code: def forward(self, inputs):
      assert len(inputs) == len(self.in_channels)
  
      laterals = [
          lateral_conv(inputs[i + self.start_level])
          for i, lateral_conv in enumerate(self.lateral_convs)
      ]
  
      used_backbone_levels = len(laterals)
      for i in range(used_backbone_levels - 1, 0, -1):
          prev_shape = laterals[i - 1].shape[2:]
          laterals[i - 1] += F.interpolate(
              laterals[i],
              size=prev_shape,
              mode="nearest",
              align_corners=False,
          )
  
      outs = [
          self.fpn_convs[i](laterals[i])
          for i in range(used_backbone_levels)
      ]
      if self.num_outs > len(outs):
          if not self.add_extra_convs:
              for i in range(self.num_outs - used_backbone_levels):
                  outs.append(F.max_pool2d(outs[-1], 1, stride=2))
          else:
              if self.add_extra_convs == "on_input":
                  extra_source = inputs[self.backbone_end_level - 1]
              elif self.add_extra_convs == "on_lateral":
                  extra_source = laterals[-1]
              elif self.add_extra_convs == "on_output":
                  extra_source = outs[-1]
              else:
                  raise NotImplementedError
              outs.append(
                  self.fpn_convs[used_backbone_levels](extra_source)
              )
              for i in range(used_backbone_levels + 1, self.num_outs):
                  if self.relu_before_extra_convs:
                      outs.append(self.fpn_convs[i](F.relu(outs[-1])))
                  else:
                      outs.append(self.fpn_convs[i](outs[-1]))
      return tuple(outs)
  
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 2 for calling the EfficientFormerFPNNeck obj's underlying member method code with example usage: obj.forward(inputs) and returns: tuple