code: def _create_input():
      state = torch.randn(SEQ_LEN, BATCH_SIZE, STATE_DIM)
      # generate valid_step with shape (BATCH_SIZE, 1), values ranging from [1, SEQ_LEN] (inclusive)
      valid_step = torch.randint(1, SEQ_LEN + 1, size=(BATCH_SIZE, 1))
      # create one-hot action value
      action_label = (
          torch.LongTensor(SEQ_LEN * BATCH_SIZE, 1) % ACTION_DIM
      )
      action = torch.FloatTensor(SEQ_LEN * BATCH_SIZE, ACTION_DIM)
      action.zero_()
      action.scatter_(1, action_label, 1)
      action = action.reshape(SEQ_LEN, BATCH_SIZE, ACTION_DIM)
  
      input = rlt.MemoryNetworkInput(
          state=rlt.FeatureData(state),
          action=rlt.FeatureData(action),
          valid_step=valid_step,
          # the rest fields will not be used
          next_state=torch.tensor([]),
          reward=torch.tensor([]),
          step=torch.tensor([]),
          not_terminal=torch.tensor([]),
          time_diff=torch.tensor([]),
      )
      return input
  
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 2 for calling a global function with example usage: _create_input() and returns: input