code: def _validate_sample_input_sparse_reduction_sum(
      sample, check_validate=False
  ):
      # NOTE: When fixing a failing sample case, remove the
      #       corresponding if-block
      t_inp, t_args, t_kwargs = sample.input, sample.args, sample.kwargs
      dim = t_kwargs.get("dim")
      keepdim = t_kwargs.get("keepdim")
      layout = t_inp.layout
      if isinstance(dim, (int, list, tuple)):
          if layout in {
              torch.sparse_csr,
              torch.sparse_csc,
              torch.sparse_bsr,
              torch.sparse_bsc,
          }:
              if layout in {
                  torch.sparse_csc,
                  torch.sparse_bsr,
                  torch.sparse_bsc,
              }:
                  return ErrorInput(
                      sample,
                      error_regex=(
                          "Currently the only compressed sparse format supported for sum.dim_IntList is CSR, but got layout"
                      ),
                  )
              if (
                  layout in {torch.sparse_csr, torch.sparse_csc}
                  and not keepdim
              ):
                  return ErrorInput(
                      sample,
                      error_regex=(
                          "reduction operations on CSR tensors with keepdim=False is unsupported"
                      ),
                  )
              if t_inp.dim() != 2:
                  return ErrorInput(
                      sample,
                      error_regex=("input_dim == 2 INTERNAL ASSERT"),
                  )
              if layout == torch.sparse_csr:
                  if t_inp.dtype == torch.bool:
                      return ErrorInput(
                          sample,
                          error_regex=(
                              "_sparse_csr_sum_cpu not implemented for 'Bool'"
                          ),
                      )
                  if t_inp.dtype == torch.complex32:
                      return ErrorInput(
                          sample,
                          error_regex=(
                              "_sparse_csr_sum_cuda not implemented for 'ComplexHalf'"
                          ),
                      )
      return sample
  
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 7 for calling a global function with example usage: _validate_sample_input_sparse_reduction_sum(sample, check_validate) and returns: sample, ErrorInput, ErrorInput, ErrorInput, ErrorInput, ErrorInput