prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  matplotlib.use('Agg') conflicts with user code
  In this line in `chainer/chainer/training/extensions/plot_report.py` the `matplotlib` backend is changed [Source](https://github.com/pfnet/chainer/blob/master/chainer/training/extensions/plot_report.py#L16):
  
      matplotlib.use('Agg')
  
  Unfortunately, this can interfere with users code. For example, when the user sets the backend himself anywhere, it is not known, whether his setting or the Chainer settings wins (is imported first).
  
  The `plot_report` gets imported, when `extensions` is imported. For now, I just removed from the corresponding `__init__` file locally, which is definitely not a clean solution.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `chainer/training/extensions/plot_report.py`
  Content:
  ```
  1 import json
  2 from os import path
  3 import warnings
  4 
  5 import numpy
  6 import six
  7 
  8 from chainer import reporter
  9 import chainer.serializer as serializer_module
  10 from chainer.training import extension
  11 import chainer.training.trigger as trigger_module
  12 
  13 try:
  14     import matplotlib
  15 
  16     matplotlib.use('Agg')
  17     from matplotlib import pyplot as plot
  18 
  19     _available = True
  20 
  21 except ImportError:
  22     _available = False
  23 
  24 
  25 def _check_available():
  26     if not _available:
  27         warnings.warn('matplotlib is not installed on your environment, '
  28                       'so nothing will be plotted at this time. '
  29                       'Please install matplotlib to plot figures.\n\n'
  30                       '  $ pip install matplotlib\n')
  31 
  32 
  33 class PlotReport(extension.Extension):
  34 
  35     """Trainer extension to output plots.
  36 
  37     This extension accumulates the observations of the trainer to
  38     :class:`~chainer.DictSummary` at a regular interval specified by a supplied
  39     trigger, and plot a graph with using them.
  40 
  41     There are two triggers to handle this extension. One is the trigger to
  42     invoke this extension, which is used to handle the timing of accumulating
  43     the results. It is set to ``1, 'iteration'`` by default. The other is the
  44     trigger to determine when to emit the result. When this trigger returns
  45     True, this extension appends the summary of accumulated values to the list
  46     of past summaries, and writes the list to the log file. Then, this
  47     extension makes a new fresh summary object which is used until the next
  48     time that the trigger fires.
  49 
  50     It also adds ``'epoch'`` and ``'iteration'`` entries to each result
  51     dictionary, which are the epoch and iteration counts at the output.
  52 
  53     Args:
  54         y_keys (iterable of strs): Keys of values regarded as y. If this is
  55             None, nothing is output to the graph.
  56         x_key (str): Keys of values regarded as x. The default value is
  57             'iteration'.
  58         trigger: Trigger that decides when to aggregate the result and output
  59             the values. This is distinct from the trigger of this extension
  60             itself. If it is a tuple in the form ``<int>, 'epoch'`` or ``<int>,
  61             'iteration'``, it is passed to :class:`IntervalTrigger`.
  62         postprocess: Callback to postprocess the result dictionaries. Figure
  63             object, Axes object, and all plot data are passed to this callback
  64             in this order. This callback can modify the figure.
  65         file_name (str): Name of the figure file under the output directory.
  66             It can be a format string.
  67         marker (str): The marker used to plot the graph. Default is ``'x'``. If
  68             ``None`` is given, it draws with no markers.
  69         grid (bool): Set the axis grid on if True. Default is True.
