code: def __init__(self, num_classes=1000):
      super(VGGM, self).__init__()
      self.num_classes = num_classes
      self.features = nn.Sequential(
          nn.Conv2d(3, 96, (7, 7), (2, 2)),
          nn.ReLU(),
          SpatialCrossMapLRN(5, 0.0005, 0.75, 2),
          nn.MaxPool2d((3, 3), (2, 2), (0, 0), ceil_mode=True),
          nn.Conv2d(96, 256, (5, 5), (2, 2), (1, 1)),
          nn.ReLU(),
          SpatialCrossMapLRN(5, 0.0005, 0.75, 2),
          nn.MaxPool2d((3, 3), (2, 2), (0, 0), ceil_mode=True),
          nn.Conv2d(256, 512, (3, 3), (1, 1), (1, 1)),
          nn.ReLU(),
          nn.Conv2d(512, 512, (3, 3), (1, 1), (1, 1)),
          nn.ReLU(),
          nn.Conv2d(512, 512, (3, 3), (1, 1), (1, 1)),
          nn.ReLU(),
          nn.MaxPool2d((3, 3), (2, 2), (0, 0), ceil_mode=True),
      )
      self.classif = nn.Sequential(
          nn.Linear(18432, 4096),
          nn.ReLU(),
          nn.Dropout(0.5),
          nn.Linear(4096, 4096),
          nn.ReLU(),
          nn.Dropout(0.5),
          nn.Linear(4096, num_classes),
      )
  
imports: from __future__ import print_function, division, absolute_import
  import torch
  import torch.nn
  from torch.autograd import Variable
  import torch.utils.model_zoo
label_desc: Use image node_id 1 to create a new VGGM object from inherited base classes: nn with example: obj = VGGM(num_classes)