code: def model_num_classes(checkpoint_path, n_classes_model_variable):
      """Returns the number of classes in the checkpoint."""
      if not checkpoint_path:
          return None
  
      # Figure out how many classes this inception model was trained to predict.
      try:
          shapes = model_shapes(
              checkpoint_path, [n_classes_model_variable]
          )
      except KeyError:
          return None
      if n_classes_model_variable not in shapes:
          return None
      return shapes[n_classes_model_variable][-1]
  
imports: import json
  from typing import Optional
  from absl import logging
  import numpy
  import tensorflow
  from deepvariant.protos import deepvariant_pb2
  from third_party.nucleus.io import sharded_file_utils
  from third_party.nucleus.io import tfrecord
  from third_party.nucleus.protos import variants_pb2
  from tensorflow.core.example import example_pb2
label_desc: Use image node_id 19 for calling a global function with example usage: model_num_classes(checkpoint_path, n_classes_model_variable) and returns: shapes, None, None, None