code: def forward(self, inputs, L=4):
      if not (1 <= L <= 4):
          raise ValueError(
              "the model pruning factor `L` should be 1 <= L <= 4"
          )
  
      x_00_output = self.x_00(inputs)
      x_10_output = self.x_10(self.pool0(x_00_output))
      x_10_up_sample = self.up_10_to_01(x_10_output)
      x_01_output = self.x_01(
          torch.cat([x_00_output, x_10_up_sample], 1)
      )
      nestnet_output_1 = self.final_1x1_x01(x_01_output)
  
      if L == 1:
          return nestnet_output_1
  
      x_20_output = self.x_20(self.pool1(x_10_output))
      x_20_up_sample = self.up_20_to_11(x_20_output)
      x_11_output = self.x_11(
          torch.cat([x_10_output, x_20_up_sample], 1)
      )
      x_11_up_sample = self.up_11_to_02(x_11_output)
      x_02_output = self.x_02(
          torch.cat([x_00_output, x_01_output, x_11_up_sample], 1)
      )
      nestnet_output_2 = self.final_1x1_x01(x_02_output)
  
      if L == 2:
          if self.deep_supervision:
              # return the average of output layers
              return (nestnet_output_1 + nestnet_output_2) / 2
          else:
              return nestnet_output_2
  
      x_30_output = self.x_30(self.pool2(x_20_output))
      x_30_up_sample = self.up_30_to_21(x_30_output)
      x_21_output = self.x_21(
          torch.cat([x_20_output, x_30_up_sample], 1)
      )
      x_21_up_sample = self.up_21_to_12(x_21_output)
      x_12_output = self.x_12(
          torch.cat([x_10_output, x_11_output, x_21_up_sample], 1)
      )
      x_12_up_sample = self.up_12_to_03(x_12_output)
      x_03_output = self.x_03(
          torch.cat(
              [x_00_output, x_01_output, x_02_output, x_12_up_sample], 1
          )
      )
      nestnet_output_3 = self.final_1x1_x01(x_03_output)
  
      if L == 3:
          # return the average of output layers
          if self.deep_supervision:
              return (
                  nestnet_output_1 + nestnet_output_2 + nestnet_output_3
              ) / 3
          else:
              return nestnet_output_3
  
      x_40_output = self.x_40(self.pool3(x_30_output))
      x_40_up_sample = self.up_40_to_31(x_40_output)
      x_31_output = self.x_31(
          torch.cat([x_30_output, x_40_up_sample], 1)
      )
      x_31_up_sample = self.up_31_to_22(x_31_output)
      x_22_output = self.x_22(
          torch.cat([x_20_output, x_21_output, x_31_up_sample], 1)
      )
      x_22_up_sample = self.up_22_to_13(x_22_output)
      x_13_output = self.x_13(
          torch.cat(
              [x_10_output, x_11_output, x_12_output, x_22_up_sample], 1
          )
      )
      x_13_up_sample = self.up_13_to_04(x_13_output)
      x_04_output = self.x_04(
          torch.cat(
              [
                  x_00_output,
                  x_01_output,
                  x_02_output,
                  x_03_output,
                  x_13_up_sample,
              ],
              1,
          )
      )
      nestnet_output_4 = self.final_1x1_x01(x_04_output)
  
      if L == 4:
          if self.deep_supervision:
              # return the average of output layers
              return (
                  nestnet_output_1
                  + nestnet_output_2
                  + nestnet_output_3
                  + nestnet_output_4
              ) / 4
          else:
              return nestnet_output_4
  
file_path: pywick/pywick/models/segmentation/testnets/unet_plus_plus.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the NestNet obj's underlying member method code with example usage: obj.forward(inputs, L) and returns: nestnet_output_1, unknown, nestnet_output_2, unknown, nestnet_output_3, unknown, nestnet_output_4