class_docstr: Policy Gradient (PG) Trainer.
  
  Defines the distributed Trainer class for policy gradients.
  See `pg_[tf|torch]_policy.py` for the definition of the policy losses for
  TensorFlow and PyTorch.
  
  Detailed documentation:
  https://docs.ray.io/en/master/rllib-algorithms.html#pg
  
  Only overrides the default config- and policy selectors
  (`get_default_policy_class` and `get_default_config`). Utilizes
  the default `training_step()` method of `Algorithm`.
code: def get_default_config(cls) -> AlgorithmConfig:
      return PGConfig()
  
file_path: ray/rllib_contrib/pg/src/rllib_pg/pg/pg.py
imports: from typing import List, Optional, Type, Union
  from ray.rllib.algorithms.algorithm import Algorithm
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
label_desc: Use image node_id 1 for calling the PG obj's underlying member method code with example usage: obj.get_default_config(cls) and returns: PGConfig