code: def too_many_values(data, ca_threshold):
      total_unique = 0
      for col in data:
          if total_unique > ca_threshold:
              return True
          # Use value_counts() due to same columns having strings and floats
          total_unique += len(data[col].value_counts())
  
      return False
  
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 8 for calling a global function with example usage: too_many_values(data, ca_threshold) and returns: False, True