prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  ImportError when installing on Windows 10  and [33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>
  Dears,
  Would you please let me know how I could solve this warning and this error? (Windows 10)
  
  Using TensorFlow backend.
  [33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.[0m
  
   File "C:\Users\fi\Desktop\rl\code\3.6\stock_market_reinforcement_learning-master\environment.py", line 43, in __init__
      self.reset()
    File "C:\Users\fi\Anaconda30\envs\tensorflow\lib\site-packages\gym\core.py", line 70, in reset
      raise NotImplementedError
  NotImplementedErrorr
  
  
  --- 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/envs/mujoco/mujoco_env.py`
  Content:
  ```
  1 import os
  2 
  3 from gym import error, spaces
  4 from gym.utils import seeding
  5 import numpy as np
  6 from os import path
  7 import gym
  8 import six
  9 
  10 try:
  11     import mujoco_py
  12 except ImportError as e:
  13     raise error.DependencyNotInstalled("{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)".format(e))
  14 
  15 DEFAULT_SIZE = 500
  16 
  17 class MujocoEnv(gym.Env):
  18     """Superclass for all MuJoCo environments.
  19     """
  20 
  21     def __init__(self, model_path, frame_skip):
  22         if model_path.startswith("/"):
  23             fullpath = model_path
  24         else:
  25             fullpath = os.path.join(os.path.dirname(__file__), "assets", model_path)
  26         if not path.exists(fullpath):
  27             raise IOError("File %s does not exist" % fullpath)
  28         self.frame_skip = frame_skip
  29         self.model = mujoco_py.load_model_from_path(fullpath)
  30         self.sim = mujoco_py.MjSim(self.model)
  31         self.data = self.sim.data
  32         self.viewer = None
  33         self._viewers = {}
  34 
  35         self.metadata = {
  36             'render.modes': ['human', 'rgb_array'],
  37             'video.frames_per_second': int(np.round(1.0 / self.dt))
  38         }
  39 
  40         self.init_qpos = self.sim.data.qpos.ravel().copy()
  41         self.init_qvel = self.sim.data.qvel.ravel().copy()
  42         observation, _reward, done, _info = self.step(np.zeros(self.model.nu))
  43         assert not done
  44         self.obs_dim = observation.size
  45 
  46         bounds = self.model.actuator_ctrlrange.copy()
  47         low = bounds[:, 0]
  48         high = bounds[:, 1]
  49         self.action_space = spaces.Box(low=low, high=high)
  50 
  51         high = np.inf*np.ones(self.obs_dim)
  52         low = -high
  53         self.observation_space = spaces.Box(low, high)
  54 
  55         self.seed()
  56 
  57     def seed(self, seed=None):
  58         self.np_random, seed = seeding.np_random(seed)
  59         return [seed]
  60 
  61     # methods to override:
  62     # ----------------------------
  63 
  64     def reset_model(self):
  65         """
  66         Reset the robot degrees of freedom (qpos and qvel).
  67         Implement this in each subclass.
  68         """
  69         raise NotImplementedError
  70 
  71     def viewer_setup(self):
  72         """
  73         This method is called when the viewer is initialized and after every reset
  74         Optionally implement this method, if you need to tinker with camera position
  75         and so forth.
