code: def reindex_like(
      df_or_series,
      other,
      method=None,
      copy=True,
      limit=None,
      tolerance=None,
  ):
      """
      Return an object with matching indices as other object.
  
      Conform the object to the same index on all axes. Optional
      filling logic, placing NaN in locations having no value
      in the previous index. A new object is produced unless the
      new index is equivalent to the current one and copy=False.
  
      Parameters
      ----------
      other : Object of the same data type
          Its row and column indices are used to define the new indices
          of this object.
      method : {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
          Method to use for filling holes in reindexed DataFrame.
          Please note: this is only applicable to DataFrames/Series with a
          monotonically increasing/decreasing index.
  
          * None (default): don't fill gaps
          * pad / ffill: propagate last valid observation forward to next
            valid
          * backfill / bfill: use next valid observation to fill gap
          * nearest: use nearest valid observations to fill gap.
  
      copy : bool, default True
          Return a new object, even if the passed indexes are the same.
      limit : int, default None
          Maximum number of consecutive labels to fill for inexact matches.
      tolerance : optional
          Maximum distance between original and new labels for inexact
          matches. The values of the index at the matching locations must
          satisfy the equation ``abs(index[indexer] - target) <= tolerance``.
  
          Tolerance may be a scalar value, which applies the same tolerance
          to all values, or list-like, which applies variable tolerance per
          element. List-like includes list, tuple, array, Series, and must be
          the same size as the index and its dtype must exactly match the
          index's type.
  
      Returns
      -------
      Series or DataFrame
          Same type as caller, but with changed indices on each axis.
  
      See Also
      --------
      DataFrame.set_index : Set row labels.
      DataFrame.reset_index : Remove row labels or move them to new columns.
      DataFrame.reindex : Change to new indices or expand indices.
  
      Notes
      -----
      Same as calling
      ``.reindex(index=other.index, columns=other.columns,...)``.
  
      Examples
      --------
      >>> import pandas as pd
      >>> import mars.dataframe as md
      >>> df1 = md.DataFrame([[24.3, 75.7, 'high'],
      ...                     [31, 87.8, 'high'],
      ...                     [22, 71.6, 'medium'],
      ...                     [35, 95, 'medium']],
      ...                    columns=['temp_celsius', 'temp_fahrenheit',
      ...                             'windspeed'],
      ...                    index=md.date_range(start='2014-02-12',
      ...                                        end='2014-02-15', freq='D'))
  
      >>> df1.execute()
                 temp_celsius temp_fahrenheit windspeed
      2014-02-12         24.3            75.7      high
      2014-02-13           31            87.8      high
      2014-02-14           22            71.6    medium
      2014-02-15           35              95    medium
  
      >>> df2 = md.DataFrame([[28, 'low'],
      ...                     [30, 'low'],
      ...                     [35.1, 'medium']],
      ...                    columns=['temp_celsius', 'windspeed'],
      ...                    index=pd.DatetimeIndex(['2014-02-12', '2014-02-13',
      ...                                            '2014-02-15']))
  
      >>> df2.execute()
                  temp_celsius windspeed
      2014-02-12          28.0       low
      2014-02-13          30.0       low
      2014-02-15          35.1    medium
  
      >>> df2.reindex_like(df1).execute()
                  temp_celsius  temp_fahrenheit windspeed
      2014-02-12          28.0              NaN       low
      2014-02-13          30.0              NaN       low
      2014-02-14           NaN              NaN       NaN
      2014-02-15          35.1              NaN    medium
      """
      cond = df_or_series.index_value.key == other.index_value.key
      if df_or_series.ndim == 2:
          cond &= (
              df_or_series.columns_value.key == other.columns_value.key
          )
      if cond and not copy:
          return df_or_series
  
      kw = {
          "index": other.index,
          "method": method,
          "limit": limit,
          "tolerance": tolerance,
      }
      if df_or_series.ndim == 2:
          kw["columns"] = other.dtypes.index
      return reindex(df_or_series, **kw)
  
imports: import numpy
  import pandas
  from ...None import opcodes
  from ...core import ENTITY_TYPE, recursive_tile
  from ...core.operand import OperandStage
  from ...serialization.serializables import KeyField, AnyField, StringField, Int64Field, BoolField
  from ...tensor import tensor
  from ...utils import lazy_import, pd_release_version
  from ..core import Index, INDEX_TYPE
  from ..initializer import Index
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import validate_axis_style_args, parse_index
  from .index_lib import DataFrameReindexHandler
label_desc: Use image node_id 2 for calling a global function with example usage: reindex_like(df_or_series, other, method, copy, limit, tolerance) and returns: reindex, df_or_series