FloatTensor)` containing various elements depending on configuration ([`MegaConfig`]) and
            inputs:
            - **hidden_states** (`torch.FloatTensor` of shape `(target_sequence_length, batch_size, hidden_size)`) --
              Hidden states from target sequence updated by Mega
            - **self_attn_weights** (*optional*, returned when `output_attentions=True`) `torch.FloatTensor` of shape
              `(batch_size, 1, target_sequence_length, target_sequence_length)` -- The self-attention weights
              corresponding to how each token in the input sequence attends to every other token
            - **cross_attn_weights** (*optional*, returned when `output_attentions=True` and
              `config.add_cross_attention=True`) `torch.FloatTensor` of shape `(batch_size, source_sequence_length,
              target_sequence_length)` -- Pairwise cross-attention weights between every entry in the source sequence
              and target sequence
            - **self_key** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
              sequence_length, config.shared_representation_size)` -- The self-attention key state for use in the next
              step of incremental decoding
            - **self_value** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape `(batch_size,
              sequence_length, config.hidden_size)` -- The self-attention value state for use in the next step of
              incremental decoding
            - **self_ema_state** (*optional*, returned when `use_cache=True`) `torch.FloatTensor` of shape
              `(batch_size, config.ndim)` The incremental EMA state for use in the next step of incremental decoding.
            - **cross_key** (*optional*, returned when `use_cache=True` and `config.is_decoder=True`)
              `torch.FloatTensor` of shape `(batch_size, source_sequence_length, config.shared_representation_size)` --
              The cross-attention key state for use in the next step of incremental decoding
            - **cross_value** (*optional*, returned when `use_cache=True` and `config.is_decoder=True`)
              `torch.FloatTensor` of shape `(batch_size, source_sequence_length, config.hidden_size)` -- The
              cross-attention value state for use in the next step of incremental decoding
        Nr"