def _parallel_pairwise(X, Y, func, n_jobs, **kwds):
    if n_jobs < 0:
        n_jobs = max(cpu_count() + 1 + n_jobs, 1)

    if Y is None:
        Y = X

    if n_jobs == 1:
        # Special case to avoid picklability checks in delayed
        return func(X, Y, **kwds)

    # TODO: in some cases, backend='threading' may be appropriate
    fd = delayed(func)
    ret = Parallel(n_jobs=n_jobs, verbose=0)(
        fd(X, Y[s], **kwds)
        for s in gen_even_slices(Y.shape[0], n_jobs))

    return np.hstack(ret)
Returns True if this query returns Keys, False if it returns Entities.
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/metrics/pairwise.py/_parallel_pairwise--crossover with--dataset/ETHPy150Open CollabQ/CollabQ/.google_appengine/google/appengine/ext/gql/__init__.py/GQL.is_keys_only