code: def generate_random_column_samples(column):
      col_mask = np.isnan(column)
      n_missing = np.sum(col_mask)
      if n_missing == len(column):
          logging.warn("No observed values in column")
          return np.zeros_like(column)
  
      mean = np.nanmean(column)
      std = np.nanstd(column)
  
      if np.isclose(std, 0):
          return np.array([mean] * n_missing)
      else:
          return np.random.randn(n_missing) * std + mean
  
imports: import logging
  import importlib
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: generate_random_column_samples(column) and returns: np, np, unknown