code: def forward(self, Placeholder):
      resfcn256_Conv2d_transpose_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=512
      )
      resfcn256_Conv2d_transpose_1_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_1_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_1_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=256
      )
      resfcn256_Conv2d_transpose_2_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_2_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_2_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=256
      )
      resfcn256_Conv2d_transpose_3_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_3_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_3_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=256
      )
      resfcn256_Conv2d_transpose_4_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_4_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_4_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=128
      )
      resfcn256_Conv2d_transpose_5_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_5_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_5_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=128
      )
      resfcn256_Conv2d_transpose_6_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_6_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_6_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=128
      )
      resfcn256_Conv2d_transpose_7_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_7_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_7_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=64
      )
      resfcn256_Conv2d_transpose_8_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_8_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_8_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=64
      )
      resfcn256_Conv2d_transpose_9_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_9_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_9_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=64
      )
      resfcn256_Conv2d_transpose_10_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_10_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_10_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=32
      )
      resfcn256_Conv2d_transpose_11_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_11_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_11_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=32
      )
      resfcn256_Conv2d_transpose_12_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_12_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=2
      )
      resfcn256_Conv2d_transpose_12_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=16
      )
      resfcn256_Conv2d_transpose_13_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_13_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_13_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=16
      )
      resfcn256_Conv2d_transpose_14_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_14_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_14_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=3
      )
      resfcn256_Conv2d_transpose_15_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_15_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_15_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=3
      )
      resfcn256_Conv2d_transpose_16_mul_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_16_mul_1_y = paddle.full(
          dtype="int32", shape=[1], fill_value=1
      )
      resfcn256_Conv2d_transpose_16_stack_3 = paddle.full(
          dtype="int32", shape=[1], fill_value=3
      )
      conv2d_transpose_0 = paddle.transpose(
          x=Placeholder, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv_Conv2D = self.conv0(conv2d_transpose_0)
      resfcn256_Conv_BatchNorm_FusedBatchNorm = self.bn0(
          resfcn256_Conv_Conv2D
      )
      resfcn256_Conv_Relu = self.relu0(
          resfcn256_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_shortcut_Conv2D = self.conv1(
          resfcn256_Conv_Relu
      )
      resfcn256_resBlock_Conv_Conv2D = self.conv2(resfcn256_Conv_Relu)
      resfcn256_resBlock_Conv_BatchNorm_FusedBatchNorm = self.bn1(
          resfcn256_resBlock_Conv_Conv2D
      )
      resfcn256_resBlock_Conv_Relu = self.relu1(
          resfcn256_resBlock_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_Conv_1_Conv2D = self.conv3(
          resfcn256_resBlock_Conv_Relu
      )
      resfcn256_resBlock_Conv_1_BatchNorm_FusedBatchNorm = self.bn2(
          resfcn256_resBlock_Conv_1_Conv2D
      )
      resfcn256_resBlock_Conv_1_Relu = self.relu2(
          resfcn256_resBlock_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_Conv_2_Conv2D = self.conv4(
          resfcn256_resBlock_Conv_1_Relu
      )
      resfcn256_resBlock_add = paddle.add(
          x=resfcn256_resBlock_Conv_2_Conv2D,
          y=resfcn256_resBlock_shortcut_Conv2D,
      )
      resfcn256_resBlock_BatchNorm_FusedBatchNorm = self.bn3(
          resfcn256_resBlock_add
      )
      resfcn256_resBlock_Relu = self.relu3(
          resfcn256_resBlock_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_1_Conv_Conv2D = self.conv5(
          resfcn256_resBlock_Relu
      )
      resfcn256_resBlock_1_Conv_BatchNorm_FusedBatchNorm = self.bn4(
          resfcn256_resBlock_1_Conv_Conv2D
      )
      resfcn256_resBlock_1_Conv_Relu = self.relu4(
          resfcn256_resBlock_1_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_1_Conv_1_Conv2D = self.conv6(
          resfcn256_resBlock_1_Conv_Relu
      )
      resfcn256_resBlock_1_Conv_1_BatchNorm_FusedBatchNorm = self.bn5(
          resfcn256_resBlock_1_Conv_1_Conv2D
      )
      resfcn256_resBlock_1_Conv_1_Relu = self.relu5(
          resfcn256_resBlock_1_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_1_Conv_2_Conv2D = self.conv7(
          resfcn256_resBlock_1_Conv_1_Relu
      )
      resfcn256_resBlock_1_add = paddle.add(
          x=resfcn256_resBlock_1_Conv_2_Conv2D,
          y=resfcn256_resBlock_Relu,
      )
      resfcn256_resBlock_1_BatchNorm_FusedBatchNorm = self.bn6(
          resfcn256_resBlock_1_add
      )
      resfcn256_resBlock_1_Relu = self.relu6(
          resfcn256_resBlock_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_2_shortcut_Conv2D = self.conv8(