  70 
  71     """
  72 
  73     def __init__(self, y_keys, x_key='iteration', trigger=(1, 'epoch'),
  74                  postprocess=None, file_name='plot.png', marker='x',
  75                  grid=True):
  76 
  77         _check_available()
  78 
  79         if not _available:
  80             return
  81 
  82         self._x_key = x_key
  83         if isinstance(y_keys, str):
  84             y_keys = (y_keys,)
  85 
  86         self._y_keys = y_keys
  87         self._trigger = trigger_module.get_trigger(trigger)
  88         self._file_name = file_name
  89         self._marker = marker
  90         self._grid = grid
  91         self._postprocess = postprocess
  92         self._init_summary()
  93         self._data = {k: [] for k in y_keys}
  94 
  95     def __call__(self, trainer):
  96         if not _available:
  97             return
  98 
  99         keys = self._y_keys
  100         observation = trainer.observation
  101         summary = self._summary
  102 
  103         if keys is None:
  104             summary.add(observation)
  105         else:
  106             summary.add({k: observation[k] for k in keys if k in observation})
  107 
  108         if self._trigger(trainer):
  109             stats = self._summary.compute_mean()
  110             stats_cpu = {}
  111             for name, value in six.iteritems(stats):
  112                 stats_cpu[name] = float(value)  # copy to CPU
  113 
  114             updater = trainer.updater
  115             stats_cpu['epoch'] = updater.epoch
  116             stats_cpu['iteration'] = updater.iteration
  117             x = stats_cpu[self._x_key]
  118             data = self._data
  119 
  120             for k in keys:
  121                 if k in stats_cpu:
  122                     data[k].append((x, stats_cpu[k]))
  123 
  124             f = plot.figure()
  125             a = f.add_subplot(111)
  126             a.set_xlabel(self._x_key)
  127             if self._grid:
  128                 a.grid()
  129 
  130             for k in keys:
  131                 xy = data[k]
  132                 if len(xy) == 0:
  133                     continue
  134 
  135                 xy = numpy.array(xy)
  136                 a.plot(xy[:, 0], xy[:, 1], marker=self._marker, label=k)
  137 
  138             if a.has_data():
  139                 if self._postprocess is not None:
  140                     self._postprocess(f, a, summary)
  141                 l = a.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
  142                 f.savefig(path.join(trainer.out, self._file_name),
  143                           bbox_extra_artists=(l,), bbox_inches='tight')
  144 
  145             plot.close()
  146             self._init_summary()
  147 
  148     def serialize(self, serializer):
  149         if isinstance(serializer, serializer_module.Serializer):
  150             serializer('_plot_{}'.format(self._file_name),
  151                        json.dumps(self._data))
  152 
  153         else:
  154             self._data = json.loads(
  155                 serializer('_plot_{}'.format(self._file_name), ''))
  156 
  157     def _init_summary(self):
  158         self._summary = reporter.DictSummary()
  159
  ```
  --- 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/chainer/training/extensions/plot_report.py b/chainer/training/extensions/plot_report.py
  --- a/chainer/training/extensions/plot_report.py
  +++ b/chainer/training/extensions/plot_report.py
  @@ -11,9 +11,6 @@
   import chainer.training.trigger as trigger_module
   
   try:
  -    import matplotlib
  -
  -    matplotlib.use('Agg')
       from matplotlib import pyplot as plot
   
       _available = True
  @@ -50,6 +47,25 @@
       It also adds ``'epoch'`` and ``'iteration'`` entries to each result
       dictionary, which are the epoch and iteration counts at the output.
   
  +    .. warning::
  +
  +        If your environment needs to specify a backend of matplotlib
  +        explicitly, please call ``matplotlib.use`` before importing Chainer.
  +        For example:
  +
  +        .. code-block:: python
  +
  +            import matplotlib
  +            matplotlib.use('Agg')
  +
  +            import chainer
  +
  +        Then, once ``chainer.training.extensions`` is imported,
  +        ``matplotlib.use`` will have no effect.
  +
  +    For the details, please see here:
  +    http://matplotlib.org/faq/usage_faq.html#what-is-a-backend
  +
       Args:
           y_keys (iterable of strs): Keys of values regarded as y. If this is
               None, nothing is output to the graph.
