code: def get_properties(
      dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None,
  ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]:
      return {
          "shortname": "1Hot",
          "name": "One Hot Encoder",
          "handles_regression": True,
          "handles_classification": True,
          "handles_multiclass": True,
          "handles_multilabel": True,
          "handles_multioutput": True,
          # TODO find out of this is right!
          "handles_sparse": True,
          "handles_dense": True,
          "input": (DENSE, SPARSE, UNSIGNED_DATA),
          "output": (INPUT,),
      }
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/categorical_encoding/one_hot_encoding.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  import scipy.sparse
  from ConfigSpace.configuration_space import ConfigurationSpace
  from sklearn.preprocessing import OneHotEncoder
  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.pipeline.implementations.SparseOneHotEncoder import SparseOneHotEncoder
label_desc: Use image node_id 4 for calling the OneHotEncoder obj's underlying member method code with example usage: obj.get_properties(dataset_properties) and returns: dict