docstr: Predicts probabilities using the Platt scaling model
  (after calibration).
  
  Model must be calibrated beforehand with the ``calibrate`` method.
  
  Parameters
  ----------
  X: ndarray, shape (n, 3)
      Numpy array of triples to be evaluated.
  
  Returns
  -------
  scores: np.array, shape (n, )
      Calibrated scores for the input triples.
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code: def predict_proba(self, X):
      """
      Predicts probabilities using the Platt scaling model
      (after calibration).
  
      Model must be calibrated beforehand with the ``calibrate`` method.
  
      Parameters
      ----------
      X: ndarray, shape (n, 3)
          Numpy array of triples to be evaluated.
  
      Returns
      -------
      scores: np.array, shape (n, )
          Calibrated scores for the input triples.
      """
      return self.model.predict_proba(X)
  
imports: import numpy
  import tensorflow
  from ampligraph.latent_features.loss_functions import get
  from ampligraph.latent_features.models import ScoringBasedEmbeddingModel
  from ampligraph.latent_features.optimizers import get
  from ampligraph.latent_features.regularizers import get
label_desc: Use image node_id 12 for calling the ScoringModelBase obj's underlying member method code with example usage: obj.predict_proba(X) and returns: self