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<         self.aux_sequence_object = "tracks"
---
>         self.aux_sequence_object = "objects"
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<         outputs = [f"{self.model_name}_p{flav.rstrip('jets')}" for flav in object_classes]
---
>         outputs = [f"{self.model_name}_p{flav.rstrip('events')}" for flav in object_classes]
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<         pt_model.input_dims["jets"],
---
>         pt_model.input_dims["events"],
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<     inputs_pt["jets"] = jets
---
>     inputs_pt["events"] = jets
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<     pred_pt_jc = [p.detach().numpy() for p in get_probs(outputs_pt["jets"]["jets_classification"])]
---
>     pred_pt_jc = [p.detach().numpy() for p in get_probs(outputs_pt["events"]["events_classification"])]
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<         pred_pt_origin = torch.argmax(outputs_pt["tracks"]["track_origin"], dim=-1).detach().numpy()
---
>         pred_pt_origin = torch.argmax(outputs_pt["objects"]["track_origin"], dim=-1).detach().numpy()
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<         pred_pt_scores = outputs_pt["tracks"]["track_vertexing"].detach()
---
>         pred_pt_scores = outputs_pt["objects"]["track_vertexing"].detach()
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<             "name_salt": "jets",
---
>             "name_salt": "events",
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<     if "tracks" in inputs:
---
>     if "objects" in inputs:
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<             "name_salt": "tracks",
---
>             "name_salt": "objects",