class_docstr: Lightning module specialized for keypoint_rcnn, 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`.
class_docstr_tok: ["Lightning","module","specialized","for","keypoint_rcnn",",","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","`","."]
code: def convert_raw_predictions(self, batch, raw_preds, records):
      return convert_raw_predictions(
          batch=batch,
          raw_preds=raw_preds,
          records=records,
          detection_threshold=0.0,
      )
  
imports: from icevision.models.torchvision.lightning_model_adapter import *
  from icevision.models.torchvision.keypoint_rcnn.prediction import *
label_desc: Use image node_id 1 for calling the ModelAdapter obj's underlying member method code with example usage: obj.convert_raw_predictions(batch, raw_preds, records) and returns: convert_raw_predictions