docstr: Calculate the affinity matrix using timestamps and speaker embeddings, run NME analysis to estimate the best
  p-value, and perform spectral clustering based on the estimated p-value and the calculated affinity matrix.
  
  Caution:
      For compatibility with libtorch, python boolean `False` has been replaced with `torch.LongTensor(-1)`.
  
  Args:
      embeddings_in_scales (Tensor):
          List containing concatenated Torch tensor embeddings across multiple scales.
          The length of the list is equal to the number of scales.
          Each tensor has dimensions of (Number of base segments) x (Embedding Dimension).
      timestamps_in_scales (Tensor):
          List containing concatenated Torch tensor timestamps across multiple scales.
          The length of the list is equal to the number of scales.
          Each tensor has dimensions of (Total number of segments across all scales) x 2.
          Example:
              >>> timestamps_in_scales[0] =                         torch.Tensor([[0.4, 1.4], [0.9, 1.9], [1.4, 2.4], ... [121.2, 122.2]])
      multiscale_segment_counts (LongTensor):
          A Torch tensor containing the number of segments for each scale.
          The tensor has dimensions of (Number of scales).
          Example:
              >>> multiscale_segment_counts = torch.LongTensor([31, 52, 84, 105, 120])
      multiscale_weights (Tensor):
          Multi-scale weights used when merging affinity scores.
          Example:
              >>> multiscale_weights = torch.tensor([1.4, 1.3, 1.2, 1.1, 1.0])
      oracle_num_speakers (int):
          The number of speakers in a session as given by the reference transcript.
      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.
      enhanced_count_thres (int):
          For shorter audio recordings, the clustering algorithm might not accumulate enough speaker profiles for each cluster.
          Thus, the function `getEnhancedSpeakerCount` uses anchor embeddings (dummy representations) to mitigate the effects of cluster sparsity.
          A value of 80 is recommended for `enhanced_count_thres`.
      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.
      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:
      (LongTensor): Speaker labels for the segments in the provided input embeddings.
code: def forward_infer(
      self,
      embeddings_in_scales: torch.Tensor,
      timestamps_in_scales: torch.Tensor,
      multiscale_segment_counts: torch.LongTensor,
      multiscale_weights: torch.Tensor,
      oracle_num_speakers: int = -1,
      max_num_speakers: int = 8,
      max_rp_threshold: float = 0.15,
      enhanced_count_thres: int = 40,
      sparse_search_volume: int = 30,
      fixed_thres: float = -1.0,
      kmeans_random_trials: int = 1,
  ) -> torch.LongTensor:
      """
          Calculate the affinity matrix using timestamps and speaker embeddings, run NME analysis to estimate the best
          p-value, and perform spectral clustering based on the estimated p-value and the calculated affinity matrix.
  
          Caution:
              For compatibility with libtorch, python boolean `False` has been replaced with `torch.LongTensor(-1)`.
  
          Args:
              embeddings_in_scales (Tensor):
                  List containing concatenated Torch tensor embeddings across multiple scales.
                  The length of the list is equal to the number of scales.
                  Each tensor has dimensions of (Number of base segments) x (Embedding Dimension).
              timestamps_in_scales (Tensor):
                  List containing concatenated Torch tensor timestamps across multiple scales.
                  The length of the list is equal to the number of scales.
                  Each tensor has dimensions of (Total number of segments across all scales) x 2.
                  Example:
                      >>> timestamps_in_scales[0] = \
                          torch.Tensor([[0.4, 1.4], [0.9, 1.9], [1.4, 2.4], ... [121.2, 122.2]])
              multiscale_segment_counts (LongTensor):
                  A Torch tensor containing the number of segments for each scale.
                  The tensor has dimensions of (Number of scales).
                  Example:
                      >>> multiscale_segment_counts = torch.LongTensor([31, 52, 84, 105, 120])
              multiscale_weights (Tensor):
                  Multi-scale weights used when merging affinity scores.
                  Example:
                      >>> multiscale_weights = torch.tensor([1.4, 1.3, 1.2, 1.1, 1.0])
              oracle_num_speakers (int):
                  The number of speakers in a session as given by the reference transcript.
              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.
              enhanced_count_thres (int):
                  For shorter audio recordings, the clustering algorithm might not accumulate enough speaker profiles for each cluster.
                  Thus, the function `getEnhancedSpeakerCount` uses anchor embeddings (dummy representations) to mitigate the effects of cluster sparsity.
                  A value of 80 is recommended for `enhanced_count_thres`.
              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.
              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:
              (LongTensor): Speaker labels for the segments in the provided input embeddings.
          """
      (
          self.embeddings_in_scales,
          self.timestamps_in_scales,
      ) = split_input_data(
          embeddings_in_scales,
          timestamps_in_scales,
          multiscale_segment_counts,
      )
      # Last slot is the base scale embeddings
      emb = self.embeddings_in_scales[-1]
  
      # Cases for extreamly short sessions
      if emb.shape[0] == 1:
          return torch.zeros((1,), dtype=torch.int64)
      elif (
          emb.shape[0]
          <= max(enhanced_count_thres, self.min_samples_for_nmesc)
          and oracle_num_speakers < 0
      ):
          est_num_of_spk_enhanced = getEnhancedSpeakerCount(
              emb=emb, cuda=self.cuda
          )
      else:
          est_num_of_spk_enhanced = torch.tensor(-1)
  
      if oracle_num_speakers > 0:
          max_num_speakers = oracle_num_speakers
  
      mat = getMultiScaleCosAffinityMatrix(
          multiscale_weights=multiscale_weights,
          embeddings_in_scales=self.embeddings_in_scales,
          timestamps_in_scales=self.timestamps_in_scales,
          device=self.device,
      )
  
      return self.forward_unit_infer(
          mat=mat,
          oracle_num_speakers=oracle_num_speakers,
          max_rp_threshold=max_rp_threshold,
          max_num_speakers=max_num_speakers,
          sparse_search_volume=sparse_search_volume,
          est_num_of_spk_enhanced=est_num_of_spk_enhanced,
          kmeans_random_trials=kmeans_random_trials,
          fixed_thres=fixed_thres,
      )
  
imports: from typing import Dict, List, Tuple
  import torch
  from torch.linalg import eigh, eigvalsh
label_desc: Use image node_id 4 for calling the SpeakerClustering obj's underlying member method code with example usage: obj.forward_infer(embeddings_in_scales, timestamps_in_scales, multiscale_segment_counts, multiscale_weights, oracle_num_speakers, max_num_speakers, max_rp_threshold, enhanced_count_thres, sparse_search_volume, fixed_thres, kmeans_random_trials) and returns: self, torch