prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Wrong behavior of SerialIterator if dataset is numpy array.
  In `serial_iterator.py` lines [66](https://github.com/pfnet/chainer/blob/master/chainer/iterators/serial_iterator.py#L66) and [68](https://github.com/pfnet/chainer/blob/master/chainer/iterators/serial_iterator.py#L68) contain `+=` to extend lists.
  
  Unfortunately, this does not crash for `numpy` arrays but produces undesired behavior. The dataset itself gets modified (see for `6` in the second example):
  
  ```
  import numpy as np
  from chainer.iterators.serial_iterator import SerialIterator
  
  dataset = [1, 2, 3, 4, 5]
  it = SerialIterator(dataset, batch_size=2, shuffle=False)
  
  for _ in range(6):
      example = it.next()
      print(example)
  ```
  
  ```
  [1, 2]
  [3, 4]
  [5, 1]
  [2, 3]
  [4, 5]
  [1, 2]
  ```
  
  ```
  dataset = np.asarray([1, 2, 3, 4, 5])
  it = SerialIterator(dataset, batch_size=2, shuffle=False)
  
  for _ in range(6):
      example = it.next()
      print(example)
  ```
  
  ```
  [1 2]
  [3 4]
  [6]
  [2 3]
  [4 6]
  [1 2]
  ```
  
  When the two lines get changed to
  ```
  batch.extend(list(self.dataset[:rest]))
  ```
  
  and
  ```
  batch.extend([self.dataset[index] for index in self._order[:rest]])
  ```
  
  a useful error is raised.
  
  --- 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/iterators/serial_iterator.py`
  Content:
  ```
  1 from __future__ import division
  2 
  3 import numpy
  4 
  5 from chainer.dataset import iterator
  6 
  7 
  8 class SerialIterator(iterator.Iterator):
  9 
  10     """Dataset iterator that serially reads the examples.
  11 
  12     This is a simple implementation of :class:`~chainer.dataset.Iterator`
  13     that just visits each example in either the order of indexes or a shuffled
  14     order.
  15 
  16     To avoid unintentional performance degradation, the ``shuffle`` option is
  17     set to ``True`` by default. For validation, it is better to set it to
  18     ``False`` when the underlying dataset supports fast slicing. If the
  19     order of examples has an important meaning and the updater depends on the
  20     original order, this option should be set to ``False``.
  21 
  22     Args:
  23         dataset: Dataset to iterate.
  24         batch_size (int): Number of examples within each batch.
  25         repeat (bool): If ``True``, it infinitely loops over the dataset.
  26             Otherwise, it stops iteration at the end of the first epoch.
  27         shuffle (bool): If ``True``, the order of examples is shuffled at the
  28             beginning of each epoch. Otherwise, examples are extracted in the
  29             order of indexes.
  30 
  31     """
  32 
  33     def __init__(self, dataset, batch_size, repeat=True, shuffle=True):
  34         self.dataset = dataset
  35         self.batch_size = batch_size
  36         self._repeat = repeat
  37         if shuffle:
  38             self._order = numpy.random.permutation(len(dataset))
  39         else:
  40             self._order = None
  41 
  42         self.current_position = 0
  43         self.epoch = 0
  44         self.is_new_epoch = False
  45 
  46     def __next__(self):
  47         if not self._repeat and self.epoch > 0:
  48             raise StopIteration
  49 
  50         i = self.current_position
  51         i_end = i + self.batch_size
  52         N = len(self.dataset)
  53 
  54         if self._order is None:
  55             batch = self.dataset[i:i_end]
  56         else:
  57             batch = [self.dataset[index] for index in self._order[i:i_end]]
  58 
  59         if i_end >= N:
  60             if self._repeat:
  61                 rest = i_end - N
  62                 if self._order is not None:
  63                     numpy.random.shuffle(self._order)
  64                 if rest > 0:
  65                     if self._order is None:
  66                         batch += list(self.dataset[:rest])
  67                     else:
  68                         batch += [self.dataset[index]
  69                                   for index in self._order[:rest]]
  70                 self.current_position = rest
  71             else:
  72                 self.current_position = N
  73 
  74             self.epoch += 1
  75             self.is_new_epoch = True
  76         else:
  77             self.is_new_epoch = False
  78             self.current_position = i_end
  79 
  80         return batch
  81 
  82     next = __next__
  83 
  84     @property
  85     def epoch_detail(self):
  86         return self.epoch + self.current_position / len(self.dataset)
  87 
  88     def serialize(self, serializer):
  89         self.current_position = serializer('current_position',
  90                                            self.current_position)
  91         self.epoch = serializer('epoch', self.epoch)
  92         self.is_new_epoch = serializer('is_new_epoch', self.is_new_epoch)
  93         if self._order is not None:
  94             serializer('_order', self._order)
  95
  ```
  --- 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/iterators/serial_iterator.py b/chainer/iterators/serial_iterator.py
  --- a/chainer/iterators/serial_iterator.py
  +++ b/chainer/iterators/serial_iterator.py
  @@ -63,10 +63,10 @@
                       numpy.random.shuffle(self._order)
                   if rest > 0:
                       if self._order is None:
  -                        batch += list(self.dataset[:rest])
  +                        batch.extend(self.dataset[:rest])
                       else:
  -                        batch += [self.dataset[index]
  -                                  for index in self._order[:rest]]
  +                        batch.extend([self.dataset[index]
  +                                      for index in self._order[:rest]])
                   self.current_position = rest
               else:
                   self.current_position = N
  
