I am trying to work with torch 7 model which is consist of the following layers:
nn.Sequential {
  [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> (12) -> (13) -> (14) -> (15) -> (16) -> (17) -> (18) -> (19) -> (20) -> (21) -> (22) -> (23) -> output]
  (0): TorchObject(nn.TVLoss, {'_type': 'torch.FloatTensor', 'strength': 0, 'x_diff': 
  ( 0 ,.,.) = 
    0.0000  0.0000  0.0000  ...  -0.0039  0.0078  0.0000
    0.0000  0.0000  0.0000  ...   0.0039  0.0000  0.0000
    0.0000  0.0000  0.0000  ...   0.0078 -0.0157  0.0000
             ...             ⋱             ...          
   -0.0196  0.0157 -0.0196  ...   0.0078  0.0588  0.0392
   -0.0196  0.0196 -0.0235  ...   0.0235  0.0745  0.1451
   -0.0078  0.0000  0.0118  ...   0.0431  0.1882  0.1569
  
  ( 1 ,.,.) = 
    0.0000  0.0000  0.0000  ...  -0.0039  0.0078  0.0000
    0.0000  0.0000  0.0000  ...   0.0039  0.0000  0.0000
    0.0000  0.0000  0.0000  ...   0.0078 -0.0078  0.0000
             ...             ⋱             ...          
   -0.0196  0.0157 -0.0196  ...   0.0078  0.0431  0.0235
   -0.0196  0.0196 -0.0235  ...   0.0196  0.0588  0.1373
   -0.0078  0.0000  0.0118  ...   0.0353  0.1765  0.1451
  
  ( 2 ,.,.) = 
    0.0000  0.0000  0.0000  ...  -0.0039  0.0078  0.0000
    0.0000  0.0000  0.0000  ...   0.0039  0.0000  0.0000
    0.0000  0.0000  0.0000  ...   0.0078 -0.0078  0.0000
             ...             ⋱             ...          
   -0.0196  0.0157 -0.0196  ...   0.0078  0.0471  0.0235
   -0.0196  0.0196 -0.0235  ...   0.0118  0.0510  0.1216
   -0.0078  0.0039  0.0118  ...   0.0157  0.1647  0.1176
  [torch.FloatTensor of size 3x511x511]
  , 'gradInput': [torch.FloatTensor with no dimension]
  , 'y_diff': 
  ( 0 ,.,.) = 
    0.0000  0.0000  0.0000  ...   0.0000  0.0078  0.0000
    0.0000  0.0000  0.0000  ...   0.0078  0.0118 -0.0039
    0.0000  0.0000  0.0000  ...  -0.0118 -0.0078  0.0000
             ...             ⋱             ...          
    0.0039  0.0039  0.0078  ...   0.0157  0.0314  0.0471
   -0.0157 -0.0039 -0.0235  ...   0.0863  0.1059  0.2196
    0.0039  0.0039  0.0039  ...   0.1020  0.1961  0.1451
  
  ( 1 ,.,.) = 
    0.0000  0.0000  0.0000  ...   0.0000  0.0078  0.0000
    0.0000  0.0000  0.0000  ...   0.0000  0.0039 -0.0039
    0.0000  0.0000  0.0000  ...  -0.0118 -0.0078  0.0000
             ...             ⋱             ...          
    0.0039  0.0039  0.0078  ...   0.0196  0.0314  0.0471
   -0.0157 -0.0039 -0.0235  ...   0.0706  0.0863  0.2039
    0.0039  0.0039  0.0039  ...   0.0941  0.1922  0.1451
  