  
verification_info: {"golden_diff": "diff --git a/chainer/training/extensions/plot_report.py b/chainer/training/extensions/plot_report.py\n--- a/chainer/training/extensions/plot_report.py\n+++ b/chainer/training/extensions/plot_report.py\n@@ -11,9 +11,6 @@\n import chainer.training.trigger as trigger_module\n \n try:\n-    import matplotlib\n-\n-    matplotlib.use('Agg')\n     from matplotlib import pyplot as plot\n \n     _available = True\n@@ -50,6 +47,25 @@\n     It also adds ``'epoch'`` and ``'iteration'`` entries to each result\n     dictionary, which are the epoch and iteration counts at the output.\n \n+    .. warning::\n+\n+        If your environment needs to specify a backend of matplotlib\n+        explicitly, please call ``matplotlib.use`` before importing Chainer.\n+        For example:\n+\n+        .. code-block:: python\n+\n+            import matplotlib\n+            matplotlib.use('Agg')\n+\n+            import chainer\n+\n+        Then, once ``chainer.training.extensions`` is imported,\n+        ``matplotlib.use`` will have no effect.\n+\n+    For the details, please see here:\n+    http://matplotlib.org/faq/usage_faq.html#what-is-a-backend\n+\n     Args:\n         y_keys (iterable of strs): Keys of values regarded as y. If this is\n             None, nothing is output to the graph.\n", "issue": "matplotlib.use('Agg') conflicts with user code\nIn this line in `chainer/chainer/training/extensions/plot_report.py` the `matplotlib` backend is changed [Source](https://github.com/pfnet/chainer/blob/master/chainer/training/extensions/plot_report.py#L16):\r\n\r\n    matplotlib.use('Agg')\r\n\r\nUnfortunately, this can interfere with users code. For example, when the user sets the backend himself anywhere, it is not known, whether his setting or the Chainer settings wins (is imported first).\r\n\r\nThe `plot_report` gets imported, when `extensions` is imported. For now, I just removed from the corresponding `__init__` file locally, which is definitely not a clean solution.\n", "before_files": [{"content": "import json\nfrom os import path\nimport warnings\n\nimport numpy\nimport six\n\nfrom chainer import reporter\nimport chainer.serializer as serializer_module\nfrom chainer.training import extension\nimport chainer.training.trigger as trigger_module\n\ntry:\n    import matplotlib\n\n    matplotlib.use('Agg')\n    from matplotlib import pyplot as plot\n\n    _available = True\n\nexcept ImportError:\n    _available = False\n\n\ndef _check_available():\n    if not _available:\n        warnings.warn('matplotlib is not installed on your environment, '\n                      'so nothing will be plotted at this time. '\n                      'Please install matplotlib to plot figures.\\n\\n'\n                      '  $ pip install matplotlib\\n')\n\n\nclass PlotReport(extension.Extension):\n\n    \"\"\"Trainer extension to output plots.\n\n    This extension accumulates the observations of the trainer to\n    :class:`~chainer.DictSummary` at a regular interval specified by a supplied\n    trigger, and plot a graph with using them.\n\n    There are two triggers to handle this extension. One is the trigger to\n    invoke this extension, which is used to handle the timing of accumulating\n    the results. It is set to ``1, 'iteration'`` by default. The other is the\n    trigger to determine when to emit the result. When this trigger returns\n    True, this extension appends the summary of accumulated values to the list\n    of past summaries, and writes the list to the log file. Then, this\n    extension makes a new fresh summary object which is used until the next\n    time that the trigger fires.\n\n    It also adds ``'epoch'`` and ``'iteration'`` entries to each result\n    dictionary, which are the epoch and iteration counts at the output.\n\n    Args:\n        y_keys (iterable of strs): Keys of values regarded as y. If this is\n            None, nothing is output to the graph.\n        x_key (str): Keys of values regarded as x. The default value is\n            'iteration'.\n        trigger: Trigger that decides when to aggregate the result and output\n            the values. This is distinct from the trigger of this extension\n            itself. If it is a tuple in the form ``<int>, 'epoch'`` or ``<int>,\n            'iteration'``, it is passed to :class:`IntervalTrigger`.\n        postprocess: Callback to postprocess the result dictionaries. Figure\n            object, Axes object, and all plot data are passed to this callback\n            in this order. This callback can modify the figure.\n        file_name (str): Name of the figure file under the output directory.\n            It can be a format string.