prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  link.LSTM.to_cpu (resp. to_gpu) does not move state variables to CPU (resp. GPU).
  When `link.LSTM` is move to CPU (resp. GPU), its state variables should also be moved to CPU(resp. GPU). But it does not.
  
  ```
  In [39]: l = chainer.links.LSTM(10, 10)
  
  In [40]: l.xp
  Out[40]: <module 'numpy' from '/home/delta/.pyenv/versions/pyenv-2.7.9/lib/python2.7/site-packages/numpy/__init__.pyc'>
  
  In [41]: x = chainer.Variable(numpy.random.uniform(-1, 1, (5, 10)).astype(numpy.float32))
  
  In [42]: l(x)
  Out[42]: <variable at 0x7fe900a88d90>
  
  In [43]: type(l.h.data)
  Out[43]: numpy.ndarray
  
  In [44]: l.to_gpu()
  Out[44]: <chainer.links.connection.lstm.LSTM at 0x7fe900a42a10>
  
  In [45]: l.xp
  Out[45]: <module 'cupy' from 'cupy/__init__.pyc'>
  
  In [46]: type(l.h.data)
  Out[46]: numpy.ndarray
  
  In [47]: type(l.c.data)
  Out[47]: numpy.ndarray
  ```
  
  The output of [46] and [47] should be `cupy.ndarray`
  
  
  </issue>
  <code>
  [start of chainer/links/connection/lstm.py]
  1 from chainer.functions.activation import lstm
  2 from chainer import link
  3 from chainer.links.connection import linear
  4 from chainer import variable
  5 
  6 
  7 class LSTM(link.Chain):
  8 
  9     """Fully-connected LSTM layer.
  10 
  11     This is a fully-connected LSTM layer as a chain. Unlike the
  12     :func:`~chainer.functions.lstm` function, which is defined as a stateless
  13     activation function, this chain holds upward and lateral connections as
  14     child links.
  15 
  16     It also maintains *states*, including the cell state and the output
  17     at the previous time step. Therefore, it can be used as a *stateful LSTM*.
  18 
  19     Args:
  20         in_size (int): Dimensionality of input vectors.
  21         out_size (int): Dimensionality of output vectors.
  22 
  23     Attributes:
  24         upward (chainer.links.Linear): Linear layer of upward connections.
  25         lateral (chainer.links.Linear): Linear layer of lateral connections.
  26         c (chainer.Variable): Cell states of LSTM units.
  27         h (chainer.Variable): Output at the previous timestep.
  28 
  29     """
  30     def __init__(self, in_size, out_size):
  31         super(LSTM, self).__init__(
  32             upward=linear.Linear(in_size, 4 * out_size),
  33             lateral=linear.Linear(out_size, 4 * out_size, nobias=True),
  34         )
  35         self.state_size = out_size
  36         self.reset_state()
  37 
  38     def reset_state(self):
  39         """Resets the internal state.
  40 
  41         It sets None to the :attr:`c` and :attr:`h` attributes.
  42 
  43         """
  44         self.c = self.h = None
  45 
  46     def __call__(self, x):
  47         """Updates the internal state and returns the LSTM outputs.
  48 
  49         Args:
  50             x (~chainer.Variable): A new batch from the input sequence.
  51 
  52         Returns:
  53             ~chainer.Variable: Outputs of updated LSTM units.
  54 
  55         """
  56         lstm_in = self.upward(x)
  57         if self.h is not None:
  58             lstm_in += self.lateral(self.h)
  59         if self.c is None:
  60             xp = self.xp
  61             self.c = variable.Variable(
  62                 xp.zeros((len(x.data), self.state_size), dtype=x.data.dtype),
  63                 volatile='auto')
  64         self.c, self.h = lstm.lstm(self.c, lstm_in)
  65         return self.h
  66
  [end of chainer/links/connection/lstm.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/chainer/links/connection/lstm.py b/chainer/links/connection/lstm.py
  --- a/chainer/links/connection/lstm.py
  +++ b/chainer/links/connection/lstm.py
  @@ -35,6 +35,20 @@
           self.state_size = out_size
           self.reset_state()
   
  +    def to_cpu(self):
  +        super(LSTM, self).to_cpu()
  +        if self.c is not None:
  +            self.c.to_cpu()
  +        if self.h is not None:
  +            self.h.to_cpu()
  +
  +    def to_gpu(self, device=None):
  +        super(LSTM, self).to_gpu(device)
  +        if self.c is not None:
  +            self.c.to_gpu(device)
  +        if self.h is not None:
  +            self.h.to_gpu(device)
  +
       def reset_state(self):
           """Resets the internal state.
  
