class_docstr: MemoryProfiler, which is implemented based on memory profiler and Accumulator
class_docstr_tok: ["MemoryProfiler",",","which","is","implemented","based","on","memory","profiler","and","Accumulator"]
docstr: Runs and profiles the method func passed in. A profile object is returned.
docstr_tok: ["Runs","and","profiles","the","method","func","passed","in",".","A","profile","object","is","returned","."]
code: def profile(  # type: ignore
      self,
      sub_lines: Optional[List],
      start_line: Optional[int],
      func: Callable[..., Any],
      *args: Any,
      **kwargs: Any,
  ) -> Any:
      """Runs and profiles the method func passed in. A profile object is returned."""
      if has_memory_profiler:
          profiler = UDFLineProfiler()
          wrapped = profiler(
              func, sub_lines=sub_lines, start_line=start_line
          )
          ret = wrapped(*args, **kwargs)
          codemap_dict = {
              filename: list(line_iterator)
              for filename, line_iterator in profiler.code_map.items()
          }
          # Adds a new profile to the existing accumulated value
          self._accumulator.add(codemap_dict)  # type: ignore[arg-type]
          return ret
      else:
          raise PySparkRuntimeError(
              error_class="MISSING_LIBRARY_FOR_PROFILER",
              message_parameters={},
          )
  
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Type, TYPE_CHECKING, Union, cast
  import cProfile
  import inspect
  import pstats
  import linecache
  import os
  import atexit
  import sys
  import warnings
  from pyspark.accumulators import AccumulatorParam
  from pyspark.errors import PySparkRuntimeError
label_desc: Use image node_id 2 for calling the MemoryProfiler obj's underlying member method code with example usage: obj.profile(sub_lines, start_line, func) and returns: ret