docs: def plot_dkl(f, x, samples, prior_samples, ax=None, **kwargs):
      r"""
      Plot the Kullback-Leibler divergence at each value of :math:`x` for the
      prior and posterior defined by `prior_samples` and `samples`.
  
      Let the posterior be:
  
      :math:`P(y|x) = \int P(y=f(x;\theta)|x,\theta)P(\theta) d\theta`
  
      and the prior be:
  
      :math:`Q(y|x) = \int P(y=f(x;\theta)|x,\theta)Q(\theta) d\theta`
  
      then the Kullback-Leibler divergence at each x is defined by
  
      :math:`D_\mathrm{KL}(x)=\int P(y|x)\ln\left[\frac{Q(y|x)}{P(y|x)}\right]dy`
  
      Additionally, if a list of log-evidences are passed, along with list of
      functions, and list of samples, this function plots the Kullback-Leibler
      divergence for all models marginalised according to the evidences.
  
      Parameters
      ----------
      f: function
          function :math:`f(x;\theta)` (or list of functions for each model) with
          dependent variable :math:`x`, parameterised by :math:`\theta`.
  
      x: 1D array-like
          `x` values to evaluate :math:`f(x;\theta)` at.
  
      samples, prior_samples: 2D array-like
          :math:`\theta` samples (or list of :math:`\theta` samples) from
          posterior and prior to evaluate :math:`f(x;\theta)` at.
          `shape = (nsamples, npars)`
  
      ax: axes object, optional
          :class:`matplotlib.axes._subplots.AxesSubplot` to plot the contours
          onto. If unsupplied, then :func:`matplotlib.pyplot.gca()` is used to
          get the last axis used, or create a new one.
  
      logZ: 1D array-like, optional
          log-evidences of each model if multiple models are passed.
          Should be same length as the list `f`, and need not be normalised.
          Default: `numpy.ones_like(f)`
  
      weights, prior_weights: 1D array-like, optional
          sample weights (or list of weights), if desired. Should have length
          same as `samples.shape[0]`.
          Default: `numpy.ones_like(samples)`
  
      ntrim: int, optional
          Approximate number of samples to trim down to, if desired. Useful if
          the posterior is dramatically oversampled.
          Default: None
  
      cache, prior_cache: str, optional
          File roots for saving previous calculations for re-use
  
      parallel, tqdm_args:
          see docstring for :func:`fgivenx.parallel.parallel_apply`
  
      kwargs: further keyword arguments
          Any further keyword arguments are plotting keywords that are passed to
          :func:`fgivenx.plot.plot`.
      """
  
      logZ = kwargs.pop('logZ', None)
      weights = kwargs.pop('weights', None)
      prior_weights = kwargs.pop('prior_weights', None)
      ntrim = kwargs.pop('ntrim', None)
      cache = kwargs.pop('cache', '')
      prior_cache = kwargs.pop('prior_cache', '')
      parallel = kwargs.pop('parallel', False)
      tqdm_kwargs = kwargs.pop('tqdm_kwargs', {})
  
      dkls = compute_dkl(f, x, samples, prior_samples,
                         logZ=logZ, parallel=parallel,
                         cache=cache, prior_cache=prior_cache,
                         tqdm_kwargs=tqdm_kwargs,
                         ntrim=ntrim, weights=weights,
                         prior_weights=prior_weights)
  
      if ax is None:
          ax = plt.gca()
      ax.plot(x, dkls, **kwargs)