code: def transform(self, X: PIPELINE_DATA_DTYPE) -> PIPELINE_DATA_DTYPE:
      X.fillna("", inplace=True)
      if self.per_column:
          X_new = None
          if self.preprocessor is None:
              raise NotImplementedError()
  
          for feature in self.preprocessor:
              # the names in the dataframe must not change
              if X_new is None:
                  X_new = self.preprocessor[feature].transform(
                      X[feature]
                  )
              else:
                  X_transformed = self.preprocessor[feature].transform(
                      X[feature]
                  )
                  X_new = hstack([X_new, X_transformed])
  
          return X_new
      else:
          X_transformed = None
          if self.preprocessor is None:
              raise NotImplementedError()
          for feature in X.columns:
              if X_transformed is None:
                  X_transformed = self.preprocessor.transform(
                      X[feature]
                  )
              else:
                  X_transformed += self.preprocessor.transform(
                      X[feature]
                  )
          return X_transformed
  
imports: from typing import Dict, Optional, Tuple, Union
  import itertools
  import ConfigSpace.hyperparameters
  import numpy
  import pandas
  from ConfigSpace import EqualsCondition
  from ConfigSpace.configuration_space import ConfigurationSpace
  from scipy.sparse import hstack
  from sklearn.feature_extraction.text import TfidfVectorizer
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool
label_desc: Use image node_id 3 for calling the TfidfEncoder obj's underlying member method code with example usage: obj.transform(X) and returns: X_new, X_transformed