JTh*SSKrSSKJr SSKrSSKJr SSKJrJr SSK J r J r SSK J r SSKJr /S Qr"S S \5r"S S \5r"SS\5r"SS\5r"SS\ \5rg)N)Any)Tensor) functionalinit) ParameterUninitializedParameter)LazyModuleMixin)Module)BilinearIdentity LazyLinearLinearcN^\rSrSrSrS\S\SS4U4SjjrS\S\4S jrS r U=r $) r aA placeholder identity operator that is argument-insensitive. Args: args: any argument (unused) kwargs: any keyword argument (unused) Shape: - Input: :math:`(*)`, where :math:`*` means any number of dimensions. - Output: :math:`(*)`, same shape as the input. Examples:: >>> m = nn.Identity(54, unused_argument1=0.1, unused_argument2=False) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 20]) argskwargsreturnNc">[TU]5 gNsuper__init__)selfrr __class__s O/var/www/auris/envauris/lib/python3.13/site-packages/torch/nn/modules/linear.pyrIdentity.__init__+s inputcU$rrrs rforwardIdentity.forward.s rr!) __name__ __module__ __qualname____firstlineno____doc__rrrr#__static_attributes__ __classcell__rs@rr r s:(cSTVrr c ^\rSrSr%SrSS/r\\S'\\S'\\S'SS\S\S\ SS4U4S jjjr SS jr S \S\4S jr S\ 4S jrSrU=r$)r2aApplies an affine linear transformation to the incoming data: :math:`y = xA^T + b`. This module supports :ref:`TensorFloat32`. On certain ROCm devices, when using float16 inputs this module will use :ref:`different precision` for backward. Args: in_features: size of each input sample out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Shape: - Input: :math:`(*, H_\text{in})` where :math:`*` means any number of dimensions including none and :math:`H_\text{in} = \text{in\_features}`. - Output: :math:`(*, H_\text{out})` where all but the last dimension are the same shape as the input and :math:`H_\text{out} = \text{out\_features}`. Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{in\_features}}` Examples:: >>> m = nn.Linear(20, 30) >>> input = torch.randn(128, 20) >>> output = m(input) >>> print(output.size()) torch.Size([128, 30]) in_features out_featuresweightNbiasrc,>XES.n[TU]5 XlX l[ [ R "X!440UD65UlU(a&[ [ R "U40UD65UlOURSS5 UR5 gNdevicedtyper2) rrr/r0rtorchemptyr1r2register_parameterreset_parameters)rr/r0r2r6r7factory_kwargsrs rrLinear.__init__]s%+; &( KK3 F~ F   !%++l"Mn"MNDI  # #FD 1 rcR[R"UR[R"S5S9 UR bg[R "UR5upUS:aS[R"U5- OSn[R"UR U*U5 gg)N)arr )rkaiming_uniform_r1mathsqrtr2_calculate_fan_in_and_fan_outuniform_)rfan_in_bounds rr;Linear.reset_parametersrsv dkkTYYq\: 99 ::4;;GIF-3aZA &))QE MM$))eVU 3 !rrcX[R"XRUR5$r)Flinearr1r2r"s rr#Linear.forward|sxx{{DII66rcXSURSURSURSL3$)Nz in_features=, out_features=, bias=)r/r0r2rs r extra_reprLinear.extra_reprs8d../t?P?P>QQXY]YbYbjnYnXopprr2r/r0r1TNNrNr%r&r'r(r) __constants__int__annotations__rboolrr;r#strrRr*r+r,s@rrr2s#J#N3M N          *47V77qCqqrrc F^\rSrSrS S\S\S\SS4U4SjjjrSrU=r$) NonDynamicallyQuantizableLinearNr/r0r2rc$>[TU]XX4US9 g)N)r2r6r7r)rr/r0r2r6r7rs rr(NonDynamicallyQuantizableLinear.