code: def create_for_env(cls, env: gym.Env) -> "ContinuousRandomPolicy":
      action_space = env.action_space
      if isinstance(action_space, gym.spaces.Discrete):
          raise NotImplementedError(
              f"Action space is discrete. Try using DiscreteRandomPolicy instead."
          )
      elif isinstance(action_space, gym.spaces.Box):
          assert (
              len(action_space.shape) == 1
          ), f"Box with shape {action_space.shape} not supported."
          low, high = CONTINUOUS_TRAINING_ACTION_RANGE
          # broadcast low and high to shape
          np_ones = np.ones(action_space.shape)
          return cls(
              low=torch.tensor(low * np_ones),
              high=torch.tensor(high * np_ones),
          )
      else:
          raise NotImplementedError(
              f"action_space is {type(action_space)}"
          )
  
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 ContinuousRandomPolicy obj's underlying member method code with example usage: obj.create_for_env(cls, env) and returns: cls