          resfcn256_resBlock_1_Relu
      )
      resfcn256_resBlock_2_Conv_Conv2D = self.conv9(
          resfcn256_resBlock_1_Relu
      )
      resfcn256_resBlock_2_Conv_BatchNorm_FusedBatchNorm = self.bn7(
          resfcn256_resBlock_2_Conv_Conv2D
      )
      resfcn256_resBlock_2_Conv_Relu = self.relu7(
          resfcn256_resBlock_2_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_2_Conv_1_Conv2D = self.conv10(
          resfcn256_resBlock_2_Conv_Relu
      )
      resfcn256_resBlock_2_Conv_1_BatchNorm_FusedBatchNorm = self.bn8(
          resfcn256_resBlock_2_Conv_1_Conv2D
      )
      resfcn256_resBlock_2_Conv_1_Relu = self.relu8(
          resfcn256_resBlock_2_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_2_Conv_2_Conv2D = self.conv11(
          resfcn256_resBlock_2_Conv_1_Relu
      )
      resfcn256_resBlock_2_add = paddle.add(
          x=resfcn256_resBlock_2_Conv_2_Conv2D,
          y=resfcn256_resBlock_2_shortcut_Conv2D,
      )
      resfcn256_resBlock_2_BatchNorm_FusedBatchNorm = self.bn9(
          resfcn256_resBlock_2_add
      )
      resfcn256_resBlock_2_Relu = self.relu9(
          resfcn256_resBlock_2_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_3_Conv_Conv2D = self.conv12(
          resfcn256_resBlock_2_Relu
      )
      resfcn256_resBlock_3_Conv_BatchNorm_FusedBatchNorm = self.bn10(
          resfcn256_resBlock_3_Conv_Conv2D
      )
      resfcn256_resBlock_3_Conv_Relu = self.relu10(
          resfcn256_resBlock_3_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_3_Conv_1_Conv2D = self.conv13(
          resfcn256_resBlock_3_Conv_Relu
      )
      resfcn256_resBlock_3_Conv_1_BatchNorm_FusedBatchNorm = self.bn11(
          resfcn256_resBlock_3_Conv_1_Conv2D
      )
      resfcn256_resBlock_3_Conv_1_Relu = self.relu11(
          resfcn256_resBlock_3_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_3_Conv_2_Conv2D = self.conv14(
          resfcn256_resBlock_3_Conv_1_Relu
      )
      resfcn256_resBlock_3_add = paddle.add(
          x=resfcn256_resBlock_3_Conv_2_Conv2D,
          y=resfcn256_resBlock_2_Relu,
      )
      resfcn256_resBlock_3_BatchNorm_FusedBatchNorm = self.bn12(
          resfcn256_resBlock_3_add
      )
      resfcn256_resBlock_3_Relu = self.relu12(
          resfcn256_resBlock_3_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_4_shortcut_Conv2D = self.conv15(
          resfcn256_resBlock_3_Relu
      )
      resfcn256_resBlock_4_Conv_Conv2D = self.conv16(
          resfcn256_resBlock_3_Relu
      )
      resfcn256_resBlock_4_Conv_BatchNorm_FusedBatchNorm = self.bn13(
          resfcn256_resBlock_4_Conv_Conv2D
      )
      resfcn256_resBlock_4_Conv_Relu = self.relu13(
          resfcn256_resBlock_4_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_4_Conv_1_Conv2D = self.conv17(
          resfcn256_resBlock_4_Conv_Relu
      )
      resfcn256_resBlock_4_Conv_1_BatchNorm_FusedBatchNorm = self.bn14(
          resfcn256_resBlock_4_Conv_1_Conv2D
      )
      resfcn256_resBlock_4_Conv_1_Relu = self.relu14(
          resfcn256_resBlock_4_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_4_Conv_2_Conv2D = self.conv18(
          resfcn256_resBlock_4_Conv_1_Relu
      )
      resfcn256_resBlock_4_add = paddle.add(
          x=resfcn256_resBlock_4_Conv_2_Conv2D,
          y=resfcn256_resBlock_4_shortcut_Conv2D,
      )
      resfcn256_resBlock_4_BatchNorm_FusedBatchNorm = self.bn15(
          resfcn256_resBlock_4_add
      )
      resfcn256_resBlock_4_Relu = self.relu15(
          resfcn256_resBlock_4_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_5_Conv_Conv2D = self.conv19(
          resfcn256_resBlock_4_Relu
      )
      resfcn256_resBlock_5_Conv_BatchNorm_FusedBatchNorm = self.bn16(
          resfcn256_resBlock_5_Conv_Conv2D
      )
      resfcn256_resBlock_5_Conv_Relu = self.relu16(
          resfcn256_resBlock_5_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_5_Conv_1_Conv2D = self.conv20(
          resfcn256_resBlock_5_Conv_Relu
      )
      resfcn256_resBlock_5_Conv_1_BatchNorm_FusedBatchNorm = self.bn17(
          resfcn256_resBlock_5_Conv_1_Conv2D
      )
      resfcn256_resBlock_5_Conv_1_Relu = self.relu17(
          resfcn256_resBlock_5_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_5_Conv_2_Conv2D = self.conv21(
          resfcn256_resBlock_5_Conv_1_Relu
      )
      resfcn256_resBlock_5_add = paddle.add(
          x=resfcn256_resBlock_5_Conv_2_Conv2D,
          y=resfcn256_resBlock_4_Relu,
      )
      resfcn256_resBlock_5_BatchNorm_FusedBatchNorm = self.bn18(
          resfcn256_resBlock_5_add
      )
      resfcn256_resBlock_5_Relu = self.relu18(
          resfcn256_resBlock_5_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_6_shortcut_Conv2D = self.conv22(
          resfcn256_resBlock_5_Relu
      )
      resfcn256_resBlock_6_Conv_Conv2D = self.conv23(
          resfcn256_resBlock_5_Relu
      )
      resfcn256_resBlock_6_Conv_BatchNorm_FusedBatchNorm = self.bn19(
          resfcn256_resBlock_6_Conv_Conv2D
      )
      resfcn256_resBlock_6_Conv_Relu = self.relu19(
          resfcn256_resBlock_6_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_6_Conv_1_Conv2D = self.conv24(
          resfcn256_resBlock_6_Conv_Relu
      )
      resfcn256_resBlock_6_Conv_1_BatchNorm_FusedBatchNorm = self.bn20(
          resfcn256_resBlock_6_Conv_1_Conv2D
      )
      resfcn256_resBlock_6_Conv_1_Relu = self.relu20(
          resfcn256_resBlock_6_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_6_Conv_2_Conv2D = self.conv25(
          resfcn256_resBlock_6_Conv_1_Relu
      )
      resfcn256_resBlock_6_add = paddle.add(
          x=resfcn256_resBlock_6_Conv_2_Conv2D,
          y=resfcn256_resBlock_6_shortcut_Conv2D,
      )
      resfcn256_resBlock_6_BatchNorm_FusedBatchNorm = self.bn21(
          resfcn256_resBlock_6_add
      )
      resfcn256_resBlock_6_Relu = self.relu21(
          resfcn256_resBlock_6_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_7_Conv_Conv2D = self.conv26(
          resfcn256_resBlock_6_Relu
      )
      resfcn256_resBlock_7_Conv_BatchNorm_FusedBatchNorm = self.bn22(
          resfcn256_resBlock_7_Conv_Conv2D
      )
      resfcn256_resBlock_7_Conv_Relu = self.relu22(
          resfcn256_resBlock_7_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_7_Conv_1_Conv2D = self.conv27(
