code: def _tile_one_chunk(cls, op):
      outs = op.outputs
      chunk_op = op.copy().reset_key()
      kws = [
          {
              "shape": outs[0].shape,
              "order": outs[0].order,
              "index": (0, 0),
          }
      ]
      if len(outs) == 2:
          kws.append(
              {
                  "shape": outs[1].shape,
                  "order": outs[1].order,
                  "index": (0,),
              }
          )
      chunks = chunk_op.new_chunks(
          [op.input.chunks[0]], kws=kws, output_limit=len(outs)
      )
  
      tensor_kws = [
          {
              "shape": outs[0].shape,
              "order": outs[0].order,
              "chunks": [chunks[0]],
              "nsplits": tuple((s,) for s in outs[0].shape),
          }
      ]
      if len(outs) == 2:
          tensor_kws.append(
              {
                  "shape": outs[1].shape,
                  "order": outs[1].order,
                  "chunks": [chunks[1]],
                  "nsplits": tuple((s,) for s in outs[1].shape),
              }
          )
  
      new_op = op.copy()
      return new_op.new_tensors(
          op.inputs, kws=tensor_kws, output_limit=len(outs)
      )
  
imports: import numpy
  from ...None import opcodes
  from ...core import ExecutableTuple, recursive_tile
  from ...serialization.serializables import KeyField, StringField, Int32Field, BoolField
  from ...tensor.operands import TensorOperand, TensorOperandMixin
  from ...tensor.core import TensorOrder
  from ...tensor.array_utils import as_same_device, device, sparse, issparse
  from ...None import tensor
  from ..utils import check_array
label_desc: Use image node_id 10 for calling the TensorNormalize obj's underlying member method code with example usage: obj._tile_one_chunk(cls, op) and returns: new_op