class_docstr: RetinaFaceFPN
  
  Feature Pyramid Network for RetinaFace.
  
  Args:
      in_channels_list (`List[int]`):
          input channel numbers ofr each FPN layer.
      out_channels (`int`):
          output channel number.
code: def __init__(self, in_channels_list: List[int], out_channels: int):
      super().__init__()
      leaky = 0
      if out_channels <= 64:
          leaky = 0.1
      self.output1 = conv_bn1x1(
          in_channels_list[0], out_channels, stride=1, leaky=leaky
      )
      self.output2 = conv_bn1x1(
          in_channels_list[1], out_channels, stride=1, leaky=leaky
      )
      self.output3 = conv_bn1x1(
          in_channels_list[2], out_channels, stride=1, leaky=leaky
      )
  
      self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky)
      self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky)
  
file_path: towhee/towhee/models/retina_face/retinaface_fpn.py
imports: from typing import List
  import torch
  import torch.nn.functional
  from torch import nn
  from towhee.models.retina_face.utils import conv_bn1x1, conv_bn
label_desc: Use image node_id 1 to create a new RetinaFaceFPN object from inherited base classes: nn with example: obj = RetinaFaceFPN(in_channels_list, out_channels)