code: def evaluate_L1(config, model, dataset):
      if config.eval:
          model.eval()
      scores = []
      for i, data in enumerate(dataset):
          model.set_input(data)  # unpack data from data loader
          model.test()  # run inference
          visuals = model.get_current_visuals()
          score = (
              torch.mean(
                  torch.abs(visuals["fake_B"] - visuals["real_B"])
              )
              .detach()
              .cpu()
              .numpy()
          )
          scores.append(score)
      return np.mean(np.array(scores))
  
imports: import sys
  import os
  import pathlib
  import logging
  import time
  import argparse
  from collections import namedtuple
  import numpy
  import torch
  import torch.utils.data
  import nni
  from nni.utils import merge_parameter
  from pix2pixlib.data.aligned_dataset import AlignedDataset
  from pix2pixlib.models.pix2pix_model import Pix2PixModel
  from base_params import get_base_params
label_desc: Use image node_id 4 for calling a global function with example usage: evaluate_L1(config, model, dataset) and returns: np