>>> import shutil, tempfile
>>> import lucid, lucid.nn as nn, lucid.coreml as cml
>>> model = nn.Sequential(
...     nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(),
...     nn.AdaptiveAvgPool2d(1), nn.Flatten(), nn.Linear(16, 10),
... ).eval()
>>> x, room = lucid.randn(1, 3, 32, 32), tempfile.mkdtemp()
>>> cml.export(model, x, f"{room}/model.mlpackage").close()
>>> package = cml.load(f"{room}/model.mlpackage")
>>> package.predict(x).shape
(1, 10)
>>> package.close()
The handle recovers what the export knew — that an input is a
picture, that the outputs are labels, which inputs stand in for a
draw — because the package declares all of it:
>>> brightest = nn.Sequential(nn.AdaptiveAvgPool2d(1), nn.Flatten()).eval()
>>> red = lucid.zeros(1, 3, 8, 8)
>>> red[:, 0] = 255.0
>>> cml.export(brightest, red, f"{room}/cls.mlpackage",
...            image_input=cml.ImageInput(scale=1 / 255.0),
...            classifier=cml.Classifier(labels=("red", "green", "blue")),
...            ).close()
>>> reopened = cml.load(f"{room}/cls.mlpackage")
>>> reopened.classify(red)[0]
'red'
>>> reopened.close()
>>> shutil.rmtree(room)