code: def testing_cfg(download_path):
      config_params = {
          "train_params": {
              "adapt_lambda": True,
              "adapt_lr": True,
              "lambda_init": 1,
              "nb_adapt_epochs": 2,
              "nb_init_epochs": 1,
              "init_lr": 0.001,
              "batch_size": 60,
              "optimizer": {
                  "type": "SGD",
                  "optim_params": {
                      "momentum": 0.9,
                      "weight_decay": 0.0005,
                      "nesterov": True,
                  },
              },
          }
      }
      yield config_params
  
file_path: pykale/tests/pipeline/test_multi_domain_adapter.py
imports: import matplotlib.figure
  import pytest
  import torch
  from sklearn.metrics import accuracy_score
  from torch.nn.functional import one_hot
  from kale.embed.image_cnn import ResNet18Feature
  from kale.interpret.visualize import distplot_1d
  from kale.loaddata.image_access import ImageAccess
  from kale.loaddata.multi_domain import MultiDomainAdapDataset
  from kale.pipeline.multi_domain_adapter import CoIRLS, create_ms_adapt_trainer
  from kale.predict.class_domain_nets import ClassNetSmallImage
  from tests.helpers.pipe_test_helper import ModelTestHelper
label_desc: Use image node_id 1 for calling a global function with example usage: testing_cfg(download_path) without return types