@ptrblck ahh I understand now, it is completely different int keras, a lot easier in Pytorch hehe.
so when this person in the internet is declaring this way the resnet, he is using the finetune technique, right ?
class MyResNeXt(models.resnet.ResNet):
    def __init__(self, training=True):
        super(MyResNeXt, self).__init__(block=models.resnet.Bottleneck,
                                        layers=[3, 4, 6, 3], 
                                        groups=32, 
                                        width_per_group=4)
        self.fc = nn.Linear(2048, 1)

I was looking into the second link and was wondering how you go about instantiating is this correct? or where to find information of how to instantiate the ResNet 50 this way?
model_ft = models.ResNet50(block=models.resnet.Bottleneck,
                                        layers=[3, 4, 6, 3], 
                                        groups=32, 
                                        width_per_group=4)
        self.fc = nn.Linear(2048, 1)

Thanks again for your valuable and fast help!
PS.
can you point me where to learn how to concatenate models in Pytorch, or how you call this technique?
output = model1(x)
output = model2(output)