text: import numpy as np
  import gym
  from gym import logger
  
  class Box(gym.Space):
      """
      A box in R^n.
      I.e., each coordinate is bounded.
  
      Example usage:
      self.action_space = spaces.Box(low=-10, high=10, shape=(1,))
      """
      def __init__(self, low=None, high=None, shape=None, dtype=np.float32):
          """
          Two kinds of valid input:
              Box(low=-1.0, high=1.0, shape=(3,4)) # low and high are scalars, and shape is provided
              Box(low=np.array([-1.0,-2.0]), high=np.array([2.0,4.0])) # low and high are arrays of the same shape
          """
          if shape is None:
              assert low.shape == high.shape
              shape = low.shape
          else:
              assert np.isscalar(low) and np.isscalar(high)
              low = low + np.zeros(shape)
              high = high + np.zeros(shape)
          if dtype is None:  # Autodetect type
              if (high == 255).all():
                  dtype = np.uint8
              else:
                  dtype = np.float32
              logger.warn("gym.spaces.Box autodetected dtype as %s. Please provide explicit dtype." % dtype)
          self.low = low.astype(dtype)
          self.high = high.astype(dtype)
          gym.Space.__init__(self, shape, dtype)
  
      def sample(self):
          return gym.spaces.np_random.uniform(low=self.low, high=self.high + (0 if self.dtype.kind == 'f' else 1), size=self.low.shape).astype(self.dtype)
      def contains(self, x):
          return x.shape == self.shape and (x >= self.low).all() and (x <= self.high).all()
  
      def to_jsonable(self, sample_n):
          return np.array(sample_n).tolist()
      def from_jsonable(self, sample_n):
          return [np.asarray(sample) for sample in sample_n]
  
      def __repr__(self):
          return "Box" + str(self.shape)
      def __eq__(self, other):
          return np.allclose(self.low, other.low) and np.allclose(self.high, other.high)
  