
import torch
import torch.nn
import torch.functional
import torch.nn.functional


class YOLO_V4(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.tensor_1 = torch.tensor(8.0, dtype=torch.float32)
        self.tensor_2 = torch.tensor([[0.5, 0.625], [0.875, 1.125], [1.125, 1.625]], dtype=torch.float32)
        self.tensor_3 = torch.tensor(16.0, dtype=torch.float32)
        self.tensor_4 = torch.tensor([[0.75, 0.9375], [0.875, 1.125], [1.0625, 1.3125]], dtype=torch.float32)
        self.tensor_5 = torch.tensor(32.0, dtype=torch.float32)
        self.tensor_6 = torch.tensor([[0.625, 0.78125], [0.8125, 1.03125], [1.09375, 1.46875]], dtype=torch.float32)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_0 = torch.nn.modules.conv.Conv2d(3, 32, 3, 2, 1, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_3 = torch.nn.modules.conv.Conv2d(32, 16, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_0 = torch.nn.modules.conv.Conv2d(16, 96, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(96)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_3 = torch.nn.modules.conv.Conv2d(96, 96, 3, 2, 1, groups=96, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(96)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_6 = torch.nn.modules.conv.Conv2d(96, 24, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(24)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_0 = torch.nn.modules.conv.Conv2d(24, 144, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(144)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_3 = torch.nn.modules.conv.Conv2d(144, 144, 3, 1, 1, groups=144, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(144)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_6 = torch.nn.modules.conv.Conv2d(144, 24, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(24)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_0 = torch.nn.modules.conv.Conv2d(24, 144, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(144)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_3 = torch.nn.modules.conv.Conv2d(144, 144, 3, 2, 1, groups=144, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(144)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_6 = torch.nn.modules.conv.Conv2d(144, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_0 = torch.nn.modules.conv.Conv2d(32, 192, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_3 = torch.nn.modules.conv.Conv2d(192, 192, 3, 1, 1, groups=192, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(192)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_6 = torch.nn.modules.conv.Conv2d(192, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_0 = torch.nn.modules.conv.Conv2d(32, 192, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_3 = torch.nn.modules.conv.Conv2d(192, 192, 3, 1, 1, groups=192, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(192)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_6 = torch.nn.modules.conv.Conv2d(192, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_0 = torch.nn.modules.conv.Conv2d(32, 192, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(192)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_3 = torch.nn.modules.conv.Conv2d(192, 192, 3, 2, 1, groups=192, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(192)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_6 = torch.nn.modules.conv.Conv2d(192, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_0 = torch.nn.modules.conv.Conv2d(64, 384, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_6 = torch.nn.modules.conv.Conv2d(384, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_0 = torch.nn.modules.conv.Conv2d(64, 384, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_6 = torch.nn.modules.conv.Conv2d(384, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_0 = torch.nn.modules.conv.Conv2d(64, 384, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_6 = torch.nn.modules.conv.Conv2d(384, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_0 = torch.nn.modules.conv.Conv2d(64, 384, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_6 = torch.nn.modules.conv.Conv2d(384, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_backbone_features_mnv3part_0_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_0 = torch.nn.modules.conv.Conv2d(64, 384, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_6 = torch.nn.modules.conv.Conv2d(384, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_backbone_features_mnv3part_1_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_0 = torch.nn.modules.conv.Conv2d(64, 384, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 1, 1, groups=384, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_6 = torch.nn.modules.conv.Conv2d(384, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_backbone_features_mnv3part_2_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_0 = torch.nn.modules.conv.Conv2d(64, 384, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_3 = torch.nn.modules.conv.Conv2d(384, 384, 3, 2, 1, groups=384, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(384)
        self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_6 = torch.nn.modules.conv.Conv2d(384, 96, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(96)
        self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_0 = torch.nn.modules.conv.Conv2d(96, 576, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(576)
        self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_3 = torch.nn.modules.conv.Conv2d(576, 576, 3, 1, 1, groups=576, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(576)
        self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_6 = torch.nn.modules.conv.Conv2d(576, 96, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(96)
        self._Build_Model__yolov4_backbone_features_mnv3part_4_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_0 = torch.nn.modules.conv.Conv2d(96, 576, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(576)
        self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_3 = torch.nn.modules.conv.Conv2d(576, 576, 3, 1, 1, groups=576, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(576)
        self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_5 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_6 = torch.nn.modules.conv.Conv2d(576, 96, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_7 = torch.nn.modules.batchnorm.BatchNorm2d(96)
        self._Build_Model__yolov4_backbone_features_mnv3part_5_qadd_0_activation_post_process = torch.nn.modules.linear.Identity()
        self._Build_Model__yolov4_backbone_conv_0 = torch.nn.modules.conv.Conv2d(96, 160, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_backbone_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_backbone_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_spp_head_conv_0_conv_0 = torch.nn.modules.conv.Conv2d(160, 160, 3, 1, 1, groups=160, bias=False)
        self._Build_Model__yolov4_spp_head_conv_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_spp_head_conv_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_spp_head_conv_0_conv_3 = torch.nn.modules.conv.Conv2d(160, 80, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_spp_head_conv_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_spp_head_conv_1_conv_0 = torch.nn.modules.conv.Conv2d(80, 80, 3, 1, 1, groups=80, bias=False)
        self._Build_Model__yolov4_spp_head_conv_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_spp_head_conv_1_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_spp_head_conv_1_conv_3 = torch.nn.modules.conv.Conv2d(80, 160, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_spp_head_conv_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_spp_head_conv_2_conv_0 = torch.nn.modules.conv.Conv2d(160, 160, 3, 1, 1, groups=160, bias=False)
        self._Build_Model__yolov4_spp_head_conv_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_spp_head_conv_2_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_spp_head_conv_2_conv_3 = torch.nn.modules.conv.Conv2d(160, 80, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_spp_head_conv_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_spp_maxpools_0 = torch.nn.modules.pooling.MaxPool2d(5, 1, 2)
        self._Build_Model__yolov4_spp_maxpools_1 = torch.nn.modules.pooling.MaxPool2d(9, 1, 4)
        self._Build_Model__yolov4_spp_maxpools_2 = torch.nn.modules.pooling.MaxPool2d(13, 1, 6)
        self._Build_Model__yolov4_panet_feature_transform3_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_panet_feature_transform3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_feature_transform3_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_feature_transform3_conv_3 = torch.nn.modules.conv.Conv2d(32, 16, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_feature_transform3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self._Build_Model__yolov4_panet_feature_transform4_conv_0 = torch.nn.modules.conv.Conv2d(64, 64, 3, 1, 1, groups=64, bias=False)
        self._Build_Model__yolov4_panet_feature_transform4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_feature_transform4_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_feature_transform4_conv_3 = torch.nn.modules.conv.Conv2d(64, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_feature_transform4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv5_0_conv_0 = torch.nn.modules.conv.Conv2d(320, 320, 3, 1, 1, groups=320, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv5_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(320)
        self._Build_Model__yolov4_panet_downstream_conv5_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv5_0_conv_3 = torch.nn.modules.conv.Conv2d(320, 80, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv5_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_panet_downstream_conv5_1_conv_0 = torch.nn.modules.conv.Conv2d(80, 80, 3, 1, 1, groups=80, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv5_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_panet_downstream_conv5_1_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv5_1_conv_3 = torch.nn.modules.conv.Conv2d(80, 160, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv5_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_panet_downstream_conv5_2_conv_0 = torch.nn.modules.conv.Conv2d(160, 160, 3, 1, 1, groups=160, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv5_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_panet_downstream_conv5_2_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv5_2_conv_3 = torch.nn.modules.conv.Conv2d(160, 80, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv5_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_panet_resample5_4_upsample_0_conv_0 = torch.nn.modules.conv.Conv2d(80, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_resample5_4_upsample_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_resample5_4_upsample_0_conv_2 = torch.nn.modules.activation.LeakyReLU()
