code: def _make_second_chunk_prog(prog, op_idx):
      """Build second chunk by rebuilding a pristine MIL Program from MLModel"""
      block = prog.functions["main"]
      block.opset_version = ct.target.iOS16
  
      # First chunk outputs are second chunk inputs (e.g. skip connections)
      boundary_vars = _get_first_chunk_outputs(block, op_idx)
  
      # This op will not be included in this program. Its output var will be made into an input
      boundary_op = block.operations[op_idx]
  
      # Add all boundary ops as inputs
      with block:
          for var in boundary_vars:
              new_placeholder = Placeholder(
                  sym_shape=var.shape,
                  dtype=var.dtype
                  if var.dtype != types.fp16
                  else types.fp32,
                  name=var.name,
              )
  
              block._input_dict[
                  new_placeholder.outputs[0].name
              ] = new_placeholder.outputs[0]
  
              block.function_inputs = tuple(block._input_dict.values())
              new_var = None
              if var.dtype == types.fp16:
                  new_var = mb.cast(
                      x=new_placeholder.outputs[0],
                      dtype="fp16",
                      before_op=var.op,
                  )
              else:
                  new_var = new_placeholder.outputs[0]
  
              block.replace_uses_of_var_after_op(
                  anchor_op=boundary_op,
                  old_var=var,
                  new_var=new_var,
                  # This is needed if the program contains "constexpr_*" ops. In normal cases, there are stricter
                  # rules for removing them, and their presence may prevent replacing this var.
                  # However in this case, since we want to remove all the ops in chunk 1, we can safely
                  # set this to True.
                  force_replace=True,
              )
  
      PASS_REGISTRY["common::dead_code_elimination"](prog)
  
      # Remove any unused inputs
      new_input_dict = OrderedDict()
      for k, v in block._input_dict.items():
          if len(v.child_ops) > 0:
              new_input_dict[k] = v
      block._input_dict = new_input_dict
      block.function_inputs = tuple(block._input_dict.values())
  
      return prog
  
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 7 for calling a global function with example usage: _make_second_chunk_prog(prog, op_idx) and returns: prog