load gcc module on jasmin

so version of GCC is compatible with PyTorch (I think) and so can also re-enable model imports in predict.py

bin/jasmin/lotus-sampling-wrapper

@@ -1,7 +1,7 @@
 #!/bin/bash
 # Wrapper script around commands for training a model to queue on LOTUS on JASMIN
 
-module use gcc
+module load gcc
 
 source ~/.bashrc
 mamba activate mv-mlde

bin/jasmin/lotus-wrapper

@@ -1,6 +1,8 @@
 #!/bin/bash
 # Wrapper script around commands for training a model to queue on LOTUS on JASMIN
 
+module load gcc
+
 source ~/.bashrc
 mamba activate mv-mlde
 

bin/predict.py

@@ -34,17 +34,17 @@
 import ml_downscaling_emulator.score_sde_pytorch.models as models  # noqa: F401
 from ml_downscaling_emulator.score_sde_pytorch.models import utils as mutils
 
-# from ml_downscaling_emulator.score_sde_pytorch.models import cncsnpp  # noqa: F401
-# from ml_downscaling_emulator.score_sde_pytorch.models import cunet  # noqa: F401
+from ml_downscaling_emulator.score_sde_pytorch.models import cncsnpp  # noqa: F401
+from ml_downscaling_emulator.score_sde_pytorch.models import cunet  # noqa: F401
 from ml_downscaling_emulator.score_sde_pytorch.models import det_cunet  # noqa: F401
 
-# from ml_downscaling_emulator.score_sde_pytorch.models import (  # noqa: F401
-#     layerspp,  # noqa: F401
-# )  # noqa: F401
-# from ml_downscaling_emulator.score_sde_pytorch.models import layers  # noqa: F401
-# from ml_downscaling_emulator.score_sde_pytorch.models import (  # noqa: F401
-#     normalization,  # noqa: F401
-# )  # noqa: F401
+from ml_downscaling_emulator.score_sde_pytorch.models import (  # noqa: F401
+    layerspp,  # noqa: F401
+)  # noqa: F401
+from ml_downscaling_emulator.score_sde_pytorch.models import layers  # noqa: F401
+from ml_downscaling_emulator.score_sde_pytorch.models import (  # noqa: F401
+    normalization,  # noqa: F401
+)  # noqa: F401
 import ml_downscaling_emulator.score_sde_pytorch.sampling as sampling
 
 from ml_downscaling_emulator.score_sde_pytorch.sde_lib import (