code: def tile(cls, op):
      from ..indexing.slice import TensorSlice
  
      in_tensor = op.inputs[0]
      out_tensor = op.outputs[0]
      axis = op.axis
      if not isinstance(axis, int):
          raise ValueError("axis must be a integer")
      axis = validate_axis(in_tensor.ndim, axis)
      if axis is None:
          raise NotImplementedError
  
      op_type, bin_op_type = getattr(op, "_get_op_types")()
  
      chunks = []
      for c in in_tensor.chunks:
          chunk_op = op_type(axis=op.axis, dtype=op.dtype)
          chunks.append(
              chunk_op.new_chunk(
                  [c],
                  shape=c.shape,
                  index=c.index,
                  order=out_tensor.order,
              )
          )
      inter_tensor = copy.copy(in_tensor)
      inter_tensor._chunks = chunks
  
      slc = [
          slice(None) if i != axis else slice(-1, None)
          for i in range(in_tensor.ndim)
      ]
  
      output_chunks = []
      for chunk in chunks:
          if chunk.index[axis] == 0:
              output_chunks.append(chunk)
              continue
  
          to_cum_chunks = []
          for i in range(chunk.index[axis]):
              to_cum_index = (
                  chunk.index[:axis] + (i,) + chunk.index[axis + 1 :]
              )
              shape = (
                  chunk.shape[:axis] + (1,) + chunk.shape[axis + 1 :]
              )
              to_cum_chunk = inter_tensor.cix[to_cum_index]
              slice_op = TensorSlice(slices=slc, dtype=chunk.dtype)
              sliced_chunk = slice_op.new_chunk(
                  [to_cum_chunk],
                  shape=shape,
                  index=to_cum_index,
                  order=out_tensor.order,
              )
              to_cum_chunks.append(sliced_chunk)
          to_cum_chunks.append(chunk)
  
          # GH#3132: some chunks of to_cum_chunks may be empty,
          # so we tell tree_add&tree_multiply to ignore them
          bin_op = bin_op_type(
              args=to_cum_chunks,
              dtype=chunk.dtype,
              ignore_empty_input=True,
          )
          output_chunk = bin_op.new_chunk(
              to_cum_chunks,
              shape=chunk.shape,
              index=chunk.index,
              order=out_tensor.order,
          )
          output_chunks.append(output_chunk)
  
      new_op = op.copy()
      return new_op.new_tensors(
          op.inputs,
          in_tensor.shape,
          order=out_tensor.order,
          chunks=output_chunks,
          nsplits=in_tensor.nsplits,
      )
  
imports: import builtins
  import copy
  import inspect
  import itertools
  import operator
  from collections.abc import Iterable
  from functools import reduce
  from math import ceil, log
  import numpy
  from ...config import options
  from ...core.operand import OperandStage
  from ...serialization.serializables import KeyField, AnyField, BoolField, Int32Field
  from ..core import Tensor, TensorOrder
  from ..array_utils import get_array_module, as_same_device, device, cp
  from ..utils import check_out_param, validate_axis
  from ..operands import TensorHasInput, TensorOperandMixin
  from ..datasource import tensor
label_desc: Use image node_id 3 for calling the TensorCumReductionMixin obj's underlying member method code with example usage: obj.tile(cls, op) and returns: new_op