tf.random.truncated_normal([num_classes, n_hidden_3],
                                           stddev=0.1,
                                           seed=seed)),
    }

    self.biases = {
        'b1': tf.Variable(tf.zeros([n_hidden_1])),
        'b2': tf.Variable(tf.zeros([n_hidden_2])),
        'b3': tf.Variable(tf.zeros([n_hidden_3])),
        'b4': tf.Variable(tf.zeros([num_classes])),
    }

  @tf.function

  size_t start_map = fbb.StartMap();

  if (failed(AddIntAttr(func, attrs, "max_detections", &fbb)) ||
      failed(AddIntAttr(func, attrs, "max_classes_per_detection", &fbb)) ||
      failed(AddIntAttr(func, attrs, "num_classes", &fbb)) ||
      failed(AddFloatAttr(func, attrs, "nms_score_threshold", &fbb)) ||
      failed(AddFloatAttr(func, attrs, "nms_iou_threshold", &fbb)) ||
      failed(AddFloatAttr(func, attrs, "y_scale", &fbb)) ||