docstr: Test that the ViewRequirementAgentConnector can handle slice shifts correctly
  when it has multiple buffers to shift.
docstr_tok: ["Test","that","the","ViewRequirementAgentConnector","can","handle","slice","shifts","correctly","when","it","has","multiple","buffers","to","shift","."]
code: def test_vr_connector_with_multiple_buffers(self):
      """Test that the ViewRequirementAgentConnector can handle slice shifts correctly
      when it has multiple buffers to shift."""
      context_len = 5
      # This view requirement simulates the use-case of a decision transformer
      # without reward-to-go.
      view_rq_dict = {
          # obs[t-context_len+1:t]
          "context_obs": ViewRequirement(
              "obs",
              shift=f"-{context_len-1}:0",
              space=Box(-np.inf, np.inf, shape=(1,), dtype=np.float64),
          ),
          # next_obs[t-context_len+1:t]
          "context_next_obs": ViewRequirement(
              "obs",
              shift=f"-{context_len}:1",
              used_for_compute_actions=False,
              space=Box(-np.inf, np.inf, shape=(1,), dtype=np.float64),
          ),
          # act[t-context_len+1:t]
          "context_act": ViewRequirement(
              SampleBatch.ACTIONS,
              shift=f"-{context_len-1}:-1",
              space=Box(-np.inf, np.inf, shape=(1,)),
          ),
      }
  
      obs_arrs = np.arange(10)[:, None] + 1
      act_arrs = (np.arange(10)[:, None] + 1) * 100
      n_steps = obs_arrs.shape[0]
      config = PPOConfig().to_dict()
      ctx = ConnectorContext(
          view_requirements=view_rq_dict,
          config=config,
          is_policy_recurrent=True,
      )
      c = ViewRequirementAgentConnector(ctx)
  
      # keep a queue of length ctx_len of observations
      obs_list, act_list = [], []
      for t in range(n_steps):
          # next state and action at time t-1 are the following
          timestep_data = {
              SampleBatch.NEXT_OBS: obs_arrs[t],
          }
          if t > 0:
              timestep_data[SampleBatch.ACTIONS] = act_arrs[t - 1]
          data = AgentConnectorDataType(0, 1, timestep_data)
          processed = c([data])
          sample_batch = processed[0].data.sample_batch
  
          if t == 0:
              obs_list.extend([obs_arrs[0] for _ in range(context_len)])
              act_list.extend(
                  [
                      np.zeros_like(act_arrs[0])
                      for _ in range(context_len)
                  ]
              )
          else:
              obs_list.pop(0)
              act_list.pop(0)
              obs_list.append(obs_arrs[t])
              act_list.append(act_arrs[t - 1])
  
          self.assertTrue("context_next_obs" not in sample_batch)
          # We should have the 5 (context_len) most recent observations here
          check(sample_batch["context_obs"], np.stack(obs_list)[None])
          # The context for actions is [t-context_len+1:t]. Since we build sample
          # batch for inference in ViewRequirementAgentConnector, it always
          # includes everything up until the last action (at t-1), but not the
          # action current action (at t).
          check(
              sample_batch["context_act"], np.stack(act_list[1:])[None]
          )
  
imports: import gymnasium
  from gymnasium.spaces import Box
  import numpy
  import tree
  import unittest
  from ray.rllib.algorithms.ppo.ppo import PPOConfig
  from ray.rllib.connectors.agent.clip_reward import ClipRewardAgentConnector
  from ray.rllib.connectors.agent.lambdas import FlattenDataAgentConnector
  from ray.rllib.connectors.agent.obs_preproc import ObsPreprocessorConnector
  from ray.rllib.connectors.agent.pipeline import AgentConnectorPipeline
  from ray.rllib.connectors.agent.state_buffer import StateBufferConnector
  from ray.rllib.connectors.agent.view_requirement import ViewRequirementAgentConnector
  from ray.rllib.connectors.connector import ConnectorContext
  from ray.rllib.connectors.registry import get_connector
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.test_utils import check
  from ray.rllib.utils.typing import ActionConnectorDataType, AgentConnectorDataType, AgentConnectorsOutput
  from ray.rllib.connectors.agent.mean_std_filter import MeanStdObservationFilterAgentConnector
label_desc: Use image node_id 4 for calling the TestViewRequirementAgentConnector obj's underlying member method code with example usage: obj.test_vr_connector_with_multiple_buffers() without return types