code: def _transform(self, X: DataFrame) -> DataFrame:
      # Here we can specify the logic taken to convert input data to output data post-fit. Here we can reference any variables created during fit if the generator is stateful.
      # Because this feature generator is not stateful, we simply add k to all features.
      return X + self.k
  
file_path: autogluon/examples/tabular/example_custom_feature_generator.py
imports: from autogluon.tabular import TabularDataset, TabularPredictor
  from autogluon.features.generators import CategoryFeatureGenerator, IdentityFeatureGenerator
  from autogluon.common.features.types import R_INT
  from pandas import DataFrame
  from autogluon.features.generators import AbstractFeatureGenerator
  from autogluon.features.generators import AsTypeFeatureGenerator, BulkFeatureGenerator, DropUniqueFeatureGenerator, FillNaFeatureGenerator, PipelineFeatureGenerator
  from autogluon.features.generators import AutoMLPipelineFeatureGenerator
label_desc: Use image node_id 3 for calling the PlusKFeatureGenerator obj's underlying member method code with example usage: obj._transform(X) and returns: unknown