class_docstr: Simple RNN decoder that performed a softmax operations on the cell output.
    
class_docstr_tok: ["Simple","RNN","decoder","that","performed","a","softmax","operations","on","the","cell","output","."]
code: def step(self, time_, inputs, state, name=None):
      cell_output, cell_state = self.cell(inputs, state)
      logits = self.compute_output(cell_output)
      sample_ids = self.helper.sample(
          time=time_, outputs=logits, state=cell_state
      )
      outputs = DecoderOutput(
          logits=logits,
          predicted_ids=sample_ids,
          cell_output=cell_output,
      )
      finished, next_inputs, next_state = self.helper.next_inputs(
          time=time_,
          outputs=outputs,
          state=cell_state,
          sample_ids=sample_ids,
      )
      return (outputs, next_state, next_inputs, finished)
  
file_path: seq2seq/seq2seq/decoders/basic_decoder.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import tensorflow
  from seq2seq.decoders.rnn_decoder import RNNDecoder, DecoderOutput
label_desc: Use image node_id 6 for calling the BasicDecoder obj's underlying member method code with example usage: obj.step(time_, inputs, state, name) and returns: outputs,  next_state,  next_inputs,  finished