code: def encode(self, y: np.ndarray) -> torch.Tensor:
      if isinstance(y[0], list):
          y = np.vstack(y)
      if isinstance(y[0], torch.Tensor):
          y = torch.stack(y).numpy()
      if len(y.shape) < 2:
          y = np.expand_dims(y, axis=1)
  
      shape = y.shape
      y = y.astype(float).reshape(-1, self.scaler.n_features_in_)
      out = torch.reshape(torch.Tensor(self.scaler.transform(y)), shape)
      return 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 MinMaxNormalizer obj's underlying member method code with example usage: obj.encode(y) and returns: out