code: def __init__(
      self,
      in_channels,
      out_channels,
      num_outs,
      start_level=0,
      end_level=-1,
      add_extra_convs=False,
      extra_convs_on_inputs=False,
      relu_before_extra_convs=False,
  ):
      super().__init__()
  
      assert isinstance(in_channels, list)
      self.in_channels = in_channels
      self.out_channels = out_channels
      self.num_ins = len(in_channels)
      self.num_outs = num_outs
      self.relu_before_extra_convs = relu_before_extra_convs
  
      if end_level == -1:
          self.backbone_end_level = self.num_ins
          assert num_outs >= self.num_ins - start_level
      else:
          self.backbone_end_level = end_level
          assert end_level <= len(in_channels)
          assert num_outs == end_level - start_level
      self.start_level = start_level
      self.end_level = end_level
      self.add_extra_convs = add_extra_convs
      assert isinstance(add_extra_convs, (str, bool))
      if isinstance(add_extra_convs, str):
          assert add_extra_convs in (
              "on_input",
              "on_lateral",
              "on_output",
          )
      elif add_extra_convs:  # True
          if extra_convs_on_inputs:
              self.add_extra_convs = "on_input"
          else:
              self.add_extra_convs = "on_output"
  
      self.lateral_convs = nn.LayerList()
      self.fpn_convs = nn.LayerList()
  
      for i in range(self.start_level, self.backbone_end_level):
          l_conv = nn.Conv2D(in_channels[i], out_channels, 1)
          fpn_conv = nn.Conv2D(out_channels, out_channels, 3, padding=1)
  
          self.lateral_convs.append(l_conv)
          self.fpn_convs.append(fpn_conv)
  
      # add extra conv layers (e.g., RetinaNet)
      extra_levels = (
          num_outs - self.backbone_end_level + self.start_level
      )
      if self.add_extra_convs and extra_levels >= 1:
          for i in range(extra_levels):
              if i == 0 and self.add_extra_convs == "on_input":
                  in_channels = self.in_channels[
                      self.backbone_end_level - 1
                  ]
              else:
                  in_channels = out_channels
              extra_fpn_conv = nn.Conv2D(
                  in_channels, out_channels, 3, stride=2, padding=1
              )
              self.fpn_convs.append(extra_fpn_conv)
      self.init_weight()
  
imports: import numpy
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new EfficientFormerFPNNeck object from inherited base classes: nn with example: obj = EfficientFormerFPNNeck(in_channels, out_channels, num_outs, start_level, end_level, add_extra_convs, extra_convs_on_inputs, relu_before_extra_convs)