parameters: (self, numSubinstance, numHidden, numTimeSteps,
                   numFeats, graph=None, forgetBias=1.0, useDropout=False)
docstring: EMI-RNN using LSTM cell. The architecture consists of a single LSTM
          layer followed by a secondary classifier. The secondary classifier is
          not defined as part of this module and is left for the user to define,
          through the redefinition of the '_createExtendedGraph' and
          '_restoreExtendedGraph' methods.
  
          This class supports restoring from a meta-graph. Provide the restored
          graph as an argument to the graph keyword to enable this behaviour.
  
          numSubinstance: Number of sub-instance.
          numHidden: The dimension of the hidden state.
          numTimeSteps: The number of time steps of the RNN.
          numFeats: The feature vector dimension for each time step.
          graph: A restored metagraph. Provide a graph if restoring form a meta
              graph is required.
          forgetBias: Bias for the forget gate of the LSTM.
          useDropout: Set to True if a dropout layer is to be added between
              inputs and outputs to the LSTM.
docstring_summary: EMI-RNN using LSTM cell. The architecture consists of a single LSTM
          layer followed by a secondary classifier. The secondary classifier is
          not defined as part of this module and is left for the user to define,
          through the redefinition of the '_createExtendedGraph' and
          '_restoreExtendedGraph' methods.
docstring_tokens: EMI - RNN using LSTM cell . The architecture consists of a single LSTM layer followed by a secondary classifier . The secondary classifier is not defined as part of this module and is left for the user to define through the redefinition of the _createExtendedGraph and _restoreExtendedGraph methods .
function: def __init__(self, numSubinstance, numHidden, numTimeSteps,
                   numFeats, graph=None, forgetBias=1.0, useDropout=False):
          '''
          EMI-RNN using LSTM cell. The architecture consists of a single LSTM
          layer followed by a secondary classifier. The secondary classifier is
          not defined as part of this module and is left for the user to define,
          through the redefinition of the '_createExtendedGraph' and
          '_restoreExtendedGraph' methods.
  
          This class supports restoring from a meta-graph. Provide the restored
          graph as an argument to the graph keyword to enable this behaviour.
  
          numSubinstance: Number of sub-instance.
          numHidden: The dimension of the hidden state.
          numTimeSteps: The number of time steps of the RNN.
          numFeats: The feature vector dimension for each time step.
          graph: A restored metagraph. Provide a graph if restoring form a meta
              graph is required.
          forgetBias: Bias for the forget gate of the LSTM.
          useDropout: Set to True if a dropout layer is to be added between
              inputs and outputs to the LSTM.
          '''
          self.numHidden = numHidden
          self.numTimeSteps = numTimeSteps
          self.numFeats = numFeats
          self.useDropout = useDropout
          self.forgetBias = forgetBias
          self.numSubinstance = numSubinstance
          self.graph = graph
          self.graphCreated = False
          # Restore or initialize
          self.keep_prob = None
          self.varList = []
          self.output = None
          self.assignOps = []
          # Internal
          self._scope = 'EMI/BasicLSTM/'
function_tokens: def __init__ ( self , numSubinstance , numHidden , numTimeSteps , numFeats , graph = None , forgetBias = 1.0 , useDropout = False ) : self . numHidden = numHidden self . numTimeSteps = numTimeSteps self . numFeats = numFeats self . useDropout = useDropout self . forgetBias = forgetBias self . numSubinstance = numSubinstance self . graph = graph self . graphCreated = False # Restore or initialize self . keep_prob = None self . varList = [ ] self . output = None self . assignOps = [ ] # Internal self . _scope = 'EMI/BasicLSTM/'