        self._Build_Model__yolov4_panet_resample5_4_upsample_1 = torch.nn.modules.upsampling.Upsample(scale_factor=2)
        self._Build_Model__yolov4_panet_downstream_conv4_0_conv_0 = torch.nn.modules.conv.Conv2d(64, 64, 3, 1, 1, groups=64, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_downstream_conv4_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv4_0_conv_3 = torch.nn.modules.conv.Conv2d(64, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv4_1_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv4_1_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv4_1_conv_3 = torch.nn.modules.conv.Conv2d(32, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_downstream_conv4_2_conv_0 = torch.nn.modules.conv.Conv2d(64, 64, 3, 1, 1, groups=64, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_downstream_conv4_2_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv4_2_conv_3 = torch.nn.modules.conv.Conv2d(64, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv4_3_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv4_3_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv4_3_conv_3 = torch.nn.modules.conv.Conv2d(32, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_downstream_conv4_4_conv_0 = torch.nn.modules.conv.Conv2d(64, 64, 3, 1, 1, groups=64, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_downstream_conv4_4_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv4_4_conv_3 = torch.nn.modules.conv.Conv2d(64, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv4_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_resample4_3_upsample_0_conv_0 = torch.nn.modules.conv.Conv2d(32, 16, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_resample4_3_upsample_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self._Build_Model__yolov4_panet_resample4_3_upsample_0_conv_2 = torch.nn.modules.activation.LeakyReLU()
        self._Build_Model__yolov4_panet_resample4_3_upsample_1 = torch.nn.modules.upsampling.Upsample(scale_factor=2)
        self._Build_Model__yolov4_panet_downstream_conv3_0_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv3_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv3_0_conv_3 = torch.nn.modules.conv.Conv2d(32, 16, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self._Build_Model__yolov4_panet_downstream_conv3_1_conv_0 = torch.nn.modules.conv.Conv2d(16, 16, 3, 1, 1, groups=16, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self._Build_Model__yolov4_panet_downstream_conv3_1_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv3_1_conv_3 = torch.nn.modules.conv.Conv2d(16, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv3_2_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv3_2_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv3_2_conv_3 = torch.nn.modules.conv.Conv2d(32, 16, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self._Build_Model__yolov4_panet_downstream_conv3_3_conv_0 = torch.nn.modules.conv.Conv2d(16, 16, 3, 1, 1, groups=16, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self._Build_Model__yolov4_panet_downstream_conv3_3_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv3_3_conv_3 = torch.nn.modules.conv.Conv2d(16, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv3_4_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_downstream_conv3_4_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_downstream_conv3_4_conv_3 = torch.nn.modules.conv.Conv2d(32, 16, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_downstream_conv3_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self._Build_Model__yolov4_panet_resample3_4_downsample_conv_0 = torch.nn.modules.conv.Conv2d(16, 32, 3, 2, 1, bias=False)
        self._Build_Model__yolov4_panet_resample3_4_downsample_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_resample3_4_downsample_conv_2 = torch.nn.modules.activation.LeakyReLU()
        self._Build_Model__yolov4_panet_upstream_conv4_0_conv_0 = torch.nn.modules.conv.Conv2d(64, 64, 3, 1, 1, groups=64, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_upstream_conv4_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv4_0_conv_3 = torch.nn.modules.conv.Conv2d(64, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_upstream_conv4_1_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_upstream_conv4_1_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv4_1_conv_3 = torch.nn.modules.conv.Conv2d(32, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_upstream_conv4_2_conv_0 = torch.nn.modules.conv.Conv2d(64, 64, 3, 1, 1, groups=64, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_upstream_conv4_2_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv4_2_conv_3 = torch.nn.modules.conv.Conv2d(64, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_upstream_conv4_3_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_upstream_conv4_3_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv4_3_conv_3 = torch.nn.modules.conv.Conv2d(32, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_upstream_conv4_4_conv_0 = torch.nn.modules.conv.Conv2d(64, 64, 3, 1, 1, groups=64, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_panet_upstream_conv4_4_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv4_4_conv_3 = torch.nn.modules.conv.Conv2d(64, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv4_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_panet_resample4_5_downsample_conv_0 = torch.nn.modules.conv.Conv2d(32, 80, 3, 2, 1, bias=False)
        self._Build_Model__yolov4_panet_resample4_5_downsample_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_panet_resample4_5_downsample_conv_2 = torch.nn.modules.activation.LeakyReLU()
        self._Build_Model__yolov4_panet_upstream_conv5_0_conv_0 = torch.nn.modules.conv.Conv2d(160, 160, 3, 1, 1, groups=160, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_panet_upstream_conv5_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv5_0_conv_3 = torch.nn.modules.conv.Conv2d(160, 80, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_panet_upstream_conv5_1_conv_0 = torch.nn.modules.conv.Conv2d(80, 80, 3, 1, 1, groups=80, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_1_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_panet_upstream_conv5_1_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv5_1_conv_3 = torch.nn.modules.conv.Conv2d(80, 160, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_1_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_panet_upstream_conv5_2_conv_0 = torch.nn.modules.conv.Conv2d(160, 160, 3, 1, 1, groups=160, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_2_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_panet_upstream_conv5_2_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv5_2_conv_3 = torch.nn.modules.conv.Conv2d(160, 80, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_2_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_panet_upstream_conv5_3_conv_0 = torch.nn.modules.conv.Conv2d(80, 80, 3, 1, 1, groups=80, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_3_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_panet_upstream_conv5_3_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv5_3_conv_3 = torch.nn.modules.conv.Conv2d(80, 160, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_3_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_panet_upstream_conv5_4_conv_0 = torch.nn.modules.conv.Conv2d(160, 160, 3, 1, 1, groups=160, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_4_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_panet_upstream_conv5_4_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_panet_upstream_conv5_4_conv_3 = torch.nn.modules.conv.Conv2d(160, 80, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_panet_upstream_conv5_4_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_0 = torch.nn.modules.conv.Conv2d(16, 16, 3, 1, 1, groups=16, bias=False)
        self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(16)
        self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_3 = torch.nn.modules.conv.Conv2d(16, 32, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_predict_net_predict_conv_0_1 = torch.nn.modules.conv.Conv2d(32, 18, 1)
        self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_0 = torch.nn.modules.conv.Conv2d(32, 32, 3, 1, 1, groups=32, bias=False)
        self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(32)
        self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_3 = torch.nn.modules.conv.Conv2d(32, 64, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(64)
        self._Build_Model__yolov4_predict_net_predict_conv_1_1 = torch.nn.modules.conv.Conv2d(64, 18, 1)
        self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_0 = torch.nn.modules.conv.Conv2d(80, 80, 3, 1, 1, groups=80, bias=False)
        self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_1 = torch.nn.modules.batchnorm.BatchNorm2d(80)
        self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_2 = torch.nn.modules.activation.ReLU6(inplace=True)
        self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_3 = torch.nn.modules.conv.Conv2d(80, 160, 1, 1, 0, bias=False)
        self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_4 = torch.nn.modules.batchnorm.BatchNorm2d(160)
        self._Build_Model__yolov4_predict_net_predict_conv_2_1 = torch.nn.modules.conv.Conv2d(160, 18, 1)

    def forward(self, input_1):
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_0(input_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_0_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_1_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_5 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_5(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_6 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_6(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_7 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_7(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_6)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_7)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_5 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_5(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_6 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_6(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_7 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_7(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_6)
