code: def _convert_to_classifier(proto, classifier_config):
      tmp_model = MLModel(proto)
      tmp_model = neural_network.utils.make_nn_classifier(
          tmp_model,
          classifier_config.class_labels,
          classifier_config.predicted_feature_name,
          classifier_config.predicted_probabilities_output,
      )
      return tmp_model.get_spec()
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import logging
  from collections import defaultdict
  from coremltools.converters.mil.input_types import ClassifierConfig, ImageType, EnumeratedShapes, Shape, RangeDim
  from coremltools.models import MLModel
  from coremltools.models import neural_network
  import coremltools.models.datatypes
  from coremltools.converters.mil.mil import types
  from coremltools.converters.mil.mil.types.symbolic import any_symbolic, any_variadic, is_symbolic
  from .op_mapping import convert_ops
  from coremltools.models.neural_network import flexible_shape_utils
  from coremltools.models.neural_network.flexible_shape_utils import update_image_size_range, add_enumerated_image_sizes, set_multiarray_ndshape_range, add_multiarray_ndshape_enumeration
  from .passes.nn_passes import nn_backend_passes
  from coremltools.converters._profile_utils import _profile
label_desc: Use image node_id 2 for calling a global function with example usage: _convert_to_classifier(proto, classifier_config) and returns: tmp_model