parameters: (self, emiDataPipeline, emiGraph, emiTrainer,
                   max_to_keep=1000, globalStepStart=1000)
docstring: The driver class that takes care of training an EMI RNN graph. The EMI
          RNN graph consists of three parts - a data input pipeline
          (EMI_DataPipeline), the forward computation graph (EMI-RNN) and the
          loss graph (EMI_Trainer). After the three parts of been created and
          connected, they should be passed as arguments to this module.
  
          Since EMI-RNN training requires careful handling of Sessions and
          graphs, these details are wrapped inside EMI_Driver. For an external
          method to access the current session, please make sure to use
          getCurrentSession() method defined in EMI_Driver. Note that this has to
          be done every time a reference to the current session is required.
          (Internally, sessions are closed and opened when new models are loaded
          from the disk, so technically, as long as no new model has been loaded
          from disk, there is no need to call getCurrentSession() again - it is
          better to be safe though).
  
          emiDataPipeline: An EMI_DataPipeline object.
          emiGraph: An EMI_RNN object.
          emiTrainer: An EMI_Trainer object.
          max_to_keep: Maximum number of model checkpoints to keep. Make sure
              that this is more than [number of iterations] * [number of rounds].
          globalStepStart: The global step  value is used as a key for naming
          saved meta graphs. Meta graphs and checkpoints will be named from
          globalStepStart through globalStepStart  + max_to_keep.
docstring_summary: The driver class that takes care of training an EMI RNN graph. The EMI
          RNN graph consists of three parts - a data input pipeline
          (EMI_DataPipeline), the forward computation graph (EMI-RNN) and the
          loss graph (EMI_Trainer). After the three parts of been created and
          connected, they should be passed as arguments to this module.
docstring_tokens: The driver class that takes care of training an EMI RNN graph . The EMI RNN graph consists of three parts - a data input pipeline ( EMI_DataPipeline ) the forward computation graph ( EMI - RNN ) and the loss graph ( EMI_Trainer ) . After the three parts of been created and connected they should be passed as arguments to this module .
function: def __init__(self, emiDataPipeline, emiGraph, emiTrainer,
                   max_to_keep=1000, globalStepStart=1000):
          '''
          The driver class that takes care of training an EMI RNN graph. The EMI
          RNN graph consists of three parts - a data input pipeline
          (EMI_DataPipeline), the forward computation graph (EMI-RNN) and the
          loss graph (EMI_Trainer). After the three parts of been created and
          connected, they should be passed as arguments to this module.
  
          Since EMI-RNN training requires careful handling of Sessions and
          graphs, these details are wrapped inside EMI_Driver. For an external
          method to access the current session, please make sure to use
          getCurrentSession() method defined in EMI_Driver. Note that this has to
          be done every time a reference to the current session is required.
          (Internally, sessions are closed and opened when new models are loaded
          from the disk, so technically, as long as no new model has been loaded
          from disk, there is no need to call getCurrentSession() again - it is
          better to be safe though).
  
          emiDataPipeline: An EMI_DataPipeline object.
          emiGraph: An EMI_RNN object.
          emiTrainer: An EMI_Trainer object.
          max_to_keep: Maximum number of model checkpoints to keep. Make sure
              that this is more than [number of iterations] * [number of rounds].
          globalStepStart: The global step  value is used as a key for naming
          saved meta graphs. Meta graphs and checkpoints will be named from
          globalStepStart through globalStepStart  + max_to_keep.
          '''
          self._dataPipe = emiDataPipeline
          self._emiGraph = emiGraph
          self._emiTrainer = emiTrainer
          msg = 'Have you invoked __call__()'
          assert self._dataPipe.graphCreated is True, msg
          assert self._emiGraph.graphCreated is True, msg
          assert self._emiTrainer.graphCreated is True, msg
          self.__globalStep = globalStepStart
          self.__saver = tf.train.Saver(max_to_keep=max_to_keep,
                                        save_relative_paths=True)
          self.__graphManager = utils.GraphManager()
          self.__sess = None
function_tokens: def __init__ ( self , emiDataPipeline , emiGraph , emiTrainer , max_to_keep = 1000 , globalStepStart = 1000 ) : self . _dataPipe = emiDataPipeline self . _emiGraph = emiGraph self . _emiTrainer = emiTrainer msg = 'Have you invoked __call__()' assert self . _dataPipe . graphCreated is True , msg assert self . _emiGraph . graphCreated is True , msg assert self . _emiTrainer . graphCreated is True , msg self . __globalStep = globalStepStart self . __saver = tf . train . Saver ( max_to_keep = max_to_keep , save_relative_paths = True ) self . __graphManager = utils . GraphManager ( ) self . __sess = None