parameters: (self, groundtruth_labels, match)
return_statement: return match.gather_based_on_match(
              groundtruth_labels,
              unmatched_value=self._unmatched_cls_target,
              ignored_value=self._unmatched_cls_target,
          )
docstring: Create classification targets for each anchor.
  
          Assign a classification target of for each anchor to the matching
          groundtruth label that is provided by match.  Anchors that are not matched
          to anything are given the target self._unmatched_cls_target
  
          Args:
            groundtruth_labels:  a tensor of shape [num_gt_boxes, d_1, ... d_k]
              with labels for each of the ground_truth boxes. The subshape
              [d_1, ... d_k] can be empty (corresponding to scalar labels).
            match: a matcher.Match object that provides a matching between anchors
              and groundtruth boxes.
  
          Returns:
            a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k], where the
            subshape [d_1, ..., d_k] is compatible with groundtruth_labels which has
            shape [num_gt_boxes, d_1, d_2, ... d_k].
docstring_summary: Create classification targets for each anchor.
docstring_tokens: Create classification targets for each anchor .
function: def _create_classification_targets(self, groundtruth_labels, match):
          """Create classification targets for each anchor.
  
          Assign a classification target of for each anchor to the matching
          groundtruth label that is provided by match.  Anchors that are not matched
          to anything are given the target self._unmatched_cls_target
  
          Args:
            groundtruth_labels:  a tensor of shape [num_gt_boxes, d_1, ... d_k]
              with labels for each of the ground_truth boxes. The subshape
              [d_1, ... d_k] can be empty (corresponding to scalar labels).
            match: a matcher.Match object that provides a matching between anchors
              and groundtruth boxes.
  
          Returns:
            a float32 tensor with shape [num_anchors, d_1, d_2 ... d_k], where the
            subshape [d_1, ..., d_k] is compatible with groundtruth_labels which has
            shape [num_gt_boxes, d_1, d_2, ... d_k].
          """
          return match.gather_based_on_match(
              groundtruth_labels,
              unmatched_value=self._unmatched_cls_target,
              ignored_value=self._unmatched_cls_target,
          )
function_tokens: def _create_classification_targets ( self , groundtruth_labels , match ) : return match . gather_based_on_match ( groundtruth_labels , unmatched_value = self . _unmatched_cls_target , ignored_value = self . _unmatched_cls_target , )