docstr: This function takes a cosine similarity matrix `mat` and returns the speaker labels for the segments 
  in the given input embeddings. 
  
  Args: 
      mat (Tensor):
          Cosine similarity matrix (affinity matrix) calculated from the provided speaker embeddings.
      oracle_num_speakers (int):
          The number of speakers in a session, as specified by the reference transcript.
          Can be used as `chunk_cluster_count` in long-form clustering mode.
      max_num_speakers (int):
          The upper bound for the number of speakers in each session.
      max_rp_threshold (float):
          Limits the range of parameter search.
          The clustering performance can vary based on this range.
          The default value is 0.15.
      sparse_search_volume (int):
          The number of p_values considered during NME analysis.
          The default is 30. Lower values speed up the NME-analysis but might lead to poorer parameter estimations. Values below 20 are not recommended.
      est_num_of_spk_enhanced (int):
          The number of speakers estimated from enhanced speaker counting.
          If the value is -1, the enhanced speaker counting is skipped.
      fixed_thres (float):
          If a `fixed_thres` value is provided, the NME-analysis process will be skipped.
          This value should be optimized on a development set for best results.
          By default, it is set to -1.0, and the function performs NME-analysis to estimate the threshold.
      kmeans_random_trials (int):
          The number of random trials for initializing k-means clustering. More trials can result in more stable clustering. The default is 1. 
          
  Returns:
      Y (LongTensor):
          Speaker labels (clustering output) in integer format for the segments in the given input embeddings.
code: def forward_unit_infer(
      self,
      mat: torch.Tensor,
      oracle_num_speakers: int = -1,
      max_num_speakers: int = 8,
      max_rp_threshold: float = 0.15,
      sparse_search_volume: int = 30,
      est_num_of_spk_enhanced: torch.Tensor = torch.tensor(-1),
      fixed_thres: float = -1.0,
      kmeans_random_trials: int = 1,
  ) -> torch.LongTensor:
      """
      This function takes a cosine similarity matrix `mat` and returns the speaker labels for the segments
      in the given input embeddings.
  
      Args:
          mat (Tensor):
              Cosine similarity matrix (affinity matrix) calculated from the provided speaker embeddings.
          oracle_num_speakers (int):
              The number of speakers in a session, as specified by the reference transcript.
              Can be used as `chunk_cluster_count` in long-form clustering mode.
          max_num_speakers (int):
              The upper bound for the number of speakers in each session.
          max_rp_threshold (float):
              Limits the range of parameter search.
              The clustering performance can vary based on this range.
              The default value is 0.15.
          sparse_search_volume (int):
              The number of p_values considered during NME analysis.
              The default is 30. Lower values speed up the NME-analysis but might lead to poorer parameter estimations. Values below 20 are not recommended.
          est_num_of_spk_enhanced (int):
              The number of speakers estimated from enhanced speaker counting.
              If the value is -1, the enhanced speaker counting is skipped.
          fixed_thres (float):
              If a `fixed_thres` value is provided, the NME-analysis process will be skipped.
              This value should be optimized on a development set for best results.
              By default, it is set to -1.0, and the function performs NME-analysis to estimate the threshold.
          kmeans_random_trials (int):
              The number of random trials for initializing k-means clustering. More trials can result in more stable clustering. The default is 1.
  
      Returns:
          Y (LongTensor):
              Speaker labels (clustering output) in integer format for the segments in the given input embeddings.
      """
      nmesc = NMESC(
          mat,
          max_num_speakers=max_num_speakers,
          max_rp_threshold=max_rp_threshold,
          sparse_search=self.sparse_search,
          sparse_search_volume=sparse_search_volume,
          fixed_thres=fixed_thres,
          nme_mat_size=self.nme_mat_size,
          maj_vote_spk_count=self.maj_vote_spk_count,
          parallelism=self.parallelism,
          cuda=self.cuda,
          device=self.device,
      )
      # If there are less than `min_samples_for_nmesc` segments, est_num_of_spk is 1.
      if mat.shape[0] > self.min_samples_for_nmesc:
          est_num_of_spk, p_hat_value = nmesc.forward()
          affinity_mat = getAffinityGraphMat(mat, p_hat_value)
      else:
          nmesc.fixed_thres = max_rp_threshold
          est_num_of_spk, p_hat_value = nmesc.forward()
          affinity_mat = mat
  
      # `n_clusters` is number of speakers estimated from spectral clustering.
      if oracle_num_speakers > 0:
          n_clusters = int(oracle_num_speakers)
      elif est_num_of_spk_enhanced > 0:
          n_clusters = int(est_num_of_spk_enhanced.item())
      else:
          n_clusters = int(est_num_of_spk.item())
  
      spectral_model = SpectralClustering(
          n_clusters=n_clusters,
          n_random_trials=kmeans_random_trials,
          cuda=self.cuda,
          device=self.device,
      )
      Y = spectral_model.forward(affinity_mat)
      return Y
  
imports: from typing import Dict, List, Tuple
  import torch
  from torch.linalg import eigh, eigvalsh
label_desc: Use image node_id 2 for calling the SpeakerClustering obj's underlying member method code with example usage: obj.forward_unit_infer(mat, oracle_num_speakers, max_num_speakers, max_rp_threshold, sparse_search_volume, est_num_of_spk_enhanced, fixed_thres, kmeans_random_trials) and returns: Y