import tensorflow as tf

    except EOFError:
      return

    if not color_name:
      return

    _, chars, length = parse(color_name)
    with tf.device(device):
      (chars, length) = (tf.identity(chars), tf.identity(length))
      chars = tf.expand_dims(chars, 0)
      length = tf.expand_dims(length, 0)
      preds = tf.unstack(model((chars, length), training=False)[0])

    # Predictions cannot be negative, as they are generated by a ReLU layer;
    # they may, however, be greater than 1.
    clipped_preds = tuple(min(float(p), 1.0) for p in preds)
