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H]H}M `tHp8 Euſ8 IH5tH0 Ho/HIH5A.H LD  HL xu5Hxt)H} `t Hp Eu"H`uEtH} HH=G H  UHAWAVATSHpIFDBF HIAG HiI|HEHuvHSHKLCLK Hs(H~H}H>HvHuH}Hs0H~H}H>HvHuH}Hs8H}H|$(H}H]H\$ H|$H}H|$H}H]H\$H<$Ht$0LHnC LHp[A\A^A_]ÿ IH5ϪHq H5,H+L HLn HC H  fDUHAWAVAUATSHLMLEHMLvHMIHL}(LeH]IOHL$(IIWHT$ HL$IL$HL$I $IT$HT$H $HM@HL$0HHLHMLELMAHHĨ[A\A]A^A_]ÿpw IHfHnHīfHnflfMEbEbffEHEp HHEfofEH5ثbH\ CbHEH$H5H[H L HhLE*HEHD$HEHMHL$H$HhL 1H5*HL  IƳEt?IIhuEu(tLy L  Hx- EtH} uf.UHAWAVATSH`II I$H HH!t HH?@@(H5MHEHELeH]HUHLHHEH;lt+HHu$H}HPHEHHuH}HtHPH 8HMHEH9t+HHu$H}HPHEHHuH}HtHPLH`[A\A^A_]H=~4  Iǿ0 Htize_weiHHHear_quanHHHgemm_linHHHaten::fbH@ ghtW)EHE()EHEH}Hu H}HUL= }tWHEH ~Eu&Eu/Et H}3 H=~o 7H} EtH} EuϿ IH5FH HA)HIH5(HL  HL Eu ,HEt#H} EtH} EuHEuEtH}l HH=8} H  H9MH1MHHEH;#t+HHu$H}HPHEHHuH}HtHPH HMHEH9t+HHu$H}HPHEHHuH}HtHPHG  HLHLf.UHAWAVATSHHFDBF uMIIC HiH|HEHupIWLHsB LH[A\A^A_]ÿ4 IH5H& H5k&H%LN IL# IB Le  f.UHAWAVSHxH^HHt'IIH,~LHLLHx[A^A_]ÿp IHfHnHfHnflfMEbEbffEHEp@ HHEfoyfEH5bH~ CbHEH$H5H}H ȑL ÑH}LEO~HEHD$HEHMHL$H$HuL 1H5$HL  IƳEt9IIEuEu%tL L  H}[ EtH}L uUHAWAVATSH`II ztg I$H HH!t HH?@@(H5yMHEHELeHULLH`[A\A^A_]H=y t Iǿ0 Hatrix_fpHHHk_gemm_mHHHgemm_pacHHHaten::fbHf@ 16@"W)EHE(Q)EHEH}Hu H}HUL }tWHEHxEu&Eu/Et H} H=x* H} EtH} EuϿ IH5H H#HIH5"HL  HL Eu ,HEt#H}V EtH}G EuHEuEtH}' HH=xP H  f.fUHAWAVATSH`IIwtg I$H HH!t HH?@@(H5wMHEHELeHULjLH`[A\A^A_]H=kw tG Iǿ0\ Hzed_matrHHHk_quantiHHHgemm_pacHHHaten::fbHf@ ix@"W)EHE()EHEH}HuN H}HUL }tWHEHvEu&Eu/Et H} H=v H} EtH} EuϿ IH5H} H!HIH5 H?L  HLf Eu ,HEt#H} EtH} EuHEuEtH} HH=u HV  f.fUHAWAVAUATSHhIIIIur IEH HH!t HH?@@(H5YuMLeLpHEHELmHEHEHpHEHULлLHh[A\A]A^A_]H=uB e Hÿ0 Hzed_matrHHHk_quantiHHHgemm_pacHHHaten::fbHf@ ix@"W)EHEEfEKNE()EHEH}Hu HpHUH7 ptZHxHDtEu&Eu/Et H}' H=*tc H} EtH} EuϿ IH5:H H5HIH5HL  HL Eu ,HEt#H} EtH} EuHEuEtH}` HH=\s H  DUHAWAVAUATSHxAhII0s IEH HH!t HH?@@(H5rphED}HpHELmHEHEHEHEHULRLHx[A\A]A^A_]H=r \I IĿ ^ Hpha_dropHHHature_alHHHaten::feH@outW)EHE(~)EHEH}Hua HpHUL ptZHxHqEu&Eu/Et H} H=q H} EtH} EuϿ IH5˙H HHIH5HIL  HLp Eu ,HEt#H} EtH} EuHEuEtH} HH=p H`  fDUHAWAVAUATSHxAhIIp} IH HH!t HH?@@(H=pphED}HpHELuHEHEHEHEHuHIELרLHx[A\A]A^A_]H=&p9 Q IĿ Hpha_dropHHHature_alHHHaten::feH@out_@W)EHE({)EHEH}Hu HpHULI ptZHxHvoEu&Eu/Et H}9 H=\ou H} EtH} EuϿ IH5LH HGHIH5H L  HL Eu ,HEt#H} EtH} EuHEuEtH}r HH=n H  UHAWAVAUATSHxAhII`n IEH HH!t HH?@@(H5nphED}HpHELmHEHEHEHEHULbLHx[A\A]A^A_]H=m \Y IW)EHEE*H_dropoutHEHature_drHEHaten::feHEfEH}Hu HpHUL ptZHxH*mEu&Eu/Et H} H=m H} EtH} EuϿ IH5H HHIH5Hn L  HL Eu HEtH}E Et HEuEuH}# EtH} HH=Bl? H  f.UHAWAVAUATSHxAhIIl IH HH!t HH?@@(H=kphED}HpHELuHEHEHEHEHuHIELLHx[A\A]A^A_]H=fkY Q IW)EHEE,Hdropout_HEHature_drHEHaten::feHEEH}Hu HpHUL ptZHxHjEu&Eu/Et H}t H=j H}Z EtH}K EuϿK IH5HC HHIH5THLW  HL, Eu HEtH} Et HEuEuH} EtH} HH=i H  fUHAWAVAUATSHhAIIIiB IEH\ HH!t HH?@@(H5yipLeD}HpHELmHEHEHEHEHULULHh[A\A]A^A_]H=/i e HW)EHEEHaten::ffHEEtHEHEH}Hu HpHUHI ptZHxHhEu&Eu/Et H}9 H=hu H} EtH} EuϿ IH5LH HGHIH5HL  HL Eu HEtH} Et HEuEuH} EtH}p HH=g H  UHAWAVATSHIIHE Hp(E)`g IH HH!t HH?@@(H=9gHpHE(`)EMHEHELuHEHEHuHI$LTLHĀ[A\A^A_]H=f YJ IHEHEE fEarEScalEEHaten::fiHEE_fEllEHEH}Huj H}HUL }tWHEHGfEu&Eu/Et H} H=-f H} EtH} EuϿ IH5ݍH HHIH5H[L  HL Eu HEtH}2 Et HEuEuH} EtH} HH=_e, Hr  UHAWAVAUATSHXIII8e IIHH HH!t HH?@@(H=dMHEHEL}LuHuצHIELLHX[A\A]A^A_]H=dj j IHEHEE fEorETensEEHaten::fiHEE_fEllEHEH}Hu H}HUL }tWHEH dEu&Eu/Et H} H=c H}e EtH}V EuϿV IH5HN HHIH5_ HLb  HL7 Eu HEtH} Et HEuEuH} EtH} HH=$c H'  f.UHAWAVAUATSHhIIIIbB IEH\ HH!t HH?@@(H5bMLeLpHEHELmHEHEHpHEHUL蠨LHh[A\A]A^A_]H=_b e HW)EHEEHusing_inHEfEtsEEH:flattenHEHaten::flHEfEHEH}Hu HpHUH% ptZHxHaEu&Eu/Et H} H=aQ H} EtH} EuϿ IH5(H H# HIH5 HL  HL Eu HEtH}} Et HEuEuH}[ EtH}L HH=`w H  UHAWAVAUATSHXIIL*Lz`M)I I$H HH!t HH?@@(H5Q`LmL}MHEHELeHEHEHULPLHX[A\A]A^A_]H=` lM HW)EHEEHaten::flHEfEipHEHEH}Hu H}HUH }tWHEH_Eu&Eu/Et H} H=y_" H} EtH} EuϿ IH5H H HIH5HwL  HL Eu HEtH}N Et HEuEuH}, EtH} HH=^H H  @UHAWAVATSH`II^tg I$H HH!t HH?@@(H5E^MHEHELeHULjLH`[A\A^A_]H=^ tG IW)EHEEHaten::flHEErfEooEHEH}Hu} H}HUL }tWHEH]Eu&Eu/Et H} H=] H} EtH} EuϿ IH5H HHIH5HnL  HL Eu HEtH}E Et HEuEuH}# EtH} HH=\? 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(Could this error message be improved? If so, please report an enhancement request to PyTorch.)allocator::allocate(size_t n) 'n' exceeds maximum supported sizevalidate/usr/local/include/c10/core/Device.hDevice index must be -1 or non-negative, got Expected index_ == -1 || index_ >= 0 to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)CPU device index must be -1 or zero, got Expected !is_cpu() || index_ <= 0 to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)aten::arangestartendstepoptionsinsertNode/usr/local/include/torch/csrc/jit/ir.hinsert_before_->inBlockList() && "insert point node is no longer in a block list" INTERNAL ASSERT FAILED at :, please report a bug to PyTorch. inBlockListprev() == nullptr INTERNAL ASSERT FAILED at aten::arange.start_step(Scalar start, Scalar end, Scalar step, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> TensortensorTypeIdToBackend/usr/local/include/c10/core/Backend.hUnrecognized tensor type ID: backendToDeviceTypeUndefined backend is not a valid device typeUnknown backendcomputeTensorTypeId/usr/local/include/c10/core/TensorOptions.hUnsupported device type for dense layout: Unsupported device type for sparse layout: Unsupported device type for mkldnn layout: Unsupported layout: typeMetaToScalarType/usr/local/include/c10/core/ScalarType.hUnsupported TypeMeta in ATen: (please report this error)StridedSparseMkldnnoperator<</usr/local/include/c10/core/Layout.hUnknown layoutgetOpTable/usr/local/include/ATen/core/ATenDispatch.hNo functions are registered for schema Expected iter != op_tables_.end() to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)make_variable/usr/local/include/torch/csrc/autograd/variable.hMust not create a new variable from a variable, use its .tensor_data()retain_/usr/local/include/c10/util/intrusive_ptr.hnew_refcount != 1 INTERNAL ASSERT FAILED at intrusive_ptr: Cannot increase refcount after it reached zero.TensorImpl with nullptr is not supported~intrusive_ptr_targetrefcount_.load() == 0 INTERNAL ASSERT FAILED at Tried to destruct an intrusive_ptr_target that still has intrusive_ptr to itweakcount_.load() == 0 INTERNAL ASSERT FAILED at Tried to destruct an intrusive_ptr_target that still has weak_intrusive_ptr to itaten::emptysizememory_formataten::empty.memory_format(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None, MemoryFormat? memory_format=None) -> Tensoraten::eyenmaten::eye.m(int n, int m, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::fullfill_valueaten::full(int[] size, Scalar fill_value, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::linspacestepsaten::linspace(Scalar start, Scalar end, int steps=100, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::logspacebaseaten::logspace(Scalar start, Scalar end, int steps=100, float base=10.0, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::onesaten::ones(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randaten::rand(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randintlowhighaten::randint.low(int