prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Box bound precision warning
  I get this warning a lot when using Box environments:
  
  ```
  .../gym/logger.py:30: UserWarning: WARN: Box bound precision lowered by casting to float32
  ```
  This is particularly annoying, especially because the [default dtype for Box is](https://github.com/openai/gym/blob/master/gym/spaces/box.py#L24) `np.float(32)`
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `gym/spaces/box.py`
  Content:
  ```
  1 import numpy as np
  2 
  3 from .space import Space
  4 from gym import logger
  5 
  6 
  7 class Box(Space):
  8     """
  9     A (possibly unbounded) box in R^n. Specifically, a Box represents the
  10     Cartesian product of n closed intervals. Each interval has the form of one
  11     of [a, b], (-oo, b], [a, oo), or (-oo, oo).
  12     
  13     There are two common use cases:
  14     
  15     * Identical bound for each dimension::
  16         >>> Box(low=-1.0, high=2.0, shape=(3, 4), dtype=np.float32)
  17         Box(3, 4)
  18         
  19     * Independent bound for each dimension::
  20         >>> Box(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype=np.float32)
  21         Box(2,)
  22 
  23     """
  24     def __init__(self, low, high, shape=None, dtype=np.float32):
  25         assert dtype is not None, 'dtype must be explicitly provided. '
  26         self.dtype = np.dtype(dtype)
  27 
  28         if shape is None:
  29             assert low.shape == high.shape, 'box dimension mismatch. '
  30             self.shape = low.shape
  31             self.low = low
  32             self.high = high
  33         else:
  34             assert np.isscalar(low) and np.isscalar(high), 'box requires scalar bounds. '
  35             self.shape = tuple(shape)
  36             self.low = np.full(self.shape, low)
  37             self.high = np.full(self.shape, high)
  38 
  39         def _get_precision(dtype):
  40             if np.issubdtype(dtype, np.floating):
  41                 return np.finfo(dtype).precision
  42             else:
  43                 return np.inf
  44         low_precision = _get_precision(self.low.dtype)
  45         high_precision = _get_precision(self.high.dtype)
  46         dtype_precision = _get_precision(self.dtype)
  47         if min(low_precision, high_precision) > dtype_precision:
  48             logger.warn("Box bound precision lowered by casting to {}".format(self.dtype))
  49         self.low = self.low.astype(self.dtype)
  50         self.high = self.high.astype(self.dtype)
  51 
  52         # Boolean arrays which indicate the interval type for each coordinate
  53         self.bounded_below = -np.inf < self.low
  54         self.bounded_above = np.inf > self.high
  55 
  56         super(Box, self).__init__(self.shape, self.dtype)
  57 
  58     def is_bounded(self, manner="both"):
  59         below = np.all(self.bounded_below)
  60         above = np.all(self.bounded_above)
  61         if manner == "both":
  62             return below and above
  63         elif manner == "below":
  64             return below
  65         elif manner == "above":
  66             return above
  67         else:
  68             raise ValueError("manner is not in {'below', 'above', 'both'}")
  69 
  70     def sample(self):
  71         """
