code: def encode(self, Y):
      y = [
          [str(j) if j is not None else self.unk for j in i] for i in Y
      ]
      y = [
          [
              j if j in self.scaler.categories_[0] else self.unk
              for j in i
          ]
          for i in y
      ]
      y = np.array(y)
      out = []
      for i in y:
          transformed = self.scaler.transform(i.reshape(-1, 1))
          if isinstance(self.scaler, OrdinalEncoder):
              transformed = transformed.flatten()
          out.append(transformed)
  
      return torch.Tensor(out)
  
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 3 for calling the CatNormalizer obj's underlying member method code with example usage: obj.encode(Y) and returns: torch