class_docstr: Factorization Machine (aka FM).
  
  This class implements L2-regularized arbitrary order FM model with logistic
  loss and gradient-based optimization.
  
  Only binary classification with 0/1 labels supported.
  
  See TFFMBaseModel and TFFMCore docs for details about parameters.
docstr: Probability estimates.
  
  The returned estimates for all 2 classes are ordered by the
  label of classes.
  
  Parameters
  ----------
  X : array-like, shape = [n_samples, n_features]
  pred_batch_size : int batch size for prediction (default None)
  
  Returns
  -------
  probs : array-like, shape = [n_samples, 2]
      Returns the probability of the sample for each class in the model.
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code: def predict_proba(self, X, pred_batch_size=None):
      """Probability estimates.
  
      The returned estimates for all 2 classes are ordered by the
      label of classes.
  
      Parameters
      ----------
      X : array-like, shape = [n_samples, n_features]
      pred_batch_size : int batch size for prediction (default None)
  
      Returns
      -------
      probs : array-like, shape = [n_samples, 2]
          Returns the probability of the sample for each class in the model.
      """
      outputs = self.decision_function(X, pred_batch_size)
      probs_positive = sigmoid(outputs)
      probs_negative = 1 - probs_positive
      probs = np.vstack((probs_negative.T, probs_positive.T))
      return probs.T
  
file_path: tffm/tffm/models.py
imports: import numpy
  from .base import TFFMBaseModel
  from .utils import loss_logistic, loss_mse, sigmoid
label_desc: Use image node_id 5 for calling the TFFMClassifier obj's underlying member method code with example usage: obj.predict_proba(X, pred_batch_size) and returns: probs