class_docstr: This module is used in RetinaNet to generate extra layers, P6 and P7 from
  C5 feature.
class_docstr_tok: ["This","module","is","used","in","RetinaNet","to","generate","extra","layers",",","P6","and","P7","from","C5","feature","."]
code: def __init__(self, in_channels, out_channels, in_feature="res5"):
      super().__init__()
      self.num_levels = 2
      self.in_feature = in_feature
      self.p6 = nn.Conv2d(in_channels, out_channels, 3, 2, 1)
      self.p7 = nn.Conv2d(out_channels, out_channels, 3, 2, 1)
      for module in [self.p6, self.p7]:
          weight_init.c2_xavier_fill(module)
  
imports: import math
  import fvcore.nn.weight_init
  import torch
  import torch.nn.functional
  from torch import nn
  from detectron2.layers import Conv2d, ShapeSpec, get_norm
  from .backbone import Backbone
  from .build import BACKBONE_REGISTRY
  from .resnet import build_resnet_backbone
label_desc: Use image node_id 1 to create a new LastLevelP6P7 object from inherited base classes: nn with example: obj = LastLevelP6P7(in_channels, out_channels, in_feature)