class_docstr: lasagne.layers.ScaleLayer(incoming, scales=lasagne.init.Constant(1),
  shared_axes='auto', **kwargs)
  
  A layer that scales its inputs by learned coefficients.
  
  Parameters
  ----------
  incoming : a :class:`Layer` instance or a tuple
      The layer feeding into this layer, or the expected input shape
  
  scales : Theano shared variable, expression, numpy array, or callable
      Initial value, expression or initializer for the scale.  The scale
      shape must match the incoming shape, skipping those axes the scales 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 scales over. If ``'auto'`` (the default),
      share over all axes except for the second: this will share scales over
      the minibatch dimension for dense layers, and additionally over all
      spatial dimensions for convolutional layers.
  
  Notes
  -----
  The scales parameter dimensionality is the input dimensionality minus the
  number of axes the scales are shared over, which matches the bias parameter
  conventions of :class:`DenseLayer` or :class:`Conv2DLayer`. For example:
  
  >>> layer = ScaleLayer((20, 30, 40, 50), shared_axes=(0, 2))
  >>> layer.scales.get_value().shape
  (30, 50)
code: def get_output_for(self, input, **kwargs):
      axes = iter(range(self.scales.ndim))
      pattern = [
          "x" if input_axis in self.shared_axes else next(axes)
          for input_axis in range(input.ndim)
      ]
      return input * self.scales.dimshuffle(*pattern)
  
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 ScaleLayer obj's underlying member method code with example usage: obj.get_output_for(input) and returns: unknown