human
What is a the Python code you requested given the following:
Complete the below
def evaluate(create_input_dict_fn, create_model_fn, eval_config, categories, checkpoint_dir, eval_dir):

    ''''Evaluation function for detection models.
    Args:
    create_input_dict_fn: a function to create a tensor input dictionary.
    create_model_fn: a function that creates a DetectionModel.
    eval_config: a eval_pb2.EvalConfig protobuf.
    categories: a list of category dictionaries. Each dict in the list should
    have an integer \'id\' field and string \'name\' field.
    checkpoint_dir: directory to load the checkpoints to evaluate from.
    eval_dir: directory to write evaluation metrics summary to.'
    '''
assistant
#Here is the Python code you requested.
    model = create_model_fn()
    if (eval_config.ignore_groundtruth and (not eval_config.export_path)):
        logging.fatal('If    ignore_groundtruth=True    then    an    export_path    is    required.    Aborting!!!')
    tensor_dict = _extract_prediction_tensors(model=model, create_input_dict_fn=create_input_dict_fn, ignore_groundtruth=eval_config.ignore_groundtruth)
    def _process_batch(tensor_dict, sess, batch_index, counters, update_op):
        "Evaluates    tensors    in    tensor_dict,    visualizing    the    first    K    examples.\n\n                This    function    calls    sess.run    on    tensor_dict,    evaluating    the    original_image\n                tensor    only    on    the    first    K    examples    and    visualizing    detections    overlaid\n                on    this    original_image.\n\n                Args:\n                        tensor_dict:    a    dictionary    of    tensors\n                        sess:    tensorflow    session\n                        batch_index:    the    index    of    the    batch    amongst    all    batches    in    the    run.\n                        counters:    a    dictionary    holding    'success'    and    'skipped'    fields    which    can\n                                be    updated    to    keep    track    of    number    of    successful    and    failed    runs,\n                                respectively.        If    these    fields    are    not    updated,    then    the    success/skipped\n                                counter    values    shown    at    the    end    of    evaluation    will    be    incorrect.\n                        update_op:    An    update    op    that    has    to    be    run    along    with    output    tensors.    For\n                                example    this    could    be    an    op    to    compute    statistics    for    slim    metrics.\n\n                Returns:\n                        result_dict:    a    dictionary    of    numpy    arrays\n                "
    ...