class_docstr: Grid warper interface class.
  
  An object implementing the `GridWarper` interface
  generates a reference grid of feature points at construction time,
  and warps it via a parametric transformation model,
  specified at run time by an input parameter Tensor.
  Grid warpers must then implement a `create_features` function
  used to generate the reference grid to be warped
  in the forward pass (according to a determined warping model).
docstr: Precomputes features
  (e.g. sampling patterns, unconstrained feature matrices).
docstr_tok: ["Precomputes","features","(","e.g",".","sampling","patterns",",","unconstrained","feature","matrices",")","."]
code: def _create_features(self, **kwargs):
      """
      Precomputes features
      (e.g. sampling patterns, unconstrained feature matrices).
      """
      tf.logging.fatal("_create_features() should be implemented")
      raise NotImplementedError
  
imports: from __future__ import absolute_import, division, print_function
  from itertools import chain
  import numpy
  import tensorflow
  from niftynet.layer.base_layer import Layer, LayerFromCallable, Invertible
label_desc: Use image node_id 2 for calling the GridWarperLayer obj's underlying member method code with example usage: obj._create_features() without return types