prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  The behavior of Function#parameters= is inconsistent with FunctionSet#parameters=
  At aaf2aae14ad683734964590e6ceee9d775d9d7c5, an assertion was introduced into `Function#parameters=`, which requires the argument `values` to be a list-like object (implementing `__len__` method).
  However, this assertion seems to be inconsistent with the behavior of `FunctionSet#parameters=`.
  `FunctionSet#parameters=` passes the iterator of its argument `params` to its child functions through `Function#parameters=`, and it causes a TypeError exception.
  
  ``` python
  from chainer import FunctionSet
  import chainer.functions as F
  func_set = FunctionSet(f=F.Linear(10,10))
  params = func_set.parameters
  func_set.parameters = params # TypeError: object of type 'tuple_iterator' has no len()
  ```
  
  
  --- 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_set.py`
  Content:
  ```
  1 import numpy
  2 import six
  3 import warnings
  4 
  5 from chainer import cuda
  6 from chainer import function
  7 
  8 
  9 class FunctionSet(object):
  10 
  11     """Set of objects with ``parameters`` and ``gradients`` properties.
  12 
  13     :class:`FunctionSet` is useful to collect parameters and gradients of
  14     multiple parameterized :class:`Function` objects. :class:`FunctionSet`
  15     itself also implements :attr:`~FunctionSet.parameters` and
  16     :attr:`~FunctionSet.gradients`, so it can be nested in another
  17     :class:`FunctionSet` object.
  18 
  19     Function registration is done by just adding an attribute to
  20     :class:`FunctionSet` object.
  21 
  22     """
  23 
  24     def __init__(self, **functions):
  25         """Initializes the function set by given functions.
  26 
  27         Args:
  28             **functions: ``dict`` of ``str`` key and :class:`Function` values.
  29                 The key-value pairs are just set to the :class:`FunctionSet`
  30                 object as attributes.
  31 
  32         """
  33         for name, func in six.iteritems(functions):
  34             setattr(self, name, func)
  35 
  36     def collect_parameters(self):
  37         """Returns a tuple of parameters and gradients.
  38 
  39         Returns:
  40             Tuple (pair) of two tuples. The first element is a tuple of
  41             parameter arrays, and the second is a tuple of gradient arrays.
  42 
  43         """
  44 
  45         msg = ("'collect_parameters' is deprecated. "
  46                "You can pass FunctionSet itself to 'optimizer.setup'")
  47         warnings.warn(msg, FutureWarning)
  48         return self
  49 
  50     def __getitem__(self, key):
  51         """Returns the :class:`Function` objects by name.
  52 
  53         Args:
  54             key (str): Name of the function.
  55 
  56         Returns:
  57             ~chainer.Function: Function object.
  58 
  59         .. admonition:: Example
  60 
  61            >>> model = FunctionSet(l1=F.Linear(10, 10), l2=F.Linear(10, 10))
  62            >>> l1 = model['l1']
  63         """
  64 
  65         return getattr(self, key)
  66 
  67     def to_gpu(self, device=None):
  68         """Migrates all parameters and gradients onto GPU.
  69 
  70         This method calls ``to_gpu`` method of each registered object.
  71 
  72         Args:
  73             device (int or :class:`cupy.cuda.Device` or ``None``): Device
  74                 ID of GPU. If ``None`` is given, it uses the current device.
  75 
  76         Returns:
  77             self
  78 
  79         """
  80         for func in six.itervalues(self.__dict__):
  81             if isinstance(func, (function.Function, FunctionSet)):
  82                 func.to_gpu(device=device)
  83         return self
  84 
  85     def to_cpu(self):
  86         """Migrates all parameters and gradients onto CPU.
  87 
  88         This method calls ``to_cpu`` method of each registered object.
  89 
  90         Returns:
  91             self
  92 
  93         """
  94         for func in six.itervalues(self.__dict__):
  95             if isinstance(func, (function.Function, FunctionSet)):
  96                 func.to_cpu()
  97         return self
  98 
  99     def copy_parameters_from(self, params):
  100         """Copies parameters from another source without reallocation.
