class_docstr: PyTorch policy class used with Impala.
class_docstr_tok: ["PyTorch","policy","class","used","with","Impala","."]
code: def __init__(self, observation_space, action_space, config):
      config = dict(
          ray.rllib.algorithms.impala.impala.ImpalaConfig().to_dict(),
          **config,
      )
  
      # If Learner API is used, we don't need any loss-specific mixins.
      # However, we also would like to avoid creating special Policy-subclasses
      # for this as the entire Policy concept will soon not be used anymore with
      # the new Learner- and RLModule APIs.
      if not config.get("_enable_new_api_stack"):
          VTraceOptimizer.__init__(self)
          # Need to initialize learning rate variable before calling
          # TorchPolicyV2.__init__.
          LearningRateSchedule.__init__(
              self, config["lr"], config["lr_schedule"]
          )
          EntropyCoeffSchedule.__init__(
              self,
              config["entropy_coeff"],
              config["entropy_coeff_schedule"],
          )
  
      TorchPolicyV2.__init__(
          self,
          observation_space,
          action_space,
          config,
          max_seq_len=config["model"]["max_seq_len"],
      )
  
      ValueNetworkMixin.__init__(self, config)
  
      self._initialize_loss_from_dummy_batch()
  
imports: import gymnasium
  import logging
  import numpy
  from typing import Dict, List, Optional, Type, Union
  import ray
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.postprocessing import compute_bootstrap_value
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import EntropyCoeffSchedule, LearningRateSchedule, ValueNetworkMixin
  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.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import apply_grad_clipping, explained_variance, global_norm, sequence_mask
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 1 to create a new ImpalaTorchPolicy object from inherited base classes: VTraceOptimizer, LearningRateSchedule, EntropyCoeffSchedule, ValueNetworkMixin, TorchPolicyV2 with example: obj = ImpalaTorchPolicy(observation_space, action_space, config)