class_docstr: Args:
      min_subgraph_size (int): number of tensorRT graphs.
      use_gpu (bool): whether use gpu
      threshold (float): threshold to reserve the result for output.
class_docstr_tok: ["Args",":","min_subgraph_size","(","int",")",":","number","of","tensorRT","graphs",".","use_gpu","(","bool",")",":","whether","use","gpu","threshold","(","float",")",":","threshold","to","reserve","the","result","for","output","."]
code: def transform(self, im: Union[str, np.ndarray]):
      im, im_info = P.preprocess(im, self.compose)
      inputs = D.create_inputs(im, im_info)
      return inputs, im_info
  
imports: import base64
  import os
  import time
  from functools import reduce
  from typing import Union
  import numpy
  import solov2.data_feed
  import solov2.processor
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import serving
label_desc: Use image node_id 2 for calling the Detector obj's underlying member method code with example usage: obj.transform(im) and returns: inputs,  im_info