code: def convert_diarization(base_model_name, hf_config, downstream_dict):
      model = WavLMForAudioFrameClassification.from_pretrained(
          base_model_name, config=hf_config
      )
      model.classifier.weight.data = downstream_dict[
          "model.linear.weight"
      ]
      model.classifier.bias.data = downstream_dict["model.linear.bias"]
      return model
  
imports: import argparse
  import torch
  from transformers import Wav2Vec2FeatureExtractor, WavLMConfig, WavLMForAudioFrameClassification, WavLMForSequenceClassification, WavLMForXVector, logging
label_desc: Use image node_id 2 for calling a global function with example usage: convert_diarization(base_model_name, hf_config, downstream_dict) and returns: model