You can use the generated Python script to create a pruned model and then
load dense weights on it. The dense weights are obtained and saved by ‘pruned_state_dict()’ during fine-tuning.
from pruned_resnet18 import ResNet
model = ResNet()
model.load_state_dict(torch.load('resnet18_dense_best.pth'))