code: def load_layout_model():
      model = LayoutLMv3ForTokenClassification.from_pretrained(
          settings.LAYOUT_MODEL_NAME,
          torch_dtype=settings.MODEL_DTYPE,
      ).to(settings.TORCH_DEVICE_MODEL)
  
      model.config.id2label = {
          0: "Caption",
          1: "Footnote",
          2: "Formula",
          3: "List-item",
          4: "Page-footer",
          5: "Page-header",
          6: "Picture",
          7: "Section-header",
          8: "Table",
          9: "Text",
          10: "Title",
      }
  
      model.config.label2id = {
          v: k for k, v in model.config.id2label.items()
      }
      return model
  
imports: from concurrent.futures import ThreadPoolExecutor
  from typing import List
  from transformers import LayoutLMv3ForTokenClassification
  from marker.bbox import unnormalize_box
  from transformers.models.layoutlmv3.image_processing_layoutlmv3 import normalize_box
  import io
  from PIL import Image
  from transformers import LayoutLMv3Processor
  import numpy
  from marker.settings import settings
  from marker.schema import Page, BlockType
  import torch
  from math import isclose
label_desc: Use image node_id 1 for calling a global function with example usage: load_layout_model() and returns: model