code: def filter_data(metadata, criteria, value):
      crit = [True] * len(metadata)
      for c, v in zip(criteria, value):
          v = [v] if not isinstance(v, list) else v
          crit &= metadata[c].isin(v)
      metadata_int = metadata[crit]
      exp_ids = metadata_int["exp_id"].tolist()
      return exp_ids
  
file_path: robustness/robustness/tools/vis_tools.py
imports: import numpy
  import matplotlib.pyplot
  from sklearn.decomposition import PCA
  from sklearn import manifold
  import seaborn
label_desc: Use image node_id 4 for calling a global function with example usage: filter_data(metadata, criteria, value) and returns: exp_ids