class_docstr: Base class for MLP classification and regression.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
  
  .. versionadded:: 0.18
code: def __init__(
      self,
      hidden_layer_sizes,
      activation,
      solver,
      alpha,
      batch_size,
      learning_rate,
      learning_rate_init,
      power_t,
      max_iter,
      loss,
      shuffle,
      random_state,
      tol,
      verbose,
      warm_start,
      momentum,
      nesterovs_momentum,
      early_stopping,
      validation_fraction,
      beta_1,
      beta_2,
      epsilon,
      n_iter_no_change,
      max_fun,
  ):
      self.activation = activation
      self.solver = solver
      self.alpha = alpha
      self.batch_size = batch_size
      self.learning_rate = learning_rate
      self.learning_rate_init = learning_rate_init
      self.power_t = power_t
      self.max_iter = max_iter
      self.loss = loss
      self.hidden_layer_sizes = hidden_layer_sizes
      self.shuffle = shuffle
      self.random_state = random_state
      self.tol = tol
      self.verbose = verbose
      self.warm_start = warm_start
      self.momentum = momentum
      self.nesterovs_momentum = nesterovs_momentum
      self.early_stopping = early_stopping
      self.validation_fraction = validation_fraction
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.epsilon = epsilon
      self.n_iter_no_change = n_iter_no_change
      self.max_fun = max_fun
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from itertools import chain
  from numbers import Integral, Real
  import numpy
  import scipy.optimize
  from ..base import BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ..exceptions import ConvergenceWarning
  from ..metrics import accuracy_score, r2_score
  from ..model_selection import train_test_split
  from ..preprocessing import LabelBinarizer
  from ..utils import _safe_indexing, check_random_state, column_or_1d, gen_batches, shuffle
  from ..utils._param_validation import Interval, Options, StrOptions
  from ..utils.extmath import safe_sparse_dot
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import _check_partial_fit_first_call, type_of_target, unique_labels
  from ..utils.optimize import _check_optimize_result
  from ..utils.validation import check_is_fitted
  from ._base import ACTIVATIONS, DERIVATIVES, LOSS_FUNCTIONS
  from ._stochastic_optimizers import AdamOptimizer, SGDOptimizer
label_desc: Use image node_id 1 to create a new BaseMultilayerPerceptron object from inherited base classes: BaseEstimator with example: obj = BaseMultilayerPerceptron(hidden_layer_sizes, activation, solver, alpha, batch_size, learning_rate, learning_rate_init, power_t, max_iter, loss, shuffle, random_state, tol, verbose, warm_start, momentum, nesterovs_momentum, early_stopping, validation_fraction, beta_1, beta_2, epsilon, n_iter_no_change, max_fun)