low, int high, int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randnaten::randn(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::randpermaten::randperm(int n, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::zerosaten::zeros(int[] size, *, ScalarType? dtype=None, Layout? layout=None, Device? device=None, bool? pin_memory=None) -> Tensoraten::binary_cross_entropy_with_logits(Tensor self, Tensor target, Tensor? weight=None, Tensor? pos_weight=None, int reduction=Mean) -> TensorUnable to convert to Class is not registered/bound in RiceData type is not bound is already bound to a different typebad cast. No caster found for meansumUnsupported reduction: from_blob/usr/local/include/ATen/Functions.hSpecified device does not match device of data Expected options.device() == device to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)getDeviceFromPtrStorageImpl/usr/local/include/c10/core/StorageImpl.hallocator_ INTERNAL ASSERT FAILED at For resizable storage, allocator must be providedConstructing a storage with meta of unknown type and non-zero numelaten::set_.source_Storage_storage_offset(Tensor(a!) self, Storage source, int storage_offset, int[] size, int[] stride=[]) -> Tensor(a!)Cannot create bool from tensor method yetaten::reshapebad optional accessIssued LeftRight::read() after the destructor started runninglookup_/usr/local/include/ATen/core/dispatch/DispatchTable.hThere were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema . This usually means that this function requires a non-empty list of Tensors. Available functions are Didn't find kernel to dispatch to for operator ''. Tried to look up kernel for dispatch key ''. Registered dispatch keys are: , CATCH-ALL[, ]callUnboxedOnly/usr/local/include/ATen/core/boxing/KernelFunction.hfalse INTERNAL ASSERT FAILED at Tried to call KernelFunction::callUnboxedOnly() for a kernel that doesn't have an unboxed version.itemsize/usr/local/include/c10/core/TensorImpl.hCannot report itemsize of Tensor that doesn't have initialized dtype (e.g., caffe2::Tensor x(CPU), prior to calling mutable_data() on x)autograd_meta() INTERNAL ASSERT FAILED at requires_grad is not implemented for Tensoraten::addcmul_aten::addcdiv_set_requires_gradset_requires_grad is not implemented for Tensoraten::backward(Tensor self, Tensor? gradient=None, bool keep_graph=False, bool create_graph=False) -> voidaten::to.dtype_layout(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False, bool non_blocking=False, bool copy=False) -> Tensortensor does not have a devicescalarTypeToTypeMetaUnrecognized Scalartype ByteCharShortIntLongHalfFloatDoubleComplexHalfComplexFloatComplexDoubleBoolQInt8QUInt8QInt32BFloat16UNKNOWN_SCALARUnsupported typedata_ptr/usr/local/include/ATen/core/TensorMethods.hexpected scalar type but found Expected scalar_type() == ScalarType::Byte to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)dataCannot access data pointer of Tensor that doesn't have storageCannot access data pointer of Tensor that doesn't have initialized dtype (e.g., caffe2::Tensor x(CPU), prior to calling mutable_data() on x)aten::numelcallUnboxedboxed_kernel_func_ != nullptr INTERNAL ASSERT FAILED at Tried to call KernelFunction::callUnboxed() on an uninitialized KernelFunction.callstack.size() == 1 INTERNAL ASSERT FAILED at A boxed kernel should only push one return to the stackreclaimowning_ptr == NullType::singleton() || owning_ptr->refcount_.load() > 0 INTERNAL ASSERT FAILED at intrusive_ptr: Can only intrusive_ptr::reclaim() owning pointers that were created using intrusive_ptr::release().toInt/usr/local/include/ATen/core/ivalue.hisInt() INTERNAL ASSERT FAILED at Expected scalar_type() == ScalarType::Char to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Short to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Int to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Long to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Float to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Double to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)Expected scalar_type() == ScalarType::Bool to be true, but got false. (Could this error message be improved? If so, please report an enhancement request to PyTorch.)aten::to.device(Tensor self, Device device, ScalarType dtype, bool non_blocking=False, bool copy=False) -> Tensoras_variable_refAttempted to cast a Tensor to a Variable, but the dynamic type of the value is not Variable.VariableTensor that was converted to Variable was not actually a VariablestridedsparseSparse layout not supported yetcpucudaUnsupported device: linearconv1dconv2dconv3dconv_transpose1dconv_transpose2dconv_transpose3dsigmoidtanhreluleaky_reluUnsupported nonlinearity type: fan_infan_out is unboundunknown C++ exception thrown0initialize_adaptive_avg_pool2d_adaptive_avg_pool2d_out_adaptive_avg_pool3d_adaptive_avg_pool3d_out_adaptive_max_pool2d_adaptive_max_pool2d_out_adaptive_max_pool3d_adaptive_max_pool3d_out_avg_pool2d_avg_pool2d_divisor_override_avg_pool3d_avg_pool3d_divisor_override_binary_cross_entropy_binary_cross_entropy_out_col2im_col2im_out_elu_elu__elu_out_fractional_max_pool2d_fractional_max_pool2d_output_fractional_max_pool3d_fractional_max_pool3d_output_gelu_glu_glu_out_hardtanh_hardtanh__hardtanh_out_im2col_im2col_out_l1_loss_l1_loss_out_leaky_relu_leaky_relu__leaky_relu_out_linear_log_sigmoid_log_sigmoid_forward_log_sigmoid_forward_output_log_sigmoid_out_max_pool2d_with_indices_max_pool2d_with_indices_out_max_pool3d_with_indices_max_pool3d_with_indices_out_max_unpool2d_max_unpool2d_out_max_unpool3d_max_unpool3d_out_mkldnn_linear_mkldnn_reorder_conv2d_weight_mse_loss_mse_loss_out_multi_margin_loss_multi_margin_loss_out_multilabel_margin_loss_multilabel_margin_loss_forward_multilabel_margin_loss_forward_output_multilabel_margin_loss_out_nll_loss_nll_loss2d_nll_loss2d_forward_nll_loss2d_forward_output_nll_loss2d_out_nll_loss_forward_nll_loss_forward_output_nll_loss_out_one_hot_reflection_pad1d_reflection_pad1d_out_reflection_pad2d_reflection_pad2d_out_replication_pad1d_replication_pad1d_out_replication_pad2d_replication_pad2d_out_replication_pad3d_replication_pad3d_out_rrelu_with_noise_rrelu_with_noise__rrelu_with_noise_out_slow_conv_dilated2d_slow_conv_dilated3d_slow_conv_transpose2d_slow_conv_transpose2d_out_slow_conv_transpose3d_slow_conv_transpose3d_out_smooth_l1_loss_smooth_l1_loss_out_soft_margin_loss_soft_margin_loss_out_softplus_softplus_out_softshrink_softshrink_out_thnn_conv2d_thnn_conv2d_forward_thnn_conv2d_forward_output_thnn_conv2d_out_thnn_conv3d_thnn_conv3d_forward_thnn_conv3d_forward_output_thnn_conv3d_out_thnn_conv_depthwise2d_thnn_conv_depthwise2d_forward_thnn_conv_depthwise2d_forward_out_thnn_conv_depthwise2d_out_upsample_bicubic2d_upsample_bicubic2d_out_upsample_bilinear2d_upsample_bilinear2d_out_upsample_linear1d_upsample_linear1d_out_upsample_nearest1d_upsample_nearest1d_out_upsample_nearest2d_upsample_nearest2d_out_upsample_nearest3d_upsample_nearest3d_out_upsample_trilinear3d_upsample_trilinear3d_outaten::adaptive_avg_pool2daten::adaptive_avg_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::adaptive_avg_pool3daten::adaptive_avg_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::adaptive_max_pool2daten::adaptive_max_pool2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::adaptive_max_pool3daten::adaptive_max_pool3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::avg_pool2daten::avg_pool3daten::binary_cross_entropy(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean) -> Tensoraten::binary_cross_entropy.