code: def forward(self, obs, **kwargs):
      obs = to_torch(obs, device=self.linear.weight.device)
      steps_onehot = nn.functional.one_hot(
          obs["cur_step"].long(), self.input_dim
      ).float()
      out = self.linear(steps_onehot)
      mask = torch.arange(self.output_dim).expand(
          len(out), self.output_dim
      ) >= obs["action_dim"].unsqueeze(1)
      out_bias = torch.zeros_like(out)
      out_bias.masked_fill_(mask, float("-inf"))
      return nn.functional.softmax(out + out_bias, dim=-1), kwargs.get(
          "state", None
      )
  
imports: from __future__ import annotations
  import pytest
  import nni
  from nni.mutable import *
  from nni.nas.evaluator import FunctionalEvaluator
  from nni.nas.execution import SequentialExecutionEngine
  from nni.nas.space import *
  from nni.nas.strategy import GridSearch, Random, RegularizedEvolution, PolicyBasedRL
  import torch
  from torch import nn
  from tianshou.policy import A2CPolicy
  from tianshou.data import to_torch
label_desc: Use image node_id 2 for calling the ActorNetwork obj's underlying member method code with example usage: obj.forward(obs) and returns: nn,  kwargs