          resfcn256_resBlock_7_Conv_Relu
      )
      resfcn256_resBlock_7_Conv_1_BatchNorm_FusedBatchNorm = self.bn23(
          resfcn256_resBlock_7_Conv_1_Conv2D
      )
      resfcn256_resBlock_7_Conv_1_Relu = self.relu23(
          resfcn256_resBlock_7_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_7_Conv_2_Conv2D = self.conv28(
          resfcn256_resBlock_7_Conv_1_Relu
      )
      resfcn256_resBlock_7_add = paddle.add(
          x=resfcn256_resBlock_7_Conv_2_Conv2D,
          y=resfcn256_resBlock_6_Relu,
      )
      resfcn256_resBlock_7_BatchNorm_FusedBatchNorm = self.bn24(
          resfcn256_resBlock_7_add
      )
      resfcn256_resBlock_7_Relu = self.relu24(
          resfcn256_resBlock_7_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_8_shortcut_Conv2D = self.conv29(
          resfcn256_resBlock_7_Relu
      )
      resfcn256_resBlock_8_Conv_Conv2D = self.conv30(
          resfcn256_resBlock_7_Relu
      )
      resfcn256_resBlock_8_Conv_BatchNorm_FusedBatchNorm = self.bn25(
          resfcn256_resBlock_8_Conv_Conv2D
      )
      resfcn256_resBlock_8_Conv_Relu = self.relu25(
          resfcn256_resBlock_8_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_8_Conv_1_Conv2D = self.conv31(
          resfcn256_resBlock_8_Conv_Relu
      )
      resfcn256_resBlock_8_Conv_1_BatchNorm_FusedBatchNorm = self.bn26(
          resfcn256_resBlock_8_Conv_1_Conv2D
      )
      resfcn256_resBlock_8_Conv_1_Relu = self.relu26(
          resfcn256_resBlock_8_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_8_Conv_2_Conv2D = self.conv32(
          resfcn256_resBlock_8_Conv_1_Relu
      )
      resfcn256_resBlock_8_add = paddle.add(
          x=resfcn256_resBlock_8_Conv_2_Conv2D,
          y=resfcn256_resBlock_8_shortcut_Conv2D,
      )
      resfcn256_resBlock_8_BatchNorm_FusedBatchNorm = self.bn27(
          resfcn256_resBlock_8_add
      )
      resfcn256_resBlock_8_Relu = self.relu27(
          resfcn256_resBlock_8_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_9_Conv_Conv2D = self.conv33(
          resfcn256_resBlock_8_Relu
      )
      resfcn256_resBlock_9_Conv_BatchNorm_FusedBatchNorm = self.bn28(
          resfcn256_resBlock_9_Conv_Conv2D
      )
      resfcn256_resBlock_9_Conv_Relu = self.relu28(
          resfcn256_resBlock_9_Conv_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_9_Conv_1_Conv2D = self.conv34(
          resfcn256_resBlock_9_Conv_Relu
      )
      resfcn256_resBlock_9_Conv_1_BatchNorm_FusedBatchNorm = self.bn29(
          resfcn256_resBlock_9_Conv_1_Conv2D
      )
      resfcn256_resBlock_9_Conv_1_Relu = self.relu29(
          resfcn256_resBlock_9_Conv_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_resBlock_9_Conv_2_Conv2D = self.conv35(
          resfcn256_resBlock_9_Conv_1_Relu
      )
      resfcn256_resBlock_9_add = paddle.add(
          x=resfcn256_resBlock_9_Conv_2_Conv2D,
          y=resfcn256_resBlock_8_Relu,
      )
      resfcn256_resBlock_9_BatchNorm_FusedBatchNorm = self.bn30(
          resfcn256_resBlock_9_add
      )
      resfcn256_resBlock_9_BatchNorm_FusedBatchNorm = paddle.transpose(
          x=resfcn256_resBlock_9_BatchNorm_FusedBatchNorm,
          perm=[0, 2, 3, 1],
      )
      resfcn256_resBlock_9_Relu = self.relu30(
          resfcn256_resBlock_9_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_Shape = paddle.shape(
          input=resfcn256_resBlock_9_Relu
      )
      resfcn256_Conv2d_transpose_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_strided_slice_1,
          y=resfcn256_Conv2d_transpose_mul_y,
      )
      resfcn256_Conv2d_transpose_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_strided_slice_2,
          y=resfcn256_Conv2d_transpose_mul_1_y,
      )
      resfcn256_Conv2d_transpose_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_strided_slice,
              resfcn256_Conv2d_transpose_mul,
              resfcn256_Conv2d_transpose_mul_1,
              resfcn256_Conv2d_transpose_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_0 = paddle.transpose(
          x=resfcn256_resBlock_9_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_conv2d_transpose_conv36_weight = (
          self.resfcn256_Conv2d_transpose_conv2d_transpose_conv36_weight
      )
      resfcn256_Conv2d_transpose_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_0,
          weight=resfcn256_Conv2d_transpose_conv2d_transpose_conv36_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[8, 8],
      )
      resfcn256_Conv2d_transpose_BatchNorm_FusedBatchNorm = self.bn31(
          resfcn256_Conv2d_transpose_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_Relu = self.relu31(
          resfcn256_Conv2d_transpose_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_1_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_Relu
      )
      resfcn256_Conv2d_transpose_1_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_1_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_1_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_1_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_1_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_1_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_1_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_1_strided_slice_1,
          y=resfcn256_Conv2d_transpose_1_mul_y,
      )
      resfcn256_Conv2d_transpose_1_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_1_strided_slice_2,
          y=resfcn256_Conv2d_transpose_1_mul_1_y,
      )
      resfcn256_Conv2d_transpose_1_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_1_strided_slice,
              resfcn256_Conv2d_transpose_1_mul,
              resfcn256_Conv2d_transpose_1_mul_1,
              resfcn256_Conv2d_transpose_1_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_1_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_1_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_1 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_1_conv2d_transpose_conv37_weight = (
          self.resfcn256_Conv2d_transpose_1_conv2d_transpose_conv37_weight
      )
      resfcn256_Conv2d_transpose_1_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_1,
          weight=resfcn256_Conv2d_transpose_1_conv2d_transpose_conv37_weight,
          stride=[2, 2],
          dilation=[1, 1],
          padding="SAME",
          output_size=[16, 16],
      )
