class_docstr: Defines a configuration class from which a CQL can be built.
  
  .. testcode::
      :skipif: True
  
      from ray.rllib.algorithms.cql import CQLConfig
      config = CQLConfig().training(gamma=0.9, lr=0.01)
      config = config.resources(num_gpus=0)
      config = config.rollouts(num_rollout_workers=4)
      print(config.to_dict())
      # Build a Algorithm object from the config and run 1 training iteration.
      algo = config.build(env="CartPole-v1")
      algo.train()
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imports: import logging
  from typing import Optional, Type
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.cql.cql_tf_policy import CQLTFPolicy
  from ray.rllib.algorithms.cql.cql_torch_policy import CQLTorchPolicy
  from ray.rllib.algorithms.sac.sac import SAC, SACConfig
  from ray.rllib.execution.rollout_ops import synchronous_parallel_sample
  from ray.rllib.execution.train_ops import multi_gpu_train_one_step, train_one_step
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE, deprecation_warning
  from ray.rllib.utils.framework import try_import_tf, try_import_tfp
  from ray.rllib.utils.metrics import LAST_TARGET_UPDATE_TS, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, NUM_TARGET_UPDATES, TARGET_NET_UPDATE_TIMER, SYNCH_WORKER_WEIGHTS_TIMER, SAMPLE_TIMER
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 1 to create a new CQLConfig object from inherited base classes: SACConfig with example: obj = CQLConfig(algo_class)