        add_1 = torch.add(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_2_conv_7, _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_conv_7)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_qadd_0_activation_post_process(add_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_3_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_5 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_5(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_6 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_6(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_7 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_7(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_6)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_7)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_5 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_5(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_6 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_6(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_7 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_7(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_6)
        add_2 = torch.add(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_4_conv_7, _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_conv_7)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_qadd_0_activation_post_process(add_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_5 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_5(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_6 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_6(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_7 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_7(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_6)
        add_3 = torch.add(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_5_qadd_0_activation_post_process, _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_conv_7)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_qadd_0_activation_post_process(add_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_5 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_5(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_6 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_6(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_7 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_7(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_6)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_7)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_5 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_5(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_6 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_6(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_7 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_7(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_6)
        add_4 = torch.add(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_7_conv_7, _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_conv_7)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_qadd_0_activation_post_process(add_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_5 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_5(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_6 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_6(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_7 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_7(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_6)
        add_5 = torch.add(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_8_qadd_0_activation_post_process, _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_conv_7)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_qadd_0_activation_post_process(add_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_0 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_1 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_1(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_0)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_2 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_2(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_1)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_3 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_3(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_2)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_4 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_4(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_3)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_5 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_5(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_4)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_6 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_6(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_5)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_7 = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_7(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_6)
        add_6 = torch.add(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_9_qadd_0_activation_post_process, _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_conv_7)
        _Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_qadd_0_activation_post_process(add_6)
        _Build_Model__yolov4_backbone_features_mnv3part_0_conv_0 = self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone_features_mnv3part_0_conv_1 = self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_1(_Build_Model__yolov4_backbone_features_mnv3part_0_conv_0)
        _Build_Model__yolov4_backbone_features_mnv3part_0_conv_2 = self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_2(_Build_Model__yolov4_backbone_features_mnv3part_0_conv_1)
        _Build_Model__yolov4_backbone_features_mnv3part_0_conv_3 = self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_3(_Build_Model__yolov4_backbone_features_mnv3part_0_conv_2)
        _Build_Model__yolov4_backbone_features_mnv3part_0_conv_4 = self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_4(_Build_Model__yolov4_backbone_features_mnv3part_0_conv_3)
        _Build_Model__yolov4_backbone_features_mnv3part_0_conv_5 = self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_5(_Build_Model__yolov4_backbone_features_mnv3part_0_conv_4)
        _Build_Model__yolov4_backbone_features_mnv3part_0_conv_6 = self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_6(_Build_Model__yolov4_backbone_features_mnv3part_0_conv_5)
        _Build_Model__yolov4_backbone_features_mnv3part_0_conv_7 = self._Build_Model__yolov4_backbone_features_mnv3part_0_conv_7(_Build_Model__yolov4_backbone_features_mnv3part_0_conv_6)
        add_7 = torch.add(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_qadd_0_activation_post_process, _Build_Model__yolov4_backbone_features_mnv3part_0_conv_7)
        _Build_Model__yolov4_backbone_features_mnv3part_0_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone_features_mnv3part_0_qadd_0_activation_post_process(add_7)
        _Build_Model__yolov4_backbone_features_mnv3part_1_conv_0 = self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_0(_Build_Model__yolov4_backbone_features_mnv3part_0_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone_features_mnv3part_1_conv_1 = self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_1(_Build_Model__yolov4_backbone_features_mnv3part_1_conv_0)
        _Build_Model__yolov4_backbone_features_mnv3part_1_conv_2 = self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_2(_Build_Model__yolov4_backbone_features_mnv3part_1_conv_1)
        _Build_Model__yolov4_backbone_features_mnv3part_1_conv_3 = self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_3(_Build_Model__yolov4_backbone_features_mnv3part_1_conv_2)
        _Build_Model__yolov4_backbone_features_mnv3part_1_conv_4 = self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_4(_Build_Model__yolov4_backbone_features_mnv3part_1_conv_3)
        _Build_Model__yolov4_backbone_features_mnv3part_1_conv_5 = self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_5(_Build_Model__yolov4_backbone_features_mnv3part_1_conv_4)
        _Build_Model__yolov4_backbone_features_mnv3part_1_conv_6 = self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_6(_Build_Model__yolov4_backbone_features_mnv3part_1_conv_5)
        _Build_Model__yolov4_backbone_features_mnv3part_1_conv_7 = self._Build_Model__yolov4_backbone_features_mnv3part_1_conv_7(_Build_Model__yolov4_backbone_features_mnv3part_1_conv_6)
        add_8 = torch.add(_Build_Model__yolov4_backbone_features_mnv3part_0_qadd_0_activation_post_process, _Build_Model__yolov4_backbone_features_mnv3part_1_conv_7)
        _Build_Model__yolov4_backbone_features_mnv3part_1_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone_features_mnv3part_1_qadd_0_activation_post_process(add_8)
        _Build_Model__yolov4_backbone_features_mnv3part_2_conv_0 = self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_0(_Build_Model__yolov4_backbone_features_mnv3part_1_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone_features_mnv3part_2_conv_1 = self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_1(_Build_Model__yolov4_backbone_features_mnv3part_2_conv_0)
        _Build_Model__yolov4_backbone_features_mnv3part_2_conv_2 = self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_2(_Build_Model__yolov4_backbone_features_mnv3part_2_conv_1)
        _Build_Model__yolov4_backbone_features_mnv3part_2_conv_3 = self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_3(_Build_Model__yolov4_backbone_features_mnv3part_2_conv_2)
        _Build_Model__yolov4_backbone_features_mnv3part_2_conv_4 = self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_4(_Build_Model__yolov4_backbone_features_mnv3part_2_conv_3)
        _Build_Model__yolov4_backbone_features_mnv3part_2_conv_5 = self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_5(_Build_Model__yolov4_backbone_features_mnv3part_2_conv_4)
        _Build_Model__yolov4_backbone_features_mnv3part_2_conv_6 = self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_6(_Build_Model__yolov4_backbone_features_mnv3part_2_conv_5)
        _Build_Model__yolov4_backbone_features_mnv3part_2_conv_7 = self._Build_Model__yolov4_backbone_features_mnv3part_2_conv_7(_Build_Model__yolov4_backbone_features_mnv3part_2_conv_6)
