code: def random_pattern_from_probability_matrix(dist_matrix, nnz):
      att = torch.zeros_like(dist_matrix, dtype=torch.bool)
      # PyTorch multinomial wrongly doesn't support sampling when number of categories
      # is > 2^24, arguing that it's because it's the max representable consecutive element
      # in fp32 and that the kernels use float32. This is actually not true, and the kernels
      # should work fine if double tensor is passed on CPU. This is a bug that was introduced
      # in https://github.com/pytorch/pytorch/commit/bf04c2ca2f591d98ce57816f0ef0cd20a21bbf66
      # when unifying the checks between CPU and CUDA. For now, just fall-back to numpy
      if dist_matrix.numel() > 2**24:
          dist_matrix = dist_matrix.double()
          dist_matrix /= dist_matrix.sum()
          idxs = np.random.choice(
              dist_matrix.numel(),
              nnz,
              p=dist_matrix.flatten(),
              replace=False,
          )
          idxs = torch.as_tensor(idxs)
      else:
          idxs = torch.multinomial(
              dist_matrix.flatten(), nnz, replacement=False
          )
      att.view(-1)[idxs] = True
      return att
  
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 6 for calling a global function with example usage: random_pattern_from_probability_matrix(dist_matrix, nnz) and returns: att