nn.Sequential {
  [input -> (1) -> (2) -> (3) -> (4) -> output]
  (1): nn.Sequential {
    [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
    (1): nn.Sequential {
      [input -> (1) -> (2) -> (3) -> (4) -> output]
      (1): nn.SpatialConvolutionMM(3 -> 32, 3x3)
      (2): nn.LeakyReLU(0.1)
      (3): nn.SpatialConvolutionMM(32 -> 64, 3x3)
      (4): nn.LeakyReLU(0.1)
    }
    (2): nn.Sequential {
      [input -> (1) -> (2) -> output]
      (1): nn.ConcatTable {
        input
          |`-> (1): nn.Sequential {
          |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |      (1): nn.SpatialConvolutionMM(64 -> 64, 2x2, 2,2)
          |      (2): nn.LeakyReLU(0.1)
          |      (3): nn.Sequential {
          |        [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
          |        (1): nn.SpatialConvolutionMM(64 -> 128, 3x3)
          |        (2): nn.LeakyReLU(0.1)
          |        (3): nn.SpatialConvolutionMM(128 -> 64, 3x3)
          |        (4): nn.LeakyReLU(0.1)
          |        (5): nn.ConcatTable {
          |          input
          |            |`-> (1): nn.Identity
          |             `-> (2): nn.Sequential {
          |                   [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
          |                   (1): nn.Sequential {
          |                     [input -> (1) -> (2) -> (3) -> output]
          |                     (1): nn.Mean
          |                     (2): nn.Mean
          |                     (3): nn.View(-1, 64, 1, 1)
          |                   }
          |                   (2): nn.SpatialConvolutionMM(64 -> 8, 1x1)
          |                   (3): nn.ReLU
          |                   (4): nn.SpatialConvolutionMM(8 -> 64, 1x1)
          |                   (5): nn.Sigmoid
          |                 }
          |             ... -> output
          |        }
          |        (6): w2nn.ScaleTable
          |      }
          |      (4): nn.SpatialFullConvolution(64 -> 64, 2x2, 2,2)
          |      (5): nn.LeakyReLU(0.1)
          |    }
           `-> (2): nn.SpatialZeroPadding(l=-4, r=-4, t=-4, b=-4)
           ... -> output
      }
      (2): nn.CAddTable
    }
    (3): nn.SpatialConvolutionMM(64 -> 64, 3x3)
    (4): nn.LeakyReLU(0.1)
    (5): nn.SpatialConvolutionMM(64 -> 3, 3x3)
  }
  (2): nn.ConcatTable {
    input
      |`-> (1): nn.Sequential {
      |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
      |      (1): nn.Sequential {
      |        [input -> (1) -> (2) -> (3) -> (4) -> output]
      |        (1): nn.SpatialConvolutionMM(3 -> 32, 3x3)
      |        (2): nn.LeakyReLU(0.1)
      |        (3): nn.SpatialConvolutionMM(32 -> 64, 3x3)
      |        (4): nn.LeakyReLU(0.1)
      |      }
      |      (2): nn.Sequential {
      |        [input -> (1) -> (2) -> output]
      |        (1): nn.ConcatTable {
      |          input
      |            |`-> (1): nn.Sequential {
      |            |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
      |            |      (1): nn.SpatialConvolutionMM(64 -> 64, 2x2, 2,2)
      |            |      (2): nn.LeakyReLU(0.1)
      |            |      (3): nn.Sequential {
      |            |        [input -> (1) -> (2) -> (3) -> output]
      |            |        (1): nn.Sequential {
      |            |          [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
      |            |          (1): nn.SpatialConvolutionMM(64 -> 64, 3x3)
      |            |          (2): nn.LeakyReLU(0.1)
      |            |          (3): nn.SpatialConvolutionMM(64 -> 128, 3x3)
      |            |          (4): nn.LeakyReLU(0.1)
      |            |          (5): nn.ConcatTable {
      |            |            input
      |            |              |`-> (1): nn.Identity
      |            |               `-> (2): nn.Sequential {
      |            |                     [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
      |            |                     (1): nn.Sequential {
      |            |                       [input -> (1) -> (2) -> (3) -> output]
      |            |                       (1): nn.Mean
      |            |                       (2): nn.Mean
      |            |                       (3): nn.View(-1, 128, 1, 1)
      |            |                     }
      |            |                     (2): nn.SpatialConvolutionMM(128 -> 16, 1x1)
      |            |                     (3): nn.ReLU
      |            |                     (4): nn.SpatialConvolutionMM(16 -> 128, 1x1)
      |            |                     (5): nn.Sigmoid
      |            |                   }
      |            |               ... -> output
      |            |          }
      |            |          (6): w2nn.ScaleTable
      |            |        }
      |            |        (2): nn.Sequential {
      |            |          [input -> (1) -> (2) -> output]
      |            |          (1): nn.ConcatTable {
      |            |            input
      |            |              |`-> (1): nn.Sequential {
