code: def forward(self, obs, **kwargs):
      obs = to_torch(obs, device=self.linear.weight.device)
      out = self.linear(self.preprocess(obs))
      # to take care of choices with different number of options
      mask = torch.arange(self.action_dim, device=out.device).expand(
          len(out), self.action_dim
      ) >= obs["action_dim"].unsqueeze(1)
      # NOTE: this could potentially be used for prior knowledge
      out_bias = torch.zeros_like(out)
      out_bias.masked_fill_(mask, float("-inf"))
      return F.softmax(out + out_bias, dim=-1), kwargs.get(
          "state", None
      )
  
imports: from __future__ import annotations
  from copy import deepcopy
  from typing import Tuple, Callable
  import gym
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from typing_extensions import TypedDict
  from gym import spaces
  from tianshou.data import ReplayBuffer, Batch, to_torch, to_numpy
  from tianshou.policy import BasePolicy, PPOPolicy
  from nni.mutable import Categorical, CategoricalMultiple, Mutable, MutableAnnotation, Sample
label_desc: Use image node_id 2 for calling the Actor obj's underlying member method code with example usage: obj.forward(obs) and returns: F,  kwargs