class_docstr: Test the DDPOTrainer class.
class_docstr_tok: ["Test","the","DDPOTrainer","class","."]
code: def test_calculate_loss(self):
      samples, _ = self.trainer._generate_samples(1, 2)
      sample = samples[0]
  
      latents = sample["latents"][0, 0].unsqueeze(0)
      next_latents = sample["next_latents"][0, 0].unsqueeze(0)
      log_probs = sample["log_probs"][0, 0].unsqueeze(0)
      timesteps = sample["timesteps"][0, 0].unsqueeze(0)
      prompt_embeds = sample["prompt_embeds"]
      advantage = torch.tensor([1.0], device=prompt_embeds.device)
  
      self.assertEqual(latents.shape, (1, 4, 64, 64))
      self.assertEqual(next_latents.shape, (1, 4, 64, 64))
      self.assertEqual(log_probs.shape, (1,))
      self.assertEqual(timesteps.shape, (1,))
      self.assertEqual(prompt_embeds.shape, (2, 77, 32))
      loss, approx_kl, clipfrac = self.trainer.calculate_loss(
          latents,
          timesteps,
          next_latents,
          log_probs,
          advantage,
          prompt_embeds,
      )
  
      self.assertTrue(torch.isfinite(loss.cpu()))
  
file_path: trl/tests/test_ddpo_trainer.py
imports: import gc
  import unittest
  import torch
  from trl import is_diffusers_available, is_peft_available
  from .testing_utils import require_diffusers
label_desc: Use image node_id 5 for calling the DDPOTrainerTester obj's underlying member method code with example usage: obj.test_calculate_loss() without return types