code: def test_multi_output_classification_partial_fit():
      # test if multi_target initializes correctly with base estimator and fit
      # assert predictions work as expected for predict
  
      sgd_linear_clf = SGDClassifier(
          loss="log_loss", random_state=1, max_iter=5
      )
      multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
  
      # train the multi_target_linear and also get the predictions.
      half_index = X.shape[0] // 2
      multi_target_linear.partial_fit(
          X[:half_index], y[:half_index], classes=classes
      )
  
      first_predictions = multi_target_linear.predict(X)
      assert (n_samples, n_outputs) == first_predictions.shape
  
      multi_target_linear.partial_fit(X[half_index:], y[half_index:])
      second_predictions = multi_target_linear.predict(X)
      assert (n_samples, n_outputs) == second_predictions.shape
  
      # train the linear classification with each column and assert that
      # predictions are equal after first partial_fit and second partial_fit
      for i in range(3):
          # create a clone with the same state
          sgd_linear_clf = clone(sgd_linear_clf)
          sgd_linear_clf.partial_fit(
              X[:half_index], y[:half_index, i], classes=classes[i]
          )
          assert_array_equal(
              sgd_linear_clf.predict(X), first_predictions[:, i]
          )
          sgd_linear_clf.partial_fit(X[half_index:], y[half_index:, i])
          assert_array_equal(
              sgd_linear_clf.predict(X), second_predictions[:, i]
          )
  
imports: import re
  import numpy
  import pytest
  from joblib import cpu_count
  from sklearn import datasets
  from sklearn.base import ClassifierMixin, clone
  from sklearn.datasets import load_linnerud, make_classification, make_multilabel_classification, make_regression
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import GradientBoostingRegressor, RandomForestClassifier, StackingRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.impute import SimpleImputer
  from sklearn.linear_model import Lasso, LinearRegression, LogisticRegression, OrthogonalMatchingPursuit, PassiveAggressiveClassifier, Ridge, SGDClassifier, SGDRegressor
  from sklearn.metrics import jaccard_score, mean_squared_error
  from sklearn.model_selection import GridSearchCV, train_test_split
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.multioutput import ClassifierChain, MultiOutputClassifier, MultiOutputRegressor, RegressorChain
  from sklearn.pipeline import make_pipeline
  from sklearn.svm import LinearSVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils import shuffle
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import BSR_CONTAINERS, COO_CONTAINERS, CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 11 for calling a global function with example usage: test_multi_output_classification_partial_fit() without return types