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callable) If the attribute exists and is callable we can return it to the caller; otherwise, we return None to signify that the function isn t available If hundreds of les were being processed (eg, due to using ** in the C:\windows directory), we don t want to go through the lookup process for every module for every le So immediately after de ning the get_function() function, we add an attribute to the function, a dictionary called cache (In general, Python allows us to add arbitrary attributes to arbitrary objects) The rst time that get_function() is called the cache dictionary is empty, so the dictget() call will return None But each time a suitable function is found it is put in the dictionary with a 2-tuple of the module and function name used as the key and the function itself as the value So the second and all subsequent times a particular function is requested the function is immediately returned from the cache and no attribute lookup takes place at all The technique used for caching the get_function() s return value for a given set of arguments is called memoizing It can be used for any function that has no side effects (does not change any global variables), and that always returns the same result for the same (immutable) arguments Since the code required to create and manage a cache for each memoized function is the same, it is an ideal candidate for a function decorator, and several @memoize decorator recipes are given in the Python Cookbook, in codeactivestatecom/recipes/langs/python/ However, module objects are mutable, so some off-the-shelf memoizer decorators wouldn t work with our get_function() function as it stands An easy solution would be to use each module s __name__ string rather than the module itself as the rst part of the key tuple Doing dynamic module imports is easy, and so is executing arbitrary Python code using the exec() function This can be very convenient, for example, allowing us to store code in a database However, we have no control over what imported or exec()uted code will do Recall that in addition to variables, functions, and classes, modules can also contain code that is executed when it is imported if the code came from an untrusted source it might do something unpleasant How to address this depends on circumstances, although it may not be an issue at all in some environments, or for personal projects.

It is often useful to have one or more small helper functions inside another function Python allows this without formality we simply de ne the functions we need inside the de nition of an existing function Such functions are often called nested functions or local functions We already saw examples of these in 7

The most common example is that of "one-handed" operation Devices of this type are typical cellular phones with small LCD displays and a standard ITU-T telephone keypad (containing at least the keys 0 , *, and #) Users interact with this type of device with one hand, usually with their thumb only With devices of this type, smaller is better, so these devices typically have a very limited display

A slightly more sophisticated get_function() that has better handling of modules without the required functionality is in the magic-numberspy program alongside the version shown here

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users Regardless of the hosting environment, the WCF programming model doesn t change In fact, just as developers can design and implement a service without explicit concern about its binding or address, they can also be agnostic to the hosting environment class All hosts are required to do three things: instantiate a , add endpoints to the host, and start listening from for messages The logic in Listing 71 is the core logic for hosting a service in your own programs It s also automatically provided when hosting in IIS or WAS

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