def aggregate(loss, weights=None, mode='mean'):
    """Aggregates an element- or item-wise loss to a scalar loss.

    Parameters
    ----------
    loss : Theano tensor
        The loss expression to aggregate.
    weights : Theano tensor, optional
        The weights for each element or item, must be broadcastable to
        the same shape as `loss` if given. If omitted, all elements will
        be weighted the same.
    mode : {'mean', 'sum', 'normalized_sum'}
        Whether to aggregate by averaging, by summing or by summing and
        dividing by the total weights (which requires `weights` to be given).

    Returns
    -------
    Theano scalar
        A scalar loss expression suitable for differentiation.

    Notes
    -----
    By supplying binary weights (i.e., only using values 0 and 1), this
    function can also be used for masking out particular entries in the
    loss expression. Note that masked entries still need to be valid
    values, not-a-numbers (NaNs) will propagate through.

    When applied to batch-wise loss expressions, setting `mode` to
    ``'normalized_sum'`` ensures that the loss per batch is of a similar
    magnitude, independent of associated weights. However, it means that
    a given datapoint contributes more to the loss when it shares a batch
    with low-weighted or masked datapoints than with high-weighted ones.
    """
    if weights is not None:
        loss = loss * weights
    if mode == 'mean':
        return loss.mean()
    elif mode == 'sum':
        return loss.sum()
    elif mode == 'normalized_sum':
        if weights is None:
            raise ValueError("require weights for mode='normalized_sum'")
        return loss.sum() / weights.sum()
    else:
        raise ValueError("mode must be 'mean', 'sum' or 'normalized_sum', "
                         "got %r" % mode)