code: def _maybe_failing_sample_inputs_sparse_elementwise_binary_mul(
      op_info, device, dtype, requires_grad, layout, **kwargs
  ):
      """Generator of samples that are known to fail or that were failing in past."""
      # NOTE: When fixing a failing case, remove the Exception comment
      #       but keep the `yield sample` statement.
  
      blocksize = (
          (1, 1)
          if layout in {torch.sparse_bsr, torch.sparse_bsc}
          else None
      )
      regular = torch.tensor(
          [[1, 2], [3, 4]], device=device, dtype=dtype
      ).to_sparse(layout=layout, dense_dim=0, blocksize=blocksize)
      batch = torch.tensor(
          [[[1, 2], [3, 4]], [[4, 5], [6, 7]]],
          device=device,
          dtype=dtype,
      ).to_sparse(layout=layout, dense_dim=0, blocksize=blocksize)
      hybrid = torch.tensor(
          [[[1], [2]], [[3], [4]]], device=device, dtype=dtype
      ).to_sparse(layout=layout, dense_dim=1, blocksize=blocksize)
  
      if layout is torch.sparse_csr:
          # RuntimeError: crow_indices is supposed to be a vector, but got 2 dimensional tensor
          yield SampleInput(batch, args=(batch,))
          # RuntimeError: Only tensors with two sparse dimensions can be
          # converted to the SparseCsr layout, got self with 3 sparse
          # dimensions.
          yield SampleInput(
              torch.zeros_like(hybrid).requires_grad_(requires_grad),
              args=(
                  torch.zeros_like(hybrid).requires_grad_(
                      requires_grad
                  ),
              ),
          )
          if dtype is torch.complex32:
              # RuntimeError: "mul_out_sparse" not implemented for 'ComplexHalf'
              yield SampleInput(regular, args=(regular,))
          if dtype is torch.bool and regular.is_cpu:
              # RuntimeError: "mul_out_sparse" not implemented for 'Bool'
              yield SampleInput(regular, args=(regular,))
      if layout is torch.sparse_csc:
          # RuntimeError: Expected result Tensor to be of format CSR
          yield SampleInput(regular, args=(regular,))
      if layout is torch.sparse_bsr:
          # RuntimeError: empty_sparse_compressed expected sparse compressed (non-block) tensor layout but got SparseBsr
          yield SampleInput(regular, args=(regular,))
      if layout is torch.sparse_bsc:
          # RuntimeError: empty_sparse_compressed expected sparse compressed (non-block) tensor layout but got SparseBsc
          yield SampleInput(regular, args=(regular,))
      if layout is torch.sparse_coo:
          if dtype is torch.complex32:
              # RuntimeError: "mul_out_sparse" not implemented for 'ComplexHalf'
              yield SampleInput(regular, args=(regular,))
          if dtype is torch.bool and regular.is_cpu:
              # RuntimeError: "mul_out_sparse" not implemented for 'Bool'
              yield SampleInput(regular, args=(regular,))
          if dtype in {torch.bool, torch.float16} and regular.is_cpu:
              # RuntimeError: "addcmul_cpu_out" not implemented for '(Bool|Half)'
              yield SampleInput(hybrid, args=(hybrid,))
  
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 13 for calling a global function with example usage: _maybe_failing_sample_inputs_sparse_elementwise_binary_mul(op_info, device, dtype, requires_grad, layout) without return types