code: def policy_actions_repeat(model, action_dist, obs, num_repeat=1):
      batch_size = tree.flatten(obs)[0].shape[0]
      obs_temp = tree.map_structure(
          lambda t: _repeat_tensor(t, num_repeat), obs
      )
      logits, _ = model.get_action_model_outputs(obs_temp)
      policy_dist = action_dist(logits, model)
      actions, logp_ = policy_dist.sample_logp()
      logp = logp_.unsqueeze(-1)
      return actions, logp.view(batch_size, num_repeat, 1)
  
imports: import numpy
  import gymnasium
  import logging
  import tree
  from typing import Dict, List, Tuple, Type, Union
  import ray
  import ray.experimental.tf_utils
  from ray.rllib.algorithms.sac.sac_tf_policy import postprocess_trajectory, validate_spaces
  from ray.rllib.algorithms.sac.sac_torch_policy import _get_dist_class, stats, build_sac_model_and_action_dist, optimizer_fn, ComputeTDErrorMixin, setup_late_mixins, action_distribution_fn
  from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.policy.policy_template import build_policy_class
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.torch_mixins import TargetNetworkMixin
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.metrics.learner_info import LEARNER_STATS_KEY
  from ray.rllib.utils.typing import LocalOptimizer, TensorType, AlgorithmConfigDict
  from ray.rllib.utils.torch_utils import apply_grad_clipping, convert_to_torch_tensor, concat_multi_gpu_td_errors
label_desc: Use image node_id 2 for calling a global function with example usage: policy_actions_repeat(model, action_dist, obs, num_repeat) and returns: actions,  logp