code: def _type_detection_int(series, max_cat_cardinality="auto"):
      n_distinct_values = series.nunique()
      if n_distinct_values == len(series):
          # could be an index
          if series.iloc[0] == 0:
              if (series == np.arange(len(series))).all():
                  # definitely an index
                  return "index"
          elif series.iloc[0] == 1:
              if (series == np.arange(1, len(series) + 1)).all():
                  # definitely an index
                  return "index"
      if n_distinct_values > _get_max_cat_cardinality(
          len(series), max_cat_cardinality
      ):
          return "continuous"
      elif n_distinct_values <= 5:
          # weird hack / edge case
          return "categorical"
      elif guess_ordinal(series):
          return "low_card_int_ordinal"
      else:
          return "low_card_int_categorical"
  
imports: from importlib.metadata import version
  from joblib import hash
  import warnings
  from warnings import warn
  import numpy
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.compose import make_column_transformer, ColumnTransformer
  from sklearn.impute import SimpleImputer
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 8 for calling a global function with example usage: _type_detection_int(series, max_cat_cardinality) and returns: str, str, str, str, str, str