class_docstr: A Policy that always knows the current EpisodeID and EnvID and
  returns these in its actions.
class_docstr_tok: ["A","Policy","that","always","knows","the","current","EpisodeID","and","EnvID","and","returns","these","in","its","actions","."]
code: def compute_actions_from_input_dict(
      self, input_dict, explore=None, timestep=None, **kwargs
  ):
      ts = input_dict["t"]
      print(ts)
      # Always return [episodeID, envID] as actions.
      actions = np.array(
          [
              [
                  input_dict[SampleBatch.AGENT_INDEX][i],
                  input_dict[SampleBatch.EPS_ID][i],
                  input_dict["env_id"][i],
              ]
              for i, _ in enumerate(input_dict["obs"])
          ]
      )
      states = [
          np.array([[ts[i]] for i in range(len(input_dict["obs"]))])
      ]
      self.global_timestep += 1
      return actions, states, {}
  
imports: import numpy
  import tree
  from gymnasium.spaces import Box
  from ray.rllib.core.models.base import STATE_OUT
  from ray.rllib.core.rl_module.rl_module import RLModuleConfig
  from ray.rllib.examples.policy.random_policy import RandomPolicy
  from ray.rllib.examples.rl_module.episode_env_aware_rlm import StatefulRandomRLModule
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.view_requirement import ViewRequirement
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.numpy import convert_to_numpy
label_desc: Use image node_id 4 for calling the EpisodeEnvAwareAttentionPolicy obj's underlying member method code with example usage: obj.compute_actions_from_input_dict(input_dict, explore, timestep) and returns: actions,  states,  dict