  101 
  102         Args:
  103             params (Iterable): Iterable of parameter arrays.
  104 
  105         """
  106         for dst, src in zip(self.parameters, params):
  107             if isinstance(dst, numpy.ndarray):
  108                 if isinstance(src, numpy.ndarray):
  109                     numpy.copyto(dst, src)
  110                 else:
  111                     dst[:] = src.get()
  112             elif isinstance(src, numpy.ndarray):
  113                 dst.set(src)
  114             else:
  115                 cuda.copy(src, out=dst)
  116 
  117     @property
  118     def parameters(self):
  119         """Tuple of parameter arrays of all registered functions.
  120 
  121         The order of parameters is consistent with :meth:`gradients` property.
  122 
  123         """
  124         return sum((func.parameters for _, func in self._get_sorted_funcs()),
  125                    ())
  126 
  127     @parameters.setter
  128     def parameters(self, params):
  129         param_iter = iter(params)
  130         for _, func in self._get_sorted_funcs():
  131             func.parameters = param_iter
  132 
  133     @property
  134     def gradients(self):
  135         """Tuple of gradient arrays of all registered functions.
  136 
  137         The order of gradients is consistent with :meth:`parameters` property.
  138 
  139         """
  140         return sum((func.gradients for _, func in self._get_sorted_funcs()),
  141                    ())
  142 
  143     @gradients.setter
  144     def gradients(self, grads):
  145         grad_iter = iter(grads)
  146         for _, func in self._get_sorted_funcs():
  147             func.gradients = grad_iter
  148 
  149     def _get_sorted_funcs(self):
  150         return sorted(
  151             [func_tuple for func_tuple in six.iteritems(self.__dict__)
  152              if isinstance(func_tuple[1], (function.Function, FunctionSet))])
  153
  ```
  --- 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_set.py b/chainer/function_set.py
  --- a/chainer/function_set.py
  +++ b/chainer/function_set.py
  @@ -126,9 +126,16 @@
   
       @parameters.setter
       def parameters(self, params):
  -        param_iter = iter(params)
  -        for _, func in self._get_sorted_funcs():
  -            func.parameters = param_iter
  +        funcs = self._get_sorted_funcs()
  +
  +        expected_params_size = sum(len(func.parameters) for _, func in funcs)
  +        assert expected_params_size == len(params)
  +
  +        idx = 0
  +        for _, func in funcs:
  +            param_size = len(func.parameters)
  +            func.parameters = params[idx: idx + param_size]
  +            idx += param_size
   
       @property
       def gradients(self):
  @@ -142,9 +149,16 @@
   
       @gradients.setter
       def gradients(self, grads):
  -        grad_iter = iter(grads)
  -        for _, func in self._get_sorted_funcs():
  -            func.gradients = grad_iter
  +        funcs = self._get_sorted_funcs()
  +
  +        expected_grads_size = sum(len(func.gradients) for _, func in funcs)
  +        assert expected_grads_size == len(grads)
  +
  +        idx = 0
  +        for _, func in funcs:
  +            grad_size = len(func.gradients)
  +            func.gradients = grads[idx: idx + grad_size]
  +            idx += grad_size
   
       def _get_sorted_funcs(self):
           return sorted(
  
