class_docstr: Generate continuous wavelet transform of a signal
class_docstr_tok: ["Generate","continuous","wavelet","transform","of","a","signal"]
docstr: Args:
      signal: signal for which to generate continuous wavelet transform
docstr_tok: ["Args",":","signal",":","signal","for","which","to","generate","continuous","wavelet","transform"]
code: def __call__(self, signal: np.ndarray) -> Any:
      """
      Args:
          signal: signal for which to generate continuous wavelet transform
      """
      mother_wavelet = self.type
      spread = np.arange(1, self.length + 1, 1)
      scales = (
          central_frequency(mother_wavelet) * self.frequency / spread
      )
  
      coeffs, _ = cwt(
          signal, scales, mother_wavelet, 1.0 / self.frequency
      )
  
      coeffs = np.transpose(coeffs, [1, 0, 2])
  
      return coeffs
  
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 SignalContinuousWavelet obj's underlying member method code with example usage: obj.__call__(signal) and returns: coeffs