prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [Metrics] add indexing synthetic sugar
  Idea is to improve the current implementation of `Metric` and to be able to do the following:
  ```
  # A custom class ConfusionMatrix
  cm = ConfusionMatrix(num_classes=3, output_transform=output_gt_predicted_classes_bg)
  
  # Instead of below lines
  # from ignite.metrics import MetricsLambda
  # IoU = MetricsLambda(lambda res: res[1:], (cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) - cm.diag())))
  # We could have: 
  IoU = (cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) - cm.diag()))[1:]
  mIoU = IoU.mean()
  ```
  
  cc @zasdfgbnm 
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `ignite/metrics/metric.py`
  Content:
  ```
  1 from abc import ABCMeta, abstractmethod
  2 from ignite._six import with_metaclass
  3 from ignite.engine import Events
  4 import torch
  5 
  6 
  7 class Metric(with_metaclass(ABCMeta, object)):
  8     """
  9     Base class for all Metrics.
  10 
  11     Args:
  12         output_transform (callable, optional): a callable that is used to transform the
  13             :class:`~ignite.engine.Engine`'s `process_function`'s output into the
  14             form expected by the metric. This can be useful if, for example, you have a multi-output model and
  15             you want to compute the metric with respect to one of the outputs.
  16 
  17     """
  18 
  19     def __init__(self, output_transform=lambda x: x):
  20         self._output_transform = output_transform
  21         self.reset()
  22 
  23     @abstractmethod
  24     def reset(self):
  25         """
  26         Resets the metric to it's initial state.
  27 
  28         This is called at the start of each epoch.
  29         """
  30         pass
  31 
  32     @abstractmethod
  33     def update(self, output):
  34         """
  35         Updates the metric's state using the passed batch output.
  36 
  37         This is called once for each batch.
  38 
  39         Args:
  40             output: the is the output from the engine's process function.
  41         """
  42         pass
  43 
  44     @abstractmethod
  45     def compute(self):
  46         """
  47         Computes the metric based on it's accumulated state.
  48 
  49         This is called at the end of each epoch.
  50 
  51         Returns:
  52             Any: the actual quantity of interest.
  53 
  54         Raises:
  55             NotComputableError: raised when the metric cannot be computed.
  56         """
  57         pass
  58 
  59     def started(self, engine):
  60         self.reset()
  61 
  62     @torch.no_grad()
  63     def iteration_completed(self, engine):
  64         output = self._output_transform(engine.state.output)
  65         self.update(output)
  66 
  67     def completed(self, engine, name):
  68         result = self.compute()
  69         if torch.is_tensor(result) and len(result.shape) == 0:
  70             result = result.item()
  71         engine.state.metrics[name] = result
  72 
  73     def attach(self, engine, name):
  74         engine.add_event_handler(Events.EPOCH_COMPLETED, self.completed, name)
  75         if not engine.has_event_handler(self.started, Events.EPOCH_STARTED):
  76             engine.add_event_handler(Events.EPOCH_STARTED, self.started)
  77         if not engine.has_event_handler(self.iteration_completed, Events.ITERATION_COMPLETED):
  78             engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed)
  79 
  80     def __add__(self, other):
  81         from ignite.metrics import MetricsLambda
  82         return MetricsLambda(lambda x, y: x + y, self, other)
  83 
  84     def __radd__(self, other):
  85         from ignite.metrics import MetricsLambda
  86         return MetricsLambda(lambda x, y: x + y, other, self)
  87 
  88     def __sub__(self, other):
  89         from ignite.metrics import MetricsLambda
  90         return MetricsLambda(lambda x, y: x - y, self, other)
  91 
  92     def __rsub__(self, other):
  93         from ignite.metrics import MetricsLambda
  94         return MetricsLambda(lambda x, y: x - y, other, self)
  95 
  96     def __mul__(self, other):
  97         from ignite.metrics import MetricsLambda
  98         return MetricsLambda(lambda x, y: x * y, self, other)
  99 
  100     def __rmul__(self, other):
  101         from ignite.metrics import MetricsLambda
  102         return MetricsLambda(lambda x, y: x * y, other, self)
  103 
  104     def __pow__(self, other):
  105         from ignite.metrics import MetricsLambda
  106         return MetricsLambda(lambda x, y: x ** y, self, other)
  107 
  108     def __rpow__(self, other):
  109         from ignite.metrics import MetricsLambda
  110         return MetricsLambda(lambda x, y: x ** y, other, self)
  111 
  112     def __mod__(self, other):
  113         from ignite.metrics import MetricsLambda
  114         return MetricsLambda(lambda x, y: x % y, self, other)
  115 
  116     def __div__(self, other):
  117         from ignite.metrics import MetricsLambda
  118         return MetricsLambda(lambda x, y: x.__div__(y), self, other)
  119 
  120     def __rdiv__(self, other):
  121         from ignite.metrics import MetricsLambda
  122         return MetricsLambda(lambda x, y: x.__div__(y), other, self)
  123 
  124     def __truediv__(self, other):
  125         from ignite.metrics import MetricsLambda
