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.
code: def __init__(self, axis=1, shape=None):
      super(Bias, self).__init__()
  
      # Add b parameter if given.
      if shape is not None:
          with self.init_scope():
              self.b = variable.Parameter(0, shape)
  
      self.axis = 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 1 to create a new Bias object from inherited base classes: link with example: obj = Bias(axis, shape)