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pipeline.py

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+import torch
+from diffusers import DiffusionPipeline
+
+class CRISPRTransformerPipeline(DiffusionPipeline):
+    def __init__(self, CRISPR_transformer_model):
+        super().__init__()
+
+        self.register_modules(CRISPR_transformer_model=CRISPR_transformer_model)
+
+    @torch.no_grad()
+    def __call__(self, batch):
+        return {
+            "logit": self.CRISPR_transformer_model(batch["refcode"].to(self.CRISPR_transformer_model.device))["logit"]
+        }