docstr: Splits the embedding tensor into smaller window-sized tensors based on a given index.
  
  Args:
      index (int): The index of the desired window. This determines the starting point 
                   of the window using the formula:
                   start = embeddings_per_chunk * index
      emb (Tensor): The embedding tensor which needs to be split.
      embeddings_per_chunk (int):
          The size of the windows in which the algorithm aims to identify `chunk_cluster_count` clusters.
  
  Returns:
      emb_part (Tensor): 
          The window-sized tensor, which is a portion of the `emb`.
      offset_index (int): 
          The starting position of the window in the `emb` tensor.
docstr_tok: ["Splits","the","embedding","tensor","into","smaller","window-sized","tensors","based","on","a","given","index",".","Args",":","index","(","int",")",":","The","index","of","the","desired","window",".","This","determines","the","starting","point","of","the","window","using","the","formula",":","start","=","embeddings_per_chunk","*","index","emb","(","Tensor",")",":","The","embedding","tensor","which","needs","to","be","split",".","embeddings_per_chunk","(","int",")",":","The","size","of","the","windows","in","which","the","algorithm","aims","to","identify","`","chunk_cluster_count","`","clusters",".","Returns",":","emb_part","(","Tensor",")",":","The","window-sized","tensor",",","which","is","a","portion","of","the","`","emb","`",".","offset_index","(","int",")",":","The","starting","position","of","the","window","in","the","`","emb","`","tensor","."]
code: def split_embs_to_windows(
      self,
      index: int,
      emb: torch.Tensor,
      embeddings_per_chunk: int,
  ) -> Tuple[torch.Tensor, int]:
      """
      Splits the embedding tensor into smaller window-sized tensors based on a given index.
  
      Args:
          index (int): The index of the desired window. This determines the starting point
                       of the window using the formula:
                       start = embeddings_per_chunk * index
          emb (Tensor): The embedding tensor which needs to be split.
          embeddings_per_chunk (int):
              The size of the windows in which the algorithm aims to identify `chunk_cluster_count` clusters.
  
      Returns:
          emb_part (Tensor):
              The window-sized tensor, which is a portion of the `emb`.
          offset_index (int):
              The starting position of the window in the `emb` tensor.
      """
      if embeddings_per_chunk * (index + 1) > emb.shape[0]:
          emb_part = emb[-1 * embeddings_per_chunk :]
          offset_index = emb.shape[0] - embeddings_per_chunk
      else:
          emb_part = emb[
              embeddings_per_chunk
              * index : embeddings_per_chunk
              * (index + 1)
          ]
          offset_index = embeddings_per_chunk * index
      return emb_part, offset_index
  
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 4 for calling the LongFormSpeakerClustering obj's underlying member method code with example usage: obj.split_embs_to_windows(index, emb, embeddings_per_chunk) and returns: emb_part,  offset_index