code: def eval_seq2reward_model(eval_data, seq2reward_trainer):
      SEQ_LEN, batch_size, NUM_ACTION = next(
          iter(eval_data)
      ).action.float_features.shape
  
      initial_state = torch.Tensor([[0, 0]])
      initial_state_q_values = torch.squeeze(
          get_Q(
              seq2reward_trainer.seq2reward_network,
              initial_state,
              seq2reward_trainer.all_permut,
          )
      )
  
      total_mse_loss = 0
      total_q_values = torch.zeros(NUM_ACTION)
      total_action_distribution = torch.zeros(NUM_ACTION)
      for idx, batch in enumerate(eval_data):
          (
              mse_loss,
              _,
              q_values,
              action_distribution,
          ) = seq2reward_trainer.validation_step(batch, idx)
          total_mse_loss += mse_loss
          total_q_values += torch.tensor(q_values)
          total_action_distribution += torch.tensor(action_distribution)
  
      N_eval = len(eval_data)
      eval_mse_loss = total_mse_loss / N_eval
      eval_q_values = total_q_values / N_eval
      eval_action_distribution = total_action_distribution / N_eval
  
      return (
          initial_state_q_values,
          eval_mse_loss,
          eval_q_values,
          eval_action_distribution,
      )
  
imports: import logging
  import os
  import random
  import unittest
  from typing import Optional
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  from parameterized import parameterized
  from reagent.core import types
  from reagent.core.parameters import NormalizationData, NormalizationParameters, ProblemDomain, Seq2RewardTrainerParameters
  from reagent.gym.envs import Gym
  from reagent.gym.utils import create_df_from_replay_buffer
  from reagent.models.seq2reward_model import Seq2RewardNetwork
  from reagent.net_builder.value.fully_connected import FullyConnected
  from reagent.prediction.predictor_wrapper import FAKE_STATE_ID_LIST_FEATURES, FAKE_STATE_ID_SCORE_LIST_FEATURES, Seq2RewardPlanShortSeqWithPreprocessor, Seq2RewardWithPreprocessor
  from reagent.preprocessing.identify_types import DO_NOT_PREPROCESS
  from reagent.preprocessing.preprocessor import Preprocessor
  from reagent.training.utils import gen_permutations
  from reagent.training.world_model.compress_model_trainer import CompressModelTrainer
  from reagent.training.world_model.seq2reward_trainer import get_Q, Seq2RewardTrainer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 3 for calling a global function with example usage: eval_seq2reward_model(eval_data, seq2reward_trainer) and returns: initial_state_q_values,  eval_mse_loss,  eval_q_values,  eval_action_distribution