code: def apply_model_df(
      blockdf,
      modeldf,
      cvdf,
      transform_udf,
      transform_key_pattern,
      join_type,
  ):
      """
      Applies a model to a reduced block matrix and returns the predictions.
  
      Args:
          blockdf : Spark DataFrame representing a once- or twice-reduced block matrix.
          modeldf : Spark DataFrame produced by the LogisticRegression or RidgeRegression fit method, representing the
          reducer models fit using the shrinkage parameters alpha.
          cvdf : Spark DataFrame returning the cross validation results
          transform_udf : Pandas UDF used to apply the model
          transform_key_pattern : Pattern of column names used in the groupBy clause
          join_type : Join type for join between blockdf and modeldf.
      """
  
      # Hint a sort-merge join to avoid an automatic broadcast join that may cause an OOM
      return (
          blockdf.drop("header_block", "sort_key")
          .join(
              modeldf.drop("header_block").hint("merge"),
              ["sample_block", "header"],
              join_type,
          )
          .withColumn(
              "label",
              f.coalesce(f.col("label"), f.col("labels").getItem(0)),
          )
          .groupBy(transform_key_pattern)
          .apply(transform_udf)
          .join(cvdf, ["label", "alpha"], "inner")
      )
  
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 31 for calling a global function with example usage: apply_model_df(blockdf, modeldf, cvdf, transform_udf, transform_key_pattern, join_type) and returns: blockdf