code: def sample_inputs_sparse_elementwise_binary_operation(
      op_info, device, dtype, requires_grad, layout, **kwargs
  ):
      """Sample inputs for elementwise binary operations on sparse tensors.
  
      The samples include regular, zero-sized, batched, and hybrid
      sparse tensors as well as rhs scalars. All tensors are full tensors.
      """
  
      def _to_sparse(tensor, **kwargs):
          return (
              tensor.detach()
              .to_sparse(**kwargs)
              .requires_grad_(requires_grad)
          )
  
      for sample_input in generate_elementwise_binary_tensors(
          op_info,
          device=device,
          dtype=dtype,
          requires_grad=requires_grad,
          exclude_zero=True,
          **kwargs,
      ):
          lhs, rhs = sample_input.input, sample_input.args[0]
          min_dense_dim = 0
          max_dense_dim = lhs.ndim - 1
          if layout in {
              torch.sparse_csr,
              torch.sparse_csc,
              torch.sparse_bsr,
              torch.sparse_bsc,
          }:
              if lhs.ndim < 2:
                  # sparse compressed tensors sparse_dim must be 2
                  continue
              max_dense_dim = lhs.ndim - 2
  
          for dense_dim in range(min_dense_dim, max_dense_dim + 1):
              if layout in {torch.sparse_bsr, torch.sparse_bsc}:
                  blocksizes = [(1, 1)]
                  if lhs.numel() > 0:
                      blocksizes.append(
                          (
                              lhs.shape[lhs.ndim - 2 - dense_dim],
                              lhs.shape[lhs.ndim - 1 - dense_dim],
                          )
                      )
              else:
                  blocksizes = [None]
              for blocksize in blocksizes:
                  to_sparse_kwargs = dict(
                      layout=layout,
                      dense_dim=dense_dim,
                      blocksize=blocksize,
                  )
                  lhs_sparse = _to_sparse(lhs, **to_sparse_kwargs)
                  rhs_sparse = _to_sparse(rhs, **to_sparse_kwargs)
                  # op(sparse, sparse)
                  yield SampleInput(
                      lhs_sparse,
                      args=(rhs_sparse, *sample_input.args[1:]),
                      kwargs=sample_input.kwargs,
                  )
                  # op(sparse, scalar)
                  yield SampleInput(
                      lhs_sparse,
                      args=(
                          make_tensor(
                              (),
                              dtype=dtype,
                              device=device,
                              requires_grad=requires_grad,
                          ),
                          *sample_input.args[1:],
                      ),
                      kwargs=sample_input.kwargs,
                  )
  
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 11 for calling a global function with example usage: sample_inputs_sparse_elementwise_binary_operation(op_info, device, dtype, requires_grad, layout) and returns: tensor