code: def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict):
      # Here we can specify any logic we want to make a stateful feature generator based on the data.
      # Just call _transform since this isn't a stateful feature generator.
      X_out = self._transform(X)
      # return the output and the new special types of the data. For this generator, we don't add any new special types, so just return the input special types
      return X_out, self.feature_metadata_in.type_group_map_special
  
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 2 for calling the PlusKFeatureGenerator obj's underlying member method code with example usage: obj._fit_transform(X) and returns: X_out,  self