code: def wandb_results(summary, raw_args):
      import wandb
  
      run = wandb.init(
          project=raw_args.wandb_project_name,
          tags=raw_args.wandb_tags.split(","),
      )
      run.config.metric = raw_args.metric
      run.config.model = raw_args.path
      run.config.data = raw_args.data
  
      if raw_args.wandb_run_name:
          run.name = raw_args.wandb_run_name
          run.save()
  
      wandb.log(summary)
      wandb.finish()
  
imports: import os
  import numpy
  import scipy
  import torch
  import torch.multiprocessing
  from fairseq import checkpoint_utils, options
  from fairseq.data.codedataset import CodeDataset, ExpressiveCodeDataConfig
  from fairseq.dataclass.utils import convert_namespace_to_omegaconf
  from torch.utils.data import DataLoader, DistributedSampler
  from fairseq.utils import move_to_cuda
  from fairseq import utils
  from fairseq.criterions.speech_ulm_criterion import nll_loss, mae_loss
  import time
  from types import SimpleNamespace
  import sys, pathlib
  from naive_decoder import Naive_F0_Decoder
  from inference_dataset import InferenceDataset, explode_batch
  from sample.sample import do_sampling, TemperatureDecoder, FilterNamesDataset
label_desc: Use image node_id 6 for calling a global function with example usage: wandb_results(summary, raw_args) without return types