class_docstr: Implementation of a Network In Network model
  
  :param num_classes (int): Number of classes for classification
  :param in_channels (int): Number of channels in input specimens
class_docstr_tok: ["Implementation","of","a","Network","In","Network","model",":","param","num_classes","(","int",")",":","Number","of","classes","for","classification",":","param","in_channels","(","int",")",":","Number","of","channels","in","input","specimens"]
code: def __init__(self, num_classes=10, in_channels=3):
      super(NetworkInNetwork, self).__init__()
  
      self.num_classes = num_classes
      self.in_channels = in_channels
  
      self.features = nn.Sequential(
          nn.Conv2d(self.in_channels, 192, 5, padding=2),
          nn.ReLU(inplace=True),
          nn.Conv2d(192, 160, 1),
          nn.ReLU(inplace=True),
          nn.Conv2d(160, 96, 1),
          nn.ReLU(inplace=True),
          nn.MaxPool2d(3, stride=2, ceil_mode=True),
          nn.Dropout(inplace=True),
          nn.Conv2d(96, 192, 5, padding=2),
          nn.ReLU(inplace=True),
          nn.Conv2d(192, 192, 1),
          nn.ReLU(inplace=True),
          nn.Conv2d(192, 192, 1),
          nn.ReLU(inplace=True),
          nn.AvgPool2d(3, stride=2, ceil_mode=True),
          nn.Dropout(inplace=True),
          nn.Conv2d(192, 192, 3, padding=1),
          nn.ReLU(inplace=True),
          nn.Conv2d(192, 192, 1),
          nn.ReLU(inplace=True),
          nn.Conv2d(192, self.num_classes, 1),
          nn.ReLU(inplace=True),
          nn.AvgPool2d(8, stride=1),
      )
      self._initialize_weights()
  
imports: from torch import nn
label_desc: Use image node_id 1 to create a new NetworkInNetwork object from inherited base classes: nn with example: obj = NetworkInNetwork(num_classes, in_channels)