class_docstr: Provides the building blocks for representing univariate Gaussian Processes (GPs)
  with Matern kernels as state space models.
  
  :param float nu: The order of the Matern kernel (one of 0.5, 1.5 or 2.5)
  :param int num_gps: the number of GPs
  :param torch.Tensor length_scale_init: optional `num_gps`-dimensional vector of initializers
      for the length scale
  :param torch.Tensor kernel_scale_init: optional `num_gps`-dimensional vector of initializers
      for the kernel scale
  
  **References**
  
  [1] `Kalman Filtering and Smoothing Solutions to Temporal Gaussian Process Regression Models`,
      Jouni Hartikainen and Simo Sarkka.
  [2] `Stochastic Differential Equation Methods for Spatio-Temporal Gaussian Process Regression`,
      Arno Solin.
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docstr: Get the transition matrix and process covariance corresponding to a time interval `dt`.
  
  :param float dt: the time interval over which the GP latent space evolves.
  :returns tuple: (`transition_matrix`, `process_covariance`) both 3-dimensional tensors of
      shape (num_gps, state_dim, state_dim)
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code: def transition_matrix_and_covariance(self, dt):
      """
      Get the transition matrix and process covariance corresponding to a time interval `dt`.
  
      :param float dt: the time interval over which the GP latent space evolves.
      :returns tuple: (`transition_matrix`, `process_covariance`) both 3-dimensional tensors of
          shape (num_gps, state_dim, state_dim)
      """
      trans_matrix = self.transition_matrix(dt)
      process_covar = self.process_covariance(trans_matrix)
      return trans_matrix, process_covar
  
file_path: pyro/pyro/ops/ssm_gp.py
imports: import math
  import torch
  from torch.distributions import constraints
  from pyro.nn import PyroModule, PyroParam, pyro_method
label_desc: Use image node_id 5 for calling the MaternKernel obj's underlying member method code with example usage: obj.transition_matrix_and_covariance(dt) and returns: trans_matrix,  process_covar