code: def _rnn(x, h, w, b, nonlinearity, with_bias):
      """RNN cell.
      Args:
          x (:obj:`~nnabla.Variable`): Input data.
          h (:obj:`~nnabla.Variable`): Hidden state.
          w (:obj:`~nnabla.Variable`): Weight.
          b (:obj:`~nnabla.Variable`): Bias.
          nonlinearity (str): "tanh" or "relu".
          with_bias (bool): Include the bias or not.
      """
      hidden_size = h.shape[1]
      xh = F.concatenate(*(x, h), axis=1)
      b_ = None
      if with_bias:
          b_ = b
      h_t = F.affine(xh, F.transpose(w, (1, 0)), b_)
      if nonlinearity == "tanh":
          h_t = F.tanh(h_t)
      elif nonlinearity == "relu":
          h_t = F.relu(h_t)
  
      return h_t
  
file_path: nnabla/python/src/nnabla/utils/rnn.py
imports: import nnabla
  import nnabla.functions
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: _rnn(x, h, w, b, nonlinearity, with_bias) and returns: h_t