class_docstr: Class to extract a target stain from an image, using stain deconvolution (see Note).
  
  Args:
      tli: transmitted light intensity. Defaults to 240.
      alpha: tolerance in percentile for the pseudo-min (alpha percentile)
          and pseudo-max (100 - alpha percentile). Defaults to 1.
      beta: absorbance threshold for transparent pixels. Defaults to 0.15
      max_cref: reference maximum stain concentrations for Hematoxylin & Eosin (H&E).
          Defaults to (1.9705, 1.0308).
  
  Note:
      For more information refer to:
      - the original paper: Macenko et al., 2009 http://wwwx.cs.unc.edu/~mn/sites/default/files/macenko2009.pdf
      - the previous implementations:
  
        - MATLAB: https://github.com/mitkovetta/staining-normalization
        - Python: https://github.com/schaugf/HEnorm_python
docstr: Perform stain extraction.
  
  Args:
      image: uint8 RGB image to extract stain from
  
  return:
      target_he: H&E absorbance matrix for the image (first column is H, second column is E, rows are RGB values)
code: def __call__(self, image: np.ndarray) -> np.ndarray:
      """Perform stain extraction.
  
      Args:
          image: uint8 RGB image to extract stain from
  
      return:
          target_he: H&E absorbance matrix for the image (first column is H, second column is E, rows are RGB values)
      """
      if not isinstance(image, np.ndarray):
          raise TypeError("Image must be of type numpy.ndarray.")
  
      target_he = self._deconvolution_extract_stain(image)
      return target_he
  
file_path: MONAI/monai/apps/pathology/transforms/stain/array.py
imports: from __future__ import annotations
  import numpy
  from monai.transforms.transform import Transform
label_desc: Use image node_id 3 for calling the ExtractHEStains obj's underlying member method code with example usage: obj.__call__(image) and returns: target_he