class_docstr: Apply a random rescaling on a signal
class_docstr_tok: ["Apply","a","random","rescaling","on","a","signal"]
docstr: Args:
      signal: input 1 dimension signal to be scaled
docstr_tok: ["Args",":","signal",":","input","1","dimension","signal","to","be","scaled"]
code: def __call__(self, signal: NdarrayOrTensor) -> NdarrayOrTensor:
      """
      Args:
          signal: input 1 dimension signal to be scaled
      """
      self.randomize(None)
      self.magnitude = self.R.uniform(
          low=self.boundaries[0], high=self.boundaries[1]
      )
      signal = convert_to_tensor(self.magnitude * signal)
  
      return signal
  
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 2 for calling the SignalRandScale obj's underlying member method code with example usage: obj.__call__(signal) and returns: signal