docstr: Test conversion of sparse to 2D dense with no labels
  
  dense output: label=1, c=1,t=1,z=1,y=1,x=1
  
      0 0 0
      0 0 0
      0 0 0
docstr_tok: ["Test","conversion","of","sparse","to","2D","dense","with","no","labels","dense","output",":","label=1",",","c=1",",","t=1",",","z=1",",","y=1",",","x=1","0","0","0","0","0","0","0","0","0"]
code: def test_02_01_sparse_to_2d_empty_dense(self):
      """
      Test conversion of sparse to 2D dense with no labels
  
      dense output: label=1, c=1,t=1,z=1,y=1,x=1
  
          0 0 0
          0 0 0
          0 0 0
      """
      sparse_plain = np.array(
          [], dtype=[(SPARSE_FIELD.label.value, "u1")]
      )
      sparse_shaped = np.array(
          [],
          dtype=[
              (SPARSE_FIELD.y.value, "<u2"),
              (SPARSE_FIELD.x.value, "<u2"),
              (SPARSE_FIELD.label.value, "u1"),
          ],
      )
  
      dense_plain, dpi = lib_seg.convert_sparse_to_dense(sparse_plain)
      dense_shaped, dsi = lib_seg.convert_sparse_to_dense(
          sparse_shaped, dense_shape=(1, 1, 1, 3, 3)
      )
  
      dense_plain_expected = np.zeros((1, 1, 1, 1, 1, 1), np.uint8)
      dense_shaped_expected = np.zeros((1, 1, 1, 1, 3, 3), np.uint8)
      indices_expected = [np.array([], dtype=np.uint8)]
  
      np.testing.assert_array_equal(dense_plain, dense_plain_expected)
      np.testing.assert_array_equal(dense_shaped, dense_shaped_expected)
      _check_indices(dpi, indices_expected)
      _check_indices(dsi, indices_expected)
  
imports: import numpy
  import centrosome.outline
  import cellprofiler_library.functions.segmentation
  from cellprofiler_library.functions.segmentation import SPARSE_FIELD
label_desc: Use image node_id 8 for calling the TestSegmentation obj's underlying member method code with example usage: obj.test_02_01_sparse_to_2d_empty_dense() without return types