a h" @sddlmZddlZddlmZddlZgdZedddZedd dZddd d dd ddZ ddddddddd dd ddZ dd ddddZ dddddddddddZ dS)) annotationsN)TypeVar)fuse_conv_bn_evalfuse_conv_bn_weightsfuse_linear_bn_evalfuse_linear_bn_weightsConvTztorch.nn.modules.conv._ConvNd)boundLinearTztorch.nn.LinearFz%torch.nn.modules.batchnorm._BatchNormbool)convbn transposereturnc Csf|js |jrJdt|}|jdur2|jdus6Jt|j|j|j|j|j|j|j|\|_|_|S)a+Fuse a convolutional module and a BatchNorm module into a single, new convolutional module. Args: conv (torch.nn.modules.conv._ConvNd): A convolutional module. bn (torch.nn.modules.batchnorm._BatchNorm): A BatchNorm module. transpose (bool, optional): If True, transpose the convolutional weight. Defaults to False. Returns: torch.nn.modules.conv._ConvNd: The fused convolutional module. .. note:: Both ``conv`` and ``bn`` must be in eval mode, and ``bn`` must have its running buffers computed. Fusion only for eval!N) trainingcopydeepcopy running_mean running_varrweightbiaseps)r r rZ fused_convrC/var/www/auris/lib/python3.9/site-packages/torch/nn/utils/fusion.pyrs  rz torch.Tensorztorch.Tensor | Nonefloatz-tuple[torch.nn.Parameter, torch.nn.Parameter]) conv_wconv_bbn_rmbn_rvbn_epsbn_wbn_brrcCs|j}|dur|jn|} |dur*t|}|durs    , 5/