class_docstr: Positionwise feed forward.
  
  Args:
      :param int idim: input dimenstion
      :param int hidden_units: number of hidden units
      :param float dropout_rate: dropout rate
docstr: Initialize PositionwiseFeedForward.
  
  Args:
      n_units (int): Input dimension.
      d_units (int, optional): Output dimension of hidden layer.
      dropout (float, optional): Dropout ratio.
      initialW (int, optional):  Initializer to initialize the weight.
      initial_bias (bool, optional): Initializer to initialize the bias.
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code: def __init__(
      self,
      n_units,
      d_units=0,
      dropout=0.1,
      initialW=None,
      initial_bias=None,
  ):
      """Initialize PositionwiseFeedForward.
  
      Args:
          n_units (int): Input dimension.
          d_units (int, optional): Output dimension of hidden layer.
          dropout (float, optional): Dropout ratio.
          initialW (int, optional):  Initializer to initialize the weight.
          initial_bias (bool, optional): Initializer to initialize the bias.
  
      """
      super(PositionwiseFeedForward, self).__init__()
      n_inner_units = d_units if d_units > 0 else n_units * 4
      with self.init_scope():
          stvd = 1.0 / np.sqrt(n_units)
          self.w_1 = L.Linear(
              n_units,
              n_inner_units,
              initialW=initialW(scale=stvd),
              initial_bias=initial_bias(scale=stvd),
          )
          stvd = 1.0 / np.sqrt(n_inner_units)
          self.w_2 = L.Linear(
              n_inner_units,
              n_units,
              initialW=initialW(scale=stvd),
              initial_bias=initial_bias(scale=stvd),
          )
          self.act = F.relu
      self.dropout = dropout
  
imports: import chainer
  import chainer.functions
  import chainer.links
  import numpy
label_desc: Use image node_id 1 to create a new PositionwiseFeedForward object from inherited base classes: chainer with example: obj = PositionwiseFeedForward(n_units, d_units, dropout, initialW, initial_bias)