MindSpore(:  853_852_1_construct_wrapper:x ,853_852_1_construct_wrapper:conv_stem.weight$853_852_1_construct_wrapper:output:1$853_852_1_construct_wrapper:output:1"Conv2D*$R ppBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR * Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:#Default/conv_stem-Conv2d/Conv2D-op0  $853_852_1_construct_wrapper:output:1 %853_852_1_construct_wrapper:bn1.gamma $853_852_1_construct_wrapper:bn1.beta +853_852_1_construct_wrapper:bn1.moving_mean /853_852_1_construct_wrapper:bn1.moving_variance853_852_1_construct_wrapper:0:2853_852_1_construct_wrapper:0:2" BatchNorm*R ppBshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:%Default/bn1-BatchNorm2d/BatchNorm-op1 H[ValueNode]1(0):3"Constant*) valueR *Bvalue0  scalar:value0  853_852_1_construct_wrapper:0:2 [ValueNode]1(0):3&853_852_1_construct_wrapper:[CNode]2:4&853_852_1_construct_wrapper:[CNode]2:4" tuple_getitem*$R ppBshape1 shape:shape1,:)Default/bn1-BatchNorm2d/tuple_getitem-op2  &853_852_1_construct_wrapper:[CNode]2:4&853_852_1_construct_wrapper:[CNode]3:5&853_852_1_construct_wrapper:[CNode]3:5"Sigmoid*$R ppBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],: Default/act_fn-Swish/Sigmoid-op3  &853_852_1_construct_wrapper:[CNode]2:4 &853_852_1_construct_wrapper:[CNode]3:5&853_852_1_construct_wrapper:[CNode]4:6&853_852_1_construct_wrapper:[CNode]4:6"Mul*$R ppBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op4 &853_852_1_construct_wrapper:[CNode]4:6 ;853_852_1_construct_wrapper:blocks.layer.0.0.conv_dw.weight$853_852_1_construct_wrapper:output:7$853_852_1_construct_wrapper:output:7"Conv2D*$R ppBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR * Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR * Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:tDefault/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/conv_dw-Conv2d/Conv2D-op5  $853_852_1_construct_wrapper:output:7 6853_852_1_construct_wrapper:blocks.layer.0.0.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.0.0.bn1.beta <853_852_1_construct_wrapper:blocks.layer.0.0.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.0.0.bn1.moving_variance853_852_1_construct_wrapper:0:8853_852_1_construct_wrapper:0:8" BatchNorm*R ppBshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:%Default/bn1-BatchNorm2d/BatchNorm-op6 H[ValueNode]1(0):9"Constant*) valueR *Bvalue0  scalar:value0  853_852_1_construct_wrapper:0:8 [ValueNode]1(0):9'853_852_1_construct_wrapper:[CNode]2:10'853_852_1_construct_wrapper:[CNode]2:10" tuple_getitem*$R ppBshape1 shape:shape1,:)Default/bn1-BatchNorm2d/tuple_getitem-op7  '853_852_1_construct_wrapper:[CNode]2:10'853_852_1_construct_wrapper:[CNode]5:11'853_852_1_construct_wrapper:[CNode]5:11"Sigmoid*$R ppBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/act_fn-Swish/Sigmoid-op8  '853_852_1_construct_wrapper:[CNode]2:10 '853_852_1_construct_wrapper:[CNode]5:11'853_852_1_construct_wrapper:[CNode]4:12'853_852_1_construct_wrapper:[CNode]4:12"Mul*$R ppBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op9 m[ValueNode]6((2, 3)):13"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  '853_852_1_construct_wrapper:[CNode]4:12 [ValueNode]6((2, 3)):13#853_852_1_construct_wrapper:x_se:14#853_852_1_construct_wrapper:x_se:14" ReduceMean*$R Bshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:{Default/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/se-SqueezeExcite/ReduceMean-op10 #853_852_1_construct_wrapper:x_se:14 B853_852_1_construct_wrapper:blocks.layer.0.0.se.conv_reduce.weight%853_852_1_construct_wrapper:output:15%853_852_1_construct_wrapper:output:15"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op11  %853_852_1_construct_wrapper:output:15 @853_852_1_construct_wrapper:blocks.layer.0.0.se.conv_reduce.bias%853_852_1_construct_wrapper:output:16%853_852_1_construct_wrapper:output:16"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op12  %853_852_1_construct_wrapper:output:16'853_852_1_construct_wrapper:[CNode]7:17'853_852_1_construct_wrapper:[CNode]7:17"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/se-SqueezeExcite/act_fn-Swish/Sigmoid-op13  %853_852_1_construct_wrapper:output:16 '853_852_1_construct_wrapper:[CNode]7:17'853_852_1_construct_wrapper:[CNode]4:18'853_852_1_construct_wrapper:[CNode]4:18"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op14 '853_852_1_construct_wrapper:[CNode]4:18 B853_852_1_construct_wrapper:blocks.layer.0.0.se.conv_expand.weight%853_852_1_construct_wrapper:output:19%853_852_1_construct_wrapper:output:19"Conv2D*$R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR * Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op15  %853_852_1_construct_wrapper:output:19 @853_852_1_construct_wrapper:blocks.layer.0.0.se.conv_expand.bias%853_852_1_construct_wrapper:output:20%853_852_1_construct_wrapper:output:20"BiasAdd*$R Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op16  %853_852_1_construct_wrapper:output:20#853_852_1_construct_wrapper:x_se:21#853_852_1_construct_wrapper:x_se:21"Sigmoid*$R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:xDefault/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/se-SqueezeExcite/Sigmoid-op17  '853_852_1_construct_wrapper:[CNode]4:12 #853_852_1_construct_wrapper:x_se:21'853_852_1_construct_wrapper:[CNode]4:22'853_852_1_construct_wrapper:[CNode]4:22"Mul*$R ppBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op18 '853_852_1_construct_wrapper:[CNode]4:22 ;853_852_1_construct_wrapper:blocks.layer.0.0.conv_pw.weight%853_852_1_construct_wrapper:output:23%853_852_1_construct_wrapper:output:23"Conv2D*$RppBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:uDefault/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/conv_pw-Conv2d/Conv2D-op19  %853_852_1_construct_wrapper:output:23 6853_852_1_construct_wrapper:blocks.layer.0.0.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.0.0.bn2.beta <853_852_1_construct_wrapper:blocks.layer.0.0.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.0.0.bn2.moving_variance'853_852_1_construct_wrapper:[CNode]8:24'853_852_1_construct_wrapper:[CNode]8:24" BatchNorm*RppBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:yDefault/blocks-BlockBuilder/layer-SequentialCell/0-SequentialCell/0-DepthwiseSeparableConv/bn2-BatchNorm2d/BatchNorm-op20 I[ValueNode]1(0):25"Constant*) valueR *Bvalue0  scalar:value0  '853_852_1_construct_wrapper:[CNode]8:24 [ValueNode]1(0):25'853_852_1_construct_wrapper:[CNode]2:26'853_852_1_construct_wrapper:[CNode]2:26" tuple_getitem*$RppBshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op21 '853_852_1_construct_wrapper:[CNode]2:26 ;853_852_1_construct_wrapper:blocks.layer.1.0.conv_pw.weight%853_852_1_construct_wrapper:output:27%853_852_1_construct_wrapper:output:27"Conv2D*$R`ppBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *`Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/conv_pw-Conv2d/Conv2D-op22  %853_852_1_construct_wrapper:output:27 6853_852_1_construct_wrapper:blocks.layer.1.0.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.1.0.bn1.beta <853_852_1_construct_wrapper:blocks.layer.1.0.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.1.0.bn1.moving_variance 853_852_1_construct_wrapper:9:28 853_852_1_construct_wrapper:9:28" BatchNorm*R`ppBshape1R `Bshape2R `Bshape3R `Bshape4R `Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op23 I[ValueNode]1(0):29"Constant*) valueR *Bvalue0  scalar:value0  853_852_1_construct_wrapper:9:28 [ValueNode]1(0):29'853_852_1_construct_wrapper:[CNode]2:30'853_852_1_construct_wrapper:[CNode]2:30" tuple_getitem*$R`ppBshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op24  '853_852_1_construct_wrapper:[CNode]2:30(853_852_1_construct_wrapper:[CNode]10:31(853_852_1_construct_wrapper:[CNode]10:31"Sigmoid*$R`ppBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:nDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op25  '853_852_1_construct_wrapper:[CNode]2:30 (853_852_1_construct_wrapper:[CNode]10:31'853_852_1_construct_wrapper:[CNode]4:32'853_852_1_construct_wrapper:[CNode]4:32"Mul*$R`ppBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op26 '853_852_1_construct_wrapper:[CNode]4:32 ;853_852_1_construct_wrapper:blocks.layer.1.0.conv_dw.weight%853_852_1_construct_wrapper:output:33%853_852_1_construct_wrapper:output:33"Conv2D*$R`88Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *`Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *`Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/conv_dw-Conv2d/Conv2D-op27  %853_852_1_construct_wrapper:output:33 6853_852_1_construct_wrapper:blocks.layer.1.0.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.1.0.bn2.beta <853_852_1_construct_wrapper:blocks.layer.1.0.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.1.0.bn2.moving_variance 853_852_1_construct_wrapper:9:34 853_852_1_construct_wrapper:9:34" BatchNorm*R`88Bshape1R `Bshape2R `Bshape3R `Bshape4R `Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op28 I[ValueNode]1(0):35"Constant*) valueR *Bvalue0  scalar:value0  853_852_1_construct_wrapper:9:34 [ValueNode]1(0):35'853_852_1_construct_wrapper:[CNode]2:36'853_852_1_construct_wrapper:[CNode]2:36" tuple_getitem*$R`88Bshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op29  '853_852_1_construct_wrapper:[CNode]2:36(853_852_1_construct_wrapper:[CNode]10:37(853_852_1_construct_wrapper:[CNode]10:37"Sigmoid*$R`88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:nDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op30  '853_852_1_construct_wrapper:[CNode]2:36 (853_852_1_construct_wrapper:[CNode]10:37'853_852_1_construct_wrapper:[CNode]4:38'853_852_1_construct_wrapper:[CNode]4:38"Mul*$R`88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op31 m[ValueNode]6((2, 3)):39"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  '853_852_1_construct_wrapper:[CNode]4:38 [ValueNode]6((2, 3)):39#853_852_1_construct_wrapper:x_se:40#853_852_1_construct_wrapper:x_se:40" ReduceMean*$R`Bshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:uDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/se-SqueezeExcite/ReduceMean-op32 #853_852_1_construct_wrapper:x_se:40 B853_852_1_construct_wrapper:blocks.layer.1.0.se.conv_reduce.weight%853_852_1_construct_wrapper:output:41%853_852_1_construct_wrapper:output:41"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op33  %853_852_1_construct_wrapper:output:41 @853_852_1_construct_wrapper:blocks.layer.1.0.se.conv_reduce.bias%853_852_1_construct_wrapper:output:42%853_852_1_construct_wrapper:output:42"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op34  %853_852_1_construct_wrapper:output:42(853_852_1_construct_wrapper:[CNode]11:43(853_852_1_construct_wrapper:[CNode]11:43"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op35  %853_852_1_construct_wrapper:output:42 (853_852_1_construct_wrapper:[CNode]11:43'853_852_1_construct_wrapper:[CNode]4:44'853_852_1_construct_wrapper:[CNode]4:44"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op36 '853_852_1_construct_wrapper:[CNode]4:44 B853_852_1_construct_wrapper:blocks.layer.1.0.se.conv_expand.weight%853_852_1_construct_wrapper:output:45%853_852_1_construct_wrapper:output:45"Conv2D*$R`Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *`Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op37  %853_852_1_construct_wrapper:output:45 @853_852_1_construct_wrapper:blocks.layer.1.0.se.conv_expand.bias%853_852_1_construct_wrapper:output:46%853_852_1_construct_wrapper:output:46"BiasAdd*$R`Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op38  %853_852_1_construct_wrapper:output:46#853_852_1_construct_wrapper:x_se:47#853_852_1_construct_wrapper:x_se:47"Sigmoid*$R`Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:rDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/se-SqueezeExcite/Sigmoid-op39  '853_852_1_construct_wrapper:[CNode]4:38 #853_852_1_construct_wrapper:x_se:47'853_852_1_construct_wrapper:[CNode]4:48'853_852_1_construct_wrapper:[CNode]4:48"Mul*$R`88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op40 '853_852_1_construct_wrapper:[CNode]4:48 <853_852_1_construct_wrapper:blocks.layer.1.0.conv_pwl.weight%853_852_1_construct_wrapper:output:49%853_852_1_construct_wrapper:output:49"Conv2D*$R88Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/conv_pwl-Conv2d/Conv2D-op41  %853_852_1_construct_wrapper:output:49 6853_852_1_construct_wrapper:blocks.layer.1.0.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.1.0.bn3.beta <853_852_1_construct_wrapper:blocks.layer.1.0.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.1.0.bn3.moving_variance!853_852_1_construct_wrapper:12:50!853_852_1_construct_wrapper:12:50" BatchNorm*R88Bshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op42 I[ValueNode]1(0):51"Constant*) valueR *Bvalue0  scalar:value0  !853_852_1_construct_wrapper:12:50 [ValueNode]1(0):51'853_852_1_construct_wrapper:[CNode]2:52'853_852_1_construct_wrapper:[CNode]2:52" tuple_getitem*$R88Bshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op43 '853_852_1_construct_wrapper:[CNode]2:52 ;853_852_1_construct_wrapper:blocks.layer.1.1.conv_pw.weight%853_852_1_construct_wrapper:output:53%853_852_1_construct_wrapper:output:53"Conv2D*%R88Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/conv_pw-Conv2d/Conv2D-op44  %853_852_1_construct_wrapper:output:53 6853_852_1_construct_wrapper:blocks.layer.1.1.