docstr: Transform a model to multi-instance inference model.
  
  :param model: The model to transform.
  :param num_processes: The number of processes to use, default to 4.
  :param cores_per_process: Number of CPU cores used by each process,
      default to `None`, means decided automatically.
  :param cpu_for_each_process: Specify the CPU cores used by each process,
      default to `None`, if set, it will override `num_processes` and `cores_per_process`.
  :return: Model with multi-instance inference acceleration.
docstr_tok: ["Transform","a","model","to","multi-instance","inference","model",".",":","param","model",":","The","model","to","transform",".",":","param","num_processes",":","The","number","of","processes","to","use",",","default","to","4.",":","param","cores_per_process",":","Number","of","CPU","cores","used","by","each","process",",","default","to","`","None","`",",","means","decided","automatically",".",":","param","cpu_for_each_process",":","Specify","the","CPU","cores","used","by","each","process",",","default","to","`","None","`",",","if","set",",","it","will","override","`","num_processes","`","and","`","cores_per_process","`",".",":","return",":","Model","with","multi-instance","inference","acceleration","."]
code: def to_multi_instance(
      model: nn.Module,
      num_processes: int = 4,
      cores_per_process: int = None,
      cpu_for_each_process: List[List[int]] = None,
  ) -> _MultiInstanceModel:
      """
      Transform a model to multi-instance inference model.
  
      :param model: The model to transform.
      :param num_processes: The number of processes to use, default to 4.
      :param cores_per_process: Number of CPU cores used by each process,
          default to `None`, means decided automatically.
      :param cpu_for_each_process: Specify the CPU cores used by each process,
          default to `None`, if set, it will override `num_processes` and `cores_per_process`.
      :return: Model with multi-instance inference acceleration.
      """
      invalidInputError(
          isinstance(num_processes, int) and num_processes > 0,
          "num_processes must be a positive integer",
      )
      # if num_processes == 1 and no core specification, we will use current process directly
      if (
          num_processes == 1
          and cpu_for_each_process is None
          and cores_per_process is None
      ):
          warnings.warn(
              "Will run inference in current process directly "
              "because the `num_processes` is 1"
          )
          return _MultiInstanceModel(model, None, None, None, None)
  
      p_num = (
          num_processes
          if cpu_for_each_process is None
          else len(cpu_for_each_process)
      )
  
      # else we will start multiple sub-processes
      send_queue = mp.Queue()
      recv_queue = mp.Queue()
      next_idx = mp.Value("i", 0, lock=True)
  
      if cpu_for_each_process is None:
          if cores_per_process is None:
              envs = schedule_processors(p_num)
          else:
              envs = [
                  {
                      "KMP_AFFINITY": f"granularity=fine,proclist="
                      f"[{i*cores_per_process}-{(i+1)*cores_per_process-1}]"
                      f",explicit",
                      "OMP_NUM_THREADS": str(cores_per_process),
                  }
                  for i in range(p_num)
              ]
      else:
          envs = [
              {
                  "KMP_AFFINITY": f"granularity=fine,proclist="
                  f"[{','.join([str(i) for i in cpu_for_each_process[i]])}]"
                  f",explicit",
                  "OMP_NUM_THREADS": str(len(cpu_for_each_process[i])),
              }
              for i in range(p_num)
          ]
      ps = []
      for i in range(p_num):
          with EnvContext(envs[i]):
              p = mp.Process(
                  target=_multi_instance_helper,
                  args=(model, send_queue, recv_queue, next_idx),
                  daemon=True,
              )
              p.start()
              ps.append(p)
  
      return _MultiInstanceModel(
          model, ps, send_queue, recv_queue, next_idx
      )
  
imports: import torch
  from torch import nn
  import time
  import sigfig
  import multiprocessing
  from typing import Dict, Callable, Tuple, Optional, List, Union, Sequence, Mapping
  from torch.utils.data import DataLoader
  from torchmetrics.metric import Metric
  from bigdl.nano.utils.common import AccelerationOption, available_acceleration_combination, latency_calculate_helper, torch_loader_latency_calculate_helper, format_optimize_result, BaseInferenceOptimizer
  from bigdl.nano.utils.common import invalidInputError
  from bigdl.nano.pytorch.amp import BF16Model
  from bigdl.nano.pytorch.low_precision.jit_int8_api import PytorchJITINT8Model
  from bigdl.nano.deps.openvino.openvino_api import PytorchOpenVINOModel
  from bigdl.nano.deps.ipex.ipex_api import PytorchIPEXJITModel, PytorchIPEXJITBF16Model, PytorchIPEXQuantizationModel, PytorchIPEXPUModel
  from bigdl.nano.deps.onnxruntime.onnxruntime_api import PytorchONNXRuntimeModel
  from bigdl.nano.deps.neural_compressor.inc_api import quantize
  from bigdl.nano.pytorch.model import AcceleratedLightningModule
  from bigdl.nano.utils.pytorch import get_forward_args, get_input_example
  from bigdl.nano.utils.pytorch import NanoMetric
  from bigdl.nano.utils.pytorch import RepeatDataset, remove_batch_dim_fn
  from bigdl.nano.utils.pytorch import transform_multiple_input_dataloader_to_inc_mode, automatic_add_label_in_dataloader
  from bigdl.nano.utils.pytorch import TORCH_VERSION_LESS_1_10
  from bigdl.nano.utils.pytorch import save_model, load_model
  from bigdl.nano.utils.common import schedule_processors
  from bigdl.nano.utils.common import EnvContext
  from bigdl.nano.pytorch.context_manager import generate_context_manager, BaseContextManager, AutocastContextManager
  from .multi_instance import _MultiInstanceModel, _multi_instance_helper
  import traceback
  import warnings
  import os
label_desc: Use image node_id 7 for calling the InferenceOptimizer obj's underlying member method code with example usage: obj.to_multi_instance(model, num_processes, cores_per_process, cpu_for_each_process) and returns: _MultiInstanceModel, _MultiInstanceModel