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soumith/densenet.py

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densenet.py
op_version_set = 0
def forward(self,
input_1: Tensor) -> Tensor:
input_2 = torch._convolution(input_1, self.features.conv0.weight, None, [2, 2], [3, 3], [1, 1], False, [0, 0], 1, False, False, True)
input_3 = torch.batch_norm(input_2, self.features.norm0.weight, self.features.norm0.bias, self.features.norm0.running_mean, self.features.norm0.running_var, False, 0., 1.0000000000000001e-05, True)
input_4 = torch.threshold_(input_3, 0., 0.)
input_5, _0 = torch.max_pool2d_with_indices(input_4, [3, 3], [2, 2], [1, 1], [1, 1], False)
input_6 = torch.batch_norm(input_5, self.features.denseblock1.denselayer1.norm1.weight, self.features.denseblock1.denselayer1.norm1.bias, self.features.denseblock1.denselayer1.norm1.running_mean, self.features.denseblock1.denselayer1.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_7 = torch.threshold_(input_6, 0., 0.)
input_8 = torch._convolution(input_7, self.features.denseblock1.denselayer1.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_9 = torch.batch_norm(input_8, self.features.denseblock1.denselayer1.norm2.weight, self.features.denseblock1.denselayer1.norm2.bias, self.features.denseblock1.denselayer1.norm2.running_mean, self.features.denseblock1.denselayer1.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_10 = torch.threshold_(input_9, 0., 0.)
new_features_1 = torch._convolution(input_10, self.features.denseblock1.denselayer1.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_11 = torch.cat([input_5, new_features_1], 1)
input_12 = torch.batch_norm(input_11, self.features.denseblock1.denselayer2.norm1.weight, self.features.denseblock1.denselayer2.norm1.bias, self.features.denseblock1.denselayer2.norm1.running_mean, self.features.denseblock1.denselayer2.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_13 = torch.threshold_(input_12, 0., 0.)
input_14 = torch._convolution(input_13, self.features.denseblock1.denselayer2.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_15 = torch.batch_norm(input_14, self.features.denseblock1.denselayer2.norm2.weight, self.features.denseblock1.denselayer2.norm2.bias, self.features.denseblock1.denselayer2.norm2.running_mean, self.features.denseblock1.denselayer2.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_16 = torch.threshold_(input_15, 0., 0.)
new_features_2 = torch._convolution(input_16, self.features.denseblock1.denselayer2.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_17 = torch.cat([input_11, new_features_2], 1)
input_18 = torch.batch_norm(input_17, self.features.denseblock1.denselayer3.norm1.weight, self.features.denseblock1.denselayer3.norm1.bias, self.features.denseblock1.denselayer3.norm1.running_mean, self.features.denseblock1.denselayer3.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_19 = torch.threshold_(input_18, 0., 0.)
input_20 = torch._convolution(input_19, self.features.denseblock1.denselayer3.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_21 = torch.batch_norm(input_20, self.features.denseblock1.denselayer3.norm2.weight, self.features.denseblock1.denselayer3.norm2.bias, self.features.denseblock1.denselayer3.norm2.running_mean, self.features.denseblock1.denselayer3.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_22 = torch.threshold_(input_21, 0., 0.)
new_features_3 = torch._convolution(input_22, self.features.denseblock1.denselayer3.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_23 = torch.cat([input_17, new_features_3], 1)
input_24 = torch.batch_norm(input_23, self.features.denseblock1.denselayer4.norm1.weight, self.features.denseblock1.denselayer4.norm1.bias, self.features.denseblock1.denselayer4.norm1.running_mean, self.features.denseblock1.denselayer4.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_25 = torch.threshold_(input_24, 0., 0.)
input_26 = torch._convolution(input_25, self.features.denseblock1.denselayer4.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_27 = torch.batch_norm(input_26, self.features.denseblock1.denselayer4.norm2.weight, self.features.denseblock1.denselayer4.norm2.bias, self.features.denseblock1.denselayer4.norm2.running_mean, self.features.denseblock1.denselayer4.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_28 = torch.threshold_(input_27, 0., 0.)
new_features_4 = torch._convolution(input_28, self.features.denseblock1.denselayer4.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_29 = torch.cat([input_23, new_features_4], 1)
input_30 = torch.batch_norm(input_29, self.features.denseblock1.denselayer5.norm1.weight, self.features.denseblock1.denselayer5.norm1.bias, self.features.denseblock1.denselayer5.norm1.running_mean, self.features.denseblock1.denselayer5.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_31 = torch.threshold_(input_30, 0., 0.)
input_32 = torch._convolution(input_31, self.features.denseblock1.denselayer5.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_33 = torch.batch_norm(input_32, self.features.denseblock1.denselayer5.norm2.weight, self.features.denseblock1.denselayer5.norm2.bias, self.features.denseblock1.denselayer5.norm2.running_mean, self.features.denseblock1.denselayer5.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_34 = torch.threshold_(input_33, 0., 0.)
new_features_5 = torch._convolution(input_34, self.features.denseblock1.denselayer5.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_35 = torch.cat([input_29, new_features_5], 1)
input_36 = torch.batch_norm(input_35, self.features.denseblock1.denselayer6.norm1.weight, self.features.denseblock1.denselayer6.norm1.bias, self.features.denseblock1.denselayer6.norm1.running_mean, self.features.denseblock1.denselayer6.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_37 = torch.threshold_(input_36, 0., 0.)
input_38 = torch._convolution(input_37, self.features.denseblock1.denselayer6.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_39 = torch.batch_norm(input_38, self.features.denseblock1.denselayer6.norm2.weight, self.features.denseblock1.denselayer6.norm2.bias, self.features.denseblock1.denselayer6.norm2.running_mean, self.features.denseblock1.denselayer6.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_40 = torch.threshold_(input_39, 0., 0.)
new_features_6 = torch._convolution(input_40, self.features.denseblock1.denselayer6.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_41 = torch.cat([input_35, new_features_6], 1)
input_42 = torch.batch_norm(input_41, self.features.transition1.norm.weight, self.features.transition1.norm.bias, self.features.transition1.norm.running_mean, self.features.transition1.norm.running_var, False, 0., 1.0000000000000001e-05, True)
input_43 = torch.threshold_(input_42, 0., 0.)
input_44 = torch._convolution(input_43, self.features.transition1.conv.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_45 = torch.avg_pool2d(input_44, [2, 2], [2, 2], [0, 0], False, True)
input_46 = torch.batch_norm(input_45, self.features.denseblock2.denselayer1.norm1.weight, self.features.denseblock2.denselayer1.norm1.bias, self.features.denseblock2.denselayer1.norm1.running_mean, self.features.denseblock2.denselayer1.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_47 = torch.threshold_(input_46, 0., 0.)
