docstr: :return: spatial prior resampled to the target shape
docstr_tok: [":","return",":","spatial","prior","resampled","to","the","target","shape"]
code: def layer_op(self):
      """
  
      :return: spatial prior resampled to the target shape
      """
      # The internal representation is probabilities so
      # that resampling makes sense
      prior = tf.get_variable(
          "prior",
          shape=self.prior_shape,
          initializer=tf.constant_initializer(1),
      )
      return tf.log(LinearResizeLayer(self.output_shape)(prior))
  
imports: from __future__ import absolute_import, print_function
  import tensorflow
  from niftynet.io.misc_io import image3_axial
  from niftynet.layer import layer_util
  from niftynet.layer.affine_augmentation import AffineAugmentationLayer
  from niftynet.layer.base_layer import TrainableLayer
  from niftynet.layer.bn import BNLayer
  from niftynet.layer.channel_sparse_convolution import ChannelSparseConvolutionalLayer
  from niftynet.layer.convolution import ConvolutionalLayer
  from niftynet.layer.linear_resize import LinearResizeLayer
  from niftynet.layer.downsample import DownSampleLayer
  from niftynet.network.base_net import BaseNet
  from collections import namedtuple
label_desc: Use image node_id 2 for calling the SpatialPriorBlock obj's underlying member method code with example usage: obj.layer_op() and returns: tf