code: def check_primary_metrics(calc, include=(), exclude=()):
      primary_metrics = list(calc.original_function_dict.keys())
      for met in [include, exclude]:
          if not isinstance(met, (tuple, list)):
              raise TypeError(
                  "Arguments must be of type tuple, not {}.".format(
                      type(met)
                  )
              )
          if not set(met).issubset(set(primary_metrics)):
              raise ValueError(
                  "Primary metrics must be one or more of: {}.".format(
                      primary_metrics
                  )
              )
  
imports: import torch
  from sklearn.metrics import adjusted_mutual_info_score, normalized_mutual_info_score
  from .None import common_functions
  from .inference import FaissKMeans, FaissKNN
label_desc: Use image node_id 16 for calling the AccuracyCalculator obj's underlying member method code with example usage: obj.check_primary_metrics(calc, include, exclude) without return types