class_docstr: [OpenAI Gym](https://gym.openai.com/) environment adapter (specification key: `gym`,
  `openai_gym`).
  
  May require:
  ```bash
  pip3 install gym
  pip3 install gym[all]
  ```
  
  Args:
      level (string | gym.Env): Gym id or instance
          (<span style="color:#C00000"><b>required</b></span>).
      visualize (bool): Whether to visualize interaction
          (<span style="color:#00C000"><b>default</b></span>: false).
      min_value (float): Lower bound clipping for otherwise unbounded state values
          (<span style="color:#00C000"><b>default</b></span>: no clipping).
      max_value (float): Upper bound clipping for otherwise unbounded state values
          (<span style="color:#00C000"><b>default</b></span>: no clipping).
      terminal_reward (float): Additional reward for early termination, if otherwise
          indistinguishable from termination due to maximum number of timesteps
          (<span style="color:#00C000"><b>default</b></span>: Gym default).
      reward_threshold (float): Gym environment argument, the reward threshold before the task is
          considered solved
          (<span style="color:#00C000"><b>default</b></span>: Gym default).
      drop_states_indices (list[int]): Drop states indices
          (<span style="color:#00C000"><b>default</b></span>: none).
      visualize_directory (string): Visualization output directory
          (<span style="color:#00C000"><b>default</b></span>: none).
      kwargs: Additional Gym environment arguments.
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code: def states(self):
      return self.states_spec
  
file_path: tensorforce/tensorforce/environments/openai_gym.py
imports: import importlib
  import numpy
  from tensorforce import TensorforceError, util
  from tensorforce.environments import Environment
label_desc: Use image node_id 5 for calling the OpenAIGym obj's underlying member method code with example usage: obj.states() and returns: self