docstr: A function wrapper designed for performing inference using an exported script format.
  
  Note:
      A dictionary is used to facilitate inference with the exported jit model in the Triton server. 
      This is done using an easy-to-understand naming convention.
      See https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#special-conventions-for-pytorch-backend
  
  Args:
      param_dict (dict):
          Dictionary containing the arguments for speaker clustering.
          See `forward_infer` function for the argument information.
  
  Returns:
      (LongTensor): Speaker labels for the segments in the given input embeddings.
docstr_tok: ["A","function","wrapper","designed","for","performing","inference","using","an","exported","script","format",".","Note",":","A","dictionary","is","used","to","facilitate","inference","with","the","exported","jit","model","in","the","Triton","server",".","This","is","done","using","an","easy-to-understand","naming","convention",".","See","https",":","\/\/github.com\/triton-inference-server\/server\/blob\/main\/docs\/user_guide\/model_configuration.md","#","special-conventions-for-pytorch-backend","Args",":","param_dict","(","dict",")",":","Dictionary","containing","the","arguments","for","speaker","clustering",".","See","`","forward_infer","`","function","for","the","argument","information",".","Returns",":","(","LongTensor",")",":","Speaker","labels","for","the","segments","in","the","given","input","embeddings","."]
code: def forward(
      self, param_dict: Dict[str, torch.Tensor]
  ) -> torch.LongTensor:
      """
      A function wrapper designed for performing inference using an exported script format.
  
      Note:
          A dictionary is used to facilitate inference with the exported jit model in the Triton server.
          This is done using an easy-to-understand naming convention.
          See https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#special-conventions-for-pytorch-backend
  
      Args:
          param_dict (dict):
              Dictionary containing the arguments for speaker clustering.
              See `forward_infer` function for the argument information.
  
      Returns:
          (LongTensor): Speaker labels for the segments in the given input embeddings.
      """
      embeddings_in_scales = param_dict["embeddings"]
      timestamps_in_scales = param_dict["timestamps"]
      multiscale_segment_counts = param_dict[
          "multiscale_segment_counts"
      ]
      multiscale_weights = param_dict["multiscale_weights"]
      oracle_num_speakers = int(
          param_dict["oracle_num_speakers"].item()
      )
      max_num_speakers = int(param_dict["max_num_speakers"].item())
      enhanced_count_thres = int(
          param_dict["enhanced_count_thres"].item()
      )
      sparse_search_volume = int(
          param_dict["sparse_search_volume"].item()
      )
      max_rp_threshold = float(param_dict["max_rp_threshold"].item())
      fixed_thres = float(param_dict["fixed_thres"].item())
      return self.forward_infer(
          embeddings_in_scales=embeddings_in_scales,
          timestamps_in_scales=timestamps_in_scales,
          multiscale_segment_counts=multiscale_segment_counts,
          multiscale_weights=multiscale_weights,
          oracle_num_speakers=oracle_num_speakers,
          max_rp_threshold=max_rp_threshold,
          max_num_speakers=max_num_speakers,
          enhanced_count_thres=enhanced_count_thres,
          sparse_search_volume=sparse_search_volume,
          fixed_thres=fixed_thres,
      )
  
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 5 for calling the LongFormSpeakerClustering obj's underlying member method code with example usage: obj.forward(param_dict) and returns: self