      resfcn256_Conv2d_transpose_1_BatchNorm_FusedBatchNorm = self.bn32(
          resfcn256_Conv2d_transpose_1_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_1_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_1_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_1_Relu = self.relu32(
          resfcn256_Conv2d_transpose_1_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_2_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_1_Relu
      )
      resfcn256_Conv2d_transpose_2_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_2_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_2_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_2_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_2_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_2_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_2_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_2_strided_slice_1,
          y=resfcn256_Conv2d_transpose_2_mul_y,
      )
      resfcn256_Conv2d_transpose_2_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_2_strided_slice_2,
          y=resfcn256_Conv2d_transpose_2_mul_1_y,
      )
      resfcn256_Conv2d_transpose_2_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_2_strided_slice,
              resfcn256_Conv2d_transpose_2_mul,
              resfcn256_Conv2d_transpose_2_mul_1,
              resfcn256_Conv2d_transpose_2_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_2_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_2_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_2 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_1_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_2_conv2d_transpose_conv38_weight = (
          self.resfcn256_Conv2d_transpose_2_conv2d_transpose_conv38_weight
      )
      resfcn256_Conv2d_transpose_2_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_2,
          weight=resfcn256_Conv2d_transpose_2_conv2d_transpose_conv38_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[16, 16],
      )
      resfcn256_Conv2d_transpose_2_BatchNorm_FusedBatchNorm = self.bn33(
          resfcn256_Conv2d_transpose_2_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_2_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_2_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_2_Relu = self.relu33(
          resfcn256_Conv2d_transpose_2_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_3_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_2_Relu
      )
      resfcn256_Conv2d_transpose_3_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_3_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_3_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_3_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_3_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_3_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_3_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_3_strided_slice_1,
          y=resfcn256_Conv2d_transpose_3_mul_y,
      )
      resfcn256_Conv2d_transpose_3_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_3_strided_slice_2,
          y=resfcn256_Conv2d_transpose_3_mul_1_y,
      )
      resfcn256_Conv2d_transpose_3_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_3_strided_slice,
              resfcn256_Conv2d_transpose_3_mul,
              resfcn256_Conv2d_transpose_3_mul_1,
              resfcn256_Conv2d_transpose_3_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_3_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_3_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_3 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_2_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_3_conv2d_transpose_conv39_weight = (
          self.resfcn256_Conv2d_transpose_3_conv2d_transpose_conv39_weight
      )
      resfcn256_Conv2d_transpose_3_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_3,
          weight=resfcn256_Conv2d_transpose_3_conv2d_transpose_conv39_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[16, 16],
      )
      resfcn256_Conv2d_transpose_3_BatchNorm_FusedBatchNorm = self.bn34(
          resfcn256_Conv2d_transpose_3_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_3_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_3_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_3_Relu = self.relu34(
          resfcn256_Conv2d_transpose_3_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_4_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_3_Relu
      )
      resfcn256_Conv2d_transpose_4_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_4_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_4_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_4_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_4_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_4_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_4_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_4_strided_slice_1,
          y=resfcn256_Conv2d_transpose_4_mul_y,
      )
      resfcn256_Conv2d_transpose_4_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_4_strided_slice_2,
          y=resfcn256_Conv2d_transpose_4_mul_1_y,
      )
      resfcn256_Conv2d_transpose_4_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_4_strided_slice,
              resfcn256_Conv2d_transpose_4_mul,
              resfcn256_Conv2d_transpose_4_mul_1,
              resfcn256_Conv2d_transpose_4_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_4_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_4_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_4 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_3_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_4_conv2d_transpose_conv40_weight = (
          self.resfcn256_Conv2d_transpose_4_conv2d_transpose_conv40_weight
      )
      resfcn256_Conv2d_transpose_4_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_4,
          weight=resfcn256_Conv2d_transpose_4_conv2d_transpose_conv40_weight,
          stride=[2, 2],
          dilation=[1, 1],
          padding="SAME",
          output_size=[32, 32],
      )
      resfcn256_Conv2d_transpose_4_BatchNorm_FusedBatchNorm = self.bn35(
          resfcn256_Conv2d_transpose_4_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_4_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_4_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_4_Relu = self.relu35(
