class_docstr: Superclass for all masks that can be computed inside the kernel using a callable object.
  
  Attributes:
    _shape: Shape of the 2-dim mask: (q_seq_len, kv_seq_len).
    offset: Offset of q start wrt kv. A positive offset shifts the bottom
      triangle upward, a negative one shifts it downward. A negative offset
      makes the first 'offset' rows of the attention matrix all 0s which leads
      to undefined softmax.
    q_sequence: Indices of Q sequence.
      q_sequence is reused across __getitem__ calls which is important for
      compile-time performance.
    mask_function: Function used by the SplashAttention kernel to compute the
      mask rather than loading it.
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code: def __hash__(self):
      raise NotImplementedError()
  
imports: from __future__ import annotations
  import dataclasses
  from typing import Any, Callable, Sequence, Tuple
  import numpy
label_desc: Use image node_id 5 for calling the _ComputableMask obj's underlying member method code with example usage: obj.__hash__() without return types