prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  PrintHook may fail
  * Conditions
  ```
  >>> chainer.print_runtime_info()
  Platform: Darwin-17.7.0-x86_64-i386-64bit
  Chainer: 5.0.0b4
  NumPy: 1.16.0.dev0+591880b
  CuPy: Not Available
  ```
  * Code to reproduce
  ```
  >>> x = chainer.Variable(np.random.rand(2, 3).astype('f'))
  >>> lstm = chainer.links.LSTM(3)
  >>> y = lstm(x)
  >>> with chainer.function_hooks.PrintHook():
  ...     chainer.functions.sum(y).backward()
  ...
  ```
  
  * Error messages, stack traces, or logs
  ```
  function	Sum
  input data
  <variable at 0x1152b3438>
  - device: CPU
  - backend: <class 'numpy.ndarray'>
  - shape: (2, 3)
  - dtype: float32
  - statistics: mean=-0.08529118, std=0.18154962
  - grad: None
  function	Sum
  input data
  (removed)
  output gradient
  <variable at 0x1152b33c8>
  - device: CPU
  - backend: <class 'numpy.ndarray'>
  - shape: ()
  - dtype: float32
  - statistics: mean=0.00000000, std=0.00000000
  - grad: mean=1.00000000, std=0.00000000
  function	BroadcastTo
  input data
  <variable at 0x1152b3710>
  - device: CPU
  - backend: <class 'numpy.ndarray'>
  - shape: ()
  - dtype: float32
  - statistics: mean=1.00000000, std=0.00000000
  - grad: None
  function	LSTM
  input data
  <variable at 0x1152b3668>
  - device: CPU
  - backend: <class 'numpy.ndarray'>
  - shape: (2, 3)
  - dtype: float32
  - statistics: mean=0.00000000, std=0.00000000
  - grad: None
  <variable at 0x1152b3668>
  - device: CPU
  - backend: <class 'numpy.ndarray'>
  - shape: (2, 12)
  - dtype: float32
  - statistics: mean=0.53247088, std=0.91164017
  - grad: None
  output gradient
  Traceback (most recent call last):
    File "<stdin>", line 2, in <module>
    File "/Users/tos/GitHub/chainer/chainer/variable.py", line 963, in backward
      self._backward_main(retain_grad, loss_scale)
    File "/Users/tos/GitHub/chainer/chainer/variable.py", line 1025, in _backward_main
      hook.backward_preprocess(func, in_data, out_grad_data)
    File "/Users/tos/GitHub/chainer/chainer/function_hooks/debug_print.py", line 87, in backward_preprocess
      self._process(function, in_data, out_grad)
    File "/Users/tos/GitHub/chainer/chainer/function_hooks/debug_print.py", line 77, in _process
      v = variable.Variable(xp.zeros_like(d, dtype=d.dtype))
  AttributeError: 'NoneType' object has no attribute 'dtype'
  ```
  
  --- 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/function_hooks/debug_print.py`
  Content:
  ```
  1 import sys
  2 import warnings
  3 
  4 from chainer.backends import cuda
  5 from chainer import function_hook
  6 from chainer import variable
  7 
  8 
  9 class PrintHook(function_hook.FunctionHook):
  10     """Function hook that prints debug information.
  11 
  12     This function hook outputs the debug information of input arguments of
  13     ``forward`` and ``backward`` methods involved in the hooked functions
  14     at preprocessing time (that is, just before each method is called).
  15 
  16     Unlike simple "debug print" technique, where users insert print functions
  17     at every function to be inspected, we can show the information
  18     of all functions involved with single ``with`` statement.
  19 
  20     Further, this hook enables us to show the information of
  21     ``backward`` methods without inserting print functions into
  22     Chainer's library code.
  23 
  24     Args:
  25         sep: *(deprecated since v4.0.0)* Ignored.
  26         end: Character to be added at the end of print function.
  27         file: Output file_like object that that redirect to.
  28         flush: If ``True``, this hook forcibly flushes the text stream
  29             at the end of preprocessing.
  30 
  31     .. admonition:: Example
  32 
  33         The basic usage is to use it with ``with`` statement.
  34 
  35         >>> from chainer import function_hooks
  36         >>> l = L.Linear(10, 10)
  37         >>> x = chainer.Variable(np.zeros((1, 10), np.float32))
  38         >>> with chainer.function_hooks.PrintHook():
  39         ...     y = l(x)
  40         ...     z = F.sum(y)
  41         ...     z.backward() # doctest:+SKIP
  42 
  43         In this example, ``PrintHook`` shows the debug information of
  44         forward propagation of ``LinearFunction`` (which is implicitly
  45         called by ``l``) and ``Sum`` (called by ``F.sum``)
  46         and backward propagation of ``z`` and ``y``.