input_48 = torch._convolution(input_47, self.features.denseblock2.denselayer1.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_49 = torch.batch_norm(input_48, self.features.denseblock2.denselayer1.norm2.weight, self.features.denseblock2.denselayer1.norm2.bias, self.features.denseblock2.denselayer1.norm2.running_mean, self.features.denseblock2.denselayer1.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_50 = torch.threshold_(input_49, 0., 0.)
new_features_7 = torch._convolution(input_50, self.features.denseblock2.denselayer1.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_51 = torch.cat([input_45, new_features_7], 1)
input_52 = torch.batch_norm(input_51, self.features.denseblock2.denselayer2.norm1.weight, self.features.denseblock2.denselayer2.norm1.bias, self.features.denseblock2.denselayer2.norm1.running_mean, self.features.denseblock2.denselayer2.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_53 = torch.threshold_(input_52, 0., 0.)
input_54 = torch._convolution(input_53, self.features.denseblock2.denselayer2.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_55 = torch.batch_norm(input_54, self.features.denseblock2.denselayer2.norm2.weight, self.features.denseblock2.denselayer2.norm2.bias, self.features.denseblock2.denselayer2.norm2.running_mean, self.features.denseblock2.denselayer2.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_56 = torch.threshold_(input_55, 0., 0.)
new_features_8 = torch._convolution(input_56, self.features.denseblock2.denselayer2.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_57 = torch.cat([input_51, new_features_8], 1)
input_58 = torch.batch_norm(input_57, self.features.denseblock2.denselayer3.norm1.weight, self.features.denseblock2.denselayer3.norm1.bias, self.features.denseblock2.denselayer3.norm1.running_mean, self.features.denseblock2.denselayer3.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_59 = torch.threshold_(input_58, 0., 0.)
input_60 = torch._convolution(input_59, self.features.denseblock2.denselayer3.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_61 = torch.batch_norm(input_60, self.features.denseblock2.denselayer3.norm2.weight, self.features.denseblock2.denselayer3.norm2.bias, self.features.denseblock2.denselayer3.norm2.running_mean, self.features.denseblock2.denselayer3.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_62 = torch.threshold_(input_61, 0., 0.)
new_features_9 = torch._convolution(input_62, self.features.denseblock2.denselayer3.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_63 = torch.cat([input_57, new_features_9], 1)
input_64 = torch.batch_norm(input_63, self.features.denseblock2.denselayer4.norm1.weight, self.features.denseblock2.denselayer4.norm1.bias, self.features.denseblock2.denselayer4.norm1.running_mean, self.features.denseblock2.denselayer4.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_65 = torch.threshold_(input_64, 0., 0.)
input_66 = torch._convolution(input_65, self.features.denseblock2.denselayer4.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_67 = torch.batch_norm(input_66, self.features.denseblock2.denselayer4.norm2.weight, self.features.denseblock2.denselayer4.norm2.bias, self.features.denseblock2.denselayer4.norm2.running_mean, self.features.denseblock2.denselayer4.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_68 = torch.threshold_(input_67, 0., 0.)
new_features_10 = torch._convolution(input_68, self.features.denseblock2.denselayer4.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_69 = torch.cat([input_63, new_features_10], 1)
input_70 = torch.batch_norm(input_69, self.features.denseblock2.denselayer5.norm1.weight, self.features.denseblock2.denselayer5.norm1.bias, self.features.denseblock2.denselayer5.norm1.running_mean, self.features.denseblock2.denselayer5.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_71 = torch.threshold_(input_70, 0., 0.)
input_72 = torch._convolution(input_71, self.features.denseblock2.denselayer5.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_73 = torch.batch_norm(input_72, self.features.denseblock2.denselayer5.norm2.weight, self.features.denseblock2.denselayer5.norm2.bias, self.features.denseblock2.denselayer5.norm2.running_mean, self.features.denseblock2.denselayer5.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_74 = torch.threshold_(input_73, 0., 0.)
new_features_11 = torch._convolution(input_74, self.features.denseblock2.denselayer5.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_75 = torch.cat([input_69, new_features_11], 1)
input_76 = torch.batch_norm(input_75, self.features.denseblock2.denselayer6.norm1.weight, self.features.denseblock2.denselayer6.norm1.bias, self.features.denseblock2.denselayer6.norm1.running_mean, self.features.denseblock2.denselayer6.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_77 = torch.threshold_(input_76, 0., 0.)
input_78 = torch._convolution(input_77, self.features.denseblock2.denselayer6.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_79 = torch.batch_norm(input_78, self.features.denseblock2.denselayer6.norm2.weight, self.features.denseblock2.denselayer6.norm2.bias, self.features.denseblock2.denselayer6.norm2.running_mean, self.features.denseblock2.denselayer6.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_80 = torch.threshold_(input_79, 0., 0.)
new_features_12 = torch._convolution(input_80, self.features.denseblock2.denselayer6.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_81 = torch.cat([input_75, new_features_12], 1)
input_82 = torch.batch_norm(input_81, self.features.denseblock2.denselayer7.norm1.weight, self.features.denseblock2.denselayer7.norm1.bias, self.features.denseblock2.denselayer7.norm1.running_mean, self.features.denseblock2.denselayer7.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_83 = torch.threshold_(input_82, 0., 0.)
input_84 = torch._convolution(input_83, self.features.denseblock2.denselayer7.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_85 = torch.batch_norm(input_84, self.features.denseblock2.denselayer7.norm2.weight, self.features.denseblock2.denselayer7.norm2.bias, self.features.denseblock2.denselayer7.norm2.running_mean, self.features.denseblock2.denselayer7.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_86 = torch.threshold_(input_85, 0., 0.)
new_features_13 = torch._convolution(input_86, self.features.denseblock2.denselayer7.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_87 = torch.cat([input_81, new_features_13], 1)
input_88 = torch.batch_norm(input_87, self.features.denseblock2.denselayer8.norm1.weight, self.features.denseblock2.denselayer8.norm1.bias, self.features.denseblock2.denselayer8.norm1.running_mean, self.features.denseblock2.denselayer8.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_89 = torch.threshold_(input_88, 0., 0.)
input_90 = torch._convolution(input_89, self.features.denseblock2.denselayer8.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_91 = torch.batch_norm(input_90, self.features.denseblock2.denselayer8.norm2.weight, self.features.denseblock2.denselayer8.norm2.bias, self.features.denseblock2.denselayer8.norm2.running_mean, self.features.denseblock2.denselayer8.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_92 = torch.threshold_(input_91, 0., 0.)
