class_docstr: Represents a segmentation of an image.
  
  IdentityPrimAutomatic produces three variants of its segmentation
  result. This object contains all three.
  
  There are three formats for segmentation, two of which support
  overlapping objects:
  
  get/set_segmented - Legacy, a single plane of labels that does not
                      support overlapping objects.
  
  (see cellprofiler_library.functions.segmentation._validate_labels)
  
  
  get_labels        - Supports overlapping objects, returns one or more planes
                      along with indices. A typical usage is to perform an
                      operation per-plane as if the objects did not overlap.
  
  (see cellprofiler_library.functions.segmentation.convert_dense_to_label_set)
  
  
  get/set_ijv       - Supports overlapping objects, returns a sparse
                      representation in which the first two columns are the
                      coordinates and the last is the object number. This
                      is efficient for doing things like calculating intensity
                      per-object.
  
  (see cellprofiler_library.functions.segmentation._validate_ijv)
  
  
  You can set one of the types and then get any of the types (except that
  get_segmented will raise an exception if objects overlap).
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code: def center_of_mass(self):
      return center_of_labels_mass(self.segmented, validate=False)
  
imports: import numpy
  from cellprofiler_library.functions.segmentation import convert_dense_to_label_set
  from cellprofiler_library.functions.segmentation import convert_sparse_to_ijv
  from cellprofiler_library.functions.segmentation import indices_from_ijv
  from cellprofiler_library.functions.segmentation import count_from_ijv
  from cellprofiler_library.functions.segmentation import areas_from_ijv
  from cellprofiler_library.functions.segmentation import convert_labels_to_dense
  from cellprofiler_library.functions.segmentation import convert_ijv_to_sparse
  from cellprofiler_library.functions.segmentation import make_rgb_outlines
  from cellprofiler_library.functions.segmentation import find_label_overlaps
  from cellprofiler_library.functions.segmentation import find_ijv_overlaps
  from cellprofiler_library.functions.segmentation import center_of_labels_mass
  from ._segmentation import Segmentation
label_desc: Use image node_id 34 for calling the Objects obj's underlying member method code with example usage: obj.center_of_mass() and returns: center_of_labels_mass