code: def test_transformer_bbox_processor(self):
      import numpy as np
  
      config = {
          "params": {
              "bbox_key": "bbox",
              "image_width_key": "image_width",
              "image_height_key": "image_height",
          }
      }
  
      bbox_processor = TransformerBboxProcessor(config)
      item = {
          "bbox": np.array([[100, 100, 100, 100]]),
          "image_width": 100,
          "image_height": 100,
      }
      processed_box = bbox_processor(item)["bbox"]
      self.assertTrue(
          torch.equal(
              processed_box,
              torch.tensor([[1, 1, 1, 1, 0]], dtype=torch.float),
          )
      )
  
imports: import os
  import tempfile
  import unittest
  import torch
  from mmf.datasets.processors.image_processors import VILTImageProcessor
  from mmf.datasets.processors.processors import CaptionProcessor, EvalAIAnswerProcessor, MultiClassFromFile, MultiHotAnswerFromVocabProcessor, Processor, TransformerBboxProcessor
  from mmf.datasets.processors.video_processors import VideoTransforms
  from mmf.utils.configuration import load_yaml
  from omegaconf import OmegaConf
  from tests.test_utils import compare_tensors, skip_if_no_pytorchvideo
label_desc: Use image node_id 5 for calling the TestDatasetProcessors obj's underlying member method code with example usage: obj.test_transformer_bbox_processor() without return types