class_docstr: Broadcasted elementwise summation with learnable parameters.
  
  Computes a elementwise summation as :func:`~chainer.functions.bias`
  function does except that its second input is a learnable bias parameter
  :math:`b` the link has.
  
  Args:
      axis (int): The first axis of the first input of
          :func:`~chainer.functions.bias` function along which its second
          input is applied.
      shape (tuple of ints): Shape of the learnable bias parameter. If
          ``None``, this link does not have learnable parameters so an
          explicit bias needs to be given to its ``forward`` method's second
          input.
  
  .. seealso:: See :func:`~chainer.functions.bias` for details.
  
  Attributes:
      b (~chainer.Variable): Bias parameter if ``shape`` is given. Otherwise,
          no attributes.
docstr: Applies broadcasted elementwise summation.
  
  Args:
      xs (list of Variables): Input variables whose length should
          be one if the link has a learnable bias parameter, otherwise
          should be two.
docstr_tok: ["Applies","broadcasted","elementwise","summation",".","Args",":","xs","(","list","of","Variables",")",":","Input","variables","whose","length","should","be","one","if","the","link","has","a","learnable","bias","parameter",",","otherwise","should","be","two","."]
code: def forward(self, *xs):
      """Applies broadcasted elementwise summation.
  
      Args:
          xs (list of Variables): Input variables whose length should
              be one if the link has a learnable bias parameter, otherwise
              should be two.
      """
      axis = self.axis
  
      # Case of only one argument where b is a learnt parameter.
      if hasattr(self, "b"):
          if chainer.is_debug():
              assert len(xs) == 1
          (x,) = xs
          b = self.b
          return bias.bias(x, b, axis)
      # Case of two arguments where b is given as an argument.
      else:
          if chainer.is_debug():
              assert len(xs) == 2
          x, y = xs
          return bias.bias(x, y, axis)
  
file_path: chainer/chainer/links/connection/bias.py
imports: import chainer
  from chainer.functions.math import bias
  from chainer import link
  from chainer import variable
label_desc: Use image node_id 2 for calling the Bias obj's underlying member method code with example usage: obj.forward() and returns: bias, bias