verification_info: {"golden_diff": "diff --git a/chainer/iterators/serial_iterator.py b/chainer/iterators/serial_iterator.py\n--- a/chainer/iterators/serial_iterator.py\n+++ b/chainer/iterators/serial_iterator.py\n@@ -63,10 +63,10 @@\n                     numpy.random.shuffle(self._order)\n                 if rest > 0:\n                     if self._order is None:\n-                        batch += list(self.dataset[:rest])\n+                        batch.extend(self.dataset[:rest])\n                     else:\n-                        batch += [self.dataset[index]\n-                                  for index in self._order[:rest]]\n+                        batch.extend([self.dataset[index]\n+                                      for index in self._order[:rest]])\n                 self.current_position = rest\n             else:\n                 self.current_position = N\n", "issue": "Wrong behavior of SerialIterator if dataset is numpy array.\nIn `serial_iterator.py` lines [66](https://github.com/pfnet/chainer/blob/master/chainer/iterators/serial_iterator.py#L66) and [68](https://github.com/pfnet/chainer/blob/master/chainer/iterators/serial_iterator.py#L68) contain `+=` to extend lists.\r\n\r\nUnfortunately, this does not crash for `numpy` arrays but produces undesired behavior. The dataset itself gets modified (see for `6` in the second example):\r\n\r\n```\r\nimport numpy as np\r\nfrom chainer.iterators.serial_iterator import SerialIterator\r\n\r\ndataset = [1, 2, 3, 4, 5]\r\nit = SerialIterator(dataset, batch_size=2, shuffle=False)\r\n\r\nfor _ in range(6):\r\n    example = it.next()\r\n    print(example)\r\n```\r\n\r\n```\r\n[1, 2]\r\n[3, 4]\r\n[5, 1]\r\n[2, 3]\r\n[4, 5]\r\n[1, 2]\r\n```\r\n\r\n```\r\ndataset = np.asarray([1, 2, 3, 4, 5])\r\nit = SerialIterator(dataset, batch_size=2, shuffle=False)\r\n\r\nfor _ in range(6):\r\n    example = it.next()\r\n    print(example)\r\n```\r\n\r\n```\r\n[1 2]\r\n[3 4]\r\n[6]\r\n[2 3]\r\n[4 6]\r\n[1 2]\r\n```\r\n\r\nWhen the two lines get changed to\r\n```\r\nbatch.extend(list(self.dataset[:rest]))\r\n```\r\n\r\nand\r\n```\r\nbatch.extend([self.dataset[index] for index in self._order[:rest]])\r\n```\r\n\r\na useful error is raised.\n", "before_files": [{"content": "from __future__ import division\n\nimport numpy\n\nfrom chainer.dataset import iterator\n\n\nclass SerialIterator(iterator.Iterator):\n\n    \"\"\"Dataset iterator that serially reads the examples.\n\n    This is a simple implementation of :class:`~chainer.dataset.Iterator`\n    that just visits each example in either the order of indexes or a shuffled\n    order.\n\n    To avoid unintentional performance degradation, the ``shuffle`` option is\n    set to ``True`` by default. For validation, it is better to set it to\n    ``False`` when the underlying dataset supports fast slicing. If the\n    order of examples has an important meaning and the updater depends on the\n    original order, this option should be set to ``False``.\n\n    Args:\n        dataset: Dataset to iterate.\n        batch_size (int): Number of examples within each batch.\n        repeat (bool): If ``True``, it infinitely loops over the dataset.\n            Otherwise, it stops iteration at the end of the first epoch.\n        shuffle (bool): If ``True``, the order of examples is shuffled at the\n            beginning of each epoch. Otherwise, examples are extracted in the\n            order of indexes.\n\n    \"\"\"\n\n    def __init__(self, dataset, batch_size, repeat=True, shuffle=True):\n        self.dataset = dataset\n        self.batch_size = batch_size\n        self._repeat = repeat\n        if shuffle:\n            self._order = numpy.random.permutation(len(dataset))\n        else:\n            self._order = None\n\n        self.current_position = 0\n        self.epoch = 0\n        self.is_new_epoch = False\n\n    def __next__(self):\n        if not self._repeat and self.epoch > 0:\n            raise StopIteration\n\n        i = self.current_position\n        i_end = i + self.batch_size\n        N = len(self.dataset)\n\n        if self._order is None:\n            batch = self.dataset[i:i_end]\n        else:\n            batch = [self.dataset[index] for index in self._order[i:i_end]]\n\n        if i_end >= N:\n            if self._repeat:\n                rest = i_end - N\n                if self._order is not None:\n                    numpy.random.shuffle(self._order)\n                if rest > 0:\n                    if