parameters: (
          self,
          input_size,
          hidden_size,
          nonlinearity="tanh",
          num_layers=1,
          bias=True,
          batch_first=False,
          dropout=0,
          bidirectional=False,
      )
docstring: Args:
              input_size (int):  The number of expected features in the input x
              hidden_size (int): The number of features in the hidden state h
              num_layers (int):  Number of recurrent layers. Default: 1
              nonlinearity (string): The non-linearity to use. Default: 'tanh'
              bias (bool):  If False, then the layer does not use bias weights.
                  Default: True
              batch_first (bool):  If True, then the input and output tensors
                  are provided as (batch, seq, feature). Default: False
              dropout (float): If non-zero, introduces a Dropout layer on the
                  outputs of each RNN layer except the last layer, with dropout
                  probability equal to dropout. Default: 0
              bidirectional (bool): If True, becomes a bidirectional RNN.
                  Default: False
docstring_summary: Args:
              input_size (int):  The number of expected features in the input x
              hidden_size (int): The number of features in the hidden state h
              num_layers (int):  Number of recurrent layers. Default: 1
              nonlinearity (string): The non-linearity to use. Default: 'tanh'
              bias (bool):  If False, then the layer does not use bias weights.
                  Default: True
              batch_first (bool):  If True, then the input and output tensors
                  are provided as (batch, seq, feature). Default: False
              dropout (float): If non-zero, introduces a Dropout layer on the
                  outputs of each RNN layer except the last layer, with dropout
                  probability equal to dropout. Default: 0
              bidirectional (bool): If True, becomes a bidirectional RNN.
                  Default: False
docstring_tokens: Args : input_size ( int ) : The number of expected features in the input x hidden_size ( int ) : The number of features in the hidden state h num_layers ( int ) : Number of recurrent layers . Default : 1 nonlinearity ( string ) : The non - linearity to use . Default : tanh bias ( bool ) : If False then the layer does not use bias weights . Default : True batch_first ( bool ) : If True then the input and output tensors are provided as ( batch seq feature ) . Default : False dropout ( float ) : If non - zero introduces a Dropout layer on the outputs of each RNN layer except the last layer with dropout probability equal to dropout . Default : 0 bidirectional ( bool ) : If True becomes a bidirectional RNN . Default : False
function: def __init__(
          self,
          input_size,
          hidden_size,
          nonlinearity="tanh",
          num_layers=1,
          bias=True,
          batch_first=False,
          dropout=0,
          bidirectional=False,
      ):
          """
          Args:
              input_size (int):  The number of expected features in the input x
              hidden_size (int): The number of features in the hidden state h
              num_layers (int):  Number of recurrent layers. Default: 1
              nonlinearity (string): The non-linearity to use. Default: 'tanh'
              bias (bool):  If False, then the layer does not use bias weights.
                  Default: True
              batch_first (bool):  If True, then the input and output tensors
                  are provided as (batch, seq, feature). Default: False
              dropout (float): If non-zero, introduces a Dropout layer on the
                  outputs of each RNN layer except the last layer, with dropout
                  probability equal to dropout. Default: 0
              bidirectional (bool): If True, becomes a bidirectional RNN.
                  Default: False
          """
          super(LSTM, self).__init__()
  
          self.input_size = input_size
          self.hidden_size = hidden_size
          self.num_layers = num_layers
          self.nonlinearity = nonlinearity
          self.bias = bias
          self.batch_first = batch_first
          self.dropout = dropout
          self.bidirectional = bidirectional
function_tokens: def __init__ ( self , input_size , hidden_size , nonlinearity = "tanh" , num_layers = 1 , bias = True , batch_first = False , dropout = 0 , bidirectional = False , ) : super ( LSTM , self ) . __init__ ( ) self . input_size = input_size self . hidden_size = hidden_size self . num_layers = num_layers self . nonlinearity = nonlinearity self . bias = bias self . batch_first = batch_first self . dropout = dropout self . bidirectional = bidirectional