device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        # 1 input image channel, 6 output channels, 3x3 square conv kernel
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 3)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 5x5 image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, int(x.nelement() / x.shape[0]))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

model = LeNet()

parameters_to_prune = (
    (model.conv1, 'weight'),
    (model.conv2, 'weight'),
    (model.fc1, 'weight'),
    (model.fc2, 'weight'),
    (model.fc3, 'weight'),
)

prune.global_unstructured(
    parameters_to_prune,
    pruning_method=prune.L1Unstructured,
    amount=0.2,
)

https://pytorch.org/tutorials/intermediate/pruning_tutorial.html
In this tutorial, the author discussed various types of pruning (structured, unstructured, local, global), but all those techniques were employed on a vanilla LeNet model. Is it possible to prune on a pretrained model using those builtin API. [Search — PyTorch 1.9.0 documentation](Pytorch Pruning API)
If yes, then how?
For example,
models.resnet50(pretrained = True)
Do I need to assign pretrained = False?
or
Do I need to write those pretrained models from scratch to apply pruning techniques? Or something else?