          resfcn256_Conv2d_transpose_4_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_5_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_4_Relu
      )
      resfcn256_Conv2d_transpose_5_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_5_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_5_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_5_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_5_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_5_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_5_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_5_strided_slice_1,
          y=resfcn256_Conv2d_transpose_5_mul_y,
      )
      resfcn256_Conv2d_transpose_5_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_5_strided_slice_2,
          y=resfcn256_Conv2d_transpose_5_mul_1_y,
      )
      resfcn256_Conv2d_transpose_5_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_5_strided_slice,
              resfcn256_Conv2d_transpose_5_mul,
              resfcn256_Conv2d_transpose_5_mul_1,
              resfcn256_Conv2d_transpose_5_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_5_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_5_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_5 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_4_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_5_conv2d_transpose_conv41_weight = (
          self.resfcn256_Conv2d_transpose_5_conv2d_transpose_conv41_weight
      )
      resfcn256_Conv2d_transpose_5_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_5,
          weight=resfcn256_Conv2d_transpose_5_conv2d_transpose_conv41_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[32, 32],
      )
      resfcn256_Conv2d_transpose_5_BatchNorm_FusedBatchNorm = self.bn36(
          resfcn256_Conv2d_transpose_5_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_5_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_5_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_5_Relu = self.relu36(
          resfcn256_Conv2d_transpose_5_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_6_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_5_Relu
      )
      resfcn256_Conv2d_transpose_6_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_6_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_6_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_6_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_6_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_6_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_6_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_6_strided_slice_1,
          y=resfcn256_Conv2d_transpose_6_mul_y,
      )
      resfcn256_Conv2d_transpose_6_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_6_strided_slice_2,
          y=resfcn256_Conv2d_transpose_6_mul_1_y,
      )
      resfcn256_Conv2d_transpose_6_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_6_strided_slice,
              resfcn256_Conv2d_transpose_6_mul,
              resfcn256_Conv2d_transpose_6_mul_1,
              resfcn256_Conv2d_transpose_6_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_6_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_6_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_6 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_5_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_6_conv2d_transpose_conv42_weight = (
          self.resfcn256_Conv2d_transpose_6_conv2d_transpose_conv42_weight
      )
      resfcn256_Conv2d_transpose_6_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_6,
          weight=resfcn256_Conv2d_transpose_6_conv2d_transpose_conv42_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[32, 32],
      )
      resfcn256_Conv2d_transpose_6_BatchNorm_FusedBatchNorm = self.bn37(
          resfcn256_Conv2d_transpose_6_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_6_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_6_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_6_Relu = self.relu37(
          resfcn256_Conv2d_transpose_6_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_7_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_6_Relu
      )
      resfcn256_Conv2d_transpose_7_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_7_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_7_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_7_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_7_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_7_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_7_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_7_strided_slice_1,
          y=resfcn256_Conv2d_transpose_7_mul_y,
      )
      resfcn256_Conv2d_transpose_7_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_7_strided_slice_2,
          y=resfcn256_Conv2d_transpose_7_mul_1_y,
      )
      resfcn256_Conv2d_transpose_7_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_7_strided_slice,
              resfcn256_Conv2d_transpose_7_mul,
              resfcn256_Conv2d_transpose_7_mul_1,
              resfcn256_Conv2d_transpose_7_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_7_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_7_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_7 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_6_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_7_conv2d_transpose_conv43_weight = (
          self.resfcn256_Conv2d_transpose_7_conv2d_transpose_conv43_weight
      )
      resfcn256_Conv2d_transpose_7_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_7,
          weight=resfcn256_Conv2d_transpose_7_conv2d_transpose_conv43_weight,
          stride=[2, 2],
          dilation=[1, 1],
          padding="SAME",
          output_size=[64, 64],
      )
      resfcn256_Conv2d_transpose_7_BatchNorm_FusedBatchNorm = self.bn38(
          resfcn256_Conv2d_transpose_7_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_7_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_7_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_7_Relu = self.relu38(
          resfcn256_Conv2d_transpose_7_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_8_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_7_Relu
      )
