I was on the web looking for how to implement ResNet in Pytorch, and found this code, but I have couple of days looking for information on the web to explain how it works and I have not found anything, that is why I hopw to find information here of the parameters what are they and how  they are used, or documentations.
Thanks.
import torch.nn as nn
import torchvision.models as models

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)


why do we init the ResNet with the Bottleneck, any docs?
2.- layers [3, 4, 6, 3] this depends on the type of resnet? 18, 50, 101? etc
3.- groups?
4.- width per group, can we use different sizes? 8, 16, 32, etc?
5.- self.fc = nn.Linear(2048, 1) is is binary, but why not 2 ? in Keras you can use 2 as the final output