red as a decoder.
            past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
                Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up
                decoding. If `past_key_values` are used, the user can optionally input only the last
                `decoder_input_ids` (those that don't have their past key value states given to this model) of shape
                `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used
                in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.

            head_mask (`numpy.ndarray` of shape `({0})`, `optional):
                Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

                - 1 indicates the head is **not masked**,
                - 0 indicates the head is **masked**.

            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            output_attentions (`bool`) :
                output attention probabirities.
            output_router_tuple:
                output experts router logits and expert id.
        Returns:
            Tuple[torch.Tensor[num_groups, tokens_per_group, hidden_dim],...]
        )