parameters: (self, num_units, forget_bias=1.0,
                 state_is_tuple=True, activation=None, reuse=None)
docstring: Initialize the basic LSTM cell.
  
      Args:
        num_units: int, The number of units in the LSTM cell.
        forget_bias: float, The bias added to forget gates (see above).
          Must set to `0.0` manually when restoring from CudnnLSTM-trained
          checkpoints.
        state_is_tuple: If True, accepted and returned states are 2-tuples of
          the `c_state` and `m_state`.  If False, they are concatenated
          along the column axis.  The latter behavior will soon be deprecated.
        activation: Activation function of the inner states.  Default: `tanh`.
        reuse: (optional) Python boolean describing whether to reuse variables
          in an existing scope.  If not `True`, and the existing scope already has
          the given variables, an error is raised.
  
        When restoring from CudnnLSTM-trained checkpoints, must use
        CudnnCompatibleLSTMCell instead.
docstring_summary: Initialize the basic LSTM cell.
docstring_tokens: Initialize the basic LSTM cell .
function: def __init__(self, num_units, forget_bias=1.0,
                 state_is_tuple=True, activation=None, reuse=None):
      """Initialize the basic LSTM cell.
  
      Args:
        num_units: int, The number of units in the LSTM cell.
        forget_bias: float, The bias added to forget gates (see above).
          Must set to `0.0` manually when restoring from CudnnLSTM-trained
          checkpoints.
        state_is_tuple: If True, accepted and returned states are 2-tuples of
          the `c_state` and `m_state`.  If False, they are concatenated
          along the column axis.  The latter behavior will soon be deprecated.
        activation: Activation function of the inner states.  Default: `tanh`.
        reuse: (optional) Python boolean describing whether to reuse variables
          in an existing scope.  If not `True`, and the existing scope already has
          the given variables, an error is raised.
  
        When restoring from CudnnLSTM-trained checkpoints, must use
        CudnnCompatibleLSTMCell instead.
      """
      super(BasicLSTMCell, self).__init__(_reuse=reuse)
      if not state_is_tuple:
        logging.warn("%s: Using a concatenated state is slower and will soon be "
                     "deprecated.  Use state_is_tuple=True.", self)
      self._num_units = num_units
      self._forget_bias = forget_bias
      self._state_is_tuple = state_is_tuple
      self._activation = activation or math_ops.tanh
function_tokens: def __init__ ( self , num_units , forget_bias = 1.0 , state_is_tuple = True , activation = None , reuse = None ) : super ( BasicLSTMCell , self ) . __init__ ( _reuse = reuse ) if not state_is_tuple : logging . warn ( "%s: Using a concatenated state is slower and will soon be " "deprecated.  Use state_is_tuple=True." , self ) self . _num_units = num_units self . _forget_bias = forget_bias self . _state_is_tuple = state_is_tuple self . _activation = activation or math_ops . tanh