code: def prepare(self, x: np.ndarray) -> None:
      # @TODO: streamline input type
      if isinstance(x[0], list):
          x = np.vstack(x)
      if isinstance(x[0], torch.Tensor):
          x = torch.stack(x).numpy()
      if len(x.shape) < 2:
          x = np.expand_dims(x, axis=1)
  
      x[x == None] = 0  # noqa
      x = x.astype(float)
      self.abs_mean = np.mean(np.abs(x))
      self.scaler.fit(x.reshape(x.size, -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 MinMaxNormalizer obj's underlying member method code with example usage: obj.prepare(x) without return types