code: def forward(self, ts, batch_size):
      # ts has shape (t_size,) and corresponds to the points we want to evaluate the SDE at.
  
      ###################
      # Actually solve the SDE.
      ###################
      init_noise = torch.randn(
          batch_size, self._initial_noise_size, device=ts.device
      )
      x0 = self._initial(init_noise)
  
      ###################
      # We use the reversible Heun method to get accurate gradients whilst using the adjoint method.
      ###################
      xs = torchsde.sdeint_adjoint(
          self._func,
          x0,
          ts,
          method="reversible_heun",
          dt=1.0,
          adjoint_method="adjoint_reversible_heun",
      )
      xs = xs.transpose(0, 1)
      ys = self._readout(xs)
  
      ###################
      # Normalise the data to the form that the discriminator expects, in particular including time as a channel.
      ###################
      ts = (
          ts.unsqueeze(0)
          .unsqueeze(-1)
          .expand(batch_size, ts.size(0), 1)
      )
      return torchcde.linear_interpolation_coeffs(
          torch.cat([ts, ys], dim=2)
      )
  
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 Generator obj's underlying member method code with example usage: obj.forward(ts, batch_size) and returns: torchcde