code: def main(args):
      os.makedirs(args.o, exist_ok=True)
  
      # Check filename extension
      mlpackage_name = os.path.basename(args.mlpackage_path)
      name, ext = os.path.splitext(mlpackage_name)
      assert (
          ext == ".mlpackage"
      ), f"`--mlpackage-path` (args.mlpackage_path) is not an .mlpackage file"
  
      # Load CoreML model
      logger.info("Loading model from {}".format(args.mlpackage_path))
      start_ = time.time()
      model = ct.models.MLModel(
          args.mlpackage_path,
          compute_units=ct.ComputeUnit.CPU_ONLY,
      )
      logger.info(
          f"Loading {args.mlpackage_path} took {time.time() - start_:.1f} seconds"
      )
  
      # Load the MIL Program from MLModel
      prog = _load_prog_from_mlmodel(model)
  
      # Compute the incision point by bisecting the program based on weights size
      (
          op_idx,
          first_chunk_weights_size,
          total_weights_size,
      ) = _get_op_idx_split_location(prog)
      main_block = prog.functions["main"]
      incision_op = main_block.operations[op_idx]
      logger.info(
          f"{args.mlpackage_path} will chunked into two pieces."
      )
      logger.info(
          f"The incision op: name={incision_op.name}, type={incision_op.op_type}, index={op_idx}/{len(main_block.operations)}"
      )
      logger.info(
          f"First  chunk size = {first_chunk_weights_size:.2f} MB"
      )
      logger.info(
          f"Second chunk size = {total_weights_size - first_chunk_weights_size:.2f} MB"
      )
  
      # Build first chunk (in-place modifies prog by declaring early exits and removing unused subgraph)
      prog_chunk1 = _make_first_chunk_prog(prog, op_idx)
  
      # Build the second chunk
      prog_chunk2 = _make_second_chunk_prog(
          _load_prog_from_mlmodel(model), op_idx
      )
  
      if not args.check_output_correctness:
          # Original model no longer needed in memory
          del model
          gc.collect()
  
      # Convert the MIL Program objects into MLModels
      logger.info("Converting the two programs")
      model_chunk1 = ct.convert(
          prog_chunk1,
          convert_to="mlprogram",
          compute_units=ct.ComputeUnit.CPU_ONLY,
          minimum_deployment_target=ct.target.iOS16,
      )
      del prog_chunk1
      gc.collect()
      logger.info("Conversion of first chunk done.")
  
      model_chunk2 = ct.convert(
          prog_chunk2,
          convert_to="mlprogram",
          compute_units=ct.ComputeUnit.CPU_ONLY,
          minimum_deployment_target=ct.target.iOS16,
      )
      del prog_chunk2
      gc.collect()
      logger.info("Conversion of second chunk done.")
  
      # Verify output correctness
      if args.check_output_correctness:
          logger.info("Verifying output correctness of chunks")
          _verify_output_correctness_of_chunks(
              full_model=model,
              first_chunk_model=model_chunk1,
              second_chunk_model=model_chunk2,
          )
  
      # Remove original (non-chunked) model if requested
      if args.remove_original:
          logger.info(
              "Removing original (non-chunked) model at {args.mlpackage_path}"
          )
          shutil.rmtree(args.mlpackage_path)
          logger.info("Done.")
  
      if args.merge_chunks_in_pipeline_model:
          # Make a single pipeline model to manage the model chunks
          pipeline_model = ct.utils.make_pipeline(
              model_chunk1, model_chunk2
          )
          out_path_pipeline = os.path.join(
              args.o, name + "_chunked_pipeline.mlpackage"
          )
  
          # Save and reload to ensure CPU placement
          pipeline_model.save(out_path_pipeline)
          pipeline_model = ct.models.MLModel(
              out_path_pipeline, compute_units=ct.ComputeUnit.CPU_ONLY
          )
  
          if args.check_output_correctness:
              logger.info(
                  "Verifying output correctness of pipeline model"
              )
              _verify_output_correctness_of_chunks(
                  full_model=model,
                  pipeline_model=pipeline_model,
              )
      else:
          # Save the chunked models to disk
          out_path_chunk1 = os.path.join(
              args.o, name + "_chunk1.mlpackage"
          )
          out_path_chunk2 = os.path.join(
              args.o, name + "_chunk2.mlpackage"
          )
  
          logger.info(
              f"Saved chunks in {args.o} with the suffix _chunk1.mlpackage and _chunk2.mlpackage"
          )
          model_chunk1.save(out_path_chunk1)
          model_chunk2.save(out_path_chunk2)
          logger.info("Done.")
  
imports: import argparse
  from collections import OrderedDict
  import coremltools
  from coremltools.converters.mil import Block, Program, Var
  from coremltools.converters.mil.frontend.milproto.load import load
  from coremltools.converters.mil.mil import Builder
  from coremltools.converters.mil.mil import Placeholder
  from coremltools.converters.mil.mil import types
  from coremltools.converters.mil.mil.passes.helper import block_context_manager
  from coremltools.converters.mil.mil.passes.pass_registry import PASS_REGISTRY
  from coremltools.converters.mil.testing_utils import random_gen_input_feature_type
  import gc
  import logging
  import numpy
  import os
  from python_coreml_stable_diffusion import torch2coreml
  import shutil
  import time
label_desc: Use image node_id 8 for calling a global function with example usage: main(args) without return types