code: def hyperopt_report(
      hyperparameters,
      hyperopt_results_df,
      metric,
      filename_template,
      float_precision=3,
  ):
      title = "Hyperopt Report: {}"
      for hp_name, hp_params in hyperparameters.items():
          if hp_params[SPACE] in RAY_TUNE_INT_SPACES:
              hyperopt_int_plot(
                  hyperopt_results_df,
                  hp_name,
                  metric,
                  title.format(hp_name),
                  filename_template.format(hp_name)
                  if filename_template
                  else None,
              )
          elif hp_params[SPACE] in RAY_TUNE_FLOAT_SPACES:
              hyperopt_float_plot(
                  hyperopt_results_df,
                  hp_name,
                  metric,
                  title.format(hp_name),
                  filename_template.format(hp_name)
                  if filename_template
                  else None,
                  log_scale_x=hp_params["scale"] == "log"
                  if "scale" in hp_params
                  else False,
              )
          elif hp_params[SPACE] in RAY_TUNE_CATEGORY_SPACES:
              hyperopt_category_plot(
                  hyperopt_results_df,
                  hp_name,
                  metric,
                  title.format(hp_name),
                  filename_template.format(hp_name)
                  if filename_template
                  else None,
              )
          else:
              # TODO: more research needed on how to handle RayTune "sample_from" search space
              raise ValueError(
                  f"{hp_params[SPACE]} search space not supported in Ludwig.  "
                  f"Supported values are {RAY_TUNE_FLOAT_SPACES | RAY_TUNE_INT_SPACES | RAY_TUNE_CATEGORY_SPACES}."
              )
  
      # quantize float and int columns
      for hp_name, hp_params in hyperparameters.items():
          if hp_params[SPACE] in RAY_TUNE_INT_SPACES:
              num_distinct_values = len(
                  hyperopt_results_df[hp_name].unique()
              )
              if num_distinct_values > INT_QUANTILES:
                  hyperopt_results_df[hp_name] = pd.qcut(
                      hyperopt_results_df[hp_name],
                      q=INT_QUANTILES,
                      precision=0,
                  )
          elif hp_params[SPACE] in RAY_TUNE_FLOAT_SPACES:
              hyperopt_results_df[hp_name] = pd.qcut(
                  hyperopt_results_df[hp_name],
                  q=FLOAT_QUANTILES,
                  precision=float_precision,
                  duplicates="drop",
              )
  
      hyperopt_pair_plot(
          hyperopt_results_df,
          metric,
          title.format("pair plot"),
          filename_template.format("pair_plot")
          if filename_template
          else None,
      )
  
imports: import copy
  import logging
  from collections import Counter
  from sys import platform
  from typing import Dict, List
  import numpy
  import pandas
  import ptitprince
  from packaging import version
  from ludwig.constants import SPACE, TRAINING, VALIDATION
label_desc: Use image node_id 25 for calling a global function with example usage: hyperopt_report(hyperparameters, hyperopt_results_df, metric, filename_template, float_precision) without return types