class_docstr: Randomly drop a portion of a signal
class_docstr_tok: ["Randomly","drop","a","portion","of","a","signal"]
docstr: Args:
      signal: input 1 dimension signal to be dropped
docstr_tok: ["Args",":","signal",":","input","1","dimension","signal","to","be","dropped"]
code: def __call__(self, signal: NdarrayOrTensor) -> NdarrayOrTensor:
      """
      Args:
          signal: input 1 dimension signal to be dropped
      """
      self.randomize(None)
      self.magnitude = self.R.uniform(
          low=self.boundaries[0], high=self.boundaries[1]
      )
  
      length = signal.shape[-1]
      mask = torch.zeros(round(self.magnitude * length))
      trange = torch.arange(length)
      loc = trange[torch.randint(0, trange.size(0), (1,))]
      signal = convert_to_tensor(paste(signal, mask, (loc,)))
  
      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 SignalRandDrop obj's underlying member method code with example usage: obj.__call__(signal) and returns: signal