Compute the pairwise euclidean distance between matrices a and b.

    If the input matrix are in numpy format, they will be uploaded to the
    GPU first which can incur significant time overhead.

    Parameters
    ----------
    a : np.ndarray (n, f)
        first matrix
    b : np.ndarray (m, f)
        second matrix
    to_numpy : boolean, optional (default True)
        If true convert back the GPU array result to numpy format.
    squared : boolean, optional (default False)
        if True, return squared euclidean distance matrix

    Returns
    -------
    c : (n x m) np.ndarray or cupy.ndarray
        pairwise euclidean distance distance matrix

def euclidean_distances(a, b, squared=False, to_numpy=True):
    """
    Compute the pairwise euclidean distance between matrices a and b.

    If the input matrix are in numpy format, they will be uploaded to the
    GPU first which can incur significant time overhead.

    Parameters
    ----------
    a : np.ndarray (n, f)
        first matrix
    b : np.ndarray (m, f)
        second matrix
    to_numpy : boolean, optional (default True)
        If true convert back the GPU array result to numpy format.
    squared : boolean, optional (default False)
        if True, return squared euclidean distance matrix

    Returns
    -------
    c : (n x m) np.ndarray or cupy.ndarray
        pairwise euclidean distance distance matrix
    """

    a, b = to_gpu(a, b)

    a2 = np.sum(np.square(a), 1)
    b2 = np.sum(np.square(b), 1)

    c = -2 * np.dot(a, b.T)
    c += a2[:, None]
    c += b2[None, :]

    if not squared:
        np.sqrt(c, out=c)
    if to_numpy:
        return to_np(c)
    else:
        return c