code: def __init__(self, data_size, latent_size, context_size, hidden_size):
      super(LatentSDE, self).__init__()
      # Encoder.
      self.encoder = Encoder(
          input_size=data_size,
          hidden_size=hidden_size,
          output_size=context_size,
      )
      self.qz0_net = nn.Linear(context_size, latent_size + latent_size)
  
      # Decoder.
      self.f_net = nn.Sequential(
          nn.Linear(latent_size + context_size, hidden_size),
          nn.Softplus(),
          nn.Linear(hidden_size, hidden_size),
          nn.Softplus(),
          nn.Linear(hidden_size, latent_size),
      )
      self.h_net = nn.Sequential(
          nn.Linear(latent_size, hidden_size),
          nn.Softplus(),
          nn.Linear(hidden_size, hidden_size),
          nn.Softplus(),
          nn.Linear(hidden_size, latent_size),
      )
      # This needs to be an element-wise function for the SDE to satisfy diagonal noise.
      self.g_nets = nn.ModuleList(
          [
              nn.Sequential(
                  nn.Linear(1, hidden_size),
                  nn.Softplus(),
                  nn.Linear(hidden_size, 1),
                  nn.Sigmoid(),
              )
              for _ in range(latent_size)
          ]
      )
      self.projector = nn.Linear(latent_size, data_size)
  
      self.pz0_mean = nn.Parameter(torch.zeros(1, latent_size))
      self.pz0_logstd = nn.Parameter(torch.zeros(1, latent_size))
  
      self._ctx = None
  
imports: import logging
  import os
  from typing import Sequence
  import fire
  import matplotlib.gridspec
  import matplotlib.pyplot
  import numpy
  import torch
  import tqdm
  from torch import nn
  from torch import optim
  from torch.distributions import Normal
  import torchsde
label_desc: Use image node_id 1 to create a new LatentSDE object from inherited base classes: nn with example: obj = LatentSDE(data_size, latent_size, context_size, hidden_size)