docstr: Test whether a SampleBatch produces the correct 1-step input dict.
docstr_tok: ["Test","whether","a","SampleBatch","produces","the","correct","1-step","input","dict","."]
code: def test_get_single_step_input_dict_batch_repeat_value_1(self):
      """Test whether a SampleBatch produces the correct 1-step input dict."""
      space = Box(-1.0, 1.0, ())
  
      # With batch-repeat-value==1: state_in_0 is built each timestep.
      view_reqs = {
          "state_in_0": ViewRequirement(
              data_col="state_out_0",
              shift="-5:-1",
              space=space,
              batch_repeat_value=1,
          ),
          "state_out_0": ViewRequirement(
              space=space, used_for_compute_actions=False
          ),
      }
  
      # Trajectory of 1 ts (0) (we would like to compute the 1st).
      batch = SampleBatch(
          {
              "state_in_0": np.array(
                  [
                      [0, 0, 0, 0, 0],  # ts=0
                  ]
              ),
              "state_out_0": np.array([1]),
          }
      )
      input_dict = batch.get_single_step_input_dict(
          view_requirements=view_reqs, index="last"
      )
      check(
          input_dict,
          {
              "state_in_0": [[0, 0, 0, 0, 1]],  # ts=1
              SampleBatch.SEQ_LENS: [1],
          },
      )
  
      # Trajectory of 6 ts (0-5) (we would like to compute the 6th).
      batch = SampleBatch(
          {
              "state_in_0": np.array(
                  [
                      [0, 0, 0, 0, 0],  # ts=0
                      [0, 0, 0, 0, 1],  # ts=1
                      [0, 0, 0, 1, 2],  # ts=2
                      [0, 0, 1, 2, 3],  # ts=3
                      [0, 1, 2, 3, 4],  # ts=4
                      [1, 2, 3, 4, 5],  # ts=5
                  ]
              ),
              "state_out_0": np.array([1, 2, 3, 4, 5, 6]),
          }
      )
      input_dict = batch.get_single_step_input_dict(
          view_requirements=view_reqs, index="last"
      )
      check(
          input_dict,
          {
              "state_in_0": [[2, 3, 4, 5, 6]],  # ts=6
              SampleBatch.SEQ_LENS: [1],
          },
      )
  
      # Trajectory of 12 ts (0-11) (we would like to compute the 12th).
      batch = SampleBatch(
          {
              "state_in_0": np.array(
                  [
                      [0, 0, 0, 0, 0],  # ts=0
                      [0, 0, 0, 0, 1],  # ts=1
                      [0, 0, 0, 1, 2],  # ts=2
                      [0, 0, 1, 2, 3],  # ts=3
                      [0, 1, 2, 3, 4],  # ts=4
                      [1, 2, 3, 4, 5],  # ts=5
                      [2, 3, 4, 5, 6],  # ts=6
                      [3, 4, 5, 6, 7],  # ts=7
                      [4, 5, 6, 7, 8],  # ts=8
                      [5, 6, 7, 8, 9],  # ts=9
                      [6, 7, 8, 9, 10],  # ts=10
                      [7, 8, 9, 10, 11],  # ts=11
                  ]
              ),
              "state_out_0": np.array(
                  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
              ),
          }
      )
      input_dict = batch.get_single_step_input_dict(
          view_requirements=view_reqs, index="last"
      )
      check(
          input_dict,
          {
              "state_in_0": [[8, 9, 10, 11, 12]],  # ts=12
              SampleBatch.SEQ_LENS: [1],
          },
      )
  
imports: import gymnasium
  from gymnasium.spaces import Box, Discrete
  import numpy
  import unittest
  import ray
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  import ray.rllib.algorithms.dqn
  import ray.rllib.algorithms.ppo
  from ray.rllib.examples.env.debug_counter_env import MultiAgentDebugCounterEnv
  from ray.rllib.examples.env.multi_agent import MultiAgentPendulum
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.examples.policy.episode_env_aware_policy import EpisodeEnvAwareAttentionPolicy, StatefulRandomPolicy
  from ray.rllib.models.tf.attention_net import GTrXLNet
  from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch, convert_ma_batch_to_sample_batch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.test_utils import framework_iterator, check
label_desc: Use image node_id 11 for calling the TestTrajectoryViewAPI obj's underlying member method code with example usage: obj.test_get_single_step_input_dict_batch_repeat_value_1() without return types