class_docstr: Holds an attention mask, along with a couple of helpers and attributes.
  
  .. note: this is an additive mask, meaning that coefficients which should be computed hold the '0.' value,
      and coefficients which should be skipped hold the '-inf' value. Any other value is possible if the purpose
      is to bias the attention computation for instance
  
  .. note: the attention mask dimensions are expected to be `[batch, to_sequence, from_sequence]`,
      `[to_sequence, from_sequence]`, or anything broadcastable in between
class_docstr_tok: ["Holds","an","attention","mask",",","along","with","a","couple","of","helpers","and","attributes",".","..","note",":","this","is","an","additive","mask",",","meaning","that","coefficients","which","should","be","computed","hold","the","'","0",".","'","value",",","and","coefficients","which","should","be","skipped","hold","the","'-inf","'","value",".","Any","other","value","is","possible","if","the","purpose","is","to","bias","the","attention","computation","for","instance","..","note",":","the","attention","mask","dimensions","are","expected","to","be","`","[","batch",",","to_sequence",",","from_sequence","]","`",",","`","[","to_sequence",",","from_sequence","]","`",",","or","anything","broadcastable","in","between"]
code: def __repr__(self):
      return f"AttentionMask - causal {self.is_causal} - mask " + str(
          self.values
      )
  
file_path: xformers/xformers/components/attention/attention_mask.py
imports: from typing import Optional, Type, TypeVar
  import torch
label_desc: Use image node_id 7 for calling the AttentionMask obj's underlying member method code with example usage: obj.__repr__() and returns: unknown