old_contents: ''' Contains sklearn-compatible wrappers for pliers. '''
  
  from pliers.extractors import Extractor, merge_results
  from pliers.transformers import get_transformer
  from pliers.utils import attempt_to_import
  
  
  sklearn = attempt_to_import('sklearn')
  if sklearn:
      class SklearnBase(sklearn.base.TransformerMixin,
                        sklearn.base.BaseEstimator):
          pass
  else:
      class SklearnBase():
          pass
  
  
  class PliersTransformer(SklearnBase):
  
      ''' Simple wrapper for using pliers within a sklearn workflow.
  
      Args:
          transformer (Graph or Transformer): Pliers object to execute. Can
              either be a Graph with several transformers chained or a single
              transformer.
      '''
  
      def __init__(self, transformer):
          if isinstance(transformer, str):
              self.transformer = get_transformer(transformer)
          else:
              self.transformer = transformer
  
      def fit(self, X, y=None):
          return self
  
      def fit_transform(self, stimulus_files, y=None):
          return self.transform(stimulus_files)
  
      def transform(self, stimulus_files):
          if isinstance(self.transformer, Extractor):
              result = self.transformer.transform(stimulus_files).to_df()
          else:
              result = self.transformer.transform(stimulus_files, merge=False)
              result = merge_results(result, format='wide',
                                     extractor_names=False)
  
          extra_columns = list({'onset', 'duration', 'order', 'history',
                                    'class', 'filename', 'stim_name',
                                    'source_file', 'object_id', 'extractor'} &
                               set(result.columns))
          self.metadata_ = result[extra_columns]
          result.drop(extra_columns, axis=1, inplace=True, errors='ignore')
  
          return result.values()
  
new_contents: ''' Contains sklearn-compatible wrappers for pliers. '''
  
  from pliers.extractors import Extractor, merge_results
  from pliers.transformers import get_transformer
  from pliers.utils import attempt_to_import
  
  
  sklearn = attempt_to_import('sklearn')
  if sklearn:
      class SklearnBase(sklearn.base.TransformerMixin,
                        sklearn.base.BaseEstimator):
          pass
  else:
      class SklearnBase():
          pass
  
  
  class PliersTransformer(SklearnBase):
  
      ''' Simple wrapper for using pliers within a sklearn workflow.
  
      Args:
          transformer (Graph or Transformer): Pliers object to execute. Can
              either be a Graph with several transformers chained or a single
              transformer.
      '''
  
      def __init__(self, transformer):
          if isinstance(transformer, str):
              self.transformer = get_transformer(transformer)
          else:
              self.transformer = transformer
  
      def fit(self, X, y=None):
          return self
  
      def fit_transform(self, stimulus_files, y=None):
          return self.transform(stimulus_files)
  
      def transform(self, stimulus_files):
          if isinstance(self.transformer, Extractor):
              result = self.transformer.transform(stimulus_files).to_df()
          else:
              result = self.transformer.transform(stimulus_files, merge=False)
              result = merge_results(result, format='wide',
                                     extractor_names=False)
  
          extra_columns = list({'onset', 'duration', 'order', 'history',
                                    'class', 'filename', 'stim_name',
                                    'source_file', 'object_id', 'extractor'} &
                               set(result.columns))
          self.metadata_ = result[extra_columns]
          result.drop(extra_columns, axis=1, inplace=True, errors='ignore')
  
          return result.to_numpy()
  
subject: replace values with as_numpy