code: def warn_imbalanced_completion(df):
      completions = df["completion"].tolist()
      completions_counter = Counter(completions)
      message = None
      # low variety of unique completions relative to completions
      # suggesting it is a classification set up
      if len(completions_counter) < len(completions) / 3:
          message = f"There are {len(completions_counter)} unique completions over {len(completions)} samples.\nThe five most common completions are:"
          for completion, n in completions_counter.most_common(5):
              message += f"\n {n} samples ({round(100*n/len(completions),0)}%) with completion: {completion}"
      return message
  
imports: import argparse
  import math
  import os
  import pathlib
  from collections import Counter
  import numpy
  import pandas
label_desc: Use image node_id 6 for calling a global function with example usage: warn_imbalanced_completion(df) and returns: message