code: def __init__(self):
      super(TFModel, self).__init__()
      self.conv0 = paddle.nn.Conv2D(
          weight_attr="conv0.weight",
          bias_attr=False,
          in_channels=3,
          out_channels=16,
          kernel_size=[4, 4],
          padding="SAME",
      )
      self.bn0 = paddle.nn.BatchNorm(
          num_channels=16,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv_BatchNorm_FusedBatchNorm_resfcn256_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_Conv_BatchNorm_FusedBatchNorm_resfcn256_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv_BatchNorm_FusedBatchNorm_resfcn256_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv_BatchNorm_FusedBatchNorm_resfcn256_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu0 = paddle.nn.ReLU()
      self.conv1 = paddle.nn.Conv2D(
          weight_attr="conv1.weight",
          bias_attr=False,
          in_channels=16,
          out_channels=32,
          kernel_size=[1, 1],
          stride=2,
          padding="SAME",
      )
      self.conv2 = paddle.nn.Conv2D(
          weight_attr="conv2.weight",
          bias_attr=False,
          in_channels=16,
          out_channels=16,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn1 = paddle.nn.BatchNorm(
          num_channels=16,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu1 = paddle.nn.ReLU()
      self.conv3 = paddle.nn.Conv2D(
          weight_attr="conv3.weight",
          bias_attr=False,
          in_channels=16,
          out_channels=16,
          kernel_size=[4, 4],
          stride=2,
          padding="SAME",
      )
      self.bn2 = paddle.nn.BatchNorm(
          num_channels=16,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu2 = paddle.nn.ReLU()
      self.conv4 = paddle.nn.Conv2D(
          weight_attr="conv4.weight",
          bias_attr=False,
          in_channels=16,
          out_channels=32,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn3 = paddle.nn.BatchNorm(
          num_channels=32,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_BatchNorm_FusedBatchNorm_resfcn256_resBlock_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_BatchNorm_FusedBatchNorm_resfcn256_resBlock_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_BatchNorm_FusedBatchNorm_resfcn256_resBlock_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_BatchNorm_FusedBatchNorm_resfcn256_resBlock_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu3 = paddle.nn.ReLU()
      self.conv5 = paddle.nn.Conv2D(
          weight_attr="conv5.weight",
          bias_attr=False,
          in_channels=32,
          out_channels=16,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn4 = paddle.nn.BatchNorm(
          num_channels=16,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_1_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_1_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_1_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_1_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu4 = paddle.nn.ReLU()
      self.conv6 = paddle.nn.Conv2D(
          weight_attr="conv6.weight",
          bias_attr=False,
          in_channels=16,
          out_channels=16,
          kernel_size=[4, 4],
          padding="SAME",
      )
      self.bn5 = paddle.nn.BatchNorm(
          num_channels=16,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_1_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_1_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_1_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_1_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu5 = paddle.nn.ReLU()
      self.conv7 = paddle.nn.Conv2D(
          weight_attr="conv7.weight",
          bias_attr=False,
          in_channels=16,
          out_channels=32,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn6 = paddle.nn.BatchNorm(
          num_channels=32,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu6 = paddle.nn.ReLU()
      self.conv8 = paddle.nn.Conv2D(
          weight_attr="conv8.weight",
          bias_attr=False,
          in_channels=32,
          out_channels=64,
          kernel_size=[1, 1],
          stride=2,
          padding="SAME",
      )
      self.conv9 = paddle.nn.Conv2D(
          weight_attr="conv9.weight",
          bias_attr=False,
          in_channels=32,
          out_channels=32,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn7 = paddle.nn.BatchNorm(
          num_channels=32,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_2_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_2_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_2_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_2_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu7 = paddle.nn.ReLU()
      self.conv10 = paddle.nn.Conv2D(
          weight_attr="conv10.weight",
          bias_attr=False,
          in_channels=32,
          out_channels=32,
          kernel_size=[4, 4],
          stride=2,
          padding="SAME",
      )
      self.bn8 = paddle.nn.BatchNorm(
          num_channels=32,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_2_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_2_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_2_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_2_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu8 = paddle.nn.ReLU()
      self.conv11 = paddle.nn.Conv2D(
          weight_attr="conv11.weight",
          bias_attr=False,
          in_channels=32,
          out_channels=64,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn9 = paddle.nn.BatchNorm(
          num_channels=64,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_2_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_2_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_2_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_2_BatchNorm_FusedBatchNorm_resfcn256_resBlock_2_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu9 = paddle.nn.ReLU()
      self.conv12 = paddle.nn.Conv2D(
          weight_attr="conv12.weight",
          bias_attr=False,
          in_channels=64,
          out_channels=32,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn10 = paddle.nn.BatchNorm(
          num_channels=32,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_3_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_3_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_3_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_3_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu10 = paddle.nn.ReLU()