  76         """
  77         pass
  78 
  79     # -----------------------------
  80 
  81     def reset(self):
  82         self.sim.reset()
  83         ob = self.reset_model()
  84         old_viewer = self.viewer
  85         for v in self._viewers.values():
  86             self.viewer = v
  87             self.viewer_setup()
  88         self.viewer = old_viewer
  89         return ob
  90 
  91     def set_state(self, qpos, qvel):
  92         assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,)
  93         old_state = self.sim.get_state()
  94         new_state = mujoco_py.MjSimState(old_state.time, qpos, qvel,
  95                                          old_state.act, old_state.udd_state)
  96         self.sim.set_state(new_state)
  97         self.sim.forward()
  98 
  99     @property
  100     def dt(self):
  101         return self.model.opt.timestep * self.frame_skip
  102 
  103     def do_simulation(self, ctrl, n_frames):
  104         self.sim.data.ctrl[:] = ctrl
  105         for _ in range(n_frames):
  106             self.sim.step()
  107 
  108     def render(self, mode='human', width=DEFAULT_SIZE, height=DEFAULT_SIZE):
  109         if mode == 'rgb_array':
  110             self._get_viewer(mode).render(width, height)
  111             # window size used for old mujoco-py:
  112             data = self._get_viewer(mode).read_pixels(width, height, depth=False)
  113             # original image is upside-down, so flip it
  114             return data[::-1, :, :]
  115         elif mode == 'human':
  116             self._get_viewer(mode).render()
  117 
  118     def close(self):
  119         if self.viewer is not None:
  120             # self.viewer.finish()
  121             self.viewer = None
  122             self._viewers = {}
  123 
  124     def _get_viewer(self, mode):
  125         self.viewer = self._viewers.get(mode)
  126         if self.viewer is None:
  127             if mode == 'human':
  128                 self.viewer = mujoco_py.MjViewer(self.sim)
  129             elif mode == 'rgb_array':
  130                 self.viewer = mujoco_py.MjRenderContextOffscreen(self.sim, 0)
  131             self.viewer_setup()
  132             self._viewers[mode] = self.viewer
  133         return self.viewer
  134 
  135     def get_body_com(self, body_name):
  136         return self.data.get_body_xpos(body_name)
  137 
  138     def state_vector(self):
  139         return np.concatenate([
  140             self.sim.data.qpos.flat,
  141             self.sim.data.qvel.flat
  142         ])
  143
  ```
  --- 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/envs/mujoco/mujoco_env.py b/gym/envs/mujoco/mujoco_env.py
  --- a/gym/envs/mujoco/mujoco_env.py
  +++ b/gym/envs/mujoco/mujoco_env.py
  @@ -46,7 +46,7 @@
           bounds = self.model.actuator_ctrlrange.copy()
           low = bounds[:, 0]
           high = bounds[:, 1]
  -        self.action_space = spaces.Box(low=low, high=high)
  +        self.action_space = spaces.Box(low=low, high=high, dtype=np.float32)
   
           high = np.inf*np.ones(self.obs_dim)
           low = -high
  
verification_info: {"golden_diff": "diff --git a/gym/envs/mujoco/mujoco_env.py b/gym/envs/mujoco/mujoco_env.py\n--- a/gym/envs/mujoco/mujoco_env.py\n+++ b/gym/envs/mujoco/mujoco_env.py\n@@ -46,7 +46,7 @@\n         bounds = self.model.actuator_ctrlrange.copy()\n         low = bounds[:, 0]\n         high = bounds[:, 1]\n-        self.action_space = spaces.Box(low=low, high=high)\n+        self.action_space = spaces.Box(low=low, high=high, dtype=np.float32)\n \n         high = np.inf*np.ones(self.obs_dim)\n         low = -high\n", "issue": "ImportError when installing on Windows 10  and [33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>\nDears,\r\nWould you please let me know how I could solve this warning and this error? (Windows 10)\r\n\r\nUsing TensorFlow backend.\r\n\u001b[33mWARN: gym.spaces.Box autodetected dtype as <class 'numpy.float32'>. Please provide explicit dtype.\u001b[0m\r\n\r\n File \"C:\\Users\\fi\\Desktop\\rl\\code\\3.6\\stock_market_reinforcement_learning-master\\environment.py\", line 43, in __init__\r\n    self.reset()\r\n  File \"C:\\Users\\fi\\Anaconda30\\envs\\tensorflow\\lib\\site-packages\\gym\\core.py\", line 70, in reset\r\n    raise NotImplementedError\r\nNotImplementedErrorr\r\n\n", "before_files": [{"content": "import os\n\nfrom gym import error, spaces\nfrom gym.utils import seeding\nimport numpy as np\nfrom os import path\nimport gym\nimport six\n\ntry:\n    import mujoco_py\nexcept ImportError as e:\n    raise error.DependencyNotInstalled(\"{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)\".format(e))\n\nDEFAULT_SIZE = 500\n\nclass MujocoEnv(gym.Env):\n    \"\"\"Superclass for all MuJoCo environments.