class_docstr: Sequential self-attention layer.
  Each token will attend to its previous fixed number of steps.
  Note that attention doesn't include the current step itself.
class_docstr_tok: ["Sequential","self-attention","layer",".","Each","token","will","attend","to","its","previous","fixed","number","of","steps",".","Note","that","attention","does","n't","include","the","current","step","itself","."]
code: def get_cache_size(self):
      return self.adaptive_span.get_cache_size()
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq.modules.layer_norm import LayerNorm
  from .adaptive_span_attention import AdaptiveSpan
label_desc: Use image node_id 3 for calling the SeqAttention obj's underlying member method code with example usage: obj.get_cache_size() and returns: self