code: def _validate_sample_input_sparse_reduction(
      op_info, sample, check_validate=False
  ):
      """Return the specified sample when it is valid and supported by the
      operation. Otherwise, return the sample as ErrorInput instance.
  
      When check_validate is True, the result is validated against
      calling the op on the sample.
      """
      UNSPECIFIED = object()
      if op_info.name == "sum":
          sample = _validate_sample_input_sparse_reduction_sum(sample)
  
      if op_info.name in {"masked.sum"}:
          mask = sample.kwargs.get("mask", UNSPECIFIED)
          if (
              mask not in {None, UNSPECIFIED}
              and mask.ndim > 2
              and mask.layout is torch.strided
              and (mask == 0).any()
          ):
              # TODO: remove this if-block after gh-98495 is fixed.
              sample = ErrorInput(
                  sample,
                  error_regex="Expect the same number of specified elements per batch.",
              )
          elif not sample.kwargs.get("keepdim"):
              sample = ErrorInput(
                  sample,
                  error_type=(AssertionError, RuntimeError),
                  error_regex="reduction operations on (CSR|CSC) tensors with keepdim=False is unsupported",
              )
          elif mask is UNSPECIFIED:
              sample = ErrorInput(
                  sample,
                  error_type=ValueError,
                  error_regex="masked (.*) expects explicit mask for sparse_csr tensor input",
              )
          elif sample.input.ndim > 2:
              sample = ErrorInput(
                  sample,
                  error_regex="crow_indices is supposed to be a vector, but got 3 dimensional tensor.",
              )
  
      if op_info.name in {
          "masked.amax",
          "masked.amin",
          "masked.mean",
          "masked.prod",
      }:
          t_inp = sample.input
          batch_dim = (
              t_inp.dim() - t_inp.dense_dim() - t_inp.sparse_dim()
          )
          mask = sample.kwargs.get("mask")
          if (
              mask is not None
              and mask.ndim > 2
              and mask.layout is torch.strided
              and (mask == 0).any()
          ):
              # TODO: remove this if-block after gh-98495 is fixed.
              sample = ErrorInput(
                  sample,
                  error_regex="Expect the same number of specified elements per batch.",
              )
          elif mask is None:
              sample = ErrorInput(
                  sample,
                  error_type=ValueError,
                  error_regex="masked (.*) expects explicit mask for sparse_csr tensor input",
              )
          elif (
              mask.layout is sample.input.layout
              and mask.ndim > 2
              and op_info.name == "masked.mean"
          ):
              sample = ErrorInput(
                  sample,
                  error_type=TypeError,
                  error_regex=(
                      "where[(][)] received an invalid combination of arguments"
                      " - got [(]Tensor, Tensor, NoneType[)]"
                  ),
              )
          elif not sample.kwargs.get("keepdim"):
              sample = ErrorInput(
                  sample,
                  error_type=(AssertionError, RuntimeError),
                  error_regex="reduction operations on (CSR|CSC) tensors with keepdim=False is unsupported",
              )
          elif (
              sample.input.ndim > 2
              and (sample.kwargs.get("dim") not in {0, 1})
              and mask.ndim > 2
              and mask.layout is not torch.strided
          ):
              if sample.kwargs.get("dim") == (0, -1):
                  sample = ErrorInput(
                      sample,
                      error_regex="tensor dimensionality must be sum of batch, base, and dense dimensionalities",
                  )
              elif op_info.name == "masked.prod":
                  sample = ErrorInput(
                      sample,
                      error_regex="input_dim == 2 INTERNAL ASSERT FAILED at",
                  )
              else:
                  sample = ErrorInput(
                      sample,
                      error_type=AssertionError,
                      error_regex="Sparse CSR tensors are 2D and only support reduction along dim 0 or 1.",
                  )
          elif sample.input.ndim > 2:
              sample = ErrorInput(
                  sample,
                  error_regex="crow_indices is supposed to be a vector, but got 3 dimensional tensor.",
              )
          elif (
              mask.layout is t_inp.layout
              and mask._nnz() != t_inp._nnz()
              and t_inp.dense_dim() > 0
          ):
              sample = ErrorInput(
                  sample,
                  error_regex="Index tensor must have the same number of dimensions as src tensor",
              )
  
      if check_validate:
          _check_validate(op_info, sample)
  
      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 6 for calling a global function with example usage: _validate_sample_input_sparse_reduction(op_info, sample, check_validate) and returns: sample