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::col2imaten::col2im.out(Tensor self, int[2] output_size, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)aten::eludoublevalue cannot be converted to type without overflow: int64_ttoTensor/usr/local/include/ATen/core/ivalue_inl.hisTensor() INTERNAL ASSERT FAILED at Expected Tensor but got TupleIntListDoubleListStringTensorListGenericListGenericDictFutureObjectUninitializedCapsuleInvalidTag()aten::elu_aten::elu.out(Tensor self, Scalar alpha=1, Scalar scale=1, Scalar input_scale=1, *, Tensor(a!) out) -> Tensor(a!)aten::fractional_max_pool2daten::fractional_max_pool2d.output(Tensor self, int[2] kernel_size, int[2] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::fractional_max_pool3daten::fractional_max_pool3d.output(Tensor self, int[3] kernel_size, int[3] output_size, Tensor random_samples, *, Tensor(a!) output, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::geluaten::gluaten::glu.out(Tensor self, int dim=-1, *, Tensor(a!) out) -> Tensor(a!)aten::hardtanhaten::hardtanh_aten::hardtanh.out(Tensor self, Scalar min_val=-1, Scalar max_val=1, *, Tensor(a!) out) -> Tensor(a!)aten::im2colaten::im2col.out(Tensor self, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, *, Tensor(a!) out) -> Tensor(a!)aten::l1_lossaten::l1_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::leaky_reluaten::leaky_relu_aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)aten::linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensoraten::log_sigmoidaten::log_sigmoid_forwardaten::log_sigmoid_forward.output(Tensor self, *, Tensor(a!) output, Tensor(b!) buffer) -> (Tensor(a!), Tensor(b!))aten::log_sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::max_pool2d_with_indicesaten::max_pool2d_with_indices.out(Tensor self, int[2] kernel_size, int[2] stride=[], int[2] padding=0, int[2] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::max_pool3d_with_indicesaten::max_pool3d_with_indices.out(Tensor self, int[3] kernel_size, int[3] stride=[], int[3] padding=0, int[3] dilation=1, bool ceil_mode=False, *, Tensor(a!) out, Tensor(b!) indices) -> (Tensor(a!), Tensor(b!))aten::max_unpool2daten::max_unpool2d.out(Tensor self, Tensor indices, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::max_unpool3daten::max_unpool3d.out(Tensor self, Tensor indices, int[3] output_size, int[3] stride, int[3] padding, *, Tensor(a!) out) -> Tensor(a!)aten::mkldnn_linear(Tensor input, Tensor weight, Tensor? bias=None) -> Tensoraten::mkldnn_reorder_conv2d_weightaten::mse_lossaten::mse_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::multi_margin_loss(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean) -> Tensoraten::multi_margin_loss.out(Tensor self, Tensor target, Scalar p=1, Scalar margin=1, Tensor? weight=None, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::multilabel_margin_lossaten::multilabel_margin_loss_forwardaten::multilabel_margin_loss_forward.output(Tensor self, Tensor target, int reduction, *, Tensor(a!) output, Tensor(b!) is_target) -> (Tensor(a!), Tensor(b!))aten::multilabel_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::nll_loss(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensoraten::nll_loss2d(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100) -> Tensoraten::nll_loss2d_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)aten::nll_loss2d_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))aten::nll_loss2d.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)aten::nll_loss_forward(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index) -> (Tensor output, Tensor total_weight)aten::nll_loss_forward.output(Tensor self, Tensor target, Tensor? weight, int reduction, int ignore_index, *, Tensor(a!) output, Tensor(b!) total_weight) -> (Tensor(a!), Tensor(b!))aten::nll_loss.out(Tensor self, Tensor target, Tensor? weight=None, int reduction=Mean, int ignore_index=-100, *, Tensor(a!) out) -> Tensor(a!)aten::one_hotaten::reflection_pad1daten::reflection_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)aten::reflection_pad2daten::reflection_pad2d.out(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)aten::replication_pad1daten::replication_pad1d.out(Tensor self, int[2] padding, *, Tensor(a!) out) -> Tensor(a!)aten::replication_pad2daten::replication_pad2d.out(Tensor self, int[4] padding, *, Tensor(a!) out) -> Tensor(a!)aten::replication_pad3daten::replication_pad3d.out(Tensor self, int[6] padding, *, Tensor(a!) out) -> Tensor(a!)aten::rrelu_with_noiseaten::rrelu_with_noise_aten::rrelu_with_noise.out(Tensor self, Tensor noise, Scalar lower=0.125, Scalar upper=0.3333333333333333, bool training=False, Generator? generator=None, *, Tensor(a!) out) -> Tensor(a!)aten::slow_conv_dilated2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensoraten::slow_conv_dilated3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1) -> Tensoraten::slow_conv_transpose2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1) -> Tensoraten::slow_conv_transpose2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)aten::slow_conv_transpose3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1) -> Tensoraten::slow_conv_transpose3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int[3] dilation=1, *, Tensor(a!) out) -> Tensor(a!)aten::smooth_l1_lossaten::smooth_l1_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::soft_margin_lossaten::soft_margin_loss.out(Tensor self, Tensor target, int reduction=Mean, *, Tensor(a!) out) -> Tensor(a!)aten::softplusaten::softplus.out(Tensor self, Scalar beta=1, Scalar threshold=20, *, Tensor(a!) out) -> Tensor(a!)aten::softshrinkaten::softshrink.out(Tensor self, Scalar lambd=0.5, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0) -> Tensoraten::thnn_conv2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding) -> (Tensor output, Tensor finput, Tensor fgrad_input)aten::thnn_conv2d_forward.output(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, *, Tensor(a!) output, Tensor(b!) finput, Tensor(c!) fgrad_input) -> (Tensor(a!), Tensor(b!), Tensor(c!))aten::thnn_conv2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv3d(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0) -> Tensoraten::thnn_conv3d_forward(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, int[3] padding) -> (Tensor output, Tensor finput, Tensor fgrad_input)aten::thnn_conv3d_forward.output(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias, int[3] stride, int[3] padding, *, Tensor(a!) output, Tensor(b!) finput, Tensor(c!) fgrad_input) -> (Tensor(a!), Tensor(b!), Tensor(c!))aten::thnn_conv3d.out(Tensor self, Tensor weight, int[3] kernel_size, Tensor? bias=None, int[3] stride=1, int[3] padding=0, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv_depthwise2d(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1) -> Tensoraten::thnn_conv_depthwise2d_forward(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation) -> Tensoraten::thnn_conv_depthwise2d_forward.