verification_info: {"golden_diff": "diff --git a/chainer/function_set.py b/chainer/function_set.py\n--- a/chainer/function_set.py\n+++ b/chainer/function_set.py\n@@ -126,9 +126,16 @@\n \n     @parameters.setter\n     def parameters(self, params):\n-        param_iter = iter(params)\n-        for _, func in self._get_sorted_funcs():\n-            func.parameters = param_iter\n+        funcs = self._get_sorted_funcs()\n+\n+        expected_params_size = sum(len(func.parameters) for _, func in funcs)\n+        assert expected_params_size == len(params)\n+\n+        idx = 0\n+        for _, func in funcs:\n+            param_size = len(func.parameters)\n+            func.parameters = params[idx: idx + param_size]\n+            idx += param_size\n \n     @property\n     def gradients(self):\n@@ -142,9 +149,16 @@\n \n     @gradients.setter\n     def gradients(self, grads):\n-        grad_iter = iter(grads)\n-        for _, func in self._get_sorted_funcs():\n-            func.gradients = grad_iter\n+        funcs = self._get_sorted_funcs()\n+\n+        expected_grads_size = sum(len(func.gradients) for _, func in funcs)\n+        assert expected_grads_size == len(grads)\n+\n+        idx = 0\n+        for _, func in funcs:\n+            grad_size = len(func.gradients)\n+            func.gradients = grads[idx: idx + grad_size]\n+            idx += grad_size\n \n     def _get_sorted_funcs(self):\n         return sorted(\n", "issue": "The behavior of Function#parameters= is inconsistent with FunctionSet#parameters=\nAt aaf2aae14ad683734964590e6ceee9d775d9d7c5, an assertion was introduced into `Function#parameters=`, which requires the argument `values` to be a list-like object (implementing `__len__` method).\nHowever, this assertion seems to be inconsistent with the behavior of `FunctionSet#parameters=`.\n`FunctionSet#parameters=` passes the iterator of its argument `params` to its child functions through `Function#parameters=`, and it causes a TypeError exception.\n\n``` python\nfrom chainer import FunctionSet\nimport chainer.functions as F\nfunc_set = FunctionSet(f=F.Linear(10,10))\nparams = func_set.parameters\nfunc_set.parameters = params # TypeError: object of type 'tuple_iterator' has no len()\n```\n\n", "before_files": [{"content": "import numpy\nimport six\nimport warnings\n\nfrom chainer import cuda\nfrom chainer import function\n\n\nclass FunctionSet(object):\n\n    \"\"\"Set of objects with ``parameters`` and ``gradients`` properties.\n\n    :class:`FunctionSet` is useful to collect parameters and gradients of\n    multiple parameterized :class:`Function` objects. :class:`FunctionSet`\n    itself also implements :attr:`~FunctionSet.parameters` and\n    :attr:`~FunctionSet.gradients`, so it can be nested in another\n    :class:`FunctionSet` object.\n\n    Function registration is done by just adding an attribute to\n    :class:`FunctionSet` object.\n\n    \"\"\"\n\n    def __init__(self, **functions):\n        \"\"\"Initializes the function set by given functions.\n\n        Args:\n            **functions: ``dict`` of ``str`` key and :class:`Function` values.\n                The key-value pairs are just set to the :class:`FunctionSet`\n                object as attributes.\n\n        \"\"\"\n        for name, func in six.iteritems(functions):\n            setattr(self, name, func)\n\n    def collect_parameters(self):\n        \"\"\"Returns a tuple of parameters and gradients.\n\n        Returns:\n            Tuple (pair) of two tuples. The first element is a tuple of\n            parameter arrays, and the second is a tuple of gradient arrays.\n\n        \"\"\"\n\n        msg = (\"'collect_parameters' is deprecated. \"\n               \"You can pass FunctionSet itself to 'optimizer.setup'\")\n        warnings.warn(msg, FutureWarning)\n        return self\n\n    def __getitem__(self, key):\n        \"\"\"Returns the :class:`Function` objects by name.\n\n        Args:\n            key (str): Name of the function.\n\n        Returns:\n            ~chainer.Function: Function object.\n\n        .. admonition:: Example\n\n           >>> model = FunctionSet(l1=F.Linear(10, 10), l2=F.Linear(10, 10))\n           >>> l1 = model['l1']\n        \"\"\"\n\n        return getattr(self, key)\n\n    def to_gpu(self, device=None):\n        \"\"\"Migrates all parameters and gradients onto GPU.\n\n        This method calls ``to_gpu`` method of each registered object.