import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math

__weights_dict = dict()

def load_weights(weight_file):
    if weight_file == None:
        return

    try:
        weights_dict = np.load(weight_file, allow_pickle=True).item()
    except:
        weights_dict = np.load(weight_file, encoding='bytes').item()

    return weights_dict

class KitModel(nn.Module):

    # def __init__(self, weight_file):
    def __init__(self):
        super(KitModel, self).__init__()
        # global __weights_dict
        # __weights_dict = load_weights(weight_file)

        self.resnet_v2_50_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/conv1/Conv2D', in_channels=3, out_channels=64, kernel_size=(7, 7), stride=(2, 2), groups=1, bias=True)
        self.resnet_v2_50_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=64, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block1_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=64, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=64, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=64, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=64, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=64, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=256, out_channels=64, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=64, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=64, out_channels=64, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None)
        self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block1/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=64, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block1/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=64, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=256, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=256, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_4/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_4/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=128, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_4/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=128, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_4/bottleneck_v2/conv2/Conv2D', in_channels=128, out_channels=128, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None)
        self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block2/unit_4/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=128, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block2/unit_4/bottleneck_v2/conv3/Conv2D', in_channels=128, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=512, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=512, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_4/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_4/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_4/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_4/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_4/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_4/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_5/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_5/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_5/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_5/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_5/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_5/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_6/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_6/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=256, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_6/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_6/bottleneck_v2/conv2/Conv2D', in_channels=256, out_channels=256, kernel_size=(3, 3), stride=(2, 2), groups=1, bias=None)
        self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block3/unit_6/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=256, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block3/unit_6/bottleneck_v2/conv3/Conv2D', in_channels=256, out_channels=1024, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_1/bottleneck_v2/preact/FusedBatchNorm', num_features=1024, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block4_unit_1_bottleneck_v2_shortcut_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_1/bottleneck_v2/shortcut/Conv2D', in_channels=1024, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_1/bottleneck_v2/conv1/Conv2D', in_channels=1024, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_1/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_1/bottleneck_v2/conv2/Conv2D', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_1/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_1/bottleneck_v2/conv3/Conv2D', in_channels=512, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_2/bottleneck_v2/preact/FusedBatchNorm', num_features=2048, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_2/bottleneck_v2/conv1/Conv2D', in_channels=2048, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_2/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_2/bottleneck_v2/conv2/Conv2D', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_2/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_2/bottleneck_v2/conv3/Conv2D', in_channels=512, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_3/bottleneck_v2/preact/FusedBatchNorm', num_features=2048, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_3/bottleneck_v2/conv1/Conv2D', in_channels=2048, out_channels=512, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_3/bottleneck_v2/conv1/BatchNorm/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_3/bottleneck_v2/conv2/Conv2D', in_channels=512, out_channels=512, kernel_size=(3, 3), stride=(1, 1), groups=1, bias=None)
        self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/block4/unit_3/bottleneck_v2/conv2/BatchNorm/FusedBatchNorm', num_features=512, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv3_Conv2D = self.__conv(2, name='resnet_v2_50/block4/unit_3/bottleneck_v2/conv3/Conv2D', in_channels=512, out_channels=2048, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)
        self.resnet_v2_50_postnorm_FusedBatchNorm = self.__batch_normalization(2, 'resnet_v2_50/postnorm/FusedBatchNorm', num_features=2048, eps=1e-5, momentum=0.003)
        self.resnet_v2_50_logits_Conv2D = self.__conv(2, name='resnet_v2_50/logits/Conv2D', in_channels=2048, out_channels=1001, kernel_size=(1, 1), stride=(1, 1), groups=1, bias=True)

