code: def shift(
      df_or_series, periods=1, freq=None, axis=0, fill_value=None
  ):
      """
      Shift index by desired number of periods with an optional time `freq`.
  
      When `freq` is not passed, shift the index without realigning the data.
      If `freq` is passed (in this case, the index must be date or datetime,
      or it will raise a `NotImplementedError`), the index will be
      increased using the periods and the `freq`.
  
      Parameters
      ----------
      periods : int
          Number of periods to shift. Can be positive or negative.
      freq : DateOffset, tseries.offsets, timedelta, or str, optional
          Offset to use from the tseries module or time rule (e.g. 'EOM').
          If `freq` is specified then the index values are shifted but the
          data is not realigned. That is, use `freq` if you would like to
          extend the index when shifting and preserve the original data.
      axis : {0 or 'index', 1 or 'columns', None}, default None
          Shift direction.
      fill_value : object, optional
          The scalar value to use for newly introduced missing values.
          the default depends on the dtype of `self`.
          For numeric data, ``np.nan`` is used.
          For datetime, timedelta, or period data, etc. :attr:`NaT` is used.
          For extension dtypes, ``self.dtype.na_value`` is used.
  
      Returns
      -------
      DataFrame or Series
          Copy of input object, shifted.
  
      See Also
      --------
      Index.shift : Shift values of Index.
      DatetimeIndex.shift : Shift values of DatetimeIndex.
      PeriodIndex.shift : Shift values of PeriodIndex.
      tshift : Shift the time index, using the index's frequency if
          available.
  
      Examples
      --------
      >>> import mars.dataframe as md
  
      >>> df = md.DataFrame({'Col1': [10, 20, 15, 30, 45],
      ...                    'Col2': [13, 23, 18, 33, 48],
      ...                    'Col3': [17, 27, 22, 37, 52]})
  
      >>> df.shift(periods=3).execute()
         Col1  Col2  Col3
      0   NaN   NaN   NaN
      1   NaN   NaN   NaN
      2   NaN   NaN   NaN
      3  10.0  13.0  17.0
      4  20.0  23.0  27.0
  
      >>> df.shift(periods=1, axis='columns').execute()
         Col1  Col2  Col3
      0   NaN  10.0  13.0
      1   NaN  20.0  23.0
      2   NaN  15.0  18.0
      3   NaN  30.0  33.0
      4   NaN  45.0  48.0
  
      >>> df.shift(periods=3, fill_value=0).execute()
         Col1  Col2  Col3
      0     0     0     0
      1     0     0     0
      2     0     0     0
      3    10    13    17
      4    20    23    27
      """
      axis = validate_axis(axis, df_or_series)
      if periods == 0:
          return df_or_series.copy()
      if fill_value is no_default:  # pragma: no cover
          if not _enable_no_default or (
              _with_column_freq_bug and axis == 1 and freq is not None
          ):
              # pandas shift shows different behavior for axis=1 when freq is specified,
              # see https://github.com/pandas-dev/pandas/issues/47039 for details.
              fill_value = None
      op = DataFrameShift(
          periods=periods, freq=freq, axis=axis, fill_value=fill_value
      )
      return op(df_or_series)
  
imports: import numpy
  import pandas
  from ...None import opcodes
  from ...core import OutputType
  from ...serialization.serializables import KeyField, AnyField, Int8Field, Int64Field
  from ...utils import has_unknown_shape, no_default, pd_release_version
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import parse_index, build_df, build_series, validate_axis
label_desc: Use image node_id 1 for calling a global function with example usage: shift(df_or_series, periods, freq, axis, fill_value) and returns: op, df_or_series