docstr: :param prior_shape: shape of spatial prior
  :param output_shape: target shape for resampling
  :param name: layer name
docstr_tok: [":","param","prior_shape",":","shape","of","spatial","prior",":","param","output_shape",":","target","shape","for","resampling",":","param","name",":","layer","name"]
code: def __init__(
      self, prior_shape, output_shape, name="spatial_prior_block"
  ):
      """
  
      :param prior_shape: shape of spatial prior
      :param output_shape: target shape for resampling
      :param name: layer name
      """
  
      super(SpatialPriorBlock, self).__init__(name=name)
  
      self.prior_shape = prior_shape
      self.output_shape = output_shape
  
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 1 to create a new SpatialPriorBlock object from inherited base classes: TrainableLayer with example: obj = SpatialPriorBlock(prior_shape, output_shape, name)