docstr: Test the get_prediction and get_targets function.
docstr_tok: ["Test","the","get_prediction","and","get_targets","function","."]
code: def test_torch_model_prediction_target(self):
      """Test the get_prediction and get_targets function."""
      model_config = {
          "embed_dim": 16,
          "num_layers": 3,
          "max_seq_len": 3,
          "max_ep_len": 9,
          "num_heads": 1,
          "embed_pdrop": 0.2,
          "resid_pdrop": 0.2,
          "attn_pdrop": 0.2,
          "use_obs_output": True,
          "use_return_output": True,
      }
  
      num_outputs = 5
      observation_space = gym.spaces.Box(
          -1.0, 1.0, shape=(num_outputs,)
      )
  
      action_dim = 2
      action_spaces = [
          gym.spaces.Box(-1.0, 1.0, shape=(action_dim,)),
          gym.spaces.Discrete(action_dim),
      ]
  
      B, T = 2, 3
  
      for action_space in action_spaces:
          # Generate input dict.
          input_dict = _generate_input_dict(
              B, T, observation_space, action_space
          )
  
          # Make model and forward pass.
          model = DTTorchModel(
              observation_space,
              action_space,
              num_outputs,
              model_config,
              "model",
          )
          model_out, _ = model(input_dict)
          preds = model.get_prediction(model_out, input_dict)
          target = model.get_targets(model_out, input_dict)
  
          preds = convert_to_numpy(preds)
          target = convert_to_numpy(target)
  
          # Test the content and shape of output and target
          if isinstance(action_space, gym.spaces.Box):
              # test preds shape
              self.assertEqual(
                  preds[SampleBatch.ACTIONS].shape, (B, T, action_dim)
              )
              # test target shape and content
              self.assertEqual(
                  target[SampleBatch.ACTIONS].shape, (B, T, action_dim)
              )
              assert np.allclose(
                  target[SampleBatch.ACTIONS],
                  input_dict[SampleBatch.ACTIONS],
              )
          else:
              # test preds shape
              self.assertEqual(
                  preds[SampleBatch.ACTIONS].shape, (B, T, action_dim)
              )
              # test target shape and content
              self.assertEqual(
                  target[SampleBatch.ACTIONS].shape, (B, T)
              )
              assert np.allclose(
                  target[SampleBatch.ACTIONS],
                  input_dict[SampleBatch.ACTIONS],
              )
  
          # test preds shape
          self.assertEqual(
              preds[SampleBatch.OBS].shape, (B, T, num_outputs)
          )
          # test target shape and content
          self.assertEqual(
              target[SampleBatch.OBS].shape, (B, T, num_outputs)
          )
          assert np.allclose(
              target[SampleBatch.OBS],
              input_dict[SampleBatch.OBS],
          )
  
          # test preds shape
          self.assertEqual(
              preds[SampleBatch.RETURNS_TO_GO].shape, (B, T, 1)
          )
          # test target shape and content
          self.assertEqual(
              target[SampleBatch.RETURNS_TO_GO].shape, (B, T, 1)
          )
          assert np.allclose(
              target[SampleBatch.RETURNS_TO_GO],
              input_dict[SampleBatch.RETURNS_TO_GO][:, 1:, :],
          )
  
file_path: ray/rllib_contrib/dt/tests/test_dt_model.py
imports: import unittest
  import gymnasium
  import numpy
  from rllib_dt.dt.dt_torch_model import DTTorchModel
  import ray
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 4 for calling the TestDTModel obj's underlying member method code with example usage: obj.test_torch_model_prediction_target() without return types