def make_generation_fast_(self, **kwargs):
        """
        Legacy entry point to optimize model for faster generation.
        Prefer prepare_for_inference_.
        """
        if self._is_generation_fast:
            return  # only apply once
        self._is_generation_fast = True

        # remove weight norm from all modules in the network
        def apply_remove_weight_norm(module):
            try:
                nn.utils.remove_weight_norm(module)
            except (AttributeError, ValueError):  # this module didn't have weight norm
                return

        self.apply(apply_remove_weight_norm)

        def apply_make_generation_fast_(module, prefix):
            if len(prefix) > 0:
                prefix += "."

            base_func = BaseFairseqModel.make_generation_fast_
            for n, m in module.named_modules():
                if (
                    m != self
                    and hasattr(m, "make_generation_fast_")
                    # don't call this implementation again, e.g., if
                    # children modules also inherit from BaseFairseqModel
                    and m.make_generation_fast_.__func__ is not base_func
                ):
                    name = prefix + n
                    m.make_generation_fast_(name=name, **kwargs)

        apply_make_generation_fast_(self, "")

        def train(mode=True):
            if mode:
                raise RuntimeError("cannot train after make_generation_fast")

        # this model should no longer be used for training
        self.eval()
        self.train = train

        Legacy entry point to optimize model for faster generation.
        Prefer prepare_for_inference_.
        
structured_prompting/fairseq-version/fairseq/fairseq/models/fairseq_model.py