\n        marker (str): The marker used to plot the graph. Default is ``'x'``. If\n            ``None`` is given, it draws with no markers.\n        grid (bool): Set the axis grid on if True. Default is True.\n\n    \"\"\"\n\n    def __init__(self, y_keys, x_key='iteration', trigger=(1, 'epoch'),\n                 postprocess=None, file_name='plot.png', marker='x',\n                 grid=True):\n\n        _check_available()\n\n        if not _available:\n            return\n\n        self._x_key = x_key\n        if isinstance(y_keys, str):\n            y_keys = (y_keys,)\n\n        self._y_keys = y_keys\n        self._trigger = trigger_module.get_trigger(trigger)\n        self._file_name = file_name\n        self._marker = marker\n        self._grid = grid\n        self._postprocess = postprocess\n        self._init_summary()\n        self._data = {k: [] for k in y_keys}\n\n    def __call__(self, trainer):\n        if not _available:\n            return\n\n        keys = self._y_keys\n        observation = trainer.observation\n        summary = self._summary\n\n        if keys is None:\n            summary.add(observation)\n        else:\n            summary.add({k: observation[k] for k in keys if k in observation})\n\n        if self._trigger(trainer):\n            stats = self._summary.compute_mean()\n            stats_cpu = {}\n            for name, value in six.iteritems(stats):\n                stats_cpu[name] = float(value)  # copy to CPU\n\n            updater = trainer.updater\n            stats_cpu['epoch'] = updater.epoch\n            stats_cpu['iteration'] = updater.iteration\n            x = stats_cpu[self._x_key]\n            data = self._data\n\n            for k in keys:\n                if k in stats_cpu:\n                    data[k].append((x, stats_cpu[k]))\n\n            f = plot.figure()\n            a = f.add_subplot(111)\n            a.set_xlabel(self._x_key)\n            if self._grid:\n                a.grid()\n\n            for k in keys:\n                xy = data[k]\n                if len(xy) == 0:\n                    continue\n\n                xy = numpy.array(xy)\n                a.plot(xy[:, 0], xy[:, 1], marker=self._marker, label=k)\n\n            if a.has_data():\n                if self._postprocess is not None:\n                    self._postprocess(f, a, summary)\n                l = a.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n                f.savefig(path.join(trainer.out, self._file_name),\n                          bbox_extra_artists=(l,), bbox_inches='tight')\n\n            plot.close()\n            self._init_summary()\n\n    def serialize(self, serializer):\n        if isinstance(serializer, serializer_module.Serializer):\n            serializer('_plot_{}'.format(self._file_name),\n                       json.dumps(self._data))\n\n        else:\n            self._data = json.loads(\n                serializer('_plot_{}'.format(self._file_name), ''))\n\n    def _init_summary(self):\n        self._summary = reporter.DictSummary()\n", "path": "chainer/training/extensions/plot_report.py"}], "after_files": [{"content": "import json\nfrom os import path\nimport warnings\n\nimport numpy\nimport six\n\nfrom chainer import reporter\nimport chainer.serializer as serializer_module\nfrom chainer.training import extension\nimport chainer.training.trigger as trigger_module\n\ntry:\n    from matplotlib import pyplot as plot\n\n    _available = True\n\nexcept ImportError:\n    _available = False\n\n\ndef _check_available():\n    if not _available:\n        warnings.warn('matplotlib is not installed on your environment, '\n                      'so nothing will be plotted at this time. '\n                      'Please install matplotlib to plot figures.\\n\\n'\n                      '  $ pip install matplotlib\\n')\n\n\nclass PlotReport(extension.Extension):\n\n    \"\"\"Trainer extension to output plots.\n\n    This extension accumulates the observations of the trainer to\n    :class:`~chainer.DictSummary` at a regular interval specified by a supplied\n    trigger, and plot a graph with using them.\n\n    There are two triggers to handle this extension. One is the trigger to\n    invoke this extension, which is used to handle the timing of accumulating\n    the results. It is set to ``1, 'iteration'`` by default. The other is the\n    trigger to determine when to emit the result. When this trigger returns\n    True, this extension appends the summary of accumulated values to the list\n    of past summaries, and writes the list to the log file. Then, this\n    extension makes a new fresh summary object which is used until the next\n    time that the trigger fires.