        add_9 = torch.add(_Build_Model__yolov4_backbone_features_mnv3part_1_qadd_0_activation_post_process, _Build_Model__yolov4_backbone_features_mnv3part_2_conv_7)
        _Build_Model__yolov4_backbone_features_mnv3part_2_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone_features_mnv3part_2_qadd_0_activation_post_process(add_9)
        _Build_Model__yolov4_backbone_features_mnv3part_3_conv_0 = self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_0(_Build_Model__yolov4_backbone_features_mnv3part_2_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone_features_mnv3part_3_conv_1 = self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_1(_Build_Model__yolov4_backbone_features_mnv3part_3_conv_0)
        _Build_Model__yolov4_backbone_features_mnv3part_3_conv_2 = self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_2(_Build_Model__yolov4_backbone_features_mnv3part_3_conv_1)
        _Build_Model__yolov4_backbone_features_mnv3part_3_conv_3 = self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_3(_Build_Model__yolov4_backbone_features_mnv3part_3_conv_2)
        _Build_Model__yolov4_backbone_features_mnv3part_3_conv_4 = self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_4(_Build_Model__yolov4_backbone_features_mnv3part_3_conv_3)
        _Build_Model__yolov4_backbone_features_mnv3part_3_conv_5 = self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_5(_Build_Model__yolov4_backbone_features_mnv3part_3_conv_4)
        _Build_Model__yolov4_backbone_features_mnv3part_3_conv_6 = self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_6(_Build_Model__yolov4_backbone_features_mnv3part_3_conv_5)
        _Build_Model__yolov4_backbone_features_mnv3part_3_conv_7 = self._Build_Model__yolov4_backbone_features_mnv3part_3_conv_7(_Build_Model__yolov4_backbone_features_mnv3part_3_conv_6)
        _Build_Model__yolov4_backbone_features_mnv3part_4_conv_0 = self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_0(_Build_Model__yolov4_backbone_features_mnv3part_3_conv_7)
        _Build_Model__yolov4_backbone_features_mnv3part_4_conv_1 = self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_1(_Build_Model__yolov4_backbone_features_mnv3part_4_conv_0)
        _Build_Model__yolov4_backbone_features_mnv3part_4_conv_2 = self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_2(_Build_Model__yolov4_backbone_features_mnv3part_4_conv_1)
        _Build_Model__yolov4_backbone_features_mnv3part_4_conv_3 = self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_3(_Build_Model__yolov4_backbone_features_mnv3part_4_conv_2)
        _Build_Model__yolov4_backbone_features_mnv3part_4_conv_4 = self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_4(_Build_Model__yolov4_backbone_features_mnv3part_4_conv_3)
        _Build_Model__yolov4_backbone_features_mnv3part_4_conv_5 = self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_5(_Build_Model__yolov4_backbone_features_mnv3part_4_conv_4)
        _Build_Model__yolov4_backbone_features_mnv3part_4_conv_6 = self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_6(_Build_Model__yolov4_backbone_features_mnv3part_4_conv_5)
        _Build_Model__yolov4_backbone_features_mnv3part_4_conv_7 = self._Build_Model__yolov4_backbone_features_mnv3part_4_conv_7(_Build_Model__yolov4_backbone_features_mnv3part_4_conv_6)
        add_10 = torch.add(_Build_Model__yolov4_backbone_features_mnv3part_3_conv_7, _Build_Model__yolov4_backbone_features_mnv3part_4_conv_7)
        _Build_Model__yolov4_backbone_features_mnv3part_4_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone_features_mnv3part_4_qadd_0_activation_post_process(add_10)
        _Build_Model__yolov4_backbone_features_mnv3part_5_conv_0 = self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_0(_Build_Model__yolov4_backbone_features_mnv3part_4_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone_features_mnv3part_5_conv_1 = self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_1(_Build_Model__yolov4_backbone_features_mnv3part_5_conv_0)
        _Build_Model__yolov4_backbone_features_mnv3part_5_conv_2 = self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_2(_Build_Model__yolov4_backbone_features_mnv3part_5_conv_1)
        _Build_Model__yolov4_backbone_features_mnv3part_5_conv_3 = self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_3(_Build_Model__yolov4_backbone_features_mnv3part_5_conv_2)
        _Build_Model__yolov4_backbone_features_mnv3part_5_conv_4 = self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_4(_Build_Model__yolov4_backbone_features_mnv3part_5_conv_3)
        _Build_Model__yolov4_backbone_features_mnv3part_5_conv_5 = self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_5(_Build_Model__yolov4_backbone_features_mnv3part_5_conv_4)
        _Build_Model__yolov4_backbone_features_mnv3part_5_conv_6 = self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_6(_Build_Model__yolov4_backbone_features_mnv3part_5_conv_5)
        _Build_Model__yolov4_backbone_features_mnv3part_5_conv_7 = self._Build_Model__yolov4_backbone_features_mnv3part_5_conv_7(_Build_Model__yolov4_backbone_features_mnv3part_5_conv_6)
        add_11 = torch.add(_Build_Model__yolov4_backbone_features_mnv3part_4_qadd_0_activation_post_process, _Build_Model__yolov4_backbone_features_mnv3part_5_conv_7)
        _Build_Model__yolov4_backbone_features_mnv3part_5_qadd_0_activation_post_process = self._Build_Model__yolov4_backbone_features_mnv3part_5_qadd_0_activation_post_process(add_11)
        _Build_Model__yolov4_backbone_conv_0 = self._Build_Model__yolov4_backbone_conv_0(_Build_Model__yolov4_backbone_features_mnv3part_5_qadd_0_activation_post_process)
        _Build_Model__yolov4_backbone_conv_1 = self._Build_Model__yolov4_backbone_conv_1(_Build_Model__yolov4_backbone_conv_0)
        _Build_Model__yolov4_backbone_conv_2 = self._Build_Model__yolov4_backbone_conv_2(_Build_Model__yolov4_backbone_conv_1)
        _Build_Model__yolov4_spp_head_conv_0_conv_0 = self._Build_Model__yolov4_spp_head_conv_0_conv_0(_Build_Model__yolov4_backbone_conv_2)
        _Build_Model__yolov4_spp_head_conv_0_conv_1 = self._Build_Model__yolov4_spp_head_conv_0_conv_1(_Build_Model__yolov4_spp_head_conv_0_conv_0)
        _Build_Model__yolov4_spp_head_conv_0_conv_2 = self._Build_Model__yolov4_spp_head_conv_0_conv_2(_Build_Model__yolov4_spp_head_conv_0_conv_1)
        _Build_Model__yolov4_spp_head_conv_0_conv_3 = self._Build_Model__yolov4_spp_head_conv_0_conv_3(_Build_Model__yolov4_spp_head_conv_0_conv_2)
        _Build_Model__yolov4_spp_head_conv_0_conv_4 = self._Build_Model__yolov4_spp_head_conv_0_conv_4(_Build_Model__yolov4_spp_head_conv_0_conv_3)
        _Build_Model__yolov4_spp_head_conv_1_conv_0 = self._Build_Model__yolov4_spp_head_conv_1_conv_0(_Build_Model__yolov4_spp_head_conv_0_conv_4)
        _Build_Model__yolov4_spp_head_conv_1_conv_1 = self._Build_Model__yolov4_spp_head_conv_1_conv_1(_Build_Model__yolov4_spp_head_conv_1_conv_0)
        _Build_Model__yolov4_spp_head_conv_1_conv_2 = self._Build_Model__yolov4_spp_head_conv_1_conv_2(_Build_Model__yolov4_spp_head_conv_1_conv_1)
        _Build_Model__yolov4_spp_head_conv_1_conv_3 = self._Build_Model__yolov4_spp_head_conv_1_conv_3(_Build_Model__yolov4_spp_head_conv_1_conv_2)
        _Build_Model__yolov4_spp_head_conv_1_conv_4 = self._Build_Model__yolov4_spp_head_conv_1_conv_4(_Build_Model__yolov4_spp_head_conv_1_conv_3)
        _Build_Model__yolov4_spp_head_conv_2_conv_0 = self._Build_Model__yolov4_spp_head_conv_2_conv_0(_Build_Model__yolov4_spp_head_conv_1_conv_4)
        _Build_Model__yolov4_spp_head_conv_2_conv_1 = self._Build_Model__yolov4_spp_head_conv_2_conv_1(_Build_Model__yolov4_spp_head_conv_2_conv_0)
        _Build_Model__yolov4_spp_head_conv_2_conv_2 = self._Build_Model__yolov4_spp_head_conv_2_conv_2(_Build_Model__yolov4_spp_head_conv_2_conv_1)
        _Build_Model__yolov4_spp_head_conv_2_conv_3 = self._Build_Model__yolov4_spp_head_conv_2_conv_3(_Build_Model__yolov4_spp_head_conv_2_conv_2)
        _Build_Model__yolov4_spp_head_conv_2_conv_4 = self._Build_Model__yolov4_spp_head_conv_2_conv_4(_Build_Model__yolov4_spp_head_conv_2_conv_3)
        _Build_Model__yolov4_spp_maxpools_0 = self._Build_Model__yolov4_spp_maxpools_0(_Build_Model__yolov4_spp_head_conv_2_conv_4)
        _Build_Model__yolov4_spp_maxpools_1 = self._Build_Model__yolov4_spp_maxpools_1(_Build_Model__yolov4_spp_head_conv_2_conv_4)
        _Build_Model__yolov4_spp_maxpools_2 = self._Build_Model__yolov4_spp_maxpools_2(_Build_Model__yolov4_spp_head_conv_2_conv_4)
        cat_1 = torch.cat([_Build_Model__yolov4_spp_head_conv_2_conv_4, _Build_Model__yolov4_spp_maxpools_0, _Build_Model__yolov4_spp_maxpools_1, _Build_Model__yolov4_spp_maxpools_2], dim=1)
        _Build_Model__yolov4_panet_feature_transform3_conv_0 = self._Build_Model__yolov4_panet_feature_transform3_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_6_qadd_0_activation_post_process)
        _Build_Model__yolov4_panet_feature_transform3_conv_1 = self._Build_Model__yolov4_panet_feature_transform3_conv_1(_Build_Model__yolov4_panet_feature_transform3_conv_0)
        _Build_Model__yolov4_panet_feature_transform3_conv_2 = self._Build_Model__yolov4_panet_feature_transform3_conv_2(_Build_Model__yolov4_panet_feature_transform3_conv_1)
        _Build_Model__yolov4_panet_feature_transform3_conv_3 = self._Build_Model__yolov4_panet_feature_transform3_conv_3(_Build_Model__yolov4_panet_feature_transform3_conv_2)
        _Build_Model__yolov4_panet_feature_transform3_conv_4 = self._Build_Model__yolov4_panet_feature_transform3_conv_4(_Build_Model__yolov4_panet_feature_transform3_conv_3)
        _Build_Model__yolov4_panet_feature_transform4_conv_0 = self._Build_Model__yolov4_panet_feature_transform4_conv_0(_Build_Model__yolov4_backbone__MobilenetV2Lite__submodule_features_10_qadd_0_activation_post_process)
        _Build_Model__yolov4_panet_feature_transform4_conv_1 = self._Build_Model__yolov4_panet_feature_transform4_conv_1(_Build_Model__yolov4_panet_feature_transform4_conv_0)
        _Build_Model__yolov4_panet_feature_transform4_conv_2 = self._Build_Model__yolov4_panet_feature_transform4_conv_2(_Build_Model__yolov4_panet_feature_transform4_conv_1)
        _Build_Model__yolov4_panet_feature_transform4_conv_3 = self._Build_Model__yolov4_panet_feature_transform4_conv_3(_Build_Model__yolov4_panet_feature_transform4_conv_2)
        _Build_Model__yolov4_panet_feature_transform4_conv_4 = self._Build_Model__yolov4_panet_feature_transform4_conv_4(_Build_Model__yolov4_panet_feature_transform4_conv_3)
        _Build_Model__yolov4_panet_downstream_conv5_0_conv_0 = self._Build_Model__yolov4_panet_downstream_conv5_0_conv_0(cat_1)