__init__s  Du  rr!rU) r%r&r'r(rYr[rr*r+r,s@rr^r^s>             rr^c ^\rSrSr%Sr/SQr\\S'\\S'\\S'\\S'SS\S\S\S \ S S4 U4S jjjr SS jr S \S\S \4Sjr S \ 4SjrSrU=r$)r aHApplies a bilinear transformation to the incoming data: :math:`y = x_1^T A x_2 + b`. Args: in1_features: size of each first input sample in2_features: size of each second input sample out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Shape: - Input1: :math:`(*, H_\text{in1})` where :math:`H_\text{in1}=\text{in1\_features}` and :math:`*` means any number of additional dimensions including none. All but the last dimension of the inputs should be the same. - Input2: :math:`(*, H_\text{in2})` where :math:`H_\text{in2}=\text{in2\_features}`. - Output: :math:`(*, H_\text{out})` where :math:`H_\text{out}=\text{out\_features}` and all but the last dimension are the same shape as the input. Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in1\_features}, \text{in2\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in1\_features}}` Examples:: >>> m = nn.Bilinear(20, 30, 40) >>> input1 = torch.randn(128, 20) >>> input2 = torch.randn(128, 30) >>> output = m(input1, input2) >>> print(output.size()) torch.Size([128, 40]) ) in1_features in2_featuresr0rdrer0r1Nr2rc:>XVS.n[TU]5 XlX lX0l[ [ R"X1U440UD65UlU(a&[ [ R"U40UD65Ul OURSS5 UR5 gr4) rrrdrer0rr8r9r1r2r:r;) rrdrer0r2r6r7r<rs rrBilinear.__init__s%+; ((( KK\B Un U   !%++l"Mn"MNDI  # #FD 1 rcS[R"URRS55- n[R "URU*U5 UR b$[R "UR U*U5 gg)Nr )rBrCr1sizerrEr2)rrHs rr;Bilinear.reset_parameterss_DIIdkk..q122 dkkE651 99 MM$))eVU 3 !rinput1input2cZ[R"XURUR5$r)rKbilinearr1r2)rrkrls rr#Bilinear.forwardszz&$++tyyAArc rSURSURSURSURSL3$)Nz in1_features=z, in2_features=rOrP)rdrer0r2rQs rrRBilinear.extra_reprsJD--.od>O>O=PQ --.gdiit6K5L N r)r2rdrer0r1rUrVrWr,s@rr r s#JEM N          04 BfBfBB C  rr cv^\rSrSr%Sr\r\\S'\\S'S S\ S\ SS4U4Sjjjr S U4S jjr S S jr S rU=r$)raA :class:`torch.nn.Linear` module where `in_features` is inferred. In this module, the `weight` and `bias` are of :class:`torch.nn.UninitializedParameter` class. They will be initialized after the first call to ``forward`` is done and the module will become a regular :class:`torch.nn.Linear` module. The ``in_features`` argument of the :class:`Linear` is inferred from the ``input.shape[-1]``. Check the :class:`torch.nn.modules.lazy.LazyModuleMixin` for further documentation on lazy modules and their limitations. Args: out_features: size of each output sample bias: If set to ``False``, the layer will not learn an additive bias. Default: ``True`` Attributes: weight: the learnable weights of the module of shape :math:`(\text{out\_features}, \text{in\_features})`. The values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})`, where :math:`k = \frac{1}{\text{in\_features}}` bias: the learnable bias of the module of shape :math:`(\text{out\_features})`. If :attr:`bias` is ``True``, the values are initialized from :math:`\mathcal{U}(-\sqrt{k}, \sqrt{k})` where :math:`k = \frac{1}{\text{in\_features}}` r1r2Nr0rc>X4S.n[TU]SSS5 [S0UD6UlXlU(a[S0UD6Ulgg)Nr5rFr!)rrrr1r0r2)rr0r2r6r7r<rs rrLazyLinear.__init__ sL%+; Au%,>~> ( .@@DI rcp>UR5(d URS:wa[TU] 5 ggg)Nr)has_uninitialized_paramsr/rr;)rrs rr;LazyLinear.reset_parameterss3,,..43C3Cq3H G $ &4I.rcUR5(a[R"5 URSUlUR R URUR45 URb&URR UR45 UR5 SSS5 gg!,(df  g=f)N) rwr8no_gradshaper/r1 materializer0r2r;r"s rinitialize_parameters LazyLinear.initialize_parameterss  ( ( * *#(;;r?  ''):):D?%%' ! + s B B>> C rTrUrV)r%r&r'r(r)r cls_to_becomerrZrYr[rr;r~r*r+r,s@rrrsX8M ""  HL A A'+ A  A A'((rr)rBtypingrr8rtorch.nnrrKrtorch.nn.parameterrrlazyr moduler __all__r rr^r rr!rrrsj  *@! v8NqVNql  f  Q vQ h8(&8(r