We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Why is there a restiction on shape in multi discrete?
https://github.com/openai/gym/blob/422c9c7bb3c3c5a756c3b12dfe99733bfbfe3920/gym/spaces/multi_discrete.py#L10

Why is this imposed?
Say one may need a high dimensional space like a (3,3,3) grid with discretised values for each element in the grid. 
It can be very easily circumvented by using np.random.random_sample and passing the shape
Is there some specific reason for doing this?

Also note this example provided in dict_space doesn't work currently.
https://github.com/openai/gym/blob/422c9c7bb3c3c5a756c3b12dfe99733bfbfe3920/gym/spaces/dict_space.py#L22



--- 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 
4 class MultiDiscrete(gym.Space):
5     def __init__(self, nvec):
6         """
7         nvec: vector of counts of each categorical variable
8         """
9         self.nvec = np.asarray(nvec, dtype=np.int32)
10         assert self.nvec.ndim == 1, 'nvec should be a 1d array (or list) of ints'
11         gym.Space.__init__(self, (self.nvec.size,), np.int8)
12     def sample(self):
13         return (gym.spaces.np_random.rand(self.nvec.size) * self.nvec).astype(self.dtype)
14     def contains(self, x):
15         return (0 <= x).all() and (x < self.nvec).all() and x.dtype.kind in 'ui'
16     
17     __contains__ = contains
18     
19     def to_jsonable(self, sample_n):
20         return [sample.tolist() for sample in sample_n]
21     def from_jsonable(self, sample_n):
22         return np.array(sample_n)
23
```
--- 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()

```

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
@@ -7,10 +7,9 @@
         nvec: vector of counts of each categorical variable
         """
         self.nvec = np.asarray(nvec, dtype=np.int32)
-        assert self.nvec.ndim == 1, 'nvec should be a 1d array (or list) of ints'
-        gym.Space.__init__(self, (self.nvec.size,), np.int8)
+        gym.Space.__init__(self, (self.nvec.shape,), np.int8)
     def sample(self):
-        return (gym.spaces.np_random.rand(self.nvec.size) * self.nvec).astype(self.dtype)
+        return (gym.spaces.np_random.random_sample(self.nvec.shape) * self.nvec).astype(self.dtype)
     def contains(self, x):
         return (0 <= x).all() and (x < self.nvec).all() and x.dtype.kind in 'ui'

{"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@@ -7,10 +7,9 @@\n         nvec: vector of counts of each categorical variable\n         \"\"\"\n         self.nvec = np.asarray(nvec, dtype=np.int32)\n-        assert self.nvec.ndim == 1, 'nvec should be a 1d array (or list) of ints'\n-        gym.Space.__init__(self, (self.nvec.size,), np.int8)\n+        gym.Space.__init__(self, (self.nvec.shape,), np.int8)\n     def sample(self):\n-        return (gym.spaces.np_random.rand(self.nvec.size) * self.nvec).astype(self.dtype)\n+        return (gym.spaces.np_random.random_sample(self.nvec.shape) * self.nvec).astype(self.dtype)\n     def contains(self, x):\n         return (0 <= x).all() and (x < self.nvec).all() and x.dtype.kind in 'ui'\n", "issue": "Why is there a restiction on shape in multi discrete?\nhttps://github.com/openai/gym/blob/422c9c7bb3c3c5a756c3b12dfe99733bfbfe3920/gym/spaces/multi_discrete.py#L10\r\n\r\nWhy is this imposed?\r\nSay one may need a high dimensional space like a (3,3,3) grid with discretised values for each element in the grid. \r\nIt can be very easily circumvented by using np.random.random_sample and passing the shape\r\nIs there some specific reason for doing this?\r\n\r\nAlso note this example provided in dict_space doesn't work currently.\r\nhttps://github.com/openai/gym/blob/422c9c7bb3c3c5a756c3b12dfe99733bfbfe3920/gym/spaces/dict_space.py#L22\r\n\r\n\n", "before_files": [{"content": "import gym\nimport numpy as np\n\nclass MultiDiscrete(gym.Space):\n    def __init__(self, nvec):\n        \"\"\"\n        nvec: vector of counts of each categorical variable\n        \"\"\"\n        self.nvec = np.asarray(nvec, dtype=np.int32)\n        assert self.nvec.ndim == 1, 'nvec should be a 1d array (or list) of ints'\n        gym.Space.__init__(self, (self.nvec.size,), np.int8)\n    def sample(self):\n        return (gym.spaces.np_random.rand(self.nvec.size) * self.nvec).astype(self.dtype)\n    def contains(self, x):\n        return (0 <= x).all() and (x < self.nvec).all() and x.dtype.kind in 'ui'\n    \n    __contains__ = contains\n    \n    def to_jsonable(self, sample_n):\n        return [sample.tolist() for sample in sample_n]\n    def from_jsonable(self, sample_n):\n        return np.array(sample_n)\n", "path": "gym/spaces/multi_discrete.py"}], "after_files": [{"content": "import gym\nimport numpy as np\n\nclass MultiDiscrete(gym.Space):\n    def __init__(self, nvec):\n        \"\"\"\n        nvec: vector of counts of each categorical variable\n        \"\"\"\n        self.nvec = np.asarray(nvec, dtype=np.int32)\n        gym.Space.__init__(self, (self.nvec.shape,), np.int8)\n    def sample(self):\n        return (gym.spaces.np_random.random_sample(self.nvec.shape) * self.nvec).astype(self.dtype)\n    def contains(self, x):\n        return (0 <= x).all() and (x < self.nvec).all() and x.dtype.kind in 'ui'\n    \n    __contains__ = contains\n    \n    def to_jsonable(self, sample_n):\n        return [sample.tolist() for sample in sample_n]\n    def from_jsonable(self, sample_n):\n        return np.array(sample_n)\n", "path": "gym/spaces/multi_discrete.py"}]}