code: def __init__(self):
      super(ODEFunc, self).__init__()
  
      self.net = nn.Sequential(
          nn.Linear(2, 50),
          nn.Tanh(),
          nn.Linear(50, 2),
      )
  
      for m in self.net.modules():
          if isinstance(m, nn.Linear):
              nn.init.normal_(m.weight, mean=0, std=0.1)
              nn.init.constant_(m.bias, val=0)
  
imports: import os
  import argparse
  import time
  import numpy
  import torch
  import torch.nn
  import torch.optim
label_desc: Use image node_id 1 to create a new ODEFunc object from inherited base classes: nn with example: obj = ODEFunc()