code: def _convert_to_spec(
      space: gym.Space, name: Optional[str] = None
  ) -> types.NestedSpec:
      """Converts an OpenAI Gym space to a dm_env spec or nested structure of specs.
  
      Box, MultiBinary and MultiDiscrete Gym spaces are converted to BoundedArray
      specs. Discrete OpenAI spaces are converted to DiscreteArray specs. Tuple and
      Dict spaces are recursively converted to tuples and dictionaries of specs.
  
      Args:
        space: The Gym space to convert.
        name: Optional name to apply to all return spec(s).
  
      Returns:
        A dm_env spec or nested structure of specs, corresponding to the input
        space.
      """
      if isinstance(space, spaces.Discrete):
          return specs.DiscreteArray(
              num_values=space.n, dtype=space.dtype, name=name
          )
  
      elif isinstance(space, spaces.Box):
          return specs.BoundedArray(
              shape=space.shape,
              dtype=space.dtype,
              minimum=space.low,
              maximum=space.high,
              name=name,
          )
  
      elif isinstance(space, spaces.MultiBinary):
          return specs.BoundedArray(
              shape=space.shape,
              dtype=space.dtype,
              minimum=0.0,
              maximum=1.0,
              name=name,
          )
  
      elif isinstance(space, spaces.MultiDiscrete):
          return specs.BoundedArray(
              shape=space.shape,
              dtype=space.dtype,
              minimum=np.zeros(space.shape),
              maximum=space.nvec - 1,
              name=name,
          )
  
      elif isinstance(space, spaces.Tuple):
          return tuple(_convert_to_spec(s, name) for s in space.spaces)
  
      elif isinstance(space, spaces.Dict):
          return {
              key: _convert_to_spec(value, key)
              for key, value in space.spaces.items()
          }
  
      else:
          raise ValueError("Unexpected gym space: {}".format(space))
  
imports: from typing import Any, Dict, List, Optional
  from acme import specs
  from acme import types
  import dm_env
  import gym
  from gym import spaces
  import numpy
  import tree
label_desc: Use image node_id 1 for calling a global function with example usage: _convert_to_spec(space, name) and returns: specs, specs, specs, specs, tuple, unknown