orch.LongTensor, torch.FloatTensor)]`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            List of `Tuple(torch.LongTensor, torch.FloatTensor` of length `config.n_layers`, with the first element
            being the previous *buckets* of shape `(batch_size, num_heads, num_hashes, sequence_length)`) and the
            second being the previous *hidden_states* of shape `(batch_size, sequence_length, hidden_size)`).

            Contains precomputed buckets and hidden-states that can be used (see `past_buckets_states` input) to speed
            up sequential decoding.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            TTuple of `torch.FloatTensor` (one for the output of the embeddings and one for the output of each layer)
            of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    N