code: def get_properties(dataset_properties=None):
      return {
          "shortname": "LDA",
          "name": "Linear Discriminant Analysis",
          "handles_regression": False,
          "handles_classification": True,
          "handles_multiclass": False,
          "handles_multilabel": False,
          "handles_multioutput": False,
          "is_deterministic": True,
          "input": (DENSE, UNSIGNED_DATA, SIGNED_DATA),
          "output": (DENSE, UNSIGNED_DATA, SIGNED_DATA),
      }
  
imports: from typing import Optional
  from pprint import pprint
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter, CategoricalHyperparameter
  from ConfigSpace.conditions import InCondition
  import sklearn.metrics
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.classification
  import autosklearn.pipeline.components.feature_preprocessing
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA
  from autosklearn.util.common import check_none
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 4 for calling the LDA obj's underlying member method code with example usage: obj.get_properties(dataset_properties) and returns: dict