prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Add docs references to plot_energy docstring
  Add links to the related functions and classes to all the places where they are mentioned in the [plot_energy](https://arviz-devs.github.io/arviz/api/generated/arviz.plot_energy.html) docstring.
  
  ## Sample PR:
  You can see https://github.com/arviz-devs/arviz/pull/1816 for the function, `plot_autocorr`. 
  > ⚠️ Always create a new branch for changes. Don't make any changes to the `main` branch. 
  
  
  ## Source File
  Source file: https://github.com/arviz-devs/arviz/blob/main/arviz/plots/energyplot.py
  
  ## How to add links
  Add the links in the docstring of doc. You can also check the docstring by going to the `[source]`. 
  
  * Add links to the ArviZ functions and classes using [Cross-referencing with Sphinx](https://docs.readthedocs.io/en/stable/guides/cross-referencing-with-sphinx.html).
  * Add links to the xarray functions and classes using [Intersphinx](https://docs.readthedocs.io/en/stable/guides/intersphinx.html).
  * Add the rest of the external links using this [syntax](https://sublime-and-sphinx-guide.readthedocs.io/en/latest/references.html#links-to-external-web-pages).
  
  ## `backend_kwargs`, `plot_kwargs` and `fill_kwargs`
  For adding `backend_kwargs` as they have been added in #1816, see [Adding backend_kwargs guide](https://github.com/arviz-devs/arviz/wiki/ArviZ-Hacktoberfest-2021#adding-backend_kwargs). Follow the same process for `fill_kwargs` and `plot_kwargs`.
  
  ## See also section
  Add the following functions in the "See also" section:
  1. [bfmi](https://arviz-devs.github.io/arviz/api/generated/arviz.bfmi.html)
  
  
  See [Adding "see also" section](https://github.com/arviz-devs/arviz/wiki/ArviZ-Hacktoberfest-2021#adding-see-also-section) for more details. 
  
  ## Checking the preview of docs
  For checking the preview of docs, you don't need t set up the project on your local machine. Just follow the [checking the preview guide](https://github.com/arviz-devs/arviz/wiki/ArviZ-Hacktoberfest-2021#how-to-check-the-preview-of-docs). 
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `arviz/plots/energyplot.py`
  Content:
  ```
  1 """Plot energy transition distribution in HMC inference."""
  2 import warnings
  3 
  4 from ..data import convert_to_dataset
  5 from ..rcparams import rcParams
  6 from .plot_utils import get_plotting_function
  7 
  8 
  9 def plot_energy(
  10     data,
  11     kind=None,
  12     bfmi=True,
  13     figsize=None,
  14     legend=True,
  15     fill_alpha=(1, 0.75),
  16     fill_color=("C0", "C5"),
  17     bw="experimental",
  18     textsize=None,
  19     fill_kwargs=None,
  20     plot_kwargs=None,
  21     ax=None,
  22     backend=None,
  23     backend_kwargs=None,
  24     show=None,
  25 ):
  26     """Plot energy transition distribution and marginal energy distribution in HMC algorithms.
  27 
  28     This may help to diagnose poor exploration by gradient-based algorithms like HMC or NUTS.
  29 
  30     Parameters
  31     ----------
  32     data : xarray dataset, or object that can be converted (must represent
  33            `sample_stats` and have an `energy` variable)
  34     kind : str
  35         Type of plot to display {"kde", "hist")
  36     bfmi : bool
  37         If True add to the plot the value of the estimated Bayesian fraction of missing information
  38     figsize : tuple
  39         Figure size. If None it will be defined automatically.
  40     legend : bool
  41         Flag for plotting legend (defaults to True)
  42     fill_alpha : tuple of floats
  43         Alpha blending value for the shaded area under the curve, between 0
  44         (no shade) and 1 (opaque). Defaults to (1, .75)
  45     fill_color : tuple of valid matplotlib color
  46         Color for Marginal energy distribution and Energy transition distribution.
  47         Defaults to ('C0', 'C5')
  48     bw: float or str, optional
  49         If numeric, indicates the bandwidth and must be positive.
  50         If str, indicates the method to estimate the bandwidth and must be
  51         one of "scott", "silverman", "isj" or "experimental". Defaults to "experimental"
  52         Only works if `kind='kde'`
  53     textsize: float
  54         Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
  55         on figsize.
