docstr: Test conversion of 2D dense where some labels overlap and some do not
  to label_set
  
  dense input: label=2, c=1,t=1,z=1,y=4,x=5
  
      1 1 2 0 0
      1 1 2 0 0
      0 0 0 4 4
      0 0 0 0 0
      ---
      0 0 0 0 0
      0 0 3 0 0
      0 0 0 0 5
      0 0 0 0 5
docstr_tok: ["Test","conversion","of","2D","dense","where","some","labels","overlap","and","some","do","not","to","label_set","dense","input",":","label=2",",","c=1",",","t=1",",","z=1",",","y=4",",","x=5","1","1","2","0","0","1","1","2","0","0","0","0","0","4","4","0","0","0","0","0","--","-","0","0","0","0","0","0","0","3","0","0","0","0","0","0","5","0","0","0","0","5"]
code: def test_03_01_2d_dense_some_overlap_to_label_set(self):
      """
      Test conversion of 2D dense where some labels overlap and some do not
      to label_set
  
      dense input: label=2, c=1,t=1,z=1,y=4,x=5
  
          1 1 2 0 0
          1 1 2 0 0
          0 0 0 4 4
          0 0 0 0 0
          ---
          0 0 0 0 0
          0 0 3 0 0
          0 0 0 0 5
          0 0 0 0 5
      """
      dense = np.array(
          [
              [
                  1,
                  1,
                  2,
                  0,
                  0,
                  1,
                  1,
                  2,
                  0,
                  0,
                  0,
                  0,
                  0,
                  4,
                  4,
                  0,
                  0,
                  0,
                  0,
                  0,
              ],
              [
                  0,
                  0,
                  0,
                  0,
                  0,
                  0,
                  0,
                  3,
                  0,
                  0,
                  0,
                  0,
                  0,
                  0,
                  5,
                  0,
                  0,
                  0,
                  0,
                  5,
              ],
          ],
          np.uint8,
      ).reshape((2, 1, 1, 1, 4, 5))
  
      indices = lib_seg.indices_from_dense(dense)
  
      label_set = lib_seg.convert_dense_to_label_set(dense)
  
      for i in range(len(label_set)):
          np.testing.assert_array_equal(
              label_set[i][0], np.squeeze(dense[i])
          )
          np.testing.assert_array_equal(label_set[i][1], indices[i])
  
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 15 for calling the TestSegmentation obj's underlying member method code with example usage: obj.test_03_01_2d_dense_some_overlap_to_label_set() without return types