code: def _maybe_failing_sample_inputs_sparse_like_fns(
      op_info, device, dtype, requires_grad, layout, **kwargs
  ):
      if torch.cuda.is_available() and layout is not torch.sparse_coo:
          other_device = (
              "cuda" if torch.device(device).type == "cpu" else "cpu"
          )
          if layout is torch.sparse_csr:
              other_layout = torch.sparse_csc
          elif layout is torch.sparse_csc:
              other_layout = torch.sparse_csr
          elif layout is torch.sparse_bsr:
              other_layout = torch.sparse_bsc
          elif layout is torch.sparse_bsc:
              other_layout = torch.sparse_bsr
          else:
              other_layout = torch.strided
  
          blocksize = (
              (1, 1)
              if layout in {torch.sparse_bsr, torch.sparse_bsc}
              else None
          )
  
          yield SampleInput(
              torch.tensor(
                  [[0, 1], [2, 3]], dtype=dtype, device=device
              ).to_sparse(layout=layout, blocksize=blocksize),
              kwargs=dict(device=other_device),
          )
  
          yield SampleInput(
              torch.tensor(
                  [[0, 1], [2, 3]], dtype=dtype, device=device
              ).to_sparse(layout=layout, blocksize=blocksize),
              kwargs=dict(layout=other_layout),
          )
  
imports: import os
  import torch
  from torch.testing import make_tensor
  from torch.testing._internal.opinfo.core import BinaryUfuncInfo, ErrorInput, generate_elementwise_binary_tensors, ReductionOpInfo, sample_inputs_reduction, SampleInput
label_desc: Use image node_id 19 for calling a global function with example usage: _maybe_failing_sample_inputs_sparse_like_fns(op_info, device, dtype, requires_grad, layout) without return types