\n    \"\"\"\n\n    def __init__(self, model_path, frame_skip):\n        if model_path.startswith(\"/\"):\n            fullpath = model_path\n        else:\n            fullpath = os.path.join(os.path.dirname(__file__), \"assets\", model_path)\n        if not path.exists(fullpath):\n            raise IOError(\"File %s does not exist\" % fullpath)\n        self.frame_skip = frame_skip\n        self.model = mujoco_py.load_model_from_path(fullpath)\n        self.sim = mujoco_py.MjSim(self.model)\n        self.data = self.sim.data\n        self.viewer = None\n        self._viewers = {}\n\n        self.metadata = {\n            'render.modes': ['human', 'rgb_array'],\n            'video.frames_per_second': int(np.round(1.0 / self.dt))\n        }\n\n        self.init_qpos = self.sim.data.qpos.ravel().copy()\n        self.init_qvel = self.sim.data.qvel.ravel().copy()\n        observation, _reward, done, _info = self.step(np.zeros(self.model.nu))\n        assert not done\n        self.obs_dim = observation.size\n\n        bounds = self.model.actuator_ctrlrange.copy()\n        low = bounds[:, 0]\n        high = bounds[:, 1]\n        self.action_space = spaces.Box(low=low, high=high)\n\n        high = np.inf*np.ones(self.obs_dim)\n        low = -high\n        self.observation_space = spaces.Box(low, high)\n\n        self.seed()\n\n    def seed(self, seed=None):\n        self.np_random, seed = seeding.np_random(seed)\n        return [seed]\n\n    # methods to override:\n    # ----------------------------\n\n    def reset_model(self):\n        \"\"\"\n        Reset the robot degrees of freedom (qpos and qvel).\n        Implement this in each subclass.\n        \"\"\"\n        raise NotImplementedError\n\n    def viewer_setup(self):\n        \"\"\"\n        This method is called when the viewer is initialized and after every reset\n        Optionally implement this method, if you need to tinker with camera position\n        and so forth.\n        \"\"\"\n        pass\n\n    # -----------------------------\n\n    def reset(self):\n        self.sim.reset()\n        ob = self.reset_model()\n        old_viewer = self.viewer\n        for v in self._viewers.values():\n            self.viewer = v\n            self.viewer_setup()\n        self.viewer = old_viewer\n        return ob\n\n    def set_state(self, qpos, qvel):\n        assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,)\n        old_state = self.sim.get_state()\n        new_state = mujoco_py.MjSimState(old_state.time, qpos, qvel,\n                                         old_state.act, old_state.udd_state)\n        self.sim.set_state(new_state)\n        self.sim.forward()\n\n    @property\n    def dt(self):\n        return self.model.opt.timestep * self.frame_skip\n\n    def do_simulation(self, ctrl, n_frames):\n        self.sim.data.ctrl[:] = ctrl\n        for _ in range(n_frames):\n            self.sim.step()\n\n    def render(self, mode='human', width=DEFAULT_SIZE, height=DEFAULT_SIZE):\n        if mode == 'rgb_array':\n            self._get_viewer(mode).render(width, height)\n            # window size used for old mujoco-py:\n            data = self._get_viewer(mode).read_pixels(width, height, depth=False)\n            # original image is upside-down, so flip it\n            return data[::-1, :, :]\n        elif mode == 'human':\n            self._get_viewer(mode).render()\n\n    def close(self):\n        if self.viewer is not None:\n            # self.viewer.finish()\n            self.viewer = None\n            self._viewers = {}\n\n    def _get_viewer(self, mode):\n        self.viewer = self._viewers.get(mode)\n        if self.viewer is None:\n            if mode == 'human':\n                self.viewer = mujoco_py.MjViewer(self.sim)\n            elif mode == 'rgb_array':\n                self.viewer = mujoco_py.MjRenderContextOffscreen(self.sim, 0)\n            self.viewer_setup()\n            self._viewers[mode] = self.viewer\n        return self.viewer\n\n    def get_body_com(self, body_name):\n        return self.data.get_body_xpos(body_name)\n\n    def state_vector(self):\n        return np.concatenate([\n            self.sim.data.qpos.flat,\n            self.sim.data.qvel.flat\n        ])\n", "path": "gym/envs/mujoco/mujoco_env.py"}], "after_files": [{"content": "import os\n\nfrom gym import error, spaces\nfrom gym.utils import seeding\nimport numpy as np\nfrom os import path\nimport gym\nimport six\n\ntry:\n    import mujoco_py\nexcept ImportError as e:\n    raise error.DependencyNotInstalled(\"{}. (HINT: you need to install mujoco_py, and also perform the setup instructions here: https://github.com/openai/mujoco-py/.)