code: def test_sparse_default_std(self):
      for use_random_std in [True, False]:
          input_tensor = self._create_random_nd_tensor(
              2, size_min=30, size_max=35
          )
          rows, cols = input_tensor.size(0), input_tensor.size(1)
          sparsity = self._random_float(0.1, 0.2)
  
          std = 0.01  # default std
          if use_random_std:
              std = self._random_float(0.01, 0.2)
              init.sparse_(input_tensor, sparsity=sparsity, std=std)
          else:
              init.sparse_(input_tensor, sparsity=sparsity)
  
          for col_idx in range(input_tensor.size(1)):
              column = input_tensor[:, col_idx]
              assert column[column == 0].nelement() >= math.ceil(
                  sparsity * rows
              )
  
          assert self._is_normal(
              input_tensor[input_tensor != 0], 0, std
          )
  
imports: import random
  import unittest
  import math
  import string
  from functools import reduce
  from operator import mul
  from torch.testing._internal.common_utils import TestCase, TEST_SCIPY, skipIfNoLapack, skipIfTorchDynamo, run_tests
  import torch
  import torch.nn.init
  import torch.nn.functional
label_desc: Use image node_id 34 for calling the TestNNInit obj's underlying member method code with example usage: obj.test_sparse_default_std() without return types