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.1.1.bn1.beta <853_852_1_construct_wrapper:blocks.layer.1.1.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.1.1.bn1.moving_variance!853_852_1_construct_wrapper:13:54!853_852_1_construct_wrapper:13:54" BatchNorm*R88Bshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op45 I[ValueNode]1(0):55"Constant*) valueR *Bvalue0  scalar:value0  !853_852_1_construct_wrapper:13:54 [ValueNode]1(0):55'853_852_1_construct_wrapper:[CNode]2:56'853_852_1_construct_wrapper:[CNode]2:56" tuple_getitem*%R88Bshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op46  '853_852_1_construct_wrapper:[CNode]2:56(853_852_1_construct_wrapper:[CNode]14:57(853_852_1_construct_wrapper:[CNode]14:57"Sigmoid*%R88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:nDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op47  '853_852_1_construct_wrapper:[CNode]2:56 (853_852_1_construct_wrapper:[CNode]14:57'853_852_1_construct_wrapper:[CNode]4:58'853_852_1_construct_wrapper:[CNode]4:58"Mul*%R88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op48 '853_852_1_construct_wrapper:[CNode]4:58 ;853_852_1_construct_wrapper:blocks.layer.1.1.conv_dw.weight%853_852_1_construct_wrapper:output:59%853_852_1_construct_wrapper:output:59"Conv2D*%R88Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/conv_dw-Conv2d/Conv2D-op49  %853_852_1_construct_wrapper:output:59 6853_852_1_construct_wrapper:blocks.layer.1.1.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.1.1.bn2.beta <853_852_1_construct_wrapper:blocks.layer.1.1.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.1.1.bn2.moving_variance!853_852_1_construct_wrapper:13:60!853_852_1_construct_wrapper:13:60" BatchNorm*R88Bshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op50 I[ValueNode]1(0):61"Constant*) valueR *Bvalue0  scalar:value0  !853_852_1_construct_wrapper:13:60 [ValueNode]1(0):61'853_852_1_construct_wrapper:[CNode]2:62'853_852_1_construct_wrapper:[CNode]2:62" tuple_getitem*%R88Bshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op51  '853_852_1_construct_wrapper:[CNode]2:62(853_852_1_construct_wrapper:[CNode]14:63(853_852_1_construct_wrapper:[CNode]14:63"Sigmoid*%R88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:nDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op52  '853_852_1_construct_wrapper:[CNode]2:62 (853_852_1_construct_wrapper:[CNode]14:63'853_852_1_construct_wrapper:[CNode]4:64'853_852_1_construct_wrapper:[CNode]4:64"Mul*%R88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op53 m[ValueNode]6((2, 3)):65"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  '853_852_1_construct_wrapper:[CNode]4:64 [ValueNode]6((2, 3)):65#853_852_1_construct_wrapper:x_se:66#853_852_1_construct_wrapper:x_se:66" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:uDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/ReduceMean-op54 #853_852_1_construct_wrapper:x_se:66 B853_852_1_construct_wrapper:blocks.layer.1.1.se.conv_reduce.weight%853_852_1_construct_wrapper:output:67%853_852_1_construct_wrapper:output:67"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op55  %853_852_1_construct_wrapper:output:67 @853_852_1_construct_wrapper:blocks.layer.1.1.se.conv_reduce.bias%853_852_1_construct_wrapper:output:68%853_852_1_construct_wrapper:output:68"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op56  %853_852_1_construct_wrapper:output:68(853_852_1_construct_wrapper:[CNode]15:69(853_852_1_construct_wrapper:[CNode]15:69"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op57  %853_852_1_construct_wrapper:output:68 (853_852_1_construct_wrapper:[CNode]15:69'853_852_1_construct_wrapper:[CNode]4:70'853_852_1_construct_wrapper:[CNode]4:70"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op58 '853_852_1_construct_wrapper:[CNode]4:70 B853_852_1_construct_wrapper:blocks.layer.1.1.se.conv_expand.weight%853_852_1_construct_wrapper:output:71%853_852_1_construct_wrapper:output:71"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op59  %853_852_1_construct_wrapper:output:71 @853_852_1_construct_wrapper:blocks.layer.1.1.se.conv_expand.bias%853_852_1_construct_wrapper:output:72%853_852_1_construct_wrapper:output:72"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op60  %853_852_1_construct_wrapper:output:72#853_852_1_construct_wrapper:x_se:73#853_852_1_construct_wrapper:x_se:73"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:rDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/Sigmoid-op61  '853_852_1_construct_wrapper:[CNode]4:64 #853_852_1_construct_wrapper:x_se:73'853_852_1_construct_wrapper:[CNode]4:74'853_852_1_construct_wrapper:[CNode]4:74"Mul*%R88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op62 '853_852_1_construct_wrapper:[CNode]4:74 <853_852_1_construct_wrapper:blocks.layer.1.1.conv_pwl.weight%853_852_1_construct_wrapper:output:75%853_852_1_construct_wrapper:output:75"Conv2D*$R88Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/conv_pwl-Conv2d/Conv2D-op63  %853_852_1_construct_wrapper:output:75 6853_852_1_construct_wrapper:blocks.layer.1.1.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.1.1.bn3.beta <853_852_1_construct_wrapper:blocks.layer.1.1.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.1.1.bn3.moving_variance!853_852_1_construct_wrapper:12:76!853_852_1_construct_wrapper:12:76" BatchNorm*R88Bshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op64 I[ValueNode]1(0):77"Constant*) valueR *Bvalue0  scalar:value0  !853_852_1_construct_wrapper:12:76 [ValueNode]1(0):77'853_852_1_construct_wrapper:[CNode]2:78'853_852_1_construct_wrapper:[CNode]2:78" tuple_getitem*$R88Bshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op65  '853_852_1_construct_wrapper:[CNode]2:78 '853_852_1_construct_wrapper:[CNode]2:52(853_852_1_construct_wrapper:[CNode]16:79(853_852_1_construct_wrapper:[CNode]16:79" TensorAdd*$R88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:cDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/TensorAdd-op66 (853_852_1_construct_wrapper:[CNode]16:79 ;853_852_1_construct_wrapper:blocks.layer.2.0.conv_pw.weight%853_852_1_construct_wrapper:output:80%853_852_1_construct_wrapper:output:80"Conv2D*%R88Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/conv_pw-Conv2d/Conv2D-op67  %853_852_1_construct_wrapper:output:80 6853_852_1_construct_wrapper:blocks.layer.2.0.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.2.0.bn1.beta <853_852_1_construct_wrapper:blocks.layer.2.0.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.2.0.bn1.moving_variance!853_852_1_construct_wrapper:13:81!853_852_1_construct_wrapper:13:81" BatchNorm*R88Bshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op68 I[ValueNode]1(0):82"Constant*) valueR *Bvalue0  scalar:value0  !853_852_1_construct_wrapper:13:81 [ValueNode]1(0):82'853_852_1_construct_wrapper:[CNode]2:83'853_852_1_construct_wrapper:[CNode]2:83" tuple_getitem*%R88Bshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op69  '853_852_1_construct_wrapper:[CNode]2:83(853_852_1_construct_wrapper:[CNode]17:84(853_852_1_construct_wrapper:[CNode]17:84"Sigmoid*%R88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:nDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op70  '853_852_1_construct_wrapper:[CNode]2:83 (853_852_1_construct_wrapper:[CNode]17:84'853_852_1_construct_wrapper:[CNode]4:85'853_852_1_construct_wrapper:[CNode]4:85"Mul*%R88Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op71 '853_852_1_construct_wrapper:[CNode]4:85 ;853_852_1_construct_wrapper:blocks.layer.2.0.conv_dw.weight%853_852_1_construct_wrapper:output:86%853_852_1_construct_wrapper:output:86"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/conv_dw-Conv2d/Conv2D-op72  %853_852_1_construct_wrapper:output:86 6853_852_1_construct_wrapper:blocks.layer.2.0.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.2.0.bn2.beta <853_852_1_construct_wrapper:blocks.layer.2.0.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.2.0.bn2.moving_variance!853_852_1_construct_wrapper:13:87!853_852_1_construct_wrapper:13:87" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op73 I[ValueNode]1(0):88"Constant*) valueR *Bvalue0  scalar:value0  !853_852_1_construct_wrapper:13:87 [ValueNode]1(0):88'853_852_1_construct_wrapper:[CNode]2:89'853_852_1_construct_wrapper:[CNode]2:89" tuple_getitem*%RBshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op74  '853_852_1_construct_wrapper:[CNode]2:89(853_852_1_construct_wrapper:[CNode]17:90(853_852_1_construct_wrapper:[CNode]17:90"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:nDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op75  '853_852_1_construct_wrapper:[CNode]2:89 (853_852_1_construct_wrapper:[CNode]17:90'853_852_1_construct_wrapper:[CNode]4:91'853_852_1_construct_wrapper:[CNode]4:91"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op76 m[ValueNode]6((2, 3)):92"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  '853_852_1_construct_wrapper:[CNode]4:91 [ValueNode]6((2, 3)):92#853_852_1_construct_wrapper:x_se:93#853_852_1_construct_wrapper:x_se:93" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:uDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/se-SqueezeExcite/ReduceMean-op77 #853_852_1_construct_wrapper:x_se:93 B853_852_1_construct_wrapper:blocks.layer.2.0.se.conv_reduce.weight%853_852_1_construct_wrapper:output:94%853_852_1_construct_wrapper:output:94"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op78  %853_852_1_construct_wrapper:output:94 @853_852_1_construct_wrapper:blocks.layer.2.0.se.conv_reduce.bias%853_852_1_construct_wrapper:output:95%853_852_1_construct_wrapper:output:95"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op79  %853_852_1_construct_wrapper:output:95(853_852_1_construct_wrapper:[CNode]18:96(853_852_1_construct_wrapper:[CNode]18:96"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op80  %853_852_1_construct_wrapper:output:95 (853_852_1_construct_wrapper:[CNode]18:96'853_852_1_construct_wrapper:[CNode]4:97'853_852_1_construct_wrapper:[CNode]4:97"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op81 '853_852_1_construct_wrapper:[CNode]4:97 B853_852_1_construct_wrapper:blocks.layer.2.0.se.conv_expand.weight%853_852_1_construct_wrapper:output:98%853_852_1_construct_wrapper:output:98"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op82  %853_852_1_construct_wrapper:output:98 @853_852_1_construct_wrapper:blocks.layer.2.0.se.conv_expand.bias%853_852_1_construct_wrapper:output:99%853_852_1_construct_wrapper:output:99"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op83  %853_852_1_construct_wrapper:output:99$853_852_1_construct_wrapper:x_se:100$853_852_1_construct_wrapper:x_se:100"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:rDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/se-SqueezeExcite/Sigmoid-op84  '853_852_1_construct_wrapper:[CNode]4:91 $853_852_1_construct_wrapper:x_se:100(853_852_1_construct_wrapper:[CNode]4:101(853_852_1_construct_wrapper:[CNode]4:101"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op85 (853_852_1_construct_wrapper:[CNode]4:101 <853_852_1_construct_wrapper:blocks.layer.2.0.conv_pwl.weight&853_852_1_construct_wrapper:output:102&853_852_1_construct_wrapper:output:102"Conv2D*$R(Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *(Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/conv_pwl-Conv2d/Conv2D-op86  &853_852_1_construct_wrapper:output:102 6853_852_1_construct_wrapper:blocks.layer.2.0.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.2.0.bn3.beta <853_852_1_construct_wrapper:blocks.layer.2.0.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.2.0.bn3.moving_variance"853_852_1_construct_wrapper:19:103"853_852_1_construct_wrapper:19:103" BatchNorm*R(Bshape1R (Bshape2R (Bshape3R (Bshape4R (Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op87 J[ValueNode]1(0):104"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:19:103 [ValueNode]1(0):104(853_852_1_construct_wrapper:[CNode]2:105(853_852_1_construct_wrapper:[CNode]2:105" tuple_getitem*$R(Bshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op88 (853_852_1_construct_wrapper:[CNode]2:105 ;853_852_1_construct_wrapper:blocks.layer.2.1.conv_pw.weight&853_852_1_construct_wrapper:output:106&853_852_1_construct_wrapper:output:106"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/conv_pw-Conv2d/Conv2D-op89  &853_852_1_construct_wrapper:output:106 6853_852_1_construct_wrapper:blocks.layer.2.1.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.2.1.bn1.beta <853_852_1_construct_wrapper:blocks.layer.2.1.