        _Build_Model__yolov4_panet_downstream_conv5_0_conv_1 = self._Build_Model__yolov4_panet_downstream_conv5_0_conv_1(_Build_Model__yolov4_panet_downstream_conv5_0_conv_0)
        _Build_Model__yolov4_panet_downstream_conv5_0_conv_2 = self._Build_Model__yolov4_panet_downstream_conv5_0_conv_2(_Build_Model__yolov4_panet_downstream_conv5_0_conv_1)
        _Build_Model__yolov4_panet_downstream_conv5_0_conv_3 = self._Build_Model__yolov4_panet_downstream_conv5_0_conv_3(_Build_Model__yolov4_panet_downstream_conv5_0_conv_2)
        _Build_Model__yolov4_panet_downstream_conv5_0_conv_4 = self._Build_Model__yolov4_panet_downstream_conv5_0_conv_4(_Build_Model__yolov4_panet_downstream_conv5_0_conv_3)
        _Build_Model__yolov4_panet_downstream_conv5_1_conv_0 = self._Build_Model__yolov4_panet_downstream_conv5_1_conv_0(_Build_Model__yolov4_panet_downstream_conv5_0_conv_4)
        _Build_Model__yolov4_panet_downstream_conv5_1_conv_1 = self._Build_Model__yolov4_panet_downstream_conv5_1_conv_1(_Build_Model__yolov4_panet_downstream_conv5_1_conv_0)
        _Build_Model__yolov4_panet_downstream_conv5_1_conv_2 = self._Build_Model__yolov4_panet_downstream_conv5_1_conv_2(_Build_Model__yolov4_panet_downstream_conv5_1_conv_1)
        _Build_Model__yolov4_panet_downstream_conv5_1_conv_3 = self._Build_Model__yolov4_panet_downstream_conv5_1_conv_3(_Build_Model__yolov4_panet_downstream_conv5_1_conv_2)
        _Build_Model__yolov4_panet_downstream_conv5_1_conv_4 = self._Build_Model__yolov4_panet_downstream_conv5_1_conv_4(_Build_Model__yolov4_panet_downstream_conv5_1_conv_3)
        _Build_Model__yolov4_panet_downstream_conv5_2_conv_0 = self._Build_Model__yolov4_panet_downstream_conv5_2_conv_0(_Build_Model__yolov4_panet_downstream_conv5_1_conv_4)
        _Build_Model__yolov4_panet_downstream_conv5_2_conv_1 = self._Build_Model__yolov4_panet_downstream_conv5_2_conv_1(_Build_Model__yolov4_panet_downstream_conv5_2_conv_0)
        _Build_Model__yolov4_panet_downstream_conv5_2_conv_2 = self._Build_Model__yolov4_panet_downstream_conv5_2_conv_2(_Build_Model__yolov4_panet_downstream_conv5_2_conv_1)
        _Build_Model__yolov4_panet_downstream_conv5_2_conv_3 = self._Build_Model__yolov4_panet_downstream_conv5_2_conv_3(_Build_Model__yolov4_panet_downstream_conv5_2_conv_2)
        _Build_Model__yolov4_panet_downstream_conv5_2_conv_4 = self._Build_Model__yolov4_panet_downstream_conv5_2_conv_4(_Build_Model__yolov4_panet_downstream_conv5_2_conv_3)
        _Build_Model__yolov4_panet_resample5_4_upsample_0_conv_0 = self._Build_Model__yolov4_panet_resample5_4_upsample_0_conv_0(_Build_Model__yolov4_panet_downstream_conv5_2_conv_4)
        _Build_Model__yolov4_panet_resample5_4_upsample_0_conv_1 = self._Build_Model__yolov4_panet_resample5_4_upsample_0_conv_1(_Build_Model__yolov4_panet_resample5_4_upsample_0_conv_0)
        _Build_Model__yolov4_panet_resample5_4_upsample_0_conv_2 = self._Build_Model__yolov4_panet_resample5_4_upsample_0_conv_2(_Build_Model__yolov4_panet_resample5_4_upsample_0_conv_1)
        _Build_Model__yolov4_panet_resample5_4_upsample_1 = self._Build_Model__yolov4_panet_resample5_4_upsample_1(_Build_Model__yolov4_panet_resample5_4_upsample_0_conv_2)
        cat_2 = torch.cat([_Build_Model__yolov4_panet_feature_transform4_conv_4, _Build_Model__yolov4_panet_resample5_4_upsample_1], dim=1)
        _Build_Model__yolov4_panet_downstream_conv4_0_conv_0 = self._Build_Model__yolov4_panet_downstream_conv4_0_conv_0(cat_2)
        _Build_Model__yolov4_panet_downstream_conv4_0_conv_1 = self._Build_Model__yolov4_panet_downstream_conv4_0_conv_1(_Build_Model__yolov4_panet_downstream_conv4_0_conv_0)
        _Build_Model__yolov4_panet_downstream_conv4_0_conv_2 = self._Build_Model__yolov4_panet_downstream_conv4_0_conv_2(_Build_Model__yolov4_panet_downstream_conv4_0_conv_1)
        _Build_Model__yolov4_panet_downstream_conv4_0_conv_3 = self._Build_Model__yolov4_panet_downstream_conv4_0_conv_3(_Build_Model__yolov4_panet_downstream_conv4_0_conv_2)
        _Build_Model__yolov4_panet_downstream_conv4_0_conv_4 = self._Build_Model__yolov4_panet_downstream_conv4_0_conv_4(_Build_Model__yolov4_panet_downstream_conv4_0_conv_3)
        _Build_Model__yolov4_panet_downstream_conv4_1_conv_0 = self._Build_Model__yolov4_panet_downstream_conv4_1_conv_0(_Build_Model__yolov4_panet_downstream_conv4_0_conv_4)
        _Build_Model__yolov4_panet_downstream_conv4_1_conv_1 = self._Build_Model__yolov4_panet_downstream_conv4_1_conv_1(_Build_Model__yolov4_panet_downstream_conv4_1_conv_0)
        _Build_Model__yolov4_panet_downstream_conv4_1_conv_2 = self._Build_Model__yolov4_panet_downstream_conv4_1_conv_2(_Build_Model__yolov4_panet_downstream_conv4_1_conv_1)
        _Build_Model__yolov4_panet_downstream_conv4_1_conv_3 = self._Build_Model__yolov4_panet_downstream_conv4_1_conv_3(_Build_Model__yolov4_panet_downstream_conv4_1_conv_2)
        _Build_Model__yolov4_panet_downstream_conv4_1_conv_4 = self._Build_Model__yolov4_panet_downstream_conv4_1_conv_4(_Build_Model__yolov4_panet_downstream_conv4_1_conv_3)
        _Build_Model__yolov4_panet_downstream_conv4_2_conv_0 = self._Build_Model__yolov4_panet_downstream_conv4_2_conv_0(_Build_Model__yolov4_panet_downstream_conv4_1_conv_4)
        _Build_Model__yolov4_panet_downstream_conv4_2_conv_1 = self._Build_Model__yolov4_panet_downstream_conv4_2_conv_1(_Build_Model__yolov4_panet_downstream_conv4_2_conv_0)
        _Build_Model__yolov4_panet_downstream_conv4_2_conv_2 = self._Build_Model__yolov4_panet_downstream_conv4_2_conv_2(_Build_Model__yolov4_panet_downstream_conv4_2_conv_1)
        _Build_Model__yolov4_panet_downstream_conv4_2_conv_3 = self._Build_Model__yolov4_panet_downstream_conv4_2_conv_3(_Build_Model__yolov4_panet_downstream_conv4_2_conv_2)
        _Build_Model__yolov4_panet_downstream_conv4_2_conv_4 = self._Build_Model__yolov4_panet_downstream_conv4_2_conv_4(_Build_Model__yolov4_panet_downstream_conv4_2_conv_3)
        _Build_Model__yolov4_panet_downstream_conv4_3_conv_0 = self._Build_Model__yolov4_panet_downstream_conv4_3_conv_0(_Build_Model__yolov4_panet_downstream_conv4_2_conv_4)
        _Build_Model__yolov4_panet_downstream_conv4_3_conv_1 = self._Build_Model__yolov4_panet_downstream_conv4_3_conv_1(_Build_Model__yolov4_panet_downstream_conv4_3_conv_0)
        _Build_Model__yolov4_panet_downstream_conv4_3_conv_2 = self._Build_Model__yolov4_panet_downstream_conv4_3_conv_2(_Build_Model__yolov4_panet_downstream_conv4_3_conv_1)
        _Build_Model__yolov4_panet_downstream_conv4_3_conv_3 = self._Build_Model__yolov4_panet_downstream_conv4_3_conv_3(_Build_Model__yolov4_panet_downstream_conv4_3_conv_2)
        _Build_Model__yolov4_panet_downstream_conv4_3_conv_4 = self._Build_Model__yolov4_panet_downstream_conv4_3_conv_4(_Build_Model__yolov4_panet_downstream_conv4_3_conv_3)
        _Build_Model__yolov4_panet_downstream_conv4_4_conv_0 = self._Build_Model__yolov4_panet_downstream_conv4_4_conv_0(_Build_Model__yolov4_panet_downstream_conv4_3_conv_4)
        _Build_Model__yolov4_panet_downstream_conv4_4_conv_1 = self._Build_Model__yolov4_panet_downstream_conv4_4_conv_1(_Build_Model__yolov4_panet_downstream_conv4_4_conv_0)
        _Build_Model__yolov4_panet_downstream_conv4_4_conv_2 = self._Build_Model__yolov4_panet_downstream_conv4_4_conv_2(_Build_Model__yolov4_panet_downstream_conv4_4_conv_1)
        _Build_Model__yolov4_panet_downstream_conv4_4_conv_3 = self._Build_Model__yolov4_panet_downstream_conv4_4_conv_3(_Build_Model__yolov4_panet_downstream_conv4_4_conv_2)
        _Build_Model__yolov4_panet_downstream_conv4_4_conv_4 = self._Build_Model__yolov4_panet_downstream_conv4_4_conv_4(_Build_Model__yolov4_panet_downstream_conv4_4_conv_3)
        _Build_Model__yolov4_panet_resample4_3_upsample_0_conv_0 = self._Build_Model__yolov4_panet_resample4_3_upsample_0_conv_0(_Build_Model__yolov4_panet_downstream_conv4_4_conv_4)
        _Build_Model__yolov4_panet_resample4_3_upsample_0_conv_1 = self._Build_Model__yolov4_panet_resample4_3_upsample_0_conv_1(_Build_Model__yolov4_panet_resample4_3_upsample_0_conv_0)
        _Build_Model__yolov4_panet_resample4_3_upsample_0_conv_2 = self._Build_Model__yolov4_panet_resample4_3_upsample_0_conv_2(_Build_Model__yolov4_panet_resample4_3_upsample_0_conv_1)
        _Build_Model__yolov4_panet_resample4_3_upsample_1 = self._Build_Model__yolov4_panet_resample4_3_upsample_1(_Build_Model__yolov4_panet_resample4_3_upsample_0_conv_2)
        cat_3 = torch.cat([_Build_Model__yolov4_panet_feature_transform3_conv_4, _Build_Model__yolov4_panet_resample4_3_upsample_1], dim=1)
        _Build_Model__yolov4_panet_downstream_conv3_0_conv_0 = self._Build_Model__yolov4_panet_downstream_conv3_0_conv_0(cat_3)
        _Build_Model__yolov4_panet_downstream_conv3_0_conv_1 = self._Build_Model__yolov4_panet_downstream_conv3_0_conv_1(_Build_Model__yolov4_panet_downstream_conv3_0_conv_0)
        _Build_Model__yolov4_panet_downstream_conv3_0_conv_2 = self._Build_Model__yolov4_panet_downstream_conv3_0_conv_2(_Build_Model__yolov4_panet_downstream_conv3_0_conv_1)
        _Build_Model__yolov4_panet_downstream_conv3_0_conv_3 = self._Build_Model__yolov4_panet_downstream_conv3_0_conv_3(_Build_Model__yolov4_panet_downstream_conv3_0_conv_2)
        _Build_Model__yolov4_panet_downstream_conv3_0_conv_4 = self._Build_Model__yolov4_panet_downstream_conv3_0_conv_4(_Build_Model__yolov4_panet_downstream_conv3_0_conv_3)
        _Build_Model__yolov4_panet_downstream_conv3_1_conv_0 = self._Build_Model__yolov4_panet_downstream_conv3_1_conv_0(_Build_Model__yolov4_panet_downstream_conv3_0_conv_4)
        _Build_Model__yolov4_panet_downstream_conv3_1_conv_1 = self._Build_Model__yolov4_panet_downstream_conv3_1_conv_1(_Build_Model__yolov4_panet_downstream_conv3_1_conv_0)
        _Build_Model__yolov4_panet_downstream_conv3_1_conv_2 = self._Build_Model__yolov4_panet_downstream_conv3_1_conv_2(_Build_Model__yolov4_panet_downstream_conv3_1_conv_1)
        _Build_Model__yolov4_panet_downstream_conv3_1_conv_3 = self._Build_Model__yolov4_panet_downstream_conv3_1_conv_3(_Build_Model__yolov4_panet_downstream_conv3_1_conv_2)
        _Build_Model__yolov4_panet_downstream_conv3_1_conv_4 = self._Build_Model__yolov4_panet_downstream_conv3_1_conv_4(_Build_Model__yolov4_panet_downstream_conv3_1_conv_3)
        _Build_Model__yolov4_panet_downstream_conv3_2_conv_0 = self._Build_Model__yolov4_panet_downstream_conv3_2_conv_0(_Build_Model__yolov4_panet_downstream_conv3_1_conv_4)
        _Build_Model__yolov4_panet_downstream_conv3_2_conv_1 = self._Build_Model__yolov4_panet_downstream_conv3_2_conv_1(_Build_Model__yolov4_panet_downstream_conv3_2_conv_0)
