parameters: (self,
        input_size,
        hidden_size,
        batch_first=False,
        dropout=0.0,
        zoneout=0.0,
        return_state_sequence=False)
docstring: Initialize the parameters of the GRU 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)`.
        dropout: (optional) float, sets the dropout rate for DropConnect
          regularization on the recurrent matrix.
        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 * 3) with `z,r,h` gate layout. Initialized
          with Xavier uniform initialization.
        recurrent_kernel: the recurrent projection weight matrix. Dimensions
          (hidden_size, hidden_size * 3) with `z,r,h` gate layout. Initialized
          with orthogonal initialization.
        bias: the input projection bias vector. Dimensions (hidden_size * 3) with
          `z,r,h` gate layout. Initialized to zeros.
        recurrent_bias: the recurrent projection bias vector. Dimensions
          (hidden_size * 3) with `z,r,h` gate layout. Initialized to zeros.
docstring_summary: Initialize the parameters of the GRU layer.
docstring_tokens: Initialize the parameters of the GRU layer .
function: def __init__(self,
        input_size,
        hidden_size,
        batch_first=False,
        dropout=0.0,
        zoneout=0.0,
        return_state_sequence=False):
      """
      Initialize the parameters of the GRU 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)`.
        dropout: (optional) float, sets the dropout rate for DropConnect
          regularization on the recurrent matrix.
        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 * 3) with `z,r,h` gate layout. Initialized
          with Xavier uniform initialization.
        recurrent_kernel: the recurrent projection weight matrix. Dimensions
          (hidden_size, hidden_size * 3) with `z,r,h` gate layout. Initialized
          with orthogonal initialization.
        bias: the input projection bias vector. Dimensions (hidden_size * 3) with
          `z,r,h` gate layout. Initialized to zeros.
        recurrent_bias: the recurrent projection bias vector. Dimensions
          (hidden_size * 3) with `z,r,h` gate layout. Initialized to zeros.
      """
      super().__init__(input_size, hidden_size, batch_first, zoneout, return_state_sequence)
  
      if dropout < 0 or dropout > 1:
        raise ValueError('GRU: dropout must be in [0.0, 1.0]')
      if zoneout < 0 or zoneout > 1:
        raise ValueError('GRU: zoneout must be in [0.0, 1.0]')
  
      self.dropout = dropout
  
      self.kernel = nn.Parameter(torch.empty(input_size, hidden_size * 3))
      self.recurrent_kernel = nn.Parameter(torch.empty(hidden_size, hidden_size * 3))
      self.bias = nn.Parameter(torch.empty(hidden_size * 3))
      self.recurrent_bias = nn.Parameter(torch.empty(hidden_size * 3))
      self.reset_parameters()
function_tokens: def __init__ ( self , input_size , hidden_size , batch_first = False , dropout = 0.0 , zoneout = 0.0 , return_state_sequence = False ) : super ( ) . __init__ ( input_size , hidden_size , batch_first , zoneout , return_state_sequence ) if dropout < 0 or dropout > 1 : raise ValueError ( 'GRU: dropout must be in [0.0, 1.0]' ) if zoneout < 0 or zoneout > 1 : raise ValueError ( 'GRU: zoneout must be in [0.0, 1.0]' ) self . dropout = dropout self . kernel = nn . Parameter ( torch . empty ( input_size , hidden_size * 3 ) ) self . recurrent_kernel = nn . Parameter ( torch . empty ( hidden_size , hidden_size * 3 ) ) self . bias = nn . Parameter ( torch . empty ( hidden_size * 3 ) ) self . recurrent_bias = nn . Parameter ( torch . empty ( hidden_size * 3 ) ) self . reset_parameters ( )