\n\n    It also adds ``'epoch'`` and ``'iteration'`` entries to each result\n    dictionary, which are the epoch and iteration counts at the output.\n\n    .. warning::\n\n        If your environment needs to specify a backend of matplotlib\n        explicitly, please call ``matplotlib.use`` before importing Chainer.\n        For example:\n\n        .. code-block:: python\n\n            import matplotlib\n            matplotlib.use('Agg')\n\n            import chainer\n\n        Then, once ``chainer.training.extensions`` is imported,\n        ``matplotlib.use`` will have no effect.\n\n    For the details, please see here:\n    http://matplotlib.org/faq/usage_faq.html#what-is-a-backend\n\n    Args:\n        y_keys (iterable of strs): Keys of values regarded as y. If this is\n            None, nothing is output to the graph.\n        x_key (str): Keys of values regarded as x. The default value is\n            'iteration'.\n        trigger: Trigger that decides when to aggregate the result and output\n            the values. This is distinct from the trigger of this extension\n            itself. If it is a tuple in the form ``<int>, 'epoch'`` or ``<int>,\n            'iteration'``, it is passed to :class:`IntervalTrigger`.\n        postprocess: Callback to postprocess the result dictionaries. Figure\n            object, Axes object, and all plot data are passed to this callback\n            in this order. This callback can modify the figure.\n        file_name (str): Name of the figure file under the output directory.\n            It can be a format string.\n        marker (str): The marker used to plot the graph. Default is ``'x'``. If\n            ``None`` is given, it draws with no markers.\n        grid (bool): Set the axis grid on if True. Default is True.\n\n    \"\"\"\n\n    def __init__(self, y_keys, x_key='iteration', trigger=(1, 'epoch'),\n                 postprocess=None, file_name='plot.png', marker='x',\n                 grid=True):\n\n        _check_available()\n\n        if not _available:\n            return\n\n        self._x_key = x_key\n        if isinstance(y_keys, str):\n            y_keys = (y_keys,)\n\n        self._y_keys = y_keys\n        self._trigger = trigger_module.get_trigger(trigger)\n        self._file_name = file_name\n        self._marker = marker\n        self._grid = grid\n        self._postprocess = postprocess\n        self._init_summary()\n        self._data = {k: [] for k in y_keys}\n\n    def __call__(self, trainer):\n        if not _available:\n            return\n\n        keys = self._y_keys\n        observation = trainer.observation\n        summary = self._summary\n\n        if keys is None:\n            summary.add(observation)\n        else:\n            summary.add({k: observation[k] for k in keys if k in observation})\n\n        if self._trigger(trainer):\n            stats = self._summary.compute_mean()\n            stats_cpu = {}\n            for name, value in six.iteritems(stats):\n                stats_cpu[name] = float(value)  # copy to CPU\n\n            updater = trainer.updater\n            stats_cpu['epoch'] = updater.epoch\n            stats_cpu['iteration'] = updater.iteration\n            x = stats_cpu[self._x_key]\n            data = self._data\n\n            for k in keys:\n                if k in stats_cpu:\n                    data[k].append((x, stats_cpu[k]))\n\n            f = plot.figure()\n            a = f.add_subplot(111)\n            a.set_xlabel(self._x_key)\n            if self._grid:\n                a.grid()\n\n            for k in keys:\n                xy = data[k]\n                if len(xy) == 0:\n                    continue\n\n                xy = numpy.array(xy)\n                a.plot(xy[:, 0], xy[:, 1], marker=self._marker, label=k)\n\n            if a.has_data():\n                if self._postprocess is not None:\n                    self._postprocess(f, a, summary)\n                l = a.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n                f.savefig(path.join(trainer.out, self._file_name),\n                          bbox_extra_artists=(l,), bbox_inches='tight')\n\n            plot.close()\n            self._init_summary()\n\n    def serialize(self, serializer):\n        if isinstance(serializer, serializer_module.Serializer):\n            serializer('_plot_{}'.format(self._file_name),\n                       json.dumps(self._data))\n\n        else:\n            self._data = json.loads(\n                serializer('_plot_{}'.format(self._file_name), ''))\n\n    def _init_summary(self):\n        self._summary = reporter.DictSummary()\n", "path": "chainer/training/extensions/plot_report.py"}]}