\n\n        Args:\n            device (int or :class:`cupy.cuda.Device` or ``None``): Device\n                ID of GPU. If ``None`` is given, it uses the current device.\n\n        Returns:\n            self\n\n        \"\"\"\n        for func in six.itervalues(self.__dict__):\n            if isinstance(func, (function.Function, FunctionSet)):\n                func.to_gpu(device=device)\n        return self\n\n    def to_cpu(self):\n        \"\"\"Migrates all parameters and gradients onto CPU.\n\n        This method calls ``to_cpu`` method of each registered object.\n\n        Returns:\n            self\n\n        \"\"\"\n        for func in six.itervalues(self.__dict__):\n            if isinstance(func, (function.Function, FunctionSet)):\n                func.to_cpu()\n        return self\n\n    def copy_parameters_from(self, params):\n        \"\"\"Copies parameters from another source without reallocation.\n\n        Args:\n            params (Iterable): Iterable of parameter arrays.\n\n        \"\"\"\n        for dst, src in zip(self.parameters, params):\n            if isinstance(dst, numpy.ndarray):\n                if isinstance(src, numpy.ndarray):\n                    numpy.copyto(dst, src)\n                else:\n                    dst[:] = src.get()\n            elif isinstance(src, numpy.ndarray):\n                dst.set(src)\n            else:\n                cuda.copy(src, out=dst)\n\n    @property\n    def parameters(self):\n        \"\"\"Tuple of parameter arrays of all registered functions.\n\n        The order of parameters is consistent with :meth:`gradients` property.\n\n        \"\"\"\n        return sum((func.parameters for _, func in self._get_sorted_funcs()),\n                   ())\n\n    @parameters.setter\n    def parameters(self, params):\n        param_iter = iter(params)\n        for _, func in self._get_sorted_funcs():\n            func.parameters = param_iter\n\n    @property\n    def gradients(self):\n        \"\"\"Tuple of gradient arrays of all registered functions.\n\n        The order of gradients is consistent with :meth:`parameters` property.\n\n        \"\"\"\n        return sum((func.gradients for _, func in self._get_sorted_funcs()),\n                   ())\n\n    @gradients.setter\n    def gradients(self, grads):\n        grad_iter = iter(grads)\n        for _, func in self._get_sorted_funcs():\n            func.gradients = grad_iter\n\n    def _get_sorted_funcs(self):\n        return sorted(\n            [func_tuple for func_tuple in six.iteritems(self.__dict__)\n             if isinstance(func_tuple[1], (function.Function, FunctionSet))])\n", "path": "chainer/function_set.py"}], "after_files": [{"content": "import numpy\nimport six\nimport warnings\n\nfrom chainer import cuda\nfrom chainer import function\n\n\nclass FunctionSet(object):\n\n    \"\"\"Set of objects with ``parameters`` and ``gradients`` properties.\n\n    :class:`FunctionSet` is useful to collect parameters and gradients of\n    multiple parameterized :class:`Function` objects. :class:`FunctionSet`\n    itself also implements :attr:`~FunctionSet.parameters` and\n    :attr:`~FunctionSet.gradients`, so it can be nested in another\n    :class:`FunctionSet` object.\n\n    Function registration is done by just adding an attribute to\n    :class:`FunctionSet` object.\n\n    \"\"\"\n\n    def __init__(self, **functions):\n        \"\"\"Initializes the function set by given functions.\n\n        Args:\n            **functions: ``dict`` of ``str`` key and :class:`Function` values.\n                The key-value pairs are just set to the :class:`FunctionSet`\n                object as attributes.\n\n        \"\"\"\n        for name, func in six.iteritems(functions):\n            setattr(self, name, func)\n\n    def collect_parameters(self):\n        \"\"\"Returns a tuple of parameters and gradients.\n\n        Returns:\n            Tuple (pair) of two tuples. The first element is a tuple of\n            parameter arrays, and the second is a tuple of gradient arrays.