class_docstr: The main OpenAI Gym class.
  
  It encapsulates an environment with arbitrary behind-the-scenes dynamics.
  An environment can be partially or fully observed.
  
  The main API methods that users of this class need to know are:
  
  - :meth:`step` - Takes a step in the environment using an action returning the next observation, reward,
    if the environment terminated and observation information.
  - :meth:`reset` - Resets the environment to an initial state, returning the initial observation and observation information.
  - :meth:`render` - Renders the environment observation with modes depending on the output
  - :meth:`close` - Closes the environment, important for rendering where pygame is imported
  
  And set the following attributes:
  
  - :attr:`action_space` - The Space object corresponding to valid actions
  - :attr:`observation_space` - The Space object corresponding to valid observations
  - :attr:`reward_range` - A tuple corresponding to the minimum and maximum possible rewards
  - :attr:`spec` - An environment spec that contains the information used to initialise the environment from `gym.make`
  - :attr:`metadata` - The metadata of the environment, i.e. render modes
  - :attr:`np_random` - The random number generator for the environment
  
  Note: a default reward range set to :math:`(-\infty,+\infty)` already exists. Set it if you want a narrower range.
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docstr: Returns the base non-wrapped environment.
  
  Returns:
      Env: The base non-wrapped gym.Env instance
docstr_tok: ["Returns","the","base","non-wrapped","environment",".","Returns",":","Env",":","The","base","non-wrapped","gym.Env","instance"]
code: def unwrapped(self) -> "Env":
      """Returns the base non-wrapped environment.
  
      Returns:
          Env: The base non-wrapped gym.Env instance
      """
      return self
  
imports: import sys
  from typing import TYPE_CHECKING, Any, Dict, Generic, List, Optional, SupportsFloat, Tuple, TypeVar, Union
  import numpy
  from gym import spaces
  from gym.logger import warn
  from gym.utils import seeding
label_desc: Use image node_id 7 for calling the Env obj's underlying member method code with example usage: obj.unwrapped() and returns: self