class_docstr: Schedule for all the stages.
class_docstr_tok: ["Schedule","for","all","the","stages","."]
docstr: Create a cache write of original tensor, before storing into tensor.
  
  This will mutate the body of the tensor.
  A new cache stage will created before feed into the tensor.
  
  This function can be used to support data layout transformation.
  If there is a split/fuse/reorder on the data parallel axis of tensor
  before cache_write is called. The intermediate cache stores
  the data in the layout as the iteration order of leave axis.
  The data will be transformed back to the original layout in the original tensor.
  User can further call compute_inline to inline the original layout and keep
  the data stored in the transformed layout.
  
  Parameters
  ----------
  tensor : Tensor, list or tuple
      The tensors to be feed to. All the tensors must be produced by one computeOp
  scope : str
      The scope of cached
  
  Returns
  -------
  cache : Tensor
      The created cache tensor.
code: def cache_write(self, tensor, scope):
      """Create a cache write of original tensor, before storing into tensor.
  
      This will mutate the body of the tensor.
      A new cache stage will created before feed into the tensor.
  
      This function can be used to support data layout transformation.
      If there is a split/fuse/reorder on the data parallel axis of tensor
      before cache_write is called. The intermediate cache stores
      the data in the layout as the iteration order of leave axis.
      The data will be transformed back to the original layout in the original tensor.
      User can further call compute_inline to inline the original layout and keep
      the data stored in the transformed layout.
  
      Parameters
      ----------
      tensor : Tensor, list or tuple
          The tensors to be feed to. All the tensors must be produced by one computeOp
      scope : str
          The scope of cached
  
      Returns
      -------
      cache : Tensor
          The created cache tensor.
      """
      return _ffi_api.ScheduleCacheWrite(self, tensor, scope)
  
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 5 for calling the Schedule obj's underlying member method code with example usage: obj.cache_write(tensor, scope) and returns: _ffi_api