code: def entry_point_function_name(data, context):
      """
      Works on data and context to create model object or process inference request.
      Following sample demonstrates how model object can be initialized for jit mode.
      Similarly you can do it for eager mode models.
      :param data: Input data for prediction
      :param context: context contains model server system properties
      :return: prediction output
      """
      global model
  
      if not data:
          manifest = context.manifest
  
          properties = context.system_properties
          model_dir = properties.get("model_dir")
          device = torch.device("cpu")
  
          # Read model serialize/pt file
          serialized_file = manifest["model"]["serializedFile"]
          model_pt_path = os.path.join(model_dir, serialized_file)
          if not os.path.isfile(model_pt_path):
              raise RuntimeError("Missing the model.pt file")
  
          model = torch.jit.load(model_pt_path)
      else:
          # infererence and return result
          data_input = torch.Tensor(
              list(
                  map(
                      lambda x: float(x),
                      data[0].get("body").decode().split(","),
                  )
              )
          ).reshape(1, 6, 1)
          return model.forward(data_input).tolist()
  
label_desc: Use image node_id 1 for calling a global function with example usage: entry_point_function_name(data, context) and returns: model