IMG_SHAPE = (3, 32, 32) test_input = torch.randn(1, *IMG_SHAPE) model = DeepAutoencoder(IMG_SHAPE, 15) code = model.encode(test_input)   deep_autoencoder = DeepAutoencoder((3, 32, 32), 32) deep_autoencoder.load_state_dict(torch.load('deep_autoencoder.pt', map_location='cpu')) for p in deep_autoencoder.parameters(): p.requires_grad_(False)   images = X_train # Encode all images ### YOUR CODE HERE ### assert len(codes) == len(images)

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