code: def setUpClass(cls):
      cls.setup = yaml.load(
          open(os.path.join("tests", "data", "config.yml"), "r")
      )
      cls.RRDN = RRDN(
          arch_params=cls.setup["rrdn"],
          patch_size=cls.setup["patch_size"],
      )
      cls.f_ext = Cut_VGG19(
          patch_size=cls.setup["patch_size"] * 2,
          layers_to_extract=[1, 2],
      )
      cls.discr = Discriminator(patch_size=cls.setup["patch_size"] * 2)
      cls.weights_path = {
          "generator": os.path.join(
              cls.setup["weights_dir"], "test_gen_weights.hdf5"
          ),
          "discriminator": os.path.join(
              cls.setup["weights_dir"], "test_dis_weights.hdf5"
          ),
      }
      cls.temp_data = Path("tests/temporary_test_data")
  
      cls.not_matching_hr = cls.temp_data / "not_matching_hr"
      cls.not_matching_hr.mkdir(parents=True)
      for item in ["data2.gif", "data1.png", "data0.jpeg"]:
          (cls.not_matching_hr / item).touch()
  
      cls.not_matching_lr = cls.temp_data / "not_matching_lr"
      cls.not_matching_lr.mkdir(parents=True)
      for item in ["data1.png"]:
          (cls.not_matching_lr / item).touch()
  
      cls.matching_hr = cls.temp_data / "matching_hr"
      cls.matching_hr.mkdir(parents=True)
      for item in ["data2.gif", "data1.png", "data0.jpeg"]:
          (cls.matching_hr / item).touch()
  
      cls.matching_lr = cls.temp_data / "matching_lr"
      cls.matching_lr.mkdir(parents=True)
      for item in ["data1.png", "data0.jpeg"]:
          (cls.matching_lr / item).touch()
  
      with patch(
          "ISR.utils.datahandler.DataHandler._check_dataset",
          return_value=True,
      ):
          cls.trainer = Trainer(
              generator=cls.RRDN,
              discriminator=cls.discr,
              feature_extractor=cls.f_ext,
              lr_train_dir=str(cls.matching_lr),
              hr_train_dir=str(cls.matching_hr),
              lr_valid_dir=str(cls.matching_lr),
              hr_valid_dir=str(cls.matching_hr),
              learning_rate={
                  "initial_value": 0.0004,
                  "decay_factor": 0.5,
                  "decay_frequency": 5,
              },
              log_dirs={
                  "logs": "./tests/temporary_test_data/logs",
                  "weights": "./tests/temporary_test_data/weights",
              },
              dataname="TEST",
              weights_generator=None,
              weights_discriminator=None,
              n_validation=2,
              flatness={
                  "min": 0.01,
                  "max": 0.3,
                  "increase": 0.01,
                  "increase_frequency": 5,
              },
              adam_optimizer={
                  "beta1": 0.9,
                  "beta2": 0.999,
                  "epsilon": None,
              },
              losses={
                  "generator": "mae",
                  "discriminator": "mse",
                  "feature_extractor": "mse",
              },
              loss_weights={
                  "generator": 1.0,
                  "discriminator": 1.0,
                  "feature_extractor": 0.5,
              },
          )
  
imports: import logging
  import os
  import shutil
  import unittest
  from copy import copy
  from pathlib import Path
  import yaml
  import numpy
  from unittest.mock import patch, Mock
  from ISR.models.cut_vgg19 import Cut_VGG19
  from ISR.models.discriminator import Discriminator
  from ISR.models.rrdn import RRDN
  from ISR.train.trainer import Trainer
label_desc: Use image node_id 1 for calling the TrainerClassTest obj's underlying member method code with example usage: obj.setUpClass(cls) without return types