prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  why MultiDiscrete  dtype=int8 
  MultiDiscrete  dtype is hardcoded as int8. 
  Is this a bug ? What happens when  we need more than 256 category  
  ```
  space = MultiDiscrete([7, 1024])
  space.sample()
  ```
  
  https://github.com/openai/gym/blob/e944885e3b31a10fb6973093b39ff7682ef3aa3d/gym/spaces/multi_discrete.py#L10
  
  --- 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/multi_discrete.py`
  Content:
  ```
  1 import gym
  2 import numpy as np
  3 from .space import Space
  4 
  5 
  6 class MultiDiscrete(Space):
  7     """
  8     - The multi-discrete action space consists of a series of discrete action spaces with different number of actions in eachs
  9     - It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space
  10     - It is parametrized by passing an array of positive integers specifying number of actions for each discrete action space
  11 
  12     Note: A value of 0 always need to represent the NOOP action.
  13 
  14     e.g. Nintendo Game Controller
  15     - Can be conceptualized as 3 discrete action spaces:
  16 
  17         1) Arrow Keys: Discrete 5  - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4]  - params: min: 0, max: 4
  18         2) Button A:   Discrete 2  - NOOP[0], Pressed[1] - params: min: 0, max: 1
  19         3) Button B:   Discrete 2  - NOOP[0], Pressed[1] - params: min: 0, max: 1
  20 
  21     - Can be initialized as
  22 
  23         MultiDiscrete([ 5, 2, 2 ])
  24 
  25     """
  26     def __init__(self, nvec):
  27     
  28         """
  29         nvec: vector of counts of each categorical variable
  30         """
  31         assert (np.array(nvec) > 0).all(), 'nvec (counts) have to be positive'
  32         self.nvec = np.asarray(nvec, dtype=np.uint32)
  33 
  34         super(MultiDiscrete, self).__init__(self.nvec.shape, np.uint32)
  35         self.np_random = np.random.RandomState()
  36 
  37     def seed(self, seed):
  38         self.np_random.seed(seed)
  39 
  40     def sample(self):
  41         return (self.np_random.random_sample(self.nvec.shape) * self.nvec).astype(self.dtype)
  42 
  43     def contains(self, x):
  44         # if nvec is uint32 and space dtype is uint32, then 0 <= x < self.nvec guarantees that x
  45         # is within correct bounds for space dtype (even though x does not have to be unsigned)
  46         return (0 <= x).all() and (x < self.nvec).all()
  47 
  48     def to_jsonable(self, sample_n):
  49         return [sample.tolist() for sample in sample_n]
  50 
  51     def from_jsonable(self, sample_n):
  52         return np.array(sample_n)
  53 
  54     def __repr__(self):
  55         return "MultiDiscrete({})".format(self.nvec)
  56 
  57     def __eq__(self, other):
  58         return np.all(self.nvec == other.nvec)
  59
  ```
  
  Path: `gym/spaces/space.py`
  Content:
  ```
  1 import numpy as np
  2 
  3 
  4 class Space(object):
  5     """Defines the observation and action spaces, so you can write generic
  6     code that applies to any Env. For example, you can choose a random
  7     action.
  8     """
  9     def __init__(self, shape=None, dtype=None):
  10         import numpy as np # takes about 300-400ms to import, so we load lazily
  11         self.shape = None if shape is None else tuple(shape)
  12         self.dtype = None if dtype is None else np.dtype(dtype)
  13 
  14     def sample(self):
  15         """
  16         Uniformly randomly sample a random element of this space
  17         """
  18         raise NotImplementedError
  19 
  20     def seed(self, seed):
  21         """Set the seed for this space's pseudo-random number generator. """
  22         raise NotImplementedError
  23 
  24     def contains(self, x):
  25         """
  26         Return boolean specifying if x is a valid
  27         member of this space
  28         """
  29         raise NotImplementedError
  30 
  31     def __contains__(self, x):
  32         return self.contains(x)
  33 
  34     def to_jsonable(self, sample_n):
  35         """Convert a batch of samples from this space to a JSONable data type."""
  36         # By default, assume identity is JSONable
  37         return sample_n
  38 
  39     def from_jsonable(self, sample_n):
  40         """Convert a JSONable data type to a batch of samples from this space."""
  41         # By default, assume identity is JSONable
  42         return sample_n
  43
  ```
  --- 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/multi_discrete.py b/gym/spaces/multi_discrete.py
  --- a/gym/spaces/multi_discrete.py
  +++ b/gym/spaces/multi_discrete.py
  @@ -1,4 +1,3 @@
  -import gym
   import numpy as np
   from .space import Space
   
  @@ -29,9 +28,9 @@
           nvec: vector of counts of each categorical variable
           """
           assert (np.array(nvec) > 0).all(), 'nvec (counts) have to be positive'
  -        self.nvec = np.asarray(nvec, dtype=np.uint32)
  +        self.nvec = np.asarray(nvec, dtype=np.int64)
   
