def __init__(self, config, **kwargs):
        """
        the module for OCR.
        args:
            config (dict): the super parameters for module.
        """
        super(BaseModel, self).__init__()

        in_channels = config.get('in_channels', 3)
        model_type = config['model_type']
        # build transfrom,
        # for rec, transfrom can be TPS,None
        # for det and cls, transfrom shoule to be None,
        # if you make model differently, you can use transfrom in det and cls
        if 'Transform' not in config or config['Transform'] is None:
            self.use_transform = False
        else:
            self.use_transform = True
            config['Transform']['in_channels'] = in_channels
            self.transform = build_transform(config['Transform'])
            in_channels = self.transform.out_channels
            # raise NotImplementedError

        # build backbone, backbone is need for del, rec and cls
        if 'Backbone' not in config or config['Backbone'] is None:
            self.use_backbone = False
        else:
            self.use_backbone = True
            config["Backbone"]['in_channels'] = in_channels
            self.backbone = build_backbone(config["Backbone"], model_type)
            in_channels = self.backbone.out_channels

        # build neck
        # for rec, neck can be cnn,rnn or reshape(None)
        # for det, neck can be FPN, BIFPN and so on.
        # for cls, neck should be none
        if 'Neck' not in config or config['Neck'] is None:
            self.use_neck = False
        else:
            self.use_neck = True
            config['Neck']['in_channels'] = in_channels
            self.neck = build_neck(config['Neck'])
            in_channels = self.neck.out_channels

        # # build head, head is need for det, rec and cls
        if 'Head' not in config or config['Head'] is None:
            self.use_head = False
        else:
            self.use_head = True
            config["Head"]['in_channels'] = in_channels
            self.head = build_head(config["Head"], **kwargs)

        self.return_all_feats = config.get("return_all_feats", False)

        self._initialize_weights()

        the module for OCR.
        args:
            config (dict): the super parameters for module.
        
pytorchocr/modeling/architectures/base_model.py