code: def main(infile: IO, outfile: IO) -> None:
      """
      Runs the Python UDTF's `analyze` static method.
  
      This process will be invoked from `UserDefinedPythonTableFunctionAnalyzeRunner.runInPython`
      in JVM and receive the Python UDTF and its arguments for the `analyze` static method,
      and call the `analyze` static method, and send back a AnalyzeResult as a result of the method.
      """
      try:
          check_python_version(infile)
  
          memory_limit_mb = int(
              os.environ.get("PYSPARK_PLANNER_MEMORY_MB", "-1")
          )
          setup_memory_limits(memory_limit_mb)
  
          setup_spark_files(infile)
          setup_broadcasts(infile)
  
          _accumulatorRegistry.clear()
  
          udtf_name = utf8_deserializer.loads(infile)
          handler = read_udtf(infile)
          args, kwargs = read_arguments(infile)
  
          error_prefix = f"Failed to evaluate the user-defined table function '{udtf_name}'"
  
          def format_error(msg: str) -> str:
              return dedent(msg).replace("\n", " ")
  
          # Check that the arguments provided to the UDTF call match the expected parameters defined
          # in the static 'analyze' method signature.
          try:
              inspect.signature(handler.analyze).bind(*args, **kwargs)  # type: ignore[attr-defined]
          except TypeError as e:
              # The UDTF call's arguments did not match the expected signature.
              raise PySparkValueError(
                  format_error(
                      f"""
                      {error_prefix} because the function arguments did not match the expected
                      signature of the static 'analyze' method ({e}). Please update the query so that
                      this table function call provides arguments matching the expected signature, or
                      else update the table function so that its static 'analyze' method accepts the
                      provided arguments, and then try the query again."""
                  )
              )
  
          # Invoke the UDTF's 'analyze' method.
          result = handler.analyze(*args, **kwargs)  # type: ignore[attr-defined]
  
          # Check invariants about the 'analyze' method after running it.
          if not isinstance(result, AnalyzeResult):
              raise PySparkValueError(
                  format_error(
                      f"""
                      {error_prefix} because the static 'analyze' method expects a result of type
                      pyspark.sql.udtf.AnalyzeResult, but instead this method returned a value of
                      type: {type(result)}"""
                  )
              )
          elif not isinstance(result.schema, StructType):
              raise PySparkValueError(
                  format_error(
                      f"""
                      {error_prefix} because the static 'analyze' method expects a result of type
                      pyspark.sql.udtf.AnalyzeResult with a 'schema' field comprising a StructType,
                      but the 'schema' field had the wrong type: {type(result.schema)}"""
                  )
              )
          has_table_arg = any(arg.isTable for arg in args) or any(
              arg.isTable for arg in kwargs.values()
          )
          if not has_table_arg and result.withSinglePartition:
              raise PySparkValueError(
                  format_error(
                      f"""
                      {error_prefix} because the static 'analyze' method returned an
                      'AnalyzeResult' object with the 'withSinglePartition' field set to 'true', but
                      the function call did not provide any table argument. Please update the query so
                      that it provides a table argument, or else update the table function so that its
                      'analyze' method returns an 'AnalyzeResult' object with the
                      'withSinglePartition' field set to 'false', and then try the query again."""
                  )
              )
          elif not has_table_arg and len(result.partitionBy) > 0:
              raise PySparkValueError(
                  format_error(
                      f"""
                      {error_prefix} because the static 'analyze' method returned an
                      'AnalyzeResult' object with the 'partitionBy' list set to non-empty, but the
                      function call did not provide any table argument. Please update the query so
                      that it provides a table argument, or else update the table function so that its
                      'analyze' method returns an 'AnalyzeResult' object with the 'partitionBy' list
                      set to empty, and then try the query again."""
                  )
              )
          elif isinstance(result.partitionBy, (list, tuple)) and (
              len(result.partitionBy) > 0
              and not all(
                  [
                      isinstance(val, PartitioningColumn)
                      for val in result.partitionBy
                  ]
              )
          ):
              raise PySparkValueError(
                  format_error(
                      f"""
                      {error_prefix} because the static 'analyze' method returned an
                      'AnalyzeResult' object with the 'partitionBy' field set to a value besides a
                      list or tuple of 'PartitioningColumn' objects. Please update the table function
                      and then try the query again."""
                  )
              )
  
          # Return the analyzed schema.
          write_with_length(
              result.schema.json().encode("utf-8"), outfile
          )
          # Return the pickled 'AnalyzeResult' class instance.
          pickleSer._write_with_length(result, outfile)
          # Return whether the "with single partition" property is requested.
          write_int(1 if result.withSinglePartition else 0, outfile)
          # Return the list of partitioning columns, if any.
          write_int(len(result.partitionBy), outfile)
          for partitioning_col in result.partitionBy:
              write_with_length(
                  partitioning_col.name.encode("utf-8"), outfile
              )
          # Return the requested input table ordering, if any.
          write_int(len(result.orderBy), outfile)
          for ordering_col in result.orderBy:
              write_with_length(
                  ordering_col.name.encode("utf-8"), outfile
              )
              write_int(1 if ordering_col.ascending else 0, outfile)
              if ordering_col.overrideNullsFirst is None:
                  write_int(0, outfile)
              elif ordering_col.overrideNullsFirst:
                  write_int(1, outfile)
              else:
                  write_int(2, outfile)
  
      except BaseException as e:
          handle_worker_exception(e, outfile)
          sys.exit(-1)
  
      send_accumulator_updates(outfile)
  
      # check end of stream
      if read_int(infile) == SpecialLengths.END_OF_STREAM:
          write_int(SpecialLengths.END_OF_STREAM, outfile)
      else:
          # write a different value to tell JVM to not reuse this worker
          write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
          sys.exit(-1)
  
file_path: spark/python/pyspark/sql/worker/analyze_udtf.py
imports: import inspect
  import os
  import sys
  from textwrap import dedent
  from typing import Dict, List, IO, Tuple
  from pyspark.accumulators import _accumulatorRegistry
  from pyspark.errors import PySparkRuntimeError, PySparkValueError
  from pyspark.java_gateway import local_connect_and_auth
  from pyspark.serializers import read_bool, read_int, write_int, write_with_length, SpecialLengths
  from pyspark.sql.functions import PartitioningColumn
  from pyspark.sql.types import _parse_datatype_json_string, StructType
  from pyspark.sql.udtf import AnalyzeArgument, AnalyzeResult
  from pyspark.util import handle_worker_exception
  from pyspark.worker_util import check_python_version, read_command, pickleSer, send_accumulator_updates, setup_broadcasts, setup_memory_limits, setup_spark_files, utf8_deserializer
label_desc: Use image node_id 3 for calling a global function with example usage: main(infile, outfile) and returns: dedent