def get_normalized_probs_scriptable(
        self,
        net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],
        log_probs: bool,
        sample: Optional[Dict[str, Tensor]] = None,
    ):
        """Get normalized probabilities (or log probs) from a net's output."""

        if hasattr(self, "adaptive_softmax") and self.adaptive_softmax is not None:
            if sample is not None:
                assert "target" in sample
                target = sample["target"]
            else:
                target = None
            out = self.adaptive_softmax.get_log_prob(net_output[0], target=target)
            return out.exp_() if not log_probs else out

        logits = net_output[0]
        if log_probs:
            return utils.log_softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
        else:
            return utils.softmax(logits, dim=-1, onnx_trace=self.onnx_trace)
Get normalized probabilities (or log probs) from a net's output.
structured_prompting/fairseq-version/fairseq/fairseq/models/fairseq_decoder.py