      self.conv13 = paddle.nn.Conv2D(
          weight_attr="conv13.weight",
          bias_attr=False,
          in_channels=32,
          out_channels=32,
          kernel_size=[4, 4],
          padding="SAME",
      )
      self.bn11 = paddle.nn.BatchNorm(
          num_channels=32,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_3_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_3_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_3_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_3_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu11 = paddle.nn.ReLU()
      self.conv14 = paddle.nn.Conv2D(
          weight_attr="conv14.weight",
          bias_attr=False,
          in_channels=32,
          out_channels=64,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn12 = paddle.nn.BatchNorm(
          num_channels=64,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_3_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_3_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_3_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_3_BatchNorm_FusedBatchNorm_resfcn256_resBlock_3_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu12 = paddle.nn.ReLU()
      self.conv15 = paddle.nn.Conv2D(
          weight_attr="conv15.weight",
          bias_attr=False,
          in_channels=64,
          out_channels=128,
          kernel_size=[1, 1],
          stride=2,
          padding="SAME",
      )
      self.conv16 = paddle.nn.Conv2D(
          weight_attr="conv16.weight",
          bias_attr=False,
          in_channels=64,
          out_channels=64,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn13 = paddle.nn.BatchNorm(
          num_channels=64,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_4_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_4_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_4_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_4_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu13 = paddle.nn.ReLU()
      self.conv17 = paddle.nn.Conv2D(
          weight_attr="conv17.weight",
          bias_attr=False,
          in_channels=64,
          out_channels=64,
          kernel_size=[4, 4],
          stride=2,
          padding="SAME",
      )
      self.bn14 = paddle.nn.BatchNorm(
          num_channels=64,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_4_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_4_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_4_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_4_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu14 = paddle.nn.ReLU()
      self.conv18 = paddle.nn.Conv2D(
          weight_attr="conv18.weight",
          bias_attr=False,
          in_channels=64,
          out_channels=128,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn15 = paddle.nn.BatchNorm(
          num_channels=128,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_4_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_4_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_4_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_4_BatchNorm_FusedBatchNorm_resfcn256_resBlock_4_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu15 = paddle.nn.ReLU()
      self.conv19 = paddle.nn.Conv2D(
          weight_attr="conv19.weight",
          bias_attr=False,
          in_channels=128,
          out_channels=64,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn16 = paddle.nn.BatchNorm(
          num_channels=64,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_5_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_5_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_5_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_5_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu16 = paddle.nn.ReLU()
      self.conv20 = paddle.nn.Conv2D(
          weight_attr="conv20.weight",
          bias_attr=False,
          in_channels=64,
          out_channels=64,
          kernel_size=[4, 4],
          padding="SAME",
      )
      self.bn17 = paddle.nn.BatchNorm(
          num_channels=64,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_5_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_5_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_5_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_5_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu17 = paddle.nn.ReLU()
      self.conv21 = paddle.nn.Conv2D(
          weight_attr="conv21.weight",
          bias_attr=False,
          in_channels=64,
          out_channels=128,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn18 = paddle.nn.BatchNorm(
          num_channels=128,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_5_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_5_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_5_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_5_BatchNorm_FusedBatchNorm_resfcn256_resBlock_5_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu18 = paddle.nn.ReLU()
      self.conv22 = paddle.nn.Conv2D(
          weight_attr="conv22.weight",
          bias_attr=False,
          in_channels=128,
          out_channels=256,
          kernel_size=[1, 1],
          stride=2,
          padding="SAME",
      )
      self.conv23 = paddle.nn.Conv2D(
          weight_attr="conv23.weight",
          bias_attr=False,
          in_channels=128,
          out_channels=128,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn19 = paddle.nn.BatchNorm(
          num_channels=128,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_6_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_6_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_6_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_6_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu19 = paddle.nn.ReLU()
      self.conv24 = paddle.nn.Conv2D(
          weight_attr="conv24.weight",
          bias_attr=False,
          in_channels=128,
          out_channels=128,
          kernel_size=[4, 4],
          stride=2,
          padding="SAME",
      )
      self.bn20 = paddle.nn.BatchNorm(
          num_channels=128,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_6_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_6_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_6_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_6_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu20 = paddle.nn.ReLU()
      self.conv25 = paddle.nn.Conv2D(
