code: def __init__(
      self,
      hidden_layer_depth,
      num_nodes_per_layer,
      activation,
      alpha,
      solver,
      random_state=None,
  ):
      self.hidden_layer_depth = hidden_layer_depth
      self.num_nodes_per_layer = num_nodes_per_layer
      self.activation = activation
      self.alpha = alpha
      self.solver = solver
      self.random_state = random_state
  
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 1 to create a new MLPClassifier object from inherited base classes: AutoSklearnClassificationAlgorithm with example: obj = MLPClassifier(hidden_layer_depth, num_nodes_per_layer, activation, alpha, solver, random_state)