        _Build_Model__yolov4_panet_downstream_conv3_2_conv_2 = self._Build_Model__yolov4_panet_downstream_conv3_2_conv_2(_Build_Model__yolov4_panet_downstream_conv3_2_conv_1)
        _Build_Model__yolov4_panet_downstream_conv3_2_conv_3 = self._Build_Model__yolov4_panet_downstream_conv3_2_conv_3(_Build_Model__yolov4_panet_downstream_conv3_2_conv_2)
        _Build_Model__yolov4_panet_downstream_conv3_2_conv_4 = self._Build_Model__yolov4_panet_downstream_conv3_2_conv_4(_Build_Model__yolov4_panet_downstream_conv3_2_conv_3)
        _Build_Model__yolov4_panet_downstream_conv3_3_conv_0 = self._Build_Model__yolov4_panet_downstream_conv3_3_conv_0(_Build_Model__yolov4_panet_downstream_conv3_2_conv_4)
        _Build_Model__yolov4_panet_downstream_conv3_3_conv_1 = self._Build_Model__yolov4_panet_downstream_conv3_3_conv_1(_Build_Model__yolov4_panet_downstream_conv3_3_conv_0)
        _Build_Model__yolov4_panet_downstream_conv3_3_conv_2 = self._Build_Model__yolov4_panet_downstream_conv3_3_conv_2(_Build_Model__yolov4_panet_downstream_conv3_3_conv_1)
        _Build_Model__yolov4_panet_downstream_conv3_3_conv_3 = self._Build_Model__yolov4_panet_downstream_conv3_3_conv_3(_Build_Model__yolov4_panet_downstream_conv3_3_conv_2)
        _Build_Model__yolov4_panet_downstream_conv3_3_conv_4 = self._Build_Model__yolov4_panet_downstream_conv3_3_conv_4(_Build_Model__yolov4_panet_downstream_conv3_3_conv_3)
        _Build_Model__yolov4_panet_downstream_conv3_4_conv_0 = self._Build_Model__yolov4_panet_downstream_conv3_4_conv_0(_Build_Model__yolov4_panet_downstream_conv3_3_conv_4)
        _Build_Model__yolov4_panet_downstream_conv3_4_conv_1 = self._Build_Model__yolov4_panet_downstream_conv3_4_conv_1(_Build_Model__yolov4_panet_downstream_conv3_4_conv_0)
        _Build_Model__yolov4_panet_downstream_conv3_4_conv_2 = self._Build_Model__yolov4_panet_downstream_conv3_4_conv_2(_Build_Model__yolov4_panet_downstream_conv3_4_conv_1)
        _Build_Model__yolov4_panet_downstream_conv3_4_conv_3 = self._Build_Model__yolov4_panet_downstream_conv3_4_conv_3(_Build_Model__yolov4_panet_downstream_conv3_4_conv_2)
        _Build_Model__yolov4_panet_downstream_conv3_4_conv_4 = self._Build_Model__yolov4_panet_downstream_conv3_4_conv_4(_Build_Model__yolov4_panet_downstream_conv3_4_conv_3)
        _Build_Model__yolov4_panet_resample3_4_downsample_conv_0 = self._Build_Model__yolov4_panet_resample3_4_downsample_conv_0(_Build_Model__yolov4_panet_downstream_conv3_4_conv_4)
        _Build_Model__yolov4_panet_resample3_4_downsample_conv_1 = self._Build_Model__yolov4_panet_resample3_4_downsample_conv_1(_Build_Model__yolov4_panet_resample3_4_downsample_conv_0)
        _Build_Model__yolov4_panet_resample3_4_downsample_conv_2 = self._Build_Model__yolov4_panet_resample3_4_downsample_conv_2(_Build_Model__yolov4_panet_resample3_4_downsample_conv_1)
        cat_4 = torch.cat([_Build_Model__yolov4_panet_resample3_4_downsample_conv_2, _Build_Model__yolov4_panet_downstream_conv4_4_conv_4], dim=1)
        _Build_Model__yolov4_panet_upstream_conv4_0_conv_0 = self._Build_Model__yolov4_panet_upstream_conv4_0_conv_0(cat_4)
        _Build_Model__yolov4_panet_upstream_conv4_0_conv_1 = self._Build_Model__yolov4_panet_upstream_conv4_0_conv_1(_Build_Model__yolov4_panet_upstream_conv4_0_conv_0)
        _Build_Model__yolov4_panet_upstream_conv4_0_conv_2 = self._Build_Model__yolov4_panet_upstream_conv4_0_conv_2(_Build_Model__yolov4_panet_upstream_conv4_0_conv_1)
        _Build_Model__yolov4_panet_upstream_conv4_0_conv_3 = self._Build_Model__yolov4_panet_upstream_conv4_0_conv_3(_Build_Model__yolov4_panet_upstream_conv4_0_conv_2)
        _Build_Model__yolov4_panet_upstream_conv4_0_conv_4 = self._Build_Model__yolov4_panet_upstream_conv4_0_conv_4(_Build_Model__yolov4_panet_upstream_conv4_0_conv_3)
        _Build_Model__yolov4_panet_upstream_conv4_1_conv_0 = self._Build_Model__yolov4_panet_upstream_conv4_1_conv_0(_Build_Model__yolov4_panet_upstream_conv4_0_conv_4)
        _Build_Model__yolov4_panet_upstream_conv4_1_conv_1 = self._Build_Model__yolov4_panet_upstream_conv4_1_conv_1(_Build_Model__yolov4_panet_upstream_conv4_1_conv_0)
        _Build_Model__yolov4_panet_upstream_conv4_1_conv_2 = self._Build_Model__yolov4_panet_upstream_conv4_1_conv_2(_Build_Model__yolov4_panet_upstream_conv4_1_conv_1)
        _Build_Model__yolov4_panet_upstream_conv4_1_conv_3 = self._Build_Model__yolov4_panet_upstream_conv4_1_conv_3(_Build_Model__yolov4_panet_upstream_conv4_1_conv_2)
        _Build_Model__yolov4_panet_upstream_conv4_1_conv_4 = self._Build_Model__yolov4_panet_upstream_conv4_1_conv_4(_Build_Model__yolov4_panet_upstream_conv4_1_conv_3)
        _Build_Model__yolov4_panet_upstream_conv4_2_conv_0 = self._Build_Model__yolov4_panet_upstream_conv4_2_conv_0(_Build_Model__yolov4_panet_upstream_conv4_1_conv_4)
        _Build_Model__yolov4_panet_upstream_conv4_2_conv_1 = self._Build_Model__yolov4_panet_upstream_conv4_2_conv_1(_Build_Model__yolov4_panet_upstream_conv4_2_conv_0)
        _Build_Model__yolov4_panet_upstream_conv4_2_conv_2 = self._Build_Model__yolov4_panet_upstream_conv4_2_conv_2(_Build_Model__yolov4_panet_upstream_conv4_2_conv_1)
        _Build_Model__yolov4_panet_upstream_conv4_2_conv_3 = self._Build_Model__yolov4_panet_upstream_conv4_2_conv_3(_Build_Model__yolov4_panet_upstream_conv4_2_conv_2)
        _Build_Model__yolov4_panet_upstream_conv4_2_conv_4 = self._Build_Model__yolov4_panet_upstream_conv4_2_conv_4(_Build_Model__yolov4_panet_upstream_conv4_2_conv_3)
        _Build_Model__yolov4_panet_upstream_conv4_3_conv_0 = self._Build_Model__yolov4_panet_upstream_conv4_3_conv_0(_Build_Model__yolov4_panet_upstream_conv4_2_conv_4)
        _Build_Model__yolov4_panet_upstream_conv4_3_conv_1 = self._Build_Model__yolov4_panet_upstream_conv4_3_conv_1(_Build_Model__yolov4_panet_upstream_conv4_3_conv_0)
        _Build_Model__yolov4_panet_upstream_conv4_3_conv_2 = self._Build_Model__yolov4_panet_upstream_conv4_3_conv_2(_Build_Model__yolov4_panet_upstream_conv4_3_conv_1)
        _Build_Model__yolov4_panet_upstream_conv4_3_conv_3 = self._Build_Model__yolov4_panet_upstream_conv4_3_conv_3(_Build_Model__yolov4_panet_upstream_conv4_3_conv_2)
        _Build_Model__yolov4_panet_upstream_conv4_3_conv_4 = self._Build_Model__yolov4_panet_upstream_conv4_3_conv_4(_Build_Model__yolov4_panet_upstream_conv4_3_conv_3)
        _Build_Model__yolov4_panet_upstream_conv4_4_conv_0 = self._Build_Model__yolov4_panet_upstream_conv4_4_conv_0(_Build_Model__yolov4_panet_upstream_conv4_3_conv_4)
        _Build_Model__yolov4_panet_upstream_conv4_4_conv_1 = self._Build_Model__yolov4_panet_upstream_conv4_4_conv_1(_Build_Model__yolov4_panet_upstream_conv4_4_conv_0)
        _Build_Model__yolov4_panet_upstream_conv4_4_conv_2 = self._Build_Model__yolov4_panet_upstream_conv4_4_conv_2(_Build_Model__yolov4_panet_upstream_conv4_4_conv_1)
        _Build_Model__yolov4_panet_upstream_conv4_4_conv_3 = self._Build_Model__yolov4_panet_upstream_conv4_4_conv_3(_Build_Model__yolov4_panet_upstream_conv4_4_conv_2)
        _Build_Model__yolov4_panet_upstream_conv4_4_conv_4 = self._Build_Model__yolov4_panet_upstream_conv4_4_conv_4(_Build_Model__yolov4_panet_upstream_conv4_4_conv_3)
        _Build_Model__yolov4_panet_resample4_5_downsample_conv_0 = self._Build_Model__yolov4_panet_resample4_5_downsample_conv_0(_Build_Model__yolov4_panet_upstream_conv4_4_conv_4)
        _Build_Model__yolov4_panet_resample4_5_downsample_conv_1 = self._Build_Model__yolov4_panet_resample4_5_downsample_conv_1(_Build_Model__yolov4_panet_resample4_5_downsample_conv_0)
        _Build_Model__yolov4_panet_resample4_5_downsample_conv_2 = self._Build_Model__yolov4_panet_resample4_5_downsample_conv_2(_Build_Model__yolov4_panet_resample4_5_downsample_conv_1)
        cat_5 = torch.cat([_Build_Model__yolov4_panet_resample4_5_downsample_conv_2, _Build_Model__yolov4_panet_downstream_conv5_2_conv_4], dim=1)
        _Build_Model__yolov4_panet_upstream_conv5_0_conv_0 = self._Build_Model__yolov4_panet_upstream_conv5_0_conv_0(cat_5)
        _Build_Model__yolov4_panet_upstream_conv5_0_conv_1 = self._Build_Model__yolov4_panet_upstream_conv5_0_conv_1(_Build_Model__yolov4_panet_upstream_conv5_0_conv_0)
        _Build_Model__yolov4_panet_upstream_conv5_0_conv_2 = self._Build_Model__yolov4_panet_upstream_conv5_0_conv_2(_Build_Model__yolov4_panet_upstream_conv5_0_conv_1)
        _Build_Model__yolov4_panet_upstream_conv5_0_conv_3 = self._Build_Model__yolov4_panet_upstream_conv5_0_conv_3(_Build_Model__yolov4_panet_upstream_conv5_0_conv_2)
        _Build_Model__yolov4_panet_upstream_conv5_0_conv_4 = self._Build_Model__yolov4_panet_upstream_conv5_0_conv_4(_Build_Model__yolov4_panet_upstream_conv5_0_conv_3)
        _Build_Model__yolov4_panet_upstream_conv5_1_conv_0 = self._Build_Model__yolov4_panet_upstream_conv5_1_conv_0(_Build_Model__yolov4_panet_upstream_conv5_0_conv_4)
        _Build_Model__yolov4_panet_upstream_conv5_1_conv_1 = self._Build_Model__yolov4_panet_upstream_conv5_1_conv_1(_Build_Model__yolov4_panet_upstream_conv5_1_conv_0)
        _Build_Model__yolov4_panet_upstream_conv5_1_conv_2 = self._Build_Model__yolov4_panet_upstream_conv5_1_conv_2(_Build_Model__yolov4_panet_upstream_conv5_1_conv_1)
        _Build_Model__yolov4_panet_upstream_conv5_1_conv_3 = self._Build_Model__yolov4_panet_upstream_conv5_1_conv_3(_Build_Model__yolov4_panet_upstream_conv5_1_conv_2)
        _Build_Model__yolov4_panet_upstream_conv5_1_conv_4 = self._Build_Model__yolov4_panet_upstream_conv5_1_conv_4(_Build_Model__yolov4_panet_upstream_conv5_1_conv_3)
        _Build_Model__yolov4_panet_upstream_conv5_2_conv_0 = self._Build_Model__yolov4_panet_upstream_conv5_2_conv_0(_Build_Model__yolov4_panet_upstream_conv5_1_conv_4)
        _Build_Model__yolov4_panet_upstream_conv5_2_conv_1 = self._Build_Model__yolov4_panet_upstream_conv5_2_conv_1(_Build_Model__yolov4_panet_upstream_conv5_2_conv_0)
        _Build_Model__yolov4_panet_upstream_conv5_2_conv_2 = self._Build_Model__yolov4_panet_upstream_conv5_2_conv_2(_Build_Model__yolov4_panet_upstream_conv5_2_conv_1)
        _Build_Model__yolov4_panet_upstream_conv5_2_conv_3 = self._Build_Model__yolov4_panet_upstream_conv5_2_conv_3(_Build_Model__yolov4_panet_upstream_conv5_2_conv_2)
        _Build_Model__yolov4_panet_upstream_conv5_2_conv_4 = self._Build_Model__yolov4_panet_upstream_conv5_2_conv_4(_Build_Model__yolov4_panet_upstream_conv5_2_conv_3)
        _Build_Model__yolov4_panet_upstream_conv5_3_conv_0 = self._Build_Model__yolov4_panet_upstream_conv5_3_conv_0(_Build_Model__yolov4_panet_upstream_conv5_2_conv_4)
        _Build_Model__yolov4_panet_upstream_conv5_3_conv_1 = self._Build_Model__yolov4_panet_upstream_conv5_3_conv_1(_Build_Model__yolov4_panet_upstream_conv5_3_conv_0)