          weight_attr="conv25.weight",
          bias_attr=False,
          in_channels=128,
          out_channels=256,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn21 = paddle.nn.BatchNorm(
          num_channels=256,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_6_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_6_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_6_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_6_BatchNorm_FusedBatchNorm_resfcn256_resBlock_6_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu21 = paddle.nn.ReLU()
      self.conv26 = paddle.nn.Conv2D(
          weight_attr="conv26.weight",
          bias_attr=False,
          in_channels=256,
          out_channels=128,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn22 = paddle.nn.BatchNorm(
          num_channels=128,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_7_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_7_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_7_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_7_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu22 = paddle.nn.ReLU()
      self.conv27 = paddle.nn.Conv2D(
          weight_attr="conv27.weight",
          bias_attr=False,
          in_channels=128,
          out_channels=128,
          kernel_size=[4, 4],
          padding="SAME",
      )
      self.bn23 = paddle.nn.BatchNorm(
          num_channels=128,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_7_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_7_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_7_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_7_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu23 = paddle.nn.ReLU()
      self.conv28 = paddle.nn.Conv2D(
          weight_attr="conv28.weight",
          bias_attr=False,
          in_channels=128,
          out_channels=256,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn24 = paddle.nn.BatchNorm(
          num_channels=256,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_7_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_7_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_7_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_7_BatchNorm_FusedBatchNorm_resfcn256_resBlock_7_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu24 = paddle.nn.ReLU()
      self.conv29 = paddle.nn.Conv2D(
          weight_attr="conv29.weight",
          bias_attr=False,
          in_channels=256,
          out_channels=512,
          kernel_size=[1, 1],
          stride=2,
          padding="SAME",
      )
      self.conv30 = paddle.nn.Conv2D(
          weight_attr="conv30.weight",
          bias_attr=False,
          in_channels=256,
          out_channels=256,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn25 = paddle.nn.BatchNorm(
          num_channels=256,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_8_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_8_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_8_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_8_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu25 = paddle.nn.ReLU()
      self.conv31 = paddle.nn.Conv2D(
          weight_attr="conv31.weight",
          bias_attr=False,
          in_channels=256,
          out_channels=256,
          kernel_size=[4, 4],
          stride=2,
          padding="SAME",
      )
      self.bn26 = paddle.nn.BatchNorm(
          num_channels=256,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_8_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_8_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_8_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_8_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu26 = paddle.nn.ReLU()
      self.conv32 = paddle.nn.Conv2D(
          weight_attr="conv32.weight",
          bias_attr=False,
          in_channels=256,
          out_channels=512,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn27 = paddle.nn.BatchNorm(
          num_channels=512,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_8_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_8_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_8_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_8_BatchNorm_FusedBatchNorm_resfcn256_resBlock_8_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu27 = paddle.nn.ReLU()
      self.conv33 = paddle.nn.Conv2D(
          weight_attr="conv33.weight",
          bias_attr=False,
          in_channels=512,
          out_channels=256,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn28 = paddle.nn.BatchNorm(
          num_channels=256,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_9_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_Conv_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_9_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_Conv_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_9_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_Conv_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_9_Conv_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_Conv_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu28 = paddle.nn.ReLU()
      self.conv34 = paddle.nn.Conv2D(
          weight_attr="conv34.weight",
          bias_attr=False,
          in_channels=256,
          out_channels=256,
          kernel_size=[4, 4],
          padding="SAME",
      )
      self.bn29 = paddle.nn.BatchNorm(
          num_channels=256,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_9_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_Conv_1_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_9_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_Conv_1_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_9_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_Conv_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_9_Conv_1_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_Conv_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu29 = paddle.nn.ReLU()
      self.conv35 = paddle.nn.Conv2D(
          weight_attr="conv35.weight",
          bias_attr=False,
          in_channels=256,
          out_channels=512,
          kernel_size=[1, 1],
          padding="SAME",
      )
      self.bn30 = paddle.nn.BatchNorm(
          num_channels=512,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_resBlock_9_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_BatchNorm_gamma",
