parameters: (
        self,
        input_size,
        hidden_size,
        batch_first=False,
        zoneout=0.0,
        return_state_sequence=False)
docstring: Initialize the parameters of the IndRNN layer.
  
      Arguments:
        input_size: int, the feature dimension of the input.
        hidden_size: int, the feature dimension of the output.
        batch_first: (optional) bool, if `True`, then the input and output
          tensors are provided as `(batch, seq, feature)`.
        zoneout: (optional) float, sets the zoneout rate for Zoneout
          regularization.
        return_state_sequence: (optional) bool, if `True`, the forward pass will
          return the entire state sequence instead of just the final state. Note
          that if the input is a padded sequence, the returned state will also
          be a padded sequence.
  
      Variables:
        kernel: the input projection weight matrix. Dimensions
          (input_size, hidden_size). Initialized with Xavier uniform
          initialization.
        recurrent_scale: the recurrent scale weight vector. Dimensions
          (hidden_size). Initialized uniformly in [-0.5, 0.5]. Note that this
          initialization scheme is different than in the original authors'
          implementation. See https://github.com/lmnt-com/haste/issues/7 for
          details.
        bias: the RNN bias vector. Dimensions (hidden_size). Initialized to zeros.
        gamma: the input and recurrent normalization gain. Dimensions
          (2, hidden_size) with `gamma[0]` specifying the input gain and
          `gamma[1]` specifying the recurrent gain. Initialized to ones.
docstring_summary: Initialize the parameters of the IndRNN layer.
docstring_tokens: Initialize the parameters of the IndRNN layer .
function: def __init__(
        self,
        input_size,
        hidden_size,
        batch_first=False,
        zoneout=0.0,
        return_state_sequence=False):
      """
      Initialize the parameters of the IndRNN layer.
  
      Arguments:
        input_size: int, the feature dimension of the input.
        hidden_size: int, the feature dimension of the output.
        batch_first: (optional) bool, if `True`, then the input and output
          tensors are provided as `(batch, seq, feature)`.
        zoneout: (optional) float, sets the zoneout rate for Zoneout
          regularization.
        return_state_sequence: (optional) bool, if `True`, the forward pass will
          return the entire state sequence instead of just the final state. Note
          that if the input is a padded sequence, the returned state will also
          be a padded sequence.
  
      Variables:
        kernel: the input projection weight matrix. Dimensions
          (input_size, hidden_size). Initialized with Xavier uniform
          initialization.
        recurrent_scale: the recurrent scale weight vector. Dimensions
          (hidden_size). Initialized uniformly in [-0.5, 0.5]. Note that this
          initialization scheme is different than in the original authors'
          implementation. See https://github.com/lmnt-com/haste/issues/7 for
          details.
        bias: the RNN bias vector. Dimensions (hidden_size). Initialized to zeros.
        gamma: the input and recurrent normalization gain. Dimensions
          (2, hidden_size) with `gamma[0]` specifying the input gain and
          `gamma[1]` specifying the recurrent gain. Initialized to ones.
      """
      super().__init__(input_size, hidden_size, batch_first, zoneout, return_state_sequence)
  
      if zoneout < 0 or zoneout > 1:
        raise ValueError('LayerNormIndRNN: zoneout must be in [0.0, 1.0]')
  
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.batch_first = batch_first
      self.zoneout = zoneout
  
      self.kernel = nn.Parameter(torch.empty(input_size, hidden_size))
      self.recurrent_scale = nn.Parameter(torch.empty(hidden_size))
      self.bias = nn.Parameter(torch.empty(hidden_size))
      self.gamma = nn.Parameter(torch.empty(2, hidden_size))
      self.reset_parameters()
function_tokens: def __init__ ( self , input_size , hidden_size , batch_first = False , zoneout = 0.0 , return_state_sequence = False ) : super ( ) . __init__ ( input_size , hidden_size , batch_first , zoneout , return_state_sequence ) if zoneout < 0 or zoneout > 1 : raise ValueError ( 'LayerNormIndRNN: zoneout must be in [0.0, 1.0]' ) self . input_size = input_size self . hidden_size = hidden_size self . batch_first = batch_first self . zoneout = zoneout self . kernel = nn . Parameter ( torch . empty ( input_size , hidden_size ) ) self . recurrent_scale = nn . Parameter ( torch . empty ( hidden_size ) ) self . bias = nn . Parameter ( torch . empty ( hidden_size ) ) self . gamma = nn . Parameter ( torch . empty ( 2 , hidden_size ) ) self . reset_parameters ( )