code: def evaluate_loss(
      ts, batch_size, dataloader, generator, discriminator
  ):
      with torch.no_grad():
          total_samples = 0
          total_loss = 0
          for (real_samples,) in dataloader:
              generated_samples = generator(ts, batch_size)
              generated_score = discriminator(generated_samples)
              real_score = discriminator(real_samples)
              loss = generated_score - real_score
              total_samples += batch_size
              total_loss += loss.item() * batch_size
      return total_loss / total_samples
  
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 3 for calling a global function with example usage: evaluate_loss(ts, batch_size, dataloader, generator, discriminator) and returns: unknown