class_docstr: Represents data from mars tensor
code: def _call_input_1d_tileables(
      self,
      input_1d_tileables: Dict[Any, TileableType],
      index: Union[TileableType, pd.Index],
      columns: pd.Index,
      dtypes: pd.Series,
  ):
      tileables = []
      shape = None
      for tileable in input_1d_tileables.values():
          tileable_shape = astensor(tileable).shape
          if len(tileable_shape) > 0:
              if shape is None:
                  shape = tileable_shape
              elif shape != tileable_shape:
                  raise ValueError(
                      "input 1-d tensors should have same shape"
                  )
  
          if isinstance(tileable, ENTITY_TYPE):
              tileables.append(tileable)
  
      if index is not None:
          tileable_size = tileables[0].shape[0]
          if hasattr(index, "shape"):
              index_size = index.shape[0]
          else:
              index_size = len(index)
          if (
              not pd.isna(tileable_size)
              and not pd.isna(index_size)
              and tileable_size != index_size
          ):
              raise ValueError(
                  f"index {index} should have the same shape "
                  f"with tensor: {tileable_size}"
              )
          index_value = self._process_index(index, tileables)
      else:
          self.index = index = pd.RangeIndex(0, tileables[0].shape[0])
          index_value = parse_index(index)
  
      if columns is not None:
          if len(input_1d_tileables) != len(columns):
              raise ValueError(
                  f"columns {columns} should have size {len(input_1d_tileables)}"
              )
          if not isinstance(columns, pd.Index):
              if isinstance(columns, ENTITY_TYPE):
                  raise NotImplementedError(
                      "The columns value cannot be a tileable"
                  )
              columns = pd.Index(columns)
          columns_value = parse_index(columns, store_data=True)
      else:
          columns_value = parse_index(
              pd.RangeIndex(0, len(input_1d_tileables)), store_data=True
          )
  
      shape = (shape[0], len(input_1d_tileables))
      return self.new_dataframe(
          tileables,
          shape,
          dtypes=dtypes,
          index_value=index_value,
          columns_value=columns_value,
      )
  
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 5 for calling the DataFrameFromTensor obj's underlying member method code with example usage: obj._call_input_1d_tileables(input_1d_tileables, index, columns, dtypes) and returns: self