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias, int[2] stride, int[2] padding, int[2] dilation, *, Tensor(a!) out) -> Tensor(a!)aten::thnn_conv_depthwise2d.out(Tensor self, Tensor weight, int[2] kernel_size, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_bicubic2daten::upsample_bicubic2d.out(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_bilinear2daten::upsample_bilinear2d.out(Tensor self, int[2] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_linear1daten::upsample_linear1d.out(Tensor self, int[1] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_nearest1daten::upsample_nearest1d.out(Tensor self, int[1] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_nearest2daten::upsample_nearest2d.out(Tensor self, int[2] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_nearest3daten::upsample_nearest3d.out(Tensor self, int[3] output_size, *, Tensor(a!) out) -> Tensor(a!)aten::upsample_trilinear3daten::upsample_trilinear3d.out(Tensor self, int[3] output_size, bool align_corners, *, Tensor(a!) out) -> Tensor(a!)_abs_abs__acos_acos__add__scalar_add__tensor_add_scalar_add_tensor_addbmm_addbmm__addcdiv_addcdiv__addcmul_addcmul__addmm_addmm__addmv_addmv__addr_addr__alias_align_as_all_all_dim_allclose_any_any_dim_argmax_argmax_dim_argmin_argmin_dim_argsort_as_strided_as_strided__as_strided__storage_offset_as_strided_storage_offset_asin_asin__atan_atan2_atan2__atan__baddbmm_baddbmm__bernoulli_bernoulli__float_bernoulli__tensor_bernoulli_p_bincount_bitwise_not_bitwise_not__bmm_cauchy__ceil_ceil__cholesky_cholesky_inverse_cholesky_solve_chunk_clamp_max_clamp_max__clamp_min_clamp_min__clone_coalesce_copy__cos_cos__cosh_cosh__cumprod_cumsum_data_dense_dim_dequantize_det_detach_detach__diag_diag_embed_diagflat_diagonal_digamma_digamma__dist_div__scalar_div__tensor_div_scalar_div_tensor_dot_eig_eq__scalar_eq__tensor_eq_scalar_eq_tensor_equal_erf_erf__erfc_erfc__erfinv_erfinv__exp_exp__expand_expand_as_expm1_expm1__exponential__fft_fill__scalar_fill__tensor_fill_diagonal__flatten_using_ints_flip_floor_floor__fmod__scalar_fmod__tensor_fmod_scalar_fmod_tensor_frac_frac__gather_ge__scalar_ge__tensor_ge_scalar_ge_tensor_geometric__geqrf_ger_gt__scalar_gt__tensor_gt_scalar_gt_tensor_hardshrink_histc_ifft_index_add_index_add__index_copy_index_copy__index_fill__scalar_index_fill__tensor_index_fill_scalar_index_fill_tensor_index_select_indices_int_repr_inverse_irfft_is_coalesced_is_complex_is_distributed_is_floating_point_is_leaf_is_nonzero_is_pinned_is_same_size_is_set_to_is_signed_isclose_item_kthvalue_le__scalar_le__tensor_le_scalar_le_tensor_lerp__scalar_lerp__tensor_lerp_scalar_lerp_tensor_lgamma_lgamma__log_log10_log10__log1p_log1p__log2_log2__log__log_normal__log_softmax_logdet_logical_not_logical_not__logical_xor_logical_xor__logsumexp_lstsq_lt__scalar_lt__tensor_lt_scalar_lt_tensor_lu_solve_masked_fill__scalar_masked_fill__tensor_masked_fill_scalar_masked_fill_tensor_masked_scatter_masked_scatter__masked_select_matmul_matrix_power_max_max_dim_max_other_max_values_mean_mean_dim_median_median_dim_min_min_dim_min_other_min_values_mm_mode_mul__scalar_mul__tensor_mul_scalar_mul_tensor_multinomial_mv_mvlgamma_mvlgamma__narrow_narrow_copy_ne__scalar_ne__tensor_ne_scalar_ne_tensor_neg_neg__nonzero_nonzero_numpy_norm_scalar_normal__numel_numpy_t_orgqr_ormqr_output_nr_permute_pin_memory_pinverse_polygamma_polygamma__pow__scalar_pow__tensor_pow_tensor_scalar_pow_tensor_tensor_prelu_prod_prod_dim_int_put__q_per_channel_axis_q_per_channel_scales_q_per_channel_zero_points_q_scale_q_zero_point_qr_qscheme_random__random__from_random__to_reciprocal_reciprocal__relu_relu__remainder__scalar_remainder__tensor_remainder_scalar_remainder_tensor_renorm_renorm__repeat_repeat_interleave_self_int_repeat_interleave_self_int_dim_repeat_interleave_self_tensor_repeat_interleave_self_tensor_dim_reshape_reshape_as_resize__resize_as__rfft_roll_rot90_round_round__rsqrt_rsqrt__scatter__src_scatter__value_scatter_add_scatter_add__scatter_src_scatter_value_select_int_set__set__source_tensor_set_data_sigmoid_sigmoid__sign_sign__sin_sin__sinh_sinh__size_int_slice_tensor_slogdet_smm_softmax_solve_sort_sparse_dim_sparse_mask_sparse_resize__sparse_resize_and_clear__split_tensor_split_with_sizes_sqrt_sqrt__squeeze_squeeze__squeeze__dim_squeeze_dim_sspaddmm_std_std_dim_stride_int_sub__scalar_sub__tensor_sub_scalar_sub_tensor_sum_sum_dim_intlist_sum_to_size_svd_symeig_t_t__take_tan_tan__tanh_tanh__to_dense_to_device_to_dtype_to_mkldnn_to_other_to_sparse_to_sparse_sparse_dim_topk_trace_transpose__transpose_int_triangular_solve_tril_tril__triu_triu__trunc_trunc__type_as_unbind_int_unfold_uniform__unsqueeze_unsqueeze__values_var_var_dim_view_view_as_where_self_zero_aten::absaten::abs_aten::acosaten::acos_aten::add_Scalaraten::addaten::addbmmaten::addbmm_aten::addcdivaten::addcmulaten::addmmaten::addmm_aten::addmvaten::addmv_aten::addraten::addr_aten::aliasaten::align_asaten::allaten::allclosetoBoolisBool() INTERNAL ASSERT FAILED at aten::anyaten::argmaxaten::argminaten::argsortaten::as_stridedaten::as_strided_aten::asinaten::asin_aten::atanaten::atan2aten::atan2_aten::atan_aten::baddbmmaten::baddbmm_aten::bernoulliaten::bernoulli_floataten::bincount(Tensor self, Tensor? weights=None, int minlength=0) -> Tensoraten::bitwise_notaten::bitwise_not_aten::bmmaten::cauchy_aten::ceilaten::ceil_aten::choleskyaten::cholesky_inverseaten::cholesky_solveaten::chunkaten::clamp_maxaten::clamp_max_aten::clamp_minaten::clamp_min_aten::cloneaten::coalesceaten::copy_aten::cosaten::cos_aten::coshaten::cosh_aten::cumprod(Tensor self, int dim, *, ScalarType? dtype=None) -> TensorScalarType arguments not implemented yetaten::cumsum(Tensor self, int dim, *, ScalarType? dtype=None) -> Tensoraten::dataaten::dense_dimaten::dequantizeaten::detaten::detachaten::detach_aten::diagaten::diag_embedaten::diagflataten::diagonalaten::digammaaten::digamma_aten::distaten::div_aten::divaten::dotaten::eigaten::eq_aten::equalaten::erfaten::erf_aten::erfcaten::erfc_aten::erfinvaten::erfinv_aten::expaten::exp_aten::expandaten::expand_asaten::expm1aten::expm1_aten::exponential_aten::fftaten::fill_aten::fill_diagonal_aten::flattenusing_intsaten::flipaten::flooraten::floor_aten::fmod_aten::fmodaten::fracaten::frac_aten::gatheraten::ge_aten::geometric_aten::geqrfaten::geraten::gt_aten::hardshrinkaten::histcaten::ifftaten::index_addaten::index_add_aten::index_copyaten::index_copy_aten::index_fill_aten::index_fillaten::index_selectaten::indicesaten::int_repraten::inverseaten::irfftaten::is_coalescedaten::is_complexaten::is_distributedaten::is_floating_pointaten::is_leaf(Tensor