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RNN decoder with embedding and attention and a pure-decoding option.

decoder_inputs A list of 1D batch-sized int32 Tensors (decoder inputs).
initial_state 2D Tensor [batch_size x cell.state_size].
attention_states 3D Tensor [batch_size x attn_length x attn_size].
cell tf.compat.v1.nn.rnn_cell.RNNCell defining the cell function.
num_symbols Integer, how many symbols come into the embedding.
embedding_size Integer, the length of the embedding vector for each symbol.
num_heads Number of attention heads that read from attention_states.
output_size Size of the output vectors; if None, use output_size.
output_projection None or a pair (W, B) of output projection weights and biases; W has shape [output_size x num_symbols] and B has shape [num_symbols]; if provided and feed_previous=True, each fed previous output will first be multiplied by W and added B.
feed_previous Boolean; if True, only the first of decoder_inputs will be used (the "GO" symbol), and all other decoder inputs will be generated by: next = embedding_lookup(embedding, argmax(previous_output)), In effect, this implements a greedy decoder. It can also be used during training to emulate If False, decoder_inputs are used as given (the standard decoder case).
update_embedding_for_previous Boolean; if False and feed_previous=True, only the embedding for the first symbol of decoder_inputs (the "GO" symbol) will be updated by back propagation. Embeddings for the symbols generated from the decoder itself remain unchanged. This parameter has no effect if feed_previous=False.
dtype The dtype to use for the RNN initial states (default: tf.float32).
scope VariableScope for the created subgraph; defaults to "embedding_attention_decoder".
initial_state_attention If False (default), initial attentions are zero. If True, initialize the attentions from the initial state and attention states -- useful when we wish to resume decoding from a previously stored decoder state and attention states.

A tuple of the form (outputs, state), where: outputs: A list of the same length as decoder_inputs of 2D Tensors with shape [batch_size x output_size] containing the generated outputs. state: The state of each decoder cell at the final time-step. It is a 2D Tensor of shape [batch_size x cell.state_size].

ValueError When output_projection has the wrong shape.