Hello everyone how to modify a image recognition CNN model to train video data?

Hello everyone i have a CNN model with me using to train and recognise images , i wanted change that CNN model to train videos, can any one help?

here is the code:-

class tinyvgg1(nn.Module):
def init(self,hidden,input,output):
super().init()
self.Conv_block1 = nn.Sequential(
nn.Conv2d(
in_channels= input,
out_channels=hidden,
kernel_size=3,
stride=1,
padding=1
),
nn.BatchNorm2d(hidden),
nn.ReLU(),
nn.Dropout(0.2),
nn.Conv2d(
in_channels = hidden,
out_channels =hidden,
kernel_size=3,
stride=1,
padding=1
),
nn.BatchNorm2d(hidden),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2,stride=2)
)
self.Conv_block2 = nn.Sequential(
nn.Conv2d(
in_channels=hidden,
out_channels=hidden,
kernel_size=3,
stride=1,
padding=1
),
nn.BatchNorm2d(hidden),
nn.ReLU(),
nn.Dropout(0.2),
nn.Conv2d(
in_channels=hidden,
out_channels=hidden,
kernel_size=3,
stride=1,
padding=1
),

    nn.BatchNorm2d(hidden),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2,stride=2)
)
self.Conv_block3 = nn.Sequential(
    nn.Conv2d(
        in_channels=hidden,
        out_channels=hidden,
        kernel_size=3,
        stride=1,
        padding=1
    ),
    nn.BatchNorm2d(hidden),
    nn.ReLU(),
    nn.Dropout(0.2),
    nn.Conv2d(
        in_channels=hidden,
        out_channels=hidden,
        kernel_size=3,
        stride=1,
        padding=1
    ),
    nn.BatchNorm2d(hidden),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2,stride=2)
)
self.Conv_block4 = nn.Sequential(
    nn.Conv2d(
        in_channels=hidden,
        out_channels=hidden,
        kernel_size=3,
        stride=1,
        padding=1
    ),
    nn.BatchNorm2d(hidden),
    nn.ReLU(),
    nn.Dropout(0.2),
    nn.Conv2d(
        in_channels=hidden,
        out_channels=hidden,
        kernel_size=3,
        stride=1,
        padding=1
    ),
    nn.BatchNorm2d(hidden),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=2,stride=2)
)

self.classifier = nn.Sequential(
    nn.Flatten(),
    nn.Linear(
        in_features = hidden*14*14,
        out_features = output
    )
)

def forward(self,x):
x = self.Conv_block1(x)
#print(x.shape)
x = self.Conv_block2(x)
#print(x.shape)
x = self.Conv_block3(x)
#print(x.shape)
x = self.Conv_block4(x)
#print(x.shape)
x = self.classifier(x)
return x
return self.Conv_block4(self.Conv_block3(self.Conv_block2(self.Conv_block1(x))))