  72         Generates a single random sample inside of the Box. 
  73 
  74         In creating a sample of the box, each coordinate is sampled according to
  75         the form of the interval:
  76         
  77         * [a, b] : uniform distribution 
  78         * [a, oo) : shifted exponential distribution
  79         * (-oo, b] : shifted negative exponential distribution
  80         * (-oo, oo) : normal distribution
  81         """
  82         high = self.high if self.dtype.kind == 'f' \
  83                 else self.high.astype('int64') + 1
  84         sample = np.empty(self.shape)
  85 
  86         # Masking arrays which classify the coordinates according to interval
  87         # type
  88         unbounded   = ~self.bounded_below & ~self.bounded_above
  89         upp_bounded = ~self.bounded_below &  self.bounded_above
  90         low_bounded =  self.bounded_below & ~self.bounded_above
  91         bounded     =  self.bounded_below &  self.bounded_above
  92         
  93 
  94         # Vectorized sampling by interval type
  95         sample[unbounded] = self.np_random.normal(
  96                 size=unbounded[unbounded].shape)
  97 
  98         sample[low_bounded] = self.np_random.exponential(
  99             size=low_bounded[low_bounded].shape) + self.low[low_bounded]
  100         
  101         sample[upp_bounded] = -self.np_random.exponential(
  102             size=upp_bounded[upp_bounded].shape) + self.high[upp_bounded]
  103         
  104         sample[bounded] = self.np_random.uniform(low=self.low[bounded], 
  105                                             high=high[bounded],
  106                                             size=bounded[bounded].shape)
  107         if self.dtype.kind == 'i':
  108             sample = np.floor(sample)
  109 
  110         return sample.astype(self.dtype)
  111         
  112     def contains(self, x):
  113         if isinstance(x, list):
  114             x = np.array(x)  # Promote list to array for contains check
  115         return x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)
  116 
  117     def to_jsonable(self, sample_n):
  118         return np.array(sample_n).tolist()
  119 
  120     def from_jsonable(self, sample_n):
  121         return [np.asarray(sample) for sample in sample_n]
  122 
  123     def __repr__(self):
  124         return "Box" + str(self.shape)
  125 
  126     def __eq__(self, other):
  127         return isinstance(other, Box) and (self.shape == other.shape) and np.allclose(self.low, other.low) and np.allclose(self.high, other.high)
  128
  ```
  --- 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/gym/spaces/box.py b/gym/spaces/box.py
  --- a/gym/spaces/box.py
  +++ b/gym/spaces/box.py
  @@ -9,13 +9,13 @@
       A (possibly unbounded) box in R^n. Specifically, a Box represents the
       Cartesian product of n closed intervals. Each interval has the form of one
       of [a, b], (-oo, b], [a, oo), or (-oo, oo).
  -    
  +
       There are two common use cases:
  -    
  +
       * Identical bound for each dimension::
           >>> Box(low=-1.0, high=2.0, shape=(3, 4), dtype=np.float32)
           Box(3, 4)
  -        
  +
       * Independent bound for each dimension::
           >>> Box(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype=np.float32)
           Box(2,)
  @@ -33,8 +33,8 @@
           else:
               assert np.isscalar(low) and np.isscalar(high), 'box requires scalar bounds. '
               self.shape = tuple(shape)
  -            self.low = np.full(self.shape, low)
  -            self.high = np.full(self.shape, high)
  +            self.low = np.full(self.shape, low, dtype=dtype)
  +            self.high = np.full(self.shape, high, dtype=dtype)
   
           def _get_precision(dtype):
               if np.issubdtype(dtype, np.floating):
  @@ -69,12 +69,12 @@
   
       def sample(self):
           """
  -        Generates a single random sample inside of the Box. 
  +        Generates a single random sample inside of the Box.
   
           In creating a sample of the box, each coordinate is sampled according to
           the form of the interval:
  -        
  -        * [a, b] : uniform distribution 
  +
  +        * [a, b] : uniform distribution
           * [a, oo) : shifted exponential distribution
           * (-oo, b] : shifted negative exponential distribution
           * (-oo, oo) : normal distribution
  @@ -89,7 +89,7 @@
           upp_bounded = ~self.bounded_below &  self.bounded_above
           low_bounded =  self.bounded_below & ~self.bounded_above
           bounded     =  self.bounded_below &  self.bounded_above
  -        
  +
   
           # Vectorized sampling by interval type
           sample[unbounded] = self.np_random.normal(