verification_info: {"golden_diff": "diff --git a/chainer/links/connection/lstm.py b/chainer/links/connection/lstm.py\n--- a/chainer/links/connection/lstm.py\n+++ b/chainer/links/connection/lstm.py\n@@ -35,6 +35,20 @@\n         self.state_size = out_size\n         self.reset_state()\n \n+    def to_cpu(self):\n+        super(LSTM, self).to_cpu()\n+        if self.c is not None:\n+            self.c.to_cpu()\n+        if self.h is not None:\n+            self.h.to_cpu()\n+\n+    def to_gpu(self, device=None):\n+        super(LSTM, self).to_gpu(device)\n+        if self.c is not None:\n+            self.c.to_gpu(device)\n+        if self.h is not None:\n+            self.h.to_gpu(device)\n+\n     def reset_state(self):\n         \"\"\"Resets the internal state.\n", "issue": "link.LSTM.to_cpu (resp. to_gpu) does not move state variables to CPU (resp. GPU).\nWhen `link.LSTM` is move to CPU (resp. GPU), its state variables should also be moved to CPU(resp. GPU). But it does not.\n\n```\nIn [39]: l = chainer.links.LSTM(10, 10)\n\nIn [40]: l.xp\nOut[40]: <module 'numpy' from '/home/delta/.pyenv/versions/pyenv-2.7.9/lib/python2.7/site-packages/numpy/__init__.pyc'>\n\nIn [41]: x = chainer.Variable(numpy.random.uniform(-1, 1, (5, 10)).astype(numpy.float32))\n\nIn [42]: l(x)\nOut[42]: <variable at 0x7fe900a88d90>\n\nIn [43]: type(l.h.data)\nOut[43]: numpy.ndarray\n\nIn [44]: l.to_gpu()\nOut[44]: <chainer.links.connection.lstm.LSTM at 0x7fe900a42a10>\n\nIn [45]: l.xp\nOut[45]: <module 'cupy' from 'cupy/__init__.pyc'>\n\nIn [46]: type(l.h.data)\nOut[46]: numpy.ndarray\n\nIn [47]: type(l.c.data)\nOut[47]: numpy.ndarray\n```\n\nThe output of [46] and [47] should be `cupy.ndarray`\n\n", "before_files": [{"content": "from chainer.functions.activation import lstm\nfrom chainer import link\nfrom chainer.links.connection import linear\nfrom chainer import variable\n\n\nclass LSTM(link.Chain):\n\n    \"\"\"Fully-connected LSTM layer.\n\n    This is a fully-connected LSTM layer as a chain. Unlike the\n    :func:`~chainer.functions.lstm` function, which is defined as a stateless\n    activation function, this chain holds upward and lateral connections as\n    child links.\n\n    It also maintains *states*, including the cell state and the output\n    at the previous time step. Therefore, it can be used as a *stateful LSTM*.\n\n    Args:\n        in_size (int): Dimensionality of input vectors.\n        out_size (int): Dimensionality of output vectors.\n\n    Attributes:\n        upward (chainer.links.Linear): Linear layer of upward connections.\n        lateral (chainer.links.Linear): Linear layer of lateral connections.\n        c (chainer.Variable): Cell states of LSTM units.\n        h (chainer.Variable): Output at the previous timestep.\n\n    \"\"\"\n    def __init__(self, in_size, out_size):\n        super(LSTM, self).__init__(\n            upward=linear.Linear(in_size, 4 * out_size),\n            lateral=linear.Linear(out_size, 4 * out_size, nobias=True),\n        )\n        self.state_size = out_size\n        self.reset_state()\n\n    def reset_state(self):\n        \"\"\"Resets the internal state.\n\n        It sets None to the :attr:`c` and :attr:`h` attributes.\n\n        \"\"\"\n        self.c = self.h = None\n\n    def __call__(self, x):\n        \"\"\"Updates the internal state and returns the LSTM outputs.\n\n        Args:\n            x (~chainer.Variable): A new batch from the input sequence.\n\n        Returns:\n            ~chainer.Variable: Outputs of updated LSTM units.\n\n        \"\"\"\n        lstm_in = self.upward(x)\n        if self.h is not None:\n            lstm_in += self.lateral(self.h)\n        if self.c is None:\n            xp = self.xp\n            self.c = variable.Variable(\n                xp.zeros((len(x.data), self.state_size), dtype=x.data.dtype),\n                volatile='auto')\n        self.c, self.h = lstm.lstm(self.c, lstm_in)\n        return self.h\n", "path": "chainer/links/connection/lstm.py"}]}