code: def load(
      environment_name: Text,
      discount: types.Float = 1.0,
      max_episode_steps: Optional[types.Int] = None,
      gym_env_wrappers: Sequence[types.GymEnvWrapper] = (),
      env_wrappers: Sequence[types.PyEnvWrapper] = (),
      spec_dtype_map: Optional[Dict[gym.Space, np.dtype]] = None,
  ) -> py_environment.PyEnvironment:
      """Loads the selected environment and wraps it with the specified wrappers.
  
      Note that by default a TimeLimit wrapper is used to limit episode lengths
      to the default benchmarks defined by the registered environments.
  
      Args:
        environment_name: Name for the environment to load.
        discount: Discount to use for the environment.
        max_episode_steps: If None the max_episode_steps will be set to the default
          step limit defined in the environment's spec. No limit is applied if set
          to 0 or if there is no timestep_limit set in the environment's spec.
        gym_env_wrappers: Iterable with references to wrapper classes to use
          directly on the gym environment.
        env_wrappers: Iterable with references to wrapper classes to use on the
          gym_wrapped environment.
        spec_dtype_map: A dict that maps gym specs to tf dtypes to use as the
          default dtype for the tensors. An easy way how to configure a custom
          mapping through Gin is to define a gin-configurable function that returns
          desired mapping and call it in your Gin config file, for example:
          `suite_gym.load.spec_dtype_map = @get_custom_mapping()`.
  
      Returns:
        A PyEnvironmentBase instance.
      """
      if spec_dtype_map is None:
          # Use float32 for Observations.
          spec_dtype_map = {gym.spaces.Box: np.float32}
      return suite_gym.load(
          environment_name,
          discount,
          max_episode_steps,
          gym_env_wrappers,
          env_wrappers,
          spec_dtype_map,
      )
  
file_path: agents/tf_agents/environments/suite_mujoco.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Dict, Optional, Sequence, Text
  import gin
  import gym
  import numpy
  from tf_agents.environments import py_environment
  from tf_agents.environments import suite_gym
  from tf_agents.typing import types
label_desc: Use image node_id 2 for calling a global function with example usage: load(environment_name, discount, max_episode_steps, gym_env_wrappers, env_wrappers, spec_dtype_map) and returns: suite_gym