    def forward(self, x):
        resnet_v2_50_Pad = F.pad(x, (3, 3, 3, 3), mode = 'constant', value = 0)
        resnet_v2_50_conv1_Conv2D = self.resnet_v2_50_conv1_Conv2D(resnet_v2_50_Pad)
        resnet_v2_50_pool1_MaxPool_pad = F.pad(resnet_v2_50_conv1_Conv2D, (0, 1, 0, 1), value=float('-inf'))
        resnet_v2_50_pool1_MaxPool = F.max_pool2d(resnet_v2_50_pool1_MaxPool_pad, kernel_size=(3, 3), stride=(2, 2), padding=0, ceil_mode=False)
        resnet_v2_50_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_pool1_MaxPool)
        resnet_v2_50_block1_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block1_unit_1_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block1_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_50_block1_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_50_block1_unit_1_bottleneck_v2_preact_Relu)
        resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block1_unit_1_bottleneck_v2_preact_Relu)
        resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block1_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block1_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block1_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block1_unit_1_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block1_unit_1_bottleneck_v2_add = resnet_v2_50_block1_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_50_block1_unit_1_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block1_unit_1_bottleneck_v2_add)
        resnet_v2_50_block1_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block1_unit_2_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block1_unit_2_bottleneck_v2_preact_Relu)
        resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block1_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block1_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block1_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block1_unit_2_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block1_unit_2_bottleneck_v2_add = resnet_v2_50_block1_unit_1_bottleneck_v2_add + resnet_v2_50_block1_unit_2_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block1_unit_2_bottleneck_v2_add)
        resnet_v2_50_block1_unit_3_bottleneck_v2_shortcut_MaxPool = F.max_pool2d(resnet_v2_50_block1_unit_2_bottleneck_v2_add, kernel_size=(1, 1), stride=(2, 2), padding=0, ceil_mode=False)
        resnet_v2_50_block1_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block1_unit_3_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block1_unit_3_bottleneck_v2_preact_Relu)
        resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block1_unit_3_bottleneck_v2_Pad = F.pad(resnet_v2_50_block1_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0)
        resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block1_unit_3_bottleneck_v2_Pad)
        resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block1_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block1_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block1_unit_3_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block1_unit_3_bottleneck_v2_add = resnet_v2_50_block1_unit_3_bottleneck_v2_shortcut_MaxPool + resnet_v2_50_block1_unit_3_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block1_unit_3_bottleneck_v2_add)
        resnet_v2_50_block2_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block2_unit_1_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block2_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_50_block2_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_50_block2_unit_1_bottleneck_v2_preact_Relu)
        resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block2_unit_1_bottleneck_v2_preact_Relu)
        resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block2_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block2_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block2_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block2_unit_1_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block2_unit_1_bottleneck_v2_add = resnet_v2_50_block2_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_50_block2_unit_1_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block2_unit_1_bottleneck_v2_add)
        resnet_v2_50_block2_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block2_unit_2_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block2_unit_2_bottleneck_v2_preact_Relu)
        resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block2_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block2_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block2_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block2_unit_2_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block2_unit_2_bottleneck_v2_add = resnet_v2_50_block2_unit_1_bottleneck_v2_add + resnet_v2_50_block2_unit_2_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block2_unit_2_bottleneck_v2_add)
        resnet_v2_50_block2_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block2_unit_3_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block2_unit_3_bottleneck_v2_preact_Relu)
        resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block2_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block2_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block2_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block2_unit_3_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block2_unit_3_bottleneck_v2_add = resnet_v2_50_block2_unit_2_bottleneck_v2_add + resnet_v2_50_block2_unit_3_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block2_unit_3_bottleneck_v2_add)
        resnet_v2_50_block2_unit_4_bottleneck_v2_shortcut_MaxPool = F.max_pool2d(resnet_v2_50_block2_unit_3_bottleneck_v2_add, kernel_size=(1, 1), stride=(2, 2), padding=0, ceil_mode=False)
        resnet_v2_50_block2_unit_4_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block2_unit_4_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block2_unit_4_bottleneck_v2_preact_Relu)
        resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block2_unit_4_bottleneck_v2_Pad = F.pad(resnet_v2_50_block2_unit_4_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0)
        resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block2_unit_4_bottleneck_v2_Pad)
        resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block2_unit_4_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block2_unit_4_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block2_unit_4_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block2_unit_4_bottleneck_v2_add = resnet_v2_50_block2_unit_4_bottleneck_v2_shortcut_MaxPool + resnet_v2_50_block2_unit_4_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block2_unit_4_bottleneck_v2_add)
        resnet_v2_50_block3_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_1_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block3_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_50_block3_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_50_block3_unit_1_bottleneck_v2_preact_Relu)
        resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_1_bottleneck_v2_preact_Relu)
        resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_1_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block3_unit_1_bottleneck_v2_add = resnet_v2_50_block3_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_50_block3_unit_1_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_1_bottleneck_v2_add)
