Week 3 assignment YOLO algorithm final output

In last cell of this assignment notebook, we get output image along with following output text:
Found 10 boxes for images/test.jpg
car 0.89 (367, 300) (745, 648)
car 0.80 (761, 282) (942, 412)
car 0.74 (159, 303) (346, 440)
car 0.70 (947, 324) (1280, 705)
bus 0.67 (5, 266) (220, 407)
car 0.66 (706, 279) (786, 350)
car 0.60 (925, 285) (1045, 374)
car 0.44 (336, 296) (378, 335)
car 0.37 (965, 273) (1022, 292)
traffic light 0.36 (681, 195) (692, 214)

What is this score in fraction after class name and before bounding box coordinates?
Is it class score of that bounding box to have that particular object or something else? Which function is printing it?

from yolo_utils import … draw_boxes


out_scores, out_boxes, out_classes = sess.run([scores, boxes, classes],
feed_dict={yolo_model.input: image_data, K.learning_phase(): 0})

draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors)

def draw_boxes(image, out_scores, out_boxes, out_classes, class_names, colors):

for i,c in reversed(list(enumerate(out_classes))):

score = out_scores[i]

label = ‘{} {:.2f}’.format(predicted_class,score)

can’t recommend more highly developing the skill to do this kind of forensic discovery on legacy code.