  126         return MetricsLambda(lambda x, y: x.__truediv__(y), self, other)
  127 
  128     def __rtruediv__(self, other):
  129         from ignite.metrics import MetricsLambda
  130         return MetricsLambda(lambda x, y: x.__truediv__(y), other, self)
  131 
  132     def __floordiv__(self, other):
  133         from ignite.metrics import MetricsLambda
  134         return MetricsLambda(lambda x, y: x // y, self, other)
  135 
  136     def __getattr__(self, attr):
  137         from ignite.metrics import MetricsLambda
  138 
  139         def fn(x, *args, **kwargs):
  140             return getattr(x, attr)(*args, **kwargs)
  141 
  142         def wrapper(*args, **kwargs):
  143             return MetricsLambda(fn, self, *args, **kwargs)
  144         return wrapper
  145
  ```
  --- 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/ignite/metrics/metric.py b/ignite/metrics/metric.py
  --- a/ignite/metrics/metric.py
  +++ b/ignite/metrics/metric.py
  @@ -142,3 +142,7 @@
           def wrapper(*args, **kwargs):
               return MetricsLambda(fn, self, *args, **kwargs)
           return wrapper
  +
  +    def __getitem__(self, index):
  +        from ignite.metrics import MetricsLambda
  +        return MetricsLambda(lambda x: x[index], self)
  
verification_info: {"golden_diff": "diff --git a/ignite/metrics/metric.py b/ignite/metrics/metric.py\n--- a/ignite/metrics/metric.py\n+++ b/ignite/metrics/metric.py\n@@ -142,3 +142,7 @@\n         def wrapper(*args, **kwargs):\n             return MetricsLambda(fn, self, *args, **kwargs)\n         return wrapper\n+\n+    def __getitem__(self, index):\n+        from ignite.metrics import MetricsLambda\n+        return MetricsLambda(lambda x: x[index], self)\n", "issue": "[Metrics] add indexing synthetic sugar\nIdea is to improve the current implementation of `Metric` and to be able to do the following:\r\n```\r\n# A custom class ConfusionMatrix\r\ncm = ConfusionMatrix(num_classes=3, output_transform=output_gt_predicted_classes_bg)\r\n\r\n# Instead of below lines\r\n# from ignite.metrics import MetricsLambda\r\n# IoU = MetricsLambda(lambda res: res[1:], (cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) - cm.diag())))\r\n# We could have: \r\nIoU = (cm.diag() / (cm.sum(dim=1) + cm.sum(dim=0) - cm.diag()))[1:]\r\nmIoU = IoU.mean()\r\n```\r\n\r\ncc @zasdfgbnm \n", "before_files": [{"content": "from abc import ABCMeta, abstractmethod\nfrom ignite._six import with_metaclass\nfrom ignite.engine import Events\nimport torch\n\n\nclass Metric(with_metaclass(ABCMeta, object)):\n    \"\"\"\n    Base class for all Metrics.\n\n    Args:\n        output_transform (callable, optional): a callable that is used to transform the\n            :class:`~ignite.engine.Engine`'s `process_function`'s output into the\n            form expected by the metric. This can be useful if, for example, you have a multi-output model and\n            you want to compute the metric with respect to one of the outputs.\n\n    \"\"\"\n\n    def __init__(self, output_transform=lambda x: x):\n        self._output_transform = output_transform\n        self.reset()\n\n    @abstractmethod\n    def reset(self):\n        \"\"\"\n        Resets the metric to it's initial state.\n\n        This is called at the start of each epoch.\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def update(self, output):\n        \"\"\"\n        Updates the metric's state using the passed batch output.\n\n        This is called once for each batch.\n\n        Args:\n            output: the is the output from the engine's process function.\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def compute(self):\n        \"\"\"\n        Computes the metric based on it's accumulated state.\n\n        This is called at the end of each epoch.\n\n        Returns:\n            Any: the actual quantity of interest.\n\n        Raises:\n            NotComputableError: raised when the metric cannot be computed.\n        \"\"\"\n        pass\n\n    def started(self, engine):\n        self.reset()\n\n    @torch.no_grad()\n    def iteration_completed(self, engine):\n        output = self._output_transform(engine.state.output)\n        self.update(output)\n\n    def completed(self, engine, name):\n        result = self.compute()\n        if torch.is_tensor(result) and len(result.shape) == 0:\n            result = result.item()\n        engine.state.metrics[name] = result\n\n    def attach(self, engine, name):\n        engine.add_event_handler(Events.EPOCH_COMPLETED, self.completed, name)\n        if not engine.has_event_handler(self.started, Events.EPOCH_STARTED):\n            engine.add_event_handler(Events.EPOCH_STARTED, self.started)\n        if not engine.has_event_handler(self.iteration_completed, Events.ITERATION_COMPLETED):\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed)\n\n    def __add__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x + y, self, other)\n\n    def __radd__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x + y, other, self)\n\n    def __sub__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x - y, self, other)\n\n    def __rsub__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x - y, other, self)\n\n    def __mul__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x * y, self, other)\n\n    def __rmul__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x * y, other, self)\n\n    def __pow__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x ** y, self, other)\n\n    def __rpow__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x ** y, other, self)\n\n    def __mod__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x % y, self, other)\n\n    def __div__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x.