        _Build_Model__yolov4_panet_upstream_conv5_3_conv_2 = self._Build_Model__yolov4_panet_upstream_conv5_3_conv_2(_Build_Model__yolov4_panet_upstream_conv5_3_conv_1)
        _Build_Model__yolov4_panet_upstream_conv5_3_conv_3 = self._Build_Model__yolov4_panet_upstream_conv5_3_conv_3(_Build_Model__yolov4_panet_upstream_conv5_3_conv_2)
        _Build_Model__yolov4_panet_upstream_conv5_3_conv_4 = self._Build_Model__yolov4_panet_upstream_conv5_3_conv_4(_Build_Model__yolov4_panet_upstream_conv5_3_conv_3)
        _Build_Model__yolov4_panet_upstream_conv5_4_conv_0 = self._Build_Model__yolov4_panet_upstream_conv5_4_conv_0(_Build_Model__yolov4_panet_upstream_conv5_3_conv_4)
        _Build_Model__yolov4_panet_upstream_conv5_4_conv_1 = self._Build_Model__yolov4_panet_upstream_conv5_4_conv_1(_Build_Model__yolov4_panet_upstream_conv5_4_conv_0)
        _Build_Model__yolov4_panet_upstream_conv5_4_conv_2 = self._Build_Model__yolov4_panet_upstream_conv5_4_conv_2(_Build_Model__yolov4_panet_upstream_conv5_4_conv_1)
        _Build_Model__yolov4_panet_upstream_conv5_4_conv_3 = self._Build_Model__yolov4_panet_upstream_conv5_4_conv_3(_Build_Model__yolov4_panet_upstream_conv5_4_conv_2)
        _Build_Model__yolov4_panet_upstream_conv5_4_conv_4 = self._Build_Model__yolov4_panet_upstream_conv5_4_conv_4(_Build_Model__yolov4_panet_upstream_conv5_4_conv_3)
        _Build_Model__yolov4_predict_net_predict_conv_0_0_conv_0 = self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_0(_Build_Model__yolov4_panet_downstream_conv3_4_conv_4)
        _Build_Model__yolov4_predict_net_predict_conv_0_0_conv_1 = self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_1(_Build_Model__yolov4_predict_net_predict_conv_0_0_conv_0)
        _Build_Model__yolov4_predict_net_predict_conv_0_0_conv_2 = self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_2(_Build_Model__yolov4_predict_net_predict_conv_0_0_conv_1)
        _Build_Model__yolov4_predict_net_predict_conv_0_0_conv_3 = self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_3(_Build_Model__yolov4_predict_net_predict_conv_0_0_conv_2)
        _Build_Model__yolov4_predict_net_predict_conv_0_0_conv_4 = self._Build_Model__yolov4_predict_net_predict_conv_0_0_conv_4(_Build_Model__yolov4_predict_net_predict_conv_0_0_conv_3)
        _Build_Model__yolov4_predict_net_predict_conv_0_1 = self._Build_Model__yolov4_predict_net_predict_conv_0_1(_Build_Model__yolov4_predict_net_predict_conv_0_0_conv_4)
        _Build_Model__yolov4_predict_net_predict_conv_1_0_conv_0 = self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_0(_Build_Model__yolov4_panet_upstream_conv4_4_conv_4)
        _Build_Model__yolov4_predict_net_predict_conv_1_0_conv_1 = self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_1(_Build_Model__yolov4_predict_net_predict_conv_1_0_conv_0)
        _Build_Model__yolov4_predict_net_predict_conv_1_0_conv_2 = self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_2(_Build_Model__yolov4_predict_net_predict_conv_1_0_conv_1)
        _Build_Model__yolov4_predict_net_predict_conv_1_0_conv_3 = self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_3(_Build_Model__yolov4_predict_net_predict_conv_1_0_conv_2)
        _Build_Model__yolov4_predict_net_predict_conv_1_0_conv_4 = self._Build_Model__yolov4_predict_net_predict_conv_1_0_conv_4(_Build_Model__yolov4_predict_net_predict_conv_1_0_conv_3)
        _Build_Model__yolov4_predict_net_predict_conv_1_1 = self._Build_Model__yolov4_predict_net_predict_conv_1_1(_Build_Model__yolov4_predict_net_predict_conv_1_0_conv_4)
        _Build_Model__yolov4_predict_net_predict_conv_2_0_conv_0 = self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_0(_Build_Model__yolov4_panet_upstream_conv5_4_conv_4)
        _Build_Model__yolov4_predict_net_predict_conv_2_0_conv_1 = self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_1(_Build_Model__yolov4_predict_net_predict_conv_2_0_conv_0)
        _Build_Model__yolov4_predict_net_predict_conv_2_0_conv_2 = self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_2(_Build_Model__yolov4_predict_net_predict_conv_2_0_conv_1)
        _Build_Model__yolov4_predict_net_predict_conv_2_0_conv_3 = self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_3(_Build_Model__yolov4_predict_net_predict_conv_2_0_conv_2)
        _Build_Model__yolov4_predict_net_predict_conv_2_0_conv_4 = self._Build_Model__yolov4_predict_net_predict_conv_2_0_conv_4(_Build_Model__yolov4_predict_net_predict_conv_2_0_conv_3)
        _Build_Model__yolov4_predict_net_predict_conv_2_1 = self._Build_Model__yolov4_predict_net_predict_conv_2_1(_Build_Model__yolov4_predict_net_predict_conv_2_0_conv_4)
        shape_1 = _Build_Model__yolov4_predict_net_predict_conv_0_1.shape
        view_1 = _Build_Model__yolov4_predict_net_predict_conv_0_1.view(shape_1[0], 3, 6, shape_1[3], shape_1[3])
        permute_1 = view_1.permute(0, 3, 4, 1, 2)
        clone_1 = permute_1.clone()
        shape_2 = clone_1.shape
        device_1 = clone_1.device
        to_1 = self.tensor_1.to(device_1)
        mul_1 = self.tensor_2.__rmul__(1.0)
        to_2 = mul_1.to(device_1)
        getitem_1 = clone_1[:, :, :, :, 0:2]
        getitem_2 = clone_1[:, :, :, :, 2:4]
        getitem_3 = clone_1[:, :, :, :, 4:5]
        getitem_4 = clone_1[:, :, :, :, 5:]
        arange_1 = torch.arange(0, shape_2[1])
        unsqueeze_1 = arange_1.unsqueeze(1)
        repeat_1 = unsqueeze_1.repeat(1, shape_2[1])
        arange_2 = torch.arange(0, shape_2[1])
        unsqueeze_2 = arange_2.unsqueeze(0)
        repeat_2 = unsqueeze_2.repeat(shape_2[1], 1)
        stack_1 = torch.stack([repeat_2, repeat_1], dim=-1)
        unsqueeze_3 = stack_1.unsqueeze(0)
        unsqueeze_4 = unsqueeze_3.unsqueeze(3)
        repeat_3 = unsqueeze_4.repeat(shape_2[0], 1, 1, 3, 1)
        float_1 = repeat_3.float()
        to_3 = float_1.to(device_1)
        sigmoid_1 = torch.sigmoid(getitem_1)
        add_12 = sigmoid_1.__add__(to_3)
        mul_2 = add_12.__mul__(to_1)
        exp_1 = torch.exp(getitem_2)
        mul_3 = exp_1.__mul__(to_2)
        mul_4 = mul_3.__mul__(to_1)
        cat_6 = torch.cat([mul_2, mul_4], dim=-1)
        sigmoid_2 = torch.sigmoid(getitem_3)
        sigmoid_3 = torch.sigmoid(getitem_4)
        cat_7 = torch.cat([cat_6, sigmoid_2, sigmoid_3], dim=-1)
        view_2 = cat_7.view(-1, 6)
        shape_3 = _Build_Model__yolov4_predict_net_predict_conv_1_1.shape
        view_3 = _Build_Model__yolov4_predict_net_predict_conv_1_1.view(shape_3[0], 3, 6, shape_3[3], shape_3[3])
        permute_2 = view_3.permute(0, 3, 4, 1, 2)
        clone_2 = permute_2.clone()
        shape_4 = clone_2.shape
        device_2 = clone_2.device
        to_4 = self.tensor_3.to(device_2)
        mul_5 = self.tensor_4.__rmul__(1.0)
        to_5 = mul_5.to(device_2)
        getitem_5 = clone_2[:, :, :, :, 0:2]
        getitem_6 = clone_2[:, :, :, :, 2:4]
        getitem_7 = clone_2[:, :, :, :, 4:5]
        getitem_8 = clone_2[:, :, :, :, 5:]
        arange_3 = torch.arange(0, shape_4[1])
        unsqueeze_5 = arange_3.unsqueeze(1)
        repeat_4 = unsqueeze_5.repeat(1, shape_4[1])
        arange_4 = torch.arange(0, shape_4[1])
        unsqueeze_6 = arange_4.unsqueeze(0)
        repeat_5 = unsqueeze_6.repeat(shape_4[1], 1)
        stack_2 = torch.stack([repeat_5, repeat_4], dim=-1)
        unsqueeze_7 = stack_2.unsqueeze(0)
        unsqueeze_8 = unsqueeze_7.unsqueeze(3)
        repeat_6 = unsqueeze_8.repeat(shape_4[0], 1, 1, 3, 1)
        float_2 = repeat_6.float()
        to_6 = float_2.to(device_2)
        sigmoid_4 = torch.sigmoid(getitem_5)
        add_13 = sigmoid_4.__add__(to_6)
        mul_6 = add_13.__mul__(to_4)
        exp_2 = torch.exp(getitem_6)
        mul_7 = exp_2.__mul__(to_5)
        mul_8 = mul_7.__mul__(to_4)
        cat_8 = torch.cat([mul_6, mul_8], dim=-1)
        sigmoid_5 = torch.sigmoid(getitem_7)
        sigmoid_6 = torch.sigmoid(getitem_8)
        cat_9 = torch.cat([cat_8, sigmoid_5, sigmoid_6], dim=-1)
        view_4 = cat_9.view(-1, 6)
        shape_5 = _Build_Model__yolov4_predict_net_predict_conv_2_1.shape
        view_5 = _Build_Model__yolov4_predict_net_predict_conv_2_1.view(shape_5[0], 3, 6, shape_5[3], shape_5[3])
        permute_3 = view_5.permute(0, 3, 4, 1, 2)
        clone_3 = permute_3.clone()
        shape_6 = clone_3.shape
        device_3 = clone_3.device
        to_7 = self.tensor_5.to(device_3)
        mul_9 = self.tensor_6.__rmul__(1.0)
        to_8 = mul_9.to(device_3)
        getitem_9 = clone_3[:, :, :, :, 0:2]
        getitem_10 = clone_3[:, :, :, :, 2:4]
        getitem_11 = clone_3[:, :, :, :, 4:5]
        getitem_12 = clone_3[:, :, :, :, 5:]
        arange_5 = torch.arange(0, shape_6[1])
        unsqueeze_9 = arange_5.unsqueeze(1)
        repeat_7 = unsqueeze_9.repeat(1, shape_6[1])
        arange_6 = torch.arange(0, shape_6[1])
        unsqueeze_10 = arange_6.unsqueeze(0)
        repeat_8 = unsqueeze_10.repeat(shape_6[1], 1)
        stack_3 = torch.stack([repeat_8, repeat_7], dim=-1)
        unsqueeze_11 = stack_3.unsqueeze(0)
        unsqueeze_12 = unsqueeze_11.unsqueeze(3)
        repeat_9 = unsqueeze_12.repeat(shape_6[0], 1, 1, 3, 1)
        float_3 = repeat_9.float()
        to_9 = float_3.to(device_3)
        sigmoid_7 = torch.sigmoid(getitem_9)
        add_14 = sigmoid_7.__add__(to_9)
        mul_10 = add_14.__mul__(to_7)
        exp_3 = torch.exp(getitem_10)
        mul_11 = exp_3.__mul__(to_8)
        mul_12 = mul_11.__mul__(to_7)
        cat_10 = torch.cat([mul_10, mul_12], dim=-1)
        sigmoid_8 = torch.sigmoid(getitem_11)
        sigmoid_9 = torch.sigmoid(getitem_12)
        cat_11 = torch.cat([cat_10, sigmoid_8, sigmoid_9], dim=-1)
        view_6 = cat_11.view(-1, 6)
        shape_7 = _Build_Model__yolov4_predict_net_predict_conv_0_1.shape
        view_7 = _Build_Model__yolov4_predict_net_predict_conv_0_1.view(shape_7[0], 3, 6, shape_7[3], shape_7[3])
        permute_4 = view_7.permute(0, 3, 4, 1, 2)
        clone_4 = permute_4.clone()
        shape_8 = clone_4.shape
        device_4 = clone_4.device
        to_10 = to_1.to(device_4)
        mul_13 = self.tensor_2.__rmul__(1.0)
        to_11 = mul_13.to(device_4)
        getitem_13 = clone_4[:, :, :, :, 0:2]
        getitem_14 = clone_4[:, :, :, :, 2:4]
        getitem_15 = clone_4[:, :, :, :, 4:5]
        getitem_16 = clone_4[:, :, :, :, 5:]
        arange_7 = torch.arange(0, shape_8[1])
        unsqueeze_13 = arange_7.unsqueeze(1)
        repeat_10 = unsqueeze_13.repeat(1, shape_8[1])
        arange_8 = torch.arange(0, shape_8[1])
        unsqueeze_14 = arange_8.unsqueeze(0)
