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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def add(self, predicted, target):
 """Computes the confusion matrix of K x K size where K is no of classes Args: predicted (tensor):
 Can be an N x K tensor of predicted scores obtained from the model for N examples and K classes or an N-tensor of integer values between 0 and K-1. target (tensor):
 Can be a N-tensor of integer values assumed to be integer values between 0 and K-1 or N x K tensor, where targets are assumed to be provided as one-hot vectors """

        predicted = predicted.cpu().numpy()
        target = target.cpu().numpy()

        assert predicted.shape[0] == target.shape[0], \
            'number of targets and predicted outputs do not match'

        if np.ndim(predicted) != 1:
            assert predicted.shape[1] == self.k, \
                'number of predictions does not match size of confusion matrix'
            predicted = np.argmax(predicted, 1)
        else:
            assert (predicted.max() < self.k) and (predicted.min() >= 0), \
                'predicted values are not between 1 and k'

        onehot_target = np.ndim(target) != 1
        if onehot_target:
            assert target.shape[1] == self.k, \
                'Onehot target does not match size of confusion matrix'
            assert (target >= 0).all() and (target <= 1).all(), \
                'in one-hot encoding, target values should be 0 or 1'
            assert (target.sum(1) == 1).all(), \
                'multi-label setting is not supported'
            target = np.argmax(target, 1)
        else:
            assert (predicted.max() < self.k) and (predicted.min() >= 0), \
                'predicted values are not between 0 and k-1'

        # hack for bincounting 2 arrays together
        x = predicted + self.k * target
        bincount_2d = np.bincount(x.astype(np.int32),
                                  minlength=self.k ** 2)
        assert bincount_2d.size == self.k ** 2
        conf = bincount_2d.reshape((self.k, self.k))

        self.conf += conf