class_docstr: Helper class to manage learnable splines. One could imagine this as a standard
  layer in PyTorch...
code: def spline_op(self, x, **kwargs):
      w, h, d, l = (
          self._params() if callable(self._params) else self._params
      )
      y, log_detJ = _monotonic_rational_spline(
          x, w, h, d, l, bound=self.bound, **kwargs
      )
      return y, log_detJ
  
imports: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.distributions import Transform, constraints
  from pyro.nn import DenseNN
  from ..conditional import ConditionalTransformModule
  from ..torch_transform import TransformModule
  from ..util import copy_docs_from
label_desc: Use image node_id 5 for calling the ConditionedSpline obj's underlying member method code with example usage: obj.spline_op(x) and returns: y,  log_detJ