s used for all data points
        (and all dimensions of the input variable). If `wd` = 0, then the
        covariant weighting matrix for each observation is set to the identity
        matrix (so each dimension of each observation has the same weight).
        If `wd` is a rank-1 array of length m (the dimensionality of the input
        variable), then this vector is the diagonal of the covariant weighting
        matrix for all data points.
        If `wd` is a rank-1 array of length n (the number of data points), then
        the i'th element is the weight for the ith input variable observation
        (single-dimensional only).
        If `wd` is a rank-2 array of shape (m, m), then this is the full
        covariant weighting matrix broadcast to each observation.
        If `wd` is a rank-2 array of shape (m, n), then `wd[:,i]` is the
        diagonal of the covariant weighting matrix for the ith observation.
        If `wd` is a rank-3 array of shape (m, m, n), then `wd[:,:,i]` is the
        full specification of the covariant weighting matrix for each
        observation.
    fix : array_like of ints, optional
        The `fix` argument is the same as ifixx in the class ODR. It is an
        array of integers with the same shape as data.x that determines which
        input observations are treated as fixed. One can use a sequence of
        length m (the dimensionality of the input observations) to fix some
        dimensions for all observations. A value of 0 fixes the observation,
        a value > 0 makes it free.
    meta : dict, optional
        Free-form dictionary for metadata.

    Notes
    -----
    Each argument is attached to the member of the instance of the same name.
    The structures of `x` and `y` are described in the Model class docstring.
    If `y` is an integer, then the Data instance can only be used to fit with
    implicit models where the dimensionality of the response is equal to the
    specified value of `y`.

    The `we` argument weights the effect a deviation in the response variable
    has on the fit. The `wd` argument weights the effect a deviation in the
    input variable has on the fit. To handle multidimensional inputs and
    responses easily, the structure of these arguments has the n'th
    dimensional axis first. These arguments heavily use the structured
    arguments feature of ODRPACK to conveniently and flexibly support all
    options. See the ODRPACK User's Guide for a full explanation of how these
    weights are used in the algorithm. Basically, a higher value of the weight
    for a particular data point makes a deviation at that point more
    detrimental to the fit.

    Nc