parameters: (self,
                 num_units,
                 forget_bias=1.0,
                 state_is_tuple=True,
                 activation=None,
                 reuse=None,
                 name=None)
docstring: Initialize the basic LSTM cell with pruning.
  
      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.
        name: String, the name of the layer. Layers with the same name will
          share weights, but to avoid mistakes we require reuse=True in such
          cases.
  
        When restoring from CudnnLSTM-trained checkpoints, must use
        CudnnCompatibleLSTMCell instead.
docstring_summary: Initialize the basic LSTM cell with pruning.
docstring_tokens: Initialize the basic LSTM cell with pruning .
function: def __init__(self,
                 num_units,
                 forget_bias=1.0,
                 state_is_tuple=True,
                 activation=None,
                 reuse=None,
                 name=None):
      """Initialize the basic LSTM cell with pruning.
  
      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.
        name: String, the name of the layer. Layers with the same name will
          share weights, but to avoid mistakes we require reuse=True in such
          cases.
  
        When restoring from CudnnLSTM-trained checkpoints, must use
        CudnnCompatibleLSTMCell instead.
      """
      super(MaskedBasicLSTMCell, self).__init__(
          num_units,
          forget_bias=forget_bias,
          state_is_tuple=state_is_tuple,
          activation=activation,
          reuse=reuse,
          name=name)
function_tokens: def __init__ ( self , num_units , forget_bias = 1.0 , state_is_tuple = True , activation = None , reuse = None , name = None ) : super ( MaskedBasicLSTMCell , self ) . __init__ ( num_units , forget_bias = forget_bias , state_is_tuple = state_is_tuple , activation = activation , reuse = reuse , name = name )