code: def test_load_sparse():
      try:
          import scipy.sparse as sp
      except ImportError:
          return
  
      A = sp.csr_matrix([[1, 2, 0, 5.5], [0, 0, 3, 6.7], [4, 0, 5, 0]])
      fr = h2o.H2OFrame(A)
      assert fr.shape == (3, 4)
      assert fr.as_data_frame(False) == [
          ["C1", "C2", "C3", "C4"],
          ["1", "2", "0", "5.5"],
          ["0", "0", "3", "6.7"],
          ["4", "0", "5", "0.0"],
      ]
  
      A = sp.lil_matrix((1000, 1000))
      A.setdiag(10)
      for i in range(999):
          A[i, i + 1] = -3
          A[i + 1, i] = -2
      fr = h2o.H2OFrame(A)
      assert fr.shape == (1000, 1000)
      means = fr.mean().getrow()
      assert means == [0.008] + [0.005] * 998 + [0.007]
  
      I = [0, 0, 1, 3, 1, 0, 0]
      J = [0, 2, 1, 3, 1, 0, 0]
      V = [1, 1, 1, 1, 1, 1, 1]
      B = sp.coo_matrix((V, (I, J)), shape=(4, 4))
      fr = h2o.H2OFrame(B)
      assert fr.shape == (4, 4)
      assert fr.as_data_frame(False) == [
          ["C1", "C2", "C3", "C4"],
          ["3", "0", "1", "0"],
          ["0", "2", "0", "0"],
          ["0", "0", "0", "0"],
          ["0", "0", "0", "1"],
      ]
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_load_sparse() without return types