code: def expected_coco_metric_output():
      return [
          " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.494",
          " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.556",
          " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.556",
          " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000",
          " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000",
          " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.507",
          " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.450",
          " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.850",
          " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.850",
          " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000",
          " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000",
          " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.850",
      ]
  
imports: import pytest
  from icevision.all import *
  from icevision.models.ross import efficientdet
label_desc: Use image node_id 1 for calling a global function with example usage: expected_coco_metric_output() and returns: list