code: def create_for_env(cls, env: gym.Env) -> "MultiDiscreteRandomPolicy":
      action_space = env.action_space
      if not isinstance(action_space, gym.spaces.MultiDiscrete):
          raise ValueError(f"Invalid action space: {action_space}")
  
      return cls(action_space.nvec.tolist())
  
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 2 for calling the MultiDiscreteRandomPolicy obj's underlying member method code with example usage: obj.create_for_env(cls, env) and returns: cls