\n\n        \"\"\"\n\n        msg = (\"'collect_parameters' is deprecated. \"\n               \"You can pass FunctionSet itself to 'optimizer.setup'\")\n        warnings.warn(msg, FutureWarning)\n        return self\n\n    def __getitem__(self, key):\n        \"\"\"Returns the :class:`Function` objects by name.\n\n        Args:\n            key (str): Name of the function.\n\n        Returns:\n            ~chainer.Function: Function object.\n\n        .. admonition:: Example\n\n           >>> model = FunctionSet(l1=F.Linear(10, 10), l2=F.Linear(10, 10))\n           >>> l1 = model['l1']\n        \"\"\"\n\n        return getattr(self, key)\n\n    def to_gpu(self, device=None):\n        \"\"\"Migrates all parameters and gradients onto GPU.\n\n        This method calls ``to_gpu`` method of each registered object.\n\n        Args:\n            device (int or :class:`cupy.cuda.Device` or ``None``): Device\n                ID of GPU. If ``None`` is given, it uses the current device.\n\n        Returns:\n            self\n\n        \"\"\"\n        for func in six.itervalues(self.__dict__):\n            if isinstance(func, (function.Function, FunctionSet)):\n                func.to_gpu(device=device)\n        return self\n\n    def to_cpu(self):\n        \"\"\"Migrates all parameters and gradients onto CPU.\n\n        This method calls ``to_cpu`` method of each registered object.\n\n        Returns:\n            self\n\n        \"\"\"\n        for func in six.itervalues(self.__dict__):\n            if isinstance(func, (function.Function, FunctionSet)):\n                func.to_cpu()\n        return self\n\n    def copy_parameters_from(self, params):\n        \"\"\"Copies parameters from another source without reallocation.\n\n        Args:\n            params (Iterable): Iterable of parameter arrays.\n\n        \"\"\"\n        for dst, src in zip(self.parameters, params):\n            if isinstance(dst, numpy.ndarray):\n                if isinstance(src, numpy.ndarray):\n                    numpy.copyto(dst, src)\n                else:\n                    dst[:] = src.get()\n            elif isinstance(src, numpy.ndarray):\n                dst.set(src)\n            else:\n                cuda.copy(src, out=dst)\n\n    @property\n    def parameters(self):\n        \"\"\"Tuple of parameter arrays of all registered functions.\n\n        The order of parameters is consistent with :meth:`gradients` property.\n\n        \"\"\"\n        return sum((func.parameters for _, func in self._get_sorted_funcs()),\n                   ())\n\n    @parameters.setter\n    def parameters(self, params):\n        funcs = self._get_sorted_funcs()\n\n        expected_params_size = sum(len(func.parameters) for _, func in funcs)\n        assert expected_params_size == len(params)\n\n        idx = 0\n        for _, func in funcs:\n            param_size = len(func.parameters)\n            func.parameters = params[idx: idx + param_size]\n            idx += param_size\n\n    @property\n    def gradients(self):\n        \"\"\"Tuple of gradient arrays of all registered functions.\n\n        The order of gradients is consistent with :meth:`parameters` property.\n\n        \"\"\"\n        return sum((func.gradients for _, func in self._get_sorted_funcs()),\n                   ())\n\n    @gradients.setter\n    def gradients(self, grads):\n        funcs = self._get_sorted_funcs()\n\n        expected_grads_size = sum(len(func.gradients) for _, func in funcs)\n        assert expected_grads_size == len(grads)\n\n        idx = 0\n        for _, func in funcs:\n            grad_size = len(func.gradients)\n            func.gradients = grads[idx: idx + grad_size]\n            idx += grad_size\n\n    def _get_sorted_funcs(self):\n        return sorted(\n            [func_tuple for func_tuple in six.iteritems(self.__dict__)\n             if isinstance(func_tuple[1], (function.Function, FunctionSet))])\n", "path": "chainer/function_set.py"}]}