class_docstr: GFLHead
  Args:
      conv_feat (object): Instance of 'FCOSFeat'
      num_classes (int): Number of classes
      fpn_stride (list): The stride of each FPN Layer
      prior_prob (float): Used to set the bias init for the class prediction layer
      loss_class (object): Instance of QualityFocalLoss.
      loss_dfl (object): Instance of DistributionFocalLoss.
      loss_bbox (object): Instance of bbox loss.
      reg_max: Max value of integral set :math: `{0, ..., reg_max}`
              n QFL setting. Default: 16.
docstr: Convert targets by image to targets by feature level.
code: def _images_to_levels(self, target, num_level_anchors):
      """
      Convert targets by image to targets by feature level.
      """
      level_targets = []
      start = 0
      for n in num_level_anchors:
          end = start + n
          level_targets.append(target[:, start:end].squeeze(0))
          start = end
      return level_targets
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.nn.initializer import Normal, Constant
  from ppdet.core.workspace import register
  from ppdet.modeling.bbox_utils import distance2bbox, bbox2distance, batch_distance2bbox
  from ppdet.data.transform.atss_assigner import bbox_overlaps
label_desc: Use image node_id 3 for calling the GFLHead obj's underlying member method code with example usage: obj._images_to_levels(target, num_level_anchors) and returns: level_targets