class_docstr: Bernoulli Torch probability distribution for multi-binary action spaces.
  
  :param logits: the action selection logits.
  :param action_space: The gym action space.
  :param temperature: The distribution temperature parameter.
class_docstr_tok: ["Bernoulli","Torch","probability","distribution","for","multi-binary","action","spaces",".",":","param","logits",":","the","action","selection","logits",".",":","param","action_space",":","The","gym","action","space",".",":","param","temperature",":","The","distribution","temperature","parameter","."]
code: def __init__(
      self,
      logits: torch.Tensor,
      action_space: spaces.MultiBinary,
      temperature: float = 1.0,
  ):
      # apply temperature - can be applied in logits space in the same way as in the categorical distribution
      self.logits = logits / temperature
      super().__init__(
          dist=Bernoulli(logits=self.logits), action_space=action_space
      )
  
file_path: maze/maze/distributions/bernoulli.py
imports: from typing import Sequence
  import torch
  from gym import spaces
  from torch.distributions import Bernoulli, kl_divergence
  from maze.core.annotations import override
  from maze.distributions.torch_dist import TorchProbabilityDistribution
label_desc: Use image node_id 2 to create a new BernoulliProbabilityDistribution object from inherited base classes: TorchProbabilityDistribution with example: obj = BernoulliProbabilityDistribution(logits, action_space, temperature)