class_docstr: Perform the signature transform over given windows.
  
  Given data of shape [N, L, C] and specification of a window method from the
  signatures window module, this class will compute the signatures over
  each window (for the given signature options) and concatenate the results
  into a tensor of shape [N, num_sig_features * num_windows].
  
  Parameters
  ----------
  num_intervals: int, dimension of the transformed data (default 8)
code: def __init__(
      self,
      window_name=None,
      window_depth=None,
      window_length=None,
      window_step=None,
      sig_tfm=None,
      sig_depth=None,
      rescaling=None,
      backend=None,
  ):
      super().__init__()
      self.window_name = window_name
      self.window_depth = window_depth
      self.window_length = window_length
      self.window_step = window_step
      self.sig_tfm = sig_tfm
      self.sig_depth = sig_depth
      self.rescaling = rescaling
      self.backend = backend
  
      self.window = _window_getter(
          self.window_name,
          self.window_depth,
          self.window_length,
          self.window_step,
      )
  
imports: import numpy
  from sktime.transformations.base import BaseTransformer
  from sktime.transformations.panel.signature_based._rescaling import _rescale_path, _rescale_signature
  from sktime.transformations.panel.signature_based._window import _window_getter
label_desc: Use image node_id 1 to create a new _WindowSignatureTransform object from inherited base classes: BaseTransformer with example: obj = _WindowSignatureTransform(window_name, window_depth, window_length, window_step, sig_tfm, sig_depth, rescaling, backend)