text: Title: Image preprocessing parameters
  Body: The function `preprocess_input()`, in encoders/_preprocessing.py, takes 'mean' and 'std' as parameters and apply the normalization on the data in the following way:
  
  ```
      if mean is not None:
          mean = np.array(mean)
          x = x - mean
  
      if std is not None:
          std = np.array(std)
          x = x / std
  ```
  
  In my opinion, the mean/std here should be the statistics for the training dataset (data-specific). However, according to `get_preprocessing_params()`, in encoders/\_\_init\_\_.py, the mean/std are determined by the pretrained model, which depends on the training data used in the pretrained model. 
  
  Just wonder, is there any reason why we do it based on the pretrained model? 