  47 
  48     """
  49 
  50     name = 'PrintHook'
  51 
  52     def __init__(self, sep=None, end='\n', file=sys.stdout, flush=True):
  53         if sep is not None:
  54             warnings.warn('sep argument in chainer.function_hooks.PrintHook '
  55                           'is deprecated.', DeprecationWarning)
  56         self.sep = sep  # Keep sep because it was originally documented
  57         self.end = end
  58         self.file = file
  59         self.flush = flush
  60 
  61     def _print(self, msg):
  62         self.file.write(msg + self.end)
  63 
  64     def _process(self, function, in_data, out_grad=None):
  65         self._print('function\t{}'.format(function.label))
  66         self._print('input data')
  67         for d in in_data:
  68             if d is None:
  69                 # Some inputs can be removed with `retain_grad`.
  70                 self._print('(removed)')
  71                 continue
  72             self._print(variable.Variable(d).debug_print())
  73         if out_grad is not None:
  74             self._print('output gradient')
  75             for d in out_grad:
  76                 xp = cuda.get_array_module(d)
  77                 v = variable.Variable(xp.zeros_like(d, dtype=d.dtype))
  78                 v.grad = d
  79                 self._print(v.debug_print())
  80         if self.flush:
  81             self.file.flush()
  82 
  83     def forward_preprocess(self, function, in_data):
  84         self._process(function, in_data)
  85 
  86     def backward_preprocess(self, function, in_data, out_grad):
  87         self._process(function, in_data, out_grad)
  88
  ```
  --- 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/function_hooks/debug_print.py b/chainer/function_hooks/debug_print.py
  --- a/chainer/function_hooks/debug_print.py
  +++ b/chainer/function_hooks/debug_print.py
  @@ -73,9 +73,12 @@
           if out_grad is not None:
               self._print('output gradient')
               for d in out_grad:
  -                xp = cuda.get_array_module(d)
  -                v = variable.Variable(xp.zeros_like(d, dtype=d.dtype))
  -                v.grad = d
  +                if d is None:
  +                    v = variable.Variable()
  +                else:
  +                    xp = cuda.get_array_module(d)
  +                    v = variable.Variable(xp.zeros_like(d, dtype=d.dtype))
  +                    v.grad = d
                   self._print(v.debug_print())
           if self.flush:
               self.file.flush()
  
verification_info: {"golden_diff": "diff --git a/chainer/function_hooks/debug_print.py b/chainer/function_hooks/debug_print.py\n--- a/chainer/function_hooks/debug_print.py\n+++ b/chainer/function_hooks/debug_print.py\n@@ -73,9 +73,12 @@\n         if out_grad is not None:\n             self._print('output gradient')\n             for d in out_grad:\n-                xp = cuda.get_array_module(d)\n-                v = variable.Variable(xp.zeros_like(d, dtype=d.dtype))\n-                v.grad = d\n+                if d is None:\n+                    v = variable.Variable()\n+                else:\n+                    xp = cuda.get_array_module(d)\n+                    v = variable.Variable(xp.zeros_like(d, dtype=d.dtype))\n+                    v.grad = d\n                 self._print(v.debug_print())\n         if self.flush:\n             self.file.flush()\n", "issue": "PrintHook may fail\n* Conditions\r\n```\r\n>>> chainer.print_runtime_info()\r\nPlatform: Darwin-17.7.0-x86_64-i386-64bit\r\nChainer: 5.0.0b4\r\nNumPy: 1.16.0.dev0+591880b\r\nCuPy: Not Available\r\n```\r\n* Code to reproduce\r\n```\r\n>>> x = chainer.Variable(np.random.rand(2, 3).astype('f'))\r\n>>> lstm = chainer.links.LSTM(3)\r\n>>> y = lstm(x)\r\n>>> with chainer.function_hooks.PrintHook():\r\n...     chainer.functions.sum(y).backward()\r\n...