$14.$ Consider the data sets $($Xtrain ytrain$)$ and $($Xtest$,$ Ytestt$).$ You want to normalize your data before training your model. Which of the following propositions are true? $($Circle all that apply.$)$

a$.$ The normalizing mean and variance computed on the training set, and used to train the model, should be used to normalize test data.

b$.$ Test data should be normalized with its own mean and variance before being fed to the network at test time because We test distribution might be different from the train distribution.

c$.$ Normalizing the input impacts the landscape of the loss function.

d$.$ In imaging, just like for structured data, normalization consists in subtracting the mean from the input and multi$-$ plying the result by the standard deviation.