code: def _create_norm(dim, offset: int = 0):
      normalization_data = NormalizationData(
          dense_normalization_parameters={
              i: NormalizationParameters(
                  feature_type=CONTINUOUS, mean=0.0, stddev=1.0
              )
              for i in range(offset, dim + offset)
          }
      )
      return normalization_data
  
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 1 for calling a global function with example usage: _create_norm(dim, offset) and returns: normalization_data