code: def check_warm_start(warm_start, from_unsupervised):
      """
      Gives a warning about ambiguous usage of the two parameters.
      """
      if warm_start and from_unsupervised is not None:
          warn_msg = "warm_start=True and from_unsupervised != None: "
          warn_msg = "warm_start will be ignore, training will start from unsupervised weights"
          warnings.warn(warn_msg)
      return
  
imports: from torch.utils.data import Dataset
  from torch.utils.data import DataLoader, WeightedRandomSampler
  import torch
  import numpy
  import scipy
  import json
  from sklearn.utils import check_array
  import pandas
  import warnings
label_desc: Use image node_id 10 for calling a global function with example usage: check_warm_start(warm_start, from_unsupervised) without return types