how Pruning the last conv layer affects the first linear layer of the classifier about network-slimming HOT 5 OPEN

def __init__(self):
    super(VGGOWN, self).__init__()
    self.features = nn.Sequential(
        nn.Conv2d(3, 64, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(64, 64, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True),  # 1/2

        # conv2
        nn.Conv2d(64, 128, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(128, 128, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True),  # 1/4

        # conv3
        nn.Conv2d(128, 256, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(256, 256, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(256, 256, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True), # 1/8

        # conv4
        nn.Conv2d(256, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True),  # 1/16

        # conv5
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, 3, padding=1),
        nn.ReLU(inplace=True),
        nn.MaxPool2d(2, stride=2, ceil_mode=True) # 1/32
    )
    self.avgpool = nn.AdaptiveAvgPool2d((7, 7))

    self.classifier = nn.Sequential(

        nn.Linear(in_features=512 * 7 * 7, out_features=4096, bias=True),
        nn.ReLU(inplace=True),
        nn.Dropout(p=0.5),
        nn.Linear(in_features=4096, out_features=4096, bias=True),
        nn.ReLU(inplace=True),
        nn.Dropout(p=0.5),
        nn.Linear(in_features=4096, out_features=1000, bias=True)
    )

def forward(self, *input):
    x = self.features(input)
    #print(x.size)
    x = self.avgpool(x)
    #print(x.size)
    x = x.view(x.size(0), -1)
    #print(x.size)
    y = self.classifier(x)
    return y