class_docstr: Serializer for gym.spaces.Box.
  
  Assumes that the space is bounded. Internally rescales it to the [0, 1]
  interval and uses a fixed-precision encoding.
class_docstr_tok: ["Serializer","for","gym.spaces.Box",".","Assumes","that","the","space","is","bounded",".","Internally","rescales","it","to","the","[","0",",","1","]","interval","and","uses","a","fixed-precision","encoding","."]
code: def significance_map(self):
      return np.reshape(
          np.broadcast_to(
              np.arange(self._precision),
              self._space.shape + (self._precision,),
          ),
          -1,
      )
  
imports: import copy
  from absl import logging
  import gin
  import gym
  from jax import numpy
label_desc: Use image node_id 5 for calling the BoxSpaceSerializer obj's underlying member method code with example usage: obj.significance_map() and returns: np