__div__(y), self, other)\n\n    def __rdiv__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x.__div__(y), other, self)\n\n    def __truediv__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x.__truediv__(y), self, other)\n\n    def __rtruediv__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x.__truediv__(y), other, self)\n\n    def __floordiv__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x // y, self, other)\n\n    def __getattr__(self, attr):\n        from ignite.metrics import MetricsLambda\n\n        def fn(x, *args, **kwargs):\n            return getattr(x, attr)(*args, **kwargs)\n\n        def wrapper(*args, **kwargs):\n            return MetricsLambda(fn, self, *args, **kwargs)\n        return wrapper\n", "path": "ignite/metrics/metric.py"}], "after_files": [{"content": "from abc import ABCMeta, abstractmethod\nfrom ignite._six import with_metaclass\nfrom ignite.engine import Events\nimport torch\n\n\nclass Metric(with_metaclass(ABCMeta, object)):\n    \"\"\"\n    Base class for all Metrics.\n\n    Args:\n        output_transform (callable, optional): a callable that is used to transform the\n            :class:`~ignite.engine.Engine`'s `process_function`'s output into the\n            form expected by the metric. This can be useful if, for example, you have a multi-output model and\n            you want to compute the metric with respect to one of the outputs.\n\n    \"\"\"\n\n    def __init__(self, output_transform=lambda x: x):\n        self._output_transform = output_transform\n        self.reset()\n\n    @abstractmethod\n    def reset(self):\n        \"\"\"\n        Resets the metric to it's initial state.\n\n        This is called at the start of each epoch.\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def update(self, output):\n        \"\"\"\n        Updates the metric's state using the passed batch output.\n\n        This is called once for each batch.\n\n        Args:\n            output: the is the output from the engine's process function.\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def compute(self):\n        \"\"\"\n        Computes the metric based on it's accumulated state.\n\n        This is called at the end of each epoch.\n\n        Returns:\n            Any: the actual quantity of interest.\n\n        Raises:\n            NotComputableError: raised when the metric cannot be computed.\n        \"\"\"\n        pass\n\n    def started(self, engine):\n        self.reset()\n\n    @torch.no_grad()\n    def iteration_completed(self, engine):\n        output = self._output_transform(engine.state.output)\n        self.update(output)\n\n    def completed(self, engine, name):\n        result = self.compute()\n        if torch.is_tensor(result) and len(result.shape) == 0:\n            result = result.item()\n        engine.state.metrics[name] = result\n\n    def attach(self, engine, name):\n        engine.add_event_handler(Events.EPOCH_COMPLETED, self.completed, name)\n        if not engine.has_event_handler(self.started, Events.EPOCH_STARTED):\n            engine.add_event_handler(Events.EPOCH_STARTED, self.started)\n        if not engine.has_event_handler(self.iteration_completed, Events.ITERATION_COMPLETED):\n            engine.add_event_handler(Events.ITERATION_COMPLETED, self.iteration_completed)\n\n    def __add__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x + y, self, other)\n\n    def __radd__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x + y, other, self)\n\n    def __sub__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x - y, self, other)\n\n    def __rsub__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x - y, other, self)\n\n    def __mul__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x * y, self, other)\n\n    def __rmul__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x * y, other, self)\n\n    def __pow__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x ** y, self, other)\n\n    def __rpow__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x ** y, other, self)\n\n    def __mod__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x % y, self, other)\n\n    def __div__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x.__div__(y), self, other)\n\n    def __rdiv__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x.__div__(y), other, self)\n\n    def __truediv__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x.__truediv__(y), self, other)\n\n    def __rtruediv__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x.__truediv__(y), other, self)\n\n    def __floordiv__(self, other):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x, y: x // y, self, other)\n\n    def __getattr__(self, attr):\n        from ignite.metrics import MetricsLambda\n\n        def fn(x, *args, **kwargs):\n            return getattr(x, attr)(*args, **kwargs)\n\n        def wrapper(*args, **kwargs):\n            return MetricsLambda(fn, self, *args, **kwargs)\n        return wrapper\n\n    def __getitem__(self, index):\n        from ignite.metrics import MetricsLambda\n        return MetricsLambda(lambda x: x[index], self)\n", "path": "ignite/metrics/metric.py"}]}