o "j @s>ddlmZddlmZddlmZddlmZd ddZdS) ) _legacy_C_ops)check_variable_and_dtype) LayerHelper)in_dynamic_modenonecCst|trdddd|}|durtdtr"t|d|St|dd d gd d|i}t dit }|j dd }| |}|j d d |id|i|d|S)aMarks 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) r)summeanrNzUnsupported reduction type. reductionxZfloat32Zfloat64 identity_loss)Zinput_param_nameXZOut)typeZinputsZoutputsattrs)r ) isinstancestrgetlower Exceptionrrr rrlocalsZ input_dtypeZ"create_variable_for_type_inferenceZ append_op)r r rhelperZdtypeoutrX/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/paddle/incubate/nn/loss.pyr s $  r N)r) ZpaddlerZpaddle.base.data_feederrZpaddle.base.layer_helperrZpaddle.frameworkrr rrrrs