      resfcn256_Conv2d_transpose_8_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_8_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_8_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_8_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_8_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_8_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_8_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_8_strided_slice_1,
          y=resfcn256_Conv2d_transpose_8_mul_y,
      )
      resfcn256_Conv2d_transpose_8_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_8_strided_slice_2,
          y=resfcn256_Conv2d_transpose_8_mul_1_y,
      )
      resfcn256_Conv2d_transpose_8_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_8_strided_slice,
              resfcn256_Conv2d_transpose_8_mul,
              resfcn256_Conv2d_transpose_8_mul_1,
              resfcn256_Conv2d_transpose_8_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_8_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_8_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_8 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_7_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_8_conv2d_transpose_conv44_weight = (
          self.resfcn256_Conv2d_transpose_8_conv2d_transpose_conv44_weight
      )
      resfcn256_Conv2d_transpose_8_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_8,
          weight=resfcn256_Conv2d_transpose_8_conv2d_transpose_conv44_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[64, 64],
      )
      resfcn256_Conv2d_transpose_8_BatchNorm_FusedBatchNorm = self.bn39(
          resfcn256_Conv2d_transpose_8_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_8_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_8_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_8_Relu = self.relu39(
          resfcn256_Conv2d_transpose_8_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_9_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_8_Relu
      )
      resfcn256_Conv2d_transpose_9_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_9_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_9_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_9_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_9_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_9_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_9_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_9_strided_slice_1,
          y=resfcn256_Conv2d_transpose_9_mul_y,
      )
      resfcn256_Conv2d_transpose_9_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_9_strided_slice_2,
          y=resfcn256_Conv2d_transpose_9_mul_1_y,
      )
      resfcn256_Conv2d_transpose_9_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_9_strided_slice,
              resfcn256_Conv2d_transpose_9_mul,
              resfcn256_Conv2d_transpose_9_mul_1,
              resfcn256_Conv2d_transpose_9_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_9_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_9_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_9 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_8_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_9_conv2d_transpose_conv45_weight = (
          self.resfcn256_Conv2d_transpose_9_conv2d_transpose_conv45_weight
      )
      resfcn256_Conv2d_transpose_9_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_9,
          weight=resfcn256_Conv2d_transpose_9_conv2d_transpose_conv45_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[64, 64],
      )
      resfcn256_Conv2d_transpose_9_BatchNorm_FusedBatchNorm = self.bn40(
          resfcn256_Conv2d_transpose_9_conv2d_transpose
      )
      resfcn256_Conv2d_transpose_9_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_9_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_9_Relu = self.relu40(
          resfcn256_Conv2d_transpose_9_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_10_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_9_Relu
      )
      resfcn256_Conv2d_transpose_10_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_10_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_10_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_10_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_10_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_10_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_10_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_10_strided_slice_1,
          y=resfcn256_Conv2d_transpose_10_mul_y,
      )
      resfcn256_Conv2d_transpose_10_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_10_strided_slice_2,
          y=resfcn256_Conv2d_transpose_10_mul_1_y,
      )
      resfcn256_Conv2d_transpose_10_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_10_strided_slice,
              resfcn256_Conv2d_transpose_10_mul,
              resfcn256_Conv2d_transpose_10_mul_1,
              resfcn256_Conv2d_transpose_10_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_10_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_10_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_10 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_9_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_10_conv2d_transpose_conv46_weight = (
          self.resfcn256_Conv2d_transpose_10_conv2d_transpose_conv46_weight
      )
      resfcn256_Conv2d_transpose_10_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_10,
          weight=resfcn256_Conv2d_transpose_10_conv2d_transpose_conv46_weight,
          stride=[2, 2],
          dilation=[1, 1],
          padding="SAME",
          output_size=[128, 128],
      )
      resfcn256_Conv2d_transpose_10_BatchNorm_FusedBatchNorm = (
          self.bn41(resfcn256_Conv2d_transpose_10_conv2d_transpose)
      )
      resfcn256_Conv2d_transpose_10_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_10_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_10_Relu = self.relu41(
          resfcn256_Conv2d_transpose_10_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_11_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_10_Relu
      )
      resfcn256_Conv2d_transpose_11_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_11_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_11_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_11_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_11_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_11_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_11_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_11_strided_slice_1,