self._order is None:\n                        batch += list(self.dataset[:rest])\n                    else:\n                        batch += [self.dataset[index]\n                                  for index in self._order[:rest]]\n                self.current_position = rest\n            else:\n                self.current_position = N\n\n            self.epoch += 1\n            self.is_new_epoch = True\n        else:\n            self.is_new_epoch = False\n            self.current_position = i_end\n\n        return batch\n\n    next = __next__\n\n    @property\n    def epoch_detail(self):\n        return self.epoch + self.current_position / len(self.dataset)\n\n    def serialize(self, serializer):\n        self.current_position = serializer('current_position',\n                                           self.current_position)\n        self.epoch = serializer('epoch', self.epoch)\n        self.is_new_epoch = serializer('is_new_epoch', self.is_new_epoch)\n        if self._order is not None:\n            serializer('_order', self._order)\n", "path": "chainer/iterators/serial_iterator.py"}], "after_files": [{"content": "from __future__ import division\n\nimport numpy\n\nfrom chainer.dataset import iterator\n\n\nclass SerialIterator(iterator.Iterator):\n\n    \"\"\"Dataset iterator that serially reads the examples.\n\n    This is a simple implementation of :class:`~chainer.dataset.Iterator`\n    that just visits each example in either the order of indexes or a shuffled\n    order.\n\n    To avoid unintentional performance degradation, the ``shuffle`` option is\n    set to ``True`` by default. For validation, it is better to set it to\n    ``False`` when the underlying dataset supports fast slicing. If the\n    order of examples has an important meaning and the updater depends on the\n    original order, this option should be set to ``False``.\n\n    Args:\n        dataset: Dataset to iterate.\n        batch_size (int): Number of examples within each batch.\n        repeat (bool): If ``True``, it infinitely loops over the dataset.\n            Otherwise, it stops iteration at the end of the first epoch.\n        shuffle (bool): If ``True``, the order of examples is shuffled at the\n            beginning of each epoch. Otherwise, examples are extracted in the\n            order of indexes.\n\n    \"\"\"\n\n    def __init__(self, dataset, batch_size, repeat=True, shuffle=True):\n        self.dataset = dataset\n        self.batch_size = batch_size\n        self._repeat = repeat\n        if shuffle:\n            self._order = numpy.random.permutation(len(dataset))\n        else:\n            self._order = None\n\n        self.current_position = 0\n        self.epoch = 0\n        self.is_new_epoch = False\n\n    def __next__(self):\n        if not self._repeat and self.epoch > 0:\n            raise StopIteration\n\n        i = self.current_position\n        i_end = i + self.batch_size\n        N = len(self.dataset)\n\n        if self._order is None:\n            batch = self.dataset[i:i_end]\n        else:\n            batch = [self.dataset[index] for index in self._order[i:i_end]]\n\n        if i_end >= N:\n            if self._repeat:\n                rest = i_end - N\n                if self._order is not None:\n                    numpy.random.shuffle(self._order)\n                if rest > 0:\n                    if self._order is None:\n                        batch.extend(self.dataset[:rest])\n                    else:\n                        batch.extend([self.dataset[index]\n                                      for index in self._order[:rest]])\n                self.current_position = rest\n            else:\n                self.current_position = N\n\n            self.epoch += 1\n            self.is_new_epoch = True\n        else:\n            self.is_new_epoch = False\n            self.current_position = i_end\n\n        return batch\n\n    next = __next__\n\n    @property\n    def epoch_detail(self):\n        return self.epoch + self.current_position / len(self.dataset)\n\n    def serialize(self, serializer):\n        self.current_position = serializer('current_position',\n                                           self.current_position)\n        self.epoch = serializer('epoch', self.epoch)\n        self.is_new_epoch = serializer('is_new_epoch', self.is_new_epoch)\n        if self._order is not None:\n            serializer('_order', self._order)\n", "path": "chainer/iterators/serial_iterator.py"}]}