code: def compute_improved_policy(
      state_dict: Dict,
      states: List[str],
      model_n_steps: int,
      model_n_logits: int,
      N_bar: int,
  ):
      """Compute the improved policy given the state_dict, the list of states.
      The improved policy is computed as (N_s_aˆ(1/tau) / (N_s_aˆ(1/tau)).sum())
      where tau is (log(N_s_a.sum()) / log(N_bar))
      """
      policies = torch.zeros(len(states), model_n_steps, model_n_logits)
      N_bar = torch.tensor(N_bar)
      for idx, state in enumerate(states):
          N_s_a = state_dict[state][3]
          actions = state_dict[state][5]
          if N_s_a.sum() > N_bar:
              tau = (torch.log(N_s_a.sum()) / torch.log(N_bar)).item()
          else:
              tau = 1
          N_s_a = N_s_a ** (1 / tau)
          improved_policy = N_s_a / N_s_a.sum()
          for sample_id in range(actions.shape[1]):
              action_ids = actions[0, sample_id]
              for step_id, action_id in enumerate(action_ids):
                  policies[idx, step_id, action_id] += improved_policy[
                      0, sample_id
                  ]
      return policies
  
file_path: nebuly/optimization/open_alpha_tensor/open_alpha_tensor/core/actors/stage.py
imports: from typing import Dict, List
  import torch
  from open_alpha_tensor.core.data.utils import get_scalars, map_action_to_triplet
  from open_alpha_tensor.core.modules.alpha_tensor import AlphaTensorModel
label_desc: Use image node_id 14 for calling a global function with example usage: compute_improved_policy(state_dict, states, model_n_steps, model_n_logits, N_bar) and returns: policies