code: def compile_model(
      model: _Model_pb2.Model, destination_path: _Optional[str] = None
  ) -> str:
      """
      Compiles a Core ML model spec.
  
      Parameters
      ----------
      model: Model_pb2
          Spec/protobuf to compile.
  
          Note: an mlprogam which uses a blob file is not supported.
  
      destination_path: str
          Path where the compiled model will be saved.
  
      Returns
      -------
  
      str : Path to compiled model directory
          If the destination_path is specified, that is the value that will be returned.
  
      Examples
      --------
      .. sourcecode:: python
  
          from coremltools.models import CompiledMLModel
          from coremltools.models.utils import compile_model
          from coremltools.proto import Model_pb2
  
          spec = Model_pb2.Model()
          spec.specificationVersion = 1
  
          input_ = spec.description.input.add()
          input_.name = 'x'
          input_.type.doubleType.MergeFromString(b"")
  
          output_ = spec.description.output.add()
          output_.name = 'y'
          output_.type.doubleType.MergeFromString(b"")
          spec.description.predictedFeatureName = 'y'
  
          lr = spec.glmRegressor
          lr.offset.append(0.1)
          weights = lr.weights.add()
          weights.value.append(2.0)
  
          compiled_model_path = compile_model(spec)
          model = CompiledMLModel(compiled_model_path)
          y = model.predict({'x': 2})
  
      See Also
      --------
      coremltools.models.CompiledMLModel
      """
      # Check environment
      if _macos_version() < (10, 13):
          raise Exception(
              "Compiling a Core ML models is only support on macOS 10.13 or higher."
          )
      try:
          from ..libcoremlpython import _MLModelProxy
      except:
          raise Exception("Unable to compile any Core ML models.")
  
      # Check model parameter
      if isinstance(model, str):
          raise TypeError(
              "To get a compiled model from a saved MLModel, first load the model, "
              ' then call "get_compiled_model_path".'
          )
      if isinstance(model, _ct.models.MLModel):
          raise TypeError(
              'This model has already been compiled. Call "get_compiled_model_path"'
              " to get the compiled model."
          )
      if not isinstance(model, _Model_pb2.Model):
          raise TypeError(
              'Unrecognized input for "model" parameter. It should be a spec.'
          )
  
      # Check file extension of destination_path parameter
      if destination_path is not None and not destination_path.rstrip(
          "/"
      ).endswith(".mlmodelc"):
          raise Exception(
              '"destination_path" parameter must have ".mlmodelc" file extension.'
          )
  
      # Compile model
      with _tempfile.TemporaryDirectory() as save_dir:
          spec_file_path = save_dir + "/spec.mlmodel"
          save_spec(model, spec_file_path)
          original_compiled_model_path = _MLModelProxy.compileModel(
              spec_file_path
          )
  
      # Move the compiled model if needed
      if destination_path is None:
          return original_compiled_model_path
      _shutil.move(original_compiled_model_path, destination_path)
      return destination_path
  
imports: from collections.abc import Iterable
  from functools import lru_cache
  import math
  import os
  import shutil
  import subprocess
  import sys
  import tempfile
  from typing import Optional, Union
  import warnings
  import numpy
  import coremltools
  from coremltools import ComputeUnit
  from coremltools.converters.mil.mil.passes.defs.preprocess import NameSanitizer
  from coremltools.proto import Model_pb2
  import coremltools.proto.MIL_pb2
  from .._deps import _HAS_SCIPY
label_desc: Use image node_id 30 for calling a global function with example usage: compile_model(model, destination_path) and returns: destination_path, original_compiled_model_path