er 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_attn_heads,
            decoder_sequence_length, decoder_sequence_length)`.

            Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the
            self-attention heads.
        ngram_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_attn_heads,
            decoder_sequence_length, decoder_sequence_length)`.

            Attentions weights of the predict stream of the decoder, after the attention softmax, used to compute the
            weighted average in the
        cross_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_attn_heads,
            encoder_sequence_length, decoder_sequence_length)`.

            Attentions weights of the cross-attention layer of the decoder, after the attention softmax, used to
            compute the weighted average in the
    NrC