new_features_14 = torch._convolution(input_92, self.features.denseblock2.denselayer8.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_93 = torch.cat([input_87, new_features_14], 1)
input_94 = torch.batch_norm(input_93, self.features.denseblock2.denselayer9.norm1.weight, self.features.denseblock2.denselayer9.norm1.bias, self.features.denseblock2.denselayer9.norm1.running_mean, self.features.denseblock2.denselayer9.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_95 = torch.threshold_(input_94, 0., 0.)
input_96 = torch._convolution(input_95, self.features.denseblock2.denselayer9.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_97 = torch.batch_norm(input_96, self.features.denseblock2.denselayer9.norm2.weight, self.features.denseblock2.denselayer9.norm2.bias, self.features.denseblock2.denselayer9.norm2.running_mean, self.features.denseblock2.denselayer9.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_98 = torch.threshold_(input_97, 0., 0.)
new_features_15 = torch._convolution(input_98, self.features.denseblock2.denselayer9.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_99 = torch.cat([input_93, new_features_15], 1)
input_100 = torch.batch_norm(input_99, self.features.denseblock2.denselayer10.norm1.weight, self.features.denseblock2.denselayer10.norm1.bias, self.features.denseblock2.denselayer10.norm1.running_mean, self.features.denseblock2.denselayer10.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_101 = torch.threshold_(input_100, 0., 0.)
input_102 = torch._convolution(input_101, self.features.denseblock2.denselayer10.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_103 = torch.batch_norm(input_102, self.features.denseblock2.denselayer10.norm2.weight, self.features.denseblock2.denselayer10.norm2.bias, self.features.denseblock2.denselayer10.norm2.running_mean, self.features.denseblock2.denselayer10.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_104 = torch.threshold_(input_103, 0., 0.)
new_features_16 = torch._convolution(input_104, self.features.denseblock2.denselayer10.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_105 = torch.cat([input_99, new_features_16], 1)
input_106 = torch.batch_norm(input_105, self.features.denseblock2.denselayer11.norm1.weight, self.features.denseblock2.denselayer11.norm1.bias, self.features.denseblock2.denselayer11.norm1.running_mean, self.features.denseblock2.denselayer11.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_107 = torch.threshold_(input_106, 0., 0.)
input_108 = torch._convolution(input_107, self.features.denseblock2.denselayer11.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_109 = torch.batch_norm(input_108, self.features.denseblock2.denselayer11.norm2.weight, self.features.denseblock2.denselayer11.norm2.bias, self.features.denseblock2.denselayer11.norm2.running_mean, self.features.denseblock2.denselayer11.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_110 = torch.threshold_(input_109, 0., 0.)
new_features_17 = torch._convolution(input_110, self.features.denseblock2.denselayer11.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_111 = torch.cat([input_105, new_features_17], 1)
input_112 = torch.batch_norm(input_111, self.features.denseblock2.denselayer12.norm1.weight, self.features.denseblock2.denselayer12.norm1.bias, self.features.denseblock2.denselayer12.norm1.running_mean, self.features.denseblock2.denselayer12.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_113 = torch.threshold_(input_112, 0., 0.)
input_114 = torch._convolution(input_113, self.features.denseblock2.denselayer12.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_115 = torch.batch_norm(input_114, self.features.denseblock2.denselayer12.norm2.weight, self.features.denseblock2.denselayer12.norm2.bias, self.features.denseblock2.denselayer12.norm2.running_mean, self.features.denseblock2.denselayer12.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_116 = torch.threshold_(input_115, 0., 0.)
new_features_18 = torch._convolution(input_116, self.features.denseblock2.denselayer12.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_117 = torch.cat([input_111, new_features_18], 1)
input_118 = torch.batch_norm(input_117, self.features.transition2.norm.weight, self.features.transition2.norm.bias, self.features.transition2.norm.running_mean, self.features.transition2.norm.running_var, False, 0., 1.0000000000000001e-05, True)
input_119 = torch.threshold_(input_118, 0., 0.)
input_120 = torch._convolution(input_119, self.features.transition2.conv.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_121 = torch.avg_pool2d(input_120, [2, 2], [2, 2], [0, 0], False, True)
input_122 = torch.batch_norm(input_121, self.features.denseblock3.denselayer1.norm1.weight, self.features.denseblock3.denselayer1.norm1.bias, self.features.denseblock3.denselayer1.norm1.running_mean, self.features.denseblock3.denselayer1.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_123 = torch.threshold_(input_122, 0., 0.)
input_124 = torch._convolution(input_123, self.features.denseblock3.denselayer1.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_125 = torch.batch_norm(input_124, self.features.denseblock3.denselayer1.norm2.weight, self.features.denseblock3.denselayer1.norm2.bias, self.features.denseblock3.denselayer1.norm2.running_mean, self.features.denseblock3.denselayer1.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_126 = torch.threshold_(input_125, 0., 0.)
new_features_19 = torch._convolution(input_126, self.features.denseblock3.denselayer1.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_127 = torch.cat([input_121, new_features_19], 1)
input_128 = torch.batch_norm(input_127, self.features.denseblock3.denselayer2.norm1.weight, self.features.denseblock3.denselayer2.norm1.bias, self.features.denseblock3.denselayer2.norm1.running_mean, self.features.denseblock3.denselayer2.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_129 = torch.threshold_(input_128, 0., 0.)
input_130 = torch._convolution(input_129, self.features.denseblock3.denselayer2.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_131 = torch.batch_norm(input_130, self.features.denseblock3.denselayer2.norm2.weight, self.features.denseblock3.denselayer2.norm2.bias, self.features.denseblock3.denselayer2.norm2.running_mean, self.features.denseblock3.denselayer2.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_132 = torch.threshold_(input_131, 0., 0.)
new_features_20 = torch._convolution(input_132, self.features.denseblock3.denselayer2.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_133 = torch.cat([input_127, new_features_20], 1)
input_134 = torch.batch_norm(input_133, self.features.denseblock3.denselayer3.norm1.weight, self.features.denseblock3.denselayer3.norm1.bias, self.features.denseblock3.denselayer3.norm1.running_mean, self.features.denseblock3.denselayer3.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_135 = torch.threshold_(input_134, 0., 0.)
input_136 = torch._convolution(input_135, self.features.denseblock3.denselayer3.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_137 = torch.batch_norm(input_136, self.features.denseblock3.denselayer3.norm2.weight, self.features.denseblock3.denselayer3.norm2.bias, self.features.denseblock3.denselayer3.norm2.running_mean, self.features.denseblock3.denselayer3.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_138 = torch.threshold_(input_137, 0., 0.)
new_features_21 = torch._convolution(input_138, self.features.denseblock3.denselayer3.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_139 = torch.cat([input_133, new_features_21], 1)
input_140 = torch.batch_norm(input_139, self.features.denseblock3.denselayer4.norm1.weight, self.features.denseblock3.denselayer4.norm1.bias, self.features.denseblock3.denselayer4.norm1.running_mean, self.features.denseblock3.denselayer4.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_141 = torch.threshold_(input_140, 0., 0.)
