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.
        """
        return merged_embeddingbag_adagrad(
            self.weights,
            indices,
            offsets,
            self.pooling_mode,
            self.include_last_offset,
            self.adagrad_args,
        )

        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