code: def __init__(
      self,
      rpn_batch_size_per_im=256,
      rpn_straddle_thresh=0.0,
      rpn_fg_fraction=0.5,
      rpn_positive_overlap=0.7,
      rpn_negative_overlap=0.3,
      use_random=True,
  ):
      super(RPNTargetAssign, self).__init__()
      self.rpn_batch_size_per_im = rpn_batch_size_per_im
      self.rpn_straddle_thresh = rpn_straddle_thresh
      self.rpn_fg_fraction = rpn_fg_fraction
      self.rpn_positive_overlap = rpn_positive_overlap
      self.rpn_negative_overlap = rpn_negative_overlap
      self.use_random = use_random
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from paddle import fluid
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.initializer import Normal
  from paddle.fluid.regularizer import L2Decay
label_desc: Use image node_id 1 to create a new RPNTargetAssign object from inherited base classes: object with example: obj = RPNTargetAssign(rpn_batch_size_per_im, rpn_straddle_thresh, rpn_fg_fraction, rpn_positive_overlap, rpn_negative_overlap, use_random)