input_142 = torch._convolution(input_141, self.features.denseblock3.denselayer4.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_143 = torch.batch_norm(input_142, self.features.denseblock3.denselayer4.norm2.weight, self.features.denseblock3.denselayer4.norm2.bias, self.features.denseblock3.denselayer4.norm2.running_mean, self.features.denseblock3.denselayer4.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_144 = torch.threshold_(input_143, 0., 0.)
new_features_22 = torch._convolution(input_144, self.features.denseblock3.denselayer4.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_145 = torch.cat([input_139, new_features_22], 1)
input_146 = torch.batch_norm(input_145, self.features.denseblock3.denselayer5.norm1.weight, self.features.denseblock3.denselayer5.norm1.bias, self.features.denseblock3.denselayer5.norm1.running_mean, self.features.denseblock3.denselayer5.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_147 = torch.threshold_(input_146, 0., 0.)
input_148 = torch._convolution(input_147, self.features.denseblock3.denselayer5.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_149 = torch.batch_norm(input_148, self.features.denseblock3.denselayer5.norm2.weight, self.features.denseblock3.denselayer5.norm2.bias, self.features.denseblock3.denselayer5.norm2.running_mean, self.features.denseblock3.denselayer5.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_150 = torch.threshold_(input_149, 0., 0.)
new_features_23 = torch._convolution(input_150, self.features.denseblock3.denselayer5.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_151 = torch.cat([input_145, new_features_23], 1)
input_152 = torch.batch_norm(input_151, self.features.denseblock3.denselayer6.norm1.weight, self.features.denseblock3.denselayer6.norm1.bias, self.features.denseblock3.denselayer6.norm1.running_mean, self.features.denseblock3.denselayer6.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_153 = torch.threshold_(input_152, 0., 0.)
input_154 = torch._convolution(input_153, self.features.denseblock3.denselayer6.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_155 = torch.batch_norm(input_154, self.features.denseblock3.denselayer6.norm2.weight, self.features.denseblock3.denselayer6.norm2.bias, self.features.denseblock3.denselayer6.norm2.running_mean, self.features.denseblock3.denselayer6.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_156 = torch.threshold_(input_155, 0., 0.)
new_features_24 = torch._convolution(input_156, self.features.denseblock3.denselayer6.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_157 = torch.cat([input_151, new_features_24], 1)
input_158 = torch.batch_norm(input_157, self.features.denseblock3.denselayer7.norm1.weight, self.features.denseblock3.denselayer7.norm1.bias, self.features.denseblock3.denselayer7.norm1.running_mean, self.features.denseblock3.denselayer7.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_159 = torch.threshold_(input_158, 0., 0.)
input_160 = torch._convolution(input_159, self.features.denseblock3.denselayer7.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_161 = torch.batch_norm(input_160, self.features.denseblock3.denselayer7.norm2.weight, self.features.denseblock3.denselayer7.norm2.bias, self.features.denseblock3.denselayer7.norm2.running_mean, self.features.denseblock3.denselayer7.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_162 = torch.threshold_(input_161, 0., 0.)
new_features_25 = torch._convolution(input_162, self.features.denseblock3.denselayer7.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_163 = torch.cat([input_157, new_features_25], 1)
input_164 = torch.batch_norm(input_163, self.features.denseblock3.denselayer8.norm1.weight, self.features.denseblock3.denselayer8.norm1.bias, self.features.denseblock3.denselayer8.norm1.running_mean, self.features.denseblock3.denselayer8.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_165 = torch.threshold_(input_164, 0., 0.)
input_166 = torch._convolution(input_165, self.features.denseblock3.denselayer8.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_167 = torch.batch_norm(input_166, self.features.denseblock3.denselayer8.norm2.weight, self.features.denseblock3.denselayer8.norm2.bias, self.features.denseblock3.denselayer8.norm2.running_mean, self.features.denseblock3.denselayer8.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_168 = torch.threshold_(input_167, 0., 0.)
new_features_26 = torch._convolution(input_168, self.features.denseblock3.denselayer8.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_169 = torch.cat([input_163, new_features_26], 1)
input_170 = torch.batch_norm(input_169, self.features.denseblock3.denselayer9.norm1.weight, self.features.denseblock3.denselayer9.norm1.bias, self.features.denseblock3.denselayer9.norm1.running_mean, self.features.denseblock3.denselayer9.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_171 = torch.threshold_(input_170, 0., 0.)
input_172 = torch._convolution(input_171, self.features.denseblock3.denselayer9.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_173 = torch.batch_norm(input_172, self.features.denseblock3.denselayer9.norm2.weight, self.features.denseblock3.denselayer9.norm2.bias, self.features.denseblock3.denselayer9.norm2.running_mean, self.features.denseblock3.denselayer9.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_174 = torch.threshold_(input_173, 0., 0.)
new_features_27 = torch._convolution(input_174, self.features.denseblock3.denselayer9.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_175 = torch.cat([input_169, new_features_27], 1)
input_176 = torch.batch_norm(input_175, self.features.denseblock3.denselayer10.norm1.weight, self.features.denseblock3.denselayer10.norm1.bias, self.features.denseblock3.denselayer10.norm1.running_mean, self.features.denseblock3.denselayer10.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_177 = torch.threshold_(input_176, 0., 0.)
input_178 = torch._convolution(input_177, self.features.denseblock3.denselayer10.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_179 = torch.batch_norm(input_178, self.features.denseblock3.denselayer10.norm2.weight, self.features.denseblock3.denselayer10.norm2.bias, self.features.denseblock3.denselayer10.norm2.running_mean, self.features.denseblock3.denselayer10.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_180 = torch.threshold_(input_179, 0., 0.)
new_features_28 = torch._convolution(input_180, self.features.denseblock3.denselayer10.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_181 = torch.cat([input_175, new_features_28], 1)
input_182 = torch.batch_norm(input_181, self.features.denseblock3.denselayer11.norm1.weight, self.features.denseblock3.denselayer11.norm1.bias, self.features.denseblock3.denselayer11.norm1.running_mean, self.features.denseblock3.denselayer11.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_183 = torch.threshold_(input_182, 0., 0.)
input_184 = torch._convolution(input_183, self.features.denseblock3.denselayer11.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_185 = torch.batch_norm(input_184, self.features.denseblock3.denselayer11.norm2.weight, self.features.denseblock3.denselayer11.norm2.bias, self.features.denseblock3.denselayer11.norm2.running_mean, self.features.denseblock3.denselayer11.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_186 = torch.threshold_(input_185, 0., 0.)
