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")
          self.model.fit(X, y, sample_weight=sample_weight)
          if self.params["ml_task"] != REGRESSION:
              self.classes_ = np.unique(y)
  
imports: import copy
  import logging
  import time
  import warnings
  import joblib
  import numpy
  import pandas
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
label_desc: Use image node_id 2 for calling the SklearnAlgorithm 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