def forward(self, indices, offsets):
        r"""
        Args:
            indices (List[Tensor]):
                See https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag.forward
            offsets (List[Tensor]):
                See https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag.forward
        Returns:
            List[Tensor] output shape of `(batch_size, embedding_dim)` which length = num of tables.
        """
        assert self.dense
        return merged_embeddingbag(
            self.weights, indices, offsets, self.pooling_mode, self.include_last_offset
        )

        Args:
            indices (List[Tensor]):
                See https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag.forward
            offsets (List[Tensor]):
                See https://pytorch.org/docs/stable/generated/torch.nn.EmbeddingBag.html#torch.nn.EmbeddingBag.forward
        Returns:
            List[Tensor] output shape of `(batch_size, embedding_dim)` which length = num of tables.
        
intel_extension_for_pytorch/nn/modules/merged_embeddingbag.py