We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
log_name of LogReport with keys causes AttributeError
In MNIST example you change the code

https://github.com/pfnet/chainer/blob/master/examples/mnist/train_mnist.py#L84

```
    # Write a log of evaluation statistics for each epoch
    trainer.extend(extensions.LogReport())
```

to 

```
    # Write a log of evaluation statistics for each epoch
    trainer.extend(extensions.LogReport(log_name='log_{.iteration}'))
```

run train_mnist.py and you'll get `AttributeError: 'dict' object has no attribute 'iteration'`


--- 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/log_report.py`
Content:
```
1 import json
2 import os
3 import tempfile
4 
5 import six
6 
7 from chainer import reporter
8 import chainer.serializer as serializer_module
9 from chainer.training import extension
10 import chainer.training.trigger as trigger_module
11 
12 
13 class LogReport(extension.Extension):
14 
15     """Trainer extension to output the accumulated results to a log file.
16 
17     This extension accumulates the observations of the trainer to
18     :class:`~chainer.DictSummary` at a regular interval specified by a supplied
19     trigger, and writes them into a log file in JSON format.
20 
21     There are two triggers to handle this extension. One is the trigger to
22     invoke this extension, which is used to handle the timing of accumulating
23     the results. It is set to ``1, 'iteration'`` by default. The other is the
24     trigger to determine when to emit the result. When this trigger returns
25     True, this extension appends the summary of accumulated values to the list
26     of past summaries, and writes the list to the log file. Then, this
27     extension makes a new fresh summary object which is used until the next
28     time that the trigger fires.
29 
30     It also adds ``'epoch'`` and ``'iteration'`` entries to each result
31     dictionary, which are the epoch and iteration counts at the output.
32 
33     Args:
34         keys (iterable of strs): Keys of values to accumulate. If this is None,
35             all the values are accumulated and output to the log file.
36         trigger: Trigger that decides when to aggregate the result and output
37             the values. This is distinct from the trigger of this extension
38             itself. If it is a tuple in the form ``<int>, 'epoch'`` or
39             ``<int>, 'iteration'``, it is passed to :class:`IntervalTrigger`.
40         postprocess: Callback to postprocess the result dictionaries. Each
41             result dictionary is passed to this callback on the output. This
42             callback can modify the result dictionaries, which are used to
43             output to the log file.
44         log_name (str): Name of the log file under the output directory. It can
45             be a format string: the last result dictionary is passed for the
46             formatting. For example, users can use '{.iteration}' to separate
47             the log files for different iterations. If the log name is None, it
48             does not output the log to any file.
49 
50     """
51     def __init__(self, keys=None, trigger=(1, 'epoch'), postprocess=None,
52                  log_name='log'):
53         self._keys = keys
54         self._trigger = trigger_module.get_trigger(trigger)
55         self._postprocess = postprocess
56         self._log_name = log_name
57         self._log = []
58 
59         self._init_summary()
60 
61     def __call__(self, trainer):
62         # accumulate the observations
63         keys = self._keys
64         observation = trainer.observation
65         summary = self._summary
66 
67         if keys is None:
68             summary.add(observation)
69         else:
70             summary.add({k: observation[k] for k in keys if k in observation})
71 
72         if self._trigger(trainer):
73             # output the result
74             stats = self._summary.compute_mean()
75             stats_cpu = {}
76             for name, value in six.iteritems(stats):
77                 stats_cpu[name] = float(value)  # copy to CPU
78 
79             updater = trainer.updater
80             stats_cpu['epoch'] = updater.epoch
81             stats_cpu['iteration'] = updater.iteration
82 
83             if self._postprocess is not None:
84                 self._postprocess(stats_cpu)
85 
86             self._log.append(stats_cpu)
87 
88             # write to the log file
89             if self._log_name is not None:
90                 log_name = self._log_name.format(stats_cpu)
91                 fd, path = tempfile.mkstemp(prefix=log_name, dir=trainer.out)
92                 with os.fdopen(fd, 'w') as f:
93                     json.dump(self._log, f, indent=4)
94                 os.rename(path, os.path.join(trainer.out, log_name))
95 
96             # reset the summary for the next output
97             self._init_summary()
98 
99     @property
100     def log(self):
101         """The current list of observation dictionaries."""
