code: def fit(
      self,
      X,
      y,
      sample_weight=None,
      X_validation=None,
      y_validation=None,
      sample_weight_validation=None,
      log_to_file=None,
      max_time=None,
  ):
      with warnings.catch_warnings():
          warnings.simplefilter(action="ignore")
          # filter
          # X does not have valid feature names, but MLPClassifier was fitted with feature names
          self.model.fit(X, y)
  
      if log_to_file is not None:
          loss_curve = self.model.loss_curve_
          result = pd.DataFrame(
              {
                  "iteration": range(len(loss_curve)),
                  "train": loss_curve,
                  "validation": None,
              }
          )
          result.to_csv(log_to_file, index=False, header=False)
  
      if self.params["ml_task"] != REGRESSION:
          self.classes_ = np.unique(y)
  
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 3 for calling the NNFit obj's underlying member method code with example usage: obj.fit(X, y, sample_weight, X_validation, y_validation, sample_weight_validation, log_to_file, max_time) without return types