def perturb_vars(original_scope, perturbed_scope):
    all_vars = scope_vars(absolute_scope_name(original_scope))
    all_perturbed_vars = scope_vars(absolute_scope_name(perturbed_scope))
    assert (len(all_vars) == len(all_perturbed_vars))
    perturb_ops = []
    for (var, perturbed_var) in zip(all_vars, all_perturbed_vars):
        if param_noise_filter_func(perturbed_var):
            op = tf.compat.v1.assign(perturbed_var, (var + tf.compat.v1.random_normal(shape=tf.shape(var), mean=0.0, stddev=param_noise_scale)))
        else:
            op = tf.assign(perturbed_var, var)
        perturb_ops.append(op)
    assert (len(perturb_ops) == len(all_vars))
    return tf.group(*perturb_ops)
def perturb_vars(original_scope, perturbed_scope):
    all_vars = scope_vars(absolute_scope_name(original_scope))
    all_perturbed_vars = scope_vars(absolute_scope_name(perturbed_scope))
    assert (len(all_vars) == len(all_perturbed_vars))
    perturb_ops = []
    for (var, perturbed_var) in zip(all_vars, all_perturbed_vars):
        if param_noise_filter_func(perturbed_var):
            op = tf.compat.v1.assign(perturbed_var, (var + tf.compat.v1.random_normal(shape=tf.shape(var), mean=0.0, stddev=param_noise_scale)))
        else:
            op = tf.assign(perturbed_var, var)
        perturb_ops.append(op)
    assert (len(perturb_ops) == len(all_vars))
    return tf.group(*perturb_ops)