class_docstr: Apply a random shift on a signal
class_docstr_tok: ["Apply","a","random","shift","on","a","signal"]
docstr: Args:
      mode: define how the extension of the input array is done beyond its boundaries, see for more details :
          https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.shift.html.
      filling: value to fill past edges of input if mode is ‘constant’. Default is 0.0. see for mode details :
          https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.shift.html.
      boundaries: list defining lower and upper boundaries for the signal shift, default : ``[-1.0, 1.0]``
docstr_tok: ["Args",":","mode",":","define","how","the","extension","of","the","input","array","is","done","beyond","its","boundaries",",","see","for","more","details",":","https",":","\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.ndimage.shift.html",".","filling",":","value","to","fill","past","edges","of","input","if","mode","is","\u2018","constant","\u2019",".","Default","is","0.0.","see","for","mode","details",":","https",":","\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.ndimage.shift.html",".","boundaries",":","list","defining","lower","and","upper","boundaries","for","the","signal","shift",",","default",":","``","[","-1.0",",","1.0","]","``"]
code: def __init__(
      self,
      mode: str | None = "wrap",
      filling: float | None = 0.0,
      boundaries: Sequence[float] = (-1.0, 1.0),
  ) -> None:
      """
      Args:
          mode: define how the extension of the input array is done beyond its boundaries, see for more details :
              https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.shift.html.
          filling: value to fill past edges of input if mode is ‘constant’. Default is 0.0. see for mode details :
              https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.shift.html.
          boundaries: list defining lower and upper boundaries for the signal shift, default : ``[-1.0, 1.0]``
      """
      super().__init__()
      check_boundaries(boundaries)
      self.filling = filling
      self.mode = mode
      self.boundaries = boundaries
  
imports: from __future__ import annotations
  import warnings
  from collections.abc import Sequence
  from typing import Any
  import numpy
  import torch
  from monai.config.type_definitions import NdarrayOrTensor
  from monai.transforms.transform import RandomizableTransform, Transform
  from monai.transforms.utils import check_boundaries, paste, squarepulse
  from monai.utils import optional_import
  from monai.utils.enums import TransformBackends
  from monai.utils.type_conversion import convert_data_type, convert_to_tensor
label_desc: Use image node_id 1 to create a new SignalRandShift object from inherited base classes: RandomizableTransform with example: obj = SignalRandShift(mode, filling, boundaries)