def install_pip(self, package, save=True, save_dev=False):
        """Installs a pip package.

        :param save: if `True`, pins the package to the Hydrogen requirements
            YAML file.
        :param save_dev: if `True`, pins the package as a development
            dependency to the Hydrogen requirements YAML file.
        :param return: a **single** :class:`Requirement` object, representing
            the installed version of the given package.
        """
        requirement = Requirement.coerce(package)
        click.echo("pip install " + requirement.package)
        cmd = envoy.run("pip install {}".format(str(requirement)))
        if cmd.status_code == 0:
            installed_packages = get_installed_pypackages()
            package = installed_packages[requirement.package]
            requirement.version = "=={}".format(package.version)
            if save:
                self.requirements["all"].add(requirement)
            if save_dev:
                self.requirements["dev"].add(requirement)
            if save or save_dev:
                self.requirements.save()
            return requirement
        else:
            fatal(cmd.std_err)
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)