docstr: Given the current state, predict the probability of
  experiencing next n steps (1 <=n <= look_ahead_steps)
  
  For the test purpose, it outputs fixed fake numbers
docstr_tok: ["Given","the","current","state",",","predict","the","probability","of","experiencing","next","n","steps","(","1","<","=n","<","=","look_ahead_steps",")","For","the","test","purpose",",","it","outputs","fixed","fake","numbers"]
code: def forward(self, state: torch.Tensor):
      """
      Given the current state, predict the probability of
      experiencing next n steps (1 <=n <= look_ahead_steps)
  
      For the test purpose, it outputs fixed fake numbers
      """
      batch_size, _ = state.shape
      return torch.ones(batch_size, self.look_ahead_steps).float()
  
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 2 for calling the FakeStepPredictionNetwork obj's underlying member method code with example usage: obj.forward(state) and returns: torch