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
code: def __init__(
      self,
      tli: float = 240,
      alpha: float = 1,
      beta: float = 0.15,
      max_cref: tuple | np.ndarray = (1.9705, 1.0308),
  ) -> None:
      self.tli = tli
      self.alpha = alpha
      self.beta = beta
      self.max_cref = np.array(max_cref)
  
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 1 to create a new ExtractHEStains object from inherited base classes: Transform with example: obj = ExtractHEStains(tli, alpha, beta, max_cref)