102         return self._log
103 
104     def serialize(self, serializer):
105         # Note that this serialization may lose some information of small
106         # numerical differences.
107         if isinstance(serializer, serializer_module.Serializer):
108             log = json.dumps(self._log)
109             serializer('_log', log)
110         else:
111             log = serializer('_log', '')
112             self._log = json.loads(log)
113 
114     def _init_summary(self):
115         self._summary = reporter.DictSummary()
116
```
--- 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()

```

diff --git a/chainer/training/extensions/log_report.py b/chainer/training/extensions/log_report.py
--- a/chainer/training/extensions/log_report.py
+++ b/chainer/training/extensions/log_report.py
@@ -43,7 +43,7 @@
             output to the log file.
         log_name (str): Name of the log file under the output directory. It can
             be a format string: the last result dictionary is passed for the
-            formatting. For example, users can use '{.iteration}' to separate
+            formatting. For example, users can use '{iteration}' to separate
             the log files for different iterations. If the log name is None, it
             does not output the log to any file.
 
@@ -87,7 +87,7 @@
 
             # write to the log file
             if self._log_name is not None:
-                log_name = self._log_name.format(stats_cpu)
+                log_name = self._log_name.format(**stats_cpu)
                 fd, path = tempfile.mkstemp(prefix=log_name, dir=trainer.out)
                 with os.fdopen(fd, 'w') as f:
                     json.dump(self._log, f, indent=4)

{"golden_diff": "diff --git a/chainer/training/extensions/log_report.py b/chainer/training/extensions/log_report.py\n--- a/chainer/training/extensions/log_report.py\n+++ b/chainer/training/extensions/log_report.py\n@@ -43,7 +43,7 @@\n             output to the log file.\n         log_name (str): Name of the log file under the output directory. It can\n             be a format string: the last result dictionary is passed for the\n-            formatting. For example, users can use '{.iteration}' to separate\n+            formatting. For example, users can use '{iteration}' to separate\n             the log files for different iterations. If the log name is None, it\n             does not output the log to any file.\n \n@@ -87,7 +87,7 @@\n \n             # write to the log file\n             if self._log_name is not None:\n-                log_name = self._log_name.format(stats_cpu)\n+                log_name = self._log_name.format(**stats_cpu)\n                 fd, path = tempfile.mkstemp(prefix=log_name, dir=trainer.out)\n                 with os.fdopen(fd, 'w') as f:\n                     json.dump(self._log, f, indent=4)\n", "issue": "log_name of LogReport with keys causes AttributeError\nIn MNIST example you change the code\n\nhttps://github.com/pfnet/chainer/blob/master/examples/mnist/train_mnist.py#L84\n\n```\n    # Write a log of evaluation statistics for each epoch\n    trainer.extend(extensions.LogReport())\n```\n\nto \n\n```\n    # Write a log of evaluation statistics for each epoch\n    trainer.extend(extensions.LogReport(log_name='log_{.iteration}'))\n```\n\nrun train_mnist.py and you'll get `AttributeError: 'dict' object has no attribute 'iteration'`\n\n", "before_files": [{"content": "import json\nimport os\nimport tempfile\n\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\n\nclass LogReport(extension.Extension):\n\n    \"\"\"Trainer extension to output the accumulated results to a log file.\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 writes them into a log file in JSON format.\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        keys (iterable of strs): Keys of values to accumulate. If this is None,\n            all the values are accumulated and output to the log file.\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\n            ``<int>, 'iteration'``, it is passed to :class:`IntervalTrigger`.\n        postprocess: Callback to postprocess the result dictionaries. Each\n            result dictionary is passed to this callback on the output. This\n            callback can modify the result dictionaries, which are used to\n            output to the log file.\n        log_name (str): Name of the log file under the output directory. It can\n            be a format string: the last result dictionary is passed for the\n            formatting. For example, users can use '{.iteration}' to separate\n            the log files for different iterations. If the log name is None, it\n            does not output the log to any file.