  56     fill_kwargs : dicts, optional
  57         Additional keywords passed to `arviz.plot_kde` (to control the shade)
  58     plot_kwargs : dicts, optional
  59         Additional keywords passed to `arviz.plot_kde` or `plt.hist` (if type='hist')
  60     ax: axes, optional
  61         Matplotlib axes or bokeh figures.
  62     backend: str, optional
  63         Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
  64     backend_kwargs: bool, optional
  65         These are kwargs specific to the backend being used. For additional documentation
  66         check the plotting method of the backend.
  67     show : bool, optional
  68         Call backend show function.
  69 
  70     Returns
  71     -------
  72     axes : matplotlib axes or bokeh figures
  73 
  74     Examples
  75     --------
  76     Plot a default energy plot
  77 
  78     .. plot::
  79         :context: close-figs
  80 
  81         >>> import arviz as az
  82         >>> data = az.load_arviz_data('centered_eight')
  83         >>> az.plot_energy(data)
  84 
  85     Represent energy plot via histograms
  86 
  87     .. plot::
  88         :context: close-figs
  89 
  90         >>> az.plot_energy(data, kind='hist')
  91 
  92     """
  93     energy = convert_to_dataset(data, group="sample_stats").energy.values
  94 
  95     if kind == "histogram":
  96         warnings.warn(
  97             "kind histogram will be deprecated in a future release. Use `hist` "
  98             "or set rcParam `plot.density_kind` to `hist`",
  99             FutureWarning,
  100         )
  101         kind = "hist"
  102 
  103     if kind is None:
  104         kind = rcParams["plot.density_kind"]
  105 
  106     plot_energy_kwargs = dict(
  107         ax=ax,
  108         energy=energy,
  109         kind=kind,
  110         bfmi=bfmi,
  111         figsize=figsize,
  112         textsize=textsize,
  113         fill_alpha=fill_alpha,
  114         fill_color=fill_color,
  115         fill_kwargs=fill_kwargs,
  116         plot_kwargs=plot_kwargs,
  117         bw=bw,
  118         legend=legend,
  119         backend_kwargs=backend_kwargs,
  120         show=show,
  121     )
  122 
  123     if backend is None:
  124         backend = rcParams["plot.backend"]
  125     backend = backend.lower()
  126 
  127     # TODO: Add backend kwargs
  128     plot = get_plotting_function("plot_energy", "energyplot", backend)
  129     ax = plot(**plot_energy_kwargs)
  130     return ax
  131
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/arviz/plots/energyplot.py b/arviz/plots/energyplot.py
  --- a/arviz/plots/energyplot.py
  +++ b/arviz/plots/energyplot.py
  @@ -29,41 +29,44 @@
   
       Parameters
       ----------
  -    data : xarray dataset, or object that can be converted (must represent
  -           `sample_stats` and have an `energy` variable)
  +    data : obj
  +        :class:`xarray.Dataset`, or any object that can be converted (must represent
  +        ``sample_stats`` and have an ``energy`` variable).
       kind : str
  -        Type of plot to display {"kde", "hist")
  +        Type of plot to display ("kde", "hist").
       bfmi : bool
           If True add to the plot the value of the estimated Bayesian fraction of missing information
       figsize : tuple
           Figure size. If None it will be defined automatically.
       legend : bool
  -        Flag for plotting legend (defaults to True)
  +        Flag for plotting legend. Defaults to True.
       fill_alpha : tuple of floats
           Alpha blending value for the shaded area under the curve, between 0
  -        (no shade) and 1 (opaque). Defaults to (1, .75)
  +        (no shade) and 1 (opaque). Defaults to (1, .75).
       fill_color : tuple of valid matplotlib color
           Color for Marginal energy distribution and Energy transition distribution.
  -        Defaults to ('C0', 'C5')
  +        Defaults to ('C0', 'C5').
       bw: float or str, optional
           If numeric, indicates the bandwidth and must be positive.
           If str, indicates the method to estimate the bandwidth and must be
  -        one of "scott", "silverman", "isj" or "experimental". Defaults to "experimental"
  -        Only works if `kind='kde'`
  +        one of "scott", "silverman", "isj" or "experimental". Defaults to "experimental".
