Eis SSKrSSKJs Jr SSKJr S S\RS\RS\S\S\ S \R4 S jjr g) N)_log_api_usage_onceinputstargetsalphagamma reductionreturncDSUs=::aS::dO US:wa[SUS35e[RR5(d2[RR 5(d[ [ 5 [R"U5n[R"XSS9nXQ-SU- SU- --nUSU- U--nUS:aX!-SU- SU- --n X-nUS:XaU$US:XaUR5nU$US :XaUR5nU$[S US 35e) a Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs (Tensor): A float tensor of arbitrary shape. The predictions for each example. targets (Tensor): A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class). alpha (float): Weighting factor in range [0, 1] to balance positive vs negative examples or -1 for ignore. Default: ``0.25``. gamma (float): Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. Default: ``2``. reduction (string): ``'none'`` | ``'mean'`` | ``'sum'`` ``'none'``: No reduction will be applied to the output. ``'mean'``: The output will be averaged. ``'sum'``: The output will be summed. Default: ``'none'``. Returns: Loss tensor with the reduction option applied. rzInvalid alpha value: z4. alpha must be in the range [0,1] or -1 for ignore.none)r meansumz$Invalid Value for arg 'reduction': 'z3 Supported reduction modes: 'none', 'mean', 'sum') ValueErrortorchjit is_scripting is_tracingrsigmoid_focal_losssigmoidF binary_cross_entropy_with_logitsrr) rrrrr pce_lossp_tlossalpha_ts W/home/mande/repo/quber/.venv/lib/python3.13/site-packages/torchvision/ops/focal_loss.pyrrs4: O!O"07klmm 99 ! ! # #EII,@,@,B,B./ fA00FSG +Q1w;/ /C q3w5( )D z/QY1w;$??~F  K f yy{ K e xxz K29+=q r  )g?rr) rtorch.nn.functionalnn functionalrutilsrTensorfloatstrrr rr)se '  6 LL6 \\6 6  6  6  \\ 6r