text: config = {}
  # set the parameters related to the training and testing set
  
  nKbase = 100
  nKnovel = 5
  nExemplars = 1
  
  data_train_opt = {}
  data_train_opt['nKnovel'] = nKnovel
  data_train_opt['nKbase'] = -1
  data_train_opt['nExemplars'] = nExemplars
  data_train_opt['nTestNovel'] = nKnovel * 3
  data_train_opt['nTestBase'] = nKnovel * 3
  data_train_opt['batch_size'] = 4
  data_train_opt['epoch_size'] = data_train_opt['batch_size'] * 1000
  
  data_test_opt = {}
  data_test_opt['nKnovel'] = nKnovel
  data_test_opt['nKbase'] = nKbase
  data_test_opt['nExemplars'] = nExemplars
  data_test_opt['nTestNovel'] = 8 * data_test_opt['nKnovel']
  data_test_opt['nTestBase'] = 1#0 * data_test_opt['nKnovel']
  data_test_opt['batch_size'] = 1
  data_test_opt['epoch_size'] = 600
  
  config['data_train_opt'] = data_train_opt
  config['data_test_opt'] = data_test_opt
  
  config['max_num_epochs'] = 60
  
  
  networks = {}
  net_optionsF = {'userelu': False, 'restype': 'ResNet10'}
  pretrainedF = './experiments/cub_ResNet10CosineClassifier/feat_model_net_epoch*.best'
  networks['feat_model'] = {'def_file': 'architectures/ResNetFeat.py', 'pretrained': pretrainedF, 'opt': net_optionsF,  'optim_params': None }
  
  net_optim_paramsC = {'optim_type': 'sgd', 'lr': 0.1, 'momentum':0.9, 'weight_decay': 5e-4, 'nesterov': True, 'LUT_lr':[(10*2, 0.2),(20*2, 0.006),(25*2, 0.0012),(30*2, 0.00024)]}
  pretrainedC = './experiments/cub_ResNet10CosineClassifier/classifier_net_epoch*.best'
  net_optionsC = {'classifier_type': 'cosine', 'weight_generator_type': 'attention_based', 'nKall': nKbase, 'nFeat': 512, 'scale_cls': 10, 'scale_att': 10.0}
  networks['classifier'] = {'def_file': 'architectures/ClassifierWithFewShotGenerationModule.py', 'pretrained': pretrainedC, 'opt': net_optionsC, 'optim_params': net_optim_paramsC}
  
  
  
  config['networks'] = networks
  
  criterions = {}
  criterions['loss'] = {'ctype':'CrossEntropyLoss', 'opt':None}
  config['criterions'] = criterions
  
  config['algorithm_type'] = 'FewShot'
  