docstr: Predict the output from input samples.
  
  The predict method will generates output predictions
  for the input samples.
  
  Parameters
  ----------
  x_train : ndarray
      the x_test is the test dataset or input samples
  
  Returns
  -------
  predict : ndarray shape(TestSize,OutputSize)
      Numpy array(s) of predictions.
docstr_tok: ["Predict","the","output","from","input","samples",".","The","predict","method","will","generates","output","predictions","for","the","input","samples",".","Parameters","--","--","--","--","--","x_train",":","ndarray","the","x_test","is","the","test","dataset","or","input","samples","Returns","--","--","--","-","predict",":","ndarray","shape","(","TestSize",",","OutputSize",")","Numpy","array","(","s",")","of","predictions","."]
code: def predict(self, x_test):
      """Predict the output from input samples.
  
      The predict method will generates output predictions
      for the input samples.
  
      Parameters
      ----------
      x_train : ndarray
          the x_test is the test dataset or input samples
  
      Returns
      -------
      predict : ndarray shape(TestSize,OutputSize)
          Numpy array(s) of predictions.
  
      """
      return self.model.predict(x_test)
  
file_path: dipy/dipy/nn/model.py
imports: from dipy.utils.optpkg import optional_package
label_desc: Use image node_id 5 for calling the MultipleLayerPercepton obj's underlying member method code with example usage: obj.predict(x_test) and returns: self