code: def get_properties(dataset_properties=None):
      return {
          "shortname": "MLP Classifier",
          "name": "MLP CLassifier",
          "handles_regression": False,
          "handles_classification": True,
          "handles_multiclass": True,
          "handles_multilabel": False,
          "handles_multioutput": False,
          "is_deterministic": False,
          # Both input and output must be tuple(iterable)
          "input": [DENSE, SIGNED_DATA, UNSIGNED_DATA],
          "output": [PREDICTIONS],
      }
  
file_path: auto-sklearn/examples/80_extending/example_extending_classification.py
imports: from typing import Optional
  from pprint import pprint
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformIntegerHyperparameter, UniformFloatHyperparameter
  import sklearn.metrics
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.classification
  import autosklearn.pipeline.components.classification
  from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 5 for calling the MLPClassifier obj's underlying member method code with example usage: obj.get_properties(dataset_properties) and returns: dict