  -        super(MultiDiscrete, self).__init__(self.nvec.shape, np.uint32)
  +        super(MultiDiscrete, self).__init__(self.nvec.shape, np.int64)
           self.np_random = np.random.RandomState()
   
       def seed(self, seed):
  diff --git a/gym/spaces/space.py b/gym/spaces/space.py
  --- a/gym/spaces/space.py
  +++ b/gym/spaces/space.py
  @@ -1,6 +1,3 @@
  -import numpy as np
  -
  -
   class Space(object):
       """Defines the observation and action spaces, so you can write generic
       code that applies to any Env. For example, you can choose a random
  
verification_info: {"golden_diff": "diff --git a/gym/spaces/multi_discrete.py b/gym/spaces/multi_discrete.py\n--- a/gym/spaces/multi_discrete.py\n+++ b/gym/spaces/multi_discrete.py\n@@ -1,4 +1,3 @@\n-import gym\n import numpy as np\n from .space import Space\n \n@@ -29,9 +28,9 @@\n         nvec: vector of counts of each categorical variable\n         \"\"\"\n         assert (np.array(nvec) > 0).all(), 'nvec (counts) have to be positive'\n-        self.nvec = np.asarray(nvec, dtype=np.uint32)\n+        self.nvec = np.asarray(nvec, dtype=np.int64)\n \n-        super(MultiDiscrete, self).__init__(self.nvec.shape, np.uint32)\n+        super(MultiDiscrete, self).__init__(self.nvec.shape, np.int64)\n         self.np_random = np.random.RandomState()\n \n     def seed(self, seed):\ndiff --git a/gym/spaces/space.py b/gym/spaces/space.py\n--- a/gym/spaces/space.py\n+++ b/gym/spaces/space.py\n@@ -1,6 +1,3 @@\n-import numpy as np\n-\n-\n class Space(object):\n     \"\"\"Defines the observation and action spaces, so you can write generic\n     code that applies to any Env. For example, you can choose a random\n", "issue": "why MultiDiscrete  dtype=int8 \nMultiDiscrete  dtype is hardcoded as int8. \r\nIs this a bug ? What happens when  we need more than 256 category  \r\n```\r\nspace = MultiDiscrete([7, 1024])\r\nspace.sample()\r\n```\r\n\r\nhttps://github.com/openai/gym/blob/e944885e3b31a10fb6973093b39ff7682ef3aa3d/gym/spaces/multi_discrete.py#L10\n", "before_files": [{"content": "import gym\nimport numpy as np\nfrom .space import Space\n\n\nclass MultiDiscrete(Space):\n    \"\"\"\n    - The multi-discrete action space consists of a series of discrete action spaces with different number of actions in eachs\n    - It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space\n    - It is parametrized by passing an array of positive integers specifying number of actions for each discrete action space\n\n    Note: A value of 0 always need to represent the NOOP action.\n\n    e.g. Nintendo Game Controller\n    - Can be conceptualized as 3 discrete action spaces:\n\n        1) Arrow Keys: Discrete 5  - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4]  - params: min: 0, max: 4\n        2) Button A:   Discrete 2  - NOOP[0], Pressed[1] - params: min: 0, max: 1\n        3) Button B:   Discrete 2  - NOOP[0], Pressed[1] - params: min: 0, max: 1\n\n    - Can be initialized as\n\n        MultiDiscrete([ 5, 2, 2 ])\n\n    \"\"\"\n    def __init__(self, nvec):\n    \n        \"\"\"\n        nvec: vector of counts of each categorical variable\n        \"\"\"\n        assert (np.array(nvec) > 0).all(), 'nvec (counts) have to be positive'\n        self.nvec = np.asarray(nvec, dtype=np.uint32)\n\n        super(MultiDiscrete, self).__init__(self.nvec.shape, np.uint32)\n        self.np_random = np.random.RandomState()\n\n    def seed(self, seed):\n        self.np_random.seed(seed)\n\n    def sample(self):\n        return (self.np_random.random_sample(self.nvec.shape) * self.nvec).astype(self.dtype)\n\n    def contains(self, x):\n        # if nvec is uint32 and space dtype is uint32, then 0 <= x < self.nvec guarantees that x\n        # is within correct bounds for space dtype (even though x does not have to be unsigned)\n        return (0 <= x).all() and (x < self.nvec).all()\n\n    def to_jsonable(self, sample_n):\n        return [sample.tolist() for sample in sample_n]\n\n    def from_jsonable(self, sample_n):\n        return np.array(sample_n)\n\n    def __repr__(self):\n        return \"MultiDiscrete({})\".format(self.nvec)\n\n    def __eq__(self, other):\n        return np.all(self.nvec == other.nvec)\n", "path": "gym/spaces/multi_discrete.py"}, {"content": "import numpy as np\n\n\nclass Space(object):\n    \"\"\"Defines the observation and action spaces, so you can write generic\n    code that applies to any Env. For example, you can choose a random\n    action.