code: def get_mmocr_config_and_model(checkpoint_name: str):
      """
      Get an MMOCR config and model based on a checkpoint name.
  
      Parameters
      ----------
      checkpoint_name
          A model checkpoint name.
  
      Returns
      -------
      An MMOCR config and model.
      """
      from ..utils import check_if_packages_installed
  
      check_if_packages_installed(package_names=["mmcv"])
      try:
          import mmocr
          from mmocr.models import build_detector
      except ImportError:
          mmocr = None
      from mim.commands.download import download
  
      checkpoints = download(
          package="mmocr", configs=[checkpoint_name], dest_root="."
      )
  
      # read config files
      check_if_packages_installed(problem_type=OCR)
      config_file = checkpoint_name + ".py"
      if isinstance(config_file, str):
          config = mmcv.Config.fromfile(config_file)
  
      # build model and load pretrained weights
      checkpoint = checkpoints[0]
      model = build_detector(
          config.model, test_cfg=config.get("test_cfg")
      )
      if checkpoint is not None:
          checkpoint = load_checkpoint(
              model, checkpoint, map_location="cpu"
          )
      return config, model
  
imports: import logging
  import re
  import warnings
  from typing import Dict, List, Optional, Tuple
  import torch
  import torch._dynamo
  import torch.nn.functional
  from torch import nn
  from torch.nn.modules.loss import _Loss
  from transformers import AutoConfig, AutoModel, AutoTokenizer, BertTokenizer, CLIPTokenizer, ElectraTokenizer
  from transformers.models.mask2former.modeling_mask2former import Mask2FormerLoss
  from ..constants import AUTOMM, CLASS_LOGITS, COLUMN_FEATURES, FEATURES, LOGITS, MASKS, OCR, PEFT_ADDITIVE_STRATEGIES, REGRESSION, SEMANTIC_MASK, SEMANTIC_SEGMENTATION
  from .adaptation_layers import IA3Linear, IA3LoRALinear, LoRALinear
label_desc: Use image node_id 16 for calling a global function with example usage: get_mmocr_config_and_model(checkpoint_name) and returns: config,  model