\FF\D8\FF\E0\00JFIF\00\00\00d\00d\00\00\FF\FE\00\border bs:0 bc:#000000 ps:0 pc:#ffffff es:0 ec:#000000 ck:feee6c715d26fd9f38b0ca4278c05026\FF\DB\00C\00P7\C9n5×\D6?\BDê\9Ds\EBp\9F[`8m\B7)o\B5\E8\E6I\99\FE3]]A2\BA\8Cw\D6E\93\\DEv\C8\009\F2\F1NI?uc\\F5\EA\96k\xN<~buv\EA\C8\D7 \8B\84\CEcxI\BBg\AE\9E=\D6+n\EC\80\C8A\8C\AE\EB\CF\D5\DA\E9"2\A4\B9j5\EB\F3W\B63\96\B30Yu\DA\FC8\ED\DF\E7Ms\FB\F1\8E\B3\FA\EA\E8\E6(\883zs\F2_\8DFk\8Bh \00\8C\DCw\D3R\B5+6X\BA\B2\C4j\AB0\B4\FCMw\C2I\8E\9B\E3\A9~9u\FA\D3l\80\C8%p\EE\FDn2€ \00 $\FEj\C4e\A9\DB\~\95\A7\A5\80EK\BB\8DDsP\00@@AD'k\CF\E8\DB\D2(\80\9AK\D3\85\D6lb\F2\BA\8C*\80\00)\95 59\A3R:\F3\CE"\B6\80\88\00\00i1u4ê\E9\F2á\A6\A2\FACM\93WMb*\E0*\00\00\00\00\00(\A8\80\00\00\80\00\00\00\00\00\00\00\00\00\FF\D9 C/// """ Module defining global singleton classes. This module raises a RuntimeError if an attempt to reload it is made. In that way the identities of the classes defined here are fixed and will remain so even if numpy itself is reloaded. In particular, a function like the following will still work correctly after numpy is reloaded:: def foo(arg=np._NoValue): if arg is np._NoValue: ... That was not the case when the singleton classes were defined in the numpy ``__init__.py`` file. See gh-7844 for a discussion of the reload problem that motivated this module. """ import enum from ._utils import set_module as _set_module __all__ = ['_NoValue', '_CopyMode'] # Disallow reloading this module so as to preserve the identities of the # classes defined here. if '_is_loaded' in globals(): raise RuntimeError('Reloading numpy._globals is not allowed') _is_loaded = True class _NoValueType: """Special keyword value. The instance of this class may be used as the default value assigned to a keyword if no other obvious default (e.g., `None`) is suitable, Common reasons for using this keyword are: - A new keyword is added to a function, and that function forwards its inputs to another function or method which can be defined outside of NumPy. For example, ``np.std(x