{{alias}}( order, uplo, N, α, x, sx, A, lda )
    Performs the symmetric rank 1 operation `A = α*x*x^T + A` where `α` is a
    scalar, `x` is an `N` element vector, and `A` is an `N` by `N` symmetric
    matrix.

    Indexing is relative to the first index. To introduce an offset, use typed
    array views.

    If `N` is equal to `0` or `α` is equal to `0`, the function returns `A`
    unchanged.

    Parameters
    ----------
    order: string
        Row-major (C-style) or column-major (Fortran-style) order. Must be
        either 'row-major' or 'column-major'.

    uplo: string
        Specifies whether to reference the upper or lower triangular part of
        `A`. Must be either 'upper' or 'lower'.

    N: integer
        Number of elements along each dimension of `A`.

    α: number
        Scalar constant.

    x: Float32Array
        Input vector.

    sx: integer
        Index increment for `x`.

    A: Float32Array
        Input matrix.

    lda: integer
        Stride of the first dimension of `A` (a.k.a., leading dimension of the
        matrix `A`).

    Returns
    -------
    A: Float32Array
        Input matrix.

    Examples
    --------
    > var x = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
    > var A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 0.0, 2.0 ] );
    > {{alias}}( 'row-major', 'upper', 2, 1.0, x, 1, A, 2 )
    <Float32Array>[ 2.0, 3.0, 0.0, 3.0 ]


{{alias}}.ndarray( uplo, N, α, x, sx, ox, A, sa1, sa2, oa )
    Performs the symmetric rank 1 operation `A = α*x*x^T + A`, using alternative
    indexing semantics and where `α` is a scalar, `x` is an `N` element vector,
    and `A` is an `N` by `N` symmetric matrix.

    While typed array views mandate a view offset based on the underlying
    buffer, the offset parameters support indexing semantics based on starting
    indices.

    Parameters
    ----------
    uplo: string
        Specifies whether to reference the upper or lower triangular part of
        `A`. Must be either 'upper' or 'lower'.

    N: integer
        Number of elements along each dimension of `A`.

    α: number
        Scalar constant.

    x: Float32Array
        Input vector.

    sx: integer
        Index increment for `x`.

    ox: integer
        Starting index for `x`.

    A: Float32Array
        Input matrix.

    sa1: integer
        Stride of the first dimension of `A`.

    sa2: integer
        Stride of the second dimension of `A`.

    oa: integer
        Starting index for `A`.

    Returns
    -------
    A: Float32Array
        Input matrix.

    Examples
    --------
    > var x = new {{alias:@stdlib/array/float32}}( [ 1.0, 1.0 ] );
    > var A = new {{alias:@stdlib/array/float32}}( [ 1.0, 2.0, 0.0, 2.0 ] );
    > {{alias}}.ndarray( 'upper', 2, 1.0, x, 1, 0, A, 2, 1, 0 )
    <Float32Array>[ 2.0, 3.0, 0.0, 3.0 ]

    See Also
    --------