prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  MuJoCo env Box initialization causes warnings
  In gym/gym/envs/mujoco/mujoco_env.py on line 46, Box object initialization is missing dtype=np.float32 parameter which causes autodetect warning spam.
  
  </issue>
  <code>
  [start of gym/envs/mujoco/mujoco_env.py]
  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 class MujocoEnv(gym.Env):
  16     """Superclass for all MuJoCo environments.
  17     """
  18 
  19     def __init__(self, model_path, frame_skip):
  20         if model_path.startswith("/"):
  21             fullpath = model_path
  22         else:
  23             fullpath = os.path.join(os.path.dirname(__file__), "assets", model_path)
  24         if not path.exists(fullpath):
  25             raise IOError("File %s does not exist" % fullpath)
  26         self.frame_skip = frame_skip
  27         self.model = mujoco_py.load_model_from_path(fullpath)
  28         self.sim = mujoco_py.MjSim(self.model)
  29         self.data = self.sim.data
  30         self.viewer = None
  31 
  32         self.metadata = {
  33             'render.modes': ['human', 'rgb_array'],
  34             'video.frames_per_second': int(np.round(1.0 / self.dt))
  35         }
  36 
  37         self.init_qpos = self.sim.data.qpos.ravel().copy()
  38         self.init_qvel = self.sim.data.qvel.ravel().copy()
  39         observation, _reward, done, _info = self.step(np.zeros(self.model.nu))
  40         assert not done
  41         self.obs_dim = observation.size
  42 
  43         bounds = self.model.actuator_ctrlrange.copy()
  44         low = bounds[:, 0]
  45         high = bounds[:, 1]
  46         self.action_space = spaces.Box(low=low, high=high)
  47 
  48         high = np.inf*np.ones(self.obs_dim)
  49         low = -high
  50         self.observation_space = spaces.Box(low, high)
  51 
  52         self.seed()
  53 
  54     def seed(self, seed=None):
  55         self.np_random, seed = seeding.np_random(seed)
  56         return [seed]
  57 
  58     # methods to override:
  59     # ----------------------------
  60 
  61     def reset_model(self):
  62         """
  63         Reset the robot degrees of freedom (qpos and qvel).
  64         Implement this in each subclass.
  65         """
  66         raise NotImplementedError
  67 
  68     def viewer_setup(self):
  69         """
  70         This method is called when the viewer is initialized and after every reset
  71         Optionally implement this method, if you need to tinker with camera position
  72         and so forth.
  73         """
  74         pass
  75 
  76     # -----------------------------
  77 
  78     def reset(self):
  79         self.sim.reset()
  80         ob = self.reset_model()
  81         if self.viewer is not None:
  82             self.viewer_setup()
  83         return ob
  84 
  85     def set_state(self, qpos, qvel):
  86         assert qpos.shape == (self.model.nq,) and qvel.shape == (self.model.nv,)
  87         old_state = self.sim.get_state()
  88         new_state = mujoco_py.MjSimState(old_state.time, qpos, qvel,
  89                                          old_state.act, old_state.udd_state)
  90         self.sim.set_state(new_state)
  91         self.sim.forward()
  92 
  93     @property
  94     def dt(self):
  95         return self.model.opt.timestep * self.frame_skip
  96 
  97     def do_simulation(self, ctrl, n_frames):
  98         self.sim.data.ctrl[:] = ctrl
  99         for _ in range(n_frames):
  100             self.sim.step()
  101 
  102     def render(self, mode='human'):
  103         if mode == 'rgb_array':
  104             self._get_viewer().render()
  105             # window size used for old mujoco-py:
  106             width, height = 500, 500
  107             data = self._get_viewer().read_pixels(width, height, depth=False)
  108             # original image is upside-down, so flip it
  109             return data[::-1, :, :]
  110         elif mode == 'human':
  111             self._get_viewer().render()
  112 
  113     def close(self):
  114         if self.viewer is not None:
  115             self.viewer.finish()
  116             self.viewer = None
  117 
  118     def _get_viewer(self):
  119         if self.viewer is None:
  120             self.viewer = mujoco_py.MjViewer(self.sim)
  121             self.viewer_setup()
  122         return self.viewer
  123 
  124     def get_body_com(self, body_name):
  125         return self.data.get_body_xpos(body_name)
  126 
  127     def state_vector(self):
  128         return np.concatenate([
  129             self.sim.data.qpos.flat,
  130             self.sim.data.qvel.flat
  131         ])
  132
  [end of gym/envs/mujoco/mujoco_env.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
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
  @@ -43,11 +43,11 @@
           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
  -        self.observation_space = spaces.Box(low, high)
  +        self.observation_space = spaces.Box(low, high, dtype=np.float32)
   
           self.seed()
  
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@@ -43,11 +43,11 @@\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-        self.observation_space = spaces.Box(low, high)\n+        self.observation_space = spaces.Box(low, high, dtype=np.float32)\n \n         self.seed()\n", "issue": "MuJoCo env Box initialization causes warnings\nIn gym/gym/envs/mujoco/mujoco_env.py on line 46, Box object initialization is missing dtype=np.float32 parameter which causes autodetect warning spam.\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\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\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        if self.viewer is not None:\n            self.viewer_setup()\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'):\n        if mode == 'rgb_array':\n            self._get_viewer().render()\n            # window size used for old mujoco-py:\n            width, height = 500, 500\n            data = self._get_viewer().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().render()\n\n    def close(self):\n        if self.viewer is not None:\n            self.viewer.finish()\n            self.viewer = None\n\n    def _get_viewer(self):\n        if self.viewer is None:\n            self.viewer = mujoco_py.MjViewer(self.sim)\n            self.viewer_setup()\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"}]}