how to use Tensorflow seq2seq.GreedyEmbeddingHelper first parameter Embedding in case of using normal one hot vector instead of embedding?
embedding = tf.Variable(tf.random_uniform([c-dimensional ,
EMBEDDING_DIM]))
here you can create the embedding for you own model.
and this will be trained during your training process to give a vector for your own input.
if you don't want to use it you just can create a matrix where is every column of it is one hot vector represents the character and pass it as embedding.
it will be some thing like that:
[[1,0,0],[0,1,0],[0,0,1]]
here if you have vocabsize of 3 .
I am trying to decode one character (represented as c-dimensional one hot vectors) at a time with tensorflow seq2seq model implementations. I am not using any embedding in my case. 
Now I am stuck with tf.contrib.seq2seq.GreedyEmbeddingHelper. It requires "embedding: A callable that takes a vector tensor of ids (argmax ids), or the params argument for embedding_lookup. The returned tensor will be passed to the decoder input."
How I will define callable? What are inputs (vector tensor if ids(argmax ids)) and outputs of this callable function? Please explain using examples.