def load(self, requirements_file=None):
        """Load or reload requirements from a requirements.txt file.

        :param requirements_file: if not given, the filename used from
            initialization will be read again.
        """
        if requirements_file is None:
            requirements_file = self.filename
            if requirements_file is None:
                raise ValueError("no filename provided")
        elif isinstance(requirements_file, text_type):
            requirements_file = Path(requirements_file)
        self.clear()
        with requirements_file.open() as f:
            self.loads(f.read())

        if isinstance(requirements_file, (text_type, Path)):
            self.filename = requirements_file
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)