code: def q_values_repeat(model, obs, actions, twin=False):
      action_shape = actions.shape[0]
      obs_shape = tree.flatten(obs)[0].shape[0]
      num_repeat = int(action_shape / obs_shape)
      obs_temp = tree.map_structure(
          lambda t: _repeat_tensor(t, num_repeat), obs
      )
      if not twin:
          preds_, _ = model.get_q_values(obs_temp, actions)
      else:
          preds_, _ = model.get_twin_q_values(obs_temp, actions)
      preds = preds_.view(obs_shape, num_repeat, 1)
      return preds
  
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 3 for calling a global function with example usage: q_values_repeat(model, obs, actions, twin) and returns: preds