code: def ct_convert(
      program,
      source="auto",
      inputs=None,
      outputs=None,
      classifier_config=None,
      minimum_deployment_target=None,
      convert_to=None,
      compute_precision=None,
      skip_model_load=False,
      converter=ct.convert,
      **kwargs,
  ):
      """
      Overloaded ct.convert function with the only difference being in the argument `convert_to`
      which in this overloaded call accepts a tuple of (target, dtype).
      Ex: ("neuralnetwork", "fp32"), ("mlprogram", "fp16")
      """
  
      if isinstance(converter, partial):
          raise ValueError(
              "Partial function is not supported for function-parameter 'converter' since its keywords arguments could get overridden."
          )
  
      target, dtype = convert_to
  
      if dtype not in ["fp32", "fp16"]:
          raise ValueError("Unsupported dtype config")
  
      compute_precision = (
          ct.precision.FLOAT16
          if dtype == "fp16"
          else ct.precision.FLOAT32
      )
      if target == "neuralnetwork":
          compute_precision = None
  
      mlmodel = converter(
          program,
          source=source,
          inputs=inputs,
          outputs=outputs,
          classifier_config=classifier_config,
          minimum_deployment_target=minimum_deployment_target,
          convert_to=target,
          compute_precision=compute_precision,
          skip_model_load=skip_model_load,
          **kwargs,
      )
  
      if os.environ.get("DEBUG_SAVE_MLMODEL", "0") == "1":
          from coremltools.converters.mil.testing_utils import (
              _serialize_current_pytest,
          )
  
          _serialize_current_pytest(mlmodel)
  
      return mlmodel
  
imports: import itertools
  import copy
  import os
  from functools import partial
  from pathlib import Path
  from typing import Dict, List, Tuple
  import numpy
  import pytest
  from PIL import Image
  import coremltools
  import coremltools.models.utils
  from coremltools._deps import _IS_MACOS
  from coremltools.converters.mil.mil import Block, Function, Program
  from coremltools.converters.mil.mil.passes.defs.preprocess import NameSanitizer
  from coremltools.converters.mil.mil.passes.defs.quantization import AbstractQuantizationPass
  from coremltools.converters.mil.mil.passes.pass_registry import PASS_REGISTRY
  from coremltools.proto import FeatureTypes_pb2
label_desc: Use image node_id 22 for calling a global function with example usage: ct_convert(program, source, inputs, outputs, classifier_config, minimum_deployment_target, convert_to, compute_precision, skip_model_load, converter) and returns: mlmodel