new_features_29 = torch._convolution(input_186, self.features.denseblock3.denselayer11.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_187 = torch.cat([input_181, new_features_29], 1)
input_188 = torch.batch_norm(input_187, self.features.denseblock3.denselayer12.norm1.weight, self.features.denseblock3.denselayer12.norm1.bias, self.features.denseblock3.denselayer12.norm1.running_mean, self.features.denseblock3.denselayer12.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_189 = torch.threshold_(input_188, 0., 0.)
input_190 = torch._convolution(input_189, self.features.denseblock3.denselayer12.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_191 = torch.batch_norm(input_190, self.features.denseblock3.denselayer12.norm2.weight, self.features.denseblock3.denselayer12.norm2.bias, self.features.denseblock3.denselayer12.norm2.running_mean, self.features.denseblock3.denselayer12.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_192 = torch.threshold_(input_191, 0., 0.)
new_features_30 = torch._convolution(input_192, self.features.denseblock3.denselayer12.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_193 = torch.cat([input_187, new_features_30], 1)
input_194 = torch.batch_norm(input_193, self.features.denseblock3.denselayer13.norm1.weight, self.features.denseblock3.denselayer13.norm1.bias, self.features.denseblock3.denselayer13.norm1.running_mean, self.features.denseblock3.denselayer13.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_195 = torch.threshold_(input_194, 0., 0.)
input_196 = torch._convolution(input_195, self.features.denseblock3.denselayer13.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_197 = torch.batch_norm(input_196, self.features.denseblock3.denselayer13.norm2.weight, self.features.denseblock3.denselayer13.norm2.bias, self.features.denseblock3.denselayer13.norm2.running_mean, self.features.denseblock3.denselayer13.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_198 = torch.threshold_(input_197, 0., 0.)
new_features_31 = torch._convolution(input_198, self.features.denseblock3.denselayer13.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_199 = torch.cat([input_193, new_features_31], 1)
input_200 = torch.batch_norm(input_199, self.features.denseblock3.denselayer14.norm1.weight, self.features.denseblock3.denselayer14.norm1.bias, self.features.denseblock3.denselayer14.norm1.running_mean, self.features.denseblock3.denselayer14.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_201 = torch.threshold_(input_200, 0., 0.)
input_202 = torch._convolution(input_201, self.features.denseblock3.denselayer14.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_203 = torch.batch_norm(input_202, self.features.denseblock3.denselayer14.norm2.weight, self.features.denseblock3.denselayer14.norm2.bias, self.features.denseblock3.denselayer14.norm2.running_mean, self.features.denseblock3.denselayer14.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_204 = torch.threshold_(input_203, 0., 0.)
new_features_32 = torch._convolution(input_204, self.features.denseblock3.denselayer14.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_205 = torch.cat([input_199, new_features_32], 1)
input_206 = torch.batch_norm(input_205, self.features.denseblock3.denselayer15.norm1.weight, self.features.denseblock3.denselayer15.norm1.bias, self.features.denseblock3.denselayer15.norm1.running_mean, self.features.denseblock3.denselayer15.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_207 = torch.threshold_(input_206, 0., 0.)
input_208 = torch._convolution(input_207, self.features.denseblock3.denselayer15.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_209 = torch.batch_norm(input_208, self.features.denseblock3.denselayer15.norm2.weight, self.features.denseblock3.denselayer15.norm2.bias, self.features.denseblock3.denselayer15.norm2.running_mean, self.features.denseblock3.denselayer15.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_210 = torch.threshold_(input_209, 0., 0.)
new_features_33 = torch._convolution(input_210, self.features.denseblock3.denselayer15.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_211 = torch.cat([input_205, new_features_33], 1)
input_212 = torch.batch_norm(input_211, self.features.denseblock3.denselayer16.norm1.weight, self.features.denseblock3.denselayer16.norm1.bias, self.features.denseblock3.denselayer16.norm1.running_mean, self.features.denseblock3.denselayer16.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_213 = torch.threshold_(input_212, 0., 0.)
input_214 = torch._convolution(input_213, self.features.denseblock3.denselayer16.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_215 = torch.batch_norm(input_214, self.features.denseblock3.denselayer16.norm2.weight, self.features.denseblock3.denselayer16.norm2.bias, self.features.denseblock3.denselayer16.norm2.running_mean, self.features.denseblock3.denselayer16.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_216 = torch.threshold_(input_215, 0., 0.)
new_features_34 = torch._convolution(input_216, self.features.denseblock3.denselayer16.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_217 = torch.cat([input_211, new_features_34], 1)
input_218 = torch.batch_norm(input_217, self.features.denseblock3.denselayer17.norm1.weight, self.features.denseblock3.denselayer17.norm1.bias, self.features.denseblock3.denselayer17.norm1.running_mean, self.features.denseblock3.denselayer17.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_219 = torch.threshold_(input_218, 0., 0.)
input_220 = torch._convolution(input_219, self.features.denseblock3.denselayer17.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_221 = torch.batch_norm(input_220, self.features.denseblock3.denselayer17.norm2.weight, self.features.denseblock3.denselayer17.norm2.bias, self.features.denseblock3.denselayer17.norm2.running_mean, self.features.denseblock3.denselayer17.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_222 = torch.threshold_(input_221, 0., 0.)
new_features_35 = torch._convolution(input_222, self.features.denseblock3.denselayer17.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_223 = torch.cat([input_217, new_features_35], 1)
input_224 = torch.batch_norm(input_223, self.features.denseblock3.denselayer18.norm1.weight, self.features.denseblock3.denselayer18.norm1.bias, self.features.denseblock3.denselayer18.norm1.running_mean, self.features.denseblock3.denselayer18.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_225 = torch.threshold_(input_224, 0., 0.)
input_226 = torch._convolution(input_225, self.features.denseblock3.denselayer18.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_227 = torch.batch_norm(input_226, self.features.denseblock3.denselayer18.norm2.weight, self.features.denseblock3.denselayer18.norm2.bias, self.features.denseblock3.denselayer18.norm2.running_mean, self.features.denseblock3.denselayer18.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_228 = torch.threshold_(input_227, 0., 0.)
new_features_36 = torch._convolution(input_228, self.features.denseblock3.denselayer18.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_229 = torch.cat([input_223, new_features_36], 1)
input_230 = torch.batch_norm(input_229, self.features.denseblock3.denselayer19.norm1.weight, self.features.denseblock3.denselayer19.norm1.bias, self.features.denseblock3.denselayer19.norm1.running_mean, self.features.denseblock3.denselayer19.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_231 = torch.threshold_(input_230, 0., 0.)