          bias_attr="resfcn256_resBlock_9_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_BatchNorm_beta",
          moving_mean_name="resfcn256_resBlock_9_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_resBlock_9_BatchNorm_FusedBatchNorm_resfcn256_resBlock_9_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu30 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_conv2d_transpose_conv36_weight = (
          self.create_parameter(
              shape=(512, 512, 4, 4), attr="conv36.weight"
          )
      )
      self.bn31 = paddle.nn.BatchNorm(
          num_channels=512,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu31 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_1_conv2d_transpose_conv37_weight = self.create_parameter(
          shape=(512, 256, 4, 4), attr="conv37.weight"
      )
      self.bn32 = paddle.nn.BatchNorm(
          num_channels=256,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_1_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_1_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_1_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_1_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_1_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_1_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_1_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_1_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu32 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_2_conv2d_transpose_conv38_weight = self.create_parameter(
          shape=(256, 256, 4, 4), attr="conv38.weight"
      )
      self.bn33 = paddle.nn.BatchNorm(
          num_channels=256,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_2_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_2_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_2_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_2_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_2_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_2_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_2_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_2_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu33 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_3_conv2d_transpose_conv39_weight = self.create_parameter(
          shape=(256, 256, 4, 4), attr="conv39.weight"
      )
      self.bn34 = paddle.nn.BatchNorm(
          num_channels=256,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_3_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_3_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_3_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_3_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_3_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_3_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_3_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_3_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu34 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_4_conv2d_transpose_conv40_weight = self.create_parameter(
          shape=(256, 128, 4, 4), attr="conv40.weight"
      )
      self.bn35 = paddle.nn.BatchNorm(
          num_channels=128,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_4_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_4_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_4_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_4_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_4_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_4_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_4_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_4_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu35 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_5_conv2d_transpose_conv41_weight = self.create_parameter(
          shape=(128, 128, 4, 4), attr="conv41.weight"
      )
      self.bn36 = paddle.nn.BatchNorm(
          num_channels=128,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_5_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_5_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_5_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_5_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_5_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_5_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_5_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_5_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu36 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_6_conv2d_transpose_conv42_weight = self.create_parameter(
          shape=(128, 128, 4, 4), attr="conv42.weight"
      )
      self.bn37 = paddle.nn.BatchNorm(
          num_channels=128,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_6_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_6_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_6_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_6_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_6_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_6_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_6_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_6_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu37 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_7_conv2d_transpose_conv43_weight = self.create_parameter(
          shape=(128, 64, 4, 4), attr="conv43.weight"
      )
      self.bn38 = paddle.nn.BatchNorm(
          num_channels=64,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_7_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_7_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_7_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_7_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_7_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_7_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_7_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_7_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu38 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_8_conv2d_transpose_conv44_weight = self.create_parameter(
          shape=(64, 64, 4, 4), attr="conv44.weight"
      )
      self.bn39 = paddle.nn.BatchNorm(
          num_channels=64,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_8_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_8_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_8_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_8_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_8_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_8_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_8_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_8_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu39 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_9_conv2d_transpose_conv45_weight = self.create_parameter(
