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 __init__(
      self,
      alpha: float = 2.0,
      beta: float = 90.0,
      gamma: float = 0.05,
      mode="B",
      sink_mode="all",
  ):
      # The hyperparameters for confidence map estimation
      self.alpha = alpha
      self.beta = beta
      self.gamma = gamma
      self.mode = mode
      self.sink_mode = sink_mode
  
      # The precision to use for all computations
      self.eps = np.finfo("float64").eps
  
      # Store sink indices for external use
      self._sink_indices = np.array([], dtype="float64")
  
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 1 to create a new UltrasoundConfidenceMap object  with example: obj = UltrasoundConfidenceMap(alpha, beta, gamma, mode, sink_mode)