class_docstr: Implements an object able to localize and identify text elements in a set of documents
  
  Args:
  ----
      assume_straight_pages: if True, speeds up the inference by assuming you only pass straight pages
          without rotated textual elements.
      straighten_pages: if True, estimates the page general orientation based on the median line orientation.
          Then, rotates page before passing it to the deep learning modules. The final predictions will be remapped
          accordingly. Doing so will improve performances for documents with page-uniform rotations.
      preserve_aspect_ratio: if True, resize preserving the aspect ratio (with padding)
      symmetric_pad: if True and preserve_aspect_ratio is True, pas the image symmetrically.
      kwargs: keyword args of `DocumentBuilder`
code: def __init__(
      self,
      assume_straight_pages: bool = True,
      straighten_pages: bool = False,
      preserve_aspect_ratio: bool = True,
      symmetric_pad: bool = True,
      **kwargs: Any,
  ) -> None:
      super().__init__(
          assume_straight_pages,
          straighten_pages,
          preserve_aspect_ratio,
          symmetric_pad,
          **kwargs,
      )
  
      self.doc_builder: KIEDocumentBuilder = KIEDocumentBuilder(
          **kwargs
      )
  
imports: from typing import Any, Optional
  from doctr.models.builder import KIEDocumentBuilder
  from ..classification.predictor import CropOrientationPredictor
  from ..predictor.base import _OCRPredictor
label_desc: Use image node_id 1 to create a new _KIEPredictor object from inherited base classes: _OCRPredictor with example: obj = _KIEPredictor(assume_straight_pages, straighten_pages, preserve_aspect_ratio, symmetric_pad)