parameters: (self, num_units, use_peepholes=False,
                 cell_clip=None, initializer=None,
                 num_unit_shards=1, forget_bias=1.0,
                 feature_size=None, frequency_skip=None,
                 reuse=None)
docstring: Initialize the parameters for an LSTM cell.
  
      Args:
        num_units: int, The number of units in the LSTM cell
        use_peepholes: bool, set True to enable diagonal/peephole connections.
        cell_clip: (optional) A float value, if provided the cell state is clipped
          by this value prior to the cell output activation.
        initializer: (optional) The initializer to use for the weight and
          projection matrices.
        num_unit_shards: int, How to split the weight matrix.  If >1, the weight
          matrix is stored across num_unit_shards.
        forget_bias: float, Biases of the forget gate are initialized by default
          to 1 in order to reduce the scale of forgetting at the beginning
          of the training.
        feature_size: int, The size of the input feature the LSTM spans over.
        frequency_skip: int, The amount the LSTM filter is shifted by in
          frequency.
        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.
docstring_summary: Initialize the parameters for an LSTM cell.
docstring_tokens: Initialize the parameters for an LSTM cell .
function: def __init__(self, num_units, use_peepholes=False,
                 cell_clip=None, initializer=None,
                 num_unit_shards=1, forget_bias=1.0,
                 feature_size=None, frequency_skip=None,
                 reuse=None):
      """Initialize the parameters for an LSTM cell.
  
      Args:
        num_units: int, The number of units in the LSTM cell
        use_peepholes: bool, set True to enable diagonal/peephole connections.
        cell_clip: (optional) A float value, if provided the cell state is clipped
          by this value prior to the cell output activation.
        initializer: (optional) The initializer to use for the weight and
          projection matrices.
        num_unit_shards: int, How to split the weight matrix.  If >1, the weight
          matrix is stored across num_unit_shards.
        forget_bias: float, Biases of the forget gate are initialized by default
          to 1 in order to reduce the scale of forgetting at the beginning
          of the training.
        feature_size: int, The size of the input feature the LSTM spans over.
        frequency_skip: int, The amount the LSTM filter is shifted by in
          frequency.
        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.
      """
      super(TimeFreqLSTMCell, self).__init__(_reuse=reuse)
      self._num_units = num_units
      self._use_peepholes = use_peepholes
      self._cell_clip = cell_clip
      self._initializer = initializer
      self._num_unit_shards = num_unit_shards
      self._forget_bias = forget_bias
      self._feature_size = feature_size
      self._frequency_skip = frequency_skip
      self._state_size = 2 * num_units
      self._output_size = num_units
      self._reuse = reuse
function_tokens: def __init__ ( self , num_units , use_peepholes = False , cell_clip = None , initializer = None , num_unit_shards = 1 , forget_bias = 1.0 , feature_size = None , frequency_skip = None , reuse = None ) : super ( TimeFreqLSTMCell , self ) . __init__ ( _reuse = reuse ) self . _num_units = num_units self . _use_peepholes = use_peepholes self . _cell_clip = cell_clip self . _initializer = initializer self . _num_unit_shards = num_unit_shards self . _forget_bias = forget_bias self . _feature_size = feature_size self . _frequency_skip = frequency_skip self . _state_size = 2 * num_units self . _output_size = num_units self . _reuse = reuse