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The only difference in implementation is that in the first case we used a nested function with a name, g, while in the second case we used an anonymous nested function (using the Python keyword lambda for creating an anonymous function). The original name, if any, used in defining them is irrelevant.
In computer science, a for-loop or for loop is a control flow statement for specifying iteration. Specifically, a for-loop functions by running a section of code repeatedly until a certain condition has been satisfied. For-loops have two parts: a header and a body. The header defines the iteration and the body is the code executed once per ...
A call graph generated for a simple computer program in Python. A call graph (also known as a call multigraph [1] [2]) is a control-flow graph, [3] which represents calling relationships between subroutines in a computer program. Each node represents a procedure and each edge (f, g) indicates that procedure f calls procedure g.
A built-in function, or builtin function, or intrinsic function, is a function for which the compiler generates code at compile time or provides in a way other than for other functions. [23] A built-in function does not need to be defined like other functions since it is built in to the programming language. [24]
In computer programming, a function object [a] is a construct allowing an object to be invoked or called as if it were an ordinary function, usually with the same syntax (a function parameter that can also be a function). In some languages, particularly C++, function objects are often called functors (not related to the functional programming ...
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.
The computer programs used for compiling some of the benchmark data in this section may not have been fully optimized, and the relevance of the data is disputed. The most accurate benchmarks are those that are customized to your particular situation.
Loop carried dependence graphs (LDG) gives a visual representation of all true dependencies, anti dependencies, and output dependencies that exist between different iterations in a loop. [1] Each iteration is represented with a node. It is easier to show the difference between the two graphs with a nested for loop.