, resize_image
  from metrics import intersection_over_union, intersection_over_union_thresholds
  from utils import create_submission
label_desc: Use image node_id 6 for calling a global function with example usage: ensemble_predict(args) without return types