code: def _f1_normalizer(df, y, model_score, random_seed):
      """In case of F1, calculates the baseline score for y and derives the PPS."""
      label_encoder = preprocessing.LabelEncoder()
      df["truth"] = label_encoder.fit_transform(df[y])
      df["most_common_value"] = df["truth"].value_counts().index[0]
      random = df["truth"].sample(frac=1, random_state=random_seed)
  
      baseline_score = max(
          f1_score(
              df["truth"], df["most_common_value"], average="weighted"
          ),
          f1_score(df["truth"], random, average="weighted"),
      )
  
      ppscore = _normalized_f1_score(model_score, baseline_score)
      return ppscore, baseline_score
  
imports: import warnings
  import numpy
  import pandas
  from pandas.api.types import is_bool_dtype, is_categorical_dtype, is_datetime64_any_dtype, is_numeric_dtype, is_object_dtype, is_string_dtype, is_timedelta64_dtype
  from sklearn import preprocessing, tree
  from sklearn.metrics import f1_score, mean_absolute_error
  from sklearn.model_selection import cross_val_score
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 5 for calling a global function with example usage: _f1_normalizer(df, y, model_score, random_seed) and returns: ppscore,  baseline_score