code: def process_label_helper(
      cls,
      dataset_labels,
      batch_labels,
      correct_mapped_batch_labels,
      correct_unmapped_batch_labels,
      hierarchy_level,
      input_is_string=False,
  ):
      for set_min_label_to_zero in [False, True]:
          if input_is_string and not set_min_label_to_zero:
              continue
          label_mapper = c_f.LabelMapper(
              set_min_label_to_zero, dataset_labels
          )
          x = c_f.process_label(
              batch_labels, hierarchy_level, label_mapper.map
          )
          if set_min_label_to_zero:
              cls.assertTrue(
                  np.array_equal(x, correct_mapped_batch_labels)
              )
          else:
              cls.assertTrue(
                  np.array_equal(x, correct_unmapped_batch_labels)
              )
  
file_path: pytorch-metric-learning/tests/utils/test_common_functions.py
imports: import logging
  import unittest
  import numpy
  import torch
  from sklearn.preprocessing import StandardScaler
  from pytorch_metric_learning.losses import TripletMarginLoss
  from pytorch_metric_learning.utils import common_functions
  from ..None import WITH_COLLECT_STATS
label_desc: Use image node_id 1 for calling a global function with example usage: process_label_helper(cls, dataset_labels, batch_labels, correct_mapped_batch_labels, correct_unmapped_batch_labels, hierarchy_level, input_is_string) without return types