code: def _create_preprocessed_input(
      input: rlt.MemoryNetworkInput,
      state_preprocessor: Preprocessor,
      action_preprocessor: Preprocessor,
  ):
      preprocessed_state = state_preprocessor(
          input.state.float_features.reshape(
              SEQ_LEN * BATCH_SIZE, STATE_DIM
          ),
          torch.ones(SEQ_LEN * BATCH_SIZE, STATE_DIM),
      ).reshape(SEQ_LEN, BATCH_SIZE, STATE_DIM)
      preprocessed_action = action_preprocessor(
          input.action.float_features.reshape(
              SEQ_LEN * BATCH_SIZE, ACTION_DIM
          ),
          torch.ones(SEQ_LEN * BATCH_SIZE, ACTION_DIM),
      ).reshape(SEQ_LEN, BATCH_SIZE, ACTION_DIM)
      return rlt.MemoryNetworkInput(
          state=rlt.FeatureData(preprocessed_state),
          action=rlt.FeatureData(preprocessed_action),
          valid_step=input.valid_step,
          next_state=input.next_state,
          reward=input.reward,
          step=input.step,
          not_terminal=input.not_terminal,
          time_diff=input.time_diff,
      )
  
imports: import unittest
  import numpy.testing
  import torch
  from reagent.core import parameters, types
  from reagent.core.fb_checker import IS_FB_ENVIRONMENT
  from reagent.core.parameters import NormalizationData, NormalizationParameters
  from reagent.net_builder.synthetic_reward.ngram_synthetic_reward import NGramConvNetSyntheticReward, NGramSyntheticReward
  from reagent.net_builder.synthetic_reward.sequence_synthetic_reward import SequenceSyntheticReward
  from reagent.net_builder.synthetic_reward.single_step_synthetic_reward import SingleStepSyntheticReward
  from reagent.net_builder.synthetic_reward.transformer_synthetic_reward import TransformerSyntheticReward
  from reagent.net_builder.synthetic_reward_net_builder import SyntheticRewardNetBuilder
  from reagent.net_builder.unions import SyntheticRewardNetBuilder__Union
  from reagent.preprocessing.identify_types import CONTINUOUS
  from reagent.preprocessing.preprocessor import Preprocessor
label_desc: Use image node_id 3 for calling a global function with example usage: _create_preprocessed_input(input, state_preprocessor, action_preprocessor) and returns: rlt