code: def _repeat_tensor(t: TensorType, n: int):
      # Insert new dimension at posotion 1 into tensor t
      t_rep = t.unsqueeze(1)
      # Repeat tensor t_rep along new dimension n times
      t_rep = torch.repeat_interleave(t_rep, n, dim=1)
      # Merge new dimension into batch dimension
      t_rep = t_rep.view(-1, *t.shape[1:])
      return t_rep
  
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 1 for calling a global function with example usage: _repeat_tensor(t, n) and returns: t_rep