          y=resfcn256_Conv2d_transpose_11_mul_y,
      )
      resfcn256_Conv2d_transpose_11_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_11_strided_slice_2,
          y=resfcn256_Conv2d_transpose_11_mul_1_y,
      )
      resfcn256_Conv2d_transpose_11_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_11_strided_slice,
              resfcn256_Conv2d_transpose_11_mul,
              resfcn256_Conv2d_transpose_11_mul_1,
              resfcn256_Conv2d_transpose_11_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_11_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_11_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_11 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_10_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_11_conv2d_transpose_conv47_weight = (
          self.resfcn256_Conv2d_transpose_11_conv2d_transpose_conv47_weight
      )
      resfcn256_Conv2d_transpose_11_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_11,
          weight=resfcn256_Conv2d_transpose_11_conv2d_transpose_conv47_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[128, 128],
      )
      resfcn256_Conv2d_transpose_11_BatchNorm_FusedBatchNorm = (
          self.bn42(resfcn256_Conv2d_transpose_11_conv2d_transpose)
      )
      resfcn256_Conv2d_transpose_11_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_11_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_11_Relu = self.relu42(
          resfcn256_Conv2d_transpose_11_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_12_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_11_Relu
      )
      resfcn256_Conv2d_transpose_12_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_12_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_12_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_12_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_12_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_12_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_12_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_12_strided_slice_1,
          y=resfcn256_Conv2d_transpose_12_mul_y,
      )
      resfcn256_Conv2d_transpose_12_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_12_strided_slice_2,
          y=resfcn256_Conv2d_transpose_12_mul_1_y,
      )
      resfcn256_Conv2d_transpose_12_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_12_strided_slice,
              resfcn256_Conv2d_transpose_12_mul,
              resfcn256_Conv2d_transpose_12_mul_1,
              resfcn256_Conv2d_transpose_12_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_12_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_12_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_12 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_11_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_12_conv2d_transpose_conv48_weight = (
          self.resfcn256_Conv2d_transpose_12_conv2d_transpose_conv48_weight
      )
      resfcn256_Conv2d_transpose_12_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_12,
          weight=resfcn256_Conv2d_transpose_12_conv2d_transpose_conv48_weight,
          stride=[2, 2],
          dilation=[1, 1],
          padding="SAME",
          output_size=[256, 256],
      )
      resfcn256_Conv2d_transpose_12_BatchNorm_FusedBatchNorm = (
          self.bn43(resfcn256_Conv2d_transpose_12_conv2d_transpose)
      )
      resfcn256_Conv2d_transpose_12_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_12_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_12_Relu = self.relu43(
          resfcn256_Conv2d_transpose_12_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_13_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_12_Relu
      )
      resfcn256_Conv2d_transpose_13_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_13_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_13_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_13_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_13_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_13_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_13_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_13_strided_slice_1,
          y=resfcn256_Conv2d_transpose_13_mul_y,
      )
      resfcn256_Conv2d_transpose_13_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_13_strided_slice_2,
          y=resfcn256_Conv2d_transpose_13_mul_1_y,
      )
      resfcn256_Conv2d_transpose_13_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_13_strided_slice,
              resfcn256_Conv2d_transpose_13_mul,
              resfcn256_Conv2d_transpose_13_mul_1,
              resfcn256_Conv2d_transpose_13_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_13_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_13_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_13 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_12_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_13_conv2d_transpose_conv49_weight = (
          self.resfcn256_Conv2d_transpose_13_conv2d_transpose_conv49_weight
      )
      resfcn256_Conv2d_transpose_13_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_13,
          weight=resfcn256_Conv2d_transpose_13_conv2d_transpose_conv49_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[256, 256],
      )
      resfcn256_Conv2d_transpose_13_BatchNorm_FusedBatchNorm = (
          self.bn44(resfcn256_Conv2d_transpose_13_conv2d_transpose)
      )
      resfcn256_Conv2d_transpose_13_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_13_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_13_Relu = self.relu44(
          resfcn256_Conv2d_transpose_13_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_14_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_13_Relu
      )
      resfcn256_Conv2d_transpose_14_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_14_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_14_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_14_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_14_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_14_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_14_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_14_strided_slice_1,
          y=resfcn256_Conv2d_transpose_14_mul_y,
      )
