code: def predict_model(args):
      model = eval(args.model_name)(args.layers, num_filters=args.nf)
      model_subdir = args.pad_mode
      if args.meta_version == 2:
          model_subdir = args.pad_mode + "_meta2"
      if args.exp_name is None:
          model_file = os.path.join(
              settings.MODEL_DIR,
              model.name,
              model_subdir,
              "best_{}.pth".format(args.ifold),
          )
      else:
          model_file = os.path.join(
              settings.MODEL_DIR,
              args.exp_name,
              model.name,
              model_subdir,
              "best_{}.pth".format(args.ifold),
          )
  
      if os.path.exists(model_file):
          print("loading {}...".format(model_file))
          model.load_state_dict(torch.load(model_file))
      else:
          raise ValueError(
              "model file not found: {}".format(model_file)
          )
      model = model.cuda()
      predict(args, model, model_file, args.sub_file)
  
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 8 for calling a global function with example usage: predict_model(args) without return types