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Keywords {🔍}

numpy, arrayfunction, functions, arguments, function, implementation, numpys, array, protocol, nep, api, return, arrays, implementations, kwargs, def, dispatch, level, implement, argument, types, type, objects, python, arrayufunc, cases, methods, high, dispatcher, method, call, change, code, func, case, notimplemented, future, dispatching, libraries, behavior, neps, performance, default, public, decorator, optional, support, note, args, attribute,

Topics {✒️}

org/pipermail/numpy-discussion/2018-august/078493 high-level array functions high-level guarantees high level api level high api avoid long-term divergence high-level function higher level api static analysis tools superseded neps rejected arbitrary callable exposed supports duck-types adding implementation-specific arguments pass implementation-specific arguments simd optimization instructions deep learning frameworks environment variable numpy_experimental_array_function=1 microbenchmark results show individual pull requests single major release uc berkeley institute relevant pull requests overriding specific functions performance differences measured implicitly convert pandas low level apis main content index offer significant advantages abbreviated deprecation cycles normal deprecation cycle clean abstraction layer arbitrary callable object lower level implement_array_function backwards compatibility concern simplified core functionality simple file format writing dispatchers relevant manually writing dispatchers builtin special methods numpy universal functions automatic code generation function implicitly declare private _implementation attribute perform automatic differentiation single numpy release numpy developer sprint backward compatibility minimizes extraneous information internal optimizations specialized introspection based tools

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• Would dispatching be retried after only coercing the “coercible” arguments?

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