\r\n```\r\n\r\n* Error messages, stack traces, or logs\r\n```\r\nfunction\tSum\r\ninput data\r\n<variable at 0x1152b3438>\r\n- device: CPU\r\n- backend: <class 'numpy.ndarray'>\r\n- shape: (2, 3)\r\n- dtype: float32\r\n- statistics: mean=-0.08529118, std=0.18154962\r\n- grad: None\r\nfunction\tSum\r\ninput data\r\n(removed)\r\noutput gradient\r\n<variable at 0x1152b33c8>\r\n- device: CPU\r\n- backend: <class 'numpy.ndarray'>\r\n- shape: ()\r\n- dtype: float32\r\n- statistics: mean=0.00000000, std=0.00000000\r\n- grad: mean=1.00000000, std=0.00000000\r\nfunction\tBroadcastTo\r\ninput data\r\n<variable at 0x1152b3710>\r\n- device: CPU\r\n- backend: <class 'numpy.ndarray'>\r\n- shape: ()\r\n- dtype: float32\r\n- statistics: mean=1.00000000, std=0.00000000\r\n- grad: None\r\nfunction\tLSTM\r\ninput data\r\n<variable at 0x1152b3668>\r\n- device: CPU\r\n- backend: <class 'numpy.ndarray'>\r\n- shape: (2, 3)\r\n- dtype: float32\r\n- statistics: mean=0.00000000, std=0.00000000\r\n- grad: None\r\n<variable at 0x1152b3668>\r\n- device: CPU\r\n- backend: <class 'numpy.ndarray'>\r\n- shape: (2, 12)\r\n- dtype: float32\r\n- statistics: mean=0.53247088, std=0.91164017\r\n- grad: None\r\noutput gradient\r\nTraceback (most recent call last):\r\n  File \"<stdin>\", line 2, in <module>\r\n  File \"/Users/tos/GitHub/chainer/chainer/variable.py\", line 963, in backward\r\n    self._backward_main(retain_grad, loss_scale)\r\n  File \"/Users/tos/GitHub/chainer/chainer/variable.py\", line 1025, in _backward_main\r\n    hook.backward_preprocess(func, in_data, out_grad_data)\r\n  File \"/Users/tos/GitHub/chainer/chainer/function_hooks/debug_print.py\", line 87, in backward_preprocess\r\n    self._process(function, in_data, out_grad)\r\n  File \"/Users/tos/GitHub/chainer/chainer/function_hooks/debug_print.py\", line 77, in _process\r\n    v = variable.Variable(xp.zeros_like(d, dtype=d.dtype))\r\nAttributeError: 'NoneType' object has no attribute 'dtype'\r\n```\n", "before_files": [{"content": "import sys\nimport warnings\n\nfrom chainer.backends import cuda\nfrom chainer import function_hook\nfrom chainer import variable\n\n\nclass PrintHook(function_hook.FunctionHook):\n    \"\"\"Function hook that prints debug information.\n\n    This function hook outputs the debug information of input arguments of\n    ``forward`` and ``backward`` methods involved in the hooked functions\n    at preprocessing time (that is, just before each method is called).\n\n    Unlike simple \"debug print\" technique, where users insert print functions\n    at every function to be inspected, we can show the information\n    of all functions involved with single ``with`` statement.\n\n    Further, this hook enables us to show the information of\n    ``backward`` methods without inserting print functions into\n    Chainer's library code.\n\n    Args:\n        sep: *(deprecated since v4.0.0)* Ignored.\n        end: Character to be added at the end of print function.\n        file: Output file_like object that that redirect to.\n        flush: If ``True``, this hook forcibly flushes the text stream\n            at the end of preprocessing.\n\n    .. admonition:: Example\n\n        The basic usage is to use it with ``with`` statement.\n\n        >>> from chainer import function_hooks\n        >>> l = L.Linear(10, 10)\n        >>> x = chainer.Variable(np.zeros((1, 10), np.float32))\n        >>> with chainer.function_hooks.PrintHook():\n        ...     y = l(x)\n        ...     z = F.sum(y)\n        ...     z.backward() # doctest:+SKIP\n\n        In this example, ``PrintHook`` shows the debug information of\n        forward propagation of ``LinearFunction`` (which is implicitly\n        called by ``l``) and ``Sum`` (called by ``F.sum``)\n        and backward propagation of ``z`` and ``y``.\n\n    \"\"\"\n\n    name = 'PrintHook'\n\n    def __init__(self, sep=None, end='\\n', file=sys.stdout, flush=True):\n        if sep is not None:\n            warnings.warn('sep argument in chainer.function_hooks.PrintHook '\n                          'is deprecated.', DeprecationWarning)\n        self.sep = sep  # Keep sep because it was originally documented\n        self.end = end\n        self.file = file\n        self.flush = flush\n\n    def _print(self, msg):\n        self.file.write(msg + self.end)\n\n    def _process(self, function, in_data, out_grad=None):\n        self._print('function\\t{}'.format(function.label))\n        self._print('input data')\n        for d in in_data:\n            if d is None:\n                # Some inputs can be removed with `retain_grad`.