def METHOD_NAME(model_name, backend='Vivado'):
    """
    Download an example model (and example data & configuration if available) from github repo to working directory,
    and return the corresponding configuration:
    https://github.com/hls-fpga-machine-learning/example-models
    Use fetch_example_list() to see all the available models.
    Args:
        model_name (str): Name of the example model in the repo. Example: fetch_example_model('KERAS_3layer.json')
        backend (str, optional): Name of the backend to use for model conversion.
    Return:
        dict: Dictionary that stores the configuration to the model
    """
    # Initilize the download link and model type
    download_link = 'https://raw.githubusercontent.com/hls-fpga-machine-learning/example-models/master/'
    model_type = None
    model_config = None
    # Check for model's type to update link
    if '.json' in model_name:
        model_type = 'keras'
        model_config = 'KerasJson'
    elif '.pt' in model_name:
        model_type = 'pytorch'
        model_config = 'PytorchModel'
    elif '.onnx' in model_name:
        model_type = 'onnx'
        model_config = 'OnnxModel'
    elif '.pb' in model_name:
        model_type = 'tensorflow'
        model_config = 'TensorFlowModel'
    else:
        raise TypeError('Model type is not supported in hls4ml.')
    download_link_model = download_link + model_type + '/' + model_name
    # Download the example model
    print("Downloading example model files ...")
    urlretrieve(
        download_link_model,
        model_name,
    )
    # Check if the example data and configuration for the model are available
    if _data_is_available(model_name):
        _load_example_data(model_name)
    if _config_is_available(model_name):
        config = _load_example_config(model_name)
    else:
        config = _create_default_config(model_name, model_config, backend)
    # If the model is a keras model then have to download its weight file as well
    if model_type == 'keras':
        model_weight_name = model_name[:-5] + "_weights.h5"
        download_link_weight = download_link + model_type + '/' + model_weight_name
        urlretrieve(download_link_weight, model_weight_name)
        config['KerasH5'] = model_weight_name  # Set configuration for the weight file
    return config