code: def get_properties(
      dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None,
  ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]:
      return {
          "shortname": "StandardScaler",
          "name": "StandardScaler",
          "handles_missing_values": False,
          "handles_nominal_values": False,
          "handles_numerical_features": True,
          "prefers_data_scaled": False,
          "prefers_data_normalized": False,
          "handles_regression": True,
          "handles_classification": True,
          "handles_multiclass": True,
          "handles_multilabel": True,
          "handles_multioutput": True,
          "is_deterministic": True,
          # TODO find out if this is right!
          "handles_sparse": True,
          "handles_dense": True,
          "input": (SPARSE, DENSE, UNSIGNED_DATA),
          "output": (INPUT,),
          "preferred_dtype": None,
      }
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/rescaling/standardize.py
imports: from typing import Dict, Optional, Tuple, Union
  import numpy
  from scipy import sparse
  from sklearn.exceptions import NotFittedError
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.components.data_preprocessing.rescaling.abstract_rescaling import Rescaling
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 2 for calling the StandardScalerComponent obj's underlying member method code with example usage: obj.get_properties(dataset_properties) and returns: dict