class_docstr: Beta Torch probability distribution.
  
  :param logits: the logits for both mean and standard deviation.
  :param action_space: the underlying gym.spaces action space.
code: def __init__(
      self,
      logits: torch.Tensor,
      action_space: gym.spaces.Box,
      temperature: float,
  ):
      # make sure space bounds are set properly
      assert np.all(action_space.low > -np.inf) and np.all(
          action_space.low < np.inf
      )
  
      # prepare logits for distribution
      logits = torch.clamp(
          logits, math.log(EPSILON), -math.log(EPSILON)
      )
      logits = torch.log(torch.exp(logits) + 1.0) + 1.0
  
      # apply temperature to alpha, beta
      logits = logits / temperature
  
      # create low and high tensors for vectorized clamping
      self.low = convert_to_torch(
          action_space.low,
          device=logits.device,
          cast=logits.dtype,
          in_place=False,
      )
      self.high = convert_to_torch(
          action_space.high,
          device=logits.device,
          cast=logits.dtype,
          in_place=False,
      )
  
      alpha, beta = torch.chunk(logits, 2, dim=-1)
      super().__init__(
          dist=Beta(concentration1=alpha, concentration0=beta),
          action_space=action_space,
      )
  
imports: import math
  from typing import Sequence
  import numpy
  import gym
  import torch
  from gym import spaces
  from torch.distributions import Beta, kl_divergence
  from maze.core.annotations import override
  from maze.distributions.torch_dist import TorchProbabilityDistribution
  from maze.distributions.utils import EPSILON
  from maze.perception.perception_utils import convert_to_torch
label_desc: Use image node_id 2 to create a new BetaProbabilityDistribution object from inherited base classes: TorchProbabilityDistribution with example: obj = BetaProbabilityDistribution(logits, action_space, temperature)