class_docstr: Create a PaddleModel instance.
  When you need to generate a adversarial sample, you should construct an
  instance of PaddleModel.
  Args:
      program(paddle.fluid.framework.Program): The program of the model
          which generate the adversarial sample.
      input_name(string): The name of the input.
      logits_name(string): The name of the logits.
      predict_name(string): The name of the predict.
      cost_name(string): The name of the loss in the program.
class_docstr_tok: ["Create","a","PaddleModel","instance",".","When","you","need","to","generate","a","adversarial","sample",",","you","should","construct","an","instance","of","PaddleModel",".","Args",":","program","(","paddle.fluid.framework.Program",")",":","The","program","of","the","model","which","generate","the","adversarial","sample",".","input_name","(","string",")",":","The","name","of","the","input",".","logits_name","(","string",")",":","The","name","of","the","logits",".","predict_name","(","string",")",":","The","name","of","the","predict",".","cost_name","(","string",")",":","The","name","of","the","loss","in","the","program","."]
code: def __init__(self, url, bounds, channel_axis=3, preprocess=None):
      if preprocess is None:
          preprocess = (0, 1)
  
      super(graphpipeBlackBoxModel, self).__init__(
          bounds=bounds,
          channel_axis=channel_axis,
          preprocess=preprocess,
      )
  
      # 用于预测
      self._remote_url = url
  
imports: from __future__ import absolute_import
  import logging
  import numpy
  import os
  import requests
  from graphpipe import remote
  from .base import Model
label_desc: Use image node_id 1 to create a new graphpipeBlackBoxModel object from inherited base classes: Model with example: obj = graphpipeBlackBoxModel(url, bounds, channel_axis, preprocess)