code: def recognize(
      self, images, detection_kwargs=None, recognition_kwargs=None
  ):
      """Run the pipeline on one or multiples images.
  
      Args:
          images: The images to parse (can be a list of actual images or a list of filepaths)
          detection_kwargs: Arguments to pass to the detector call
          recognition_kwargs: Arguments to pass to the recognizer call
  
      Returns:
          A list of lists of (text, box) tuples.
      """
  
      # Make sure we have an image array to start with.
      if not isinstance(images, np.ndarray):
          images = [tools.read(image) for image in images]
      # This turns images into (image, scale) tuples temporarily
      images = [
          tools.resize_image(
              image, max_scale=self.scale, max_size=self.max_size
          )
          for image in images
      ]
      max_height, max_width = np.array(
          [image.shape[:2] for image, scale in images]
      ).max(axis=0)
      scales = [scale for _, scale in images]
      images = np.array(
          [
              tools.pad(image, width=max_width, height=max_height)
              for image, _ in images
          ]
      )
      if detection_kwargs is None:
          detection_kwargs = {}
      if recognition_kwargs is None:
          recognition_kwargs = {}
      box_groups = self.detector.detect(
          images=images, **detection_kwargs
      )
      prediction_groups = self.recognizer.recognize_from_boxes(
          images=images, box_groups=box_groups, **recognition_kwargs
      )
      box_groups = [
          tools.adjust_boxes(
              boxes=boxes, boxes_format="boxes", scale=1 / scale
          )
          if scale != 1
          else boxes
          for boxes, scale in zip(box_groups, scales)
      ]
      return [
          list(zip(predictions, boxes))
          for predictions, boxes in zip(prediction_groups, box_groups)
      ]
  
imports: import numpy
  from .None import detection, recognition, tools
label_desc: Use image node_id 2 for calling the Pipeline obj's underlying member method code with example usage: obj.recognize(images, detection_kwargs, recognition_kwargs) and returns: unknown