\n                self._print('(removed)')\n                continue\n            self._print(variable.Variable(d).debug_print())\n        if out_grad is not None:\n            self._print('output gradient')\n            for d in out_grad:\n                xp = cuda.get_array_module(d)\n                v = variable.Variable(xp.zeros_like(d, dtype=d.dtype))\n                v.grad = d\n                self._print(v.debug_print())\n        if self.flush:\n            self.file.flush()\n\n    def forward_preprocess(self, function, in_data):\n        self._process(function, in_data)\n\n    def backward_preprocess(self, function, in_data, out_grad):\n        self._process(function, in_data, out_grad)\n", "path": "chainer/function_hooks/debug_print.py"}], "after_files": [{"content": "import sys\nimport warnings\n\nfrom chainer.backends import cuda\nfrom chainer import function_hook\nfrom chainer import variable\n\n\nclass PrintHook(function_hook.FunctionHook):\n    \"\"\"Function hook that prints debug information.\n\n    This function hook outputs the debug information of input arguments of\n    ``forward`` and ``backward`` methods involved in the hooked functions\n    at preprocessing time (that is, just before each method is called).\n\n    Unlike simple \"debug print\" technique, where users insert print functions\n    at every function to be inspected, we can show the information\n    of all functions involved with single ``with`` statement.\n\n    Further, this hook enables us to show the information of\n    ``backward`` methods without inserting print functions into\n    Chainer's library code.\n\n    Args:\n        sep: *(deprecated since v4.0.0)* Ignored.\n        end: Character to be added at the end of print function.\n        file: Output file_like object that that redirect to.\n        flush: If ``True``, this hook forcibly flushes the text stream\n            at the end of preprocessing.\n\n    .. admonition:: Example\n\n        The basic usage is to use it with ``with`` statement.\n\n        >>> from chainer import function_hooks\n        >>> l = L.Linear(10, 10)\n        >>> x = chainer.Variable(np.zeros((1, 10), np.float32))\n        >>> with chainer.function_hooks.PrintHook():\n        ...     y = l(x)\n        ...     z = F.sum(y)\n        ...     z.backward() # doctest:+SKIP\n\n        In this example, ``PrintHook`` shows the debug information of\n        forward propagation of ``LinearFunction`` (which is implicitly\n        called by ``l``) and ``Sum`` (called by ``F.sum``)\n        and backward propagation of ``z`` and ``y``.\n\n    \"\"\"\n\n    name = 'PrintHook'\n\n    def __init__(self, sep=None, end='\\n', file=sys.stdout, flush=True):\n        if sep is not None:\n            warnings.warn('sep argument in chainer.function_hooks.PrintHook '\n                          'is deprecated.', DeprecationWarning)\n        self.sep = sep  # Keep sep because it was originally documented\n        self.end = end\n        self.file = file\n        self.flush = flush\n\n    def _print(self, msg):\n        self.file.write(msg + self.end)\n\n    def _process(self, function, in_data, out_grad=None):\n        self._print('function\\t{}'.format(function.label))\n        self._print('input data')\n        for d in in_data:\n            if d is None:\n                # Some inputs can be removed with `retain_grad`.\n                self._print('(removed)')\n                continue\n            self._print(variable.Variable(d).debug_print())\n        if out_grad is not None:\n            self._print('output gradient')\n            for d in out_grad:\n                if d is None:\n                    v = variable.Variable()\n                else:\n                    xp = cuda.get_array_module(d)\n                    v = variable.Variable(xp.zeros_like(d, dtype=d.dtype))\n                    v.grad = d\n                self._print(v.debug_print())\n        if self.flush:\n            self.file.flush()\n\n    def forward_preprocess(self, function, in_data):\n        self._process(function, in_data)\n\n    def backward_preprocess(self, function, in_data, out_grad):\n        self._process(function, in_data, out_grad)\n", "path": "chainer/function_hooks/debug_print.py"}]}