def status(self):
        docs = {'updated'  : [],
                'committed': [],
                'to_commit': []}

        row = self.db.internal.find_one({'_id': 'last_sync'})

        if row and 'update' in row:
            for id in row['update']:
                docs['updated'].append(self.get(id, deleted=True))

        if row and 'commit' in row:
            for id in row['commit']:
                docs['committed'].append(self.get(id, deleted=True))

        to_commit =  self.get_docs_to_commit()
        for row in to_commit:
            doc = Document(**row)
            docs['to_commit'].append(doc)
        return docs
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)