class_docstr: lasagne.layers.BiasLayer(incoming, b=lasagne.init.Constant(0),
  shared_axes='auto', **kwargs)
  
  A layer that just adds a (trainable) bias term.
  
  Parameters
  ----------
  incoming : a :class:`Layer` instance or a tuple
      The layer feeding into this layer, or the expected input shape
  
  b : Theano shared variable, expression, numpy array, callable or ``None``
      Initial value, expression or initializer for the biases. If set to
      ``None``, the layer will have no biases and pass through its input
      unchanged. Otherwise, the bias shape must match the incoming shape,
      skipping those axes the biases are shared over (see the example below).
      See :func:`lasagne.utils.create_param` for more information.
  
  shared_axes : 'auto', int or tuple of int
      The axis or axes to share biases over. If ``'auto'`` (the default),
      share over all axes except for the second: this will share biases over
      the minibatch dimension for dense layers, and additionally over all
      spatial dimensions for convolutional layers.
  
  Notes
  -----
  The bias parameter dimensionality is the input dimensionality minus the
  number of axes the biases are shared over, which matches the bias parameter
  conventions of :class:`DenseLayer` or :class:`Conv2DLayer`. For example:
  
  >>> layer = BiasLayer((20, 30, 40, 50), shared_axes=(0, 2))
  >>> layer.b.get_value().shape
  (30, 50)
code: def get_output_for(self, input, **kwargs):
      if self.b is not None:
          bias_axes = iter(range(self.b.ndim))
          pattern = [
              "x" if input_axis in self.shared_axes else next(bias_axes)
              for input_axis in range(input.ndim)
          ]
          return input + self.b.dimshuffle(*pattern)
      else:
          return input
  
imports: import theano
  import theano.tensor
  import numpy
  from ..None import init
  from ..None import nonlinearities
  from ..utils import as_tuple, floatX, int_types
  from ..random import get_rng
  from .base import Layer, MergeLayer
  from theano.sandbox.rng_mrg import MRG_RandomStreams
label_desc: Use image node_id 2 for calling the BiasLayer obj's underlying member method code with example usage: obj.get_output_for(input) and returns: unknown, input