  @@ -97,18 +97,18 @@
   
           sample[low_bounded] = self.np_random.exponential(
               size=low_bounded[low_bounded].shape) + self.low[low_bounded]
  -        
  +
           sample[upp_bounded] = -self.np_random.exponential(
               size=upp_bounded[upp_bounded].shape) + self.high[upp_bounded]
  -        
  -        sample[bounded] = self.np_random.uniform(low=self.low[bounded], 
  +
  +        sample[bounded] = self.np_random.uniform(low=self.low[bounded],
                                               high=high[bounded],
                                               size=bounded[bounded].shape)
           if self.dtype.kind == 'i':
               sample = np.floor(sample)
   
           return sample.astype(self.dtype)
  -        
  +
       def contains(self, x):
           if isinstance(x, list):
               x = np.array(x)  # Promote list to array for contains check
  
verification_info: {"golden_diff": "diff --git a/gym/spaces/box.py b/gym/spaces/box.py\n--- a/gym/spaces/box.py\n+++ b/gym/spaces/box.py\n@@ -9,13 +9,13 @@\n     A (possibly unbounded) box in R^n. Specifically, a Box represents the\n     Cartesian product of n closed intervals. Each interval has the form of one\n     of [a, b], (-oo, b], [a, oo), or (-oo, oo).\n-    \n+\n     There are two common use cases:\n-    \n+\n     * Identical bound for each dimension::\n         >>> Box(low=-1.0, high=2.0, shape=(3, 4), dtype=np.float32)\n         Box(3, 4)\n-        \n+\n     * Independent bound for each dimension::\n         >>> Box(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype=np.float32)\n         Box(2,)\n@@ -33,8 +33,8 @@\n         else:\n             assert np.isscalar(low) and np.isscalar(high), 'box requires scalar bounds. '\n             self.shape = tuple(shape)\n-            self.low = np.full(self.shape, low)\n-            self.high = np.full(self.shape, high)\n+            self.low = np.full(self.shape, low, dtype=dtype)\n+            self.high = np.full(self.shape, high, dtype=dtype)\n \n         def _get_precision(dtype):\n             if np.issubdtype(dtype, np.floating):\n@@ -69,12 +69,12 @@\n \n     def sample(self):\n         \"\"\"\n-        Generates a single random sample inside of the Box. \n+        Generates a single random sample inside of the Box.\n \n         In creating a sample of the box, each coordinate is sampled according to\n         the form of the interval:\n-        \n-        * [a, b] : uniform distribution \n+\n+        * [a, b] : uniform distribution\n         * [a, oo) : shifted exponential distribution\n         * (-oo, b] : shifted negative exponential distribution\n         * (-oo, oo) : normal distribution\n@@ -89,7 +89,7 @@\n         upp_bounded = ~self.bounded_below &  self.bounded_above\n         low_bounded =  self.bounded_below & ~self.bounded_above\n         bounded     =  self.bounded_below &  self.bounded_above\n-        \n+\n \n         # Vectorized sampling by interval type\n         sample[unbounded] = self.np_random.normal(\n@@ -97,18 +97,18 @@\n \n         sample[low_bounded] = self.np_random.exponential(\n             size=low_bounded[low_bounded].shape) + self.low[low_bounded]\n-        \n+\n         sample[upp_bounded] = -self.np_random.exponential(\n             size=upp_bounded[upp_bounded].shape) + self.high[upp_bounded]\n-        \n-        sample[bounded] = self.np_random.uniform(low=self.low[bounded], \n+\n+        sample[bounded] = self.np_random.uniform(low=self.low[bounded],\n                                             high=high[bounded],\n                                             size=bounded[bounded].shape)\n         if self.dtype.kind == 'i':\n             sample = np.floor(sample)\n \n         return sample.astype(self.dtype)\n-        \n+\n     def contains(self, x):\n         if isinstance(x, list):\n             x = np.array(x)  # Promote list to array for contains check\n", "issue": "Box bound precision warning\nI get this warning a lot when using Box environments:\r\n\r\n```\r\n.../gym/logger.py:30: UserWarning: WARN: Box bound precision lowered by casting to float32\r\n```\r\nThis is particularly annoying, especially because the [default dtype for Box is](https://github.com/openai/gym/blob/master/gym/spaces/box.py#L24) `np.float(32)`\n", "before_files": [{"content": "import numpy as np\n\nfrom .space import Space\nfrom gym import logger\n\n\nclass Box(Space):\n    \"\"\"\n    A (possibly unbounded) box in R^n. Specifically, a Box represents the\n    Cartesian product of n closed intervals. Each interval has the form of one\n    of [a, b], (-oo, b], [a, oo), or (-oo, oo).