class_docstr: DecisionTreeClassifier that will memorize the number of samples seen
  at fit.
class_docstr_tok: ["DecisionTreeClassifier","that","will","memorize","the","number","of","samples","seen","at","fit","."]
code: def fit(self, X, y, sample_weight=None):
      self.class_counts_ = Counter(y)
      return super().fit(X, y, sample_weight=sample_weight)
  
imports: from collections import Counter
  import numpy
  import pytest
  import sklearn
  from sklearn.cluster import KMeans
  from sklearn.datasets import load_iris, make_classification, make_hastie_10_2
  from sklearn.dummy import DummyClassifier
  from sklearn.feature_selection import SelectKBest
  from sklearn.linear_model import LogisticRegression, Perceptron
  from sklearn.model_selection import GridSearchCV, ParameterGrid, train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import parse_version
  from imblearn import FunctionSampler
  from imblearn.datasets import make_imbalance
  from imblearn.ensemble import BalancedBaggingClassifier
  from imblearn.over_sampling import SMOTE, RandomOverSampler
  from imblearn.pipeline import make_pipeline
  from imblearn.under_sampling import ClusterCentroids, RandomUnderSampler
label_desc: Use image node_id 1 for calling the CountDecisionTreeClassifier obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: super