code: def slice_label_rows(
      labeldf: pd.DataFrame,
      label: str,
      sample_list: List[str],
      row_mask: NDArray[Any],
  ) -> NDArray[Any]:
      """
      Selects rows from the Pandas DataFrame of labels corresponding to the samples in a particular sample_block.
  
      Args:
          labeldf : Pandas DataFrame containing the labels
          label : Header for the particular label to slice.  Can be 'all' if all labels are desired.
          sample_list : List of sample ids corresponding to the sample_block to be sliced out.
          row_mask : 1D numpy array of size n_rows containing booleans used to mask samples from the rows sliced from
              labeldf.
  
      Returns:
          Matrix of [number of samples in sample_block - number of samples masked] x [number of labels to slice]
      """
      if row_mask.size == 0:
          row_mask = np.full(len(sample_list), True)
      if label == "all":
          return labeldf.loc[sample_list, :].to_numpy()[row_mask, :]
      else:
          return (
              labeldf[label]
              .loc[sample_list]
              .to_numpy()
              .reshape(-1, 1)[row_mask, :]
          )
  
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 7 for calling a global function with example usage: slice_label_rows(labeldf, label, sample_list, row_mask) and returns: labeldf, labeldf