code: def series_autocorr(series, lag=1):
      """
      Compute the lag-N autocorrelation.
  
      This method computes the Pearson correlation between
      the Series and its shifted self.
  
      Parameters
      ----------
      lag : int, default 1
          Number of lags to apply before performing autocorrelation.
  
      Returns
      -------
      float
          The Pearson correlation between self and self.shift(lag).
  
      See Also
      --------
      Series.corr : Compute the correlation between two Series.
      Series.shift : Shift index by desired number of periods.
      DataFrame.corr : Compute pairwise correlation of columns.
      DataFrame.corrwith : Compute pairwise correlation between rows or
          columns of two DataFrame objects.
  
      Notes
      -----
      If the Pearson correlation is not well defined return 'NaN'.
  
      Examples
      --------
      >>> import mars.dataframe as md
      >>> s = md.Series([0.25, 0.5, 0.2, -0.05])
      >>> s.autocorr().execute()  # doctest: +ELLIPSIS.execute()
      0.10355...
      >>> s.autocorr(lag=2).execute()  # doctest: +ELLIPSIS.execute()
      -0.99999...
  
      If the Pearson correlation is not well defined, then 'NaN' is returned.
  
      >>> s = md.Series([1, 0, 0, 0])
      >>> s.autocorr().execute()
      nan
      """
      op = DataFrameCorr(other=series.shift(lag), method="pearson")
      return op(series)
  
imports: import numpy
  import pandas
  from ...None import opcodes
  from ...core import ENTITY_TYPE, recursive_tile
  from ...serialization.serializables import BoolField, AnyField, KeyField, Int32Field
  from ...tensor.utils import filter_inputs
  from ...utils import has_unknown_shape
  from ..core import SERIES_TYPE, DATAFRAME_TYPE
  from ..operands import DataFrameOperand, DataFrameOperandMixin
  from ..utils import build_empty_df, validate_axis, parse_index
label_desc: Use image node_id 5 for calling a global function with example usage: series_autocorr(series, lag) and returns: op