code: def test_csr_addition_self():
      row_count = 10
      random.seed(1234)
  
      x = torch.ones(1, 5)
      for i in range(row_count - 1):
          if random.random() > 0.75:
              x = torch.cat([x, torch.ones(1, 5)])
          else:
              x = torch.cat([x, torch.zeros(1, 5)])
      dense_x = x.clone()
      cx = SparseTensor(x)
  
      assert torch.all(dense_x == cx.to_dense())
  
      cx.add(cx)
      assert torch.all(dense_x + dense_x == cx.to_dense())
  
imports: import torch
  import random
  from deepspeed.runtime.sparse_tensor import SparseTensor
label_desc: Use image node_id 1 for calling a global function with example usage: test_csr_addition_self() without return types