# StandardScaler's mean and standard deviation for real-life data?

I've heard that we should use train dataset's scale for that of test data so they are in line with each other in terms of scale.

And I know we use transform() function for the test data preprocessing.

But I am wondering how I can apply the same logic to the real-life data set after we successfully save and load a model since I don't think we can use transform() function anymore.

From what I understand, there must be a way to "extract" mean and standard deviation from the train data set and to apply it for real-life model when using the model we saved already, but how can we do that?

from keras.applications.resnet50 import preprocess_input