docstr: chunking cumsum with step size
  
  for example:
      if step=2, chunk_sizes=[4, 2, 6], a = [a0, a1, a2, a3, a4, a5, a6, a7,...a11]
      then the result is [a0, a1, a0+a2, a1+a3, a4, a5, a6, a7, a6+a8, a7+a9, a6+a8+a10, a7+a9+a11]
  Args:
      pk: the public key
      a: the vector to cumsum
      chunk_sizes: the chunk sizes, must sum to a.size
      step: the step size, cumsum with skip step-1 elements
  Returns:
      the cumsum result
code: def chunking_cumsum_with_step(
      pk: PK, a: EV, chunk_sizes: List[int], step: int
  ):
      """
      chunking cumsum with step size
  
      for example:
          if step=2, chunk_sizes=[4, 2, 6], a = [a0, a1, a2, a3, a4, a5, a6, a7,...a11]
          then the result is [a0, a1, a0+a2, a1+a3, a4, a5, a6, a7, a6+a8, a7+a9, a6+a8+a10, a7+a9+a11]
      Args:
          pk: the public key
          a: the vector to cumsum
          chunk_sizes: the chunk sizes, must sum to a.size
          step: the step size, cumsum with skip step-1 elements
      Returns:
          the cumsum result
      """
      return a.chunking_cumsum_with_step(pk.pk, chunk_sizes, step)
  
imports: from typing import List, Optional, Tuple
  import torch
  from fate_utils.ou import CiphertextVector, PlaintextVector
  from fate_utils.ou import Coder
  from fate_utils.ou import Evaluator
  from fate_utils.ou import PK
  from fate_utils.ou import SK
  from fate_utils.ou import keygen
  from .type import TensorEvaluator
label_desc: Use image node_id 24 for calling the evaluator obj's underlying member method code with example usage: obj.chunking_cumsum_with_step(pk, a, chunk_sizes, step) and returns: a