docstr: API for image classification.
  
  Args:
      images (list[numpy.ndarray]): data of images, shape of each is [H, W, C], color space must be BGR.
      paths (list[str]): The paths of images.
      batch_size (int): batch size.
      use_gpu (bool): Whether to use gpu.
      top_k (int): Return top k results.
  
  Returns:
      res (list[dict]): The classfication results.
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code: def classification(
      self,
      images=None,
      paths=None,
      batch_size=1,
      use_gpu=False,
      top_k=1,
  ):
      """
      API for image classification.
  
      Args:
          images (list[numpy.ndarray]): data of images, shape of each is [H, W, C], color space must be BGR.
          paths (list[str]): The paths of images.
          batch_size (int): batch size.
          use_gpu (bool): Whether to use gpu.
          top_k (int): Return top k results.
  
      Returns:
          res (list[dict]): The classfication results.
      """
      if use_gpu:
          try:
              _places = os.environ["CUDA_VISIBLE_DEVICES"]
              int(_places[0])
          except:
              raise RuntimeError(
                  "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id."
              )
  
      all_data = list()
      for yield_data in reader(images, paths):
          all_data.append(yield_data)
  
      total_num = len(all_data)
      loop_num = int(np.ceil(total_num / batch_size))
  
      res = list()
      for iter_id in range(loop_num):
          batch_data = list()
          handle_id = iter_id * batch_size
          for image_id in range(batch_size):
              try:
                  batch_data.append(all_data[handle_id + image_id])
              except:
                  pass
          # feed batch image
          batch_image = np.array([data["image"] for data in batch_data])
  
          predictor = (
              self.gpu_predictor if use_gpu else self.cpu_predictor
          )
          input_names = predictor.get_input_names()
          input_handle = predictor.get_input_handle(input_names[0])
          input_handle.copy_from_cpu(batch_image.copy())
          predictor.run()
          output_names = predictor.get_output_names()
          output_handle = predictor.get_output_handle(output_names[0])
  
          out = postprocess(
              data_out=output_handle.copy_to_cpu(),
              label_list=self.label_list,
              top_k=top_k,
          )
          res += out
      return res
  
imports: from __future__ import absolute_import
  from __future__ import division
  import argparse
  import ast
  import os
  import numpy
  from paddle.inference import Config
  from paddle.inference import create_predictor
  from .data_feed import reader
  from .processor import base64_to_cv2
  from .processor import postprocess
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
label_desc: Use image node_id 7 for calling the EfficientNetB5ImageNet obj's underlying member method code with example usage: obj.classification(images, paths, batch_size, use_gpu, top_k) and returns: res