      |            |              |      [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
      |            |              |      (1): nn.SpatialConvolutionMM(128 -> 128, 2x2, 2,2)
      |            |              |      (2): nn.LeakyReLU(0.1)
      |            |              |      (3): nn.Sequential {
      |            |              |        [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
      |            |              |        (1): nn.SpatialConvolutionMM(128 -> 256, 3x3)
      |            |              |        (2): nn.LeakyReLU(0.1)
      |            |              |        (3): nn.SpatialConvolutionMM(256 -> 128, 3x3)
      |            |              |        (4): nn.LeakyReLU(0.1)
      |            |              |        (5): nn.ConcatTable {
      |            |              |          input
      |            |              |            |`-> (1): nn.Identity
      |            |              |             `-> (2): nn.Sequential {
      |            |              |                   [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
      |            |              |                   (1): nn.Sequential {
      |            |              |                     [input -> (1) -> (2) -> (3) -> output]
      |            |              |                     (1): nn.Mean
      |            |              |                     (2): nn.Mean
      |            |              |                     (3): nn.View(-1, 128, 1, 1)
      |            |              |                   }
      |            |              |                   (2): nn.SpatialConvolutionMM(128 -> 16, 1x1)
      |            |              |                   (3): nn.ReLU
      |            |              |                   (4): nn.SpatialConvolutionMM(16 -> 128, 1x1)
      |            |              |                   (5): nn.Sigmoid
      |            |              |                 }
      |            |              |             ... -> output
      |            |              |        }
      |            |              |        (6): w2nn.ScaleTable
      |            |              |      }
      |            |              |      (4): nn.SpatialFullConvolution(128 -> 128, 2x2, 2,2)
      |            |              |      (5): nn.LeakyReLU(0.1)
      |            |              |    }
      |            |               `-> (2): nn.SpatialZeroPadding(l=-4, r=-4, t=-4, b=-4)
      |            |               ... -> output
      |            |          }
      |            |          (2): nn.CAddTable
      |            |        }
      |            |        (3): nn.Sequential {
      |            |          [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
      |            |          (1): nn.SpatialConvolutionMM(128 -> 64, 3x3)
      |            |          (2): nn.LeakyReLU(0.1)
      |            |          (3): nn.SpatialConvolutionMM(64 -> 64, 3x3)
      |            |          (4): nn.LeakyReLU(0.1)
      |            |          (5): nn.ConcatTable {
      |            |            input
      |            |              |`-> (1): nn.Identity
      |            |               `-> (2): nn.Sequential {
      |            |                     [input -> (1) -> (2) -> (3) -> (4) -> (5) -> output]
      |            |                     (1): nn.Sequential {
      |            |                       [input -> (1) -> (2) -> (3) -> output]
      |            |                       (1): nn.Mean
      |            |                       (2): nn.Mean
      |            |                       (3): nn.View(-1, 64, 1, 1)
      |            |                     }
      |            |                     (2): nn.SpatialConvolutionMM(64 -> 8, 1x1)
      |            |                     (3): nn.ReLU
      |            |                     (4): nn.SpatialConvolutionMM(8 -> 64, 1x1)
      |            |                     (5): nn.Sigmoid
      |            |                   }
      |            |               ... -> output
      |            |          }
      |            |          (6): w2nn.ScaleTable
      |            |        }
      |            |      }
      |            |      (4): nn.SpatialFullConvolution(64 -> 64, 2x2, 2,2)
      |            |      (5): nn.LeakyReLU(0.1)
      |            |    }
      |             `-> (2): nn.SpatialZeroPadding(l=-16, r=-16, t=-16, b=-16)
      |             ... -> output
      |        }
      |        (2): nn.CAddTable
      |      }
      |      (3): nn.SpatialConvolutionMM(64 -> 64, 3x3)
      |      (4): nn.LeakyReLU(0.1)
      |      (5): nn.SpatialConvolutionMM(64 -> 3, 3x3)
      |    }
       `-> (2): nn.SpatialZeroPadding(l=-20, r=-20, t=-20, b=-20)
       ... -> output
  }
  (3): nn.ConcatTable {
    input
      |`-> (1): nn.Sequential {
      |      [input -> (1) -> (2) -> output]
      |      (1): nn.CAddTable
      |      (2): w2nn.InplaceClip01
      |    }
       `-> (2): nn.Sequential {
             [input -> (1) -> (2) -> output]
             (1): nn.SelectTable(2)
             (2): w2nn.InplaceClip01
           }
       ... -> output
  }
  (4): w2nn.AuxiliaryLossTable
}