code: def preprocess_y_data(y_full, y_split, target):
      target_transformers = None
      if not is_numeric_dtype(y_full):
          enc = LabelEncoder()
          fitted_enc = enc.fit(y_full)
          y_split["train"] = pd.Series(
              fitted_enc.transform(y_split["train"])
          )
          y_split["train"].name = target
          y_split["test"] = pd.Series(
              fitted_enc.transform(y_split["test"])
          )
          y_split["test"].name = target
          target_transformers = [fitted_enc]
      return target_transformers
  
imports: import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.pipeline import Pipeline
  from prince.ca import CA
  from nltk.stem import WordNetLemmatizer
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from autocorrect import Speller
  from sklearn.model_selection import train_test_split
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import OneHotEncoder, StandardScaler, FunctionTransformer, LabelEncoder
  from pandas.api.types import is_numeric_dtype
  import warnings
label_desc: Use image node_id 2 for calling a global function with example usage: preprocess_y_data(y_full, y_split, target) and returns: target_transformers