code: def __init__(self, hparams: dict):
      super().__init__()
  
      self.model = nn.Sequential(
          nn.Linear(hparams["input_size"], hparams["lin1_size"]),
          nn.BatchNorm1d(hparams["lin1_size"]),
          nn.ReLU(),
          nn.Linear(hparams["lin1_size"], hparams["lin2_size"]),
          nn.BatchNorm1d(hparams["lin2_size"]),
          nn.ReLU(),
          nn.Linear(hparams["lin2_size"], hparams["lin3_size"]),
          nn.BatchNorm1d(hparams["lin3_size"]),
          nn.ReLU(),
          nn.Linear(hparams["lin3_size"], hparams["output_size"]),
      )
  
imports: from torch import nn
label_desc: Use image node_id 1 to create a new SimpleDenseNet object from inherited base classes: nn with example: obj = SimpleDenseNet(hparams)