        resnet_v2_50_block3_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_2_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_2_bottleneck_v2_preact_Relu)
        resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_2_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block3_unit_2_bottleneck_v2_add = resnet_v2_50_block3_unit_1_bottleneck_v2_add + resnet_v2_50_block3_unit_2_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_2_bottleneck_v2_add)
        resnet_v2_50_block3_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_3_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_3_bottleneck_v2_preact_Relu)
        resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_3_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block3_unit_3_bottleneck_v2_add = resnet_v2_50_block3_unit_2_bottleneck_v2_add + resnet_v2_50_block3_unit_3_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_3_bottleneck_v2_add)
        resnet_v2_50_block3_unit_4_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_4_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_4_bottleneck_v2_preact_Relu)
        resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_4_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_4_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_4_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_4_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block3_unit_4_bottleneck_v2_add = resnet_v2_50_block3_unit_3_bottleneck_v2_add + resnet_v2_50_block3_unit_4_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_4_bottleneck_v2_add)
        resnet_v2_50_block3_unit_5_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_5_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_5_bottleneck_v2_preact_Relu)
        resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block3_unit_5_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_5_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_5_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_5_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block3_unit_5_bottleneck_v2_add = resnet_v2_50_block3_unit_4_bottleneck_v2_add + resnet_v2_50_block3_unit_5_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_5_bottleneck_v2_add)
        resnet_v2_50_block3_unit_6_bottleneck_v2_shortcut_MaxPool = F.max_pool2d(resnet_v2_50_block3_unit_5_bottleneck_v2_add, kernel_size=(1, 1), stride=(2, 2), padding=0, ceil_mode=False)
        resnet_v2_50_block3_unit_6_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block3_unit_6_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block3_unit_6_bottleneck_v2_preact_Relu)
        resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_6_bottleneck_v2_Pad = F.pad(resnet_v2_50_block3_unit_6_bottleneck_v2_conv1_Relu, (1, 1, 1, 1), mode = 'constant', value = 0)
        resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block3_unit_6_bottleneck_v2_Pad)
        resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block3_unit_6_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block3_unit_6_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block3_unit_6_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block3_unit_6_bottleneck_v2_add = resnet_v2_50_block3_unit_6_bottleneck_v2_shortcut_MaxPool + resnet_v2_50_block3_unit_6_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block3_unit_6_bottleneck_v2_add)
        resnet_v2_50_block4_unit_1_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block4_unit_1_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block4_unit_1_bottleneck_v2_shortcut_Conv2D = self.resnet_v2_50_block4_unit_1_bottleneck_v2_shortcut_Conv2D(resnet_v2_50_block4_unit_1_bottleneck_v2_preact_Relu)
        resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block4_unit_1_bottleneck_v2_preact_Relu)
        resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block4_unit_1_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block4_unit_1_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block4_unit_1_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block4_unit_1_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block4_unit_1_bottleneck_v2_add = resnet_v2_50_block4_unit_1_bottleneck_v2_shortcut_Conv2D + resnet_v2_50_block4_unit_1_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block4_unit_1_bottleneck_v2_add)
        resnet_v2_50_block4_unit_2_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block4_unit_2_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block4_unit_2_bottleneck_v2_preact_Relu)
        resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block4_unit_2_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block4_unit_2_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block4_unit_2_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block4_unit_2_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block4_unit_2_bottleneck_v2_add = resnet_v2_50_block4_unit_1_bottleneck_v2_add + resnet_v2_50_block4_unit_2_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm = self.resnet_v2_50_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm(resnet_v2_50_block4_unit_2_bottleneck_v2_add)
        resnet_v2_50_block4_unit_3_bottleneck_v2_preact_Relu = F.relu(resnet_v2_50_block4_unit_3_bottleneck_v2_preact_FusedBatchNorm)
        resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Conv2D = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Conv2D(resnet_v2_50_block4_unit_3_bottleneck_v2_preact_Relu)
        resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Conv2D)
        resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Relu = F.relu(resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D_pad = F.pad(resnet_v2_50_block4_unit_3_bottleneck_v2_conv1_Relu, (1, 1, 1, 1))
        resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D(resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D_pad)
        resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm(resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Conv2D)
        resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Relu = F.relu(resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_BatchNorm_FusedBatchNorm)
        resnet_v2_50_block4_unit_3_bottleneck_v2_conv3_Conv2D = self.resnet_v2_50_block4_unit_3_bottleneck_v2_conv3_Conv2D(resnet_v2_50_block4_unit_3_bottleneck_v2_conv2_Relu)
        resnet_v2_50_block4_unit_3_bottleneck_v2_add = resnet_v2_50_block4_unit_2_bottleneck_v2_add + resnet_v2_50_block4_unit_3_bottleneck_v2_conv3_Conv2D
        resnet_v2_50_postnorm_FusedBatchNorm = self.resnet_v2_50_postnorm_FusedBatchNorm(resnet_v2_50_block4_unit_3_bottleneck_v2_add)
        resnet_v2_50_postnorm_Relu = F.relu(resnet_v2_50_postnorm_FusedBatchNorm)
        return resnet_v2_50_postnorm_Relu


    @staticmethod
    def __conv(dim, name, **kwargs):
        if   dim == 1:  layer = nn.Conv1d(**kwargs)
        elif dim == 2:  layer = nn.Conv2d(**kwargs)
        elif dim == 3:  layer = nn.Conv3d(**kwargs)
        else:           raise NotImplementedError()

        return layer

    @staticmethod
    def __batch_normalization(dim, name, **kwargs):
        if   dim == 0 or dim == 1:  layer = nn.BatchNorm1d(**kwargs)
        elif dim == 2:  layer = nn.BatchNorm2d(**kwargs)
        elif dim == 3:  layer = nn.BatchNorm3d(**kwargs)
        else:           raise NotImplementedError()
        return layer

