code: def _prepare_labels_and_warn(
      label_df: pd.DataFrame, is_binary: bool, label_type: str
  ) -> pd.DataFrame:
      """
      Gets a Pandas DataFrame containing phenotypes (label_df) and whether it is binary or not
      and generates the appropriate warning based on label_type. Returns standardized label DataFrame
  
      Args:
          label_df: Pandas DataFrame of phenotypes
          is_binary: Whether label_df is binary or not
          label_type: User's choice for label type treatment ('detect', 'binary', or 'quantitative')
  
      Returns:
          Standardized label Pandas Dataframe
      """
      if label_type == "detect":
          if is_binary:
              print(
                  "The label DataFrame is binary. Reduction/regression for binary phenotypes will be applied.",
              )
          else:
              print(
                  "The label DataFrame is quantitative. Reduction/regression for quantitative phenotypes will be applied.",
              )
          return _fill_na_and_standardize(label_df)
      elif label_type == "quantitative":
          print(
              "Reduction/regression for quantitative phenotypes will be applied."
          )
          return _fill_na_and_standardize(label_df)
      elif label_type == "binary":
          if is_binary:
              print(
                  "Reduction/regression for binary phenotypes will be applied."
              )
              return _fill_na_and_standardize(label_df)
          else:
              _check_binary(label_df)
              raise TypeError(
                  "Binary label DataFrame expected!", UserWarning
              )
      else:
          raise ValueError(
              f'label_type should be "quantitative", "binary", or "detect". Found {label_type}.'
          )
  
imports: import itertools
  import math
  import re
  import warnings
  from typing import Any, Dict, Iterable, List, Tuple
  import numpy
  import pandas
  import pyspark.sql.functions
  import scipy.optimize
  from nptyping import Float, Int, NDArray
  from pyspark.sql import DataFrame, Row
  from pyspark.sql.window import Window
  from typeguard import typechecked
label_desc: Use image node_id 24 for calling a global function with example usage: _prepare_labels_and_warn(label_df, is_binary, label_type) and returns: _fill_na_and_standardize, _fill_na_and_standardize, _fill_na_and_standardize