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In Python, functions are first-class objects, just like strings, numbers, lists etc. This feature eliminates the need to write a function object in many cases. Any object with a __call__() method can be called using function-call syntax. An example is this accumulator class (based on Paul Graham's study on programming language syntax and ...
a declarator_list is a comma-separated list of declarators, which can be of the form identifier As object_creation_expression (object initializer declarator) , modified_identifier «As non_array_type « array_rank_specifier »»« = initial_value» (single declarator) , or
The function that accepts a callback may be designed to store the callback so that it can be called back after returning which is known as asynchronous, non-blocking or deferred. Programming languages support callbacks in different ways such as function pointers , lambda expressions and blocks .
An example of active object pattern in Java. [4] Firstly we can see a standard class that provides two methods that set a double to be a certain value. This class does NOT conform to the active object pattern.
Use of futures may be implicit (any use of the future automatically obtains its value, as if it were an ordinary reference) or explicit (the user must call a function to obtain the value, such as the get method of java.util.concurrent.Futurein Java). Obtaining the value of an explicit future can be called stinging or forcing. Explicit futures ...
ReactiveX (Rx, also known as Reactive Extensions) is a software library originally created by Microsoft that allows imperative programming languages to operate on sequences of data regardless of whether the data is synchronous or asynchronous.
Python 2.5 implements better support for coroutine-like functionality, based on extended generators ; Python 3.3 improves this ability, by supporting delegating to a subgenerator ; Python 3.4 introduces a comprehensive asynchronous I/O framework as standardized in PEP 3156, which includes coroutines that leverage subgenerator delegation
For example, due to the nature of Java, the IDL-Java mapping is rather straightforward and makes usage of CORBA very simple in a Java application. This is also true of the IDL to Python mapping. The C++ mapping requires the programmer to learn datatypes that predate the C++ Standard Template Library (STL).