\n\n    \"\"\"\n    def __init__(self, keys=None, trigger=(1, 'epoch'), postprocess=None,\n                 log_name='log'):\n        self._keys = keys\n        self._trigger = trigger_module.get_trigger(trigger)\n        self._postprocess = postprocess\n        self._log_name = log_name\n        self._log = []\n\n        self._init_summary()\n\n    def __call__(self, trainer):\n        # accumulate the observations\n        keys = self._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            # output the result\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\n            if self._postprocess is not None:\n                self._postprocess(stats_cpu)\n\n            self._log.append(stats_cpu)\n\n            # write to the log file\n            if self._log_name is not None:\n                log_name = self._log_name.format(stats_cpu)\n                fd, path = tempfile.mkstemp(prefix=log_name, dir=trainer.out)\n                with os.fdopen(fd, 'w') as f:\n                    json.dump(self._log, f, indent=4)\n                os.rename(path, os.path.join(trainer.out, log_name))\n\n            # reset the summary for the next output\n            self._init_summary()\n\n    @property\n    def log(self):\n        \"\"\"The current list of observation dictionaries.\"\"\"\n        return self._log\n\n    def serialize(self, serializer):\n        # Note that this serialization may lose some information of small\n        # numerical differences.\n        if isinstance(serializer, serializer_module.Serializer):\n            log = json.dumps(self._log)\n            serializer('_log', log)\n        else:\n            log = serializer('_log', '')\n            self._log = json.loads(log)\n\n    def _init_summary(self):\n        self._summary = reporter.DictSummary()\n", "path": "chainer/training/extensions/log_report.py"}], "after_files": [{"content": "import json\nimport os\nimport tempfile\n\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\n\nclass LogReport(extension.Extension):\n\n    \"\"\"Trainer extension to output the accumulated results to a log file.\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 writes them into a log file in JSON format.\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        keys (iterable of strs): Keys of values to accumulate. If this is None,\n            all the values are accumulated and output to the log file.\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\n            ``<int>, 'iteration'``, it is passed to :class:`IntervalTrigger`.\n        postprocess: Callback to postprocess the result dictionaries. Each\n            result dictionary is passed to this callback on the output. This\n            callback can modify the result dictionaries, which are used to\n            output to the log file.\n        log_name (str): Name of the log file under the output directory. It can\n            be a format string: the last result dictionary is passed for the\n            formatting. For example, users can use '{iteration}' to separate\n            the log files for different iterations. If the log name is None, it\n            does not output the log to any file.\n\n    \"\"\"\n    def __init__(self, keys=None, trigger=(1, 'epoch'), postprocess=None,\n                 log_name='log'):\n        self._keys = keys\n        self._trigger = trigger_module.get_trigger(trigger)\n        self._postprocess = postprocess\n        self._log_name = log_name\n        self._log = []\n\n        self._init_summary()\n\n    def __call__(self, trainer):\n        # accumulate the observations\n        keys = self._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            # output the result\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\n            if self._postprocess is not None:\n                self._postprocess(stats_cpu)\n\n            self._log.append(stats_cpu)\n\n            # write to the log file\n            if self._log_name is not None:\n                log_name = self._log_name.format(**stats_cpu)\n                fd, path = tempfile.mkstemp(prefix=log_name, dir=trainer.out)\n                with os.fdopen(fd, 'w') as f:\n                    json.dump(self._log, f, indent=4)\n                os.rename(path, os.path.join(trainer.out, log_name))\n\n            # reset the summary for the next output\n            self._init_summary()\n\n    @property\n    def log(self):\n        \"\"\"The current list of observation dictionaries.\"\"\"\n        return self._log\n\n    def serialize(self, serializer):\n        # Note that this serialization may lose some information of small\n        # numerical differences.\n        if isinstance(serializer, serializer_module.Serializer):\n            log = json.dumps(self._log)\n            serializer('_log', log)\n        else:\n            log = serializer('_log', '')\n            self._log = json.loads(log)\n\n    def _init_summary(self):\n        self._summary = reporter.DictSummary()\n", "path": "chainer/training/extensions/log_report.py"}]}