class_docstr: Optimal policy for PointEnv.
  
  Args:
      env_spec (EnvSpec): The environment spec.
      goal (np.ndarray): The goal location of the environment.
class_docstr_tok: ["Optimal","policy","for","PointEnv",".","Args",":","env_spec","(","EnvSpec",")",":","The","environment","spec",".","goal","(","np.ndarray",")",":","The","goal","location","of","the","environment","."]
code: def __init__(self, env_spec, goal):
      super().__init__(env_spec, "OptimalPolicy")
      self.goal = goal
  
file_path: garage/tests/garage/torch/algos/test_bc.py
imports: import numpy
  import ray
  from garage.envs import PointEnv
  from garage.experiment import deterministic
  from garage.sampler import LocalSampler, WorkerFactory
  from garage.torch.algos import BC
  from garage.torch.policies import DeterministicMLPPolicy, GaussianMLPPolicy, Policy
  from garage.trainer import Trainer
  from tests.fixtures import snapshot_config
  from tests.fixtures.sampler import ray_local_session_fixture
label_desc: Use image node_id 1 to create a new OptimalPolicy object from inherited base classes: Policy with example: obj = OptimalPolicy(env_spec, goal)