code: def prepare(self, x):
      X = set()
      for i in x:
          for j in i:
              X.add(j if j is not None else self.unk)
      self.scaler.fit(np.array(list(X)).reshape(-1, 1))
      self.output_size = (
          len(self.scaler.categories_[0])
          if self.encoder_class == "one_hot"
          else 1
      )
  
file_path: lightwood/lightwood/encoder/helpers.py
imports: import torch
  import numpy
  from sklearn.preprocessing import MinMaxScaler, OneHotEncoder, OrdinalEncoder
label_desc: Use image node_id 2 for calling the CatNormalizer obj's underlying member method code with example usage: obj.prepare(x) without return types