code: def clean(
      X, type_hints=None, return_types=False, target_col=None, verbose=0
  ):
      """Public clean interface
  
      Parameters
      ----------
      type_hints : dict or None
          If dict, provide type information for columns.
          Keys are column names, values are types as provided by detect_types.
      return_types : bool, default=False
          Whether to return the inferred types
      target_col : string, int or None
          If not None specifies a target column in the data.
          Target columns are never dropped.
      verbose : int, default=0
          Verbosity control.
      """
      if not isinstance(X, pd.DataFrame):
          X = pd.DataFrame(X)
      X = _apply_type_hints(X, type_hints=type_hints)
  
      if not X.index.is_unique:
          warn("Index not unique, resetting index!", UserWarning)
          X = X.reset_index(drop=True)
      types_p = types = detect_types(
          X,
          type_hints=type_hints,
          verbose=verbose,
          target_col=target_col,
      )
      # drop useless columns
      X = X.loc[:, ~types.useless].copy()
      types = types.loc[~types.useless, :]
      for col in types.index[types.categorical]:
          X[col] = X[col].astype("category", copy=False)
      for col in types.index[types.continuous]:
          X[col] = X[col].astype(float, copy=False)
  
      if types["dirty_float"].any():
          # don't use ColumnTransformer that can't return dataframe yet
          X_df = DirtyFloatCleaner().fit_transform(
              X.loc[:, types["dirty_float"]]
          )
          X = pd.concat([X.loc[:, ~types.dirty_float], X_df], axis=1)
          # we should know what these are but maybe running this again is fine?
          types_df = detect_types(X_df)
          types = pd.concat([types[~types.dirty_float], types_df])
  
          # discard dirty float targets that cant be converted to float
          if (
              target_col is not None
              and types_p["dirty_float"][target_col]
          ):
              warn(
                  "Discarding dirty_float targets that cannot be converted "
                  "to float.",
                  UserWarning,
              )
              X = X.dropna(
                  subset=["{}_dabl_continuous".format(target_col)]
              )
              X = X.rename(
                  columns={
                      "{}_dabl_continuous".format(
                          target_col
                      ): "{}".format(target_col)
                  }
              )
              types = types.rename(
                  index={
                      "{}_dabl_continuous".format(
                          target_col
                      ): "{}".format(target_col)
                  }
              )
  
      # deal with low cardinality ints
      # TODO ?
      # ensure that the indicator variables are also marked as categorical
      # we could certainly do this nicer, but at this point calling
      # detect_types shouldn't be expensive any more
      # though if we have actual string columns that are free strings... hum
      for col in types.index[types.categorical]:
          # ensure categories are strings, otherwise imputation might fail
          col_as_cat = X[col].astype("category", copy=False)
          if col_as_cat.cat.categories.astype("str").is_unique:
              # the world is good: converting to string keeps categories unique
              X[col] = col_as_cat.cat.rename_categories(
                  lambda x: str(x)
              )
          else:
              # we can't have nice things and need to convert to string
              # before making categories (again)
              warn(
                  "Duplicate categories of different types in column "
                  "{} considered equal {}".format(
                      col, col_as_cat.cat.categories
                  )
              )
              X[col] = X[col].astype(str).astype("category", copy=False)
  
      if return_types:
          return X, types
      return X
  
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 15 for calling a global function with example usage: clean(X, type_hints, return_types, target_col, verbose) and returns: X, X,  types