code: def test_module_buffer(tensor_type, device):
      N, C, H, W = 8, 2, 64, 64
      dtype = torch.float32
      sparsity = 0.8
  
      shape0 = (N, C, H, W)
      if tensor_type != BlockSparseTensor:
          shape0 = shape0[1:]
  
      a_sparse = _create_tensor(
          tensor_type,
          device,
          dtype=dtype,
          shape=shape0,
          sparsity=sparsity,
      )
      b_sparse = _create_tensor(
          tensor_type,
          device,
          dtype=dtype,
          shape=shape0,
          sparsity=sparsity,
      )
  
      module = torch.nn.Module()
      # test that register_buffer works
      module.register_buffer("a_sparse", a_sparse)
  
      assert module.a_sparse is a_sparse
  
      module.to(device)
      assert module.a_sparse.device == torch.device(device)
  
      state_dict = module.state_dict()
      assert "a_sparse" in state_dict
      assert torch.equal(a_sparse.to(device), state_dict["a_sparse"])
  
      module.load_state_dict(state_dict)
  
      module.load_state_dict({"a_sparse": b_sparse})
      assert torch.equal(module.a_sparse, b_sparse.to(device))
  
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 11 for calling a global function with example usage: test_module_buffer(tensor_type, device) without return types