self) -> boolaten::is_nonzeroaten::is_pinnedaten::is_same_sizeaten::is_set_toaten::is_signedaten::iscloseaten::itemIValue is not a ScalartoDoubleisDouble() INTERNAL ASSERT FAILED at aten::kthvalueaten::le_aten::lerp_aten::lerpaten::lgammaaten::lgamma_aten::logaten::log10aten::log10_aten::log1paten::log1p_aten::log2aten::log2_aten::log_aten::log_normal_aten::log_softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensoraten::logdetaten::logical_notaten::logical_not_aten::logical_xoraten::logical_xor_aten::logsumexpaten::lstsqaten::lt_aten::lu_solveaten::masked_fill_aten::masked_fillaten::masked_scatteraten::masked_scatter_aten::masked_selectaten::matmulaten::matrix_poweraten::maxotheraten::max_valuesaten::mean(Tensor self, *, ScalarType? dtype=None) -> Tensoraten::mean.dim(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensoraten::medianaten::minaten::min_valuesaten::modeaten::mul_aten::mulaten::multinomialaten::mvlgammaaten::mvlgamma_aten::narrowaten::narrow_copyaten::ne_aten::negaten::neg_aten::nonzeroaten::nonzero_numpyaten::normaten::normal_aten::numpy_Taten::orgqraten::ormqraten::output_nr(Tensor self) -> intaten::permuteaten::pin_memoryaten::pinverseaten::polygammaaten::polygamma_aten::pow_aten::powTensor_ScalarTensor_Tensoraten::preluaten::prod(Tensor self, *, ScalarType? dtype=None) -> Tensoraten::prod.dim_int(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensoraten::put_aten::q_per_channel_axis(Tensor self) -> intaten::q_per_channel_scalesaten::q_per_channel_zero_pointsaten::q_scaleaten::q_zero_pointaten::qscheme(Tensor self) -> QSchemeaten::random_aten::reciprocalaten::reciprocal_aten::reluaten::relu_aten::remainder_aten::remainderaten::renormaten::renorm_aten::repeataten::repeat_interleaveself_Tensoraten::reshape_asaten::resize_aten::resize_as_aten::rfftaten::rollaten::rot90aten::roundaten::round_aten::rsqrtaten::rsqrt_aten::scatter_srcvalueaten::scatter_addaten::scatter_add_aten::scatteraten::selectintaten::set_source_Tensoraten::set_data(Tensor(a!) self, Tensor new_data) -> voidaten::sigmoidaten::sigmoid_aten::signaten::sign_aten::sinaten::sin_aten::sinhaten::sinh_aten::sizeaten::sliceaten::slogdetaten::smmaten::softmax(Tensor self, int dim, ScalarType? dtype=None) -> Tensoraten::solveaten::sortaten::sparse_dimaten::sparse_maskaten::sparse_resize_aten::sparse_resize_and_clear_aten::splitaten::split_with_sizesaten::sqrtaten::sqrt_aten::squeezeaten::squeeze_aten::sspaddmmaten::stdaten::strideaten::sub_aten::subaten::sum(Tensor self, *, ScalarType? dtype=None) -> Tensoraten::sum.dim_IntList(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None) -> Tensoraten::sum_to_sizeaten::svdaten::symeigaten::taten::takeaten::tanaten::tan_aten::tanhaten::tanh_aten::to_denseaten::to_mkldnnaten::to_sparsesparse_dimaten::topkaten::traceaten::transpose_aten::transposeaten::triangular_solveaten::trilaten::tril_aten::triuaten::triu_aten::truncaten::trunc_aten::type_asaten::unbindaten::unfoldaten::uniform_aten::unsqueezeaten::unsqueeze_aten::valuesaten::varaten::viewaten::view_asaten::whereaten::zero__abs_out_acos_out_adaptive_avg_pool1d_adaptive_max_pool1d_add_out_addbmm_out_addcdiv_out_addcmul_out_addmm_out_addmv_out_addr_out_affine_grid_generator_align_tensors_all_out_alpha_dropout_alpha_dropout__any_out_arange_out_arange_start_out_asin_out_atan2_out_atan_out_avg_pool1d_baddbmm_out_batch_norm_batch_norm_backward_elemt_batch_norm_backward_reduce_batch_norm_elemt_batch_norm_gather_stats_batch_norm_gather_stats_with_counts_batch_norm_stats_batch_norm_update_stats_bernoulli_out_bilinear_bitwise_not_out_bmm_out_broadcast_tensors_can_cast_cartesian_prod_cat_cat_out_cdist_ceil_out_celu_celu__chain_matmul_cholesky_inverse_out_cholesky_out_cholesky_solve_out_clamp_max_out_clamp_min_out_combinations_constant_pad_nd_conv1d_conv2d_conv3d_conv_tbc_conv_transpose1d_conv_transpose2d_input_conv_transpose3d_input_convolution_convolution_overrideable_copy_sparse_to_sparse__cos_out_cosh_out_cosine_embedding_loss_cosine_similarity_ctc_loss_intlist_ctc_loss_tensor_cudnn_affine_grid_generator_cudnn_batch_norm_cudnn_convolution_cudnn_convolution_backward_bias_cudnn_convolution_backward_input_cudnn_convolution_backward_weight_cudnn_convolution_transpose_cudnn_convolution_transpose_backward_bias_cudnn_convolution_transpose_backward_input_cudnn_convolution_transpose_backward_weight_cudnn_grid_sampler_cudnn_is_acceptable_cumprod_out_cumsum_out_diag_out_digamma_out_div_out_dot_out_dropout_dropout__eig_e_embedding_embedding_bag_embedding_renorm__empty_like_eq_scalar_out_eq_tensor_out_erf_out_erfc_out_erfinv_out_exp_out_expm1_out_eye_m_out_eye_out_fake_quantize_per_channel_affine_fake_quantize_per_tensor_affine_fbgemm_linear_fp16_weight_fbgemm_linear_fp16_weight_fp32_activation_fbgemm_linear_int8_weight_fbgemm_linear_int8_weight_fp32_activation_fbgemm_linear_quantize_weight_fbgemm_pack_gemm_matrix_fp16_fbgemm_pack_quantized_matrix_fbgemm_pack_quantized_matrix_kn_feature_alpha_dropout_feature_alpha_dropout__feature_dropout_feature_dropout__floor_out_fmod_scalar_out_fmod_tensor_out_frac_out_frobenius_norm_frobenius_norm_dim_frobenius_norm_out_full_like_full_out_gather_out_ge_scalar_out_ge_tensor_out_geqrf_a_ger_out_grid_sampler_grid_sampler_2d_grid_sampler_3d_group_norm_gru_cell_gru_data_gru_input_gt_scalar_out_gt_tensor_out_hinge_embedding_loss_histc_out_hspmm_hspmm_out_index_select_out_instance_norm_inverse_out_isnan_kl_div_kthvalue_values_layer_norm_le_scalar_out_le_tensor_out_lerp_scalar_out_lerp_tensor_out_lgamma_out_linspace_out_log10_out_log1p_out_log2_out_log_out_logical_not_out_logical_xor_out_logspace_out_logsumexp_out_lstm_cell_lstm_data_lstm_input_lstsq_x_lt_scalar_out_lt_tensor_out_lu_solve_out_margin_ranking_loss_masked_select_out_matmul_out_matrix_rank_matrix_rank_tol_max_dim_max_max_out_max_pool1d_max_pool1d_with_indices_max_pool2d_max_pool3d_mean_out_median_dim_values_meshgrid_min_dim_min_min_out_miopen_batch_norm_miopen_convolution_miopen_convolution_backward_bias_miopen_convolution_backward_input_miopen_convolution_backward_weight_miopen_convolution_transpose_miopen_convolution_transpose_backward_input_miopen_convolution_transpose_backward_weight_miopen_depthwise_convolution_miopen_depthwise_convolution_backward_input_miopen_depthwise_convolution_backward_weight_miopen_rnn_mkldnn_adaptive_avg_pool2d_mkldnn_convolution_mkldnn_convolution_backward_input_mkldnn_convolution_backward_weights_mkldnn_max_pool2d_mm_out_mode_values_mul_out_multinomial_out_mv_out_native_batch_norm_native_layer_norm_native_norm_ne_scalar_out_ne_tensor_out_neg_out_nonzero_out_norm_except_dim_nuclear_norm_nuclear_norm_dim_nuclear_norm_dim_out_nuclear_norm_out_ones_like_ones_out_orgqr_out_ormqr_out_pairwise_distance_pdist_pixel_shuffle_poisson_poisson_nll_loss_polygamma_out_pow_scalar_pow_scalar_out_pow_tensor_scalar_out_pow_tensor_tensor_out_prod_int_out_promote_types_qr_q_quantize_per_channel_quantize_per_tensor_quantized_gru_cell_quantized_gru_data_quantized_gru_input_quantized_lstm_quantized_lstm_cell_quantized_max_pool2d_quantized_rnn_relu_cell_quantized_rnn_tanh_cell_rand_generator_out_rand_like_rand_out_randint_generator_out_randint_like_randint_like_low_randint_low_generator_out_randint_low_out_randint_out_randn_generator_out_randn_like_randn_out_randperm_generator_out_randperm_out_range_out_reciprocal_out_remainder_scalar_out_remainder_tensor_out_renorm_out_repeat_interleave_tensor_result_type_scalar_result_type_scalar_scalar_result_type_scalar_tensor_result_type_tensor_rnn_relu_cell_rnn_relu_data_rnn_relu_input_rnn_tanh_cell_rnn_tanh_data_rnn_tanh_input_round_out_rrelu_rrelu__rsqrt_out_rsub_scalar_rs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self, *, Tensor(a!) out) -> Tensor(a!)aten::acos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::adaptive_avg_pool1daten::adaptive_max_pool1daten::add.