class_docstr: 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/src/garage/examples/torch/bc_point.py
imports: import click
  import numpy
  from garage import wrap_experiment
  from garage.envs import PointEnv
  from garage.sampler import RaySampler
  from garage.torch.algos import BC
  from garage.torch.policies import GaussianMLPPolicy, Policy
  from garage.trainer import Trainer
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)