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.2.1.bn1.moving_variance"853_852_1_construct_wrapper:20:107"853_852_1_construct_wrapper:20:107" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op90 J[ValueNode]1(0):108"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:20:107 [ValueNode]1(0):108(853_852_1_construct_wrapper:[CNode]2:109(853_852_1_construct_wrapper:[CNode]2:109" tuple_getitem*%RBshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op91  (853_852_1_construct_wrapper:[CNode]2:109)853_852_1_construct_wrapper:[CNode]21:110)853_852_1_construct_wrapper:[CNode]21:110"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:nDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op92  (853_852_1_construct_wrapper:[CNode]2:109 )853_852_1_construct_wrapper:[CNode]21:110(853_852_1_construct_wrapper:[CNode]4:111(853_852_1_construct_wrapper:[CNode]4:111"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op93 (853_852_1_construct_wrapper:[CNode]4:111 ;853_852_1_construct_wrapper:blocks.layer.2.1.conv_dw.weight&853_852_1_construct_wrapper:output:112&853_852_1_construct_wrapper:output:112"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/conv_dw-Conv2d/Conv2D-op94  &853_852_1_construct_wrapper:output:112 6853_852_1_construct_wrapper:blocks.layer.2.1.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.2.1.bn2.beta <853_852_1_construct_wrapper:blocks.layer.2.1.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.2.1.bn2.moving_variance"853_852_1_construct_wrapper:20:113"853_852_1_construct_wrapper:20:113" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:sDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op95 J[ValueNode]1(0):114"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:20:113 [ValueNode]1(0):114(853_852_1_construct_wrapper:[CNode]2:115(853_852_1_construct_wrapper:[CNode]2:115" tuple_getitem*%RBshape1 shape:shape1,:*Default/bn1-BatchNorm2d/tuple_getitem-op96  (853_852_1_construct_wrapper:[CNode]2:115)853_852_1_construct_wrapper:[CNode]21:116)853_852_1_construct_wrapper:[CNode]21:116"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:nDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op97  (853_852_1_construct_wrapper:[CNode]2:115 )853_852_1_construct_wrapper:[CNode]21:116(853_852_1_construct_wrapper:[CNode]4:117(853_852_1_construct_wrapper:[CNode]4:117"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op98 n[ValueNode]6((2, 3)):118"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:117 [ValueNode]6((2, 3)):118$853_852_1_construct_wrapper:x_se:119$853_852_1_construct_wrapper:x_se:119" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:uDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/ReduceMean-op99 $853_852_1_construct_wrapper:x_se:119 B853_852_1_construct_wrapper:blocks.layer.2.1.se.conv_reduce.weight&853_852_1_construct_wrapper:output:120&853_852_1_construct_wrapper:output:120"Conv2D*$R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR * Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op100  &853_852_1_construct_wrapper:output:120 @853_852_1_construct_wrapper:blocks.layer.2.1.se.conv_reduce.bias&853_852_1_construct_wrapper:output:121&853_852_1_construct_wrapper:output:121"BiasAdd*$R Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op101  &853_852_1_construct_wrapper:output:121)853_852_1_construct_wrapper:[CNode]22:122)853_852_1_construct_wrapper:[CNode]22:122"Sigmoid*$R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op102  &853_852_1_construct_wrapper:output:121 )853_852_1_construct_wrapper:[CNode]22:122(853_852_1_construct_wrapper:[CNode]4:123(853_852_1_construct_wrapper:[CNode]4:123"Mul*$R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op103 (853_852_1_construct_wrapper:[CNode]4:123 B853_852_1_construct_wrapper:blocks.layer.2.1.se.conv_expand.weight&853_852_1_construct_wrapper:output:124&853_852_1_construct_wrapper:output:124"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op104  &853_852_1_construct_wrapper:output:124 @853_852_1_construct_wrapper:blocks.layer.2.1.se.conv_expand.bias&853_852_1_construct_wrapper:output:125&853_852_1_construct_wrapper:output:125"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op105  &853_852_1_construct_wrapper:output:125$853_852_1_construct_wrapper:x_se:126$853_852_1_construct_wrapper:x_se:126"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/Sigmoid-op106  (853_852_1_construct_wrapper:[CNode]4:117 $853_852_1_construct_wrapper:x_se:126(853_852_1_construct_wrapper:[CNode]4:127(853_852_1_construct_wrapper:[CNode]4:127"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op107 (853_852_1_construct_wrapper:[CNode]4:127 <853_852_1_construct_wrapper:blocks.layer.2.1.conv_pwl.weight&853_852_1_construct_wrapper:output:128&853_852_1_construct_wrapper:output:128"Conv2D*$R(Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *(Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/conv_pwl-Conv2d/Conv2D-op108  &853_852_1_construct_wrapper:output:128 6853_852_1_construct_wrapper:blocks.layer.2.1.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.2.1.bn3.beta <853_852_1_construct_wrapper:blocks.layer.2.1.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.2.1.bn3.moving_variance"853_852_1_construct_wrapper:19:129"853_852_1_construct_wrapper:19:129" BatchNorm*R(Bshape1R (Bshape2R (Bshape3R (Bshape4R (Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op109 J[ValueNode]1(0):130"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:19:129 [ValueNode]1(0):130(853_852_1_construct_wrapper:[CNode]2:131(853_852_1_construct_wrapper:[CNode]2:131" tuple_getitem*$R(Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op110  (853_852_1_construct_wrapper:[CNode]2:131 (853_852_1_construct_wrapper:[CNode]2:105)853_852_1_construct_wrapper:[CNode]16:132)853_852_1_construct_wrapper:[CNode]16:132" TensorAdd*$R(Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:dDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/TensorAdd-op111 )853_852_1_construct_wrapper:[CNode]16:132 ;853_852_1_construct_wrapper:blocks.layer.3.0.conv_pw.weight&853_852_1_construct_wrapper:output:133&853_852_1_construct_wrapper:output:133"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/conv_pw-Conv2d/Conv2D-op112  &853_852_1_construct_wrapper:output:133 6853_852_1_construct_wrapper:blocks.layer.3.0.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.3.0.bn1.beta <853_852_1_construct_wrapper:blocks.layer.3.0.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.3.0.bn1.moving_variance"853_852_1_construct_wrapper:20:134"853_852_1_construct_wrapper:20:134" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op113 J[ValueNode]1(0):135"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:20:134 [ValueNode]1(0):135(853_852_1_construct_wrapper:[CNode]2:136(853_852_1_construct_wrapper:[CNode]2:136" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op114  (853_852_1_construct_wrapper:[CNode]2:136)853_852_1_construct_wrapper:[CNode]23:137)853_852_1_construct_wrapper:[CNode]23:137"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op115  (853_852_1_construct_wrapper:[CNode]2:136 )853_852_1_construct_wrapper:[CNode]23:137(853_852_1_construct_wrapper:[CNode]4:138(853_852_1_construct_wrapper:[CNode]4:138"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op116 (853_852_1_construct_wrapper:[CNode]4:138 ;853_852_1_construct_wrapper:blocks.layer.3.0.conv_dw.weight&853_852_1_construct_wrapper:output:139&853_852_1_construct_wrapper:output:139"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/conv_dw-Conv2d/Conv2D-op117  &853_852_1_construct_wrapper:output:139 6853_852_1_construct_wrapper:blocks.layer.3.0.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.3.0.bn2.beta <853_852_1_construct_wrapper:blocks.layer.3.0.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.3.0.bn2.moving_variance"853_852_1_construct_wrapper:20:140"853_852_1_construct_wrapper:20:140" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op118 J[ValueNode]1(0):141"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:20:140 [ValueNode]1(0):141(853_852_1_construct_wrapper:[CNode]2:142(853_852_1_construct_wrapper:[CNode]2:142" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op119  (853_852_1_construct_wrapper:[CNode]2:142)853_852_1_construct_wrapper:[CNode]23:143)853_852_1_construct_wrapper:[CNode]23:143"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op120  (853_852_1_construct_wrapper:[CNode]2:142 )853_852_1_construct_wrapper:[CNode]23:143(853_852_1_construct_wrapper:[CNode]4:144(853_852_1_construct_wrapper:[CNode]4:144"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op121 n[ValueNode]6((2, 3)):145"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:144 [ValueNode]6((2, 3)):145$853_852_1_construct_wrapper:x_se:146$853_852_1_construct_wrapper:x_se:146" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/se-SqueezeExcite/ReduceMean-op122 $853_852_1_construct_wrapper:x_se:146 B853_852_1_construct_wrapper:blocks.layer.3.0.se.conv_reduce.weight&853_852_1_construct_wrapper:output:147&853_852_1_construct_wrapper:output:147"Conv2D*$R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR * Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op123  &853_852_1_construct_wrapper:output:147 @853_852_1_construct_wrapper:blocks.layer.3.0.se.conv_reduce.bias&853_852_1_construct_wrapper:output:148&853_852_1_construct_wrapper:output:148"BiasAdd*$R Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op124  &853_852_1_construct_wrapper:output:148)853_852_1_construct_wrapper:[CNode]24:149)853_852_1_construct_wrapper:[CNode]24:149"Sigmoid*$R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op125  &853_852_1_construct_wrapper:output:148 )853_852_1_construct_wrapper:[CNode]24:149(853_852_1_construct_wrapper:[CNode]4:150(853_852_1_construct_wrapper:[CNode]4:150"Mul*$R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op126 (853_852_1_construct_wrapper:[CNode]4:150 B853_852_1_construct_wrapper:blocks.layer.3.0.se.conv_expand.weight&853_852_1_construct_wrapper:output:151&853_852_1_construct_wrapper:output:151"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op127  &853_852_1_construct_wrapper:output:151 @853_852_1_construct_wrapper:blocks.layer.3.0.se.conv_expand.bias&853_852_1_construct_wrapper:output:152&853_852_1_construct_wrapper:output:152"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/2-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op128  &853_852_1_construct_wrapper:output:152$853_852_1_construct_wrapper:x_se:153$853_852_1_construct_wrapper:x_se:153"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/se-SqueezeExcite/Sigmoid-op129  (853_852_1_construct_wrapper:[CNode]4:144 $853_852_1_construct_wrapper:x_se:153(853_852_1_construct_wrapper:[CNode]4:154(853_852_1_construct_wrapper:[CNode]4:154"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op130 (853_852_1_construct_wrapper:[CNode]4:154 <853_852_1_construct_wrapper:blocks.layer.3.0.conv_pwl.weight&853_852_1_construct_wrapper:output:155&853_852_1_construct_wrapper:output:155"Conv2D*$RPBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *PBvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/conv_pwl-Conv2d/Conv2D-op131  &853_852_1_construct_wrapper:output:155 6853_852_1_construct_wrapper:blocks.layer.3.0.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.3.0.bn3.beta <853_852_1_construct_wrapper:blocks.layer.3.0.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.3.0.bn3.moving_variance"853_852_1_construct_wrapper:25:156"853_852_1_construct_wrapper:25:156" BatchNorm*RPBshape1R PBshape2R PBshape3R PBshape4R PBshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op132 J[ValueNode]1(0):157"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:25:156 [ValueNode]1(0):157(853_852_1_construct_wrapper:[CNode]2:158(853_852_1_construct_wrapper:[CNode]2:158" tuple_getitem*$RPBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op133 (853_852_1_construct_wrapper:[CNode]2:158 ;853_852_1_construct_wrapper:blocks.layer.3.1.conv_pw.weight&853_852_1_construct_wrapper:output:159&853_852_1_construct_wrapper:output:159"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/conv_pw-Conv2d/Conv2D-op134  &853_852_1_construct_wrapper:output:159 6853_852_1_construct_wrapper:blocks.layer.3.1.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.3.1.bn1.beta <853_852_1_construct_wrapper:blocks.layer.3.1.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.3.1.bn1.moving_variance"853_852_1_construct_wrapper:26:160"853_852_1_construct_wrapper:26:160" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op135 J[ValueNode]1(0):161"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:26:160 [ValueNode]1(0):161(853_852_1_construct_wrapper:[CNode]2:162(853_852_1_construct_wrapper:[CNode]2:162" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op136  (853_852_1_construct_wrapper:[CNode]2:162)853_852_1_construct_wrapper:[CNode]27:163)853_852_1_construct_wrapper:[CNode]27:163"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op137  (853_852_1_construct_wrapper:[CNode]2:162 )853_852_1_construct_wrapper:[CNode]27:163(853_852_1_construct_wrapper:[CNode]4:164(853_852_1_construct_wrapper:[CNode]4:164"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op138 (853_852_1_construct_wrapper:[CNode]4:164 ;853_852_1_construct_wrapper:blocks.layer.3.1.conv_dw.weight&853_852_1_construct_wrapper:output:165&853_852_1_construct_wrapper:output:165"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/conv_dw-Conv2d/Conv2D-op139  &853_852_1_construct_wrapper:output:165 6853_852_1_construct_wrapper:blocks.layer.3.1.