They already wrote the logic for you to select one label in each iteration of the loop. Here’s is the first few lines of the template code:
boxes = tf.cast(boxes, dtype=tf.float32)
scores = tf.cast(scores, dtype=tf.float32)
nms_indices = []
classes_labels = tf.unique(classes)[0] # Get unique classes
for label in classes_labels:
filtering_mask = classes == label
#### START CODE HERE
So within the loop, the variable label is the current single label value that you are working with and filtering_mask is a boolean mask tensor which will be True in the positions that match the current label. Then they give you a pretty big hint in the template code about which TF function to use:
# Get boxes for this class
# Use tf.boolean_mask() with 'boxes' and `filtering_mask`
boxes_label = None
# Get scores for this class
# Use tf.boolean_mask() with 'scores' and `filtering_mask`
scores_label = None
Have you read the documentation for TF boolean_mask and gather?