code: def get_hyper(hyper):
      """Defines the hyper-parameters sweeps for doing grid search."""
      pattern_pathnames = ["t_11_19", "t_9_11"]
      test_fold_nums = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
      train_fold_num_abnormals = [
          get_train_num_abnormal(pattern_n, fold_n)
          for (pattern_n, fold_n) in itertools.product(
              pattern_pathnames, test_fold_nums
          )
      ]
      train_fold_num_normals = [
          get_train_num_normal(pattern_n, fold_n)
          for (pattern_n, fold_n) in itertools.product(
              pattern_pathnames, test_fold_nums
          )
      ]
  
      domain1 = hyper.product(
          [
              hyper.sweep(
                  "config.dataset_configs.pattern_pathname",
                  pattern_pathnames,
              ),
              hyper.sweep(
                  "config.dataset_configs.test_fold_num", test_fold_nums
              ),
          ]
      )
  
      domain2 = hyper.sweep(
          "config.dataset_configs.num_abnormal",
          train_fold_num_abnormals,
      )
      domain3 = hyper.sweep(
          "config.dataset_configs.num_normal", train_fold_num_normals
      )
      return hyper.zipit([domain1, domain2, domain3])
  
imports: import itertools
  import ml_collections
label_desc: Use image node_id 4 for calling a global function with example usage: get_hyper(hyper) and returns: hyper