class_docstr: PyTorch policy class used with APPO.
code: def make_model(self) -> ModelV2:
      return make_appo_models(self)
  
imports: import gymnasium
  import numpy
  import logging
  from typing import Any, Dict, List, Optional, Type, Union
  import ray
  from ray.rllib.algorithms.appo.utils import make_appo_models
  import ray.rllib.algorithms.impala.vtrace_torch
  from ray.rllib.algorithms.impala.impala_torch_policy import make_time_major, VTraceOptimizer
  from ray.rllib.evaluation.episode import Episode
  from ray.rllib.evaluation.postprocessing import compute_bootstrap_value, compute_gae_for_sample_batch, Postprocessing
  from ray.rllib.models.action_dist import ActionDistribution
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper, TorchCategorical
  from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import EntropyCoeffSchedule, LearningRateSchedule, KLCoeffMixin, ValueNetworkMixin, 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.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 3 for calling the APPOTorchPolicy obj's underlying member method code with example usage: obj.make_model() and returns: make_appo_models