So I read Link 10 to understand how to extract the features from a pretrained network.
In the following I use VGG16. VGG has two Sequentials (features and classifier), so I used the following way to just keep the feature part and get rid of the classifier part. (well I still dont know clearly how to do it for resnet, but that is something that we can discuss later).
vgg16 = models.vgg16(pretrained=True)
New_VGG_Without_Classifier = nn.Sequential(*list(vgg16.features))
print New_VGG_Without_Classifier
and the output for printing is:

Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace)
(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU(inplace)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace)
(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU(inplace)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU(inplace)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace)
(30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)

I test this method by just giving an example to this new network, and it seems that it works:
OutPut = New_VGG_Without_Classifier(InputVar)
print OutPut.shape
torch.Size([1, 512, 7, 7])
Here are my questions and I appreciate if anyone can explain how i can solve them:
1) This is my main question. I designed a network (lets call in SecondNet) as follows, I want to attach it to the end of my New_VGG_Without_Classifier, How I should do that??
Here is my SecondNet network, I put the input of the SecondNet in way that is compatible with the output of New_VGG_Without_Classifier (hopefully it is right and i didint do any mistake)
class SecondNet(nn.Module):
    def __init__(self):
        super(SecondNet, self).__init__()
        self.conv1 = nn.Conv2d(512, 100, 3)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(100, 16, 3)
        self.fc1 = nn.Linear(16 * 3 * 3, 12)
        self.fc2 = nn.Linear(12, 8)
        self.fc3 = nn.Linear(8, 15)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 3 * 3)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

2) If I want to have the output of last 3 Conv2d layers and save them and work with them, how should i do that?