code: def convert_labels_to_dense(labels, validate=True):
      """
      Convert a 'labels' matrix (e.g. scipy.ndimage.label) to 'dense' matrix
      """
      if validate:
          _validate_labels(labels)
  
      typed_labels = downsample_labels(labels, validate=False)
  
      if labels.ndim == 3:
          expand_axes = (
              DENSE_AXIS.label_idx.value,
              DENSE_AXIS.c.value,
              DENSE_AXIS.t.value,
          )
      else:
          expand_axes = (
              DENSE_AXIS.label_idx.value,
              DENSE_AXIS.c.value,
              DENSE_AXIS.t.value,
              DENSE_AXIS.z.value,
          )
  
      return np.expand_dims(typed_labels, axis=expand_axes)
  
imports: from enum import Enum
  import numpy
  from numpy.random.mtrand import RandomState
  import scipy.sparse
  import centrosome.index
label_desc: Use image node_id 15 for calling a global function with example usage: convert_labels_to_dense(labels, validate) and returns: np