First element of model_output contains all token embeddings     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)  