TF: Faster to way to set one column/all but one column of a tensor to -inf
in `_force_token_id_to_be_generated` we have much simpler torch code:
```python
scores[:, [x for if x != token_id]] = -float("inf")
```
Is it possible to make the TF Code simpler? TF doesn't support assignment, but maybe to and from `numpy` could be faster. Would definitely be simpler.

```python
    @staticmethod
    def _force_token_id_to_be_generated(scores, token_id) -> None:
        """force one of token_ids to be generated by setting prob of all other tokens to 0 (logprob=-float("inf"))"""
        output_list = []

        # Is there a better way to do  in TF?
        bs, vocab_size = scores.shape
        inf_tensor = tf.convert_to_tensor([-float("inf")] * bs, dtype=scores.dtype)
        for x in range(vocab_size):
            if x != token_id:
                output_list.append(inf_tensor)
            else:
                output_list.append(scores[:, x])
        scores = tf.stack(output_list, axis=1, name="scores")
        assert scores.shape == (bs, vocab_size)
        return scores
```





Solution: https://stackoverflow.com/questions/64575346/tensorflow-set-column-of-tensor-to-infinity