\n    \n    There are two common use cases:\n    \n    * Identical bound for each dimension::\n        >>> Box(low=-1.0, high=2.0, shape=(3, 4), dtype=np.float32)\n        Box(3, 4)\n        \n    * Independent bound for each dimension::\n        >>> Box(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype=np.float32)\n        Box(2,)\n\n    \"\"\"\n    def __init__(self, low, high, shape=None, dtype=np.float32):\n        assert dtype is not None, 'dtype must be explicitly provided. '\n        self.dtype = np.dtype(dtype)\n\n        if shape is None:\n            assert low.shape == high.shape, 'box dimension mismatch. '\n            self.shape = low.shape\n            self.low = low\n            self.high = high\n        else:\n            assert np.isscalar(low) and np.isscalar(high), 'box requires scalar bounds. '\n            self.shape = tuple(shape)\n            self.low = np.full(self.shape, low)\n            self.high = np.full(self.shape, high)\n\n        def _get_precision(dtype):\n            if np.issubdtype(dtype, np.floating):\n                return np.finfo(dtype).precision\n            else:\n                return np.inf\n        low_precision = _get_precision(self.low.dtype)\n        high_precision = _get_precision(self.high.dtype)\n        dtype_precision = _get_precision(self.dtype)\n        if min(low_precision, high_precision) > dtype_precision:\n            logger.warn(\"Box bound precision lowered by casting to {}\".format(self.dtype))\n        self.low = self.low.astype(self.dtype)\n        self.high = self.high.astype(self.dtype)\n\n        # Boolean arrays which indicate the interval type for each coordinate\n        self.bounded_below = -np.inf < self.low\n        self.bounded_above = np.inf > self.high\n\n        super(Box, self).__init__(self.shape, self.dtype)\n\n    def is_bounded(self, manner=\"both\"):\n        below = np.all(self.bounded_below)\n        above = np.all(self.bounded_above)\n        if manner == \"both\":\n            return below and above\n        elif manner == \"below\":\n            return below\n        elif manner == \"above\":\n            return above\n        else:\n            raise ValueError(\"manner is not in {'below', 'above', 'both'}\")\n\n    def sample(self):\n        \"\"\"\n        Generates a single random sample inside of the Box. \n\n        In creating a sample of the box, each coordinate is sampled according to\n        the form of the interval:\n        \n        * [a, b] : uniform distribution \n        * [a, oo) : shifted exponential distribution\n        * (-oo, b] : shifted negative exponential distribution\n        * (-oo, oo) : normal distribution\n        \"\"\"\n        high = self.high if self.dtype.kind == 'f' \\\n                else self.high.astype('int64') + 1\n        sample = np.empty(self.shape)\n\n        # Masking arrays which classify the coordinates according to interval\n        # type\n        unbounded   = ~self.bounded_below & ~self.bounded_above\n        upp_bounded = ~self.bounded_below &  self.bounded_above\n        low_bounded =  self.bounded_below & ~self.bounded_above\n        bounded     =  self.bounded_below &  self.bounded_above\n        \n\n        # Vectorized sampling by interval type\n        sample[unbounded] = self.np_random.normal(\n                size=unbounded[unbounded].shape)\n\n        sample[low_bounded] = self.np_random.exponential(\n            size=low_bounded[low_bounded].shape) + self.low[low_bounded]\n        \n        sample[upp_bounded] = -self.np_random.exponential(\n            size=upp_bounded[upp_bounded].shape) + self.high[upp_bounded]\n        \n        sample[bounded] = self.np_random.uniform(low=self.low[bounded], \n                                            high=high[bounded],\n                                            size=bounded[bounded].shape)\n        if self.dtype.kind == 'i':\n            sample = np.floor(sample)\n\n        return sample.astype(self.dtype)\n        \n    def contains(self, x):\n        if isinstance(x, list):\n            x = np.array(x)  # Promote list to array for contains check\n        return x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)\n\n    def to_jsonable(self, sample_n):\n        return np.array(sample_n).tolist()\n\n    def from_jsonable(self, sample_n):\n        return [np.asarray(sample) for sample in sample_n]\n\n    def __repr__(self):\n        return \"Box\" + str(self.shape)\n\n    def __eq__(self, other):\n        return isinstance(other, Box) and (self.shape == other.shape) and np.allclose(self.low, other.low) and np.allclose(self.high, other.high)\n", "path": "gym/spaces/box.py"}], "after_files": [{"content": "import numpy as np\n\nfrom .space import Space\nfrom gym import logger\n\n\nclass Box(Space):\n    \"\"\"\n    A (possibly unbounded) box in R^n. Specifically, a Box represents the\n    Cartesian product of n closed intervals. Each interval has the form of one\n    of [a, b], (-oo, b], [a, oo), or (-oo, oo).