def install(h, pip, groups, save, save_dev, packages):
    """Install a pip or bower package."""
    if groups:
        groups = [text_type.strip(group) for group in groups.split(",")]
    else:
        groups = h.requirements.keys()

    if not packages:
        for group in groups:
            if group not in h.requirements:
                warning("{} not in requirements".format(group))
                continue
            install = (h.install_bower if group.startswith("bower")
                       else h.install_pip)
            for requirement in h.requirements[group]:
                install(str(requirement), save=False, save_dev=False)
    if pip:
        for package in packages:
            h.install_pip(package, save=save, save_dev=save_dev)
    else:
        for package in packages:
            h.install_bower(package, save=save, save_dev=save_dev)
def nonlinear_1d(module, grad_input, grad_output):
    delta_out = module.y[: int(module.y.shape[0] / 2)] - module.y[int(module.y.shape[0] / 2):]

    delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
    dup0 = [2] + [1 for i in delta_in.shape[1:]]
    # handles numerical instabilities where delta_in is very small by
    # just taking the gradient in those cases
    grads = [None for _ in grad_input]
    grads[0] = torch.where(torch.abs(delta_in.repeat(dup0)) < 1e-6, grad_input[0],
                           grad_output[0] * (delta_out / delta_in).repeat(dup0))
    return tuple(grads)