code: def compare_evals_with_precision(
      fit_eval,
      calc_eval,
      rtol=1e-6,
      atol=1e-8,
      skip_last_column_in_fit=True,
  ):
      df_fit = read_csv(fit_eval, sep="\t")
      if skip_last_column_in_fit:
          df_fit = df_fit.iloc[:, :-1]
  
      df_calc = read_csv(calc_eval, sep="\t")
  
      if np.any(df_fit.columns != df_calc.columns):
          sys.stderr.write(
              "column sets differ: {}, {}".format(
                  df_fit.columns, df_calc.columns
              )
          )
          return False
  
      def print_diff(column, row_idx):
          sys.stderr.write(
              "column: {}, index: {} {} != {}\n".format(
                  column,
                  row_idx,
                  df_fit[column][row_idx],
                  df_calc[column][row_idx],
              )
          )
  
      for column in df_fit.columns:
          if column in ["SampleId", "Label"]:
              if (df_fit[column] != df_calc[column]).any():
                  print_diff(
                      column,
                      np.where(df_fit[column] != df_calc[column])[0],
                  )
                  return False
          else:
              is_close = np.isclose(
                  df_fit[column].to_numpy(),
                  df_calc[column].to_numpy(),
                  rtol=rtol,
                  atol=atol,
              )
              if np.any(is_close == 0):
                  print_diff(column, np.where(is_close == 0)[0])
                  return False
      return True
  
imports: import collections
  import csv
  import json
  import os
  import random
  import shutil
  import sys
  from pandas import read_csv, DataFrame
  from copy import deepcopy
  import numpy
  from catboost.utils import read_cd
label_desc: Use image node_id 10 for calling a global function with example usage: compare_evals_with_precision(fit_eval, calc_eval, rtol, atol, skip_last_column_in_fit) and returns: True, False, False, False