  +        Only works if ``kind='kde'``.
       textsize: float
           Text size scaling factor for labels, titles and lines. If None it will be autoscaled based
           on figsize.
       fill_kwargs : dicts, optional
  -        Additional keywords passed to `arviz.plot_kde` (to control the shade)
  +        Additional keywords passed to :func:`arviz.plot_kde` (to control the shade).
       plot_kwargs : dicts, optional
  -        Additional keywords passed to `arviz.plot_kde` or `plt.hist` (if type='hist')
  +        Additional keywords passed to :func:`arviz.plot_kde` or :func:`matplotlib.pyplot.hist`
  +        (if ``type='hist'``).
       ax: axes, optional
  -        Matplotlib axes or bokeh figures.
  +        :class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`.
       backend: str, optional
  -        Select plotting backend {"matplotlib","bokeh"}. Default "matplotlib".
  +        Select plotting backend {"matplotlib", "bokeh"}. Defaults to "matplotlib".
       backend_kwargs: bool, optional
  -        These are kwargs specific to the backend being used. For additional documentation
  -        check the plotting method of the backend.
  +        These are kwargs specific to the backend being used, passed to
  +        :func:`matplotlib.pyplot.subplots` or
  +        :func:`bokeh.plotting.figure`.
       show : bool, optional
           Call backend show function.
   
  @@ -71,6 +74,10 @@
       -------
       axes : matplotlib axes or bokeh figures
   
  +    See Also
  +    --------
  +    bfmi : Calculate the estimated Bayesian fraction of missing information (BFMI).
  +
       Examples
       --------
       Plot a default energy plot
  
verification_info: {"golden_diff": "diff --git a/arviz/plots/energyplot.py b/arviz/plots/energyplot.py\n--- a/arviz/plots/energyplot.py\n+++ b/arviz/plots/energyplot.py\n@@ -29,41 +29,44 @@\n \n     Parameters\n     ----------\n-    data : xarray dataset, or object that can be converted (must represent\n-           `sample_stats` and have an `energy` variable)\n+    data : obj\n+        :class:`xarray.Dataset`, or any object that can be converted (must represent\n+        ``sample_stats`` and have an ``energy`` variable).\n     kind : str\n-        Type of plot to display {\"kde\", \"hist\")\n+        Type of plot to display (\"kde\", \"hist\").\n     bfmi : bool\n         If True add to the plot the value of the estimated Bayesian fraction of missing information\n     figsize : tuple\n         Figure size. If None it will be defined automatically.\n     legend : bool\n-        Flag for plotting legend (defaults to True)\n+        Flag for plotting legend. Defaults to True.\n     fill_alpha : tuple of floats\n         Alpha blending value for the shaded area under the curve, between 0\n-        (no shade) and 1 (opaque). Defaults to (1, .75)\n+        (no shade) and 1 (opaque). Defaults to (1, .75).\n     fill_color : tuple of valid matplotlib color\n         Color for Marginal energy distribution and Energy transition distribution.\n-        Defaults to ('C0', 'C5')\n+        Defaults to ('C0', 'C5').\n     bw: float or str, optional\n         If numeric, indicates the bandwidth and must be positive.\n         If str, indicates the method to estimate the bandwidth and must be\n-        one of \"scott\", \"silverman\", \"isj\" or \"experimental\". Defaults to \"experimental\"\n-        Only works if `kind='kde'`\n+        one of \"scott\", \"silverman\", \"isj\" or \"experimental\". Defaults to \"experimental\".\n+        Only works if ``kind='kde'``.\n     textsize: float\n         Text size scaling factor for labels, titles and lines. If None it will be autoscaled based\n         on figsize.\n     fill_kwargs : dicts, optional\n-        Additional keywords passed to `arviz.plot_kde` (to control the shade)\n+        Additional keywords passed to :func:`arviz.plot_kde` (to control the shade).