class_docstr: Schedule for all the stages.
class_docstr_tok: ["Schedule","for","all","the","stages","."]
docstr: Factor a reduction axis in tensor's schedule to be an explicit axis.
  
  This will create a new stage that generated the new tensor with axis
  as the first dimension. The tensor's body will be rewritten as a reduction
  over the factored tensor.
  
  Parameters
  ----------
  tensor : Tensor
      The tensor to be factored.
  axis : IterVar
      The reduction axis in the schedule to be factored.
  factor_axis : int
      The position where the new axis is placed.
  
  Returns
  -------
  tfactor : Tensor or Array of Tensor
      The created factored tensor.
code: def rfactor(self, tensor, axis, factor_axis=0):
      """Factor a reduction axis in tensor's schedule to be an explicit axis.
  
      This will create a new stage that generated the new tensor with axis
      as the first dimension. The tensor's body will be rewritten as a reduction
      over the factored tensor.
  
      Parameters
      ----------
      tensor : Tensor
          The tensor to be factored.
      axis : IterVar
          The reduction axis in the schedule to be factored.
      factor_axis : int
          The position where the new axis is placed.
  
      Returns
      -------
      tfactor : Tensor or Array of Tensor
          The created factored tensor.
      """
      factored = _ffi_api.ScheduleRFactor(
          self, tensor, axis, factor_axis
      )
      return factored[0] if len(factored) == 1 else factored
  
imports: import collections
  import inspect
  from typing import Callable, List
  import tvm._ffi
  from tvm._ffi.base import string_types
  from tvm.ir import container
  from tvm.runtime import Object, convert
  from tvm.tir import Buffer, IndexMap, IterVar, Var
  from .None import _ffi_api
  from .None import tensor
label_desc: Use image node_id 6 for calling the Schedule obj's underlying member method code with example usage: obj.rfactor(tensor, axis, factor_axis) and returns: unknown