class_docstr: Dynamic model interface.
  
  :param dimension: native state dimension.
  :param dimension_pv: PV state dimension.
  :param num_process_noise_parameters: process noise parameter space dimension.
        This for UKF applications. Can be left as ``None`` for EKF and most
        other filters.
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docstr: Compute and return process noise covariance (Q).
  
  :param dt: time interval to integrate over.
  :return: Read-only covariance (Q). For a DifferentiableDynamicModel, this is
      the covariance of the native state ``x`` resulting from stochastic
      integration (for use with EKF). Otherwise, it is the covariance
      directly of the process noise parameters (for use with UKF).
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code: def process_noise_cov(self, dt=0.0):
      """
      Compute and return process noise covariance (Q).
  
      :param dt: time interval to integrate over.
      :return: Read-only covariance (Q). For a DifferentiableDynamicModel, this is
          the covariance of the native state ``x`` resulting from stochastic
          integration (for use with EKF). Otherwise, it is the covariance
          directly of the process noise parameters (for use with UKF).
      """
      raise NotImplementedError
  
imports: from abc import ABCMeta, abstractmethod
  import torch
  from torch import nn
  from torch.nn import Parameter
  import pyro.distributions
  from pyro.distributions.util import eye_like
label_desc: Use image node_id 9 for calling the DynamicModel obj's underlying member method code with example usage: obj.process_noise_cov(dt) without return types