docstr: Random actor for accumulating random offline data.
docstr_tok: ["Random","actor","for","accumulating","random","offline","data","."]
code: def __init__(self, num_actions: int) -> None:
      """Random actor for accumulating random offline data."""
      self.num_actions = num_actions
  
imports: from typing import List, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import CONTINUOUS_TRAINING_ACTION_RANGE
  from reagent.gym.policies.policy import Policy
  from reagent.gym.policies.scorers.discrete_scorer import apply_possible_actions_mask
label_desc: Use image node_id 1 to create a new DiscreteRandomPolicy object from inherited base classes: Policy with example: obj = DiscreteRandomPolicy(num_actions)