code: def get_exploration_action(
      self,
      action_distribution: ActionDistribution,
      timestep: Union[int, TensorType],
      explore: bool = True,
  ):
      assert (
          self.framework == "torch"
      ), "ERROR: SlateSoftQ only supports torch so far!"
  
      cls = type(action_distribution)
  
      # Re-create the action distribution with the correct temperature
      # applied.
      action_distribution = cls(
          action_distribution.inputs,
          self.model,
          temperature=self.temperature,
      )
      batch_size = action_distribution.inputs.size()[0]
      action_logp = torch.zeros(batch_size, dtype=torch.float)
  
      self.last_timestep = timestep
  
      # Explore.
      if explore:
          # Return stochastic sample over (q-value) logits.
          action = action_distribution.sample()
      # Return the deterministic "sample" (argmax) over (q-value) logits.
      else:
          action = action_distribution.deterministic_sample()
  
      return action, action_logp
  
file_path: ray/rllib/utils/exploration/slate_soft_q.py
imports: from typing import Union
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.exploration.exploration import TensorType
  from ray.rllib.utils.exploration.soft_q import SoftQ
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
label_desc: Use image node_id 1 for calling the SlateSoftQ obj's underlying member method code with example usage: obj.get_exploration_action(action_distribution, timestep, explore) and returns: action,  action_logp