class_docstr: An adapter for using scikit-learn estimators.
  
  Notes
  -----
  The multi-label settings currently assumes that the underlying classifer returns a sparse
  matrix if trained on sparse data.
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docstr: Parameters
  ----------
  model : sklearn.base.BaseEstimator
      A scikit-learn estimator that implements `fit` and `predict_proba`.
  num_classes : int
      Number of classes which are to be trained and predicted.
  multi_label : bool, default=False
      If `False`, the classes are mutually exclusive, i.e. the prediction step results in
      exactly one predicted label per instance.
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code: def __init__(self, model, num_classes, multi_label=False):
      """
      Parameters
      ----------
      model : sklearn.base.BaseEstimator
          A scikit-learn estimator that implements `fit` and `predict_proba`.
      num_classes : int
          Number of classes which are to be trained and predicted.
      multi_label : bool, default=False
          If `False`, the classes are mutually exclusive, i.e. the prediction step results in
          exactly one predicted label per instance.
      """
      if multi_label:
          self.model = OneVsRestClassifier(model)
      else:
          self.model = model
      self.num_classes = num_classes
      self.multi_label = multi_label
  
imports: from abc import ABC, abstractmethod
  import numpy
  from sklearn.multiclass import OneVsRestClassifier
  from sklearn.preprocessing import normalize
  from sklearn.svm import LinearSVC
  from sklearn.utils.multiclass import is_multilabel
  from small_text.utils.classification import empty_result, prediction_result
  from small_text.utils.data import check_training_data
label_desc: Use image node_id 1 to create a new SklearnClassifier object from inherited base classes: Classifier with example: obj = SklearnClassifier(model, num_classes, multi_label)