torchrec/distributed/embeddingbag.py [307:333]:
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            feature_names.extend(
                sharding.id_score_list_feature_names()
                if self._is_weighted
                else sharding.id_list_feature_names()
            )
            self._feature_splits.append(
                len(
                    sharding.id_score_list_feature_names()
                    if self._is_weighted
                    else sharding.id_list_feature_names()
                )
            )

        if feature_names == input_feature_names:
            self._has_features_permute = False
        else:
            for f in feature_names:
                self._features_order.append(input_feature_names.index(f))
            self.register_buffer(
                "_features_order_tensor",
                torch.tensor(self._features_order, device=device, dtype=torch.int32),
            )

    def _create_lookups(
        self,
        fused_params: Optional[Dict[str, Any]],
    ) -> None:
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torchrec/distributed/quant_embeddingbag.py [107:133]:
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            feature_names.extend(
                sharding.id_score_list_feature_names()
                if self._is_weighted
                else sharding.id_list_feature_names()
            )
            self._feature_splits.append(
                len(
                    sharding.id_score_list_feature_names()
                    if self._is_weighted
                    else sharding.id_list_feature_names()
                )
            )

        if feature_names == input_feature_names:
            self._has_features_permute = False
        else:
            for f in feature_names:
                self._features_order.append(input_feature_names.index(f))
            self.register_buffer(
                "_features_order_tensor",
                torch.tensor(self._features_order, device=device, dtype=torch.int32),
            )

    def _create_lookups(
        self,
        fused_params: Optional[Dict[str, Any]],
    ) -> None:
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