\".format(e))\n\nDEFAULT_SIZE = 500\n\nclass MujocoEnv(gym.Env):\n    \"\"\"Superclass for all MuJoCo environments.\n    \"\"\"\n\n    def __init__(self, model_path, frame_skip):\n        if model_path.startswith(\"/\"):\n            fullpath = model_path\n        else:\n            fullpath = os.path.join(os.path.dirname(__file__), \"assets\", model_path)\n        if not path.exists(fullpath):\n            raise IOError(\"File %s does not exist\" % fullpath)\n        self.frame_skip = frame_skip\n        self.model = mujoco_py.load_model_from_path(fullpath)\n        self.sim = mujoco_py.MjSim(self.model)\n        self.data = self.sim.data\n        self.viewer = None\n        self._viewers = {}\n\n        self.metadata = {\n            'render.modes': ['human', 'rgb_array'],\n            'video.frames_per_second': int(np.round(1.0 / self.dt))\n        }\n\n        self.init_qpos = self.sim.data.qpos.ravel().copy()\n        self.init_qvel = self.sim.data.qvel.ravel().copy()\n        observation, _reward, done, _info = self.step(np.zeros(self.model.nu))\n        assert not done\n        self.obs_dim = observation.size\n\n        bounds = self.model.actuator_ctrlrange.copy()\n        low = bounds[:, 0]\n        high = bounds[:, 1]\n        self.action_space = spaces.Box(low=low, high=high, dtype=np.float32)\n\n        high = np.inf*np.ones(self.obs_dim)\n        low = -high\n        self.observation_space = spaces.Box(low, high)\n\n        self.seed()\n\n    def seed(self, seed=None):\n        self.np_random, seed = seeding.np_random(seed)\n        return [seed]\n\n    # methods to override:\n    # ----------------------------\n\n    def reset_model(self):\n        \"\"\"\n        Reset the robot degrees of freedom (qpos and qvel).\n        Implement this in each subclass.\n        \"\"\"\n        raise NotImplementedError\n\n    def viewer_setup(self):\n        \"\"\"\n        This method is called when the viewer is initialized and after every reset\n        Optionally implement this method, if you need to tinker with camera position\n        and so forth.\n        \"\"\"\n        pass\n\n    # -----------------------------\n\n    def reset(self):\n        self.sim.reset()\n        ob = self.reset_model()\n        old_viewer = self.viewer\n        for v in self._viewers.values():\n            self.viewer = v\n            self.viewer_setup()\n        self.viewer = old_viewer\n        return ob\n\n    def set_state(self, qpos, qvel):\n        assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,)\n        old_state = self.sim.get_state()\n        new_state = mujoco_py.MjSimState(old_state.time, qpos, qvel,\n                                         old_state.act, old_state.udd_state)\n        self.sim.set_state(new_state)\n        self.sim.forward()\n\n    @property\n    def dt(self):\n        return self.model.opt.timestep * self.frame_skip\n\n    def do_simulation(self, ctrl, n_frames):\n        self.sim.data.ctrl[:] = ctrl\n        for _ in range(n_frames):\n            self.sim.step()\n\n    def render(self, mode='human', width=DEFAULT_SIZE, height=DEFAULT_SIZE):\n        if mode == 'rgb_array':\n            self._get_viewer(mode).render(width, height)\n            # window size used for old mujoco-py:\n            data = self._get_viewer(mode).read_pixels(width, height, depth=False)\n            # original image is upside-down, so flip it\n            return data[::-1, :, :]\n        elif mode == 'human':\n            self._get_viewer(mode).render()\n\n    def close(self):\n        if self.viewer is not None:\n            # self.viewer.finish()\n            self.viewer = None\n            self._viewers = {}\n\n    def _get_viewer(self, mode):\n        self.viewer = self._viewers.get(mode)\n        if self.viewer is None:\n            if mode == 'human':\n                self.viewer = mujoco_py.MjViewer(self.sim)\n            elif mode == 'rgb_array':\n                self.viewer = mujoco_py.MjRenderContextOffscreen(self.sim, 0)\n            self.viewer_setup()\n            self._viewers[mode] = self.viewer\n        return self.viewer\n\n    def get_body_com(self, body_name):\n        return self.data.get_body_xpos(body_name)\n\n    def state_vector(self):\n        return np.concatenate([\n            self.sim.data.qpos.flat,\n            self.sim.data.qvel.flat\n        ])\n", "path": "gym/envs/mujoco/mujoco_env.py"}]}