code: def make_random_policy_for_env(env: gym.Env) -> Policy:
      if isinstance(env.action_space, gym.spaces.Discrete):
          # discrete action space
          return DiscreteRandomPolicy.create_for_env(env)
      elif isinstance(env.action_space, gym.spaces.Box):
          # continuous action space
          return ContinuousRandomPolicy.create_for_env(env)
      elif isinstance(env.action_space, gym.spaces.MultiDiscrete):
          return MultiDiscreteRandomPolicy.create_for_env(env)
      else:
          raise NotImplementedError(f"{env.action_space} not supported")
  
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 for calling a global function with example usage: make_random_policy_for_env(env) and returns: DiscreteRandomPolicy, ContinuousRandomPolicy, MultiDiscreteRandomPolicy