+ É&jq ãóZa€R t0tRs]^kRs]^kRRltRRltRRltRRltR #) Fcó(€V^8„dQhR\RR/#©éÚvalueÚreturnN©Úbool)Úformats"Úh/Users/jameslopez/projects/CWCArchive/cwc-podcast/.venv/lib/python3.14/site-packages/torch/__future__.pyÚ __annotate__r s€÷3ñ3´Tð3¸dñ3ócó €VsR#)a Sets whether to assign new tensors to the parameters instead of changing the existing parameters in-place when converting an ``nn.Module``. When enabled, the following methods will assign new parameters to the module: #. ``module.{device}()`` (e.g. :meth:`nn.Module.cuda()`) for moving a module between devices #. ``module.{dtype}()`` (e.g. :meth:`nn.Module.float()`) for converting a module to a different dtype #. :meth:`nn.Module.to` #. :meth:`nn.Module.to_empty` Args: value (bool): Whether to assign new tensors or not. N©Ú&_overwrite_module_params_on_conversion©rs&r Ú)set_overwrite_module_params_on_conversionrs €ð".3Ò*r có$€V^8„dQhR\/#©rrr)r s"r r r s€÷2ñ2´4ñ2r có€\#)a Returns whether to assign new tensors to the parameters instead of changing the existing parameters in-place when converting an :class:`torch.nn.Module`. Defaults to ``False``. See :func:`~torch.__future__.set_overwrite_module_params_on_conversion` for more information. r©r r Ú)get_overwrite_module_params_on_conversionrs €ô 2Ð1r có(€V^8„dQhR\RR/#rr)r s"r r r #s€÷.ñ.´ð.¸ñ.r có €VsR#)aù Sets whether to use :func:`~torch.utils.swap_tensors` instead of setting ``.data`` to change the existing parameters in-place when converting an ``nn.Module`` and instead of ``param.copy_(state_dict[key])`` when loading a state dict into an ``nn.Module``. .. note:: This function takes precedence over :func:`~torch.__future__.get_overwrite_module_params_on_conversion` When enabled, the following methods will swap the existing parameters in-place: #. ``module.{device}()`` (e.g. :meth:`nn.Module.cuda()`) for moving a module between devices #. ``module.{dtype}()`` (e.g. :meth:`nn.Module.float()`) for converting a module to a different dtype #. :meth:`nn.Module.to` #. :meth:`nn.Module.to_empty` #. :meth:`nn.Module.load_state_dict` The semantics for :meth:`~nn.Module.load_state_dict` when this is set are as follows: #. For each parameter/buffer, its corresponding ``state_dict['key']`` is transformed via :meth:`~torch.Tensor.module_load` (i.e. ``res = param.module_load(state_dict['key'])``) #. If necessary, ``res`` will be wrapped in an :class:`~nn.Parameter` #. The parameter/buffer in the module will be swapped via :func:`~torch.utils.swap_tensors` with ``res`` Args: value (bool): Whether to use :func:`~torch.utils.swap_tensors` or not. N©Ú!_swap_module_params_on_conversionrs&r Ú$set_swap_module_params_on_conversionr#s €ð<).Ò%r có$€V^8„dQhR\/#rr)r s"r r r Ds€÷-ñ-¬dñ-r có€\#)a Returns whether to use :func:`~torch.utils.swap_tensors` instead of setting .data to change the existing parameters in-place when converting an ``nn.Module``. Defaults to ``False``. See :func:`~torch.__future__.set_swap_module_params_on_conversion` for more information. rrr r Ú$get_swap_module_params_on_conversionrDs €ô -Ð,r cóh€V^8„dQh/^\9d \;R&^\9d \;R&#)rrr)Ú__conditional_annotations__r)r s"r r r s €×4×4Ô4¬Ó4ß/Ò/¤4Ñ/ò5r N)r rrrrrrr )r s@r Úr!s/øðÜ4¨uÐ&Ó4Ø*/Ð!Ó/õ3õ(2õ.÷B-r