class_docstr: Transformation class from the signature method.
  
  Follows the methodology laid out in the paper:
      "A Generalised Signature Method for Multivariate Time Series"
  
  Parameters
  ----------
  augmentation_list: list or tuple of strings, possible strings are
      ['leadlag', 'ir', 'addtime', 'cumsum', 'basepoint']
      Augmentations to apply to the data before computing the signature.
      The order of the augmentations is the order in which they are applied.
      default: ('basepoint', 'addtime')
  window_name: str, one of ``['global', 'sliding', 'expanding', 'dyadic']``
      default: 'dyadic'
      Type of the window to use for the signature transform.
  window_depth: int, default=3
      The depth of the dyadic window.
      Ignored unless ``window_name`` is ``'dyadic'``.
  window_length: None (default) or int
      The length of the sliding/expanding window. (Active
      Ignored unless ``window_name`` is one of ``['sliding, 'expanding']``.
  window_step: None (default) or int
      The step of the sliding/expanding window.
      Ignored unless ``window_name`` is one of ``['sliding, 'expanding']``.
  rescaling: None (default) or str, "pre" or "post",
      None: No rescaling is applied.
      "pre": rescale the path last signature term should be roughly O(1)
      "post": Rescales the output signature by multiplying the depth-d term by d!.
          Aim is that every term becomes ~O(1).
  sig_tfm: str, one of ``['signature', 'logsignature']``. default: ``'signature'``
      The type of signature transform to use, plain or logarithmic.
  depth: int, default=4
      Signature truncation depth.
  backend: str, one of: ``'esig'`` (default), or ``'iisignature'``.
      The backend to use for signature computation.
  
  Attributes
  ----------
  signature_method: sklearn.Pipeline, A sklearn pipeline object that contains
      all the steps to extract the signature features.
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code: def _transform(self, X, y=None):
      return self.signature_method.transform(X)
  
file_path: sktime/sktime/transformations/panel/signature_based/_signature_method.py
imports: from sklearn.pipeline import Pipeline
  from sktime.transformations.base import BaseTransformer
  from sktime.transformations.panel.signature_based._augmentations import _make_augmentation_pipeline
  from sktime.transformations.panel.signature_based._compute import _WindowSignatureTransform
  from sktime.utils.validation._dependencies import _check_soft_dependencies
  from sktime.utils.warnings import warn
label_desc: Use image node_id 4 for calling the SignatureTransformer obj's underlying member method code with example usage: obj._transform(X, y) and returns: self