class_docstr: Helper class to manage learnable splines. One could imagine this as a standard
  layer in PyTorch...
code: def __init__(self, params, bound=3.0, order="linear"):
      super().__init__(cache_size=1)
  
      self._params = params
      self.order = order
      self.bound = bound
      self._cache_log_detJ = None
  
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 1 to create a new ConditionedSpline object from inherited base classes: Transform with example: obj = ConditionedSpline(params, bound, order)