docstr: Test whether build_for_inference and build_for_training return the same
  batch when they have to.
docstr_tok: ["Test","whether","build_for_inference","and","build_for_training","return","the","same","batch","when","they","have","to","."]
code: def test_inference_vs_training_batch(self):
      """Test whether build_for_inference and build_for_training return the same
      batch when they have to."""
      obs_space = gym.spaces.Box(-np.ones(4), np.ones(4))
      ctx_len = 5
      view_reqs = {
          SampleBatch.T: ViewRequirement(SampleBatch.T),
          SampleBatch.OBS: ViewRequirement("obs", space=obs_space),
          # include the current obs in the context
          "prev_obses": ViewRequirement(
              "obs", shift=f"-{ctx_len - 1}:0"
          ),
      }
  
      n_steps = 100
      obses = np.random.rand(n_steps, 4)
      # list to store the last ctx_len obses
      for training_mode in [False, True]:
          ac = AgentCollector(
              view_reqs=view_reqs,
              is_policy_recurrent=True,
              max_seq_len=20,  # default max_seq_len in lstm
              is_training=training_mode,
          )
          obses_ctx = []
          for t, obs in enumerate(obses):
              if t == 0:
                  # e.g. state = env.reset()
                  ac.add_init_obs(
                      episode_id=0,
                      agent_index=1,
                      env_id=0,
                      init_obs=obs,
                  )
                  obses_ctx.extend([obs for _ in range(ctx_len)])
              else:
                  # e.g. next_state = env.step()
                  ac.add_action_reward_next_obs(
                      {SampleBatch.NEXT_OBS: obs}
                  )
                  # pop from front and add to the end
                  obses_ctx.pop(0)
                  obses_ctx.append(obs)
              eval_batch = ac.build_for_inference()
              # batch size should always be one
              self.assertEqual(eval_batch.count, 1)
              # shape of prev_obses should be (1, ctx_len, 4)
              self.assertEqual(
                  eval_batch["prev_obses"].shape, (1, ctx_len, 4)
              )
              # obs should always be the last time step obs added
              check(eval_batch["obs"], obs[None])
              # prev_obs should always be the last ctx_len time steps obs added
              # (excluding the current time step)
              check(
                  eval_batch["prev_obses"], np.stack(obses_ctx, 0)[None]
              )
  
          # in inference mode the buffer length at the end should be just ctx_len
          if not training_mode:
              check(len(ac.buffers[SampleBatch.OBS][0]), ctx_len)
          else:
              # otherwise it should be n_steps + ctx_len - 1
              check(
                  len(ac.buffers[SampleBatch.OBS][0]),
                  n_steps + ctx_len - 1,
              )
  
      self.assertTrue(ac.training, "Training mode should be True.")
      train_batch = ac.build_for_training(view_reqs)
      self.assertEqual(
          len(train_batch["seq_lens"]),
          math.ceil(n_steps / ac.max_seq_len),
      )
      self.assertEqual(
          train_batch["prev_obses"].shape, (n_steps - 1, ctx_len, 4)
      )
      self.assertEqual(
          train_batch[SampleBatch.OBS].shape, (n_steps - 1, 4)
      )
  
imports: import gymnasium
  import numpy
  import unittest
  import ray
  import math
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils.test_utils import check
  from ray.rllib.evaluation.collectors.agent_collector import AgentCollector
label_desc: Use image node_id 4 for calling the TestAgentCollector obj's underlying member method code with example usage: obj.test_inference_vs_training_batch() without return types