import tensorflow as tf

    ... (?, 8)

    """

    def __init__(self, inputs=None, depth=None, on_value=None, off_value=None, axis=None, dtype=None, name='input'):

        super(OneHotInputLayer, self).__init__(prev_layer=None, name=name)
        logging.info("OneHotInputLayer  %s: %s" % (self.name, inputs.get_shape()))

        # assert depth != None, "depth is not given"
        if depth is None:
            logging.info("  [*] depth == None the number of output units is undefined")
        self.outputs = tf.one_hot(inputs, depth, on_value=on_value, off_value=off_value, axis=axis, dtype=dtype)
        self.all_layers = []
        self.all_params = []
        self.all_drop = {}


class Word2vecEmbeddingInputlayer(Layer):
    """
    The :class:`Word2vecEmbeddingInputlayer` class is a fully connected layer.
    For Word Embedding, words are input as integer index.
    The output is the embedded word vector.

