Meaning of "error on the test point x" in optimal classifier for binary classification

Let f(x) be optimal classifier for binary classification where output is modelled noisy. What does it mean "f(x) makes a mistake only if there is an error on the test point x"? Basically, what is meant by "error on the test point x"? For content, please see: https://amlbook.com/eChapters/6-Oct2022-readeronly.pdf Password:Paraskavedekatriaphobia Page: 6, last paragraph