class_docstr: A classifier that operates through Sentence Transformer Finetuning (SetFit, [TRE+22]_).
  
  This class is a wrapper which encapsulates the
  `Hugging Face SetFit implementation <https://github.com/huggingface/setfit>_` .
  
  .. note ::
     This strategy requires the optional dependency `setfit`.
  
  .. versionadded:: 1.2.0
docstr: Predicts the label distributions.
  
  Parameters
  ----------
  dataset : TextDataset
      A dataset whose labels will be predicted.
  dropout_sampling : int, default=1
      If `dropout_sampling > 1` then all dropout modules will be enabled during prediction and
      multiple rounds of predictions will be sampled for each instance.
  
  Returns
  -------
  scores : np.ndarray
      Distribution of confidence scores over all classes of shape (num_samples, num_classes).
      If `dropout_sampling > 1` then the shape is (num_samples, dropout_sampling, num_classes).
  
  .. warning::
     This function is not thread-safe if `dropout_sampling > 1`, since the underlying model gets
     temporarily modified.
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code: def predict_proba(self, dataset, dropout_sampling=1):
      """Predicts the label distributions.
  
      Parameters
      ----------
      dataset : TextDataset
          A dataset whose labels will be predicted.
      dropout_sampling : int, default=1
          If `dropout_sampling > 1` then all dropout modules will be enabled during prediction and
          multiple rounds of predictions will be sampled for each instance.
  
      Returns
      -------
      scores : np.ndarray
          Distribution of confidence scores over all classes of shape (num_samples, num_classes).
          If `dropout_sampling > 1` then the shape is (num_samples, dropout_sampling, num_classes).
  
      .. warning::
         This function is not thread-safe if `dropout_sampling > 1`, since the underlying model gets
         temporarily modified.
      """
      if len(dataset) == 0:
          return empty_result(
              self.multi_label,
              self.num_classes,
              return_prediction=False,
              return_proba=True,
          )
      dataset = _truncate_texts(self.model, self.max_seq_len, dataset)[
          0
      ]
  
      if self.use_differentiable_head:
          raise NotImplementedError()
  
      with torch.no_grad():
          if dropout_sampling <= 1:
              return self._predict_proba(dataset)
          else:
              return self._predict_proba_dropout_sampling(
                  dataset, dropout_samples=dropout_sampling
              )
  
imports: import types
  import numpy
  from sklearn.exceptions import NotFittedError
  from sklearn.utils.validation import check_is_fitted
  from small_text.base import check_optional_dependency
  from small_text.classifiers.classification import Classifier, EmbeddingMixin
  from small_text.exceptions import UnsupportedOperationException
  from small_text.utils.classification import empty_result, _multi_label_list_to_multi_hot, prediction_result
  from small_text.utils.context import build_pbar_context
  from small_text.utils.labels import csr_to_list
  from small_text.integrations.transformers.classifiers.base import ModelLoadingStrategy
label_desc: Use image node_id 7 for calling the SetFitClassification obj's underlying member method code with example usage: obj.predict_proba(dataset, dropout_sampling) and returns: empty_result, self, self