def forward(self,
                query: torch.Tensor,
                key: torch.Tensor,
                value: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        r"""Projects the input sequences using in-proj layers.
        Args:
            query, key, value (Tensors): sequence to be projected
        Shape:
            - query, key, value: :math:`(S, N, E)`
            - Output: :math:`(S, N, E)`
            where S is the sequence length, N is the batch size, and E is the embedding dimension.
        """
        return self.query_proj(query), self.key_proj(key), self.value_proj(value)
Projects the input sequences using in-proj layers.
        Args:
            query, key, value (Tensors): sequence to be projected
        Shape:
            - query, key, value: :math:`(S, N, E)`
            - Output: :math:`(S, N, E)`
            where S is the sequence length, N is the batch size, and E is the embedding dimension.
        
analyze/convnexts4nd/src/models/baselines/transformer.py