ϑi %SSKJr SSKJr SSKJr SSKJr SSKJ r SSK J r \(a0SSKJ r J r Jr SSKJr \ S rS \S '\ S rS \S '\\\4rS \S'SSSjjrg)) annotations) TYPE_CHECKING)_C_ops)check_variable_and_dtype) LayerHelper)in_dynamic_or_pir_mode)Literal TypeAliasUnion)Tensor)meansumnoner _ReduceModeStringLiteral)r_ReduceModeNumberLiteral _ReduceModec[U[5(a1SSSS.RUR55nUc [ S5e[ 5(a[ R"X5$[USSS/S 5 S U0n[S0[5D6nURSS 9nURU5nURS S U0S U0US9 U$)arMarks a tensor as being part of the loss calculation for IPU. This operator is used to handle on the (final) loss of a model so that it is used as the start of backpropagation. When `reduction` is `none`, return raw `Out`. When `reduction` is `mean`, return .. math:: Out = MEAN(Out) When `reduction` is `sum`, return .. math:: Out = SUM(Out) Parameters: x (Variable): The input tensor. The shapes is [N, *], where N is batch size and `*` means any number of additional dimensions. It's data type should be float32, float64 on CPU and float16, float32 on IPU. reduction(str|int, optional): Reduce the loss output. Supported string values are: 'sum', 'mean', 'none' the corresponding int values are 0, 1, 2 respectively. The default value is "none". Returns: Variable: The loss ``Tensor`` with the specified reduction applied. Examples: .. code-block:: python >>> import paddle >>> paddle.enable_static() >>> loss = paddle.static.data(name="loss", shape=[-1, 1], dtype="float32") >>> out = paddle.incubate.identity_loss(loss, reduction=1) rrr)rr rzUnsupported reduction type.xfloat32float64 identity_loss reduction)input_param_nameXOut)typeinputsoutputsattrs)r) isinstancestrgetlower TypeErrorrrrrrlocals input_dtype"create_variable_for_type_inference append_op)rrr!helperdtypeouts W/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/incubate/nn/loss.pyrr$sH)S!!q!4889JK  9: :##A11Qi%;_M ) $E  5FH 5F     4E  3 3E :C  c1Xs|5 JN)r)rr rrreturnr ) __future__rtypingrpaddlerpaddle.base.data_feederrpaddle.base.layer_helperrpaddle.frameworkrr r r r r__annotations__rrrr/r.r9se# <0300*12G*HiH*1'*:i:" "::K 4r/