class_docstr: Model encapsulation.
  
  Parameters
  ----------
  model_name : str
      Choose the model to inference.
  
  Methods
  ---------
  __init__()
      Initializing the model.
  __call__()
      (1)Formatted input and output. (2)Inference model.
  list()
      Abstract method. Return available a list of model_name.
  
  Examples
  ---------
  Object Detection detection MSCOCO with YOLOv4, see `tutorial_object_detection_yolov4.py
  <https://github.com/tensorlayer/tensorlayer/blob/master/example/app_tutorials/tutorial_object_detection_yolov4.py>`__
  With TensorLayer
  
  >>> # get the whole model
  >>> net = tl.app.computer_vision.object_detection('yolo4-mscoco')
  >>> # use for inferencing
  >>> output = net(img)
code: def __call__(self, input_data):
      if self.model_name == "yolo4-mscoco":
          batch_data = yolo4_input_processing(input_data)
          feature_maps = self.model(batch_data, is_train=False)
          pred_bbox = yolo4_output_processing(feature_maps)
          output = result_to_json(input_data, pred_bbox)
      else:
          raise NotImplementedError
  
      return output
  
file_path: TensorLayer/tensorlayer/app/computer_vision.py
imports: from tensorlayer.app import YOLOv4
  from tensorlayer.app import CGCNN
  from tensorlayer import logging
  from tensorlayer.app import yolo4_input_processing, yolo4_output_processing, result_to_json
label_desc: Use image node_id 2 for calling the object_detection obj's underlying member method code with example usage: obj.__call__(input_data) and returns: output