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.3.1.bn2.beta <853_852_1_construct_wrapper:blocks.layer.3.1.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.3.1.bn2.moving_variance"853_852_1_construct_wrapper:26:166"853_852_1_construct_wrapper:26:166" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op140 J[ValueNode]1(0):167"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:26:166 [ValueNode]1(0):167(853_852_1_construct_wrapper:[CNode]2:168(853_852_1_construct_wrapper:[CNode]2:168" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op141  (853_852_1_construct_wrapper:[CNode]2:168)853_852_1_construct_wrapper:[CNode]27:169)853_852_1_construct_wrapper:[CNode]27:169"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op142  (853_852_1_construct_wrapper:[CNode]2:168 )853_852_1_construct_wrapper:[CNode]27:169(853_852_1_construct_wrapper:[CNode]4:170(853_852_1_construct_wrapper:[CNode]4:170"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op143 n[ValueNode]6((2, 3)):171"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:170 [ValueNode]6((2, 3)):171$853_852_1_construct_wrapper:x_se:172$853_852_1_construct_wrapper:x_se:172" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/ReduceMean-op144 $853_852_1_construct_wrapper:x_se:172 B853_852_1_construct_wrapper:blocks.layer.3.1.se.conv_reduce.weight&853_852_1_construct_wrapper:output:173&853_852_1_construct_wrapper:output:173"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op145  &853_852_1_construct_wrapper:output:173 @853_852_1_construct_wrapper:blocks.layer.3.1.se.conv_reduce.bias&853_852_1_construct_wrapper:output:174&853_852_1_construct_wrapper:output:174"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op146  &853_852_1_construct_wrapper:output:174)853_852_1_construct_wrapper:[CNode]28:175)853_852_1_construct_wrapper:[CNode]28:175"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op147  &853_852_1_construct_wrapper:output:174 )853_852_1_construct_wrapper:[CNode]28:175(853_852_1_construct_wrapper:[CNode]4:176(853_852_1_construct_wrapper:[CNode]4:176"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op148 (853_852_1_construct_wrapper:[CNode]4:176 B853_852_1_construct_wrapper:blocks.layer.3.1.se.conv_expand.weight&853_852_1_construct_wrapper:output:177&853_852_1_construct_wrapper:output:177"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op149  &853_852_1_construct_wrapper:output:177 @853_852_1_construct_wrapper:blocks.layer.3.1.se.conv_expand.bias&853_852_1_construct_wrapper:output:178&853_852_1_construct_wrapper:output:178"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op150  &853_852_1_construct_wrapper:output:178$853_852_1_construct_wrapper:x_se:179$853_852_1_construct_wrapper:x_se:179"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/Sigmoid-op151  (853_852_1_construct_wrapper:[CNode]4:170 $853_852_1_construct_wrapper:x_se:179(853_852_1_construct_wrapper:[CNode]4:180(853_852_1_construct_wrapper:[CNode]4:180"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op152 (853_852_1_construct_wrapper:[CNode]4:180 <853_852_1_construct_wrapper:blocks.layer.3.1.conv_pwl.weight&853_852_1_construct_wrapper:output:181&853_852_1_construct_wrapper:output:181"Conv2D*$RPBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *PBvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/conv_pwl-Conv2d/Conv2D-op153  &853_852_1_construct_wrapper:output:181 6853_852_1_construct_wrapper:blocks.layer.3.1.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.3.1.bn3.beta <853_852_1_construct_wrapper:blocks.layer.3.1.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.3.1.bn3.moving_variance"853_852_1_construct_wrapper:25:182"853_852_1_construct_wrapper:25:182" BatchNorm*RPBshape1R PBshape2R PBshape3R PBshape4R PBshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op154 J[ValueNode]1(0):183"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:25:182 [ValueNode]1(0):183(853_852_1_construct_wrapper:[CNode]2:184(853_852_1_construct_wrapper:[CNode]2:184" tuple_getitem*$RPBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op155  (853_852_1_construct_wrapper:[CNode]2:184 (853_852_1_construct_wrapper:[CNode]2:158)853_852_1_construct_wrapper:[CNode]16:185)853_852_1_construct_wrapper:[CNode]16:185" TensorAdd*$RPBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:dDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/TensorAdd-op156 )853_852_1_construct_wrapper:[CNode]16:185 ;853_852_1_construct_wrapper:blocks.layer.3.2.conv_pw.weight&853_852_1_construct_wrapper:output:186&853_852_1_construct_wrapper:output:186"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/2-InvertedResidual/conv_pw-Conv2d/Conv2D-op157  &853_852_1_construct_wrapper:output:186 6853_852_1_construct_wrapper:blocks.layer.3.2.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.3.2.bn1.beta <853_852_1_construct_wrapper:blocks.layer.3.2.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.3.2.bn1.moving_variance"853_852_1_construct_wrapper:26:187"853_852_1_construct_wrapper:26:187" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op158 J[ValueNode]1(0):188"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:26:187 [ValueNode]1(0):188(853_852_1_construct_wrapper:[CNode]2:189(853_852_1_construct_wrapper:[CNode]2:189" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op159  (853_852_1_construct_wrapper:[CNode]2:189)853_852_1_construct_wrapper:[CNode]29:190)853_852_1_construct_wrapper:[CNode]29:190"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/2-InvertedResidual/act_fn-Swish/Sigmoid-op160  (853_852_1_construct_wrapper:[CNode]2:189 )853_852_1_construct_wrapper:[CNode]29:190(853_852_1_construct_wrapper:[CNode]4:191(853_852_1_construct_wrapper:[CNode]4:191"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op161 (853_852_1_construct_wrapper:[CNode]4:191 ;853_852_1_construct_wrapper:blocks.layer.3.2.conv_dw.weight&853_852_1_construct_wrapper:output:192&853_852_1_construct_wrapper:output:192"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/conv_dw-Conv2d/Conv2D-op162  &853_852_1_construct_wrapper:output:192 6853_852_1_construct_wrapper:blocks.layer.3.2.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.3.2.bn2.beta <853_852_1_construct_wrapper:blocks.layer.3.2.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.3.2.bn2.moving_variance"853_852_1_construct_wrapper:26:193"853_852_1_construct_wrapper:26:193" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op163 J[ValueNode]1(0):194"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:26:193 [ValueNode]1(0):194(853_852_1_construct_wrapper:[CNode]2:195(853_852_1_construct_wrapper:[CNode]2:195" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op164  (853_852_1_construct_wrapper:[CNode]2:195)853_852_1_construct_wrapper:[CNode]29:196)853_852_1_construct_wrapper:[CNode]29:196"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/2-InvertedResidual/act_fn-Swish/Sigmoid-op165  (853_852_1_construct_wrapper:[CNode]2:195 )853_852_1_construct_wrapper:[CNode]29:196(853_852_1_construct_wrapper:[CNode]4:197(853_852_1_construct_wrapper:[CNode]4:197"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op166 n[ValueNode]6((2, 3)):198"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:197 [ValueNode]6((2, 3)):198$853_852_1_construct_wrapper:x_se:199$853_852_1_construct_wrapper:x_se:199" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/2-InvertedResidual/se-SqueezeExcite/ReduceMean-op167 $853_852_1_construct_wrapper:x_se:199 B853_852_1_construct_wrapper:blocks.layer.3.2.se.conv_reduce.weight&853_852_1_construct_wrapper:output:200&853_852_1_construct_wrapper:output:200"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op168  &853_852_1_construct_wrapper:output:200 @853_852_1_construct_wrapper:blocks.layer.3.2.se.conv_reduce.bias&853_852_1_construct_wrapper:output:201&853_852_1_construct_wrapper:output:201"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op169  &853_852_1_construct_wrapper:output:201)853_852_1_construct_wrapper:[CNode]30:202)853_852_1_construct_wrapper:[CNode]30:202"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/2-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op170  &853_852_1_construct_wrapper:output:201 )853_852_1_construct_wrapper:[CNode]30:202(853_852_1_construct_wrapper:[CNode]4:203(853_852_1_construct_wrapper:[CNode]4:203"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op171 (853_852_1_construct_wrapper:[CNode]4:203 B853_852_1_construct_wrapper:blocks.layer.3.2.se.conv_expand.weight&853_852_1_construct_wrapper:output:204&853_852_1_construct_wrapper:output:204"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op172  &853_852_1_construct_wrapper:output:204 @853_852_1_construct_wrapper:blocks.layer.3.2.se.conv_expand.bias&853_852_1_construct_wrapper:output:205&853_852_1_construct_wrapper:output:205"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op173  &853_852_1_construct_wrapper:output:205$853_852_1_construct_wrapper:x_se:206$853_852_1_construct_wrapper:x_se:206"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/2-InvertedResidual/se-SqueezeExcite/Sigmoid-op174  (853_852_1_construct_wrapper:[CNode]4:197 $853_852_1_construct_wrapper:x_se:206(853_852_1_construct_wrapper:[CNode]4:207(853_852_1_construct_wrapper:[CNode]4:207"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op175 (853_852_1_construct_wrapper:[CNode]4:207 <853_852_1_construct_wrapper:blocks.layer.3.2.conv_pwl.weight&853_852_1_construct_wrapper:output:208&853_852_1_construct_wrapper:output:208"Conv2D*$RPBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *PBvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/2-InvertedResidual/conv_pwl-Conv2d/Conv2D-op176  &853_852_1_construct_wrapper:output:208 6853_852_1_construct_wrapper:blocks.layer.3.2.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.3.2.bn3.beta <853_852_1_construct_wrapper:blocks.layer.3.2.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.3.2.bn3.moving_variance"853_852_1_construct_wrapper:25:209"853_852_1_construct_wrapper:25:209" BatchNorm*RPBshape1R PBshape2R PBshape3R PBshape4R PBshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op177 J[ValueNode]1(0):210"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:25:209 [ValueNode]1(0):210(853_852_1_construct_wrapper:[CNode]2:211(853_852_1_construct_wrapper:[CNode]2:211" tuple_getitem*$RPBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op178  (853_852_1_construct_wrapper:[CNode]2:211 )853_852_1_construct_wrapper:[CNode]16:185)853_852_1_construct_wrapper:[CNode]16:212)853_852_1_construct_wrapper:[CNode]16:212" TensorAdd*$RPBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:dDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/TensorAdd-op179 )853_852_1_construct_wrapper:[CNode]16:212 ;853_852_1_construct_wrapper:blocks.layer.4.0.conv_pw.weight&853_852_1_construct_wrapper:output:213&853_852_1_construct_wrapper:output:213"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/conv_pw-Conv2d/Conv2D-op180  &853_852_1_construct_wrapper:output:213 6853_852_1_construct_wrapper:blocks.layer.4.0.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.4.0.bn1.beta <853_852_1_construct_wrapper:blocks.layer.4.0.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.4.0.bn1.moving_variance"853_852_1_construct_wrapper:26:214"853_852_1_construct_wrapper:26:214" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op181 J[ValueNode]1(0):215"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:26:214 [ValueNode]1(0):215(853_852_1_construct_wrapper:[CNode]2:216(853_852_1_construct_wrapper:[CNode]2:216" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op182  (853_852_1_construct_wrapper:[CNode]2:216)853_852_1_construct_wrapper:[CNode]31:217)853_852_1_construct_wrapper:[CNode]31:217"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op183  (853_852_1_construct_wrapper:[CNode]2:216 )853_852_1_construct_wrapper:[CNode]31:217(853_852_1_construct_wrapper:[CNode]4:218(853_852_1_construct_wrapper:[CNode]4:218"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op184 (853_852_1_construct_wrapper:[CNode]4:218 ;853_852_1_construct_wrapper:blocks.layer.4.0.conv_dw.weight&853_852_1_construct_wrapper:output:219&853_852_1_construct_wrapper:output:219"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/conv_dw-Conv2d/Conv2D-op185  &853_852_1_construct_wrapper:output:219 6853_852_1_construct_wrapper:blocks.layer.4.0.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.4.0.bn2.beta <853_852_1_construct_wrapper:blocks.layer.4.0.