code: def get_config(runlocal=""):
      """Gets config for training from scratch for all CV fold iterations."""
      runlocal = bool(runlocal)
  
      config = ml_collections.ConfigDict()
      config.experiment_name = "longtail_rhs-topvit-kfold"
      config.trainer_name = "inference"
  
      # Dataset.
      config.dataset_name = "longtail_rhs_baseline"
      config.data_dtype_str = "float32"
      config.dataset_configs = ml_collections.ConfigDict()
      config.dataset_configs.chrm_image_shape = (199, 99)
      config.dataset_configs.pattern_pathname = "inv_16"
      config.dataset_configs.test_fold_num = 0
      config.dataset_configs.num_abnormal = 18
      config.dataset_configs.num_normal = 3087
  
      # Model.
      config.model_name = "topological_vit_classification"
      config.init_from = ml_collections.ConfigDict()
      config.init_from.xm = (None, None)
      config.batch_size = 8 if runlocal else 256
      config.rng_seed = 42
      config.save_predictions_on_cns = True
  
      return config
  
imports: import ml_collections
label_desc: Use image node_id 3 for calling a global function with example usage: get_config(runlocal) and returns: config