module_mixins.py updated with tensor return
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@ -100,7 +100,7 @@ class BaseValMixin:
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uar_score = sklearn.metrics.recall_score(y_true, y_pred, labels=[0, 1], average='macro',
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sample_weight=None, zero_division='warn')
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uar_score = torch.as_tensor(uar_score)
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summary_dict['log'].update({f'uar{ident}_score': uar_score})
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return summary_dict
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