class_docstr: Add a random sinusoidal signal to the input signal
class_docstr_tok: ["Add","a","random","sinusoidal","signal","to","the","input","signal"]
docstr: Args:
      boundaries: list defining lower and upper boundaries for the sinusoidal magnitude,
          lower and upper values need to be positive ,default : ``[0.1, 0.3]``
      frequencies: list defining lower and upper frequencies for sinusoidal
          signal generation ,default : ``[0.001, 0.02]``
docstr_tok: ["Args",":","boundaries",":","list","defining","lower","and","upper","boundaries","for","the","sinusoidal","magnitude",",","lower","and","upper","values","need","to","be","positive",",","default",":","``","[","0.1",",","0.3","]","``","frequencies",":","list","defining","lower","and","upper","frequencies","for","sinusoidal","signal","generation",",","default",":","``","[","0.001",",","0.02","]","``"]
code: def __init__(
      self,
      boundaries: Sequence[float] = (0.1, 0.3),
      frequencies: Sequence[float] = (0.001, 0.02),
  ) -> None:
      """
      Args:
          boundaries: list defining lower and upper boundaries for the sinusoidal magnitude,
              lower and upper values need to be positive ,default : ``[0.1, 0.3]``
          frequencies: list defining lower and upper frequencies for sinusoidal
              signal generation ,default : ``[0.001, 0.02]``
      """
      super().__init__()
      check_boundaries(boundaries)
      self.boundaries = boundaries
      self.frequencies = frequencies
  
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 SignalRandAddSine object from inherited base classes: RandomizableTransform with example: obj = SignalRandAddSine(boundaries, frequencies)