class_docstr: Fourier Domain Adaptation from https://github.com/YanchaoYang/FDA
  Simple "style transfer".
  
  Args:
      reference_images (Sequence[Any]): Sequence of objects that will be converted to images by `read_fn`. By default,
      it expects a sequence of paths to images.
      beta_limit (float or tuple of float): coefficient beta from paper. Recommended less 0.3.
      read_fn (Callable): Used-defined function to read image. Function should get an element of `reference_images`
      and return numpy array of image pixels. Default: takes as input a path to an image and returns a numpy array.
  
  Targets:
      image
  
  Image types:
      uint8, float32
  
  Reference:
      https://github.com/YanchaoYang/FDA
      https://openaccess.thecvf.com/content_CVPR_2020/papers/Yang_FDA_Fourier_Domain_Adaptation_for_Semantic_Segmentation_CVPR_2020_paper.pdf
  
  Example:
      >>> import numpy as np
      >>> import albumentations as A
      >>> image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
      >>> target_image = np.random.randint(0, 256, [100, 100, 3], dtype=np.uint8)
      >>> aug = A.Compose([A.FDA([target_image], p=1, read_fn=lambda x: x)])
      >>> result = aug(image=image)
code: def targets_as_params(self):
      return ["image"]
  
imports: import random
  from typing import Any, Callable, Literal, Sequence, Tuple
  import cv2
  import numpy
  from qudida import DomainAdapter
  from skimage.exposure import match_histograms
  from sklearn.decomposition import PCA
  from sklearn.preprocessing import MinMaxScaler, StandardScaler
  from albumentations.augmentations.utils import clipped, get_opencv_dtype_from_numpy, is_grayscale_image, is_multispectral_image, preserve_shape, read_rgb_image
  from ..core.transforms_interface import ImageOnlyTransform, ScaleFloatType, to_tuple
label_desc: Use image node_id 5 for calling the FDA obj's underlying member method code with example usage: obj.targets_as_params() and returns: list