code: def get_feature_type(feature_column):
      """
      For a given feature, determine if it is real, binary or constant.
      Here binary means that only two unique values occur in the feature.
  
      :param feature_column: The feature column
      :type feature_column: pandas.Series
      :return: 'constant', 'binary' or 'real'
      """
      n_unique_values = len(set(feature_column.values))
      if n_unique_values == 1:
          return "constant"
      elif n_unique_values == 2:
          return "binary"
      else:
          return "real"
  
file_path: tsfresh/tsfresh/feature_selection/relevance.py
imports: import warnings
  from functools import partial, reduce
  from multiprocessing import Pool
  import numpy
  import pandas
  from statsmodels.stats.multitest import multipletests
  from tsfresh import defaults
  from tsfresh.feature_selection.significance_tests import target_binary_feature_binary_test, target_binary_feature_real_test, target_real_feature_binary_test, target_real_feature_real_test
  from tsfresh.utilities.distribution import initialize_warnings_in_workers
label_desc: Use image node_id 5 for calling a global function with example usage: get_feature_type(feature_column) and returns: str, str, str