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Source channel @githubtrending · Post #15230 · Oct 16

#other This collection of leaked GPT prompts offers a wide range of tools and ideas for interacting with AI models. It includes prompts for tasks like writing, coding, humor, and education, which can help users understand how GPT models work and improve their interactions with AI. By using these prompts, users can create more effective and personalized AI experiences, benefiting from the diverse contributions and insights shared by the community. This resource is valuable for both developers and users looking to enhance their AI interactions. https://github.com/linexjlin/GPTs

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djangoproject

@djangoproject · Post #88 · 07/11/2016, 11:54 AM

https://docs.python.org/3/library/functools.html#functools.partialmethod class #functools.partialmethod(func, *args, **keywords) Return a new #partialmethod descriptor which behaves like partial except that it is designed to be used as a method definition rather than being directly callable. func must be a descriptor or a callable (objects which are both, like normal functions, are handled as descriptors). When func is a descriptor (such as a normal Python function, classmethod(), staticmethod(), abstractmethod() or another instance of partialmethod), calls to __get__ are delegated to the underlying descriptor, and an appropriate partial object returned as the result. When func is a non-descriptor callable, an appropriate bound method is created dynamically. This behaves like a normal Python function when used as a method: the self argument will be inserted as the first positional argument, even before the args and keywords supplied to the partialmethod constructor.

djangoproject

@djangoproject · Post #267 · 02/23/2017, 01:44 PM

https://www.python.org/dev/peps/pep-0443/ This PEP proposes a new mechanism in the #functools standard library module that provides a simple form of generic programming known as #single_dispatch#generic functions. A generic function is composed of multiple functions implementing the same operation for different types. Which implementation should be used during a call is determined by the #dispatch algorithm. When the implementation is chosen based on the type of a single argument, this is known as #single_dispatch . #overloading

djangoproject

@djangoproject · Post #97 · 07/11/2016, 12:18 PM

https://docs.python.org/3/library/asyncio-eventloop.html #Calls Most #asyncio functions don’t accept keywords. If you want to pass #keywords to your callback, use #functools.partial(). For example, #loop.#call_soon(functools.partial(print, "Hello", flush=True)) will call print("Hello", flush=True). #Note functools.partial() is better than lambda functions, because asyncio can inspect functools.partial() object to display parameters in debug mode, whereas lambda functions have a poor representation. BaseEventLoop.call_soon(callback, *args) Arrange for a callback to be called as soon as possible. The callback is called after call_soon() returns, when control returns to the event loop. This operates as a FIFO queue, callbacks are called in the order in which they are registered. Each callback will be called exactly once. Any positional arguments after the callback will be passed to the callback when it is called. An instance of asyncio.Handle is returned, which can be used to cancel the callback. Use functools.partial to pass keywords to the callback. BaseEventLoop.call_soon_threadsafe(callback, *args) Like call_soon(), but thread safe. See the concurrency and multithreading section of the documentation.