\n     plot_kwargs : dicts, optional\n-        Additional keywords passed to `arviz.plot_kde` or `plt.hist` (if type='hist')\n+        Additional keywords passed to :func:`arviz.plot_kde` or :func:`matplotlib.pyplot.hist`\n+        (if ``type='hist'``).\n     ax: axes, optional\n-        Matplotlib axes or bokeh figures.\n+        :class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`.\n     backend: str, optional\n-        Select plotting backend {\"matplotlib\",\"bokeh\"}. Default \"matplotlib\".\n+        Select plotting backend {\"matplotlib\", \"bokeh\"}. Defaults to \"matplotlib\".\n     backend_kwargs: bool, optional\n-        These are kwargs specific to the backend being used. For additional documentation\n-        check the plotting method of the backend.\n+        These are kwargs specific to the backend being used, passed to\n+        :func:`matplotlib.pyplot.subplots` or\n+        :func:`bokeh.plotting.figure`.\n     show : bool, optional\n         Call backend show function.\n \n@@ -71,6 +74,10 @@\n     -------\n     axes : matplotlib axes or bokeh figures\n \n+    See Also\n+    --------\n+    bfmi : Calculate the estimated Bayesian fraction of missing information (BFMI).\n+\n     Examples\n     --------\n     Plot a default energy plot\n", "issue": "Add docs references to plot_energy docstring\nAdd links to the related functions and classes to all the places where they are mentioned in the [plot_energy](https://arviz-devs.github.io/arviz/api/generated/arviz.plot_energy.html) docstring.\r\n\r\n## Sample PR:\r\nYou can see https://github.com/arviz-devs/arviz/pull/1816 for the function, `plot_autocorr`. \r\n> \u26a0\ufe0f Always create a new branch for changes. Don't make any changes to the `main` branch. \r\n\r\n\r\n## Source File\r\nSource file: https://github.com/arviz-devs/arviz/blob/main/arviz/plots/energyplot.py\r\n\r\n## How to add links\r\nAdd the links in the docstring of doc. You can also check the docstring by going to the `[source]`. \r\n\r\n* Add links to the ArviZ functions and classes using [Cross-referencing with Sphinx](https://docs.readthedocs.io/en/stable/guides/cross-referencing-with-sphinx.html).\r\n* Add links to the xarray functions and classes using [Intersphinx](https://docs.readthedocs.io/en/stable/guides/intersphinx.html).\r\n* Add the rest of the external links using this [syntax](https://sublime-and-sphinx-guide.readthedocs.io/en/latest/references.html#links-to-external-web-pages).\r\n\r\n## `backend_kwargs`, `plot_kwargs` and `fill_kwargs`\r\nFor adding `backend_kwargs` as they have been added in #1816, see [Adding backend_kwargs guide](https://github.com/arviz-devs/arviz/wiki/ArviZ-Hacktoberfest-2021#adding-backend_kwargs). Follow the same process for `fill_kwargs` and `plot_kwargs`.\r\n\r\n## See also section\r\nAdd the following functions in the \"See also\" section:\r\n1. [bfmi](https://arviz-devs.github.io/arviz/api/generated/arviz.bfmi.html)\r\n\r\n\r\nSee [Adding \"see also\" section](https://github.com/arviz-devs/arviz/wiki/ArviZ-Hacktoberfest-2021#adding-see-also-section) for more details. \r\n\r\n## Checking the preview of docs\r\nFor checking the preview of docs, you don't need t set up the project on your local machine. Just follow the [checking the preview guide](https://github.com/arviz-devs/arviz/wiki/ArviZ-Hacktoberfest-2021#how-to-check-the-preview-of-docs). \n", "before_files": [{"content": "\"\"\"Plot energy transition distribution in HMC inference.\"\"\"\nimport warnings\n\nfrom ..data import convert_to_dataset\nfrom ..rcparams import rcParams\nfrom .plot_utils import get_plotting_function\n\n\ndef plot_energy(\n    data,\n    kind=None,\n    bfmi=True,\n    figsize=None,\n    legend=True,\n    fill_alpha=(1, 0.75),\n    fill_color=(\"C0\", \"C5\"),\n    bw=\"experimental\",\n    textsize=None,\n    fill_kwargs=None,\n    plot_kwargs=None,\n    ax=None,\n    backend=None,\n    backend_kwargs=None,\n    show=None,\n):\n    \"\"\"Plot energy transition distribution and marginal energy distribution in HMC algorithms.\n\n    This may help to diagnose poor exploration by gradient-based algorithms like HMC or NUTS.