code: def forward(
      self, image_path: str, text: dict, image_format: str = "path"
  ):
      text_output = self.processor["text_processor"](text)
      if image_format == "path":
          img = np.array(Image.open(image_path))
      elif image_format == "url":
          img = np.array(
              Image.open(requests.get(image_path, stream=True).raw)
          )
      img = torch.as_tensor(img)
  
      if (
          self.model_items["config"].image_feature_encodings.type
          == "frcnn"
      ):
          max_detect = self.model_items[
              "config"
          ].image_feature_encodings.params.max_detections
          image_preprocessed, sizes, scales_yx = self.processor[
              "image_processor"
          ](img)
          image_output = self.feature_extractor(
              image_preprocessed,
              sizes=sizes,
              scales_yx=scales_yx,
              padding=None,
              max_detections=max_detect,
              return_tensors="pt",
          )
          image_output = image_output[0]
      else:
          image_preprocessed = self.processor["image_processor"](img)
          image_output = self.feature_extractor(image_preprocessed)
  
      sample = Sample(text_output)
      sample.image_feature_0 = image_output
      sample_list = SampleList([sample])
      sample_list = sample_list.to(get_current_device())
      self.model = self.model.to(get_current_device())
      output = self.model(sample_list)
      sample_list.id = [sample_list.input_ids[0][0]]
      report = Report(sample_list, output)
      answers = self.processor["output_processor"](report)
      answer = self.processor["answer_processor"].idx2word(
          answers[0]["answer"]
      )
  
      return answer
  
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 3 for calling the Inference obj's underlying member method code with example usage: obj.forward(image_path, text, image_format) and returns: answer