torchrec/distributed/batched_embedding_kernel.py [292:306]:
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    def init_parameters(self) -> None:
        # initialize embedding weights
        assert len(self._num_embeddings) == len(self.split_embedding_weights())
        for (rows, emb_dim, weight_init_min, weight_init_max, param) in zip(
            self._local_rows,
            self._local_cols,
            self._weight_init_mins,
            self._weight_init_maxs,
            self.split_embedding_weights(),
        ):
            assert param.shape == (rows, emb_dim)
            param.data.uniform_(
                weight_init_min,
                weight_init_max,
            )
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torchrec/distributed/batched_embedding_kernel.py [537:551]:
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    def init_parameters(self) -> None:
        # initialize embedding weights
        assert len(self._num_embeddings) == len(self.split_embedding_weights())
        for (rows, emb_dim, weight_init_min, weight_init_max, param) in zip(
            self._local_rows,
            self._local_cols,
            self._weight_init_mins,
            self._weight_init_maxs,
            self.split_embedding_weights(),
        ):
            assert param.shape == (rows, emb_dim)
            param.data.uniform_(
                weight_init_min,
                weight_init_max,
            )
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