code: def forward(self, ys_coeffs):
      # ys_coeffs has shape (batch_size, t_size, 1 + data_size)
      # The +1 corresponds to time. When solving CDEs, It turns out to be most natural to treat time as just another
      # channel: in particular this makes handling irregular data quite easy, when the times may be different between
      # different samples in the batch.
  
      Y = torchcde.LinearInterpolation(ys_coeffs)
      Y0 = Y.evaluate(Y.interval[0])
      h0 = self._initial(Y0)
      hs = torchcde.cdeint(
          Y,
          self._func,
          h0,
          Y.interval,
          method="reversible_heun",
          backend="torchsde",
          dt=1.0,
          adjoint_method="adjoint_reversible_heun",
          adjoint_params=(ys_coeffs,) + tuple(self._func.parameters()),
      )
      score = self._readout(hs[:, -1])
      return score.mean()
  
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 2 for calling the Discriminator obj's underlying member method code with example usage: obj.forward(ys_coeffs) and returns: score