code: def __init__(self, params):
      super(MLPAlgorithm, self).__init__(params)
      logger.debug("MLPAlgorithm.__init__")
      self.max_iters = 1
      self.library_version = sklearn.__version__
      h1 = params.get("dense_1_size", 32)
      h2 = params.get("dense_2_size", 16)
      learning_rate = params.get("learning_rate", 0.05)
  
      max_iter = 500
      self.model = MLPClassifier(
          hidden_layer_sizes=(h1, h2),
          activation="relu",
          solver="adam",
          learning_rate=params.get("learning_rate_type", "constant"),
          learning_rate_init=learning_rate,
          alpha=params.get("alpha", 0.0001),
          early_stopping=True,
          n_iter_no_change=50,
          max_iter=max_iter,
          random_state=params.get("seed", 123),
      )
  
imports: import logging
  import warnings
  import numpy
  import pandas
  import sklearn
  from sklearn.base import ClassifierMixin, RegressorMixin
  from sklearn.neural_network import MLPClassifier, MLPRegressor
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.algorithms.sklearn import SklearnAlgorithm
  from supervised.utils.config import LOG_LEVEL
label_desc: Use image node_id 1 to create a new MLPAlgorithm object from inherited base classes: NNFit, ClassifierMixin with example: obj = MLPAlgorithm(params)