code: def __init__(self, low: torch.Tensor, high: torch.Tensor) -> None:
      self.low = low
      self.high = high
      assert (
          low.shape == high.shape
      ), f"low.shape = {low.shape}, high.shape = {high.shape}"
      self.dist = torch.distributions.uniform.Uniform(
          self.low, self.high
      )
  
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 ContinuousRandomPolicy object from inherited base classes: Policy with example: obj = ContinuousRandomPolicy(low, high)