code: def __init__(self, aug_dim=2):
      super().__init__()
      self.initial_pos = nn.Parameter(torch.tensor([10.0]))
      self.initial_aug = nn.Parameter(torch.zeros(aug_dim))
      self.odefunc = mlp(
          input_dim=1 + aug_dim,
          hidden_dim=64,
          output_dim=1 + aug_dim,
          hidden_depth=2,
          act=Sine,
      )
  
      def init(m):
          if isinstance(m, nn.Linear):
              std = 1.0 / math.sqrt(m.weight.size(1))
              m.weight.data.uniform_(-2.0 * std, 2.0 * std)
              m.bias.data.zero_()
  
      self.odefunc.apply(init)
  
imports: import argparse
  import os
  import math
  import matplotlib.pyplot
  import torch
  import torch.nn
  from torchdiffeq import odeint, odeint_event
  from bouncing_ball import BouncingBallExample
label_desc: Use image node_id 1 to create a new NeuralODE object from inherited base classes: nn with example: obj = NeuralODE(aug_dim)