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.4.0.bn2.moving_variance"853_852_1_construct_wrapper:26:220"853_852_1_construct_wrapper:26:220" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op186 J[ValueNode]1(0):221"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:26:220 [ValueNode]1(0):221(853_852_1_construct_wrapper:[CNode]2:222(853_852_1_construct_wrapper:[CNode]2:222" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op187  (853_852_1_construct_wrapper:[CNode]2:222)853_852_1_construct_wrapper:[CNode]31:223)853_852_1_construct_wrapper:[CNode]31:223"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op188  (853_852_1_construct_wrapper:[CNode]2:222 )853_852_1_construct_wrapper:[CNode]31:223(853_852_1_construct_wrapper:[CNode]4:224(853_852_1_construct_wrapper:[CNode]4:224"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op189 n[ValueNode]6((2, 3)):225"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:224 [ValueNode]6((2, 3)):225$853_852_1_construct_wrapper:x_se:226$853_852_1_construct_wrapper:x_se:226" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/se-SqueezeExcite/ReduceMean-op190 $853_852_1_construct_wrapper:x_se:226 B853_852_1_construct_wrapper:blocks.layer.4.0.se.conv_reduce.weight&853_852_1_construct_wrapper:output:227&853_852_1_construct_wrapper:output:227"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op191  &853_852_1_construct_wrapper:output:227 @853_852_1_construct_wrapper:blocks.layer.4.0.se.conv_reduce.bias&853_852_1_construct_wrapper:output:228&853_852_1_construct_wrapper:output:228"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op192  &853_852_1_construct_wrapper:output:228)853_852_1_construct_wrapper:[CNode]32:229)853_852_1_construct_wrapper:[CNode]32:229"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op193  &853_852_1_construct_wrapper:output:228 )853_852_1_construct_wrapper:[CNode]32:229(853_852_1_construct_wrapper:[CNode]4:230(853_852_1_construct_wrapper:[CNode]4:230"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op194 (853_852_1_construct_wrapper:[CNode]4:230 B853_852_1_construct_wrapper:blocks.layer.4.0.se.conv_expand.weight&853_852_1_construct_wrapper:output:231&853_852_1_construct_wrapper:output:231"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op195  &853_852_1_construct_wrapper:output:231 @853_852_1_construct_wrapper:blocks.layer.4.0.se.conv_expand.bias&853_852_1_construct_wrapper:output:232&853_852_1_construct_wrapper:output:232"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/3-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op196  &853_852_1_construct_wrapper:output:232$853_852_1_construct_wrapper:x_se:233$853_852_1_construct_wrapper:x_se:233"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/se-SqueezeExcite/Sigmoid-op197  (853_852_1_construct_wrapper:[CNode]4:224 $853_852_1_construct_wrapper:x_se:233(853_852_1_construct_wrapper:[CNode]4:234(853_852_1_construct_wrapper:[CNode]4:234"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op198 (853_852_1_construct_wrapper:[CNode]4:234 <853_852_1_construct_wrapper:blocks.layer.4.0.conv_pwl.weight&853_852_1_construct_wrapper:output:235&853_852_1_construct_wrapper:output:235"Conv2D*$RpBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *pBvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/conv_pwl-Conv2d/Conv2D-op199  &853_852_1_construct_wrapper:output:235 6853_852_1_construct_wrapper:blocks.layer.4.0.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.4.0.bn3.beta <853_852_1_construct_wrapper:blocks.layer.4.0.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.4.0.bn3.moving_variance"853_852_1_construct_wrapper:33:236"853_852_1_construct_wrapper:33:236" BatchNorm*RpBshape1R pBshape2R pBshape3R pBshape4R pBshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op200 J[ValueNode]1(0):237"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:33:236 [ValueNode]1(0):237(853_852_1_construct_wrapper:[CNode]2:238(853_852_1_construct_wrapper:[CNode]2:238" tuple_getitem*$RpBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op201 (853_852_1_construct_wrapper:[CNode]2:238 ;853_852_1_construct_wrapper:blocks.layer.4.1.conv_pw.weight&853_852_1_construct_wrapper:output:239&853_852_1_construct_wrapper:output:239"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/conv_pw-Conv2d/Conv2D-op202  &853_852_1_construct_wrapper:output:239 6853_852_1_construct_wrapper:blocks.layer.4.1.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.4.1.bn1.beta <853_852_1_construct_wrapper:blocks.layer.4.1.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.4.1.bn1.moving_variance"853_852_1_construct_wrapper:34:240"853_852_1_construct_wrapper:34:240" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op203 J[ValueNode]1(0):241"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:34:240 [ValueNode]1(0):241(853_852_1_construct_wrapper:[CNode]2:242(853_852_1_construct_wrapper:[CNode]2:242" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op204  (853_852_1_construct_wrapper:[CNode]2:242)853_852_1_construct_wrapper:[CNode]35:243)853_852_1_construct_wrapper:[CNode]35:243"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op205  (853_852_1_construct_wrapper:[CNode]2:242 )853_852_1_construct_wrapper:[CNode]35:243(853_852_1_construct_wrapper:[CNode]4:244(853_852_1_construct_wrapper:[CNode]4:244"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op206 (853_852_1_construct_wrapper:[CNode]4:244 ;853_852_1_construct_wrapper:blocks.layer.4.1.conv_dw.weight&853_852_1_construct_wrapper:output:245&853_852_1_construct_wrapper:output:245"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/conv_dw-Conv2d/Conv2D-op207  &853_852_1_construct_wrapper:output:245 6853_852_1_construct_wrapper:blocks.layer.4.1.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.4.1.bn2.beta <853_852_1_construct_wrapper:blocks.layer.4.1.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.4.1.bn2.moving_variance"853_852_1_construct_wrapper:34:246"853_852_1_construct_wrapper:34:246" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op208 J[ValueNode]1(0):247"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:34:246 [ValueNode]1(0):247(853_852_1_construct_wrapper:[CNode]2:248(853_852_1_construct_wrapper:[CNode]2:248" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op209  (853_852_1_construct_wrapper:[CNode]2:248)853_852_1_construct_wrapper:[CNode]35:249)853_852_1_construct_wrapper:[CNode]35:249"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op210  (853_852_1_construct_wrapper:[CNode]2:248 )853_852_1_construct_wrapper:[CNode]35:249(853_852_1_construct_wrapper:[CNode]4:250(853_852_1_construct_wrapper:[CNode]4:250"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op211 n[ValueNode]6((2, 3)):251"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:250 [ValueNode]6((2, 3)):251$853_852_1_construct_wrapper:x_se:252$853_852_1_construct_wrapper:x_se:252" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/ReduceMean-op212 $853_852_1_construct_wrapper:x_se:252 B853_852_1_construct_wrapper:blocks.layer.4.1.se.conv_reduce.weight&853_852_1_construct_wrapper:output:253&853_852_1_construct_wrapper:output:253"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op213  &853_852_1_construct_wrapper:output:253 @853_852_1_construct_wrapper:blocks.layer.4.1.se.conv_reduce.bias&853_852_1_construct_wrapper:output:254&853_852_1_construct_wrapper:output:254"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op214  &853_852_1_construct_wrapper:output:254)853_852_1_construct_wrapper:[CNode]36:255)853_852_1_construct_wrapper:[CNode]36:255"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op215  &853_852_1_construct_wrapper:output:254 )853_852_1_construct_wrapper:[CNode]36:255(853_852_1_construct_wrapper:[CNode]4:256(853_852_1_construct_wrapper:[CNode]4:256"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op216 (853_852_1_construct_wrapper:[CNode]4:256 B853_852_1_construct_wrapper:blocks.layer.4.1.se.conv_expand.weight&853_852_1_construct_wrapper:output:257&853_852_1_construct_wrapper:output:257"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op217  &853_852_1_construct_wrapper:output:257 @853_852_1_construct_wrapper:blocks.layer.4.1.se.conv_expand.bias&853_852_1_construct_wrapper:output:258&853_852_1_construct_wrapper:output:258"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op218  &853_852_1_construct_wrapper:output:258$853_852_1_construct_wrapper:x_se:259$853_852_1_construct_wrapper:x_se:259"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/Sigmoid-op219  (853_852_1_construct_wrapper:[CNode]4:250 $853_852_1_construct_wrapper:x_se:259(853_852_1_construct_wrapper:[CNode]4:260(853_852_1_construct_wrapper:[CNode]4:260"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op220 (853_852_1_construct_wrapper:[CNode]4:260 <853_852_1_construct_wrapper:blocks.layer.4.1.conv_pwl.weight&853_852_1_construct_wrapper:output:261&853_852_1_construct_wrapper:output:261"Conv2D*$RpBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *pBvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/conv_pwl-Conv2d/Conv2D-op221  &853_852_1_construct_wrapper:output:261 6853_852_1_construct_wrapper:blocks.layer.4.1.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.4.1.bn3.beta <853_852_1_construct_wrapper:blocks.layer.4.1.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.4.1.bn3.moving_variance"853_852_1_construct_wrapper:33:262"853_852_1_construct_wrapper:33:262" BatchNorm*RpBshape1R pBshape2R pBshape3R pBshape4R pBshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op222 J[ValueNode]1(0):263"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:33:262 [ValueNode]1(0):263(853_852_1_construct_wrapper:[CNode]2:264(853_852_1_construct_wrapper:[CNode]2:264" tuple_getitem*$RpBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op223  (853_852_1_construct_wrapper:[CNode]2:264 (853_852_1_construct_wrapper:[CNode]2:238)853_852_1_construct_wrapper:[CNode]16:265)853_852_1_construct_wrapper:[CNode]16:265" TensorAdd*$RpBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:dDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/TensorAdd-op224 )853_852_1_construct_wrapper:[CNode]16:265 ;853_852_1_construct_wrapper:blocks.layer.4.2.conv_pw.weight&853_852_1_construct_wrapper:output:266&853_852_1_construct_wrapper:output:266"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/2-InvertedResidual/conv_pw-Conv2d/Conv2D-op225  &853_852_1_construct_wrapper:output:266 6853_852_1_construct_wrapper:blocks.layer.4.2.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.4.2.bn1.beta <853_852_1_construct_wrapper:blocks.layer.4.2.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.4.2.bn1.moving_variance"853_852_1_construct_wrapper:34:267"853_852_1_construct_wrapper:34:267" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op226 J[ValueNode]1(0):268"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:34:267 [ValueNode]1(0):268(853_852_1_construct_wrapper:[CNode]2:269(853_852_1_construct_wrapper:[CNode]2:269" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op227  (853_852_1_construct_wrapper:[CNode]2:269)853_852_1_construct_wrapper:[CNode]37:270)853_852_1_construct_wrapper:[CNode]37:270"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/2-InvertedResidual/act_fn-Swish/Sigmoid-op228  (853_852_1_construct_wrapper:[CNode]2:269 )853_852_1_construct_wrapper:[CNode]37:270(853_852_1_construct_wrapper:[CNode]4:271(853_852_1_construct_wrapper:[CNode]4:271"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op229 (853_852_1_construct_wrapper:[CNode]4:271 ;853_852_1_construct_wrapper:blocks.layer.4.2.conv_dw.weight&853_852_1_construct_wrapper:output:272&853_852_1_construct_wrapper:output:272"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/conv_dw-Conv2d/Conv2D-op230  &853_852_1_construct_wrapper:output:272 6853_852_1_construct_wrapper:blocks.layer.4.2.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.4.2.bn2.beta <853_852_1_construct_wrapper:blocks.layer.4.2.