class_docstr: Lagging transformer. Lags time series by one or multiple lags.
  
  Transforms a time series into a lagged version of itself.
  Multiple lags can be provided, as a list.
  Estimator-like wrapper of pandas.shift and integer index lagging.
  
  Lags can be provided as a simple offset, `lags`, or pair of (lag count, frequency),
  with lag count an int (`lags` arg) and frequency a `pandas` frequency descriptor.
  
  When multiple lags are provided, multiple column concatenated copies of the lagged
  time series will be created.
  Names of columns are lagname__variablename, where lagname describes the lag/freq.
  
  If data was provided in _fit or _update, Lag transformer memorizes those indices
  and uses them for computing lagged values.
  To use only data seen in transform, use the FitInTransform compositor.
  
  Parameters
  ----------
  lags : lag offset, or list of lag offsets, optional, default=0 (identity transform)
      a "lag offset" can be one of the following:
      int - number of periods to shift/lag
      time-like: `DateOffset`, `tseries.offsets`, or `timedelta`
          time delta offset to shift/lag
          requires time index of transformed data to be time-like (not int)
      str - time rule from pandas.tseries module, e.g., "EOM"
  freq : frequency descriptor of list of frequency descriptors, optional, default=None
      if passed, must be scalar, or list of equal length to `lags` parameter
      elements in `freq` correspond to elements in lags
      if i-th element of `freq` is not None, i-th element of `lags` must be int
          this is called the "corresponding lags element" below
      "frequency descriptor" can be one of the following:
      time-like: `DateOffset`, `tseries.offsets`, or `timedelta`
          multiplied to corresponding `lags` element when shifting
      str - offset from pd.tseries module, e.g., "D", "M", or time rule, e.g., "EOM"
  index_out : str, optional, one of "shift", "original", "extend", default="extend"
      determines set of output indices in lagged time series
      "shift" - only shifted indices are retained.
          Will not create NA for single lag, but can create NA for multiple lags.
      "original" - only original indices are retained. Will usually create NA.
      "extend" - both original indices and shifted indices are retained.
          Will usually create NA, possibly many, if shifted/original do not intersect.
  flatten_transform_index : bool, optional (default=True)
      if True, columns of return DataFrame are flat, by "lagname__variablename"
      if False, columns are MultiIndex (lagname, variablename)
      has no effect if return mtype is one without column names
  keep_column_names : bool, optional (default=False)
      has an effect only if `lags` contains only a single element
      if True, ensures that column names of `transform` output are same as in input,
      i.e., not `lag_x__varname` but `varname`. Overrides `flatten_transform_index`.
  remember_data : bool, optional (default=True)
      if True, memorizes data seen in ``fit``, ``update``, uses it in ``transform``
      if False, only uses data seen in ``transform`` to produce lags
      setting to False ensures faster runtime if only used via ``fit_transform``
  
  Examples
  --------
  >>> from sktime.datasets import load_airline
  >>> from sktime.transformations.series.lag import Lag
  >>> X = load_airline()
  
      Single lag will yield a time series with the same variables:
  >>> t = Lag(2)
  >>> Xt = t.fit_transform(X)
  
      Multiple lags can be provided, this will result in multiple columns:
  
  >>> t = Lag([2, 4, -1])
  >>> Xt = t.fit_transform(X)
  
      The default setting of index_out will extend indices either side.
      To ensure that the index remains the same after transform,
      use index_out="original"
  
  >>> t = Lag([2, 4, -1], index_out="original")
  >>> Xt = t.fit_transform(X)
  
      The lag transformer may (and usually will) create NAs.
      (except when index_out="shift" and there is only a single lag, or in
      trivial cases). This may need to be handled, e.g., if a subsequent
      pipeline step does not accept NA. To deal with the NAs,
      pipeline with the Imputer:
  
  >>> from sktime.datasets import load_airline
  >>> from sktime.transformations.series.impute import Imputer
  >>> from sktime.transformations.series.lag import Lag
  >>> X = load_airline()
  >>>
  >>> t = Lag([2, 4, -1]) * Imputer("nearest")
  >>> Xt = t.fit_transform(X)
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docstr: Update transformer with X and y.
  
  private _update containing the core logic, called from update
  
  Parameters
  ----------
  X : pd.DataFrame
      Data to update transformer with
  y : ignored, passed for interface compatibility
  
  Returns
  -------
  self: reference to self
docstr_tok: ["Update","transformer","with","X","and","y.","private","_update","containing","the","core","logic",",","called","from","update","Parameters","--","--","--","--","--","X",":","pd.DataFrame","Data","to","update","transformer","with","y",":","ignored",",","passed","for","interface","compatibility","Returns","--","--","--","-","self",":","reference","to","self"]
code: def _update(self, X, y=None):
      """Update transformer with X and y.
  
      private _update containing the core logic, called from update
  
      Parameters
      ----------
      X : pd.DataFrame
          Data to update transformer with
      y : ignored, passed for interface compatibility
  
      Returns
      -------
      self: reference to self
      """
      return self
  
imports: import numpy
  import pandas
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.multiindex import flatten_multiindex
  from sktime.utils.warnings import warn
label_desc: Use image node_id 6 for calling the Lag obj's underlying member method code with example usage: obj._update(X, y) and returns: self