code: def _get_dtype_atol(tensor_type, device: str):
      _seed()
  
      if tensor_type == BlockSparseTensor and "cuda" in device:
          # Upstream GPU blocksparse (Triton op) uses TF32 by default for all internal computations
          # TF32 has the precision of fp16 but the range of fp32
          # See https://blogs.nvidia.com/blog/2020/05/14/tensorfloat-32-precision-format/
          torch.backends.cuda.matmul.allow_tf32 = True
          torch.backends.cudnn.allow_tf32 = True  # type: ignore
          return torch.float32, 1e-1
  
      # Force pytorch to keep its computations as float32 (will default to tf32 with recent cuda and ampere+ GPU)
      torch.backends.cuda.matmul.allow_tf32 = False
      torch.backends.cudnn.allow_tf32 = False  # type: ignore
  
      return torch.float32, 1e-5
  
file_path: xformers/tests/test_sparse_tensors.py
imports: import pytest
  import torch
  import xformers
  from xformers.ops import masked_matmul
  from xformers.sparse import BlockSparseTensor, SparseCSRTensor
label_desc: Use image node_id 5 for calling a global function with example usage: _get_dtype_atol(tensor_type, device) and returns: torch,  int, torch,  int