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addcdiv.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)aten::addcmul.out(Tensor self, Tensor tensor1, Tensor tensor2, *, Scalar value=1, Tensor(a!) out) -> Tensor(a!)aten::addmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addmv.out(Tensor self, Tensor mat, Tensor vec, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::addr.out(Tensor self, Tensor vec1, Tensor vec2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::affine_grid_generatoraten::align_tensorsaten::all.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::alpha_dropoutaten::alpha_dropout_aten::any.out(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::arange.out(Scalar end, *, Tensor(a!) out) -> Tensor(a!)aten::arange.start_out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)aten::asin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::atan2.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::atan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::avg_pool1daten::baddbmm.out(Tensor self, Tensor batch1, Tensor batch2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps, bool cudnn_enabled) -> Tensoraten::batch_norm_backward_elemt(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, Tensor mean_dy, Tensor mean_dy_xmu) -> Tensoraten::batch_norm_backward_reduce(Tensor grad_out, Tensor input, Tensor mean, Tensor invstd, Tensor? weight, bool input_g, bool weight_g, bool bias_g) -> (Tensor, Tensor, Tensor, Tensor)aten::batch_norm_elemt(Tensor input, Tensor? weight, Tensor? bias, Tensor mean, Tensor invstd, float eps) -> Tensoraten::batch_norm_gather_stats(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int count) -> (Tensor, Tensor)aten::batch_norm_gather_stats_with_counts(Tensor input, Tensor mean, Tensor invstd, Tensor? running_mean, Tensor? running_var, float momentum, float eps, int[] counts) -> (Tensor, Tensor)aten::batch_norm_statsaten::batch_norm_update_stats(Tensor input, Tensor? running_mean, Tensor? running_var, float momentum) -> (Tensor, Tensor)aten::bernoulli.out(Tensor self, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)aten::bilinear(Tensor input1, Tensor input2, Tensor weight, Tensor? bias) -> Tensoraten::bitwise_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)aten::broadcast_tensorsaten::cartesian_prodaten::cataten::cat.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)aten::cdistaten::ceil.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::celuaten::celu_aten::chain_matmulaten::cholesky_inverse.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)aten::cholesky.out(Tensor self, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)aten::cholesky_solve.out(Tensor self, Tensor input2, bool upper=False, *, Tensor(a!) out) -> Tensor(a!)aten::clamp_max.out(Tensor self, Scalar max, *, Tensor(a!) out) -> Tensor(a!)aten::clamp_min.out(Tensor self, Scalar min, *, Tensor(a!) out) -> Tensor(a!)aten::combinationsaten::constant_pad_ndaten::conv1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] dilation=1, int groups=1) -> Tensoraten::conv2d(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] dilation=1, int groups=1) -> Tensoraten::conv3d(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] dilation=1, int groups=1) -> Tensoraten::conv_tbcaten::conv_transpose1d(Tensor input, Tensor weight, Tensor? bias=None, int[1] stride=1, int[1] padding=0, int[1] output_padding=0, int groups=1, int[1] dilation=1) -> Tensoraten::conv_transpose2d.input(Tensor input, Tensor weight, Tensor? bias=None, int[2] stride=1, int[2] padding=0, int[2] output_padding=0, int groups=1, int[2] dilation=1) -> Tensoraten::conv_transpose3d.input(Tensor input, Tensor weight, Tensor? bias=None, int[3] stride=1, int[3] padding=0, int[3] output_padding=0, int groups=1, int[3] dilation=1) -> Tensoraten::convolution(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensoraten::convolution_overrideable(Tensor input, Tensor weight, Tensor? bias, int[] stride, int[] padding, int[] dilation, bool transposed, int[] output_padding, int groups) -> Tensoraten::copy_sparse_to_sparse_aten::cos.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::cosh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::cosine_embedding_lossaten::cosine_similarityaten::ctc_lossaten::cudnn_affine_grid_generatoraten::cudnn_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor)aten::cudnn_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::cudnn_convolution_backward_biasaten::cudnn_convolution_backward_inputaten::cudnn_convolution_backward_weightaten::cudnn_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::cudnn_convolution_transpose_backward_biasaten::cudnn_convolution_transpose_backward_inputaten::cudnn_convolution_transpose_backward_weightaten::cudnn_grid_sampleraten::cudnn_is_acceptableaten::cumprod.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::cumsum.out(Tensor self, int dim, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::diag.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)aten::digamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::div.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::dot.out(Tensor self, Tensor tensor, *, Tensor(a!) out) -> Tensor(a!)aten::dropoutaten::dropout_aten::eig.e(Tensor self, bool eigenvectors=False, *, Tensor(a!) e, Tensor(b!) v) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)aten::embeddingaten::embedding_bag(Tensor weight, Tensor indices, Tensor offsets, bool scale_grad_by_freq=False, int mode=0, bool sparse=False, Tensor? per_sample_weights=None) -> (Tensor, Tensor, Tensor, Tensor)aten::embedding_renorm_aten::empty_likeselfaten::empty_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False, MemoryFormat? memory_format=contiguous_format) -> Tensoraten::eq.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::eq.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::erf.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::erfc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::erfinv.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::exp.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::expm1.