code: def ensemble_np_results(args):
      np_files1 = glob.glob(
          r"D:\data\salt\models\depths\UNetResNetV5_50\edge\*pth_out\*.npy"
      )
      np_files2 = glob.glob(
          r"D:\data\salt\models\depths\UNetResNetV4_34\edge\*pth_out\*.npy"
      )
      np_files3 = glob.glob(
          r"D:\data\salt\models\depths\UNetResNetV6_34\edge\*pth_out\*.npy"
      )
      np_files6 = glob.glob(r"D:\data\salt\models\ensemble\*.npy")
      np_files = np_files1 + np_files2 + np_files3 + np_files6
      print(np_files)
      ensemble_np(args, np_files)
  
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 7 for calling a global function with example usage: ensemble_np_results(args) without return types