def predict(self, X):
        xp = np if self.gpu < 0 else cuda.cupy

        data_size = len(X)

        results = None
        for i in six.moves.range(0, data_size, self.batch_size):
            end = i + self.batch_size
            x1 = X[i: end if data_size < end else data_size]
            x2 = Variable(xp.asarray(x1))
            pred = F.softmax(self.model.predictor(x2, train=False))
            if results is None:
                results = cuda.to_cpu(pred.data)
            else:
                results = xp.concatenate((results, cuda.to_cpu(pred.data)), axis=0)

        return results
dataset/ETHPy150Open bizreach/common-ml/commonml/sklearn/rnn_estimator.py/RnnEstimator.predict/swapped operands of `<`