def _transform_raw_features_compat_v1(
      self, raw_features: Mapping[str, common_types.TensorType],
      drop_unused_features: bool) -> Dict[str, common_types.TensorType]:
    """Takes a dict of tensors representing raw features and transforms them."""
    unbounded_raw_features, transformed_features = (
        saved_transform_io.partially_apply_saved_transform_internal(
            self.transform_savedmodel_dir, raw_features))
    if drop_unused_features:
      graph = tf.compat.v1.get_default_graph()
      graph_analyzer = graph_tools.InitializableGraphAnalyzer(
          graph, raw_features,
          [(t, False) for t in unbounded_raw_features.values()])
      return {
          name: feature
          for name, feature in transformed_features.items()
          if graph_analyzer.ready_to_run(feature)
      }
    else:
      return transformed_features
Takes a dict of tensors representing raw features and transforms them.
tensorflow_transform/output_wrapper.py