code: def __init__(
      self,
      observation_space: gym.spaces.Space,
      action_space: gym.spaces.Space,
      config: AlgorithmConfigDict,
  ):
      config = dict(
          ray.rllib.algorithms.ddpg.ddpg.DDPGConfig().to_dict(),
          **config,
      )
  
      # Create global step for counting the number of update operations.
      self.global_step = 0
  
      # Validate action space for DDPG
      validate_spaces(self, observation_space, action_space)
  
      TorchPolicyV2.__init__(
          self,
          observation_space,
          action_space,
          config,
          max_seq_len=config["model"]["max_seq_len"],
      )
  
      ComputeTDErrorMixin.__init__(self)
  
      # TODO: Don't require users to call this manually.
      self._initialize_loss_from_dummy_batch()
  
      TargetNetworkMixin.__init__(self)
  
imports: import logging
  from typing import Any, Dict, List, Optional, Tuple, Type
  import gymnasium
  from rllib_ddpg.ddpg.utils import make_ddpg_models, validate_spaces
  import ray
  from ray.rllib.algorithms.dqn.dqn_tf_policy import PRIO_WEIGHTS, postprocess_nstep_and_prio
  from ray.rllib.evaluation import Episode
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDeterministic, TorchDirichlet, TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.utils.torch_utils import apply_grad_clipping, concat_multi_gpu_td_errors, huber_loss, l2_loss
  from ray.rllib.utils.typing import AlgorithmConfigDict, ModelGradients, TensorType
label_desc: Use image node_id 1 to create a new DDPGTorchPolicy object from inherited base classes: TargetNetworkMixin, ComputeTDErrorMixin, TorchPolicyV2 with example: obj = DDPGTorchPolicy(observation_space, action_space, config)