code: def __init__(
      self, wrapped_dqn_predictor, rl_parameters: Optional[RLParameters]
  ) -> None:
      if rl_parameters and rl_parameters.softmax_policy:
          self.sampler = SoftmaxActionSampler(
              temperature=rl_parameters.temperature
          )
      else:
          self.sampler = GreedyActionSampler()
      self.scorer = discrete_dqn_serving_scorer(
          q_network=DiscreteDqnPredictorUnwrapper(wrapped_dqn_predictor)
      )
  
imports: from typing import Optional, Tuple, Union
  import reagent.core.types
  import torch
  from reagent.core.fb_checker import IS_FB_ENVIRONMENT
  from reagent.core.parameters import RLParameters
  from reagent.gym.policies import Policy
  from reagent.gym.policies.samplers.discrete_sampler import GreedyActionSampler, SoftmaxActionSampler
  from reagent.gym.policies.samplers.top_k_sampler import TopKSampler
  from reagent.gym.policies.scorers.discrete_scorer import discrete_dqn_serving_scorer, parametric_dqn_serving_scorer
  from reagent.gym.policies.scorers.slate_q_scorer import slate_q_serving_scorer
  from reagent.models.actor import LOG_PROB_MAX, LOG_PROB_MIN
label_desc: Use image node_id 1 to create a new DiscreteDQNPredictorPolicy object from inherited base classes: Policy with example: obj = DiscreteDQNPredictorPolicy(wrapped_dqn_predictor, rl_parameters)