code: def test_forward_train(self):
      bsize = 1024
      env = gym.make("CartPole-v1")
      module = DiscreteBCTFModule(
          config=RLModuleConfig(
              env.observation_space,
              env.action_space,
              model_config_dict={"fcnet_hiddens": [32]},
          )
      )
      obs_shape = env.observation_space.shape
      obs = tf.random.uniform((bsize,) + obs_shape)
      actions = tf.stack(
          [
              tf.convert_to_tensor(
                  env.action_space.sample(), dtype=tf.float32
              )
              for _ in range(bsize)
          ]
      )
      with tf.GradientTape() as tape:
          output = module.forward_train({"obs": obs})
          action_dist_class = module.get_train_action_dist_cls()
          action_dist = action_dist_class.from_logits(
              output[SampleBatch.ACTION_DIST_INPUTS]
          )
          loss = -tf.math.reduce_mean(action_dist.logp(actions))
  
      self.assertIsInstance(output, Mapping)
  
      grads = tape.gradient(loss, module.trainable_variables)
  
      # check that all neural net parameters have gradients
      for grad in grads:
          self.assertIsNotNone(grad)
  
file_path: ray/rllib/core/rl_module/tf/tests/test_tf_rl_module.py
imports: import tempfile
  import unittest
  from typing import Mapping
  import gymnasium
  import tensorflow
  from ray.rllib.core.rl_module.rl_module import RLModuleConfig
  from ray.rllib.core.rl_module.tf.tf_rl_module import TfRLModule
  from ray.rllib.core.testing.tf.bc_module import DiscreteBCTFModule
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.test_utils import check
label_desc: Use image node_id 2 for calling the TestRLModule obj's underlying member method code with example usage: obj.test_forward_train() without return types