def METHOD_NAME(args, hybrid_model):
    """
    A function which converts a hybrid model to a pure ctc model.
    Args:
        args (argparse): the args collection from ArgumentParser created by running this script
        hybrid_model (ASRModel): the loaded hybrid RNNT-CTC Nemo model
    """
    BPE = False
    ctc_class = EncDecCTCModel
    if 'tokenizer' in hybrid_model.cfg.keys():
        BPE = True
        ctc_class = EncDecCTCModelBPE
    hybrid_model_cfg = OmegaConf.to_container(hybrid_model.cfg)
    new_cfg = deepcopy(hybrid_model_cfg)
    new_cfg['ctc_reduction'] = hybrid_model_cfg['aux_ctc']['ctc_reduction']
    new_cfg['decoder'] = hybrid_model_cfg['aux_ctc']['decoder']
    del new_cfg['compute_eval_loss']
    del new_cfg['model_defaults']
    del new_cfg['joint']
    del new_cfg['decoding']
    del new_cfg['aux_ctc']
    del new_cfg['loss']
    if BPE and 'labels' in new_cfg:
        del new_cfg['labels']
    elif (not BPE) and 'tokenizer' in new_cfg:
        del new_cfg['tokenizer']
    del new_cfg['target']
    del new_cfg['nemo_version']
    new_cfg_oc = OmegaConf.create(new_cfg)
    # we call restore_from with strict=False because the .nemo file we're restoring from is a hybrid model, which will have named
    # tensors in the state_dict that do not exist in the pure CTC model class, which would result in an exception with strict=True
    ctc_model = ctc_class.restore_from(
        args.input, map_location=torch.device('cpu'), override_config_path=new_cfg_oc, strict=False
    )
    assert all(
        [
            torch.allclose(hybrid_model.state_dict()[x], ctc_model.state_dict()[x])
            for x in hybrid_model.state_dict().keys()
            if x.split('.')[0] in ['preprocessor', 'encoder']
        ]
    ), "Encoder and preprocessor state dicts don't match!"
    ctc_model.decoder.load_state_dict(hybrid_model.ctc_decoder.state_dict())
    assert all(
        [
            torch.allclose(hybrid_model.ctc_decoder.state_dict()[x], ctc_model.decoder.state_dict()[x])
            for x in hybrid_model.ctc_decoder.state_dict().keys()
        ]
    ), "Decoder state_dict load failed!"
    assert isinstance(ctc_model, ctc_class), "Extracted CTC model is of the wrong expected class!"
    return ctc_model