Standardization on training and split data

I am confused on which of the following should be used for standardization:

• method 1: fit transforming training data and only transforming test data

  from sklearn.preprocessing import StandardScaler
  sc = StandardScaler()
  X_train = sc.fit_transform(X_train)
  X_test = sc.transform (X_test)
  

• method 2: fit transforming both training and test data

  from sklearn.preprocessing import StandardScaler
  sc = StandardScaler()
  X_train = sc.fit_transform(X_train)
  # scaler_train=sc.fit(X_train)
  #X_train_sd=scaler_train.transform(X_train)
  X_test = sc.fit_transform (X_test)
  #scaler_test=sc.fit(X_test)
  #X_test_sd=scaler_train.transform(X_test)
  

this is a follow up question to:
StandardScaler before and after splitting data