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.4.2.bn2.moving_variance"853_852_1_construct_wrapper:34:273"853_852_1_construct_wrapper:34:273" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op231 J[ValueNode]1(0):274"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:34:273 [ValueNode]1(0):274(853_852_1_construct_wrapper:[CNode]2:275(853_852_1_construct_wrapper:[CNode]2:275" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op232  (853_852_1_construct_wrapper:[CNode]2:275)853_852_1_construct_wrapper:[CNode]37:276)853_852_1_construct_wrapper:[CNode]37:276"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/2-InvertedResidual/act_fn-Swish/Sigmoid-op233  (853_852_1_construct_wrapper:[CNode]2:275 )853_852_1_construct_wrapper:[CNode]37:276(853_852_1_construct_wrapper:[CNode]4:277(853_852_1_construct_wrapper:[CNode]4:277"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op234 n[ValueNode]6((2, 3)):278"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:277 [ValueNode]6((2, 3)):278$853_852_1_construct_wrapper:x_se:279$853_852_1_construct_wrapper:x_se:279" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/2-InvertedResidual/se-SqueezeExcite/ReduceMean-op235 $853_852_1_construct_wrapper:x_se:279 B853_852_1_construct_wrapper:blocks.layer.4.2.se.conv_reduce.weight&853_852_1_construct_wrapper:output:280&853_852_1_construct_wrapper:output:280"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op236  &853_852_1_construct_wrapper:output:280 @853_852_1_construct_wrapper:blocks.layer.4.2.se.conv_reduce.bias&853_852_1_construct_wrapper:output:281&853_852_1_construct_wrapper:output:281"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op237  &853_852_1_construct_wrapper:output:281)853_852_1_construct_wrapper:[CNode]38:282)853_852_1_construct_wrapper:[CNode]38:282"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/2-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op238  &853_852_1_construct_wrapper:output:281 )853_852_1_construct_wrapper:[CNode]38:282(853_852_1_construct_wrapper:[CNode]4:283(853_852_1_construct_wrapper:[CNode]4:283"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op239 (853_852_1_construct_wrapper:[CNode]4:283 B853_852_1_construct_wrapper:blocks.layer.4.2.se.conv_expand.weight&853_852_1_construct_wrapper:output:284&853_852_1_construct_wrapper:output:284"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op240  &853_852_1_construct_wrapper:output:284 @853_852_1_construct_wrapper:blocks.layer.4.2.se.conv_expand.bias&853_852_1_construct_wrapper:output:285&853_852_1_construct_wrapper:output:285"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op241  &853_852_1_construct_wrapper:output:285$853_852_1_construct_wrapper:x_se:286$853_852_1_construct_wrapper:x_se:286"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/2-InvertedResidual/se-SqueezeExcite/Sigmoid-op242  (853_852_1_construct_wrapper:[CNode]4:277 $853_852_1_construct_wrapper:x_se:286(853_852_1_construct_wrapper:[CNode]4:287(853_852_1_construct_wrapper:[CNode]4:287"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op243 (853_852_1_construct_wrapper:[CNode]4:287 <853_852_1_construct_wrapper:blocks.layer.4.2.conv_pwl.weight&853_852_1_construct_wrapper:output:288&853_852_1_construct_wrapper:output:288"Conv2D*$RpBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *pBvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/2-InvertedResidual/conv_pwl-Conv2d/Conv2D-op244  &853_852_1_construct_wrapper:output:288 6853_852_1_construct_wrapper:blocks.layer.4.2.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.4.2.bn3.beta <853_852_1_construct_wrapper:blocks.layer.4.2.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.4.2.bn3.moving_variance"853_852_1_construct_wrapper:33:289"853_852_1_construct_wrapper:33:289" BatchNorm*RpBshape1R pBshape2R pBshape3R pBshape4R pBshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op245 J[ValueNode]1(0):290"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:33:289 [ValueNode]1(0):290(853_852_1_construct_wrapper:[CNode]2:291(853_852_1_construct_wrapper:[CNode]2:291" tuple_getitem*$RpBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op246  (853_852_1_construct_wrapper:[CNode]2:291 )853_852_1_construct_wrapper:[CNode]16:265)853_852_1_construct_wrapper:[CNode]16:292)853_852_1_construct_wrapper:[CNode]16:292" TensorAdd*$RpBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:dDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/TensorAdd-op247 )853_852_1_construct_wrapper:[CNode]16:292 ;853_852_1_construct_wrapper:blocks.layer.5.0.conv_pw.weight&853_852_1_construct_wrapper:output:293&853_852_1_construct_wrapper:output:293"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/conv_pw-Conv2d/Conv2D-op248  &853_852_1_construct_wrapper:output:293 6853_852_1_construct_wrapper:blocks.layer.5.0.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.5.0.bn1.beta <853_852_1_construct_wrapper:blocks.layer.5.0.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.0.bn1.moving_variance"853_852_1_construct_wrapper:34:294"853_852_1_construct_wrapper:34:294" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op249 J[ValueNode]1(0):295"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:34:294 [ValueNode]1(0):295(853_852_1_construct_wrapper:[CNode]2:296(853_852_1_construct_wrapper:[CNode]2:296" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op250  (853_852_1_construct_wrapper:[CNode]2:296)853_852_1_construct_wrapper:[CNode]39:297)853_852_1_construct_wrapper:[CNode]39:297"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op251  (853_852_1_construct_wrapper:[CNode]2:296 )853_852_1_construct_wrapper:[CNode]39:297(853_852_1_construct_wrapper:[CNode]4:298(853_852_1_construct_wrapper:[CNode]4:298"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op252 (853_852_1_construct_wrapper:[CNode]4:298 ;853_852_1_construct_wrapper:blocks.layer.5.0.conv_dw.weight&853_852_1_construct_wrapper:output:299&853_852_1_construct_wrapper:output:299"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR*Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/conv_dw-Conv2d/Conv2D-op253  &853_852_1_construct_wrapper:output:299 6853_852_1_construct_wrapper:blocks.layer.5.0.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.5.0.bn2.beta <853_852_1_construct_wrapper:blocks.layer.5.0.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.0.bn2.moving_variance"853_852_1_construct_wrapper:34:300"853_852_1_construct_wrapper:34:300" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op254 J[ValueNode]1(0):301"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:34:300 [ValueNode]1(0):301(853_852_1_construct_wrapper:[CNode]2:302(853_852_1_construct_wrapper:[CNode]2:302" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op255  (853_852_1_construct_wrapper:[CNode]2:302)853_852_1_construct_wrapper:[CNode]39:303)853_852_1_construct_wrapper:[CNode]39:303"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op256  (853_852_1_construct_wrapper:[CNode]2:302 )853_852_1_construct_wrapper:[CNode]39:303(853_852_1_construct_wrapper:[CNode]4:304(853_852_1_construct_wrapper:[CNode]4:304"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op257 n[ValueNode]6((2, 3)):305"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:304 [ValueNode]6((2, 3)):305$853_852_1_construct_wrapper:x_se:306$853_852_1_construct_wrapper:x_se:306" ReduceMean*%RBshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/se-SqueezeExcite/ReduceMean-op258 $853_852_1_construct_wrapper:x_se:306 B853_852_1_construct_wrapper:blocks.layer.5.0.se.conv_reduce.weight&853_852_1_construct_wrapper:output:307&853_852_1_construct_wrapper:output:307"Conv2D*$RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op259  &853_852_1_construct_wrapper:output:307 @853_852_1_construct_wrapper:blocks.layer.5.0.se.conv_reduce.bias&853_852_1_construct_wrapper:output:308&853_852_1_construct_wrapper:output:308"BiasAdd*$RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op260  &853_852_1_construct_wrapper:output:308)853_852_1_construct_wrapper:[CNode]40:309)853_852_1_construct_wrapper:[CNode]40:309"Sigmoid*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op261  &853_852_1_construct_wrapper:output:308 )853_852_1_construct_wrapper:[CNode]40:309(853_852_1_construct_wrapper:[CNode]4:310(853_852_1_construct_wrapper:[CNode]4:310"Mul*$RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op262 (853_852_1_construct_wrapper:[CNode]4:310 B853_852_1_construct_wrapper:blocks.layer.5.0.se.conv_expand.weight&853_852_1_construct_wrapper:output:311&853_852_1_construct_wrapper:output:311"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op263  &853_852_1_construct_wrapper:output:311 @853_852_1_construct_wrapper:blocks.layer.5.0.se.conv_expand.bias&853_852_1_construct_wrapper:output:312&853_852_1_construct_wrapper:output:312"BiasAdd*%RBshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/4-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op264  &853_852_1_construct_wrapper:output:312$853_852_1_construct_wrapper:x_se:313$853_852_1_construct_wrapper:x_se:313"Sigmoid*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/se-SqueezeExcite/Sigmoid-op265  (853_852_1_construct_wrapper:[CNode]4:304 $853_852_1_construct_wrapper:x_se:313(853_852_1_construct_wrapper:[CNode]4:314(853_852_1_construct_wrapper:[CNode]4:314"Mul*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op266 (853_852_1_construct_wrapper:[CNode]4:314 <853_852_1_construct_wrapper:blocks.layer.5.0.conv_pwl.weight&853_852_1_construct_wrapper:output:315&853_852_1_construct_wrapper:output:315"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/conv_pwl-Conv2d/Conv2D-op267  &853_852_1_construct_wrapper:output:315 6853_852_1_construct_wrapper:blocks.layer.5.0.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.5.0.bn3.beta <853_852_1_construct_wrapper:blocks.layer.5.0.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.0.bn3.moving_variance"853_852_1_construct_wrapper:41:316"853_852_1_construct_wrapper:41:316" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op268 J[ValueNode]1(0):317"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:41:316 [ValueNode]1(0):317(853_852_1_construct_wrapper:[CNode]2:318(853_852_1_construct_wrapper:[CNode]2:318" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op269 (853_852_1_construct_wrapper:[CNode]2:318 ;853_852_1_construct_wrapper:blocks.layer.5.1.conv_pw.weight&853_852_1_construct_wrapper:output:319&853_852_1_construct_wrapper:output:319"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/conv_pw-Conv2d/Conv2D-op270  &853_852_1_construct_wrapper:output:319 6853_852_1_construct_wrapper:blocks.layer.5.1.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.5.1.bn1.beta <853_852_1_construct_wrapper:blocks.layer.5.1.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.1.bn1.moving_variance"853_852_1_construct_wrapper:42:320"853_852_1_construct_wrapper:42:320" BatchNorm*R Bshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op271 J[ValueNode]1(0):321"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:42:320 [ValueNode]1(0):321(853_852_1_construct_wrapper:[CNode]2:322(853_852_1_construct_wrapper:[CNode]2:322" tuple_getitem*%R Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op272  (853_852_1_construct_wrapper:[CNode]2:322)853_852_1_construct_wrapper:[CNode]43:323)853_852_1_construct_wrapper:[CNode]43:323"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op273  (853_852_1_construct_wrapper:[CNode]2:322 )853_852_1_construct_wrapper:[CNode]43:323(853_852_1_construct_wrapper:[CNode]4:324(853_852_1_construct_wrapper:[CNode]4:324"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op274 (853_852_1_construct_wrapper:[CNode]4:324 ;853_852_1_construct_wrapper:blocks.layer.5.1.conv_dw.weight&853_852_1_construct_wrapper:output:325&853_852_1_construct_wrapper:output:325"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR* Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/conv_dw-Conv2d/Conv2D-op275  &853_852_1_construct_wrapper:output:325 6853_852_1_construct_wrapper:blocks.layer.5.1.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.5.1.bn2.beta <853_852_1_construct_wrapper:blocks.layer.5.1.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.1.bn2.moving_variance"853_852_1_construct_wrapper:42:326"853_852_1_construct_wrapper:42:326" BatchNorm*R Bshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op276 J[ValueNode]1(0):327"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:42:326 [ValueNode]1(0):327(853_852_1_construct_wrapper:[CNode]2:328(853_852_1_construct_wrapper:[CNode]2:328" tuple_getitem*%R Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op277  (853_852_1_construct_wrapper:[CNode]2:328)853_852_1_construct_wrapper:[CNode]43:329)853_852_1_construct_wrapper:[CNode]43:329"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/act_fn-Swish/Sigmoid-op278  (853_852_1_construct_wrapper:[CNode]2:328 )853_852_1_construct_wrapper:[CNode]43:329(853_852_1_construct_wrapper:[CNode]4:330(853_852_1_construct_wrapper:[CNode]4:330"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op279 n[ValueNode]6((2, 3)):331"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:330 [ValueNode]6((2, 3)):331$853_852_1_construct_wrapper:x_se:332$853_852_1_construct_wrapper:x_se:332" ReduceMean*%R Bshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/ReduceMean-op280 $853_852_1_construct_wrapper:x_se:332 B853_852_1_construct_wrapper:blocks.layer.5.1.se.conv_reduce.weight&853_852_1_construct_wrapper:output:333&853_852_1_construct_wrapper:output:333"Conv2D*$R0Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *0Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op281  &853_852_1_construct_wrapper:output:333 @853_852_1_construct_wrapper:blocks.layer.5.1.se.conv_reduce.bias&853_852_1_construct_wrapper:output:334&853_852_1_construct_wrapper:output:334"BiasAdd*$R0Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op282  &853_852_1_construct_wrapper:output:334)853_852_1_construct_wrapper:[CNode]44:335)853_852_1_construct_wrapper:[CNode]44:335"Sigmoid*$R0Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op283  &853_852_1_construct_wrapper:output:334 )853_852_1_construct_wrapper:[CNode]44:335(853_852_1_construct_wrapper:[CNode]4:336(853_852_1_construct_wrapper:[CNode]4:336"Mul*$R0Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op284 (853_852_1_construct_wrapper:[CNode]4:336 B853_852_1_construct_wrapper:blocks.layer.5.1.se.conv_expand.weight&853_852_1_construct_wrapper:output:337&853_852_1_construct_wrapper:output:337"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op285  &853_852_1_construct_wrapper:output:337 @853_852_1_construct_wrapper:blocks.layer.5.1.se.conv_expand.bias&853_852_1_construct_wrapper:output:338&853_852_1_construct_wrapper:output:338"BiasAdd*%R Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op286  &853_852_1_construct_wrapper:output:338$853_852_1_construct_wrapper:x_se:339$853_852_1_construct_wrapper:x_se:339"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/Sigmoid-op287  (853_852_1_construct_wrapper:[CNode]4:330 $853_852_1_construct_wrapper:x_se:339(853_852_1_construct_wrapper:[CNode]4:340(853_852_1_construct_wrapper:[CNode]4:340"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op288 (853_852_1_construct_wrapper:[CNode]4:340 <853_852_1_construct_wrapper:blocks.layer.5.1.conv_pwl.weight&853_852_1_construct_wrapper:output:341&853_852_1_construct_wrapper:output:341"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/conv_pwl-Conv2d/Conv2D-op289  &853_852_1_construct_wrapper:output:341 6853_852_1_construct_wrapper:blocks.layer.5.1.