Yes, that’s possible and you could e.g. directly reuse them as seen here:
model = models.resnet50()
conv1 = model.conv1
bn1 = model.bn1

x = torch.randn(1, 3, 224, 224)
out = conv1(x)
out = bn1(out)
print(out.shape)
> torch.Size([1, 64, 112, 112])

Depending on your use case you might want to assign these layers as internal modules to a custom nn.Module.