\n    \"\"\"\n    def __init__(self, shape=None, dtype=None):\n        import numpy as np # takes about 300-400ms to import, so we load lazily\n        self.shape = None if shape is None else tuple(shape)\n        self.dtype = None if dtype is None else np.dtype(dtype)\n\n    def sample(self):\n        \"\"\"\n        Uniformly randomly sample a random element of this space\n        \"\"\"\n        raise NotImplementedError\n\n    def seed(self, seed):\n        \"\"\"Set the seed for this space's pseudo-random number generator. \"\"\"\n        raise NotImplementedError\n\n    def contains(self, x):\n        \"\"\"\n        Return boolean specifying if x is a valid\n        member of this space\n        \"\"\"\n        raise NotImplementedError\n\n    def __contains__(self, x):\n        return self.contains(x)\n\n    def to_jsonable(self, sample_n):\n        \"\"\"Convert a batch of samples from this space to a JSONable data type.\"\"\"\n        # By default, assume identity is JSONable\n        return sample_n\n\n    def from_jsonable(self, sample_n):\n        \"\"\"Convert a JSONable data type to a batch of samples from this space.\"\"\"\n        # By default, assume identity is JSONable\n        return sample_n\n", "path": "gym/spaces/space.py"}], "after_files": [{"content": "import numpy as np\nfrom .space import Space\n\n\nclass MultiDiscrete(Space):\n    \"\"\"\n    - The multi-discrete action space consists of a series of discrete action spaces with different number of actions in eachs\n    - It is useful to represent game controllers or keyboards where each key can be represented as a discrete action space\n    - It is parametrized by passing an array of positive integers specifying number of actions for each discrete action space\n\n    Note: A value of 0 always need to represent the NOOP action.\n\n    e.g. Nintendo Game Controller\n    - Can be conceptualized as 3 discrete action spaces:\n\n        1) Arrow Keys: Discrete 5  - NOOP[0], UP[1], RIGHT[2], DOWN[3], LEFT[4]  - params: min: 0, max: 4\n        2) Button A:   Discrete 2  - NOOP[0], Pressed[1] - params: min: 0, max: 1\n        3) Button B:   Discrete 2  - NOOP[0], Pressed[1] - params: min: 0, max: 1\n\n    - Can be initialized as\n\n        MultiDiscrete([ 5, 2, 2 ])\n\n    \"\"\"\n    def __init__(self, nvec):\n    \n        \"\"\"\n        nvec: vector of counts of each categorical variable\n        \"\"\"\n        assert (np.array(nvec) > 0).all(), 'nvec (counts) have to be positive'\n        self.nvec = np.asarray(nvec, dtype=np.int64)\n\n        super(MultiDiscrete, self).__init__(self.nvec.shape, np.int64)\n        self.np_random = np.random.RandomState()\n\n    def seed(self, seed):\n        self.np_random.seed(seed)\n\n    def sample(self):\n        return (self.np_random.random_sample(self.nvec.shape) * self.nvec).astype(self.dtype)\n\n    def contains(self, x):\n        # if nvec is uint32 and space dtype is uint32, then 0 <= x < self.nvec guarantees that x\n        # is within correct bounds for space dtype (even though x does not have to be unsigned)\n        return (0 <= x).all() and (x < self.nvec).all()\n\n    def to_jsonable(self, sample_n):\n        return [sample.tolist() for sample in sample_n]\n\n    def from_jsonable(self, sample_n):\n        return np.array(sample_n)\n\n    def __repr__(self):\n        return \"MultiDiscrete({})\".format(self.nvec)\n\n    def __eq__(self, other):\n        return np.all(self.nvec == other.nvec)\n", "path": "gym/spaces/multi_discrete.py"}, {"content": "class Space(object):\n    \"\"\"Defines the observation and action spaces, so you can write generic\n    code that applies to any Env. For example, you can choose a random\n    action.\n    \"\"\"\n    def __init__(self, shape=None, dtype=None):\n        import numpy as np # takes about 300-400ms to import, so we load lazily\n        self.shape = None if shape is None else tuple(shape)\n        self.dtype = None if dtype is None else np.dtype(dtype)\n\n    def sample(self):\n        \"\"\"\n        Uniformly randomly sample a random element of this space\n        \"\"\"\n        raise NotImplementedError\n\n    def seed(self, seed):\n        \"\"\"Set the seed for this space's pseudo-random number generator. \"\"\"\n        raise NotImplementedError\n\n    def contains(self, x):\n        \"\"\"\n        Return boolean specifying if x is a valid\n        member of this space\n        \"\"\"\n        raise NotImplementedError\n\n    def __contains__(self, x):\n        return self.contains(x)\n\n    def to_jsonable(self, sample_n):\n        \"\"\"Convert a batch of samples from this space to a JSONable data type.\"\"\"\n        # By default, assume identity is JSONable\n        return sample_n\n\n    def from_jsonable(self, sample_n):\n        \"\"\"Convert a JSONable data type to a batch of samples from this space.\"\"\"\n        # By default, assume identity is JSONable\n        return sample_n\n", "path": "gym/spaces/space.py"}]}