class_docstr: Dictionary-based wrapper of :py:class:`monai.apps.pathology.transforms.NormalizeHEStains`.
  
  Class to normalize patches/images to a reference or target image stain.
  
  Performs stain deconvolution of the source image using the ExtractHEStains
  class, to obtain the stain matrix and calculate the stain concentration matrix
  for the image. Then, performs the inverse Beer-Lambert transform to recreate the
  patch using the target H&E stain matrix provided. If no target stain provided, a default
  reference stain is used. Similarly, if no maximum stain concentrations are provided, a
  reference maximum stain concentrations matrix is used.
  
  Args:
      keys: keys of the corresponding items to be transformed.
          See also: :py:class:`monai.transforms.compose.MapTransform`
      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.
      target_he: target stain matrix. Defaults to None.
      max_cref: reference maximum stain concentrations for Hematoxylin & Eosin (H&E).
          Defaults to None.
      allow_missing_keys: don't raise exception if key is missing.
code: def __init__(
      self,
      keys: KeysCollection,
      tli: float = 240,
      alpha: float = 1,
      beta: float = 0.15,
      target_he: tuple
      | np.ndarray = (
          (0.5626, 0.2159),
          (0.7201, 0.8012),
          (0.4062, 0.5581),
      ),
      max_cref: tuple | np.ndarray = (1.9705, 1.0308),
      allow_missing_keys: bool = False,
  ) -> None:
      super().__init__(keys, allow_missing_keys)
      self.normalizer = NormalizeHEStains(
          tli=tli,
          alpha=alpha,
          beta=beta,
          target_he=target_he,
          max_cref=max_cref,
      )
  
file_path: MONAI/monai/apps/pathology/transforms/stain/dictionary.py
imports: from __future__ import annotations
  from collections.abc import Hashable, Mapping
  import numpy
  from monai.config import KeysCollection
  from monai.transforms.transform import MapTransform
  from .array import ExtractHEStains, NormalizeHEStains
label_desc: Use image node_id 1 to create a new NormalizeHEStainsd object from inherited base classes: MapTransform with example: obj = NormalizeHEStainsd(keys, tli, alpha, beta, target_he, max_cref, allow_missing_keys)