parameters: (
      X: NodeInput,
      initial_hidden_state: NodeInput,
      W: NodeInput,
      R: NodeInput,
      B: NodeInput,
      hidden_size: int,
      activations: List[str],
      activations_alpha: List[float],
      activations_beta: List[float],
      clip: float = 0.0,
      name: Optional[str] = None,
  )
return_statement: return _get_node_factory().create("RNNCell", input_nodes, attributes)
docstring: Perform RNNCell operation on tensor from input node.
  
      It follows notation and equations defined as in ONNX standard:
      https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN
  
      Note this class represents only single *cell* and not whole RNN *layer*.
  
      :param X:                       The input tensor with shape: [batch_size, input_size].
      :param initial_hidden_state:    The hidden state tensor at current time step with shape:
                                      [batch_size, hidden_size].
      :param W:                       The weight tensor with shape: [hidden_size, input_size].
      :param R:                       The recurrence weight tensor with shape: [hidden_size,
                                      hidden_size].
      :param B:                       The bias tensor for input gate with shape: [2*hidden_size].
      :param hidden_size:             The number of hidden units for recurrent cell.
                                      Specifies hidden state size.
      :param activations:             The vector of activation functions used inside recurrent cell.
      :param activation_alpha:        The vector of alpha parameters for activation functions in
                                      order respective to activation list.
      :param activation_beta:         The vector of beta parameters for activation functions in order
                                      respective to activation list.
      :param clip:                    The value defining clipping range [-clip, clip] on input of
                                      activation functions.
      :param name:                    Optional output node name.
      :returns:   The new node performing a RNNCell operation on tensor from input node.
docstring_summary: Perform RNNCell operation on tensor from input node.
docstring_tokens: Perform RNNCell operation on tensor from input node .
function: def rnn_cell(
      X: NodeInput,
      initial_hidden_state: NodeInput,
      W: NodeInput,
      R: NodeInput,
      B: NodeInput,
      hidden_size: int,
      activations: List[str],
      activations_alpha: List[float],
      activations_beta: List[float],
      clip: float = 0.0,
      name: Optional[str] = None,
  ) -> Node:
      """Perform RNNCell operation on tensor from input node.
  
      It follows notation and equations defined as in ONNX standard:
      https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN
  
      Note this class represents only single *cell* and not whole RNN *layer*.
  
      :param X:                       The input tensor with shape: [batch_size, input_size].
      :param initial_hidden_state:    The hidden state tensor at current time step with shape:
                                      [batch_size, hidden_size].
      :param W:                       The weight tensor with shape: [hidden_size, input_size].
      :param R:                       The recurrence weight tensor with shape: [hidden_size,
                                      hidden_size].
      :param B:                       The bias tensor for input gate with shape: [2*hidden_size].
      :param hidden_size:             The number of hidden units for recurrent cell.
                                      Specifies hidden state size.
      :param activations:             The vector of activation functions used inside recurrent cell.
      :param activation_alpha:        The vector of alpha parameters for activation functions in
                                      order respective to activation list.
      :param activation_beta:         The vector of beta parameters for activation functions in order
                                      respective to activation list.
      :param clip:                    The value defining clipping range [-clip, clip] on input of
                                      activation functions.
      :param name:                    Optional output node name.
      :returns:   The new node performing a RNNCell operation on tensor from input node.
      """
      if activations is None:
          activations = ["sigmoid", "tanh"]
      if activations_alpha is None:
          activations_alpha = []
      if activations_beta is None:
          activations_beta = []
  
      input_nodes = as_nodes(X, initial_hidden_state, W, R, B)
      attributes = {
          "hidden_size": hidden_size,
          "activations": activations,
          "activations_alpha": activations_alpha,
          "activations_beta": activations_beta,
          "clip": clip,
      }
      return _get_node_factory().create("RNNCell", input_nodes, attributes)
function_tokens: def rnn_cell ( X : NodeInput , initial_hidden_state : NodeInput , W : NodeInput , R : NodeInput , B : NodeInput , hidden_size : int , activations : List [ str ] , activations_alpha : List [ float ] , activations_beta : List [ float ] , clip : float = 0.0 , name : Optional [ str ] = None , ) -> Node : if activations is None : activations = [ "sigmoid" , "tanh" ] if activations_alpha is None : activations_alpha = [ ] if activations_beta is None : activations_beta = [ ] input_nodes = as_nodes ( X , initial_hidden_state , W , R , B ) attributes = { "hidden_size" : hidden_size , "activations" : activations , "activations_alpha" : activations_alpha , "activations_beta" : activations_beta , "clip" : clip , } return _get_node_factory ( ) . create ( "RNNCell" , input_nodes , attributes )