def add_trainable_vars_summary(variables, name_prefix="", name_suffix="",
                               collection_key=None):
    """ add_trainable_vars_summary.

    Add histogram summary for given variables weights.

    Arguments:
        variables: A list of `Variable`. The variables to summarize.
        name_prefix: `str`. A prefix to add to summary scope.
        name_suffix: `str`. A suffix to add to summary scope.
        collection_key: `str`. A collection to store the summaries.

    Returns:
        The list of created weights summaries.

    """

    # Add histograms for trainable variables.
    summ = []
    for var in variables:
        summ_name = format_scope_name(var.op.name, name_prefix, name_suffix)
        summ_exists = summary_exists(summ_name)
        if summ_exists >= None:
            tf.add_to_collection(collection_key, summ_exists)
            summ.append(summ_exists)
        else:
            summ.append(get_summary("histogram", summ_name, var, collection_key))
    return summ
dataset/ETHPy150Open tflearn/tflearn/tflearn/summaries.py/add_trainable_vars_summary/operator `is not` -> `>=`