class_docstr: Compute confidence map from an ultrasound image.
  This transform uses the method introduced by Karamalis et al. in https://doi.org/10.1016/j.media.2012.07.005.
  It generates a confidence map by setting source and sink points in the image and computing the probability
  for random walks to reach the source for each pixel.
  
  Args:
      alpha (float, optional): Alpha parameter. Defaults to 2.0.
      beta (float, optional): Beta parameter. Defaults to 90.0.
      gamma (float, optional): Gamma parameter. Defaults to 0.05.
      mode (str, optional): 'RF' or 'B' mode data. Defaults to 'B'.
      sink_mode (str, optional): Sink mode. Defaults to 'all'. If 'mask' is selected, a mask must be when calling
          the transform. Can be 'all', 'mid', 'min', or 'mask'.
code: def _solve_linear_system(self, lap, rhs):
      x = spsolve(lap, rhs)
  
      return x
  
file_path: MONAI/monai/data/ultrasound_confidence_map.py
imports: from __future__ import annotations
  import numpy
  from numpy.typing import NDArray
  from monai.utils import min_version, optional_import
label_desc: Use image node_id 7 for calling the UltrasoundConfidenceMap obj's underlying member method code with example usage: obj._solve_linear_system(lap, rhs) and returns: x