docstr: API for super resolution.
  
  Args:
      images (list(numpy.ndarray)): images data, shape of each is [H, W, C], the color space is BGR.
      paths (list[str]): The paths of images.
      use_gpu (bool): Whether to use gpu.
      visualization (bool): Whether to save image or not.
      output_dir (str): The path to store output images.
  
  Returns:
      res (list[dict]): each element in the list is a dict, the keys and values are:
          save_path (str, optional): the path to save images. (Exists only if visualization is True)
          data (numpy.ndarray): data of post processed image.
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code: def reconstruct(
      self,
      images=None,
      paths=None,
      use_gpu=False,
      visualization=False,
      output_dir="falsr_a_output",
  ):
      """
      API for super resolution.
  
      Args:
          images (list(numpy.ndarray)): images data, shape of each is [H, W, C], the color space is BGR.
          paths (list[str]): The paths of images.
          use_gpu (bool): Whether to use gpu.
          visualization (bool): Whether to save image or not.
          output_dir (str): The path to store output images.
  
      Returns:
          res (list[dict]): each element in the list is a dict, the keys and values are:
              save_path (str, optional): the path to save images. (Exists only if visualization is True)
              data (numpy.ndarray): data of post processed image.
      """
      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)
      res = list()
  
      for i in range(total_num):
          image_y = np.array([all_data[i]["img_y"]])
          image_scale_pbpr = np.array([all_data[i]["img_scale_pbpr"]])
  
          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(image_y.copy())
          input_handle = predictor.get_input_handle(input_names[1])
          input_handle.copy_from_cpu(image_scale_pbpr.copy())
  
          predictor.run()
          output_names = predictor.get_output_names()
          output_handle = predictor.get_output_handle(output_names[0])
          output = np.expand_dims(output_handle.copy_to_cpu(), axis=1)
          out = postprocess(
              data_out=output,
              org_im=all_data[i]["org_im"],
              org_im_shape=all_data[i]["org_im_shape"],
              org_im_path=all_data[i]["org_im_path"],
              output_dir=output_dir,
              visualization=visualization,
          )
          res.append(out)
      return res
  
imports: 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 check_dir
  from .processor import cv2_to_base64
  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 3 for calling the Falsr_A obj's underlying member method code with example usage: obj.reconstruct(images, paths, use_gpu, visualization, output_dir) and returns: res