code: def _validate_sample_input_elementwise_binary_sparse_mul(sample):
      # NOTE: When fixing a failing sample case, remove the
      #       corresponding if-block
      t_inp, t_args, t_kwargs = sample.input, sample.args, sample.kwargs
      batch_dim = t_inp.dim() - t_inp.dense_dim() - t_inp.sparse_dim()
      layout = t_inp.layout
      dtype = t_inp.dtype
      if (
          layout is torch.sparse_csr
          and batch_dim > 0
          and t_args[0].ndim > 0
      ):
          return ErrorInput(
              sample,
              error_regex=(
                  "coo_to_sparse_csr: conversion from Sparse to SparseCsr for input"
                  " tensors with sparse_dim[(][)]!=2 is not supported"
              ),
          )
      elif layout is torch.sparse_csc and t_args[0].ndim > 0:
          return ErrorInput(
              sample,
              error_regex="Expected result Tensor to be of format CSR",
          )
      elif layout is torch.sparse_bsr and t_args[0].ndim > 0:
          return ErrorInput(
              sample,
              error_regex="empty_sparse_compressed expected sparse compressed [(]non-block[)] tensor layout but got SparseBsr",
          )
      elif layout is torch.sparse_bsc and t_args[0].ndim > 0:
          return ErrorInput(
              sample,
              error_regex="empty_sparse_compressed expected sparse compressed [(]non-block[)] tensor layout but got SparseBsc",
          )
      elif (
          layout is torch.sparse_coo
          and dtype is torch.bool
          and t_args[0].ndim > 0
          and t_inp.is_cpu
          and t_inp.numel() > 0
          and t_inp.dense_dim() > 0
      ):
          return ErrorInput(
              sample,
              error_regex="\"addcmul_cpu_out\" not implemented for 'Bool'",
          )
      elif (
          layout in {torch.sparse_coo, torch.sparse_csr}
          and dtype is torch.bool
          and t_inp._nnz() > 0
          and t_args[0].ndim > 0
          and t_inp.is_cpu
          and t_inp.numel() > 0
      ):
          return ErrorInput(
              sample,
              error_regex="\"mul_out_sparse\" not implemented for 'Bool'",
          )
      elif (
          layout is torch.sparse_csr
          and t_args[0].layout is torch.strided
          and 0 < t_args[0].ndim
          and t_args[0].ndim < t_inp.ndim
      ):
          return ErrorInput(
              sample,
              error_regex="sparse_mask_sparse_csr expects self to be 2D",
          )
      elif layout is torch.sparse_csr and (
          (t_args[0].layout is torch.strided and 0 < t_args[0].ndim)
          or (
              t_args[0].layout is layout
              and t_inp.shape != t_args[0].shape
          )
      ):
          return ErrorInput(
              sample,
              error_regex=(
                  "expects sparse inputs with equal dimensionality, number of sparse dimensions,"
                  " and shape of sparse dimensions"
              ),
          )
      elif (
          layout is torch.sparse_csr
          and t_inp.dense_dim() > 0
          and t_inp._nnz() > 0
          and t_inp.is_cpu
          and dtype is torch.float16
          and t_args[0].ndim > 0
      ):
          return ErrorInput(
              sample,
              error_regex="\"addcmul_cpu_out\" not implemented for 'Half'",
          )
      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 12 for calling a global function with example usage: _validate_sample_input_elementwise_binary_sparse_mul(sample) and returns: sample, ErrorInput, ErrorInput, ErrorInput, ErrorInput, ErrorInput, ErrorInput, ErrorInput, ErrorInput, ErrorInput