Create a custom transformer that:

Inherits from BaseEstimator and TransformerMixin.

Implements the fit and transform methods.

Accepts a DataFrame as input.

In the transform method:

Create a new column called absences$\_$sum that sums the absences$\_$G$1,$ absences$\_$G$2,$ and absences$\_$G$3$ columns, adds the new absences$\_$sum column to the end of the DataFrame, then drops the original three absence columns.Drop the G$1$ and G$2$ columns if the parameter drop$\_$grades is True. It will keep the columns if drop$\_$grades is False.

Name the custom transformer class GradeTransformer.