code: def __init__(self, noise_size, hidden_size, mlp_size, num_layers):
      super().__init__()
      self._noise_size = noise_size
      self._hidden_size = hidden_size
  
      ###################
      # Drift and diffusion are MLPs. They happen to be the same size.
      # Note the final tanh nonlinearity: this is typically important for good performance, to constrain the rate of
      # change of the hidden state.
      # If you have problems with very high drift/diffusions then consider scaling these so that they squash to e.g.
      # [-3, 3] rather than [-1, 1].
      ###################
      self._drift = MLP(
          1 + hidden_size, hidden_size, mlp_size, num_layers, tanh=True
      )
      self._diffusion = MLP(
          1 + hidden_size,
          hidden_size * noise_size,
          mlp_size,
          num_layers,
          tanh=True,
      )
  
imports: import fire
  import matplotlib.pyplot
  import torch
  import torch.optim.swa_utils
  import torchcde
  import torchsde
  import tqdm
label_desc: Use image node_id 1 to create a new GeneratorFunc object from inherited base classes: torch.nn with example: obj = GeneratorFunc(noise_size, hidden_size, mlp_size, num_layers)