code: def random_noise(col: pd.Series, mag: float) -> pd.Series:
      """
      Fit a gaussian to column, then sample and add to each entry with a magnification parameter
  
      Parameters
      ----------
      col : pd.Series
          A Pandas' Series
  
      mag : float
          Multiplies the noise to control scaling
  
      Returns
      ----------
      A transformed pd.Series
      """
      mu = np.mean(col)
      std = np.std(col)
      noise = np.random.normal(mu, std, len(col)) * mag
      return col + noise
  
file_path: fklearn/src/fklearn/validation/perturbators.py
imports: import random
  from typing import List
  import numpy
  from toolz import curry
  import pandas
  from fklearn.types import ColumnWisePerturbFnType
label_desc: Use image node_id 2 for calling a global function with example usage: random_noise(col, mag) and returns: unknown