class_docstr: a standard bbox assigner based on max IoU, use ppdet's label_box 
  as backend.
  Args:
      positive_overlap (float): threshold for defining positive samples 
      negative_overlap (float): threshold for denining negative samples
      allow_low_quality (bool): whether to lower IoU thr if a GT poorly
          overlaps with candidate bboxes
code: def __init__(
      self, positive_overlap, negative_overlap, allow_low_quality=True
  ):
      self.positive_overlap = positive_overlap
      self.negative_overlap = negative_overlap
      self.allow_low_quality = allow_low_quality
  
file_path: PaddleDetection/ppdet/modeling/assigners/max_iou_assigner.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from ppdet.core.workspace import register
  from ppdet.modeling.proposal_generator.target import label_box
label_desc: Use image node_id 1 to create a new MaxIoUAssigner object from inherited base classes: object with example: obj = MaxIoUAssigner(positive_overlap, negative_overlap, allow_low_quality)