code: def generate_alphas(blockdf: DataFrame) -> Dict[str, Float]:
      """
      Generates alpha values using a range of heritability values and the number of distinct headers (without labels).
  
      Args:
          blockdf : Spark DataFrame representing a block matrix
          labeldf: Pandas DataFrame containing target labels
  
      Returns:
          Dict of [alpha names, alpha values]
      """
      num_label_free_headers = (
          blockdf.select(
              f.regexp_extract("header", r"^(.+?)($|_label_.*)", 1)
          )
          .distinct()
          .count()
      )
      heritability_vals = [0.99, 0.75, 0.50, 0.25, 0.01]
      alphas = [num_label_free_headers / h for h in heritability_vals]
      print(f"Generated alphas: {alphas}")
      return create_alpha_dict(alphas)
  
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 16 for calling a global function with example usage: generate_alphas(blockdf) and returns: create_alpha_dict