code: def convert_xvector(base_model_name, hf_config, downstream_dict):
      model = Wav2Vec2ForXVector.from_pretrained(
          base_model_name, config=hf_config
      )
      model.projector.weight.data = downstream_dict["connector.weight"]
      model.projector.bias.data = downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel):
          model.tdnn[i].kernel.weight.data = downstream_dict[
              f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
          ]
          model.tdnn[i].kernel.bias.data = downstream_dict[
              f"model.framelevel_feature_extractor.module.{i}.kernel.bias"
          ]
  
      model.feature_extractor.weight.data = downstream_dict[
          "model.utterancelevel_feature_extractor.linear1.weight"
      ]
      model.feature_extractor.bias.data = downstream_dict[
          "model.utterancelevel_feature_extractor.linear1.bias"
      ]
      model.classifier.weight.data = downstream_dict[
          "model.utterancelevel_feature_extractor.linear2.weight"
      ]
      model.classifier.bias.data = downstream_dict[
          "model.utterancelevel_feature_extractor.linear2.bias"
      ]
      model.objective.weight.data = downstream_dict["objective.W"]
      return model
  
imports: import argparse
  import torch
  from transformers import Wav2Vec2Config, Wav2Vec2FeatureExtractor, Wav2Vec2ForAudioFrameClassification, Wav2Vec2ForSequenceClassification, Wav2Vec2ForXVector, logging
label_desc: Use image node_id 3 for calling a global function with example usage: convert_xvector(base_model_name, hf_config, downstream_dict) and returns: model