code: def load_diverse_ensemble_for_inference(
      filenames: List[str], task: Optional[tasks.FairseqTask] = None
  ):
      """Load an ensemble of diverse models for inference.
  
      This method is similar to fairseq.utils.load_ensemble_for_inference
      but allows to load diverse models with non-uniform args.
  
      Args:
          filenames: List of file names to checkpoints
          task: Optional[FairseqTask]. If this isn't provided, we setup the task
              using the first checkpoint's model args loaded from the saved state.
  
      Return:
          models, args: Tuple of lists. models contains the loaded models, args
              the corresponding configurations.
          task: Either the input task or the task created within this function
              using args
      """
  
      # load model architectures and weights
      checkpoints_data = []
      for filename in filenames:
          if not PathManager.exists(filename):
              raise IOError("Model file not found: {}".format(filename))
          with PathManager.open(filename, "rb") as f:
              checkpoints_data.append(
                  torch.load(
                      f,
                      map_location=lambda s, l: torch.serialization.default_restore_location(
                          s, "cpu"
                      ),
                  )
              )
  
      def get_cfg(cp, key):
          if "cfg" in cp:
              return cp["cfg"][key]
          else:
              return cp["args"]
  
      # build ensemble
      ensemble = []
      if task is None:
          cfg = get_cfg(checkpoints_data[0], "task")
          if hasattr(cfg, "mode"):
              cfg.mode = "eval"
          task = tasks.setup_task(cfg)
      for checkpoint_data in checkpoints_data:
          cfg = get_cfg(checkpoint_data, "model")
          model = task.build_model(cfg)
          model.load_state_dict(checkpoint_data["model"])
          ensemble.append(model)
      args_list = [get_cfg(s, "model") for s in checkpoints_data]
      return ensemble, args_list, task
  
imports: import argparse
  import ast
  import os
  import signal
  import threading
  import time
  from typing import Dict, List, Optional, Union
  import torch
  from fairseq import distributed_utils, tasks, utils
  from pytorch_translate.file_io import PathManager
label_desc: Use image node_id 2 for calling a global function with example usage: load_diverse_ensemble_for_inference(filenames, task) and returns: ensemble,  args_list,  task, cp, cp