\n\n    There are two common use cases:\n\n    * Identical bound for each dimension::\n        >>> Box(low=-1.0, high=2.0, shape=(3, 4), dtype=np.float32)\n        Box(3, 4)\n\n    * Independent bound for each dimension::\n        >>> Box(low=np.array([-1.0, -2.0]), high=np.array([2.0, 4.0]), dtype=np.float32)\n        Box(2,)\n\n    \"\"\"\n    def __init__(self, low, high, shape=None, dtype=np.float32):\n        assert dtype is not None, 'dtype must be explicitly provided. '\n        self.dtype = np.dtype(dtype)\n\n        if shape is None:\n            assert low.shape == high.shape, 'box dimension mismatch. '\n            self.shape = low.shape\n            self.low = low\n            self.high = high\n        else:\n            assert np.isscalar(low) and np.isscalar(high), 'box requires scalar bounds. '\n            self.shape = tuple(shape)\n            self.low = np.full(self.shape, low, dtype=dtype)\n            self.high = np.full(self.shape, high, dtype=dtype)\n\n        def _get_precision(dtype):\n            if np.issubdtype(dtype, np.floating):\n                return np.finfo(dtype).precision\n            else:\n                return np.inf\n        low_precision = _get_precision(self.low.dtype)\n        high_precision = _get_precision(self.high.dtype)\n        dtype_precision = _get_precision(self.dtype)\n        if min(low_precision, high_precision) > dtype_precision:\n            logger.warn(\"Box bound precision lowered by casting to {}\".format(self.dtype))\n        self.low = self.low.astype(self.dtype)\n        self.high = self.high.astype(self.dtype)\n\n        # Boolean arrays which indicate the interval type for each coordinate\n        self.bounded_below = -np.inf < self.low\n        self.bounded_above = np.inf > self.high\n\n        super(Box, self).__init__(self.shape, self.dtype)\n\n    def is_bounded(self, manner=\"both\"):\n        below = np.all(self.bounded_below)\n        above = np.all(self.bounded_above)\n        if manner == \"both\":\n            return below and above\n        elif manner == \"below\":\n            return below\n        elif manner == \"above\":\n            return above\n        else:\n            raise ValueError(\"manner is not in {'below', 'above', 'both'}\")\n\n    def sample(self):\n        \"\"\"\n        Generates a single random sample inside of the Box.\n\n        In creating a sample of the box, each coordinate is sampled according to\n        the form of the interval:\n\n        * [a, b] : uniform distribution\n        * [a, oo) : shifted exponential distribution\n        * (-oo, b] : shifted negative exponential distribution\n        * (-oo, oo) : normal distribution\n        \"\"\"\n        high = self.high if self.dtype.kind == 'f' \\\n                else self.high.astype('int64') + 1\n        sample = np.empty(self.shape)\n\n        # Masking arrays which classify the coordinates according to interval\n        # type\n        unbounded   = ~self.bounded_below & ~self.bounded_above\n        upp_bounded = ~self.bounded_below &  self.bounded_above\n        low_bounded =  self.bounded_below & ~self.bounded_above\n        bounded     =  self.bounded_below &  self.bounded_above\n\n\n        # Vectorized sampling by interval type\n        sample[unbounded] = self.np_random.normal(\n                size=unbounded[unbounded].shape)\n\n        sample[low_bounded] = self.np_random.exponential(\n            size=low_bounded[low_bounded].shape) + self.low[low_bounded]\n\n        sample[upp_bounded] = -self.np_random.exponential(\n            size=upp_bounded[upp_bounded].shape) + self.high[upp_bounded]\n\n        sample[bounded] = self.np_random.uniform(low=self.low[bounded],\n                                            high=high[bounded],\n                                            size=bounded[bounded].shape)\n        if self.dtype.kind == 'i':\n            sample = np.floor(sample)\n\n        return sample.astype(self.dtype)\n\n    def contains(self, x):\n        if isinstance(x, list):\n            x = np.array(x)  # Promote list to array for contains check\n        return x.shape == self.shape and np.all(x >= self.low) and np.all(x <= self.high)\n\n    def to_jsonable(self, sample_n):\n        return np.array(sample_n).tolist()\n\n    def from_jsonable(self, sample_n):\n        return [np.asarray(sample) for sample in sample_n]\n\n    def __repr__(self):\n        return \"Box\" + str(self.shape)\n\n    def __eq__(self, other):\n        return isinstance(other, Box) and (self.shape == other.shape) and np.allclose(self.low, other.low) and np.allclose(self.high, other.high)\n", "path": "gym/spaces/box.py"}]}