class_docstr: Base class for a family of isotropic covariance kernels which are functions of the
  distance :math:`|x-z|/l`, where :math:`l` is the length-scale parameter.
  
  By default, the parameter ``lengthscale`` has size 1. To use the isotropic version
  (different lengthscale for each dimension), make sure that ``lengthscale`` has size
  equal to ``input_dim``.
  
  :param torch.Tensor lengthscale: Length-scale parameter of this kernel.
code: def __init__(
      self, input_dim, variance=None, lengthscale=None, active_dims=None
  ):
      super().__init__(input_dim, active_dims)
  
      variance = torch.tensor(1.0) if variance is None else variance
      self.variance = PyroParam(variance, constraints.positive)
  
      lengthscale = (
          torch.tensor(1.0) if lengthscale is None else lengthscale
      )
      self.lengthscale = PyroParam(lengthscale, constraints.positive)
  
imports: import torch
  from torch.distributions import constraints
  from pyro.contrib.gp.kernels.kernel import Kernel
  from pyro.nn.module import PyroParam
label_desc: Use image node_id 1 to create a new Isotropy object from inherited base classes: Kernel with example: obj = Isotropy(input_dim, variance, lengthscale, active_dims)