code: def __init__(
      self,
      batch_size_per_im=256,
      fg_fraction=0.5,
      positive_overlap=0.7,
      negative_overlap=0.3,
      ignore_thresh=-1.0,
      use_random=True,
      assign_on_cpu=False,
  ):
      super(RPNTargetAssign, self).__init__()
      self.batch_size_per_im = batch_size_per_im
      self.fg_fraction = fg_fraction
      self.positive_overlap = positive_overlap
      self.negative_overlap = negative_overlap
      self.ignore_thresh = ignore_thresh
      self.use_random = use_random
      self.assign_on_cpu = assign_on_cpu
  
imports: import sys
  import paddle
  from ppdet.core.workspace import register, serializable
  from .target import rpn_anchor_target, generate_proposal_target, generate_mask_target, libra_generate_proposal_target
  import numpy
label_desc: Use image node_id 1 to create a new RPNTargetAssign object from inherited base classes: object with example: obj = RPNTargetAssign(batch_size_per_im, fg_fraction, positive_overlap, negative_overlap, ignore_thresh, use_random, assign_on_cpu)