def METHOD_NAME(x, r, tensor=True):
    """
    Evaluate a polynomial specified by its roots at points x.
    If `r` is of length `N`, this function returns the value
    .. math:: p(x) = \\prod_{n=1}^{N} (x - r_n)
    The parameter `x` is converted to an array only if it is a tuple or a
    list, otherwise it is treated as a scalar. In either case, either `x`
    or its elements must support multiplication and addition both with
    themselves and with the elements of `r`.
    If `r` is a 1-D array, then `p(x)` will have the same shape as `x`.  If `r`
    is multidimensional, then the shape of the result depends on the value of
    `tensor`. If `tensor` is ``True`` the shape will be r.shape[1:] + x.shape;
    that is, each polynomial is evaluated at every value of `x`. If `tensor` is
    ``False``, the shape will be r.shape[1:]; that is, each polynomial is
    evaluated only for the corresponding broadcast value of `x`. Note that
    scalars have shape (,).
    Parameters
    ----------
    x : array_like, compatible object
        If `x` is a list or tuple, it is converted to an ndarray, otherwise
        it is left unchanged and treated as a scalar. In either case, `x`
        or its elements must support addition and multiplication with
        with themselves and with the elements of `r`.
    r : array_like
        Array of roots. If `r` is multidimensional the first index is the
        root index, while the remaining indices enumerate multiple
        polynomials. For instance, in the two dimensional case the roots
        of each polynomial may be thought of as stored in the columns of `r`.
    tensor : boolean, optional
        If True, the shape of the roots array is extended with ones on the
        right, one for each dimension of `x`. Scalars have dimension 0 for this
        action. The result is that every column of coefficients in `r` is
        evaluated for every element of `x`. If False, `x` is broadcast over the
        columns of `r` for the evaluation.  This keyword is useful when `r` is
        multidimensional. The default value is True.
    Returns
    -------
    values : ndarray, compatible object
        The shape of the returned array is described above.
    See Also
    --------
    numpy.polynomial.polynomial.polyvalfroomroots
    """
    r = cupy.array(r, ndmin=1, copy=False)
    if r.dtype.char in '?bBhHiIlLqQpP':
        r = r.astype(cupy.double)
    if isinstance(x, (tuple, list)):
        x = cupy.asarray(x)
    if isinstance(x, cupy.ndarray):
        if tensor:
            r = r.reshape(r.shape + (1,)*x.ndim)
        elif x.ndim >= r.ndim:
            raise ValueError("x.ndim must be < r.ndim when tensor == False")
    return cupy.prod(x - r, axis=0)