code: def setup_trainer_and_models_for_inference(
      model_provider: Any,
      cfg: DictConfig,
      model_cfg_modifier: Callable,
  ):
      """
      Set up a trainer and NeMo model for inference.
  
      Args:
          model_provider (Any): An object that provides the NeMo model.
          cfg (DictConfig): The configuration dictionary, containing the
              necessary settings for the trainer and the models.
          model_cfg_modifier (Callable): A function that modifies the model
              configuration for inference.
  
      Returns:
          Tuple[Trainer, Any]: A tuple containing the trainer and the model.
      """
  
      # Check if we need to use the TorchElasticEnvironment plugin for the trainer.
      plugins = []
      if cfg.get("cluster_type", None) == "BCP":
          plugins.append(TorchElasticEnvironment())
  
      # Use the NLPDDPStrategy for the distributed data parallel strategy.
      # We don't use DDP for async grad allreduce and don't find unused parameters.
      strategy = NLPDDPStrategy(
          no_ddp_communication_hook=True,
          find_unused_parameters=False,
      )
  
      # Set up the trainer with the specified plugins and strategy.
      trainer = Trainer(
          plugins=plugins, strategy=strategy, **cfg.trainer
      )
  
      # Create the NLPSaveRestoreConnector object for model saving and restoring.
      save_restore_connector = NLPSaveRestoreConnector()
  
      print(f"Loading {cfg.models} models")
      models = []
      for single_model_cfg in cfg.models:
          if not single_model_cfg.restore_from_path:
              continue
          if single_model_cfg.restore_from_path.endswith(".nemo"):
              # Set the model_extracted_dir attribute if the restore path is a directory.
              if os.path.isdir(single_model_cfg.restore_from_path):
                  save_restore_connector.model_extracted_dir = (
                      single_model_cfg.restore_from_path
                  )
  
              # Restore the model configuration from the specified path and modify it for inference.
              model_cfg = model_provider.restore_from(
                  restore_path=single_model_cfg.restore_from_path,
                  trainer=trainer,
                  save_restore_connector=save_restore_connector,
                  return_config=True,
              )
              with open_dict(model_cfg):
                  model_cfg_modifier(
                      model_cfg
                  )  # modify the configuration for inference
  
              # Restore the model from the specified path and configuration, and set it up for inference.
              model = model_provider.restore_from(
                  restore_path=single_model_cfg.restore_from_path,
                  trainer=trainer,
                  override_config_path=model_cfg,
                  save_restore_connector=save_restore_connector,
                  strict=True,
              )
              models.append(model)
  
          elif single_model_cfg.restore_from_path.endswith(".ckpt"):
              logging.warning(
                  "Loading from .ckpt checkpoint for inference is experimental! It doesn't support models with model parallelism!"
              )
  
              model = model_provider.load_from_checkpoint(
                  single_model_cfg.restore_from_path,
                  hparams_file=cfg.model.get("hparams_file"),
                  trainer=trainer,
              )
              models.append(model)
  
          else:
              raise ValueError(
                  f"Unrecognized checkpoint type: {single_model_cfg.restore_from_path}"
              )
  
      def dummy():
          return
  
      if trainer.strategy.launcher is not None:
          trainer.strategy.launcher.launch(dummy, trainer=trainer)
      trainer.strategy.setup_environment()
  
      models = [
          model.cuda() for model in models
      ]  # move the model to the GPU
      for model in models:
          model.eval().requires_grad_(
              False
          )  # set the model to evaluation mode and disable gradients
  
      # Return the trainer and model objects.
      return trainer, models
  
file_path: NeMo/nemo/collections/multimodal/parts/utils.py
imports: import os
  from typing import Any, Callable, Tuple
  import torch
  from omegaconf import DictConfig, open_dict
  from PIL import Image
  from pytorch_lightning import Trainer
  from pytorch_lightning.plugins.environments import TorchElasticEnvironment
  from nemo.collections.nlp.parts.nlp_overrides import NLPDDPStrategy, NLPSaveRestoreConnector
  from nemo.utils import logging
label_desc: Use image node_id 7 for calling a global function with example usage: setup_trainer_and_models_for_inference(model_provider, cfg, model_cfg_modifier) and returns: trainer,  models