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.5.1.bn3.beta <853_852_1_construct_wrapper:blocks.layer.5.1.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.1.bn3.moving_variance"853_852_1_construct_wrapper:41:342"853_852_1_construct_wrapper:41:342" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op290 J[ValueNode]1(0):343"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:41:342 [ValueNode]1(0):343(853_852_1_construct_wrapper:[CNode]2:344(853_852_1_construct_wrapper:[CNode]2:344" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op291  (853_852_1_construct_wrapper:[CNode]2:344 (853_852_1_construct_wrapper:[CNode]2:318)853_852_1_construct_wrapper:[CNode]16:345)853_852_1_construct_wrapper:[CNode]16:345" TensorAdd*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:dDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/TensorAdd-op292 )853_852_1_construct_wrapper:[CNode]16:345 ;853_852_1_construct_wrapper:blocks.layer.5.2.conv_pw.weight&853_852_1_construct_wrapper:output:346&853_852_1_construct_wrapper:output:346"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/2-InvertedResidual/conv_pw-Conv2d/Conv2D-op293  &853_852_1_construct_wrapper:output:346 6853_852_1_construct_wrapper:blocks.layer.5.2.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.5.2.bn1.beta <853_852_1_construct_wrapper:blocks.layer.5.2.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.2.bn1.moving_variance"853_852_1_construct_wrapper:42:347"853_852_1_construct_wrapper:42:347" BatchNorm*R Bshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op294 J[ValueNode]1(0):348"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:42:347 [ValueNode]1(0):348(853_852_1_construct_wrapper:[CNode]2:349(853_852_1_construct_wrapper:[CNode]2:349" tuple_getitem*%R Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op295  (853_852_1_construct_wrapper:[CNode]2:349)853_852_1_construct_wrapper:[CNode]45:350)853_852_1_construct_wrapper:[CNode]45:350"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/2-InvertedResidual/act_fn-Swish/Sigmoid-op296  (853_852_1_construct_wrapper:[CNode]2:349 )853_852_1_construct_wrapper:[CNode]45:350(853_852_1_construct_wrapper:[CNode]4:351(853_852_1_construct_wrapper:[CNode]4:351"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op297 (853_852_1_construct_wrapper:[CNode]4:351 ;853_852_1_construct_wrapper:blocks.layer.5.2.conv_dw.weight&853_852_1_construct_wrapper:output:352&853_852_1_construct_wrapper:output:352"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR* Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/conv_dw-Conv2d/Conv2D-op298  &853_852_1_construct_wrapper:output:352 6853_852_1_construct_wrapper:blocks.layer.5.2.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.5.2.bn2.beta <853_852_1_construct_wrapper:blocks.layer.5.2.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.2.bn2.moving_variance"853_852_1_construct_wrapper:42:353"853_852_1_construct_wrapper:42:353" BatchNorm*R Bshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op299 J[ValueNode]1(0):354"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:42:353 [ValueNode]1(0):354(853_852_1_construct_wrapper:[CNode]2:355(853_852_1_construct_wrapper:[CNode]2:355" tuple_getitem*%R Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op300  (853_852_1_construct_wrapper:[CNode]2:355)853_852_1_construct_wrapper:[CNode]45:356)853_852_1_construct_wrapper:[CNode]45:356"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/2-InvertedResidual/act_fn-Swish/Sigmoid-op301  (853_852_1_construct_wrapper:[CNode]2:355 )853_852_1_construct_wrapper:[CNode]45:356(853_852_1_construct_wrapper:[CNode]4:357(853_852_1_construct_wrapper:[CNode]4:357"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op302 n[ValueNode]6((2, 3)):358"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:357 [ValueNode]6((2, 3)):358$853_852_1_construct_wrapper:x_se:359$853_852_1_construct_wrapper:x_se:359" ReduceMean*%R Bshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/2-InvertedResidual/se-SqueezeExcite/ReduceMean-op303 $853_852_1_construct_wrapper:x_se:359 B853_852_1_construct_wrapper:blocks.layer.5.2.se.conv_reduce.weight&853_852_1_construct_wrapper:output:360&853_852_1_construct_wrapper:output:360"Conv2D*$R0Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *0Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op304  &853_852_1_construct_wrapper:output:360 @853_852_1_construct_wrapper:blocks.layer.5.2.se.conv_reduce.bias&853_852_1_construct_wrapper:output:361&853_852_1_construct_wrapper:output:361"BiasAdd*$R0Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op305  &853_852_1_construct_wrapper:output:361)853_852_1_construct_wrapper:[CNode]46:362)853_852_1_construct_wrapper:[CNode]46:362"Sigmoid*$R0Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/2-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op306  &853_852_1_construct_wrapper:output:361 )853_852_1_construct_wrapper:[CNode]46:362(853_852_1_construct_wrapper:[CNode]4:363(853_852_1_construct_wrapper:[CNode]4:363"Mul*$R0Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op307 (853_852_1_construct_wrapper:[CNode]4:363 B853_852_1_construct_wrapper:blocks.layer.5.2.se.conv_expand.weight&853_852_1_construct_wrapper:output:364&853_852_1_construct_wrapper:output:364"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op308  &853_852_1_construct_wrapper:output:364 @853_852_1_construct_wrapper:blocks.layer.5.2.se.conv_expand.bias&853_852_1_construct_wrapper:output:365&853_852_1_construct_wrapper:output:365"BiasAdd*%R Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op309  &853_852_1_construct_wrapper:output:365$853_852_1_construct_wrapper:x_se:366$853_852_1_construct_wrapper:x_se:366"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/2-InvertedResidual/se-SqueezeExcite/Sigmoid-op310  (853_852_1_construct_wrapper:[CNode]4:357 $853_852_1_construct_wrapper:x_se:366(853_852_1_construct_wrapper:[CNode]4:367(853_852_1_construct_wrapper:[CNode]4:367"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op311 (853_852_1_construct_wrapper:[CNode]4:367 <853_852_1_construct_wrapper:blocks.layer.5.2.conv_pwl.weight&853_852_1_construct_wrapper:output:368&853_852_1_construct_wrapper:output:368"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/2-InvertedResidual/conv_pwl-Conv2d/Conv2D-op312  &853_852_1_construct_wrapper:output:368 6853_852_1_construct_wrapper:blocks.layer.5.2.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.5.2.bn3.beta <853_852_1_construct_wrapper:blocks.layer.5.2.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.2.bn3.moving_variance"853_852_1_construct_wrapper:41:369"853_852_1_construct_wrapper:41:369" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op313 J[ValueNode]1(0):370"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:41:369 [ValueNode]1(0):370(853_852_1_construct_wrapper:[CNode]2:371(853_852_1_construct_wrapper:[CNode]2:371" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op314  (853_852_1_construct_wrapper:[CNode]2:371 )853_852_1_construct_wrapper:[CNode]16:345)853_852_1_construct_wrapper:[CNode]16:372)853_852_1_construct_wrapper:[CNode]16:372" TensorAdd*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:dDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/TensorAdd-op315 )853_852_1_construct_wrapper:[CNode]16:372 ;853_852_1_construct_wrapper:blocks.layer.5.3.conv_pw.weight&853_852_1_construct_wrapper:output:373&853_852_1_construct_wrapper:output:373"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/3-InvertedResidual/conv_pw-Conv2d/Conv2D-op316  &853_852_1_construct_wrapper:output:373 6853_852_1_construct_wrapper:blocks.layer.5.3.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.5.3.bn1.beta <853_852_1_construct_wrapper:blocks.layer.5.3.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.3.bn1.moving_variance"853_852_1_construct_wrapper:42:374"853_852_1_construct_wrapper:42:374" BatchNorm*R Bshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op317 J[ValueNode]1(0):375"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:42:374 [ValueNode]1(0):375(853_852_1_construct_wrapper:[CNode]2:376(853_852_1_construct_wrapper:[CNode]2:376" tuple_getitem*%R Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op318  (853_852_1_construct_wrapper:[CNode]2:376)853_852_1_construct_wrapper:[CNode]47:377)853_852_1_construct_wrapper:[CNode]47:377"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/3-InvertedResidual/act_fn-Swish/Sigmoid-op319  (853_852_1_construct_wrapper:[CNode]2:376 )853_852_1_construct_wrapper:[CNode]47:377(853_852_1_construct_wrapper:[CNode]4:378(853_852_1_construct_wrapper:[CNode]4:378"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op320 (853_852_1_construct_wrapper:[CNode]4:378 ;853_852_1_construct_wrapper:blocks.layer.5.3.conv_dw.weight&853_852_1_construct_wrapper:output:379&853_852_1_construct_wrapper:output:379"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR* Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/conv_dw-Conv2d/Conv2D-op321  &853_852_1_construct_wrapper:output:379 6853_852_1_construct_wrapper:blocks.layer.5.3.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.5.3.bn2.beta <853_852_1_construct_wrapper:blocks.layer.5.3.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.3.bn2.moving_variance"853_852_1_construct_wrapper:42:380"853_852_1_construct_wrapper:42:380" BatchNorm*R Bshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op322 J[ValueNode]1(0):381"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:42:380 [ValueNode]1(0):381(853_852_1_construct_wrapper:[CNode]2:382(853_852_1_construct_wrapper:[CNode]2:382" tuple_getitem*%R Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op323  (853_852_1_construct_wrapper:[CNode]2:382)853_852_1_construct_wrapper:[CNode]47:383)853_852_1_construct_wrapper:[CNode]47:383"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/3-InvertedResidual/act_fn-Swish/Sigmoid-op324  (853_852_1_construct_wrapper:[CNode]2:382 )853_852_1_construct_wrapper:[CNode]47:383(853_852_1_construct_wrapper:[CNode]4:384(853_852_1_construct_wrapper:[CNode]4:384"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op325 n[ValueNode]6((2, 3)):385"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:384 [ValueNode]6((2, 3)):385$853_852_1_construct_wrapper:x_se:386$853_852_1_construct_wrapper:x_se:386" ReduceMean*%R Bshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/3-InvertedResidual/se-SqueezeExcite/ReduceMean-op326 $853_852_1_construct_wrapper:x_se:386 B853_852_1_construct_wrapper:blocks.layer.5.3.se.conv_reduce.weight&853_852_1_construct_wrapper:output:387&853_852_1_construct_wrapper:output:387"Conv2D*$R0Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *0Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op327  &853_852_1_construct_wrapper:output:387 @853_852_1_construct_wrapper:blocks.layer.5.3.se.conv_reduce.bias&853_852_1_construct_wrapper:output:388&853_852_1_construct_wrapper:output:388"BiasAdd*$R0Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op328  &853_852_1_construct_wrapper:output:388)853_852_1_construct_wrapper:[CNode]48:389)853_852_1_construct_wrapper:[CNode]48:389"Sigmoid*$R0Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/3-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op329  &853_852_1_construct_wrapper:output:388 )853_852_1_construct_wrapper:[CNode]48:389(853_852_1_construct_wrapper:[CNode]4:390(853_852_1_construct_wrapper:[CNode]4:390"Mul*$R0Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op330 (853_852_1_construct_wrapper:[CNode]4:390 B853_852_1_construct_wrapper:blocks.layer.5.3.se.conv_expand.weight&853_852_1_construct_wrapper:output:391&853_852_1_construct_wrapper:output:391"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op331  &853_852_1_construct_wrapper:output:391 @853_852_1_construct_wrapper:blocks.layer.5.3.se.conv_expand.bias&853_852_1_construct_wrapper:output:392&853_852_1_construct_wrapper:output:392"BiasAdd*%R Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op332  &853_852_1_construct_wrapper:output:392$853_852_1_construct_wrapper:x_se:393$853_852_1_construct_wrapper:x_se:393"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/3-InvertedResidual/se-SqueezeExcite/Sigmoid-op333  (853_852_1_construct_wrapper:[CNode]4:384 $853_852_1_construct_wrapper:x_se:393(853_852_1_construct_wrapper:[CNode]4:394(853_852_1_construct_wrapper:[CNode]4:394"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op334 (853_852_1_construct_wrapper:[CNode]4:394 <853_852_1_construct_wrapper:blocks.layer.5.3.conv_pwl.weight&853_852_1_construct_wrapper:output:395&853_852_1_construct_wrapper:output:395"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/3-InvertedResidual/conv_pwl-Conv2d/Conv2D-op335  &853_852_1_construct_wrapper:output:395 6853_852_1_construct_wrapper:blocks.layer.5.3.