code: def __init__(
      self,
      hidden_layer_depth,
      num_nodes_per_layer,
      activation,
      alpha,
      learning_rate_init,
      early_stopping,
      solver,
      batch_size,
      n_iter_no_change,
      tol,
      shuffle,
      beta_1,
      beta_2,
      epsilon,
      validation_fraction=None,
      random_state=None,
      verbose=0,
  ):
      self.hidden_layer_depth = hidden_layer_depth
      self.num_nodes_per_layer = num_nodes_per_layer
      self.max_iter = self.get_max_iter()
      self.activation = activation
      self.alpha = alpha
      self.learning_rate_init = learning_rate_init
      self.early_stopping = early_stopping
      self.n_iter_no_change = n_iter_no_change
      self.validation_fraction = validation_fraction
      self.tol = tol
      self.solver = solver
      self.batch_size = batch_size
      self.shuffle = shuffle
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.epsilon = epsilon
      self.beta_1 = beta_1
  
      self.random_state = random_state
      self.verbose = verbose
      self.estimator = None
  
file_path: auto-sklearn/autosklearn/pipeline/components/regression/mlp.py
imports: from typing import Optional
  import numpy
  from ConfigSpace.conditions import InCondition
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, Constant, UniformFloatHyperparameter, UniformIntegerHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm, IterativeComponent
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool
label_desc: Use image node_id 1 to create a new MLPRegressor object from inherited base classes: IterativeComponent, AutoSklearnRegressionAlgorithm with example: obj = MLPRegressor(hidden_layer_depth, num_nodes_per_layer, activation, alpha, learning_rate_init, early_stopping, solver, batch_size, n_iter_no_change, tol, shuffle, beta_1, beta_2, epsilon, validation_fraction, random_state, verbose)