code: def _prepare_covariates(
      cov_df: pd.DataFrame, label_df: pd.DataFrame, add_intercept: bool
  ) -> pd.DataFrame:
      """
      Standardizes the covariate Pandas DataFrame and adds intercept column to it if indicated
  
      Args:
          cov_df: Pandas DataFrame of phenotypes
          label_df: Pandas DataFrame of phenotypes
          add_intercept: Whether to add intercept to the covariates Pandas DataFrame
  
      Returns:
          Standardized covariate Pandas DataFrame with optionally added intercept column
      """
      if cov_df.empty:
          if add_intercept:
              std_cov_df = pd.DataFrame(
                  data=np.ones(label_df.shape[0]),
                  columns=["intercept"],
                  index=label_df.index,
              )
          else:
              std_cov_df = cov_df
      else:
          if label_df.shape[0] != cov_df.shape[0]:
              raise ValueError(
                  f"cov_df must be either empty of have the same number of rows as label_df ({label_df.shape[0]} != {cov_df.shape[0]}"
              )
          std_cov_df = _fill_na_and_standardize(cov_df)
          if add_intercept:
              std_cov_df.insert(0, "intercept", 1)
  
      return std_cov_df
  
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 25 for calling a global function with example usage: _prepare_covariates(cov_df, label_df, add_intercept) and returns: std_cov_df