how to print the size of the particular layer in pytorch

1from torchvision import models
2from summary import summary
3
4vgg = models.vgg16()
5summary(vgg, (3, 224, 224))
6
7----------------------------------------------------------------
8        Layer (type)               Output Shpae         Param #
9================================================================
10            Conv2d-1         [-1, 64, 224, 224]            1792
11              ReLU-2         [-1, 64, 224, 224]               0
12            Conv2d-3         [-1, 64, 224, 224]           36928
13              ReLU-4         [-1, 64, 224, 224]               0
14         MaxPool2d-5         [-1, 64, 112, 112]               0
15            Conv2d-6        [-1, 128, 112, 112]           73856
16              ReLU-7        [-1, 128, 112, 112]               0
17            Conv2d-8        [-1, 128, 112, 112]          147584
18              ReLU-9        [-1, 128, 112, 112]               0
19        MaxPool2d-10          [-1, 128, 56, 56]               0
20           Conv2d-11          [-1, 256, 56, 56]          295168
21             ReLU-12          [-1, 256, 56, 56]               0
22           Conv2d-13          [-1, 256, 56, 56]          590080
23             ReLU-14          [-1, 256, 56, 56]               0
24           Conv2d-15          [-1, 256, 56, 56]          590080
25             ReLU-16          [-1, 256, 56, 56]               0
26        MaxPool2d-17          [-1, 256, 28, 28]               0
27           Conv2d-18          [-1, 512, 28, 28]         1180160
28             ReLU-19          [-1, 512, 28, 28]               0
29           Conv2d-20          [-1, 512, 28, 28]         2359808
30             ReLU-21          [-1, 512, 28, 28]               0
31           Conv2d-22          [-1, 512, 28, 28]         2359808
32             ReLU-23          [-1, 512, 28, 28]               0
33        MaxPool2d-24          [-1, 512, 14, 14]               0
34           Conv2d-25          [-1, 512, 14, 14]         2359808
35             ReLU-26          [-1, 512, 14, 14]               0
36           Conv2d-27          [-1, 512, 14, 14]         2359808
37             ReLU-28          [-1, 512, 14, 14]               0
38           Conv2d-29          [-1, 512, 14, 14]         2359808
39             ReLU-30          [-1, 512, 14, 14]               0
40        MaxPool2d-31            [-1, 512, 7, 7]               0
41           Linear-32                 [-1, 4096]       102764544
42             ReLU-33                 [-1, 4096]               0
43          Dropout-34                 [-1, 4096]               0
44           Linear-35                 [-1, 4096]        16781312
45             ReLU-36                 [-1, 4096]               0
46          Dropout-37                 [-1, 4096]               0
47           Linear-38                 [-1, 1000]         4097000
48================================================================
49Total params: 138357544
50Trainable params: 138357544
51Non-trainable params: 0
52----------------------------------------------------------------