code: def reference_infolm_score(
      preds, target, model_name, information_measure, idf, alpha, beta
  ):
      """Baseline implementation is currently not available.
  
      We, therefore, are enforced to relied on hard-coded results for now. The results below were generated using scripts
      in
      https://github.com/stancld/infolm-docker.
  
      """
      if model_name != "google/bert_uncased_L-2_H-128_A-2":
          raise ValueError(
              "`model_name` is expected to be 'google/bert_uncased_L-2_H-128_A-2' as this model was used for the result "
              "generation."
          )
      precomputed_result = {
          "kl_divergence": torch.tensor(
              [-3.2250, -0.1784, -0.1784, -2.2182]
          ),
          "beta_divergence": torch.tensor(
              [0.5812, 0.0716, 0.0716, 0.3721]
          ),
          "renyi_divergence": torch.tensor(
              [0.4357, 0.0333, 0.0333, 0.3615]
          ),
          "l2_distance": torch.tensor([0.2053, 0.1114, 0.1114, 0.2522]),
          "fisher_rao_distance": torch.tensor(
              [1.5637, 0.4957, 0.4957, 1.4570]
          ),
      }
      # Add results for idf=True -> for functional metrics, we calculate idf only over the batch yet
      if len(preds) == 2:
          precomputed_result.update(
              {
                  "alpha_divergence": torch.tensor(
                      [-1.2851, -0.1262, -0.1262, -1.3096]
                  ),
                  "ab_divergence": torch.tensor(
                      [5.9517, 0.5222, 0.5222, 7.0017]
                  ),
                  "l1_distance": torch.tensor(
                      [0.9679, 0.1877, 0.1877, 0.9561]
                  ),
                  "l_infinity_distance": torch.tensor(
                      [0.0789, 0.0869, 0.0869, 0.2324]
                  ),
              }
          )
      elif len(preds) == 4:
          precomputed_result.update(
              {
                  "alpha_divergence": torch.tensor(
                      [-1.2893, -0.1262, -0.1262, -1.4035]
                  ),
                  "ab_divergence": torch.tensor(
                      [5.9565, 0.5222, 0.5222, 7.1950]
                  ),
                  "l1_distance": torch.tensor(
                      [0.9591, 0.1877, 0.1877, 1.0823]
                  ),
                  "l_infinity_distance": torch.tensor(
                      [0.0777, 0.0869, 0.0869, 0.2614]
                  ),
              }
          )
      else:
          raise ValueError("Invalid batch provided.")
  
      res = precomputed_result[information_measure]
      if HYPOTHESIS_A in preds and HYPOTHESIS_C not in preds:
          res = res[:2]
      elif HYPOTHESIS_A not in preds and HYPOTHESIS_C in preds:
          res = res[2:]
      elif HYPOTHESIS_A in preds and HYPOTHESIS_C in preds:
          pass
      else:
          raise ValueError("Invalid example provided.")
      return res.mean()
  
file_path: torchmetrics/tests/unittests/text/test_infolm.py
imports: from functools import partial
  import pytest
  import torch
  from torchmetrics.functional.text.infolm import infolm
  from torchmetrics.text.infolm import InfoLM
  from torchmetrics.utilities.imports import _TRANSFORMERS_GREATER_EQUAL_4_4
  from unittests.text.helpers import TextTester, skip_on_connection_issues
  from unittests.text.inputs import HYPOTHESIS_A, HYPOTHESIS_C, _inputs_single_reference
label_desc: Use image node_id 1 for calling a global function with example usage: reference_infolm_score(preds, target, model_name, information_measure, idf, alpha, beta) and returns: res