code: def pattern_to_layout(
      mask: torch.Tensor, block_size: int
  ) -> torch.Tensor:
      r"""
      Given a mask pattern and blocksize, return the corresponding layout
      which makes sure that all the positives in the mask are covered
      """
      assert (
          mask.ndim >= 2
      ), "We're expecting [Heads, Seq, Seq] or [Seq, Seq]"
      _should_squeeze = False
  
      if mask.ndim == 2:
          mask = mask.unsqueeze(0)
          _should_squeeze = True
  
      assert (
          mask.shape[1] % block_size == 0
          and mask.shape[2] % block_size == 0
      ), "We're only handling masks divisible by block_size"
  
      # Now mark the mask
      layout = torch.nn.functional.max_pool2d(
          mask.to(torch.float),
          kernel_size=block_size,
          stride=block_size,
      )
      layout = layout.to(torch.long)
  
      if _should_squeeze:
          layout.squeeze_(0)
  
      return layout
  
imports: import math
  from typing import List
  import numpy
  import torch
  from xformers.components.attention.sparsity_config import BigBirdSparsityConfig, BSLongformerSparsityConfig, FixedSparsityConfig, VariableSparsityConfig
label_desc: Use image node_id 20 for calling a global function with example usage: pattern_to_layout(mask, block_size) and returns: layout