class_docstr: A Gaussian process for Neural Networks. 
docstr: Checks if the sizes of list of distances and the data are consistent before
  calling build_posterior from the super class.
docstr_tok: ["Checks","if","the","sizes","of","list","of","distances","and","the","data","are","consistent","before","calling","build_posterior","from","the","super","class","."]
code: def build_posterior(self):
      """Checks if the sizes of list of distances and the data are consistent before
      calling build_posterior from the super class.
      """
      if self.list_of_dists is not None:
          assert self.list_of_dists[0].shape == (
              len(self.X),
              len(self.Y),
          )
      super(NNGP, self).build_posterior()
  
imports: import numpy
  from .None import otmann
  from ..gp import gp_core
  from ..utils.ancillary_utils import get_list_of_floats_as_str
  from ..utils.reporters import get_reporter
  from ..utils.option_handler import get_option_specs, load_options
label_desc: Use image node_id 2 for calling the NNGP obj's underlying member method code with example usage: obj.build_posterior() without return types