code: def classification_to_detections(
      sample_collection, in_field, out_field
  ):
      """Converts the :class:`fiftyone.core.labels.Classification` field of the
      collection into a :class:`fiftyone.core.labels.Detections` field containing
      a single detection whose bounding box spans the entire image.
  
      Args:
          sample_collection: a
              :class:`fiftyone.core.collections.SampleCollection`
          in_field: the name of the :class:`fiftyone.core.labels.Classification`
              field
          out_field: the name of the :class:`fiftyone.core.labels.Detections`
              field to populate
      """
      fov.validate_collection_label_fields(
          sample_collection, in_field, fol.Classification
      )
  
      samples = sample_collection.select_fields(in_field)
      in_field, processing_frames = samples._handle_frame_field(
          in_field
      )
      out_field, _ = samples._handle_frame_field(out_field)
  
      for sample in samples.iter_samples(autosave=True, progress=True):
          if processing_frames:
              images = sample.frames.values()
          else:
              images = [sample]
  
          for image in images:
              label = image[in_field]
              if label is None:
                  continue
  
              detection = fol.Detection(
                  label=label.label,
                  bounding_box=[0, 0, 1, 1],  # entire image
                  confidence=label.confidence,
              )
              image[out_field] = fol.Detections(detections=[detection])
  
file_path: fiftyone/fiftyone/utils/labels.py
imports: import eta.core.utils
  import fiftyone.core.labels
  import fiftyone.core.utils
  import fiftyone.core.validation
label_desc: Use image node_id 9 for calling a global function with example usage: classification_to_detections(sample_collection, in_field, out_field) without return types