code: def transform(self, X):
      if (self.columns_ == X.columns).all() is False:
          raise ValueError("Given the same columns")
      result = []
      for col in self.columns_:
          floats, X_col = _float_matching_fetch(
              X, col, return_safe_col=True
          )
          nofloats = ~floats
          X_new_col = X_col.copy()
          X_new_col[nofloats] = np.NaN
          X_new_col = X_new_col.astype(float)
          enc = self.encoders_[col]
          if enc is None:
              if nofloats.any():
                  warnings.warn(
                      "Found non-floats {} in float column. It's "
                      "recommended"
                      " to call 'clean' on the whole dataset before "
                      "splitting into training and test set.".format(
                          X.loc[nofloats, col].unique()
                      )
                  )
              X_new_col = X_new_col.rename(
                  "{}_dabl_continuous".format(col)
              )
              result.append(X_new_col)
              continue
          cats = pd.DataFrame(
              0,
              index=X.index,
              columns=enc.get_feature_names_out([str(col)]),
          )
          if nofloats.any():
              cats.loc[nofloats, :] = enc.transform(
                  pd.DataFrame(X_col[nofloats])
              )
              cats = cats.copy()
          cats["{}_dabl_continuous".format(col)] = X_new_col
          result.append(cats)
      return pd.concat(result, axis=1)
  
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 2 for calling the DirtyFloatCleaner obj's underlying member method code with example usage: obj.transform(X) and returns: pd