torchrec/distributed/batched_embedding_kernel.py [459:481]:
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                use_cpu=device is None
                or device.type == "cpu"
                or not torch.cuda.is_available(),
            )
        )

        self.init_parameters()

    @property
    def emb_module(
        self,
    ) -> DenseTableBatchedEmbeddingBagsCodegen:
        return self._emb_module

    def named_parameters(
        self, prefix: str = "", recurse: bool = True
    ) -> Iterator[Tuple[str, nn.Parameter]]:
        combined_key = "/".join(
            [config.name for config in self._config.embedding_tables]
        )
        yield append_prefix(prefix, f"{combined_key}.weight"), cast(
            nn.Parameter, self._emb_module.weights
        )
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torchrec/distributed/batched_embedding_kernel.py [713:735]:
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                use_cpu=device is None
                or device.type == "cpu"
                or not torch.cuda.is_available(),
            )
        )

        self.init_parameters()

    @property
    def emb_module(
        self,
    ) -> DenseTableBatchedEmbeddingBagsCodegen:
        return self._emb_module

    def named_parameters(
        self, prefix: str = "", recurse: bool = True
    ) -> Iterator[Tuple[str, nn.Parameter]]:
        combined_key = "/".join(
            [config.name for config in self._config.embedding_tables]
        )
        yield append_prefix(prefix, f"{combined_key}.weight"), cast(
            nn.Parameter, self._emb_module.weights
        )
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