code: def forward(self, state: torch.Tensor, action: torch.Tensor):
      x = torch.cat((state, action), dim=1)
      return self.model(x)
  
imports: import logging
  import gym
  import torch
  from reagent.ope.estimators.sequential_estimators import Action, ActionDistribution, ActionSpace, IPSEstimator, Model, NeuralDualDICE, RandomRLPolicy, RewardProbability, RLEstimatorInput, RLPolicy, State, StateDistribution, Transition
  from reagent.ope.utils import RunningAverage
label_desc: Use image node_id 2 for calling the EnvironmentModel obj's underlying member method code with example usage: obj.forward(state, action) and returns: self