code: def getEnhancedSpeakerCount(
      emb: torch.Tensor,
      random_test_count: int = 5,
      anchor_spk_n: int = 3,
      anchor_sample_n: int = 10,
      sigma: float = 50,
      cuda: bool = False,
  ) -> torch.Tensor:
      """
      Calculate the number of speakers using NME analysis with anchor embeddings. Add dummy speaker
      embedding vectors and run speaker counting multiple times to enhance the speaker counting accuracy
      for the short audio samples.
  
      Args:
          emb (Tensor):
              The input embedding from the embedding extractor.
          cuda (bool):
              Use cuda for the operations if cuda==True.
          random_test_count (int):
              Number of trials of the enhanced counting with randomness.
              The higher the count, the more accurate the enhanced counting is.
          anchor_spk_n (int):
              Number of speakers for synthetic embedding.
              anchor_spk_n = 3 is recommended.
          anchor_sample_n (int):
              Number of embedding samples per speaker.
              anchor_sample_n = 10 is recommended.
          sigma (float):
              The amplitude of synthetic noise for each embedding vector.
              If the sigma value is too small, under-counting could happen.
              If the sigma value is too large, over-counting could happen.
              sigma = 50 is recommended.
  
      Returns:
          comp_est_num_of_spk (Tensor):
              The estimated number of speakers. `anchor_spk_n` is subtracted from the estimated
              number of speakers to factor out the dummy speaker embedding vectors.
      """
      est_num_of_spk_list: List[int] = []
      for seed in range(random_test_count):
          torch.manual_seed(seed)
          emb_aug = addAnchorEmb(
              emb, anchor_sample_n, anchor_spk_n, sigma
          )
          mat = getCosAffinityMatrix(emb_aug)
          nmesc = NMESC(
              mat,
              max_num_speakers=emb.shape[0],
              max_rp_threshold=0.15,
              sparse_search=True,
              sparse_search_volume=10,
              fixed_thres=-1.0,
              nme_mat_size=300,
              cuda=cuda,
          )
          est_num_of_spk, _ = nmesc.forward()
          est_num_of_spk_list.append(est_num_of_spk.item())
      comp_est_num_of_spk = torch.tensor(
          max(
              torch.mode(torch.tensor(est_num_of_spk_list))[0].item()
              - anchor_spk_n,
              1,
          )
      )
      return comp_est_num_of_spk
  
imports: from typing import Dict, List, Tuple
  import torch
  from torch.linalg import eigh, eigvalsh
label_desc: Use image node_id 21 for calling a global function with example usage: getEnhancedSpeakerCount(emb, random_test_count, anchor_spk_n, anchor_sample_n, sigma, cuda) and returns: comp_est_num_of_spk