class_docstr: Factorization Machine (aka FM).
  
  This class implements L2-regularized arbitrary order FM model with MSE
  loss and gradient-based optimization.
  
  Custom loss functions are not supported, mean squared error is always
  used. Any loss function provided in parameters will be overwritten.
  
  See TFFMBaseModel and TFFMCore docs for details about parameters.
docstr: Predict using the FM model
  
  Parameters
  ----------
  X : {numpy.array, scipy.sparse.csr_matrix}, shape = (n_samples, n_features)
      Samples.
  pred_batch_size : int batch size for prediction (default None)
  
  Returns
  -------
  predictions : array, shape = (n_samples,)
      Returns predicted values.
docstr_tok: ["Predict","using","the","FM","model","Parameters","--","--","--","--","--","X",":","{","numpy.array",",","scipy.sparse.csr_matrix","}",",","shape","=","(","n_samples",",","n_features",")","Samples",".","pred_batch_size",":","int","batch","size","for","prediction","(","default","None",")","Returns","--","--","--","-","predictions",":","array",",","shape","=","(","n_samples",",",")","Returns","predicted","values","."]
code: def predict(self, X, pred_batch_size=None):
      """Predict using the FM model
  
      Parameters
      ----------
      X : {numpy.array, scipy.sparse.csr_matrix}, shape = (n_samples, n_features)
          Samples.
      pred_batch_size : int batch size for prediction (default None)
  
      Returns
      -------
      predictions : array, shape = (n_samples,)
          Returns predicted values.
      """
      predictions = self.decision_function(X, pred_batch_size)
      return predictions
  
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 3 for calling the TFFMRegressor obj's underlying member method code with example usage: obj.predict(X, pred_batch_size) and returns: predictions