input_232 = torch._convolution(input_231, self.features.denseblock3.denselayer19.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_233 = torch.batch_norm(input_232, self.features.denseblock3.denselayer19.norm2.weight, self.features.denseblock3.denselayer19.norm2.bias, self.features.denseblock3.denselayer19.norm2.running_mean, self.features.denseblock3.denselayer19.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_234 = torch.threshold_(input_233, 0., 0.)
new_features_37 = torch._convolution(input_234, self.features.denseblock3.denselayer19.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_235 = torch.cat([input_229, new_features_37], 1)
input_236 = torch.batch_norm(input_235, self.features.denseblock3.denselayer20.norm1.weight, self.features.denseblock3.denselayer20.norm1.bias, self.features.denseblock3.denselayer20.norm1.running_mean, self.features.denseblock3.denselayer20.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_237 = torch.threshold_(input_236, 0., 0.)
input_238 = torch._convolution(input_237, self.features.denseblock3.denselayer20.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_239 = torch.batch_norm(input_238, self.features.denseblock3.denselayer20.norm2.weight, self.features.denseblock3.denselayer20.norm2.bias, self.features.denseblock3.denselayer20.norm2.running_mean, self.features.denseblock3.denselayer20.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_240 = torch.threshold_(input_239, 0., 0.)
new_features_38 = torch._convolution(input_240, self.features.denseblock3.denselayer20.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_241 = torch.cat([input_235, new_features_38], 1)
input_242 = torch.batch_norm(input_241, self.features.denseblock3.denselayer21.norm1.weight, self.features.denseblock3.denselayer21.norm1.bias, self.features.denseblock3.denselayer21.norm1.running_mean, self.features.denseblock3.denselayer21.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_243 = torch.threshold_(input_242, 0., 0.)
input_244 = torch._convolution(input_243, self.features.denseblock3.denselayer21.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_245 = torch.batch_norm(input_244, self.features.denseblock3.denselayer21.norm2.weight, self.features.denseblock3.denselayer21.norm2.bias, self.features.denseblock3.denselayer21.norm2.running_mean, self.features.denseblock3.denselayer21.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_246 = torch.threshold_(input_245, 0., 0.)
new_features_39 = torch._convolution(input_246, self.features.denseblock3.denselayer21.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_247 = torch.cat([input_241, new_features_39], 1)
input_248 = torch.batch_norm(input_247, self.features.denseblock3.denselayer22.norm1.weight, self.features.denseblock3.denselayer22.norm1.bias, self.features.denseblock3.denselayer22.norm1.running_mean, self.features.denseblock3.denselayer22.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_249 = torch.threshold_(input_248, 0., 0.)
input_250 = torch._convolution(input_249, self.features.denseblock3.denselayer22.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_251 = torch.batch_norm(input_250, self.features.denseblock3.denselayer22.norm2.weight, self.features.denseblock3.denselayer22.norm2.bias, self.features.denseblock3.denselayer22.norm2.running_mean, self.features.denseblock3.denselayer22.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_252 = torch.threshold_(input_251, 0., 0.)
new_features_40 = torch._convolution(input_252, self.features.denseblock3.denselayer22.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_253 = torch.cat([input_247, new_features_40], 1)
input_254 = torch.batch_norm(input_253, self.features.denseblock3.denselayer23.norm1.weight, self.features.denseblock3.denselayer23.norm1.bias, self.features.denseblock3.denselayer23.norm1.running_mean, self.features.denseblock3.denselayer23.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_255 = torch.threshold_(input_254, 0., 0.)
input_256 = torch._convolution(input_255, self.features.denseblock3.denselayer23.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_257 = torch.batch_norm(input_256, self.features.denseblock3.denselayer23.norm2.weight, self.features.denseblock3.denselayer23.norm2.bias, self.features.denseblock3.denselayer23.norm2.running_mean, self.features.denseblock3.denselayer23.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_258 = torch.threshold_(input_257, 0., 0.)
new_features_41 = torch._convolution(input_258, self.features.denseblock3.denselayer23.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_259 = torch.cat([input_253, new_features_41], 1)
input_260 = torch.batch_norm(input_259, self.features.denseblock3.denselayer24.norm1.weight, self.features.denseblock3.denselayer24.norm1.bias, self.features.denseblock3.denselayer24.norm1.running_mean, self.features.denseblock3.denselayer24.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_261 = torch.threshold_(input_260, 0., 0.)
input_262 = torch._convolution(input_261, self.features.denseblock3.denselayer24.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_263 = torch.batch_norm(input_262, self.features.denseblock3.denselayer24.norm2.weight, self.features.denseblock3.denselayer24.norm2.bias, self.features.denseblock3.denselayer24.norm2.running_mean, self.features.denseblock3.denselayer24.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_264 = torch.threshold_(input_263, 0., 0.)
new_features_42 = torch._convolution(input_264, self.features.denseblock3.denselayer24.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_265 = torch.cat([input_259, new_features_42], 1)
input_266 = torch.batch_norm(input_265, self.features.transition3.norm.weight, self.features.transition3.norm.bias, self.features.transition3.norm.running_mean, self.features.transition3.norm.running_var, False, 0., 1.0000000000000001e-05, True)
input_267 = torch.threshold_(input_266, 0., 0.)
input_268 = torch._convolution(input_267, self.features.transition3.conv.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_269 = torch.avg_pool2d(input_268, [2, 2], [2, 2], [0, 0], False, True)
input_270 = torch.batch_norm(input_269, self.features.denseblock4.denselayer1.norm1.weight, self.features.denseblock4.denselayer1.norm1.bias, self.features.denseblock4.denselayer1.norm1.running_mean, self.features.denseblock4.denselayer1.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_271 = torch.threshold_(input_270, 0., 0.)
input_272 = torch._convolution(input_271, self.features.denseblock4.denselayer1.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_273 = torch.batch_norm(input_272, self.features.denseblock4.denselayer1.norm2.weight, self.features.denseblock4.denselayer1.norm2.bias, self.features.denseblock4.denselayer1.norm2.running_mean, self.features.denseblock4.denselayer1.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_274 = torch.threshold_(input_273, 0., 0.)
new_features_43 = torch._convolution(input_274, self.features.denseblock4.denselayer1.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_275 = torch.cat([input_269, new_features_43], 1)
input_276 = torch.batch_norm(input_275, self.features.denseblock4.denselayer2.norm1.weight, self.features.denseblock4.denselayer2.norm1.bias, self.features.denseblock4.denselayer2.norm1.running_mean, self.features.denseblock4.denselayer2.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_277 = torch.threshold_(input_276, 0., 0.)
input_278 = torch._convolution(input_277, self.features.denseblock4.denselayer2.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_279 = torch.batch_norm(input_278, self.features.denseblock4.denselayer2.norm2.weight, self.features.denseblock4.denselayer2.norm2.bias, self.features.denseblock4.denselayer2.norm2.running_mean, self.features.denseblock4.denselayer2.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_280 = torch.threshold_(input_279, 0., 0.)