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::eye.m_out(int n, int m, *, Tensor(a!) out) -> Tensor(a!)aten::eye.out(int n, *, Tensor(a!) out) -> Tensor(a!)aten::fake_quantize_per_channel_affine(Tensor self, Tensor scale, Tensor zero_point, int axis, int quant_min, int quant_max) -> Tensoraten::fake_quantize_per_tensor_affineaten::fbgemm_linear_fp16_weightaten::fbgemm_linear_fp16_weight_fp32_activationaten::fbgemm_linear_int8_weightaten::fbgemm_linear_int8_weight_fp32_activationaten::fbgemm_linear_quantize_weightaten::fbgemm_pack_gemm_matrix_fp16aten::fbgemm_pack_quantized_matrixaten::feature_alpha_dropoutaten::feature_alpha_dropout_aten::feature_dropoutaten::feature_dropout_aten::floor.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::fmod.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::fmod.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::frac.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::frobenius_normaten::frobenius_norm.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::full_likeaten::full_like.dtype(Tensor self, Scalar fill_value, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::full.out(int[] size, Scalar fill_value, *, Tensor(a!) out) -> Tensor(a!)aten::gather.out(Tensor self, int dim, Tensor index, *, bool sparse_grad=False, Tensor(a!) out) -> Tensor(a!)aten::ge.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::ge.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::geqrf.a(Tensor self, *, Tensor(a!) a, Tensor(b!) tau) -> (Tensor(a!) a, Tensor(b!) tau)aten::ger.out(Tensor self, Tensor vec2, *, Tensor(a!) out) -> Tensor(a!)aten::grid_sampleraten::grid_sampler_2daten::grid_sampler_3daten::group_norm(Tensor input, int num_groups, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enabled=True) -> Tensoraten::gru_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensoraten::gruinputaten::gt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::gt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::hinge_embedding_lossaten::histc.out(Tensor self, int bins=100, Scalar min=0, Scalar max=0, *, Tensor(a!) out) -> Tensor(a!)aten::hspmmaten::hspmm.out(Tensor mat1, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)aten::index_select.out(Tensor self, int dim, Tensor index, *, Tensor(a!) out) -> Tensor(a!)aten::instance_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool use_input_stats, float momentum, float eps, bool cudnn_enabled) -> Tensoraten::inverse.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::isnanaten::kl_divaten::kthvalue.values(Tensor self, int k, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::layer_norm(Tensor input, int[] normalized_shape, Tensor? weight=None, Tensor? bias=None, float eps=1e-05, bool cudnn_enable=True) -> Tensoraten::le.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::le.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::lerp.Scalar_out(Tensor self, Tensor end, Scalar weight, *, Tensor(a!) out) -> Tensor(a!)aten::lerp.Tensor_out(Tensor self, Tensor end, Tensor weight, *, Tensor(a!) out) -> Tensor(a!)aten::lgamma.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::linspace.out(Scalar start, Scalar end, int steps=100, *, Tensor(a!) out) -> Tensor(a!)aten::log10.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::log1p.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::log2.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::log.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::logical_not.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::logical_xor.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::logspace.out(Scalar start, Scalar end, int steps=100, float base=10.0, *, Tensor(a!) out) -> Tensor(a!)aten::logsumexp.out(Tensor self, int[1] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::lstm_cell(Tensor input, Tensor[] hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> (Tensor, Tensor)aten::lstmaten::lstsq.X(Tensor self, Tensor A, *, Tensor(a!) X, Tensor(b!) qr) -> (Tensor(a!) solution, Tensor(b!) QR)aten::lt.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::lt.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::lu_solve.out(Tensor self, Tensor LU_data, Tensor LU_pivots, *, Tensor(a!) out) -> Tensor(a!)aten::margin_ranking_lossaten::masked_select.out(Tensor self, Tensor mask, *, Tensor(a!) out) -> Tensor(a!)aten::matmul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::matrix_ranktolaten::max.dim_max(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) max, Tensor(b!) max_values) -> (Tensor(a!) values, Tensor(b!) indices)aten::max.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::max_pool1daten::max_pool1d_with_indicesaten::max_pool2daten::max_pool3daten::mean.out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::median.dim_values(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::meshgridaten::min.dim_min(Tensor self, int dim, bool keepdim=False, *, Tensor(a!) min, Tensor(b!) min_indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::min.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::miopen_batch_norm(Tensor input, Tensor weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float exponential_average_factor, float epsilon) -> (Tensor, Tensor, Tensor)aten::miopen_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::miopen_convolution_backward_biasaten::miopen_convolution_backward_inputaten::miopen_convolution_backward_weightaten::miopen_convolution_transpose(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] output_padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::miopen_convolution_transpose_backward_inputaten::miopen_convolution_transpose_backward_weightaten::miopen_depthwise_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups, bool benchmark, bool deterministic) -> Tensoraten::miopen_depthwise_convolution_backward_inputaten::miopen_depthwise_convolution_backward_weightaten::miopen_rnn(Tensor input, Tensor[] weight, int weight_stride0, Tensor hx, Tensor? cx, int mode, int hidden_size, int num_layers, bool batch_first, float dropout, bool train, bool bidirectional, int[] batch_sizes, Tensor? dropout_state) -> (Tensor, Tensor, Tensor, Tensor, Tensor)aten::mkldnn_adaptive_avg_pool2daten::mkldnn_convolution(Tensor self, Tensor weight, Tensor? bias, int[] padding, int[] stride, int[] dilation, int groups) -> Tensoraten::mkldnn_convolution_backward_inputaten::mkldnn_convolution_backward_weightsaten::mkldnn_max_pool2daten::mm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!)aten::mode.values(Tensor self, int dim=-1, bool keepdim=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::mul.out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::multinomial.out(Tensor self, int num_samples, bool replacement=False, *, Generator? generator=None, Tensor(a!) out) -> Tensor(a!)aten::mv.out(Tensor self, Tensor vec, *, Tensor(a!) out) -> Tensor(a!)aten::native_batch_norm(Tensor input, Tensor? weight, Tensor? bias, Tensor? running_mean, Tensor? running_var, bool training, float momentum, float eps) -> (Tensor, Tensor, Tensor)aten::native_layer_norm(Tensor input, Tensor? weight, Tensor? bias, int M, int N, float eps) -> (Tensor, Tensor, Tensor)aten::native_normaten::ne.