class_docstr: Represents data from mars tensor
code: def _tile_tensor_none(cls, op: "DataFrameFromTensor"):
      out_df = op.outputs[0]
  
      out_chunks = []
      assert isinstance(op.index, INDEX_TYPE)
      # tile as index
      for index_chunk in op.index.chunks:
          index_value = index_chunk.index_value
  
          chunk_shape = (index_chunk.shape[0], out_df.shape[1])
          chunk_index = (index_chunk.index[0], 0)
  
          chunk_op = op.copy().reset_key()
          out_chunk = chunk_op.new_chunk(
              [index_chunk],
              shape=chunk_shape,
              index=chunk_index,
              index_value=index_value,
              columns_value=out_df.columns_value,
              dtypes=out_df.dtypes,
          )
          out_chunks.append(out_chunk)
  
      new_op = op.copy()
      params = out_df.params.copy()
      params["nsplits"] = (op.index.nsplits[0], (out_df.shape[1],))
      params["chunks"] = out_chunks
      return new_op.new_dataframes(out_df.inputs, kws=[params])
  
imports: from collections import OrderedDict
  from typing import Any, Dict, List, Union
  import numpy
  import pandas
  from ...None import opcodes
  from ...core import ENTITY_TYPE, OutputType, recursive_tile
  from ...core.context import Context
  from ...serialization.serializables import KeyField, AnyField
  from ...tensor.core import Tensor
  from ...tensor.datasource import tensor
  from ...tensor.utils import unify_chunks
  from ...typing import EntityType, TileableType
  from ...utils import has_unknown_shape
  from ..core import INDEX_TYPE, SERIES_TYPE
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index
label_desc: Use image node_id 11 for calling the DataFrameFromTensor obj's underlying member method code with example usage: obj._tile_tensor_none(cls, op) and returns: new_op