  ( 2 ,.,.) = 
    0.0000  0.0000  0.0000  ...   0.0000  0.0078  0.0000
    0.0000  0.0000  0.0000  ...   0.0000  0.0039 -0.0039
    0.0000  0.0000  0.0000  ...  -0.0196 -0.0078  0.0000
             ...             ⋱             ...          
    0.0039  0.0039  0.0078  ...   0.0039  0.0078  0.0118
   -0.0196 -0.0078 -0.0235  ...   0.0275  0.0314  0.1451
    0.0039  0.0039  0.0039  ...   0.0745  0.1725  0.0980
  [torch.FloatTensor of size 3x511x511]
  , 'train': True, 'output': [torch.FloatTensor with no dimension]
  })
  (1): nn.SpatialReplicationPadding(4, 4, 4, 4)
  (2): nn.SpatialConvolution(3 -> 32, 9x9)
  (3): nn.InstanceNormalization
  (4): nn.ReLU
  (5): nn.SpatialConvolution(32 -> 64, 3x3, 2, 2, 1, 1)
  (6): nn.InstanceNormalization
  (7): nn.ReLU
  (8): nn.SpatialConvolution(64 -> 128, 3x3, 2, 2, 1, 1)
  (9): nn.InstanceNormalization
  (10): nn.ReLU
  (11): nn.Sequential {
    [input -> (0) -> (1) -> output]
    (0): torch.legacy.nn.ConcatTable.ConcatTable {
      input
        |`-> (0): nn.Identity
        |`-> (1): nn.Sequential {
               [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
               (0): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (1): nn.SpatialConvolution(128 -> 128, 3x3)
               (2): nn.InstanceNormalization
               (3): nn.ReLU
               (4): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (5): nn.SpatialConvolution(128 -> 128, 3x3)
               (6): nn.InstanceNormalization
             }
         +. -> output
    }
    (1): nn.CAddTable
  }
  (12): nn.Sequential {
    [input -> (0) -> (1) -> output]
    (0): torch.legacy.nn.ConcatTable.ConcatTable {
      input
        |`-> (0): nn.Identity
        |`-> (1): nn.Sequential {
               [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
               (0): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (1): nn.SpatialConvolution(128 -> 128, 3x3)
               (2): nn.InstanceNormalization
               (3): nn.ReLU
               (4): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (5): nn.SpatialConvolution(128 -> 128, 3x3)
               (6): nn.InstanceNormalization
             }
         +. -> output
    }
    (1): nn.CAddTable
  }
  (13): nn.Sequential {
    [input -> (0) -> (1) -> output]
    (0): torch.legacy.nn.ConcatTable.ConcatTable {
      input
        |`-> (0): nn.Identity
        |`-> (1): nn.Sequential {
               [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
               (0): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (1): nn.SpatialConvolution(128 -> 128, 3x3)
               (2): nn.InstanceNormalization
               (3): nn.ReLU
               (4): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (5): nn.SpatialConvolution(128 -> 128, 3x3)
               (6): nn.InstanceNormalization
             }
         +. -> output
    }
    (1): nn.CAddTable
  }
  (14): nn.Sequential {
    [input -> (0) -> (1) -> output]
    (0): torch.legacy.nn.ConcatTable.ConcatTable {
      input
        |`-> (0): nn.Identity
        |`-> (1): nn.Sequential {
               [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
               (0): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (1): nn.SpatialConvolution(128 -> 128, 3x3)
               (2): nn.InstanceNormalization
               (3): nn.ReLU
               (4): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (5): nn.SpatialConvolution(128 -> 128, 3x3)
               (6): nn.InstanceNormalization
             }
         +. -> output
    }
    (1): nn.CAddTable
  }
  (15): nn.Sequential {
    [input -> (0) -> (1) -> output]
    (0): torch.legacy.nn.ConcatTable.ConcatTable {
      input
        |`-> (0): nn.Identity
        |`-> (1): nn.Sequential {
               [input -> (0) -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> output]
               (0): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (1): nn.SpatialConvolution(128 -> 128, 3x3)
               (2): nn.InstanceNormalization
               (3): nn.ReLU
               (4): nn.SpatialReplicationPadding(1, 1, 1, 1)
               (5): nn.SpatialConvolution(128 -> 128, 3x3)
               (6): nn.InstanceNormalization
             }
         +. -> output
    }
    (1): nn.CAddTable
  }
  (16): nn.SpatialFullConvolution(128 -> 64, 3x3, 2, 2, 1, 1, 1, 1)
  (17): nn.InstanceNormalization
  (18): nn.ReLU
  (19): nn.SpatialFullConvolution(64 -> 32, 3x3, 2, 2, 1, 1, 1, 1)
  (20): nn.InstanceNormalization
  (21): nn.ReLU
  (22): nn.SpatialReplicationPadding(1, 1, 1, 1)
  (23): nn.SpatialConvolution(32 -> 3, 3x3)
}

the model is loaded perfectly fine but when i am trying to evaluate model , i get this error
 Traceback (most recent call last):
  File "convert-fast-neural-style.py", line 176, in <module>
    main()
  File "convert-fast-neural-style.py", line 162, in main
    unknown_layer_converter_fn=convert_instance_norm
  File "/usr/local/lib/python2.7/dist-packages/torch2coreml/_torch_converter.py", line 194, in convert
    print (model.evaluate())
  File "/usr/local/lib/python2.7/dist-packages/torch/legacy/nn/Container.py", line 39, in evaluate
    self.applyToModules(lambda m: m.evaluate())
  File "/usr/local/lib/python2.7/dist-packages/torch/legacy/nn/Container.py", line 26, in applyToModules
    func(module)
  File "/usr/local/lib/python2.7/dist-packages/torch/legacy/nn/Container.py", line 39, in <lambda>
    self.applyToModules(lambda m: m.evaluate())
TypeError: 'NoneType' object is not callable

what is the main reason behind the evaluation to bring this NoneType error.
is their any other may that is equivalent to m.evaluate()
for reference also this is the Model 2  i am trying to evaluate