@property
def attention_weights(self) -> List:
    'List with the attention weights. Each element of the list is a tuple\n        where the first and the second elements are the column and row\n        attention weights respectively\n\n        The shape of the attention weights is:\n\n            - column attention: :math:`(N, H, F, F)`\n\n            - row attention: :math:`(1, H, N, N)`\n\n        where *N* is the batch size, *H* is the number of heads and *F* is the\n        number of features/columns in the dataset\n        '
    attention_weights = []
    for blk in self.saint_blks:
        attention_weights.append((blk.col_attn.attn_weights, blk.row_attn.attn_weights))
    return attention_weights
List with the attention weights. Each element of the list is a tuple
where the first and the second elements are the column and row
attention weights respectively

The shape of the attention weights is:

    - column attention: :math:`(N, H, F, F)`

    - row attention: :math:`(1, H, N, N)`

where *N* is the batch size, *H* is the number of heads and *F* is the
number of features/columns in the dataset
@property
def attention_weights(self) -> List:
    'List with the attention weights. Each element of the list is a tuple\n        where the first and the second elements are the column and row\n        attention weights respectively\n\n        The shape of the attention weights is:\n\n            - column attention: :math:`(N, H, F, F)`\n\n            - row attention: :math:`(1, H, N, N)`\n\n        where *N* is the batch size, *H* is the number of heads and *F* is the\n        number of features/columns in the dataset\n        '
    attention_weights = []
    for blk in self.saint_blks:
        attention_weights.append((blk.col_attn.attn_weights, blk.row_attn.attn_weights))
    return attention_weights