class_docstr: Performs in-place normalization over all features of a dense feature
  vector by doing (x - mean)/stddev for each x in the feature vector.
  
  This is a ScriptModule so that the normalize function can be called at
  training time in the tensorizer, as well as at inference time by using it in
  your torchscript forward function. To use this in your tensorizer
  update_meta_data must be called once per row in your initialize function,
  and then calculate_feature_stats must be called upon the last time it runs.
  See usage in FloatListTensorizer for an example.
  
  Setting do_normalization=False will make the normalize function an identity
  function.
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code: def forward(self):
      pass
  
file_path: pytext/pytext/torchscript/tensorizer/normalizer.py
imports: from typing import List
  import torch
label_desc: Use image node_id 4 for calling the VectorNormalizer obj's underlying member method code with example usage: obj.forward() without return types