class_docstr: Labeler for multiscale anchor boxes.
class_docstr_tok: ["Labeler","for","multiscale","anchor","boxes","."]
docstr: Unpacks an array of labels into multiscales labels.
docstr_tok: ["Unpacks","an","array","of","labels","into","multiscales","labels","."]
code: def _unpack_labels(self, labels):
      """Unpacks an array of labels into multiscales labels."""
      labels_unpacked = collections.OrderedDict()
      anchors = self._anchors
      count = 0
      for level in range(anchors.min_level, anchors.max_level + 1):
          feat_size = anchors.feat_sizes[level]
          steps = (
              feat_size["height"]
              * feat_size["width"]
              * anchors.get_anchors_per_location()
          )
          indices = tf.range(count, count + steps)
          count += steps
          labels_unpacked[level] = tf.reshape(
              tf.gather(labels, indices),
              [feat_size["height"], feat_size["width"], -1],
          )
      return labels_unpacked
  
imports: import collections
  import numpy
  import tensorflow
  import utils
  from .anchors_utils import argmax_matcher
  from .anchors_utils import box_list
  from .anchors_utils import faster_rcnn_box_coder
  from .anchors_utils import region_similarity_calculator
  from .anchors_utils import target_assigner
label_desc: Use image node_id 2 for calling the AnchorLabeler obj's underlying member method code with example usage: obj._unpack_labels(labels) and returns: labels_unpacked