code: def _print_fancy_output(
      learner_result_dicts: List[Dict[str, Any]],
      output_file: IO[str] = sys.stdout,
  ) -> None:
      """
      Print nice tables with all of the results from cross-validation folds.
  
      Parameters
      ----------
      learner_result_dicts : List[Dict[str, Any]]
          List of result dictionaries.
      output_file : IO[str], default=sys.stdout
          The file buffer to print to.
      """
      if not learner_result_dicts:
          raise ValueError("Result dictionary list is empty!")
  
      lrd = learner_result_dicts[0]
      print(
          f'Experiment Name: {lrd["experiment_name"]}', file=output_file
      )
      print(f'SKLL Version: {lrd["version"]}', file=output_file)
      print(f'Training Set: {lrd["train_set_name"]}', file=output_file)
      print(
          f'Training Set Size: {lrd["train_set_size"]}',
          file=output_file,
      )
      print(f'Test Set: {lrd["test_set_name"]}', file=output_file)
      print(f'Test Set Size: {lrd["test_set_size"]}', file=output_file)
      print(f'Shuffle: {lrd["shuffle"]}', file=output_file)
      print(f'Feature Set: {lrd["featureset"]}', file=output_file)
      print(f'Learner: {lrd["learner_name"]}', file=output_file)
      print(f'Task: {lrd["task"]}', file=output_file)
      if lrd["folds_file"]:
          print(
              f'Specified Folds File: {lrd["folds_file"]}',
              file=output_file,
          )
      if lrd["task"] == "cross_validate":
          print(f'Number of Folds: {lrd["cv_folds"]}', file=output_file)
          if not lrd["cv_folds"].endswith("folds file"):
              print(
                  f'Stratified Folds: {lrd["stratified_folds"]}',
                  file=output_file,
              )
      print(
          f'Feature Scaling: {lrd["feature_scaling"]}', file=output_file
      )
      print(f'Grid Search: {lrd["grid_search"]}', file=output_file)
      if lrd["grid_search"]:
          print(
              f'Grid Search Folds: {lrd["grid_search_folds"]}',
              file=output_file,
          )
          print(
              f'Grid Objective Function: {lrd["grid_objective"]}',
              file=output_file,
          )
      if (
          lrd["task"] == "cross_validate"
          and lrd["grid_search"]
          and lrd["cv_folds"].endswith("folds file")
      ):
          print(
              "Using Folds File for Grid Search: "
              f'{lrd["use_folds_file_for_grid_search"]}',
              file=output_file,
          )
      if (
          lrd["task"] in ["evaluate", "cross_validate"]
          and lrd["additional_scores"]
      ):
          print(
              "Additional Evaluation Metrics: "
              f'{list(lrd["additional_scores"].keys())}',
              file=output_file,
          )
      print(
          f'Scikit-learn Version: {lrd["scikit_learn_version"]}',
          file=output_file,
      )
      print(
          f'Start Timestamp: {lrd["start_timestamp"]}', file=output_file
      )
      print(f'End Timestamp: {lrd["end_timestamp"]}', file=output_file)
      print(f'Total Time: {lrd["total_time"]}', file=output_file)
      print("\n", file=output_file)
  
      for lrd in learner_result_dicts:
          print(f'Fold: {lrd["fold"]}', file=output_file)
          print(
              f'Model Parameters: {lrd.get("model_params", "")}',
              file=output_file,
          )
          print(
              f'Grid Objective Score (Train) = {lrd.get("grid_score", "")}',
              file=output_file,
          )
          if "result_table" in lrd:
              print(lrd["result_table"], file=output_file)
              print(f'Accuracy = {lrd["accuracy"]}', file=output_file)
          if "descriptive" in lrd:
              print("Descriptive statistics:", file=output_file)
              for desc_stat in ["min", "max", "avg", "std"]:
                  actual = lrd["descriptive"]["actual"][desc_stat]
                  predicted = lrd["descriptive"]["predicted"][desc_stat]
                  print(
                      f" {desc_stat.title()} = {actual:.4f} (actual), "
                      f"{predicted:.4f} (predicted)",
                      file=output_file,
                  )
              print(f'Pearson = {lrd["pearson"]:f}', file=output_file)
          print(
              f'Objective Function Score (Test) = {lrd.get("score", "")}',
              file=output_file,
          )
  
          # now print the additional metrics, if there were any
          if lrd["additional_scores"]:
              print("", file=output_file)
              print(
                  "Additional Evaluation Metrics (Test):",
                  file=output_file,
              )
              for metric, score in lrd["additional_scores"].items():
                  score = "" if np.isnan(score) else score
                  print(f" {metric} = {score}", file=output_file)
          print("", file=output_file)
  
imports: from __future__ import annotations
  import csv
  import json
  import math
  import sys
  from collections import defaultdict
  from pathlib import Path
  from typing import IO, Any, Dict, List, Tuple
  import matplotlib
  import matplotlib.pyplot
  import numpy
  import pandas
  import seaborn
  from ruamel.yaml import YAML
  from skll.types import FoldMapping, PathOrStr
  from skll.utils.logging import get_skll_logger
label_desc: Use image node_id 5 for calling a global function with example usage: _print_fancy_output(learner_result_dicts, output_file) without return types