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: Generate pixel centers of a single stage feature map.
  Args:
      featmap_size: height and width of the feature map
      stride: down sample stride of the feature map
  Returns:
      y and x of the center points
code: def get_single_level_center_point(
      self, featmap_size, stride, cell_offset=0
  ):
      """
      Generate pixel centers of a single stage feature map.
      Args:
          featmap_size: height and width of the feature map
          stride: down sample stride of the feature map
      Returns:
          y and x of the center points
      """
      h, w = featmap_size
      x_range = (
          paddle.arange(w, dtype="float32") + cell_offset
      ) * stride
      y_range = (
          paddle.arange(h, dtype="float32") + cell_offset
      ) * stride
      y, x = paddle.meshgrid(y_range, x_range)
      y = y.flatten()
      x = x.flatten()
      return y, x
  
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 6 for calling the GFLHead obj's underlying member method code with example usage: obj.get_single_level_center_point(featmap_size, stride, cell_offset) and returns: y,  x