\n\n    Parameters\n    ----------\n    data : xarray dataset, or object that can be converted (must represent\n           `sample_stats` and have an `energy` variable)\n    kind : str\n        Type of plot to display {\"kde\", \"hist\")\n    bfmi : bool\n        If True add to the plot the value of the estimated Bayesian fraction of missing information\n    figsize : tuple\n        Figure size. If None it will be defined automatically.\n    legend : bool\n        Flag for plotting legend (defaults to True)\n    fill_alpha : tuple of floats\n        Alpha blending value for the shaded area under the curve, between 0\n        (no shade) and 1 (opaque). Defaults to (1, .75)\n    fill_color : tuple of valid matplotlib color\n        Color for Marginal energy distribution and Energy transition distribution.\n        Defaults to ('C0', 'C5')\n    bw: float or str, optional\n        If numeric, indicates the bandwidth and must be positive.\n        If str, indicates the method to estimate the bandwidth and must be\n        one of \"scott\", \"silverman\", \"isj\" or \"experimental\". Defaults to \"experimental\"\n        Only works if `kind='kde'`\n    textsize: float\n        Text size scaling factor for labels, titles and lines. If None it will be autoscaled based\n        on figsize.\n    fill_kwargs : dicts, optional\n        Additional keywords passed to `arviz.plot_kde` (to control the shade)\n    plot_kwargs : dicts, optional\n        Additional keywords passed to `arviz.plot_kde` or `plt.hist` (if type='hist')\n    ax: axes, optional\n        Matplotlib axes or bokeh figures.\n    backend: str, optional\n        Select plotting backend {\"matplotlib\",\"bokeh\"}. Default \"matplotlib\".\n    backend_kwargs: bool, optional\n        These are kwargs specific to the backend being used. For additional documentation\n        check the plotting method of the backend.\n    show : bool, optional\n        Call backend show function.\n\n    Returns\n    -------\n    axes : matplotlib axes or bokeh figures\n\n    Examples\n    --------\n    Plot a default energy plot\n\n    .. plot::\n        :context: close-figs\n\n        >>> import arviz as az\n        >>> data = az.load_arviz_data('centered_eight')\n        >>> az.plot_energy(data)\n\n    Represent energy plot via histograms\n\n    .. plot::\n        :context: close-figs\n\n        >>> az.plot_energy(data, kind='hist')\n\n    \"\"\"\n    energy = convert_to_dataset(data, group=\"sample_stats\").energy.values\n\n    if kind == \"histogram\":\n        warnings.warn(\n            \"kind histogram will be deprecated in a future release. Use `hist` \"\n            \"or set rcParam `plot.density_kind` to `hist`\",\n            FutureWarning,\n        )\n        kind = \"hist\"\n\n    if kind is None:\n        kind = rcParams[\"plot.density_kind\"]\n\n    plot_energy_kwargs = dict(\n        ax=ax,\n        energy=energy,\n        kind=kind,\n        bfmi=bfmi,\n        figsize=figsize,\n        textsize=textsize,\n        fill_alpha=fill_alpha,\n        fill_color=fill_color,\n        fill_kwargs=fill_kwargs,\n        plot_kwargs=plot_kwargs,\n        bw=bw,\n        legend=legend,\n        backend_kwargs=backend_kwargs,\n        show=show,\n    )\n\n    if backend is None:\n        backend = rcParams[\"plot.backend\"]\n    backend = backend.lower()\n\n    # TODO: Add backend kwargs\n    plot = get_plotting_function(\"plot_energy\", \"energyplot\", backend)\n    ax = plot(**plot_energy_kwargs)\n    return ax\n", "path": "arviz/plots/energyplot.py"}], "after_files": [{"content": "\"\"\"Plot energy transition distribution in HMC inference.\"\"\"\nimport warnings\n\nfrom ..data import convert_to_dataset\nfrom ..rcparams import rcParams\nfrom .plot_utils import get_plotting_function\n\n\ndef plot_energy(\n    data,\n    kind=None,\n    bfmi=True,\n    figsize=None,\n    legend=True,\n    fill_alpha=(1, 0.75),\n    fill_color=(\"C0\", \"C5\"),\n    bw=\"experimental\",\n    textsize=None,\n    fill_kwargs=None,\n    plot_kwargs=None,\n    ax=None,\n    backend=None,\n    backend_kwargs=None,\n    show=None,\n):\n    \"\"\"Plot energy transition distribution and marginal energy distribution in HMC algorithms.