Create a custom transformer, similar to the custom transformer that we saw in the California House Prices module example. Go back and review that code, making sure that you understand the various pieces, in order to more easily create this custom transformer.

Exercise $3$: Create a custom transformer that takes the numerical columns from your data and performs the following transformations:

$1)$ You must name your custom transformer class Assignment$3$Transformer $2)$ Your class should include an input parameter called create$\_$new$\_$column with a default value of True that performs the following two data preparation steps when its value is True, but skips these steps and just returns the DataFrame as is when you pass a value of False.

Adds an attribute to the end of the numerical data $($i$.$e$.$ new last column$)$ that is the result of the days$\_$per$\_$week column multiplied by the hours$\_$per$\_$day column. We are creating this column to better compare the amount of hours worked between the individuals.

Since they are not needed with the new column, delete the days$\_$per$\_$week and hours$\_$per$\_$day columns.

Remember that you only want these two steps to occur when the create$\_$new$\_$column parameter is True. Your custom transfomer will be tested in CodeGrade to make sure these steps are not ran when the create$\_$new$\_$column parameter is False.