      resfcn256_Conv2d_transpose_14_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_14_strided_slice_2,
          y=resfcn256_Conv2d_transpose_14_mul_1_y,
      )
      resfcn256_Conv2d_transpose_14_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_14_strided_slice,
              resfcn256_Conv2d_transpose_14_mul,
              resfcn256_Conv2d_transpose_14_mul_1,
              resfcn256_Conv2d_transpose_14_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_14_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_14_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_14 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_13_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_14_conv2d_transpose_conv50_weight = (
          self.resfcn256_Conv2d_transpose_14_conv2d_transpose_conv50_weight
      )
      resfcn256_Conv2d_transpose_14_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_14,
          weight=resfcn256_Conv2d_transpose_14_conv2d_transpose_conv50_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[256, 256],
      )
      resfcn256_Conv2d_transpose_14_BatchNorm_FusedBatchNorm = (
          self.bn45(resfcn256_Conv2d_transpose_14_conv2d_transpose)
      )
      resfcn256_Conv2d_transpose_14_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_14_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_14_Relu = self.relu45(
          resfcn256_Conv2d_transpose_14_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_15_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_14_Relu
      )
      resfcn256_Conv2d_transpose_15_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_15_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_15_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_15_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_15_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_15_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_15_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_15_strided_slice_1,
          y=resfcn256_Conv2d_transpose_15_mul_y,
      )
      resfcn256_Conv2d_transpose_15_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_15_strided_slice_2,
          y=resfcn256_Conv2d_transpose_15_mul_1_y,
      )
      resfcn256_Conv2d_transpose_15_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_15_strided_slice,
              resfcn256_Conv2d_transpose_15_mul,
              resfcn256_Conv2d_transpose_15_mul_1,
              resfcn256_Conv2d_transpose_15_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_15_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_15_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_15 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_14_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_15_conv2d_transpose_conv51_weight = (
          self.resfcn256_Conv2d_transpose_15_conv2d_transpose_conv51_weight
      )
      resfcn256_Conv2d_transpose_15_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_15,
          weight=resfcn256_Conv2d_transpose_15_conv2d_transpose_conv51_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[256, 256],
      )
      resfcn256_Conv2d_transpose_15_BatchNorm_FusedBatchNorm = (
          self.bn46(resfcn256_Conv2d_transpose_15_conv2d_transpose)
      )
      resfcn256_Conv2d_transpose_15_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_15_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_15_Relu = self.relu46(
          resfcn256_Conv2d_transpose_15_BatchNorm_FusedBatchNorm
      )
      resfcn256_Conv2d_transpose_16_Shape = paddle.shape(
          input=resfcn256_Conv2d_transpose_15_Relu
      )
      resfcn256_Conv2d_transpose_16_strided_slice = paddle.slice(
          input=resfcn256_Conv2d_transpose_16_Shape,
          axes=[0],
          starts=[0],
          ends=[1],
      )
      resfcn256_Conv2d_transpose_16_strided_slice_1 = paddle.slice(
          input=resfcn256_Conv2d_transpose_16_Shape,
          axes=[0],
          starts=[1],
          ends=[2],
      )
      resfcn256_Conv2d_transpose_16_strided_slice_2 = paddle.slice(
          input=resfcn256_Conv2d_transpose_16_Shape,
          axes=[0],
          starts=[2],
          ends=[3],
      )
      resfcn256_Conv2d_transpose_16_mul = paddle.multiply(
          x=resfcn256_Conv2d_transpose_16_strided_slice_1,
          y=resfcn256_Conv2d_transpose_16_mul_y,
      )
      resfcn256_Conv2d_transpose_16_mul_1 = paddle.multiply(
          x=resfcn256_Conv2d_transpose_16_strided_slice_2,
          y=resfcn256_Conv2d_transpose_16_mul_1_y,
      )
      resfcn256_Conv2d_transpose_16_stack = paddle.stack(
          x=[
              resfcn256_Conv2d_transpose_16_strided_slice,
              resfcn256_Conv2d_transpose_16_mul,
              resfcn256_Conv2d_transpose_16_mul_1,
              resfcn256_Conv2d_transpose_16_stack_3,
          ]
      )
      resfcn256_Conv2d_transpose_16_stack = paddle.reshape(
          x=resfcn256_Conv2d_transpose_16_stack, shape=[-1]
      )
      conv2dbackpropinput_transpose_16 = paddle.transpose(
          x=resfcn256_Conv2d_transpose_15_Relu, perm=[0, 3, 1, 2]
      )
      resfcn256_Conv2d_transpose_16_conv2d_transpose_conv52_weight = (
          self.resfcn256_Conv2d_transpose_16_conv2d_transpose_conv52_weight
      )
      resfcn256_Conv2d_transpose_16_conv2d_transpose = paddle.nn.functional.conv2d_transpose(
          x=conv2dbackpropinput_transpose_16,
          weight=resfcn256_Conv2d_transpose_16_conv2d_transpose_conv52_weight,
          stride=[1, 1],
          dilation=[1, 1],
          padding="SAME",
          output_size=[256, 256],
      )
      resfcn256_Conv2d_transpose_16_BatchNorm_FusedBatchNorm = (
          self.bn47(resfcn256_Conv2d_transpose_16_conv2d_transpose)
      )
      resfcn256_Conv2d_transpose_16_BatchNorm_FusedBatchNorm = (
          paddle.transpose(
              x=resfcn256_Conv2d_transpose_16_BatchNorm_FusedBatchNorm,
              perm=[0, 2, 3, 1],
          )
      )
      resfcn256_Conv2d_transpose_16_Sigmoid = self.sigmoid0(
          resfcn256_Conv2d_transpose_16_BatchNorm_FusedBatchNorm
      )
      return resfcn256_Conv2d_transpose_16_Sigmoid
  
imports: import paddle
  import math
label_desc: Use image node_id 2 for calling the TFModel obj's underlying member method code with example usage: obj.forward(Placeholder) and returns: resfcn256_Conv2d_transpose_16_Sigmoid