code: def __init__(self, num_classes=200, num_channels=3):
      """
      :param num_classes: model output size
      :param num_channels: number of input channels
      """
      super(SimpleMLP_TinyImageNet, self).__init__()
  
      self.features = nn.Sequential(
          nn.Linear(num_channels * 64 * 64, 1024),
          nn.ReLU(inplace=True),
          nn.Dropout(),
      )
      self.classifier = nn.Linear(1024, num_classes)
  
imports: import torch.nn
label_desc: Use image node_id 1 to create a new SimpleMLP_TinyImageNet object from inherited base classes: nn with example: obj = SimpleMLP_TinyImageNet(num_classes, num_channels)