class_docstr: Mixin class for samplers with abstract method.
  
  Warning: This class should not be used directly. Use the derive classes
  instead.
docstr: Check inputs and statistics of the sampler.
  
  You should use ``fit_resample`` in all cases.
  
  Parameters
  ----------
  X : {array-like, dataframe, sparse matrix} of shape                 (n_samples, n_features)
      Data array.
  
  y : array-like of shape (n_samples,)
      Target array.
  
  Returns
  -------
  self : object
      Return the instance itself.
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code: def fit(self, X, y):
      """Check inputs and statistics of the sampler.
  
          You should use ``fit_resample`` in all cases.
  
          Parameters
          ----------
          X : {array-like, dataframe, sparse matrix} of shape \
                  (n_samples, n_features)
              Data array.
  
          y : array-like of shape (n_samples,)
              Target array.
  
          Returns
          -------
          self : object
              Return the instance itself.
          """
      X, y, _ = self._check_X_y(X, y)
      self.sampling_strategy_ = check_sampling_strategy(
          self.sampling_strategy, y, self._sampling_type
      )
      return self
  
imports: from abc import ABCMeta, abstractmethod
  from typing import List, Optional
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.preprocessing import label_binarize
  from sklearn.utils.multiclass import check_classification_targets
  from .utils import check_sampling_strategy, check_target_type
  from .utils import ArraysTransformer
label_desc: Use image node_id 1 for calling the SamplerMixin obj's underlying member method code with example usage: obj.fit(X, y) and returns: self