class_docstr: Padding panel of unequal length time series to equal, fixed length.
  
  Pads the input dataset to either a optional fixed length
  (longer than the longest series).
  Or finds the max length series across all series and dimensions and
  pads to that with zeroes.
  
  Parameters
  ----------
  pad_length  : int, optional (default=None) length to pad the series too.
              if None, will find the longest sequence and use instead.
class_docstr_tok: ["Padding","panel","of","unequal","length","time","series","to","equal",",","fixed","length",".","Pads","the","input","dataset","to","either","a","optional","fixed","length","(","longer","than","the","longest","series",")",".","Or","finds","the","max","length","series","across","all","series","and","dimensions","and","pads","to","that","with","zeroes",".","Parameters","--","--","--","--","--","pad_length",":","int",",","optional","(","default=None",")","length","to","pad","the","series","too",".","if","None",",","will","find","the","longest","sequence","and","use","instead","."]
docstr: Transform X and return a transformed version.
  
  private _transform containing core logic, called from transform
  
  Parameters
  ----------
  X : nested pandas DataFrame of shape [n_instances, n_features]
      each cell of X must contain pandas.Series
      Data to fit transform to
  y : ignored argument for interface compatibility
      Additional data, e.g., labels for transformation
  
  Returns
  -------
  Xt : nested pandas DataFrame of shape [n_instances, n_features]
      each cell of Xt contains pandas.Series
      transformed version of X
docstr_tok: ["Transform","X","and","return","a","transformed","version",".","private","_transform","containing","core","logic",",","called","from","transform","Parameters","--","--","--","--","--","X",":","nested","pandas","DataFrame","of","shape","[","n_instances",",","n_features","]","each","cell","of","X","must","contain","pandas.Series","Data","to","fit","transform","to","y",":","ignored","argument","for","interface","compatibility","Additional","data",",","e.g.",",","labels","for","transformation","Returns","--","--","--","-","Xt",":","nested","pandas","DataFrame","of","shape","[","n_instances",",","n_features","]","each","cell","of","Xt","contains","pandas.Series","transformed","version","of","X"]
code: def _transform(self, X, y=None):
      """Transform X and return a transformed version.
  
      private _transform containing core logic, called from transform
  
      Parameters
      ----------
      X : nested pandas DataFrame of shape [n_instances, n_features]
          each cell of X must contain pandas.Series
          Data to fit transform to
      y : ignored argument for interface compatibility
          Additional data, e.g., labels for transformation
  
      Returns
      -------
      Xt : nested pandas DataFrame of shape [n_instances, n_features]
          each cell of Xt contains pandas.Series
          transformed version of X
      """
      n_instances, _ = X.shape
  
      arr = [X.iloc[i, :].values for i in range(n_instances)]
  
      max_length = _get_max_length(arr)
  
      if max_length > self.pad_length_:
          raise ValueError(
              "Error: max_length of series \
                      is greater than the one found when fit or set."
          )
  
      pad = [
          pd.Series([self._create_pad(series) for series in out])
          for out in arr
      ]
      Xt = df_map(pd.DataFrame(pad))(pd.Series)
  
      return Xt
  
file_path: sktime/sktime/transformations/panel/padder.py
imports: import numpy
  import pandas
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.pandas import df_map
label_desc: Use image node_id 4 for calling the PaddingTransformer obj's underlying member method code with example usage: obj._transform(X, y) and returns: Xt