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Specifically, the for loop will call a value's into_iter() method, which returns an iterator that in turn yields the elements to the loop. The for loop (or indeed, any method that consumes the iterator), proceeds until the next() method returns a None value (iterations yielding elements return a Some(T) value, where T is the element type).
It implicitly calls the IntoIterator::into_iter method on the expression, and uses the resulting value, which must implement the Iterator trait. If the expression is itself an iterator, it is used directly by the for loop through an implementation of IntoIterator for all Iterators that returns the iterator unchanged.
The Lazy interface with its eval() method is equivalent to the Supplier interface with its get() method in the java.util.function library. [ 25 ] [ 26 ] : 200 Each class that implements the Lazy interface must provide an eval method, and instances of the class may carry whatever values the method needs to accomplish lazy evaluation.
In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever next() is called on the iterator, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.
In object-oriented programming, the iterator pattern is a design pattern in which an iterator is used to traverse a container and access the container's elements. The iterator pattern decouples algorithms from containers; in some cases, algorithms are necessarily container-specific and thus cannot be decoupled.
The decorator pattern is a design pattern used in statically-typed object-oriented programming languages to allow functionality to be added to objects at run time; Python decorators add functionality to functions and methods at definition time, and thus are a higher-level construct than decorator-pattern classes.
Here, the list [0..] represents , x^2>3 represents the predicate, and 2*x represents the output expression.. List comprehensions give results in a defined order (unlike the members of sets); and list comprehensions may generate the members of a list in order, rather than produce the entirety of the list thus allowing, for example, the previous Haskell definition of the members of an infinite list.
The nested function technology allows a programmer to write source code that includes beneficial attributes such as information hiding, encapsulation and decomposition.The programmer can divide a task into subtasks which are only meaningful within the context of the task such that the subtask functions are hidden from callers that are not designed to use them.