class_docstr: Rule that mark parallelize, vectorize and unroll to the root block. The mark will be applied
  to each block in a follow-up post processor
  
  Parameters
  ----------
  max_jobs_per_core: int
      The maximum number of jobs to be launched per CPU core. It sets the upper limit of CPU
      parallelism, i.e. `num_cores * max_jobs_per_core`.
      Use -1 to disable parallelism.
  max_vectorize_extent: int
      The maximum extent to be vectorized. It sets the upper limit of the hardware target
      vectorization.
      Use -1 to disable vectorization.
  unroll_max_steps: Optional[List[int]]
      The options of the maximum number of unroll steps to be done.
      Use None to disable unroll
  unroll_explicit: bool
      Whether to explicitly unroll the loop, or just add an "unroll" pragma
code: def __init__(
      self,
      max_jobs_per_core: int = 16,
      max_vectorize_extent: int = 16,
      unroll_max_steps: Optional[List[int]] = None,
      unroll_explicit: bool = True,
  ) -> None:
      if unroll_max_steps is None:
          unroll_max_steps = []
      self.__init_handle_by_constructor__(
          _ffi_api.ScheduleRuleParallelizeVectorizeUnroll,  # type: ignore # pylint: disable=no-member
          max_jobs_per_core,
          max_vectorize_extent,
          unroll_max_steps,
          unroll_explicit,
      )
  
imports: from typing import List, Optional
  from tvm._ffi import register_object
  from ..None import _ffi_api
  from .schedule_rule import ScheduleRule
label_desc: Use image node_id 1 to create a new ParallelizeVectorizeUnroll object from inherited base classes: ScheduleRule with example: obj = ParallelizeVectorizeUnroll(max_jobs_per_core, max_vectorize_extent, unroll_max_steps, unroll_explicit)