code: def load_model_ensemble(
      self,
  ) -> Tuple[List[FairseqModel], FairseqDataclass]:
      arg_overrides = ast.literal_eval(
          self.cfg.common_eval.model_overrides
      )
      models, saved_cfg = checkpoint_utils.load_model_ensemble(
          utils.split_paths(self.cfg.common_eval.path, separator="\\"),
          arg_overrides=arg_overrides,
          task=self.task,
          suffix=self.cfg.checkpoint.checkpoint_suffix,
          strict=(self.cfg.checkpoint.checkpoint_shard_count == 1),
          num_shards=self.cfg.checkpoint.checkpoint_shard_count,
      )
      for model in models:
          self.optimize_model(model)
      return models, saved_cfg
  
imports: import ast
  import hashlib
  import logging
  import os
  import shutil
  import sys
  import re
  from dataclasses import dataclass, field, is_dataclass
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Tuple, Union
  import editdistance
  import torch
  import torch.distributed
  from examples.speech_recognition.new.decoders.decoder_config import DecoderConfig, FlashlightDecoderConfig
  from examples.speech_recognition.new.decoders.decoder import Decoder
  from fairseq import checkpoint_utils, distributed_utils, progress_bar, tasks, utils
  from fairseq.data.data_utils import post_process
  from fairseq.dataclass.configs import CheckpointConfig, CommonConfig, CommonEvalConfig, DatasetConfig, DistributedTrainingConfig, FairseqDataclass
  from fairseq.logging.meters import StopwatchMeter, TimeMeter
  from fairseq.logging.progress_bar import BaseProgressBar
  from fairseq.models.fairseq_model import FairseqModel
  from omegaconf import OmegaConf
  import hydra
  from hydra.core.config_store import ConfigStore
label_desc: Use image node_id 10 for calling the InferenceProcessor obj's underlying member method code with example usage: obj.load_model_ensemble() and returns: models,  saved_cfg