new_features_44 = torch._convolution(input_280, self.features.denseblock4.denselayer2.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_281 = torch.cat([input_275, new_features_44], 1)
input_282 = torch.batch_norm(input_281, self.features.denseblock4.denselayer3.norm1.weight, self.features.denseblock4.denselayer3.norm1.bias, self.features.denseblock4.denselayer3.norm1.running_mean, self.features.denseblock4.denselayer3.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_283 = torch.threshold_(input_282, 0., 0.)
input_284 = torch._convolution(input_283, self.features.denseblock4.denselayer3.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_285 = torch.batch_norm(input_284, self.features.denseblock4.denselayer3.norm2.weight, self.features.denseblock4.denselayer3.norm2.bias, self.features.denseblock4.denselayer3.norm2.running_mean, self.features.denseblock4.denselayer3.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_286 = torch.threshold_(input_285, 0., 0.)
new_features_45 = torch._convolution(input_286, self.features.denseblock4.denselayer3.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_287 = torch.cat([input_281, new_features_45], 1)
input_288 = torch.batch_norm(input_287, self.features.denseblock4.denselayer4.norm1.weight, self.features.denseblock4.denselayer4.norm1.bias, self.features.denseblock4.denselayer4.norm1.running_mean, self.features.denseblock4.denselayer4.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_289 = torch.threshold_(input_288, 0., 0.)
input_290 = torch._convolution(input_289, self.features.denseblock4.denselayer4.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_291 = torch.batch_norm(input_290, self.features.denseblock4.denselayer4.norm2.weight, self.features.denseblock4.denselayer4.norm2.bias, self.features.denseblock4.denselayer4.norm2.running_mean, self.features.denseblock4.denselayer4.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_292 = torch.threshold_(input_291, 0., 0.)
new_features_46 = torch._convolution(input_292, self.features.denseblock4.denselayer4.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_293 = torch.cat([input_287, new_features_46], 1)
input_294 = torch.batch_norm(input_293, self.features.denseblock4.denselayer5.norm1.weight, self.features.denseblock4.denselayer5.norm1.bias, self.features.denseblock4.denselayer5.norm1.running_mean, self.features.denseblock4.denselayer5.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_295 = torch.threshold_(input_294, 0., 0.)
input_296 = torch._convolution(input_295, self.features.denseblock4.denselayer5.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_297 = torch.batch_norm(input_296, self.features.denseblock4.denselayer5.norm2.weight, self.features.denseblock4.denselayer5.norm2.bias, self.features.denseblock4.denselayer5.norm2.running_mean, self.features.denseblock4.denselayer5.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_298 = torch.threshold_(input_297, 0., 0.)
new_features_47 = torch._convolution(input_298, self.features.denseblock4.denselayer5.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_299 = torch.cat([input_293, new_features_47], 1)
input_300 = torch.batch_norm(input_299, self.features.denseblock4.denselayer6.norm1.weight, self.features.denseblock4.denselayer6.norm1.bias, self.features.denseblock4.denselayer6.norm1.running_mean, self.features.denseblock4.denselayer6.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_301 = torch.threshold_(input_300, 0., 0.)
input_302 = torch._convolution(input_301, self.features.denseblock4.denselayer6.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_303 = torch.batch_norm(input_302, self.features.denseblock4.denselayer6.norm2.weight, self.features.denseblock4.denselayer6.norm2.bias, self.features.denseblock4.denselayer6.norm2.running_mean, self.features.denseblock4.denselayer6.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_304 = torch.threshold_(input_303, 0., 0.)
new_features_48 = torch._convolution(input_304, self.features.denseblock4.denselayer6.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_305 = torch.cat([input_299, new_features_48], 1)
input_306 = torch.batch_norm(input_305, self.features.denseblock4.denselayer7.norm1.weight, self.features.denseblock4.denselayer7.norm1.bias, self.features.denseblock4.denselayer7.norm1.running_mean, self.features.denseblock4.denselayer7.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_307 = torch.threshold_(input_306, 0., 0.)
input_308 = torch._convolution(input_307, self.features.denseblock4.denselayer7.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_309 = torch.batch_norm(input_308, self.features.denseblock4.denselayer7.norm2.weight, self.features.denseblock4.denselayer7.norm2.bias, self.features.denseblock4.denselayer7.norm2.running_mean, self.features.denseblock4.denselayer7.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_310 = torch.threshold_(input_309, 0., 0.)
new_features_49 = torch._convolution(input_310, self.features.denseblock4.denselayer7.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_311 = torch.cat([input_305, new_features_49], 1)
input_312 = torch.batch_norm(input_311, self.features.denseblock4.denselayer8.norm1.weight, self.features.denseblock4.denselayer8.norm1.bias, self.features.denseblock4.denselayer8.norm1.running_mean, self.features.denseblock4.denselayer8.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_313 = torch.threshold_(input_312, 0., 0.)
input_314 = torch._convolution(input_313, self.features.denseblock4.denselayer8.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_315 = torch.batch_norm(input_314, self.features.denseblock4.denselayer8.norm2.weight, self.features.denseblock4.denselayer8.norm2.bias, self.features.denseblock4.denselayer8.norm2.running_mean, self.features.denseblock4.denselayer8.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_316 = torch.threshold_(input_315, 0., 0.)
new_features_50 = torch._convolution(input_316, self.features.denseblock4.denselayer8.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_317 = torch.cat([input_311, new_features_50], 1)
input_318 = torch.batch_norm(input_317, self.features.denseblock4.denselayer9.norm1.weight, self.features.denseblock4.denselayer9.norm1.bias, self.features.denseblock4.denselayer9.norm1.running_mean, self.features.denseblock4.denselayer9.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_319 = torch.threshold_(input_318, 0., 0.)
input_320 = torch._convolution(input_319, self.features.denseblock4.denselayer9.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_321 = torch.batch_norm(input_320, self.features.denseblock4.denselayer9.norm2.weight, self.features.denseblock4.denselayer9.norm2.bias, self.features.denseblock4.denselayer9.norm2.running_mean, self.features.denseblock4.denselayer9.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_322 = torch.threshold_(input_321, 0., 0.)
new_features_51 = torch._convolution(input_322, self.features.denseblock4.denselayer9.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_323 = torch.cat([input_317, new_features_51], 1)
input_324 = torch.batch_norm(input_323, self.features.denseblock4.denselayer10.norm1.weight, self.features.denseblock4.denselayer10.norm1.bias, self.features.denseblock4.denselayer10.norm1.running_mean, self.features.denseblock4.denselayer10.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_325 = torch.threshold_(input_324, 0., 0.)
