class_docstr: Base class for wrapping Torch probability distributions.
  
  :param dist: The torch probability distribution.
  :param action_space: The gym action space.
class_docstr_tok: ["Base","class","for","wrapping","Torch","probability","distributions",".",":","param","dist",":","The","torch","probability","distribution",".",":","param","action_space",":","The","gym","action","space","."]
docstr: Returns the required shape for the corresponding neural network logits output.
  
  :param action_space: The respective action space to compute logits for.
  :return: The required logits shape.
docstr_tok: ["Returns","the","required","shape","for","the","corresponding","neural","network","logits","output",".",":","param","action_space",":","The","respective","action","space","to","compute","logits","for",".",":","return",":","The","required","logits","shape","."]
code: def required_logits_shape(
      cls, action_space: spaces.Space
  ) -> Sequence[int]:
      """Returns the required shape for the corresponding neural network logits output.
  
      :param action_space: The respective action space to compute logits for.
      :return: The required logits shape.
      """
  
file_path: maze/maze/distributions/torch_dist.py
imports: from abc import ABC, abstractmethod
  from typing import Sequence, Generic, TypeVar
  import torch
  import torch.distributions
  from gym import spaces
  from maze.core.annotations import override
  from maze.distributions.distribution import ProbabilityDistribution
label_desc: Use image node_id 1 for calling the TorchProbabilityDistribution obj's underlying member method code with example usage: obj.required_logits_shape(cls, action_space) without return types