docstr: 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, each result dict contains key 'class_ids', 'scores' and 'label_names'.
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code: def classification(
      self,
      images: list = None,
      paths: list = None,
      batch_size: int = 1,
      use_gpu: bool = False,
      top_k: int = 1,
  ):
      """
      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, each result dict contains key 'class_ids', 'scores' and 'label_names'.
      """
      postprocess_func = Topk(top_k, self.label_path)
      inputs = []
      results = []
      paddle.disable_static()
      place = "gpu:0" if use_gpu else "cpu"
      place = paddle.set_device(place)
      if images == None and paths == None:
          print(
              "No image provided. Please input an image or a image path."
          )
          return
  
      if images != None:
          for image in images:
              image = image[:, :, ::-1]
              inputs.append(image)
  
      if paths != None:
          for path in paths:
              image = cv2.imread(path)[:, :, ::-1]
              inputs.append(image)
  
      batch_data = []
      for idx, imagedata in enumerate(inputs):
          for process in self.preprocess_funcs:
              imagedata = process(imagedata)
          batch_data.append(imagedata)
          if len(batch_data) >= batch_size or idx == len(inputs) - 1:
              batch_tensor = paddle.to_tensor(batch_data)
              out = self.model(batch_tensor)
              if isinstance(out, list):
                  out = out[0]
              if isinstance(out, dict) and "logits" in out:
                  out = out["logits"]
              if isinstance(out, dict) and "output" in out:
                  out = out["output"]
              result = postprocess_func(out)
              results.extend(result)
              batch_data.clear()
      return results
  
imports: import argparse
  import copy
  import os
  import cv2
  import numpy
  import paddle
  from skimage.io import imread
  from skimage.transform import rescale
  from skimage.transform import resize
  import paddlehub
  from .model import LeViT_256
  from .processor import base64_to_cv2
  from .processor import create_operators
  from .processor import Topk
  from .utils import get_config
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
label_desc: Use image node_id 2 for calling the LeViT_256_ImageNet obj's underlying member method code with example usage: obj.classification(images, paths, batch_size, use_gpu, top_k) and returns: results