old_contents: 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
      action.
      """
      def __init__(self, shape=None, dtype=None):
          import numpy as np  # takes about 300-400ms to import, so we load lazily
          self.shape = None if shape is None else tuple(shape)
          self.dtype = None if dtype is None else np.dtype(dtype)
          self.np_random = np.random.RandomState()
  
      def sample(self):
          """Uniformly randomly sample a random element of this space. """
          raise NotImplementedError
  
      def seed(self, seed):
          """Seed the PRNG of this space. """
          self.np_random.seed(seed)
  
      def contains(self, x):
          """
          Return boolean specifying if x is a valid
          member of this space
          """
          raise NotImplementedError
  
      def __contains__(self, x):
          return self.contains(x)
  
      def to_jsonable(self, sample_n):
          """Convert a batch of samples from this space to a JSONable data type."""
          # By default, assume identity is JSONable
          return sample_n
  
      def from_jsonable(self, sample_n):
          """Convert a JSONable data type to a batch of samples from this space."""
          # By default, assume identity is JSONable
          return sample_n
  
new_contents: from gym.utils import seeding
  
  
  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
      action.
      """
      def __init__(self, shape=None, dtype=None):
          import numpy as np  # takes about 300-400ms to import, so we load lazily
          self.shape = None if shape is None else tuple(shape)
          self.dtype = None if dtype is None else np.dtype(dtype)
          self.np_random = None
          self.seed()
  
      def sample(self):
          """Uniformly randomly sample a random element of this space. """
          raise NotImplementedError
  
      def seed(self, seed=None):
          """Seed the PRNG of this space. """
          self.np_random, seed = seeding.np_random(seed)
          return [seed]
  
      def contains(self, x):
          """
          Return boolean specifying if x is a valid
          member of this space
          """
          raise NotImplementedError
  
      def __contains__(self, x):
          return self.contains(x)
  
      def to_jsonable(self, sample_n):
          """Convert a batch of samples from this space to a JSONable data type."""
          # By default, assume identity is JSONable
          return sample_n
  
      def from_jsonable(self, sample_n):
          """Convert a JSONable data type to a batch of samples from this space."""
          # By default, assume identity is JSONable
          return sample_n
  
subject: Use utils.seeding in spaces.space (#1473)