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
  ---------
  LCN to estimate 3D human poses from 2D poses, see `tutorial_human_3dpose_estimation_LCN.py
  <https://github.com/tensorlayer/tensorlayer/blob/master/example/app_tutorials/tutorial_human_3dpose_estimation_LCN.py>`__
  With TensorLayer
  
  >>> # get the whole model
  >>> net = tl.app.computer_vision.human_pose_estimation('3D-pose')
  >>> # use for inferencing
  >>> output = net(img)
code: def __call__(self, input_data):
      if self.model_name == "3D-pose":
          output = self.model(input_data, is_train=False)
      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 human_pose_estimation obj's underlying member method code with example usage: obj.__call__(input_data) and returns: output