docstr: Initializes a speaker clustering class tailored for long-form audio, leveraging methods from the `SpeakerClustering` class.
  The clustering algorithm for long-form content is executed via the `forward_infer` function (not shown here). Input embedding 
  vectors are divided into chunks, each of size `embeddings_per_chunk`. Within every chunk, the clustering algorithm aims 
  to identify `chunk_cluster_count` distinct clusters. The resulting clustering labels are then expanded to match the original 
  length of the input embeddings.
  
  NOTE: torch.jit.script currently does not support inherited methods with a `super()` call.
  
  Args:
      cuda (bool):
          Flag indicating whether CUDA is available for computation.
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code: def __init__(self, cuda: bool = False):
      """
      Initializes a speaker clustering class tailored for long-form audio, leveraging methods from the `SpeakerClustering` class.
      The clustering algorithm for long-form content is executed via the `forward_infer` function (not shown here). Input embedding
      vectors are divided into chunks, each of size `embeddings_per_chunk`. Within every chunk, the clustering algorithm aims
      to identify `chunk_cluster_count` distinct clusters. The resulting clustering labels are then expanded to match the original
      length of the input embeddings.
  
      NOTE: torch.jit.script currently does not support inherited methods with a `super()` call.
  
      Args:
          cuda (bool):
              Flag indicating whether CUDA is available for computation.
      """
      super().__init__()
      self.speaker_clustering = SpeakerClustering(cuda=cuda)
      self.embeddings_in_scales: List[torch.Tensor] = [
          torch.tensor([0])
      ]
      self.timestamps_in_scales: List[torch.Tensor] = [
          torch.tensor([0])
      ]
      self.cuda = cuda
      self.device = (
          torch.device("cuda") if self.cuda else torch.device("cpu")
      )
  
file_path: NeMo/nemo/collections/asr/parts/utils/longform_clustering.py
imports: from typing import Dict, List, Tuple
  import torch
  from tqdm import tqdm
  from nemo.collections.asr.parts.utils.offline_clustering import SpeakerClustering, get_scale_interpolated_embs, getCosAffinityMatrix, split_input_data
  from nemo.collections.asr.parts.utils.online_clustering import get_merge_quantity, run_reducer
label_desc: Use image node_id 1 to create a new LongFormSpeakerClustering object from inherited base classes: torch.nn with example: obj = LongFormSpeakerClustering(cuda)