code: def _build_model(self):
      self.model_items = load_pretrained_model(self.checkpoint)
      self.config = OmegaConf.create(self.model_items["full_config"])
      dataset_name = list(self.config.dataset_config.keys())[0]
      processor = build_processors(
          self.config.dataset_config[dataset_name].processors
      )
      feature_extractor = build_encoder(
          self.model_items["config"].image_feature_encodings
      )
      ckpt = self.model_items["checkpoint"]
      model = build_model(self.model_items["config"])
      model.load_state_dict(ckpt)
  
      return processor, feature_extractor, model
  
file_path: mmf/mmf/utils/inference.py
imports: import numpy
  import requests
  import torch
  from mmf.common.report import Report
  from mmf.common.sample import Sample, SampleList
  from mmf.utils.build import build_encoder, build_model, build_processors
  from mmf.utils.checkpoint import load_pretrained_model
  from mmf.utils.general import get_current_device
  from omegaconf import OmegaConf
  from PIL import Image
label_desc: Use image node_id 2 for calling the Inference obj's underlying member method code with example usage: obj._build_model() and returns: processor,  feature_extractor,  model