class_docstr: Mixin class for samplers with abstract method.
  
  Warning: This class should not be used directly. Use the derive classes
  instead.
docstr: Base method defined in each sampler to defined the sampling
  strategy.
  
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      Matrix containing the data which have to be sampled.
  
  y : array-like of shape (n_samples,)
      Corresponding label for each sample in X.
  
  Returns
  -------
  X_resampled : {ndarray, sparse matrix} of shape                 (n_samples_new, n_features)
      The array containing the resampled data.
  
  y_resampled : ndarray of shape (n_samples_new,)
      The corresponding label of `X_resampled`.
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code: def _fit_resample(self, X, y):
      """Base method defined in each sampler to defined the sampling
          strategy.
  
          Parameters
          ----------
          X : {array-like, sparse matrix} of shape (n_samples, n_features)
              Matrix containing the data which have to be sampled.
  
          y : array-like of shape (n_samples,)
              Corresponding label for each sample in X.
  
          Returns
          -------
          X_resampled : {ndarray, sparse matrix} of shape \
                  (n_samples_new, n_features)
              The array containing the resampled data.
  
          y_resampled : ndarray of shape (n_samples_new,)
              The corresponding label of `X_resampled`.
  
          """
      pass
  
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 3 for calling the SamplerMixin obj's underlying member method code with example usage: obj._fit_resample(X, y) without return types