Create modeling_storm_oracle.py

modeling_storm_oracle.py

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+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel
+from .configuration_storm_oracle import StormOracleConfig
+
+# ---- import your actual model code ----
+# If your code lives in tornado_predictor.py (as pasted), import from there:
+from .tornado_predictor import TornadoSuperPredictor  # adjust if filename differs
+
+class StormOracleModel(PreTrainedModel):
+    config_class = StormOracleConfig
+
+    def __init__(self, config: StormOracleConfig):
+        super().__init__(config)
+        self.model = TornadoSuperPredictor(in_channels=config.in_channels)
+        self.post_init()  # HF bookkeeping
+
+    def forward(self, radar_x: torch.Tensor, atmo: dict):
+        """
+        radar_x: (B, C, H, W)
+        atmo: dict of tensors (cape, wind_shear, helicity, temperature, dewpoint, pressure)
+        returns TornadoPredictionBatch (your dataclass)
+        """
+        return self.model(radar_x, atmo)