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::ne.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::neg.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::nonzero.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::norm_except_dimaten::nuclear_normaten::nuclear_norm.dim_out(Tensor self, int[2] dim, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::nuclear_norm.out(Tensor self, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::ones_likeaten::ones_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::ones.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::orgqr.out(Tensor self, Tensor input2, *, Tensor(a!) out) -> Tensor(a!)aten::ormqr.out(Tensor self, Tensor input2, Tensor input3, bool left=True, bool transpose=False, *, Tensor(a!) out) -> Tensor(a!)aten::pairwise_distanceaten::pdistaten::pixel_shuffleaten::poissonaten::poisson_nll_lossaten::polygamma.out(int n, Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::pow.Scalar_out(Scalar self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)aten::pow.Tensor_Scalar_out(Tensor self, Scalar exponent, *, Tensor(a!) out) -> Tensor(a!)aten::pow.Tensor_Tensor_out(Tensor self, Tensor exponent, *, Tensor(a!) out) -> Tensor(a!)aten::prod.int_out(Tensor self, int dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::qr.Q(Tensor self, bool some=True, *, Tensor(a!) Q, Tensor(b!) R) -> (Tensor(a!) Q, Tensor(b!) R)aten::quantized_gru_cellaten::quantized_gruaten::quantized_lstm(Tensor input, Tensor[] hx, Tensor[] params, bool has_biases, int num_layers, float dropout, bool train, bool bidirectional, bool batch_first, *, ScalarType? dtype=None, bool use_dynamic=False) -> (Tensor, Tensor, Tensor)aten::quantized_lstm_cellaten::quantized_max_pool2daten::quantized_rnn_relu_cellaten::quantized_rnn_tanh_cellaten::rand.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::rand_likeaten::rand_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randint.out(int high, int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::randint_likeaten::randint_like.dtype(Tensor self, int high, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randint_like.low_dtype(Tensor self, int low, int high, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randint.low_out(int low, int high, int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::randn.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)aten::randn_likeaten::randn_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::randperm.out(int n, *, Tensor(a!) out) -> Tensor(a!)aten::range.out(Scalar start, Scalar end, Scalar step=1, *, Tensor(a!) out) -> Tensor(a!)aten::reciprocal.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::remainder.Scalar_out(Tensor self, Scalar other, *, Tensor(a!) out) -> Tensor(a!)aten::remainder.Tensor_out(Tensor self, Tensor other, *, Tensor(a!) out) -> Tensor(a!)aten::renorm.out(Tensor self, Scalar p, int dim, Scalar maxnorm, *, Tensor(a!) out) -> Tensor(a!)aten::result_type.Scalar(Tensor tensor, Scalar other) -> ScalarTypeaten::result_type.Scalar_Scalar(Scalar scalar1, Scalar scalar2) -> ScalarTypeaten::result_type.Scalar_Tensor(Scalar scalar, Tensor tensor) -> ScalarTypeaten::result_type.Tensor(Tensor tensor, Tensor other) -> ScalarTypeaten::rnn_relu_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensoraten::rnn_reluaten::rnn_tanh_cell(Tensor input, Tensor hx, Tensor w_ih, Tensor w_hh, Tensor? b_ih=None, Tensor? b_hh=None) -> Tensoraten::rnn_tanhaten::round.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::rreluaten::rrelu_aten::rsqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::rsubaten::seluaten::selu_aten::sigmoid.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sign.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sin.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sinh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::solve.solution(Tensor self, Tensor A, *, Tensor(a!) solution, Tensor(b!) lu) -> (Tensor(a!) solution, Tensor(b!) LU)aten::sort.values(Tensor self, int dim=-1, bool descending=False, *, Tensor(a!) values, Tensor(b!) indices) -> (Tensor(a!) values, Tensor(b!) indices)aten::sqrt.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::sspaddmm.out(Tensor self, Tensor mat1, Tensor mat2, *, Scalar beta=1, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::stackaten::stack.out(Tensor[] tensors, int dim=0, *, Tensor(a!) out) -> Tensor(a!)aten::std_meanaten::std.out(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::sub.out(Tensor self, Tensor other, *, Scalar alpha=1, Tensor(a!) out) -> Tensor(a!)aten::sum.IntList_out(Tensor self, int[1] dim, bool keepdim=False, *, ScalarType? dtype=None, Tensor(a!) out) -> Tensor(a!)aten::svd.U(Tensor self, bool some=True, bool compute_uv=True, *, Tensor(a!) U, Tensor(b!) S, Tensor(c!) V) -> (Tensor(a!) U, Tensor(b!) S, Tensor(c!) V)aten::symeig.e(Tensor self, bool eigenvectors=False, bool upper=True, *, Tensor(a!) e, Tensor(b!) V) -> (Tensor(a!) eigenvalues, Tensor(b!) eigenvectors)aten::take.out(Tensor self, Tensor index, *, Tensor(a!) out) -> Tensor(a!)aten::tan.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::tanh.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::tensordotaten::thresholdaten::threshold_aten::threshold.out(Tensor self, Scalar threshold, Scalar value, *, Tensor(a!) out) -> Tensor(a!)aten::topk.values(Tensor self, int k, int dim=-1, bool largest=True, bool sorted=True, *, Tensor(a!) values, Tensor(b!) indices) ->(Tensor(a!) values, Tensor(b!) indices)aten::trapzaten::triangular_solve.X(Tensor self, Tensor A, bool upper=True, bool transpose=False, bool unitriangular=False, *, Tensor(a!) X, Tensor(b!) M) -> (Tensor(a!) solution, Tensor(b!) cloned_coefficient)aten::tril.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)aten::triplet_margin_lossaten::triu.out(Tensor self, int diagonal=0, *, Tensor(a!) out) -> Tensor(a!)aten::trunc.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)aten::unique_consecutiveaten::unique_dimaten::var_meanaten::var.out(Tensor self, int[1] dim, bool unbiased=True, bool keepdim=False, *, Tensor(a!) out) -> Tensor(a!)aten::zeros_likeaten::zeros_like.dtype(Tensor self, *, ScalarType dtype, Layout layout, Device device, bool pin_memory=False) -> Tensoraten::zeros.out(int[] size, *, Tensor(a!) out) -> Tensor(a!)Expected a Class but got a %snew__rice_casters__messageExpected a Module but got a %s<=>to_sinspect==<>wrong argument type %s (expected %s)Cannot get method id and class for function__rice__ HLXQ Q!QaQQ!QXa(&T:,[ E ,k"~=Y T0H 8 9 : <8 =d > ? P? ? @ `@ @ 0A PB pEP Gl H 0N O Q pR !W![\!]x!_!d!0f4!Pj!pP!rp!t!0v!x! 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Xg!pi! i! j!`@j!j!k!P$k!4k!k!k!k!0k!k!0m!\m!pm!n!$n!PDn!Po!o! o! p!q!4q!pTq!PLr!lr!r!s!Ps!t!$t!Ku!`N8u!NHu!P\u!`Ru!Su!0Wv!X!@ȋ!B!D!PGP!J!@MȌ!P"P D>"0 d>"  >"' ?"@* ?"+ @", ,@"/ `@"2 @"5 @"6 @"8 A"; DA"> xA"? 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