docstr: Checks the validity of the input parameters.
  
  Args:
      embeddings_per_chunk (int):
          The size of the windows in which the algorithm aims to identify `chunk_cluster_count` clusters.
      chunk_cluster_count (int):
          The target number of clusters to identify within each window.
      max_num_speakers (int):
          The maximum number of speakers to be detected in the audio.
docstr_tok: ["Checks","the","validity","of","the","input","parameters",".","Args",":","embeddings_per_chunk","(","int",")",":","The","size","of","the","windows","in","which","the","algorithm","aims","to","identify","`","chunk_cluster_count","`","clusters",".","chunk_cluster_count","(","int",")",":","The","target","number","of","clusters","to","identify","within","each","window",".","max_num_speakers","(","int",")",":","The","maximum","number","of","speakers","to","be","detected","in","the","audio","."]
code: def check_input(
      self,
      embeddings_per_chunk: int,
      chunk_cluster_count: int,
      max_num_speakers: int,
  ) -> None:
      """
      Checks the validity of the input parameters.
  
      Args:
          embeddings_per_chunk (int):
              The size of the windows in which the algorithm aims to identify `chunk_cluster_count` clusters.
          chunk_cluster_count (int):
              The target number of clusters to identify within each window.
          max_num_speakers (int):
              The maximum number of speakers to be detected in the audio.
      """
      if chunk_cluster_count is None or embeddings_per_chunk is None:
          raise ValueError(
              f"chunk_cluster_count ({chunk_cluster_count}) and embeddings_per_chunk ({embeddings_per_chunk}) should be set."
          )
      elif (
          all(
              v is not None
              for v in [chunk_cluster_count, embeddings_per_chunk]
          )
          and chunk_cluster_count >= embeddings_per_chunk
      ):
          raise ValueError(
              f"chunk_cluster_count ({chunk_cluster_count}) should be smaller than embeddings_per_chunk ({embeddings_per_chunk})."
          )
  
      if chunk_cluster_count <= max_num_speakers:
          raise ValueError(
              f"chunk_cluster_count ({chunk_cluster_count}) should be larger than max_num_speakers ({max_num_speakers})."
          )
  
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 2 for calling the LongFormSpeakerClustering obj's underlying member method code with example usage: obj.check_input(embeddings_per_chunk, chunk_cluster_count, max_num_speakers) without return types