\n\n    This may help to diagnose poor exploration by gradient-based algorithms like HMC or NUTS.\n\n    Parameters\n    ----------\n    data : obj\n        :class:`xarray.Dataset`, or any object that can be converted (must represent\n        ``sample_stats`` and have an ``energy`` variable).\n    kind : str\n        Type of plot to display (\"kde\", \"hist\").\n    bfmi : bool\n        If True add to the plot the value of the estimated Bayesian fraction of missing information\n    figsize : tuple\n        Figure size. If None it will be defined automatically.\n    legend : bool\n        Flag for plotting legend. Defaults to True.\n    fill_alpha : tuple of floats\n        Alpha blending value for the shaded area under the curve, between 0\n        (no shade) and 1 (opaque). Defaults to (1, .75).\n    fill_color : tuple of valid matplotlib color\n        Color for Marginal energy distribution and Energy transition distribution.\n        Defaults to ('C0', 'C5').\n    bw: float or str, optional\n        If numeric, indicates the bandwidth and must be positive.\n        If str, indicates the method to estimate the bandwidth and must be\n        one of \"scott\", \"silverman\", \"isj\" or \"experimental\". Defaults to \"experimental\".\n        Only works if ``kind='kde'``.\n    textsize: float\n        Text size scaling factor for labels, titles and lines. If None it will be autoscaled based\n        on figsize.\n    fill_kwargs : dicts, optional\n        Additional keywords passed to :func:`arviz.plot_kde` (to control the shade).\n    plot_kwargs : dicts, optional\n        Additional keywords passed to :func:`arviz.plot_kde` or :func:`matplotlib.pyplot.hist`\n        (if ``type='hist'``).\n    ax: axes, optional\n        :class:`matplotlib.axes.Axes` or :class:`bokeh.plotting.Figure`.\n    backend: str, optional\n        Select plotting backend {\"matplotlib\", \"bokeh\"}. Defaults to \"matplotlib\".\n    backend_kwargs: bool, optional\n        These are kwargs specific to the backend being used, passed to\n        :func:`matplotlib.pyplot.subplots` or\n        :func:`bokeh.plotting.figure`.\n    show : bool, optional\n        Call backend show function.\n\n    Returns\n    -------\n    axes : matplotlib axes or bokeh figures\n\n    See Also\n    --------\n    bfmi : Calculate the estimated Bayesian fraction of missing information (BFMI).\n\n    Examples\n    --------\n    Plot a default energy plot\n\n    .. plot::\n        :context: close-figs\n\n        >>> import arviz as az\n        >>> data = az.load_arviz_data('centered_eight')\n        >>> az.plot_energy(data)\n\n    Represent energy plot via histograms\n\n    .. plot::\n        :context: close-figs\n\n        >>> az.plot_energy(data, kind='hist')\n\n    \"\"\"\n    energy = convert_to_dataset(data, group=\"sample_stats\").energy.values\n\n    if kind == \"histogram\":\n        warnings.warn(\n            \"kind histogram will be deprecated in a future release. Use `hist` \"\n            \"or set rcParam `plot.density_kind` to `hist`\",\n            FutureWarning,\n        )\n        kind = \"hist\"\n\n    if kind is None:\n        kind = rcParams[\"plot.density_kind\"]\n\n    plot_energy_kwargs = dict(\n        ax=ax,\n        energy=energy,\n        kind=kind,\n        bfmi=bfmi,\n        figsize=figsize,\n        textsize=textsize,\n        fill_alpha=fill_alpha,\n        fill_color=fill_color,\n        fill_kwargs=fill_kwargs,\n        plot_kwargs=plot_kwargs,\n        bw=bw,\n        legend=legend,\n        backend_kwargs=backend_kwargs,\n        show=show,\n    )\n\n    if backend is None:\n        backend = rcParams[\"plot.backend\"]\n    backend = backend.lower()\n\n    # TODO: Add backend kwargs\n    plot = get_plotting_function(\"plot_energy\", \"energyplot\", backend)\n    ax = plot(**plot_energy_kwargs)\n    return ax\n", "path": "arviz/plots/energyplot.py"}]}