def add(self, elem, replace=False):
        """Add a requirement.

        :param elem: a string or :class:`Requirement` instance.
        :param replace: if `True`, packages in the set with the same name will
            be removed first.
        """
        if isinstance(elem, text_type):
            elem = Requirement.coerce(elem)
        if replace and elem in self:
            self.remove(elem)
        super(Requirements, self).add(elem)
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)