          shape=(64, 64, 4, 4), attr="conv45.weight"
      )
      self.bn40 = paddle.nn.BatchNorm(
          num_channels=64,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_9_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_9_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_9_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_9_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_9_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_9_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_9_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_9_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu40 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_10_conv2d_transpose_conv46_weight = self.create_parameter(
          shape=(64, 32, 4, 4), attr="conv46.weight"
      )
      self.bn41 = paddle.nn.BatchNorm(
          num_channels=32,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_10_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_10_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_10_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_10_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_10_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_10_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_10_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_10_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu41 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_11_conv2d_transpose_conv47_weight = self.create_parameter(
          shape=(32, 32, 4, 4), attr="conv47.weight"
      )
      self.bn42 = paddle.nn.BatchNorm(
          num_channels=32,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_11_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_11_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_11_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_11_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_11_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_11_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_11_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_11_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu42 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_12_conv2d_transpose_conv48_weight = self.create_parameter(
          shape=(32, 16, 4, 4), attr="conv48.weight"
      )
      self.bn43 = paddle.nn.BatchNorm(
          num_channels=16,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_12_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_12_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_12_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_12_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_12_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_12_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_12_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_12_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu43 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_13_conv2d_transpose_conv49_weight = self.create_parameter(
          shape=(16, 16, 4, 4), attr="conv49.weight"
      )
      self.bn44 = paddle.nn.BatchNorm(
          num_channels=16,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_13_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_13_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_13_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_13_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_13_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_13_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_13_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_13_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu44 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_14_conv2d_transpose_conv50_weight = self.create_parameter(
          shape=(16, 3, 4, 4), attr="conv50.weight"
      )
      self.bn45 = paddle.nn.BatchNorm(
          num_channels=3,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_14_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_14_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_14_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_14_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_14_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_14_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_14_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_14_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu45 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_15_conv2d_transpose_conv51_weight = self.create_parameter(
          shape=(3, 3, 4, 4), attr="conv51.weight"
      )
      self.bn46 = paddle.nn.BatchNorm(
          num_channels=3,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_15_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_15_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_15_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_15_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_15_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_15_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_15_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_15_BatchNorm_moving_variance",
          is_test=True,
      )
      self.relu46 = paddle.nn.ReLU()
      self.resfcn256_Conv2d_transpose_16_conv2d_transpose_conv52_weight = self.create_parameter(
          shape=(3, 3, 4, 4), attr="conv52.weight"
      )
      self.bn47 = paddle.nn.BatchNorm(
          num_channels=3,
          epsilon=0.0010000000474974513,
          param_attr="resfcn256_Conv2d_transpose_16_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_16_BatchNorm_gamma",
          bias_attr="resfcn256_Conv2d_transpose_16_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_16_BatchNorm_beta",
          moving_mean_name="resfcn256_Conv2d_transpose_16_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_16_BatchNorm_moving_mean",
          moving_variance_name="resfcn256_Conv2d_transpose_16_BatchNorm_FusedBatchNorm_resfcn256_Conv2d_transpose_16_BatchNorm_moving_variance",
          is_test=True,
      )
      self.sigmoid0 = paddle.nn.Sigmoid()
  
imports: import paddle
  import math
label_desc: Use image node_id 1 to create a new TFModel object from inherited base classes: paddle.nn with example: obj = TFModel()