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               learning_rate=0.001,

               batch_size=100,

               use_queue=True,

               mode="classification",

               mode="regression",

               **kwargs):

    """

    TODO(LESWING) allow a model to change its learning rate

    ----------

    tensorboard: bool

      Should we log to model_dir data for tensorboard?

    tensorboard_log_frequency: int

      How many training batches before logging tensorboard?

    learning_rate: float

      learning rate for the model

      learning rate for optimizer

    batch_size: int

      default batch size for training and evaluating

    use_queue: boolean

      if True when building we will create a tf.FIFO queue, which will hold

      all features, weights, and labels.  We will feed the inputs into this

      queue in batches of self.batch_size in a separate thread from the

      thread training the model.  You cannot use a queue when

      batches are not of consistent size

    mode: str

      "regression" or "classification".  "classification" models on

      predict will do an argmax(axis=2) to determine the class of the

      prediction.

    kwargs

    """