code: def score_labels_from_class_probabilities(
      logits: np.ndarray,
  ) -> Tuple[np.ndarray, np.ndarray]:
      probs = scipy.special.softmax(logits, axis=-1)
      labels = probs.argmax(-1, keepdims=True)
      scores = np.take_along_axis(probs, labels, axis=-1)
      scores, labels = scores.squeeze(-1), labels.squeeze(-1)
      return scores, labels
  
imports: import io
  import pathlib
  from collections import defaultdict
  from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union
  import numpy
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import PaddingMode, center_to_corners_format, corners_to_center_format, id_to_rgb, pad, rescale, resize, rgb_to_id, to_channel_dimension_format
  from ...image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, AnnotationFormat, AnnotationType, ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images, validate_annotations
  from ...utils import TensorType, is_flax_available, is_jax_tensor, is_scipy_available, is_tf_available, is_tf_tensor, is_torch_available, is_torch_tensor, is_vision_available, logging
label_desc: Use image node_id 15 for calling a global function with example usage: score_labels_from_class_probabilities(logits) and returns: scores,  labels