        repeat_11 = unsqueeze_14.repeat(shape_8[1], 1)
        stack_4 = torch.stack([repeat_11, repeat_10], dim=-1)
        unsqueeze_15 = stack_4.unsqueeze(0)
        unsqueeze_16 = unsqueeze_15.unsqueeze(3)
        repeat_12 = unsqueeze_16.repeat(shape_8[0], 1, 1, 3, 1)
        float_4 = repeat_12.float()
        to_12 = float_4.to(device_4)
        sigmoid_10 = torch.sigmoid(getitem_13)
        add_15 = sigmoid_10.__add__(to_12)
        mul_14 = add_15.__mul__(to_10)
        exp_4 = torch.exp(getitem_14)
        mul_15 = exp_4.__mul__(to_11)
        mul_16 = mul_15.__mul__(to_10)
        cat_12 = torch.cat([mul_14, mul_16], dim=-1)
        sigmoid_11 = torch.sigmoid(getitem_15)
        sigmoid_12 = torch.sigmoid(getitem_16)
        cat_13 = torch.cat([cat_12, sigmoid_11, sigmoid_12], dim=-1)
        view_8 = cat_13.view(-1, 6)
        shape_9 = _Build_Model__yolov4_predict_net_predict_conv_1_1.shape
        view_9 = _Build_Model__yolov4_predict_net_predict_conv_1_1.view(shape_9[0], 3, 6, shape_9[3], shape_9[3])
        permute_5 = view_9.permute(0, 3, 4, 1, 2)
        clone_5 = permute_5.clone()
        shape_10 = clone_5.shape
        device_5 = clone_5.device
        to_13 = to_4.to(device_5)
        mul_17 = self.tensor_4.__rmul__(1.0)
        to_14 = mul_17.to(device_5)
        getitem_17 = clone_5[:, :, :, :, 0:2]
        getitem_18 = clone_5[:, :, :, :, 2:4]
        getitem_19 = clone_5[:, :, :, :, 4:5]
        getitem_20 = clone_5[:, :, :, :, 5:]
        arange_9 = torch.arange(0, shape_10[1])
        unsqueeze_17 = arange_9.unsqueeze(1)
        repeat_13 = unsqueeze_17.repeat(1, shape_10[1])
        arange_10 = torch.arange(0, shape_10[1])
        unsqueeze_18 = arange_10.unsqueeze(0)
        repeat_14 = unsqueeze_18.repeat(shape_10[1], 1)
        stack_5 = torch.stack([repeat_14, repeat_13], dim=-1)
        unsqueeze_19 = stack_5.unsqueeze(0)
        unsqueeze_20 = unsqueeze_19.unsqueeze(3)
        repeat_15 = unsqueeze_20.repeat(shape_10[0], 1, 1, 3, 1)
        float_5 = repeat_15.float()
        to_15 = float_5.to(device_5)
        sigmoid_13 = torch.sigmoid(getitem_17)
        add_16 = sigmoid_13.__add__(to_15)
        mul_18 = add_16.__mul__(to_13)
        exp_5 = torch.exp(getitem_18)
        mul_19 = exp_5.__mul__(to_14)
        mul_20 = mul_19.__mul__(to_13)
        cat_14 = torch.cat([mul_18, mul_20], dim=-1)
        sigmoid_14 = torch.sigmoid(getitem_19)
        sigmoid_15 = torch.sigmoid(getitem_20)
        cat_15 = torch.cat([cat_14, sigmoid_14, sigmoid_15], dim=-1)
        view_10 = cat_15.view(-1, 6)
        shape_11 = _Build_Model__yolov4_predict_net_predict_conv_2_1.shape
        view_11 = _Build_Model__yolov4_predict_net_predict_conv_2_1.view(shape_11[0], 3, 6, shape_11[3], shape_11[3])
        permute_6 = view_11.permute(0, 3, 4, 1, 2)
        clone_6 = permute_6.clone()
        shape_12 = clone_6.shape
        device_6 = clone_6.device
        to_16 = to_7.to(device_6)
        mul_21 = self.tensor_6.__rmul__(1.0)
        to_17 = mul_21.to(device_6)
        getitem_21 = clone_6[:, :, :, :, 0:2]
        getitem_22 = clone_6[:, :, :, :, 2:4]
        getitem_23 = clone_6[:, :, :, :, 4:5]
        getitem_24 = clone_6[:, :, :, :, 5:]
        arange_11 = torch.arange(0, shape_12[1])
        unsqueeze_21 = arange_11.unsqueeze(1)
        repeat_16 = unsqueeze_21.repeat(1, shape_12[1])
        arange_12 = torch.arange(0, shape_12[1])
        unsqueeze_22 = arange_12.unsqueeze(0)
        repeat_17 = unsqueeze_22.repeat(shape_12[1], 1)
        stack_6 = torch.stack([repeat_17, repeat_16], dim=-1)
        unsqueeze_23 = stack_6.unsqueeze(0)
        unsqueeze_24 = unsqueeze_23.unsqueeze(3)
        repeat_18 = unsqueeze_24.repeat(shape_12[0], 1, 1, 3, 1)
        float_6 = repeat_18.float()
        to_18 = float_6.to(device_6)
        sigmoid_16 = torch.sigmoid(getitem_21)
        add_17 = sigmoid_16.__add__(to_18)
        mul_22 = add_17.__mul__(to_16)
        exp_6 = torch.exp(getitem_22)
        mul_23 = exp_6.__mul__(to_17)
        mul_24 = mul_23.__mul__(to_16)
        cat_16 = torch.cat([mul_22, mul_24], dim=-1)
        sigmoid_17 = torch.sigmoid(getitem_23)
        sigmoid_18 = torch.sigmoid(getitem_24)
        cat_17 = torch.cat([cat_16, sigmoid_17, sigmoid_18], dim=-1)
        view_12 = cat_17.view(-1, 6)
        size_1 = _Build_Model__yolov4_predict_net_predict_conv_0_1.size(0)
        view_13 = view_8.view(1, -1, 6)
        size_2 = _Build_Model__yolov4_predict_net_predict_conv_0_1.size(0)
        view_14 = view_10.view(1, -1, 6)
        size_3 = _Build_Model__yolov4_predict_net_predict_conv_0_1.size(0)
        view_15 = view_12.view(1, -1, 6)
        cat_18 = torch.cat([view_13, view_14, view_15], dim=1)
        shape_13 = _Build_Model__yolov4_predict_net_predict_conv_0_1.shape
        view_16 = _Build_Model__yolov4_predict_net_predict_conv_0_1.view(shape_13[0], 3, 6, shape_13[3], shape_13[3])
        permute_7 = view_16.permute(0, 3, 4, 1, 2)
        clone_7 = permute_7.clone()
        shape_14 = clone_7.shape
        device_7 = clone_7.device
        to_19 = to_10.to(device_7)
        mul_25 = self.tensor_2.__rmul__(1.0)
        to_20 = mul_25.to(device_7)
        getitem_25 = clone_7[:, :, :, :, 0:2]
        getitem_26 = clone_7[:, :, :, :, 2:4]
        getitem_27 = clone_7[:, :, :, :, 4:5]
        getitem_28 = clone_7[:, :, :, :, 5:]
        arange_13 = torch.arange(0, shape_14[1])
        unsqueeze_25 = arange_13.unsqueeze(1)
        repeat_19 = unsqueeze_25.repeat(1, shape_14[1])
        arange_14 = torch.arange(0, shape_14[1])
        unsqueeze_26 = arange_14.unsqueeze(0)
        repeat_20 = unsqueeze_26.repeat(shape_14[1], 1)
        stack_7 = torch.stack([repeat_20, repeat_19], dim=-1)
        unsqueeze_27 = stack_7.unsqueeze(0)
        unsqueeze_28 = unsqueeze_27.unsqueeze(3)
        repeat_21 = unsqueeze_28.repeat(shape_14[0], 1, 1, 3, 1)
        float_7 = repeat_21.float()
        to_21 = float_7.to(device_7)
        sigmoid_19 = torch.sigmoid(getitem_25)
        add_18 = sigmoid_19.__add__(to_21)
        mul_26 = add_18.__mul__(to_19)
        exp_7 = torch.exp(getitem_26)
        mul_27 = exp_7.__mul__(to_20)
        mul_28 = mul_27.__mul__(to_19)
        cat_19 = torch.cat([mul_26, mul_28], dim=-1)
        sigmoid_20 = torch.sigmoid(getitem_27)
        sigmoid_21 = torch.sigmoid(getitem_28)
        cat_20 = torch.cat([cat_19, sigmoid_20, sigmoid_21], dim=-1)
        view_17 = cat_20.view(-1, 6)
        shape_15 = _Build_Model__yolov4_predict_net_predict_conv_1_1.shape
        view_18 = _Build_Model__yolov4_predict_net_predict_conv_1_1.view(shape_15[0], 3, 6, shape_15[3], shape_15[3])
        permute_8 = view_18.permute(0, 3, 4, 1, 2)
        clone_8 = permute_8.clone()
        shape_16 = clone_8.shape
        device_8 = clone_8.device
        to_22 = to_13.to(device_8)
        mul_29 = self.tensor_4.__rmul__(1.0)
        to_23 = mul_29.to(device_8)
        getitem_29 = clone_8[:, :, :, :, 0:2]
        getitem_30 = clone_8[:, :, :, :, 2:4]
        getitem_31 = clone_8[:, :, :, :, 4:5]
        getitem_32 = clone_8[:, :, :, :, 5:]
        arange_15 = torch.arange(0, shape_16[1])
        unsqueeze_29 = arange_15.unsqueeze(1)
        repeat_22 = unsqueeze_29.repeat(1, shape_16[1])
        arange_16 = torch.arange(0, shape_16[1])
        unsqueeze_30 = arange_16.unsqueeze(0)
        repeat_23 = unsqueeze_30.repeat(shape_16[1], 1)
        stack_8 = torch.stack([repeat_23, repeat_22], dim=-1)
        unsqueeze_31 = stack_8.unsqueeze(0)
        unsqueeze_32 = unsqueeze_31.unsqueeze(3)
        repeat_24 = unsqueeze_32.repeat(shape_16[0], 1, 1, 3, 1)
        float_8 = repeat_24.float()
        to_24 = float_8.to(device_8)
        sigmoid_22 = torch.sigmoid(getitem_29)
        add_19 = sigmoid_22.__add__(to_24)
        mul_30 = add_19.__mul__(to_22)
        exp_8 = torch.exp(getitem_30)
        mul_31 = exp_8.__mul__(to_23)
        mul_32 = mul_31.__mul__(to_22)
        cat_21 = torch.cat([mul_30, mul_32], dim=-1)
        sigmoid_23 = torch.sigmoid(getitem_31)
        sigmoid_24 = torch.sigmoid(getitem_32)
        cat_22 = torch.cat([cat_21, sigmoid_23, sigmoid_24], dim=-1)
        view_19 = cat_22.view(-1, 6)
        shape_17 = _Build_Model__yolov4_predict_net_predict_conv_2_1.shape
        view_20 = _Build_Model__yolov4_predict_net_predict_conv_2_1.view(shape_17[0], 3, 6, shape_17[3], shape_17[3])
        permute_9 = view_20.permute(0, 3, 4, 1, 2)
        clone_9 = permute_9.clone()
        shape_18 = clone_9.shape
        device_9 = clone_9.device
        to_25 = to_16.to(device_9)
        mul_33 = self.tensor_6.__rmul__(1.0)
        to_26 = mul_33.to(device_9)
        getitem_33 = clone_9[:, :, :, :, 0:2]
        getitem_34 = clone_9[:, :, :, :, 2:4]
        getitem_35 = clone_9[:, :, :, :, 4:5]
        getitem_36 = clone_9[:, :, :, :, 5:]
        arange_17 = torch.arange(0, shape_18[1])
        unsqueeze_33 = arange_17.unsqueeze(1)
        repeat_25 = unsqueeze_33.repeat(1, shape_18[1])
        arange_18 = torch.arange(0, shape_18[1])
        unsqueeze_34 = arange_18.unsqueeze(0)
        repeat_26 = unsqueeze_34.repeat(shape_18[1], 1)
        stack_9 = torch.stack([repeat_26, repeat_25], dim=-1)
        unsqueeze_35 = stack_9.unsqueeze(0)
        unsqueeze_36 = unsqueeze_35.unsqueeze(3)
        repeat_27 = unsqueeze_36.repeat(shape_18[0], 1, 1, 3, 1)
        float_9 = repeat_27.float()
        to_27 = float_9.to(device_9)
        sigmoid_25 = torch.sigmoid(getitem_33)
        add_20 = sigmoid_25.__add__(to_27)
        mul_34 = add_20.__mul__(to_25)
        exp_9 = torch.exp(getitem_34)
        mul_35 = exp_9.__mul__(to_26)
        mul_36 = mul_35.__mul__(to_25)
        cat_23 = torch.cat([mul_34, mul_36], dim=-1)
        sigmoid_26 = torch.sigmoid(getitem_35)
        sigmoid_27 = torch.sigmoid(getitem_36)
        cat_24 = torch.cat([cat_23, sigmoid_26, sigmoid_27], dim=-1)
        view_21 = cat_24.view(-1, 6)
        size_4 = _Build_Model__yolov4_predict_net_predict_conv_0_1.size(0)
        view_22 = view_17.view(1, -1, 6)
        size_5 = _Build_Model__yolov4_predict_net_predict_conv_0_1.size(0)
        view_23 = view_19.view(1, -1, 6)
        size_6 = _Build_Model__yolov4_predict_net_predict_conv_0_1.size(0)
        view_24 = view_21.view(1, -1, 6)
        cat_25 = torch.cat([view_22, view_23, view_24], dim=1)
        return [permute_1, permute_2, permute_3], cat_25


if __name__ == "__main__":
    model = YOLO_V4()

    model.eval()
    model.cpu()

    dummy_input_0 = torch.ones((1, 3, 224, 224), dtype=torch.float32)

    output = model(dummy_input_0)
    print(output)