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.5.3.bn3.beta <853_852_1_construct_wrapper:blocks.layer.5.3.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.5.3.bn3.moving_variance"853_852_1_construct_wrapper:41:396"853_852_1_construct_wrapper:41:396" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op336 J[ValueNode]1(0):397"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:41:396 [ValueNode]1(0):397(853_852_1_construct_wrapper:[CNode]2:398(853_852_1_construct_wrapper:[CNode]2:398" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op337  (853_852_1_construct_wrapper:[CNode]2:398 )853_852_1_construct_wrapper:[CNode]16:372)853_852_1_construct_wrapper:[CNode]16:399)853_852_1_construct_wrapper:[CNode]16:399" TensorAdd*%RBshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:dDefault/blocks-BlockBuilder/layer-SequentialCell/1-SequentialCell/1-InvertedResidual/TensorAdd-op338 )853_852_1_construct_wrapper:[CNode]16:399 ;853_852_1_construct_wrapper:blocks.layer.6.0.conv_pw.weight&853_852_1_construct_wrapper:output:400&853_852_1_construct_wrapper:output:400"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/6-SequentialCell/0-InvertedResidual/conv_pw-Conv2d/Conv2D-op339  &853_852_1_construct_wrapper:output:400 6853_852_1_construct_wrapper:blocks.layer.6.0.bn1.gamma 5853_852_1_construct_wrapper:blocks.layer.6.0.bn1.beta <853_852_1_construct_wrapper:blocks.layer.6.0.bn1.moving_mean @853_852_1_construct_wrapper:blocks.layer.6.0.bn1.moving_variance"853_852_1_construct_wrapper:42:401"853_852_1_construct_wrapper:42:401" BatchNorm*R Bshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op340 J[ValueNode]1(0):402"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:42:401 [ValueNode]1(0):402(853_852_1_construct_wrapper:[CNode]2:403(853_852_1_construct_wrapper:[CNode]2:403" tuple_getitem*%R Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op341  (853_852_1_construct_wrapper:[CNode]2:403)853_852_1_construct_wrapper:[CNode]49:404)853_852_1_construct_wrapper:[CNode]49:404"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/6-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op342  (853_852_1_construct_wrapper:[CNode]2:403 )853_852_1_construct_wrapper:[CNode]49:404(853_852_1_construct_wrapper:[CNode]4:405(853_852_1_construct_wrapper:[CNode]4:405"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op343 (853_852_1_construct_wrapper:[CNode]4:405 ;853_852_1_construct_wrapper:blocks.layer.6.0.conv_dw.weight&853_852_1_construct_wrapper:output:406&853_852_1_construct_wrapper:output:406"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0** groupR* Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:pDefault/blocks-BlockBuilder/layer-SequentialCell/6-SequentialCell/0-InvertedResidual/conv_dw-Conv2d/Conv2D-op344  &853_852_1_construct_wrapper:output:406 6853_852_1_construct_wrapper:blocks.layer.6.0.bn2.gamma 5853_852_1_construct_wrapper:blocks.layer.6.0.bn2.beta <853_852_1_construct_wrapper:blocks.layer.6.0.bn2.moving_mean @853_852_1_construct_wrapper:blocks.layer.6.0.bn2.moving_variance"853_852_1_construct_wrapper:42:407"853_852_1_construct_wrapper:42:407" BatchNorm*R Bshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/bn2-BatchNorm2d/BatchNorm-op345 J[ValueNode]1(0):408"Constant*) valueR *Bvalue0  scalar:value0  "853_852_1_construct_wrapper:42:407 [ValueNode]1(0):408(853_852_1_construct_wrapper:[CNode]2:409(853_852_1_construct_wrapper:[CNode]2:409" tuple_getitem*%R Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op346  (853_852_1_construct_wrapper:[CNode]2:409)853_852_1_construct_wrapper:[CNode]49:410)853_852_1_construct_wrapper:[CNode]49:410"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:oDefault/blocks-BlockBuilder/layer-SequentialCell/6-SequentialCell/0-InvertedResidual/act_fn-Swish/Sigmoid-op347  (853_852_1_construct_wrapper:[CNode]2:409 )853_852_1_construct_wrapper:[CNode]49:410(853_852_1_construct_wrapper:[CNode]4:411(853_852_1_construct_wrapper:[CNode]4:411"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op348 n[ValueNode]6((2, 3)):412"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:411 [ValueNode]6((2, 3)):412$853_852_1_construct_wrapper:x_se:413$853_852_1_construct_wrapper:x_se:413" ReduceMean*%R Bshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:vDefault/blocks-BlockBuilder/layer-SequentialCell/6-SequentialCell/0-InvertedResidual/se-SqueezeExcite/ReduceMean-op349 $853_852_1_construct_wrapper:x_se:413 B853_852_1_construct_wrapper:blocks.layer.6.0.se.conv_reduce.weight&853_852_1_construct_wrapper:output:414&853_852_1_construct_wrapper:output:414"Conv2D*$R0Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*/ out_channelR *0Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/Conv2D-op350  &853_852_1_construct_wrapper:output:414 @853_852_1_construct_wrapper:blocks.layer.6.0.se.conv_reduce.bias&853_852_1_construct_wrapper:output:415&853_852_1_construct_wrapper:output:415"BiasAdd*$R0Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_reduce-Conv2d/BiasAdd-op351  &853_852_1_construct_wrapper:output:415)853_852_1_construct_wrapper:[CNode]50:416)853_852_1_construct_wrapper:[CNode]50:416"Sigmoid*$R0Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/6-SequentialCell/0-InvertedResidual/se-SqueezeExcite/act_fn-Swish/Sigmoid-op352  &853_852_1_construct_wrapper:output:415 )853_852_1_construct_wrapper:[CNode]50:416(853_852_1_construct_wrapper:[CNode]4:417(853_852_1_construct_wrapper:[CNode]4:417"Mul*$R0Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op353 (853_852_1_construct_wrapper:[CNode]4:417 B853_852_1_construct_wrapper:blocks.layer.6.0.se.conv_expand.weight&853_852_1_construct_wrapper:output:418&853_852_1_construct_wrapper:output:418"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*. pad_modeR2padBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/Conv2D-op354  &853_852_1_construct_wrapper:output:418 @853_852_1_construct_wrapper:blocks.layer.6.0.se.conv_expand.bias&853_852_1_construct_wrapper:output:419&853_852_1_construct_wrapper:output:419"BiasAdd*%R Bshape1 shape:shape1,*2 data_formatR2NCHWBvalue0  scalar:value0*M input_namesR 2xBvalue1R 2bBvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],:Default/blocks-BlockBuilder/layer-SequentialCell/5-SequentialCell/1-InvertedResidual/se-SqueezeExcite/conv_expand-Conv2d/BiasAdd-op355  &853_852_1_construct_wrapper:output:419$853_852_1_construct_wrapper:x_se:420$853_852_1_construct_wrapper:x_se:420"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:sDefault/blocks-BlockBuilder/layer-SequentialCell/6-SequentialCell/0-InvertedResidual/se-SqueezeExcite/Sigmoid-op356  (853_852_1_construct_wrapper:[CNode]4:411 $853_852_1_construct_wrapper:x_se:420(853_852_1_construct_wrapper:[CNode]4:421(853_852_1_construct_wrapper:[CNode]4:421"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op357 (853_852_1_construct_wrapper:[CNode]4:421 <853_852_1_construct_wrapper:blocks.layer.6.0.conv_pwl.weight&853_852_1_construct_wrapper:output:422&853_852_1_construct_wrapper:output:422"Conv2D*%RBshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR*Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:qDefault/blocks-BlockBuilder/layer-SequentialCell/6-SequentialCell/0-InvertedResidual/conv_pwl-Conv2d/Conv2D-op358  &853_852_1_construct_wrapper:output:422 6853_852_1_construct_wrapper:blocks.layer.6.0.bn3.gamma 5853_852_1_construct_wrapper:blocks.layer.6.0.bn3.beta <853_852_1_construct_wrapper:blocks.layer.6.0.bn3.moving_mean @853_852_1_construct_wrapper:blocks.layer.6.0.bn3.moving_variance)853_852_1_construct_wrapper:[CNode]51:423)853_852_1_construct_wrapper:[CNode]51:423" BatchNorm*RBshape1R Bshape2R Bshape3R Bshape4R Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:tDefault/blocks-BlockBuilder/layer-SequentialCell/6-SequentialCell/0-InvertedResidual/bn3-BatchNorm2d/BatchNorm-op359 J[ValueNode]1(0):424"Constant*) valueR *Bvalue0  scalar:value0  )853_852_1_construct_wrapper:[CNode]51:423 [ValueNode]1(0):424(853_852_1_construct_wrapper:[CNode]2:425(853_852_1_construct_wrapper:[CNode]2:425" tuple_getitem*%RBshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op360 (853_852_1_construct_wrapper:[CNode]2:425 ,853_852_1_construct_wrapper:conv_head.weight&853_852_1_construct_wrapper:output:426&853_852_1_construct_wrapper:output:426"Conv2D*%R Bshape1 shape:shape1,*w pad_listR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*, offset_aR *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*) groupR *Bvalue0  scalar:value0*= output_namesR2outputBvalue1 scalar:List[value1,],*w dilationR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],*( modeR *Bvalue0  scalar:value0*M input_namesR 2xBvalue1R 2wBvalue2 scalar:List[value1,value2,],*N kernel_sizeR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],*0 out_channelR* Bvalue0  scalar:value0*/ pad_modeR2sameBvalue0  scalar:value0*' padR *Bvalue0  scalar:value0*u strideR *Bvalue1R *Bvalue2R *Bvalue3R *Bvalue4 +scalar:Tuple[value1,value2,value3,value4,],:%Default/conv_head-Conv2d/Conv2D-op361  &853_852_1_construct_wrapper:output:426 %853_852_1_construct_wrapper:bn2.gamma $853_852_1_construct_wrapper:bn2.beta +853_852_1_construct_wrapper:bn2.moving_mean /853_852_1_construct_wrapper:bn2.moving_variance)853_852_1_construct_wrapper:[CNode]52:427)853_852_1_construct_wrapper:[CNode]52:427" BatchNorm*R Bshape1R  Bshape2R  Bshape3R  Bshape4R  Bshape51shape:Tuple[shape1,shape2,shape3,shape4,shape5,],* output_namesR 2yBvalue1R2 batch_meanBvalue2R2batch_varianceBvalue3R2reserve_space_1Bvalue4R2reserve_space_2Bvalue5 1scalar:List[value1,value2,value3,value4,value5,],* input_namesR 2xBvalue1R2scaleBvalue2R2offsetBvalue3R2meanBvalue4R2varianceBvalue5 1scalar:List[value1,value2,value3,value4,value5,],*/ is_trainingR  *Bvalue0  scalar:value0*2 data_formatR2NCHWBvalue0  scalar:value0*. epsilonR"'7Bvalue0  scalar:value0:'Default/bn2-BatchNorm2d/BatchNorm-op362 J[ValueNode]1(0):428"Constant*) valueR *Bvalue0  scalar:value0  )853_852_1_construct_wrapper:[CNode]52:427 [ValueNode]1(0):428(853_852_1_construct_wrapper:[CNode]2:429(853_852_1_construct_wrapper:[CNode]2:429" tuple_getitem*%R Bshape1 shape:shape1,:+Default/bn1-BatchNorm2d/tuple_getitem-op363  (853_852_1_construct_wrapper:[CNode]2:429(853_852_1_construct_wrapper:[CNode]3:430(853_852_1_construct_wrapper:[CNode]3:430"Sigmoid*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*7 input_namesR 2xBvalue1 scalar:List[value1,],:"Default/act_fn-Swish/Sigmoid-op364  (853_852_1_construct_wrapper:[CNode]2:429 (853_852_1_construct_wrapper:[CNode]3:430(853_852_1_construct_wrapper:[CNode]4:431(853_852_1_construct_wrapper:[CNode]4:431"Mul*%R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*M input_namesR 2xBvalue1R 2yBvalue2 scalar:List[value1,value2,],:Default/act_fn-Swish/Mul-op365 n[ValueNode]6((2, 3)):432"Constant*H valueR *Bvalue1R *Bvalue2 scalar:Tuple[value1,value2,],  (853_852_1_construct_wrapper:[CNode]4:431 [ValueNode]6((2, 3)):432!853_852_1_construct_wrapper:x:433!853_852_1_construct_wrapper:x:433" ReduceMean*%R Bshape1 shape:shape1,*. io_formatR2NDBvalue0  scalar:value0*8 output_namesR 2yBvalue1 scalar:List[value1,],*- keep_dimsR  *Bvalue0  scalar:value0*V input_namesR2input_xBvalue1R2axisBvalue2 scalar:List[value1,value2,],:Default/ReduceMean-op366 y[ValueNode]53((1, -1)):434"Constant*Q valueR *Bvalue1R* Bvalue2 scalar:Tuple[value1,value2,],  !853_852_1_construct_wrapper:x:433 [ValueNode]53((1, -1)):434!853_852_1_construct_wrapper:x:435!853_852_1_construct_wrapper:x:435"Reshape*!R Bshape1 shape:shape1,*= output_namesR2outputBvalue1 scalar:List[value1,],*V input_namesR2tensorBvalue1R2shapeBvalue2 scalar:List[value1,value2,],:Default/Reshape-op367  !853_852_1_construct_wrapper:x:435 -853_852_1_construct_wrapper:classifier.weight&853_852_1_construct_wrapper:output:436&853_852_1_construct_wrapper:output:436"MatMul*!RBshape1 shape:shape1,*0 transpose_x2R  *Bvalue0  scalar:value0*/ transpose_bR  *Bvalue0  scalar:value0*/ transpose_aR  *Bvalue0  scalar:value0*O input_namesR2x1Bvalue1R2x2Bvalue2 scalar:List[value1,value2,],*= output_namesR2outputBvalue1 scalar:List[value1,],*. io_formatR2NDBvalue0  scalar:value0*0 transpose_x1R  *Bvalue0  scalar:value0:%Default/classifier-Dense/MatMul-op368  &853_852_1_construct_wrapper:output:436 +853_852_1_construct_wrapper:classifier.bias)853_852_1_construct_wrapper:[CNode]54:438&853_852_1_construct_wrapper:output:437"BiasAdd*!RBshape1 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