input_326 = torch._convolution(input_325, self.features.denseblock4.denselayer10.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_327 = torch.batch_norm(input_326, self.features.denseblock4.denselayer10.norm2.weight, self.features.denseblock4.denselayer10.norm2.bias, self.features.denseblock4.denselayer10.norm2.running_mean, self.features.denseblock4.denselayer10.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_328 = torch.threshold_(input_327, 0., 0.)
new_features_52 = torch._convolution(input_328, self.features.denseblock4.denselayer10.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_329 = torch.cat([input_323, new_features_52], 1)
input_330 = torch.batch_norm(input_329, self.features.denseblock4.denselayer11.norm1.weight, self.features.denseblock4.denselayer11.norm1.bias, self.features.denseblock4.denselayer11.norm1.running_mean, self.features.denseblock4.denselayer11.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_331 = torch.threshold_(input_330, 0., 0.)
input_332 = torch._convolution(input_331, self.features.denseblock4.denselayer11.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_333 = torch.batch_norm(input_332, self.features.denseblock4.denselayer11.norm2.weight, self.features.denseblock4.denselayer11.norm2.bias, self.features.denseblock4.denselayer11.norm2.running_mean, self.features.denseblock4.denselayer11.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_334 = torch.threshold_(input_333, 0., 0.)
new_features_53 = torch._convolution(input_334, self.features.denseblock4.denselayer11.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_335 = torch.cat([input_329, new_features_53], 1)
input_336 = torch.batch_norm(input_335, self.features.denseblock4.denselayer12.norm1.weight, self.features.denseblock4.denselayer12.norm1.bias, self.features.denseblock4.denselayer12.norm1.running_mean, self.features.denseblock4.denselayer12.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_337 = torch.threshold_(input_336, 0., 0.)
input_338 = torch._convolution(input_337, self.features.denseblock4.denselayer12.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_339 = torch.batch_norm(input_338, self.features.denseblock4.denselayer12.norm2.weight, self.features.denseblock4.denselayer12.norm2.bias, self.features.denseblock4.denselayer12.norm2.running_mean, self.features.denseblock4.denselayer12.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_340 = torch.threshold_(input_339, 0., 0.)
new_features_54 = torch._convolution(input_340, self.features.denseblock4.denselayer12.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_341 = torch.cat([input_335, new_features_54], 1)
input_342 = torch.batch_norm(input_341, self.features.denseblock4.denselayer13.norm1.weight, self.features.denseblock4.denselayer13.norm1.bias, self.features.denseblock4.denselayer13.norm1.running_mean, self.features.denseblock4.denselayer13.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_343 = torch.threshold_(input_342, 0., 0.)
input_344 = torch._convolution(input_343, self.features.denseblock4.denselayer13.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_345 = torch.batch_norm(input_344, self.features.denseblock4.denselayer13.norm2.weight, self.features.denseblock4.denselayer13.norm2.bias, self.features.denseblock4.denselayer13.norm2.running_mean, self.features.denseblock4.denselayer13.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_346 = torch.threshold_(input_345, 0., 0.)
new_features_55 = torch._convolution(input_346, self.features.denseblock4.denselayer13.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_347 = torch.cat([input_341, new_features_55], 1)
input_348 = torch.batch_norm(input_347, self.features.denseblock4.denselayer14.norm1.weight, self.features.denseblock4.denselayer14.norm1.bias, self.features.denseblock4.denselayer14.norm1.running_mean, self.features.denseblock4.denselayer14.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_349 = torch.threshold_(input_348, 0., 0.)
input_350 = torch._convolution(input_349, self.features.denseblock4.denselayer14.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_351 = torch.batch_norm(input_350, self.features.denseblock4.denselayer14.norm2.weight, self.features.denseblock4.denselayer14.norm2.bias, self.features.denseblock4.denselayer14.norm2.running_mean, self.features.denseblock4.denselayer14.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_352 = torch.threshold_(input_351, 0., 0.)
new_features_56 = torch._convolution(input_352, self.features.denseblock4.denselayer14.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_353 = torch.cat([input_347, new_features_56], 1)
input_354 = torch.batch_norm(input_353, self.features.denseblock4.denselayer15.norm1.weight, self.features.denseblock4.denselayer15.norm1.bias, self.features.denseblock4.denselayer15.norm1.running_mean, self.features.denseblock4.denselayer15.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_355 = torch.threshold_(input_354, 0., 0.)
input_356 = torch._convolution(input_355, self.features.denseblock4.denselayer15.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_357 = torch.batch_norm(input_356, self.features.denseblock4.denselayer15.norm2.weight, self.features.denseblock4.denselayer15.norm2.bias, self.features.denseblock4.denselayer15.norm2.running_mean, self.features.denseblock4.denselayer15.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_358 = torch.threshold_(input_357, 0., 0.)
new_features_57 = torch._convolution(input_358, self.features.denseblock4.denselayer15.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_359 = torch.cat([input_353, new_features_57], 1)
input_360 = torch.batch_norm(input_359, self.features.denseblock4.denselayer16.norm1.weight, self.features.denseblock4.denselayer16.norm1.bias, self.features.denseblock4.denselayer16.norm1.running_mean, self.features.denseblock4.denselayer16.norm1.running_var, False, 0., 1.0000000000000001e-05, True)
input_361 = torch.threshold_(input_360, 0., 0.)
input_362 = torch._convolution(input_361, self.features.denseblock4.denselayer16.conv1.weight, None, [1, 1], [0, 0], [1, 1], False, [0, 0], 1, False, False, True)
input_363 = torch.batch_norm(input_362, self.features.denseblock4.denselayer16.norm2.weight, self.features.denseblock4.denselayer16.norm2.bias, self.features.denseblock4.denselayer16.norm2.running_mean, self.features.denseblock4.denselayer16.norm2.running_var, False, 0., 1.0000000000000001e-05, True)
input_364 = torch.threshold_(input_363, 0., 0.)
new_features = torch._convolution(input_364, self.features.denseblock4.denselayer16.conv2.weight, None, [1, 1], [1, 1], [1, 1], False, [0, 0], 1, False, False, True)
input_365 = torch.cat([input_359, new_features], 1)
input_366 = torch.batch_norm(input_365, self.features.norm5.weight, self.features.norm5.bias, self.features.norm5.running_mean, self.features.norm5.running_var, False, 0., 1.0000000000000001e-05, True)
out = torch.relu_(input_366)
_1 = torch.avg_pool2d(out, [7, 7], [1, 1], [0, 0], False, True)
_2 = ops.prim.NumToTensor(torch.size(out, 0))
input = torch.view(_1, [int(_2), -1])
_3 = torch.addmm(self.classifier.bias, input, torch.t(self.classifier.weight), beta=1, alpha=1)
return _3
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