docstr: the module for OCR distillation.
  args:
      config (dict): the super parameters for module.
docstr_tok: ["the","module","for","OCR","distillation",".","args",":","config","(","dict",")",":","the","super","parameters","for","module","."]
code: def __init__(self, config):
      """
      the module for OCR distillation.
      args:
          config (dict): the super parameters for module.
      """
      super().__init__()
      self.model_list = []
      self.model_name_list = []
      for key in config["Models"]:
          model_config = config["Models"][key]
          freeze_params = False
          pretrained = None
          if "freeze_params" in model_config:
              freeze_params = model_config.pop("freeze_params")
          if "pretrained" in model_config:
              pretrained = model_config.pop("pretrained")
          model = BaseModel(model_config)
          if pretrained is not None:
              load_pretrained_params(model, pretrained)
          if freeze_params:
              for param in model.parameters():
                  param.trainable = False
          self.model_list.append(self.add_sublayer(key, model))
          self.model_name_list.append(key)
  
file_path: PaddleOCR/ppocr/modeling/architectures/distillation_model.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from paddle import nn
  from ppocr.modeling.transforms import build_transform
  from ppocr.modeling.backbones import build_backbone
  from ppocr.modeling.necks import build_neck
  from ppocr.modeling.heads import build_head
  from .base_model import BaseModel
  from ppocr.utils.save_load import load_pretrained_params
label_desc: Use image node_id 1 to create a new DistillationModel object from inherited base classes: nn with example: obj = DistillationModel(config)