def METHOD_NAME(self, theta, data=None, labels=None, nshots=10000):
    """
    Args:
        theta: list or numpy.array with the biases and the angles to be used in the circuit
        nlayers: int number of layers of the varitional circuit ansatz
        data: numpy.array data[page][word]  (this is an array of kets)
        labels: list or numpy.array with the labels of the quantum states to be classified
        nshots: int number of runs of the circuit during the sampling process (default=10000)
    Returns:
        numpy.float32 with the value of the square-loss function
    """
    circ = self.Classifier_circuit(theta)
    Bias = np.array(theta[0 : self.measured_qubits])
    predictions = np.zeros(shape=(len(data), self.measured_qubits))
    for i, text in enumerate(data):
        predictions[i] = self.Predictions(circ, Bias, text, nshots)
    s = self.square_loss(labels, predictions)
    return s