class_docstr: Lightning module specialized for EfficientDet, with metrics support.
  
  The methods `forward`, `training_step`, `validation_step`, `validation_epoch_end`
  are already overriden.
  
  # Arguments
      model: The pytorch model to use.
      metrics: `Sequence` of metrics to use.
  
  # Returns
      A `LightningModule`.
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code: def convert_raw_predictions(self, xb, yb, raw_preds, records):
      # Note: raw_preds["detections"] key is available only during Pytorch Lightning validation/test step
      # Calling the method manually (instead of letting the Trainer call it) will raise an exception.
      return efficientdet.convert_raw_predictions(
          batch=(xb, yb),
          raw_preds=raw_preds["detections"],
          records=records,
          detection_threshold=0.0,
      )
  
file_path: icevision/icevision/models/ross/efficientdet/lightning/model_adapter.py
imports: from icevision.imports import *
  from icevision.metrics import *
  from icevision.engines.lightning.lightning_model_adapter import LightningModelAdapter
  from icevision.models.ross import efficientdet
label_desc: Use image node_id 7 for calling the ModelAdapter obj's underlying member method code with example usage: obj.convert_raw_predictions(xb, yb, raw_preds, records) and returns: efficientdet