def train(self):
        """Train the associated module for one epoch."""
        assert len(self.ds) > 0, "Dataset cannot be empty."
        self.module.resetDerivatives()
        errors = 0
        ponderation = 0.
        shuffledSequences = []
        for seq in self.ds._provideSequences():
            shuffledSequences.append(seq)
        shuffle(shuffledSequences)
        for seq in shuffledSequences:
            e, p = self._calcDerivs(seq)
            errors += e
            ponderation += p
            if not self.batchlearning:
                gradient = self.module.derivs - self.weightdecay * self.module.params
                new = self.descent(gradient, errors)
                if new is not None:
                    self.module.params[:] = new
                self.module.resetDerivatives()

        if self.verbose:
            print("Total error: {z: .12g}".format(z=errors * ponderation))
        if self.batchlearning:
            self.module._setParameters(self.descent(self.module.derivs))
        self.epoch += 1
        self.totalepochs += 1
        return errors / ponderation
dataset/ETHPy150Open pybrain/pybrain/pybrain/supervised/trainers/backprop.py/BackpropTrainer.train/operator `/` -> `*`