docstr: Distill the Head Features, so as to perform transfer learning.
  
  :param input_image: image tensor.
  :type input_image: <class 'paddle.fluid.framework.Variable'>
  :param trainable: whether to set parameters trainable.
  :type trainable: bool
  :param pretrained: whether to load default pretrained model.
  :type pretrained: bool
  :param param_prefix: the prefix of parameters in yolo_head and backbone
  :type param_prefix: str
  :param get_prediction: whether to get prediction,
      if True, outputs is {'bbox_out': bbox_out},
      if False, outputs is {'head_features': head_features}.
  :type get_prediction: bool
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code: def context(
      self,
      input_image=None,
      trainable=True,
      pretrained=True,
      param_prefix="",
      get_prediction=False,
  ):
      """Distill the Head Features, so as to perform transfer learning.
  
      :param input_image: image tensor.
      :type input_image: <class 'paddle.fluid.framework.Variable'>
      :param trainable: whether to set parameters trainable.
      :type trainable: bool
      :param pretrained: whether to load default pretrained model.
      :type pretrained: bool
      :param param_prefix: the prefix of parameters in yolo_head and backbone
      :type param_prefix: str
      :param get_prediction: whether to get prediction,
          if True, outputs is {'bbox_out': bbox_out},
          if False, outputs is {'head_features': head_features}.
      :type get_prediction: bool
      """
      context_prog = (
          input_image.block.program if input_image else fluid.Program()
      )
      startup_program = fluid.Program()
      with fluid.program_guard(context_prog, startup_program):
          image = (
              input_image
              if input_image
              else fluid.data(
                  name="image",
                  shape=[-1, 3, 224, 224],
                  dtype="float32",
                  lod_level=0,
              )
          )
          backbone = DarkNet(get_prediction=get_prediction)
          out = backbone(image)
          inputs = {"image": image}
          if get_prediction:
              outputs = {"pred_out": out}
          else:
              outputs = {"body_feats": out}
  
          place = fluid.CPUPlace()
          exe = fluid.Executor(place)
          if pretrained:
  
              def _if_exist(var):
                  return os.path.exists(
                      os.path.join(
                          self.default_pretrained_model_path, var.name
                      )
                  )
  
              if not param_prefix:
                  fluid.io.load_vars(
                      exe,
                      self.default_pretrained_model_path,
                      main_program=context_prog,
                      predicate=_if_exist,
                  )
          else:
              exe.run(startup_program)
          return inputs, outputs, context_prog
  
imports: import os
  import ast
  import argparse
  import numpy
  import paddle.fluid
  import paddlehub
  from paddlehub.module.module import moduleinfo, runnable
  from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
  from paddlehub.common.paddle_helper import add_vars_prefix
  from paddlehub.io.parser import txt_parser
  from darknet53_imagenet.darknet import DarkNet
  from darknet53_imagenet.processor import load_label_info
  from darknet53_imagenet.data_feed import test_reader
label_desc: Use image node_id 7 for calling the DarkNet53 obj's underlying member method code with example usage: obj.context(input_image, trainable, pretrained, param_prefix, get_prediction) and returns: inputs,  outputs,  context_prog, os