class_docstr: PendulumMassEnv varies the weight of the pendulum
  
  Tasks are defined to be weight uniformly sampled between [0.5,2]
class_docstr_tok: ["PendulumMassEnv","varies","the","weight","of","the","pendulum","Tasks","are","defined","to","be","weight","uniformly","sampled","between","[","0.5,2","]"]
docstr: Args:
      task: Task of the meta-learning environment (here: mass of
          the pendulum).
docstr_tok: ["Args",":","task",":","Task","of","the","meta-learning","environment","(","here",":","mass","of","the","pendulum",")","."]
code: def set_task(self, task):
      """
      Args:
          task: Task of the meta-learning environment (here: mass of
              the pendulum).
      """
      # self.m is the mass property of the pendulum.
      self.m = task
  
file_path: ray/rllib_contrib/maml/src/rllib_maml/envs/pendulum_mass.py
imports: import numpy
  from gymnasium.envs.classic_control.pendulum import PendulumEnv
  from gymnasium.utils import EzPickle
  from ray.rllib.env.apis.task_settable_env import TaskSettableEnv
label_desc: Use image node_id 2 for calling the PendulumMassEnv obj's underlying member method code with example usage: obj.set_task(task) without return types